From 3369e8b688d48b37cce89fef5640816f4a784511 Mon Sep 17 00:00:00 2001 From: Pablo Rodriguez Mier Date: Wed, 23 Sep 2026 13:30:18 +0200 Subject: [PATCH 1/5] Sync public dev snapshot --- .github/workflows/build-and-publish.yml | 14 + .github/workflows/deploy-docs.yml | 157 +- .github/workflows/unit-tests.yml | 9 +- CONTRIBUTING.md | 4 +- README.md | 2 + RELEASE.md | 295 +- corneto/_plotting.py | 309 +- corneto/backend/_base.py | 573 +- corneto/backend/_picos_backend.py | 83 +- corneto/datasets/__init__.py | 5 + corneto/datasets/_catalog.py | 61 + corneto/datasets/_fetch.py | 269 + corneto/methods/__init__.py | 2 + corneto/methods/_flow_utils.py | 64 - corneto/methods/_predictive.py | 65 + corneto/methods/bidirectional_phonemes.py | 66 +- corneto/methods/causal/__init__.py | 5 + corneto/methods/causal/linear_dag.py | 1705 +++ corneto/methods/imat.py | 10 +- corneto/methods/pcst.py | 129 +- corneto/methods/phonemes.py | 41 +- corneto/methods/signal/__init__.py | 11 - corneto/methods/signal/cellnopt_ilp.py | 17 - corneto/methods/signaling/__init__.py | 21 + corneto/methods/signaling/annnet.py | 363 + corneto/methods/signaling/cellnopt_dag.py | 931 ++ corneto/methods/signaling/cellnopt_ilp.py | 620 -- .../methods/signaling/cellnopt_plotting.py | 880 ++ corneto/release.py | 50 +- datasets/sachs/v1/README.md | 60 + datasets/sachs/v1/condition_manifest.csv | 10 + datasets/sachs/v1/measurements.tsv | 7467 +++++++++++++ datasets/sachs/v1/sachs-v1.tar.gz | Bin 0 -> 270786 bytes docs/api/corneto.datasets.rst | 13 + docs/api/corneto.methods.rst | 57 +- docs/api/index.rst | 1 + docs/custom-index.html | 431 - docs/guide/causal/index.md | 12 + docs/guide/causal/linear-dag-discovery.ipynb | 2423 +++++ docs/guide/index.md | 2 +- docs/guide/intro/data.ipynb | 524 +- .../metabolism/flux-balance-analysis.ipynb | 9264 +---------------- docs/guide/metabolism/imat.ipynb | 663 ++ docs/guide/metabolism/index.md | 11 +- .../metabolism/multicondition-imat.ipynb | 1913 +--- .../metabolism/multicondition-sfba.ipynb | 582 +- docs/guide/method-inputs.md | 118 +- docs/guide/signaling/cellnopt_ILP.ipynb | 2280 ---- docs/guide/signaling/cellnopt_dag.ipynb | 3346 ++++++ docs/guide/signaling/index.md | 6 +- docs/install.md | 2 + docs/landing/assets/carnival-schematic.png | Bin 0 -> 56709 bytes docs/landing/assets/corneto-abstract.jpg | Bin 0 -> 288855 bytes docs/landing/assets/corneto-logo-512px.png | Bin 0 -> 27003 bytes docs/landing/assets/decider-eu-logo.png | Bin 0 -> 38554 bytes docs/landing/assets/embl-ebi-logo.png | Bin 0 -> 11838 bytes docs/landing/assets/favicon.ico | Bin 0 -> 15406 bytes docs/landing/assets/fba-schematic.png | Bin 0 -> 68896 bytes docs/landing/assets/feasible-space.png | Bin 0 -> 269707 bytes docs/landing/assets/permedcoe-eu-logo.png | Bin 0 -> 6481 bytes docs/landing/assets/saezlab-logo.png | Bin 0 -> 24461 bytes docs/landing/assets/ukhd-logo.jpg | Bin 0 -> 309749 bytes docs/landing/index.html | 1295 +++ docs/releases/index.md | 5 +- docs/releases/migration-1.0.md | 8 +- docs/releases/v1.0.0-rc.1.md | 8 +- docs/releases/v1.0.0-rc.3.md | 27 + docs/releases/v1.0.0-rc.4.md | 31 + docs/releases/v1.0.0-rc.5.md | 23 + docs/tutorials/README.md | 6 +- .../annnet-cellnopt-dag.ipynb | 2088 ++++ docs/tutorials/annnet-signaling/pixi.toml | 16 + docs/tutorials/causal/README.md | 56 + .../causal/linear-dag-discovery-sachs.ipynb | 2071 ++++ docs/tutorials/causal/pixi.toml | 21 + .../multi_condition_carnival.ipynb | 8944 ++++++++-------- .../multi_condition_tutorial/pixi.lock | 6369 ++++++++---- .../multi_condition_tutorial/pixi.toml | 20 +- docs/tutorials/index.md | 2 + pyproject.toml | 5 + scripts/build_all_docs_local.sh | 4 +- scripts/generate_switcher.py | 7 +- scripts/prepare_docs_pages.py | 209 + scripts/publish_docs_tag.sh | 31 +- tests/graph/test_plotting.py | 86 +- tests/methods/causal/test_linear_dag.py | 757 ++ tests/methods/causal/test_linear_dag_audit.py | 560 + .../methods/causal/test_linear_dag_sparse.py | 175 + .../methods/signaling/test_cellnopt_annnet.py | 48 + tests/methods/signaling/test_cellnopt_dag.py | 266 + tests/methods/signaling/test_cellnopt_ilp.py | 116 - .../signaling/test_cellnopt_plotting.py | 269 + tests/methods/test_bidirectional_phonemes.py | 35 + tests/methods/test_pcst_edge_cases.py | 106 +- tests/methods/test_phonemes.py | 21 + tests/methods/test_predictive_api.py | 382 + tests/methods/test_user_inputs.py | 21 +- tests/test_api_contract.py | 7 + tests/test_backend.py | 216 + tests/test_datasets.py | 194 + tests/test_deprecations.py | 13 - tests/test_generate_switcher.py | 24 + tests/test_prepare_docs_pages.py | 173 + tests/test_release.py | 56 +- 104 files changed, 38678 insertions(+), 22039 deletions(-) create mode 100644 corneto/datasets/__init__.py create mode 100644 corneto/datasets/_catalog.py create mode 100644 corneto/datasets/_fetch.py create mode 100644 corneto/methods/_predictive.py create mode 100644 corneto/methods/causal/__init__.py create mode 100644 corneto/methods/causal/linear_dag.py delete mode 100644 corneto/methods/signal/__init__.py delete mode 100644 corneto/methods/signal/cellnopt_ilp.py create mode 100644 corneto/methods/signaling/annnet.py create mode 100644 corneto/methods/signaling/cellnopt_dag.py delete mode 100644 corneto/methods/signaling/cellnopt_ilp.py create mode 100644 corneto/methods/signaling/cellnopt_plotting.py create mode 100644 datasets/sachs/v1/README.md create mode 100644 datasets/sachs/v1/condition_manifest.csv create mode 100644 datasets/sachs/v1/measurements.tsv create mode 100644 datasets/sachs/v1/sachs-v1.tar.gz create mode 100644 docs/api/corneto.datasets.rst delete mode 100644 docs/custom-index.html create mode 100644 docs/guide/causal/index.md create mode 100644 docs/guide/causal/linear-dag-discovery.ipynb create mode 100644 docs/guide/metabolism/imat.ipynb delete mode 100644 docs/guide/signaling/cellnopt_ILP.ipynb create mode 100644 docs/guide/signaling/cellnopt_dag.ipynb create mode 100644 docs/landing/assets/carnival-schematic.png create mode 100644 docs/landing/assets/corneto-abstract.jpg create mode 100644 docs/landing/assets/corneto-logo-512px.png create mode 100644 docs/landing/assets/decider-eu-logo.png create mode 100644 docs/landing/assets/embl-ebi-logo.png create mode 100644 docs/landing/assets/favicon.ico create mode 100644 docs/landing/assets/fba-schematic.png create mode 100644 docs/landing/assets/feasible-space.png create mode 100644 docs/landing/assets/permedcoe-eu-logo.png create mode 100644 docs/landing/assets/saezlab-logo.png create mode 100644 docs/landing/assets/ukhd-logo.jpg create mode 100644 docs/landing/index.html create mode 100644 docs/releases/v1.0.0-rc.3.md create mode 100644 docs/releases/v1.0.0-rc.4.md create mode 100644 docs/releases/v1.0.0-rc.5.md create mode 100644 docs/tutorials/annnet-signaling/annnet-cellnopt-dag.ipynb create mode 100644 docs/tutorials/annnet-signaling/pixi.toml create mode 100644 docs/tutorials/causal/README.md create mode 100644 docs/tutorials/causal/linear-dag-discovery-sachs.ipynb create mode 100644 docs/tutorials/causal/pixi.toml create mode 100644 scripts/prepare_docs_pages.py create mode 100644 tests/methods/causal/test_linear_dag.py create mode 100644 tests/methods/causal/test_linear_dag_audit.py create mode 100644 tests/methods/causal/test_linear_dag_sparse.py create mode 100644 tests/methods/signaling/test_cellnopt_annnet.py create mode 100644 tests/methods/signaling/test_cellnopt_dag.py delete mode 100644 tests/methods/signaling/test_cellnopt_ilp.py create mode 100644 tests/methods/signaling/test_cellnopt_plotting.py create mode 100644 tests/methods/test_predictive_api.py create mode 100644 tests/test_datasets.py create mode 100644 tests/test_generate_switcher.py create mode 100644 tests/test_prepare_docs_pages.py diff --git a/.github/workflows/build-and-publish.yml b/.github/workflows/build-and-publish.yml index fc5cf44ff..a2d68678f 100644 --- a/.github/workflows/build-and-publish.yml +++ b/.github/workflows/build-and-publish.yml @@ -82,6 +82,11 @@ jobs: ;; esac + - name: Validate committed release notes + if: steps.validate_tag.outputs.should_publish == 'true' + run: | + poetry run python -c "from corneto.release import _ensure_release_notes; _ensure_release_notes('${{ steps.validate_tag.outputs.version }}')" + # 7. Build source & wheel (only if should publish) - name: Build package if: steps.validate_tag.outputs.should_publish == 'true' @@ -89,6 +94,15 @@ jobs: poetry build poetry run twine check dist/* + # The tag is already covered by the merged PR's full test gate. Validate + # only the tag-specific artifact here, before it is published. + - name: Smoke-test the release wheel + if: steps.validate_tag.outputs.should_publish == 'true' + run: | + python -m venv /tmp/corneto-release-wheel-smoke + /tmp/corneto-release-wheel-smoke/bin/pip install dist/*.whl + /tmp/corneto-release-wheel-smoke/bin/python -c 'import corneto; print(corneto.__version__)' + # 8. Create GitHub Release with automated notes - name: Create GitHub Release if: steps.validate_tag.outputs.should_publish == 'true' diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml index e365d6b50..c05ecaec4 100644 --- a/.github/workflows/deploy-docs.yml +++ b/.github/workflows/deploy-docs.yml @@ -7,28 +7,19 @@ env: on: push: branches: - - dev - main - tags: - - '*' workflow_dispatch: - inputs: - target: - description: "What to build/deploy (dev=latest, main=stable, or tag)" - type: choice - required: true - options: - - dev - - main - - tag - tag: - description: "Tag to deploy when target=tag (e.g., v1.0.0-beta.3)" - type: string - required: false workflow_run: workflows: ["Publish Python Package"] types: [completed] +permissions: + contents: write + +concurrency: + group: docs-pages + cancel-in-progress: false + jobs: build-and-deploy: runs-on: ubuntu-latest @@ -43,14 +34,7 @@ jobs: with: fetch-depth: 0 fetch-tags: true - ref: >- - ${{ - github.event_name == 'workflow_dispatch' && - (github.event.inputs.target == 'tag' && format('refs/tags/{0}', github.event.inputs.tag) || - format('refs/heads/{0}', github.event.inputs.target)) - || github.event_name == 'workflow_run' && github.event.workflow_run.head_sha - || github.ref - }} + ref: ${{ github.event_name == 'workflow_run' && github.event.workflow_run.head_sha || github.ref }} - name: Set up Python & Graphviz uses: actions/setup-python@v5 @@ -89,29 +73,24 @@ jobs: - name: Determine version_folder id: set_version run: | - case "${GITHUB_REF}" in - refs/heads/dev) - echo "version_folder=latest" >> $GITHUB_OUTPUT - ;; - refs/heads/main) - echo "version_folder=stable" >> $GITHUB_OUTPUT - ;; - refs/tags/*) - v="${GITHUB_REF#refs/tags/}" - echo "version_folder=${v}" >> $GITHUB_OUTPUT - ;; - *) - echo "No docs to deploy for ${GITHUB_REF}" >&2 - exit 0 - ;; - esac + if [ "${{ github.event_name }}" = "workflow_run" ]; then + if [[ "${{ github.event.workflow_run.head_branch }}" == v* ]]; then + echo "version_folder=${{ github.event.workflow_run.head_branch }}" >> $GITHUB_OUTPUT + else + echo "Publish workflow did not run for a release tag" >&2 + exit 1 + fi + elif [ "${GITHUB_REF}" = "refs/heads/main" ]; then + echo "version_folder=stable" >> $GITHUB_OUTPUT + else + echo "No documentation target for ${GITHUB_REF}" >&2 + exit 1 + fi - name: Sync GitHub Releases to Docs env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - run: | - sleep 30 - python scripts/sync_releases.py --repo saezlab/corneto --docs-dir docs + run: python scripts/sync_releases.py --repo saezlab/corneto --docs-dir docs - name: Build Docs env: @@ -119,66 +98,46 @@ jobs: DOCS_BASE_URL: https://corneto.org run: poetry run sphinx-build -W --keep-going -b html docs docs/_build/html - - name: Deploy “latest” docs - if: ${{ steps.set_version.outputs.version_folder == 'latest' }} - uses: peaceiris/actions-gh-pages@v4 - with: - github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: ./docs/_build/html - destination_dir: latest - keep_files: true - - - name: Deploy “stable” docs - if: ${{ steps.set_version.outputs.version_folder == 'stable' }} - uses: peaceiris/actions-gh-pages@v4 - with: - github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: ./docs/_build/html - destination_dir: stable - keep_files: true - - - name: Deploy versioned docs for tag - if: ${{ startsWith(github.ref, 'refs/tags/') }} - uses: peaceiris/actions-gh-pages@v4 - with: - github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: ./docs/_build/html - destination_dir: ${{ steps.set_version.outputs.version_folder }} - keep_files: true - - - name: Prepare root redirect + - name: Check out gh-pages run: | - mkdir -p temp_root - touch temp_root/.nojekyll - cp docs/custom-index.html temp_root/index.html - - name: Generate switcher.json + set -euo pipefail + git fetch origin gh-pages:refs/remotes/origin/gh-pages + git worktree add --detach gh-pages-worktree refs/remotes/origin/gh-pages + + - name: Prepare Pages tree env: DOCS_BASE_URL: https://corneto.org run: | - python scripts/generate_switcher.py --output temp_root/switcher.json - - - name: Deploy root redirect - uses: peaceiris/actions-gh-pages@v4 - with: - github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: temp_root - destination_dir: ./ - keep_files: true - - - name: Patch switcher URL in published HTML + set -euo pipefail + root_args=() + if [ "${{ steps.set_version.outputs.version_folder }}" = "stable" ]; then + root_args+=(--update-root) + fi + poetry run python scripts/prepare_docs_pages.py \ + --pages-dir gh-pages-worktree \ + --build-dir docs/_build/html \ + --landing-dir docs/landing \ + --version-folder "${{ steps.set_version.outputs.version_folder }}" \ + --base-url "${DOCS_BASE_URL}" \ + "${root_args[@]}" + + - name: Commit and deploy Pages tree + env: + VERSION_FOLDER: ${{ steps.set_version.outputs.version_folder }} run: | - git fetch origin gh-pages - git worktree add gh-pages-worktree gh-pages - python scripts/patch_switcher_urls.py \ - --root gh-pages-worktree \ - --new-url https://corneto.org/switcher.json + set -euo pipefail cd gh-pages-worktree - if [ -n "$(git status --porcelain)" ]; then - git config user.name "github-actions[bot]" - git config user.email "github-actions[bot]@users.noreply.github.com" - git add -A - git commit -m "chore: update switcher json url" - git push origin gh-pages - else - echo "No switcher URL changes detected." + if [ -z "$(git status --porcelain)" ]; then + echo "No Pages changes to deploy." + exit 0 + fi + git config user.name "github-actions[bot]" + git config user.email "41898282+github-actions[bot]@users.noreply.github.com" + git add -A + git commit -m "docs: deploy ${VERSION_FOLDER}" + git fetch origin gh-pages + if ! git merge --ff-only origin/gh-pages; then + echo "gh-pages advanced during deployment; refusing to overwrite it." >&2 + exit 1 fi + git push origin HEAD:gh-pages diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml index f213c1b4f..7b3d9819d 100644 --- a/.github/workflows/unit-tests.yml +++ b/.github/workflows/unit-tests.yml @@ -4,8 +4,15 @@ name: Unit tests on: - push: pull_request: + branches: + - main + +# A PR into the public trunk is the release gate. Do not repeat this matrix +# for every temporary branch push, merge to main, or release tag. +concurrency: + group: unit-tests-${{ github.event.pull_request.number }} + cancel-in-progress: true env: POETRY_VERSION: "2.4.1" diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 43712a505..e14e0b8e2 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -13,7 +13,7 @@ For notebook execution in tutorials, we also support [Pixi](https://pixi.sh/) en Once installed, clone the repository and install it with poetry. This will create a virtual environment ready for development: ```bash -git clone git+https://github.com/saezlab/corneto.git@dev +git clone https://github.com/saezlab/corneto.git cd corneto poetry install --with dev ``` @@ -243,7 +243,7 @@ poetry run python docs/tutorials/run_notebooks.py - **Pixi integration**: Tutorial notebooks can use individual `pixi.toml` files for isolated execution environments with specific dependencies. - **Poetry and PEP 621**: The project uses both Poetry (legacy) and modern PEP 621 project configuration in `pyproject.toml`. -Please see [contributing with tutorials](https://github.com/saezlab/corneto/blob/dev/docs/tutorials/README.md) for more information on how to contribute tutorials. +Please see [contributing with tutorials](https://github.com/saezlab/corneto/blob/main/docs/tutorials/README.md) for more information on how to contribute tutorials. ## Releases diff --git a/README.md b/README.md index bb697839e..587dac6fb 100644 --- a/README.md +++ b/README.md @@ -61,12 +61,14 @@ pip install corneto CORNETO provides several optional dependency groups: +- **`plot`**: Graphviz, NetworkX, and Matplotlib visualization support - **`research`**: Full research stack with Gurobi, PICOS, visualization, and network tools - **`os`**: Open-source solvers (SCIP, HiGHS) with visualization and network tools - **`ml`**: Machine learning dependencies (JAX, Keras, scikit-learn) Install any combination with: ```bash +pip install corneto[plot] # Visualization without the full research stack pip install corneto[research,ml] # Multiple extras ``` diff --git a/RELEASE.md b/RELEASE.md index 41dc719cb..ad53f294e 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,264 +1,121 @@ # Release Process -CORNETO uses an automated tag-based release process powered by Poetry Dynamic Versioning and GitHub Actions. Git tags serve as the single source of truth for versioning - no manual version bumping in files is required. +CORNETO uses public `main` as its trunk. Every public change reaches it through a +pull request, and an annotated Git tag is the single source of truth for a +release version. Poetry Dynamic Versioning reads that tag; do not edit a version +in project files for a release. -**Note**: This document covers the release process for maintainers. For development setup and contribution guidelines, see [CONTRIBUTING.md](CONTRIBUTING.md). +## Public workflow -## Release Workflow +1. Develop and validate changes in the private workbench as needed. +2. Review the exact publishable snapshot for confidential material. +3. Push that snapshot to a temporary public `publication/` branch and + open one pull request into public `main`. +4. The public-main pull request is the release gate. Its CI validates the merge + result with the Python test matrix, quality checks, documentation build, and + package smoke test. +5. After the PR merges, fast-forward the private mirror's `main` branch to + public `main`. +6. Create the release tag from that public `main` commit. -To create a new release: +There is no public `dev` branch in this workflow. The private `dev` branch may +remain an unpublished integration line, but it is never a public promotion +target. -1. **Integrate `dev` into `main`**. - - Merge via pull request (`dev` -> `main`) or fast-forward merge locally. - ```bash - git checkout dev - git pull --ff-only origin dev - git checkout main - git pull --ff-only origin main - git merge --ff-only dev - git push origin main - ``` +## Creating a release -2. **Create and push the release tag from `main`**: - ```bash - poetry install - git checkout main - git pull --ff-only origin main - poetry run release v1.2.3 - ``` - - Use pre-release tags as needed: `v1.2.3-alpha.0`, `v1.2.3-beta.0`, `v1.2.3-rc.0`. +Only tag a clean local checkout on `main` after the public-main PR has passed +and merged: -3. **Automatic pipeline execution**: - - GitHub Actions detects the new tag - - Builds the package using Poetry - - **Creates GitHub Release with automated release notes** - - Publishes to PyPI via OIDC trusted publishing - - Deploys versioned documentation to GitHub Pages +```bash +git checkout main +git pull --ff-only origin main +poetry install +poetry run release v1.2.3 --dry-run +poetry run release v1.2.3 +``` -4. **Sync `main` back into `dev` after tagging**: - - This keeps `dev` reachable from the latest release tags used by dynamic versioning. - - Without this step, `dev` may continue reporting an older pre-release base (e.g., `1.0.0b3+...`). - ```bash - git checkout dev - git pull --ff-only origin dev - git merge --ff-only origin/main - git push origin dev - ``` +Pre-release tags use the same flow, for example `v1.2.3-alpha.0`, +`v1.2.3-beta.0`, or `v1.2.3-rc.0`. The helper confirms that the tree is clean, +the checkout is on and matches the selected remote's `main`, and the tag does +not already exist. It also validates the committed release page before a tag +can be created. -5. **Version resolution**: - - Poetry Dynamic Versioning automatically extracts the version from the Git tag - - The package version in `pyproject.toml` remains at `0.0.0` (placeholder) - - Built packages use the actual tag version (e.g., `1.2.3`) +## Preparing release notes -6. **Automated Release Notes**: - - GitHub automatically generates release notes based on merged PRs and commits - - Uses conventional commit patterns to categorize changes - - Includes contributor acknowledgments and change summaries - - Release notes can be manually edited after creation if needed +Add `docs/releases/.md` and include it in `docs/releases/index.md` before +opening the public release pull request. Use the tag exactly as written; do not +rewrite `v1.0.0-rc.4` as `CORNETO 1.0.0 RC4`. Pre-releases include the same +badge used by the documentation release synchronizer: -## Example Release Process +```markdown +# Release v1.0.0-rc.4 {bdg-warning}`Pre-release` -```bash -# Integrate dev into main -git checkout dev -git pull --ff-only origin dev -git checkout main -git pull --ff-only origin main -git merge --ff-only dev -git push origin main +One or two sentences describing the purpose of the release. -# Create and push a release tag explicitly -poetry run release v1.0.0-beta.4 +## Highlights -# Sync main back into dev so dev sees the latest release tag ancestry -git checkout dev -git pull --ff-only origin dev -git merge --ff-only origin/main -git push origin dev -``` +- The most important user-facing change. +- Another important change. -The release pipeline (`.github/workflows/build-and-publish.yml`) will automatically: -- Build source and wheel distributions -- Create a GitHub Release with automated release notes -- Publish to PyPI using trusted publishing -- Deploy documentation with version switcher +## Additional context -### Release Helper +Optional installation, migration, compatibility, or method-specific details. +``` -Use the helper command to create and push an explicit tag: +For a stable release, omit the pre-release badge: -```bash -poetry run release v1.2.3 -poetry run release v1.0.0-beta.4 -poetry run release 1.0.0-rc.1 # 'v' prefix is optional +```markdown +# Release v1.0.0 ``` -It validates release preconditions (clean tree, on `main`, in sync with `origin/main`, -`origin/dev` merged into `main`, tag not already present), then creates an annotated tag and pushes it to `origin`. +The release helper and tag publication workflow both enforce the canonical +title and the `## Highlights` section. This keeps manually prepared pages +consistent with pages produced by `scripts/sync_releases.py`. Useful options: ```bash -poetry run release v1.0.0-beta.4 --dry-run # validate only -poetry run release v1.0.0-beta.4 --yes # skip confirmation prompt -poetry run release v1.0.0-beta.4 --remote public # use an explicit release remote +poetry run release v1.0.0-rc.1 --dry-run +poetry run release v1.0.0-rc.1 --yes ``` -The remote defaults to `origin`, which is appropriate for a normal clone of -the public CORNETO repository. In a private development workbench where -`origin` points to a private mirror, configure a separate release-only remote -for the public repository and pass it explicitly: +In the private workbench, the public repository is configured as `public` and +must be named explicitly: ```bash -git remote add public https://github.com/saezlab/corneto.git -poetry run release v1.0.0-beta.8 --remote public --dry-run -poetry run release v1.0.0-beta.8 --remote public +poetry run release v1.0.0-rc.1 --remote public --dry-run +poetry run release v1.0.0-rc.1 --remote public ``` -Before tagging, publish selected private changes through a clean branch and a -pull request into public `dev`, followed by a release pull request from public -`dev` into public `main`. Never release a private development branch directly. - -### Customizing Release Notes +## CI responsibilities -After the automated release is created, you can: -1. Go to the [GitHub Releases page](https://github.com/saezlab/corneto/releases) -2. Edit the release to add additional context, migration guides, or breaking change notices -3. The automated notes will serve as the foundation, with your manual additions +The release process intentionally gives each workflow one job: -## Version Numbering +- Pull requests into `main` run the complete release gate. +- Pushes to `main` deploy the stable documentation only. +- Release tags build the exact tagged distributions, check them, publish to + PyPI, create the GitHub Release, and deploy versioned documentation. -Follow [Semantic Versioning](https://semver.org/): -- `MAJOR.MINOR.PATCH` (e.g., `v1.2.3`) -- Use `v` prefix for tags (e.g., `v1.0.0`, not `1.0.0`) -- Pre-releases: `v1.0.0-alpha.0`, `v1.0.0-beta.0`, `v1.0.0-rc.0` +This avoids repeating the full test matrix for temporary branch pushes, the +post-merge `main` push, and the release tag. The tagged package remains +protected by artifact-specific validation, while the merged PR is the code +quality gate. -## Prerequisites for Releases +## Local validation -Before creating a release, ensure the development environment and code quality standards are met: +Before opening the public-main PR, run the checks relevant to the change. A +full release-candidate check normally includes: -### Development Setup -All maintainers should have the development environment properly configured as described in [CONTRIBUTING.md](CONTRIBUTING.md), including: -- Poetry for dependency management -- Pre-commit hooks installed and active - -### Pre-commit Requirements -**Critical**: Pre-commit hooks must be installed and passing for all commits that will be included in the release. The pre-commit configuration ensures: - -- **Conventional commit messages**: Required for automated GitHub release notes generation (feat:, fix:, docs:, etc.) -- **Code quality**: Linting, formatting, and style checks via Ruff -- **Repository hygiene**: File validation, trailing whitespace removal, etc. - -**Important**: Conventional commits are essential for the automated release notes feature. Each commit should follow the pattern: -``` -(): - -[optional body] - -[optional footer(s)] -``` - -Common types: `feat`, `fix`, `docs`, `chore`, `refactor`, `test`, `ci`, `build`, `perf`, `style`, `revert` - -To set up pre-commit hooks: -```bash -poetry run pre-commit install --hook-type pre-commit --hook-type commit-msg -``` - -### Code Quality Validation -Before releasing, run comprehensive quality checks: ```bash -# Tests poetry run pytest - -# Linting and formatting -poetry run ruff check corneto tests +poetry run ruff check --no-fix corneto tests poetry run ruff format --check corneto tests - -# Package metadata poetry check - -# Advisory type-checking baseline (309 errors in Pyrefly 0.60.0 for RC1) -# This report is tracked but is not an RC blocker. -poetry run pyrefly check corneto - -# Documentation and distributions -poetry run sphinx-build -E -a -W --keep-going -b html docs docs/_build/html +poetry run sphinx-build -W --keep-going -b html docs docs/_build/html poetry build poetry run twine check dist/* ``` -## Development Workflow - -The typical development workflow involves: - -1. **Work in the `dev` branch** for ongoing development -2. **Ensure all commits follow conventional commit format** (enforced by pre-commit hooks) -3. **Run quality checks** before merging to main -4. **Integrate into `main`** when ready for release (via pull request or fast-forward merge) -5. **Create release tag** on the `main` branch to trigger automated publishing -6. **Sync `main` back into `dev`** immediately after tagging - -## Technical Details - -### Poetry Dynamic Versioning Configuration - -The project uses Poetry Dynamic Versioning (configured in `pyproject.toml`): - -```toml -[tool.poetry-dynamic-versioning] -enable = true -vcs = "git" -pattern = "default" -style = "pep440" -tagged-metadata = false -``` - -### GitHub Actions Workflow - -The release workflow (`.github/workflows/build-and-publish.yml`) is triggered by: -- Push events to tags matching `v*` pattern -- Uses OIDC trusted publishing for secure PyPI uploads -- Requires no manual secrets or tokens - -### Branch Strategy - -- **`main`**: Stable releases and release tags -- **`dev`**: Active development branch -- Recommended integration: `dev` -> `main` for releases, then `main` -> `dev` immediately after tagging - -### Documentation Version Switcher - -The documentation uses a centralized version switcher that allows users to navigate between different documentation versions (stable, latest, and specific releases). This system is designed to work across all deployed documentation versions automatically. - -#### Architecture - -The version switcher uses a **single, centrally-updated file hosted on GitHub Pages**: - -1. **Switcher Hosting**: `switcher.json` is deployed to the root of the docs site - - URL: `https://corneto.org/switcher.json` (configured via `DOCS_BASE_URL`) - - This URL is configured in `docs/conf.py` via `_switcher_url_with_ts()` - - A timestamp parameter is added to bypass browser caching - -2. **Automated Updates**: When documentation is deployed to GitHub Pages: - - The docs workflow generates `switcher.json` (`scripts/generate_switcher.py`) - - It is published to the site root alongside the redirect page - - A post-deploy patch step updates existing HTML in `gh-pages` to: - - point `theme_switcher_json_url` to `https://corneto.org/switcher.json` - - correct `theme_switcher_version_match` based on folder (`stable/`, `latest/`, `vX.Y.Z/`) - -3. **Version Mapping**: Documentation deployments use the following version identifiers: - - `dev` branch → deployed as `"latest"` - - `main` branch → deployed as `"stable"` (typically marked as preferred) - - Git tags (e.g., `v1.0.0`) → deployed using the tag name - - The `SPHINX_VERSION_MATCH` environment variable (set in `.github/workflows/deploy-docs.yml`) tells the PyData Sphinx Theme which version is currently being viewed. - -4. **Deployment Behavior (Why `destination_dir` is used without `keep_files`)**: - - Each docs build replaces only its own folder (`latest/`, `stable/`, or `vX.Y.Z/`) on the `gh-pages` branch. - - This ensures clean regeneration for a given version (no mixing of old/new files) while leaving other versions untouched. - - The root redirect is deployed with `keep_files: true` so it doesn’t delete any versioned folders. - -#### Manual Switcher Updates - -If the automatic workflow fails or manual intervention is needed, re-run the docs workflow or regenerate and deploy the root artifacts (redirect + `switcher.json`) using the same workflow steps. +The GitHub release pipeline uses OIDC trusted publishing, creates release notes +automatically, and marks alpha, beta, and RC tags as pre-releases. diff --git a/corneto/_plotting.py b/corneto/_plotting.py index 9e4b08a2e..8bf7a7751 100644 --- a/corneto/_plotting.py +++ b/corneto/_plotting.py @@ -60,6 +60,52 @@ def _as_values(value: Any) -> Optional[np.ndarray]: return np.array(value) +def _scaled_magnitudes( + values: Any, + *, + zero_threshold: float = 1e-6, + scale: Optional[str] = "log", + clip_quantile: Optional[float] = 0.05, +) -> np.ndarray: + """Return finite edge magnitudes normalized to ``[0, 1]``. + + Zeros remain zero, including after quantile clipping. ``clip_quantile`` is + the fraction clipped from the upper tail, matching the historical plotting + option while avoiding lower-tail inflation of small or zero values. + """ + array = np.asarray(values, dtype=float) + if array.ndim != 1: + raise ValueError(f"Plot values must be one-dimensional; got shape {array.shape}.") + if not np.all(np.isfinite(array)): + raise ValueError("Plot values must be finite.") + if zero_threshold < 0: + raise ValueError("zero_threshold must be nonnegative.") + + magnitudes = np.abs(array) + magnitudes[magnitudes < zero_threshold] = 0.0 + if scale == "log": + magnitudes = np.log1p(magnitudes) + elif scale == "std": + std = float(np.std(magnitudes)) + if std > 0: + magnitudes = magnitudes / std + elif scale is not None: + raise ValueError(f"Unknown scale '{scale}'. Use None, 'log', or 'std'.") + + nonzero = magnitudes > 0 + if clip_quantile is not None: + if not 0 <= clip_quantile <= 1: + raise ValueError(f"Clipping value must be between 0 and 1, got {clip_quantile}") + if np.any(nonzero): + upper = float(np.quantile(magnitudes[nonzero], 1 - clip_quantile)) + magnitudes[nonzero] = np.minimum(magnitudes[nonzero], upper) + + maximum = float(np.max(magnitudes)) if magnitudes.size else 0.0 + if maximum <= 0: + return np.zeros_like(magnitudes) + return magnitudes / maximum + + def _processor_sign_magnitude(graph: BaseGraph, data: Dict[str, Any], theme: Theme) -> ProcessorOutput: edge_attrs: Dict[int, Dict[str, str]] = {} vertex_attrs: Dict[Union[int, str], Dict[str, str]] = {} @@ -101,28 +147,21 @@ def _processor_metabolism_flux(graph: BaseGraph, data: Dict[str, Any], theme: Th scale = data.get("scale", "log") clip_quantil = data.get("clip_quantil", 0.05) - flow = np.array(flux_values, dtype=float) - flow[np.abs(flow) < zero_flow_threshold] = 0 - if scale is not None: - if scale == "log": - flow = np.sign(flow) * np.log10(np.abs(flow) + 1.0) - elif scale == "std": - std = float(np.std(flow)) - if std > 0: - flow = flow / std - else: - raise ValueError(f"Unknown scale '{scale}'. Use None, 'log', or 'std'.") - if clip_quantil is not None: - flow = clip_quantiles(flow, float(clip_quantil)) - max_flow = max(float(np.max(np.abs(flow))), 1e-6) + flow = np.asarray(flux_values, dtype=float) + magnitudes = _scaled_magnitudes( + flow, + zero_threshold=zero_flow_threshold, + scale=scale, + clip_quantile=None if clip_quantil is None else float(clip_quantil), + ) min_w = float(theme["min_edge_width"]) max_w = float(theme["max_edge_width"]) - for i, v in enumerate(flow): + for i, (v, magnitude) in enumerate(zip(flow, magnitudes)): if scale is None: - edge_width = max_w if abs(v) > 0 else min_w + edge_width = max_w if magnitude > 0 else min_w else: - edge_width = min_w + (max_w - min_w) * abs(v / max_flow) + edge_width = min_w + (max_w - min_w) * magnitude edge_attrs[i] = {"penwidth": str(edge_width)} if v > 0: edge_attrs[i]["color"] = str(theme["positive_color"]) @@ -236,6 +275,59 @@ def _merge_solution_data( return merged +def _resolve_sample_index( + sample: Optional[Union[int, str]], + *, + feature_data: Any, + num_samples: int, +) -> Optional[int]: + if sample is None: + return None + if isinstance(sample, int): + if sample < 0 or sample >= num_samples: + raise ValueError(f"sample index {sample} out of range for {num_samples} samples.") + return sample + samples = getattr(feature_data, "samples", None) + if samples is None: + raise ValueError("A string sample requires feature_data with named samples.") + names = list(samples) + if sample not in samples: + raise ValueError(f"sample '{sample}' not found in feature_data samples.") + index = names.index(sample) + if index >= num_samples: + raise ValueError(f"sample '{sample}' maps to column {index}, but solution data has only {num_samples} columns.") + return index + + +def _select_solution_sample( + data: Dict[str, Any], + *, + sample: Optional[Union[int, str]], + feature_data: Any, +) -> Dict[str, Any]: + selected = dict(data) + for key in ("vertex_values", "edge_values", "flux_values"): + if key not in selected: + continue + values = _as_values(selected[key]) + if values is None or values.ndim <= 1: + continue + if values.ndim != 2: + raise ValueError(f"Plot data '{key}' must be a vector or matrix; got shape {values.shape}.") + if values.shape[1] == 1 and sample is None: + selected[key] = values[:, 0] + continue + index = _resolve_sample_index( + sample, + feature_data=feature_data, + num_samples=values.shape[1], + ) + if index is None: + raise ValueError(f"Plot data '{key}' contains {values.shape[1]} samples; select one with sample=.") + selected[key] = values[:, index] + return selected + + def _resolve_role_styles( preset: Optional[str], role_styles: Optional[Dict[str, Union[str, Dict[str, str]]]], @@ -348,27 +440,144 @@ def _merge_attrs( def _plot_with_networkx(graph: BaseGraph, **kwargs) -> Any: - import matplotlib.pyplot as plt - import networkx as nx - - from corneto.contrib.networkx import corneto_graph_to_networkx - - skip_unsupported_edges = bool(kwargs.get("skip_unsupported_edges", True)) - nx_graph = corneto_graph_to_networkx(graph, skip_unsupported_edges=skip_unsupported_edges) + try: + import matplotlib.colors as mcolors + import matplotlib.pyplot as plt + import networkx as nx + except ImportError as exc: + raise ImportError( + "The NetworkX renderer requires optional plotting dependencies. " + "Install CORNETO with `pip install 'corneto[plot]'`." + ) from exc + + model_keys = { + "graph_attr", + "node_attr", + "edge_attr", + "custom_edge_attr", + "custom_vertex_attr", + "edge_indexes", + "orphan_edges", + } + model = _build_plot_model(graph, **{key: kwargs[key] for key in model_keys if key in kwargs}) + nx_graph = nx.MultiDiGraph() + node_defaults = dict(model["node_defaults"] or {}) + edge_defaults = dict(model["edge_attr"] or {}) + for name, attrs in model["nodes"]: + merged = dict(node_defaults) + merged.update(attrs) + nx_graph.add_node(name, **merged) + for edge_index, (source, target, attrs) in enumerate(model["edges"]): + merged = dict(edge_defaults) + merged.update(attrs) + nx_graph.add_edge(source, target, key=edge_index, **merged) pos = kwargs.get("pos") - if pos is None: - pos = nx.spring_layout(nx_graph) + if pos is not None: + pos = {str(key): value for key, value in pos.items()} + else: + pos = nx.spring_layout(nx_graph, seed=kwargs.get("seed", 0)) ax = kwargs.get("ax") if ax is None: - fig, ax = plt.subplots(figsize=kwargs.get("figsize")) + fig, ax = plt.subplots(figsize=kwargs.get("figsize"), constrained_layout=True) else: fig = ax.figure - nx.draw_networkx(nx_graph, pos=pos, ax=ax, with_labels=kwargs.get("node_labels") is None) - if kwargs.get("node_labels") is not None: - nx.draw_networkx_labels(nx_graph, pos=pos, labels=kwargs["node_labels"], ax=ax) + graphviz_colors = { + "firebrick4": "#8b1a1a", + "dodgerblue4": "#00688b", + "olivedrab3": "#9acd32", + "orangered": "#ff4500", + } + + def mpl_color(value: Any, default: str) -> str: + color = str(value or default) + color = graphviz_colors.get(color.lower(), color) + return color if mcolors.is_color_like(color) else default + + shape_map = { + "box": "s", + "rect": "s", + "rectangle": "s", + "square": "s", + "diamond": "D", + "triangle": "^", + "point": ".", + "circle": "o", + "ellipse": "o", + } + grouped_nodes: Dict[str, List[str]] = {} + for node, attrs in nx_graph.nodes(data=True): + shape = shape_map.get(str(attrs.get("shape", "circle")).lower(), "o") + grouped_nodes.setdefault(shape, []).append(node) + for shape, nodes in grouped_nodes.items(): + attrs = [nx_graph.nodes[node] for node in nodes] + sizes = [float(attr.get("nodesize", 60 if shape == "." else 900)) for attr in attrs] + nx.draw_networkx_nodes( + nx_graph, + pos, + nodelist=nodes, + node_shape=shape, + node_size=sizes, + node_color=[ + mpl_color(attr.get("fillcolor"), "white") if "filled" in str(attr.get("style", "")) else "white" + for attr in attrs + ], + edgecolors=[mpl_color(attr.get("color"), "black") for attr in attrs], + linewidths=[float(attr.get("penwidth", 1.0)) for attr in attrs], + ax=ax, + ) + + for source, target, key, attrs in nx_graph.edges(keys=True, data=True): + style = str(attrs.get("style", "solid")).split(",")[0] + arrowhead = str(attrs.get("arrowhead", "normal")) + arrowstyle = "-[" if arrowhead == "tee" else ("-" if arrowhead == "none" else "-|>") + nx.draw_networkx_edges( + nx_graph, + pos, + edgelist=[(source, target, key)], + edge_color=mpl_color(attrs.get("color"), "black"), + width=float(attrs.get("penwidth", 1.0)), + style=style if style in {"solid", "dashed", "dotted", "dashdot"} else "solid", + arrows=arrowhead != "none", + arrowstyle=arrowstyle, + connectionstyle="arc3,rad=0.05", + ax=ax, + ) + + requested_labels = kwargs.get("node_labels") + if requested_labels is not None: + requested_labels = {str(node): label for node, label in requested_labels.items()} + labels = {} + for node, attrs in nx_graph.nodes(data=True): + if requested_labels is not None: + labels[node] = requested_labels.get(node, node) + else: + labels[node] = attrs.get("label", node) + font_colors = {mpl_color(attrs.get("fontcolor"), "black") for _, attrs in nx_graph.nodes(data=True)} + nx.draw_networkx_labels( + nx_graph, + pos, + labels=labels, + font_color=next(iter(font_colors)) if len(font_colors) == 1 else "black", + font_size=kwargs.get("font_size", 9), + ax=ax, + ) + + edge_labels = { + (source, target, key): attrs["label"] + for source, target, key, attrs in nx_graph.edges(keys=True, data=True) + if attrs.get("label") not in (None, "") + } + if edge_labels: + nx.draw_networkx_edge_labels( + nx_graph, + pos, + edge_labels=edge_labels, + font_size=kwargs.get("edge_font_size", 8), + ax=ax, + ) ax.set_axis_off() return fig @@ -410,6 +619,7 @@ def plot_graph(graph: BaseGraph, renderer: str = "auto", **kwargs) -> Any: if solution_map is not None: merged_solution_map.update(solution_map) data = _merge_solution_data(data, solution, merged_solution_map) + data = _select_solution_sample(data, sample=sample, feature_data=feature_data) if node_roles is None and feature_data is not None: node_roles = _infer_node_roles_from_feature_data(feature_data, sample=sample, role_key=role_key) @@ -613,27 +823,22 @@ def flow_style( scale: Optional[Literal["log", "std"]] = "log", clip_quantil: Optional[float] = 0.05, ): - flow = np.array(P.expr[flow_name].value) - flow[np.abs(flow) < zero_flow_threshold] = 0 - if scale is not None: - if scale == "log": - flow = np.sign(flow) * np.log10(np.abs(flow) + 1.0) - elif scale == "std": - flow = flow / np.std(flow) - else: - raise ValueError(f"Unknown normalization method: {scale}") - if clip_quantil is not None: - flow = clip_quantiles(flow, clip_quantil) - max_flow = max(np.max(np.abs(flow)), 1e-6) + flow = np.asarray(P.expr[flow_name].value, dtype=float) + magnitudes = _scaled_magnitudes( + flow, + zero_threshold=zero_flow_threshold, + scale=scale, + clip_quantile=clip_quantil, + ) edge_attrs = dict() - for i, v in enumerate(flow): + for i, (v, magnitude) in enumerate(zip(flow, magnitudes)): # Apply threshold edge width - if abs(v) > 0: + if magnitude > 0: edge_width = max_edge_width else: edge_width = min_edge_width if scale is not None: - edge_width = min_edge_width + (max_edge_width - min_edge_width) * abs(v / max_flow) + edge_width = min_edge_width + (max_edge_width - min_edge_width) * magnitude edge_attrs[i] = {"penwidth": str(edge_width)} if flow[i] > 0: edge_attrs[i]["color"] = positive_color @@ -726,6 +931,8 @@ def _build_plot_model( edges: List[Tuple[str, str, Dict[str, str]]] = [] is_hypergraph = False + included_nodes = set() + connected_nodes = {str(vertex) for source, target in graph.E for vertex in (*source, *target)} for i, e in enumerate(graph.edges()): if edge_indexes is not None and i not in edge_indexes: continue @@ -743,6 +950,7 @@ def add_node(v_name: str, default_shape: str) -> None: if v_name in custom_vertex_attr_str: attrs.update(custom_vertex_attr_str[v_name]) nodes.append((v_name, attrs)) + included_nodes.add(v_name) if len(s) == 0: v_name = f"e_{i}_source" @@ -785,6 +993,17 @@ def add_node(v_name: str, default_shape: str) -> None: attrs.update(custom_edge_attr.get(i, {})) edges.append((v_s[0], v_t[0], attrs)) + for vertex in graph.V: + vertex_name = str(vertex) + if vertex_name in included_nodes or vertex_name in connected_nodes: + continue + attrs = {} + if "shape" not in node_defaults: + attrs["shape"] = "circle" + if vertex_name in custom_vertex_attr_str: + attrs.update(custom_vertex_attr_str[vertex_name]) + nodes.append((vertex_name, attrs)) + return { "graph_attr": graph_attr, "node_defaults": node_defaults, diff --git a/corneto/backend/_base.py b/corneto/backend/_base.py index 70a2f5145..c70abf6b6 100644 --- a/corneto/backend/_base.py +++ b/corneto/backend/_base.py @@ -51,6 +51,34 @@ def _get_unique_name(prefix: str = "_var") -> str: return prefix + hex(hash(uuid4())) +def _sparse_vector_replication(vector_size: int, repetitions: int): + """Return a sparse map from a vector to repeated Fortran-order columns.""" + from scipy.sparse import csr_matrix + + if vector_size <= 0 or repetitions <= 0: + return csr_matrix((vector_size * repetitions, vector_size)) + rows = np.arange(vector_size * repetitions, dtype=int) + columns = np.tile(np.arange(vector_size, dtype=int), repetitions) + return csr_matrix( + (np.ones(rows.size, dtype=float), (rows, columns)), + shape=(vector_size * repetitions, vector_size), + ) + + +def _sparse_vector_entry_repetition(vector_size: int, repetitions: int): + """Return a sparse map that repeats each vector entry consecutively.""" + from scipy.sparse import csr_matrix + + if vector_size <= 0 or repetitions <= 0: + return csr_matrix((vector_size * repetitions, vector_size)) + rows = np.arange(vector_size * repetitions, dtype=int) + columns = np.repeat(np.arange(vector_size, dtype=int), repetitions) + return csr_matrix( + (np.ones(rows.size, dtype=float), (rows, columns)), + shape=(vector_size * repetitions, vector_size), + ) + + class CExpression(abc.ABC): # Arithmetic operator overloading with Numpy # See: https://www.cvxpy.org/_modules/cvxpy/expressions/expression.html#Expression @@ -370,6 +398,16 @@ def __init__( self._shape = shape self._is_variable = variable + # Constants already carry their exact value in ``expr``. Creating + # synthetic +/-infinity bounds for them would allocate dense arrays + # with the full expression shape, defeating sparse constants. + if not variable and lb is None and ub is None: + self._lb = None + self._ub = None + self._name = name + self._vartype = vartype + return + if lb is None: if vartype == VarType.CONTINUOUS: lb_r = np.full(expr.shape, -np.inf) @@ -910,7 +948,7 @@ def Flow( if isinstance(ub, np.ndarray) and len(ub.shape) == 1: ub = np.tile(ub, (n_flows, 1)).T F = self.Variable(name=varname, shape=shape, lb=lb, ub=ub) - A = self._sparse(g.get_vertex_incidence_matrix_as_lists(values=values)) + A = self.Constant(self._sparse(g.get_vertex_incidence_matrix_as_lists(values=values))) P = self.Problem(A @ F == 0) if shared_bounds and n_flows > 1: # check num dims of lb @@ -922,7 +960,7 @@ def Flow( P += S <= ub[:, 0] P += S >= lb[:, 0] if create_nonzero_indicators: - P += NonZeroIndicator(tolerance=indicator_tolerance) + P += NonZeroIndicator(var_name=varname, tolerance=indicator_tolerance) Ip = P.get_symbol(varname + "_ipos") In = P.get_symbol(varname + "_ineg") P.register(alias_flow_ipos, Ip) @@ -1143,13 +1181,56 @@ def Acyclic( else: raise ValueError("At least one indicator variable name is required") - # Limit the number of parents per node, if requested + # Limit the number of parents per node, if requested. A negative + # selection traverses an edge in reverse, so it contributes a parent + # at the edge's source rather than at its target. if max_parents is not None: - for v, max_val in max_parents.items(): - edges_idx = [i for i, _ in g.in_edges(v)] - if edges_idx: - # Sum selected parent edges - P += np.ones((len(edges_idx),)) @ indicator[edges_idx] <= max_val + from scipy.sparse import csr_matrix + + parent_vertices = list(max_parents) + parent_vertex_index = {vertex: index for index, vertex in enumerate(parent_vertices)} + positive_rows = [] + positive_columns = [] + negative_rows = [] + negative_columns = [] + for edge_index, (source, target) in enumerate(g.E): + if target: + target_vertex = next(iter(target)) + if target_vertex in parent_vertex_index: + positive_rows.append(parent_vertex_index[target_vertex]) + positive_columns.append(edge_index) + if source: + source_vertex = next(iter(source)) + if source_vertex in parent_vertex_index: + negative_rows.append(parent_vertex_index[source_vertex]) + negative_columns.append(edge_index) + + parent_count = None + if Ip is not None and positive_rows: + positive_selector = csr_matrix( + ( + np.ones(len(positive_rows), dtype=float), + (positive_rows, positive_columns), + ), + shape=(len(parent_vertices), g.num_edges), + ) + parent_count = self.Constant(positive_selector) @ Ip + if In is not None and negative_rows: + negative_selector = csr_matrix( + ( + np.ones(len(negative_rows), dtype=float), + (negative_rows, negative_columns), + ), + shape=(len(parent_vertices), g.num_edges), + ) + negative_parent_count = self.Constant(negative_selector) @ In + parent_count = negative_parent_count if parent_count is None else parent_count + negative_parent_count + if parent_count is not None: + parent_count = parent_count.reshape((len(parent_vertices), 1)) + max_values = np.asarray([max_parents[vertex] for vertex in parent_vertices], dtype=float).reshape( + (-1, 1) + ) + P += parent_count <= max_values # Determine number of samples (if the indicator is 1D, assume 1 sample) if len(indicator.shape) == 1: @@ -1361,6 +1442,480 @@ def Indicator( c += [S >= indicator.multiply(lb), S <= indicator.multiply(ub)] return self.Problem(c) + def ExactSupport( + self, + V: CSymbol, + *, + selected: Optional[CExpression] = None, + indexes: Optional[Union[int, slice, Tuple, List, np.ndarray]] = None, + epsilon: Union[float, List[float], np.ndarray] = 1.0, + nonnegative: bool = False, + name: Optional[str] = None, + positive_name: Optional[str] = None, + negative_name: Optional[str] = None, + ) -> ProblemDef: + """Link a bounded value to binary structural support with a gap. + + ``selected == 0`` forces the value to zero. ``selected == 1`` forces + its magnitude to be at least ``epsilon``. For nonnegative values this + uses one binary per entry. Signed values use mutually exclusive + positive and negative binaries and expose their sum as ``selected``. + + ``epsilon`` may be a scalar or an array explicitly broadcastable to + the selected value shape. "Exact" refers to this mathematical + minimum-magnitude gap; it is not a solver-tolerance guarantee, and + values smaller than the requested gap may still be returned by a + numerically inaccurate solve and should be checked by the caller. + + An existing binary selector symbol can be supplied to avoid + introducing a redundant binary variable, which is useful when a + method already has a shared structural edge-selection variable. An + arbitrary selector expression is linked to an auxiliary binary + selector, so fractional expression values make the model infeasible + instead of relaxing the support constraints. + """ + if isinstance(epsilon, (bool, np.bool_)): + raise TypeError("epsilon must be a finite positive number or array.") + epsilon_input = np.asarray(epsilon) + if epsilon_input.dtype.kind in {"b", "c", "U", "S"}: + raise TypeError("epsilon must be a finite positive number or array.") + if epsilon_input.dtype.kind == "O" and any( + isinstance(value, (bool, np.bool_)) or not isinstance(value, numbers.Real) for value in epsilon_input.flat + ): + raise TypeError("epsilon must be a finite positive number or array.") + try: + epsilon_input = np.asarray(epsilon, dtype=float) + except (TypeError, ValueError) as error: + raise TypeError("epsilon must be a finite positive number or array.") from error + + if V._provided_lb is None or V._provided_ub is None: + raise ValueError(f"The continuous variable {V.name} is unbounded, exact support cannot be created.") + + S = V if indexes is None else V[indexes] + lb = np.asarray(V.lb if indexes is None else V.lb[indexes], dtype=float) + ub = np.asarray(V.ub if indexes is None else V.ub[indexes], dtype=float) + if np.shape(lb) != S.shape or np.shape(ub) != S.shape: + expected_size = int(np.prod(S.shape)) + if lb.size == expected_size: + lb = lb.reshape(S.shape) + else: + lb = np.broadcast_to(lb, S.shape) + if ub.size == expected_size: + ub = ub.reshape(S.shape) + else: + ub = np.broadcast_to(ub, S.shape) + if not np.all(np.isfinite(lb)) or not np.all(np.isfinite(ub)): + raise ValueError("ExactSupport requires finite lower and upper bounds.") + if np.any(lb > ub): + raise ValueError("ExactSupport requires lower bounds not to exceed upper bounds.") + try: + epsilon_values = np.broadcast_to(epsilon_input, S.shape) + except ValueError as error: + raise ValueError( + f"epsilon with shape {epsilon_input.shape} cannot be broadcast to selected value shape {S.shape}." + ) from error + if not np.all(np.isfinite(epsilon_values)) or np.any(epsilon_values <= 0): + raise ValueError("epsilon must be a finite positive number or array.") + + support_name = name or f"{V.name}_support" + constraints = [] + selector = None + if selected is not None: + if not isinstance(selected, CExpression): + raise TypeError("selected must be a binary-valued CExpression.") + if selected.shape != S.shape: + raise ValueError(f"selected has shape {selected.shape}; expected {S.shape}.") + if isinstance(selected, CSymbol): + if selected._vartype != VarType.BINARY: + raise TypeError("selected must be a binary-valued selector.") + selector = selected + else: + selector = self.Variable( + f"{support_name}_selector", + S.shape, + 0, + 1, + vartype=VarType.BINARY, + ) + constraints.append(selector == selected) + + if nonnegative: + if np.any(lb < 0): + raise ValueError("ExactSupport(..., nonnegative=True) requires nonnegative lower bounds.") + if selected is None: + selector = self.Variable(support_name, S.shape, 0, 1, vartype=VarType.BINARY) + + possible = np.asarray(ub >= epsilon_values, dtype=float) + if not np.all(possible): + constraints.append(selector.multiply(1 - possible) == 0) + constraints += [S >= selector.multiply(epsilon_values), S <= selector.multiply(ub)] + problem = self.Problem(constraints) + if selector.name != support_name: + problem.register(support_name, selector) + return problem + + if positive_name is None: + positive_name = f"{support_name}_positive" + if negative_name is None: + negative_name = f"{support_name}_negative" + positive = self.Variable(positive_name, S.shape, 0, 1, vartype=VarType.BINARY) + negative = self.Variable(negative_name, S.shape, 0, 1, vartype=VarType.BINARY) + support = positive + negative + if selector is not None: + constraints.append(support == selector) + support = selector + + positive_possible = np.asarray(ub >= epsilon_values, dtype=float) + negative_possible = np.asarray(lb <= -epsilon_values, dtype=float) + if not np.all(positive_possible): + constraints.append(positive.multiply(1 - positive_possible) == 0) + if not np.all(negative_possible): + constraints.append(negative.multiply(1 - negative_possible) == 0) + constraints += [ + positive + negative <= 1, + S >= negative.multiply(lb) + positive.multiply(epsilon_values), + S <= positive.multiply(ub) - negative.multiply(epsilon_values), + ] + problem = self.Problem(constraints) + if support.name != support_name: + problem.register(support_name, support) + return problem + + def SelectedFlow( + self, + g: BaseGraph, + *, + lb: Optional[Union[float, List, np.ndarray]] = 0, + ub: Optional[Union[float, List, np.ndarray]] = DEFAULT_UB, + n_flows: int = 1, + edge_indices: Optional[Iterable[int]] = None, + flow_blocks: Optional[Iterable[Tuple[Iterable[int], Union[slice, Iterable[int]]]]] = None, + selector_groups: Optional[Iterable[int]] = None, + epsilon: float = 1.0, + exact_support: bool = True, + selected: Optional[CExpression] = None, + acyclic_graph: Optional[BaseGraph] = None, + max_parents: Optional[Union[int, Dict[Any, int]]] = None, + flow_name: str = EXPR_NAME_FLOW, + selected_by_flow_name: str = "selected_by_flow", + selected_by_group_name: Optional[str] = None, + selected_any_name: Optional[str] = "selected_any", + dag_name: str = VAR_DAG, + ) -> ProblemDef: + """Create bounded flows with exact per-flow and shared support. + + Flow conservation is imposed on ``g``. Exact support binaries are + created only for ``edge_indices`` so boundary edges do not consume + unnecessary integer variables. Rows whose selected flow bounds are + nonnegative use one binary per flow; rows that permit negative flow + automatically use mutually exclusive positive and negative binaries. + Set ``exact_support=False`` to use one bounded indicator per entry: + nonzero flow still implies selection, but a selected entry may carry + zero flow. This smaller formulation is suitable when a minimizing + objective makes such false selections unattractive and flow direction + is not otherwise required. + + ``flow_blocks`` can map multiple rectangular regions of the flow + matrix to the same logical edge ordering. ``selector_groups`` can then + map block columns to shared selector columns. This supports layouts + such as forward and reversed edge blocks without indicators on the + unused off-diagonal blocks. Set ``selected_any_name=None`` when a union + across selector columns is not needed. + + The union across flows can be linked to an existing shared selector. + With signed acyclic flows, direction-specific unions are used so a + negative flow orders the edge in reverse. No redundant structural-union + binary is created in that case. + """ + if not isinstance(n_flows, int) or isinstance(n_flows, bool) or n_flows <= 0: + raise ValueError("n_flows must be a positive integer.") + if not isinstance(exact_support, bool): + raise TypeError("exact_support must be a boolean.") + if flow_blocks is not None and edge_indices is not None: + raise ValueError("Provide either edge_indices or flow_blocks, not both.") + + problem = self.Flow( + g, + lb=lb, + ub=ub, + n_flows=n_flows, + shared_bounds=False, + alias_flow=flow_name, + force_matrix=True, + ) + flow = problem.expr[flow_name] + + if flow_blocks is None: + rows = range(g.num_edges) if edge_indices is None else edge_indices + flow_blocks = [(rows, slice(0, n_flows))] + + normalized_blocks = [] + used_flow_columns = set() + num_selected_edges = None + for block_index, (block_rows, block_columns) in enumerate(flow_blocks): + rows = np.asarray(list(block_rows), dtype=int) + if rows.ndim != 1: + raise ValueError(f"flow_blocks[{block_index}] edge indices must be one-dimensional.") + if np.any(rows < 0) or np.any(rows >= g.num_edges): + raise ValueError(f"flow_blocks[{block_index}] contains an edge outside the flow graph.") + if num_selected_edges is None: + num_selected_edges = len(rows) + elif len(rows) != num_selected_edges: + raise ValueError("Every flow block must map the same number of logical edges.") + + if isinstance(block_columns, slice): + start, stop, step = block_columns.indices(n_flows) + columns = np.arange(start, stop, step, dtype=int) + else: + columns = np.asarray(list(block_columns), dtype=int) + if columns.ndim != 1 or not columns.size: + raise ValueError(f"flow_blocks[{block_index}] flow columns must be a non-empty one-dimensional set.") + if np.any(columns < 0) or np.any(columns >= n_flows): + raise ValueError(f"flow_blocks[{block_index}] contains a flow column outside [0, {n_flows}).") + if columns.size > 1 and np.any(np.diff(columns) != 1): + raise ValueError("Flow columns within each block must be contiguous and increasing.") + duplicate_columns = used_flow_columns.intersection(columns.tolist()) + if duplicate_columns: + raise ValueError("Flow blocks must use disjoint flow columns.") + used_flow_columns.update(columns.tolist()) + normalized_blocks.append((rows, slice(int(columns[0]), int(columns[-1]) + 1), len(columns))) + + if not normalized_blocks or num_selected_edges is None or num_selected_edges == 0: + raise ValueError("SelectedFlow requires at least one mapped edge.") + if selected is not None and selected.shape != (num_selected_edges,): + raise ValueError(f"selected has shape {selected.shape}; expected {(num_selected_edges,)}.") + + def combine_rows(parts): + """Stack row groups and restore the caller's edge order.""" + if len(parts) == 1: + return parts[0][1] + positions = np.concatenate([part_positions for part_positions, _ in parts]) + stacked = self.vstack([expression for _, expression in parts]) + return stacked[np.argsort(positions), :] + + selection_blocks = [] + positive_blocks = [] + negative_blocks = [] + has_signed_rows = False + multiple_blocks = len(normalized_blocks) > 1 + for block_index, (block_rows, block_columns, _) in enumerate(normalized_blocks): + block_flow = flow[block_rows, block_columns] + selected_lb = np.asarray(flow.lb[block_rows, block_columns], dtype=float) + if selected_lb.shape != block_flow.shape: + selected_lb = np.broadcast_to(selected_lb, block_flow.shape) + signed_rows = np.any(selected_lb < 0, axis=1) + nonnegative_positions = np.flatnonzero(~signed_rows) + signed_positions = np.flatnonzero(signed_rows) + has_signed_rows |= exact_support and bool(signed_positions.size) + + block_base_name = ( + f"{selected_by_flow_name}_block_{block_index}" if multiple_blocks else selected_by_flow_name + ) + selection_parts = [] + positive_parts = [] + negative_parts = [] + + if not exact_support: + if signed_positions.size and acyclic_graph is not None: + raise ValueError("Signed acyclic flows require exact_support=True.") + problem += self.Indicator( + flow, + indexes=(block_rows, block_columns), + name=block_base_name, + ) + support = problem.expr[block_base_name] + selection_blocks.append(support) + positive_blocks.append(support) + negative_blocks.append(self.Constant(np.zeros(support.shape))) + continue + + if nonnegative_positions.size: + nonnegative_edges = block_rows[nonnegative_positions] + support_name = block_base_name if not signed_positions.size else f"{block_base_name}_nonnegative" + problem += self.ExactSupport( + flow, + indexes=(nonnegative_edges, block_columns), + epsilon=epsilon, + nonnegative=True, + name=support_name, + ) + support = problem.expr[support_name] + selection_parts.append((nonnegative_positions, support)) + positive_parts.append((nonnegative_positions, support)) + negative_parts.append( + ( + nonnegative_positions, + self.Constant(np.zeros(support.shape)), + ) + ) + + if signed_positions.size: + signed_edges = block_rows[signed_positions] + support_name = block_base_name if not nonnegative_positions.size else f"{block_base_name}_signed" + positive_name = f"{support_name}_positive" + negative_name = f"{support_name}_negative" + problem += self.ExactSupport( + flow, + indexes=(signed_edges, block_columns), + epsilon=epsilon, + name=support_name, + positive_name=positive_name, + negative_name=negative_name, + ) + selection_parts.append((signed_positions, problem.expr[support_name])) + positive_parts.append((signed_positions, problem.expr[positive_name])) + negative_parts.append((signed_positions, problem.expr[negative_name])) + + selection_blocks.append(combine_rows(selection_parts)) + positive_blocks.append(combine_rows(positive_parts)) + negative_blocks.append(combine_rows(negative_parts)) + + selected_by_flow = selection_blocks[0] if len(selection_blocks) == 1 else self.hstack(selection_blocks) + if selected_by_flow_name not in problem.expressions: + problem.register(selected_by_flow_name, selected_by_flow) + + positive_by_flow = negative_by_flow = None + if has_signed_rows: + positive_by_flow_name = f"{selected_by_flow_name}_positive" + negative_by_flow_name = f"{selected_by_flow_name}_negative" + positive_by_flow = positive_blocks[0] if len(positive_blocks) == 1 else self.hstack(positive_blocks) + negative_by_flow = negative_blocks[0] if len(negative_blocks) == 1 else self.hstack(negative_blocks) + if positive_by_flow_name not in problem.expressions: + problem.register(positive_by_flow_name, positive_by_flow) + if negative_by_flow_name not in problem.expressions: + problem.register(negative_by_flow_name, negative_by_flow) + + num_mapped_flows = selected_by_flow.shape[1] + if selector_groups is None: + selector_groups = np.arange(num_mapped_flows, dtype=int) + else: + selector_groups = np.asarray(list(selector_groups), dtype=int) + if selector_groups.shape != (num_mapped_flows,): + raise ValueError(f"selector_groups has shape {selector_groups.shape}; expected {(num_mapped_flows,)}.") + if np.any(selector_groups < 0): + raise ValueError("selector_groups must contain nonnegative integers.") + unique_groups = np.unique(selector_groups) + if not np.array_equal(unique_groups, np.arange(len(unique_groups))): + raise ValueError("selector_groups must use consecutive group indexes starting at zero.") + + num_selector_groups = int(np.max(selector_groups)) + 1 + identity_groups = np.array_equal(selector_groups, np.arange(num_mapped_flows)) + if identity_groups: + selected_by_group = selected_by_flow + else: + group_name = selected_by_group_name or f"{selected_by_flow_name}_grouped" + selected_by_group = self.Variable( + group_name, + (num_selected_edges, num_selector_groups), + vartype=VarType.BINARY, + ) + from scipy.sparse import csr_matrix + + group_matrix = csr_matrix( + ( + np.ones(num_mapped_flows, dtype=float), + (np.arange(num_mapped_flows), selector_groups), + ), + shape=(num_mapped_flows, num_selector_groups), + ) + expanded_groups = selected_by_group @ group_matrix.T + problem += selected_by_flow <= expanded_groups + problem += selected_by_group <= selected_by_flow @ group_matrix + if selected_by_group_name is not None and selected_by_group_name not in problem.expressions: + problem.register(selected_by_group_name, selected_by_group) + + if selected_any_name is None: + if selected is not None: + raise ValueError("selected requires selected_any_name.") + if acyclic_graph is not None: + raise ValueError("acyclic_graph requires selected_any_name.") + return problem + + def union_across_flows(values, name): + num_value_columns = values.shape[1] + if num_value_columns == 1: + union = values[:, 0] + else: + union = self.Variable( + name, + (num_selected_edges,), + vartype=VarType.BINARY, + ) + union_matrix = self.Constant(_sparse_vector_replication(num_selected_edges, num_value_columns)) @ union + union_matrix = union_matrix.reshape((num_selected_edges, num_value_columns)) + problem.add_constraints(values <= union_matrix) + problem.add_constraints(union <= values.sum(axis=1)) + if name not in problem.expressions: + problem.register(name, union) + return union + + signed_acyclic = has_signed_rows and acyclic_graph is not None + positive_any = negative_any = None + if signed_acyclic: + positive_any_name = f"{selected_any_name}_positive" + negative_any_name = f"{selected_any_name}_negative" + positive_any = union_across_flows(positive_by_flow, positive_any_name) + negative_any = union_across_flows(negative_by_flow, negative_any_name) + # A shared DAG cannot use the same structural edge in both + # directions, even when the directions occur in different flows. + problem += positive_any + negative_any <= 1 + directional_union = positive_any + negative_any + if selected is None: + selected_any = directional_union + else: + selected_any = selected + problem += selected_any == directional_union + else: + if selected is None: + selected_any = ( + selected_by_group[:, 0] + if num_selector_groups == 1 + else self.Variable( + selected_any_name, + (num_selected_edges,), + vartype=VarType.BINARY, + ) + ) + else: + selected_any = selected + + if num_selector_groups == 1: + problem += selected_any == selected_by_group[:, 0] + else: + selected_matrix = ( + self.Constant(_sparse_vector_replication(num_selected_edges, num_selector_groups)) @ selected_any + ) + selected_matrix = selected_matrix.reshape((num_selected_edges, num_selector_groups)) + problem += selected_by_group <= selected_matrix + problem += selected_any <= selected_by_group.sum(axis=1) + + if selected_any_name not in problem.expressions: + problem.register(selected_any_name, selected_any) + + if acyclic_graph is not None: + if acyclic_graph.num_edges != num_selected_edges: + raise ValueError("acyclic_graph must have one edge for each selected flow edge.") + if signed_acyclic: + self.Acyclic( + acyclic_graph, + problem, + indicator_positive_var_name=f"{selected_any_name}_positive", + indicator_negative_var_name=f"{selected_any_name}_negative", + acyclic_var_name=dag_name, + max_parents=max_parents, + ) + else: + indicator_name = f"_{selected_any_name}_acyclic" + problem.register(indicator_name, selected_any) + self.Acyclic( + acyclic_graph, + problem, + indicator_positive_var_name=indicator_name, + acyclic_var_name=dag_name, + max_parents=max_parents, + ) + return problem + def NonZeroIndicator( self, V: CSymbol, @@ -1535,7 +2090,7 @@ def __getattr__(self, name): def _find_continuous_var(p): # Search for continous vars - cvars = [k for k, v in p.symbols.items() if v._vartype == VarType.CONTINUOUS] + cvars = [k for k, v in p.symbols.items() if v.is_variable and v._vartype == VarType.CONTINUOUS] if len(cvars) == 0: raise ValueError("No available continuous vars for creating indicator vars") if len(cvars) == 1: diff --git a/corneto/backend/_picos_backend.py b/corneto/backend/_picos_backend.py index 588a8347a..074362b74 100644 --- a/corneto/backend/_picos_backend.py +++ b/corneto/backend/_picos_backend.py @@ -2,9 +2,10 @@ from typing import Any, List, Optional, Set, Tuple, Union import numpy as np +from scipy.sparse import csr_matrix from corneto._constants import Direction, Solver, VarType -from corneto._settings import _numpy_array +from corneto._decorators import _delegate from corneto.backend._base import ( Backend, CExpression, @@ -20,9 +21,13 @@ def _get_shape(a: Any): + if hasattr(a, "_corneto_shape"): + return a._corneto_shape if hasattr(a, "_csymbol_shape"): return a._csymbol_shape if hasattr(a, "_expr"): + if hasattr(a._expr, "_corneto_shape"): + return a._expr._corneto_shape return a._expr._csymbol_shape if hasattr(a._expr, "_csymbol_shape") else a._expr.shape if hasattr(a, "shape"): return a.shape @@ -32,6 +37,8 @@ def _get_shape(a: Any): def _infer_shape(c: Any): shape = _get_shape(c) if len(shape) == 2 and (shape[0] == 1 or shape[1] == 1): + if hasattr(c, "_corneto_shape") or (hasattr(c, "_expr") and hasattr(c._expr, "_corneto_shape")): + return shape # This is the problematic case, as any transformation # using PICOS will result in a 2D array. if hasattr(c, "_proxy_symbols"): @@ -58,6 +65,12 @@ def shape(self) -> Tuple[int, ...]: def _create_proxy_expr(self, expr: Any, symbols: Optional[Set["CSymbol"]] = None) -> "PicosExpression": return PicosExpression(expr, symbols) + @property + def T(self): + result = self._expr.T + result._corneto_shape = tuple(result.shape) + return self._create(result, {}) + def _elementwise_mul(self, other: Any) -> Any: return self._expr ^ other @@ -65,7 +78,64 @@ def _norm(self, p: int = 2) -> CExpression: return pc.Norm(self._expr, p=p) def _sum(self, axis: Optional[int] = None) -> Any: - return pc.sum(self._expr, axis=axis) + shape = _infer_shape(self) + if len(shape) > 2: + return pc.sum(self._expr, axis=axis) + + if len(shape) == 0: + return self._expr + + if len(shape) == 1: + if axis not in (None, 0, -1): + return pc.sum(self._expr, axis=axis) + rows, columns = shape[0], 1 + else: + rows, columns = shape + if axis is not None and axis not in (0, 1, -1, -2): + return pc.sum(self._expr, axis=axis) + + if axis is None or len(shape) == 1: + size = rows * columns + operator = csr_matrix( + ( + np.ones(size, dtype=float), + (np.zeros(size, dtype=int), np.arange(size, dtype=int)), + ), + shape=(1, size), + ) + flattened = self._expr.reshaped((size, 1)) + result = pc.Constant(_get_unique_name("sum"), value=operator) * flattened + output_shape = (1,) if len(shape) == 1 else (1, 1) + result._corneto_shape = output_shape + return result + + if axis in (0, -2): + operator = csr_matrix( + ( + np.ones(rows, dtype=float), + (np.zeros(rows, dtype=int), np.arange(rows, dtype=int)), + ), + shape=(1, rows), + ) + result = pc.Constant(_get_unique_name("sum"), value=operator) * self._expr + result._corneto_shape = (1, columns) + return result + + operator = csr_matrix( + ( + np.ones(columns, dtype=float), + (np.arange(columns, dtype=int), np.zeros(columns, dtype=int)), + ), + shape=(columns, 1), + ) + result = self._expr * pc.Constant(_get_unique_name("sum"), value=operator) + result._corneto_shape = (rows, 1) + return result + + @_delegate(override=True) + def sum(self, axis: Optional[int] = None) -> CExpression: + """Reduce with sparse affine operators instead of PICOS dense sums.""" + return self._sum(axis=axis) def _max(self, axis: Optional[int] = None) -> Any: raise NotImplementedError() @@ -100,7 +170,9 @@ def _vstack(self, other: Any) -> Any: return a // b def _reshape(self, shape: Tuple[int, ...]) -> Any: - return self._expr.reshaped(shape) + result = self._expr.reshaped(shape) + result._corneto_shape = tuple(shape) + return result @property def value(self) -> np.ndarray: @@ -153,7 +225,10 @@ def value(self, value: Any) -> None: class PicosBackend(Backend): def __init__(self, default_solver: Optional[str] = None) -> None: - super().__init__(default_solver, _numpy_array) + # PICOS accepts scipy CSR matrices as affine constants. Keep the + # backend's graph operators sparse; dense conversion is only needed + # for the data-loading helpers used by older callers. + super().__init__(default_solver, csr_matrix) def _load(self): import picos diff --git a/corneto/datasets/__init__.py b/corneto/datasets/__init__.py new file mode 100644 index 000000000..cea3cccf5 --- /dev/null +++ b/corneto/datasets/__init__.py @@ -0,0 +1,5 @@ +"""Small, versioned datasets fetched on demand.""" + +from corneto.datasets._fetch import DatasetError, DatasetIntegrityError, fetch_dataset + +__all__ = ["DatasetError", "DatasetIntegrityError", "fetch_dataset"] diff --git a/corneto/datasets/_catalog.py b/corneto/datasets/_catalog.py new file mode 100644 index 000000000..ec40ad0f0 --- /dev/null +++ b/corneto/datasets/_catalog.py @@ -0,0 +1,61 @@ +"""Built-in dataset metadata for the CORNETO dataset loader.""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import Mapping + + +@dataclass(frozen=True) +class DatasetSpec: + """Immutable metadata required to locate and validate one dataset revision.""" + + name: str + version: str + remote_url: str | None + archive_sha256: str + file_sha256: Mapping[str, str] + provenance: str = "" + license: str = "" + + @property + def relative_path(self) -> Path: + return Path(self.name) / self.version + + +_SACHS_SPEC = DatasetSpec( + name="sachs", + version="v1", + # This path becomes available when the dataset revision is published. The + # checksum prevents a mutable hosting path from silently changing data. + remote_url="https://raw.githubusercontent.com/saezlab/corneto/main/datasets/sachs/v1/sachs-v1.tar.gz", + archive_sha256="78c23eb79223a9b03cea9f4ce4e832151be3c99c54b31e2ee3ea7ec983bdaca3", + file_sha256={ + "measurements.tsv": "a40650c0fe6aa6581dca556202cd4e94fb0dfa1e6d121c710123bbc164509bcd", + "condition_manifest.csv": "9f3083b5a2b20b8b2d4cbfab1f00153765c400fd76c94c5392c81b004ebf6fcb", + "README.md": "979d829cbc105873c38b00d99733d0563c190bbb44f873b4697f13d8acccd777", + }, + provenance="https://doi.org/10.5281/zenodo.7681811", + license="CC BY 4.0", +) + +DATASETS: dict[tuple[str, str], DatasetSpec] = { + (_SACHS_SPEC.name, _SACHS_SPEC.version): _SACHS_SPEC, +} + + +def _validate_component(value: str, label: str) -> None: + if not isinstance(value, str) or not value or value in {".", ".."} or Path(value).name != value: + raise ValueError(f"{label} must be a single non-empty path component.") + + +def get_dataset_spec(name: str, version: str) -> DatasetSpec: + """Return the registered specification for a dataset revision.""" + _validate_component(name, "name") + _validate_component(version, "version") + try: + return DATASETS[(name, version)] + except KeyError as error: + known = ", ".join(f"{dataset}:{revision}" for dataset, revision in sorted(DATASETS)) + raise ValueError(f"Unknown dataset revision {name!r}, {version!r}; known revisions: {known}.") from error diff --git a/corneto/datasets/_fetch.py b/corneto/datasets/_fetch.py new file mode 100644 index 000000000..36cb5b109 --- /dev/null +++ b/corneto/datasets/_fetch.py @@ -0,0 +1,269 @@ +"""Source-first fetching and caching for small CORNETO datasets.""" + +from __future__ import annotations + +import hashlib +import json +import os +import shutil +import sys +import tarfile +import tempfile +from pathlib import Path +from urllib.request import Request, urlopen + +from ._catalog import DatasetSpec, get_dataset_spec + +DEFAULT_VERSION = "v1" +_DATASETS_ENV = "CORNETO_DATASETS_DIR" +_DATA_HOME_ENV = "CORNETO_DATA_HOME" +_CACHE_MARKER = ".complete.json" +_DOWNLOAD_TIMEOUT = 30 + + +class DatasetError(RuntimeError): + """Base error for dataset discovery and fetching failures.""" + + +class DatasetIntegrityError(DatasetError): + """Raised when a dataset does not match its recorded checksums.""" + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + while chunk := stream.read(1024 * 1024): + digest.update(chunk) + return digest.hexdigest() + + +def _default_data_home() -> Path: + if os.name == "nt": + root = Path(os.environ.get("LOCALAPPDATA", Path.home() / "AppData" / "Local")) / "corneto" / "Cache" + elif sys.platform == "darwin": + root = Path.home() / "Library" / "Caches" / "corneto" + else: + root = Path(os.environ.get("XDG_CACHE_HOME", Path.home() / ".cache")) / "corneto" + return root / "datasets" + + +def _cache_root(data_home: Path | str | None) -> Path: + if data_home is not None: + return Path(data_home).expanduser() + configured = os.environ.get(_DATA_HOME_ENV) + if configured: + return Path(configured).expanduser() + return _default_data_home() + + +def _as_dataset_root(path: Path) -> Path: + path = path.expanduser() + if (path / "datasets").is_dir(): + return path / "datasets" + return path + + +def _source_roots(source_dir: Path | str | None) -> list[Path]: + roots: list[Path] = [] + + def add(path: Path) -> None: + candidate = _as_dataset_root(path).resolve() + if candidate not in roots: + roots.append(candidate) + + if source_dir is not None: + add(Path(source_dir)) + configured = os.environ.get(_DATASETS_ENV) + if configured: + add(Path(configured)) + + for base in (Path(__file__).resolve().parent, Path.cwd().resolve()): + for parent in (base, *base.parents): + add(parent / "datasets") + return roots + + +def _dataset_dir(root: Path, spec: DatasetSpec) -> Path: + return root / spec.relative_path + + +def _validate_files(directory: Path, spec: DatasetSpec) -> bool: + missing = [] + for filename in spec.file_sha256: + path = directory / filename + if not path.is_file() or path.is_symlink(): + missing.append(filename) + if missing: + return False + + for filename, expected in spec.file_sha256.items(): + actual = _sha256(directory / filename) + if actual != expected: + raise DatasetIntegrityError( + f"Checksum mismatch for {spec.name} {spec.version} file {filename!r}: " + f"expected {expected}, got {actual}." + ) + return True + + +def _marker_payload(spec: DatasetSpec) -> dict[str, object]: + return { + "name": spec.name, + "version": spec.version, + "archive_sha256": spec.archive_sha256, + "files": dict(spec.file_sha256), + } + + +def _valid_cache(directory: Path, spec: DatasetSpec) -> bool: + marker = directory / _CACHE_MARKER + if not marker.is_file(): + return False + try: + payload = json.loads(marker.read_text(encoding="utf-8")) + except (OSError, ValueError): + return False + if payload != _marker_payload(spec): + return False + return _validate_files(directory, spec) + + +def _write_marker(directory: Path, spec: DatasetSpec) -> None: + directory.mkdir(parents=True, exist_ok=True) + descriptor = tempfile.NamedTemporaryFile( + mode="w", + encoding="utf-8", + dir=directory, + prefix=".complete-", + suffix=".tmp", + delete=False, + ) + temporary = Path(descriptor.name) + try: + with descriptor: + json.dump(_marker_payload(spec), descriptor, indent=2, sort_keys=True) + descriptor.write("\n") + descriptor.flush() + os.fsync(descriptor.fileno()) + os.replace(temporary, directory / _CACHE_MARKER) + finally: + temporary.unlink(missing_ok=True) + + +def _extract_archive(archive_path: Path, target: Path, spec: DatasetSpec) -> Path: + extraction_dir = Path(tempfile.mkdtemp(prefix=f".{spec.name}-{spec.version}-", dir=target.parent)) + try: + seen: set[str] = set() + expected = set(spec.file_sha256) + with tarfile.open(archive_path, mode="r:gz") as archive: + for member in archive.getmembers(): + if member.name not in expected or member.name in seen or not member.isfile(): + raise DatasetIntegrityError( + f"Archive for {spec.name} {spec.version} contains an unexpected or unsafe member " + f"{member.name!r}." + ) + seen.add(member.name) + source = archive.extractfile(member) + if source is None: + raise DatasetIntegrityError(f"Could not read archive member {member.name!r}.") + destination = extraction_dir / member.name + with source, destination.open("wb") as output: + shutil.copyfileobj(source, output) + + if seen != expected or not _validate_files(extraction_dir, spec): + missing = sorted(expected - seen) + raise DatasetIntegrityError(f"Archive for {spec.name} {spec.version} is missing files: {missing}.") + + target.mkdir(parents=True, exist_ok=True) + (target / _CACHE_MARKER).unlink(missing_ok=True) + for filename in expected: + os.replace(extraction_dir / filename, target / filename) + _write_marker(target, spec) + return target + finally: + shutil.rmtree(extraction_dir, ignore_errors=True) + + +def _download_to_cache(target: Path, spec: DatasetSpec) -> Path: + if not spec.remote_url: + raise DatasetError(f"Dataset {spec.name!r} {spec.version!r} has no configured remote source.") + + target.parent.mkdir(parents=True, exist_ok=True) + descriptor = tempfile.NamedTemporaryFile( + mode="wb", + dir=target.parent, + prefix=f".{spec.name}-{spec.version}-", + suffix=".download", + delete=False, + ) + archive_path = Path(descriptor.name) + digest = hashlib.sha256() + try: + with descriptor: + request = Request(spec.remote_url, headers={"User-Agent": "corneto-datasets"}) + with urlopen(request, timeout=_DOWNLOAD_TIMEOUT) as response: + while chunk := response.read(1024 * 1024): + digest.update(chunk) + descriptor.write(chunk) + descriptor.flush() + os.fsync(descriptor.fileno()) + actual = digest.hexdigest() + if actual != spec.archive_sha256: + raise DatasetIntegrityError( + f"Checksum mismatch for downloaded {spec.name} {spec.version}: " + f"expected {spec.archive_sha256}, got {actual}." + ) + return _extract_archive(archive_path, target, spec) + finally: + archive_path.unlink(missing_ok=True) + + +def fetch_dataset( + name: str, + version: str = DEFAULT_VERSION, + *, + source_dir: Path | str | None = None, + data_home: Path | str | None = None, + auto_download: bool = True, +) -> Path: + """Locate or fetch a versioned CORNETO dataset. + + Local repository data takes precedence over the persistent cache and the + configured remote source. Dataset files are validated by SHA-256 before + they are returned. + + Args: + name: Registered dataset name. + version: Immutable dataset revision. Defaults to ``"v1"``. + source_dir: Optional directory containing dataset directories, or a + repository root containing a ``datasets`` directory. + data_home: Optional persistent cache directory. + auto_download: Whether to fetch a missing dataset from its remote URL. + + Returns: + The path containing the validated dataset files. + + Raises: + DatasetIntegrityError: If available data do not match their checksums. + DatasetError: If no local data exists and the dataset has no remote URL. + FileNotFoundError: If data are missing and ``auto_download`` is false. + ValueError: If the dataset revision is not registered. + """ + if not isinstance(auto_download, bool): + raise TypeError("auto_download must be a boolean.") + spec = get_dataset_spec(name, version) + + for root in _source_roots(source_dir): + candidate = _dataset_dir(root, spec) + if candidate.exists() and _validate_files(candidate, spec): + return candidate + + cache_path = _dataset_dir(_cache_root(data_home), spec) + if _valid_cache(cache_path, spec): + return cache_path + + if not auto_download: + raise FileNotFoundError( + f"Dataset {name!r} {version!r} was not found locally or in the cache, and auto_download is false." + ) + return _download_to_cache(cache_path, spec) diff --git a/corneto/methods/__init__.py b/corneto/methods/__init__.py index dae677b09..72be33a62 100644 --- a/corneto/methods/__init__.py +++ b/corneto/methods/__init__.py @@ -15,6 +15,7 @@ CarnivalILP, milp_carnival, ) +from corneto.methods.causal import LinearDAGDiscovery from corneto.methods.fba import MultiSampleFBA from corneto.methods.imat import MultiSampleIMAT from corneto.methods.pcst import PrizeCollectingSteinerTree @@ -32,6 +33,7 @@ "BidirectionalPHONEMeS", "CarnivalFlow", "CarnivalILP", + "LinearDAGDiscovery", "MultiSampleFBA", "MultiSampleIMAT", "PHONEMeS", diff --git a/corneto/methods/_flow_utils.py b/corneto/methods/_flow_utils.py index 8ef2d8bf3..0a171dafb 100644 --- a/corneto/methods/_flow_utils.py +++ b/corneto/methods/_flow_utils.py @@ -22,70 +22,6 @@ class VertexSelection: incoming: Any -@dataclass(frozen=True) -class SelectedFlow: - """Flow-selection expressions over all and biological edges.""" - - all_edges: Any - biological_edges: Any - dag_layer: Any | None - - -def add_acyclic_flow_selection( - backend: Backend, - problem: ProblemDef, - graph: BaseGraph, - *, - epsilon: float, -): - """Add flow indicators and DAG constraints, returning edge selection.""" - problem += backend.NonZeroIndicator(problem.expr._flow, tolerance=epsilon) - backend.Acyclic( - graph, - problem, - indicator_negative_var_name="_flow_ineg", - indicator_positive_var_name="_flow_ipos", - ) - return problem.expr._flow_ipos + problem.expr._flow_ineg - - -def add_selected_flow( - backend: Backend, - problem: ProblemDef, - graph: BaseGraph, - *, - biological_edge_indices: Iterable[int], - epsilon: float, - acyclic: bool, -) -> SelectedFlow: - """Attach edge-selection indicators and optionally DAG constraints to a flow.""" - biological_edge_indices = list(biological_edge_indices) - if acyclic: - selected_all = add_acyclic_flow_selection( - backend, - problem, - graph, - epsilon=epsilon, - ) - dag_layer = problem.expr._dag_layer - else: - flow = problem.expr._flow - indicator_indexes = biological_edge_indices if len(flow.shape) == 1 else (biological_edge_indices, slice(None)) - problem += backend.Indicator(flow, indexes=indicator_indexes) - selected_all = problem.expr._flow_i - dag_layer = None - biological = ( - selected_all[biological_edge_indices] - if len(selected_all.shape) == 1 - else selected_all[biological_edge_indices, :] - ) - return SelectedFlow( - all_edges=selected_all, - biological_edges=biological, - dag_layer=dag_layer, - ) - - def add_vertex_selection( backend: Backend, problem: ProblemDef, diff --git a/corneto/methods/_predictive.py b/corneto/methods/_predictive.py new file mode 100644 index 000000000..80e79e766 --- /dev/null +++ b/corneto/methods/_predictive.py @@ -0,0 +1,65 @@ +"""Small private helpers shared by reusable mechanistic methods. + +The public :class:`~corneto.methods._base.Method` deliberately remains an +optimization-only abstraction. These helpers are used by the few method +classes that expose a fitted mechanistic model as well as the normal CORNETO +``build``/``ProblemDef`` lifecycle. +""" + +from __future__ import annotations + +from collections.abc import Mapping +from typing import Any + +import numpy as np + + +def solve_options(value: Mapping[str, Any] | None, extra: Mapping[str, Any]) -> dict[str, Any]: + """Normalize the keyword options accepted by a convenience ``fit`` call.""" + if value is not None and not isinstance(value, Mapping): + raise TypeError("solve_options must be a mapping or None.") + if value and extra: + raise TypeError("Pass solver arguments either in solve_options or as keyword arguments, not both.") + return dict(value or extra) + + +def validate_solve_result(result: Any, expressions: Any, names: tuple[str, ...], method: str) -> Any: + """Validate a solver result while accepting time-limited incumbents. + + Backends use different result classes and status strings. A non-failure + status is therefore not enough: every expression needed for prediction + must also contain a finite incumbent value. This permits useful + ``user_limit``/``time_limit`` results when the solver supplied primals. + """ + status = str(getattr(result, "status", "")).lower() + failed = ("infeasible", "unbounded", "error", "invalid", "unknown") + if not status or any(token in status for token in failed): + raise ValueError(f"{method}.fit() did not produce a usable solution (status={status or 'unknown'!r}).") + for name in names: + try: + value = getattr(expressions, name).value + except (AttributeError, KeyError) as error: + raise ValueError(f"{method}.fit() solution does not expose required expression {name!r}.") from error + if value is None: + raise ValueError(f"{method}.fit() solver returned no value for required expression {name!r}.") + array = np.asarray(value, dtype=float) + if not np.all(np.isfinite(array)): + raise ValueError(f"{method}.fit() solver returned non-finite values for {name!r}.") + return result + + +def require_expression_value(problem: Any, name: str, method: str) -> np.ndarray: + """Return a solved expression value with a consistent public error.""" + if problem is None: + raise ValueError(f"{method} has not been built; call build/build_many or fit first.") + try: + value = getattr(problem.expr, name).value + except (AttributeError, KeyError) as error: + raise ValueError(f"{method} problem does not expose required expression {name!r}.") from error + if value is None: + raise ValueError(f"{method} has not been solved or has no usable solution for {name!r}.") + array = np.asarray(value, dtype=float) + if not np.all(np.isfinite(array)): + raise ValueError(f"{method} has no usable finite solution for {name!r}.") + return array + diff --git a/corneto/methods/bidirectional_phonemes.py b/corneto/methods/bidirectional_phonemes.py index a45d1e924..ca421c317 100644 --- a/corneto/methods/bidirectional_phonemes.py +++ b/corneto/methods/bidirectional_phonemes.py @@ -18,7 +18,6 @@ validate_numeric, ) from corneto.methods._network_utils import prune_to_paths -from corneto.methods._optimization_utils import add_condition_union from corneto.methods._phonemes_preprocessing import ( normalize_phonemes_score_mapping, ) @@ -457,31 +456,34 @@ def create_problem(self, graph: BaseGraph, data: Data): """Create the global vectorized bidirectional problem.""" num_conditions = len(self._condition_names) num_edges = self._biological_num_edges - flow_problem = self.backend.Flow( + flow_problem = self.backend.SelectedFlow( graph, lb=self._flow_lb, ub=self._flow_ub, n_flows=2 * num_conditions, - shared_bounds=False, - force_matrix=True, + flow_blocks=( + (range(num_edges), slice(0, num_conditions)), + ( + range(self._reverse_edge_offset, self._reverse_edge_offset + num_edges), + slice(num_conditions, 2 * num_conditions), + ), + ), + selector_groups=np.tile(np.arange(num_conditions), 2), + epsilon=self.epsilon, + selected_by_flow_name="edge_selected_directional", + selected_by_group_name="edge_selected", + selected_any_name="edge_selected_any", ) flow = flow_problem.expr._flow - flow_problem += self.backend.NonZeroIndicator( - flow, - tolerance=self.epsilon, - ) - positive = flow_problem.expr._flow_ipos + directional_selection = flow_problem.expr.edge_selected_directional flow_downstream = flow[:num_edges, :num_conditions] flow_upstream = flow[ self._reverse_edge_offset : self._reverse_edge_offset + num_edges, num_conditions:, ] - edge_downstream = positive[:num_edges, :num_conditions] - edge_upstream = positive[ - self._reverse_edge_offset : self._reverse_edge_offset + num_edges, - num_conditions:, - ] + edge_downstream = directional_selection[:, :num_conditions] + edge_upstream = directional_selection[:, num_conditions:] flow_problem.register("flow_downstream", flow_downstream) flow_problem.register("flow_upstream", flow_upstream) flow_problem.register("edge_selected_downstream", edge_downstream) @@ -515,14 +517,24 @@ def create_problem(self, graph: BaseGraph, data: Data): anchor_vertices = tuple(self._anchor_inflow_edges) anchor_rows = [self._anchor_inflow_edges[vertex] for vertex in anchor_vertices] anchor_indexes = [vertex_index[vertex] for vertex in anchor_vertices] - downstream_inflow = flow[anchor_rows, :num_conditions] - upstream_inflow = flow[anchor_rows, num_conditions:] downstream_active = anchor_downstream[anchor_indexes, :] upstream_active = anchor_upstream[anchor_indexes, :] - flow_problem += downstream_inflow >= self.epsilon * downstream_active - flow_problem += downstream_inflow <= self._flow_max * downstream_active - flow_problem += upstream_inflow >= self.epsilon * upstream_active - flow_problem += upstream_inflow <= self._flow_max * upstream_active + flow_problem += self.backend.ExactSupport( + flow, + indexes=(anchor_rows, slice(0, num_conditions)), + selected=downstream_active, + epsilon=self.epsilon, + nonnegative=True, + name="anchor_flow_downstream", + ) + flow_problem += self.backend.ExactSupport( + flow, + indexes=(anchor_rows, slice(num_conditions, 2 * num_conditions)), + selected=upstream_active, + epsilon=self.epsilon, + nonnegative=True, + name="anchor_flow_upstream", + ) internal_downstream = ~(self._anchor_mask | self._measured_downstream_mask) downstream_vertices = add_vertex_selection( @@ -549,13 +561,6 @@ def create_problem(self, graph: BaseGraph, data: Data): name="vertex_selected_upstream", reverse=True, ) - edge_selected = _pairwise_or( - self.backend, - flow_problem, - edge_downstream, - edge_upstream, - name="edge_selected", - ) vertex_selected = _pairwise_or( self.backend, flow_problem, @@ -563,12 +568,7 @@ def create_problem(self, graph: BaseGraph, data: Data): upstream_vertices.selected, name="vertex_selected", ) - edge_selected_any = add_condition_union( - self.backend, - flow_problem, - edge_selected, - name="edge_selected_any", - ) + edge_selected_any = flow_problem.expr.edge_selected_any assert self._biological_graph is not None self.backend.Acyclic( diff --git a/corneto/methods/causal/__init__.py b/corneto/methods/causal/__init__.py new file mode 100644 index 000000000..94dfcb068 --- /dev/null +++ b/corneto/methods/causal/__init__.py @@ -0,0 +1,5 @@ +"""Causal structure-discovery methods.""" + +from corneto.methods.causal.linear_dag import LinearDAGDiscovery + +__all__ = ["LinearDAGDiscovery"] diff --git a/corneto/methods/causal/linear_dag.py b/corneto/methods/causal/linear_dag.py new file mode 100644 index 000000000..3fd093044 --- /dev/null +++ b/corneto/methods/causal/linear_dag.py @@ -0,0 +1,1705 @@ +"""Flow-supported linear DAG discovery over a prior knowledge network.""" + +from __future__ import annotations + +from dataclasses import dataclass +from numbers import Real +from types import MappingProxyType +from typing import Any, Mapping, Optional + +import numpy as np +from scipy.sparse import csr_matrix + +from corneto._constants import VarType +from corneto.backend._base import ( + Backend, + ProblemDef, + _sparse_vector_entry_repetition, + _sparse_vector_replication, +) +from corneto.data import Data +from corneto.graph import Attr, BaseGraph, EdgeType +from corneto.methods._base import FlowMethod +from corneto.methods._network_utils import BoundaryFlowLayout, augment_with_boundaries +from corneto.methods._predictive import ( + require_expression_value, + validate_solve_result, +) +from corneto.methods._predictive import ( + solve_options as normalize_solve_options, +) + +__all__ = ["LinearDAGDiscovery"] + + +_MISSING = object() + + +@dataclass(frozen=True) +class _InterventionDesign: + """Validated intervention semantics aligned to vertex-by-sample arrays. + + ``mapping`` maps estimated shift parameters to a Fortran-order flattened + vertex-by-sample matrix. Its rows are therefore indexed as + ``vertex_index + num_vertices * sample_index``. Keeping this indexing in + one object avoids duplicating the grouping and shape bookkeeping in the + optimization model. + """ + + hard: np.ndarray + shift: np.ndarray + groups: np.ndarray + known_shift: np.ndarray + mapping: csr_matrix + effect_keys: tuple[tuple[Any, Any], ...] + + @staticmethod + def _validate_group(group: Any) -> None: + if group is _MISSING or group is None: + raise ValueError("Estimated shift interventions require a non-null intervention group.") + if isinstance(group, Real) and not isinstance(group, bool) and not np.isfinite(float(group)): + raise ValueError("Estimated shift intervention groups must be finite when numeric.") + try: + hash(group) + except TypeError as error: + raise TypeError("Intervention groups must be hashable.") from error + + @classmethod + def build( + cls, + vertex_keys: tuple[Any, ...], + num_vertices: int, + num_samples: int, + annotations: Mapping[tuple[int, int], tuple[str, Any, Any]], + ) -> "_InterventionDesign": + hard = np.zeros((num_vertices, num_samples), dtype=bool) + shift = np.zeros((num_vertices, num_samples), dtype=bool) + groups = np.full((num_vertices, num_samples), _MISSING, dtype=object) + known_shift = np.zeros((num_vertices, num_samples), dtype=float) + effect_indices: dict[tuple[int, Any], int] = {} + effect_keys: list[tuple[Any, Any]] = [] + mapping_rows: list[int] = [] + mapping_columns: list[int] = [] + + for (vertex_index, sample_index), (kind, shift_value, group) in annotations.items(): + if kind != "none" and group is not _MISSING: + groups[vertex_index, sample_index] = group + if kind == "hard": + hard[vertex_index, sample_index] = True + continue + if kind != "shift": + continue + + shift[vertex_index, sample_index] = True + if shift_value is not None: + known_shift[vertex_index, sample_index] = float(shift_value) + continue + cls._validate_group(group) + effect_key = (vertex_index, group) + effect_index = effect_indices.get(effect_key) + if effect_index is None: + effect_index = len(effect_keys) + effect_indices[effect_key] = effect_index + effect_keys.append((vertex_keys[vertex_index], group)) + mapping_rows.append(vertex_index + num_vertices * sample_index) + mapping_columns.append(effect_index) + + mapping = csr_matrix( + ( + np.ones(len(mapping_rows), dtype=float), + (mapping_rows, mapping_columns), + ), + shape=(num_vertices * num_samples, len(effect_keys)), + ) + return cls( + hard=hard, + shift=shift, + groups=groups, + known_shift=known_shift, + mapping=mapping, + effect_keys=tuple(effect_keys), + ) + + +@dataclass(frozen=True) +class _EvidenceUnit: + """One distinct intervention unit contributing to a flow signature.""" + + source: Any + intervention_group: Any + sample_indices: tuple[int, ...] + explicit_group: bool + + +@dataclass(frozen=True) +class _Commodity: + """Immutable structural flow column and its evidence units. + + Several intervention groups can share one structural column. In that + case ``evidence_units`` retains the distinct groups for coverage and + unexplained-commodity weighting, while the flow itself is represented + only once. + """ + + source: Any + source_edge: int + sink_edges: tuple[int, ...] + hard_blocked_edges: tuple[int, ...] + evidence_units: tuple[_EvidenceUnit, ...] + + @property + def group_count(self) -> int: + """Number of distinct intervention evidence units represented.""" + return len(self.evidence_units) + + +class LinearDAGDiscovery(FlowMethod): + """Discover a sparse linear DAG inside a directed prior network. + + A linear structural equation is fitted for every vertex. One nonnegative + flow commodity is created for each unique intervention-to-measurement + structural signature. Repeated cells and explicitly grouped intervention + evidence therefore share a flow column when their source, eligible sinks, + and hard-intervention edge blocks agree. Every selected biological edge + must be used by at least one commodity, so the learned DAG is structurally + supported by intervention-to-measurement paths without requiring every + intervention to be explained. + + Missing outcomes are excluded from the loss. If a candidate parent is + missing in a sample, the corresponding child equation is also excluded; + this conservative complete-equation rule prevents missing predictors from + being silently treated as zero while preserving a linear formulation. + + Feature metadata may declare ``intervention="shift"`` for an additive + soft intervention. Its structural equation remains in the loss and its + measured value remains a flow source. Provide ``shift`` (or + ``intervention_shift``) for a known effect, or provide + ``intervention_group`` to estimate one bounded effect shared by its + replicates. The legacy ``intervened=True`` metadata remains a hard + intervention. + + Args: + lambda_edges: Penalty for every selected prior edge. + coefficient_bound: Absolute bound on raw linear edge coefficients. + fit_intercept: Whether to fit one intercept per vertex. + intercept_bound: Absolute bound on vertex intercepts. + max_parents: Optional global or vertex-specific parent limit. + loss: ``"absolute"`` for a MILP or ``"squared"`` for a MIQP. + standardize_loss: Divide residuals by the observed response standard + deviation of each vertex. Coefficients remain in the original + measurement units. + vertex_weights: Optional positive loss weight per PKN vertex. + sample_weights: Optional positive loss weight per sample. + enforce_signs: Constrain coefficients using numeric PKN interactions + ``+1`` and ``-1`` when present. + intervention_type_key: Feature metadata key containing ``"none"``, + ``"hard"``, or ``"shift"``. A boolean ``intervened=True`` under + ``intervention_key`` remains a hard intervention for compatibility. + intervention_shift_key: Feature metadata key containing a known + additive shift. If it is absent for a shift intervention, the + shift is estimated and shared by ``intervention_group_key``. + intervention_group_key: Feature metadata key identifying an explicit + intervention evidence group. The target vertex is always part of + the group key, so one group cannot couple different sources. For + estimated shifts, the same key also shares one bounded effect + parameter across its replicates. + intervention_shift_bound: Absolute bound for each estimated shift. + lambda_intervention_shifts: Optional L1 penalty on estimated shifts. + coefficient_support: ``"exact"`` requires every selected edge to have + a fitted coefficient with magnitude at least ``min_abs_coefficient``; + ``"structural"`` permits selected connector edges with zero + coefficients. + min_abs_coefficient: Minimum absolute fitted coefficient for selected + edges in ``"exact"`` mode, in normalized coefficient units. The + normalized coefficient for ``u -> v`` is + ``beta[u -> v] * scale[u] / scale[v]``. + The default ``0.25`` is a modeling assumption about normalized + direct effects, not a solver tolerance or a guarantee of a + nonzero total intervention effect. + interaction_attribute: Edge attribute containing PKN interaction signs. + intervention_key: Feature metadata key marking legacy hard + interventions. + flow_capacity: Upper bound on every commodity flow. By default, the + number of PKN edges multiplied by ``flow_epsilon`` is used. + flow_epsilon: Minimum positive flow on every used biological edge. + min_commodity_coverage: Minimum fraction of distinct intervention + evidence units that must have a selected-edge path to a measured, + non-intervened response. Repeated cells in one explicit group count + once, while separate groups sharing one flow signature each count + once. If the unique flows represent ``K`` evidence units in total, + a value ``f`` requires at least ``ceil(f * K)`` of those units to + be connected. This does not measure prediction accuracy. Zero does + not force any intervention to be connected. + lambda_unexplained: Optional objective penalty for every intervention + evidence unit without such a structural path. Unlike + ``min_commodity_coverage``, this is a soft preference rather than + a minimum feasibility requirement. + backend: Optimization backend. + """ + + def __init__( + self, + lambda_edges: float = 1e-2, + coefficient_bound: float = 5.0, + fit_intercept: bool = True, + intercept_bound: float = 10.0, + max_parents: Optional[int | dict[Any, int]] = None, + loss: str = "absolute", + standardize_loss: bool = True, + vertex_weights: Optional[Mapping[Any, float]] = None, + sample_weights: Optional[Mapping[Any, float]] = None, + enforce_signs: bool = False, + interaction_attribute: str = "interaction", + intervention_key: str = "intervened", + flow_capacity: Optional[float] = None, + flow_epsilon: float = 1.0, + min_commodity_coverage: float = 0.0, + lambda_unexplained: float = 0.0, + backend: Optional[Backend] = None, + coefficient_support: str = "exact", + min_abs_coefficient: float = 0.25, + *, + intervention_type_key: str = "intervention", + intervention_shift_key: str = "shift", + intervention_group_key: str = "intervention_group", + intervention_shift_bound: float = 10.0, + lambda_intervention_shifts: float = 0.0, + ): + self._validate_nonnegative(lambda_edges, "lambda_edges") + self._validate_positive(coefficient_bound, "coefficient_bound") + self._validate_positive(intercept_bound, "intercept_bound") + self._validate_positive(intervention_shift_bound, "intervention_shift_bound") + self._validate_positive(flow_epsilon, "flow_epsilon") + if coefficient_support not in {"exact", "structural"}: + raise ValueError("coefficient_support must be 'exact' or 'structural'.") + self._validate_positive(min_abs_coefficient, "min_abs_coefficient") + if flow_capacity is not None: + self._validate_positive(flow_capacity, "flow_capacity") + if float(flow_capacity) < float(flow_epsilon): + raise ValueError("flow_capacity must be greater than or equal to flow_epsilon.") + if loss not in {"absolute", "squared"}: + raise ValueError("loss must be 'absolute' or 'squared'.") + if not isinstance(standardize_loss, bool): + raise TypeError("standardize_loss must be boolean.") + if vertex_weights is not None and not isinstance(vertex_weights, Mapping): + raise TypeError("vertex_weights must be a mapping or None.") + if sample_weights is not None and not isinstance(sample_weights, Mapping): + raise TypeError("sample_weights must be a mapping or None.") + if isinstance(max_parents, int) and not isinstance(max_parents, bool): + if max_parents < 0: + raise ValueError("max_parents must be nonnegative.") + elif max_parents is not None: + if not isinstance(max_parents, dict): + raise TypeError("max_parents must be an integer, mapping, or None.") + for vertex, value in max_parents.items(): + if isinstance(value, bool) or not isinstance(value, int) or value < 0: + raise ValueError(f"max_parents[{vertex!r}] must be a nonnegative integer.") + if isinstance(min_commodity_coverage, bool) or not isinstance(min_commodity_coverage, Real): + raise TypeError("min_commodity_coverage must be a finite number in [0, 1].") + if not np.isfinite(min_commodity_coverage) or not 0 <= min_commodity_coverage <= 1: + raise ValueError("min_commodity_coverage must be in [0, 1].") + self._validate_nonnegative(lambda_unexplained, "lambda_unexplained") + self._validate_nonnegative(lambda_intervention_shifts, "lambda_intervention_shifts") + + super().__init__( + flow_lower_bound=0, + flow_upper_bound=1, + num_flows=1, + shared_flow_bounds=False, + lambda_reg=0, + disable_structured_sparsity=True, + backend=backend, + ) + self.lambda_edges = float(lambda_edges) + self.coefficient_bound = float(coefficient_bound) + self.fit_intercept = fit_intercept + self.intercept_bound = float(intercept_bound) + self.max_parents = max_parents + self.loss = loss + self.standardize_loss = standardize_loss + self.vertex_weights = dict(vertex_weights or {}) + self.sample_weights = dict(sample_weights or {}) + self.enforce_signs = enforce_signs + self.coefficient_support = coefficient_support + self.min_abs_coefficient = float(min_abs_coefficient) + self.interaction_attribute = interaction_attribute + self.intervention_key = intervention_key + self.intervention_type_key = intervention_type_key + self.intervention_shift_key = intervention_shift_key + self.intervention_group_key = intervention_group_key + self.intervention_shift_bound = float(intervention_shift_bound) + self.lambda_intervention_shifts = float(lambda_intervention_shifts) + self.flow_capacity = None if flow_capacity is None else float(flow_capacity) + self.flow_epsilon = float(flow_epsilon) + self.min_commodity_coverage = float(min_commodity_coverage) + self.lambda_unexplained = float(lambda_unexplained) + + self._original_graph: Optional[BaseGraph] = None + self._layout: Optional[BoundaryFlowLayout] = None + self._vertex_index: dict[Any, int] = {} + self._sample_names: tuple[Any, ...] = () + self._values = np.empty((0, 0)) + self._observed = np.empty((0, 0), dtype=bool) + self._valid_residual = np.empty((0, 0), dtype=bool) + self._loss_scales = np.empty((0,)) + self._coefficient_scales = np.empty((0,)) + self._observation_weights = np.empty((0, 0)) + self._intervened = np.empty((0, 0), dtype=bool) + self._hard_intervened = np.empty((0, 0), dtype=bool) + self._soft_intervened = np.empty((0, 0), dtype=bool) + self._intervention_design: Optional[_InterventionDesign] = None + self._edge_sources = np.empty((0,), dtype=int) + self._edge_targets = np.empty((0,), dtype=int) + self._commodities: tuple[_Commodity, ...] = () + self._flow_lb = np.empty((0, 0)) + self._flow_ub = np.empty((0, 0)) + self._resolved_flow_capacity = 0.0 + # ``fit`` records the backend result for callers who use the + # convenience API. Manual ``build``/``problem.solve`` remains fully + # supported; prediction checks expression values directly in that + # case, because ProblemDef.solve cannot notify its owning method. + self.solve_result = None + + @property + def _commodity_samples(self) -> list[int]: + """Compatibility view of the first sample supporting each flow.""" + return [unit.sample_indices[0] for commodity in self._commodities for unit in commodity.evidence_units[:1]] + + @property + def _commodity_sources(self) -> list[Any]: + """Compatibility view of structural flow sources.""" + return [commodity.source for commodity in self._commodities] + + @property + def _commodity_source_edges(self) -> list[int]: + """Compatibility view of structural flow source boundary edges.""" + return [commodity.source_edge for commodity in self._commodities] + + @property + def _commodity_sink_edges(self) -> list[list[int]]: + """Compatibility view of structural flow sink boundary edges.""" + return [list(commodity.sink_edges) for commodity in self._commodities] + + @staticmethod + def _validate_positive(value: Any, name: str) -> None: + if isinstance(value, bool) or not isinstance(value, Real): + raise TypeError(f"{name} must be a finite positive number.") + if not np.isfinite(value) or value <= 0: + raise ValueError(f"{name} must be a finite positive number.") + + @staticmethod + def _validate_nonnegative(value: Any, name: str) -> None: + if isinstance(value, bool) or not isinstance(value, Real): + raise TypeError(f"{name} must be a finite nonnegative number.") + if not np.isfinite(value) or value < 0: + raise ValueError(f"{name} must be a finite nonnegative number.") + + @classmethod + def _resolve_positive_weights( + cls, + keys: tuple[Any, ...], + supplied: Mapping[Any, float], + name: str, + ) -> np.ndarray: + unknown = set(supplied).difference(keys) + if unknown: + raise ValueError(f"{name} contains unknown keys: {sorted(unknown, key=repr)!r}.") + weights = np.ones((len(keys),), dtype=float) + index = {key: position for position, key in enumerate(keys)} + for key, value in supplied.items(): + cls._validate_positive(value, f"{name}[{key!r}]") + weights[index[key]] = float(value) + return weights + + def build(self, pkn: BaseGraph, data: Data) -> ProblemDef: + """Build a discovery problem from vertex measurements and interventions.""" + if not isinstance(data, Data): + raise TypeError("data must be a corneto.data.Data object.") + self.solve_result = None + return self.build_from_data(pkn, data) + + def fit( + self, + pkn: BaseGraph, + data: Data, + *, + solve_options: Optional[Mapping[str, Any]] = None, + **solver_options: Any, + ) -> "LinearDAGDiscovery": + """Build and solve the model, returning this reusable estimator. + + ``fit`` is intentionally a thin convenience wrapper. Use + ``solve_options`` (or solver keyword arguments) only for backend + options; scientific inputs remain the ``pkn`` and ``data`` arguments. + The constructed :attr:`problem` is retained, and the backend result is + available as :attr:`solve_result`. A time-limited incumbent is + accepted when all expressions needed by prediction have finite values. + """ + options = normalize_solve_options(solve_options, solver_options) + problem = self.build(pkn, data) + result = problem.solve(**options) + required = ["edge_selected", "edge_coefficient"] + if self._intervention_design is not None and self._intervention_design.effect_keys: + required.append("intervention_shift_parameters") + validate_solve_result(result, problem.expr, tuple(required), self.name()) + # ``intercept`` is a symbol when fitting is enabled and a registered + # constant otherwise; both are checked before prediction is allowed. + if self.fit_intercept: + require_expression_value(problem, "intercept", self.name()) + self.solve_result = result + return self + + def _validate_graph(self, graph: BaseGraph) -> tuple[np.ndarray, np.ndarray]: + if graph.num_vertices == 0 or graph.num_edges == 0: + raise ValueError("LinearDAGDiscovery requires a non-empty directed PKN.") + vertex_index = {vertex: index for index, vertex in enumerate(graph.V)} + sources = [] + targets = [] + for edge_index, (source, target) in enumerate(graph.E): + if len(source) != 1 or len(target) != 1: + raise ValueError( + "LinearDAGDiscovery accepts simple directed PKN edges only; " + f"edge {edge_index} has {len(source)} sources and {len(target)} targets." + ) + attributes = graph.get_attr_edge(edge_index) + if not attributes.has_attr(Attr.EDGE_TYPE, EdgeType.DIRECTED): + raise ValueError(f"LinearDAGDiscovery requires directed edges; edge {edge_index} is not directed.") + sources.append(vertex_index[next(iter(source))]) + targets.append(vertex_index[next(iter(target))]) + return np.asarray(sources, dtype=int), np.asarray(targets, dtype=int) + + @staticmethod + def _normalize_intervention_type(raw: Any, source: str) -> str: + if raw is None: + return "none" + if isinstance(raw, (bool, np.bool_)): + return "hard" if bool(raw) else "none" + if not isinstance(raw, str): + raise TypeError(f"{source} must be boolean or one of 'none', 'hard', and 'shift'.") + normalized = raw.strip().lower() + if normalized == "soft": + normalized = "shift" + if normalized not in {"none", "hard", "shift"}: + raise ValueError(f"{source} must be one of 'none', 'hard', and 'shift'; got {raw!r}.") + return normalized + + def _get_intervention_type(self, feature: Any, sample_name: Any) -> str: + explicit_values = [] + for key in (self.intervention_type_key, "intervention_type"): + if key not in explicit_values and key in feature.data: + explicit_values.append(key) + explicit = _MISSING + if explicit_values: + explicit = self._normalize_intervention_type( + feature.data[explicit_values[0]], + f"{explicit_values[0]!r} for vertex {feature.id!r} in sample {sample_name!r}", + ) + for key in explicit_values[1:]: + other = self._normalize_intervention_type( + feature.data[key], + f"{key!r} for vertex {feature.id!r} in sample {sample_name!r}", + ) + if other != explicit: + raise ValueError( + f"Conflicting intervention types for vertex {feature.id!r} in sample {sample_name!r}." + ) + + legacy = _MISSING + if self.intervention_key in feature.data: + legacy = self._normalize_intervention_type( + feature.data[self.intervention_key], + f"{self.intervention_key!r} for vertex {feature.id!r} in sample {sample_name!r}", + ) + if explicit is _MISSING: + return "none" if legacy is _MISSING else legacy + if legacy is not _MISSING and legacy != "none" and legacy != explicit: + raise ValueError(f"Conflicting intervention types for vertex {feature.id!r} in sample {sample_name!r}.") + return explicit + + def _get_shift_value(self, feature: Any, sample_name: Any) -> Any: + keys = tuple(dict.fromkeys((self.intervention_shift_key, "shift", "shift_value", "intervention_shift"))) + present = [key for key in keys if key in feature.data] + if not present: + return None + values = [feature.data[key] for key in present] + if any(value != values[0] for value in values[1:]): + raise ValueError(f"Conflicting shift values for vertex {feature.id!r} in sample {sample_name!r}.") + value = values[0] + if value is None: + return None + if isinstance(value, bool) or not isinstance(value, Real): + raise TypeError( + f"Intervention shift for vertex {feature.id!r} in sample {sample_name!r} " + "must be numeric or omitted for estimation." + ) + value = float(value) + if not np.isfinite(value): + raise ValueError(f"Intervention shift for vertex {feature.id!r} in sample {sample_name!r} must be finite.") + return value + + def _get_intervention_group(self, feature: Any) -> Any: + for key in (self.intervention_group_key, "shift_group", "intervention_id"): + if key in feature.data: + return feature.data[key] + return _MISSING + + # Keep the old private helper name for downstream code that may have used + # it while making the broader intervention-group semantics explicit. + _get_shift_group = _get_intervention_group + + def _extract_data( + self, + graph: BaseGraph, + data: Data, + ) -> tuple[np.ndarray, np.ndarray, _InterventionDesign]: + if not data.samples: + raise ValueError("LinearDAGDiscovery requires at least one sample.") + values = np.full((graph.num_vertices, len(data.samples)), np.nan, dtype=float) + observed = np.zeros_like(values, dtype=bool) + annotations: dict[tuple[int, int], tuple[str, Any, Any]] = {} + graph_vertices = set(graph.V) + + for sample_index, (sample_name, sample) in enumerate(data.samples.items()): + seen = set() + for feature in sample.features: + if feature.mapping != "vertex": + continue + if feature.id not in graph_vertices: + raise ValueError(f"Unknown vertex {feature.id!r} in sample {sample_name!r}.") + if feature.id in seen: + raise ValueError(f"Duplicate vertex {feature.id!r} in sample {sample_name!r}.") + seen.add(feature.id) + vertex_index = self._vertex_index[feature.id] + intervention_type = self._get_intervention_type(feature, sample_name) + shift_value = None + shift_group = _MISSING + if intervention_type != "none": + shift_group = self._get_intervention_group(feature) + if shift_group is None: + shift_group = _MISSING + if shift_group is not _MISSING: + _InterventionDesign._validate_group(shift_group) + if intervention_type == "shift": + shift_value = self._get_shift_value(feature, sample_name) + elif any( + key in feature.data + for key in dict.fromkeys( + (self.intervention_shift_key, "shift", "shift_value", "intervention_shift") + ) + ): + raise ValueError( + f"A shift value is only valid for a 'shift' intervention " + f"(vertex {feature.id!r}, sample {sample_name!r})." + ) + annotations[(vertex_index, sample_index)] = ( + intervention_type, + shift_value, + shift_group, + ) + value = feature.value + if value is None or ( + isinstance(value, Real) and not isinstance(value, bool) and np.isnan(float(value)) + ): + if intervention_type != "none": + raise ValueError( + f"Intervened vertex {feature.id!r} in sample {sample_name!r} requires an observed value." + ) + continue + if isinstance(value, bool) or not isinstance(value, Real): + raise TypeError( + f"Measurement for vertex {feature.id!r} in sample {sample_name!r} must be numeric or missing." + ) + value = float(value) + if not np.isfinite(value): + raise ValueError( + f"Measurement for vertex {feature.id!r} in sample {sample_name!r} must be finite or missing." + ) + values[vertex_index, sample_index] = value + observed[vertex_index, sample_index] = True + + design = _InterventionDesign.build( + tuple(graph.V), + graph.num_vertices, + len(data.samples), + annotations, + ) + if not np.any(design.hard | design.shift): + raise ValueError("LinearDAGDiscovery requires at least one intervened vertex.") + never_observed = [graph.V[index] for index in np.flatnonzero(~observed.any(axis=1))] + if never_observed: + raise ValueError(f"PKN vertices {never_observed!r} are never observed; latent vertices are not supported.") + return values, observed, design + + def _build_commodities(self, graph: BaseGraph) -> tuple[_Commodity, ...]: + """Resolve intervention cells into unique structural flow signatures. + + Cells remain independent observations for regression, but flow + evidence is deduplicated by explicit ``(source, group)`` units or by + one implicit unit per structural signature. A repeated explicit unit + is only valid when all of its cells have the same signature. + """ + if self._layout is None or self._intervention_design is None: + raise ValueError("LinearDAGDiscovery intervention preprocessing has not been initialized.") + + vertices = tuple(graph.V) + measured_responses = self._observed & ~self._intervened + explicit_signatures: dict[tuple[int, Any], tuple[int, tuple[int, ...], tuple[int, ...]]] = {} + explicit_samples: dict[tuple[int, Any], list[int]] = {} + implicit_samples: dict[tuple[int, tuple[int, ...], tuple[int, ...]], list[int]] = {} + signature_refs: dict[ + tuple[int, tuple[int, ...], tuple[int, ...]], + list[tuple[str, Any]], + ] = {} + signature_order: list[tuple[int, tuple[int, ...], tuple[int, ...]]] = [] + + for sample_index in range(len(self._sample_names)): + sink_indices = np.flatnonzero(measured_responses[:, sample_index]) + sink_edges = tuple(self._layout.outflow_edges[vertices[index]] for index in sink_indices) + blocked_edges = tuple(np.flatnonzero(self._hard_intervened[self._edge_targets, sample_index]).tolist()) + source_indices = np.flatnonzero(self._intervened[:, sample_index]) + for source_index in source_indices: + source = vertices[source_index] + signature = (int(source_index), sink_edges, blocked_edges) + if signature not in signature_refs: + signature_refs[signature] = [] + signature_order.append(signature) + + group = self._intervention_design.groups[source_index, sample_index] + if group is _MISSING: + if signature not in implicit_samples: + implicit_samples[signature] = [] + signature_refs[signature].append(("implicit", signature)) + implicit_samples[signature].append(sample_index) + continue + + unit_key = (int(source_index), group) + prior_signature = explicit_signatures.get(unit_key) + if prior_signature is not None and prior_signature != signature: + raise ValueError( + "Intervention evidence unit for source " + f"{source!r} and intervention_group {group!r} has inconsistent " + "structural signatures across samples. Repeated explicit groups " + "must have the same eligible sinks and hard-intervention blocks." + ) + if prior_signature is None: + explicit_signatures[unit_key] = signature + explicit_samples[unit_key] = [] + signature_refs[signature].append(("explicit", unit_key)) + explicit_samples[unit_key].append(sample_index) + + commodities: list[_Commodity] = [] + for signature in signature_order: + source_index, sink_edges, blocked_edges = signature + units = [] + for kind, reference in signature_refs[signature]: + if kind == "explicit": + unit_source_index, group = reference + units.append( + _EvidenceUnit( + source=vertices[unit_source_index], + intervention_group=group, + sample_indices=tuple(explicit_samples[reference]), + explicit_group=True, + ) + ) + else: + units.append( + _EvidenceUnit( + source=vertices[source_index], + intervention_group=None, + sample_indices=tuple(implicit_samples[reference]), + explicit_group=False, + ) + ) + commodities.append( + _Commodity( + source=vertices[source_index], + source_edge=self._layout.inflow_edges[vertices[source_index]], + sink_edges=sink_edges, + hard_blocked_edges=blocked_edges, + evidence_units=tuple(units), + ) + ) + return tuple(commodities) + + def preprocess(self, graph: BaseGraph, data: Data): + """Validate data and construct signature-specific flow boundaries.""" + self.solve_result = None + self._original_graph = graph.copy() + self._vertex_index = {vertex: index for index, vertex in enumerate(graph.V)} + self._sample_names = tuple(data.samples) + self._edge_sources, self._edge_targets = self._validate_graph(graph) + self._values, self._observed, self._intervention_design = self._extract_data(graph, data) + self._hard_intervened = self._intervention_design.hard + self._soft_intervened = self._intervention_design.shift + self._intervened = self._hard_intervened | self._soft_intervened + vertices = tuple(graph.V) + self._valid_residual = self._observed & ~self._hard_intervened + for source, target in zip(self._edge_sources, self._edge_targets, strict=True): + self._valid_residual[target, :] &= self._observed[source, :] + if not np.any(self._valid_residual): + raise ValueError("No complete, non-intervened structural equations are available.") + self._values = np.where(self._observed, self._values, 0.0) + + vertex_weights = self._resolve_positive_weights( + tuple(graph.V), + self.vertex_weights, + "vertex_weights", + ) + sample_weights = self._resolve_positive_weights( + self._sample_names, + self.sample_weights, + "sample_weights", + ) + self._observation_weights = vertex_weights[:, None] * sample_weights[None, :] + self._loss_scales = np.ones((graph.num_vertices,), dtype=float) + if self.standardize_loss: + for vertex_index in range(graph.num_vertices): + fitted_values = self._values[vertex_index, self._valid_residual[vertex_index, :]] + if fitted_values.size > 1: + scale = float(np.std(fitted_values)) + if np.isfinite(scale) and scale > np.finfo(float).eps: + self._loss_scales[vertex_index] = scale + predictor_scale_mask = np.zeros_like(self._observed) + for source, target in zip(self._edge_sources, self._edge_targets, strict=True): + predictor_scale_mask[source, :] |= self._valid_residual[target, :] & self._observed[source, :] + + # A variable can appear on either side of an equation. Use both roles + # so that its coefficient scale transforms with its measurement units + # even when it is a terminal response or has no usable child equation. + coefficient_scale_mask = self._valid_residual | predictor_scale_mask + self._coefficient_scales = np.ones((graph.num_vertices,), dtype=float) + for vertex_index in range(graph.num_vertices): + coefficient_values = self._values[vertex_index, coefficient_scale_mask[vertex_index, :]] + if coefficient_values.size: + scale = float(np.std(coefficient_values)) if coefficient_values.size > 1 else 0.0 + if not np.isfinite(scale) or scale <= np.finfo(float).eps: + scale = float(np.max(np.abs(coefficient_values))) + if np.isfinite(scale) and scale > np.finfo(float).eps: + self._coefficient_scales[vertex_index] = scale + + intervened_union = self._intervened.any(axis=1) + measured_responses = self._observed & ~self._intervened + sink_union = measured_responses.any(axis=1) + self._layout = augment_with_boundaries( + graph, + inflow_vertices=(vertex for vertex, include in zip(vertices, intervened_union, strict=True) if include), + outflow_vertices=(vertex for vertex, include in zip(vertices, sink_union, strict=True) if include), + ) + + self._commodities = self._build_commodities(graph) + num_commodities = len(self._commodities) + num_flow_edges = self._layout.graph.num_edges + flow_capacity = self.flow_capacity + if flow_capacity is None: + flow_capacity = float(max(graph.num_edges, 1)) * self.flow_epsilon + if flow_capacity < self.flow_epsilon: + raise ValueError("Resolved flow capacity must be greater than or equal to flow_epsilon.") + self._resolved_flow_capacity = float(flow_capacity) + self._flow_lb = np.zeros((num_flow_edges, num_commodities), dtype=float) + self._flow_ub = np.zeros((num_flow_edges, num_commodities), dtype=float) + self._flow_ub[: graph.num_edges, :] = self._resolved_flow_capacity + for commodity_index, commodity in enumerate(self._commodities): + self._flow_ub[commodity.source_edge, commodity_index] = self._resolved_flow_capacity + self._flow_ub[np.asarray(commodity.sink_edges, dtype=int), commodity_index] = self._resolved_flow_capacity + self._flow_ub[np.asarray(commodity.hard_blocked_edges, dtype=int), commodity_index] = 0.0 + return self._layout.graph, data.copy() + + def get_flow_bounds(self, graph: BaseGraph, data: Data): + """Return the precomputed per-edge, per-commodity bounds.""" + return { + "lb": self._flow_lb, + "ub": self._flow_ub, + "n_flows": len(self._commodities), + "shared_bounds": False, + } + + def create_problem(self, graph: BaseGraph, data: Data): + """Build an efficient matrix-valued commodity-flow formulation.""" + if self._original_graph is None or self._layout is None: + raise ValueError("LinearDAGDiscovery preprocessing has not been initialized.") + num_edges = self._original_graph.num_edges + edge_selected = self.backend.Variable( + "edge_selected", + (num_edges,), + vartype=VarType.BINARY, + ) + problem = self.backend.SelectedFlow( + graph, + lb=self._flow_lb, + ub=self._flow_ub, + n_flows=len(self._commodities), + edge_indices=range(num_edges), + epsilon=self.flow_epsilon, + selected=edge_selected, + acyclic_graph=self._original_graph, + max_parents=self.max_parents, + selected_by_flow_name="edge_used_by_commodity", + selected_any_name="edge_selected", + ) + return self.create_flow_based_problem(problem, graph, data) + + def create_flow_based_problem(self, flow_problem: ProblemDef, graph: BaseGraph, data: Data) -> ProblemDef: + """Add commodity coverage and linear structural-equation fitting.""" + if self._original_graph is None: + raise ValueError("LinearDAGDiscovery preprocessing has not been initialized.") + if self._intervention_design is None: + raise ValueError("LinearDAGDiscovery intervention preprocessing has not been initialized.") + problem = flow_problem + num_vertices = self._original_graph.num_vertices + num_edges = self._original_graph.num_edges + num_samples = self._values.shape[1] + num_commodities = len(self._commodities) + + flow = problem.expr.flow + edge_selected = problem.expr.edge_selected + edge_used = problem.expr.edge_used_by_commodity + coverage_enabled = self.min_commodity_coverage > 0 or self.lambda_unexplained > 0 + if coverage_enabled: + commodity_active = self.backend.Variable( + "commodity_active", + (num_commodities,), + vartype=VarType.BINARY, + ) + active_matrix = ( + self.backend.Constant(_sparse_vector_entry_repetition(num_commodities, num_edges)) @ commodity_active + ) + active_matrix = active_matrix.reshape((num_edges, num_commodities)) + problem += edge_used <= active_matrix + problem += commodity_active.reshape((num_commodities, 1)) <= edge_used.sum(axis=0).reshape( + (num_commodities, 1) + ) + + num_flow_edges = graph.num_edges + flow_flat = flow.reshape((num_flow_edges * num_commodities, 1)) + commodity_column = commodity_active.reshape((num_commodities, 1)) + commodity_indices = np.arange(num_commodities, dtype=int) + source_edges = np.asarray([commodity.source_edge for commodity in self._commodities], dtype=int) + source_positions = source_edges + num_flow_edges * commodity_indices + source_selector = csr_matrix( + ( + np.ones(num_commodities, dtype=float), + (commodity_indices, source_positions), + ), + shape=(num_commodities, num_flow_edges * num_commodities), + ) + source_flow = self.backend.Constant(source_selector) @ flow_flat + problem += source_flow >= self.flow_epsilon * commodity_column + problem += source_flow <= self._resolved_flow_capacity * commodity_column + + sink_commodities = np.asarray( + [ + commodity_index + for commodity_index, commodity in enumerate(self._commodities) + for _ in commodity.sink_edges + ], + dtype=int, + ) + sink_edges = np.asarray( + [edge for commodity in self._commodities for edge in commodity.sink_edges], + dtype=int, + ) + sink_positions = sink_edges + num_flow_edges * sink_commodities + sink_selector = csr_matrix( + ( + np.ones(sink_positions.size, dtype=float), + (sink_commodities, sink_positions), + ), + shape=(num_commodities, num_flow_edges * num_commodities), + ) + sink_flow = self.backend.Constant(sink_selector) @ flow_flat + problem += sink_flow >= self.flow_epsilon * commodity_column + + if sink_positions.size: + sink_entry_rows = np.arange(sink_positions.size, dtype=int) + sink_entry_selector = csr_matrix( + ( + np.ones(sink_positions.size, dtype=float), + (sink_entry_rows, sink_positions), + ), + shape=(sink_positions.size, num_flow_edges * num_commodities), + ) + sink_entry_flow = self.backend.Constant(sink_entry_selector) @ flow_flat + sink_active_selector = csr_matrix( + ( + np.ones(sink_positions.size, dtype=float), + (sink_entry_rows, sink_commodities), + ), + shape=(sink_positions.size, num_commodities), + ) + sink_entry_active = self.backend.Constant(sink_active_selector) @ commodity_column + problem += sink_entry_flow <= self._resolved_flow_capacity * sink_entry_active + + group_counts = np.asarray([commodity.group_count for commodity in self._commodities], dtype=float) + problem.register( + "commodity_group_count", + self.backend.Constant(group_counts, name="commodity_group_count"), + ) + minimum_active = int(np.ceil(self.min_commodity_coverage * group_counts.sum())) + if minimum_active: + weighted_active = self.backend.Constant(csr_matrix(group_counts.reshape((1, num_commodities)))) @ ( + commodity_active.reshape((num_commodities, 1)) + ) + problem += weighted_active >= minimum_active + + if self.lambda_unexplained: + group_counts = np.asarray([commodity.group_count for commodity in self._commodities], dtype=float) + unexplained = self.backend.Constant(group_counts).multiply( + self.backend.Constant(np.ones((num_commodities,))) - commodity_active + ) + problem.register("commodity_unexplained", unexplained) + problem.add_objective( + unexplained.sum(), + weight=self.lambda_unexplained, + name="commodity_coverage", + ) + + coefficient_normalization = ( + self._coefficient_scales[self._edge_sources] / self._coefficient_scales[self._edge_targets] + ) + if self.coefficient_support == "exact": + # Translate the raw coefficient bound to normalized units. This + # preserves the raw estimator bound without imposing a second + # normalized upper bound. + normalized_bound = self.coefficient_bound * coefficient_normalization + normalized_coefficient = self.backend.Variable( + "edge_coefficient_normalized", + (num_edges,), + lb=-normalized_bound, + ub=normalized_bound, + ) + edge_coefficient = normalized_coefficient.multiply(1.0 / coefficient_normalization) + problem.register("edge_coefficient", edge_coefficient) + problem += self.backend.ExactSupport( + normalized_coefficient, + selected=edge_selected, + epsilon=self.min_abs_coefficient, + name="coefficient_support", + positive_name="coefficient_support_positive", + negative_name="coefficient_support_negative", + ) + else: + edge_coefficient = self.backend.Variable( + "edge_coefficient", + (num_edges,), + lb=-self.coefficient_bound, + ub=self.coefficient_bound, + ) + normalized_coefficient = edge_coefficient.multiply(coefficient_normalization) + problem.register("edge_coefficient_normalized", normalized_coefficient) + problem += edge_coefficient <= self.coefficient_bound * edge_selected + problem += edge_coefficient >= -self.coefficient_bound * edge_selected + + if self.enforce_signs: + positive = [] + negative = [] + for edge_index in range(num_edges): + interaction = self._original_graph.get_attr_edge(edge_index).get( + self.interaction_attribute, + None, + ) + if interaction in {1, 1.0}: + positive.append(edge_index) + elif interaction in {-1, -1.0}: + negative.append(edge_index) + if positive: + problem += edge_coefficient[np.asarray(positive, dtype=int)] >= 0 + if negative: + problem += edge_coefficient[np.asarray(negative, dtype=int)] <= 0 + + if self.fit_intercept: + intercept = self.backend.Variable( + "intercept", + (num_vertices,), + lb=-self.intercept_bound, + ub=self.intercept_bound, + ) + else: + intercept = self.backend.Constant(np.zeros((num_vertices,)), name="intercept_zero") + # The fitted case is already visible as the ``intercept`` symbol. A + # stable registered alias also makes the fixed zero-intercept case + # inspectable through the normal CORNETO expression API. + problem.register("intercept_value", intercept) + if not self.fit_intercept: + problem.register("intercept", intercept) + + parent_values = self._values[self._edge_sources, :] + parent_rows = np.arange(num_edges * num_samples, dtype=int) + parent_columns = np.tile(np.arange(num_edges, dtype=int), num_samples) + weighted_parent_operator = csr_matrix( + ( + parent_values.reshape(-1, order="F"), + (parent_rows, parent_columns), + ), + shape=(num_edges * num_samples, num_edges), + ) + weighted_parent_operator.eliminate_zeros() + weighted_parents = self.backend.Constant(weighted_parent_operator) @ edge_coefficient + weighted_parents = weighted_parents.reshape((num_edges, num_samples)) + target_incidence = csr_matrix( + ( + np.ones(num_edges, dtype=float), + (self._edge_targets, np.arange(num_edges, dtype=int)), + ), + shape=(num_vertices, num_edges), + ) + prediction = self.backend.Constant(target_incidence) @ weighted_parents + if self.fit_intercept: + intercept_matrix = self.backend.Constant(_sparse_vector_replication(num_vertices, num_samples)) @ intercept + prediction = prediction + intercept_matrix.reshape((num_vertices, num_samples)) + + intervention_design = self._intervention_design + known_shift = self.backend.Constant( + csr_matrix(intervention_design.known_shift), + name="known_intervention_shift", + ) + shift_prediction = known_shift + estimated_shift_parameters = None + if intervention_design.effect_keys: + estimated_shift_parameters = self.backend.Variable( + "intervention_shift_parameters", + (len(intervention_design.effect_keys), 1), + lb=-self.intervention_shift_bound, + ub=self.intervention_shift_bound, + ) + shift_mapping = self.backend.Constant( + intervention_design.mapping, + name="intervention_shift_mapping", + ) + estimated_shift = shift_mapping @ estimated_shift_parameters + shift_prediction = shift_prediction + estimated_shift.reshape((num_vertices, num_samples)) + prediction = prediction + shift_prediction + + problem.register("known_intervention_shift", known_shift) + problem.register("intervention_shift", shift_prediction) + if estimated_shift_parameters is not None: + problem.register("intervention_shift_mapping", shift_mapping) + problem.register("intervention_shift_parameters", estimated_shift_parameters) + if self.lambda_intervention_shifts: + problem.add_objective( + estimated_shift_parameters.norm(1), + weight=self.lambda_intervention_shifts, + name="intervention_shift_sparsity", + ) + + residual = self._values - prediction + problem.register("prediction", prediction) + problem.register("residual", residual) + problem.register( + "observed_mask", + self.backend.Constant(self._observed.astype(float), name="observed_mask"), + ) + problem.register( + "fitted_mask", + self.backend.Constant(self._valid_residual.astype(float), name="fitted_mask"), + ) + problem.register( + "loss_scale", + self.backend.Constant(self._loss_scales, name="loss_scale"), + ) + problem.register( + "observation_weight", + self.backend.Constant( + self._observation_weights, + name="observation_weight", + ), + ) + residual_vector = residual.reshape((num_vertices * num_samples,)) + valid_indices = np.flatnonzero(self._valid_residual.reshape(-1, order="F")) + fitted_residual = residual_vector[valid_indices] + observation_weights = self._observation_weights.reshape(-1, order="F")[valid_indices] + scales = np.broadcast_to( + self._loss_scales[:, None], + (num_vertices, num_samples), + ).reshape(-1, order="F")[valid_indices] + if self.loss == "absolute": + absolute_residual = self.backend.Variable( + "absolute_residual", + (valid_indices.size,), + lb=0, + ) + problem += fitted_residual <= absolute_residual + problem += -fitted_residual <= absolute_residual + fit = absolute_residual.multiply(observation_weights / scales).sum() + problem.add_objective(fit, name="absolute_fit") + else: + standardized_residual = fitted_residual.multiply(np.sqrt(observation_weights) / scales) + fit = standardized_residual.norm(2) ** 2 + problem.add_objective(fit, name="squared_fit") + + if self.lambda_edges: + problem.add_objective( + edge_selected.sum(), + weight=self.lambda_edges, + name="edge_sparsity", + ) + problem.register("dag_layer", problem.expr._dag_layer) + return problem + + # ------------------------------------------------------------------ + # Fixed-model prediction and evaluation + # ------------------------------------------------------------------ + @property + def loss_scales(self) -> np.ndarray: + """Training-derived response scales used by held-out losses.""" + if self._loss_scales.size == 0: + raise ValueError("LinearDAGDiscovery has not been built.") + return self._loss_scales.copy() + + @property + def intercepts(self) -> np.ndarray: + """Return fitted vertex intercepts in graph-vertex order.""" + if self._original_graph is None: + raise ValueError("LinearDAGDiscovery has not been built.") + require_expression_value(self.problem, "edge_selected", self.name()) + if not self.fit_intercept: + return np.zeros((self._original_graph.num_vertices,), dtype=float) + return require_expression_value(self.problem, "intercept", self.name()).reshape(-1).copy() + + @property + def edge_coefficients(self) -> np.ndarray: + """Return fitted coefficients in original PKN edge order.""" + return require_expression_value(self.problem, "edge_coefficient", self.name()).reshape(-1).copy() + + def _fitted_parameters(self) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Return selected edges, coefficients, and intercepts after solving.""" + selected = require_expression_value(self.problem, "edge_selected", self.name()).reshape(-1) > 0.5 + coefficients = require_expression_value(self.problem, "edge_coefficient", self.name()).reshape(-1) + intercept = ( + require_expression_value(self.problem, "intercept", self.name()).reshape(-1) + if self.fit_intercept + else np.zeros((self._original_graph.num_vertices,), dtype=float) + ) + if self._original_graph is None or coefficients.size != self._original_graph.num_edges: + raise ValueError("LinearDAGDiscovery has no usable fitted edge parameters.") + return selected, coefficients, intercept + + def _prediction_input_arrays( + self, + data: Data, + ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, tuple[Any, ...]]: + """Parse new observations without applying training-time fitting rules.""" + if not isinstance(data, Data): + raise TypeError("data must be a corneto.data.Data object.") + if self._original_graph is None or self._intervention_design is None: + raise ValueError("LinearDAGDiscovery has not been built; call build or fit first.") + if not data.samples: + raise ValueError("Prediction data must contain at least one sample.") + + n_vertices = self._original_graph.num_vertices + n_samples = len(data.samples) + values = np.full((n_vertices, n_samples), np.nan, dtype=float) + observed = np.zeros((n_vertices, n_samples), dtype=bool) + hard = np.zeros((n_vertices, n_samples), dtype=bool) + soft = np.zeros((n_vertices, n_samples), dtype=bool) + shifts = np.zeros((n_vertices, n_samples), dtype=float) + explicit_shift = np.zeros((n_vertices, n_samples), dtype=bool) + groups = np.full((n_vertices, n_samples), _MISSING, dtype=object) + vertices = set(self._original_graph.V) + + for sample_index, (sample_name, sample) in enumerate(data.samples.items()): + seen = set() + for feature in sample.features: + if feature.mapping != "vertex": + continue + if feature.id not in vertices: + raise ValueError(f"Unknown vertex {feature.id!r} in sample {sample_name!r}.") + if feature.id in seen: + raise ValueError(f"Duplicate vertex {feature.id!r} in sample {sample_name!r}.") + seen.add(feature.id) + vertex_index = self._vertex_index[feature.id] + intervention_type = self._get_intervention_type(feature, sample_name) + if intervention_type == "hard": + hard[vertex_index, sample_index] = True + elif intervention_type == "shift": + soft[vertex_index, sample_index] = True + elif any( + key in feature.data + for key in dict.fromkeys( + (self.intervention_shift_key, "shift", "shift_value", "intervention_shift") + ) + ): + raise ValueError( + f"A shift value is only valid for a 'shift' intervention " + f"(vertex {feature.id!r}, sample {sample_name!r})." + ) + + if intervention_type != "none": + group = self._get_intervention_group(feature) + if group is not _MISSING and group is not None: + _InterventionDesign._validate_group(group) + groups[vertex_index, sample_index] = group + shift_value = None + if intervention_type == "shift": + shift_value = self._get_shift_value(feature, sample_name) + if shift_value is not None: + shifts[vertex_index, sample_index] = shift_value + explicit_shift[vertex_index, sample_index] = True + value = feature.value + if value is None or ( + isinstance(value, Real) and not isinstance(value, bool) and np.isnan(float(value)) + ): + if intervention_type == "hard": + raise ValueError( + f"Hard-intervened vertex {feature.id!r} in sample {sample_name!r} " + "requires an observed value." + ) + continue + if isinstance(value, bool) or not isinstance(value, Real): + raise TypeError( + f"Measurement for vertex {feature.id!r} in sample {sample_name!r} must be numeric or missing." + ) + value = float(value) + if not np.isfinite(value): + raise ValueError( + f"Measurement for vertex {feature.id!r} in sample {sample_name!r} must be finite or missing." + ) + values[vertex_index, sample_index] = value + observed[vertex_index, sample_index] = True + + # Fill omitted estimated shifts only from their fitted training group. + fitted_shifts: dict[tuple[Any, Any], float] = {} + if self._intervention_design.effect_keys: + parameters = require_expression_value( + self.problem, + "intervention_shift_parameters", + self.name(), + ).reshape(-1) + fitted_shifts = dict(zip(self._intervention_design.effect_keys, parameters, strict=True)) + for vertex_index, sample_index in zip(*np.where(soft), strict=True): + # An explicit numeric shift wins and is already in ``shifts``. + feature_group = groups[vertex_index, sample_index] + if explicit_shift[vertex_index, sample_index]: + continue + if feature_group is _MISSING: + raise ValueError( + "Soft-intervention prediction requires an explicit shift or a fitted intervention_group; " + f"none was supplied for vertex {self._original_graph.V[vertex_index]!r}." + ) + key = (self._original_graph.V[vertex_index], feature_group) + if key not in fitted_shifts: + raise ValueError( + f"No fitted soft-intervention shift is available for vertex/group {key!r}; " + "test outcomes are never used to estimate it." + ) + shifts[vertex_index, sample_index] = float(fitted_shifts[key]) + return values, observed, hard, soft, shifts, tuple(data.samples) + + def _selected_order(self, selected: np.ndarray) -> tuple[list[int], list[list[int]]]: + """Topologically order the fitted selected DAG.""" + if self._original_graph is None: + raise ValueError("LinearDAGDiscovery has not been built.") + incoming = [[] for _ in range(self._original_graph.num_vertices)] + outgoing = [[] for _ in range(self._original_graph.num_vertices)] + indegree = np.zeros((self._original_graph.num_vertices,), dtype=int) + for edge_index in np.flatnonzero(selected): + source = int(self._edge_sources[edge_index]) + target = int(self._edge_targets[edge_index]) + incoming[target].append(int(edge_index)) + outgoing[source].append(target) + indegree[target] += 1 + queue = [index for index, degree in enumerate(indegree) if degree == 0] + order = [] + while queue: + vertex = queue.pop(0) + order.append(vertex) + for target in outgoing[vertex]: + indegree[target] -= 1 + if indegree[target] == 0: + queue.append(target) + if len(order) != self._original_graph.num_vertices: + raise ValueError("The fitted LinearDAGDiscovery support is cyclic and cannot be predicted.") + return order, incoming + + def _forward_prediction_arrays( + self, + data: Optional[Data] = None, + parsed: Optional[tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, tuple[Any, ...]]] = None, + ) -> tuple[np.ndarray, np.ndarray, np.ndarray, tuple[Any, ...]]: + if parsed is None: + if data is None: + raise ValueError("Prediction data is required.") + parsed = self._prediction_input_arrays(data) + values, observed, hard, soft, shifts, sample_names = parsed + selected, coefficients, intercept = self._fitted_parameters() + order, incoming = self._selected_order(selected) + predictions = np.zeros_like(values) + n_samples = values.shape[1] + for sample_index in range(n_samples): + for vertex in order: + if hard[vertex, sample_index]: + predictions[vertex, sample_index] = values[vertex, sample_index] + continue + edge_indices = incoming[vertex] + prediction = intercept[vertex] + for edge_index in edge_indices: + source = self._edge_sources[edge_index] + prediction += coefficients[edge_index] * predictions[source, sample_index] + if soft[vertex, sample_index]: + prediction += shifts[vertex, sample_index] + predictions[vertex, sample_index] = prediction + return predictions, observed, hard, sample_names + + @staticmethod + def _data_from_arrays( + vertices: tuple[Any, ...], + sample_names: tuple[Any, ...], + values: np.ndarray, + *, + observed: Optional[np.ndarray] = None, + valid: Optional[np.ndarray] = None, + predicted_mask: Optional[np.ndarray] = None, + clamped: Optional[np.ndarray] = None, + equation_prediction: Optional[np.ndarray] = None, + ) -> Data: + samples = {} + for sample_index, sample_name in enumerate(sample_names): + features = {} + for vertex_index, vertex in enumerate(vertices): + value = values[vertex_index, sample_index] + features[vertex] = { + "mapping": "vertex", + "value": None if not np.isfinite(value) else float(value), + "predicted": bool(predicted_mask[vertex_index, sample_index]) + if predicted_mask is not None + else False, + } + if observed is not None: + features[vertex]["observed"] = bool(observed[vertex_index, sample_index]) + if valid is not None: + features[vertex]["valid"] = bool(valid[vertex_index, sample_index]) + if clamped is not None: + features[vertex]["clamped"] = bool(clamped[vertex_index, sample_index]) + if equation_prediction is not None and np.isfinite(equation_prediction[vertex_index, sample_index]): + features[vertex]["equation_prediction"] = float(equation_prediction[vertex_index, sample_index]) + samples[sample_name] = features + return Data.from_cdict(samples) + + def predict(self, data: Data) -> Data: + """Predict held-out vertex values using the fixed fitted DAG. + + All non-hard measurements, including roots, are ignored as predictors. + Roots use their fitted intercept baseline; hard interventions are + clamped, soft shifts are applied to their equations, and descendants + use previously predicted parents. This method never calls a solver. + """ + predictions, observed, _hard, sample_names = self._forward_prediction_arrays(data) + if self._original_graph is None: + raise ValueError("LinearDAGDiscovery has not been built.") + return self._data_from_arrays( + tuple(self._original_graph.V), + sample_names, + predictions, + observed=observed, + predicted_mask=~_hard, + clamped=_hard, + ) + + def _local_prediction_arrays( + self, + data: Optional[Data] = None, + parsed: Optional[tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, tuple[Any, ...]]] = None, + ) -> tuple[np.ndarray, np.ndarray, np.ndarray, tuple[Any, ...]]: + if parsed is None: + if data is None: + raise ValueError("Prediction data is required.") + parsed = self._prediction_input_arrays(data) + values, observed, hard, soft, shifts, sample_names = parsed + _selected, coefficients, intercept = self._fitted_parameters() + # Training's complete-equation mask is based on every candidate PKN + # parent, even when its fitted coefficient is zero. Retain that + # conservative mask here so local diagnostics remain comparable to + # the optimization objective. + all_incoming = [[] for _ in range(values.shape[0])] + for edge_index, target in enumerate(self._edge_targets): + all_incoming[int(target)].append(edge_index) + local = np.full_like(values, np.nan) + valid = observed & ~hard + for sample_index in range(values.shape[1]): + for vertex in range(values.shape[0]): + if not valid[vertex, sample_index]: + continue + parent_edges = all_incoming[vertex] + if any(not observed[self._edge_sources[e], sample_index] for e in parent_edges): + valid[vertex, sample_index] = False + continue + value = intercept[vertex] + for edge_index in parent_edges: + value += coefficients[edge_index] * values[self._edge_sources[edge_index], sample_index] + if soft[vertex, sample_index]: + value += shifts[vertex, sample_index] + local[vertex, sample_index] = value + return local, values, valid, sample_names + + def residuals(self, data: Data) -> Data: + """Return observed-minus-local-equation residuals. + + Unlike :meth:`predict`, this diagnostic intentionally uses observed + parent values. It mirrors the structural-equation training objective, + including its complete candidate-parent missingness mask, and is useful + for local fit auditing but is not a forward intervention forecast. + """ + local, values, valid, sample_names = self._local_prediction_arrays(data) + residual = np.full_like(values, np.nan) + residual[valid] = values[valid] - local[valid] + if self._original_graph is None: + raise ValueError("LinearDAGDiscovery has not been built.") + return self._data_from_arrays( + tuple(self._original_graph.V), + sample_names, + residual, + observed=np.isfinite(values), + valid=valid, + equation_prediction=local, + ) + + def evaluate( + self, + data: Data, + *, + sample_weights: Optional[Mapping[Any, float]] = None, + ) -> dict[str, Any]: + """Evaluate fixed forward and local losses on supplied measurements. + + Forward loss excludes hard-intervention targets (which are clamped) and + never uses observed descendants as predictors. Local loss uses observed + values for every candidate PKN parent (with zero coefficients for + unselected edges), matching the training equation objective. Both use + training-derived response scales and report valid counts plus + per-vertex and per-sample normalized losses. Reported losses are + weighted means (configured vertex weights and optional held-out + ``sample_weights``), not the raw summed optimization objective; training + sample weights are never reused for unrelated held-out names. + """ + parsed = self._prediction_input_arrays(data) + values, observed, hard, _soft, _shifts, sample_names = parsed + predictions, observed, hard, sample_names = self._forward_prediction_arrays(parsed=parsed) + local, values, local_valid, _ = self._local_prediction_arrays(parsed=parsed) + forward_valid = observed & ~hard + if self._original_graph is None: + raise ValueError("LinearDAGDiscovery has not been built.") + + if sample_weights is not None and not isinstance(sample_weights, Mapping): + raise TypeError("sample_weights must be a mapping or None.") + evaluation_sample_weights = self._resolve_positive_weights( + tuple(sample_names), + dict(sample_weights or {}), + "sample_weights", + ) + evaluation_vertex_weights = self._resolve_positive_weights( + tuple(self._original_graph.V), + self.vertex_weights, + "vertex_weights", + ) + + def metrics( + errors: np.ndarray, + valid: np.ndarray, + ) -> tuple[float, int, dict[Any, float], dict[Any, float]]: + normalized = errors / self._loss_scales[:, None] + score = np.abs(normalized) if self.loss == "absolute" else normalized**2 + weights = evaluation_vertex_weights[:, None] * evaluation_sample_weights[None, :] + count = int(np.count_nonzero(valid)) + denominator = float(np.sum(weights[valid])) if count else 0.0 + loss = float(np.sum(score[valid] * weights[valid]) / denominator) if denominator else float("nan") + per_vertex = { + vertex: ( + float( + np.sum(score[index, valid[index]] * evaluation_sample_weights[valid[index]]) + / np.sum(evaluation_sample_weights[valid[index]]) + ) + if np.any(valid[index]) + else float("nan") + ) + for index, vertex in enumerate(self._original_graph.V) + } + per_sample = { + sample: ( + float( + np.sum(score[valid[:, index], index] * evaluation_vertex_weights[valid[:, index]]) + / np.sum(evaluation_vertex_weights[valid[:, index]]) + ) + if np.any(valid[:, index]) + else float("nan") + ) + for index, sample in enumerate(sample_names) + } + return loss, count, per_vertex, per_sample + + forward_loss, forward_count, forward_by_vertex, forward_by_sample = metrics( + values - predictions, + forward_valid, + ) + local_loss, local_count, local_by_vertex, local_by_sample = metrics(values - local, local_valid) + residual = np.full_like(values, np.nan) + residual[local_valid] = values[local_valid] - local[local_valid] + return { + "forward_loss": forward_loss, + "forward_count": forward_count, + "forward_by_vertex": forward_by_vertex, + "forward_by_sample": forward_by_sample, + "forward_by_condition": forward_by_sample, + "local_loss": local_loss, + "local_count": local_count, + "local_by_vertex": local_by_vertex, + "local_by_sample": local_by_sample, + "local_by_condition": local_by_sample, + "loss_is_weighted_mean": True, + "sample_weights": dict(zip(sample_names, evaluation_sample_weights, strict=True)), + "predictions": self._data_from_arrays( + tuple(self._original_graph.V), + sample_names, + predictions, + observed=observed, + predicted_mask=~hard, + clamped=hard, + ), + "residuals": self._data_from_arrays( + tuple(self._original_graph.V), + sample_names, + residual, + observed=np.isfinite(values), + valid=local_valid, + equation_prediction=local, + ), + } + + def get_commodity_info(self) -> tuple[Mapping[str, Any], ...]: + """Return read-only metadata for the resolved structural flows. + + The result is a tuple of mapping-like records, one per flow column. + ``group_count`` is the number of distinct intervention evidence units + represented by that column; repeated cells in one explicit group are + listed in that unit's ``sample_names`` but do not increase the count. + An absent group is reported as ``None`` and is represented by one + implicit unit per structural signature. + """ + if self._original_graph is None or self._layout is None: + raise ValueError("The method has not been preprocessed.") + vertex_by_outflow = {edge: vertex for vertex, edge in self._layout.outflow_edges.items()} + records = [] + for index, commodity in enumerate(self._commodities): + evidence_units = [] + for unit in commodity.evidence_units: + evidence_units.append( + MappingProxyType( + { + "source": unit.source, + "intervention_group": unit.intervention_group, + "explicit_group": unit.explicit_group, + "sample_names": tuple(self._sample_names[i] for i in unit.sample_indices), + } + ) + ) + records.append( + MappingProxyType( + { + "index": index, + "source": commodity.source, + "sink_vertices": tuple(vertex_by_outflow[edge] for edge in commodity.sink_edges), + "sink_edges": commodity.sink_edges, + "hard_blocked_edges": commodity.hard_blocked_edges, + "group_count": commodity.group_count, + "evidence_units": tuple(evidence_units), + } + ) + ) + return tuple(records) + + def get_selected_edge_indices(self, threshold: float = 0.5) -> np.ndarray: + """Return selected edge indices in the original PKN.""" + if self.problem is None: + raise ValueError("The method has not been built.") + self.validate_solution_support() + values = self.problem.expr.edge_selected.value + if values is None: + raise ValueError("The problem has not been solved.") + return np.flatnonzero(np.asarray(values).reshape(-1) > threshold) + + def validate_solution_support(self) -> None: + """Reject solved exact-support models that violate coefficient support.""" + if self.problem is None: + raise ValueError("The method has not been built.") + selected_values = self.problem.expr.edge_selected.value + coefficient_values = self.problem.expr.edge_coefficient.value + if selected_values is None or coefficient_values is None: + raise ValueError("The problem has not been solved.") + if self.coefficient_support != "exact": + return + + selected = np.asarray(selected_values).reshape(-1) > 0.5 + coefficients = np.asarray(coefficient_values).reshape(-1) + normalized = coefficients * ( + self._coefficient_scales[self._edge_sources] / self._coefficient_scales[self._edge_targets] + ) + tolerance = min(1e-9, self.min_abs_coefficient * 0.5) + magnitude = np.abs(normalized) + invalid_selected = selected & (magnitude < self.min_abs_coefficient - tolerance) + invalid_unselected = ~selected & (magnitude > tolerance) + invalid = invalid_selected | invalid_unselected + if np.any(invalid): + edges = np.flatnonzero(invalid).tolist() + raise ValueError( + "Solved exact coefficient support is invalid for edges " + f"{edges!r}: selected edges must have normalized magnitude at least " + f"min_abs_coefficient={self.min_abs_coefficient:g} and unselected " + "edges must have zero normalized coefficients." + ) + + def get_edge_usage(self, threshold: float = 0.5) -> np.ndarray: + """Return the edge-by-commodity support matrix.""" + if self.problem is None: + raise ValueError("The method has not been built.") + self.validate_solution_support() + values = self.problem.expr.edge_used_by_commodity.value + if values is None: + raise ValueError("The problem has not been solved.") + return np.asarray(values).reshape((self._original_graph.num_edges, len(self._commodities))) > threshold + + def get_solution_graph(self, threshold: float = 0.5): + """Return the selected PKN subgraph with fitted edge metadata. + + Each solution edge receives ``coefficient`` and + ``normalized_coefficient`` attributes. The configured interaction + attribute is set to the sign of the fitted coefficient and, when the + original graph provided that attribute, its prior value is preserved + as ``prior_``. Structural-support edges whose + fitted coefficient is numerically zero receive ``connector=True``. + """ + if self._original_graph is None: + raise ValueError("The method has not been built.") + selected = self.get_selected_edge_indices(threshold) + extract_keep_order = getattr(self._original_graph, "_extract_subgraph_keep_order", None) + if callable(extract_keep_order): + solution_graph = extract_keep_order(edges=selected) + else: + solution_graph = self._original_graph.edge_subgraph(selected) + + coefficients = np.asarray(self.problem.expr.edge_coefficient.value).reshape(-1) + normalized = coefficients * ( + self._coefficient_scales[self._edge_sources] / self._coefficient_scales[self._edge_targets] + ) + for solution_index, edge_index in enumerate(selected): + attributes = solution_graph.get_attr_edge(solution_index) + if self.interaction_attribute in attributes: + attributes[f"prior_{self.interaction_attribute}"] = attributes[self.interaction_attribute] + inferred_interaction = int(np.sign(coefficients[edge_index])) + attributes[self.interaction_attribute] = inferred_interaction + attributes["coefficient"] = float(coefficients[edge_index]) + attributes["normalized_coefficient"] = float(normalized[edge_index]) + attributes["inferred_interaction"] = inferred_interaction + attributes["connector"] = bool( + self.coefficient_support == "structural" + and np.isclose(coefficients[edge_index], 0.0, atol=1e-9, rtol=0.0) + ) + return solution_graph + + @staticmethod + def name() -> str: + """Return the method name.""" + return "LinearDAGDiscovery" + + @staticmethod + def description() -> str: + """Return a short method description.""" + return "Commodity-flow-supported linear DAG discovery over a prior network" diff --git a/corneto/methods/imat.py b/corneto/methods/imat.py index 88595fecd..667fd22c8 100644 --- a/corneto/methods/imat.py +++ b/corneto/methods/imat.py @@ -40,7 +40,9 @@ class MultiSampleIMAT(MultiSampleFBA): Used when both types of regularization are needed. Defaults to 0.0. eps (float, optional): Tolerance for considering a flux as non-zero. Defaults to 1e-2. - scale (bool, optional): Whether to scale the weights. Defaults to False. + scale (bool, optional): If True, normalize the nonzero iMAT score + weights independently for each condition so their absolute values + sum to 100. Defaults to False. gpr_field (str, optional): Name of the attribute field containing GPR rules. Defaults to "GPR". high_expression_threshold (Optional[float], optional): Threshold above @@ -198,8 +200,10 @@ def preprocess(self, graph: BaseGraph, data: Data) -> Tuple[BaseGraph, Data]: # Count features with mapping="edge" for feature in sample.features: - if feature.mapping == "edge" and ( - feature.data.get("role") != "objective" or "imat_score" in feature.data + if ( + feature.mapping == "edge" + and (feature.value is not None or "imat_score" in feature.data) + and (feature.data.get("role") != "objective" or "imat_score" in feature.data) ): edge_features_count += 1 diff --git a/corneto/methods/pcst.py b/corneto/methods/pcst.py index 97c9abe27..2298c780b 100644 --- a/corneto/methods/pcst.py +++ b/corneto/methods/pcst.py @@ -9,7 +9,6 @@ from corneto.data import Data from corneto.graph import Attr, BaseGraph, EdgeType from corneto.methods._base import FlowMethod -from corneto.methods._flow_utils import add_selected_flow from corneto.methods._input_utils import ( DEFAULT_CONDITION, data_from_features, @@ -74,6 +73,8 @@ def __init__( self.flow_edges_out = dict() self.prized_flow_edges = dict() self._candidate_vertices_per_sample: List[Tuple[Any, ...]] = [] + self._biological_graph: Optional[BaseGraph] = None + self._biological_num_edges = 0 # Internal storage for per-sample values. self._max_flow: Optional[float] = None @@ -169,6 +170,8 @@ def preprocess(self, graph: BaseGraph, data: Data) -> Tuple[BaseGraph, Data]: self.flow_edges_out = dict() self.prized_flow_edges = dict() self._candidate_vertices_per_sample = [] + self._biological_graph = graph + self._biological_num_edges = graph.num_edges graph_vertices = tuple(graph.V) all_vertices = tuple( @@ -292,13 +295,27 @@ def _first_vertex_choice(): def get_flow_bounds(self, graph: BaseGraph, data: Data) -> Dict[str, Any]: """Get the flow bounds for the optimization problem.""" if self._max_flow_list is not None: - lb = [ - np.array( - [0 if prop.has_attr(Attr.EDGE_TYPE, EdgeType.DIRECTED) else -mf for prop in graph.get_attr_edges()] - ) - for mf in self._max_flow_list - ] - ub = self._max_flow_list + lb = np.column_stack( + [ + np.array( + [ + 0 if prop.has_attr(Attr.EDGE_TYPE, EdgeType.DIRECTED) else -max_flow + for prop in graph.get_attr_edges() + ] + ) + for max_flow in self._max_flow_list + ] + ) + ub = np.column_stack( + [ + np.full( + graph.num_edges, + max_flow, + dtype=float, + ) + for max_flow in self._max_flow_list + ] + ) else: lb = np.array( [ @@ -315,24 +332,44 @@ def get_flow_bounds(self, graph: BaseGraph, data: Data) -> Dict[str, Any]: "shared_bounds": False, } - def create_flow_based_problem(self, flow_problem: ProblemDef, graph: BaseGraph, data: Data): - """Create the flow-based optimization problem.""" - flow_edge_ids = list(set(self.flow_edges_in.values()) | set(self.flow_edges_out.values())) - edge_ids = list(set(range(graph.num_edges)) - set(flow_edge_ids)) - - selected_flow = add_selected_flow( - self.backend, - flow_problem, + def create_problem(self, graph: BaseGraph, data: Data): + """Create exact biological support without auxiliary-edge indicators.""" + flow_params = self.get_flow_bounds(graph, data) + flow_problem = self.backend.SelectedFlow( graph, - biological_edge_indices=edge_ids, + lb=flow_params["lb"], + ub=flow_params["ub"], + n_flows=flow_params["n_flows"], + edge_indices=range(self._biological_num_edges), epsilon=self.epsilon, - acyclic=self.strict_acyclic, + exact_support=self.strict_acyclic, + selected_by_flow_name="with_flow", + selected_any_name=None, ) - with_flow = selected_flow.all_edges + return self.create_flow_based_problem(flow_problem, graph, data) - flow_problem.register("with_flow", with_flow) + def create_flow_based_problem(self, flow_problem: ProblemDef, graph: BaseGraph, data: Data): + """Create the flow-based optimization problem.""" + edge_ids = list(range(self._biological_num_edges)) + with_flow = flow_problem.expr.with_flow self._reg_varname = "with_flow" + if self.strict_acyclic: + assert self._biological_graph is not None + if "with_flow_negative" in flow_problem.expr: + self.backend.Acyclic( + self._biological_graph, + flow_problem, + indicator_positive_var_name="with_flow_positive", + indicator_negative_var_name="with_flow_negative", + ) + else: + self.backend.Acyclic( + self._biological_graph, + flow_problem, + indicator_positive_var_name="with_flow", + ) + for i, sample_data in enumerate(data.samples.values()): sample_max_flow = self._max_flow_list[i] if self._max_flow_list is not None else self._max_flow sample_selected_root = self._selected_roots[i] @@ -375,6 +412,7 @@ def create_flow_based_problem(self, flow_problem: ProblemDef, graph: BaseGraph, if other_flow_edges: flow_problem += F[other_flow_edges] == 0 + candidate_support = None # Root Flow Constraints if sample_selected_root is not None: if self.force_flow_through_root: @@ -386,16 +424,18 @@ def create_flow_based_problem(self, flow_problem: ProblemDef, graph: BaseGraph, flow_problem += F[terminals_edgeflow_idx] >= 1 else: if candidate_vertices: - flow_problem += self.backend.NonZeroIndicator( + terminal_support = f"{self.flow_name}_terminal_support_{i}" + terminal_pos = f"{self.flow_name}_terminal_pos_{i}" + terminal_neg = f"{self.flow_name}_terminal_neg_{i}" + flow_problem += self.backend.ExactSupport( flow_problem.expr.flow, - vertices_edgeflow_idx, - i, - tolerance=self.epsilon, - suffix_pos=f"_terminal_pos_{i}", - suffix_neg=f"_terminal_neg_{i}", + indexes=(vertices_edgeflow_idx, i), + epsilon=self.epsilon, + name=terminal_support, + positive_name=terminal_pos, + negative_name=terminal_neg, ) - terminal_pos = self.flow_name + f"_terminal_pos_{i}" - terminal_neg = self.flow_name + f"_terminal_neg_{i}" + candidate_support = flow_problem.expr[terminal_support] flow_problem += flow_problem.expr[terminal_neg].sum() == 1 @@ -404,9 +444,7 @@ def create_flow_based_problem(self, flow_problem: ProblemDef, graph: BaseGraph, if t_idx: # Enforce all terminals selected, whether root is a terminal # (negative sign) or not (all terminals positive). - flow_problem += flow_problem.expr[terminal_pos][t_idx].sum() + flow_problem.expr[ - terminal_neg - ][t_idx].sum() == len(t_idx) + flow_problem += candidate_support[t_idx].sum() == len(t_idx) # Costs Objective edge_costs = np.ones((graph.num_edges)) * self.default_edge_cost @@ -437,23 +475,26 @@ def create_flow_based_problem(self, flow_problem: ProblemDef, graph: BaseGraph, prized_vertices.append(prized) if prized_idx: prizes = np.array([prized_terminals[prized] for prized in prized_vertices]) - if self.strict_acyclic: - selected_for_prizes = with_flow if len(with_flow.shape) == 1 else with_flow[:, i] - selected_prized_flow_edges = selected_for_prizes[prized_idx] + if candidate_support is not None: + candidate_position = {edge: position for position, edge in enumerate(vertices_edgeflow_idx)} + selected_prized_flow_edges = candidate_support[ + [candidate_position[edge] for edge in prized_idx] + ] else: flow_variable = flow_problem.expr._flow - indicator_args = (prized_idx,) if len(flow_variable.shape) == 1 else (prized_idx, i) - flow_problem += self.backend.NonZeroIndicator( + prize_support = f"{self.flow_name}_prize_support_{i}" + prize_lb = np.asarray(flow_variable.lb[prized_idx, i], dtype=float) + nonnegative = bool(np.all(prize_lb >= 0)) + flow_problem += self.backend.ExactSupport( flow_variable, - *indicator_args, - tolerance=self.epsilon, - suffix_pos=f"_prize_pos_{i}", - suffix_neg=f"_prize_neg_{i}", - ) - selected_prized_flow_edges = ( - flow_problem.expr[f"{self.flow_name}_prize_pos_{i}"] - + flow_problem.expr[f"{self.flow_name}_prize_neg_{i}"] + indexes=(prized_idx, i), + epsilon=self.epsilon, + nonnegative=nonnegative, + name=prize_support, + positive_name=f"{self.flow_name}_prize_pos_{i}", + negative_name=f"{self.flow_name}_prize_neg_{i}", ) + selected_prized_flow_edges = flow_problem.expr[prize_support] flow_problem.register(f"selected_prized_flow_edges_{i}", selected_prized_flow_edges) flow_problem.add_objective(prizes @ selected_prized_flow_edges, weight=-1, name="prizes") diff --git a/corneto/methods/phonemes.py b/corneto/methods/phonemes.py index 14950551b..db5685d11 100644 --- a/corneto/methods/phonemes.py +++ b/corneto/methods/phonemes.py @@ -11,10 +11,7 @@ from corneto.data import Data from corneto.graph import Attr, BaseGraph, EdgeType from corneto.methods._base import FlowMethod -from corneto.methods._flow_utils import ( - add_selected_flow, - add_vertex_selection, -) +from corneto.methods._flow_utils import add_vertex_selection from corneto.methods._input_utils import ( DEFAULT_CONDITION, data_from_features, @@ -26,7 +23,6 @@ validate_vertices, ) from corneto.methods._network_utils import augment_with_boundaries, prune_to_paths -from corneto.methods._optimization_utils import add_condition_union from corneto.methods._phonemes_preprocessing import ( compute_phonemes_scores, normalize_phonemes_score_mapping, @@ -132,6 +128,7 @@ def __init__( raise ValueError("max_flow must be greater than or equal to epsilon.") self._condition_names: tuple[str, ...] = () + self._biological_graph: BaseGraph | None = None self._biological_num_edges = 0 self._flow_max = 0.0 self._target_inflow_edges: dict[Any, int] = {} @@ -357,6 +354,7 @@ def preprocess(self, graph: BaseGraph, data: Data): node_scores[measured_indexes, condition_index] = [measured[vertices[index]] for index in measured_indexes] self._condition_names = condition_names + self._biological_graph = pruned_graph self._biological_num_edges = pruned_graph.num_edges self._target_mask = target_mask self._measured_mask = measured_mask @@ -431,31 +429,31 @@ def get_flow_bounds(self, graph: BaseGraph, data: Data): } def create_problem(self, graph: BaseGraph, data: Data): - """Create a consistently two-dimensional flow problem.""" + """Create selected biological flows without auxiliary-edge binaries.""" flow_params = self.get_flow_bounds(graph, data) - flow_problem = self.backend.Flow( + flow_problem = self.backend.SelectedFlow( graph, lb=flow_params["lb"], ub=flow_params["ub"], n_flows=flow_params["n_flows"], - shared_bounds=False, - force_matrix=True, + edge_indices=range(self._biological_num_edges), + epsilon=self.epsilon, + selected_by_flow_name="edge_selected", + selected_any_name="edge_selected_any", ) return self.create_flow_based_problem(flow_problem, graph, data) def create_flow_based_problem(self, flow_problem: ProblemDef, graph: BaseGraph, data: Data): """Add vectorized PHONEMeS selection, role, and objective terms.""" - selected_flow = add_selected_flow( - self.backend, + edge_selected = flow_problem.expr.edge_selected + assert self._biological_graph is not None + self.backend.Acyclic( + self._biological_graph, flow_problem, - graph, - biological_edge_indices=range(self._biological_num_edges), - epsilon=self.epsilon, - acyclic=True, + indicator_positive_var_name="edge_selected", + acyclic_var_name="_dag_layer", ) - edge_selected = selected_flow.biological_edges - flow_problem.register("edge_selected", edge_selected) - flow_problem.register("dag_layer", selected_flow.dag_layer) + flow_problem.register("dag_layer", flow_problem.expr._dag_layer) internal_mask = ~(self._target_mask | self._measured_mask) require_outgoing = (internal_mask | self._target_mask).astype(float) @@ -471,12 +469,7 @@ def create_flow_based_problem(self, flow_problem: ProblemDef, graph: BaseGraph, require_incoming=require_incoming, ) vertex_selected = vertex_selection.selected - edge_selected_any = add_condition_union( - self.backend, - flow_problem, - edge_selected, - name="edge_selected_any", - ) + edge_selected_any = flow_problem.expr.edge_selected_any flow_problem.add_objective( vertex_selected.multiply(self._node_scores).sum(), diff --git a/corneto/methods/signal/__init__.py b/corneto/methods/signal/__init__.py deleted file mode 100644 index 21c233edc..000000000 --- a/corneto/methods/signal/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -"""Deprecated compatibility namespace for signaling methods.""" - -import warnings - -warnings.warn( - "corneto.methods.signal is deprecated since CORNETO 1.0.0rc1; use " - "corneto.methods.signaling instead. The compatibility package will be " - "removed in CORNETO 2.0.", - FutureWarning, - stacklevel=2, -) diff --git a/corneto/methods/signal/cellnopt_ilp.py b/corneto/methods/signal/cellnopt_ilp.py deleted file mode 100644 index 63941b6ce..000000000 --- a/corneto/methods/signal/cellnopt_ilp.py +++ /dev/null @@ -1,17 +0,0 @@ -"""Deprecated compatibility imports for the CellNOpt ILP implementation.""" - -from corneto.methods.signaling.cellnopt_ilp import ( - cellnoptILP, - cno_style, - expand_graph_for_flows, - plot_data, - plot_fitness, -) - -__all__ = [ - "cellnoptILP", - "cno_style", - "expand_graph_for_flows", - "plot_data", - "plot_fitness", -] diff --git a/corneto/methods/signaling/__init__.py b/corneto/methods/signaling/__init__.py index 1ba1a1b95..baf83ee5f 100644 --- a/corneto/methods/signaling/__init__.py +++ b/corneto/methods/signaling/__init__.py @@ -1 +1,22 @@ """Signaling-network inference methods.""" + +from corneto.methods.signaling.annnet import ( + add_cellnopt_conditions, + add_cellnopt_results, + build_cellnopt_from_annnet, +) +from corneto.methods.signaling.cellnopt_dag import BooleanReaction, CellNOptDAG +from corneto.methods.signaling.cellnopt_plotting import ( + plot_cellnopt_fit, + plot_cellnopt_model, +) + +__all__ = [ + "BooleanReaction", + "CellNOptDAG", + "add_cellnopt_conditions", + "add_cellnopt_results", + "build_cellnopt_from_annnet", + "plot_cellnopt_fit", + "plot_cellnopt_model", +] diff --git a/corneto/methods/signaling/annnet.py b/corneto/methods/signaling/annnet.py new file mode 100644 index 000000000..0f209a5ca --- /dev/null +++ b/corneto/methods/signaling/annnet.py @@ -0,0 +1,363 @@ +"""AnnNet integration for CellNOptDAG signaling analyses.""" + +import warnings +from copy import deepcopy +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any, Mapping + +import numpy as np + +from corneto.contrib.annnet import from_annnet +from corneto.utils import import_optional_module + +if TYPE_CHECKING: + from annnet import AnnNet + + from corneto.backend._base import ProblemDef + from corneto.methods.signaling import CellNOptDAG + + +@dataclass(frozen=True) +class _CellNOptAnnNetContext: + condition_layers: dict[str, tuple[str, ...]] + source_edge_ids: tuple[str, ...] + source_edges: dict[str, tuple[str, str, int]] + + +def _matching_condition_keys(**collections: Mapping[str, Any]) -> tuple[str, ...]: + names: tuple[str, ...] | None = None + for argument, values in collections.items(): + if not isinstance(values, Mapping): + raise TypeError(f"{argument} must be a mapping from condition names to protein values.") + current = tuple(values) + if names is None: + names = current + elif set(current) != set(names): + raise ValueError(f"{argument} must contain the same conditions as inputs.") + return names or () + + +def _condition_layer_map( + graph: "AnnNet", + *, + condition_aspect: str, + condition_layers: Mapping[str, tuple[str, ...]] | None, +) -> dict[str, tuple[str, ...]]: + aspects = tuple(graph.layers.list_aspects()) + if condition_layers is not None: + result = {str(name): tuple(layer) for name, layer in condition_layers.items()} + if not result: + raise ValueError("condition_layers must contain at least one condition.") + if any(len(layer) != len(aspects) for layer in result.values()): + raise ValueError("Each condition layer must provide one value for every AnnNet layer aspect.") + return result + + if aspects != (condition_aspect,): + raise ValueError( + f"Automatic condition discovery requires one AnnNet layer aspect named {condition_aspect!r}. " + "Pass condition_layers explicitly when the network uses several aspects." + ) + names = graph.layers.list_layers(aspect=condition_aspect) + if not names: + raise ValueError(f"The AnnNet object has no layers for the {condition_aspect!r} aspect.") + return {str(name): (str(name),) for name in names} + + +def add_cellnopt_conditions( + graph: "AnnNet", + *, + inputs: Mapping[str, Mapping[str, Any]], + measurements: Mapping[str, Mapping[str, Any]], + inhibitors: Mapping[str, Mapping[str, Any]] | None = None, + condition_aspect: str = "condition", + input_attr: str = "input", + inhibitor_attr: str = "inhibited", + measurement_attr: str = "observed", +) -> dict[str, tuple[str, ...]]: + """Add CellNOpt perturbations and measurements as AnnNet condition layers. + + Every condition receives a copy of the graph's existing vertices. Input, + inhibitor, and measurement values are stored as vertex-layer attributes. + The returned mapping can be passed to :func:`build_cellnopt_from_annnet` + when a caller wants to name or select layers explicitly. + """ + import_optional_module("annnet") + if inhibitors is None: + inhibitors = {condition: {} for condition in inputs} + condition_names = _matching_condition_keys( + inputs=inputs, + measurements=measurements, + inhibitors=inhibitors, + ) + if not condition_names: + raise ValueError("At least one experimental condition is required.") + + aspects = tuple(graph.layers.list_aspects()) + if aspects and aspects != (condition_aspect,): + raise ValueError( + "add_cellnopt_conditions can initialize an unlayered AnnNet object or reuse its single " + f"{condition_aspect!r} aspect. Use AnnNet directly for experiments with several layer aspects." + ) + if not aspects: + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="Declared aspects") + graph.layers.set_aspects( + [condition_aspect], + {condition_aspect: list(condition_names)}, + ) + + known_vertices = set(graph.vertices()) + base_vertices = list(graph.vertices()) + layers = {condition: (condition,) for condition in condition_names} + for condition, layer in layers.items(): + graph.add_vertices(base_vertices, layer=layer) + values_by_role = ( + (inputs[condition], input_attr), + (inhibitors[condition], inhibitor_attr), + (measurements[condition], measurement_attr), + ) + for values, attribute in values_by_role: + if not isinstance(values, Mapping): + raise TypeError(f"Values for condition {condition!r} must be mappings keyed by protein.") + unknown = set(values) - known_vertices + if unknown: + protein = sorted(unknown, key=str)[0] + raise ValueError(f"Unknown protein {protein!r} in condition {condition!r}.") + for protein, value in values.items(): + graph.layers.set_vertex_layer_attrs(str(protein), layer, **{attribute: value}) + return layers + + +def _cellnopt_source_graph(graph: "AnnNet", network_slice: str | None): + selected = set(graph.edges()) if network_slice is None else set(graph.slices.edges(network_slice)) + edge_ids = [edge_id for edge_id in graph.edges() if edge_id in selected] + directed_edge_ids = set(graph.get_edges_by_direction(True)) + source = graph.__class__(directed=None) + + def biological_id(vertex): + if isinstance(vertex, tuple) and len(vertex) == 2 and isinstance(vertex[1], tuple): + return str(vertex[0]) + return str(vertex) + + for edge_id in edge_ids: + edge_source, edge_target = graph.get_edge(edge_id) + sources = [biological_id(vertex) for vertex in edge_source] + targets = [biological_id(vertex) for vertex in edge_target] + if len(sources) != 1 or len(targets) != 1: + raise ValueError( + "CellNOptDAG requires signed interactions with one source and one target; " + f"AnnNet edge {edge_id!r} has {len(sources)} sources and {len(targets)} targets." + ) + source.add_edges( + sources[0], + targets[0], + edge_id=edge_id, + directed=edge_id in directed_edge_ids, + parallel="parallel", + ) + attributes = dict(graph.attrs.get_edge_attrs(edge_id)) + attributes.pop("edge_id", None) + if attributes: + source.attrs.set_edge_attrs(edge_id, **deepcopy(attributes)) + + source.uns.update(deepcopy(dict(graph.uns))) + return source + + +def build_cellnopt_from_annnet( + method: "CellNOptDAG", + graph: "AnnNet", + *, + network_slice: str | None = None, + condition_aspect: str = "condition", + condition_layers: Mapping[str, tuple[str, ...]] | None = None, + input_attr: str = "input", + inhibitor_attr: str = "inhibited", + measurement_attr: str = "observed", +) -> "ProblemDef": + """Build a CellNOptDAG problem from a signed network and data in AnnNet. + + The signed interactions are read from ``network_slice`` when supplied. + Inputs, inhibitors, and measurements are read from vertex attributes in + the condition layers. Internal graph preparation is kept inside this + integration function so that AnnNet remains the user-facing graph object. + """ + from corneto.methods.signaling import CellNOptDAG + + if not isinstance(method, CellNOptDAG): + raise TypeError("method must be a CellNOptDAG instance.") + layers = _condition_layer_map( + graph, + condition_aspect=condition_aspect, + condition_layers=condition_layers, + ) + source = _cellnopt_source_graph(graph, network_slice) + pkn = from_annnet(source) + + inputs: dict[str, dict[str, Any]] = {} + inhibitors: dict[str, dict[str, Any]] = {} + measurements: dict[str, dict[str, Any]] = {} + for condition, layer in layers.items(): + inputs[condition] = {} + inhibitors[condition] = {} + measurements[condition] = {} + for protein in graph.layers.layer_vertex_set(layer): + protein_id = str(protein[0]) if isinstance(protein, tuple) else str(protein) + attributes = graph.layers.get_vertex_layer_attrs(protein_id, layer) + if input_attr in attributes: + inputs[condition][protein_id] = attributes[input_attr] + if attributes.get(inhibitor_attr): + inhibitors[condition][protein_id] = 1 + if measurement_attr in attributes: + measurements[condition][protein_id] = attributes[measurement_attr] + + source_edge_ids = tuple(source.edges()) + source_edges = {} + for edge_id in source_edge_ids: + edge_source, edge_target = source.get_edge(edge_id) + if len(edge_source) != 1 or len(edge_target) != 1: + continue + attributes = source.attrs.get_edge_attrs(edge_id) + source_edges[edge_id] = ( + str(next(iter(edge_source))), + str(next(iter(edge_target))), + int(attributes.get("interaction", 1)), + ) + + problem = method.build_many( + pkn, + inputs=inputs, + measurements=measurements, + inhibitors=inhibitors, + ) + problem._annnet_cellnopt_context = _CellNOptAnnNetContext( + condition_layers=layers, + source_edge_ids=source_edge_ids, + source_edges=source_edges, + ) + return problem + + +def _reaction_text(reaction) -> str: + literals = [str(node) for node in reaction.positive_literals] + literals.extend(f"NOT {node}" for node in reaction.negative_literals) + return f"{' AND '.join(literals)} -> {reaction.product}" + + +def add_cellnopt_results( + graph: "AnnNet", + method: "CellNOptDAG", + problem: "ProblemDef", + *, + solution: Any = None, + model_slice: str = "cellnopt_selected", + prediction_attr: str = "predicted", + activity_attr: str = "active", + measurement_attr: str = "observed", + error_attr: str = "absolute_error", +) -> dict[str, Any]: + """Add a solved CellNOptDAG model and its condition results to AnnNet. + + The selected interactions are added to ``model_slice``. Predictions are + stored on protein-layer pairs, while copies of selected interactions in + each condition layer record whether their reaction is active. + """ + context = getattr(problem, "_annnet_cellnopt_context", None) + if context is None: + raise ValueError("Build the problem with build_cellnopt_from_annnet before adding its results.") + required = ("reaction_selected", "reaction_active", "vertex_value") + missing = [name for name in required if getattr(problem.expr, name).value is None] + if missing: + raise ValueError("Solve the CellNOptDAG problem before adding results to AnnNet.") + if graph.slices.exists(model_slice): + raise ValueError(f"AnnNet slice {model_slice!r} already exists.") + + selected = np.rint(np.asarray(problem.expr.reaction_selected.value)).astype(int).reshape(-1) + activity = np.rint(np.asarray(problem.expr.reaction_active.value)).astype(int) + predictions = np.rint(np.asarray(problem.expr.vertex_value.value)).astype(int) + selected_indices = np.flatnonzero(selected) + condition_names = tuple(context.condition_layers) + vertices = [str(vertex) for vertex in method.processed_graph.V] + + graph.slices.add(model_slice, role="inferred_model", method="CellNOptDAG") + selected_source_edges = { + context.source_edge_ids[edge_index] + for reaction_index in selected_indices + for edge_index in method.reactions[reaction_index].source_edges + } + for edge_id in selected_source_edges: + graph.slices.add_edge_to_slice(model_slice, edge_id) + + result_edge_ids = [] + condition_errors = {} + for condition_index, condition in enumerate(condition_names): + layer = context.condition_layers[condition] + graph.add_vertices(vertices, layer=layer) + endpoint_error = 0.0 + for vertex_index, protein in enumerate(vertices): + predicted = float(predictions[vertex_index, condition_index]) + attributes = {prediction_attr: predicted} + existing = graph.layers.get_vertex_layer_attrs(protein, layer) + if measurement_attr in existing: + error = abs(predicted - float(existing[measurement_attr])) + attributes[error_attr] = error + endpoint_error += error + graph.layers.set_vertex_layer_attrs(protein, layer, **attributes) + + condition_edges = [] + for reaction_index in selected_indices: + reaction = method.reactions[reaction_index] + is_active = bool(activity[reaction_index, condition_index]) + for source_edge_index in reaction.source_edges: + prior_edge_id = context.source_edge_ids[source_edge_index] + source, target, interaction = context.source_edges[prior_edge_id] + condition_edges.append( + { + "source": (source, layer), + "target": (target, layer), + "edge_id": ( + f"{model_slice}__condition_{condition_index}__reaction_{reaction_index}" + f"__edge_{source_edge_index}" + ), + "interaction": interaction, + "prior_edge_id": prior_edge_id, + "reaction_index": int(reaction_index), + "reaction": _reaction_text(reaction), + "selected": True, + activity_attr: is_active, + "source_method": "CellNOptDAG", + } + ) + result_edge_ids.extend( + graph.add_edges( + condition_edges, + slice=model_slice, + default_edge_directed=True, + ) + ) + + active_count = int(activity[selected_indices, condition_index].sum()) + layer_attributes = { + "endpoint_absolute_error": float(endpoint_error), + "selected_reactions": len(selected_indices), + "active_reactions": active_count, + } + if solution is not None and getattr(solution, "status", None) is not None: + layer_attributes["solver_status"] = str(solution.status) + graph.layers.set_layer_attrs(layer, **layer_attributes) + condition_errors[condition] = endpoint_error + + graph.history.snapshot("cellnopt_results_added") + return { + "selected_reactions": len(selected_indices), + "selected_prior_edges": len(selected_source_edges), + "condition_edges": len(result_edge_ids), + "condition_errors": condition_errors, + } + + +__all__ = [ + "add_cellnopt_conditions", + "add_cellnopt_results", + "build_cellnopt_from_annnet", +] diff --git a/corneto/methods/signaling/cellnopt_dag.py b/corneto/methods/signaling/cellnopt_dag.py new file mode 100644 index 000000000..b8367299e --- /dev/null +++ b/corneto/methods/signaling/cellnopt_dag.py @@ -0,0 +1,931 @@ +"""Flow-based Boolean network inference in the style of CellNOpt.""" + +from __future__ import annotations + +import re +from collections.abc import Mapping +from dataclasses import dataclass +from numbers import Real +from typing import Any, Optional + +import numpy as np +from scipy.sparse import csr_matrix + +from corneto._constants import VarType +from corneto.backend._base import Backend, ProblemDef +from corneto.data import Data +from corneto.graph import Attr, BaseGraph, EdgeType, Graph +from corneto.methods._base import FlowMethod +from corneto.methods._input_utils import ( + DEFAULT_CONDITION, + data_from_features, + require_mapping, + validate_condition_keys, +) +from corneto.methods._network_utils import augment_with_boundaries +from corneto.methods._predictive import ( + require_expression_value, + validate_solve_result, +) +from corneto.methods._predictive import ( + solve_options as normalize_solve_options, +) + +__all__ = ["BooleanReaction", "CellNOptDAG"] + +_AND_PATTERN = re.compile(r"^and[0-9]+$", re.IGNORECASE) +_ROLE_INPUT = "input" +_ROLE_OUTPUT = "output" +_ROLE_INPUT_OUTPUT = "input_output" + + +@dataclass(frozen=True) +class BooleanReaction: + """A normalized Boolean reaction with one product. + + Positive literals must be active and negative literals must be inactive + for the reaction to propagate in a condition. ``source_edges`` records the + PKN edges from which the reaction was compiled. + """ + + positive_literals: tuple[Any, ...] + negative_literals: tuple[Any, ...] + product: Any + source_edges: tuple[int, ...] + + @property + def literals(self) -> tuple[Any, ...]: + """Return positive and negative literals in deterministic order.""" + return self.positive_literals + self.negative_literals + + +@dataclass(frozen=True) +class _ReactionNetwork: + graph: Graph + reactions: tuple[BooleanReaction, ...] + dependency_reactions: np.ndarray + positive_literals: csr_matrix + negative_literals: csr_matrix + products: csr_matrix + + +def _is_and_vertex(vertex: Any) -> bool: + return isinstance(vertex, str) and _AND_PATTERN.fullmatch(vertex) is not None + + +def _interaction(graph: BaseGraph, edge_index: int) -> int: + value = graph.get_attr_edge(edge_index).get("interaction") + if isinstance(value, bool) or not isinstance(value, Real) or value not in (-1, 1): + raise ValueError(f"CellNOptDAG requires interaction +1 or -1 on edge {edge_index}; got {value!r}.") + return int(value) + + +def _compile_reactions(graph: BaseGraph) -> _ReactionNetwork: + """Compile SIF-style dummy AND vertices into reaction-level dependencies.""" + if graph.num_vertices == 0 or graph.num_edges == 0: + raise ValueError("CellNOptDAG requires a non-empty directed PKN.") + + and_vertices = {vertex for vertex in graph.V if _is_and_vertex(vertex)} + vertex_order = {vertex: index for index, vertex in enumerate(graph.V)} + incoming_by_and = {vertex: [] for vertex in and_vertices} + outgoing_by_and = {vertex: [] for vertex in and_vertices} + + for edge_index, ((source, target), attributes) in enumerate(zip(graph.E, graph.get_attr_edges())): + if len(source) != 1 or len(target) != 1: + raise ValueError( + "CellNOptDAG accepts simple directed PKN edges only; " + f"edge {edge_index} has {len(source)} sources and {len(target)} targets." + ) + if not attributes.has_attr(Attr.EDGE_TYPE, EdgeType.DIRECTED): + raise ValueError(f"CellNOptDAG requires directed edges; edge {edge_index} is not directed.") + _interaction(graph, edge_index) + source_vertex = next(iter(source)) + target_vertex = next(iter(target)) + if source_vertex in and_vertices and target_vertex in and_vertices: + raise ValueError(f"Nested dummy AND gates are not supported (edge {edge_index}).") + if target_vertex in and_vertices: + incoming_by_and[target_vertex].append(edge_index) + if source_vertex in and_vertices: + outgoing_by_and[source_vertex].append(edge_index) + + reactions: list[BooleanReaction] = [] + reaction_keys: set[tuple[frozenset, frozenset, Any]] = set() + + def ordered(vertices): + return tuple(sorted(vertices, key=vertex_order.__getitem__)) + + def add_reaction(positive, negative, product, source_edges): + positive = set(positive) + negative = set(negative) + contradictory = positive & negative + if contradictory: + literal = min(contradictory, key=vertex_order.__getitem__) + raise ValueError(f"Reaction producing {product!r} contains both {literal!r} and !{literal!r}.") + key = (frozenset(positive), frozenset(negative), product) + if key in reaction_keys: + return + reaction_keys.add(key) + reactions.append( + BooleanReaction( + positive_literals=ordered(positive), + negative_literals=ordered(negative), + product=product, + source_edges=tuple(source_edges), + ) + ) + + for edge_index, (source, target) in enumerate(graph.E): + source_vertex = next(iter(source)) + target_vertex = next(iter(target)) + if source_vertex in and_vertices or target_vertex in and_vertices: + continue + sign = _interaction(graph, edge_index) + add_reaction( + [source_vertex] if sign > 0 else [], + [source_vertex] if sign < 0 else [], + target_vertex, + [edge_index], + ) + + for gate in (vertex for vertex in graph.V if vertex in and_vertices): + incoming = incoming_by_and[gate] + outgoing = outgoing_by_and[gate] + if len(incoming) < 2: + raise ValueError(f"Dummy AND gate {gate!r} must have at least two incoming edges.") + if not outgoing: + raise ValueError(f"Dummy AND gate {gate!r} must have at least one outgoing edge.") + positive = [] + negative = [] + for edge_index in incoming: + source_vertex = next(iter(graph.get_edge(edge_index)[0])) + if _interaction(graph, edge_index) > 0: + positive.append(source_vertex) + else: + negative.append(source_vertex) + for edge_index in outgoing: + if _interaction(graph, edge_index) < 0: + raise ValueError( + f"Dummy AND gate {gate!r} must activate its product; edge {edge_index} has interaction -1." + ) + product = next(iter(graph.get_edge(edge_index)[1])) + add_reaction( + positive, + negative, + product, + [*incoming, edge_index], + ) + + if not reactions: + raise ValueError("CellNOptDAG preprocessing produced no Boolean reactions.") + + dependency_graph = Graph() + species = [vertex for vertex in graph.V if vertex not in and_vertices] + for vertex in species: + dependency_graph.add_vertex(vertex) + + dependency_reactions = [] + for reaction_index, reaction in enumerate(reactions): + for literal in reaction.positive_literals: + dependency_graph.add_edge( + literal, + reaction.product, + interaction=1, + reaction=reaction_index, + ) + dependency_reactions.append(reaction_index) + for literal in reaction.negative_literals: + dependency_graph.add_edge( + literal, + reaction.product, + interaction=-1, + reaction=reaction_index, + ) + dependency_reactions.append(reaction_index) + + vertex_index = {vertex: index for index, vertex in enumerate(dependency_graph.V)} + num_reactions = len(reactions) + positive_rows = [] + positive_columns = [] + negative_rows = [] + negative_columns = [] + product_rows = [] + product_columns = [] + for reaction_index, reaction in enumerate(reactions): + positive_rows.extend([reaction_index] * len(reaction.positive_literals)) + positive_columns.extend(vertex_index[vertex] for vertex in reaction.positive_literals) + negative_rows.extend([reaction_index] * len(reaction.negative_literals)) + negative_columns.extend(vertex_index[vertex] for vertex in reaction.negative_literals) + product_rows.append(vertex_index[reaction.product]) + product_columns.append(reaction_index) + + literal_shape = (num_reactions, dependency_graph.num_vertices) + product_shape = (dependency_graph.num_vertices, num_reactions) + positive_literals = csr_matrix( + ( + np.ones(len(positive_rows)), + (positive_rows, positive_columns), + ), + shape=literal_shape, + ) + negative_literals = csr_matrix( + ( + np.ones(len(negative_rows)), + (negative_rows, negative_columns), + ), + shape=literal_shape, + ) + products = csr_matrix( + ( + np.ones(num_reactions), + (product_rows, product_columns), + ), + shape=product_shape, + ) + return _ReactionNetwork( + graph=dependency_graph, + reactions=tuple(reactions), + dependency_reactions=np.asarray(dependency_reactions, dtype=int), + positive_literals=positive_literals, + negative_literals=negative_literals, + products=products, + ) + + +def _bounded_number( + value: Any, + *, + argument: str, + identifier: Any, + condition: str, + binary: bool, +) -> float: + if isinstance(value, (bool, np.bool_)): + number = float(value) + elif isinstance(value, Real): + number = float(value) + else: + raise TypeError(f"{argument}[{identifier!r}] for condition {condition!r} must be numeric, got {value!r}.") + if not np.isfinite(number): + raise ValueError(f"{argument}[{identifier!r}] for condition {condition!r} must be finite.") + allowed = number in (0, 1) if binary else 0 <= number <= 1 + if not allowed: + domain = "0 or 1" if binary else "between 0 and 1" + raise ValueError(f"{argument}[{identifier!r}] for condition {condition!r} must be {domain}; got {value!r}.") + return number + + +def _cellnopt_data( + graph: BaseGraph, + *, + inputs, + measurements, + inhibitors=None, +) -> Data: + condition_names = validate_condition_keys( + inputs=inputs, + measurements=measurements, + inhibitors=inhibitors, + ) + if not condition_names: + raise ValueError("CellNOptDAG requires at least one named condition.") + if inhibitors is None: + inhibitors = {condition: {} for condition in condition_names} + + graph_vertices = set(graph.V) + features_by_condition = {} + for condition in condition_names: + condition_inputs = require_mapping( + inputs[condition], + argument="inputs", + condition=condition, + ) + condition_measurements = require_mapping( + measurements[condition], + argument="measurements", + condition=condition, + ) + condition_inhibitors = require_mapping( + inhibitors[condition], + argument="inhibitors", + condition=condition, + ) + if not condition_inputs and not condition_inhibitors: + raise ValueError(f"Condition {condition!r} must contain an input or active inhibitor.") + if not condition_measurements: + raise ValueError(f"measurements for condition {condition!r} must not be empty.") + + input_values = {} + measurement_values = {} + active_inhibitors = set() + for vertex, value in condition_inputs.items(): + if vertex not in graph_vertices: + raise ValueError(f"Unknown vertex {vertex!r} in inputs for condition {condition!r}.") + input_values[vertex] = _bounded_number( + value, + argument="inputs", + identifier=vertex, + condition=condition, + binary=True, + ) + for vertex, value in condition_measurements.items(): + if vertex not in graph_vertices: + raise ValueError(f"Unknown vertex {vertex!r} in measurements for condition {condition!r}.") + measurement_values[vertex] = _bounded_number( + value, + argument="measurements", + identifier=vertex, + condition=condition, + binary=False, + ) + for vertex, value in condition_inhibitors.items(): + if vertex not in graph_vertices: + raise ValueError(f"Unknown vertex {vertex!r} in inhibitors for condition {condition!r}.") + inhibitor = _bounded_number( + value, + argument="inhibitors", + identifier=vertex, + condition=condition, + binary=True, + ) + if inhibitor: + active_inhibitors.add(vertex) + + overlap = active_inhibitors & set(input_values) + if overlap: + vertex = next(vertex for vertex in graph.V if vertex in overlap) + raise ValueError( + f"Vertex {vertex!r} cannot be both an input and an active inhibitor in condition {condition!r}." + ) + for vertex in active_inhibitors: + input_values[vertex] = 0.0 + + features = [] + included = set(input_values) | set(measurement_values) + for vertex in (vertex for vertex in graph.V if vertex in included): + is_input = vertex in input_values + is_output = vertex in measurement_values + role = _ROLE_INPUT_OUTPUT if is_input and is_output else (_ROLE_INPUT if is_input else _ROLE_OUTPUT) + feature = { + "id": vertex, + "mapping": "vertex", + "role": role, + "value": (measurement_values[vertex] if is_output else input_values[vertex]), + } + if is_input: + feature["input_value"] = input_values[vertex] + if vertex in active_inhibitors: + feature["intervention"] = "inhibitor" + features.append(feature) + features_by_condition[condition] = features + return data_from_features(features_by_condition) + + +class CellNOptDAG(FlowMethod): + """Infer a shared acyclic Boolean model from multiple conditions. + + The method selects reactions globally and evaluates their Boolean truth in + every condition. A single nonnegative flow has exactly the selected + reaction dependencies as its internal support. Conservation, positive + support, and acyclicity therefore require every selected dependency to lie + on a path from a controlled input or inhibitor to a measured output. + + Dummy vertices named ``AND`` are compiled into one reaction per + product. All operands of such a reaction share one selection variable and + are evaluated conjunctively. + + Args: + lambda_reg: Penalty for every selected reaction. + max_flow: Upper bound for structural flow. By default, the number of + compiled dependency edges is used. + epsilon: Minimum flow on every selected dependency edge. + backend: Optimization backend. + """ + + def __init__( + self, + lambda_reg: float = 1e-3, + max_flow: Optional[float] = None, + epsilon: float = 1.0, + backend: Optional[Backend] = None, + ): + if isinstance(lambda_reg, bool) or not isinstance(lambda_reg, Real): + raise TypeError("lambda_reg must be a finite nonnegative number.") + if not np.isfinite(lambda_reg) or lambda_reg < 0: + raise ValueError("lambda_reg must be a finite nonnegative number.") + if isinstance(epsilon, bool) or not isinstance(epsilon, Real): + raise TypeError("epsilon must be a finite positive number.") + if not np.isfinite(epsilon) or epsilon <= 0: + raise ValueError("epsilon must be a finite positive number.") + if max_flow is not None: + if isinstance(max_flow, bool) or not isinstance(max_flow, Real): + raise TypeError("max_flow must be a finite positive number.") + if not np.isfinite(max_flow) or max_flow <= 0: + raise ValueError("max_flow must be a finite positive number.") + if max_flow < epsilon: + raise ValueError("max_flow must be greater than or equal to epsilon.") + + super().__init__( + lambda_reg=lambda_reg, + reg_varname="reaction_selected", + flow_lower_bound=0, + flow_upper_bound=1, + backend=backend, + ) + self.max_flow = None if max_flow is None else float(max_flow) + self.epsilon = float(epsilon) + self.reactions: tuple[BooleanReaction, ...] = () + self._condition_names: tuple[str, ...] = () + self._biological_num_edges = 0 + self._flow_max = 0.0 + self._dependency_to_reaction = csr_matrix((0, 0)) + self._positive_literals = csr_matrix((0, 0)) + self._negative_literals = csr_matrix((0, 0)) + self._products = csr_matrix((0, 0)) + self._forced_mask = np.empty((0, 0), dtype=bool) + self._forced_values = np.empty((0, 0), dtype=float) + self._measurement_mask = np.empty((0, 0), dtype=bool) + self._measurements = np.empty((0, 0), dtype=float) + self._network_graph: Optional[Graph] = None + self._species: tuple[Any, ...] = () + self.solve_result = None + + def build( + self, + pkn: BaseGraph, + *, + inputs, + measurements, + inhibitors=None, + ) -> ProblemDef: + """Build a single-condition CellNOpt problem.""" + return self.build_many( + pkn, + inputs={DEFAULT_CONDITION: inputs}, + measurements={DEFAULT_CONDITION: measurements}, + inhibitors=(None if inhibitors is None else {DEFAULT_CONDITION: inhibitors}), + ) + + def build_many( + self, + pkn: BaseGraph, + *, + inputs, + measurements, + inhibitors=None, + ) -> ProblemDef: + """Build a problem for multiple named experimental conditions.""" + self.solve_result = None + data = _cellnopt_data( + pkn, + inputs=inputs, + measurements=measurements, + inhibitors=inhibitors, + ) + return self.build_from_data(pkn, data) + + def fit( + self, + pkn: BaseGraph, + *, + inputs, + measurements, + inhibitors=None, + solve_options: Optional[Mapping[str, Any]] = None, + **solver_options: Any, + ) -> "CellNOptDAG": + """Build and solve a shared Boolean model, returning ``self``. + + Scientific arguments intentionally mirror :meth:`build_many`; backend + options belong in ``solve_options`` (or can be supplied as solver + keyword arguments). The solved :attr:`problem` remains available for + adding constraints and inspecting expressions. Time-limited + incumbents are accepted when they include finite reaction selections. + """ + options = normalize_solve_options(solve_options, solver_options) + problem = self.build_many( + pkn, + inputs=inputs, + measurements=measurements, + inhibitors=inhibitors, + ) + result = problem.solve(**options) + validate_solve_result(result, problem.expr, ("reaction_selected",), self.name()) + self.solve_result = result + return self + + def preprocess(self, graph: BaseGraph, data: Data): + """Compile reactions, validate condition data, and add flow boundaries.""" + self.solve_result = None + network = _compile_reactions(graph) + if not data.samples: + raise ValueError("CellNOptDAG requires at least one condition.") + + condition_names = tuple(data.samples) + if any(not isinstance(name, str) or not name for name in condition_names): + raise ValueError("CellNOptDAG condition names must be non-empty strings.") + + vertex_index = {vertex: index for index, vertex in enumerate(network.graph.V)} + num_vertices = network.graph.num_vertices + num_conditions = len(condition_names) + forced_mask = np.zeros((num_vertices, num_conditions), dtype=bool) + forced_values = np.zeros((num_vertices, num_conditions), dtype=float) + measurement_mask = np.zeros((num_vertices, num_conditions), dtype=bool) + measurements = np.zeros((num_vertices, num_conditions), dtype=float) + + for condition_index, (condition, sample) in enumerate(data.samples.items()): + condition_inputs = 0 + condition_measurements = 0 + for feature in sample.features: + if feature.mapping != "vertex": + raise ValueError( + f"CellNOptDAG only accepts vertex features; got mapping " + f"{feature.mapping!r} in condition {condition!r}." + ) + if feature.id not in vertex_index: + raise ValueError( + f"Unknown species {feature.id!r} in CellNOptDAG data for " + f"condition {condition!r}; dummy AND vertices cannot be measured " + "or perturbed." + ) + role = feature.data.get("role") + if role not in { + _ROLE_INPUT, + _ROLE_OUTPUT, + _ROLE_INPUT_OUTPUT, + }: + raise ValueError( + f"Vertex feature {feature.id!r} for condition {condition!r} " + "must have role 'input', 'output', or 'input_output'." + ) + index = vertex_index[feature.id] + if role in {_ROLE_INPUT, _ROLE_INPUT_OUTPUT}: + input_value = feature.data.get( + "input_value", + feature.value if role == _ROLE_INPUT else None, + ) + forced_mask[index, condition_index] = True + forced_values[index, condition_index] = _bounded_number( + input_value, + argument="input_value", + identifier=feature.id, + condition=condition, + binary=True, + ) + condition_inputs += 1 + if role in {_ROLE_OUTPUT, _ROLE_INPUT_OUTPUT}: + measurement_mask[index, condition_index] = True + measurements[index, condition_index] = _bounded_number( + feature.value, + argument="measurement", + identifier=feature.id, + condition=condition, + binary=False, + ) + condition_measurements += 1 + if condition_inputs == 0: + raise ValueError(f"Condition {condition!r} must contain at least one input.") + if condition_measurements == 0: + raise ValueError(f"Condition {condition!r} must contain at least one output.") + + input_union = forced_mask.any(axis=1) + output_union = measurement_mask.any(axis=1) + layout = augment_with_boundaries( + network.graph, + inflow_vertices=(vertex for vertex, include in zip(network.graph.V, input_union) if include), + outflow_vertices=(vertex for vertex, include in zip(network.graph.V, output_union) if include), + ) + flow_graph = layout.graph + num_dependencies = network.graph.num_edges + num_reactions = len(network.reactions) + dependency_rows = np.arange(num_dependencies) + dependency_to_reaction = csr_matrix( + ( + np.ones(num_dependencies), + (dependency_rows, network.dependency_reactions), + ), + shape=(flow_graph.num_edges, num_reactions), + ) + + default_max_flow = float(max(num_dependencies, 1)) + flow_max = default_max_flow if self.max_flow is None else self.max_flow + if flow_max < self.epsilon: + raise ValueError(f"max_flow ({flow_max:g}) must be greater than or equal to epsilon ({self.epsilon:g}).") + + self.reactions = network.reactions + self._network_graph = network.graph.copy() + self._species = tuple(network.graph.V) + self._condition_names = condition_names + self._biological_num_edges = num_dependencies + self._flow_max = float(flow_max) + self._dependency_to_reaction = dependency_to_reaction + self._positive_literals = network.positive_literals + self._negative_literals = network.negative_literals + self._products = network.products + self._forced_mask = forced_mask + self._forced_values = forced_values + self._measurement_mask = measurement_mask + self._measurements = measurements + return flow_graph, data.copy() + + def get_flow_bounds(self, graph: BaseGraph, data: Data): + """Return bounds for the single shared structural flow.""" + return { + "lb": 0, + "ub": self._flow_max, + "n_flows": 1, + "shared_bounds": False, + } + + def create_flow_based_problem( + self, + flow_problem: ProblemDef, + graph: BaseGraph, + data: Data, + ) -> ProblemDef: + """Add vectorized Boolean propagation and shared-flow selection.""" + problem = flow_problem + num_vertices = graph.num_vertices + num_reactions = len(self.reactions) + num_conditions = len(self._condition_names) + condition_ones = np.ones((1, num_conditions)) + + reaction_selected = self.backend.Variable( + "reaction_selected", + (num_reactions,), + vartype=VarType.BINARY, + ) + reaction_active = self.backend.Variable( + "reaction_active", + (num_reactions, num_conditions), + vartype=VarType.BINARY, + ) + vertex_value = self.backend.Variable( + "vertex_value", + (num_vertices, num_conditions), + vartype=VarType.BINARY, + ) + + dependency_selected_all = self.backend.Constant(self._dependency_to_reaction) @ reaction_selected + dependency_selected = dependency_selected_all[: self._biological_num_edges] + flow = problem.expr.flow + # The shared flow and shared reaction selection have exactly the same + # support on biological dependencies. Boundary flow remains free to + # choose which controlled species and measurements connect that support. + problem += self.backend.ExactSupport( + flow, + indexes=np.arange(self._biological_num_edges, dtype=int), + selected=dependency_selected, + epsilon=self.epsilon, + nonnegative=True, + name="dependency_selected", + ) + + problem.register("_dependency_selected_all", dependency_selected_all) + self.backend.Acyclic( + graph, + problem, + indicator_positive_var_name="_dependency_selected_all", + ) + + positive_literals = self.backend.Constant(self._positive_literals) + negative_literals = self.backend.Constant(self._negative_literals) + products = self.backend.Constant(self._products) + negative_count = np.asarray(self._negative_literals.sum(axis=1)).reshape(-1, 1) + literal_count = np.asarray((self._positive_literals + self._negative_literals).sum(axis=1)).reshape(-1, 1) + negative_count = negative_count @ condition_ones + literal_count = literal_count @ condition_ones + # S[r, c] counts true literals of reaction r in condition c. With k + # literals, the three inequalities below impose + # Z[r, c] = Y[r] AND (S[r, c] == k[r]) without a big-M constant. + literal_satisfaction = positive_literals @ vertex_value + negative_count - negative_literals @ vertex_value + selected_by_condition = reaction_selected.reshape((num_reactions, 1)) @ condition_ones + + problem += reaction_active <= selected_by_condition + problem += reaction_active.multiply(literal_count) <= literal_satisfaction + problem += reaction_active >= selected_by_condition + literal_satisfaction - literal_count + + producing_reactions = products @ reaction_active + producer_count = np.asarray(self._products.sum(axis=1)).reshape(-1, 1) + producer_count = producer_count @ condition_ones + free_mask = (~self._forced_mask).astype(float) + # A non-intervened species is the OR of its active producing reactions. + # Interventions mask only this product relation; upstream reaction truth + # remains intact when a product is experimentally forced to zero. + problem += vertex_value.multiply(free_mask) <= producing_reactions.multiply(free_mask) + problem += producing_reactions.multiply(free_mask) <= vertex_value.multiply(producer_count * free_mask) + problem += vertex_value.multiply(self._forced_mask.astype(float)) == self._forced_values + + error_coefficients = self._measurement_mask.astype(float) * (1 - 2 * self._measurements) + error_constant = float(np.sum(self._measurement_mask * self._measurements)) + # For binary x and measurement m in [0, 1], + # |x - m| = m + (1 - 2m)x exactly. + measurement_error = vertex_value.multiply(error_coefficients).sum() + error_constant + problem.add_objective( + measurement_error, + name="measurement_error", + ) + + problem.register("literal_satisfaction", literal_satisfaction) + problem.register("dag_layer", problem.expr._dag_layer) + return problem + + # ------------------------------------------------------------------ + # Fixed reaction-selection prediction and evaluation + # ------------------------------------------------------------------ + def _prediction_conditions(self, inputs, inhibitors=None): + if self._network_graph is None or not self._species: + raise ValueError("CellNOptDAG has not been built; call build/build_many or fit first.") + require_mapping(inputs, argument="inputs") + condition_names = validate_condition_keys(inputs=inputs, inhibitors=inhibitors) + if not condition_names: + raise ValueError("CellNOptDAG prediction requires at least one named condition.") + if inhibitors is None: + inhibitors = {condition: {} for condition in condition_names} + species = set(self._species) + forced = np.zeros((len(self._species), len(condition_names)), dtype=bool) + forced_values = np.zeros_like(forced, dtype=float) + for condition_index, condition in enumerate(condition_names): + condition_inputs = require_mapping(inputs[condition], argument="inputs", condition=condition) + condition_inhibitors = require_mapping(inhibitors[condition], argument="inhibitors", condition=condition) + input_values = {} + for vertex, value in condition_inputs.items(): + if vertex not in species: + raise ValueError(f"Unknown species {vertex!r} in inputs for condition {condition!r}.") + input_values[vertex] = _bounded_number( + value, + argument="inputs", + identifier=vertex, + condition=condition, + binary=True, + ) + active_inhibitors = set() + for vertex, value in condition_inhibitors.items(): + if vertex not in species: + raise ValueError(f"Unknown species {vertex!r} in inhibitors for condition {condition!r}.") + active = _bounded_number( + value, + argument="inhibitors", + identifier=vertex, + condition=condition, + binary=True, + ) + if active: + active_inhibitors.add(vertex) + overlap = active_inhibitors & set(input_values) + if overlap: + vertex = next(vertex for vertex in self._species if vertex in overlap) + raise ValueError( + f"Vertex {vertex!r} cannot be both an input and an active inhibitor in condition {condition!r}." + ) + for vertex in active_inhibitors: + input_values[vertex] = 0.0 + for vertex, value in input_values.items(): + index = self._species.index(vertex) + forced[index, condition_index] = True + forced_values[index, condition_index] = value + return tuple(condition_names), forced, forced_values + + def _selected_reaction_values(self) -> np.ndarray: + return require_expression_value(self.problem, "reaction_selected", self.name()).reshape(-1) > 0.5 + + def get_selected_reaction_indices(self, threshold: float = 0.5) -> np.ndarray: + """Return indices of reactions in the fixed solved model.""" + values = require_expression_value(self.problem, "reaction_selected", self.name()).reshape(-1) + return np.flatnonzero(values > threshold) + + def _reaction_order(self, selected: np.ndarray) -> tuple[list[Any], dict[Any, list[int]]]: + """Return a stable topological species order for selected reactions.""" + incoming_count = {vertex: 0 for vertex in self._species} + outgoing = {vertex: [] for vertex in self._species} + producers = {vertex: [] for vertex in self._species} + for reaction_index, reaction in enumerate(self.reactions): + if not selected[reaction_index]: + continue + producers[reaction.product].append(reaction_index) + for literal in reaction.literals: + incoming_count[reaction.product] += 1 + outgoing[literal].append(reaction.product) + queue = [vertex for vertex in self._species if incoming_count[vertex] == 0] + order = [] + while queue: + vertex = queue.pop(0) + order.append(vertex) + for target in outgoing[vertex]: + incoming_count[target] -= 1 + if incoming_count[target] == 0: + queue.append(target) + if len(order) != len(self._species): + raise ValueError("The fitted CellNOptDAG reaction support is cyclic and cannot be predicted.") + return order, producers + + def _predict_arrays(self, inputs, inhibitors=None): + condition_names, forced, forced_values = self._prediction_conditions(inputs, inhibitors) + selected = self._selected_reaction_values() + if selected.size != len(self.reactions): + raise ValueError("CellNOptDAG has no usable fitted reaction selection.") + order, producers = self._reaction_order(selected) + species_index = {vertex: index for index, vertex in enumerate(self._species)} + values = np.zeros((len(self._species), len(condition_names)), dtype=float) + active = np.zeros((len(self.reactions), len(condition_names)), dtype=bool) + for condition_index in range(len(condition_names)): + for vertex in order: + vertex_index = species_index[vertex] + for reaction_index in producers[vertex]: + reaction = self.reactions[reaction_index] + active[reaction_index, condition_index] = bool( + all( + values[species_index[literal], condition_index] >= 0.5 + for literal in reaction.positive_literals + ) + and all( + values[species_index[literal], condition_index] < 0.5 + for literal in reaction.negative_literals + ) + ) + if forced[vertex_index, condition_index]: + values[vertex_index, condition_index] = forced_values[vertex_index, condition_index] + else: + values[vertex_index, condition_index] = float( + any(active[reaction_index, condition_index] for reaction_index in producers[vertex]) + ) + return values, forced, tuple(condition_names) + + def _prediction_data(self, values: np.ndarray, condition_names: tuple[str, ...], forced: np.ndarray) -> Data: + samples = {} + for condition_index, condition in enumerate(condition_names): + features = {} + for vertex_index, vertex in enumerate(self._species): + features[vertex] = { + "mapping": "vertex", + "value": float(values[vertex_index, condition_index]), + "predicted": not bool(forced[vertex_index, condition_index]), + "forced": bool(forced[vertex_index, condition_index]), + } + samples[condition] = features + return Data.from_cdict(samples) + + def predict(self, *, inputs, inhibitors=None) -> Data: + """Predict conditions from the fixed selected reactions. + + Inputs and active inhibitors are clamped exactly as during training; + every other species is the OR of its active selected reactions, whose + literals are evaluated in a topological order. Held-out measurements + are not accepted or consulted, and prediction never invokes a solver. + """ + values, forced, condition_names = self._predict_arrays(inputs, inhibitors) + return self._prediction_data(values, condition_names, forced) + + def evaluate(self, *, inputs, measurements, inhibitors=None) -> dict[str, Any]: + """Compare fixed-model predictions with supplied Boolean measurements.""" + predictions, forced, condition_names = self._predict_arrays(inputs, inhibitors) + measurement_names = validate_condition_keys(measurements=measurements) + if set(measurement_names) != set(condition_names): + raise ValueError( + "Condition names in measurements must match inputs/inhibitors: " + f"expected {list(condition_names)!r}, got {list(measurement_names)!r}." + ) + species_index = {vertex: index for index, vertex in enumerate(self._species)} + measurement_values = np.full((len(self._species), len(condition_names)), np.nan, dtype=float) + measurement_mask = np.zeros_like(measurement_values, dtype=bool) + for condition_index, condition in enumerate(condition_names): + values = require_mapping(measurements[condition], argument="measurements", condition=condition) + for vertex, value in values.items(): + if vertex not in species_index: + raise ValueError(f"Unknown species {vertex!r} in measurements for condition {condition!r}.") + measurement_values[species_index[vertex], condition_index] = _bounded_number( + value, + argument="measurements", + identifier=vertex, + condition=condition, + binary=False, + ) + measurement_mask[species_index[vertex], condition_index] = True + valid = measurement_mask & ~forced + errors = np.abs(predictions - measurement_values) + count = int(np.count_nonzero(valid)) + loss = float(np.mean(errors[valid])) if count else float("nan") + by_species = { + vertex: (float(np.mean(errors[index, valid[index]])) if np.any(valid[index]) else float("nan")) + for index, vertex in enumerate(self._species) + } + by_condition = { + condition: (float(np.mean(errors[valid[:, index], index])) if np.any(valid[:, index]) else float("nan")) + for index, condition in enumerate(condition_names) + } + return { + "loss": loss, + "count": count, + "by_species": by_species, + "by_condition": by_condition, + "predictions": self._prediction_data(predictions, condition_names, forced), + } + + @staticmethod + def name() -> str: + """Return the method name.""" + return "CellNOptDAG" + + @staticmethod + def description() -> str: + """Return a short method description.""" + return "Shared acyclic Boolean-reaction inference with structural flow connectivity" diff --git a/corneto/methods/signaling/cellnopt_ilp.py b/corneto/methods/signaling/cellnopt_ilp.py deleted file mode 100644 index 942c66144..000000000 --- a/corneto/methods/signaling/cellnopt_ilp.py +++ /dev/null @@ -1,620 +0,0 @@ -"""CellNOpt integer-linear programming method and visualization helpers.""" - -import re -from typing import Literal, Optional - -import numpy as np - -__all__ = [ - "cellnoptILP", - "cno_style", - "expand_graph_for_flows", - "plot_data", - "plot_fitness", -] - -import corneto as cn -from corneto.backend._base import EXPR_NAME_FLOW - - -def clip_quantiles(arr, q): - """Clip an array to its lower and upper quantiles.""" - if q < 0 or q > 1: - raise ValueError(f"Clipping value must be between 0 and 1, got {q}") - # compute the quantiles at clipping and 1-clipping and clip the flow - q = np.quantile(arr, [q, 1 - q]) - return np.clip(arr, q[0], q[1]) - - -def cno_style( - P, - max_edge_width: float = 5, - min_edge_width: float = 0.25, - flow_name: str = EXPR_NAME_FLOW, - positive_color: str = "dodgerblue4", - negative_color: str = "firebrick4", - zero_flow_threshold: float = 1e-6, - scale: Optional[Literal["log", "std"]] = "log", - clip_quantil: Optional[float] = 0.05, - iexp=0, -): - """Return graph-plotting attributes derived from a CellNOpt solution.""" - flow = np.array(P.expr[flow_name].value)[:, iexp] - flow[np.abs(flow) < zero_flow_threshold] = 0 - if scale is not None: - if scale == "log": - flow = np.log10(np.abs(flow) + 1e-6) * np.sign(flow) - elif scale == "std": - flow = flow / np.std(flow) - else: - raise ValueError(f"Unknown normalization method: {scale}") - if clip_quantil is not None: - flow = clip_quantiles(flow, clip_quantil) - max_flow = max(np.max(np.abs(flow)), 1e-6) - edge_attrs = dict() - for i, v in enumerate(flow): - # Apply threshold edge width - if abs(v) > 0: - edge_width = max_edge_width - else: - edge_width = min_edge_width - if scale is not None: - edge_width = min_edge_width + (max_edge_width - min_edge_width) * abs(v / max_flow) - # bound = P.expr[flow_name].ub[i] if v >= 0 else P.expr[flow_name].lb[i] - edge_attrs[i] = {"penwidth": str(edge_width)} - if flow[i] > 0: - edge_attrs[i]["color"] = positive_color - elif flow[i] < 0: - edge_attrs[i]["color"] = negative_color - else: - edge_attrs[i]["color"] = "black" - return edge_attrs - - -def get_interactions(G): - """Get the sign of interactions from the graph G. I in [1, -1]""" - return np.array(G.get_attr_from_edges("interaction", 1)) - - -def get_AND_gate_nodes(G): - """Get the indices of nodes that represent AND gates in the graph G. - - Parameters: - - G (Graph): The input graph. - - Returns: - - np.array: An array containing the indices of nodes that represent AND gates. - """ - # find AND gates with regular expression: AND[0+9]+ - pattern = re.compile(r"^(AND|And|and)[0-9]+$") - V_is_and = [bool(pattern.match(v)) for v in G.V] - - return np.array(V_is_and) - - -def get_incidence_matrices_of_edges(G, as_dataframe=False): - """Get the mapping matrices A, At, Ah from the graph G. - - Parameters: - - G: The graph object. - - as_dataframe: Whether to return the matrices as pandas DataFrames. Default is False. - - Returns: - - At: The tail-incidence matrix. - - Ah: The head-incidence matrix. - """ - A = G.vertex_incidence_matrix().astype(int) # V x E - At = np.clip(A, 0, 1) # V x E, 1 if vertex appears as tail of edge - Ah = np.clip(-A, 0, 1) # V x E, 1 if vertex appears as head of edge - - if as_dataframe: - import pandas as pd - - Ah = pd.DataFrame(Ah, index=G.V, columns=G.E) - At = pd.DataFrame(At, index=G.V, columns=G.E) - - return At, Ah - - -def get_egdes_with_head(G): - """Get the indices of edges with a head node. - - Parameters: - G (graph): The input graph. - - Returns: - edges_with_head (array): An array containing the indices of edges with a head node. - """ - _At, Ah = get_incidence_matrices_of_edges(G) - edges_with_head = np.flatnonzero(np.sum(np.abs(Ah), axis=0) > 0) - return edges_with_head - - -def get_inhibited_nodes(G, exp_list): - """Returns an array, with shape = (len(G.V), len(exp_list)), where each column is a boolean array - indicating if the node is inhibited in the corresponding experiment. - """ - V_is_inhibited = np.full((len(G.V), len(exp_list)), False) - - for exp, iexp in zip(exp_list, range(len(exp_list))): - if "inhibition" not in exp_list[exp]: - continue - i_nodes = list(exp_list[exp]["inhibition"].keys()) - V_is_inhibited[:, iexp] = np.array([v in i_nodes for v in G.V]) - return V_is_inhibited - - -def presolve_report(G, exp_list): - At, Ah = get_incidence_matrices_of_edges(G, as_dataframe=True) - interaction = get_interactions(G) - edges_with_head = get_egdes_with_head(G) - V_is_and = get_AND_gate_nodes(G) - - print("Vertex order:") - print(G.V) - print("AND gates:") - print(V_is_and) - print("Tails of interactions:") - print(At) - print("Head of interactions:") - print(Ah) - print("Sign of interactions:") - print(interaction) - print("Edges with head:") - print(edges_with_head) - - -def expand_graph_for_flows(G, exp_list): - """Expand the graph G with the perturbations and measurements from the experiments in exp_list.""" - G1 = G.copy() - output_names = list({key for exp in exp_list.values() for key in exp["output"].keys()}) - input_names = list({key for exp in exp_list.values() for key in exp["input"].keys()}) - - output_names = list(set(output_names)) - input_names = list(set(input_names)) - - for node in output_names: - G1.add_edge(node, ()) - for node in input_names: - G1.add_edge((), node) - - return G1 - - -def check_exp_graph_consistency(G, exp_list): - """Check if the experiments are consistent with the graph G.""" - for exp in exp_list: - for node in exp_list[exp]["input"]: - if node not in G.V: - raise ValueError(f"Node {node} in experiment {exp} is not in the graph.") - for node in exp_list[exp]["output"]: - if node not in G.V: - raise ValueError(f"Node {node} in experiment {exp} is not in the graph.") - if "inhibition" in exp_list[exp]: - for node in exp_list[exp]["inhibition"]: - if node not in G.V: - raise ValueError(f"Node {node} in experiment {exp} is not in the graph.") - - -def cellnoptILP(G, exp_list, solver=None, alpha_flow=1e-3, verbose=False, backend=None): - """Create and solves the ILP model for the given graph G and the list of experiments exp_list. - - Parameters: - - G: The graph representing the network. - - exp_list: The list of experiments. - - solver: The solver to use for solving the ILP problem. Default is None. - - alpha_flow: The weight of the penalty of flow in the objective. Default is 1e-3. - - verbose: Whether to print verbose output. Default is False. - - Returns: - - P: The ILP model. - """ - if backend is None: - backend = cn.DEFAULT_BACKEND - check_exp_graph_consistency(G, exp_list) - - At, Ah = get_incidence_matrices_of_edges(G) - interaction = get_interactions(G) - edges_with_head = get_egdes_with_head(G) - edges_with_head = np.flatnonzero(np.sum(np.abs(Ah), axis=0) > 0) - V_is_and = get_AND_gate_nodes(G) - and_idx = np.flatnonzero(V_is_and) - V_is_inhibited = get_inhibited_nodes(G, exp_list) - - # let's start with acyclic flow - P = backend.AcyclicFlow(G) - - # vertex value is binary (0 and 1) - V = backend.Variable("vertex_value", (G.num_vertices, len(exp_list)), vartype=cn.VarType.BINARY) - # edge activation is also binary: - Eact = backend.Variable("edge_activates", (G.num_edges, len(exp_list)), vartype=cn.VarType.BINARY) - - M = 100 # a large number, so sum(incoming edges)/M is always less than 1 - - # Dummy variable for the linearization of the absolute deviation objective function - Z = backend.Variable("dummy", (G.num_vertices, len(exp_list)), vartype=cn.VarType.CONTINUOUS) - P += Z >= 0 - - # some basic constraints - P += V >= 0 - P += V <= 1 - - # Rule 1: Edge can be activated only if they carry flow - for exp, iexp in zip(exp_list, range(len(exp_list))): - P += Eact[:, iexp] <= P.expr.with_flow - - # Rule 2: Edges take the upstream value for a positive sign and its - # complement for a negative sign. - for exp, iexp in zip(exp_list, range(len(exp_list))): - # this should keep Eact in [0,1] interval: - - # signed value of source/head node for an edge: - V_head = (Ah.T @ V)[edges_with_head, iexp].multiply((interaction[edges_with_head] > 0).astype(int)) + ( - 1 - (Ah.T @ V)[edges_with_head, iexp] - ).multiply((interaction[edges_with_head] < 0).astype(int)) - # an edge is active if there is flow AND there head node is also active - # logical AND is translated to ILP as: - # y >= x1 + x2 - 1 ; y <= x1 ; y <= x2 - P += Eact[edges_with_head, iexp] >= P.expr.with_flow[edges_with_head] + V_head - 1 - P += Eact[edges_with_head, iexp] <= V_head - # The with-flow upper bound is already enforced by Rule 1. - - # Rule 3: propagate the active edges to the vertices - - # This is for general nodes that are not AND gates - # - for exp, iexp in zip(exp_list, range(len(exp_list))): - is_regular_node = np.logical_and(~V_is_and, ~V_is_inhibited[:, iexp]) - is_regular_node = np.flatnonzero(is_regular_node) - - P += ( - V[is_regular_node, iexp] >= ((At @ Eact) / M)[is_regular_node, iexp] - ) # when there is at least one active edge, the vertex becomes 1 (larger than someValue/M) - P += ( - V[is_regular_node, iexp] <= (At @ Eact)[is_regular_node, iexp] - ) # but it has an upper constraint, so it takes 0 when all input are 0 - if V_is_inhibited[:, iexp].any(): - inh_idx = np.flatnonzero(V_is_inhibited[:, iexp]) - P += V[inh_idx, iexp] == np.zeros(inh_idx.size) - - # AND relation expressed as follows: - # - we only define these constraints for the AND gates - # - we count the sum of flows (selected edges) and sum of activated edges - # - if flow equals incoming-edge activation, all edges are activated and so - # is the vertex; - # - a second constraint prevents activation when no flow is present. - if V_is_and.any(): - # and_idx = np.flatnonzero(V_is_and) - sum_of_flow = At[and_idx, :] @ P.expr.with_flow - sum_of_edge_activation = At[and_idx, :] @ Eact - - # (sum_of_flow - sum_of_edge_activation)/M is always less than 1 and it is 0 if all edges are activated. - for exp, iexp in zip(exp_list, range(len(exp_list))): - P += V[and_idx, iexp] <= 1 - (sum_of_flow - sum_of_edge_activation[:, iexp]) / M - P += V[and_idx, iexp] <= sum_of_flow - - P.register("sum_of_flow", sum_of_flow) - P.register("sum_of_edge_activation", sum_of_edge_activation) - - for exp, iexp in zip(exp_list, range(len(exp_list))): - # activation: - p_nodes = list(exp_list[exp]["input"].keys()) - p_values = list(exp_list[exp]["input"].values()) - p_nodes_positions = np.array([G.V.index(key) for key in p_nodes]) - - P += V[p_nodes_positions, iexp] == p_values - - # measurements: - m_nodes = list(exp_list[exp]["output"].keys()) - m_values = np.array(list(exp_list[exp]["output"].values())) - m_nodes_positions = np.array([G.V.index(key) for key in m_nodes]) - - # linearization of the ABS function: https://lpsolve.sourceforge.net/5.1/absolute.htm - P += V[m_nodes_positions, iexp] - m_values <= Z[m_nodes_positions, iexp] - P += -(V[m_nodes_positions, iexp] - m_values) <= Z[m_nodes_positions, iexp] - - P.add_objectives(sum(Z[m_nodes_positions, iexp])) - - P.add_objectives(alpha_flow * sum(P.expr.with_flow)) - - P.solve(solver=solver, verbosity=verbose) - - return P - - -def report_solution_tables(G, exp_list, P): - import pandas as pd - - for iexp in range(len(exp_list)): - print("--------- iexp: ", iexp, " ---------") - print(pd.DataFrame({"V": G.V, "value": P.expr.vertex_value.value[:, iexp]})) - print( - pd.DataFrame( - { - "E": G.E, - "flow": P.expr.with_flow.value, - "Eact": P.expr.edge_activates.value[:, iexp], - } - ) - ) - - -def plot_solution_network_active_edges(G, P, iexp): - G.plot(custom_edge_attr=cno_style(P, flow_name="edge_activates", scale=None, iexp=iexp)) - - -def plot_fitness(G, exp_list, P, measured_only=False, **kwargs): - """Plot the fitness of the model simulation vs measurements. - - PARAMETERS: - - G: corneto.Graph object - - exp_list: dictionary of experiments - - P: solution of the ILP model - - measured_only: if True, plot only the measured nodes, otherwise plot all nodes - - **kwargs arguments are passed to subplots and figures of matplotlib - - TODO: there are some assumptions, like the first experiment is the reference experiment and it is called 'exp0' - """ - import matplotlib.pyplot as plt - - N_exps = len(exp_list) - - # Ensure that all experiments have the input and output variables - for exp in exp_list.values(): - if "input" not in exp: - raise ValueError("Input not found in experiment") - if "output" not in exp: - raise ValueError("Output not found in experiment") - - # Collect the input and output variables - input_matrix, input_vars = collect_field_into_matrix(exp_list, "input") - _output_matrix, _output_vars = collect_field_into_matrix(exp_list, "output") - - # Check if inhibition is present in any of the experiments - inhibition_present = any("inhibition" in exp for exp in exp_list.values()) - if inhibition_present: - inhibition_matrix, inhibition_vars = collect_field_into_matrix(exp_list, "inhibition") - perturbation_matrix = np.hstack((input_matrix, inhibition_matrix)) - perturbation_vars = input_vars + inhibition_vars - else: - perturbation_matrix = input_matrix - perturbation_vars = input_vars - - # Create the figure - # Set colors: input colors are blue, inhibition colors are red - perturbation_colors = ["blue"] * len(input_vars) - if inhibition_present: - perturbation_colors += ["red"] * len(inhibition_vars) - - N_nodes = len(G.V) - output_names = list({key for exp in exp_list.values() for key in exp["output"].keys()}) - - # depending on the flag measured_only, we can plot only the measured nodes or all nodes - if measured_only: - fig, axs = plt.subplots(N_exps - 1, len(output_names) + 1, squeeze=False, **kwargs) - else: - fig, axs = plt.subplots(N_exps - 1, N_nodes + 1, squeeze=False, **kwargs) - - fig.tight_layout(pad=0.0) - - # Adjust the space between subplots - plt.subplots_adjust(wspace=0.1, hspace=0.1) - - for exp, iexp in zip(exp_list, range(N_exps)): - if iexp == 0: - continue - - if measured_only: - for imarker in range(len(output_names)): - # output_names[imarker] is the name of the output node, find the position in the graph - imarker_inG = G.V.index(output_names[imarker]) - - axs[iexp - 1, imarker].plot( - [0, 10], - [ - P.expr.vertex_value.value[imarker_inG, 0], - min(P.expr.vertex_value.value[imarker_inG, iexp], 1), - ], - "bo-", - label=G.V[imarker_inG], - ) - - if G.V[imarker_inG] in exp_list[exp]["output"].keys(): - axs[iexp - 1, imarker].plot( - [0, 10], - [ - exp_list["exp0"]["output"][G.V[imarker_inG]], - exp_list[exp]["output"][G.V[imarker_inG]], - ], - "ro-", - ) - axs[iexp - 1, imarker].set_ylim([-0.05, 1.1]) - if iexp == 1: - axs[iexp - 1, imarker].set_title(output_names[imarker]) - if iexp != N_exps - 1: - axs[iexp - 1, imarker].set_xticks([]) - if imarker == 0: - axs[iexp - 1, imarker].set_ylabel(f"Exp. {iexp}") - else: - axs[iexp - 1, imarker].set_yticks([]) - else: - for imarker in range(N_nodes): - axs[iexp - 1, imarker].plot( - [0, 10], - [ - P.expr.vertex_value.value[imarker, 0], - min(P.expr.vertex_value.value[imarker, iexp], 1), - ], - "bo-", - label=G.V[imarker], - # color="blue", - # linestyle="o-", - ) - - if G.V[imarker] in exp_list[exp]["output"].keys(): - axs[iexp - 1, imarker].plot( - [0, 10], - [ - exp_list["exp0"]["output"][G.V[imarker]], - exp_list[exp]["output"][G.V[imarker]], - ], - "ro-", - ) - axs[iexp - 1, imarker].set_ylim([-0.05, 1.1]) - if iexp == 1: - axs[iexp - 1, imarker].set_title(G.V[imarker]) - if iexp != N_exps - 1: - axs[iexp - 1, imarker].set_xticks([]) - if imarker == 0: - axs[iexp - 1, imarker].set_ylabel(f"Exp. {iexp}") - else: - axs[iexp - 1, imarker].set_yticks([]) - # Plot perturbation - if measured_only: - plot_location = len(output_names) - else: - plot_location = N_nodes - - axs[iexp - 1, plot_location].bar( - range(len(perturbation_vars)), - perturbation_matrix[iexp], - color=perturbation_colors, - ) - if iexp == N_exps - 1: - axs[iexp - 1, plot_location].set_xticks(range(len(perturbation_vars))) - axs[iexp - 1, plot_location].set_xticklabels(perturbation_vars, rotation=45) - else: - # No xtick label - axs[iexp - 1, plot_location].set_xticks([]) - - axs[iexp - 1, plot_location].set_ylim([-0.01, 1.1]) - axs[iexp - 1, plot_location].set_yticks([]) - if iexp == 1: - axs[iexp - 1, plot_location].set_title("Pert.") - - plt.show() - - -def collect_field_into_matrix(experiments, field_name="input"): - """Collects the field_name values into matrix. - - Collects the field_name values (input, inhibition etc) from a dictionary - of experiments and returns them as a numpy array. - - PARAMETERS: - - experiments: dictionary of experiments containing input values - - field_name: name of the field to collect (default: 'input') - - Returns: - - input_matrix: numpy array of input values - - input_vars: list of unique input variable names - - """ - # Collect all unique input names - input_vars = set() - for exp in experiments.values(): - # ensure field_name exists - if field_name not in exp: - raise ValueError("Field name (" + field_name + ") not found in experiment") - input_vars.update(exp[field_name].keys()) - - input_vars = sorted(input_vars) # Sorting to keep a consistent order - data = [] - - # Collect input values for each experiment - for exp in experiments.values(): - row = [exp[field_name].get(var, 0) for var in input_vars] - data.append(row) - - # Convert the data to a numpy array - input_matrix = np.array(data) - - return input_matrix, input_vars - - -def plot_data(exp_list): - """Plot the data. - - PARAMETERS: - - exp_list: dictionary of experiments - - """ - import matplotlib.pyplot as plt - - N_exps = len(exp_list) - - # Ensure that all experiments have the input and output variables - for exp in exp_list.values(): - if "input" not in exp: - raise ValueError("Input not found in experiment") - if "output" not in exp: - raise ValueError("Output not found in experiment") - - # Collect the input and output variables - input_matrix, input_vars = collect_field_into_matrix(exp_list, "input") - output_matrix, output_vars = collect_field_into_matrix(exp_list, "output") - - # Check if inhibition is present in any of the experiments - inhibition_present = any("inhibition" in exp for exp in exp_list.values()) - if inhibition_present: - inhibition_matrix, inhibition_vars = collect_field_into_matrix(exp_list, "inhibition") - perturbation_matrix = np.hstack((input_matrix, inhibition_matrix)) - perturbation_vars = input_vars + inhibition_vars - else: - perturbation_matrix = input_matrix - perturbation_vars = input_vars - - # Create the figure - # Set colors: input colors are blue, inhibition colors are red - perturbation_colors = ["blue"] * len(input_vars) - if inhibition_present: - perturbation_colors += ["red"] * len(inhibition_vars) - - fig, axs = plt.subplots(N_exps - 1, len(output_vars) + 1, squeeze=False) - - fig.tight_layout(pad=0.0) - # Adjust the space between subplots - plt.subplots_adjust(wspace=0.1, hspace=0.1) - - for exp, iexp in zip(exp_list, range(N_exps)): - if iexp == 0: - continue - - for imarker in range(len(output_vars)): - # output_names[imarker] is the name of the output node, find the position in the graph - imarker_name = output_vars[imarker] - - axs[iexp - 1, imarker].plot( - [0, 10], - [output_matrix[0, imarker], output_matrix[iexp, imarker]], - "ro-", - ) - axs[iexp - 1, imarker].set_ylim([-0.01, 1.1]) - - if iexp == 1: - axs[iexp - 1, imarker].set_title(imarker_name) - if iexp != N_exps - 1: - axs[iexp - 1, imarker].set_xticks([]) - if imarker == 0: - axs[iexp - 1, imarker].set_ylabel(f"Exp. {iexp}") - else: - axs[iexp - 1, imarker].set_yticks([]) - - # Plot perturbation - axs[iexp - 1, len(output_vars)].bar( - range(len(perturbation_vars)), - perturbation_matrix[iexp], - color=perturbation_colors, - ) - if iexp == N_exps - 1: - axs[iexp - 1, len(output_vars)].set_xticks(range(len(perturbation_vars))) - axs[iexp - 1, len(output_vars)].set_xticklabels(perturbation_vars, rotation=45) - else: - # No xtick label - axs[iexp - 1, len(output_vars)].set_xticks([]) - - axs[iexp - 1, len(output_vars)].set_ylim([-0.01, 1.1]) - axs[iexp - 1, len(output_vars)].set_yticks([]) - if iexp == 1: - axs[iexp - 1, len(output_vars)].set_title("Pert.") - plt.show() diff --git a/corneto/methods/signaling/cellnopt_plotting.py b/corneto/methods/signaling/cellnopt_plotting.py new file mode 100644 index 000000000..ca2df369e --- /dev/null +++ b/corneto/methods/signaling/cellnopt_plotting.py @@ -0,0 +1,880 @@ +"""Visualization utilities for :class:`~corneto.methods.signaling.CellNOptDAG`.""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Literal, Optional, Sequence, Union + +import numpy as np + +from corneto._plotting import _scaled_magnitudes +from corneto.graph import Graph +from corneto.methods.signaling.cellnopt_dag import CellNOptDAG + +__all__ = ["plot_cellnopt_fit", "plot_cellnopt_model"] + +_STIMULUS_COLOR = "#9ACD32" +_MEASUREMENT_COLOR = "#ADD8E6" +_INHIBITOR_COLOR = "#FF6846" +_POSITIVE_COLOR = "#222222" +_NEGATIVE_COLOR = "#C43C39" +_INACTIVE_POSITIVE_COLOR = "#A8A8A8" +_INACTIVE_NEGATIVE_COLOR = "#E3A19F" +_UNSELECTED_POSITIVE_COLOR = "#D5D5D5" +_UNSELECTED_NEGATIVE_COLOR = "#F0CECD" + + +@dataclass(frozen=True) +class _CellNOptPlotData: + conditions: tuple[str, ...] + vertices: tuple[Any, ...] + selected: np.ndarray + active: np.ndarray + states: np.ndarray + flow_by_reaction: tuple[np.ndarray, ...] + measurement_mask: np.ndarray + measurements: np.ndarray + input_mask: np.ndarray + input_values: np.ndarray + inhibitor_mask: np.ndarray + + +@dataclass(frozen=True) +class _FitSeries: + """Observed and predicted responses on an explicit time axis. + + CellNOptDAG currently infers one steady-state endpoint per condition, so + extraction produces a singleton axis labelled ``Endpoint``. Keeping time + as a real array dimension lets a future time-resolved formulation reuse + the renderer without assigning scientific meaning to series identity. + """ + + times: np.ndarray + time_labels: tuple[str, ...] + observed: np.ndarray + measured: np.ndarray + predicted: np.ndarray + + +@dataclass(frozen=True) +class _ModelPlotSpec: + graph: Graph + vertex_attributes: dict[Any, dict[str, str]] + edge_attributes: dict[int, dict[str, str]] + graph_attributes: dict[str, str] + node_attributes: dict[str, str] + + +def _expression_values(problem: Any, name: str, shape: tuple[int, ...]) -> np.ndarray: + if problem is None: + raise ValueError("CellNOptDAG has not been built. Build and solve the method before plotting.") + try: + expression = problem.expr[name] + except (KeyError, TypeError, AttributeError): + try: + expression = getattr(problem.expr, name) + except AttributeError as exc: + raise ValueError(f"CellNOpt solution does not contain expression {name!r}.") from exc + value = getattr(expression, "value", None) + if value is None: + raise ValueError(f"CellNOpt expression {name!r} has no value. Solve a feasible problem before plotting.") + array = np.asarray(value, dtype=float) + try: + array = array.reshape(shape) + except ValueError as exc: + raise ValueError(f"CellNOpt expression {name!r} has shape {array.shape}; expected {shape}.") from exc + if not np.all(np.isfinite(array)): + raise ValueError(f"CellNOpt expression {name!r} contains non-finite values.") + return array + + +def _validate_binary_values(values: np.ndarray, name: str, tolerance: float = 1e-5) -> np.ndarray: + if np.any(values < -tolerance) or np.any(values > 1 + tolerance): + raise ValueError(f"CellNOpt expression {name!r} contains values outside the binary domain.") + return values >= 0.5 + + +def _extract_plot_data(method: CellNOptDAG, problem: Any = None) -> _CellNOptPlotData: + if not isinstance(method, CellNOptDAG): + raise TypeError("method must be a CellNOptDAG instance.") + if problem is None: + problem = method.problem + conditions = tuple(method._condition_names) + vertices = tuple(method.processed_graph.V) if method.processed_graph is not None else () + if not conditions or not vertices or not method.reactions: + raise ValueError("CellNOptDAG has not been built. Build and solve the method before plotting.") + + num_reactions = len(method.reactions) + num_conditions = len(conditions) + num_vertices = len(vertices) + selected_values = _expression_values(problem, "reaction_selected", (num_reactions,)) + active_values = _expression_values(problem, "reaction_active", (num_reactions, num_conditions)) + state_values = _expression_values(problem, "vertex_value", (num_vertices, num_conditions)) + flow_values = _expression_values(problem, "flow", tuple(problem.expr.flow.shape)).reshape(-1) + if flow_values.size < method._biological_num_edges: + raise ValueError("CellNOpt flow vector is shorter than the compiled biological dependency graph.") + + selected = _validate_binary_values(selected_values, "reaction_selected") + active = _validate_binary_values(active_values, "reaction_active") + states = _validate_binary_values(state_values, "vertex_value") + + dependency_flow = flow_values[: method._biological_num_edges] + flow_by_reaction = [] + cursor = 0 + for reaction in method.reactions: + next_cursor = cursor + len(reaction.literals) + flow_by_reaction.append(dependency_flow[cursor:next_cursor].copy()) + cursor = next_cursor + if cursor != method._biological_num_edges: + raise ValueError("Compiled reactions do not align with CellNOpt dependency-flow variables.") + + input_mask = np.zeros((num_vertices, num_conditions), dtype=bool) + input_values = np.zeros((num_vertices, num_conditions), dtype=float) + inhibitor_mask = np.zeros((num_vertices, num_conditions), dtype=bool) + vertex_index = {vertex: index for index, vertex in enumerate(vertices)} + for condition_index, condition in enumerate(conditions): + sample = method.processed_data.samples[condition] + for feature in sample.features: + index = vertex_index[feature.id] + role = feature.data.get("role") + if role in {"input", "input_output"}: + input_mask[index, condition_index] = True + input_values[index, condition_index] = float(feature.data.get("input_value", feature.value)) + if feature.data.get("intervention") == "inhibitor": + inhibitor_mask[index, condition_index] = True + + return _CellNOptPlotData( + conditions=conditions, + vertices=vertices, + selected=selected, + active=active, + states=states, + flow_by_reaction=tuple(flow_by_reaction), + measurement_mask=np.asarray(method._measurement_mask, dtype=bool), + measurements=np.asarray(method._measurements, dtype=float), + input_mask=input_mask, + input_values=input_values, + inhibitor_mask=inhibitor_mask, + ) + + +def _condition_index( + data: _CellNOptPlotData, + condition: Optional[Union[int, str]], +) -> Optional[int]: + if condition is None: + return None + if isinstance(condition, int): + if condition < 0 or condition >= len(data.conditions): + raise ValueError(f"condition index {condition} out of range for {len(data.conditions)} conditions.") + return condition + if condition not in data.conditions: + raise ValueError(f"Unknown condition {condition!r}; expected one of {list(data.conditions)!r}.") + return data.conditions.index(condition) + + +def _reaction_text(reaction: Any) -> str: + literals = [str(vertex) for vertex in reaction.positive_literals] + literals.extend(f"NOT {vertex}" for vertex in reaction.negative_literals) + return f"{' AND '.join(literals)} -> {reaction.product}" + + +def _node_roles( + data: _CellNOptPlotData, + condition_index: Optional[int], +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + if condition_index is None: + inputs = data.input_mask.any(axis=1) + outputs = data.measurement_mask.any(axis=1) + inhibitors = data.inhibitor_mask.any(axis=1) + else: + inputs = data.input_mask[:, condition_index] + outputs = data.measurement_mask[:, condition_index] + inhibitors = data.inhibitor_mask[:, condition_index] + return inputs, outputs, inhibitors + + +def _merge_attributes( + generated: dict[Any, dict[str, str]], + supplied: Optional[dict[Any, dict[str, str]]], +) -> dict[Any, dict[str, str]]: + merged = {key: dict(value) for key, value in generated.items()} + for key, attrs in (supplied or {}).items(): + merged.setdefault(key, {}).update({str(name): str(value) for name, value in attrs.items()}) + return merged + + +def _build_cellnopt_model_plot( + method: CellNOptDAG, + problem: Any = None, + *, + condition: Optional[Union[int, str]] = None, + show_unselected: bool = False, + show_inactive: bool = True, + width_by: Literal["selection", "flow"] = "selection", +) -> _ModelPlotSpec: + if width_by not in {"selection", "flow"}: + raise ValueError("width_by must be 'selection' or 'flow'.") + data = _extract_plot_data(method, problem) + condition_index = _condition_index(data, condition) + + included_reactions = [] + segment_flows = [] + for reaction_index, reaction in enumerate(method.reactions): + selected = bool(data.selected[reaction_index]) + active = selected if condition_index is None else bool(data.active[reaction_index, condition_index]) + include = selected or show_unselected + if condition_index is not None and selected and not active and not show_inactive: + include = False + if not include: + continue + included_reactions.append((reaction_index, reaction, selected, active)) + flows = data.flow_by_reaction[reaction_index] + segment_flows.extend(flows.tolist()) + if len(reaction.literals) > 1: + segment_flows.append(float(np.sum(flows))) + + flow_scale = ( + _scaled_magnitudes( + np.asarray(segment_flows, dtype=float), + zero_threshold=1e-9, + scale="log", + clip_quantile=0.0, + ) + if width_by == "flow" + else np.empty(0) + ) + flow_scale_cursor = 0 + + graph = Graph() + included_species = set() + for _, reaction, _, _ in included_reactions: + included_species.update(reaction.literals) + included_species.add(reaction.product) + inputs, outputs, inhibitors = _node_roles(data, condition_index) + experimental_species = { + vertex for vertex, is_experimental in zip(data.vertices, inputs | outputs | inhibitors) if is_experimental + } + included_species.update(experimental_species) + for vertex in data.vertices: + if vertex in included_species: + graph.add_vertex(vertex) + + existing_names = {str(vertex) for vertex in data.vertices} + and_nodes = {} + for reaction_index, reaction, _, _ in included_reactions: + if len(reaction.literals) <= 1: + continue + candidate = f"__cellnopt_and_{reaction_index}" + while candidate in existing_names: + candidate = f"_{candidate}" + existing_names.add(candidate) + and_nodes[reaction_index] = candidate + graph.add_vertex(candidate) + + vertex_attributes: dict[Any, dict[str, str]] = {} + vertex_index = {vertex: index for index, vertex in enumerate(data.vertices)} + for vertex in included_species: + index = vertex_index[vertex] + is_input = bool(inputs[index]) + is_output = bool(outputs[index]) + is_inhibitor = bool(inhibitors[index]) + is_stimulus = is_input and not is_inhibitor + if is_inhibitor: + fillcolor = _INHIBITOR_COLOR + role = "inhibitor target" + elif is_stimulus: + fillcolor = _STIMULUS_COLOR + role = "stimulus" + elif is_output: + fillcolor = _MEASUREMENT_COLOR + role = "measurement" + else: + fillcolor = "white" + role = "internal species" + attrs = { + "shape": "box", + "style": "rounded,filled", + "fillcolor": fillcolor, + "color": "#4C4C4C", + "fontcolor": "#111111", + "label": str(vertex), + "tooltip": role, + "margin": "0.08,0.05", + } + if is_stimulus and is_output: + attrs["peripheries"] = "2" + attrs["color"] = "#4C78A8" + attrs["tooltip"] = "stimulus and measurement" + elif is_inhibitor and is_output: + attrs["peripheries"] = "2" + attrs["color"] = "#4C78A8" + attrs["tooltip"] = "inhibitor target and measurement" + if condition_index is not None: + value = int(data.states[index, condition_index]) + attrs["penwidth"] = "2.5" if value else "1.0" + attrs["tooltip"] += f"; state={value}" + vertex_attributes[str(vertex)] = attrs + for reaction_index, node in and_nodes.items(): + vertex_attributes[node] = { + "shape": "circle", + "style": "filled", + "fillcolor": "white", + "color": "#333333", + "label": "AND", + "width": "0.38", + "height": "0.38", + "fixedsize": "true", + "fontsize": "8", + "tooltip": _reaction_text(method.reactions[reaction_index]), + } + + edge_attributes: dict[int, dict[str, str]] = {} + + def state_attributes( + *, + interaction: int, + selected: bool, + active: bool, + raw_flow: float, + aggregate: bool, + reaction_text: str, + ) -> dict[str, str]: + nonlocal flow_scale_cursor + if not selected: + color = _UNSELECTED_NEGATIVE_COLOR if interaction < 0 else _UNSELECTED_POSITIVE_COLOR + style = "dotted" + width = 0.6 + elif condition_index is not None and not active: + color = _INACTIVE_NEGATIVE_COLOR if interaction < 0 else _INACTIVE_POSITIVE_COLOR + style = "dashed" + width = 1.0 + else: + color = _NEGATIVE_COLOR if interaction < 0 else _POSITIVE_COLOR + style = "solid" + width = 2.8 if condition_index is not None else 2.2 + if width_by == "flow": + magnitude = float(flow_scale[flow_scale_cursor]) + flow_scale_cursor += 1 + if selected: + width = 0.8 + 4.2 * magnitude + tooltip = ( + f"{reaction_text}; selected={int(selected)}" + + ("" if condition_index is None else f"; active={int(active)}") + + f"; {'aggregate ' if aggregate else ''}structural flow={raw_flow:.5g}" + ) + return { + "color": color, + "fontcolor": color, + "style": style, + "penwidth": f"{width:.3g}", + "arrowhead": "tee" if interaction < 0 else "normal", + "tooltip": tooltip, + } + + for reaction_index, reaction, selected, active in included_reactions: + reaction_text = _reaction_text(reaction) + flows = data.flow_by_reaction[reaction_index] + literals = [ + *((vertex, 1) for vertex in reaction.positive_literals), + *((vertex, -1) for vertex in reaction.negative_literals), + ] + if len(literals) == 1: + literal, interaction = literals[0] + edge_index = graph.add_edge( + literal, + reaction.product, + interaction=interaction, + reaction=reaction_index, + ) + edge_attributes[edge_index] = state_attributes( + interaction=interaction, + selected=selected, + active=active, + raw_flow=float(flows[0]), + aggregate=False, + reaction_text=reaction_text, + ) + continue + + and_node = and_nodes[reaction_index] + for literal_index, (literal, interaction) in enumerate(literals): + edge_index = graph.add_edge( + literal, + and_node, + interaction=interaction, + reaction=reaction_index, + ) + edge_attributes[edge_index] = state_attributes( + interaction=interaction, + selected=selected, + active=active, + raw_flow=float(flows[literal_index]), + aggregate=False, + reaction_text=reaction_text, + ) + aggregate_flow = float(np.sum(flows)) + edge_index = graph.add_edge( + and_node, + reaction.product, + interaction=1, + reaction=reaction_index, + ) + edge_attributes[edge_index] = state_attributes( + interaction=1, + selected=selected, + active=active, + raw_flow=aggregate_flow, + aggregate=True, + reaction_text=reaction_text, + ) + + return _ModelPlotSpec( + graph=graph, + vertex_attributes=vertex_attributes, + edge_attributes=edge_attributes, + graph_attributes={"rankdir": "LR", "pad": "0.2", "nodesep": "0.35", "ranksep": "0.5"}, + node_attributes={"fixedsize": "false", "fontname": "Helvetica", "fontsize": "10"}, + ) + + +def plot_cellnopt_model( + method: CellNOptDAG, + problem: Any = None, + *, + condition: Optional[Union[int, str]] = None, + show_unselected: bool = False, + show_inactive: bool = True, + width_by: Literal["selection", "flow"] = "selection", + renderer: str = "auto", + **plot_kwargs: Any, +) -> Any: + """Plot a solved CellNOpt model using CORNETO's graph renderers. + + ``condition=None`` shows the shared selected structure. Selecting a + condition overlays reaction activity and predicted species states. + Structural flow may optionally control edge widths, but never edge color. + """ + spec = _build_cellnopt_model_plot( + method, + problem, + condition=condition, + show_unselected=show_unselected, + show_inactive=show_inactive, + width_by=width_by, + ) + graph_attributes = dict(spec.graph_attributes) + graph_attributes.update(plot_kwargs.pop("graph_attr", {}) or {}) + node_attributes = dict(spec.node_attributes) + node_attributes.update(plot_kwargs.pop("node_attr", {}) or {}) + edge_attributes = _merge_attributes( + spec.edge_attributes, + plot_kwargs.pop("custom_edge_attr", None), + ) + vertex_attributes = _merge_attributes( + spec.vertex_attributes, + {str(vertex): attrs for vertex, attrs in (plot_kwargs.pop("custom_vertex_attr", None) or {}).items()}, + ) + return spec.graph.plot( + renderer=renderer, + graph_attr=graph_attributes, + node_attr=node_attributes, + custom_edge_attr=edge_attributes, + custom_vertex_attr=vertex_attributes, + **plot_kwargs, + ) + + +def _selected_indices( + values: Optional[Union[Any, Sequence[Any]]], + available: Sequence[Any], + *, + argument: str, +) -> list[int]: + if values is None: + return list(range(len(available))) + if isinstance(values, (str, int)) or values in available: + values = [values] + indices = [] + for value in values: + if isinstance(value, int) and argument == "conditions": + if value < 0 or value >= len(available): + raise ValueError(f"{argument} index {value} out of range.") + indices.append(value) + else: + if value not in available: + raise ValueError(f"Unknown {argument[:-1]} {value!r}; expected one of {list(available)!r}.") + indices.append(available.index(value)) + if not indices: + raise ValueError(f"{argument} must select at least one value.") + return indices + + +def _fit_selection( + data: _CellNOptPlotData, + conditions: Optional[Union[Union[int, str], Sequence[Union[int, str]]]], + signals: Optional[Union[Any, Sequence[Any]]], +) -> tuple[list[int], list[int]]: + condition_indices = _selected_indices(conditions, data.conditions, argument="conditions") + measured_vertices = [ + vertex for vertex, measured in zip(data.vertices, data.measurement_mask.any(axis=1)) if measured + ] + selected_signals = measured_vertices if signals is None else signals + signal_indices = _selected_indices(selected_signals, data.vertices, argument="signals") + return condition_indices, signal_indices + + +def _cue_data( + data: _CellNOptPlotData, + condition_indices: Sequence[int], +) -> tuple[np.ndarray, list[str], list[str]]: + selected_inputs = data.input_mask[:, condition_indices] + selected_inhibitors = data.inhibitor_mask[:, condition_indices] + stimulus_vertices = [ + index for index in range(len(data.vertices)) if np.any(selected_inputs[index] & ~selected_inhibitors[index]) + ] + inhibitor_vertices = [index for index in range(len(data.vertices)) if np.any(selected_inhibitors[index])] + labels = [str(data.vertices[index]) for index in stimulus_vertices] + labels.extend(f"{data.vertices[index]} (inh)" for index in inhibitor_vertices) + kinds = ["stimulus"] * len(stimulus_vertices) + ["inhibitor"] * len(inhibitor_vertices) + cues = np.zeros((len(condition_indices), len(labels)), dtype=float) + for row, condition_index in enumerate(condition_indices): + for column, vertex_index in enumerate(stimulus_vertices): + if not data.inhibitor_mask[vertex_index, condition_index]: + cues[row, column] = data.input_values[vertex_index, condition_index] + offset = len(stimulus_vertices) + for column, vertex_index in enumerate(inhibitor_vertices): + cues[row, offset + column] = float(data.inhibitor_mask[vertex_index, condition_index]) + return cues, labels, kinds + + +def _import_matplotlib(): + try: + import matplotlib.colors as colors + import matplotlib.pyplot as plt + from matplotlib.lines import Line2D + except ImportError as exc: + raise ImportError( + "CellNOpt fit plots require Matplotlib. Install CORNETO with `pip install 'corneto[plot]'`." + ) from exc + return plt, colors, Line2D + + +def _endpoint_fit_series( + data: _CellNOptPlotData, + condition_indices: Sequence[int], + signal_indices: Sequence[int], +) -> _FitSeries: + """Return current steady-state results with an explicit endpoint axis.""" + observed = data.measurements[np.ix_(signal_indices, condition_indices)].T + measured = data.measurement_mask[np.ix_(signal_indices, condition_indices)].T + predicted = data.states[np.ix_(signal_indices, condition_indices)].T.astype(float) + return _FitSeries( + times=np.array([0.0]), + time_labels=("Endpoint",), + observed=observed[..., np.newaxis], + measured=measured[..., np.newaxis], + predicted=predicted[..., np.newaxis], + ) + + +def _plot_fit_grid( + data: _CellNOptPlotData, + condition_indices: Sequence[int], + signal_indices: Sequence[int], + *, + figsize: Optional[tuple[float, float]], +): + plt, colors, Line2D = _import_matplotlib() + num_conditions = len(condition_indices) + num_signals = len(signal_indices) + cues, cue_labels, cue_kinds = _cue_data(data, condition_indices) + series = _endpoint_fit_series(data, condition_indices, signal_indices) + if figsize is None: + condition_label_width = min( + 4.0, + 0.09 * max(len(data.conditions[index]) for index in condition_indices), + ) + figsize = ( + max( + 6.0, + 2.15 * num_signals + max(2.6, 0.65 * len(cue_labels)) + condition_label_width, + ), + max(3.2, 1.65 * num_conditions + 0.45), + ) + width_ratios = [1.0] * num_signals + [max(1.2, 0.35 * max(len(cue_labels), 1))] + figure = plt.figure(figsize=figsize, constrained_layout=True) + layout = figure.add_gridspec(2, 1, height_ratios=[0.12, 1]) + legend_axis = figure.add_subplot(layout[0]) + legend_axis.axis("off") + panel_layout = layout[1].subgridspec( + num_conditions, + num_signals + 1, + width_ratios=width_ratios, + ) + axes = np.empty((num_conditions, num_signals + 1), dtype=object) + for row in range(num_conditions): + for column in range(num_signals + 1): + axes[row, column] = figure.add_subplot(panel_layout[row, column]) + error_cmap = colors.LinearSegmentedColormap.from_list( + "cellnopt_absolute_error", + ["#E7F5E7", "#FFF0B3", "#F4B7B2"], + ) + error_norm = colors.Normalize(vmin=0, vmax=1) + + for row, condition_index in enumerate(condition_indices): + for column, signal_index in enumerate(signal_indices): + axis = axes[row, column] + predicted = series.predicted[row, column] + observed = series.observed[row, column] + measured = series.measured[row, column] + if np.any(measured): + error = float(np.mean(np.abs(observed[measured] - predicted[measured]))) + axis.set_facecolor(error_cmap(error_norm(error))) + for time, observed_value, predicted_value, is_measured in zip( + series.times, + observed, + predicted, + measured, + ): + if is_measured: + axis.plot( + [time, time], + [observed_value, predicted_value], + color="#777777", + linewidth=1.0, + label="_nolegend_", + zorder=1, + ) + else: + axis.set_facecolor("#F2F2F2") + axis.patch.set_hatch("//") + axis.patch.set_edgecolor("#C8C8C8") + + prediction_line = "-" if len(series.times) > 1 else "none" + axis.plot( + series.times, + predicted, + color="#2C7FB8", + marker="s", + markersize=5, + linewidth=1.5, + linestyle=prediction_line, + label="Model", + zorder=2, + ) + if np.any(measured): + observed_values = np.ma.array(observed, mask=~measured) + observation_line = "-" if np.count_nonzero(measured) > 1 else "none" + axis.plot( + series.times, + observed_values, + color=_NEGATIVE_COLOR, + marker="o", + markerfacecolor="none", + markeredgewidth=1.5, + markersize=7, + linewidth=1.2, + linestyle=observation_line, + label="Observed", + zorder=3, + ) + + if len(series.times) == 1: + axis.set_xlim(series.times[0] - 0.5, series.times[0] + 0.5) + else: + span = float(series.times[-1] - series.times[0]) + margin = max(0.03 * span, 1e-6) + axis.set_xlim(series.times[0] - margin, series.times[-1] + margin) + axis.set_ylim(-0.05, 1.05) + axis.set_yticks([0, 0.5, 1]) + if row == 0: + axis.set_title(str(data.vertices[signal_index]), fontsize=10) + if row == num_conditions - 1: + axis.set_xticks(series.times, series.time_labels) + else: + axis.set_xticks([]) + if column == 0: + axis.set_ylabel( + data.conditions[condition_index], + rotation=0, + ha="right", + va="center", + labelpad=10, + ) + else: + axis.set_yticklabels([]) + axis.grid(axis="y", color="#DDDDDD", linewidth=0.5) + + cue_axis = axes[row, -1] + if cue_labels: + colors = [_STIMULUS_COLOR if kind == "stimulus" else _INHIBITOR_COLOR for kind in cue_kinds] + cue_axis.bar( + np.arange(len(cue_labels)), + cues[row], + color=colors, + edgecolor="#555555", + linewidth=0.5, + ) + cue_axis.set_xlim(-0.6, len(cue_labels) - 0.4) + cue_axis.set_ylim(0, 1.05) + cue_axis.set_yticks([]) + if row == 0: + cue_axis.set_title("Cues", fontsize=10) + if row == num_conditions - 1: + cue_axis.set_xticks( + np.arange(len(cue_labels)), + cue_labels, + rotation=45, + ha="right", + ) + else: + cue_axis.set_xticks([]) + + legend_axis.legend( + handles=[ + Line2D( + [], + [], + color=_NEGATIVE_COLOR, + marker="o", + markerfacecolor="none", + linestyle="none", + label="Observed", + ), + Line2D([], [], color="#2C7FB8", marker="s", linestyle="none", label="Model"), + ], + loc="center", + ncols=2, + frameon=False, + ) + error_scale = plt.cm.ScalarMappable(norm=error_norm, cmap=error_cmap) + error_scale.set_array([]) + colorbar = figure.colorbar( + error_scale, + ax=axes.ravel().tolist(), + fraction=0.018, + pad=0.02, + ) + colorbar.set_label("Mean absolute error") + return figure, axes + + +def _annotate_heatmap(axis: Any, values: np.ndarray, mask: Optional[np.ndarray] = None) -> None: + if values.size > 100: + return + for row in range(values.shape[0]): + for column in range(values.shape[1]): + if mask is not None and mask[row, column]: + continue + axis.text( + column, + row, + f"{values[row, column]:.2g}", + ha="center", + va="center", + fontsize=8, + ) + + +def _plot_fit_heatmaps( + data: _CellNOptPlotData, + condition_indices: Sequence[int], + signal_indices: Sequence[int], + *, + figsize: Optional[tuple[float, float]], +): + plt, colors, _ = _import_matplotlib() + observed = data.measurements[np.ix_(signal_indices, condition_indices)].T + measured = data.measurement_mask[np.ix_(signal_indices, condition_indices)].T + predicted = data.states[np.ix_(signal_indices, condition_indices)].T.astype(float) + error = np.abs(observed - predicted) + cues, cue_labels, cue_kinds = _cue_data(data, condition_indices) + condition_labels = [data.conditions[index] for index in condition_indices] + signal_labels = [str(data.vertices[index]) for index in signal_indices] + + if figsize is None: + figsize = ( + max(9.5, 1.0 * len(signal_labels) + 0.55 * len(cue_labels)), + max(3.0, 0.52 * len(condition_labels) + 1.8), + ) + figure, axes = plt.subplots( + 1, + 4, + figsize=figsize, + squeeze=False, + constrained_layout=True, + gridspec_kw={"width_ratios": [1, 1, 1, max(0.7, len(cue_labels) / max(len(signal_labels), 1))]}, + ) + axes = axes[0] + + value_cmap = plt.get_cmap("viridis").copy() + value_cmap.set_bad("#E8E8E8") + error_cmap = plt.get_cmap("RdYlGn_r").copy() + error_cmap.set_bad("#E8E8E8") + observed_masked = np.ma.array(observed, mask=~measured) + error_masked = np.ma.array(error, mask=~measured) + matrices = [observed_masked, predicted, error_masked] + titles = ["Observed", "Model", "Absolute error"] + cmaps = [value_cmap, value_cmap, error_cmap] + for axis, matrix, title, cmap in zip(axes[:3], matrices, titles, cmaps): + image = axis.imshow(matrix, aspect="auto", vmin=0, vmax=1, cmap=cmap) + axis.set_title(title) + axis.set_xticks(np.arange(len(signal_labels)), signal_labels, rotation=45, ha="right") + axis.set_yticks(np.arange(len(condition_labels)), condition_labels) + _annotate_heatmap( + axis, + np.asarray(matrix.filled(0) if np.ma.isMaskedArray(matrix) else matrix), + np.ma.getmaskarray(matrix) if np.ma.isMaskedArray(matrix) else None, + ) + figure.colorbar(image, ax=axis, fraction=0.046, pad=0.03) + + cue_axis = axes[3] + if cue_labels: + cue_codes = np.zeros_like(cues) + for column, kind in enumerate(cue_kinds): + cue_codes[:, column] = cues[:, column] * (1 if kind == "stimulus" else 2) + cue_cmap = colors.ListedColormap(["#FFFFFF", _STIMULUS_COLOR, _INHIBITOR_COLOR]) + cue_axis.imshow(cue_codes, aspect="auto", vmin=0, vmax=2, cmap=cue_cmap) + else: + cue_axis.imshow(np.zeros((len(condition_labels), 1)), aspect="auto", vmin=0, vmax=1, cmap="Greys") + cue_labels = ["None"] + cue_axis.set_title("Cues") + cue_axis.set_xticks(np.arange(len(cue_labels)), cue_labels, rotation=45, ha="right") + cue_axis.set_yticks(np.arange(len(condition_labels)), condition_labels) + return figure, axes + + +def plot_cellnopt_fit( + method: CellNOptDAG, + problem: Any = None, + *, + view: Literal["cellnopt", "heatmap"] = "cellnopt", + conditions: Optional[Union[Union[int, str], Sequence[Union[int, str]]]] = None, + signals: Optional[Union[Any, Sequence[Any]]] = None, + figsize: Optional[tuple[float, float]] = None, +): + """Compare CellNOpt measurements and predictions across conditions. + + The ``cellnopt`` view overlays observations and model predictions at the + inferred endpoint for every selected condition and signal. Unmeasured + signals show model predictions only. ``heatmap`` provides aligned matrices + for larger datasets. + """ + data = _extract_plot_data(method, problem) + condition_indices, signal_indices = _fit_selection(data, conditions, signals) + if view == "cellnopt": + return _plot_fit_grid( + data, + condition_indices, + signal_indices, + figsize=figsize, + ) + if view == "heatmap": + return _plot_fit_heatmaps( + data, + condition_indices, + signal_indices, + figsize=figsize, + ) + raise ValueError("view must be 'cellnopt' or 'heatmap'.") diff --git a/corneto/release.py b/corneto/release.py index 420f6a8dc..71736591b 100644 --- a/corneto/release.py +++ b/corneto/release.py @@ -6,6 +6,7 @@ import re import subprocess import sys +from pathlib import Path from typing import Sequence VERSION_RE = re.compile(r"^v\d+\.\d+\.\d+(?:-(?:alpha|beta|rc)\.\d+)?$") @@ -55,25 +56,11 @@ def _ensure_remote_exists(remote: str) -> None: def _ensure_up_to_date_with_remote_main(remote: str) -> None: - _run(["git", "fetch", remote, "main", "dev"], check=True) + _run(["git", "fetch", remote, "main"], check=True) head = _run(["git", "rev-parse", "HEAD"], check=True) remote_main = _run(["git", "rev-parse", f"{remote}/main"], check=True) if head != remote_main: - raise ReleaseError(f"HEAD is not at {remote}/main. Pull main after merging dev -> main.") - - -def _ensure_dev_is_merged(remote: str) -> None: - # `merge-base --is-ancestor A B` succeeds when A is reachable from B. - result = subprocess.run( - ["git", "merge-base", "--is-ancestor", f"{remote}/dev", "HEAD"], - text=True, - capture_output=True, - check=False, - ) - if result.returncode != 0: - raise ReleaseError( - f"{remote}/dev is not merged into current main commit yet. Merge dev -> main before releasing." - ) + raise ReleaseError(f"HEAD is not at {remote}/main. Pull main before releasing.") def _ensure_tag_does_not_exist(version: str, remote: str) -> None: @@ -86,6 +73,27 @@ def _ensure_tag_does_not_exist(version: str, remote: str) -> None: raise ReleaseError(f"Tag already exists on {remote}: {version}") +def _expected_release_heading(version: str) -> str: + heading = f"# Release {version}" + if re.search(r"-(?:alpha|beta|rc)\.\d+$", version): + heading += " {bdg-warning}`Pre-release`" + return heading + + +def _ensure_release_notes(version: str, *, root: Path = Path(".")) -> None: + notes_path = root / "docs" / "releases" / f"{version}.md" + if not notes_path.is_file(): + raise ReleaseError(f"Missing release notes: {notes_path}") + + content = notes_path.read_text(encoding="utf-8") + first_line = next((line.strip() for line in content.splitlines() if line.strip()), "") + expected_heading = _expected_release_heading(version) + if first_line != expected_heading: + raise ReleaseError(f"Release notes must start with {expected_heading!r}; found {first_line!r} in {notes_path}.") + if not re.search(r"^## Highlights\s*$", content, flags=re.MULTILINE): + raise ReleaseError(f"Release notes must contain a '## Highlights' section: {notes_path}") + + def _create_and_push_tag(version: str, remote: str) -> None: _run(["git", "tag", "-a", version, "-m", version], check=True) _run(["git", "push", remote, version], check=True) @@ -103,7 +111,7 @@ def parse_args(argv: Sequence[str]) -> argparse.Namespace: parser.add_argument( "--remote", default="origin", - help="Git remote containing the release main/dev branches (default: origin).", + help="Git remote containing the release main branch (default: origin).", ) parser.add_argument( "--yes", @@ -127,8 +135,8 @@ def main(argv: Sequence[str] | None = None) -> int: _ensure_clean_tree() _ensure_on_main() _ensure_up_to_date_with_remote_main(args.remote) - _ensure_dev_is_merged(args.remote) _ensure_tag_does_not_exist(version, args.remote) + _ensure_release_notes(version) if args.dry_run: print(f"[dry-run] All checks passed. Would create and push tag: {version}") @@ -143,12 +151,6 @@ def main(argv: Sequence[str] | None = None) -> int: _create_and_push_tag(version, args.remote) print(f"Release tag pushed to {args.remote}: {version}") print("GitHub Actions will now build, publish, and create the GitHub release.") - print("Post-release sync reminder:") - print(" git checkout dev") - print(f" git pull --ff-only {args.remote} dev") - print(f" git merge --ff-only {args.remote}/main") - print(f" git push {args.remote} dev") - print("This keeps dev aligned with the latest release tag ancestry for dynamic versioning.") return 0 except ReleaseError as exc: print(f"Release aborted: {exc}", file=sys.stderr) diff --git a/datasets/sachs/v1/README.md b/datasets/sachs/v1/README.md new file mode 100644 index 000000000..312d1d181 --- /dev/null +++ b/datasets/sachs/v1/README.md @@ -0,0 +1,60 @@ +# Sachs continuous cytometry data (`v1`) + +This revision contains the continuous measurements used by the CORNETO Sachs +tutorial: 7,466 observations of 11 signaling proteins and phospholipids. The +measurements are natural-log transformed and the first column, +`intervention_label`, preserves the condition grouping used by the tutorial. + +The files are intentionally kept outside the `corneto` Python package. A +cloned repository can use them directly; installed users can obtain the same +version through `corneto.datasets.fetch_dataset("sachs")`. + +## Files + +- `measurements.tsv` contains the transformed measurements and condition + labels. +- `condition_manifest.csv` records the source condition, row interval, legacy + archive label, and intervention target used by the tutorial. +- `README.md` documents the source, transformation, citation, and license. + +## Provenance and transformation + +The source is the Zenodo record +[Sachs: Protein and Phospholipids Expressions](https://doi.org/10.5281/zenodo.7681811), +version 2, specifically its `sachs.zip` file +([direct download](https://zenodo.org/records/7681811/files/sachs.zip)). The +source archive is identified by MD5 +`d50ccd7f88bb3705bcf6eda3978bcc41`. + +The CORNETO `v1` files are a deterministic derived representation of the nine +measured experimental conditions in that archive, concatenated in this order: +`cd3cd28`, `cd3cd28_icam2`, `cd3cd28_aktinhib`, `cd3cd28_g0076`, +`cd3cd28_psitect`, `cd3cd28_u0126`, `cd3cd28_ly`, `pma`, and `b2camp`. +For each condition, the 11 numeric source columns were transformed with the +natural logarithm. The source column `Plcg` is named `plc` in the CORNETO +file, and the `intervention_label` column plus the manifest were added for the +tutorial. + +The five simulated conditions and `GroundTruth.csv` from the Zenodo archive +are not included here. This explains why the CORNETO file is not byte-for-byte +identical to the Zenodo ZIP. The previous CORNETO archive had no recorded +external source; comparison with the Zenodo files establishes the derivation +described above. + +## Citation and license + +Please cite both the data record and the original study: + +> Mathematical Research Data Initiative (2023). *Sachs: Protein and +> Phospholipids Expressions*. Zenodo. https://doi.org/10.5281/zenodo.7681811 + +> Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D. A., & Nolan, G. P. +> (2005). Causal protein-signaling networks derived from multiparameter +> single-cell data. *Science*, 308(5721), 523–529. +> https://doi.org/10.1126/science.1105809 + +The Zenodo record identifies the data as [Creative Commons Attribution 4.0 +International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/), +granted by G. Nolan and D. Lauffenburger. The derived CORNETO dataset is +distributed under the same license with the attribution and source links +above. diff --git a/datasets/sachs/v1/condition_manifest.csv b/datasets/sachs/v1/condition_manifest.csv new file mode 100644 index 000000000..cee2debd3 --- /dev/null +++ b/datasets/sachs/v1/condition_manifest.csv @@ -0,0 +1,10 @@ +condition,start_row,stop_row,archive_label,intervention_target +cd3cd28,0,853,observational, +cd3cd28_icam2,853,1755,observational, +cd3cd28_aktinhib,1755,2666,raf,akt +cd3cd28_g0076,2666,3389,pkc,pkc +cd3cd28_psitect,3389,4199,erk,pip2 +cd3cd28_u0126,4199,4998,pip2,mek +cd3cd28_ly,4998,5846,observational, +pma,5846,6759,pkc,pkc +b2camp,6759,7466,pka,pka diff --git a/datasets/sachs/v1/measurements.tsv b/datasets/sachs/v1/measurements.tsv new file mode 100644 index 000000000..e067a19b7 --- /dev/null +++ b/datasets/sachs/v1/measurements.tsv @@ -0,0 +1,7467 @@ +intervention_label raf mek plc pip2 pip3 erk akt pka pkc p38 jnk +observational 3.27336401015227 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After +a usable solution has been obtained, ``predict(...)`` applies the fixed model +to new externally specified conditions and ``evaluate(...)`` compares those +predictions with supplied measurements. ``LinearDAGDiscovery`` also exposes +``residuals(...)`` for local structural-equation diagnostics: its forward loss +and local loss have different semantics. ``CellNOptDAG`` evaluates its fixed +selected reactions by deterministic Boolean propagation. Most other +``Method`` subclasses remain explanatory optimization methods; the base class +does not expose an sklearn-style fit/predict API. + .. autosummary:: :toctree: generated/ CarnivalFlow CarnivalILP + signaling.CellNOptDAG BidirectionalPHONEMeS PHONEMeS compute_phonemes_scores milp_carnival - signaling.cellnopt_ilp.cellnoptILP + +CellNOpt visualization +~~~~~~~~~~~~~~~~~~~~~~ + +CellNOpt plotting utilities use the standard CORNETO graph renderers for +network views and return Matplotlib figure/axes objects for data-fit views. + +.. autosummary:: + :toctree: generated/ + + signaling.plot_cellnopt_model + signaling.plot_cellnopt_fit + +Causal discovery +---------------- + +``LinearDAGDiscovery`` infers a sparse, acyclic linear structural model from +continuous observational and perfect-intervention measurements. Candidate +interactions come from a directed prior-knowledge network, and commodity flows +ensure that selected edges are supported by intervention-to-response paths. + +.. autosummary:: + :toctree: generated/ + + LinearDAGDiscovery + +CellNOpt and AnnNet +~~~~~~~~~~~~~~~~~~~ + +These helpers keep the signed network, perturbation conditions, and fitted +CellNOptDAG results in one AnnNet object. + +.. autosummary:: + :toctree: generated/ + + signaling.add_cellnopt_conditions + signaling.build_cellnopt_from_annnet + signaling.add_cellnopt_results Metabolism ---------- diff --git a/docs/api/index.rst b/docs/api/index.rst index a30a0b02d..8b88f1129 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -31,6 +31,7 @@ as raw specifications may not always provide sufficient context. corneto.backend corneto.data + corneto.datasets corneto.io corneto.graph corneto.methods diff --git a/docs/custom-index.html b/docs/custom-index.html deleted file mode 100644 index d42cb2d23..000000000 --- a/docs/custom-index.html +++ /dev/null @@ -1,431 +0,0 @@ - - - - - - CORNETO: Unified network inference from omics data - - - - - - - - - - - - - - - - - -

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CORNETO

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Unified knowledge-driven network inference from omics data

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- - - - - - - - - - - - - - - - diff --git a/docs/guide/causal/index.md b/docs/guide/causal/index.md new file mode 100644 index 000000000..0b0da9318 --- /dev/null +++ b/docs/guide/causal/index.md @@ -0,0 +1,12 @@ +# Causal and structural modeling + +These methods combine observational or intervention data with directed prior +knowledge to fit and analyze structural models. They are applicable beyond +signaling, including gene regulatory networks and other directed biological +systems. + +```{toctree} +:maxdepth: 3 + +linear-dag-discovery.ipynb +``` diff --git a/docs/guide/causal/linear-dag-discovery.ipynb b/docs/guide/causal/linear-dag-discovery.ipynb new file mode 100644 index 000000000..bd9a28e86 --- /dev/null +++ b/docs/guide/causal/linear-dag-discovery.ipynb @@ -0,0 +1,2423 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dc272ccb", + "metadata": { + "papermill": { + "duration": 0.008932, + "end_time": "2026-09-21T22:58:59.522800", + "exception": false, + "start_time": "2026-09-21T22:58:59.513868", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Discovering a linear causal signaling network from perturbation data\n", + "\n", + "Perturbation experiments often measure continuous molecular responses: log-fold changes in phosphoproteins, reporter intensities, or expression signatures after stimulating or inhibiting a pathway component.\n", + "\n", + "This guide starts from one question:\n", + "\n", + "> **Which directed interactions from a prior-knowledge network produce a linear model that fits the measured responses across observational and intervention conditions?**\n", + "\n", + "`LinearDAGDiscovery` selects a subset of edges from a directed prior-knowledge network (PKN), requires the selected graph to contain no directed cycles, and fits one linear equation for each measured vertex. Each selected edge has one coefficient that is shared across all samples. A hard intervention removes the equation for its target in that sample; a shift intervention keeps the equation and adds a known or fitted offset.\n", + "\n", + "As many other methods implemented in CORNETO, the optimization uses auxiliary flow variables for connectivity. Every selected edge must belong to at least one directed route from an intervened vertex to a measured response. These variables enforce connectivity only; they are not biochemical fluxes, effect sizes, or confidence scores.\n", + "\n", + "CORNETO calls each row in `Data` a **sample**. A sample may be one bulk experimental condition or one cell. The intervention metadata is attached to the measured target within each sample, as shown below." + ] + }, + { + "cell_type": "markdown", + "id": "45113bfc", + "metadata": { + "papermill": { + "duration": 0.003684, + "end_time": "2026-09-21T22:58:59.532890", + "exception": false, + "start_time": "2026-09-21T22:58:59.529206", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## When is this method useful?\n", + "\n", + "| Experimental setting | Interpretation |\n", + "|---|---|\n", + "| Continuous molecular measurements | Values may be signed and need not be discretized |\n", + "| Several perturbation conditions | Perturbations help orient otherwise ambiguous associations |\n", + "| Directed PKN with alternatives or feedback | The method chooses a subset with no directed cycles |\n", + "| One linear relationship shared across samples | Each edge coefficient is the same in every sample or condition |\n", + "\n", + "Use `CellNOptDAG` instead when you have a more mechanistic prior knowledge of a pathway from perturbations to readouts or phenotypes\n", + "and you want to use boolean logic.\n", + "\n", + "## What CORNETO adds\n", + "\n", + "Many causal-discovery methods are run on a dataset and return a DAG. CORNETO instead uses the graph as part of a model of the experiment. In `LinearDAGDiscovery`, the linear equations fit the measured responses, the PKN defines which biological interactions are possible, and the intervention annotations specify which equations are modified by the experiment. The result therefore contains both a network and a fitted explanation of the experimental data.\n", + "\n", + "Prior knowledge does not have to stop at the initial PKN. Before solving, users can add assumptions such as requiring or forbidding an interaction, constraining its sign or coefficient, or limiting which mechanisms may be selected. The complete model is then fitted again under that hypothesis. Comparing the original and constrained models shows whether the hypothesis is compatible with the data, how much it changes held-out prediction, and which other interactions must change to accommodate it.\n", + "\n", + "This modeling flexibility is the main reason to use CORNETO instead. The trade-off is computational cost: enforcing graph structure, interventions, prior knowledge, and additional constraints together requires mixed-integer optimization. It is worthwhile when these modeling choices are central to the analysis; if the goal is only to obtain a graph quickly, a more scalable causal-discovery method may be preferable." + ] + }, + { + "cell_type": "markdown", + "id": "44d4e22b", + "metadata": { + "papermill": { + "duration": 0.003071, + "end_time": "2026-09-21T22:58:59.539180", + "exception": false, + "start_time": "2026-09-21T22:58:59.536109", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Example: separating the AKT and ERK branches downstream of EGFR\n", + "\n", + "Consider a small perturbation panel measuring five signaling activities. The biological hypothesis is that EGFR activates both AKT and ERK, AKT inhibits FOXO, and ERK activates ELK1.\n", + "\n", + "The PKN is intentionally less certain. It also contains reverse interactions and AKT–ERK cross-talk that could form cycles. The fit will show which of these candidate edges are selected under the chosen loss and penalties.\n", + "\n", + "The values below are simulated, centered log-fold changes coming from the assumed linear signaling in every condition, with a small noise." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "07b14fc5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T22:58:59.545881Z", + "iopub.status.busy": "2026-09-21T22:58:59.545729Z", + "iopub.status.idle": "2026-09-21T22:59:01.099318Z", + "shell.execute_reply": "2026-09-21T22:59:01.098801Z" + }, + "papermill": { + "duration": 1.558086, + "end_time": "2026-09-21T22:59:01.100095", + "exception": false, + "start_time": "2026-09-21T22:58:59.542009", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import corneto as cn\n", + "from corneto.data import Data\n", + "from corneto.methods import LinearDAGDiscovery" + ] + }, + { + "cell_type": "markdown", + "id": "7f862c20", + "metadata": { + "papermill": { + "duration": 0.001544, + "end_time": "2026-09-21T22:59:01.103471", + "exception": false, + "start_time": "2026-09-21T22:59:01.101927", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Define the candidate biology\n", + "\n", + "Interaction signs are stored in the PKN. When `enforce_signs=True`, an activating sign constrains the fitted coefficient to be nonnegative and an inhibitory sign constrains it to be nonpositive. The candidate graph contains several pairs of edges pointing in opposite directions; the selected graph, but not the PKN, is required to have no directed cycles." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4cd07528", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T22:59:01.107118Z", + "iopub.status.busy": "2026-09-21T22:59:01.106999Z", + "iopub.status.idle": "2026-09-21T22:59:01.115167Z", + "shell.execute_reply": "2026-09-21T22:59:01.114747Z" + }, + "papermill": { + "duration": 0.010688, + "end_time": "2026-09-21T22:59:01.115660", + "exception": false, + "start_time": "2026-09-21T22:59:01.104972", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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condition
obs_1-1.4-1.394-1.3661.331-1.634observational
obs_2-0.5-0.445-0.3690.546-0.440observational
obs_30.70.7290.606-0.6210.699observational
obs_41.51.6301.258-1.4111.470observational
EGFR_low-2.0-2.229-1.7452.141-2.111EGFR
EGFR_high2.12.4171.713-2.0202.179EGFR
AKT_on-0.71.800-0.535-1.502NaNAKT
AKT_off1.0-1.5000.9011.2321.108AKT
ERK_on-0.8-0.7951.7000.7332.011ERK
ERK_off0.60.596-1.600-0.563-1.836ERK
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" + ], + "text/plain": [ + " EGFR AKT ERK FOXO ELK1 intervention\n", + "condition \n", + "obs_1 -1.4 -1.394 -1.366 1.331 -1.634 observational\n", + "obs_2 -0.5 -0.445 -0.369 0.546 -0.440 observational\n", + "obs_3 0.7 0.729 0.606 -0.621 0.699 observational\n", + "obs_4 1.5 1.630 1.258 -1.411 1.470 observational\n", + "EGFR_low -2.0 -2.229 -1.745 2.141 -2.111 EGFR\n", + "EGFR_high 2.1 2.417 1.713 -2.020 2.179 EGFR\n", + "AKT_on -0.7 1.800 -0.535 -1.502 NaN AKT\n", + "AKT_off 1.0 -1.500 0.901 1.232 1.108 AKT\n", + "ERK_on -0.8 -0.795 1.700 0.733 2.011 ERK\n", + "ERK_off 0.6 0.596 -1.600 -0.563 -1.836 ERK" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng = np.random.default_rng(13)\n", + "noise_sd = 0.08\n", + "\n", + "\n", + "def signaling_state(egfr, *, akt=None, erk=None):\n", + " # Intervened values are fixed by the experiment; endogenous values include noise.\n", + " akt = 1.1 * egfr + rng.normal(0, noise_sd) if akt is None else akt\n", + " erk = 0.8 * egfr + rng.normal(0, noise_sd) if erk is None else erk\n", + " return {\n", + " \"EGFR\": egfr,\n", + " \"AKT\": akt,\n", + " \"ERK\": erk,\n", + " \"FOXO\": -0.9 * akt + rng.normal(0, noise_sd),\n", + " \"ELK1\": 1.2 * erk + rng.normal(0, noise_sd),\n", + " }\n", + "\n", + "\n", + "conditions = [\n", + " (\"obs_1\", signaling_state(-1.4), None),\n", + " (\"obs_2\", signaling_state(-0.5), None),\n", + " (\"obs_3\", signaling_state(0.7), None),\n", + " (\"obs_4\", signaling_state(1.5), None),\n", + " (\"EGFR_low\", signaling_state(-2.0), \"EGFR\"),\n", + " (\"EGFR_high\", signaling_state(2.1), \"EGFR\"),\n", + " (\"AKT_on\", signaling_state(-0.7, akt=1.8), \"AKT\"),\n", + " (\"AKT_off\", signaling_state(1.0, akt=-1.5), \"AKT\"),\n", + " (\"ERK_on\", signaling_state(-0.8, erk=1.7), \"ERK\"),\n", + " (\"ERK_off\", signaling_state(0.6, erk=-1.6), \"ERK\"),\n", + "]\n", + "\n", + "# Demonstrate a genuinely missing response.\n", + "conditions[6][1][\"ELK1\"] = None\n", + "\n", + "experiment = pd.DataFrame(\n", + " [values | {\"intervention\": target or \"observational\"} for _, values, target in conditions],\n", + " index=[name for name, _, _ in conditions],\n", + ")\n", + "experiment.index.name = \"condition\"\n", + "experiment.round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "2fbdc2a1", + "metadata": { + "papermill": { + "duration": 0.001703, + "end_time": "2026-09-21T22:59:01.559767", + "exception": false, + "start_time": "2026-09-21T22:59:01.558064", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "(linear-dag-intervention-metadata)=\n", + "### Mark interventions in `Data`\n", + "\n", + "Each sample contains a feature for each measured PKN vertex. The feature's `value` is the measurement; its metadata tells `LinearDAGDiscovery` whether that vertex was directly intervened on in that sample. Put intervention metadata on the target feature only.\n", + "\n", + "| Feature field | Expected value | When to use it |\n", + "|---|---|---|\n", + "| `mapping` | `\"vertex\"` | Required for a feature to represent a PKN vertex. |\n", + "| `value` | Numeric measurement | Required for an intervened target when fitting. |\n", + "| `intervention` | Omit for an observational feature; use `\"hard\"` or `\"shift\"` for an intervened target | `\"hard\"` removes the target's equation for that sample. `\"shift\"` keeps the equation and adds an offset. |\n", + "| `shift` | Numeric offset | Supply with `intervention=\"shift\"` when the offset is known; otherwise omit it so the model estimates the offset. |\n", + "| `intervention_group` | A condition identifier shared by replicate samples | Optional for hard interventions; required when estimating a shift shared by replicates. It groups their connectivity evidence while each sample still contributes its own measurement to the fit. |\n", + "\n", + "The `intervention` column in the table above names the target for display. In a `Data` feature, `intervention` instead names the intervention type. `intervened=True` is also accepted as an older spelling of `intervention=\"hard\"`; use one form per feature. The example below uses `intervention=\"hard\"`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3296bd15", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T22:59:01.563663Z", + "iopub.status.busy": "2026-09-21T22:59:01.563591Z", + "iopub.status.idle": "2026-09-21T22:59:01.565567Z", + "shell.execute_reply": "2026-09-21T22:59:01.565252Z" + }, + "papermill": { + "duration": 0.004566, + "end_time": "2026-09-21T22:59:01.566011", + "exception": false, + "start_time": "2026-09-21T22:59:01.561445", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "samples = {}\n", + "for condition, values, target in conditions:\n", + " samples[condition] = {}\n", + " for vertex, value in values.items():\n", + " feature = {\"mapping\": \"vertex\", \"value\": value}\n", + " if vertex == target:\n", + " feature[\"intervention\"] = \"hard\"\n", + " samples[condition][vertex] = feature\n", + "\n", + "data = Data.from_cdict(samples)" + ] + }, + { + "cell_type": "markdown", + "id": "77d70e12", + "metadata": { + "papermill": { + "duration": 0.001727, + "end_time": "2026-09-21T22:59:01.569481", + "exception": false, + "start_time": "2026-09-21T22:59:01.567754", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Fit one linear DAG to all conditions\n", + "\n", + "The objective adds `lambda_edges` for every selected edge, so the optimizer trades a lower fitting error against selecting fewer edges. `max_parents=2` imposes a literal graph constraint: no vertex may have more than two selected incoming edges. `enforce_signs=True` requires coefficients on PKN edges marked `+1` to be nonnegative and coefficients on edges marked `-1` to be nonpositive.\n", + "\n", + "The default `coefficient_support=\"exact\"` requires every selected edge to have a fitted coefficient whose normalized magnitude is at least `min_abs_coefficient=0.25`. This is a model choice about the smallest allowed coefficient, not a solver tolerance. With `coefficient_support=\"structural\"`, an edge may be selected to complete an intervention-to-response route even when its regression coefficient is zero.\n", + "\n", + "Standardized loss is enabled by default. For each target, CORNETO divides residuals by the standard deviation computed from training rows where the target is observed, is not hard-intervened, and has all candidate parents observed. This puts errors on a scale relative to that target's observed variation; it does not standardize the stored measurements or the fitted coefficients.\n", + "\n", + "For this simulated dataset, `lambda_edges=0.1` yields the four generating edges. At `0.05`, the selected optimum also contains ERK → AKT with a raw coefficient near `0.03`. The penalty does not declare an edge biologically real or unreal; users should rerun plausible values and report which edges remain selected." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f93a9934", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T22:59:01.573305Z", + "iopub.status.busy": "2026-09-21T22:59:01.573225Z", + "iopub.status.idle": "2026-09-21T22:59:01.626715Z", + "shell.execute_reply": "2026-09-21T22:59:01.626301Z" + }, + "papermill": { + "duration": 0.056153, + "end_time": "2026-09-21T22:59:01.627287", + "exception": false, + "start_time": "2026-09-21T22:59:01.571134", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'optimal'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "method = LinearDAGDiscovery(\n", + " lambda_edges=0.1,\n", + " coefficient_bound=3.0,\n", + " fit_intercept=False,\n", + " max_parents=2,\n", + " enforce_signs=True,\n", + " coefficient_support=\"exact\",\n", + " min_abs_coefficient=0.25,\n", + ")\n", + "problem = method.build(pkn, data)\n", + "result = problem.solve(solver=\"SCIPY\")\n", + "result.status" + ] + }, + { + "cell_type": "markdown", + "id": "13324eaf", + "metadata": { + "papermill": { + "duration": 0.001801, + "end_time": "2026-09-21T22:59:01.631033", + "exception": false, + "start_time": "2026-09-21T22:59:01.629232", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Which PKN edges were selected?\n", + "\n", + "A fitted edge coefficient is the number multiplying the source value in the target's linear equation. Holding the other included parent values fixed, increasing the source by one measurement unit changes the model's predicted target by that coefficient. This is a model slope; interpreting it as a causal effect requires the assumptions discussed at the end of the guide.\n", + "\n", + "Only the loss is standardized, so the displayed coefficients remain in the original source and target units. For thresholding, CORNETO rescales a coefficient as `coefficient × source_scale / target_scale`. In exact mode, a selected edge must have normalized magnitude at least `min_abs_coefficient`; every unselected edge has coefficient zero. This threshold can exclude weak but genuine relationships." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c101301f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T22:59:01.635082Z", + "iopub.status.busy": "2026-09-21T22:59:01.634998Z", + "iopub.status.idle": "2026-09-21T22:59:01.638775Z", + "shell.execute_reply": "2026-09-21T22:59:01.638455Z" + }, + "papermill": { + "duration": 0.00657, + "end_time": "2026-09-21T22:59:01.639284", + "exception": false, + "start_time": "2026-09-21T22:59:01.632714", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sourcetargetselectedcoefficient
0EGFRAKTTrue1.086810
1EGFRERKTrue0.866341
2AKTFOXOTrue-0.865437
3ERKELK1True1.195922
4AKTEGFRFalse-0.000000
5ERKEGFRFalse-0.000000
6FOXOAKTFalse0.000000
7ELK1ERKFalse-0.000000
8AKTERKFalse-0.000000
9ERKAKTFalse-0.000000
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" + ], + "text/plain": [ + " source target selected coefficient\n", + "0 EGFR AKT True 1.086810\n", + "1 EGFR ERK True 0.866341\n", + "2 AKT FOXO True -0.865437\n", + "3 ERK ELK1 True 1.195922\n", + "4 AKT EGFR False -0.000000\n", + "5 ERK EGFR False -0.000000\n", + "6 FOXO AKT False 0.000000\n", + "7 ELK1 ERK False -0.000000\n", + "8 AKT ERK False -0.000000\n", + "9 ERK AKT False -0.000000" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selected = np.asarray(problem.expr.edge_selected.value).reshape(-1) > 0.5\n", + "coefficients = np.asarray(problem.expr.edge_coefficient.value).reshape(-1)\n", + "\n", + "edge_rows = []\n", + "for edge_index, (source_set, target_set) in enumerate(pkn.E):\n", + " edge_rows.append(\n", + " {\n", + " \"source\": next(iter(source_set)),\n", + " \"target\": next(iter(target_set)),\n", + " \"selected\": bool(selected[edge_index]),\n", + " \"coefficient\": coefficients[edge_index],\n", + " }\n", + " )\n", + "edge_table = pd.DataFrame(edge_rows)\n", + "edge_table" + ] + }, + { + "cell_type": "markdown", + "id": "60e5c223", + "metadata": { + "papermill": { + "duration": 0.001771, + "end_time": "2026-09-21T22:59:01.642910", + "exception": false, + "start_time": "2026-09-21T22:59:01.641139", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Compare the candidate network with the inferred model\n", + "\n", + "Selected activating edges are blue, the selected inhibitory edge is orange, and rejected candidates remain visible as pale dotted arrows. Labels show fitted coefficients." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "322a8e47", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T22:59:01.646966Z", + "iopub.status.busy": "2026-09-21T22:59:01.646879Z", + "iopub.status.idle": "2026-09-21T22:59:01.840777Z", + "shell.execute_reply": "2026-09-21T22:59:01.840348Z" + }, + "papermill": { + "duration": 0.196721, + "end_time": "2026-09-21T22:59:01.841386", + "exception": false, + "start_time": "2026-09-21T22:59:01.644665", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "EGFR\n", + "\n", + "EGFR\n", + "\n", + "\n", + "\n", + "AKT\n", + "\n", + "AKT\n", + "\n", + "\n", + "\n", + "EGFR->AKT\n", + "\n", + "\n", + "+1.09\n", + "\n", + "\n", + "\n", + "ERK\n", + "\n", + "ERK\n", + "\n", + "\n", + "\n", + "EGFR->ERK\n", + "\n", + "\n", + "+0.87\n", + "\n", + "\n", + "\n", + "AKT->EGFR\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "AKT->ERK\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "FOXO\n", + "\n", + "FOXO\n", + "\n", + "\n", + "\n", + "AKT->FOXO\n", + "\n", + "\n", + "\n", + "-0.87\n", + "\n", + "\n", + "\n", + "ERK->EGFR\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ERK->AKT\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ELK1\n", + "\n", + "ELK1\n", + "\n", + "\n", + "\n", + "ERK->ELK1\n", + "\n", + "\n", + "+1.20\n", + "\n", + "\n", + "\n", + "FOXO->AKT\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ELK1->ERK\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "edge_styles = {}\n", + "for edge_index, row in edge_table.iterrows():\n", + " if row[\"selected\"]:\n", + " edge_styles[edge_index] = {\n", + " \"color\": \"#D55E00\" if row[\"coefficient\"] < 0 else \"#0072B2\",\n", + " \"fontcolor\": \"#333333\",\n", + " \"label\": f'{row[\"coefficient\"]:+.2f}',\n", + " \"penwidth\": \"3\",\n", + " }\n", + " else:\n", + " edge_styles[edge_index] = {\n", + " \"color\": \"#C7C7C7\",\n", + " \"style\": \"dotted\",\n", + " }\n", + "\n", + "pkn.plot(custom_edge_attr=edge_styles, graph_attr={\"rankdir\": \"LR\"})" + ] + }, + { + "cell_type": "markdown", + "id": "6616841b", + "metadata": { + "papermill": { + "duration": 0.001852, + "end_time": "2026-09-21T22:59:01.845262", + "exception": false, + "start_time": "2026-09-21T22:59:01.843410", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "(linear-dag-edge-metadata)=\n", + "### Inspect fitted edge metadata\n", + "\n", + "`get_solution_graph()` makes the interpretation explicit. It preserves a prior interaction sign as `prior_interaction`, records the fitted sign as `inferred_interaction`, and marks structural-only zero-coefficient edges as `connector`. In exact mode, connectors should be absent because selected edges must have a nonzero normalized coefficient." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b412ae39", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T22:59:01.849583Z", + "iopub.status.busy": "2026-09-21T22:59:01.849485Z", + "iopub.status.idle": "2026-09-21T22:59:01.854701Z", + "shell.execute_reply": "2026-09-21T22:59:01.854414Z" + }, + "papermill": { + "duration": 0.008216, + "end_time": "2026-09-21T22:59:01.855257", + "exception": false, + "start_time": "2026-09-21T22:59:01.847041", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sourcetargetcoefficientnormalized_coefficientprior_interactioninferred_interactionconnector
0EGFRAKT1.0868100.91039911False
1EGFRERK0.8663410.84981311False
2AKTFOXO-0.865437-0.975949-1-1False
3ERKELK11.1959220.95673811False
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" + ], + "text/plain": [ + " source target coefficient normalized_coefficient prior_interaction \\\n", + "0 EGFR AKT 1.086810 0.910399 1 \n", + "1 EGFR ERK 0.866341 0.849813 1 \n", + "2 AKT FOXO -0.865437 -0.975949 -1 \n", + "3 ERK ELK1 1.195922 0.956738 1 \n", + "\n", + " inferred_interaction connector \n", + "0 1 False \n", + "1 1 False \n", + "2 -1 False \n", + "3 1 False " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solution_graph = method.get_solution_graph()\n", + "solution_rows = []\n", + "for edge_index, (source_set, target_set) in enumerate(solution_graph.E):\n", + " attributes = solution_graph.get_attr_edge(edge_index)\n", + " solution_rows.append(\n", + " {\n", + " \"source\": next(iter(source_set)),\n", + " \"target\": next(iter(target_set)),\n", + " \"coefficient\": attributes[\"coefficient\"],\n", + " \"normalized_coefficient\": attributes[\"normalized_coefficient\"],\n", + " \"prior_interaction\": attributes.get(\"prior_interaction\"),\n", + " \"inferred_interaction\": attributes[\"inferred_interaction\"],\n", + " \"connector\": attributes[\"connector\"],\n", + " }\n", + " )\n", + "pd.DataFrame(solution_rows)" + ] + }, + { + "cell_type": "markdown", + "id": "74764b05", + "metadata": { + "papermill": { + "duration": 0.0019, + "end_time": "2026-09-21T22:59:01.859164", + "exception": false, + "start_time": "2026-09-21T22:59:01.857264", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "At these parameter values, the optimizer selects the four interactions used to generate the synthetic data. Their coefficients are close to, but not exactly, the generating values $1.1$, $0.8$, $-0.9$, and $1.2$. The available reverse edges, feedback edges, and AKT–ERK cross-talk are not selected in this fit; that result does not establish that such interactions are absent in real biology. The selected graph has no directed cycles even though the PKN contains them." + ] + }, + { + "cell_type": "markdown", + "id": "0173eb2b", + "metadata": { + "papermill": { + "duration": 0.001912, + "end_time": "2026-09-21T22:59:01.862922", + "exception": false, + "start_time": "2026-09-21T22:59:01.861010", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "(linear-dag-prediction)=\n", + "## Reuse the fitted model for held-out prediction\n", + "\n", + "The example above calls `build()` and `solve()` separately. Use that form when you need to add constraints to the optimization problem before solving it. If you do not need custom constraints, `fit()` performs those two steps and returns the fitted `LinearDAGDiscovery` object.\n", + "\n", + "| Operation | Values used from the new data | Meaning |\n", + "|---|---|---|\n", + "| `predict(data)` | Hard-intervention target values and explicit or previously fitted shift interventions | Applies the fixed selected graph recursively. It does not solve again and does not use ordinary observed values as inputs. |\n", + "| `evaluate(data)` forward loss | The predictions above, followed by the observed values used only for scoring | Measures recursive forward prediction error without feeding observed descendants into the model. Every observed vertex that is not hard-intervened is scored, including roots. |\n", + "| `evaluate(data)` local loss | Observed target and observed values for every incoming PKN parent | Measures how well each fitted equation reconstructs its target when its parent measurements are supplied. This is a diagnostic, not an independent forward prediction. |\n", + "| `residuals(data)` | The same observed-parent calculation as local loss | Returns `observed target − equation reconstruction` for each usable target and sample. |\n", + "\n", + "A **root** is a vertex with no selected incoming edge. `predict()` uses its fitted intercept, not an ordinary observed value. This example sets `fit_intercept=False`, so a root that is not hard-intervened is predicted as zero. In the held-out `ERK_on` row, ERK is fixed by the intervention but EGFR is not; the observed EGFR value is therefore ignored as an input and its prediction is zero. Because `evaluate()` scores observed non-hard vertices, the EGFR error contributes to `forward_loss`. If a root value is an externally supplied input rather than an outcome to predict, mark it as a hard intervention; `predict()` will then fix it to the supplied value and `evaluate()` will exclude that target from the loss.\n", + "\n", + "The reported losses are weighted means after dividing errors by the response scales learned from the training data; they are not the raw summed optimization objective. Configured `vertex_weights` apply. Evaluation `sample_weights` must be supplied using the held-out sample names—training weights are not automatically transferred to different samples. Select penalties and other model settings using training or validation data, not the final test report. Holding out replicate cells tests prediction for new cells under familiar interventions; holding out an entire condition tests prediction for an intervention condition not used in fitting." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cbbfc568", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T22:59:01.867155Z", + "iopub.status.busy": "2026-09-21T22:59:01.867081Z", + "iopub.status.idle": "2026-09-21T22:59:01.945020Z", + "shell.execute_reply": "2026-09-21T22:59:01.944681Z" + }, + "papermill": { + "duration": 0.080876, + "end_time": "2026-09-21T22:59:01.945606", + "exception": false, + "start_time": "2026-09-21T22:59:01.864730", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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forward_losslocal_loss
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" + ], + "text/plain": [ + " forward_loss local_loss\n", + "ERK_on 0.473 0.232" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Hold out one complete hard-intervention condition while retaining the\n", + "# observational and other intervention conditions for training.\n", + "heldout_name = \"ERK_on\"\n", + "train_data = data.query.filter(lambda item: item[0] != heldout_name).collect()\n", + "test_data = data.query.filter(lambda item: item[0] == heldout_name).collect()\n", + "\n", + "heldout_model = LinearDAGDiscovery(\n", + " lambda_edges=0.1,\n", + " coefficient_bound=3.0,\n", + " fit_intercept=False,\n", + " max_parents=2,\n", + " enforce_signs=True,\n", + " coefficient_support=\"exact\",\n", + " min_abs_coefficient=0.25,\n", + ").fit(pkn, train_data, solve_options={\"solver\": \"SCIPY\"})\n", + "\n", + "predictions = heldout_model.predict(test_data)\n", + "report = heldout_model.evaluate(test_data)\n", + "pd.DataFrame(\n", + " {\n", + " \"forward_loss\": report[\"forward_by_condition\"],\n", + " \"local_loss\": report[\"local_by_condition\"],\n", + " }\n", + ").round(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c845cf91", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T22:59:01.950325Z", + "iopub.status.busy": "2026-09-21T22:59:01.950233Z", + "iopub.status.idle": "2026-09-21T22:59:01.956017Z", + "shell.execute_reply": "2026-09-21T22:59:01.955685Z" + }, + "papermill": { + "duration": 0.008734, + "end_time": "2026-09-21T22:59:01.956531", + "exception": false, + "start_time": "2026-09-21T22:59:01.947797", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " value predicted clamped observed\n", + "id \n", + "ERK 1.700000 False True True\n", + "ELK1 2.033068 True False True" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Prediction Data keeps sample/vertex labels and identifies clamped values.\n", + "pd.DataFrame(\n", + " [feature.to_dict() for feature in predictions.samples[heldout_name].features]\n", + ").set_index(\"id\")[[\"value\", \"predicted\", \"clamped\", \"observed\"]].loc[[\"ERK\", \"ELK1\"]]" + ] + }, + { + "cell_type": "markdown", + "id": "1b54c518", + "metadata": { + "papermill": { + "duration": 0.001944, + "end_time": "2026-09-21T22:59:01.960527", + "exception": false, + "start_time": "2026-09-21T22:59:01.958583", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Prediction and residual results retain their sample and vertex labels. Their feature metadata states exactly how each value was produced:\n", + "\n", + "| Metadata | Meaning |\n", + "|---|---|\n", + "| `predicted` | The value was produced by the fixed forward model |\n", + "| `clamped` | The value was fixed to a supplied hard-intervention value rather than predicted |\n", + "| `observed` | The input data contained a value. `predict()` ignores it unless it belongs to a hard intervention; `evaluate()` may use it as the value to score. |\n", + "| `valid` | The target and every required parent were observed, so the local residual can be computed |\n", + "| `equation_prediction` | The target value reconstructed from its observed parents for the local residual |\n", + "\n", + "For a shift intervention, the target measurement may be missing when the shift value is supplied explicitly. A shift estimated during fitting can be reused only for the same `intervention_group`. A numeric shift supplied in the training data is not memorized for later samples; supply it again at prediction time. For a new group, also supply the shift explicitly because test outcomes are never used to estimate it." + ] + }, + { + "cell_type": "markdown", + "id": "28a21f37", + "metadata": { + "papermill": { + "duration": 0.001959, + "end_time": "2026-09-21T22:59:01.964438", + "exception": false, + "start_time": "2026-09-21T22:59:01.962479", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Inspect the in-sample equation reconstructions\n", + "\n", + "This plot is an in-sample fitting diagnostic, not a held-out prediction test. For each target, `problem.expr.prediction` substitutes the observed parent measurements into that target's fitted equation. A point near the diagonal therefore shows that one equation reconstructs its observed target; it does not show that the complete graph can predict the sample without those parent measurements.\n", + "\n", + "The plot includes only vertices with at least one selected incoming edge and only rows marked by `fitted_mask`. A hard-intervention target, a missing target, or a target with any missing candidate parent is excluded. The table reports mean absolute reconstruction error in the original measurement units.\n", + "\n", + "EGFR is omitted because it has no selected incoming edge. In this example, `fit_intercept=False` makes its non-hard equation simply `EGFR = 0`; those residuals are constant with respect to edge selection and are not informative about downstream fit." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "459021dd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T22:59:01.968974Z", + "iopub.status.busy": "2026-09-21T22:59:01.968892Z", + "iopub.status.idle": "2026-09-21T22:59:02.063679Z", + "shell.execute_reply": "2026-09-21T22:59:02.063295Z" + }, + "papermill": { + "duration": 0.097735, + "end_time": "2026-09-21T22:59:02.064156", + "exception": false, + "start_time": "2026-09-21T22:59:01.966421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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/8dqLZF+cKmiSgKly5cq2bgYRETmp8+fPq8WPyT45VdAkPUz6D60sYUBERGRNsjCzLH4t61HKH+367yGyT04VNOmH5CRgYtBERETWJOtNynqBsl5k//79Tb6HyD45VdBERERUEq5du4ZZs2apXCZZ7JgcA7PRiIiIilFMTAw++OAD9f8pU6YgNDSU59dBsKeJiIioGO3btw/u7u6YNGkSgoKCeG4diFMto3Ljxg0EBASoxDzmNBERUXGSZG8vLy81JCf/lwRwfv84Fg7PERER3aaLFy9i6tSpOHDggEr2Ng6YyHEwaCIiIroN586dUzlMMpIhM+XIcTGniYiIqIjOnj2LOXPmoGzZspgwYQJ8fX15Lh0YgyYiIqIikNyl77//HuHh4Spg4pCc42PQREREVIS1TGUNuTFjxsDDw0MlgJPjY04TERFRIRw/fhwzZsxQM7JlJjYDJufBniYiIiILHTlyBB9//DFq1aplCJZSMlOw5vQaRJ6KxPXU6wjxDkGfmn3Qu0Zv+Lj78Nw6EPY0ERERWeDQoUP46KOPULduXTz55JNqWO50wmn0/rE33vj9DRyMOYgLSRfUtdyW7bKfgJ07d8LV1RU9evTIk0gvJRr2799v2JaYmIhOnTqp8yz7CrrI/UsSe5qIiIhuQYoif/rpp2jYsCFGjx4NNzc31cM06tdRiE2LVcdo0EyuZbvsX9NvTak4v7bsEVu0aBHGjx+PBQsWqBINVapUyXcJmp49e6r/b9y4UZ1nvRYtWuDxxx9X518vLCwMJYlBExER0S1IDSaZISd1mKTHREgAEpMak+99srVstX/tmbXoHt7dpudYerwkgJP26KBTgd3FpIs4EHMA8w/Mx4LuC1AjoIZVnjs5OVnNMvzzzz8RHR2NxYsX45VXXslz3Pnz59G1a1eUL18ekZGR8Pf3N9kv5122yWxFW+HwHBERUT52796Nn376SZUXqF27tiFgEtJjIwFIQWR/5MlIm55fS3vE5DhrWLZsGerUqaMujzzyCL744gt1PnMn17dt21YNya1bty5PwFRaMGgiIiIyY9euXWpYKT4+Ps+XvJAhLn3gkR/Zfy31mk3Pr75HTHq+btUjZg0LFy5UwZKQnKakpCRs2rTJ5Jhhw4apXrwVK1bA09MTpRWDJiIioly2bduGL7/8UvV+yBe61GTKTXKCLOlpCvUOten5tWWP2PHjx1Vv3aBBg9RtyVEaOHCgCkaN9e3bF9u3b1dBU2nGnCYiIiIjBw8exNdff61mcMkXvLmASUgSteQE3aqnqU9EH5ueX1v2iC1cuBBZWVmoWLHif8+laXB3d0dcXJxh24svvojGjRtjyJAhar+c99KIQRMREZGRevXqqeGkdu3aqWnt+ZFZZ5JELTlB5oa+XHWuCPYKRq/qvZCVmmWzcyw9YpL0XVDgZI0esaysLCxZskQtZtytWzeTfQ888AC++eYb9O7d27Dt5ZdfVj1REjhJxfXBgwejtGHQREREBKg8GylaKdPh27dvf8tzItP0ZdZZ7llp+msJmGS/HHcj9YbNzrGtesTWrFmjepNGjhypZh8ae/DBB1UvlHHQJJ5//nmVbD906FAVOEkAVZowaCIiIqe3du1aNc29X79++dYQMkem6UsdJkmiXnVylRoKkx4bCUCkh6k0VAQvTI9YcVq4cCHuueeePAGTvqfprbfeQmzszRl9xp599lkVOA0fPlwFThJAlRY6zdyUAAcl6wTJmydFymS9ICIicm7yFbh69WoVNPXp0we9ehVv4FBavn/M1WnSX4d5h1m1TpMjYU8TERE5LQmW5NK/f390727bApTWZA89YvaAPU1EROS0zpw5o9Yv69y5s1Wfx9Y9TVQ8WKeJiIiciuTJbN26Vc3uql69utUDJnIcHJ4jIiKnCphkqvuOHTsQGhqK+vXr27pJZEcYNBERkdMETFI36Pfff1czsxgwUWExaCIiIqcImGSh2D179uCxxx5Dy5Ytbd0kskMMmoiIyOFJZW9JxB41ahSaNWtm6+aQnWLQREREDiszM1PNjpNK31KFWi85KxsrrsTh++hYxGRkIczDDQ+FB+OBckHwdXO1aZup9GLQREREdulWgY8ETJ9++ilOnDihqk/7+fmp+/2dnIYB+08iOiMLsrKcVHg+l5aBPTdSMPNsNJY3jUAtXy9bvzwqhRg0ERGR3blV4LO0fhVsWPIF/v77bzz55JOGgEkCLblfTObNBXT1S2Lor2W77N/Rqh57nCgP1mkiIiK7cqvAJzY1Fa/PnoOTJ09i3LhxJrPkpGdKAq3sfBYQk+2yf+XVOGu/DKcwYsQIlU+W+9KjRw+1v1q1aoZt3t7eqFu3Lt577z21vI2eDK/K/v379xu2JSYmolOnTur48+fPl9jrsZuephkzZmDlypU4duyYOrFt2rTBO++8gzp16ti6aUREVIL0gU9+XDIyoKWloc7Dw9WXqjEZytP3TOVH9i+7HIuhFULhSDLSUnF02xYc2boJyfHx8A0MRP0OXVCvfSd4eHlb7Xl79OihZi4a8/T0NPz/9ddfx+jRo5GWloaNGzdi7Nixqmr6E088YfbxYmJi0LNnT/X/7du3q3pbJcVuepqioqLw1FNPqfoaGzZsUJVcu3XrhuTkZFs3jYiISpA+8MnNLSMdHumpSPP2xdbuD2KdR97lSiT36Var1Gv/HudIrl88j0UTn8DGBR/h0t/HkXA1Wl3Lbdku+63F09MT4eHhJpegoCDDfn9/f7VNep1kdmPjxo2xfv16s48lvUrt27dX99m8eXOJBkx2FTStW7dOdfM1aNAATZo0UVHruXPnsHfvXls3jYiISpC5wMc9Ix1ttqxG8x3rAU2DptOZDXwkWdxcwGVM9+9xjtTDtPyNl5CSEH9zg37o699r2S775Thb0jQNW7ZswdGjR+Hu7p5n//Hjx9G2bVvVeygxgQROJc1ugqbcZNFDERwcbOumEBFRCcod+EjvUpvfIuGTlIjDd7SRokz5Bj4yu86SnqaB5R3nu0WG5JLjYqHl5JjdL9tl/7HtUVZ5/jVr1qhEfOPLG2+8Ydj/3HPPqW3SIyXrAErwNGHChDyPM2zYMNSsWRMrVqwwGd4rSXYZNMkJnTx5Mtq1a4eGDRvme1x6erpaWdr4QkRE9s048PFIS0Hb3yLhlZqMHXf3QUJQWIGBj5QjCPdwg2s+3U2yXfb3L/vf8JG9kxwmCSQLpNPhcNQmqzx/586dVRK38UXSbfSeffZZtU3ScOTYl156SeUt59a3b1+VwyRBk63YZf+jzIY4ePCgOnm3Sh5/7bXXSqxdRERkfRL4SFkBmT0XevWyGprbcXdfJAUEGQKfMHfzgY/Ub5I6TLnLFeiv5X6y35EKXErSt2FILj+ahuQE68wY9PX1RURERL77JS9J9stFAiK5vuuuu3DPPfeYHPfiiy+qfKchQ4aozpOBAweipNld0DR+/HhERkZi69atqFSpUoHHvvDCC6pHSk96mipXrlwCrSQiImuRgObrupXwyLELuFSlJq6Wr4Isd/cCAx8ZeTh06JDq0ZAJRKP9/BFXrwl2evjhema2GsqTnikJtBwpYBIySy4h5krBgZNOB99/g05bCgoKUt/zzzzzDPbt26dKDRh7+eWX4ebmpgInWU9w8ODBJdo+uwmaJKqUE/njjz+qRLHq1avf8j4y5mmrcU8iIrKO69ev49uZM/F257sRU7+JKg+grwhuLvCRKepLlixRtX0M4uKA8+fQxd9f5cqEhd0c1nNEUlbg0oljBR+kaWjQsYtVnj89PR3R0dEm2yTwyW/mmwzdSUkh6XUyXvpG7/nnn4erqyuGDh2qAicJoEqK3QRNchKXLl2KVatWqYx5/RsgCzBK3SYiInJ8EgDNmjVL9UC0uKMpQkJCCqynJF/YEjAlJSWZ3S/bZb+kfTjqH9lSh2nXim/VLDlzyeA6Fxf4BASibruOVnn+devWoXz58ibbpMai1F00RwJYCYimTZuG/v37mz1G8qAkcBo+fLgKnOT4kqDTjMtulmK5u+j0pPSAlCKwhAzPSZAlM++kcBYREdmPK1euqIBJpqNL6oVxrZ/87NmzR83eupXevXujefPmsBZbf/9IHSYpKyCz5FRSuHz1/3vtGxSMAVOnI6Qi01ccpqfJTmI7IiKyktWrV6veIAmYJACxhPHSG7c6zppBk61JQPTY7E9VWYHDURuRnBCvcphkSE56mKxZEdyR2E3QREREzkmGX1xcXNQQjAy3FaanxtJVI5xhdQkJjBrf00NdyInqNBERkXOQZTNkbTLJY5VepsIObcl09+I8jpwbgyYiIiqVZHX7mTNnwsPDo8hLZjRt2rRYjyPnxqCJiIhKndOnT6uk73LlymHSpElF7glq1KiRCrjym0wk22W/HEd0K8xpIiIiq5AFYGXdM1nGQ6pSS5FFqRkkU+ALSjzOyMjA/PnzVQFjqc/n5eVV5DbIkJ7UYcpTp+lfsuaZ7HfUcgPkpCUHHGHKJxGRs7jdKe4yNCe1fYormMldEVx6rmRITnqYSiJg4vePY2DQRERExd7DtGjiE7cspihT4I17nI4cOYI///xTzZKT2XKOhEGTY3CsTyUREdmcDMlJD5O5gEnIdtkvNYP0pBfoo48+UsFFdnZ2CbaWyHIMmoiIqFhJDpMaiiuITofDUZvUf2XI7JNPPkGDBg0wZswYVfGbqDRi0ERERMVKkr5VDlNBNA3JCXH4559/8Omnn6JJkyZ4/PHHGTA5mBEjRqgZirkvJ0+eNNThGjlyJCpUqKBKS1StWhVPP/20WpRZSK9jmzZt8MADD5g8ruQmV65cGS+//LJhmyyX06lTJzUb0sfHBy1atMDixYuL9fUwaCIiomIls+Qs6WmSZTzki09WqR81apRa+Z6sR5LhZS2+BQsWYM6cOepabst2a+rRowcuX75scqlevboqKyFL15w4cQLffvutCqRk1uSmTZvQunVrxMbGqkV5v/zyS7Xo7zfffGN4TJlVGRwcjFdeeUXdnjdvHvr27asCrD/++AMHDx7EoEGDVM/lM888U2yvhZ9QIiIqVlJW4NIJ8yvY691w80Lt2g1Uwne7du34DlhZTExMnrILcXFxuHDhAqKiolTZhbCwMKs8t6enJ8LDw/Nsf+qpp1Tv0vr16+HtfXNCQJUqVXDHHXegZs2aeOmll9Swba1atTBjxgwVKHXu3FlNFvjuu++we/dudX/prZoyZQomTpyIt956y/D4sk32T5gwAQMGDECrVq1u+7Wwp4mIiIqV1GGSsgIyS86cBE9fRPsGIUHnyjNfAqQnSQKmpKQks/tlu+y3do+TMelF+vXXX/Hkk08aAiY9CbCk93HZsmXQV0WSgEmGcCW4k2Fc6WHSV3H/4YcfkJmZabZH6YknnlC1uKQnqzgwaCIiomIlZQSkDpOUFVD0Q3U6HeI9fHHFOxCtmjfHww8P4ZkvATIzUXqY8ivLKNtlvxxnDWvWrFGBi/4ivT5///23et569eqZvY9sl54w6SETkgclvU4ydCdV4p9//nnDsTK8JzUYpa5XbtLTVKNGDXVMceDwHBERFTspXCl1mKSswOGojUhOiMcND19cTUxDp44dMGjww/kubULFS2YnWnqc5BgVt86dO6uAR08Ki547d67A++gDPOPPyKJFi1SC95kzZ9SwYrVq1Sx6fnms4vqsMWgiIiKr9Tg1vqeHugjpNZB8lJ49ezJgKkFSAb04jyssCZIiIiLy9ABJICMFTe+///489zl27BiCgoIQGhqqbu/atUutRfjLL7/g3XffVTPuNm7cqB6jdu3aajbdpUuX1Cy83EvySML53XffjeLA4TkiIrKq7du3IyUlRSUa33vvvQyYSpilix0XdVHkoggJCUHXrl3x8ccfIzU11WRfdHS0mik3cOBA9VmR/cOHD1f5Sffcc4+a9SfBt5SqEFKOQGZefvDBB3meR2bjSTA4ePBgFAcGTUREZBUyLBIZGYmvvvoK+/bt41m2EX3CdHEdV1w+/PBDlXzevXt3bN26Vc2Ck9ICEkxVrFgR06dPV8dJ/lJOTg7eeecdwww7CZCeffZZtUah3Jbep9mzZ6sZd9JLderUKcycORP/93//p2bRFcfMOcGgiYiIrBIw/fTTT1i7di369euHtm3b8izbiCxKLAUf88vrke2yX44rSbVq1VJ1oqS8gPQqybXMjJMcKBmOkzpMUg5BlteRIpXGPWGjR49WNZlkmE4+a5MmTcKPP/6Ibdu2qbyshg0bYunSpSqX6v333y+2NnPBXiIiKrKUzBSsOb0GkacicT31OkK8Q9CnZh9k78/Glt+2qJlSMqTi7Gy9YK+5Ok16EjBZs06TI2HQRERERXI64TRG/ToKMakx0EEHDZrhOuJaBB6u9zAG3DuAZ7cUBE1ChsKkrIDMkpM8H+m5kSE56WGSApR0awyaiIioSD1MvX/sjdi0WGRr2Tc3akBAQgASAhPgqnNFsFcw1vRbAx93H6c/w6UhaKLbx5wmIiIqNBmSkx4m44Cp5qmaqH+kPrxTvNV22b/2zFqeXXIYDJqIiKjQJIdJhuIUDaj1dy2UvVoWf9f6G6k+N6eQy/7Ik5E8u+QwWNySiIgKTZK+VQ5Tjk4FTCHXQ3Ci9glcD71uOEb2X0u9xrNLDoM9TUREVGgyS056knSaDu6Z7jhe57hJwCRkf6j3zYrORI6AQRMRERVa76q94Z7ujhzXHBxucBixIbF5jpGepj4RfXh2yWEwaCIiokKR9bwub7iMxkcawxWu0qWUh8yeC/MOQ6/qvXh2yWEwaCIiokLV+pHlL06fOo1hQ4ch2DtYbdcnheuvpdzAgu4LWG6AHAoTwYmICMlZ2VhxJQ7fR8ciJiMLYR5ueCg8GA+UC4Kvm6s6Q2lpaSpgOnfuHMaPH69Wl2/VuJUqK7Dq5CqVHC45TDIkJz1MrM9EjoY9TURETu7v5DS0/eMo/u/EBey9kYJ/0jLUtdyW7bJfXLhwQa1A//TTT6uASUhgNKD2AHx979f45YFf8NW9X6nbDJhIjBgxQq1tl/vSo0cPtb9atWpqoV1zZDFeOVYqmOvJMjCdOnVC3bp11QK/Qhb2lXXofHx8EBgYCGtiTxMRkZP3MA3YfxIxmVnqtvbvdv21bB/45xFEtWmEiIgI9QXFJTcctzfRGnr06IEvvvjCZFtRPkOyfl7Pnj3V/7dv347Q0FBDjp2scdi6dWssXLgQ1sSgiYjIicmXaHTGzYDJHJe0NNTashqzzh7GKyOGMmCyU9JbKMGxvNe6f4Pic2kZ2HMjBTPPRmN50wjU8vWyynN7enoiPDz8th5DepW6du2K8uXLIzIyUi0yrPfaa6+p68WLF8PaODxHROTEpNfBzOQ3xSMtBW03R8I7JQn/q1CjhFtGJdmbKPvluNLo+PHjaNu2rRqSW7dunUnAVNIYNBEROTEZptF/eRrzTE1B298i4ZmWgh1398Vl/yAbtI6Kszcx29wbDajtsn/l1TirnPA1a9bAz8/P5PLGG29YfP9hw4ahZs2aWLFihc17Ojk8R0TkxCSvRYZpcn+f1jhxEO6ZGSpgSi4ThDoe/Lqw997EfGImRfYvuxyLoRWKv4J7586d8cknn5hsCw6+WarCEn379sWPP/6ogqaHHnoItsSfAiIiJyaJwJLXYpCTA7i44FijljhTqyHSfPzU5oHlLf+SI/voTTSm/XucNfj6+qpJBEX14osvonHjxhgyZAg0TcPAgQNhKwyaiIicmMyckkRgyWvxTLyBVlt/xoEWHREbVl4FTK46IMzdDf3LcnjO0XoTc/c0yXGl1csvvww3NzcVOOXk5GDw4ME2aUfpPUNERGR1MtVcZk49snUP6mz6Cdmubkjx8TcM50jAJPutOSWdSrg30QzNir2J6enpqr6XMQmA9CUDLl68aFKLSVSpUiXP4zz//PNwdXXF0KFDVeAkAZSQYquxsbHqOjs72/BY0rsl+VPFiUETEZGT80+MR4fNkcj08cbpHg+gnLuX6nWQL1HpYWLA5Di9ieaSwa3dm7hu3TpVKsBYnTp1cOzYMfX/999/X12MSV0nKWKZ27PPPqsCp+HDh6vASQKoV155BV9++aXhmDvuuENdb9682exj3A6dJgOETuLGjRsICAhAQkICypQpY+vmEBHZnHwFSMFK+Qt90qRJ/N3ooN8/5uo06a/DPdysWqfJkTBoIiJycjJ0Ism6tqx/4+hsHTQJqcMkZQVklpy+Ijh7EwuHw3NERE5I8j/Wrl2Lxx577LarNZN9kGFWKSlgjbICzoLFLYmInMyZM2cwc+ZM1eshw3JEZBn2NBEROZGTJ09i3rx5qFixIsaPHw9vb29bN4nIbjBoIiJyEnFxcZg7dy6qVq2Kp556Cl5eTPwlKgwGTUREdkrq3xw6dAj79u1D/LVryE5LgVfyDYR5u6Nhhy6o174TPLz+60kKCgpStW1kSraHh4dN205kjzh7jojIDsXExGDJkiVITEyUugGATnfzWn6xZ2XC+9wJlPH1wYCp03E5LgHx8fFo166drZvttErD7Dm6fexpIiKywx4mCZiSkpJubpCAyehac3NHapXacD1zBAten4pzrj5o2KgR2rZtC53+WCIqNAZNRER2RobkVA9TfnQ6FTjF+QcjRnNDzfByGD16NAMmotvEoImIyM7kXqfLnLT0dMTBHf6ZKaiYkajW+iKi28M6TUREdiY5OfmWx0iit5+3F8JT4pB6I75E2kXk6Bg0ERHZGVnyJD+pqamqYKWLTocADzc1JOcbYJ2FWImcDftriYjsTNOmTXHhwgWzPVAyS8vPzw/+fn5wT4hFRkAIYgLLYc6cOSrYkvs2atQInp6eNmk7kT1jTxMRkZ2RoEcW1zWeCacPmHx8fOAnPVHZWUgPLY/08KqITUxShS0l0FqzZg0+/PBDVbKAiAqHQRMRkZ2RXqJhw4apHiWRnJSkAiZfHx+U8feHS3bWzZpNbu7/lSMwIqUKpGSBlC4gIssxaCIiskNhYWEYN24cevfujbCyZREUGIAwf18EpSYg1FUD3D3MBkxC0zRVskBKFxCR5ZjTRERkhyTwOXHiBJo1a4bmzZub7FuwYAFgJufJXOmC3Pclovyxp4mIyA4DplWrVqncpGPHjhWpJEFhjiOimxg0ERHZWcC0YsUK/PLLL3jwwQdRr169QpUkKMpxRHQTgyYiIjsKmL7//nts2LABAwcORNeuXc0eJ2UFLGHpcUR0E4MmIiI7kZOTo0oHDBkyBHfffXehShIYk+2yX44jIssxaCIisoNg6cqVK3B1dcUTTzyBDh06FKokQW6yXfazwCVR4eg06e91ElLHJCAgAAkJCShTpoytm0NEZFHA9OWXX+LgwYOYPn26Kl5pKanDJGUFZJacJH2zIrjt8PvHMbDkABFRKZKclY0VV+LwfXQsYtLSUXvHBvic/htDHx1RqIBJSE+SlBRgWQGi4sGgiYiolPg7OQ0D9p9EdEYWXLKz0WzXRnhdOovdbbrijyxfLE9OQy1fL1s3k8hpMaeJiKiU9DBJwBSTmaVue6ckIvhaNHa37Y7LlWuo7bJfjiMi22DQRERUCsiQnPQwaZlZcMnKQrJ/IDb0HoIrFaup/dka1P6VV+Ns3VQip8WgiYioFJAcJresTLTa9gua/b5RbctxM82gkAICyy7H2qiFRMSgiYioFLienIJWUT8j+Ho0TtdubPYYmeock3Fz+I6ISh4TwYmIbCw1NRV1Nq6CS1wMdnXsjdiw8maPk56mMA/+2iayFbvqadq6dSvuu+8+VKhQQVW0/emnn2zdJCKi2/bnn3/CJz4Wuzrfl2/ApO9pGlg+mGecyEbsKmiS4mxNmjRRK3sTEdm77OybM+Hat2+Pl6ZOhWf5inA1v/KJ2h7u4Yb+ZYNKtpFEZGBX/bw9e/ZUFyIie5eYmIi5c+eie/fuqvhkxbBQLPfxM9Rp0v3bs6S/DnN3w/KmEfB1c7V104mcll0FTYUlSwjIxbiMPRGRrcnvolmzZiEpKQnly/83HCeFK3e0qqfKCsgsOUn6lhwmGZKTHiYGTES25dBB04wZM/Daa6/ZuhlERAbx8fEqYJLk7ylTpiA8PNzk7EhgNLRCqLoQUeliVzlNhfXCCy+oxXn1l/Pnz9u6SUTk5JYuXap6wJ955pk8ARMRlW4O3dMki1XKhYiotBgyZAgyMzMRGsqeJCJ749A9TUREpUFMTAzmzJmjcpkCAgIYMBHZKbvqaZKkyZMnTxpunzlzBvv370dwcDCqVKli07YREZkTHR2tcpik11tfYoCI7JNdBU179uxB586dDbcnT56srocPH47FixfbsGVERHldunRJBUy+vr6YNGmS6mUiIvtlV0FTp06doGlSsYSIqPTJSEvF0W1bcGTrJtyIi8df2W7w8/fH+KeeZMBE5ADsKmgiIiqtrl88j+VvvITkuFhApwM0DaHuXvCOu4TlU5/BgKnTEVKxsq2bSUS3gYngRETF0MMkAVNKQjxSXd1x3cNPVfH2y0yDq5ajtst+OY6I7BeDJiKi2yRDctLDlKJzw0W/UCS7e6qgSU/LyVH7j22P4rkmsmMMmoiIbpPkMKW4eeKCXwg8szNRKel63l+uOh0OR23iuSayY8xpIiK6TVdj43HRNxheWRmomBwLF5N+pn9pGpIT4niuiewYe5qIiG5TSFAAgtKTUTFZepjymeGr08E3IIjnmsiOMWgiIiqiQ4cO4dy5c2jU8R6Ept0o+BeqpqFBxy4810R2jEETEVER7Nu3D5988gm2bNmCeu07wTcoGDoX879SZbvsr9uuI881kR1j0ERElEtKZgq+P/49Hvn5EfRc0VNdy23Zrl+d4LPPPkPTpk3VArweXt6qDpNPQODNB5A6TUbXsl32y3FEZL90mhOV2NYvlpmQkIAyZcrYujlEVAqdTjiNUb+OQkxqDHTQQYNmuA7zDsPkcpOx5ts1aNGiBUaMGAFXV1fDfaUOk5QVOBy1EckJ8SqHSYbkpIeJAZNz4/ePY2DQRET0L+lJ6v1jb8SmxSJby7u4rqvOFeXTy+Nhn4cxZPAQuOQzHEeUG4Mmx8CSA0REAJKzsvHioW044T8GOYEBcMlOgFfKdngl74ROS0dgXCASAhJwweMCvFp4MWAickL8M4mInN7fyWlo+8dRLIsviyzPCOS4l1PXSUGPIrb8uyh7pTbqH62PsJgwNVQXeTLS6c8ZkTNiTxMRwdl7mAbsP4mYzKx/E7f1Sdw3/6asdvIfRJwKxcUKV3C17FW17VrqNVs2mYhshEETETm1FVfiEJ2RZXZfxNF9aHDgd5yodwdSQ6LhmnUWObochHqHlng7icj2ODxHRE7t++hYfd+SKU2DV2oyjjVohqONWiLVvz26X+gOvww/9InoU/INJSKbY08TETm1mIws04VPNA3+N+KQGBCMv+5oa6i1lOrhCc9sT3S80hFdK3a1VXOJyN6Cpvj4eOzevRtXr15FTk6Oyb5hw4YVV9uIiIq1nMCa02sQeSoS11OvI8Q7BH1q9kGIe2OcS8PNwEnTUP/A76h54iA29RyEFP+Am3fWNHhnpMMFLvDM8sTJoyfRvHlzvjtETqbQQdPq1atVBdzk5GT4+/tDp698q4rf6hg0EZFdFKy8mHQRB2IOwC2oDzT/ASowarhvB2qeOIRDd7T9L2D6V50r5wz/379/P4MmIidU6JymKVOm4LHHHkNiYqLqcYqLizNcYmNjrdNKIqLb6GGSgEkKVgoJmIyvsxM2wi0rHk32blMB04Fm7XG6TmPD/XU5OfDJSEOtKxcM2+SPRiJyPoXuabp48SImTJgAHx8f67SIiKgYyZCc9DAJtywdalzyRcQFX3hluCLNIxsnKyXjdMY7KBvdEPtadMS5GvVu3vHfFaa8M9Nx34EdcM/5r0K4r68v3yMiJ1TooKl79+5qscoaNWpYp0VERMVIcphkSK5Mkiu6/VEOPuk314qTbX4pbghO8EKTv7NwuvtV9KleB8uvxqqkb8lhkiE56WEyDpiELNRLRM6n0EFTr1698Oyzz+LIkSNo1KgR3N3dTfb36cOpuERUekjSt2sWVMDkneGqgiUh/UhXfIKR4eKGSslXUWv9NTw9LwLeny9AUlISzK1lLnmbfn5+6ncfETmfQgdNo0ePVtevv/662V8o2dl5F7kkIrIVmSXneyRB9TAZB0yXfYKR5O6F8imxcNV0cEkDTu/epSazLFmyROVt5iYBk+z39PS0wSshIrsLmnKXGCAiKs2krMDeVZ8ZbstvsMu+wUh280KF5Fj4ZaWp7RJOHY7ahMb39MC4ceNw6NAhNUtOkr4lh0mG5KSHiQETkfNicUsicmi9a/TGsczFhl6mVDdPpKiA6Tr8stJNjk1OiFPXEhhJHSbWYiKi215GJSoqCvfddx8iIiJQq1Ytlce0bdu2ojwUEZFV+bj7oHK5msj5t8SAb1Y6qt+IzhMwSeVv34AgvhtEVHxB09dff4177rlHlRyQ0gPSje3t7Y0uXbpg6dKlhX04IiKra9ShGy76hiHW00/ddtPMpBloGhp07MJ3g4jypdPMTREpQL169fD4449j0qRJJttnzpyJzz//HEePHkVpdePGDQQEBCAhIQFlypSxdXOIqASkpqZi7ty5OHv6FComX1d1l3LTubjAJyAQj83+FB5e3nxfqNjx+8dJe5pOnz6thuZykyG6M2fOFFe7iIhuW0pKCubMmYPLly/j8cceRajfv0Up9cs//XstAdOAqdMZMBFR8SaCV65cGZs2bVL5TMZkm+wjIiotIiMjceXKFUycOBHVqlVDgyZNcWx7FA5HbURyQrzKYZIhubrtOjJgIqLiD5pk7TnJZZKpuG3atFG1mbZv347Fixerv+iIiEqL+++/Hx06dECFChXUbRl6k5ICciEisnrQNHbsWISHh+ODDz7A999/b8hzWrZsGfr27VvoBhARFXfuyKJFizBw4ECUL1/eEDAREdmkTlO/fv3UhYioNJFJHjIpRZK/iYiKG4tbEpFDiIuLUwFTZmamSiMoV66crZtERM4YNAUHB+PEiRMIDQ1FUFCQymPKT2xsbHG2j4jIouWd5s2bp9a+lIApLCyMZ42IbBM0zZo1C/7+/ob/FxQ0ERGVNBcXFwwePFj9gRcSEsI3gIhKR3FLe8biYkSORcoJbNmyBQMGDFCBE1Fpxe8fJ81pcnV1VYXiypYta7L9+vXrapt0jxMRWZv8HpKeb1nSSYpY+vndXCJFLz09HYcOHVLlUZKTk+Hr64umTZuiUaNGakFeIiKrB035dUzJLygPD49CN4CIqLAuXryoAiZZDkkKV+YOmGJiYrBkyRIkJiaaJIpfuHBBLTg+bNgw5j0RkfWCJlm7SUg+04IFC0x+SUnv0tatW1G3bt3Ct4CIqBCuXbum6sRJ/pK5gEn+gJOAKSkpyez9Zbvsl8XG2eNERFYJmuSvOn1P0/z589UwnZ70MMkSBbKdiMiaJFjq2rWrqvQtQ265yZCccQ9TbvI7TPbLcc2bN+ebRUTFHzTpF+Pt3LkzVq5cqUoPEBGVFFksXHKXGjZsiJ49e+Z7nOQwWUKOY9BERFbNadq8eXNh70JEdFtOnjypUgRq1qyJBg0aFFj2RJK+LWHpcUREeoWeo/vggw/i7bffzrP9vffeU9N+iYiK0/Hjx9Vi4JICMGbMmFvWiTM3ZHc7xxERFTlokpknvXr1yrO9R48eKhmciKi4HDt2TFX6joiIsDhxW8oKWMLS44iIihw0ycwTc6UF3N3dVfEuIqLiItW9W7ZsiSeffNLikiZSh0lWMMivR0q2y345jojIqkGTJGEuW7Ysz/bvvvsO9evXL+zDERHlcfToUaSmpqpaSlJTSf4os5T0Rsl9cpci0JPtsp/lBojI6ongU6dOxQMPPIBTp07h7rvvVts2bdqEb7/9FsuXLy90A4iIjO3du1fVgrv33ntx3333FenkSLAlw3msCE5ENl97bu3atXjrrbfUlF1vb280btwYr776Kjp27IjSjGv/EJVuu3fvxqJFi9CiRQuMGDHCpB4ckT3j949j4IK9RFQq7Nq1C19++SXuuusuNXzGBXjJkTBoctLhOSIia5DClW3btsWQIUMYMBGR/QZNsmzBiRMnEBoaqiqBF1QnJTY2tjjbR0QOThbRrVSpErp06aKWOLlVHSYiolIdNMm6czJFV8yePdvabSIiJ7Fx40Y1geSZZ55BrVq1GDARUanGnCYisolff/1VrWPZvXt39OvXjwETOTTmNDlRT1NhilaWKVPmdtpDRE7g559/xqpVq9TqAlJWgENyROQwQVNgYKDFv9Sys7Nvt01E5MAyMzNx4MAB9OnTx+ySTEREdh00bd682fD/s2fP4vnnn1c1VFq3bm0yVXjGjBnWaykR2TVJ8pZlmCQ/UnKYClPlm4jILnOaZIbLqFGjMHjwYJPtS5cuxWeffYYtW7agtOKYMpFtyK8ZSfj+3//+h2nTpsHLy4tvBTkVfv846dpz0qvUvHnzPNtlm1TzJSIylpOTo9amlOWWJOmbARMROU3QVLlyZcyfPz/P9k8//VTtIyIyDpi++eYbREVF4ZFHHkHnzp15cojIeSqCS80mWbBXpgvLcgfi999/Vwv4rlixwhptJCI7denSJfzxxx8YPny4IQeSiMip6jSdP38en3zyCY4dO6ZyFerXr48xY8aU+p4mjikTlQyZRSszbmX9uISEBAQEBPDUk1Pj949jYHFLIir2gGnhwoXw8/PDww8/zLNLxKDJeXOaxLZt21R+Qps2bXDx4kW17auvvsL27duLu31EZEeysrLULNr9+/erHmgiIqcOmiRvSWbAeHt7q+nD6enpantiYiLeeusta7SRiOykaKVMEvnrr78wduxYNG3a1NZNIiKybdD05ptvql+Mn3/+uUlxOul1kiCKiJyT1GiTPMennnoKjRo1snVziIhsP3vu+PHj6NChg9k15+Lj44urXURkZ+6++27UrVu31E8IISIqsZ6m8uXL4+TJk3m2Sz5TjRo1itwQIrI/aWlpmDt3Lk6cOAFXV1cGTETk0AodND3xxBN4+umnVe0VmVIsdVikeJ2sJfXkk09ap5VEVOqkpqZizpw5qkabBExERI6u0EHT//3f/+H+++9XlX1l8U0ZqpO16CSYGjduHKzt448/RvXq1dVSDM2aNVMz+YioZCUnJ2P27NmIjo7GpEmTULNmTb4FROTwClWnSeqvyDCcJHlK0HLkyBG1TIJMLZaaLNa2bNkyDB06VAVObdu2VUu3LFiwQLWjSpUqt7w/i4sRFY958+bhzJkzmDhxokU/e0TOjt8/TlrcUoKlo0ePqt6ektaqVSvceeedqhq5Xr169VTP14wZM255f35oiYqHDMvLr46KFSvylBJZgN8/Tjo8J71Mp0+fRknLyMjA3r170a1bN5Ptcnvnzp0l3h4iZyPLoUgRW6nNVqFCBQZMROR0Ch00TZ8+XSV9r1mzBpcvX1bRs/HFWq5du6aGB8uVK2eyXW5LXoU58su9pNpH5Mji4uLw/vvvq8KV/DkiImdV6DpNPXr0UNd9+vRRs+f0pKtebktgY03Gz2n8vObIkN1rr71m1fYQObrr169j5syZKn9R/mAKCwuzdZOIiOwjaNq8eTNsITQ0VE1rzt2rdPXq1Ty9T3ovvPACJk+ebLgtfyGz8B5R4coKfPDBB+oPkylTpqifQyIiZ1XooKljx46wBQ8PD1ViYMOGDejXr59hu9zu27ev2ft4enqqCxEVjawx2bVrVzRp0gTBwcE8jUTk1AodNOnzGxYuXKhm0clfoDKD7dFHH7X6L1XpNZKSA82bN0fr1q3Vaurnzp3DmDFjrPq8RM5GenTPnj2Lu+66S9VkIyKiIgRNUVFRKp8pICBABS9CllF4/fXXERkZadWeqIEDB6r8CnkuSUJv2LAhfv75Z1StWtVqz0nkbC5evIhZs2ap9STlZ9zNrUh/WxEROZxC12mSQKVNmzaqVpJ+6QRJ/pYlVHbs2KFm15RWrJNBVLDz58+rgCkoKEgVrvT39+cpI+L3DxW15ICsMyUJocZrTcn/ZehM9hGR/QZMMksuJCRE/TwzYCIius2gSSpySy5TbrKtadOmhX04IiolpHdJfoZlLTlfX19bN4eIqNQpdLLChAkT8PTTT+PkyZMqSVT8/vvv+Oijj/D222/j4MGDhmMbN25cvK0lomInPcQSMMlEjuHDh/MMExEVV06Ti0vBnVMym66kCl0WFnOaiEwdP35c/cEj5TwYMBHx+4eKuadJVjYnIvt35MgRfPzxx4iIiMDgwYNt3RwiIscLmji9n8j+ySxXmQFbt25dVefM3d3d1k0iIir1WICFyAllZmaiUaNGGDlyJAMmIiILMWgiciJSQV/WX7zjjjvUTLn8FrsmIqJiKDlARPbpzz//xIwZM7B79251mwETEVHhsKeJyAlIWZDFixejVatWaNGiha2bQ0Rklxg0ETk4Wd7oq6++UssfPfLII7csG0JERLcRNEnhO0u78mNjYy06joisT2qmScHZDh06YNCgQQyYiIisHTTNnj37dp6DiGxUzLVMmTJ4/PHHVbDEHCYiohIImlgpmMi+rF+/Hj///DNeeeUVtTwKERHdPpeirlX18ssvqyrCV69eVdvWrVuHw4cPF0OTiOh2SLC0YsUKdOrUSQ2tExGRjYKmqKgoVRTvjz/+wMqVK5GUlKS2S97Eq6++WkzNIqKi5C+tXr0aq1atwn333Yf777+fQ3JERLYMmp5//nm8+eab2LBhAzw8PAzbO3fujF27dhVn24iokDlMmzdvRr9+/dC7d2+eOyIiW5ccOHToEJYuXZpne1hYGK5fv15c7SKiQvQwZWdnIyAgANOmTVPJ30REVAp6mgIDA3H58uU82/ft24eKFSsWV7uIyMKAadmyZWrx3ZycHAZMRESlKWh6+OGH8dxzzyE6OlrlS8gvaime98wzz2DYsGHWaSUR5SE/e9LrK0NyTZo0YQ0mIqLSFjRNnz4dVapUUb1KkgRev359VThPqg3LjDoiKpmASap8b9u2Tf2xIj+DRERkXTpN+veLWHZAhuTkl7esmF6rVi3YQ6Ks5H0kJCRwGIPs2t69e/H5559jxIgRuOuuu2zdHCK6BX7/OHnQZI/4oSV7Jz+uMiwu1xcuXEDlypVt3SQisgC/f5xo9tzkyZMtfsCZM2feTnuIKB9ZWVlYtGgRmjZtipYtWzJgIiIqjUGTDMPlHhqQKc516tRRt0+cOAFXV1c0a9bMOq0kchLJWdlYcSUO30fHIiYjC2EebngoPBg9/dyxYNZM/HM5GjcO7Map1d+jfocuqNe+Ezy8vG3dbCIip2BR0CSzc4x7kvz9/fHll18almiIi4vDo48+ivbt21uvpUQO7u/kNAzYfxLRGVnQyVAcgHNpGfhf7A1ERq1FSMxlVEyOhS4zDZeuX8GlE8ewa8W3GDB1OkIqcpiOiKjU5TTJrDlZDLRBgwYm2//66y9069YNly5dQmnFMWUqzT1Mbf84ipjMLGTn+ols/GcUKp89joMtOqH/xu/gkZVh2KdzcYFPQCAem/0pe5yISjF+/zhpyQF5469cuZJnuyzcm5iYWFztInIqMiQnPUy5AyZxvGFz7Op0H85XrYWjtRqb7NNycpAcF4tj26NKrrFERE6q0EGTrGslQ3E//PCDmr0jF/n/yJEj0b9/f+u0ksjBSQ6TDMnpuWVm4I4/foNnWgrSvX0RG1ZeDdj9VeeOvHfW6XA4alNJNpeIyCkVeu25+fPnq+rfjzzyCDIzM28+iJubCpree+89a7SRyOFJ0re+k8ktIx1ttqyBX2I8TtdqhHQvn5s7dC5I9vbLe2dNQ3JCXIm2l4jIGRU6aPLx8cHHH3+sAiQpcCkpUREREfD19bVOC4kcQHp6ulrsev/+/UhOTlY/L1I6oFGjRvD09FSz5CTp2y09DW22rIZPciJ2dO6DhOCw/x5Ey4FvalLeB9fp4Btwc1IGERGVoqBJT37pBwcHq0J7DJiI8hcTE4MlS5aY5PzJjFMZ2o6KilLLoEhZgb3xSSpg8k5Jwo67++JGYEiuR9Kh4XHT8h+KpqFBxy58C4iISltOkyyb8vrrr6vlSKpWrarWoQsMDMQbb7yh9hGRaQ+TBEyyTqM5sl329w7yRTkvD5yqd4fZgEmXkw2/lETU+/ug6XYXF/gGBaNuu4487UREpa2n6aWXXsLChQvx9ttvo23btmp4bseOHZg2bRrS0tLUgr5EdJMMyRU0q1R+fuLj47Fq2Xf4fsAgDICGxIwsNRQnOUw3r3XwS0vBgNWL4JF9M49QtkkPk5QbkDpNLHBJRFQKgyYparlgwQL06dPHsK1JkyaqftOTTz7JoInIiOQwFUQq61+/fl0FTi17tETQxWeR7FIbab7tkeNaBi7ZCfBK3o7KmSfQpfcQXN19AMkJ8SqHSYbkpIeJARMRUSkNmmJjY1G3bt0822Wb7COi/0jSd0Fryel/ZmTh3fHbxiM+LRbe2kV4J282/bnTuWJ66hdY88oa+Lj/O5uOiIhKd06T9Cp9+OGHebbLNtlHRP/Jb5KEvodJhISEQPPREJMag2wt2/zxWrbav/bMWp5eIiJ76Wl699130atXL2zcuBGtW7dWs+d27tyJ8+fP4+eff7ZOK4nslJQVkFlyubm4uMDLywt+fn5qset//P6BDjpohmpNecn+yJORGFB7gJVbTURExdLT1LFjR5w4cUJVBpc8DBlekErgx48f54K9RLlIHSZZ4Fr+uBBSEFYucltmoEphWNn/j88/BQZMQvZfS73Gc0xEZE91mipUqMCEbyILSOFKqcMkZQXkDwy5SKAkQ3JCeppk/74/9+F86vlb9jSFeofyvBMR2VPQJKUFDh48qBbpzV2byXhWHZEzMlf9u06dOli/fr0akqtUqZLqZTKuCN6nZh8ciDlQ4ONKQNUngj9fRER2EzStW7dO/WV87VreYQIZcpAEVyJnZa7695UrV7Bnzx4VHE2aNEkVhM2td43emH9gPmLTYs0mg7vqXBHsFYxe1XtZ/TUQEVEx5TSNGzcOAwYMwOXLl1Uvk/GFARM5s4Kqf3t4eKjK+cuXL1fH5SZlBBZ0X6ACI/1QnPG1bJf9LDdARGRHPU0yJDd58mSUK1fOOi0icpDq35LwLflLEjDJOo1C9stxzZs3z3P/GgE1sKbfGlVWYNXJVbieel3lMMmQnPQwMWAiIrKzoOnBBx/Eli1bULNmTeu0iMgBqn9Lb5IkfcvMOEn2zn2cuaBJSGAkJQVYVoCIyAGCJiliKcNz27ZtU0ms7u7uJvsnTJhQnO0jsrvq3zJRIi4uTuUwmStuWVCVcCIicqCgaenSpfj111/h7e2tepz09WeE/J9BEzkrCZAk108fMAUFBZn8fBgfR0REThA0vfzyy3j99dfx/PPPq6rGRHSTlBA4cuSIKisgSd/mAib9cURE5ARBU0ZGBgYOHMiAichIQkKCGq6Wwq/mZs8JCaIkv0mOIyIi+1PorqLhw4dj2bJl1mkNkR36/fff8dJLL6mhOfn5kORvc/TVv2XojoiInKCnSWoxyaK9ktfUuHHjPIngM2fOLM72EZVqO3bswFdffYU2bdqoopUyZC21zHJXBDeu/k1ERPZJp2lawauE5tK5c+f8H0ynw2+//YbS6saNG2r5ChlKKVOmjK2bQ3Zu69at+Oabb9ChQwcMHjyYQ9ZElC9+/zhpT9PmzZut0xIiOyK5fdLbKn9ESI5ffknfRETk5Av2EjkzqfQtVb5feOEFNfTGgImIyDmwZgBRIfzyyy945513VE+TJHYzYCIich4MmogsIKl/q1evxk8//YQmTZrkmQBBRESOj8NzRBYETKtWrVK9TH379sW9997Lc0ZE5IQYNBHdwtmzZ7Fu3Tq1WHXXrl15voiInFShSw7YM075pMKQHw19ztLFixdRsWJFnkAi4vePE2NPE5EZOTk5+Hrp14jWonEw5CCup15HiHcI+tTsg941esPH3YfnjYjIybCnichMwPTJok9w4M8DOFXzFGLKxUCDBh106jrMOwwLui9AjYAaPHdEZBGOdDgGzp4jyhUwLfxioQqYTtc6javlrqpASeivY9NiMerXUUjJTOG5IyJyIgyaiIxIle+9f+7FidoncCXsitlzk61lIyY1BmvPrOW5IyJyIgyaiIzIsigprVMQGxpb4HmRobrIk5E8d0REToRBEzk9WRZlyZIliI6OhpeXF2L8buYwFUT2X0u95vTnjojImTBoIqcmy6HMnz8ff/zxB2Jjb/YuySw56UkqiOwP9Q4toVYSEVFpwKCJnDpg+vjjj3H8+HGMGzcO9evXV9ulrIAlPU19IvqUUEuJiKg0YNBETuuzzz7D6dOnMWHCBNSrV8+wXeowSVkBV52r2fvJdtnfq3qvEmwtERHZGoMmclqyJIoETLVr1zbZLoUrpQ5TsFewuq0fqtNfy3bZzwKXRETOhcUtyamkpKRg8+bN6NmzJ1xcCv6bQeowSVmBVSdXqYrgksMkQ3LSw8SAiYgKg8UtHYPdLKMyffp0rF27Fvv374eHhwfi4+Nt3SSyM8nJyZg9ezauXbuGFi1aoGzZsgUeL4HRgNoD1IWIiMjFnpJ2BwwYgLFjx9q6KWSHkpKSMHPmTDVDbvLkybcMmIiIiOy2p+m1115T14sXL7Z1U8gOe5g++OADJCYmqoCpYsWKtm4SERHZIbsJmoiKytvbGw0aNEC7du0QHh7OE0lEREXi0EFTenq6uhgn4pHziIuLw5UrV1C3bl08+OCDtm4OERHZOZvmNE2bNg06na7Ay549e4r8+DNmzEBAQIDhUrly5WJtP5Vekuz9/vvv49tvv0V2dratm0NERA7ApiUH5ItNLgWpVq2aWg9MT3KaJk6caNHsOXM9TRI4JSQkoEyZMrfZeiqtYmJiVNK3lBSQHKaQkBBbN4mInBxLDjgGmw7PhYaGqou1eHp6qgs5DxmOk4BJ3vdJkyYhKCjI1k0iIiIHYTc5TefOnVPTxeVahlukXpOIiIiAn5+frZtHpYQM6ZYvXx6PPvqoGpIlIiJyuorgI0aMwJdffplnu1R37tSpk0WPwe5Rx3X58mUEBgaqmXJERKUNv38cg90Ut5RcJonvcl8sDZjIcUnv43vvvYcVK1bYuilEROTA7GZ4jsics2fPYs6cOarCd79+/XiSiIjIahg0kd06deoU5s6diwoVKmDChAkcmiMiIqti0ER2PSxXqVIljB8/3qQsBRERkVMnghcHJuI5TqVvfSkBmUnp6upq6yYRERWI3z+OwW4SwYnE4cOHMXXqVEPJCQZMRERUUjg8R3bj4MGD+PTTT1GvXj21AC8REVFJYtBEVpORloqj27bgyNZNSI6Ph29gIOp36IJ67TvBw6tw9ZT27duHzz//HI0aNcLo0aPh5saPLhERlSx+85BVXL94HsvfeAnJcbFSphvQNCTEXMGlE8ewa8W3GDB1OkIqWraAck5ODjZs2ICmTZti5MiRHJIjIiKbYCI4WaWHadHEJ5CSEA8tJyfvh87FBT4BgXhs9qe37HHKyMiAh4cHUlNT1TVzmIjIHjER3DEwEZyKnQzJSQ+TuYBJyHbZf2x7VIGPs2vXLkybNg0JCQmqBhMDJiIisiUGTVTsJIdJDckVRKfD4ahN+e7etm2bWmtQkr79/f2Lv5FERESFxJwmKnaS9C05TAXSNCQnxJndJYswf/fdd2pdwYEDB8LFhbE9ERHZHr+NqNjJLDlLepp8A24WqDQWGxuLH374AV26dMGgQYMYMBERUanBniYqdlJWQGbJFUjT0KBjl1ybNAQHB+Oll15C+fLlobtV4EVERFSC2NNExU7qMPkGBatZcubIdtlft11Hw7a1a9fim2++UYGTLMDLgImIiEobBk1U7KSMgNRhkrICir7H6N9r2S775TgJkiIjI9VF1pNjsERERKUV6zSRVes1SVmBw1EbkZwQr3KYZEhOepj0AdPKlSuxfv169O/fH927d+e7QUQOiXWaHANzmshqJDBqfE8PdTFn9+7dKmB66KGHVOI3ERFRacagiWymefPm8PX1RcOGDfkuEBFRqcecJipRso7csmXL8Pfff6sK3wyYiIjIXjBoohINmJYsWaKKV16/fp1nnoiI7AqH56hEZGdnY/HixdizZw8ee+wxtGzZkmeeiIjsCoMmKjbp6ek4dOgQ9u/fj+TkZJWv1LRpUzRq1AgrVqxQAdOoUaPQrFkznnUiIrI7LDlAxSImJkYNvSUmJubZJwvuSjkB6W1q0qQJzzgROR2WHHAMzGmiYulhkoApKSnJZLvUYZIgSn5Z/Prrr6hbty7PNhER2S0GTXTbZEhOgiMJkvTk/7L4rgRSmZmZar8cR0REZK8YNNFtkxym3LPkJGCSYEkW4PXw8DB7HBERkT1hIjjdNkn6Nu5hiouLyxMw5T6OiIjI3rCniW6bzJLTkwV3PT098wRMuY8jIiKyNwya6LZJWQEZkktLS1O3/fz88gRM+uOIiIjsFYfn6LZVr14d8fHxakhOgiUXF9NYXHqfJJCSek1ERET2ikET3RYpJ/DRRx/Bzc0N4eHhyMjIMOzTcHM2XZZbFsp2KItsl2yebSIislssbklFlpCQgFmzZqkE78mTJ6s8ps27N+PXnb/CNdMV6a7p+MfvH5z3O48slyyEeYdhQfcFqBFQg2ediJwKi1s6BvY0UZG5uroiJCQEY8eORbly5ZCSmYJ3L7+L2PKxyNby9irFpsVi1K+jsKbfGvi4+/DMExGRXWHQRIWu/r1z504cOXIEWVlZakbc+fPnERgYiDVn1yAmNSbf+0ogJfvXnlmLAbUH8MwTEZFdYdBEhVpfbsGCBSpIkhwmGY6TmkwXLlxAVFQU/qr2F3TQGXKZzJH9kScjGTQREZHdYckBsriHSQKmc+fOqdsBAQEm+2W5lPJHy8Mlp+CPlARU11Kv8awTEZHdYdBEFtm6dasKmKR8gOQxST6TMakE7p7ljipJVQp8HOlpCvUO5VknIiK7w6CJLCLrxkn9JXMBk3FAdKugSXqa+kT04VknIiK7w6CJCiQL70ovkhStDA0NzTdg0vPN8YWrzvwxsl3KDvSq3otnnYiI7A4TwQnJWdlYcSUO30fHIiYjC2EebngoPBgt027g0w/n4b777lOz5CTp+1aqhFZBsFewmiWnTwrXX8t2qdPEcgNERGSPGDQ5ub+T0zBg/0lEZ2RBp4bPgHNpGTh1+jTaRK1FlYoV0Lp1a5QpU0bNkruVVs1a4dEmj6qyAqtOrsL11Osqh0mG5KSHiQETERHZKwZNTt7DJAFTTGaWuq0vFBAUcxmto9YiISgEq1p3xzPuHmrdOCkrILPkZLguN+P15TzdPVVJAdZiIiIiR8KcJicmQ3LSw5SdKwaqdvIw4oLDsKtjb1zSXLDyahw8PT0xbNgwFRiZI9tlvxxHRETkiLj2nBPrvfcE9t5IMfQwuWZlItvNHS7Z2dBpOer/MmTXrIwP1jSrbajXdOjQITWbTtack1ynpk2b3uxhYsBERGQW155zDByec2KS9K0PmMpe+gd37N6MnZ37IDEgWEIotV379zg9CYyaN2+uLkRERM6Ew3NOTGbJSU9S+IUzaLV9HeJCwpHkZ1rpW/fvcURERM6OQZMTk7IC4edPocWO9bhcsTr+bNsVWu5K3wAGlpeeJyIiIufGLgQHl5GWiqPbtuDI1k1Ijo+Hb2Ag6nfognrtO6F3gDe27N2Gy1VqYm+ru6G5mMbQrjogzN0N/csG2az9REREpQUTwR3Y9YvnsfyNl5AcFys1AWSBOMO1T2AwHnplOk5lAo+fj0N0Vo6hTpP+OtzDDcubRqCWr5etXwoRkV1jIrhjYNDkwD1MiyY+gZSEeGg5OSb7Ejx8kOTujQh3DaPmfIpMNw9VVmDZ5f8qgsuQnPQw+boVvGwKERHdGoMmx8DhOQclQ3KqhymXeA9fXPUJREB6ElLiE3BsexQa39MDQyuEqgsRERGZx0RwByU5TGoozkjcvwFTYHoSyqYmqCreh6M22ayNRERE9oQ9TQ5Kkr5VDtO/Ulw9EOMTiKC0RISm3VB5S7I/OeHWi/ASERERe5oclsySM+5p8s7OQMWka/8FTEKng28AZ8YRERFZgsNzDkrKCsjCutc9/XHD3VsFSr5Z6f8FTELT0KBjF9s1koiIyI4waHJQddt1xI3AsrjuXQZZLnlnwOlcXOAbFKyOIyIioltjTpMDkh6myDVrcQXuqIgM+KYn5a3TFBCIAVOnw8PL29bNJSIisgsMmhzQunXrsHHjRgwaNAhtW9+lygocjtqI5IR4lcMkQ3LSw8SAiYiIyHIsbumAEhIScPz4cbRs2dLWTSEiIha3dBjMaXIQOTk5iIyMNFSdZcBERERUvBg0OYDs7Gx88cUX+Pnnn3H69GlbN4eIiMghMafJAQKmhQsXYt++fRg1ahSaNm1q6yYRERE5JPY02fksuU8//xz/27cfMV1646lMH/TeewJLLl5Dcla2rZtHRETkUNjTZMdOpqRjpU8oYtp2w9WQitDSMnAuLQN7bqRg5tloLG8agVq+XrZuJhERkUNg0GSHMjIysPPPPzERAYipEoHs/5aYg/6/MZlZGLD/JHa0qgdft7zFLYmIiKhwODxnZ9LT0/Hhhx9i2bff4kZsrEnAZEy2R2dkYeVVLshLRERUHBg02ZG0tDTMnTsXZ8+exZUe/ZHqV6bA42WduWWXY0usfURERI6MQZOdSE1NVQHThQsX8PTTT+NiSLhhKC4/sj8mI6uEWkhEROTYGDTZCVdXV1W0cuLEiahZsybCPNxUT1JBZL8cR0RERLePQVMpl5SUhIsXL8LDwwNPPPEEqlevrrY/FB5sUU/TwPLBJdJOIiIiR2cXQZPk8IwcOVIFDN7e3qqn5dVXX1WzyByZLIkyc+ZMfP7552qZFGMPlAtCuIcbXPPpbpLtsr9/2aCSaSwREZGDs4uxm2PHjqmg4dNPP0VERAT++usvjB49GsnJyXj//ffhqIvuzpo1S73GSZMmwcXFNL6VMgJSh0nKCsgsOd2/PUv66zB3N7Wf5QaIiIiKh06TstJ26L333sMnn3xSqLXW9IvZSkBSpkzBM89sKS4uTgVMUl5AAqbw8PB8j5XK31JWQGbJSdK35DDJkJz0MDFgIiIqHezl+4ccoKfJHPngBQcXnK8jQYdcjD+09iA+Pl4lfk+ZMgVly5Yt8FgJjIZWCFUXIiIicvKcptxOnTqFefPmYcyYMQUeN2PGDBXZ6y+VK1dGaSY9TLIAr+RuTZ069ZYBExERETlJ0DRt2jTodLoCL3v27DG5z6VLl9CjRw8MGDAAo0aNKvDxX3jhBdUjpb+cP38epdXVq1fxzjvvYMWKFep27hwmIiIicuLhuXHjxmHQoEEFHlOtWjWTgKlz585o3bo1Pvvss1s+vqenp7rYmuQdrbgSh++j/8s7kpIBMgNOhteio6PVLDkvLy9069bN1s0lIiIie04El1pFEjA1a9YMX3/9tcr5sYdEvL+T0/Kd4SYlAT4u54OV8z+Gm5sbqlatqsoo+Pr6omnTpmjUqFGpCPqIiOj2MBHcMdhF0CQ9TB07dkSVKlWwZMkSk4CpoJlltv7QSg9T2z+OIiYzy+zCulJLqemh3aj+918ICgjIEwj6+/tj2LBhCAsLs3pbiYjIehg0OQa7mD23fv16nDx5Ul0qVapksq80x3wyJCc9TOa4ZmYi290de+s3h5+rK0KvnjdbDVyCRBnGZI8TERGRbdlFtvGIESNUcGTuUppJDpO5gt1B16+g65qvEXrlIqDT4ViF//K2jMnrS0xMxKFDh6zeViIiInKAoMleSdJ37rAuOOYyWm9ejST/QMQHh6mgKdWj4Lyl/fv3W7WdREREdGsMmqxIZskZ9zSFXLmI1lFrkBAUhl0deyPL3UO6k+Cd8V8BTnNkKRUiIiKyLbvIaSqtUjJTsOb0GkSeisT11OsI8Q5Bn5p90LtGb/i4+6iyAntupNw8OCcHjf+3DbEh4djdvgey3dwNj1PnyrkCn0dm0xEREZFtMWgqotMJpzHq11GISY2BDjpo0HAx6SIOxBzA/APzsaD7AjxQripmno1GTEYmsl1cVO9ShocXctxunnYXaPDKSEOtKxcKfC4pP0BERES2xeG5IvYwScAUmxarbkvAZHwt22W/TkvH2+5p6LjxJ7hlpCPdx08FTPohuzAPdww6uR8eWo7Z55GK6FJ2QOo1ERERkW2xp6kIZEhOepjyk61lq/1frP8Cf635C20aN0b3+lWx/OoNQ0XwgeWD0b9sEFJql1NlBWSWXG5+fn6qThPLDRAREdmeXRS3LG3FxR75+REcjDlo6FkyJ/RaKGr9XQstm7fEo48+WmAF8/T0dFVWQGbJSdI3K4ITETkWFrd0DOxpKgJJ+i4oYPJM80StE7WQUj7llgGTOt7TE82bN1cXIiIiKp2Y01QEMktOkr/zk+6VjiMNjkDXQlekNfKIiIio9GHQVARSVsBcT1O56HKocLGC+n9CQAL61Opz++8QERERlQoMmopA6jCFeYfBVWe0cPDlcNQ8XRMeGR5whava36t6r+J8r4iIiMiGGDQVgRSulDpMwV7B6rb0LtU4UwOXKlzC2WpnEewdrPbLcUREROQYOHvuNus1LVi7AId/OYyE6glwb+SuhuSkh4kBExER6XH2nGPg7LnbIIHR4z0ex96wvWjTpo0qRklERESOicNzRSClrdatW4fo6Gh4eXmhbdu2DJiIiIgcHHuaihAwrVixAhs2bIC3tzfCw8Ot884QERFRqcKgqZAB0/fff4/ffvsNgwYNQseOHa33zhAREVGpwqCpEJYvX64CpiFDhqBDhw7We1eIiIio1GHQVAiNGjVCxYoVVQ4TERERORcGTbeQnZ2NHTt2oF27dqhXr17JvCtERERU6jBoukXA9MUXX2Dv3r2oVKkSatSoUXLvDBEREZUqDJrykZWVhYULF+LAgQMYPXo0AyYiIiInx6Apn4Dps88+w+HDh/HEE0+gSZMmJf/OEBERUanCoMkMFxcXBAQEYOzYsWjYsGHJvytERERU6jBoMpKRkYELFy6ooTgpK0BERESkx2VU/pWWloZ58+bho48+Uv8nIiIiMsagCUBqairmzp2Lc+fOqSE5WU+OiIiIyJjTD8+lpKSogEkW3504cSKqV69ucoKIiIiIGDQBSE5OVrlMkydPRpUqVfipICIiIrN0mqxC6yRu3LihZsUlJCSoGXKurq7w9vZGTk6Ouk1ERGTt758yZcrwJNspF2f98H7wwQdYvHixus2AiYiIiG7FKXOaPvzwQ0gHW79+/WzdFCIiIrITTtnTlJ6ejilTpiA8PNzWTSEiIiI74VQ9Tfr0rUcffVSVFZBhOiIiImvTf984URqxQ3KqoCkxMVFdcy05IiKy1feQJISTfXKq2XMyS+7SpUvw9/eHTqdDaf1rpHLlyjh//jxnWPB8lir8bPJcllb28NmUr1oJmCpUqMDJR3bMqXqaZJZcpUqVYA/kB7+0/vDbI55PnsvSiJ9L5zqf7GGyf06ZCE5ERERUWAyaiIiIiCzAoKmU8fT0xKuvvqquieezNOFnk+eytOJnk0qKUyWCExERERUVe5qIiIiILMCgiYiIiMgCDJqIiIiILMCgqRQ7e/YsRo4cierVq8Pb2xs1a9ZUSeIZGRm2bppdmj59Otq0aQMfHx8EBgbaujl25eOPP1afQ1l+qFmzZti2bZutm2SXtm7divvuu08VOJQCuz/99JOtm2S3ZsyYgRYtWqhixWXLlsX999+P48eP27pZ5OAYNJVix44dU1XMP/30Uxw+fBizZs3C/Pnz8eKLL9q6aXZJgs0BAwZg7Nixtm6KXVm2bBkmTpyIl156Cfv27UP79u3Rs2dPnDt3ztZNszvJyclqGacPP/zQ1k2xe1FRUXjqqafw+++/Y8OGDcjKykK3bt3UOSayFs6eszPvvfcePvnkE5w+fdrWTbFbixcvVkFAfHy8rZtiF1q1aoU777xTfe706tWrp/6yl7/2qWikp+nHH39U55FuX0xMjOpxkmCqQ4cOPKVkFexpsjMJCQkIDg62dTPIiXrn9u7dq/6CNya3d+7cabN2EZn73Sj4+5GsiUGTHTl16hTmzZuHMWPG2Lop5CSuXbuG7OxslCtXzmS73I6OjrZZu4iMSbnByZMno127dmjYsCFPDlkNgyYbmDZtmuqaL+iyZ88ek/tcunQJPXr0UDk5o0aNskWzHeZcUuHJecz9JZV7G5GtjBs3DgcPHsS3337LN4Gsys26D0/5/YAPGjSowJNTrVo1k4Cpc+fOaN26NT777DOe1Ns4l1Q4oaGhcHV1zdOrdPXq1Ty9T0S2MH78eERGRqqZiZUqVeKbQFbFoMlGX0RyscTFixdVwCTTvL/44gu4uLBzsKjnkgrPw8NDffZkdlK/fv0M2+V23759eUrJZqS3UwImSabfsmWLKolBZG0Mmkox6WHq1KkTqlSpgvfff1/NDtELDw+3advskUyRj42NVdeSp7N//361PSIiAn5+frZuXqkluSJDhw5F8+bNDb2dcg6ZW1d4SUlJOHnypOH2mTNn1OdQkpfl55wsJ+UGli5dilWrVqlaTfre0ICAAFXXjsgaWHKglE+Nf/TRR83u4zrLhTdixAh8+eWXebZv3rxZBadUcHHLd999F5cvX1aJtlIzjNO6C096RKTnOLfhw4ern3eyXH45ddIjLz/rRNbAoImIiIjIAkyQISIiIrIAgyYiIiIiCzBoIiIiIrIAgyYiIiIiCzBoIiIiIrIAgyYiIiIiCzBoIiIiIrIAgyYiIiIiCzBoIirGas9SpTg+Pt4hzqmjvR4iotvFoImIiIjIAgyaiOxYRkYGSpPMzExbN4GIyGoYNBFZKD09HRMmTEDZsmXh5eWFdu3a4c8//8xz3I4dO9CkSRN1TKtWrXDo0CHDvn/++Qf33XcfgoKC4OvriwYNGuDnn3827D9y5Ajuvfde+Pn5oVy5chg6dCiuXbtm2C8LC48bNw6TJ09GaGgounbtisGDB2PQoEF5ghfZL4uX6hd4lgV3a9SooVaAl/b98MMPJveRdtSuXVvtl0Vlz549e8tzIsN38+fPR9++fdXrefPNN9X21atXo1mzZuocyHO+9tpryMrKMtxv2rRpqFKlCjw9PVGhQgV1XvWqVauGN954Aw8//LA6D7J/3rx5Js977tw59Zyyv0yZMnjooYdw5coVk8dv2rQpvvrqK/V4svK9nKPExETDMfL6GzVqpF5vSEgI7rnnHiQnJxv2y7mrV6+eeg1169ZVixYTkZPTiMgiEyZM0CpUqKD9/PPP2uHDh7Xhw4drQUFB2vXr19X+zZs3a/IjVa9ePW39+vXawYMHtd69e2vVqlXTMjIy1DG9evXSunbtqvadOnVKW716tRYVFaX2Xbp0SQsNDdVeeOEF7ejRo9r//vc/dWznzp0NbejYsaPm5+enPfvss9qxY8fUcfIY3t7eWmJiouE42ebl5aUlJCSo2y+++KJWt25dbd26dep5v/jiC83T01PbsmWL2n/u3Dl1++mnn1aP+/XXX2vlypVTrycuLi7fcyL7y5Ytqy1cuFA97tmzZ9VzlClTRlu8eLHaJudCzsG0adPUfZYvX672y3n8559/tD/++EP77LPPDI9ZtWpVzd/fX5sxY4Z2/Phxbe7cuZqrq6t6HJGTk6PdcccdWrt27bQ9e/Zov//+u3bnnXeqc6P36quvqvPUv39/7dChQ9rWrVu18PBwdR7059rNzU2bOXOmdubMGfV+fPTRR4ZzKO0pX768tmLFCu306dPqOjg4WL0mInJeDJqILJCUlKS5u7tr33zzjWGbBEISRL377rsmQdN3331nOEYCKgloli1bpm43atTIEDzkNnX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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "prediction = np.asarray(problem.expr.prediction.value)\n", + "fitted_mask = np.asarray(problem.expr.fitted_mask.value).astype(bool)\n", + "modeled_vertices = set(edge_table.loc[selected, \"target\"])\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 5))\n", + "colors = plt.cm.tab10(np.linspace(0, 1, pkn.num_vertices))\n", + "fit_summary = []\n", + "for vertex_index, vertex in enumerate(pkn.V):\n", + " if vertex not in modeled_vertices:\n", + " continue\n", + " keep = fitted_mask[vertex_index]\n", + " observed = np.full(len(data.samples), np.nan)\n", + " for sample_index, sample_name in enumerate(data.samples):\n", + " feature = next((feature for feature in data.samples[sample_name].features if feature.id == vertex), None)\n", + " if feature is not None and feature.value is not None:\n", + " observed[sample_index] = float(feature.value)\n", + " observed = observed[keep]\n", + " modeled = prediction[vertex_index, keep]\n", + " ax.scatter(\n", + " observed,\n", + " modeled,\n", + " label=vertex,\n", + " color=colors[vertex_index],\n", + " s=45,\n", + " )\n", + " fit_summary.append(\n", + " {\n", + " \"protein\": vertex,\n", + " \"fitted observations\": int(keep.sum()),\n", + " \"mean absolute error\": np.mean(np.abs(observed - modeled)),\n", + " }\n", + " )\n", + "limits = [-2.5, 2.5]\n", + "ax.plot(limits, limits, \"--\", color=\"0.4\", linewidth=1)\n", + "ax.set(xlabel=\"observed response\", ylabel=\"model prediction\", xlim=limits, ylim=limits)\n", + "ax.legend(frameon=False, bbox_to_anchor=(1.02, 1), loc=\"upper left\")\n", + "ax.set_title(\"Modeled downstream equations\")\n", + "fig.tight_layout()\n", + "\n", + "pd.DataFrame(fit_summary).set_index(\"protein\").round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "a4ef2ecc", + "metadata": { + "papermill": { + "duration": 0.002101, + "end_time": "2026-09-21T22:59:02.068719", + "exception": false, + "start_time": "2026-09-21T22:59:02.066618", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Check what was actually fitted\n", + "\n", + "This table shows which values enter the fitting loss. `observed_mask` is true whenever a measurement is present. `fitted_mask` is more restrictive: it is false for hard-intervention targets and whenever the target or any of its candidate parents is missing. Here a **candidate parent** means any vertex with an incoming PKN edge to the target, even when that edge is not selected.\n", + "\n", + "Consequently, one missing candidate-parent value can remove a target equation from that sample. This uses fewer observations than a workflow that first fills in missing values, but it guarantees that CORNETO never silently replaces a missing parent with zero." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "93718dc3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T22:59:02.073423Z", + "iopub.status.busy": "2026-09-21T22:59:02.073345Z", + "iopub.status.idle": "2026-09-21T22:59:02.078211Z", + "shell.execute_reply": "2026-09-21T22:59:02.077880Z" + }, + "papermill": { + "duration": 0.007886, + "end_time": "2026-09-21T22:59:02.078670", + "exception": false, + "start_time": "2026-09-21T22:59:02.070784", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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observedused in loss
EGFRAKTERKFOXOELK1EGFRAKTERKFOXOELK1
obs_1TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue
obs_2TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue
obs_3TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue
obs_4TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue
EGFR_lowTrueTrueTrueTrueTrueFalseTrueTrueTrueTrue
EGFR_highTrueTrueTrueTrueTrueFalseTrueTrueTrueTrue
AKT_onTrueTrueTrueTrueFalseTrueFalseFalseTrueFalse
AKT_offTrueTrueTrueTrueTrueTrueFalseTrueTrueTrue
ERK_onTrueTrueTrueTrueTrueTrueTrueFalseTrueTrue
ERK_offTrueTrueTrueTrueTrueTrueTrueFalseTrueTrue
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" + ], + "text/plain": [ + " observed used in loss \\\n", + " EGFR AKT ERK FOXO ELK1 EGFR AKT ERK FOXO \n", + "obs_1 True True True True True True True True True \n", + "obs_2 True True True True True True True True True \n", + "obs_3 True True True True True True True True True \n", + "obs_4 True True True True True True True True True \n", + "EGFR_low True True True True True False True True True \n", + "EGFR_high True True True True True False True True True \n", + "AKT_on True True True True False True False False True \n", + "AKT_off True True True True True True False True True \n", + "ERK_on True True True True True True True False True \n", + "ERK_off True True True True True True True False True \n", + "\n", + " \n", + " ELK1 \n", + "obs_1 True \n", + "obs_2 True \n", + "obs_3 True \n", + "obs_4 True \n", + "EGFR_low True \n", + "EGFR_high True \n", + "AKT_on False \n", + "AKT_off True \n", + "ERK_on True \n", + "ERK_off True " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "observed_mask = pd.DataFrame(\n", + " np.asarray(problem.expr.observed_mask.value).astype(bool).T,\n", + " index=tuple(data.samples),\n", + " columns=pkn.V,\n", + ")\n", + "used_for_fit = pd.DataFrame(\n", + " fitted_mask.T,\n", + " index=tuple(data.samples),\n", + " columns=pkn.V,\n", + ")\n", + "\n", + "pd.concat(\n", + " {\"observed\": observed_mask, \"used in loss\": used_for_fit},\n", + " axis=1,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0f4a88f3", + "metadata": { + "papermill": { + "duration": 0.002158, + "end_time": "2026-09-21T22:59:02.083144", + "exception": false, + "start_time": "2026-09-21T22:59:02.080986", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Adapting the workflow to your experiment\n", + "\n", + "For a real perturbation panel:\n", + "\n", + "1. use centered continuous measurements, such as log-fold changes relative to an appropriate control;\n", + "2. map every feature identifier to the same identifiers used by the PKN;\n", + "3. annotate the intervened target feature using the metadata described above;\n", + "4. represent missing measurements as absent features, `None`, or `NaN`;\n", + "5. inspect `fitted_mask`, then rerun plausible `lambda_edges` and `min_abs_coefficient` values and report which edges change.\n", + "\n", + "Optional weights can reflect assay reliability or unequal sample importance:\n", + "\n", + "```python\n", + "method = LinearDAGDiscovery(\n", + " vertex_weights={\"FOXO\": 0.5}, # noisier readout\n", + " sample_weights={\"replicate_3\": 0.7},\n", + ")\n", + "```\n", + "\n", + "Weights must be positive and multiply the corresponding contribution to the fitting loss. Standardization can be disabled with `standardize_loss=False`; leave it enabled when errors should be judged relative to each target's observed training variation, and disable it when the raw measurement scale itself should determine their relative importance.\n", + "\n", + "With exact coefficient support, `min_abs_coefficient` applies to `coefficient × source_scale / target_scale`, not to the displayed raw coefficient. With structural support, a selected edge may instead have coefficient zero and serve only to complete a connectivity route." + ] + }, + { + "cell_type": "markdown", + "id": "48240ea5", + "metadata": { + "papermill": { + "duration": 0.002167, + "end_time": "2026-09-21T22:59:02.087493", + "exception": false, + "start_time": "2026-09-21T22:59:02.085326", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Advanced options and formulation\n", + "\n", + "The sections below are useful when tuning the method or diagnosing a difficult dataset; they are not required for the basic workflow.\n", + "\n", + "(linear-dag-model-choices)=\n", + "### Model choices and their exact effects\n", + "\n", + "| Parameter | Effect |\n", + "|---|---|\n", + "| `lambda_edges` | Adds this amount to the objective for every selected edge. Increasing it trades more fitting error for fewer selected edges. |\n", + "| `max_parents` | Maximum number of selected incoming edges allowed at each vertex. An integer applies to every vertex; a mapping can set vertex-specific limits. |\n", + "| `enforce_signs` | When true, a PKN sign of `+1` requires a nonnegative coefficient and `-1` requires a nonpositive coefficient. |\n", + "| `coefficient_support` | `exact` requires every selected edge to have a sufficiently large nonzero coefficient; `structural` allows a selected connectivity edge to have coefficient zero. |\n", + "| `min_abs_coefficient` | In exact mode, the minimum of `abs(coefficient × source_scale / target_scale)` for a selected edge. |\n", + "| `min_commodity_coverage` | Minimum fraction of intervention evidence units whose shared flow column must contain a complete route to at least one measured, non-intervened response. |\n", + "| `lambda_unexplained` | Adds a penalty for evidence units whose shared flow column has no complete route; unlike the minimum above, it does not make that coverage mandatory. |\n", + "| `loss=\"absolute\"` | Default: minimizes absolute standardized errors, which reduces the influence of individual large errors. |\n", + "| `loss=\"squared\"` | Minimizes squared standardized errors, so large errors receive much more weight. This also changes the problem from a mixed-integer linear program to a mixed-integer quadratic program. |\n", + "\n", + "`flow_epsilon` and `flow_capacity` are technical bounds for the auxiliary connectivity variables, not descriptions of biology. `flow_epsilon` is the smallest positive value assigned to an edge used by a flow; `flow_capacity` is its upper bound. The default capacity is the number of PKN edges multiplied by `flow_epsilon`. Normally these values should not be used to express biological confidence or effect size.\n", + "\n", + "For a real analysis, solve several scientifically plausible edge penalties and coefficient thresholds and report which edges remain selected. One optimized graph is not an uncertainty estimate.\n", + "\n", + "### What does `min_commodity_coverage` mean?\n", + "\n", + "The parameter retains the internal term *commodity* from flow problems, but it controls intervention-to-response connectivity. Three levels must be distinguished:\n", + "\n", + "| Level | Exact meaning |\n", + "|---|---|\n", + "| Sample | One row in `Data`, such as one cell or one bulk condition. Every sample remains a separate regression observation. |\n", + "| Evidence unit | One explicit `(intervened source, intervention_group)` pair. Replicate cells in that group count as one unit. Samples without a group are pooled into one implicit unit when they have the same flow pattern. |\n", + "| Flow column | One auxiliary connectivity problem for a unique pattern: the intervened source, the measured non-intervened responses available as endpoints, and the edges blocked by hard interventions. |\n", + "\n", + "Several explicit evidence units can have the same pattern and therefore share one flow column. They are counted separately through that column's `group_count`, but they cannot receive different coverage decisions: when the shared flow is active, all those units count as covered; when it is inactive, none do. Ungrouped samples with the same pattern are pooled into one implicit evidence unit and therefore count once.\n", + "\n", + "An evidence unit counts as covered when its shared flow column contains a directed route from the intervened source to at least one measured response that is not itself intervened, using at least one selected PKN edge. This says only that the route exists. It does not use prediction error or require a route to every response.\n", + "\n", + "If there are ten evidence units:\n", + "\n", + "- `min_commodity_coverage=0` does not require any evidence unit to be connected;\n", + "- `min_commodity_coverage=0.6` requires shared active flow columns representing at least `ceil(0.6 × 10) = 6` evidence units;\n", + "- `min_commodity_coverage=1` requires flow columns representing all ten evidence units and makes the problem infeasible if the PKN cannot provide every required route.\n", + "\n", + "Coverage is only a graph-connectivity requirement. A covered unit may still be predicted poorly, and coverage is not a significance test or confidence score. Use `1` only when every evidence unit should have a route to at least one measured downstream vertex in the PKN. Otherwise leave the minimum at zero, choose a smaller fraction, or use `lambda_unexplained` to penalize missing routes without making them mandatory." + ] + }, + { + "cell_type": "markdown", + "id": "9ae3bf7f", + "metadata": { + "papermill": { + "duration": 0.002184, + "end_time": "2026-09-21T22:59:02.091864", + "exception": false, + "start_time": "2026-09-21T22:59:02.089680", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Method details\n", + "\n", + "### Linear structural equations\n", + "\n", + "For every usable measurement whose target is not hard-intervened, the fitted equation for vertex $v$ in sample $s$ is\n", + "\n", + "$$\n", + "\\hat{x}_{v,s} = \\alpha_v + \\sum_{u\\to v} \\beta_{u\\to v}x_{u,s} + d_{v,s}.\n", + "$$\n", + "\n", + "Here $\\alpha_v$ is the optional intercept, $\\beta_{u\\to v}$ is the fitted edge coefficient, and $d_{v,s}$ is zero unless that target has a shift intervention. A shift can be supplied numerically or estimated from an `intervention_group`. An unselected edge has coefficient zero. A hard-intervention target has no fitted equation in that sample; its supplied value can still appear as a parent value in downstream equations.\n", + "\n", + "With the default absolute-error loss and no optional shift or coverage penalties, the objective is\n", + "\n", + "$$\n", + "\\sum_{(v,s)\\,\\mathrm{fitted}}\n", + "\\frac{w_v w_s}{\\sigma_v}\\left|x_{v,s}-\\hat{x}_{v,s}\\right|\n", + "+ \\lambda_E\\sum_e z_e.\n", + "$$\n", + "\n", + "In this expression, $w_v$ and $w_s$ are the optional vertex and sample weights, $\\sigma_v$ is the training-derived response scale, and $z_e$ is 1 when edge $e$ is selected. The target scale is computed from observations where the target is not hard-intervened and every candidate parent is present. A missing parent is never replaced with zero.\n", + "\n", + "In exact coefficient-support mode, selecting edge $e:u\\to v$ also requires $|\\tilde{\\beta}_e|\\ge\\delta$, where $\\tilde{\\beta}_e=\\beta_e\\,\\mathrm{scale}(u)/\\mathrm{scale}(v)$ and $\\delta$ is `min_abs_coefficient`.\n", + "\n", + "During fitting and local residual calculation, $x_{u,s}$ is the observed parent value. During forward prediction, it is the model's previously predicted parent value. Ordinary observed values in the new data—including observed roots—are not substituted into the forward calculation. A hard-intervention target is fixed to its supplied value, and a root that is not hard-intervened uses only its fitted intercept." + ] + }, + { + "cell_type": "markdown", + "id": "a6051fbb", + "metadata": {}, + "source": [ + "### Relationship to SEMs and SCMs\n", + "\n", + "A structural equation model (SEM) describes each variable using an equation based on its parent variables. In a linear SEM, the equation for vertex $v$ is\n", + "\n", + "$$\n", + "X_v = \\alpha_v + \\sum_{u\\in\\mathrm{Pa}(v)} \\beta_{uv}X_u + \\varepsilon_v.\n", + "$$\n", + "\n", + "Here, $\\mathrm{Pa}(v)$ is the set of parents of $v$, $\\alpha_v$ is its baseline value, $\\beta_{uv}$ is the coefficient for the edge $u\\rightarrow v$, and $\\varepsilon_v$ represents variation not explained by the modeled parents. An SEM is not necessarily causal: the equations may describe statistical relationships without claiming what would happen if a variable were manipulated.\n", + "\n", + "A structural causal model (SCM) gives the equations a causal interpretation. Each equation is treated as the mechanism that produces its variable. An intervention replaces or modifies one mechanism while the remaining equations stay unchanged. For example, a hard intervention $\\operatorname{do}(X_v=x)$ replaces the equation for $X_v$ with the fixed value $x$.\n", + "\n", + "`LinearDAGDiscovery` selects the parent relationships from the candidate edges in the PKN and estimates their linear coefficients. It therefore fits a PKN-constrained linear SEM. Hard interventions fix the target and remove its usual equation for that sample, while shift interventions add an offset to the equation. Under suitable causal assumptions, these intervention semantics allow the fitted model to be interpreted as a linear acyclic SCM.\n", + "\n", + "| Result | What it contains | Causal interpretation | Interventions |\n", + "|---|---|---|---|\n", + "| Directed graph or DAG | Variables and directed edges | Not established by the graph alone | Not defined by the graph alone |\n", + "| Linear SEM | Graph, linear equations, coefficients, and unexplained variation | Optional; equations may be statistical | Not defined unless causal meaning is added |\n", + "| Linear SCM | Linear equations interpreted as causal mechanisms | Yes, by assumption | Replace or modify the target equation |\n", + "| `LinearDAGDiscovery` | A DAG selected from the PKN, fitted linear equations, and intervention annotations | Valid when the PKN, intervention design, absence of important hidden common causes, and mechanism stability assumptions are appropriate | Hard interventions replace the target equation; shift interventions modify it |\n", + "\n", + "The optimization cannot verify the assumptions required for the SCM interpretation. Without them, the result should be treated as an intervention-aware linear SEM rather than proof of a causal model. CORNETO's auxiliary flows are not part of the SEM or SCM; they only restrict graph selection by requiring selected edges to participate in intervention-to-measurement routes." + ] + }, + { + "cell_type": "markdown", + "id": "424a67de", + "metadata": { + "papermill": { + "duration": 0.002273, + "end_time": "2026-09-21T22:59:02.096347", + "exception": false, + "start_time": "2026-09-21T22:59:02.094074", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### How the connectivity constraints work\n", + "\n", + "For each unique flow pattern described above, CORNETO creates auxiliary variables that must obey these rules:\n", + "\n", + "1. flow enters at that pattern's intervened source;\n", + "2. it can leave only through responses measured and not intervened in the corresponding samples;\n", + "3. it can use only selected PKN edges;\n", + "4. every selected PKN edge must be used by at least one flow column; and\n", + "5. an incoming edge to a hard-intervention target is unavailable in that flow column, although it remains available in other intervention patterns and in the regression equations where applicable.\n", + "\n", + "These rules ensure that selected edges form intervention-to-measurement routes rather than disconnected graph fragments. By default, `min_commodity_coverage=0`, so a particular flow column may remain inactive. When coverage or unexplained-route penalties are enabled, each flow column is weighted by the number of evidence units it represents (`group_count`), including explicit groups and any pooled implicit unit. The table below shows which auxiliary flow columns use each selected edge." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "38cf4556", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T22:59:02.101212Z", + "iopub.status.busy": "2026-09-21T22:59:02.101133Z", + "iopub.status.idle": "2026-09-21T22:59:02.104585Z", + "shell.execute_reply": "2026-09-21T22:59:02.104309Z" + }, + "papermill": { + "duration": 0.00665, + "end_time": "2026-09-21T22:59:02.105153", + "exception": false, + "start_time": "2026-09-21T22:59:02.098503", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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flow 0: do(EGFR) [implicit: EGFR_low, EGFR_high]flow 1: do(AKT) [implicit: AKT_on]flow 2: do(AKT) [implicit: AKT_off]flow 3: do(ERK) [implicit: ERK_on, ERK_off]
EGFR → AKT1000
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" + ], + "text/plain": [ + " flow 0: do(EGFR) [implicit: EGFR_low, EGFR_high] \\\n", + "EGFR → AKT 1 \n", + "EGFR → ERK 1 \n", + "AKT → FOXO 1 \n", + "ERK → ELK1 1 \n", + "\n", + " flow 1: do(AKT) [implicit: AKT_on] \\\n", + "EGFR → AKT 0 \n", + "EGFR → ERK 0 \n", + "AKT → FOXO 0 \n", + "ERK → ELK1 0 \n", + "\n", + " flow 2: do(AKT) [implicit: AKT_off] \\\n", + "EGFR → AKT 0 \n", + "EGFR → ERK 0 \n", + "AKT → FOXO 0 \n", + "ERK → ELK1 0 \n", + "\n", + " flow 3: do(ERK) [implicit: ERK_on, ERK_off] \n", + "EGFR → AKT 0 \n", + "EGFR → ERK 0 \n", + "AKT → FOXO 0 \n", + "ERK → ELK1 0 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "usage = method.get_edge_usage()\n", + "commodity_labels = []\n", + "for info in method.get_commodity_info():\n", + " evidence = [\n", + " f\"{unit['intervention_group'] or 'implicit'}: {', '.join(map(str, unit['sample_names']))}\"\n", + " for unit in info['evidence_units']\n", + " ]\n", + " commodity_labels.append(f\"flow {info['index']}: do({info['source']}) [{'; '.join(evidence)}]\")\n", + "selected_labels = [\n", + " f'{row.source} → {row.target}'\n", + " for row in edge_table.loc[selected].itertuples()\n", + "]\n", + "\n", + "pd.DataFrame(\n", + " usage[selected].astype(int),\n", + " index=selected_labels,\n", + " columns=commodity_labels,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "35231820", + "metadata": { + "papermill": { + "duration": 0.002163, + "end_time": "2026-09-21T22:59:02.109510", + "exception": false, + "start_time": "2026-09-21T22:59:02.107347", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "A `1` means that the auxiliary connectivity route uses that edge. It does **not** measure signaling strength, confidence, molecule abundance, or causal effect size. The fitted coefficient—not the flow value—determines how an edge contributes to a linear equation." + ] + }, + { + "cell_type": "markdown", + "id": "681b07ee", + "metadata": { + "papermill": { + "duration": 0.002219, + "end_time": "2026-09-21T22:59:02.126893", + "exception": false, + "start_time": "2026-09-21T22:59:02.124674", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Interpretation and limitations\n", + "\n", + "The result is one acyclic linear model minimizing the configured combination of fitting error, edge penalty, and any enabled intervention-shift or missing-route penalties. It is not necessarily the graph with the fewest edges. A time-limited solver result may be only the best feasible graph found before the limit, rather than a proven optimum.\n", + "\n", + "With exact coefficient support, every selected edge has a normalized coefficient at least as large as the chosen threshold. With structural support, an edge may be selected only to complete a connectivity route and still have coefficient zero. Neither choice guarantees a nonzero total effect from an intervention to a response: effects along different paths can cancel.\n", + "\n", + "Its interpretation depends on several assumptions:\n", + "\n", + "- coefficients can be learned only from samples containing the required target and parent measurements; a fully unobserved internal vertex has no data from which to estimate its equation;\n", + "- coefficients are shared across conditions and effects are additive and linear;\n", + "- `intervention=\"hard\"` fixes the target and removes its equation for that sample;\n", + "- hidden common causes, feedback dynamics, and condition-specific coefficients are not modeled;\n", + "- an edge can be selected only if at least one auxiliary intervention-to-response route uses it, even if an excluded edge is biologically real;\n", + "- alternative graphs may fit noisy data similarly.\n", + "\n", + "Before drawing conclusions, repeat the analysis across plausible edge penalties and coefficient thresholds, resample conditions or cells where appropriate, and test reasonable PKN variants. Report which interactions remain selected and which alternative graphs explain the data similarly.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + }, + "papermill": { + "default_parameters": {}, + "duration": 3.502592, + "end_time": "2026-09-21T22:59:02.347565", + "environment_variables": {}, + "exception": null, + "input_path": "docs/guide/causal/linear-dag-discovery.ipynb", + "output_path": "/private/tmp/linear-dag-discovery-executed.ipynb", + "parameters": {}, + "start_time": "2026-09-21T22:58:58.844973", + "version": "2.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/guide/index.md b/docs/guide/index.md index 53197bb1c..5b87710c2 100644 --- a/docs/guide/index.md +++ b/docs/guide/index.md @@ -15,9 +15,9 @@ The library is designed with minimal dependencies and is easily extendable, maki ```{toctree} :maxdepth: 3 -method-inputs intro/index networks/index +causal/index metabolism/index signaling/index interoperability/index diff --git a/docs/guide/intro/data.ipynb b/docs/guide/intro/data.ipynb index 04e3c0b75..4a595f6c7 100644 --- a/docs/guide/intro/data.ipynb +++ b/docs/guide/intro/data.ipynb @@ -2,532 +2,226 @@ "cells": [ { "cell_type": "markdown", - "id": "0a2cb032", + "id": "working-with-data", "metadata": {}, "source": [ + "(guide-working-with-data)=\n", "# Working with data\n", "\n", - "CORNETO provides a flexible way to work with data in a convenient way for network problems. You can use the `Data` class to create a new dataset, or you can load an existing dataset from a file. The `Data` class provides methods for querying a given dataset. The main goal of this class is to provide a simple interface for working with datasets that are typically sparse and can be stored in memory, while still allowing for complex queries and transformations." + "A CORNETO analysis brings together a biological network and experimental observations. The network describes what can interact; the observations describe what was measured in one or more samples or experimental conditions.\n", + "\n", + "CORNETO represents these observations with the `Data` class. A `Data` object keeps related measurements together, records which sample they belong to, and provides a common data model for CORNETO methods." ] }, { "cell_type": "code", - "execution_count": 2, - "id": "56be29e1", + "execution_count": null, + "id": "import-corneto", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "import corneto as cn\n", - "\n", - "cn.info()" + "import corneto as cn" ] }, { "cell_type": "markdown", - "id": "ff4ef0e0", + "id": "data-model", "metadata": {}, "source": [ - "## Creating a new dataset from a dictionary\n", + "## Samples and features\n", "\n", - "Most of the network inference approaches in CORNETO require some type of measurements to be mapped to prior knowledge networks. For convenience, datasets used in CORNETO are represented as dictionaries with features (any measurement that is mapped, or will be mapped to a prior knowledge network), and samples, which are collections of features. The simplest way to create a dataset is to define a dictionary where keys are sample names (e.g. conditions like different perturbed cells). Each sample has features, which are lists of dictionaries containing information about the feature. Every feature has at least an id (e.g., gene name, protein name, metabolite name, etc), a value for that sample, and a mapping attribute which indicates if this feature maps to a vertex, edge, or none (for features that are not mapped to the network, or needs pre-processing to determine the mapping values). Every method may require additional attributes, which are described in the documentation of each method. The following example shows how to create a simple dataset:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3b913364", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Data(n_samples=2, n_feats=[3 3])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# A simple dataset with two samples, and three features per sample.\n", + "A `Data` object contains named samples. Each sample contains features, such as measured proteins, genes, metabolites, or phenotypes. Every feature has three common fields:\n", "\n", - "samples = {\n", - " \"sample1\": {\n", - " \"features\": [\n", - " {\"id\": \"receptor1\", \"value\": 1, \"mapping\": \"vertex\", \"role\": \"input\"},\n", - " {\"id\": \"tf1\", \"value\": 1, \"mapping\": \"vertex\", \"role\": \"output\"},\n", - " {\"id\": \"tf2\", \"value\": 1, \"mapping\": \"vertex\", \"role\": \"output\"},\n", - " ]\n", - " },\n", - " \"sample2\": {\n", - " \"features\": [\n", - " {\"id\": \"receptor2\", \"value\": 1, \"mapping\": \"vertex\", \"role\": \"input\"},\n", - " {\"id\": \"tf1\", \"value\": 1, \"mapping\": \"vertex\", \"role\": \"output\"},\n", - " {\"id\": \"tf2\", \"value\": -1, \"mapping\": \"vertex\", \"role\": \"output\"},\n", - " ]\n", - " },\n", - "}\n", + "- `id` identifies the measured entity.\n", + "- `value` stores its observed value.\n", + "- `mapping` says whether the feature corresponds to a network `vertex`, a network `edge`, or is currently mapped to `none`.\n", "\n", - "data = cn.Data.from_dict(samples)\n", - "data" + "Features can also carry information such as the assay, units, or a biological role. These additional fields are kept with the feature. Their meaning depends on the method that uses the data, so each method documents the fields it requires." ] }, { "cell_type": "markdown", - "id": "e3d71dbb", + "id": "create-data", "metadata": {}, "source": [ - "For convenience, data can be imported from a more compacted dictionary definition where features are also indexed by id." + "## Creating a dataset\n", + "\n", + "Here we record measurements from untreated cells and cells stimulated with EGF. Protein measurements map to vertices in a network. Cell viability is retained as part of the experiment but is not mapped to the network." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "f4a41dd9", + "execution_count": null, + "id": "create-data-code", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Data(n_samples=2, n_feats=[3 4])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "samples = {\n", - " \"sample1\": {\n", - " \"receptor1\": {\n", - " \"value\": 1,\n", - " \"mapping\": \"vertex\",\n", - " \"role\": \"input\",\n", - " },\n", - " \"tf1\": {\n", - " \"value\": 1,\n", - " \"mapping\": \"vertex\",\n", - " \"role\": \"output\",\n", - " },\n", - " \"tf2\": {\n", - " \"value\": 1,\n", - " \"mapping\": \"vertex\",\n", - " \"role\": \"output\",\n", + "observations = {\n", + " \"untreated\": {\n", + " \"EGFR\": {\"value\": 0.18, \"mapping\": \"vertex\"},\n", + " \"MAPK1\": {\"value\": 0.22, \"mapping\": \"vertex\"},\n", + " \"cell_viability\": {\n", + " \"value\": 0.96, \"mapping\": \"none\", \"assay\": \"phenotype\"\n", " },\n", " },\n", - " \"sample2\": {\n", - " \"receptor2\": {\n", - " \"value\": 1,\n", - " \"mapping\": \"vertex\",\n", - " \"role\": \"input\",\n", - " },\n", - " \"tf1\": {\n", - " \"value\": 1,\n", - " \"mapping\": \"vertex\",\n", - " \"role\": \"output\",\n", - " },\n", - " \"tf3\": {\n", - " \"value\": -1,\n", - " \"mapping\": \"vertex\",\n", - " \"role\": \"output\",\n", - " },\n", - " \"tf4\": {\n", - " \"value\": 1,\n", - " \"mapping\": \"vertex\",\n", - " \"role\": \"output\",\n", + " \"EGF\": {\n", + " \"EGFR\": {\"value\": 0.91, \"mapping\": \"vertex\"},\n", + " \"MAPK1\": {\"value\": 0.84, \"mapping\": \"vertex\"},\n", + " \"cell_viability\": {\n", + " \"value\": 0.94, \"mapping\": \"none\", \"assay\": \"phenotype\"\n", " },\n", " },\n", "}\n", "\n", - "data = cn.Data.from_cdict(samples)\n", + "data = cn.Data.from_cdict(observations)\n", "data" ] }, - { - "cell_type": "code", - "execution_count": 5, - "id": "6eaae82b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Feature(id=receptor1, value=1, mapping=vertex, role=input),\n", - " Feature(id=tf1, value=1, mapping=vertex, role=output),\n", - " Feature(id=tf2, value=1, mapping=vertex, role=output)]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Samples are just lists of features\n", - "data.samples[\"sample1\"].features" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "6d261bde", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'sample1': {'features': [{'id': 'receptor1',\n", - " 'value': 1,\n", - " 'mapping': 'vertex',\n", - " 'role': 'input'},\n", - " {'id': 'tf1', 'value': 1, 'mapping': 'vertex', 'role': 'output'},\n", - " {'id': 'tf2', 'value': 1, 'mapping': 'vertex', 'role': 'output'},\n", - " {'id': 'tf3', 'value': 1, 'mapping': 'vertex', 'role': 'output'}]},\n", - " 'sample2': {'features': [{'id': 'receptor2',\n", - " 'value': 1,\n", - " 'mapping': 'vertex',\n", - " 'role': 'input'},\n", - " {'id': 'tf1', 'value': 1, 'mapping': 'vertex', 'role': 'output'},\n", - " {'id': 'tf3', 'value': -1, 'mapping': 'vertex', 'role': 'output'},\n", - " {'id': 'tf4', 'value': 1, 'mapping': 'vertex', 'role': 'output'}]}}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# We can add a new feature to a sample. These are stored as lists.\n", - "data.samples[\"sample1\"].features.append(cn.Feature(id=\"tf3\", value=1, mapping=\"vertex\", role=\"output\"))\n", - "data.to_dict()" - ] - }, { "cell_type": "markdown", - "id": "a9f7a259", + "id": "inspect-data", "metadata": {}, "source": [ - "## Querying features\n", - "\n", - "To inspect or obtain features from a dataset, you can use the `query` method. This method allows you to filter features based on their attributes. For example, you can find all features that are mapped to a specific vertex in the network, or all features that have a specific value." + "The sample names are preserved, and each sample contains its own feature values." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "c454e1aa", + "execution_count": null, + "id": "inspect-samples", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Sample(n_feats=3)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.samples[\"sample1\"].query.filter(lambda f: f.id.startswith(\"tf\")).collect()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f218854b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tf1', 'tf2', 'tf3'}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# We can get all the unique ids from the samples\n", - "data.samples[\"sample1\"].query.filter(lambda f: f.id.startswith(\"tf\")).pluck()" + "list(data.samples)" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "67f27401", + "execution_count": null, + "id": "inspect-features", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Feature(id=receptor1, value=1, mapping=vertex, role=input),\n", - " Feature(id=tf1, value=1, mapping=vertex, role=output),\n", - " Feature(id=tf2, value=1, mapping=vertex, role=output),\n", - " Feature(id=tf3, value=1, mapping=vertex, role=output)]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# Get features with unique ids\n", - "data.samples[\"sample1\"].query.unique().to_list()" + "data.samples[\"EGF\"].features" ] }, { "cell_type": "markdown", - "id": "8ed64112", + "id": "query-data", "metadata": {}, "source": [ - "The `unique()` method can use different keys to determine uniqueness. By default, it uses the feature's `id` field, but you can specify other fields like `mapping`, `role`, or any combination of keys." + "## Querying measurements\n", + "\n", + "Queries make it possible to select features without changing the original dataset. For example, we can retain only measurements that map to network vertices." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "84746f4f", + "execution_count": null, + "id": "query-vertices", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Feature(id=receptor1, value=1, mapping=vertex, role=input),\n", - " Feature(id=tf1, value=1, mapping=vertex, role=output)]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# Get features with unique roles\n", - "data.samples[\"sample1\"].query.unique([\"role\"]).to_list()" + "network_measurements = (\n", + " data.query.filter_features(\n", + " lambda feature: feature.mapping == \"vertex\"\n", + " ).collect()\n", + ")\n", + "network_measurements" ] }, { "cell_type": "markdown", - "id": "7fe04517", + "id": "copy-data", "metadata": {}, "source": [ - "## Working across multiple samples\n", + "## Copying and extending data\n", "\n", - "The `Data` class also provides a query interface that works across all samples. This allows you to perform operations on all features in all samples at once." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "ed2d114e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'receptor1', 'receptor2', 'tf1', 'tf2', 'tf3', 'tf4'}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get all unique feature IDs across all samples\n", - "data.query.pluck()" + "Use `copy()` when you want to modify a dataset while keeping the original unchanged. Features can then be added to an individual sample." ] }, { "cell_type": "code", - "execution_count": 12, - "id": "9e540bbc", + "execution_count": null, + "id": "copy-data-code", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tf1', 'tf2', 'tf3', 'tf4'}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get features with unique IDs across all samples\n", - "data.query.filter_features(lambda f: f.id.startswith(\"tf\")).unique().pluck()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "351775a7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Data(n_samples=2, n_feats=[3 3])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# Filter features across all samples\n", - "dataq = data.query.filter_features(lambda f: f.id.startswith(\"tf\")).collect()\n", + "extended_data = data.copy()\n", + "extended_data.samples[\"EGF\"].add(\n", + " cn.Feature(\n", + " id=\"AKT1\",\n", + " value=0.73,\n", + " mapping=\"vertex\",\n", + " assay=\"phosphoproteomics\",\n", + " )\n", + ")\n", "\n", - "dataq" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "dd79a300", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'sample1': {'features': [{'id': 'tf1',\n", - " 'value': 1,\n", - " 'mapping': 'vertex',\n", - " 'role': 'output'},\n", - " {'id': 'tf2', 'value': 1, 'mapping': 'vertex', 'role': 'output'},\n", - " {'id': 'tf3', 'value': 1, 'mapping': 'vertex', 'role': 'output'}]},\n", - " 'sample2': {'features': [{'id': 'tf1',\n", - " 'value': 1,\n", - " 'mapping': 'vertex',\n", - " 'role': 'output'},\n", - " {'id': 'tf3', 'value': -1, 'mapping': 'vertex', 'role': 'output'},\n", - " {'id': 'tf4', 'value': 1, 'mapping': 'vertex', 'role': 'output'}]}}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataq.to_dict()" + "len(data.samples[\"EGF\"].features), len(extended_data.samples[\"EGF\"].features)" ] }, { "cell_type": "markdown", - "id": "d47b7938", + "id": "save-data", "metadata": {}, "source": [ - "## Saving and loading datasets" + "## Saving and loading data\n", + "\n", + "A `Data` object can be saved and loaded without losing sample names, feature values, mappings, or additional annotations." ] }, { "cell_type": "code", - "execution_count": 20, - "id": "475e808b", + "execution_count": null, + "id": "save-data-code", "metadata": {}, "outputs": [], "source": [ - "import tempfile\n", + "from pathlib import Path\n", + "from tempfile import TemporaryDirectory\n", "\n", - "file = tempfile.NamedTemporaryFile(delete=False).name\n", - "dataq.save(file, compression=\"xz\")" + "with TemporaryDirectory() as directory:\n", + " data_path = Path(directory) / \"experiment.json.xz\"\n", + " data.save(data_path)\n", + " restored_data = cn.Data.load(data_path)\n", + "\n", + "restored_data.to_dict() == data.to_dict()" ] }, { - "cell_type": "code", - "execution_count": 22, - "id": "2386db6a", + "cell_type": "markdown", + "id": "using-data-with-methods", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'sample1': {'features': [{'id': 'tf1',\n", - " 'value': 1,\n", - " 'mapping': 'vertex',\n", - " 'role': 'output'},\n", - " {'id': 'tf2', 'value': 1, 'mapping': 'vertex', 'role': 'output'},\n", - " {'id': 'tf3', 'value': 1, 'mapping': 'vertex', 'role': 'output'}]},\n", - " 'sample2': {'features': [{'id': 'tf1',\n", - " 'value': 1,\n", - " 'mapping': 'vertex',\n", - " 'role': 'output'},\n", - " {'id': 'tf3', 'value': -1, 'mapping': 'vertex', 'role': 'output'},\n", - " {'id': 'tf4', 'value': 1, 'mapping': 'vertex', 'role': 'output'}]}}" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "dataq.load(file, compression=\"xz\").to_dict()" + "(guide-data-methods)=\n", + "## Using data with methods\n", + "\n", + "`Data` is the general representation used by CORNETO methods. If your observations are already stored as a `Data` object, pass them to the method through `build_from_data`: \n", + "\n", + "```python\n", + "problem = method.build_from_data(network, data)\n", + "```\n", + "\n", + "For common analyses, methods also provide shorter interfaces. `build` accepts the scientific inputs for one condition, while `build_many` accepts inputs for several named conditions:\n", + "\n", + "```python\n", + "problem = method.build(network, ...)\n", + "problem = method.build_many(network, ...)\n", + "```\n", + "\n", + "These convenience methods validate their inputs, create the corresponding `Data` object, and then build the optimization problem. The biological meaning and names of the inputs differ between methods, so they are explained in each method's guide." ] } ], "metadata": { "kernelspec": { - "display_name": "corneto-hVfbzgbT-py3.12", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.12" } }, "nbformat": 4, diff --git a/docs/guide/metabolism/flux-balance-analysis.ipynb b/docs/guide/metabolism/flux-balance-analysis.ipynb index 7fbf1451c..3f15dab00 100644 --- a/docs/guide/metabolism/flux-balance-analysis.ipynb +++ b/docs/guide/metabolism/flux-balance-analysis.ipynb @@ -2,9212 +2,300 @@ "cells": [ { "cell_type": "markdown", - "id": "f2d7b3c9", + "id": "fba-introduction", "metadata": {}, "source": [ - "# Flux Balance Analysis (FBA)\n", - " \n", - "Flux Balance Analysis (FBA) is a computational method used primarily in systems biology and metabolic engineering. It's a way to understand and predict the flow or \"flux\" of metabolites in a metabolic network under certain conditions.\n", + "# Flux balance analysis\n", "\n", - "In this tutorial, we will see how to create FBA problems with CORNETO for metabolic analysis, how CORNETO can interoperate with other FBA-based libraries such as COBRApy or MIOM, and how CORNETO's FBA problem can be used as a building block for more advanced FBA methods. \n", + "Flux balance analysis (FBA) predicts steady-state fluxes in a metabolic network. For a stoichiometric matrix $S$ and reaction-flux vector $v$, FBA imposes\n", "\n", - "## What is FBA?\n", + "$$\n", + "S v = 0, \\qquad v_{\\mathrm{lower}} \\leq v \\leq v_{\\mathrm{upper}},\n", + "$$\n", "\n", - "The basis of FBA lies in the principle of steady-state, where the concentration of internal metabolites doesn't change over time even though they may be consumed and produced. This means that the total rate at which a metabolite is produced equals the rate at which it's consumed. \n", + "and optimizes an objective such as biomass production. These models are commonly used to predict growth, compare media, simulate knockouts, and identify metabolic-engineering strategies.\n", "\n", - "In the realm of metabolic network analysis, this concept translates to steady-state metabolic fluxes. A flux is a rate at which a substance moves through a system, and in metabolic systems, flux represents the rate of turnover of a metabolite through a specific pathway or reaction. When considering the steady-state, each reaction's flux in the metabolic network is balanced, meaning that, for every internal metabolite, the sum of fluxes leading to its production is equal to the sum of fluxes leading to its consumption. This balance ensures that, over time, there's no accumulation or depletion of any metabolite in the system.\n", + "CORNETO brings FBA into the same optimization framework used for other network-inference problems. It interoperates with COBRApy models and supports standard FBA, while exposing the resulting optimization problem so that constraints, conditions, sparsity, and omics-derived evidence can be combined through a common interface.\n", "\n", - "For quantifying these fluxes, the typical units used are millimoles per gram dry weight per hour (mmol/gDW/h). The \"mmol\" part represents the quantity of the metabolite, the \"gDW\" (gram dry weight) normalizes this value to the biomass of the organism, and the \"/h\" provides a temporal dimension, indicating how fast the metabolic turnover occurs. This unit allows researchers and practitioners to make meaningful comparisons across different metabolic systems and conditions. For example, a flux value of 5 mmol/gDW/h for a particular reaction indicates that 5 millimoles of the relevant metabolite are produced or consumed per gram of the organism's dry weight every hour.\n", - "\n", - "
\n", - " \"Flux\n", - "
\n", - " \n", - " Figure 1: Vivek-Ananth, R. P., and Areejit Samal. \"Advances in the integration of transcriptional regulatory information into genome-scale metabolic models.\" Biosystems 147 (2016): 1-10.\n", - " \n", - "
\n", - "\n", - "\n", - "\n", - "## FBA with CORNETO\n", - "\n", - "In order to define a FBA problem, you need to know the following:\n", - "\n", - "- **Genome-scale metabolic network**: This is the prior knowledge, usually known as genome-scale metabolic models (GEMs). These models represent the metabolic network through a stoichiometric matrix, where each row represents a metabolite and each column represents a reaction. The entries in the matrix are the stoichiometric coefficients of the metabolites in the reactions. These models also contain annotations such as Gene-Protein-Rules (GPRs) and default reaction bounds.\n", - "\n", - "- **Constraints**: For each reaction, there are limits or \"constraints\" on the flux based on factors like enzyme capacities. Basically, it’s setting the maximum and minimum fluxes for the reactions.\n", - "\n", - "- **Objective function**: You then define an objective to optimize, like maximizing the production of a specific metabolite or maximizing growth rate.\n", - "\n", - "- **LP Solver**: Using linear programming techniques, you can find the flux distribution that meets the constraints and optimizes the objective function.\n", - "\n", - "\n", - "CORNETO naturally supports FBA-based problems by transforming a genome scale metabolic network into a prior knowledge graph, and then modelling the FBA problem as a network flow problem. This is the main building block for creating more complex problems, such as multicondition Sparse FBA or multicondition iMAT for context-specific reconstruction.\n" + "COBRApy remains a natural tool for curating metabolic models and running established COBRA workflows. CORNETO is useful when the formulation itself must be extended or several network-inference problems must be expressed together." ] }, { "cell_type": "markdown", - "id": "83bd70ba", + "id": "concept-map", "metadata": {}, "source": [ - "## Using COBRApy\n", + "## From COBRApy to CORNETO\n", "\n", - "Here we show how can we use COBRApy to import the `textbook` metabolic network and how can we import it in CORNETO. We will also compare the FBA solution from COBRApy and CORNETO." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "662e7b90", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set parameter Username\n", - "Set parameter LicenseID to value 2593994\n", - "Academic license - for non-commercial use only - expires 2025-12-02\n" - ] - }, - { - "data": { - "text/plain": [ - "(72, 95)" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cobra.io import load_model\n", + "| COBRApy concept | CORNETO concept |\n", + "|---|---|\n", + "| `cobra.Model` | metabolic hypergraph |\n", + "| metabolite | vertex |\n", + "| reaction and stoichiometry | directed hyperedge and its coefficients |\n", + "| reaction bounds | edge-flow bounds |\n", + "| objective coefficients | reaction objectives |\n", + "| `solution.fluxes` | `problem.expr.flow.value` |\n", + "| optimize a configured model | build and extend an optimization problem |\n", "\n", - "model = load_model(\"textbook\")\n", - "len(model.metabolites), len(model.reactions)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "86322a8f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Reaction identifierBiomass_Ecoli_core
NameBiomass Objective Function with GAM
Memory address0x165a9e840
Stoichiometry\n", - "

1.496 3pg_c + 3.7478 accoa_c + 59.81 atp_c + 0.361 e4p_c + 0.0709 f6p_c + 0.129 g3p_c + 0.205 g6p_c + 0.2557 gln__L_c + 4.9414 glu__L_c + 59.81 h2o_c + 3.547 nad_c + 13.0279 nadph_c + 1.7867 oaa_c...

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1.496 3-Phospho-D-glycerate + 3.7478 Acetyl-CoA + 59.81 ATP + 0.361 D-Erythrose 4-phosphate + 0.0709 D-Fructose 6-phosphate + 0.129 Glyceraldehyde 3-phosphate + 0.205 D-Glucose 6-phosphate + 0.2557...

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GPR
Lower bound0.0
Upper bound1000.0
\n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "biomass_rxn = model.reactions.get_by_id(\"Biomass_Ecoli_core\")\n", - "biomass_rxn" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1f1db2c3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "solution = model.optimize()\n", - "print(solution)" + "The graph representation preserves reaction identifiers, stoichiometry, default bounds, and gene–protein–reaction annotations imported from the COBRApy model." ] }, { "cell_type": "markdown", - "id": "55cdaff4-984d-4b34-abcc-3edabd54ca44", + "id": "load-cobrapy-model", "metadata": {}, "source": [ - "## Using CORNETO" + "### Solve the COBRApy model\n", + "\n", + "We start with the familiar *E. coli* core model distributed with COBRApy." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "3b2af9de", + "execution_count": null, + "id": "imports", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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Installed version:v1.0.0.dev5 (latest stable: v1.0.0-alpha)
Available backends:CVXPY v1.6.4, PICOS v2.6.0
Default backend (corneto.opt):CVXPY
Installed solvers:CLARABEL, CVXOPT, GLOP, GLPK, GLPK_MI, GUROBI, HIGHS, OSQP, PDLP, PROXQP, SCIP, SCIPY, SCS
Graphviz version:v0.20.3
Installed path:/Users/pablorodriguezmier/Documents/work/repos/pablormier/corneto/corneto
Repository:https://github.com/saezlab/corneto
\n", - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "import numpy as np\n", + "from contextlib import redirect_stdout\n", + "from io import StringIO\n", "\n", - "import corneto as cn\n", + "import numpy as np\n", + "import pandas as pd\n", + "from cobra.io import load_model\n", "\n", - "cn.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "67301de4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(72, 95)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ "from corneto.io import cobra_model_to_graph\n", + "from corneto.methods import MultiSampleFBA\n", "\n", - "G = cobra_model_to_graph(model)\n", - "G.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d2efe710", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'__edge_type': 'directed',\n", - " 'id': 'Biomass_Ecoli_core',\n", - " '__source_attr': {'3pg_c': {'__value': -1.496},\n", - " 'accoa_c': {'__value': -3.7478},\n", - " 'atp_c': {'__value': -59.81},\n", - " 'e4p_c': {'__value': -0.361},\n", - " 'f6p_c': {'__value': -0.0709},\n", - " 'g3p_c': {'__value': -0.129},\n", - " 'g6p_c': {'__value': -0.205},\n", - " 'gln__L_c': {'__value': -0.2557},\n", - " 'glu__L_c': {'__value': -4.9414},\n", - " 'h2o_c': {'__value': -59.81},\n", - " 'nad_c': {'__value': -3.547},\n", - " 'nadph_c': {'__value': -13.0279},\n", - " 'oaa_c': {'__value': -1.7867},\n", - " 'pep_c': {'__value': -0.5191},\n", - " 'pyr_c': {'__value': -2.8328},\n", - " 'r5p_c': {'__value': -0.8977}},\n", - " '__target_attr': {'adp_c': {'__value': 59.81},\n", - " 'akg_c': {'__value': 4.1182},\n", - " 'coa_c': {'__value': 3.7478},\n", - " 'h_c': {'__value': 59.81},\n", - " 'nadh_c': {'__value': 3.547},\n", - " 'nadp_c': {'__value': 13.0279},\n", - " 'pi_c': {'__value': 59.81}},\n", - " 'default_lb': 0.0,\n", - " 'default_ub': 1000.0,\n", - " 'GPR': ''}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rid = list(G.get_edges_by_attr(\"id\", biomass_rxn.id))[0]\n", - "G.get_attr_edge(rid)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8d57a2f4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "coa_c\n", - "\n", - "coa_c\n", - "\n", - "\n", - "\n", - "e_0_center\n", - "\n", - "\n", - "\n", - "\n", - "coa_c->e_0_center\n", - "\n", - "\n", - "\n", - "\n", - "e_7_center\n", - "\n", - "\n", - "\n", - "\n", - "coa_c->e_7_center\n", - "\n", - "\n", - "\n", - "\n", - "e_70_center\n", - "\n", - "\n", - "\n", - "\n", - "coa_c->e_70_center\n", - "\n", - "\n", - "\n", - "\n", - "e_72_center\n", - "\n", - "\n", - "\n", - "\n", - "coa_c->e_72_center\n", - "\n", - "\n", - "\n", - "\n", - "e_89_center\n", - "\n", - "\n", - "\n", - "\n", - "coa_c->e_89_center\n", - "\n", - "\n", - 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"\n", - "\n", - "\n", - "\n", - "e_90_center->f6p_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_91_center->nad_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_91_center->h_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_91_center->nadph_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_92_center->g3p_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_92_center->s7p_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_93_center->g3p_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_93_center->f6p_c\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G.plot(layout=\"fdp\")" + "with redirect_stdout(StringIO()):\n", + " model = load_model(\"textbook\")\n", + "biomass_id = \"Biomass_Ecoli_core\"\n", + "cobra_solution = model.optimize()\n", + "\n", + "pd.Series(\n", + " {\n", + " \"metabolites\": len(model.metabolites),\n", + " \"reactions\": len(model.reactions),\n", + " \"biomass flux\": cobra_solution.fluxes[biomass_id],\n", + " },\n", + " name=\"COBRApy model\",\n", + ")" ] }, { - "cell_type": "code", - "execution_count": 8, - "id": "1b29ccc3", + "cell_type": "markdown", + "id": "convert-and-solve", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'_flow': _flow: Variable((95,), _flow),\n", - " 'edge_has_flux': edge_has_flux: Variable((95,), edge_has_flux, boolean=True),\n", - " 'flow': _flow: Variable((95,), _flow)}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "from corneto.methods import MultiSampleFBA as FBA\n", + "### Convert the model and solve with CORNETO\n", "\n", - "d = cn.Data.from_cdict({\"fba_example\": {\"Biomass_Ecoli_core\": {\"role\": \"objective\"}}})\n", + "`cobra_model_to_graph` converts each reaction into a stoichiometric hyperedge. `MultiSampleFBA.build` then constructs the mass-balance constraints, applies the imported bounds, and adds the requested reaction objective.\n", "\n", - "m = FBA()\n", - "P = m.build(G, d)\n", - "P.expr" + "`MultiSampleFBA` minimizes a weighted objective. Therefore, the coefficient `-1` below maximizes biomass. CORNETO's general optimization API also supports an explicit `Direction.MAX` when constructing a problem directly, but `direction` is not an argument of `MultiSampleFBA.build`." ] }, { "cell_type": "code", - "execution_count": 9, - "id": "fa9bda70-6811-4fb8-86c6-18d60a7a0e2e", + "execution_count": null, + "id": "build-corneto-fba", "metadata": {}, "outputs": [], "source": [ - "P.solve(solver=\"scipy\");" + "G = cobra_model_to_graph(model)\n", + "reaction_ids = list(G.get_attr_from_edges(\"id\"))\n", + "reaction_index = {reaction_id: i for i, reaction_id in enumerate(reaction_ids)}\n", + "biomass_idx = reaction_index[biomass_id]\n", + "\n", + "problem = MultiSampleFBA().build(\n", + " G,\n", + " objectives={biomass_id: -1},\n", + ")\n", + "problem.solve(solver=\"scipy\")\n", + "\n", + "corneto_biomass = float(problem.expr.flow[biomass_idx].value)\n", + "comparison = pd.Series(\n", + " {\n", + " \"COBRApy\": cobra_solution.fluxes[biomass_id],\n", + " \"CORNETO\": corneto_biomass,\n", + " },\n", + " name=\"optimal biomass flux\",\n", + ")\n", + "\n", + "assert np.isclose(comparison[\"COBRApy\"], comparison[\"CORNETO\"], atol=1e-7)\n", + "comparison" ] }, { - "cell_type": "code", - "execution_count": 10, - "id": "9782359c-e4e5-49a7-8474-a8904abb11f9", + "cell_type": "markdown", + "id": "inspect-results", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8739215069684303" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "opt_flux = P.expr.flow[rid].value\n", - "opt_flux" + "Fluxes follow the graph's edge order. Mapping that order back to reaction identifiers gives a labeled result analogous to `solution.fluxes`." ] }, { "cell_type": "code", - "execution_count": 11, - "id": "54dfa3d3-6e47-4430-8d78-c41f6841f0e6", + "execution_count": null, + "id": "flux-table", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "48" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "sum(np.abs(P.expr.flow.value) >= 1e-6)" + "fluxes = pd.Series(\n", + " np.asarray(problem.expr.flow.value),\n", + " index=reaction_ids,\n", + " name=\"flux\",\n", + ")\n", + "fluxes.loc[[biomass_id, \"EX_glc__D_e\", \"EX_o2_e\", \"ATPM\"]]" ] }, { - "cell_type": "code", - "execution_count": 12, - "id": "d088eaaa-9e73-4447-ab83-c0f644bbd997", + "cell_type": "markdown", + "id": "compose-constraints", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " fluxes\n", - "ACALD -0.000000\n", - "ACALDt 0.000000\n", - "ACKr 0.000000\n", - "ACONTa 6.007250\n", - "ACONTb 6.007250\n", - "... ...\n", - "TALA 1.496984\n", - "THD2 0.000000\n", - "TKT1 1.496984\n", - "TKT2 1.181498\n", - "TPI 7.477382\n", - "\n", - "[95 rows x 1 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "import pandas as pd\n", + "## Extend the optimization problem\n", "\n", - "pd.DataFrame(P.expr.flow.value, index=G.get_attr_from_edges(\"id\"), columns=[\"fluxes\"])" + "A built CORNETO problem is still editable. For example, the following constraint limits oxygen uptake to 10 mmol gDW$^{-1}$ h$^{-1}$. In this model, exchange uptake is a negative flux, so a less-negative lower limit permits less uptake." ] }, { "cell_type": "code", - "execution_count": 13, - "id": "e0771fcb", + "execution_count": null, + "id": "oxygen-constraint", "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "coa_c\n", - "\n", - "coa_c\n", - "\n", - "\n", - "\n", - "e_0_center\n", - "\n", - "\n", - "\n", - "\n", - "coa_c->e_0_center\n", - "\n", - "\n", - "\n", - "\n", - "e_7_center\n", - "\n", - "\n", - "\n", - "\n", - "coa_c->e_7_center\n", - "\n", - "\n", - "\n", - "\n", - "e_70_center\n", - "\n", - "\n", - "\n", - "\n", - "coa_c->e_70_center\n", - "\n", - "\n", - "\n", - "\n", - "e_72_center\n", - "\n", - "\n", - "\n", - "\n", - "coa_c->e_72_center\n", - "\n", - "\n", - "\n", - "\n", - "e_89_center\n", - "\n", - "\n", - "\n", - "\n", - "coa_c->e_89_center\n", - "\n", - "\n", - "\n", - "\n", - "acald_c\n", - "\n", - "acald_c\n", - 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"id": "custom-media-01", + "id": "multiple-media", "metadata": {}, "source": [ - "## Passing a custom media composition\n", + "## Solve multiple media conditions\n", "\n", - "Media composition is represented as sample-specific reaction bounds. In CORNETO, each sample passed to `MultiSampleFBA` can include exchange reactions with `lower_bound` and `upper_bound` values. For exchange reactions in this model, uptake is a negative flux, so a more negative lower bound allows more uptake.\n", - "\n", - "The same data object can also include the biomass objective, which lets us solve several media conditions in one FBA problem." + "`build_many` creates one flux vector per named condition. Objectives and reaction bounds use an outer mapping with the same condition names. Conditions are solved in one problem, which also makes it possible for advanced formulations to couple them." ] }, { "cell_type": "code", "execution_count": null, - "id": "custom-media-02", + "id": "build-many", "metadata": {}, "outputs": [], "source": [ - "media = {\n", + "condition_objectives = {\n", + " \"glucose_rich\": {biomass_id: -1},\n", + " \"glucose_limited\": {biomass_id: -1},\n", + "}\n", + "condition_bounds = {\n", " \"glucose_rich\": {\n", - " \"EX_glc__D_e\": {\"lower_bound\": -10.0, \"upper_bound\": 1000.0},\n", - " \"EX_o2_e\": {\"lower_bound\": -20.0, \"upper_bound\": 1000.0},\n", + " \"EX_glc__D_e\": (-10.0, 1000.0),\n", + " \"EX_o2_e\": (-20.0, 1000.0),\n", " },\n", " \"glucose_limited\": {\n", - " \"EX_glc__D_e\": {\"lower_bound\": -2.0, \"upper_bound\": 1000.0},\n", - " \"EX_o2_e\": {\"lower_bound\": -20.0, \"upper_bound\": 1000.0},\n", + " \"EX_glc__D_e\": (-2.0, 1000.0),\n", + " \"EX_o2_e\": (-20.0, 1000.0),\n", " },\n", "}\n", "\n", - "media_data = cn.Data.from_cdict(\n", + "media_problem = MultiSampleFBA().build_many(\n", + " G,\n", + " objectives=condition_objectives,\n", + " reaction_bounds=condition_bounds,\n", + ")\n", + "media_problem.solve(solver=\"scipy\")\n", + "\n", + "condition_names = list(condition_objectives)\n", + "reported_reactions = [biomass_id, \"EX_glc__D_e\", \"EX_o2_e\"]\n", + "media_fluxes = pd.DataFrame(\n", " {\n", " condition: {\n", - " \"Biomass_Ecoli_core\": {\"role\": \"objective\"},\n", - " **bounds,\n", + " reaction_id: float(media_problem.expr.flow[reaction_index[reaction_id], column].value)\n", + " for reaction_id in reported_reactions\n", " }\n", - " for condition, bounds in media.items()\n", + " for column, condition in enumerate(condition_names)\n", " }\n", ")\n", "\n", - "media_data" + "assert media_problem.expr.flow.value.shape == (G.num_edges, len(condition_names))\n", + "assert media_fluxes.loc[biomass_id, \"glucose_limited\"] < media_fluxes.loc[biomass_id, \"glucose_rich\"]\n", + "media_fluxes" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "custom-media-03", + "cell_type": "markdown", + "id": "sparse-fba", "metadata": {}, - "outputs": [], "source": [ - "media_problem = FBA().build(G, media_data)\n", - "media_problem.solve(solver=\"scipy\");" + "## Sparse FBA\n", + "\n", + "CORNETO can associate each reaction with a binary indicator that records whether its flux is nonzero. With `lambda_reg > 0`, it penalizes the number of active reactions. Because of these indicators, sparse FBA is a mixed-integer linear program (MILP), rather than the linear program used by the standard mathematical FBA formulation.\n", + "\n", + "Here we require at least 90% of the optimal biomass and optimize biomass together with a penalty on the number of active reactions. `lambda_reg` controls that trade-off; larger values give reaction count more influence. The biomass floor prevents sparsity from being achieved by suppressing growth." ] }, { "cell_type": "code", "execution_count": null, - "id": "custom-media-04", + "id": "solve-sparse-fba", "metadata": {}, "outputs": [], "source": [ - "reaction_ids = [\"Biomass_Ecoli_core\", \"EX_glc__D_e\", \"EX_o2_e\"]\n", - "reaction_index = {rid: next(iter(G.get_edges_by_attr(\"id\", rid))) for rid in reaction_ids}\n", + "biomass_floor = 0.90 * corneto_biomass\n", + "sparse_problem = MultiSampleFBA(lambda_reg=0.1).build(\n", + " G,\n", + " objectives={biomass_id: -1},\n", + " reaction_bounds={biomass_id: (biomass_floor, None)},\n", + ")\n", + "sparse_problem.solve(solver=\"highs\")\n", + "\n", + "sparse_biomass = float(sparse_problem.expr.flow[biomass_idx].value)\n", + "standard_active = int(np.count_nonzero(np.abs(problem.expr.flow.value) > 1e-6))\n", + "sparse_active = int(np.count_nonzero(np.abs(sparse_problem.expr.flow.value) > 1e-6))\n", + "\n", + "assert sparse_biomass >= biomass_floor - 1e-7\n", + "assert sparse_active <= standard_active\n", "\n", - "pd.DataFrame(\n", + "pd.Series(\n", " {\n", - " condition: {rid: media_problem.expr.flow[reaction_index[rid], i].value for rid in reaction_ids}\n", - " for i, condition in enumerate(media_data.samples)\n", + " \"biomass floor\": biomass_floor,\n", + " \"sparse biomass\": sparse_biomass,\n", + " \"active reactions in standard solution\": standard_active,\n", + " \"active reactions in sparse solution\": sparse_active,\n", " }\n", ")" ] }, { "cell_type": "markdown", - "id": "custom-media-05", + "id": "pfba-and-next-steps", "metadata": {}, "source": [ - "The custom bounds override the default exchange bounds stored in the graph for the matching sample. Here, glucose-limited medium restricts `EX_glc__D_e` uptake and the optimal biomass flux decreases accordingly." - ] - }, - { - "cell_type": "markdown", - "id": "3f320215-867e-4882-bd9d-5dc94f629054", - "metadata": {}, - "source": [ - "## Sparse FBA" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "71523d1d-8742-493d-a07e-e56bb3821c40", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "===============================================================================\n", - " CVXPY \n", - " v1.6.4 \n", - "===============================================================================\n", - "(CVXPY) Apr 14 05:10:44 PM: Your problem has 190 variables, 453 constraints, and 1 parameters.\n", - "(CVXPY) Apr 14 05:10:44 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Apr 14 05:10:44 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Apr 14 05:10:44 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "-------------------------------------------------------------------------------\n", - " Compilation \n", - "-------------------------------------------------------------------------------\n", - "(CVXPY) Apr 14 05:10:44 PM: Compiling problem (target solver=HIGHS).\n", - "(CVXPY) Apr 14 05:10:44 PM: Reduction chain: Dcp2Cone -> CvxAttr2Constr -> ConeMatrixStuffing -> HIGHS\n", - "(CVXPY) Apr 14 05:10:44 PM: Applying reduction Dcp2Cone\n", - "(CVXPY) Apr 14 05:10:44 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Apr 14 05:10:44 PM: Applying reduction ConeMatrixStuffing\n", - "(CVXPY) Apr 14 05:10:44 PM: Applying reduction HIGHS\n", - "(CVXPY) Apr 14 05:10:44 PM: Finished problem compilation (took 5.011e-03 seconds).\n", - "(CVXPY) Apr 14 05:10:44 PM: (Subsequent compilations of this problem, using the same arguments, should take less time.)\n", - "-------------------------------------------------------------------------------\n", - " Numerical solver \n", - "-------------------------------------------------------------------------------\n", - "(CVXPY) Apr 14 05:10:44 PM: Invoking solver HIGHS to obtain a solution.\n", - "Running HiGHS 1.10.0 (git hash: fd86653): Copyright (c) 2025 HiGHS under MIT licence terms\n", - "MIP has 453 rows; 190 cols; 883 nonzeros; 95 integer variables (95 binary)\n", - "Coefficient ranges:\n", - " Matrix [7e-02, 1e+03]\n", - " Cost [1e-01, 1e+00]\n", - " Bound [1e+00, 1e+00]\n", - " RHS [8e-01, 1e+03]\n", - "Presolving model\n", - "153 rows, 144 cols, 493 nonzeros 0s\n", - "123 rows, 123 cols, 415 nonzeros 0s\n", - "120 rows, 119 cols, 408 nonzeros 0s\n", - "\n", - "Solving MIP model with:\n", - " 120 rows\n", - " 119 cols (78 binary, 0 integer, 0 implied int., 41 continuous)\n", - " 408 nonzeros\n", - "\n", - "Src: B => Branching; C => Central rounding; F => Feasibility pump; H => Heuristic; L => Sub-MIP;\n", - " P => Empty MIP; R => Randomized rounding; S => Solve LP; T => Evaluate node; U => Unbounded;\n", - " z => Trivial zero; l => Trivial lower; u => Trivial upper; p => Trivial point; X => User solution\n", - "\n", - " Nodes | B&B Tree | Objective Bounds | Dynamic Constraints | Work \n", - "Src Proc. InQueue | Leaves Expl. | BestBound BestSol Gap | Cuts InLp Confl. | LpIters Time\n", - "\n", - " 0 0 0 0.00% -64.93809816 inf inf 0 0 0 0 0.0s\n", - " S 0 0 0 0.00% -64.93809816 3.685702492 1861.89% 0 0 0 0 0.0s\n", - " 0 0 0 0.00% 0.5684850774 3.685702492 84.58% 0 0 0 55 0.0s\n", - "\n", - "3.8% inactive integer columns, restarting\n", - "Model after restart has 114 rows, 116 cols (75 bin., 0 int., 0 impl., 41 cont.), and 396 nonzeros\n", - "\n", - " 0 0 0 0.00% 2.003873385 3.685702492 45.63% 40 0 0 697 0.0s\n", - " S 0 0 0 0.00% 2.003873385 3.685702492 45.63% 40 0 0 697 0.0s\n", - " R 0 0 0 0.00% 2.003921104 3.685702492 45.63% 40 39 0 748 0.1s\n", - "\n", - "Symmetry detection completed in 0.0s\n", - "Found 20 full orbitope(s) acting on 46 columns\n", - "\n", - " 64 0 32 100.00% 3.685702492 3.685702492 0.00% 2216 83 456 8185 0.3s\n", - "\n", - "Solving report\n", - " Status Optimal\n", - " Primal bound 3.68570249247\n", - " Dual bound 3.68570249247\n", - " Gap 0% (tolerance: 0.01%)\n", - " P-D integral 0.0711361691274\n", - " Solution status feasible\n", - " 3.68570249247 (objective)\n", - " 0 (bound viol.)\n", - " 0 (int. viol.)\n", - " 0 (row viol.)\n", - " Timing 0.27 (total)\n", - " 0.00 (presolve)\n", - " 0.00 (solve)\n", - " 0.00 (postsolve)\n", - " Max sub-MIP depth 3\n", - " Nodes 64\n", - " Repair LPs 0 (0 feasible; 0 iterations)\n", - " LP iterations 8185 (total)\n", - " 3720 (strong br.)\n", - " 791 (separation)\n", - " 2539 (heuristics)\n", - "-------------------------------------------------------------------------------\n", - " Summary \n", - "-------------------------------------------------------------------------------\n", - "(CVXPY) Apr 14 05:10:44 PM: Problem status: optimal\n", - "(CVXPY) Apr 14 05:10:44 PM: Optimal value: 3.686e+00\n", - "(CVXPY) Apr 14 05:10:44 PM: Compilation took 5.011e-03 seconds\n", - "(CVXPY) Apr 14 05:10:44 PM: Solver (including time spent in interface) took 2.758e-01 seconds\n" - ] - } - ], - "source": [ - "m = FBA(lambda_reg=0.1)\n", - 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"\n", - "\n", - "\n", - "\n", - "e_90_center->f6p_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_91_center->nad_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_91_center->h_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_91_center->nadph_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_92_center->g3p_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_92_center->s7p_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_93_center->g3p_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_93_center->f6p_c\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G.plot(custom_edge_attr=cn.pl.flow_style(P), layout=\"fdp\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "9df7ab19-7e71-4a64-a26e-154f74a0bd5a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "45" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sum(P.expr.edge_has_flux.value > 0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "155b23d2-8b66-40cf-84aa-d40c402d5012", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[objective_Biomass_Ecoli_core: Expression(AFFINE, UNKNOWN, ()),\n", - " regularization_edge_has_flux: Expression(AFFINE, NONNEGATIVE, ())]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "P.objectives" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "c6a38e48-3fae-4b00-9b60-66bde46d01d0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "45.0" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "P.objectives[1].value" + "This reaction-count criterion is not the same as parsimonious FBA (pFBA), which conventionally minimizes total flux after fixing the primary objective. Sparse FBA instead penalizes how many reactions are active.\n", + "\n", + "For several conditions, CORNETO can apply structured sparsity to the union of active reactions. A reaction shared by several conditions is then counted once, encouraging compact shared metabolic programs while retaining a separate flux vector for every condition. Continue with [Multi-condition FBA](multicondition-sfba.ipynb) for the general formulation and a worked example, followed by [gene expression integration](imat.ipynb) for context-specific metabolic inference.\n", + "\n", + "The examples above use the method-specific `build` and `build_many` interfaces. The general {ref}`Data interface ` remains available for workflows that require custom feature metadata or already represent measurements as CORNETO data objects." ] } ], diff --git a/docs/guide/metabolism/imat.ipynb b/docs/guide/metabolism/imat.ipynb new file mode 100644 index 000000000..29afe39dd --- /dev/null +++ b/docs/guide/metabolism/imat.ipynb @@ -0,0 +1,663 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "introduction", + "metadata": {}, + "source": [ + "# Integrating gene expression into metabolic models\n", + "\n", + "The preceding [FBA](flux-balance-analysis.ipynb) and [Multi-condition FBA](multicondition-sfba.ipynb) guides inferred feasible metabolic states from stoichiometry, reaction bounds, and required phenotypes. Gene expression provides another source of information: it can help choose which of the many feasible pathways best represents a particular biological context.\n", + "\n", + "This guide introduces gene-expression integration for one condition using **iMAT**, the method currently implemented in CORNETO. We will extract an expression-consistent metabolic state from the same COBRApy *E. coli* core model used in the FBA guides." + ] + }, + { + "cell_type": "markdown", + "id": "what-imat-adds", + "metadata": {}, + "source": [ + "## One established approach: iMAT\n", + "\n", + "[iMAT was introduced by Shlomi and colleagues](https://www.nature.com/articles/nbt.1487) to infer context-specific metabolism from expression evidence. It retains the FBA feasible space:\n", + "\n", + "$$\n", + "Sv=0, \\qquad \\ell \\leq v \\leq u.\n", + "$$\n", + "\n", + "It then uses qualitative expression evidence to select a flux state:\n", + "\n", + "- reactions supported by **high** expression are encouraged to carry flux;\n", + "- reactions associated with **low** expression are encouraged to remain inactive;\n", + "- reactions with intermediate or missing evidence are decided by network feasibility and the other objectives.\n", + "\n", + "The optimization minimizes disagreement with this evidence. It does not convert transcript abundance into a flux value, and expression cannot override mass balance, reaction bounds, or a required phenotype. Because reaction activity is represented with indicators, iMAT is a mixed-integer optimization problem.\n", + "\n", + "CORNETO provides this formulation through `MultiSampleIMAT`, but the framework is not restricted to the original iMAT objective. The built problem exposes its constraints, activity variables, and objective terms, making it straightforward to add biological constraints, compose other penalties, or implement methodological variations." + ] + }, + { + "cell_type": "markdown", + "id": "from-genes-to-reactions", + "metadata": {}, + "source": [ + "## From genes to reaction evidence\n", + "\n", + "Metabolic models associate genes with reactions through gene–protein–reaction (GPR) rules. CORNETO applies these rules before building iMAT: an `and` relationship uses the least-supported required subunit, while an `or` relationship uses the best-supported alternative enzyme.\n", + "\n", + "| Normalized expression | iMAT interpretation |\n", + "|---:|---|\n", + "| At or above the high threshold | Encourage the mapped reaction to be active |\n", + "| At or below the low threshold | Encourage the mapped reaction to be inactive |\n", + "| Between thresholds or missing | Do not score the reaction |\n", + "\n", + "`eps` defines the minimum absolute flux used to call a supported reaction active. The optional `lambda_reg` adds a small reaction-count penalty, favoring a compact network when several solutions fit the expression evidence equally well." + ] + }, + { + "cell_type": "markdown", + "id": "cobrapy-relationship", + "metadata": {}, + "source": [ + "## Relationship to COBRApy\n", + "\n", + "COBRApy remains the model-management layer in this workflow: it loads the metabolic model, exposes reactions and GPR rules, and supports standard FBA and pFBA analyses. CORNETO converts that model into its graph representation and composes the FBA constraints, expression-fit terms, reaction-activity indicators, and optional regularization in one optimization problem." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "load-model", + "metadata": {}, + "outputs": [], + "source": [ + "from contextlib import redirect_stdout\n", + "from io import StringIO\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from cobra.flux_analysis import pfba\n", + "from cobra.io import load_model\n", + "\n", + "from corneto.io import cobra_model_to_graph\n", + "from corneto.methods import MultiSampleIMAT\n", + "from corneto.methods.metabolism import evaluate_gpr_expression\n", + "\n", + "with redirect_stdout(StringIO()):\n", + " model = load_model(\"textbook\")\n", + "\n", + "model.solver = \"glpk\"\n", + "G = cobra_model_to_graph(model)\n", + "\n", + "reaction_ids = list(G.get_attr_from_edges(\"id\"))\n", + "reaction_index = {reaction_id: i for i, reaction_id in enumerate(reaction_ids)}\n", + "biomass_id = \"Biomass_Ecoli_core\"\n", + "\n", + "pd.Series(\n", + " {\n", + " \"metabolites\": len(model.metabolites),\n", + " \"reactions\": len(model.reactions),\n", + " \"genes\": len(model.genes),\n", + " \"reactions with GPR rules\": sum(bool(reaction.gene_reaction_rule) for reaction in model.reactions),\n", + " },\n", + " name=\"E. coli core model\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "example-question", + "metadata": {}, + "source": [ + "## Example: selecting an anaerobic fermentation program\n", + "\n", + "We now address a small biological question:\n", + "\n", + "> At a viable anaerobic growth rate, does the expression evidence support ethanol fermentation rather than alternative lactate or acetate routes?\n", + "\n", + "Glucose uptake is limited to 10 units and oxygen uptake is blocked. We require a biomass flux of at least `0.1`, but we do not maximize growth inside iMAT: the phenotype defines what the cell must accomplish, and expression evidence helps select how it accomplishes it.\n", + "\n", + "The expression values below are deliberately simple, synthetic normalized log-expression values. They resemble the positive scale commonly obtained after transforming expression measurements, but they do not represent a particular experiment." + ] + }, + { + "cell_type": "markdown", + "id": "growth-feasibility", + "metadata": {}, + "source": [ + "### Check that the required phenotype is feasible\n", + "\n", + "Before integrating expression, standard FBA establishes the maximum anaerobic growth supported by the medium." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "maximum-growth", + "metadata": {}, + "outputs": [], + "source": [ + "anaerobic_model = model.copy()\n", + "anaerobic_model.reactions.EX_glc__D_e.lower_bound = -10.0\n", + "anaerobic_model.reactions.EX_o2_e.bounds = (0.0, 1000.0)\n", + "\n", + "maximum_growth = anaerobic_model.optimize().fluxes[biomass_id]\n", + "minimum_growth = 0.1\n", + "\n", + "assert maximum_growth > minimum_growth\n", + "\n", + "pd.Series(\n", + " {\n", + " \"maximum anaerobic biomass\": maximum_growth,\n", + " \"minimum biomass required for inference\": minimum_growth,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "expression-profile", + "metadata": {}, + "source": [ + "### Define a qualitative expression profile\n", + "\n", + "Genes linked to ethanol production have expression values around `11–12`. Genes linked to competing lactate and acetate production have values around `4–5`. Values at or above `10` are classified as high, values at or below `6` as low, and values between the thresholds would remain unclassified." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "gene-expression", + "metadata": {}, + "outputs": [], + "source": [ + "high_expression_threshold = 10.0\n", + "low_expression_threshold = 6.0\n", + "\n", + "gene_expression = {\n", + " # High expression: acetaldehyde and alcohol dehydrogenases\n", + " \"b0351\": 12.1,\n", + " \"b1241\": 11.6,\n", + " \"b0356\": 10.9,\n", + " \"b1478\": 11.4,\n", + " # Low expression: lactate dehydrogenase\n", + " \"b1380\": 4.2,\n", + " \"b2133\": 5.1,\n", + " # Low expression: phosphotransacetylase and acetate kinase\n", + " \"b2297\": 4.8,\n", + " \"b2458\": 5.3,\n", + " \"b1849\": 3.9,\n", + " \"b2296\": 4.6,\n", + " \"b3115\": 5.0,\n", + "}\n", + "\n", + "expression_modules = pd.DataFrame(\n", + " [\n", + " {\n", + " \"module\": \"ethanol formation\",\n", + " \"reactions\": \"ACALD, ALCD2x\",\n", + " \"genes\": \"b0351, b1241, b0356, b1478\",\n", + " \"expression range\": \"10.9-12.1\",\n", + " \"class\": \"high\",\n", + " },\n", + " {\n", + " \"module\": \"lactate formation\",\n", + " \"reactions\": \"LDH_D\",\n", + " \"genes\": \"b1380, b2133\",\n", + " \"expression range\": \"4.2-5.1\",\n", + " \"class\": \"low\",\n", + " },\n", + " {\n", + " \"module\": \"acetate formation\",\n", + " \"reactions\": \"PTAr, ACKr\",\n", + " \"genes\": \"b2297, b2458, b1849, b2296, b3115\",\n", + " \"expression range\": \"3.9-5.3\",\n", + " \"class\": \"low\",\n", + " },\n", + " ]\n", + ")\n", + "\n", + "expression_modules" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "gpr-rules", + "metadata": {}, + "outputs": [], + "source": [ + "scored_reactions = [\"ACALD\", \"ALCD2x\", \"LDH_D\", \"PTAr\", \"ACKr\"]\n", + "gpr_table = pd.DataFrame(\n", + " {\n", + " \"reaction\": scored_reactions,\n", + " \"GPR rule\": [\n", + " model.reactions.get_by_id(reaction_id).gene_reaction_rule\n", + " for reaction_id in scored_reactions\n", + " ],\n", + " }\n", + ")\n", + "\n", + "gpr_table" + ] + }, + { + "cell_type": "markdown", + "id": "pfba-reference", + "metadata": {}, + "source": [ + "### An expression-free reference\n", + "\n", + "For comparison, COBRApy pFBA fixes biomass at the required value and minimizes total flux without using expression. This provides one compact feasible state, not a context-specific expression fit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "solve-pfba", + "metadata": {}, + "outputs": [], + "source": [ + "reference_model = model.copy()\n", + "reference_model.reactions.EX_glc__D_e.lower_bound = -10.0\n", + "reference_model.reactions.EX_o2_e.bounds = (0.0, 1000.0)\n", + "reference_model.reactions.get_by_id(biomass_id).bounds = (minimum_growth, minimum_growth)\n", + "\n", + "pfba_solution = pfba(reference_model)\n", + "\n", + "assert np.isclose(pfba_solution.fluxes[biomass_id], minimum_growth)\n", + "pfba_solution.fluxes[[biomass_id, \"EX_glc__D_e\", \"EX_lac__D_e\", \"EX_etoh_e\", \"EX_ac_e\"]].to_frame(\n", + " \"pFBA flux\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "build-imat", + "metadata": {}, + "source": [ + "### Build and solve iMAT\n", + "\n", + "`MultiSampleIMAT.build` accepts gene scores, reaction bounds, and optional reaction objectives directly. Here the biomass lower bound represents viability, so no biomass objective is needed.\n", + "\n", + "The small `lambda_reg` value breaks ties in favor of a compact active network. Expression disagreement remains the primary biological criterion in this example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "solve-imat", + "metadata": {}, + "outputs": [], + "source": [ + "eps = 1e-3\n", + "imat = MultiSampleIMAT(\n", + " eps=eps,\n", + " lambda_reg=0.01,\n", + " high_expression_threshold=high_expression_threshold,\n", + " low_expression_threshold=low_expression_threshold,\n", + ")\n", + "\n", + "imat_problem = imat.build(\n", + " G,\n", + " gene_expression=gene_expression,\n", + " reaction_bounds={\n", + " \"EX_glc__D_e\": (-10.0, 1000.0),\n", + " \"EX_o2_e\": (0.0, 1000.0),\n", + " biomass_id: (minimum_growth, None),\n", + " },\n", + ")\n", + "imat_problem.solve(solver=\"highs\")\n", + "\n", + "imat_fluxes = pd.Series(\n", + " np.asarray(imat_problem.expr.flow.value),\n", + " index=reaction_ids,\n", + " name=\"iMAT flux\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "reaction-results", + "metadata": {}, + "source": [ + "### Did the inferred state agree with expression?\n", + "\n", + "For iMAT, the main interpretation is whether a scored reaction is active, not whether its flux magnitude matches its transcript abundance. Flux signs follow the direction in which each reaction is written in the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "expression-fit", + "metadata": {}, + "outputs": [], + "source": [ + "reaction_evidence = pd.Series(\n", + " {\n", + " \"ACALD\": \"high\",\n", + " \"ALCD2x\": \"high\",\n", + " \"LDH_D\": \"low\",\n", + " \"PTAr\": \"low\",\n", + " \"ACKr\": \"low\",\n", + " },\n", + " name=\"expression evidence\",\n", + ")\n", + "\n", + "expression_fit = pd.DataFrame(\n", + " {\n", + " \"expression evidence\": reaction_evidence,\n", + " \"flux\": imat_fluxes[reaction_evidence.index],\n", + " \"active\": imat_fluxes[reaction_evidence.index].abs() >= eps * (1 - eps),\n", + " }\n", + ")\n", + "\n", + "assert expression_fit.loc[[\"ACALD\", \"ALCD2x\"], \"active\"].all()\n", + "assert not expression_fit.loc[[\"LDH_D\", \"PTAr\", \"ACKr\"], \"active\"].any()\n", + "assert imat_fluxes[biomass_id] >= minimum_growth - 1e-7\n", + "\n", + "expression_fit" + ] + }, + { + "cell_type": "markdown", + "id": "product-comparison", + "metadata": {}, + "source": [ + "### Compare the selected fermentation routes\n", + "\n", + "At the same biomass value, pFBA and iMAT answer different questions. pFBA minimizes total flux and uses both lactate and ethanol routes. iMAT instead selects a state consistent with the supplied high-ethanol and low-lactate/acetate evidence.\n", + "\n", + "The bars show representative flux solutions. Their magnitudes should not be interpreted as predictions from transcript abundance; the iMAT evidence distinguishes active from inactive reactions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "compare-products", + "metadata": {}, + "outputs": [], + "source": [ + "fermentation_products = {\n", + " \"lactate\": \"EX_lac__D_e\",\n", + " \"ethanol\": \"EX_etoh_e\",\n", + " \"acetate\": \"EX_ac_e\",\n", + "}\n", + "\n", + "product_fluxes = pd.DataFrame(\n", + " {\n", + " \"pFBA (no expression)\": {\n", + " product: pfba_solution.fluxes[reaction_id]\n", + " for product, reaction_id in fermentation_products.items()\n", + " },\n", + " \"iMAT (expression integrated)\": {\n", + " product: imat_fluxes[reaction_id]\n", + " for product, reaction_id in fermentation_products.items()\n", + " },\n", + " }\n", + ")\n", + "\n", + "assert product_fluxes.loc[\"ethanol\", \"iMAT (expression integrated)\"] > eps\n", + "assert np.isclose(product_fluxes.loc[\"lactate\", \"iMAT (expression integrated)\"], 0.0, atol=1e-7)\n", + "assert product_fluxes.loc[\"lactate\", \"pFBA (no expression)\"] > eps\n", + "\n", + "product_fluxes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "plot-products", + "metadata": {}, + "outputs": [], + "source": [ + "ax = product_fluxes.plot.bar(\n", + " figsize=(7, 3.5),\n", + " color=[\"#9aa0a6\", \"#2a9d8f\"],\n", + " width=0.75,\n", + ")\n", + "ax.set_ylabel(\"exchange flux\")\n", + "ax.set_xlabel(\"\")\n", + "ax.set_title(\"Fermentation products at the same biomass requirement\")\n", + "ax.tick_params(axis=\"x\", rotation=0)\n", + "ax.spines[[\"top\", \"right\"]].set_visible(False)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "network-plot-intro", + "metadata": {}, + "source": [ + "### Visualize the contextualized metabolic network\n", + "\n", + "The graph below contains the active iMAT network plus scored reactions that were kept inactive. High-expression active reactions are green, low-expression inactive reactions are dashed red, and active reactions without expression evidence are gray.\n", + "\n", + "| Meaning | Color and style |\n", + "|---|---|\n", + "| Active and supported by high expression | Green |\n", + "| Inactive and associated with low expression | Dashed red |\n", + "| Active without supplied expression evidence | Gray |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "plot-network", + "metadata": {}, + "outputs": [], + "source": [ + "active_reactions = imat_fluxes.abs() >= eps * (1 - eps)\n", + "scored_reaction_set = set(reaction_evidence.index)\n", + "displayed_reactions = np.array(\n", + " [\n", + " active_reactions.iloc[i] or reaction_id in scored_reaction_set\n", + " for i, reaction_id in enumerate(reaction_ids)\n", + " ]\n", + ")\n", + "\n", + "selected_indices = np.flatnonzero(displayed_reactions)\n", + "context_network = G.edge_subgraph(selected_indices)\n", + "edge_style = {}\n", + "\n", + "for displayed_index, original_index in enumerate(selected_indices):\n", + " reaction_id = reaction_ids[original_index]\n", + " evidence = reaction_evidence.get(reaction_id)\n", + "\n", + " if evidence == \"high\":\n", + " edge_style[displayed_index] = {\"color\": \"#2a9d8f\", \"penwidth\": \"4\"}\n", + " elif evidence == \"low\":\n", + " edge_style[displayed_index] = {\n", + " \"color\": \"#e76f51\",\n", + " \"penwidth\": \"3\",\n", + " \"style\": \"dashed\",\n", + " }\n", + " else:\n", + " edge_style[displayed_index] = {\"color\": \"#b0b0b0\", \"penwidth\": \"1.5\"}\n", + "\n", + "context_network.plot(\n", + " graph_attr={\"rankdir\": \"LR\"},\n", + " node_attr={\n", + " \"fixedsize\": \"false\",\n", + " \"shape\": \"box\",\n", + " \"style\": \"rounded\",\n", + " \"margin\": \"0.05,0.03\",\n", + " },\n", + " custom_edge_attr=edge_style,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "advanced-reaction-scores", + "metadata": {}, + "source": [ + "## Advanced: compute and supply reaction scores directly\n", + "\n", + "Passing `gene_expression=` is convenient because `MultiSampleIMAT` performs thresholding and GPR mapping automatically. For method development or custom preprocessing, it can be useful to inspect that transformation and pass the final reaction-level evidence through `reaction_scores=` instead.\n", + "\n", + "At this level, signed values no longer represent expression abundance. They encode the iMAT classification:\n", + "\n", + "| Reaction score | Meaning in the optimization |\n", + "|---:|---|\n", + "| `+1` | High-expression evidence: favor an active reaction |\n", + "| `−1` | Low-expression evidence: favor an inactive reaction |\n", + "| `0` or omitted | No expression-fit term for the reaction |\n", + "\n", + "First, threshold the positive expression measurements into categorical gene evidence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "classify-gene-expression", + "metadata": {}, + "outputs": [], + "source": [ + "gene_scores = {}\n", + "\n", + "for gene_id, expression in gene_expression.items():\n", + " if expression >= high_expression_threshold:\n", + " gene_scores[gene_id] = 1.0\n", + " elif expression <= low_expression_threshold:\n", + " gene_scores[gene_id] = -1.0\n", + " else:\n", + " gene_scores[gene_id] = 0.0\n", + "\n", + "pd.DataFrame(\n", + " {\n", + " \"expression\": pd.Series(gene_expression),\n", + " \"gene score\": pd.Series(gene_scores),\n", + " }\n", + ").sort_values(\"expression\", ascending=False)" + ] + }, + { + "cell_type": "markdown", + "id": "apply-gpr-rules", + "metadata": {}, + "source": [ + "Next, CORNETO's `evaluate_gpr_expression` applies every model GPR rule to the gene scores in one call. Reactions with a final score of zero are omitted, so they remain governed by stoichiometry, bounds, and regularization rather than expression-fit terms. For several samples, the related `evaluate_gpr_rules` function evaluates the same rule list against multiple gene-score mappings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "compute-reaction-scores", + "metadata": {}, + "outputs": [], + "source": [ + "gpr_rules = list(G.get_attr_from_edges(\"GPR\"))\n", + "reaction_score_values = evaluate_gpr_expression(gpr_rules, gene_scores)\n", + "\n", + "reaction_scores = {\n", + " reaction_id: float(score)\n", + " for reaction_id, score in zip(reaction_ids, reaction_score_values)\n", + " if not np.isclose(score, 0.0)\n", + "}\n", + "\n", + "reaction_score_table = pd.DataFrame(\n", + " {\n", + " \"GPR rule\": {\n", + " reaction_id: gpr_rules[reaction_index[reaction_id]]\n", + " for reaction_id in reaction_scores\n", + " },\n", + " \"reaction score\": reaction_scores,\n", + " }\n", + ")\n", + "\n", + "reaction_score_table" + ] + }, + { + "cell_type": "markdown", + "id": "solve-from-reaction-scores", + "metadata": {}, + "source": [ + "The reaction-score interface is closer to the optimization problem and is useful when reaction evidence comes from another mapping method or has already been curated. Threshold arguments are unnecessary because the scores already encode high and low reaction evidence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "reaction-score-problem", + "metadata": {}, + "outputs": [], + "source": [ + "reaction_score_problem = MultiSampleIMAT(\n", + " eps=eps,\n", + " lambda_reg=0.01,\n", + ").build(\n", + " G,\n", + " reaction_scores=reaction_scores,\n", + " reaction_bounds={\n", + " \"EX_glc__D_e\": (-10.0, 1000.0),\n", + " \"EX_o2_e\": (0.0, 1000.0),\n", + " biomass_id: (minimum_growth, None),\n", + " },\n", + ")\n", + "reaction_score_problem.solve(solver=\"highs\")\n", + "\n", + "reaction_score_fluxes = pd.Series(\n", + " np.asarray(reaction_score_problem.expr.flow.value),\n", + " index=reaction_ids,\n", + ")\n", + "\n", + "interface_comparison = pd.DataFrame(\n", + " {\n", + " \"reaction score\": pd.Series(reaction_scores),\n", + " \"active from gene_expression\": (\n", + " imat_fluxes[list(reaction_scores)].abs() >= eps * (1 - eps)\n", + " ),\n", + " \"active from reaction_scores\": (\n", + " reaction_score_fluxes[list(reaction_scores)].abs() >= eps * (1 - eps)\n", + " ),\n", + " }\n", + ")\n", + "\n", + "assert (\n", + " interface_comparison[\"active from gene_expression\"]\n", + " == interface_comparison[\"active from reaction_scores\"]\n", + ").all()\n", + "\n", + "interface_comparison" + ] + }, + { + "cell_type": "markdown", + "id": "interpretation-limitations", + "metadata": {}, + "source": [ + "## Interpretation and limitations\n", + "\n", + "The inferred state is stoichiometrically feasible, satisfies the anaerobic medium and growth requirement, activates the expression-supported ethanol route, and avoids the low-expression lactate and acetate routes. This illustrates the role of iMAT: expression resolves part of the ambiguity left by FBA by ranking feasible reaction-activity patterns.\n", + "\n", + "The result remains a modeling hypothesis:\n", + "\n", + "- expression is a cue for likely activity, not an activity or flux measurement; protein abundance, post-translational modification, and metabolite-level regulation can decouple expression from flux;\n", + "- thresholds determine which genes contribute evidence and should be checked for sensitivity;\n", + "- iMAT infers reaction activity, not flux magnitude from expression;\n", + "- low-expression reactions may remain active when network constraints or the required phenotype need them;\n", + "- alternative expression-consistent optima may still exist.\n", + "\n", + "The [original iMAT paper](https://www.nature.com/articles/nbt.1487) reports a central role for post-transcriptional regulation and uses disagreements between expression and predicted activity to generate hypotheses about regulation beyond transcript abundance. They do not identify a mechanism on their own: additional protein, enzyme-activity, metabolite, or flux measurements are needed to distinguish biological regulation from data and model assumptions.\n", + "\n", + "Real analyses should inspect threshold choices, biological constraints, expression coverage, and alternative solutions. Continue with [multi-condition gene expression integration](multicondition-imat.ipynb) to learn how CORNETO couples expression-informed metabolic inference across several contexts." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/guide/metabolism/index.md b/docs/guide/metabolism/index.md index f4a2bd44d..dba7ae4da 100644 --- a/docs/guide/metabolism/index.md +++ b/docs/guide/metabolism/index.md @@ -1,11 +1,20 @@ # Metabolism -CORNETO supports flux balance analysis workflows while adding modeling flexibility to define custom constraints, objectives, and coupled formulations in a single optimization problem. For users familiar with COBRA-style modeling, this enables extension from standard FBA to explicit use cases such as multi-condition FBA and integration of omics data into metabolic networks to detect stoichiometrically consistent steady-state metabolic states. +CORNETO brings constraint-based metabolic modeling into the same optimization +framework used for other network-inference problems. It interoperates with +COBRApy models and supports standard flux balance analysis while exposing the +formulation for custom constraints, coupled conditions, shared sparsity, and +omics integration. + +Start with standard FBA and model interoperability, continue with +multi-condition FBA and shared reaction selection, and then integrate +expression evidence first in one condition and later across several conditions. ```{toctree} :maxdepth: 3 flux-balance-analysis.ipynb multicondition-sfba.ipynb +imat.ipynb multicondition-imat.ipynb ``` diff --git a/docs/guide/metabolism/multicondition-imat.ipynb b/docs/guide/metabolism/multicondition-imat.ipynb index 706a3214b..ae412eabe 100644 --- a/docs/guide/metabolism/multicondition-imat.ipynb +++ b/docs/guide/metabolism/multicondition-imat.ipynb @@ -2,1586 +2,617 @@ "cells": [ { "cell_type": "markdown", - "id": "5138cc3a", + "id": "introduction", "metadata": {}, "source": [ - "# Multi-condition iMAT\n", + "# Multi-condition gene expression integration\n", "\n", - "This tutorial shows how to use the multi-condition iMAT method.\n", + "The [single-condition expression guide](imat.ipynb) showed how iMAT converts expression evidence into reaction-level preferences, while the [Multi-condition FBA guide](multicondition-sfba.ipynb) explained why reaction selection should be performed jointly when shared and context-specific metabolism are the biological quantities of interest.\n", "\n", - "We use a branched network with weak and conflicting evidence across two\n", - "conditions to illustrate how structured regularization can align solutions\n", - "across samples.\n" + "This guide combines those ideas without repeating their derivations. Each condition receives its own reaction scores, medium bounds, phenotype constraints, and flux vector. CORNETO then fits the condition-specific expression evidence while selecting one compact reaction union across the complete experiment." + ] + }, + { + "cell_type": "markdown", + "id": "what-changes", + "metadata": {}, + "source": [ + "## What changes in the multi-condition formulation?\n", + "\n", + "`MultiSampleIMAT.build_many` accepts mappings whose first level contains condition names:\n", + "\n", + "| Input | Condition-specific information |\n", + "|---|---|\n", + "| `reaction_scores` | Reactions favored active (`+1`), favored inactive (`−1`), or unscored |\n", + "| `reaction_bounds` | Medium, perturbations, and required phenotypes |\n", + "| `objectives` | Optional reaction objectives |\n", + "\n", + "Every condition keeps a separate flux vector and a separate expression-fit objective. With `lambda_reg > 0`, a reaction active in one or several conditions contributes once to the union penalty. Reuse is therefore encouraged, but conflicting or condition-specific expression evidence can still select different pathways.\n", + "\n", + "This is the same union-level coupling introduced in Multi-condition FBA, now applied while every condition also fits its own expression evidence. The joint model does not average expression profiles and does not force equal fluxes. Reaction-level agreement must therefore be inspected alongside the inferred network: an unmatched score can reveal tension between expression, network feasibility, phenotype constraints, and shared regularization." ] }, { "cell_type": "code", - "execution_count": 1, - "id": "e7dd7a9f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-02-15T11:15:35.458243Z", - "iopub.status.busy": "2026-02-15T11:15:35.458079Z", - "iopub.status.idle": "2026-02-15T11:15:36.890934Z", - "shell.execute_reply": "2026-02-15T11:15:36.890647Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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Installed version:v1.0.0b3.post1.dev0+048af53c
Available backends:CVXPY v1.8.1, PICOS v2.6.2
Default backend (corneto.opt):CVXPY
Installed solvers:CVXOPT, GLPK, GLPK_MI, HIGHS, SCIP, SCIPY
Plot backend (default):auto -> graphviz
Available plot backends:graphviz v0.20.3; networkx v3.6+mpl v3.10.8; graphviz-wasm
Installed path:/Users/pablorodriguezmier/Documents/work/projects/corneto/corneto
Repository:https://github.com/saezlab/corneto
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edge_indexreaction_id
00EX_A
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" - ], - "text/plain": [ - " edge_index reaction_id\n", - "0 0 EX_A\n", - "1 1 R_AB\n", - "2 2 R_BC\n", - "3 3 R_AD\n", - "4 4 R_DC\n", - "5 5 R_CE\n", - "6 6 R_EF\n", - "7 7 BIOMASS\n", - "8 8 R_BX\n", - "9 9 R_X_sink\n", - "10 10 R_DY\n", - "11 11 R_Y_sink" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "markdown", + "id": "biological-question", + "metadata": {}, "source": [ - "G = cn.Graph()\n", + "## Example: aerobic respiration and anaerobic fermentation\n", "\n", - "# Uptake\n", - "G.add_edge((), \"A\", id=\"EX_A\", default_lb=0, default_ub=10)\n", + "We ask:\n", "\n", - "# Branch 1\n", - "G.add_edge(\"A\", \"B\", id=\"R_AB\", default_lb=0, default_ub=100)\n", - "G.add_edge(\"B\", \"C\", id=\"R_BC\", default_lb=0, default_ub=100)\n", + "> Which reactions form a shared expression-consistent program for growth on glucose, and which distinguish aerobic respiration from anaerobic ethanol fermentation?\n", "\n", - "# Branch 2\n", - "G.add_edge(\"A\", \"D\", id=\"R_AD\", default_lb=0, default_ub=100)\n", - "G.add_edge(\"D\", \"C\", id=\"R_DC\", default_lb=0, default_ub=100)\n", + "The example uses two synthetic expression contexts in the *E. coli* core model. Both grow on glucose. Oxygen is available only in the aerobic condition, and biomass is fixed at approximately 60% of the maximum supported by each environment. The fixed phenotypes keep the independent and joint comparisons biologically equivalent.\n", "\n", - "# Shared pathway to objective\n", - "G.add_edge(\"C\", \"E\", id=\"R_CE\", default_lb=0, default_ub=100)\n", - "G.add_edge(\"E\", \"F\", id=\"R_EF\", default_lb=0, default_ub=100)\n", - "G.add_edge(\"F\", (), id=\"BIOMASS\", default_lb=0, default_ub=100)\n", + "We start from reaction scores, the advanced interface introduced at the end of the single-condition guide. In a real workflow these scores could be produced from expression measurements and GPR rules with `evaluate_gpr_expression` or supplied by another curated mapping procedure." + ] + }, + { + "cell_type": "markdown", + "id": "define-evidence", + "metadata": {}, + "source": [ + "### Define condition-specific reaction evidence\n", "\n", - "# Side drains\n", - "G.add_edge(\"B\", \"X\", id=\"R_BX\", default_lb=0, default_ub=100)\n", - "G.add_edge(\"X\", (), id=\"R_X_sink\", default_lb=0, default_ub=100)\n", - "G.add_edge(\"D\", \"Y\", id=\"R_DY\", default_lb=0, default_ub=100)\n", - "G.add_edge(\"Y\", (), id=\"R_Y_sink\", default_lb=0, default_ub=100)\n", + "The aerobic profile supports respiration and pyruvate oxidation while arguing against fermentation. The anaerobic profile supports ethanol formation while arguing against oxygen-dependent respiration and lactate production.\n", "\n", - "pd.DataFrame(\n", - " {\n", - " \"edge_index\": range(G.num_edges),\n", - " \"reaction_id\": [G.get_attr_edge(i).get(\"id\") for i in range(G.num_edges)],\n", - " }\n", - ")" + "To make model–data disagreement visible, the synthetic anaerobic profile deliberately classifies `PFL` as low even though pyruvate-formate lyase provides the route from pyruvate to acetyl-CoA and formate in the selected fermentative state. This controlled conflict lets us distinguish a reaction that is fitted from one that remains active despite low-expression evidence." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "78790088", - "metadata": { - "execution": { - "iopub.execute_input": "2026-02-15T11:15:36.899476Z", - "iopub.status.busy": "2026-02-15T11:15:36.899391Z", - "iopub.status.idle": "2026-02-15T11:15:36.913618Z", - "shell.execute_reply": "2026-02-15T11:15:36.913277Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "digraph {\n", - "\tnode [fixedsize=true]\n", - "\te_0_source [shape=point]\n", - "\tA [shape=circle]\n", - "\tA [shape=circle]\n", - "\tB [shape=circle]\n", - "\tB [shape=circle]\n", - "\tC [shape=circle]\n", - "\tA [shape=circle]\n", - "\tD [shape=circle]\n", - "\tD [shape=circle]\n", - "\tC [shape=circle]\n", - "\tC [shape=circle]\n", - "\tE [shape=circle]\n", - "\tE [shape=circle]\n", - "\tF [shape=circle]\n", - "\te_7_target [shape=point]\n", - "\tF [shape=circle]\n", - "\tB [shape=circle]\n", - "\tX [shape=circle]\n", - "\te_9_target [shape=point]\n", - "\tX [shape=circle]\n", - "\tD [shape=circle]\n", - "\tY [shape=circle]\n", - "\te_11_target [shape=point]\n", - "\tY [shape=circle]\n", - "\te_0_source -> A [arrowhead=normal]\n", - "\tA -> B [arrowhead=normal]\n", - "\tB -> C [arrowhead=normal]\n", - "\tA -> D [arrowhead=normal]\n", - "\tD -> C [arrowhead=normal]\n", - "\tC -> E [arrowhead=normal]\n", - "\tE -> F [arrowhead=normal]\n", - "\tF -> e_7_target [arrowhead=normal]\n", - "\tB -> X [arrowhead=normal]\n", - "\tX -> e_9_target [arrowhead=normal]\n", - "\tD -> Y [arrowhead=normal]\n", - "\tY -> e_11_target [arrowhead=normal]\n", - "}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "reaction-scores", + "metadata": {}, + "outputs": [], + "source": [ + "condition_names = [\"glucose aerobic\", \"glucose anaerobic\"]\n", + "\n", + "reaction_scores = {\n", + " \"glucose aerobic\": {\n", + " \"CYTBD\": 1.0,\n", + " \"NADH16\": 1.0,\n", + " \"PDH\": 1.0,\n", + " \"CS\": 1.0,\n", + " \"ATPS4r\": 1.0,\n", + " \"PFL\": -1.0,\n", + " \"LDH_D\": -1.0,\n", + " \"ACALD\": -1.0,\n", + " \"ALCD2x\": -1.0,\n", + " },\n", + " \"glucose anaerobic\": {\n", + " \"PFL\": -1.0,\n", + " \"ACALD\": 1.0,\n", + " \"ALCD2x\": 1.0,\n", + " \"CYTBD\": -1.0,\n", + " \"NADH16\": -1.0,\n", + " \"PDH\": -1.0,\n", + " \"LDH_D\": -1.0,\n", + " },\n", + "}\n", + "\n", + "score_table = pd.DataFrame(reaction_scores).fillna(0.0)\n", + "score_table.index.name = \"reaction\"\n", + "score_table" + ] + }, + { + "cell_type": "markdown", + "id": "define-phenotypes", + "metadata": {}, "source": [ - "G.plot()" + "### Define the media and required phenotypes\n", + "\n", + "A negative exchange lower bound allows uptake. Biomass is fixed rather than maximized because expression agreement and shared reaction selection are the objectives of this analysis." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "582902c0", - "metadata": { - "execution": { - "iopub.execute_input": "2026-02-15T11:15:36.915144Z", - "iopub.status.busy": "2026-02-15T11:15:36.915031Z", - "iopub.status.idle": "2026-02-15T11:15:36.917329Z", - "shell.execute_reply": "2026-02-15T11:15:36.917081Z" - } - }, + "execution_count": null, + "id": "reaction-bounds", + "metadata": {}, "outputs": [], "source": [ - "w = 0.2\n", - "\n", - "scores_condition_1 = {\n", - " \"R_AB\": +w,\n", - " \"R_BC\": +w,\n", - " \"R_AD\": -w,\n", - " \"R_DC\": -w,\n", - " \"R_BX\": -0.1,\n", - " \"R_DY\": -0.1,\n", + "biomass_targets = {\n", + " \"glucose aerobic\": 0.50,\n", + " \"glucose anaerobic\": 0.12,\n", "}\n", "\n", - "scores_condition_2 = {\n", - " \"R_AB\": -w,\n", - " \"R_BC\": -w,\n", - " \"R_AD\": +w,\n", - " \"R_DC\": +w,\n", - " \"R_BX\": -0.1,\n", - " \"R_DY\": -0.1,\n", + "reaction_bounds = {\n", + " \"glucose aerobic\": {\n", + " \"EX_glc__D_e\": (-10.0, 1000.0),\n", + " \"EX_o2_e\": (-20.0, 1000.0),\n", + " biomass_id: (biomass_targets[\"glucose aerobic\"], biomass_targets[\"glucose aerobic\"]),\n", + " },\n", + " \"glucose anaerobic\": {\n", + " \"EX_glc__D_e\": (-10.0, 1000.0),\n", + " \"EX_o2_e\": (0.0, 1000.0),\n", + " biomass_id: (biomass_targets[\"glucose anaerobic\"], biomass_targets[\"glucose anaerobic\"]),\n", + " },\n", "}\n", "\n", + "pd.DataFrame(\n", + " {\n", + " \"glucose lower bound\": {condition: bounds[\"EX_glc__D_e\"][0] for condition, bounds in reaction_bounds.items()},\n", + " \"oxygen lower bound\": {condition: bounds[\"EX_o2_e\"][0] for condition, bounds in reaction_bounds.items()},\n", + " \"fixed biomass\": biomass_targets,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "scale-condition-scores", + "metadata": {}, + "source": [ + "## Balance expression evidence across conditions\n", + "\n", + "Before coupling conditions, compare their total evidence weight. Without scaling, a condition with more scored reactions or larger score magnitudes contributes more to the joint objective. The shared sparsity penalty can then overwhelm a weakly weighted condition, especially when zero flux is feasible.\n", + "\n", + "`MultiSampleIMAT(scale=True)` applies L1 normalization independently within each condition:\n", + "\n", + "$$\n", + "\\widetilde{w}_{r,c} = 100\\,\\frac{w_{r,c}}{\\sum_j |w_{j,c}|}.\n", + "$$\n", + "\n", + "The signs and relative weights within a condition are preserved, while the absolute weights sum to `100`. Each condition therefore has the same maximum expression-mismatch budget.\n", + "\n", + "The value `100` is not mathematically special: normalizing to `1` and dividing `lambda_reg` by `100` would define the same trade-off. A budget of `100` usually keeps individual objective coefficients closer to order one when tens or hundreds of reactions are scored, avoiding unnecessarily tiny coefficients in the MILP.\n", + "\n", + "Scaling and phenotype constraints solve different problems:\n", + "\n", + "- **scaling** balances the influence of expression evidence across conditions;\n", + "- **phenotype constraints** prevent biologically required conditions from collapsing to a zero or otherwise trivial flux state.\n", "\n", - "def sample_from_scores(scores):\n", - " sample = {\"BIOMASS\": {\"role\": \"objective\"}}\n", - " for rid, value in scores.items():\n", - " sample[rid] = {\"mapping\": \"edge\", \"value\": float(value)}\n", - " return sample\n", + "Use scaling when score ranges or expression coverage differ for technical reasons. Leave scores unscaled when their absolute magnitudes are already comparable and intentionally encode confidence across conditions.\n", "\n", + "After scaling, compare the objective scales explicitly. For $R$ model reactions, the expression-mismatch term is bounded by `100` per condition, while the union penalty is bounded by $\\lambda R$. If $\\lambda R$ approaches `100`, sparsity can compete with an entire condition's evidence budget. Collapse can occur earlier because an all-zero condition mismatches only its positive evidence; low-expression evidence already favors zero flux.\n", "\n", - "multi_data = cn.Data.from_cdict(\n", + "Objective scaling does not correct loose big-M constraints. iMAT activity indicators depend on reaction flux bounds, so finite, biologically meaningful bounds are also important for MILP numerical stability and performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "score-weight-comparison", + "metadata": {}, + "outputs": [], + "source": [ + "eps = 1e-3\n", + "lambda_reg = 0.01\n", + "activity_tolerance = eps * (1 - eps)\n", + "\n", + "score_weight_comparison = pd.DataFrame(\n", " {\n", - " \"condition_1\": sample_from_scores(scores_condition_1),\n", - " \"condition_2\": sample_from_scores(scores_condition_2),\n", + " \"unscaled absolute score sum\": score_table.abs().sum(),\n", + " \"absolute score sum with scale=True\": 100.0,\n", " }\n", ")\n", + "score_weight_comparison" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "objective-scale", + "metadata": {}, + "outputs": [], + "source": [ + "pd.Series(\n", + " {\n", + " \"expression-mismatch budget per condition\": 100.0,\n", + " \"regularization cost per union reaction\": lambda_reg,\n", + " \"maximum union penalty in this model\": lambda_reg * len(reaction_ids),\n", + " },\n", + " name=\"objective scale\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "independent-reference", + "metadata": {}, + "source": [ + "## Independent iMAT as a reference\n", "\n", - "single_data_c1 = cn.Data.from_cdict({\"condition_1\": sample_from_scores(scores_condition_1)})\n", - "single_data_c2 = cn.Data.from_cdict({\"condition_2\": sample_from_scores(scores_condition_2)})" + "We first solve each condition separately with the same small reaction-count penalty used below. These runs fit each expression profile independently and provide the post-hoc union against which the joint analysis will be compared." ] }, { "cell_type": "code", - "execution_count": 5, - "id": "40470e3b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-02-15T11:15:36.918357Z", - "iopub.status.busy": "2026-02-15T11:15:36.918264Z", - "iopub.status.idle": "2026-02-15T11:15:36.984038Z", - "shell.execute_reply": "2026-02-15T11:15:36.983730Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Problem(Minimize(Expression(AFFINE, UNKNOWN, ())), [Inequality(Constant(CONSTANT, ZERO, (12,))), Inequality(Variable((12,), _flow)), Equality(Expression(AFFINE, UNKNOWN, (8,)), Constant(CONSTANT, ZERO, ())), Inequality(Expression(AFFINE, ZERO, (12,))), Inequality(Variable((12,), _flow)), Inequality(Expression(AFFINE, NONNEGATIVE, (6,))), Equality(Expression(AFFINE, NONNEGATIVE, (6,)), Constant(CONSTANT, ZERO, ())), Inequality(Expression(AFFINE, NONNEGATIVE, (6,))), Inequality(Expression(AFFINE, UNKNOWN, (6,))), Inequality(Expression(AFFINE, NONNEGATIVE, (6,)))])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "solve-independent", + "metadata": {}, + "outputs": [], "source": [ - "imat_multi_uncoupled = MultiSampleIMAT(lambda_reg=0.0, eps=1e-3)\n", - "P_multi_uncoupled = imat_multi_uncoupled.build(G, multi_data)\n", - "P_multi_uncoupled.solve(solver=\"highs\")\n", + "independent_fluxes = {}\n", "\n", - "imat_multi_coupled = MultiSampleIMAT(lambda_reg=0.5, eps=1e-3)\n", - "P_multi_coupled = imat_multi_coupled.build(G, multi_data)\n", - "P_multi_coupled.solve(solver=\"highs\")\n", + "for condition in condition_names:\n", + " problem = MultiSampleIMAT(eps=eps, lambda_reg=lambda_reg, scale=True).build(\n", + " G,\n", + " reaction_scores=reaction_scores[condition],\n", + " reaction_bounds=reaction_bounds[condition],\n", + " )\n", + " problem.solve(solver=\"highs\")\n", + " independent_fluxes[condition] = np.asarray(problem.expr.flow.value)\n", "\n", - "imat_single_c1 = MultiSampleIMAT(lambda_reg=0.5, eps=1e-3)\n", - "P_single_c1 = imat_single_c1.build(G, single_data_c1)\n", - "P_single_c1.solve(solver=\"highs\")\n", + "independent_fluxes = pd.DataFrame(independent_fluxes, index=reaction_ids)\n", + "independent_activity = independent_fluxes.abs() > activity_tolerance\n", "\n", - "imat_single_c2 = MultiSampleIMAT(lambda_reg=0.5, eps=1e-3)\n", - "P_single_c2 = imat_single_c2.build(G, single_data_c2)\n", - "P_single_c2.solve(solver=\"highs\")" + "pd.DataFrame(\n", + " {\n", + " \"biomass\": independent_fluxes.loc[biomass_id],\n", + " \"active reactions\": independent_activity.sum(),\n", + " }\n", + ")" ] }, { "cell_type": "markdown", - "id": "103264dc", + "id": "joint-analysis", "metadata": {}, "source": [ - "## Objective values\n", + "## Solve both expression contexts jointly\n", "\n", - "`P.objectives` provides the solved objective terms and values directly.\n" + "The scientific inputs have the same readable structure as the independent calls. `build_many` creates one flux column per named condition, and `lambda_reg` now penalizes their reaction union." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "ad7bdf16", - "metadata": { - "execution": { - "iopub.execute_input": "2026-02-15T11:15:36.985288Z", - "iopub.status.busy": "2026-02-15T11:15:36.985211Z", - "iopub.status.idle": "2026-02-15T11:15:36.991998Z", - "shell.execute_reply": "2026-02-15T11:15:36.991688Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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settingterm_indexterm_namevalueweightweighted_value
0multi lambda=00objective_condition_1__BIOMASS-10.01.0-10.0
1multi lambda=01objective_condition_2__BIOMASS-10.01.0-10.0
2multi lambda=02imat_fit_pos_condition_1_00.01.00.0
3multi lambda=03imat_fit_neg_condition_1_00.01.00.0
4multi lambda=04imat_fit_pos_condition_2_10.01.00.0
5multi lambda=05imat_fit_neg_condition_2_10.01.00.0
6multi lambda=06regularization_edge_has_flux_OR10.00.00.0
7multi lambda=0.50objective_condition_1__BIOMASS-10.01.0-10.0
8multi lambda=0.51objective_condition_2__BIOMASS-10.01.0-10.0
9multi lambda=0.52imat_fit_pos_condition_1_00.01.00.0
10multi lambda=0.53imat_fit_neg_condition_1_00.01.00.0
11multi lambda=0.54imat_fit_pos_condition_2_10.41.00.4
12multi lambda=0.55imat_fit_neg_condition_2_10.41.00.4
13multi lambda=0.56regularization_edge_has_flux_OR6.00.53.0
14single c1 lambda=0.50objective_condition_1__BIOMASS-10.01.0-10.0
15single c1 lambda=0.51imat_fit_pos_condition_1_00.01.00.0
16single c1 lambda=0.52imat_fit_neg_condition_1_00.01.00.0
17single c1 lambda=0.53regularization_edge_has_flux6.00.53.0
18single c2 lambda=0.50objective_condition_2__BIOMASS-10.01.0-10.0
19single c2 lambda=0.51imat_fit_pos_condition_2_00.01.00.0
20single c2 lambda=0.52imat_fit_neg_condition_2_00.01.00.0
21single c2 lambda=0.53regularization_edge_has_flux6.00.53.0
\n", - "
" - ], - "text/plain": [ - " setting term_index term_name value \\\n", - "0 multi lambda=0 0 objective_condition_1__BIOMASS -10.0 \n", - "1 multi lambda=0 1 objective_condition_2__BIOMASS -10.0 \n", - "2 multi lambda=0 2 imat_fit_pos_condition_1_0 0.0 \n", - "3 multi lambda=0 3 imat_fit_neg_condition_1_0 0.0 \n", - "4 multi lambda=0 4 imat_fit_pos_condition_2_1 0.0 \n", - "5 multi lambda=0 5 imat_fit_neg_condition_2_1 0.0 \n", - "6 multi lambda=0 6 regularization_edge_has_flux_OR 10.0 \n", - "7 multi lambda=0.5 0 objective_condition_1__BIOMASS -10.0 \n", - "8 multi lambda=0.5 1 objective_condition_2__BIOMASS -10.0 \n", - "9 multi lambda=0.5 2 imat_fit_pos_condition_1_0 0.0 \n", - "10 multi lambda=0.5 3 imat_fit_neg_condition_1_0 0.0 \n", - "11 multi lambda=0.5 4 imat_fit_pos_condition_2_1 0.4 \n", - "12 multi lambda=0.5 5 imat_fit_neg_condition_2_1 0.4 \n", - "13 multi lambda=0.5 6 regularization_edge_has_flux_OR 6.0 \n", - "14 single c1 lambda=0.5 0 objective_condition_1__BIOMASS -10.0 \n", - "15 single c1 lambda=0.5 1 imat_fit_pos_condition_1_0 0.0 \n", - "16 single c1 lambda=0.5 2 imat_fit_neg_condition_1_0 0.0 \n", - "17 single c1 lambda=0.5 3 regularization_edge_has_flux 6.0 \n", - "18 single c2 lambda=0.5 0 objective_condition_2__BIOMASS -10.0 \n", - "19 single c2 lambda=0.5 1 imat_fit_pos_condition_2_0 0.0 \n", - "20 single c2 lambda=0.5 2 imat_fit_neg_condition_2_0 0.0 \n", - "21 single c2 lambda=0.5 3 regularization_edge_has_flux 6.0 \n", - "\n", - " weight weighted_value \n", - "0 1.0 -10.0 \n", - "1 1.0 -10.0 \n", - "2 1.0 0.0 \n", - "3 1.0 0.0 \n", - "4 1.0 0.0 \n", - "5 1.0 0.0 \n", - "6 0.0 0.0 \n", - "7 1.0 -10.0 \n", - "8 1.0 -10.0 \n", - "9 1.0 0.0 \n", - "10 1.0 0.0 \n", - "11 1.0 0.4 \n", - "12 1.0 0.4 \n", - "13 0.5 3.0 \n", - "14 1.0 -10.0 \n", - "15 1.0 0.0 \n", - "16 1.0 0.0 \n", - "17 0.5 3.0 \n", - "18 1.0 -10.0 \n", - "19 1.0 0.0 \n", - "20 1.0 0.0 \n", - "21 0.5 3.0 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "solve-joint", + "metadata": {}, + "outputs": [], "source": [ - "def objectives_table(problem, setting):\n", - " rows = []\n", - " for i, (obj, weight) in enumerate(zip(problem.objectives, problem.weights)):\n", - " name = obj.name if obj.name else f\"unnamed_{i}\"\n", - " w = float(weight.value) if hasattr(weight, \"value\") else float(weight)\n", - " rows.append(\n", - " {\n", - " \"setting\": setting,\n", - " \"term_index\": i,\n", - " \"term_name\": name,\n", - " \"value\": float(obj.value),\n", - " \"weight\": w,\n", - " \"weighted_value\": float(obj.value) * w,\n", - " }\n", - " )\n", - " return pd.DataFrame(rows)\n", - "\n", + "joint_problem = MultiSampleIMAT(\n", + " eps=eps,\n", + " lambda_reg=lambda_reg,\n", + " scale=True,\n", + ").build_many(\n", + " G,\n", + " reaction_scores=reaction_scores,\n", + " reaction_bounds=reaction_bounds,\n", + ")\n", + "joint_problem.solve(solver=\"highs\")\n", "\n", - "obj_table = pd.concat(\n", - " [\n", - " objectives_table(P_multi_uncoupled, \"multi lambda=0\"),\n", - " objectives_table(P_multi_coupled, \"multi lambda=0.5\"),\n", - " objectives_table(P_single_c1, \"single c1 lambda=0.5\"),\n", - " objectives_table(P_single_c2, \"single c2 lambda=0.5\"),\n", - " ],\n", - " ignore_index=True,\n", + "joint_fluxes = pd.DataFrame(\n", + " np.asarray(joint_problem.expr.flow.value),\n", + " index=reaction_ids,\n", + " columns=condition_names,\n", ")\n", + "joint_activity = joint_fluxes.abs() > activity_tolerance\n", "\n", - "obj_table" + "assert joint_problem.expr.flow.value.shape == (G.num_edges, len(condition_names))" + ] + }, + { + "cell_type": "markdown", + "id": "fit-expression", + "metadata": {}, + "source": [ + "### Inspect reaction-level expression agreement\n", + "\n", + "Each condition must reach its fixed biomass. A positive reaction score is fitted when the reaction is active; a negative score is fitted when it is inactive. In the [original iMAT formulation](https://www.nature.com/articles/nbt.1487), these agreements contribute to a discrete consistency score. They are better described as **fits and mismatches** than as continuous residuals." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "8816ca3b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-02-15T11:15:36.993145Z", - "iopub.status.busy": "2026-02-15T11:15:36.993067Z", - "iopub.status.idle": "2026-02-15T11:15:36.996793Z", - "shell.execute_reply": "2026-02-15T11:15:36.996529Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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settingcondition_1_penaltycondition_2_penalty
0multi lambda=00.00.0
1multi lambda=0.50.00.8
2single lambda=0.50.00.0
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" - ], - "text/plain": [ - " setting condition_1_penalty condition_2_penalty\n", - "0 multi lambda=0 0.0 0.0\n", - "1 multi lambda=0.5 0.0 0.8\n", - "2 single lambda=0.5 0.0 0.0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "fit-summary", + "metadata": {}, + "outputs": [], "source": [ - "def fit_penalty_for_sample(problem, sample_name):\n", - " sample_name = str(sample_name).replace(\" \", \"_\")\n", - " sample_marker = f\"_{sample_name}_\"\n", - " return float(\n", - " sum(\n", - " float(o.value)\n", - " for o in problem.objectives\n", - " if o.name and o.name.startswith(\"imat_fit_\") and sample_marker in o.name\n", - " )\n", + "fit_rows = []\n", + "fit_details = []\n", + "\n", + "for condition in condition_names:\n", + " scores = pd.Series(reaction_scores[condition])\n", + " joint_scored_activity = joint_activity.loc[scores.index, condition]\n", + " independent_scored_activity = independent_activity.loc[scores.index, condition]\n", + " joint_fitted = ((scores > 0) & joint_scored_activity) | ((scores < 0) & ~joint_scored_activity)\n", + " independent_fitted = ((scores > 0) & independent_scored_activity) | (\n", + " (scores < 0) & ~independent_scored_activity\n", " )\n", "\n", + " for reaction_id in scores.index:\n", + " fit_details.append(\n", + " {\n", + " \"condition\": condition,\n", + " \"reaction\": reaction_id,\n", + " \"reaction score\": scores[reaction_id],\n", + " \"expected\": \"active\" if scores[reaction_id] > 0 else \"inactive\",\n", + " \"joint flux\": joint_fluxes.loc[reaction_id, condition],\n", + " \"joint state\": \"active\" if joint_scored_activity[reaction_id] else \"inactive\",\n", + " \"fitted independently\": bool(independent_fitted[reaction_id]),\n", + " \"fitted jointly\": bool(joint_fitted[reaction_id]),\n", + " }\n", + " )\n", "\n", - "fit_table = pd.DataFrame(\n", - " [\n", - " {\n", - " \"setting\": \"multi lambda=0\",\n", - " \"condition_1_penalty\": fit_penalty_for_sample(P_multi_uncoupled, \"condition_1\"),\n", - " \"condition_2_penalty\": fit_penalty_for_sample(P_multi_uncoupled, \"condition_2\"),\n", - " },\n", + " fit_rows.append(\n", " {\n", - " \"setting\": \"multi lambda=0.5\",\n", - " \"condition_1_penalty\": fit_penalty_for_sample(P_multi_coupled, \"condition_1\"),\n", - " \"condition_2_penalty\": fit_penalty_for_sample(P_multi_coupled, \"condition_2\"),\n", - " },\n", - " {\n", - " \"setting\": \"single lambda=0.5\",\n", - " \"condition_1_penalty\": fit_penalty_for_sample(P_single_c1, \"condition_1\"),\n", - " \"condition_2_penalty\": fit_penalty_for_sample(P_single_c2, \"condition_2\"),\n", - " },\n", - " ]\n", - ")\n", + " \"condition\": condition,\n", + " \"biomass\": joint_fluxes.loc[biomass_id, condition],\n", + " \"active reactions\": int(joint_activity[condition].sum()),\n", + " \"evidence fitted\": f\"{int(joint_fitted.sum())}/{len(scores)}\",\n", + " \"not fitted\": int((~joint_fitted).sum()),\n", + " }\n", + " )\n", "\n", - "fit_table" + " assert np.isclose(joint_fluxes.loc[biomass_id, condition], biomass_targets[condition])\n", + "\n", + "fit_summary = pd.DataFrame(fit_rows).set_index(\"condition\")\n", + "fit_details = pd.DataFrame(fit_details).set_index([\"reaction\", \"condition\"])\n", + "fit_summary" ] }, { "cell_type": "markdown", - "id": "81711827", + "id": "fit-matrix-intro", "metadata": {}, "source": [ - "## Shared reactions" + "A condition-by-reaction agreement matrix is especially useful in multi-condition analyses. It separates reactions that were not scored from scored reactions that the network could or could not fit." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "374ff582", - "metadata": { - "execution": { - "iopub.execute_input": "2026-02-15T11:15:36.998101Z", - "iopub.status.busy": "2026-02-15T11:15:36.998016Z", - "iopub.status.idle": "2026-02-15T11:15:37.002805Z", - "shell.execute_reply": "2026-02-15T11:15:37.002594Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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settingselected_branch
0multi lambda=0 (condition_1)branch_1 (A->B->C)
1multi lambda=0 (condition_2)branch_2 (A->D->C)
2multi lambda=0.5 (condition_1)branch_1 (A->B->C)
3multi lambda=0.5 (condition_2)branch_1 (A->B->C)
4single condition_1 lambda=0.5branch_1 (A->B->C)
5single condition_2 lambda=0.5branch_2 (A->D->C)
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" - ], - "text/plain": [ - " setting selected_branch\n", - "0 multi lambda=0 (condition_1) branch_1 (A->B->C)\n", - "1 multi lambda=0 (condition_2) branch_2 (A->D->C)\n", - "2 multi lambda=0.5 (condition_1) branch_1 (A->B->C)\n", - "3 multi lambda=0.5 (condition_2) branch_1 (A->B->C)\n", - "4 single condition_1 lambda=0.5 branch_1 (A->B->C)\n", - "5 single condition_2 lambda=0.5 branch_2 (A->D->C)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "fit-matrix", + "metadata": {}, + "outputs": [], "source": [ - "idx = {rid: next(iter(G.get_edges_by_attr(\"id\", rid))) for rid in [\"R_AB\", \"R_AD\"]}\n", + "all_scored_reactions = score_table.index\n", + "fit_matrix = pd.DataFrame(\"not scored\", index=all_scored_reactions, columns=condition_names)\n", "\n", + "for (reaction_id, condition), row in fit_details.iterrows():\n", + " fit_matrix.loc[reaction_id, condition] = \"fitted\" if row[\"fitted jointly\"] else \"not fitted\"\n", "\n", - "def branch_label(ab_flux, ad_flux, tol=1e-6):\n", - " ab_on = abs(ab_flux) > tol\n", - " ad_on = abs(ad_flux) > tol\n", - " if ab_on and not ad_on:\n", - " return \"branch_1 (A->B->C)\"\n", - " if ad_on and not ab_on:\n", - " return \"branch_2 (A->D->C)\"\n", - " if ab_on and ad_on:\n", - " return \"both\"\n", - " return \"none\"\n", + "fit_matrix" + ] + }, + { + "cell_type": "markdown", + "id": "mismatch-interpretation", + "metadata": {}, + "source": [ + "The mismatch table should be examined before interpreting shared and condition-specific pathways. A reaction mismatched both independently and jointly points to expression–feasibility tension within that condition. A reaction fitted independently but mismatched only in the joint model instead points to a trade-off introduced by shared regularization.\n", "\n", + "Expression is evidence about the likelihood of reaction activity, not a measurement of activity or flux. The [original iMAT paper](https://www.nature.com/articles/nbt.1487) reports a central role for post-transcriptional regulation and uses disagreements between expression and predicted activity to generate hypotheses about regulation beyond transcript abundance. Possible mechanisms include protein translation or degradation, phosphorylation and other post-translational effects, and metabolite-level control such as substrate availability and allosteric regulation. A disagreement can also arise from expression thresholds, GPR mapping, missing or incorrect model reactions, medium composition, or an imposed phenotype. iMAT alone cannot distinguish these explanations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "mismatched-reactions", + "metadata": {}, + "outputs": [], + "source": [ + "mismatches = fit_details[~fit_details[\"fitted jointly\"]].copy()\n", "\n", - "branch_usage = pd.DataFrame(\n", - " {\n", - " \"setting\": [\n", - " \"multi lambda=0 (condition_1)\",\n", - " \"multi lambda=0 (condition_2)\",\n", - " \"multi lambda=0.5 (condition_1)\",\n", - " \"multi lambda=0.5 (condition_2)\",\n", - " \"single condition_1 lambda=0.5\",\n", - " \"single condition_2 lambda=0.5\",\n", - " ],\n", - " \"selected_branch\": [\n", - " branch_label(\n", - " P_multi_uncoupled.expr.flow[idx[\"R_AB\"], 0].value, P_multi_uncoupled.expr.flow[idx[\"R_AD\"], 0].value\n", - " ),\n", - " branch_label(\n", - " P_multi_uncoupled.expr.flow[idx[\"R_AB\"], 1].value, P_multi_uncoupled.expr.flow[idx[\"R_AD\"], 1].value\n", - " ),\n", - " branch_label(\n", - " P_multi_coupled.expr.flow[idx[\"R_AB\"], 0].value, P_multi_coupled.expr.flow[idx[\"R_AD\"], 0].value\n", - " ),\n", - " branch_label(\n", - " P_multi_coupled.expr.flow[idx[\"R_AB\"], 1].value, P_multi_coupled.expr.flow[idx[\"R_AD\"], 1].value\n", - " ),\n", - " branch_label(P_single_c1.expr.flow[idx[\"R_AB\"]].value, P_single_c1.expr.flow[idx[\"R_AD\"]].value),\n", - " branch_label(P_single_c2.expr.flow[idx[\"R_AB\"]].value, P_single_c2.expr.flow[idx[\"R_AD\"]].value),\n", - " ],\n", - " }\n", - ")\n", + "assert list(mismatches.index) == [(\"PFL\", \"glucose anaerobic\")]\n", + "assert not mismatches.iloc[0][\"fitted independently\"]\n", + "mismatches" + ] + }, + { + "cell_type": "markdown", + "id": "pfl-mismatch", + "metadata": {}, + "source": [ + "`PFL` is the only unmatched reaction. It is classified as low but remains active in both the independent and joint anaerobic solutions. This is not caused by cross-condition coupling: under the imposed anaerobic growth phenotype and high ethanol evidence, the selected network uses `PFL` to supply acetyl-CoA and formate.\n", + "\n", + "In real data, this would be a hypothesis-generating result—not proof that `PFL` is active despite low expression, and not proof of post-translational regulation. The first checks should separate two broad possibilities:\n", "\n", - "single_c1_branch = branch_usage.loc[\n", - " branch_usage[\"setting\"] == \"single condition_1 lambda=0.5\", \"selected_branch\"\n", - "].item()\n", - "single_c2_branch = branch_usage.loc[\n", - " branch_usage[\"setting\"] == \"single condition_2 lambda=0.5\", \"selected_branch\"\n", - "].item()\n", - "multi_c1_branch = branch_usage.loc[\n", - " branch_usage[\"setting\"] == \"multi lambda=0.5 (condition_1)\", \"selected_branch\"\n", - "].item()\n", - "multi_c2_branch = branch_usage.loc[\n", - " branch_usage[\"setting\"] == \"multi lambda=0.5 (condition_2)\", \"selected_branch\"\n", - "].item()\n", - "\n", - "assert single_c1_branch != single_c2_branch, \"Single-sample runs should select different branches.\"\n", - "assert multi_c1_branch == multi_c2_branch, \"Coupled multi-sample run should align branch usage.\"\n", - "\n", - "branch_usage" + "- **biology not captured by transcript abundance**, including enzyme abundance or modification and metabolite-level regulation;\n", + "- **data or model assumptions**, including thresholds, GPR rules, network completeness, medium bounds, and the required phenotype.\n", + "\n", + "Proteomics, enzyme-activity, metabolomics, or flux measurements are needed to discriminate among these explanations." ] }, { - "cell_type": "code", - "execution_count": 9, - "id": "3f066a68", - "metadata": { - "execution": { - "iopub.execute_input": "2026-02-15T11:15:37.003910Z", - "iopub.status.busy": "2026-02-15T11:15:37.003842Z", - "iopub.status.idle": "2026-02-15T11:15:37.007924Z", - "shell.execute_reply": "2026-02-15T11:15:37.007671Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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settingn_sharedsharedonly_condition_1only_condition_2
0multi lambda=0.56[BIOMASS, EX_A, R_AB, R_BC, R_CE, R_EF][][]
1single lambda=0.54[BIOMASS, EX_A, R_CE, R_EF][R_AB, R_BC][R_AD, R_DC]
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" - ], - "text/plain": [ - " setting n_shared shared \\\n", - "0 multi lambda=0.5 6 [BIOMASS, EX_A, R_AB, R_BC, R_CE, R_EF] \n", - "1 single lambda=0.5 4 [BIOMASS, EX_A, R_CE, R_EF] \n", - "\n", - " only_condition_1 only_condition_2 \n", - "0 [] [] \n", - "1 [R_AB, R_BC] [R_AD, R_DC] " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "markdown", + "id": "joint-versus-independent", + "metadata": {}, "source": [ - "all_edge_ids = [G.get_attr_edge(i).get(\"id\") for i in range(G.num_edges)]\n", - "\n", - "\n", - "def active_ids(problem, condition=None, tol=1e-6):\n", - " flow = np.asarray(problem.expr.flow.value)\n", - " if condition is not None:\n", - " flow = flow[:, condition]\n", - " return {all_edge_ids[i] for i in np.where(np.abs(flow) > tol)[0]}\n", - "\n", - "\n", - "def shared_unique(active_c1, active_c2, setting):\n", - " shared = sorted(active_c1 & active_c2)\n", - " only_c1 = sorted(active_c1 - active_c2)\n", - " only_c2 = sorted(active_c2 - active_c1)\n", - " return {\n", - " \"setting\": setting,\n", - " \"n_shared\": len(shared),\n", - " \"shared\": shared,\n", - " \"only_condition_1\": only_c1,\n", - " \"only_condition_2\": only_c2,\n", - " }\n", + "### What did joint inference change?\n", "\n", + "The independent solutions are compared only after both optimizations have finished. The joint solution considers reuse while selecting the flux states, producing a smaller union and a larger internally consistent shared set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "union-comparison", + "metadata": {}, + "outputs": [], + "source": [ + "independent_union = independent_activity.any(axis=1)\n", + "independent_shared = independent_activity.all(axis=1)\n", + "joint_union = joint_activity.any(axis=1)\n", + "joint_shared = joint_activity.all(axis=1)\n", "\n", - "shared_table = pd.DataFrame(\n", - " [\n", - " shared_unique(active_ids(P_multi_coupled, 0), active_ids(P_multi_coupled, 1), \"multi lambda=0.5\"),\n", - " shared_unique(active_ids(P_single_c1), active_ids(P_single_c2), \"single lambda=0.5\"),\n", - " ]\n", + "selection_comparison = pd.DataFrame(\n", + " {\n", + " \"shared reactions\": {\n", + " \"independent inference, compared afterward\": int(independent_shared.sum()),\n", + " \"one joint inference\": int(joint_shared.sum()),\n", + " },\n", + " \"reaction union\": {\n", + " \"independent inference, compared afterward\": int(independent_union.sum()),\n", + " \"one joint inference\": int(joint_union.sum()),\n", + " },\n", + " }\n", ")\n", "\n", - "shared_table" + "assert joint_union.sum() < independent_union.sum()\n", + "assert joint_shared.sum() > independent_shared.sum()\n", + "selection_comparison" ] }, { - "cell_type": "code", - "execution_count": 10, - "id": "0ec4a31d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-02-15T11:15:37.008954Z", - "iopub.status.busy": "2026-02-15T11:15:37.008893Z", - "iopub.status.idle": "2026-02-15T11:15:37.020063Z", - "shell.execute_reply": "2026-02-15T11:15:37.019772Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "digraph {\n", - "\tgraph [center=1 rankdir=LR]\n", - "\tnode [fixedsize=true]\n", - "\te_0_source [shape=point]\n", - "\tA [shape=circle]\n", - "\tA [shape=circle]\n", - "\tB [shape=circle]\n", - "\tB [shape=circle]\n", - "\tC [shape=circle]\n", - "\tC [shape=circle]\n", - "\tE [shape=circle]\n", - "\tE [shape=circle]\n", - "\tF [shape=circle]\n", - "\te_5_target [shape=point]\n", - "\tF [shape=circle]\n", - "\te_0_source -> A [arrowhead=normal]\n", - "\tA -> B [arrowhead=normal]\n", - "\tB -> C [arrowhead=normal]\n", - "\tC -> E [arrowhead=normal]\n", - "\tE -> F [arrowhead=normal]\n", - "\tF -> e_5_target [arrowhead=normal]\n", - "}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "markdown", + "id": "usage-patterns", + "metadata": {}, "source": [ - "active_multi_c1 = np.abs(P_multi_coupled.expr.flow.value[:, 0]) > 1e-6\n", - "active_multi_c2 = np.abs(P_multi_coupled.expr.flow.value[:, 1]) > 1e-6\n", + "## Shared and condition-specific metabolism\n", "\n", - "G.edge_subgraph(active_multi_c1).plot(graph_attr={\"rankdir\": \"LR\", \"center\": \"1\"})" + "As in Multi-condition FBA, categories are derived from the joint activity matrix. With two conditions, every reaction in the union is either shared, aerobic-specific, or anaerobic-specific." ] }, { "cell_type": "code", - "execution_count": 11, - "id": "1a42f91d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-02-15T11:15:37.021194Z", - "iopub.status.busy": "2026-02-15T11:15:37.021107Z", - "iopub.status.idle": "2026-02-15T11:15:37.031511Z", - "shell.execute_reply": "2026-02-15T11:15:37.031205Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "digraph {\n", - "\tgraph [center=1 rankdir=LR]\n", - "\tnode [fixedsize=true]\n", - "\te_0_source [shape=point]\n", - "\tA [shape=circle]\n", - "\tA [shape=circle]\n", - "\tB [shape=circle]\n", - "\tB [shape=circle]\n", - "\tC [shape=circle]\n", - "\tC [shape=circle]\n", - "\tE [shape=circle]\n", - "\tE [shape=circle]\n", - "\tF [shape=circle]\n", - "\te_5_target [shape=point]\n", - "\tF [shape=circle]\n", - "\te_0_source -> A [arrowhead=normal]\n", - "\tA -> B [arrowhead=normal]\n", - "\tB -> C [arrowhead=normal]\n", - "\tC -> E [arrowhead=normal]\n", - "\tE -> F [arrowhead=normal]\n", - "\tF -> e_5_target [arrowhead=normal]\n", - "}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "categorize-reactions", + "metadata": {}, + "outputs": [], + "source": [ + "reaction_groups = pd.Series(\"inactive\", index=reaction_ids, name=\"usage group\")\n", + "reaction_groups.loc[joint_shared] = \"shared\"\n", + "reaction_groups.loc[joint_activity[\"glucose aerobic\"] & ~joint_activity[\"glucose anaerobic\"]] = (\n", + " \"glucose aerobic only\"\n", + ")\n", + "reaction_groups.loc[~joint_activity[\"glucose aerobic\"] & joint_activity[\"glucose anaerobic\"]] = (\n", + " \"glucose anaerobic only\"\n", + ")\n", + "\n", + "group_table = (\n", + " reaction_groups[joint_union]\n", + " .groupby(reaction_groups[joint_union], sort=False)\n", + " .agg(\n", + " reactions=\"size\",\n", + " reaction_ids=lambda values: \", \".join(values.index),\n", + " )\n", + ")\n", + "\n", + "assert int(group_table[\"reactions\"].sum()) == int(joint_union.sum())\n", + "group_table" + ] + }, + { + "cell_type": "markdown", + "id": "biological-interpretation", + "metadata": {}, "source": [ - "G.edge_subgraph(active_multi_c2).plot(graph_attr={\"rankdir\": \"LR\", \"center\": \"1\"})" + "### Biological interpretation\n", + "\n", + "| Usage group | Characteristic reactions | Interpretation |\n", + "|---|---|---|\n", + "| Shared | `GLCpts`, `PFK`, `GAPD`, `ENO`, `PYK`, biomass | Central glucose utilization and precursor production required in both states |\n", + "| Glucose aerobic only | `EX_o2_e`, `O2t`, `CYTBD`, `NADH16`, `PDH` | Oxygen uptake, respiratory electron transfer, and oxidative pyruvate metabolism |\n", + "| Glucose anaerobic only | `PFL`, `FORti`, `EX_for_e`, `ACALD`, `ALCD2x`, `ETOHt2r`, `EX_etoh_e` | Pyruvate-formate cleavage followed by formate and ethanol secretion |\n", + "\n", + "Most condition-specific reactions agree with the supplied evidence; `PFL` is the explicit exception identified by the mismatch analysis. The inferred sets also include the transport and exchange reactions required to complete each biological route. That is an advantage of network inference over listing high-expression reactions alone: the output is a stoichiometrically connected hypothesis that makes conflicting evidence visible.\n", + "\n", + "As noted in the Multi-condition FBA guide, shared activity does not imply equal magnitude or direction. For example, `ATPS4r` is active in both selected states but carries flux in opposite directions in this solution." ] }, { "cell_type": "markdown", - "id": "ae1c6d9a", + "id": "network-visualization", "metadata": {}, "source": [ - "## Manual check of objectives per condition" + "### Visualize the jointly inferred network\n", + "\n", + "The plot shows the complete joint union. Shared reactions are gray, aerobic-specific reactions are blue, and anaerobic-specific reactions are red.\n", + "\n", + "| Usage group | Color |\n", + "|---|---|\n", + "| Shared | Gray |\n", + "| Glucose aerobic only | Blue |\n", + "| Glucose anaerobic only | Red |" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "3f372c79", - "metadata": { - "execution": { - "iopub.execute_input": "2026-02-15T11:15:37.032792Z", - "iopub.status.busy": "2026-02-15T11:15:37.032689Z", - "iopub.status.idle": "2026-02-15T11:15:37.037532Z", - "shell.execute_reply": "2026-02-15T11:15:37.037292Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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settingcondition_1_ratiocondition_2_ratio
0multi lambda=01.01.0
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" - ], - "text/plain": [ - " setting condition_1_ratio condition_2_ratio\n", - "0 multi lambda=0 1.0 1.0\n", - "1 multi lambda=0.5 1.0 0.2\n", - "2 single lambda=0.5 1.0 1.0" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "plot-joint-network", + "metadata": {}, + "outputs": [], "source": [ - "edge_index_by_id = {rid: i for i, rid in enumerate(all_edge_ids)}\n", + "group_colors = {\n", + " \"shared\": \"#b0b0b0\",\n", + " \"glucose aerobic only\": \"#277da1\",\n", + " \"glucose anaerobic only\": \"#e63946\",\n", + "}\n", + "\n", + "selected_reactions = np.flatnonzero(joint_union.to_numpy())\n", + "joint_network = G.edge_subgraph(selected_reactions)\n", + "edge_style = {}\n", "\n", + "for displayed_index, original_index in enumerate(selected_reactions):\n", + " reaction_id = reaction_ids[original_index]\n", + " group = reaction_groups[reaction_id]\n", + " edge_style[displayed_index] = {\n", + " \"color\": group_colors[group],\n", + " \"penwidth\": \"1.5\" if group == \"shared\" else \"4\",\n", + " }\n", + "\n", + "joint_network.plot(\n", + " graph_attr={\"rankdir\": \"LR\"},\n", + " node_attr={\n", + " \"fixedsize\": \"false\",\n", + " \"shape\": \"box\",\n", + " \"style\": \"rounded\",\n", + " \"margin\": \"0.05,0.03\",\n", + " },\n", + " custom_edge_attr=edge_style,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "interpretation-limitations", + "metadata": {}, + "source": [ + "## Interpretation and limitations\n", "\n", - "def manual_fit_ratio(problem, scores, condition=None, tol=1e-6):\n", - " flow = np.asarray(problem.expr.flow.value)\n", - " if condition is not None:\n", - " flow = flow[:, condition]\n", + "By fitting the conditions together, we can compare them against the same metabolic solution. The model reuses reactions when it can, but still keeps the respiratory pathway in the aerobic condition and fermentation in the anaerobic condition. This is different from averaging the expression profiles or comparing networks inferred in separate runs.\n", "\n", - " matched = 0.0\n", - " total = 0.0\n", - " for rid, score in scores.items():\n", - " i = edge_index_by_id[rid]\n", - " active = abs(float(flow[i])) > tol\n", - " weight = abs(float(score))\n", - " ok = (score > 0 and active) or (score < 0 and not active)\n", - " total += weight\n", - " if ok:\n", - " matched += weight\n", - " return matched / total if total > 0 else np.nan\n", + "The fit table is an important part of the biological interpretation. It shows where the expression data support the selected network and where the model cannot satisfy both the expression evidence and the metabolic constraints. Comparing the independent and joint fits also reveals whether a mismatch was already present in one condition or appeared when the conditions were analyzed together.\n", "\n", + "Keep in mind that RNA abundance does not directly measure enzyme activity or flux. Protein abundance, post-translational regulation, and metabolite-level control can all separate expression from metabolic activity. A mismatch is therefore something to investigate, not evidence for a particular regulatory mechanism.\n", "\n", - "manual_fit_table = pd.DataFrame(\n", - " [\n", - " {\n", - " \"setting\": \"multi lambda=0\",\n", - " \"condition_1_ratio\": manual_fit_ratio(P_multi_uncoupled, scores_condition_1, condition=0),\n", - " \"condition_2_ratio\": manual_fit_ratio(P_multi_uncoupled, scores_condition_2, condition=1),\n", - " },\n", - " {\n", - " \"setting\": \"multi lambda=0.5\",\n", - " \"condition_1_ratio\": manual_fit_ratio(P_multi_coupled, scores_condition_1, condition=0),\n", - " \"condition_2_ratio\": manual_fit_ratio(P_multi_coupled, scores_condition_2, condition=1),\n", - " },\n", - " {\n", - " \"setting\": \"single lambda=0.5\",\n", - " \"condition_1_ratio\": manual_fit_ratio(P_single_c1, scores_condition_1),\n", - " \"condition_2_ratio\": manual_fit_ratio(P_single_c2, scores_condition_2),\n", - " },\n", - " ]\n", - ")\n", + "In practice, it is worth checking different expression thresholds and comparing the joint result with the independently inferred networks. Shared reactions may still carry different fluxes—or even operate in opposite directions—and alternative solutions may exist. The choice of `lambda_reg` also matters: larger values favor more reaction reuse, but can eventually outweigh weaker expression evidence.\n", "\n", - "manual_fit_table" + "The synthetic profiles isolate the multi-condition formulation. For a workflow using measured expression data, continue with the [context-specific metabolic omics tutorial](../../tutorials/fba/context-specific-metabolic-omics.ipynb)." ] } ], "metadata": { "kernelspec": { - "display_name": "default", + "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.12" + "version": "3.11" } }, "nbformat": 4, diff --git a/docs/guide/metabolism/multicondition-sfba.ipynb b/docs/guide/metabolism/multicondition-sfba.ipynb index 93cbd34b7..44b3f30d2 100644 --- a/docs/guide/metabolism/multicondition-sfba.ipynb +++ b/docs/guide/metabolism/multicondition-sfba.ipynb @@ -2,321 +2,558 @@ "cells": [ { "cell_type": "markdown", - "id": "mc_sfba_01", + "id": "multicondition-introduction", "metadata": {}, "source": [ - "# Multi-condition sparse FBA\n", + "# Multi-condition FBA\n", "\n", - "This tutorial extends the sparse FBA workflow from `flux-balance-analysis.ipynb` to multiple conditions. We first solve standard multi-condition FBA to get each condition's optimal biomass. Then, as in single-condition sparse FBA, we constrain biomass to at least 90% of optimum and minimize the number of active reactions.\n", + "Multi-condition flux balance analysis represents several metabolic contexts in one optimization problem. Every condition has its own flux vector, reaction bounds, and objectives, so media, perturbations, or required phenotypes can differ while the underlying metabolic model remains the same.\n", "\n", - "To make the effect visible, we use a toy network where each condition has a small preference for a different branch." + "In CORNETO, `MultiSampleFBA.build_many` constructs this condition-by-reaction flux matrix directly. This supports two related uses:\n", + "\n", + "- solve and inspect several condition-specific FBA problems through one consistent interface;\n", + "- couple reaction selection across conditions to identify a compact shared program and the reactions needed by particular contexts.\n", + "\n", + "The second use is the main advantage of the multi-condition formulation: all conditions influence reaction selection before the networks are compared." + ] + }, + { + "cell_type": "markdown", + "id": "independent-and-coupled", + "metadata": {}, + "source": [ + "## Independent and coupled formulations\n", + "\n", + "Without regularization, the condition-specific flux vectors obey separate steady-state constraints and can be interpreted as several FBA problems built together. With `lambda_reg > 0`, CORNETO adds structured sparsity across the complete flux matrix.\n", + "\n", + "For each reaction, CORNETO records whether it is active in each condition and whether it is active in the union. A reaction used by one or several conditions contributes once to the union-level penalty. Reusing a reaction is therefore cheaper than introducing another alternative, while condition-specific reactions remain available when required by their bounds or phenotypes.\n", + "\n", + "Structured sparsity penalizes the union; it does not force identical fluxes and does not directly maximize the intersection. Shared and context-specific reactions are summaries derived from the jointly selected condition networks." + ] + }, + { + "cell_type": "markdown", + "id": "joint-versus-pairwise", + "metadata": {}, + "source": [ + "## Why is this different from pairwise comparisons?\n", + "\n", + "A metabolic model usually admits many equivalent or near-equivalent flux distributions. Consider an experiment with a control and two perturbed conditions, A and B. One common strategy is to run two independent optimizations:\n", + "\n", + "| Optimization | Condition-specific flux solutions | Reactions coupled by one union penalty |\n", + "|---|---|---|\n", + "| Separate analysis 1 | control and condition A | reactions used by control or A |\n", + "| Separate analysis 2 | a new control solution and condition B | reactions used by control or B |\n", + "\n", + "The media are not combined: every condition keeps its own bounds and flux vector. However, the control is solved twice, once inside each independent optimization. Its **selected control reaction set**—the reactions carrying flux in that solution—can differ between the two runs even though its bounds and required phenotype are unchanged.\n", + "\n", + "Comparing the two results afterward can therefore mix genuine biological differences with independent selection among alternative pathways. In a joint CORNETO analysis, one optimization contains one flux vector for control, one for A, and one for B, and applies a single union penalty across all three. The resulting shared and condition-dependent reaction sets are internally consistent with the complete experiment." + ] + }, + { + "cell_type": "markdown", + "id": "cobrapy-comparison", + "metadata": {}, + "source": [ + "## Relation to COBRApy\n", + "\n", + "COBRApy naturally solves FBA or pFBA independently for each medium. A user can reproduce joint selection by manually constructing a coupled mixed-integer formulation with optlang. CORNETO provides the condition-specific flux matrix and union-level structured sparsity directly through `MultiSampleFBA.build_many`." + ] + }, + { + "cell_type": "markdown", + "id": "shared-context-example", + "metadata": {}, + "source": [ + "## Example: shared and context-specific metabolism\n", + "\n", + "The [FBA guide](flux-balance-analysis.ipynb) showed how to solve several media conditions in one CORNETO problem. Here we use that interface to address a small biological question:\n", + "\n", + "> Which reactions form a compact metabolic program shared across metabolic conditions, and which reactions are required only in particular contexts?\n", + "\n", + "We will compare aerobic growth on glucose (the control), aerobic growth on acetate, and anaerobic growth on glucose in the COBRApy *E. coli* core model. The conditions are deliberately simple: they illustrate joint reaction selection rather than reproduce a particular experiment. After solving them jointly, reactions can be described as:\n", + "\n", + "- **shared core**: active in all conditions;\n", + "- **partially shared**: active in more than one condition, but not all;\n", + "- **context-specific**: active in exactly one condition;\n", + "- **union**: active in at least one condition." ] }, { "cell_type": "code", "execution_count": null, - "id": "mc_sfba_02", + "id": "load-model", "metadata": {}, "outputs": [], "source": [ + "from contextlib import redirect_stdout\n", + "from io import StringIO\n", + "\n", "import numpy as np\n", "import pandas as pd\n", + "from cobra.io import load_model\n", "\n", - "import corneto as cn\n", + "from corneto.io import cobra_model_to_graph\n", "from corneto.methods import MultiSampleFBA\n", "\n", - "cn.info()" + "with redirect_stdout(StringIO()):\n", + " cobra_model = load_model(\"textbook\")\n", + "\n", + "G = cobra_model_to_graph(cobra_model)\n", + "reaction_ids = list(G.get_attr_from_edges(\"id\"))\n", + "biomass_id = \"Biomass_Ecoli_core\"\n", + "biomass_index = reaction_ids.index(biomass_id)" + ] + }, + { + "cell_type": "markdown", + "id": "oxygen-conditions", + "metadata": {}, + "source": [ + "## Define the metabolic conditions\n", + "\n", + "The conditions change either the carbon source or oxygen availability. As in the previous guide, uptake is a negative exchange flux and `0` blocks uptake.\n", + "\n", + "| Condition | Glucose uptake | Acetate uptake | Oxygen uptake |\n", + "|---|---:|---:|---:|\n", + "| Glucose aerobic | −10 | 0 | −20 |\n", + "| Acetate aerobic | 0 | −10 | −20 |\n", + "| Glucose anaerobic | −10 | 0 | 0 |" ] }, { "cell_type": "code", "execution_count": null, - "id": "mc_sfba_03", + "id": "condition-inputs", "metadata": {}, "outputs": [], "source": [ - "G = cn.Graph()\n", + "condition_names = [\"glucose aerobic\", \"acetate aerobic\", \"glucose anaerobic\"]\n", "\n", - "# Uptake\n", - "G.add_edge((), \"A\", id=\"EX_A\", default_lb=0, default_ub=10)\n", - "\n", - "# Two alternative branches\n", - "G.add_edge(\"A\", \"B\", id=\"R_AB\", default_lb=0, default_ub=100)\n", - "G.add_edge(\"B\", \"C\", id=\"R_BC\", default_lb=0, default_ub=100)\n", - "G.add_edge(\"A\", \"D\", id=\"R_AD\", default_lb=0, default_ub=100)\n", - "G.add_edge(\"D\", \"C\", id=\"R_DC\", default_lb=0, default_ub=100)\n", - "\n", - "# Shared pathway to biomass\n", - "G.add_edge(\"C\", \"E\", id=\"R_CE\", default_lb=0, default_ub=100)\n", - "G.add_edge(\"E\", \"F\", id=\"R_EF\", default_lb=0, default_ub=100)\n", - "G.add_edge(\"F\", (), id=\"BIOMASS\", default_lb=0, default_ub=100)\n", + "media_bounds = {\n", + " \"glucose aerobic\": {\n", + " \"EX_glc__D_e\": (-10.0, 1000.0),\n", + " \"EX_ac_e\": (0.0, 1000.0),\n", + " \"EX_o2_e\": (-20.0, 1000.0),\n", + " },\n", + " \"acetate aerobic\": {\n", + " \"EX_glc__D_e\": (0.0, 1000.0),\n", + " \"EX_ac_e\": (-10.0, 1000.0),\n", + " \"EX_o2_e\": (-20.0, 1000.0),\n", + " },\n", + " \"glucose anaerobic\": {\n", + " \"EX_glc__D_e\": (-10.0, 1000.0),\n", + " \"EX_ac_e\": (0.0, 1000.0),\n", + " \"EX_o2_e\": (0.0, 1000.0),\n", + " },\n", + "}\n", + "biomass_objectives = {name: {biomass_id: -1} for name in condition_names}" + ] + }, + { + "cell_type": "markdown", + "id": "maximum-biomass", + "metadata": {}, + "source": [ + "## Establish each condition's growth capacity\n", "\n", - "pd.DataFrame(\n", - " {\n", - " \"edge_index\": range(G.num_edges),\n", - " \"reaction_id\": [G.get_attr_edge(i).get(\"id\") for i in range(G.num_edges)],\n", - " }\n", - ")" + "First, standard multi-condition FBA determines the maximum biomass supported by each medium. At this stage the flux vectors share one problem object but are not coupled by sparsity." ] }, { "cell_type": "code", "execution_count": null, - "id": "mc_sfba_04", + "id": "solve-maximum-biomass", "metadata": {}, "outputs": [], "source": [ - "G.plot()" + "standard_problem = MultiSampleFBA().build_many(\n", + " G,\n", + " objectives=biomass_objectives,\n", + " reaction_bounds=media_bounds,\n", + ")\n", + "standard_problem.solve(solver=\"scipy\")\n", + "\n", + "maximum_biomass = pd.Series(\n", + " standard_problem.expr.flow.value[biomass_index, :],\n", + " index=condition_names,\n", + " name=\"maximum biomass\",\n", + ")\n", + "\n", + "assert (maximum_biomass > 0).all()\n", + "maximum_biomass.to_frame()" + ] + }, + { + "cell_type": "markdown", + "id": "sparse-setup", + "metadata": {}, + "source": [ + "## Require growth and favor a compact reaction union\n", + "\n", + "For this controlled comparison, biomass is fixed at exactly 90% of each condition's optimum. Pairwise and joint analyses therefore explain the same predefined phenotypes: network differences cannot be attributed to different achieved growth rates.\n", + "\n", + "`lambda_reg=0.1` penalizes reactions in the union of the selected condition networks. Because biomass is fixed, any positive regularization weight ranks feasible solutions by their union size instead of trading additional growth for sparsity.\n", + "\n", + "The two helpers below keep the scientific inputs visible: one builds a sparse problem for named conditions and the other labels its flux matrix." ] }, { "cell_type": "code", "execution_count": null, - "id": "mc_sfba_05", + "id": "sparse-helpers", "metadata": {}, "outputs": [], "source": [ - "def make_condition(preferred_branch):\n", - " # Main objective: maximize biomass (negative value because objectives are minimized).\n", - " sample = {\"BIOMASS\": {\"role\": \"objective\", \"value\": -1.0}}\n", - " # Small condition-specific preference for one branch.\n", - " sample[preferred_branch] = {\"role\": \"objective\", \"value\": -0.01}\n", - " return sample\n", + "biomass_fraction = 0.90\n", + "biomass_targets = biomass_fraction * maximum_biomass\n", + "activity_tolerance = 1e-6\n", "\n", "\n", - "condition_specs = {\n", - " \"condition_1\": make_condition(\"R_BC\"),\n", - " \"condition_2\": make_condition(\"R_DC\"),\n", - "}\n", + "def build_sparse_problem(names):\n", + " \"\"\"Build and solve sparse FBA for the requested conditions.\"\"\"\n", + " objectives = {name: biomass_objectives[name] for name in names}\n", + " bounds = {\n", + " name: {\n", + " **media_bounds[name],\n", + " biomass_id: (biomass_targets[name], biomass_targets[name]),\n", + " }\n", + " for name in names\n", + " }\n", + " problem = MultiSampleFBA(lambda_reg=0.1).build_many(\n", + " G,\n", + " objectives=objectives,\n", + " reaction_bounds=bounds,\n", + " )\n", + " problem.solve(solver=\"highs\")\n", + " return problem\n", + "\n", "\n", - "multi_data = cn.Data.from_cdict(condition_specs)\n", - "sample_names = list(condition_specs)" + "def labeled_fluxes(problem, names):\n", + " \"\"\"Return the solved flux matrix with reaction and condition labels.\"\"\"\n", + " return pd.DataFrame(\n", + " problem.expr.flow.value,\n", + " index=reaction_ids,\n", + " columns=names,\n", + " )" ] }, { "cell_type": "markdown", - "id": "mc_sfba_06", + "id": "separate-comparisons", "metadata": {}, "source": [ - "## Standard multi-condition FBA\n", + "## What happens in separate two-condition analyses?\n", "\n", - "We start with regular multi-condition FBA to obtain the maximal biomass in each condition. These values are later used as lower bounds for sparse FBA." + "First we build one optimization containing glucose-aerobic and acetate-aerobic flux vectors. Separately, we build another containing glucose-aerobic and glucose-anaerobic flux vectors. Each condition retains its own medium bounds; they are columns in the same optimization, not a combined medium.\n", + "\n", + "Because these are independent optimizations, glucose aerobic is solved twice. We compare the active reactions selected for that identical glucose-aerobic condition in the two results." ] }, { "cell_type": "code", "execution_count": null, - "id": "mc_sfba_07", + "id": "solve-pairs", "metadata": {}, "outputs": [], "source": [ - "idx = {rid: next(iter(G.get_edges_by_attr(\"id\", rid))) for rid in [\"BIOMASS\", \"R_AB\", \"R_AD\"]}\n", + "control_acetate_names = [\"glucose aerobic\", \"acetate aerobic\"]\n", + "control_anaerobic_names = [\"glucose aerobic\", \"glucose anaerobic\"]\n", "\n", - "P_opt = MultiSampleFBA(lambda_reg=0.0).build(G, multi_data)\n", - "P_opt.solve(solver=\"highs\")\n", + "control_acetate_problem = build_sparse_problem(control_acetate_names)\n", + "control_anaerobic_problem = build_sparse_problem(control_anaerobic_names)\n", "\n", - "biomass_opt = {name: float(P_opt.expr.flow[idx[\"BIOMASS\"], i].value) for i, name in enumerate(sample_names)}\n", + "control_acetate_fluxes = labeled_fluxes(control_acetate_problem, control_acetate_names)\n", + "control_anaerobic_fluxes = labeled_fluxes(control_anaerobic_problem, control_anaerobic_names)\n", "\n", - "pd.DataFrame(\n", + "pairwise_control_summary = pd.DataFrame(\n", " {\n", - " \"condition\": sample_names,\n", - " \"optimal_biomass\": [biomass_opt[name] for name in sample_names],\n", + " \"analyzed with acetate aerobic\": {\n", + " \"biomass\": control_acetate_fluxes.loc[biomass_id, \"glucose aerobic\"],\n", + " \"active reactions\": (\n", + " control_acetate_fluxes[\"glucose aerobic\"].abs() > activity_tolerance\n", + " ).sum(),\n", + " },\n", + " \"analyzed with glucose anaerobic\": {\n", + " \"biomass\": control_anaerobic_fluxes.loc[biomass_id, \"glucose aerobic\"],\n", + " \"active reactions\": (\n", + " control_anaerobic_fluxes[\"glucose aerobic\"].abs() > activity_tolerance\n", + " ).sum(),\n", + " },\n", " }\n", - ")" + ")\n", + "\n", + "assert np.isclose(pairwise_control_summary.loc[\"biomass\"].iloc[0], pairwise_control_summary.loc[\"biomass\"].iloc[1])\n", + "pairwise_control_summary" ] }, { - "cell_type": "markdown", - "id": "mc_sfba_08", + "cell_type": "code", + "execution_count": null, + "id": "compare-controls", "metadata": {}, + "outputs": [], "source": [ - "## Sparse FBA per condition (independent runs)\n", + "control_activity = pd.DataFrame(\n", + " {\n", + " \"analyzed with acetate aerobic\": (\n", + " control_acetate_fluxes[\"glucose aerobic\"].abs() > activity_tolerance\n", + " ),\n", + " \"analyzed with glucose anaerobic\": (\n", + " control_anaerobic_fluxes[\"glucose aerobic\"].abs() > activity_tolerance\n", + " ),\n", + " }\n", + ")\n", "\n", - "This mirrors the single-condition sparse FBA pattern from `flux-balance-analysis.ipynb`:\n", + "different_control_reactions = control_activity[\n", + " control_activity.iloc[:, 0] != control_activity.iloc[:, 1]\n", + "].copy()\n", + "different_control_reactions.insert(0, \"reaction\", different_control_reactions.index)\n", "\n", - "1. Solve standard FBA to get optimum biomass.\n", - "2. Add a biomass lower bound (90% of optimum).\n", - "3. Solve sparse FBA (`lambda_reg > 0`)." + "assert not different_control_reactions.empty\n", + "different_control_reactions.reset_index(drop=True)" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "mc_sfba_09", + "cell_type": "markdown", + "id": "pairwise-interpretation", "metadata": {}, - "outputs": [], "source": [ - "lambda_sparse = 0.15\n", - "biomass_fraction = 0.90\n", - "biomass_targets = {name: biomass_fraction * biomass_opt[name] for name in sample_names}\n", - "\n", - "single_sparse = {}\n", - "for name in sample_names:\n", - " d = cn.Data.from_cdict({name: condition_specs[name]})\n", - " P = MultiSampleFBA(lambda_reg=lambda_sparse).build(G, d)\n", - " P += P.expr.flow[idx[\"BIOMASS\"]] >= biomass_targets[name]\n", - " P.solve(solver=\"highs\")\n", - " single_sparse[name] = P" + "Both columns in the table describe the active reaction set for **glucose aerobic**, not the other condition in each analysis. Its medium and fixed biomass are identical in both runs, yet the selected reactions differ. The difference arises because each two-condition optimization can choose a different feasible pathway for glucose aerobic while minimizing its own reaction union. It is not a change in control biology. A post-hoc comparison cannot distinguish this optimization variability from genuine context specificity." ] }, { "cell_type": "markdown", - "id": "mc_sfba_10", + "id": "joint-analysis", "metadata": {}, "source": [ - "## Coupled multi-condition sparse FBA\n", + "## Solve all conditions jointly\n", "\n", - "Now we solve both conditions together with structured sparsity. The same biomass lower bounds are enforced, but regularization is applied jointly across conditions." + "We now place all three condition-specific flux vectors in one optimization. Glucose aerobic appears only once, alongside acetate aerobic and glucose anaerobic, and a single reaction-union penalty couples the three solutions." ] }, { "cell_type": "code", "execution_count": null, - "id": "mc_sfba_11", + "id": "solve-joint", "metadata": {}, "outputs": [], "source": [ - "P_multi_sparse = MultiSampleFBA(lambda_reg=lambda_sparse).build(G, multi_data)\n", - "for i, name in enumerate(sample_names):\n", - " P_multi_sparse += P_multi_sparse.expr.flow[idx[\"BIOMASS\"], i] >= biomass_targets[name]\n", - "P_multi_sparse.solve(solver=\"highs\")" + "joint_problem = build_sparse_problem(condition_names)\n", + "joint_fluxes = labeled_fluxes(joint_problem, condition_names)\n", + "joint_activity = joint_fluxes.abs() > activity_tolerance\n", + "\n", + "condition_summary = pd.DataFrame(\n", + " {\n", + " \"biomass target\": biomass_targets,\n", + " \"solved biomass\": joint_fluxes.loc[biomass_id],\n", + " \"active reactions\": joint_activity.sum(axis=0),\n", + " }\n", + ")\n", + "\n", + "assert (condition_summary[\"solved biomass\"] >= condition_summary[\"biomass target\"] - 1e-7).all()\n", + "condition_summary" ] }, { "cell_type": "markdown", - "id": "mc_sfba_12", + "id": "classify-reactions", "metadata": {}, "source": [ - "## Compare independent vs coupled sparse solutions" + "### Shared and context-specific reaction usage\n", + "\n", + "The activity matrix has one Boolean column per condition. Counting active columns assigns every reaction in the union to exactly one usage category." ] }, { "cell_type": "code", "execution_count": null, - "id": "mc_sfba_13", + "id": "reaction-usage", "metadata": {}, "outputs": [], "source": [ - "edge_ids = [G.get_attr_edge(i).get(\"id\") for i in range(G.num_edges)]\n", - "\n", - "\n", - "def active_ids(problem, condition=None, tol=1e-6):\n", - " flow = np.asarray(problem.expr.flow.value)\n", - " if condition is not None:\n", - " flow = flow[:, condition]\n", - " return {edge_ids[i] for i in np.where(np.abs(flow) > tol)[0]}\n", - "\n", - "\n", - "def branch_label(ab_flux, ad_flux, tol=1e-6):\n", - " ab_on = abs(float(ab_flux)) > tol\n", - " ad_on = abs(float(ad_flux)) > tol\n", - " if ab_on and not ad_on:\n", - " return \"branch_1 (A->B->C)\"\n", - " if ad_on and not ab_on:\n", - " return \"branch_2 (A->D->C)\"\n", - " if ab_on and ad_on:\n", - " return \"both\"\n", - " return \"none\"\n", - "\n", - "\n", - "rows = []\n", - "for i, name in enumerate(sample_names):\n", - " P_single = single_sparse[name]\n", - " rows.append(\n", - " {\n", - " \"setting\": f\"single sparse ({name})\",\n", - " \"selected_branch\": branch_label(\n", - " P_single.expr.flow[idx[\"R_AB\"]].value, P_single.expr.flow[idx[\"R_AD\"]].value\n", - " ),\n", - " \"biomass\": float(P_single.expr.flow[idx[\"BIOMASS\"]].value),\n", - " \"active_reactions\": len(active_ids(P_single)),\n", - " }\n", - " )\n", - " rows.append(\n", - " {\n", - " \"setting\": f\"multi sparse coupled ({name})\",\n", - " \"selected_branch\": branch_label(\n", - " P_multi_sparse.expr.flow[idx[\"R_AB\"], i].value, P_multi_sparse.expr.flow[idx[\"R_AD\"], i].value\n", - " ),\n", - " \"biomass\": float(P_multi_sparse.expr.flow[idx[\"BIOMASS\"], i].value),\n", - " \"active_reactions\": len(active_ids(P_multi_sparse, condition=i)),\n", - " }\n", - " )\n", + "active_condition_count = joint_activity.sum(axis=1)\n", + "joint_union = active_condition_count > 0\n", "\n", - "comparison = pd.DataFrame(rows)\n", - "comparison" + "reaction_usage = pd.DataFrame(\n", + " {\n", + " \"reaction\": joint_activity.index[joint_union],\n", + " \"active in\": [\n", + " \", \".join(joint_activity.columns[row])\n", + " for row in joint_activity.loc[joint_union].to_numpy()\n", + " ],\n", + " \"category\": np.select(\n", + " [\n", + " active_condition_count[joint_union] == len(condition_names),\n", + " active_condition_count[joint_union] == 1,\n", + " ],\n", + " [\"shared core\", \"context-specific\"],\n", + " default=\"partially shared\",\n", + " ),\n", + " }\n", + ")\n", + "\n", + "category_counts = reaction_usage[\"category\"].value_counts().rename(\"reactions\")\n", + "expected_categories = {\"shared core\", \"partially shared\", \"context-specific\"}\n", + "\n", + "assert set(reaction_usage[\"category\"]) == expected_categories\n", + "assert int(category_counts.sum()) == int(joint_union.sum())\n", + "category_counts.to_frame()" ] }, { "cell_type": "code", "execution_count": null, - "id": "mc_sfba_14", + "id": "usage-patterns", "metadata": {}, "outputs": [], "source": [ - "single_union = set().union(*(active_ids(single_sparse[name]) for name in sample_names))\n", - "multi_union = set().union(*(active_ids(P_multi_sparse, condition=i) for i in range(len(sample_names))))\n", - "\n", - "summary = pd.DataFrame(\n", - " [\n", - " {\n", - " \"single_sparse_union\": len(single_union),\n", - " \"multi_sparse_union\": len(multi_union),\n", - " \"union_reduction\": len(single_union) - len(multi_union),\n", - " }\n", - " ]\n", + "usage_patterns = (\n", + " reaction_usage.groupby([\"category\", \"active in\"], sort=False)\n", + " .agg(\n", + " reactions=(\"reaction\", \"size\"),\n", + " examples=(\"reaction\", lambda values: \", \".join(values.iloc[:6])),\n", + " )\n", + " .reset_index()\n", ")\n", + "usage_patterns" + ] + }, + { + "cell_type": "markdown", + "id": "biological-interpretation", + "metadata": {}, + "source": [ + "### Biological interpretation\n", + "\n", + "The selected groups recover recognizable metabolic adaptations rather than isolated reactions:\n", + "\n", + "| Usage pattern | Representative selected reactions | Interpretation |\n", + "|---|---|---|\n", + "| Glucose anaerobic only | `ACALD`, `ALCD2x`, `ETOHt2r`, `EX_etoh_e` | A complete ethanol-fermentation route, from acetyl-CoA reduction to ethanol secretion, that supports redox balancing without respiration. |\n", + "| Acetate aerobic only | `ICL`, `MALS`, `FBP`, `PPCK`, `NADTRHD` | The glyoxylate shunt and gluconeogenesis conserve acetate carbon and produce biomass precursors; transhydrogenase contributes cofactor balancing. |\n", + "| Both aerobic conditions | `CYTBD`, `NADH16`, `EX_o2_e`, `AKGDH` | Oxygen uptake together with respiratory-chain and TCA-cycle activity shared by aerobic growth. |\n", "\n", - "# Expected in this setup: coupled sparse FBA uses fewer reactions in the union across conditions.\n", - "assert len(multi_union) < len(single_union)\n", + "These are interpretations of one sparse model solution, not evidence that the reactions are biologically exclusive to those conditions.\n", "\n", - "summary" + "> **Activity does not imply the same flux direction.** The categories above use `abs(flux) > activity_tolerance`. A reaction shared by two conditions may run in opposite directions or serve different roles. For example, the acetate transport and exchange reactions shared by acetate-aerobic and glucose-anaerobic solutions support acetate uptake in one context and acetate production or secretion in the other. Here, **shared** means that the same reaction is active—not that its direction, flux magnitude, or physiological role is identical." + ] + }, + { + "cell_type": "markdown", + "id": "network-visualization", + "metadata": {}, + "source": [ + "### Visualize the joint metabolic program\n", + "\n", + "The plot shows the complete reaction union. Shared-core reactions are gray and de-emphasized; colored reactions show how the core is adapted to carbon source and oxygen availability.\n", + "\n", + "| Active conditions | Color |\n", + "|---|---|\n", + "| All conditions | Gray |\n", + "| Both aerobic conditions | Blue |\n", + "| Both glucose conditions | Green |\n", + "| Acetate aerobic and glucose anaerobic | Purple |\n", + "| Acetate aerobic only | Orange |\n", + "| Glucose anaerobic only | Red |" ] }, { "cell_type": "code", "execution_count": null, - "id": "mc_sfba_15", + "id": "plot-joint-network", "metadata": {}, "outputs": [], "source": [ - "pd.DataFrame(\n", - " {\n", - " \"single_sparse_union\": pd.Series(sorted(single_union)),\n", - " \"multi_sparse_union\": pd.Series(sorted(multi_union)),\n", + "reaction_groups = reaction_usage.set_index(\"reaction\")[\"active in\"].replace(\n", + " {\", \".join(condition_names): \"shared core\"}\n", + ")\n", + "\n", + "group_colors = {\n", + " \"shared core\": \"#b0b0b0\",\n", + " \"glucose aerobic, acetate aerobic\": \"#277da1\",\n", + " \"glucose aerobic, glucose anaerobic\": \"#43aa8b\",\n", + " \"acetate aerobic, glucose anaerobic\": \"#7b2cbf\",\n", + " \"acetate aerobic\": \"#f8961e\",\n", + " \"glucose anaerobic\": \"#e63946\",\n", + "}\n", + "\n", + "selected_reactions = np.flatnonzero(joint_union.to_numpy())\n", + "joint_network = G.edge_subgraph(selected_reactions)\n", + "edge_style = {}\n", + "\n", + "for displayed_index, original_index in enumerate(selected_reactions):\n", + " reaction_id = reaction_ids[original_index]\n", + " group = reaction_groups[reaction_id]\n", + " is_shared_core = group == \"shared core\"\n", + "\n", + " edge_style[displayed_index] = {\n", + " \"color\": group_colors[group],\n", + " \"penwidth\": \"1.5\" if is_shared_core else \"3\",\n", " }\n", + "\n", + "joint_network.plot(\n", + " graph_attr={\"rankdir\": \"LR\"},\n", + " node_attr={\n", + " \"fixedsize\": \"false\",\n", + " \"shape\": \"box\",\n", + " \"style\": \"rounded\",\n", + " \"margin\": \"0.05,0.03\",\n", + " },\n", + " custom_edge_attr=edge_style,\n", ")" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "mc_sfba_16", + "cell_type": "markdown", + "id": "compare-unions", "metadata": {}, - "outputs": [], "source": [ - "active_multi_c1 = np.abs(P_multi_sparse.expr.flow.value[:, 0]) > 1e-6\n", - "active_multi_c2 = np.abs(P_multi_sparse.expr.flow.value[:, 1]) > 1e-6\n", + "### Compare the selected unions\n", "\n", - "G.edge_subgraph(active_multi_c1).plot(graph_attr={\"rankdir\": \"LR\", \"center\": \"1\"})" + "The aggregate from the separate analyses includes every reaction selected in either two-condition optimization, including both alternative glucose-aerobic reaction sets. The joint analysis instead selects one reaction union while considering all three conditions simultaneously." ] }, { "cell_type": "code", "execution_count": null, - "id": "mc_sfba_17", + "id": "union-comparison", "metadata": {}, "outputs": [], "source": [ - "G.edge_subgraph(active_multi_c2).plot(graph_attr={\"rankdir\": \"LR\", \"center\": \"1\"})" + "pairwise_activity = pd.concat(\n", + " [\n", + " control_acetate_fluxes.abs() > activity_tolerance,\n", + " control_anaerobic_fluxes.abs() > activity_tolerance,\n", + " ],\n", + " axis=1,\n", + ")\n", + "pairwise_union = pairwise_activity.any(axis=1)\n", + "\n", + "union_comparison = pd.Series(\n", + " {\n", + " \"aggregate union from separate comparisons\": int(pairwise_union.sum()),\n", + " \"union from one joint analysis\": int(joint_union.sum()),\n", + " },\n", + " name=\"active reactions\",\n", + ")\n", + "\n", + "assert joint_union.sum() < pairwise_union.sum()\n", + "union_comparison.to_frame()" ] }, { "cell_type": "markdown", - "id": "mc_sfba_18", + "id": "interpretation-limitations", "metadata": {}, "source": [ - "In this toy example, independent sparse runs choose different branches, while the coupled multi-condition sparse run aligns branch usage and reduces the union of active reactions across conditions." + "## Interpretation and limitations\n", + "\n", + "The joint result provides one internally consistent reference across all three conditions. Reactions in the shared core support every selected state; partially shared and context-specific reactions describe how that core is adapted to carbon source and oxygen availability. Because a reused reaction contributes once to the union penalty, alternative pathways are selected consistently instead of independently in each comparison.\n", + "\n", + "These conditions are designed to illustrate joint reaction selection. The resulting shared and context-specific sets are properties of this model and formulation, not experimental evidence of pathway activity. Structured sparsity assumes that reusing reactions is preferable when several feasible explanations exist, and alternative joint optima may still exist.\n", + "\n", + "Continue with [gene expression integration](imat.ipynb) to learn how expression evidence selects a feasible metabolic state for one condition. The following [Multi-condition iMAT](multicondition-imat.ipynb) guide will extend that formulation across several contexts." ] } ], @@ -337,19 +574,6 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.4" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false } }, "nbformat": 4, diff --git a/docs/guide/method-inputs.md b/docs/guide/method-inputs.md index 77ba2e618..673b651eb 100644 --- a/docs/guide/method-inputs.md +++ b/docs/guide/method-inputs.md @@ -1,114 +1,8 @@ -# Building method problems from explicit inputs +--- +orphan: true +--- -CORNETO methods accept ordinary Python mappings and collections for their -common scientific inputs. You do not need to construct `Data`, `Sample`, or -`Feature` objects for standard workflows. +# Building method problems from data -## One condition - -Use `build` with a positional network and keyword-only scientific inputs: - -```python -from corneto.methods import CarnivalILP - -problem = CarnivalILP().build( - pkn, - perturbations={"EGFR": 1}, - transcription_factors={"JUN": 1, "FOXO3": -1}, -) -``` - -Metabolic and network methods expose inputs using their domain terminology: - -```python -from corneto.methods import MultiSampleFBA, SteinerTreeFlow - -fba_problem = MultiSampleFBA().build( - model, - objectives={"BIOMASS": -1}, - reaction_bounds={"EX_glc": (-10, 0)}, -) - -tree_problem = SteinerTreeFlow().build( - graph, - terminals=["EGFR", "JUN"], - edge_costs={0: 1.5, 4: 0.8}, -) -``` - -PHONEMeS uses perturbation targets and signed phosphosite scores. Because the -method minimizes its objective, negative scores favor including a site and -positive scores discourage it; zero-valued keys still identify measured sites: - -```python -from corneto.methods import PHONEMeS - -phonemes_problem = PHONEMeS().build( - pkn, - perturbations=["EGFR"], - phosphosite_scores={"ERK1_S123": -2.4, "AKT1_S473": 0.0}, -) -``` - -See the [PHONEMeS guide](signaling/phonemes.ipynb) for score interpretation, edge -costs, computing scores from differential results, optional pandas inputs, and -extracting the inferred subnetwork. - -When the starting points are activity-derived regulated kinases rather than -known experimental perturbations, use `BidirectionalPHONEMeS`. It optimizes -upstream and downstream explanations together: - -```python -from corneto.methods import BidirectionalPHONEMeS - -bidirectional_problem = BidirectionalPHONEMeS().build( - pkn, - regulated_kinases=["AKT1", "MTOR"], - phosphosite_scores={"AKT1_S473": -2.4, "RPS6_S235": -3.1}, -) -``` - -See the -[global bidirectional PHONEMeS guide](signaling/bidirectional-phonemes.ipynb) for -anchor policies, directional results, and the distinction from the original -post-hoc upside-down union. - -## Multiple named conditions - -Use `build_many` and add one outer mapping keyed by condition name: - -```python -problem = CarnivalILP().build_many( - pkn, - perturbations={ - "control": {"EGFR": 1}, - "treated": {"EGFR": -1}, - }, - transcription_factors={ - "control": {"JUN": 1}, - "treated": {"JUN": -1}, - }, -) -``` - -All per-condition arguments must contain the same condition names. CORNETO -validates condition names, graph identifiers, numeric values, bounds, and edge -indices before constructing the optimization problem. - -PHONEMeS also uses named mappings for `perturbations` and -`phosphosite_scores`. It infers one subnetwork per condition but charges edge -costs over their combined network: an interaction used by one or several -conditions is charged once. For this reason, `edge_costs` is one global mapping -rather than a mapping per condition. - -## Advanced data interface - -Use `build_from_data(graph, data)` when custom feature metadata or arbitrary -measurement roles require the general `Data` representation: - -```python -problem = CarnivalILP().build_from_data(pkn, data) -``` - -Passing `Data` as the second positional argument to `build` remains temporarily -supported with a deprecation warning. +This documentation has moved to {ref}`guide-data-methods` in the +{ref}`guide-working-with-data` guide. diff --git a/docs/guide/signaling/cellnopt_ILP.ipynb b/docs/guide/signaling/cellnopt_ILP.ipynb deleted file mode 100644 index 84eb8b2c1..000000000 --- a/docs/guide/signaling/cellnopt_ILP.ipynb +++ /dev/null @@ -1,2280 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Acyclic boolean models of cell signaling (experimental)\n", - "\n", - "This guide shows the basics of modeling intracellular signaling using acyclic boolean models for multi-perturbation experiments. For this, we will use an extension of the CellNopt ILP method implemented with CORNETO.\n", - "\n", - "- Author: Attila Gabor (Heidelberg University, SaezLab)\n", - "- Reviewers: Pablo Rodriguez Mier (Heidelberg University, SaezLab)\n", - "\n", - "> NOTE: This notebook is still experimental." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - " \n", - "
Installed version:v1.0.0.dev3 (latest stable: v1.0.0-alpha)
Available backends:CVXPY v1.6.0, PICOS v2.5.1
Default backend (corneto.opt):CVXPY
Installed solvers:CLARABEL, CVXOPT, GLPK, GLPK_MI, GUROBI, HIGHS, SCIP, SCIPY
Graphviz version:v0.20.3
Installed path:/Users/pablorodriguezmier/Documents/work/projects/corneto/corneto
Repository:https://github.com/saezlab/corneto
\n", - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import corneto as cn\n", - "import corneto.methods.signaling.cellnopt_ilp as cno\n", - "\n", - "cn.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction to a Boolean model\n", - "\n", - "As a first step we define start with a small network and corresponding datasets just to illustrate the concepts. \n", - "The prior knowledge network of 'G1' contains three nodes, EGF, TNFa and Ras. Edges show the potential activation of the RAS protein. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "EGF\n", - "\n", - "EGF\n", - "\n", - "\n", - "\n", - "AND1\n", - "\n", - "AND1\n", - "\n", - "\n", - "\n", - "EGF->AND1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Ras\n", - "\n", - "Ras\n", - "\n", - "\n", - "\n", - "EGF->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AND1->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa\n", - "\n", - "TNFa\n", - "\n", - "\n", - "\n", - "TNFa->AND1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Definition of the prior knowledge network\n", - "G1 = cn.Graph.from_sif_tuples(\n", - " [\n", - " (\"EGF\", 1, \"AND1\"),\n", - " (\"TNFa\", 1, \"AND1\"),\n", - " (\"AND1\", 1, \"Ras\"),\n", - " (\"EGF\", 1, \"Ras\"),\n", - " (\"TNFa\", 1, \"Ras\"),\n", - " ]\n", - ")\n", - "G1.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we organize some in-silico experimental data into a format that can be used by the CNO toolbox.\n", - "\n", - "First, we define 4 conditions (exp0 ... exp3). In each condition, the activated nodes (EGF or TNFa) are set to 1 and the non-activated nodes to 0.\n", - "We also define the values of the corresponding outputs (Ras).\n", - "\n", - "Note that the output, Ras is only active (1.0) in the 4th condition, when both of the inputs are also activated. This corresponds to an AND relationship: RAS is active only if both EGF and TNFa are activated.\n", - "We expect that the optimization finds the correct subgraph of the above network, which contains only the 3 nodes with the AND gates, while individual edges from EGF and TNFa to RAS are removed. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# RAS is only active iff both EGF and TNFa are active -> we need to identify the AND gate\n", - "exp_list_G1_and = {\n", - " \"exp0\": {\"input\": {\"EGF\": 0, \"TNFa\": 0}, \"output\": {\"Ras\": 0}},\n", - " \"exp1\": {\"input\": {\"EGF\": 1, \"TNFa\": 0}, \"output\": {\"Ras\": 0}},\n", - " \"exp2\": {\"input\": {\"EGF\": 0, \"TNFa\": 1}, \"output\": {\"Ras\": 0}},\n", - " \"exp3\": {\"input\": {\"EGF\": 1, \"TNFa\": 1}, \"output\": {\"Ras\": 1}},\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "EGF\n", - "\n", - "EGF\n", - "\n", - "\n", - "\n", - "AND1\n", - "\n", - "AND1\n", - "\n", - "\n", - "\n", - "EGF->AND1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Ras\n", - "\n", - "Ras\n", - "\n", - "\n", - "\n", - "EGF->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AND1->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa\n", - "\n", - "TNFa\n", - "\n", - "\n", - "\n", - "TNFa->AND1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_5_target\n", - "\n", - "\n", - "\n", - "\n", - "Ras->e_5_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_6_source\n", - "\n", - "\n", - "\n", - "\n", - "e_6_source->EGF\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_7_source\n", - "\n", - "\n", - "\n", - "\n", - "e_7_source->TNFa\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G1_prep = cno.expand_graph_for_flows(G1, exp_list_G1_and)\n", - "G1_prep.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cno.plot_data(exp_list_G1_and)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set parameter Username\n", - "Set parameter LicenseID to value 2593994\n", - "Academic license - for non-commercial use only - expires 2025-12-02\n" - ] - } - ], - "source": [ - "P = cno.cellnoptILP(G1_prep, exp_list_G1_and, verbose=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cno.plot_fitness(G1, exp_list_G1_and, P, measured_only=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "EGF\n", - "\n", - "EGF\n", - "\n", - "\n", - "\n", - "AND1\n", - "\n", - "AND1\n", - "\n", - "\n", - "\n", - "EGF->AND1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Ras\n", - "\n", - "Ras\n", - "\n", - "\n", - "\n", - "EGF->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AND1->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa\n", - "\n", - "TNFa\n", - "\n", - "\n", - "\n", - "TNFa->AND1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_5_target\n", - "\n", - "\n", - "\n", - "\n", - "Ras->e_5_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_6_source\n", - "\n", - "\n", - "\n", - "\n", - "e_6_source->EGF\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_7_source\n", - "\n", - "\n", - "\n", - "\n", - "e_7_source->TNFa\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G1_prep.plot(custom_edge_attr=cno.cno_style(P, flow_name=\"edge_activates\", scale=None, iexp=3))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Tasks: \n", - "1. modify the experiments so that it corresponds to a scenario where only EGF could activate RAS, but not TNFa. \n", - "2. modify the experiments so that it corresponds to a scenario where both TNFa or EGF could activate RAS.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Inhibition: !EGF activates RAS\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define the same network but now we will add an inhibition of EGF on RAS. This means that if EGF is active, RAS should be inactive.\n", - "Note the difference in the definition of the prior knowledge network.\n", - "\n", - "We also change the experiments so that the output RAS is active only when TNFa is active." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Test graph\n", - "G2 = cn.Graph.from_sif_tuples(\n", - " [\n", - " (\"EGF\", -1, \"AND1\"),\n", - " (\"TNFa\", 1, \"AND1\"),\n", - " (\"AND1\", 1, \"Ras\"),\n", - " (\"EGF\", -1, \"Ras\"),\n", - " (\"TNFa\", 1, \"Ras\"),\n", - " ]\n", - ")\n", - "\n", - "# RAS is only active iff both EGF and TNFa are active -> we need to idetify the AND gate\n", - "exp_list_G2_egf = {\n", - " \"exp0\": {\"input\": {\"EGF\": 0, \"TNFa\": 0}, \"output\": {\"Ras\": 1}},\n", - " \"exp1\": {\"input\": {\"EGF\": 1, \"TNFa\": 0}, \"output\": {\"Ras\": 0}},\n", - " \"exp2\": {\"input\": {\"EGF\": 0, \"TNFa\": 1}, \"output\": {\"Ras\": 1}},\n", - " \"exp3\": {\"input\": {\"EGF\": 1, \"TNFa\": 1}, \"output\": {\"Ras\": 0}},\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "EGF\n", - "\n", - "EGF\n", - "\n", - "\n", - "\n", - "AND1\n", - "\n", - "AND1\n", - "\n", - "\n", - "\n", - "EGF->AND1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Ras\n", - "\n", - "Ras\n", - "\n", - "\n", - "\n", - "EGF->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AND1->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa\n", - "\n", - "TNFa\n", - "\n", - "\n", - "\n", - "TNFa->AND1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_5_target\n", - "\n", - "\n", - "\n", - "\n", - "Ras->e_5_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_6_source\n", - "\n", - "\n", - "\n", - "\n", - "e_6_source->EGF\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_7_source\n", - "\n", - "\n", - "\n", - "\n", - "e_7_source->TNFa\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G2_prep = cno.expand_graph_for_flows(G2, exp_list_G2_egf)\n", - "G2_prep.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "P2 = cno.cellnoptILP(G2_prep, exp_list_G2_egf, verbose=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cno.plot_fitness(G2_prep, exp_list_G2_egf, P2, measured_only=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "EGF\n", - "\n", - "EGF\n", - "\n", - "\n", - "\n", - "AND1\n", - "\n", - "AND1\n", - "\n", - "\n", - "\n", - "EGF->AND1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Ras\n", - "\n", - "Ras\n", - "\n", - "\n", - "\n", - "EGF->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AND1->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa\n", - "\n", - "TNFa\n", - "\n", - "\n", - "\n", - "TNFa->AND1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_5_target\n", - "\n", - "\n", - "\n", - "\n", - "Ras->e_5_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_6_source\n", - "\n", - "\n", - "\n", - "\n", - "e_6_source->EGF\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_7_source\n", - "\n", - "\n", - "\n", - "\n", - "e_7_source->TNFa\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G2_prep.plot(custom_edge_attr=cno.cno_style(P2, flow_name=\"edge_activates\", scale=None, iexp=0))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Inhibition with AND gates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Please remember, inhibitions are added to the incoming edge of the AND node. The outgoing edges of the AND node are always activating by convention." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Test graph\n", - "G2 = cn.Graph.from_sif_tuples(\n", - " [\n", - " (\"EGF\", -1, \"AND1\"),\n", - " (\"TNFa\", 1, \"AND1\"),\n", - " (\"AND1\", 1, \"Ras\"),\n", - " (\"EGF\", -1, \"Ras\"),\n", - " (\"TNFa\", 1, \"Ras\"),\n", - " ]\n", - ")\n", - "\n", - "# RAS is only active iff both EGF and TNFa are active -> we need to idetify the AND gate\n", - "exp_list_G2_and = {\n", - " \"exp0\": {\"input\": {\"EGF\": 0, \"TNFa\": 0}, \"output\": {\"Ras\": 0}},\n", - " \"exp1\": {\"input\": {\"EGF\": 1, \"TNFa\": 0}, \"output\": {\"Ras\": 0}},\n", - " \"exp2\": {\"input\": {\"EGF\": 0, \"TNFa\": 1}, \"output\": {\"Ras\": 1}},\n", - " \"exp3\": {\"input\": {\"EGF\": 1, \"TNFa\": 1}, \"output\": {\"Ras\": 0}},\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "EGF\n", - "\n", - "EGF\n", - "\n", - "\n", - "\n", - "AND1\n", - "\n", - "AND1\n", - "\n", - "\n", - "\n", - "EGF->AND1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Ras\n", - "\n", - "Ras\n", - "\n", - "\n", - "\n", - "EGF->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AND1->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa\n", - "\n", - "TNFa\n", - "\n", - "\n", - "\n", - "TNFa->AND1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_5_target\n", - "\n", - "\n", - "\n", - "\n", - "Ras->e_5_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_6_source\n", - "\n", - "\n", - "\n", - "\n", - "e_6_source->EGF\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_7_source\n", - "\n", - "\n", - "\n", - "\n", - "e_7_source->TNFa\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G2_prep = cno.expand_graph_for_flows(G2, exp_list_G2_and)\n", - "G2_prep.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "EGF\n", - "\n", - "EGF\n", - "\n", - "\n", - "\n", - "AND1\n", - "\n", - "AND1\n", - "\n", - "\n", - "\n", - "EGF->AND1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Ras\n", - "\n", - "Ras\n", - "\n", - "\n", - "\n", - "EGF->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AND1->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa\n", - "\n", - "TNFa\n", - "\n", - "\n", - "\n", - "TNFa->AND1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_5_target\n", - "\n", - "\n", - "\n", - "\n", - "Ras->e_5_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_6_source\n", - "\n", - "\n", - "\n", - "\n", - "e_6_source->EGF\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_7_source\n", - "\n", - "\n", - "\n", - "\n", - "e_7_source->TNFa\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "P2 = cno.cellnoptILP(G2_prep, exp_list_G2_and, verbose=False)\n", - "G2_prep.plot(custom_edge_attr=cno.cno_style(P2, flow_name=\"edge_activates\", scale=None, iexp=2))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cno.plot_fitness(G2_prep, exp_list_G2_and, P2, measured_only=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Complex case study with inhibition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at a more realistic case study which contains a larger network and more experiments.\n", - "\n", - "Also note that the we define inhibition in the experimental description. This could be, for example, the effect of small molecule kinase inhibitor. \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning: node 'TRAF6', graph '%3' size too small for label\n", - "Warning: node 'p90RSK', graph '%3' size too small for label\n", - "Warning: node 'TRAF6', graph '%3' size too small for label\n", - "Warning: node 'p90RSK', graph '%3' size too small for label\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "EGF\n", - "\n", - "EGF\n", - "\n", - "\n", - "\n", - "Ras\n", - "\n", - "Ras\n", - "\n", - "\n", - "\n", - "EGF->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PI3K\n", - "\n", - "PI3K\n", - "\n", - "\n", - "\n", - "EGF->PI3K\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Raf\n", - "\n", - "Raf\n", - "\n", - "\n", - "\n", - "Ras->Raf\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Akt\n", - "\n", - "Akt\n", - "\n", - "\n", - "\n", - "PI3K->Akt\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa\n", - "\n", - "TNFa\n", - "\n", - "\n", - "\n", - "TNFa->PI3K\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TRAF6\n", - "\n", - "TRAF6\n", - "\n", - "\n", - "\n", - "TNFa->TRAF6\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "p38\n", - "\n", - "p38\n", - "\n", - "\n", - "\n", - "TRAF6->p38\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Jnk\n", - "\n", - "Jnk\n", - "\n", - "\n", - "\n", - "TRAF6->Jnk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "NFkB\n", - "\n", - "NFkB\n", - "\n", - "\n", - "\n", - "TRAF6->NFkB\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Hsp27\n", - "\n", - "Hsp27\n", - "\n", - "\n", - "\n", - "p38->Hsp27\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "cJun\n", - "\n", - "cJun\n", - "\n", - "\n", - "\n", - "Jnk->cJun\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Mek\n", - "\n", - "Mek\n", - "\n", - "\n", - "\n", - "Akt->Mek\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Raf->Mek\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "p90RSK\n", - "\n", - "p90RSK\n", - "\n", - "\n", - "\n", - "Mek->p90RSK\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Erk\n", - "\n", - "Erk\n", - "\n", - "\n", - "\n", - "Mek->Erk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Erk->Hsp27\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G3 = cn.Graph.from_sif_tuples(\n", - " [\n", - " (\"EGF\", 1, \"Ras\"),\n", - " (\"EGF\", 1, \"PI3K\"),\n", - " (\"TNFa\", 1, \"PI3K\"),\n", - " (\"TNFa\", 1, \"TRAF6\"),\n", - " (\"TRAF6\", 1, \"p38\"),\n", - " (\"TRAF6\", 1, \"Jnk\"),\n", - " (\"TRAF6\", 1, \"NFkB\"),\n", - " (\"Jnk\", 1, \"cJun\"),\n", - " (\"p38\", 1, \"Hsp27\"),\n", - " (\"PI3K\", 1, \"Akt\"),\n", - " (\"Ras\", 1, \"Raf\"),\n", - " (\"Raf\", 1, \"Mek\"),\n", - " (\"Akt\", -1, \"Mek\"),\n", - " (\"Mek\", 1, \"p90RSK\"),\n", - " (\"Mek\", 1, \"Erk\"),\n", - " (\"Erk\", 1, \"Hsp27\"),\n", - " ]\n", - ")\n", - "# G3.add_edge((), 'EGF')\n", - "# G3.add_edge((), 'TNFa')\n", - "# G3.add_edge('Akt', ())\n", - "# G3.add_edge('cJun', ())\n", - "# G3.add_edge('Hsp27', ())\n", - "# G3.add_edge('NFkB', ())\n", - "# G3.add_edge('Erk', ())\n", - "# G3.add_edge('p90RSK', ())\n", - "# G3.add_edge('Jnk', ())\n", - "\n", - "exp_list_toy_full = {\n", - " \"exp0\": {\n", - " \"input\": {\"EGF\": 0, \"TNFa\": 0},\n", - " \"inhibition\": {},\n", - " \"output\": {\n", - " \"Akt\": 0,\n", - " \"Hsp27\": 0,\n", - " \"NFkB\": 0,\n", - " \"Erk\": 0,\n", - " \"p90RSK\": 0,\n", - " \"Jnk\": 0,\n", - " \"cJun\": 0,\n", - " },\n", - " },\n", - " \"exp1\": {\n", - " \"input\": {\"EGF\": 1, \"TNFa\": 0},\n", - " \"inhibition\": {},\n", - " \"output\": {\n", - " \"Akt\": 0.91,\n", - " \"Hsp27\": 0,\n", - " \"NFkB\": 0.86,\n", - " \"Erk\": 0.8,\n", - " \"p90RSK\": 0.88,\n", - " \"Jnk\": 0,\n", - " \"cJun\": 0,\n", - " },\n", - " },\n", - " \"exp2\": {\n", - " \"input\": {\"EGF\": 0, \"TNFa\": 1},\n", - " \"inhibition\": {},\n", - " \"output\": {\n", - " \"Akt\": 0.82,\n", - " \"Hsp27\": 0.7,\n", - " \"NFkB\": 0.90,\n", - " \"Erk\": 0.0,\n", - " \"p90RSK\": 0.0,\n", - " \"Jnk\": 0.25,\n", - " \"cJun\": 0.4,\n", - " },\n", - " },\n", - " \"exp3\": {\n", - " \"input\": {\"EGF\": 1, \"TNFa\": 1},\n", - " \"inhibition\": {},\n", - " \"output\": {\n", - " \"Akt\": 0.91,\n", - " \"Hsp27\": 0.7,\n", - " \"NFkB\": 0.90,\n", - " \"Erk\": 0.8,\n", - " \"p90RSK\": 0.88,\n", - " \"Jnk\": 0.25,\n", - " \"cJun\": 0.4,\n", - " },\n", - " },\n", - " \"exp4\": {\n", - " \"input\": {\"EGF\": 1, \"TNFa\": 0},\n", - " \"inhibition\": {\"Raf\": 1},\n", - " \"output\": {\n", - " \"Akt\": 0.91,\n", - " \"Hsp27\": 0,\n", - " \"NFkB\": 0.86,\n", - " \"Erk\": 0.0,\n", - " \"p90RSK\": 0.0,\n", - " \"Jnk\": 0,\n", - " \"cJun\": 0,\n", - " },\n", - " },\n", - " \"exp5\": {\n", - " \"input\": {\"EGF\": 0, \"TNFa\": 1},\n", - " \"inhibition\": {\"Raf\": 1},\n", - " \"output\": {\n", - " \"Akt\": 0.82,\n", - " \"Hsp27\": 0.7,\n", - " \"NFkB\": 0.90,\n", - " \"Erk\": 0.0,\n", - " \"p90RSK\": 0.0,\n", - " \"Jnk\": 0.25,\n", - " \"cJun\": 0.4,\n", - " },\n", - " },\n", - " \"exp6\": {\n", - " \"input\": {\"EGF\": 1, \"TNFa\": 1},\n", - " \"inhibition\": {\"Raf\": 1},\n", - " \"output\": {\n", - " \"Akt\": 0.91,\n", - " \"Hsp27\": 0.7,\n", - " \"NFkB\": 0.90,\n", - " \"Erk\": 0.0,\n", - " \"p90RSK\": 0.0,\n", - " \"Jnk\": 0.25,\n", - " \"cJun\": 0.4,\n", - " },\n", - " },\n", - " \"exp7\": {\n", - " \"input\": {\"EGF\": 1, \"TNFa\": 0},\n", - " \"inhibition\": {\"PI3K\": 1},\n", - " \"output\": {\n", - " \"Akt\": 0.0,\n", - " \"Hsp27\": 0,\n", - " \"NFkB\": 0.0,\n", - " \"Erk\": 0.8,\n", - " \"p90RSK\": 0.88,\n", - " \"Jnk\": 0,\n", - " \"cJun\": 0,\n", - " },\n", - " },\n", - " \"exp8\": {\n", - " \"input\": {\"EGF\": 0, \"TNFa\": 1},\n", - " \"inhibition\": {\"PI3K\": 1},\n", - " \"output\": {\n", - " \"Akt\": 0.0,\n", - " \"Hsp27\": 0.7,\n", - " \"NFkB\": 0.90,\n", - " \"Erk\": 0.0,\n", - " \"p90RSK\": 0.0,\n", - " \"Jnk\": 0.25,\n", - " \"cJun\": 0.4,\n", - " },\n", - " },\n", - " \"exp9\": {\n", - " \"input\": {\"EGF\": 1, \"TNFa\": 1},\n", - " \"inhibition\": {\"PI3K\": 1},\n", - " \"output\": {\n", - " \"Akt\": 0.0,\n", - " \"Hsp27\": 0.7,\n", - " \"NFkB\": 0.90,\n", - " \"Erk\": 0.8,\n", - " \"p90RSK\": 0.88,\n", - " \"Jnk\": 0.25,\n", - " \"cJun\": 0.4,\n", - " },\n", - " },\n", - "}\n", - "\n", - "\n", - "G3.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cno.plot_data(exp_list_toy_full)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning: node 'TRAF6', graph '%3' size too small for label\n", - "Warning: node 'p90RSK', graph '%3' size too small for label\n", - "Warning: node 'TRAF6', graph '%3' size too small for label\n", - "Warning: node 'p90RSK', graph '%3' size too small for label\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "EGF\n", - "\n", - "EGF\n", - "\n", - "\n", - "\n", - "Ras\n", - "\n", - "Ras\n", - "\n", - "\n", - "\n", - "EGF->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PI3K\n", - "\n", - "PI3K\n", - "\n", - "\n", - "\n", - "EGF->PI3K\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Raf\n", - "\n", - "Raf\n", - "\n", - "\n", - "\n", - "Ras->Raf\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Akt\n", - "\n", - "Akt\n", - "\n", - "\n", - "\n", - "PI3K->Akt\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa\n", - "\n", - "TNFa\n", - "\n", - "\n", - "\n", - "TNFa->PI3K\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TRAF6\n", - "\n", - "TRAF6\n", - "\n", - "\n", - "\n", - "TNFa->TRAF6\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "p38\n", - "\n", - "p38\n", - "\n", - "\n", - "\n", - "TRAF6->p38\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Jnk\n", - "\n", - "Jnk\n", - "\n", - "\n", - "\n", - "TRAF6->Jnk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "NFkB\n", - "\n", - "NFkB\n", - "\n", - "\n", - "\n", - "TRAF6->NFkB\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Hsp27\n", - "\n", - "Hsp27\n", - "\n", - "\n", - "\n", - "p38->Hsp27\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "cJun\n", - "\n", - "cJun\n", - "\n", - "\n", - "\n", - "Jnk->cJun\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_22_target\n", - "\n", - "\n", - "\n", - "\n", - "Jnk->e_22_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_20_target\n", - "\n", - "\n", - "\n", - "\n", - "NFkB->e_20_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_19_target\n", - "\n", - "\n", - "\n", - "\n", - "cJun->e_19_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_16_target\n", - "\n", - "\n", - "\n", - "\n", - "Hsp27->e_16_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Mek\n", - "\n", - "Mek\n", - "\n", - "\n", - "\n", - "Akt->Mek\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_21_target\n", - "\n", - "\n", - "\n", - "\n", - "Akt->e_21_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Raf->Mek\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "p90RSK\n", - "\n", - "p90RSK\n", - "\n", - "\n", - "\n", - "Mek->p90RSK\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Erk\n", - "\n", - "Erk\n", - "\n", - "\n", - "\n", - "Mek->Erk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_17_target\n", - "\n", - "\n", - "\n", - "\n", - "p90RSK->e_17_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Erk->Hsp27\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_18_target\n", - "\n", - "\n", - "\n", - "\n", - "Erk->e_18_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_23_source\n", - "\n", - "\n", - "\n", - "\n", - "e_23_source->EGF\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_24_source\n", - "\n", - "\n", - "\n", - "\n", - "e_24_source->TNFa\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G3_prep = cno.expand_graph_for_flows(G3, exp_list_toy_full)\n", - "G3_prep.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "P_G3 = cno.cellnoptILP(G3_prep, exp_list_toy_full, verbose=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning: node 'TRAF6', graph '%3' size too small for label\n", - "Warning: node 'p90RSK', graph '%3' size too small for label\n", - "Warning: node 'TRAF6', graph '%3' size too small for label\n", - "Warning: node 'p90RSK', graph '%3' size too small for label\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "EGF\n", - "\n", - "EGF\n", - "\n", - "\n", - "\n", - "Ras\n", - "\n", - "Ras\n", - "\n", - "\n", - "\n", - "EGF->Ras\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PI3K\n", - "\n", - "PI3K\n", - "\n", - "\n", - "\n", - "EGF->PI3K\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Raf\n", - "\n", - "Raf\n", - "\n", - "\n", - "\n", - "Ras->Raf\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Akt\n", - "\n", - "Akt\n", - "\n", - "\n", - "\n", - "PI3K->Akt\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TNFa\n", - "\n", - "TNFa\n", - "\n", - "\n", - "\n", - "TNFa->PI3K\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TRAF6\n", - "\n", - "TRAF6\n", - "\n", - "\n", - "\n", - "TNFa->TRAF6\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "p38\n", - "\n", - "p38\n", - "\n", - "\n", - "\n", - "TRAF6->p38\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Jnk\n", - "\n", - "Jnk\n", - "\n", - "\n", - "\n", - "TRAF6->Jnk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "NFkB\n", - "\n", - "NFkB\n", - "\n", - "\n", - "\n", - "TRAF6->NFkB\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Hsp27\n", - "\n", - "Hsp27\n", - "\n", - "\n", - "\n", - "p38->Hsp27\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "cJun\n", - "\n", - "cJun\n", - "\n", - "\n", - "\n", - "Jnk->cJun\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_22_target\n", - "\n", - "\n", - "\n", - "\n", - "Jnk->e_22_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_20_target\n", - "\n", - "\n", - "\n", - "\n", - "NFkB->e_20_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_19_target\n", - "\n", - "\n", - "\n", - "\n", - "cJun->e_19_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_16_target\n", - "\n", - "\n", - "\n", - "\n", - "Hsp27->e_16_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Mek\n", - "\n", - "Mek\n", - "\n", - "\n", - "\n", - "Akt->Mek\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_21_target\n", - "\n", - "\n", - "\n", - "\n", - "Akt->e_21_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Raf->Mek\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "p90RSK\n", - "\n", - "p90RSK\n", - "\n", - "\n", - "\n", - "Mek->p90RSK\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Erk\n", - "\n", - "Erk\n", - "\n", - "\n", - "\n", - "Mek->Erk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_17_target\n", - "\n", - "\n", - "\n", - "\n", - "p90RSK->e_17_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Erk->Hsp27\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_18_target\n", - "\n", - "\n", - "\n", - "\n", - "Erk->e_18_target\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_23_source\n", - "\n", - "\n", - "\n", - "\n", - "e_23_source->EGF\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_24_source\n", - "\n", - "\n", - "\n", - "\n", - "e_24_source->TNFa\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G3_prep.plot(custom_edge_attr=cno.cno_style(P_G3, flow_name=\"edge_activates\", scale=None, iexp=3))" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cno.plot_fitness(G3_prep, exp_list_toy_full, P_G3)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/guide/signaling/cellnopt_dag.ipynb b/docs/guide/signaling/cellnopt_dag.ipynb new file mode 100644 index 000000000..15aeadd58 --- /dev/null +++ b/docs/guide/signaling/cellnopt_dag.ipynb @@ -0,0 +1,3346 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d0d6ea50", + "metadata": { + "papermill": { + "duration": 0.008751, + "end_time": "2026-09-21T23:05:18.033111", + "exception": false, + "start_time": "2026-09-21T23:05:18.024360", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Inferring intracellular signaling models with CellNOptDAG\n", + "\n", + "Many signaling experiments follow the same design: stimulate a receptor or inhibit a signaling protein, wait until a chosen endpoint, and measure a panel of phosphoproteins or reporters. Each experimental condition is therefore a **snapshot of the cellular response to an intervention**.\n", + "\n", + "This method is built around one question:\n", + "\n", + "> **What is the smallest connected feed-forward signaling logic sufficient to explain the observed endpoint responses across all interventions?**\n", + "\n", + "A prior-knowledge network (PKN) supplies the candidate biology. It collects signaling interactions reported in the literature or databases and is deliberately inclusive: it may contain alternative routes, AND gates, inhibitory effects, and feedback loops. `CellNOptDAG` searches this network for a small Boolean signaling model that answers the question above across the complete experiment.\n", + "\n", + "Proteins are represented as inactive (`0`) or active (`1`), and the same pathway structure is used in every condition. The interventions change which parts of that pathway can respond. This is a simplified model of intracellular signal transmission—not a reconstruction of every molecular event between stimulation and measurement.\n", + "\n", + "The worked example develops the answer in three steps:\n", + "\n", + "1. identify which candidate signaling reactions are needed;\n", + "2. examine how the predicted pathway response changes after each stimulation or inhibition;\n", + "3. compare the predicted activities with the measured readouts.\n", + "\n", + "The main workflow explains these steps visually. The final **Under the hood** section connects the biological intuition to the Boolean and optimization formulation." + ] + }, + { + "cell_type": "markdown", + "id": "4b824f5a", + "metadata": { + "papermill": { + "duration": 0.003951, + "end_time": "2026-09-21T23:05:18.042580", + "exception": false, + "start_time": "2026-09-21T23:05:18.038629", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## One signaling model, several perturbation conditions\n", + "\n", + "CellNOpt learns one pathway diagram from the complete experiment. What happens on that diagram changes from one condition to another.\n", + "\n", + "| Part of the experiment | Biological meaning | Shared across conditions? |\n", + "|---|---|---|\n", + "| Selected reactions | The signaling interactions retained in the inferred pathway | Yes |\n", + "| Stimuli and inhibitors | Which proteins are experimentally forced on or off | No |\n", + "| Predicted protein activity | The model's active/inactive response after each intervention | No |\n", + "| Active reactions | The retained interactions through which signal can pass in that condition | No |\n", + "| Measured readouts | The observed endpoint response used to evaluate the prediction | No |\n", + "\n", + "A reaction may therefore belong to the inferred pathway but remain inactive in a particular condition. For example, an `A → X` reaction can be part of the shared model even when `A` was not stimulated in one experiment.\n", + "\n", + "CORNETO also checks that the retained pathway connects experimental interventions to measured responses. That computational check is explained later; it should not be interpreted as a measured biochemical flux." + ] + }, + { + "cell_type": "markdown", + "id": "5e4ca25c", + "metadata": { + "papermill": { + "duration": 0.003213, + "end_time": "2026-09-21T23:05:18.049434", + "exception": false, + "start_time": "2026-09-21T23:05:18.046221", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Relationship to CellNOptR\n", + "\n", + "[CellNOptR](https://doi.org/10.1186/1752-0509-6-133) established the idea of training logic models of intracellular signaling against perturbation data. It can iteratively propagate Boolean states toward a steady state and can represent successive pseudo-steady states when several experimental times are available.\n", + "\n", + "This guide addresses the simpler case of one response snapshot per intervention. It retains the CellNOpt ideas of selecting reactions, evaluating Boolean responses in every condition, and balancing data fit against model size. CORNETO adds two restrictions for this endpoint setting: the selected model must be acyclic, and every selected reaction must belong to a connected route from an intervention to a readout. These additions are summarized in **Under the hood**." + ] + }, + { + "cell_type": "markdown", + "id": "adb8ac7f", + "metadata": { + "papermill": { + "duration": 0.002864, + "end_time": "2026-09-21T23:05:18.055525", + "exception": false, + "start_time": "2026-09-21T23:05:18.052661", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Example: perturbations and two signaling readouts\n", + "\n", + "We use abstract labels so that the logic is easy to follow. `A`, `C`, and `D` play the role of experimentally controlled upstream signals; `B`, `X`, and `W` are intracellular signaling proteins; and `Y` and `Z` are measured downstream responses.\n", + "\n", + "The candidate network includes several plausible mechanisms:\n", + "\n", + "- `D → B`, so activating `D` can indirectly block `A AND NOT B → X`;\n", + "- a branch from `X` toward both readouts;\n", + "- `W AND C → Z`, representing a response that needs two signals simultaneously;\n", + "- alternative ways to activate `Y` through `X`, `C`, or the absence of `D`.\n", + "\n", + "The PKN also contains shortcuts and feedback interactions. They remain legitimate biological hypotheses. The question is whether the snapshot experiment contains enough information to retain them in the fitted endpoint model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3ec145a6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:18.062075Z", + "iopub.status.busy": "2026-09-21T23:05:18.061947Z", + "iopub.status.idle": "2026-09-21T23:05:18.831543Z", + "shell.execute_reply": "2026-09-21T23:05:18.831017Z" + }, + "papermill": { + "duration": 0.773943, + "end_time": "2026-09-21T23:05:18.832301", + "exception": false, + "start_time": "2026-09-21T23:05:18.058358", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from IPython.display import display\n", + "\n", + "import corneto as cn\n", + "from corneto.methods.signaling import (\n", + " CellNOptDAG,\n", + " plot_cellnopt_fit,\n", + " plot_cellnopt_model,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ccf4fa07", + "metadata": { + "papermill": { + "duration": 0.001771, + "end_time": "2026-09-21T23:05:18.836144", + "exception": false, + "start_time": "2026-09-21T23:05:18.834373", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Define the prior-knowledge network\n", + "\n", + "Positive interactions use sign `+1`; inhibitory interactions use sign `−1`. The temporary nodes `AND1` and `AND2` are the usual SIF representation of two combinatorial signaling rules:\n", + "\n", + "- `A AND NOT B → X`: `A` must be active while `B` must be inactive;\n", + "- `W AND C → Z`: both `W` and `C` must be active.\n", + "\n", + "CORNETO treats every complete AND rule as one candidate reaction. The network also includes direct routes to `Z` and the feedback candidates `Y → Z → X` and `Z → W → Z`. Thus the PKN contains cycles even though the fitted endpoint model will not." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "29fbd626", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:18.840235Z", + "iopub.status.busy": "2026-09-21T23:05:18.840122Z", + "iopub.status.idle": "2026-09-21T23:05:18.845761Z", + "shell.execute_reply": "2026-09-21T23:05:18.845358Z" + }, + "papermill": { + "duration": 0.008365, + "end_time": "2026-09-21T23:05:18.846228", + "exception": false, + "start_time": "2026-09-21T23:05:18.837863", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " source sign target\n", + "0 A 1 AND1\n", + "1 B -1 AND1\n", + "2 AND1 1 X\n", + "3 D 1 B\n", + "4 X 1 Y\n", + "5 X 1 W\n", + "6 W 1 AND2\n", + "7 C 1 AND2\n", + "8 AND2 1 Z\n", + "9 C 1 Y\n", + "10 D -1 Y\n", + "11 W 1 Z\n", + "12 C 1 Z\n", + "13 Y 1 Z\n", + "14 Z 1 X\n", + "15 Z -1 W" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pkn_edges = [\n", + " (\"A\", 1, \"AND1\"),\n", + " (\"B\", -1, \"AND1\"),\n", + " (\"AND1\", 1, \"X\"),\n", + " (\"D\", 1, \"B\"),\n", + " (\"X\", 1, \"Y\"),\n", + " (\"X\", 1, \"W\"),\n", + " (\"W\", 1, \"AND2\"),\n", + " (\"C\", 1, \"AND2\"),\n", + " (\"AND2\", 1, \"Z\"),\n", + " (\"C\", 1, \"Y\"),\n", + " (\"D\", -1, \"Y\"),\n", + " (\"W\", 1, \"Z\"),\n", + " (\"C\", 1, \"Z\"),\n", + " (\"Y\", 1, \"Z\"),\n", + " (\"Z\", 1, \"X\"),\n", + " (\"Z\", -1, \"W\"),\n", + "]\n", + "pkn = cn.Graph.from_tuples(pkn_edges)\n", + "\n", + "pd.DataFrame(pkn_edges, columns=[\"source\", \"sign\", \"target\"])" + ] + }, + { + "cell_type": "markdown", + "id": "fbad6a30", + "metadata": { + "papermill": { + "duration": 0.001789, + "end_time": "2026-09-21T23:05:18.849938", + "exception": false, + "start_time": "2026-09-21T23:05:18.848149", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Define the perturbation experiment\n", + "\n", + "Each row below corresponds to one experimental condition. It records what was done to the cells and what was measured afterward:\n", + "\n", + "- **input:** `1` forces an upstream signal on and `0` keeps it off, representing stimulus present or absent in this simplified experiment;\n", + "- **inhibit:** `1` applies an inhibitor and forces its target off;\n", + "- **readout:** the observed activity at the endpoint.\n", + "\n", + "`Y` and `Z` are the only measured responses. Interventions target the upstream or internal signaling proteins `A–D` and `W`, so the roles of experimental cue and downstream readout remain distinct. The interpretation column is a short label for the reader and is not used by the model.\n", + "\n", + "The informative part of the design is the contrast between rows. `C1` and `C2` receive the same upstream inputs, but only `C1` inhibits `B`; their different `Y` responses test the route `D → B` followed by the first AND gate. `C3` and `C4` test whether `C` alone is sufficient for `Z`, or whether `W` is also required. `C4` and `C6` have the same inputs, but inhibiting `W` in `C6` turns `Z` off while leaving `Y` active, directly testing the `W AND C → Z` branch." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "47eeb812", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:18.854156Z", + "iopub.status.busy": "2026-09-21T23:05:18.854087Z", + "iopub.status.idle": "2026-09-21T23:05:18.856522Z", + "shell.execute_reply": "2026-09-21T23:05:18.856211Z" + }, + "papermill": { + "duration": 0.005151, + "end_time": "2026-09-21T23:05:18.857022", + "exception": false, + "start_time": "2026-09-21T23:05:18.851871", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "inputs = {\n", + " \"C1\": {\"A\": 1, \"C\": 0, \"D\": 1},\n", + " \"C2\": {\"A\": 1, \"C\": 0, \"D\": 1},\n", + " \"C3\": {\"A\": 0, \"C\": 1, \"D\": 1},\n", + " \"C4\": {\"A\": 1, \"C\": 1, \"D\": 0},\n", + " \"C5\": {\"A\": 0, \"C\": 0, \"D\": 0},\n", + " \"C6\": {\"A\": 1, \"C\": 1, \"D\": 0},\n", + "}\n", + "measurements = {\n", + " \"C1\": {\"Y\": 1, \"Z\": 0},\n", + " \"C2\": {\"Y\": 0, \"Z\": 0},\n", + " \"C3\": {\"Y\": 1, \"Z\": 0},\n", + " \"C4\": {\"Y\": 1, \"Z\": 1},\n", + " \"C5\": {\"Y\": 1, \"Z\": 0},\n", + " \"C6\": {\"Y\": 1, \"Z\": 0},\n", + "}\n", + "inhibitors = {\n", + " \"C1\": {\"B\": 1},\n", + " \"C2\": {},\n", + " \"C3\": {},\n", + " \"C4\": {},\n", + " \"C5\": {},\n", + " \"C6\": {\"W\": 1},\n", + "}\n", + "behavior = {\n", + " \"C1\": \"B inhibition enables AND1\",\n", + " \"C2\": \"D activates B and blocks AND1\",\n", + " \"C3\": \"C alone\",\n", + " \"C4\": \"AND2 enabled\",\n", + " \"C5\": \"negative-literal route\",\n", + " \"C6\": \"W inhibited; Z lost\",\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "426761ba", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:18.861150Z", + "iopub.status.busy": "2026-09-21T23:05:18.861081Z", + "iopub.status.idle": "2026-09-21T23:05:18.865413Z", + "shell.execute_reply": "2026-09-21T23:05:18.865151Z" + }, + "papermill": { + "duration": 0.007075, + "end_time": "2026-09-21T23:05:18.865943", + "exception": false, + "start_time": "2026-09-21T23:05:18.858868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " interpretation input A input C input D \\\n", + "condition \n", + "C1 B inhibition enables AND1 1 0 1 \n", + "C2 D activates B and blocks AND1 1 0 1 \n", + "C3 C alone 0 1 1 \n", + "C4 AND2 enabled 1 1 0 \n", + "C5 negative-literal route 0 0 0 \n", + "C6 W inhibited; Z lost 1 1 0 \n", + "\n", + " inhibit B inhibit W readout Y readout Z \n", + "condition \n", + "C1 1 0 1 0 \n", + "C2 0 0 0 0 \n", + "C3 0 0 1 0 \n", + "C4 0 0 1 1 \n", + "C5 0 0 1 0 \n", + "C6 0 1 1 0 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "experiment_table = pd.DataFrame(index=inputs)\n", + "experiment_table.index.name = \"condition\"\n", + "experiment_table[\"interpretation\"] = pd.Series(behavior)\n", + "for species in [\"A\", \"C\", \"D\"]:\n", + " experiment_table[f\"input {species}\"] = [inputs[name][species] for name in inputs]\n", + "for species in [\"B\", \"W\"]:\n", + " experiment_table[f\"inhibit {species}\"] = [inhibitors[name].get(species, 0) for name in inputs]\n", + "for species in [\"Y\", \"Z\"]:\n", + " experiment_table[f\"readout {species}\"] = [measurements[name][species] for name in inputs]\n", + "\n", + "experiment_table" + ] + }, + { + "cell_type": "markdown", + "id": "013eca6a", + "metadata": { + "papermill": { + "duration": 0.001825, + "end_time": "2026-09-21T23:05:18.869625", + "exception": false, + "start_time": "2026-09-21T23:05:18.867800", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Fit one pathway to the complete experiment\n", + "\n", + "All six conditions are fitted together because they are assumed to probe the same underlying signaling network. The model first tries to reproduce the measured `Y` and `Z` responses. Among similarly fitting explanations, it prefers the one using fewer reactions.\n", + "\n", + "`lambda_reg` controls this preference for a smaller model. Its value is deliberately low here: agreement with the experimental readouts is more important than removing one additional reaction." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "92d6dbf8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:18.873798Z", + "iopub.status.busy": "2026-09-21T23:05:18.873736Z", + "iopub.status.idle": "2026-09-21T23:05:18.920545Z", + "shell.execute_reply": "2026-09-21T23:05:18.920159Z" + }, + "papermill": { + "duration": 0.049617, + "end_time": "2026-09-21T23:05:18.921072", + "exception": false, + "start_time": "2026-09-21T23:05:18.871455", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'optimal'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "method = CellNOptDAG(lambda_reg=1e-3)\n", + "problem = method.build_many(\n", + " pkn,\n", + " inputs=inputs,\n", + " measurements=measurements,\n", + " inhibitors=inhibitors,\n", + ")\n", + "result = problem.solve(solver=\"SCIPY\")\n", + "result.status" + ] + }, + { + "cell_type": "markdown", + "id": "28a99a87", + "metadata": { + "papermill": { + "duration": 0.001916, + "end_time": "2026-09-21T23:05:18.925109", + "exception": false, + "start_time": "2026-09-21T23:05:18.923193", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Which signaling reactions are supported?\n", + "\n", + "The fitted model retains `D → B`, both AND gates, and three alternative routes to `Y`. Together, these reactions explain why different interventions can produce different combinations of `Y` and `Z` activity.\n", + "\n", + "The direct shortcuts to `Z` and the feedback reactions are not needed for these observations. They remain in the PKN as biological possibilities, but this particular endpoint experiment does not support including them in the smaller fitted model." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "810e64c4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:18.929419Z", + "iopub.status.busy": "2026-09-21T23:05:18.929338Z", + "iopub.status.idle": "2026-09-21T23:05:18.932441Z", + "shell.execute_reply": "2026-09-21T23:05:18.932106Z" + }, + "papermill": { + "duration": 0.005982, + "end_time": "2026-09-21T23:05:18.932927", + "exception": false, + "start_time": "2026-09-21T23:05:18.926945", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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reactionselected
0D -> B1
1X -> Y1
2X -> W1
3C -> Y1
4NOT D -> Y1
5W -> Z0
6C -> Z0
7Y -> Z0
8Z -> X0
9NOT Z -> W0
10A AND NOT B -> X1
11W AND C -> Z1
\n", + "
" + ], + "text/plain": [ + " reaction selected\n", + "0 D -> B 1\n", + "1 X -> Y 1\n", + "2 X -> W 1\n", + "3 C -> Y 1\n", + "4 NOT D -> Y 1\n", + "5 W -> Z 0\n", + "6 C -> Z 0\n", + "7 Y -> Z 0\n", + "8 Z -> X 0\n", + "9 NOT Z -> W 0\n", + "10 A AND NOT B -> X 1\n", + "11 W AND C -> Z 1" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def reaction_label(reaction):\n", + " literals = [str(node) for node in reaction.positive_literals]\n", + " literals += [f\"NOT {node}\" for node in reaction.negative_literals]\n", + " return f\"{' AND '.join(literals)} -> {reaction.product}\"\n", + "\n", + "reaction_labels = [reaction_label(reaction) for reaction in method.reactions]\n", + "selected = np.rint(problem.expr.reaction_selected.value).astype(int).reshape(-1)\n", + "pd.DataFrame({\"reaction\": reaction_labels, \"selected\": selected})" + ] + }, + { + "cell_type": "markdown", + "id": "43c08897", + "metadata": { + "papermill": { + "duration": 0.001856, + "end_time": "2026-09-21T23:05:18.936680", + "exception": false, + "start_time": "2026-09-21T23:05:18.934824", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Compare the candidate network with the fitted model\n", + "\n", + "This plot shows the complete candidate network. Reactions retained in the fitted model use the main styling; alternatives that were available but not selected are pale and dotted.\n", + "\n", + "Keeping the unselected reactions visible is useful here. It shows that the feedback loops and shortcuts were offered to the model rather than silently removed from the PKN." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "95f30c93", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:18.940952Z", + "iopub.status.busy": "2026-09-21T23:05:18.940879Z", + "iopub.status.idle": "2026-09-21T23:05:19.140666Z", + "shell.execute_reply": "2026-09-21T23:05:19.140230Z" + }, + "papermill": { + "duration": 0.202722, + "end_time": "2026-09-21T23:05:19.141257", + "exception": false, + "start_time": "2026-09-21T23:05:18.938535", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D->B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Y\n", + "\n", + "\n", + "Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_10\n", + "\n", + "\n", + "AND\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->__cellnopt_and_10\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Z\n", + "\n", + "\n", + "Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Y->Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "W->Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_11\n", + "\n", + "\n", + "AND\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "W->__cellnopt_and_11\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->__cellnopt_and_11\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Z->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Z->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->__cellnopt_and_10\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_10->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_11->Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_cellnopt_model(method, show_unselected=True)" + ] + }, + { + "cell_type": "markdown", + "id": "269f1115", + "metadata": { + "papermill": { + "duration": 0.002053, + "end_time": "2026-09-21T23:05:19.145612", + "exception": false, + "start_time": "2026-09-21T23:05:19.143559", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Inspect the fitted signaling model\n", + "\n", + "Hiding unselected reactions leaves the signaling hypothesis supported by this experiment. `D → B` controls the first AND gate, `X` branches toward `Y` and `W`, and the second AND gate combines `W` with `C` to activate `Z`. Separate routes can activate `Y` under different perturbations.\n", + "\n", + "This is the shared pathway diagram. The next plots show how much of it is active in each condition." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "386f1f50", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:19.150276Z", + "iopub.status.busy": "2026-09-21T23:05:19.150185Z", + "iopub.status.idle": "2026-09-21T23:05:19.340113Z", + "shell.execute_reply": "2026-09-21T23:05:19.339666Z" + }, + "papermill": { + "duration": 0.193052, + "end_time": "2026-09-21T23:05:19.340659", + "exception": false, + "start_time": "2026-09-21T23:05:19.147607", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D->B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Y\n", + "\n", + "\n", + "Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_10\n", + "\n", + "\n", + "AND\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->__cellnopt_and_10\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_11\n", + "\n", + "\n", + "AND\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "W->__cellnopt_and_11\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->__cellnopt_and_11\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->__cellnopt_and_10\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_10->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Z\n", + "\n", + "\n", + "Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_11->Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_cellnopt_model(method)" + ] + }, + { + "cell_type": "markdown", + "id": "08919801", + "metadata": { + "papermill": { + "duration": 0.002145, + "end_time": "2026-09-21T23:05:19.345199", + "exception": false, + "start_time": "2026-09-21T23:05:19.343054", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Reuse the selected reactions on unseen conditions\n", + "\n", + "The `build_many`/`solve` example above is the advanced, composable route: add optional CORNETO constraints to the `ProblemDef` before solving. When no custom constraints are needed, `fit` is the convenience wrapper and returns the same method instance. The selected `reaction_selected` values can then be applied to named conditions that were not used during fitting:\n", + "\n", + "- `predict` accepts only inputs and inhibitors. It does not accept measurements, never solves, and deterministically propagates the fixed selected reactions in topological order. Inputs and active inhibitors clamp their targets exactly as in training; every other species is the OR of its active selected producing reactions.\n", + "- `evaluate` compares those fixed predictions with measurements afterward. Measurement mappings may use the same condition-key set in a different insertion order. Forced species are excluded from the reported loss.\n", + "\n", + "Prediction and evaluation use named outer condition mappings, as in `build_many`; the single-condition `build` convenience accepts an inner mapping for one condition." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6aeab7d6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:19.350156Z", + "iopub.status.busy": "2026-09-21T23:05:19.350071Z", + "iopub.status.idle": "2026-09-21T23:05:19.380037Z", + "shell.execute_reply": "2026-09-21T23:05:19.379658Z" + }, + "papermill": { + "duration": 0.033193, + "end_time": "2026-09-21T23:05:19.380497", + "exception": false, + "start_time": "2026-09-21T23:05:19.347304", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 X -> Y\n", + "1 X -> W\n", + "2 C -> Y\n", + "3 NOT D -> Y\n", + "4 A AND NOT B -> X\n", + "5 W AND C -> Z\n", + "Name: selected held-out reactions, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "loss 0.25\n", + "count 4.00\n", + "Name: global report, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "C2 0.5\n", + "C6 0.0\n", + "Name: by_condition, dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Hold out C2 and C6: together they test an uninhibited negative literal\n", + "# condition and W-inhibitor clamping, while C1, C3-C5 retain the positive,\n", + "# negative-literal, OR, and AND mechanisms needed to identify the pathway.\n", + "heldout_conditions = (\"C2\", \"C6\")\n", + "train_inputs = {name: values for name, values in inputs.items() if name not in heldout_conditions}\n", + "train_measurements = {name: values for name, values in measurements.items() if name not in heldout_conditions}\n", + "train_inhibitors = {name: values for name, values in inhibitors.items() if name not in heldout_conditions}\n", + "test_inputs = {name: inputs[name] for name in heldout_conditions}\n", + "test_inhibitors = {name: inhibitors[name] for name in heldout_conditions}\n", + "# Deliberately reverse the condition insertion order to show key-based alignment.\n", + "test_measurements = {name: measurements[name] for name in reversed(heldout_conditions)}\n", + "\n", + "heldout_model = CellNOptDAG(lambda_reg=1e-3).fit(\n", + " pkn,\n", + " inputs=train_inputs,\n", + " measurements=train_measurements,\n", + " inhibitors=train_inhibitors,\n", + " solve_options={\"solver\": \"SCIPY\"},\n", + ")\n", + "heldout_predictions = heldout_model.predict(\n", + " inputs=test_inputs,\n", + " inhibitors=test_inhibitors,\n", + ")\n", + "heldout_report = heldout_model.evaluate(\n", + " inputs=test_inputs,\n", + " measurements=test_measurements,\n", + " inhibitors=test_inhibitors,\n", + ")\n", + "heldout_selected = [\n", + " reaction_label(heldout_model.reactions[index])\n", + " for index in heldout_model.get_selected_reaction_indices()\n", + "]\n", + "display(pd.Series(heldout_selected, name=\"selected held-out reactions\"))\n", + "display(pd.Series(\n", + " {\"loss\": heldout_report[\"loss\"], \"count\": heldout_report[\"count\"]},\n", + " name=\"global report\",\n", + ").round(3))\n", + "pd.Series(heldout_report[\"by_condition\"], name=\"by_condition\").round(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "aa70a7bf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:19.385608Z", + "iopub.status.busy": "2026-09-21T23:05:19.385518Z", + "iopub.status.idle": "2026-09-21T23:05:19.389357Z", + "shell.execute_reply": "2026-09-21T23:05:19.388995Z" + }, + "papermill": { + "duration": 0.006917, + "end_time": "2026-09-21T23:05:19.389789", + "exception": false, + "start_time": "2026-09-21T23:05:19.382872", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " value predicted forced\n", + "id \n", + "A 1.0 False True\n", + "W 0.0 False True\n", + "Y 1.0 True False\n", + "Z 0.0 True False" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(\n", + " [feature.to_dict() for feature in heldout_predictions.samples[\"C6\"].features]\n", + ").set_index(\"id\")[[\"value\", \"predicted\", \"forced\"]].loc[[\"A\", \"W\", \"Y\", \"Z\"]]" + ] + }, + { + "cell_type": "markdown", + "id": "8f217405", + "metadata": { + "papermill": { + "duration": 0.002228, + "end_time": "2026-09-21T23:05:19.394370", + "exception": false, + "start_time": "2026-09-21T23:05:19.392142", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The returned `Data` preserves condition/species labels and distinguishes externally forced values from model predictions: input and inhibitor targets have `forced=True, predicted=False`, while propagated species have `predicted=True`. The `predictions` entry in the evaluation report carries the same metadata. This held-out example is separate from the in-sample fit plot below; no held-out measurement is supplied to `predict`." + ] + }, + { + "cell_type": "markdown", + "id": "f0aa9784", + "metadata": { + "papermill": { + "duration": 0.00222, + "end_time": "2026-09-21T23:05:19.398849", + "exception": false, + "start_time": "2026-09-21T23:05:19.396629", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Does the model reproduce the measured responses?\n", + "\n", + "Each row is one intervention condition, and the `Y` and `Z` panels match the readout columns in the experiment table:\n", + "\n", + "- red open circles show the measured endpoint response;\n", + "- blue squares show the model prediction;\n", + "- a vertical segment would show a disagreement;\n", + "- the background color summarizes the prediction error.\n", + "\n", + "Only measured signaling responses are shown in this main fit view. Predicted activities of unmeasured internal proteins are inspected later, where they are clearly distinguished from experimental observations." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9438b958", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:19.403851Z", + "iopub.status.busy": "2026-09-21T23:05:19.403771Z", + "iopub.status.idle": "2026-09-21T23:05:19.914732Z", + "shell.execute_reply": "2026-09-21T23:05:19.914310Z" + }, + "papermill": { + "duration": 0.514146, + "end_time": "2026-09-21T23:05:19.915218", + "exception": false, + "start_time": "2026-09-21T23:05:19.401072", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit_figure, fit_axes = plot_cellnopt_fit(method)" + ] + }, + { + "cell_type": "markdown", + "id": "9a672bb9", + "metadata": { + "papermill": { + "duration": 0.002365, + "end_time": "2026-09-21T23:05:19.920227", + "exception": false, + "start_time": "2026-09-21T23:05:19.917862", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The horizontal position is labelled **Endpoint** because these data contain one post-intervention snapshot, not a time course. The plot does not create an artificial starting value or assume that the first condition is a baseline. If future formulations model several measured time points, observed and predicted values can instead be placed at their actual sampling times." + ] + }, + { + "cell_type": "markdown", + "id": "012732df", + "metadata": { + "papermill": { + "duration": 0.00228, + "end_time": "2026-09-21T23:05:19.924810", + "exception": false, + "start_time": "2026-09-21T23:05:19.922530", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## A pathway reaction can be present but inactive\n", + "\n", + "It helps to separate the **pathway map** from the **response in one experiment**. A road can be present on a map even when no traffic uses it; likewise, a reaction can belong to the fitted signaling model even when its upstream requirements are not satisfied in one condition.\n", + "\n", + "`C1` and `C2` have the same upstream inputs: `A=1`, `C=0`, and `D=1`. In both conditions, `D` can activate `B`. In `C1`, however, the experimental inhibitor forces `B` off. This allows the rule `A AND NOT B → X` to transmit the signal. In `C2`, `B` remains active, the `NOT B` requirement fails, and the same pathway branch is inactive and drawn with dashed lines." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9546c203", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:19.930033Z", + "iopub.status.busy": "2026-09-21T23:05:19.929920Z", + "iopub.status.idle": "2026-09-21T23:05:20.121373Z", + "shell.execute_reply": "2026-09-21T23:05:20.120940Z" + }, + "papermill": { + "duration": 0.194874, + "end_time": "2026-09-21T23:05:20.121965", + "exception": false, + "start_time": "2026-09-21T23:05:19.927091", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D->B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", 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+ "C->__cellnopt_and_11\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->__cellnopt_and_10\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_10->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Z\n", + "\n", + "\n", + "Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_11->Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_cellnopt_model(method, condition=\"C1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c2a5b5cc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:20.127810Z", + "iopub.status.busy": "2026-09-21T23:05:20.127718Z", + "iopub.status.idle": "2026-09-21T23:05:20.322843Z", + "shell.execute_reply": "2026-09-21T23:05:20.322351Z" + }, + "papermill": { + "duration": 0.19874, + "end_time": "2026-09-21T23:05:20.323457", + "exception": false, + "start_time": "2026-09-21T23:05:20.124717", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D->B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Y\n", + "\n", + "\n", + "Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_10\n", + "\n", + "\n", + "AND\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->__cellnopt_and_10\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_11\n", + "\n", + "\n", + "AND\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "W->__cellnopt_and_11\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->__cellnopt_and_11\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->__cellnopt_and_10\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_10->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Z\n", + "\n", + "\n", + "Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_11->Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_cellnopt_model(method, condition=\"C2\")" + ] + }, + { + "cell_type": "markdown", + "id": "e1299723", + "metadata": { + "papermill": { + "duration": 0.002537, + "end_time": "2026-09-21T23:05:20.328809", + "exception": false, + "start_time": "2026-09-21T23:05:20.326272", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### An inhibitor changes its target, not the whole cell\n", + "\n", + "`C4` and `C6` have the same upstream inputs. Without the inhibitor in `C4`, `X` activates `W`, the rule `W AND C → Z` is satisfied, and both readouts are active.\n", + "\n", + "In `C6`, the inhibitor specifically forces internal protein `W` off. Signaling to `Y` remains active through `X` and `C`, but the AND rule producing `Z` is no longer satisfied, matching the observed `Y=1, Z=0`. The intervention therefore blocks one branch rather than being interpreted as a shutdown of the whole pathway." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8d1af7bd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:20.334576Z", + "iopub.status.busy": "2026-09-21T23:05:20.334491Z", + "iopub.status.idle": "2026-09-21T23:05:20.526354Z", + "shell.execute_reply": "2026-09-21T23:05:20.525981Z" + }, + "papermill": { + "duration": 0.195678, + "end_time": "2026-09-21T23:05:20.527048", + "exception": false, + "start_time": "2026-09-21T23:05:20.331370", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D->B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Y\n", + "\n", + "\n", + "Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_10\n", + "\n", + "\n", + "AND\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->__cellnopt_and_10\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_11\n", + "\n", + "\n", + "AND\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "W->__cellnopt_and_11\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->__cellnopt_and_11\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->__cellnopt_and_10\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_10->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Z\n", + "\n", + "\n", + "Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_11->Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_cellnopt_model(method, condition=\"C6\")" + ] + }, + { + "cell_type": "markdown", + "id": "9b4ee4e6", + "metadata": { + "papermill": { + "duration": 0.002622, + "end_time": "2026-09-21T23:05:20.532564", + "exception": false, + "start_time": "2026-09-21T23:05:20.529942", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## A compact view for larger signaling experiments\n", + "\n", + "When an experiment contains many perturbations or readouts, aligned heatmaps are easier to scan than one panel per response. Observed activities, predicted activities, absolute errors, and experimental cues remain aligned by condition. Unmeasured entries stay blank rather than being treated as zeros." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f12e3db5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:20.538590Z", + "iopub.status.busy": "2026-09-21T23:05:20.538471Z", + "iopub.status.idle": "2026-09-21T23:05:20.728462Z", + "shell.execute_reply": "2026-09-21T23:05:20.728040Z" + }, + "papermill": { + "duration": 0.193718, + "end_time": "2026-09-21T23:05:20.728945", + "exception": false, + "start_time": "2026-09-21T23:05:20.535227", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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X5FFLaHUoID3h60WJ3p3XoVK09Emf1+EVQumFjla71DHPNLDuvvtu0yW7tp1VWqqlgazV6PT9tI2he1f8e9/7nvlXE2G9WNfqlzoWsbZl0OodeqGj7Z4uuuiiShf8NdEq0VpNTds+6sWTjsEaKty/pW2qdLgn3Wd9Xrf1k08+kalTp1YayxWoS1WHIKtKq4pqPGqSrM0TTj31VPnggw/Mhbb+pt2YGjBggJx33nlmiC+9C6zD02hi8cc//vGw99SmCRrzmpzozS49GWvColVgNWnR+IW9NLnVkkU9rlZ3c1PbvE6fPt20GQ5NgjXR1fPCbbfdZtrQ6ut1uCAdysMdBkxvwOixWm+Mai0cTeD0otAd0k4TUD2n6LAe+tvX9m1aMqLvocm1lmzURG+K6uu1hoW2u9dkVrdVh/Orug9KhzLR7dD31qRVk1ctBdZ402rPmnRrtUId2knHS9Z/dRiSI9HzqQ51prF46623mhtN+lnoeVWHPNJxQmt747djx46mrw3dvoSEBHORq8cE3V9tM61/D0Ds6E08PT5o4ZIWpuj5Vasta/KrN571eKPHHk2Cwz1W3X///aZKs16L6w1uPQ7odbXGvibHes2tpc6aeOs1sF7ba1MIrYWjQ6Hq+4XeNAOOyLGQDg3xox/9yPSorMOutGjRwvnhD39YaViL0J44V6xYYXrO1N7qGjVqZIax0N6jQ9/vkksuMT3caW+exx13nOmp0h2yyKW9N2vvdtrjpvZIq+/XqVMn0xPehg0bguvp+wwdOrTGfejdu7fZtiuuuKLa58P9W4WFhWaoGP0MdL1zzjnH7E/V3vqAY+0duiahvUOrr7/+2rnhhhvMUGEao/p7nDBhglNQUFDpdXv37nVGjhxpeojUoRp0mLK1a9dW2wul9uir6+owLDqMjQ6zonH04IMPVlqHIZLso0MDac/77pBB1dEeUPW3qD2iur8T7bFch6HTnsf19Tp0ng6l59Lfq/6OdUggHXZOe4Hu2LGj+W1qr8yhdDgmXU/fR3tQvuiii4K9oNbUO7Qew3/xi1+YIUX0/TWOdKSC6o7hjz76qJOVlWVGH6j6O9fhhPS8oz1Fa3xonOi89ip9NAcOHHDuuusus2/u9utwZDrygH5eLv2bY8aMieg4ob1U6+fcoUMHs106FNOVV17pbNu2rdJ6ut96HAFQ//SYo8ebdu3amWOAO5SoDgfnHlcjOVbp8Gc61JEerzTu9biUk5Pj3HnnneZ4o/7whz+YYRL1Wl7/pvbWr6Ol/Oc//+EngLAE9H9HTpEBAAAAAPAPa3qHBgAAAACAJBgAAAAAYA2SYAAAAACANUiCAQAAAADWIAkGAAAAAFiDJBgAAAAAYI0k8aCysjLZvn27NGrUSAKBQKw3B3VIR+Tav3+/tG7dWhISuAdTl4gb/yJuooe48SdiJnqIGQB+OP57MgnWBLht27ax3gxE0bZt26RNmzZ8xnWIuPE/4qbuETf+RszUPWIGgB+O/55MgrUEWG1ZeaJkZvi/tPCSDqeKLUqkWD6QucHvGHWHuPEv4iZ6gseiK3uIpCRG8S+hXhWVirywmnNNFGPm4bndJK0hMeM31/ZYLNa49RKxyu9eFRvk5eWZwtSj5RqeTILdKtCaAGc28n8SnBRIFms45f9Qzb3uETc+RtxETfBYpAlwiidPiTgGnGui95lqApyeQRLsN5mZmWIN2475Nn23cvTjv/8zTAAAAAAAKpAEAwAAAACsQRIMAAAAALAGSTAAAAAAwBokwQAAAAAAa5AEAwAAAACsQRIMAAAAALAGSTAAAAAAwBokwQAAAAAAa5AEAwAAAACsQRIMAAAAALAGSTAAAAAAwBokwQAAAAAAa5AEAwAAAACsQRIMAAAAALAGSTAAAAAAwBokwQAAAAAAa5AEAwAAAACsQRIMAAAAALAGSTAAAAAAwBokwQAAAAAAa5AEAwAAAACsQRIMAAAAALAGSTAAAAAAwBokwQAAAAAAa5AEAwAAAACskRTrDfCKm+/6St6Yny9bviiRNe+2lW6dUsXPDjr75VP5SIqlSJIkWbrIWZIRyIz1ZiGOEDPEDGphb4HIu5+LFJSIpCSK9M8WadrAvx+lbfsLAPBvSfDOnTtl7Nixkp2dLampqdK2bVsZNmyYLFy40Dz/1FNPSb9+/SQzM1MCgYDs3btXvG740AxZ/Pc20r6NHfcFcmWlnCDZ0jswSNpLR8mV5bHeJF8jZuIfMVP//Bg3sniTSOcWIpd3F+nRSmTRJvE12/bXA3wZNwAQ6yR48+bNkpOTI++8845MmTJFPv74Y5k3b570799fxowZY9Y5ePCgDBo0SCZOnCjx4rxe6dKmtR0JcJFTIPtlrxwv7cx8CzlBDkm+HHLyY71pvkTMxD9ipv75Mm4OFYvszhfp0Kx8PrupSF5h+eRHtu2vB/gybgAgCiLO+kaPHm3uHC5btkwaNmwYXN61a1cZOXKkeXzLLbeYf99777263FbUkQI5JKmSJgmB8nsg+n2mOQ2kQA5Kunz7naJuEDPxj5ipf76MmwNFIg2TRRIC5fOBgEhGisiBQpFMHzbBsW1/PcCXcQMAsU6C9+zZY+4oTpo0qdLB1dWkSZNabURhYaGZXHl5ebV6H0Si4qIEURWtmFHETX0jZuqLv+PGtt+RbfsbO1yjAUCUqkN/9tln4jiOdOrUSerS5MmTpXHjxsFJ268getIk3ZT6ljllZl6/U51PEzorqWvRihlF3NQfYqZ++TZutBQ0v0ikzCmfd5zy0tIMn5aK2ra/McY1GgBEKQnWixKlVW3q0oQJE2Tfvn3Badu2bXX6/qgsJZAmjaSJ7JStZn6XfClp0lDSA1SFrmvRihlF3NQfYqZ++TZu0pNFjmsgsn53+fzGPSKNUvxbNdi2/Y0xrtEAIEpJ8CmnnGIuSnJzc6Uuae+F2kth6FTfbprwlbQ7Y5N8saNEBozYLh16bRE/6yw58qVslCXOPNks66SL5MR6k3wpWjHjhbghZoiZaPFz3EjfLJHcXSIvrRFZvUOkX7b4mm37G0N+vkYDgJgmwU2bNpWBAwfKjBkzJD//8J6E47mb/emTm8vWlVlS9MXJsv0/WbJ+aXvxs4aBRnJW4HwzRNLZgQskI9A41pvkS8SMfxAz9cfPcSNN0kUu6Vo+ZNDwbv4fM9e2/Y0hX8cNAMR6iKSZM2dKaWmp9OzZU+bMmSMbNmwwdx2nTZsmvXr1Co5Rt3r1atM+RWkX/TqvnTYAtiFmAOIG4HwDAHGcBGdlZcnKlSvNmHPjx4+Xbt26yYUXXmgGYZ81a5ZZ54knnpDTTz9dRo0aZebPO+88M//666/X/R4AHkfMAMQNwPkGALwj4Lg9KXiIDlmhvXZ+sz5bMhtFnKfHnYGte4gtSpxieU/+bjqkoV1R3SJu/Iu4iX7cyMgckZSIRg2ElxWViMxewbkmijHz2KLukp6RGI0/gRgadcYKez7/GweKVWbNF5uOUfuOkmv4P8MEAAAAAKACSTAAAAAAwBokwQAAAAAAa5AEAwAAAACsQRIMAAAAALAGSTAAAAAAwBokwQAAAAAAa5AEAwAAAACsQRIMAAAAALAGSTAAAAAAwBokwQAAAAAAa5AEAwAAAACsQRIMAAAAALAGSTAAAAAAwBokwQAAAAAAa5AEAwAAAACsQRIMAAAAALAGSTAAAAAAwBokwQAAAAAAa5AEAwAAAACsQRIMAAAAALAGSTAAAAAAwBokwQAAAAAAa5AEAwAAAACsQRIMAAAAALAGSTAAAAAAwBpJ4mGXdDhVkgLJsd4MIK4QNwAAAEcxaz4fkcUoCQYAAAAAWIMkGAAAAABgDZJgAAAAAIA1SIIBAAAAANYgCQYAAAAAWIMkGAAAAABgDZJgAAAAAIA1SIIBAAAAANYgCQYAAAAAWIMkGAAAAABgDZJgAAAAAIA1SIIBAAAAANYgCQYAAAAAWIMkGAAAAABgDZJgAAAAAIA1SIIBAAAAANYgCQYAAAAAWIMkGAAAAABgDZJgAAAAAIA1SIIBAAAAANYgCQYAAAAAWIMkGAAAAABgDZJgAAAAAIA1SIIBAAAAANYgCQYAAAAAWIMkGAAAAABgDZJgAAAAAIA1SIIBAAAAANYgCQYAAAAAWCMp1hvgFQed/fKpfCTFUiRJkixd5CzJCGSKX9m2v4gOm35HNu0romhvgci7n4sUlIikJIr0zxZp2sC/H7lt+wsA8G9J8M6dO2Xs2LGSnZ0tqamp0rZtWxk2bJgsXLhQ9uzZY57r2LGjNGjQQNq1ayfjxo2Tffv2iZflyko5QbKld2CQtJeOkivLxc9s299Y82PM2PY7smlfvcKXcbN4k0jnFiKXdxfp0Upk0SbxNdv21wN8GTcAEOskePPmzZKTkyPvvPOOTJkyRT7++GOZN2+e9O/fX8aMGSPbt28309SpU81zzz33nHn+uuuuE68qcgpkv+yV46WdmW8hJ8ghyZdDTr74kW37G2t+jBnbfkc27atX+DJuDhWL7M4X6dCsfD67qUheYfnkR7btrwf4Mm4AwAvVoUePHi2BQECWLVsmDRs2DC7v2rWrjBw5Upo0aSJz5swJLj/ppJNk0qRJcuWVV0pJSYkkJXmvBnaBHJJUSZOEQPk9Ad2/NKeBFMhBSZdv99EvbNvfWPNjzNj2O7JpX73Cl3FzoEikYbJIQqB8PhAQyUgROVAokpkqvmPb/nqAL+MGAKIgoqOdVqPRO4Z6wAw9uLr04FodrWaTmZl5xINrYWGhmVx5eXlS/ypO0tawbX9jI1oxo4ib+kbM1Bd/x41tvyPb9jd2/H2NBgAxrA792WefieM40qlTp7Bf8/XXX8sDDzwg119//RHXmTx5sjRu3Dg4afuV+pQm6aZEp8wpM/O6jzqfJv7svMO2/Y2laMWMIm7qDzFTv3wbN1oKml8kUuaUzztOeWlphk9LRW3b3xjz6zUaAMQ8CdaDq9KqNuHQu4VDhw6VLl26yD333HPE9SZMmGDuRLrTtm3bpD6lBNKkkTSRnbLVzO+SLyVNGkp6wJ/VHG3b31iKVswo4qb+EDP1y7dxk54sclwDkfW7y+c37hFplOLfqsG27W+M+fUaDQBiXh36lFNOMQfX3Nxcufjii2tcd//+/TJo0CDJyMiQV199VZKTk4+4rvZeqFMsdZYc+a98JJudtZIoydJVzhQ/s21/YyVaMaOIm/pFzNQfX8dN3yyRdzeKrNr+7ZBBfmbb/saQn6/RAKCuBRz31mGYBg8ebHoUXLdu3WFtTvbu3WvanOjdxYEDB5qD5ty5c003/JHQ12uVm35ykSQFar6gQXwpcYrlPfl7sA2SDeojZhRx41/ETfTjRkbmiKTQKZBvFJWIzF5h1bmmvq/RHlvUXdIzEut4DxBro85YEetNAI6Je4w62vE/4iGSZs6cKaWlpdKzZ0/Tw+CGDRvMXcdp06ZJr169zN3FAQMGSH5+vjz77LNmQ3TMOp30dYBtiBmAuAE43wCAd0R82zsrK0tWrlxpeh8cP3687NixQ5o3b27GpZs1a5asWLFCPvzwQ7PuySefXOm1mzZtkhNPPLHuth6IA8QMQNwAnG8AII6rQ9cHqnX6l43VOusLceNfxE30UB3apyytDl0fqA7tb1SHRryLWnVoAAAAAADiFUkwAAAAAMAaJMEAAAAAAGuQBAMAAAAArEESDAAAAACwBkkwAAAAAMAaJMEAAAAAAGuQBAMAAAAArEESDAAAAACwBkkwAAAAAMAaJMEAAAAAAGuQBAMAAAAArEESDAAAAACwBkkwAAAAAMAaJMEAAAAAAGuQBAMAAAAArEESDAAAAACwBkkwAAAAAMAaJMEAAAAAAGuQBAMAAAAArEESDAAAAACwBkkwAAAAAMAaJMEAAAAAAGuQBAMAAAAArEESDAAAAACwBkkwAAAAAMAaSeJhr67/WDIb+T9PH9i6R6w3AT5C3AAAou3aHoslMzPT9x/00ytzxCY27e+oM1bEehMQQ/7PMAEAAAAAqEASDAAAAACwBkkwAAAAAMAaJMEAAAAAAGuQBAMAAAAArEESDAAAAACwBkkwAAAAAMAaJMEAAAAAAGuQBAMAAAAArEESjLi3ePFiGTZsmLRu3VoCgYC89tprR33NokWLJCcnR9LS0iQ7O1ueeOKJetlWwCuIG4CYAQBbkQQj7uXn50v37t1l+vTpYa2/adMmGTJkiJx77rmyatUqmThxoowbN07mzJkT9W0FvIK4AYgZALBVUqw3ADhWgwcPNlO4tNS3Xbt28uijj5r5zp07y/Lly2Xq1KkyfPhwvhBYgbgBiBkAsBVJMGKuoKBAioqKKi1zHMdUbQ6VmppqpmO1dOlSGTBgQKVlAwcOlGeffVaKi4slOTn5mP8GUN8xo4gbILK4IWYAwE5Uh0bML0qy2mdI48aNK01t2rQ5bNnkyZPr5G/u3LlTWrZsWWmZzpeUlMju3bvr5G8A9R0zxA1Qc9y0SudcAwAoR0kwYkrvyu/cVSrrl7eRzEbl92Ty9pdJhzO/kG3btklmZmZw3booBXZVLWXW0oDqlgPxEDOKuAFqjpu9UipPJGdJesX9/0NSJjcc2MS5BgAsRBIMT0jPcMykiisSUk2AQ5PgunL88ceb0uBQu3btkqSkJDnuuOPq/O8B0Y4ZRdwAR9coMUEaBBLN4yQNn2LONQBgI6pDwxNKpEyKKyZ9HE29evWSBQsWVFr29ttvy5lnnkl7YMRlzBA3QHiSkipP0cS5BgC8iyQYnlDolFWaInHgwAFZvXq1mdwhkPTx1q1bzfyECRPk6quvDq5/ww03yJYtW+S2226T3NxcmT17tukU6/bbb6/jvQLqL2aIG+DotN/DlJTyKdI+EDnXAIB/UB0anlDkOGZyH0dChzfq379/cF6TW3XNNdfIc889Jzt27AgmxCorK0vmzp0rt956q8yYMUNat24t06ZNY3gkxG3MuPORIG5gI1MCXHH7PynCSkfEDAD4B0kwPKFEAlIsgeDjSPTr1y/YsVV1NBGuqm/fvrJy5cpabCngvZhx5yNB3MBGiYkBSUwoj5XECDtCJGYAwD9IguEJBU6CJDnlt+cLIivQAsT2mCmfj+nmAHHBVIWuCJvi6HY/AQDwMJJgeEKxk2Cm8sex3hogvmKmfD6mmwPEhcSQ6tCJJMEAYC2SYHhCiZMYvKAvcRirF4gkZogbIDxJieWTeVzKpwYAtiIJhicUOEmS6JRfmRQ4XJkAkcQMcQOEJ0V7h64IG7daNADAPiTB8IQiJ1GSKy7oi6jWCUQUM8QNEJ6kpIAkJZbXNkqKsGMsAIB/kATDE0okUYql/IK+JNYbA8RZzJTPAziahOQESUgsLwJOSOCOKwDYiiQYnlBQliwJZRXVocuoowZEEjPEDRCehJRESaxoFJzAnSMAsBZJMDyh2EmS4oqqncV0jAVEFDPEDRCexOQESazoHjoxQEkwANiKJBieUChJkuCU/xwLhXZaQCQxQ9wA4UlMSZLE5PKbR4kJnGsAwFYkwRVuvusreWN+vmz5okTWvNtWunVKFT876OyXT+UjKZYiSZJk6SJnSUYgM2bboyVaScGSYO7OxwNixjsxUz5P3MSFvQUi734uUlBS3k1x/2yRpg3Etzy2v6ZNcHJFm2AhZgDAVrVqfLlz504ZO3asZGdnS2pqqrRt21aGDRsmCxcuNM9ff/31ctJJJ0l6ero0b95cLrroIlm7dq142fChGbL4722kfRs77gvkyko5QbKld2CQtJeOkivLY7o9RU5SpclviJn45/WYIW7i41wjizeJdG4hcnl3kR6tRBZtEl/z2P4mJAeCibA+9iM/nm8AIOZJ8ObNmyUnJ0feeecdmTJlinz88ccyb9486d+/v4wZM8aso8///ve/l9zcXJk/f744jiMDBgyQ0lLvjv96Xq90adPaf8lXdYqcAtkve+V4aWfmW8gJckjy5ZCTH7ttKkuWwopJH/sJMRP/vB4zxE2cnGsOFYvszhfp0Kx8PrupSF5h+eRHHtzfpNTESpPf+PV8AwB1LeKsb/To0RIIBGTZsmXSsGHD4PKuXbvKyJEjzeOf//znweUnnniiPPjgg9K9e3dzcNa7j4itAjkkqZImCYHyeyD6faY5DaRADkq6fPud1nfVzkSfVocmZuKf12OmfJ648fy55kCRSMNkEbctqo5Tm5EicqBQJNOHTXA8uL+BpG+rQwcc/41EwPkGAKKQBO/Zs8fcUZw0aVKlBNjVpEmTw5bl5+ebO45ZWVmmSk51CgsLzeTKy8uLZLNQK96qBlbsJIQkwWXiF9GKGUXc1Dfvxkz5PHETH3Hjrd+RbfubkJRoJvO4zF83jrhGA4DwRXQb9LPPPjPVZjp16nTUdWfOnCkZGRlm0iRgwYIFkpKSUu26kydPlsaNGwenmi5gcOzSJN2UYJVVXDTrd6rzaRK7zkq0OmdBxaSP/SJaMaOIm/rj9ZghbuIkbrQUNL9IxE2+tPReS0szfFgK7NH9TUpLrDT5CddoABClJFgv/NyqgEdzxRVXyKpVq2TRokVyyimnyIgRI6SgoKDadSdMmCD79u0LTtu2bYtksxChlECaNJImslO2mvld8qWkSUNJD8SmWqdbtTN08otoxYwibupPPMQMcRMHcZOeLHJcA5H1u8vnN+4RaZTiz6rQHt1frQ4dSK6YKsYL9guu0QAgfBGdAfQCQy/mtTOFo9E77Lr+eeedJy+//LLpefDVV1+tdl3tvTAzM7PSVN9umvCVtDtjk3yxo0QGjNguHXptET/rLDnypWyUJc482SzrpIvkxHR7SpzESpNfRCtmvBA3xIy3Yoa4iY+4kb5ZIrm7RF5aI7J6h0i/bPE1j+1vIDmx0uQnfr5GA4CYtglu2rSpDBw4UGbMmCHjxo07rI3j3r17q23j6N6hDG2H5TXTJzc3ky0aBhrJWXK+eEVRaZKITuaxf9ppETP+4eWYKZ8nbuLhXCNN0kUu6SrW8Nj+BlITJFDRK3RA/NOO3u/nGwCoaxHXBdJ2i9qNfs+ePWXOnDmyYcMGc9dx2rRp0qtXL9m4caNpc7VixQrZunWrLF261FRP0/HohgwZUuc7AH8odgKmo5/yyVsdqRwrYgbRjxnihnMNbK8OrTjfAECUhkjSnjdXrlxpersdP3687Nixwwy2ruPOzZo1S9LS0uT999+XRx99VL755htp2bKlqW6zZMkSadGiRaR/DpbQqpwJFdWg/VStUxEziHbMuPN+QtwgGkKrQQdK/VUSrIgbAIhSEqxatWol06dPN1N15s6dW5u3hcUKy5LEcatDl/nvwoSYQTRjRhE3wNEF0hLN5Mfq0C7ONwAQpSQYqGulToKUOAnBxwDCjxniBgiTjhHsdohV4s8kGABwdCTB8ISSskQJlFVUh674F0B4MUPcAOFx2wObxyXccAUAW5EEwxOKyhLFqbigLyYJBiKKGeIGCE8gLclM5nHFOO4AAPuQBMMTSsoSJFCWEHwMIPyYIW6A8ASSAt+WBBf7ayQCAED4SILhCaVOQALBNsFcmACRxAxxA4RJE+CKJDj4LwDAOiTB8ISi0kQpK61oE1zxL4DwYoa4AcITSE0yk1+HSAIAhIckGJ7p6fbbkmDuzgORxAxxA4QpKal8Mo9JggHAViTB8ITSkPaN+hhA+DFD3ABhSkwsHybJfQwAsBJJMDxBq0A7FVU7S6kODUQUM8QNEKaUJJGU5PLHxZQEA4CtSILhCaUSEKnoEMs8BhB2zBA3QJiSQkqC3X8BANYhCYYnmCrQVIcGahUzwXkAEbQJLuXTAgBLkQTDE0pLE0R0ch8DCDtmiBsgPIGkZAkkl1eHDtAxFgBYiyQYnlBaFhDRyX0MIOyYIW6AMFEdGgBAEgxvXdC7vUOTBAORxAxxA9SiY6wUqkMDgK0oCYYnOGUBM7mPAYQfM8QNECZKggEAJMHwCr2YLyMJBmoVM+48gKNgnGAAAEkwvKIspJMf8xhA2DFD3ABhSkopn8xjxgkGAFtRHRqeQHVooPYx484DCKMkODHp28cAACuRBMMTHCekTbDDxTwQScwQN0CYEpLKJ/cxAMBKnAHgDaHDvVCiBUQWM8QNUIvq0PQODQC2IgmGN5AEA7WPGXceQM0CISXB+hgAYCXOAPAEp6x8ch8DCD9miBsgPIHEJAkklo8THEgs5mMDAEuRBMMTAqUBM7mPAYQfM8QNEKbElPLJPC7hYwMASzEWDbxVtbNqFc8IzJw5U7KysiQtLU1ycnLk/fffP+K67733ngQCgcOmtWvXHsNOADGMmVrEDTED62jP0KFTLRA3ABD/KAmGN2i1TrdqZy2qQ//lL3+RW265xVyc9OnTR5588kkZPHiw/Pe//5V27dod8XXr1q2TzMzM4Hzz5s1rs/VAbGPGnY8AMQMrHWPv0MQNAPgDJcHwVNXOqlU8w/XII4/IddddJz/72c+kc+fO8uijj0rbtm1l1qxZNb6uRYsWcvzxxwenRMaNRJzGTKRxQ8zASknJ3/YQrY8jRNwAgD9QEuwB87evFlvk7S+T73Q4fLlevgecbx+bdfPyKq2TmppqpqqKiopkxYoV8stf/rLS8gEDBsiSJUtq3J7TTz9dCgoKpEuXLnLXXXdJ//79I94nxIbtcRMaM+58uHFDzMBa1ZQEc66pnedWnyfpGYnid6POWCE2eXplTqw3AagXlATDG6pp26gluY0bNw5OkydPrvalu3fvltLSUmnZsmWl5Tq/c+fOal/TqlUreeqpp2TOnDnyyiuvSMeOHeWCCy6QxYsXR2HngPprExxO3BAzsFVZlf8U5xoAsA8lwfCEQGn55D5W27Ztq9Ret7pS4ErvEahcHdRxnMOWuTTp1cnVq1cv8/emTp0q55133jHsCVD/MePORxo3xAxsU1xWLMVlRcHHinMNANiHkmB4QqCs8qT0Qj50OtLFfLNmzUxb3qqlvrt27TqsdLgm55xzjmzYsOHYdgSIUcxEEjfEDGxV6pRUmhTnGgCwD0kwvOEYhnpJSUkxQyItWLCg0nKd7927d9jvs2rVKlNNGvD7EEnEDGxVXRIcLuIGAPyD6tDwBK3KmVBRndMJqeIZrttuu02uuuoqOfPMM03VZm3vu3XrVrnhhhvM8xMmTJAvv/xSnn/+eTOvvUefeOKJ0rVrV9NJ0AsvvGDaB+sExFvM1CZuiBnYqKSsSErKkoKPI0XcAIA/kATDF+MEX3rppfL111/L/fffLzt27JBu3brJ3LlzpX379uZ5XaZJsUsT39tvv90kxunp6SYZfvPNN2XIkCF1tUeAp8cJJmZgozJTAlwcfBwp4gYA/CHgaO9BHqPDFWivpt+sz5bMRtTY9t9QLxtl3759ph2W+12fNPEhSUxLM+uUFhTI5w9NDK6DMD9b4saKuFFVY0YRN8cWNzIyRySF+8K+UVQiMnvFYeeaDTtmSqPMdLPK/rxDckqr0ZxrIuR+lo8t6s4QST5k0xBJtg1/Zdsxat9R8gjO+PAE07GP2zt0LUqCAZtjxp0HUDPtGbq4rHxsW7eXaACAfUiC4QmhvdtyMQ9EFjPEDRCe0A6xIu0YCwDgHyTB8ASSYKD2MePOA6hZqVMakgTXohdGAIAvkATDE7RaZ7A6NNclQEQxQ9wA4Sk1vUMnBB8DAOxEEgxf9A4NWOcYe4cGbFRSVmom9zEAwE4kwfAEqkMDtY8Zdx5AzUqkTEqcsuBjAICdSILhCVSHBmofM+48gJoVlZaYyX0MALATSTA8IeCUT+5jAOHHDHEDhKe0rCxYDVofAwDsRBIMT2CcYKD2MePOA6hZUVmJJFeME6yPAQB2IgmGJ9AmGKh9zLjzAGpWYkqCK9oEUxIMANYiCYYnkAQDtY8Zdx5AzegdGgCgSILhCXSMBdQ+Ztx5ADUrLiuRoorq0PoYAGAnkmB4AiXBQO1jxp0HUDOqQwMAFEkwPCFQ5pjJfQwg/JghboBIkuDyahO0CQYAe5EEwxMSSssn5VCtE4goZogbIDxFZaWSVFoeOPoYAGAnkmB4AtWhgdrHjDsPoGYlZY4UV9Sg0McAADuRBMMbnJCLeK5LgMhihrgBwlJcJpJU9u1jAICdSILhCYFSRwIJFW2CS8mCgUhihrgBwlOgcVNxjtHHAAA7kQTDO8O9JHz7GED4MUPcAOGhOjQAQJEEwxPoHRqofcy48wBqVlwqklj67WMAgJ1IguGdnm4rSrVCe7wFcPSYIW6A8BSGVIfWxwAAO5EEwxMoCQZqHzPuPICalTghvUM7xAwA2CqkHMFuN9/1lWSftVkSW30mn6wtFD/z4r66w71UHfYF3uXF35FN+1s1ZoibOLG3QOTVT0VeWiMy5xORPQfF1zy2v9ojdOgEALBTrZLgnTt3ytixYyU7O1tSU1Olbdu2MmzYMFm4cGGl9RzHkcGDB0sgEJDXXntNvGz40AxZ/Pc20r6N/wvHvbivpqfbkorJh1XUiJn457W4qRQzxE3cnGtk8SaRzi1ELu8u0qOVyKJN4mse29+CEqfS5Ed+PN8AQF2L+Gpu8+bN0qdPH2nSpIlMmTJFTjvtNCkuLpb58+fLmDFjZO3atcF1H330UXNwjQfn9UoXW3hxX/1cHZqY8QevxY3fq0P7Mm4OFYvszhf5fqfy+eymIh9sEckrFMlMFd/x4P6WlIkklH372G98GTcA4IUkePTo0eaguWzZMmnYsGFwedeuXWXkyJHB+TVr1sgjjzwiH330kbRq1aruthi+5OckmJhBNPg9CfZl3BwoEmmYLJJQkXhoApKRInLAp0mwB/dX2wO7seK2DfYTX8YNAMQ6Cd6zZ4/MmzdPJk2aVOng6tI7j+rgwYNy+eWXy/Tp0+X4448/6vsWFhaayZWXlxfJZsEHTHVOqUiCfVRFLVoxo4gbu4XGjDvvF/6OG9tK3ry1v4dKyqS0ogi4yGdFwVyjAUCU2gR/9tlnpg1Jp04VVZuO4NZbb5XevXvLRRddFNb7Tp48WRo3bhyctP0K7CzVqlq6Fe+iFTOKuLFb1ZghbuIgbrQUNL9IxD3Gae/EWlqa4cNSYI/ub3FZmRSVlk/62E+4RgOAKCXBejGvampD8vrrr8s777xj2pqEa8KECbJv377gtG3btkg2Cz5gOvkJmfwiWjGjiBu7VY0Z4iYO4iY9WeS4BiLrd5fPb9wj0ijFn1WhPbq/RaVOpclPuEYDgCglwaeccoq5mM/NzT3iOnox//nnn5vqaklJSWZSw4cPl379+lX7Gu29MDMzs9JU326a8JW0O2OTfLGjRAaM2C4dem0Rv/LivgZKyypNfhGtmPFC3Hjxd2TT/laNGeImPuJG+maJ5O4qHzJo9Q6Rftniax7b34LiMjlUMeljP/HzNRoA1LWA4946DJN2p//xxx/LunXrDmurtXfvXikoKJDduyvu+lY49dRT5bHHHjNd9GdlZR31b2gbLa2m9s36bMlsxFDGfpK3v0y+02GjKYHRE6n7Xfc/Y4IkJaaZdUpKC+TdlZOD68S7+ogZRdzYETeqaswo4ubY4kZG5oikeGP4K9SBohKR2SsOO9ec9/yPJalBslml5GCxLL76b74519T3Ndpji7pLekai+N2oM1aITZ5emSO2sO27tUVexTHqaMf2iM/4M2fONG0Xe/bsKffff7/pfr+kpEQWLFggs2bNMncgq+ugpF27dmFflMA+pk1joPyuvJ/aNipiBtGOGXfeT4gbRINWgS6rqAZd4rPq0Iq4AYDwRJwEayK7cuVK02vn+PHjZceOHdK8eXPJyckxSTBQG4GSMgk4FUmwj6pDK2IG0Y4ZM0/cAEdVUFwqicXlNcxKi0t994lxvgGA8NSq7peOKadDUugUjghrXMNG2kunW6rlsx47FTGDqMaMO+8zxA3qWlGZI4kVJcClPqs94SJuAODoaAAFT/BzSTAQDX4vCQaioaCkTBIqxgcu89k4wQCA8JEEwxvMBXzFBQkX80BkMUPcAGHR8YETKs4xZZxrAMBaJMHwBi3RcqtzhpRuAQgjZogbICzFpY4kVFSHdjvIAgDYhyQY3lBSIpJQMdRCWUmstwaIr5hRxA1wVNoZlpNY3jFWmQ87xgIAhIckGN6g1dLcEmAfdvADRDVmFHEDHJWpAk11aACwHkkwvMH00ukmwVRRAyKKGeIGCItTUhbsEEsfAwDsRBIMbzBVO8urqFGtE4gwZogbICylRWUSSCivBu0UkQQDgK1IguENVIcGah8ziurQwFFpKbAOL6YoCQYAe5EEwxOcslJxnIq78xX/AggvZogbIDxlZWXBMbUdbhwBgLVC6tIBMa7aGTrVwsyZMyUrK0vS0tIkJydH3n///RrXX7RokVlP18/OzpYnnniilhsPeCBmahE3xAxsU1pUWmmqDeIGAOIfSTA8wSktrTRF6i9/+Yvccsstcuedd8qqVavk3HPPlcGDB8vWrVurXX/Tpk0yZMgQs56uP3HiRBk3bpzMmTOnDvYGqP+YiTRuiBnYWh06dIoUcQMA/kASDG8ww1aUVkyRX5g88sgjct1118nPfvYz6dy5szz66KPStm1bmTVrVrXra6lvu3btzHq6vr5u5MiRMnXq1DrYGaC+YybyuCFmYOsQSaFTpIgbAPAHkmB4QlHxISkqqpiKD5lleXl5labCwsLqX1tUJCtWrJABAwZUWq7zS5YsqfY1S5cuPWz9gQMHyvLly6W4uLjO9guol5iJMG6IGdiqNL9ISg4UmkkfK841AGAfOsZCTKWkpMjxxx8vH+z8R6XlGRkZpiQ31D333CP33nvvYe+xe/duKS0tlZYtW1ZarvM7d+6s9u/q8urWLykpMe/XqlWrY9groP5jJpK4IWZga9zsfH5VpeWcawDATiTBiCntlErb52rJVCjHcSQQCFRalpqaWuN7VV2/uvc42vrVLQfiIWZqEzfEDGzBuQYAEIokGJ64ONGptpo1ayaJiYmHlfru2rXrsNJelykRqGb9pKQkOe6442q9LUB9IGYA4gYAUHu0CYYvqrnpUEcLFiyotFzne/fuXe1revXqddj6b7/9tpx55pmSnJwc1e0FYo2YAYgbALAZSTB84bbbbpNnnnlGZs+eLbm5uXLrrbea4ZFuuOEG8/yECRPk6quvDq6vy7ds2WJep+vr65599lm5/fbbY7gXQP0hZgDiBgBsRXVo+MKll14qX3/9tdx///2yY8cO6datm8ydO1fat29vntdloWMGZ2Vlmec1WZ4xY4a0bt1apk2bJsOHD4/hXgD1h5gBiBsAsFXAcXsD8hAdrqBx48byzfpsyWxEYbWf5O0vk+902Cj79u2TzMzMWG+OrxA3/kXcRP98IyNzRFK4L+wbRSUis1dwrolizDy2qLukZySK3406Y4XY5OmVOWIL275b245R+46Sa5BhAgAAAACsQRIMAAAAALAGSTAAAAAAwBokwQAAAAAAa5AEAwAAAACsQRIMAAAAALAGSTAAAAAAwBoMiugBA1v3EFuUOMUisjHWmwEfIG4AIHau7bG4xjE4EZ8YOxe2oCQYAAAAAGANkmAAAAAAgDVIggEAAAAA1iAJBgAAAABYgyQYAAAAAGANkmAAAAAAgDVIggEAAAAA1iAJBgAAAABYgyQYAAAAAGANkmAAAAAAgDVIggEAAAAA1iAJBgAAAABYgyQYAAAAAGANkmAAAAAAgDVIggEAAAAA1iAJBgAAAABYgyQYAAAAAGANkmAAAAAAgDVIggEAAAAA1iAJBgAAAABYgyQYAAAAAGANkmAAAAAAgDVIggEAAAAA1iAJBgAAAABYgyQYAAAAAGANkmAAAAAAgDVIggEAAAAA1iAJBgAAAABYIynWG+AVN9/1lbwxP1+2fFEia95tK906pYqfHXT2y6fykRRLkSRJsnSRsyQjkBnrzUIcIWaIGdTC3gKRdz8XKSgRSUkU6Z8t0rSBfz9K2/YXAODfkuCdO3fK2LFjJTs7W1JTU6Vt27YybNgwWbhwoXm+X79+EggEKk2XXXaZeNnwoRmy+O9tpH0bO+4L5MpKOUGypXdgkLSXjpIry2O9Sb5GzMQ/Yqb++TFuZPEmkc4tRC7vLtKjlciiTeJrtu2vB/gybgCgjkWc8W3evFn69OkjTZo0kSlTpshpp50mxcXFMn/+fBkzZoysXbvWrDdq1Ci5//77g69LT08XLzuvl7e3ry4VOQWyX/bK6XKumW8hJ8g6WSWHnHxJDzSM9eb5DjET/4iZ+ufLuDlULLI7X+T7ncrns5uKfLBFJK9QJNOHtY9s218P8GXcAIAXkuDRo0ebu4bLli2Thg2/TZi6du0qI0eODM43aNBAjj/++LrbUtSZAjkkqZImCYHyigD6faY5DaRADkq6kATXNWIm/hEz9c+XcXOgSKRhskhCoHw+EBDJSBE54NOk0Lb99QBfxg0AxLo69J49e2TevHnmbmLowdWldx5dL774ojRr1swceG+//XbZv3//Ed+3sLBQ8vLyKk2ItoqLEkRVtGJGETf1jZipL/6OG9t+R7btb+xwjQYAUSoJ/uyzz8RxHOnUqaJq0xFcccUVkpWVZe4yfvLJJzJhwgRZs2aNLFiwoNr1J0+eLPfdd18km4JjkCbpptS3zCkzpcH6nep8mtBZSV2LVswo4qb+EDP1y7dxo6Wg+UUiZU556ajjlJeWZvi0VNS2/Y0xrtEAIEpJsF6UKK1qUxNta+Lq1q2bnHLKKXLmmWfKypUr5Ywzzjhsfb1wue2224LzemdeO3JAdKQE0qSR00R2ylZpLSfKLvlS0qQh7YGjIFoxo4ib+kPM1C/fxk16sshxDUTW7xbp1Fxk4x6RRin+rRps2/7GGNdoABCl6tB6gaEXJbm5uZG8zFyMJCcny4YNG6p9XnsvzMzMrDTVt5smfCXtztgkX+wokQEjtkuHXlvEzzpLjnwpG2WJM082yzrpIjmx3iRfilbMeCFuiBliJlr8HDfSN0skd5fIS2tEVu8Q6Zctvmbb/saQn6/RACCmSXDTpk1l4MCBMmPGDMnPzz/s+b1791b7uk8//dT0TtiqVSvxqumTm8vWlVlS9MXJsv0/WbJ+aXvxs4aBRnJW4HwzRNLZgQskI9A41pvkS8SMfxAz9cfPcSNN0kUu6Vo+ZNDwbv4fM9e2/Y0hX8cNAMR6nOCZM2dKaWmp9OzZU+bMmWPuHOpdx2nTpkmvXr3k888/N93uL1++3HTVP3fuXPnxj38sp59+uum2H7ANMQMQNwDnGwCI4yGStBMSbW81adIkGT9+vOzYsUOaN28uOTk5MmvWLElJSTEDsj/22GNy4MAB09Zq6NChcs8990hiYmJ09gLwMGIGIG4AzjcA4B0Bx+1JwUO0o5LGjRvLN+uzJbNRxIXVcWdg6x5iixKnWN6Tv8u+fftoV1THiBv/Im6iHzcyMkckJeL7wvCqohKR2Ss410QxZjiPA4jnY5T/M0wAAAAAACqQBAMAAAAArEESDAAAAACwBkkwAAAAAMAaJMEAAAAAAGuQBAMAAAAArEESDAAAAACwBkkwAAAAAMAaJMEAAAAAAGuQBAMAAAAArEESDAAAAACwBkkwAAAAAMAaJMEAAAAAAGuQBAMAAAAArEESDAAAAACwBkkwAAAAAMAaJMEAAAAAAGuQBAMAAAAArEESDAAAAACwBkkwAAAAAMAaJMEAAAAAAGuQBAMAAAAArEESDAAAAACwBkkwAAAAAMAaJMEAAAAAAGuQBAMAAAAArEESDAAAAACwRlKsNwAAAADx5bnV50l6RqL43agzVohNnl6ZI7aw7buVGweKFYpKwlqNkmAAAAAAgDVIggEAAAAA1iAJBgAAAABYgyQYAAAAAGANkmAAAAAAgDVIggEAAAAA1iAJBgAAAABYgyQYAAAAAGANkmAAAAAAgDVIggEAAAAA1iAJBgAAAABYgyQYAAAAAGANkmAAAAAAgDVIggEAAAAA1iAJBgAAAABYgyQYAAAAAGANkmAAAAAAgDVIggEAAAAA1iAJBgAAAABYgyQYAAAAAGANkmAAAAAAgDVIggEAAAAA1iAJBgAAAABYgyQYAAAAAGANkmAAAAAAgDVIggEAAAAA1iAJBgAAAABYgyQYAAAAAGCNpFhvgFfcfNdX8sb8fNnyRYmsebetdOuUKn520Nkvn8pHUixFkiTJ0kXOkoxAZqw3C3GEmCFmUAt7C0Te/VykoEQkJVGkf7ZI0wb+/Sht218AgH9Lgnfu3Cljx46V7OxsSU1NlbZt28qwYcNk4cKFwXWWLl0q559/vjRs2FCaNGki/fr1k0OHDolXDR+aIYv/3kbat7HjvkCurJQTJFt6BwZJe+koubI81pvka8RM/CNm6p8f40YWbxLp3ELk8u4iPVqJLNokvmbb/nqAL+MGAOpYxBnf5s2bpU+fPuagOWXKFDnttNOkuLhY5s+fL2PGjJG1a9eag+ugQYNkwoQJ8vjjj0tKSoqsWbNGEhK8W/v6vF7pYosip0D2y145Xc418y3kBFknq+SQky/pgYax3jzfIWbiHzFT/3wZN4eKRXbni3y/U/l8dlORD7aI5BWKZPqw9pFt++sBvowbAPBCEjx69GgJBAKybNkycwfR1bVrVxk5cqR5fOutt8q4cePkl7/8ZfD5U045pa62GceoQA5JqqRJQqD8hKffZ5rTQArkoKQLSXBdI2biHzFT/3wZNweKRBomiyQEyucDAZGMFJEDPk0KbdtfD/Bl3ABAFER022/Pnj0yb948czcx9ODq0juPu3btkg8//FBatGghvXv3lpYtW0rfvn3lgw8+OOL7FhYWSl5eXqUJ0VZxUYKoilbMKOKmvhEz9cXfcWPb78i2/Y0drtEAIEpJ8GeffSaO40inThVVm6qxceNG8++9994ro0aNMhcyZ5xxhlxwwQWyYcOGal8zefJkady4cXDS9iuInjRJN6W+ZU6ZmdfvVOfThM5K6lq0YkYRN/WHmKlfvo0bLQXNLxIpc8rnHae8tDTDp6Witu1vjHGNBgBRSoL1okRpVZsjKSsrT6yuv/56+elPfyqnn366/O53v5OOHTvK7Nmzq32NtkvZt29fcNq2bVskm4UIpQTSpJE0kZ2y1czvki8lTRrSHjgKohUziripP8RM/fJt3KQnixzXQGT97vL5jXtEGqX4t2qwbfsbY1yjAUCUkmBtM6IXJbm5uUdcp1WrVubfLl26VFreuXNn2bq1POmqSnsvzMzMrDTVt5smfCXtztgkX+wokQEjtkuHXlvEzzpLjnwpG2WJM082yzrpIjmx3iRfilbMeCFuiBliJlr8HDfSN0skd5fIS2tEVu8Q6Zctvmbb/saQn6/RACCmSXDTpk1l4MCBMmPGDMnPzz/s+b1798qJJ54orVu3lnXr1lV6bv369dK+fXvxqumTm8vWlVlS9MXJsv0/WbJ+qXe3tS40DDSSswLnmyGSzg5cIBmBxrHeJF8iZvyDmKk/fo4baZIucknX8iGDhnfz/5i5tu1vDPk6bgCgjkXcH/7MmTOltLRUevbsKXPmzDFtr/Su47Rp06RXr17mLuQdd9xh5l9++WXTRuXuu+823fJfd911db39gOcRMwBxA3C+AQDviHiIpKysLFm5cqVMmjRJxo8fLzt27JDmzZtLTk6OzJo1y6xzyy23SEFBgemGX3sr7N69uyxYsEBOOumkaOwD4GnEDEDcAJxvAMA7Ao7bk4KH6JAV2mvnN+uzJbOR/wdvH9i6h9iixCmW9+TvpkMa2hXVLeLGv4ib6MeNjMwRSYn4vjC8qqhEZPYKzjVRjJnHFnWX9IxE8btRZ6wQmzy90p7+Lmz7buXGgWKDvKISaTz7naMe//2fYQIAAAAAUIEkGAAAAABgDZJgAAAAAIA1SIIBAAAAANYgCQYAAAAAWIMkGAAAAABgDZJgAAAAAIA1SIIBAAAAANYgCQYAAAAAWIMkGAAAAABgDZJgAAAAAIA1SIIBAAAAANYgCQYAAAAAWIMkGAAAAABgDZJgAAAAAIA1SIIBAAA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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "heatmap_figure, heatmap_axes = plot_cellnopt_fit(method, view=\"heatmap\")" + ] + }, + { + "cell_type": "markdown", + "id": "f4d57529", + "metadata": { + "papermill": { + "duration": 0.002729, + "end_time": "2026-09-21T23:05:20.734725", + "exception": false, + "start_time": "2026-09-21T23:05:20.731996", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Under the hood\n", + "\n", + "The biological workflow above rests on a few modeling assumptions. This section states only the assumptions needed to interpret the result and separates the CellNOpt core from the acyclicity and connectivity requirements added by CORNETO." + ] + }, + { + "cell_type": "markdown", + "id": "499bc91f", + "metadata": { + "papermill": { + "duration": 0.002701, + "end_time": "2026-09-21T23:05:20.740159", + "exception": false, + "start_time": "2026-09-21T23:05:20.737458", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Endpoint Boolean assumptions\n", + "\n", + "One reaction set is selected for the whole experiment, while protein and reaction activities are predicted separately for each condition.\n", + "\n", + "For every condition:\n", + "\n", + "- a selected reaction is active only when all its positive inputs are active and all its negative inputs are inactive;\n", + "- several active reactions producing the same protein act as OR alternatives;\n", + "- a stimulus or inhibitor overrides the usual production rule by fixing its target on or off;\n", + "- internal activities are predictions at the measured endpoint, not simulated intermediate time steps.\n", + "\n", + "CORNETO represents these rules with vectorized binary variables: $Y_r$ for shared reaction selection, $Z_{rc}$ for condition-specific reaction activity, and $X_{vc}$ for condition-specific protein activity. The implementation uses exact linear constraints for AND, OR, and negative literals, but those linearization details do not change the biological assumptions above." + ] + }, + { + "cell_type": "markdown", + "id": "6b6b6971", + "metadata": { + "papermill": { + "duration": 0.002703, + "end_time": "2026-09-21T23:05:20.745673", + "exception": false, + "start_time": "2026-09-21T23:05:20.742970", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Fit and model size\n", + "\n", + "The model prioritizes agreement with observed readouts and uses a smaller penalty to prefer compact signaling explanations:\n", + "\n", + "$$\n", + "\\min\\; \\sum_{(v,c)\\,\\mathrm{observed}} |X_{vc}-m_{vc}| + \\lambda\\sum_r Y_r.\n", + "$$\n", + "\n", + "Only measured entries contribute to the first term. Unmeasured proteins are not treated as zero. The second term counts selected Boolean reactions, so an AND gate is penalized once rather than once per input leg." + ] + }, + { + "cell_type": "markdown", + "id": "180b830e", + "metadata": { + "papermill": { + "duration": 0.002633, + "end_time": "2026-09-21T23:05:20.750941", + "exception": false, + "start_time": "2026-09-21T23:05:20.748308", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### What CORNETO adds\n", + "\n", + "| Modeling element | Role in this formulation |\n", + "|---|---|\n", + "| Shared reaction selection across conditions | CellNOpt core |\n", + "| Condition-specific Boolean AND, OR, and inhibitory logic | CellNOpt core |\n", + "| Readout mismatch plus a model-size penalty | CellNOpt core |\n", + "| Acyclic selected reaction dependencies | CORNETO endpoint restriction |\n", + "| One shared structural flow connecting interventions to readouts | CORNETO connectivity extension |\n", + "\n", + "Acyclicity is imposed on the **selected model**, not on the PKN. CORNETO assigns an ordering level $h_v$ to every protein and requires each selected dependency $u\\rightarrow v$ to point forward:\n", + "\n", + "$$\n", + "Y_r=1 \\;\\Longrightarrow\\; h_u < h_v\n", + "\\qquad\\text{for every dependency }u\\rightarrow v\\text{ in reaction }r.\n", + "$$\n", + "\n", + "This makes the endpoint response evaluable from the experimental inputs without choosing an update order or allowing a selected feedback loop to support itself.\n", + "\n", + "Connectivity is a separate requirement. CORNETO sends an artificial bookkeeping flow from controlled proteins, through every selected dependency, to measured readouts. Selected dependencies must carry positive flow, unselected dependencies carry none, and flow is conserved at internal proteins. The resulting flow is a structural certificate that the selected model contains no disconnected or dangling reactions." + ] + }, + { + "cell_type": "markdown", + "id": "e7f13c08", + "metadata": { + "papermill": { + "duration": 0.0027, + "end_time": "2026-09-21T23:05:20.756313", + "exception": false, + "start_time": "2026-09-21T23:05:20.753613", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Inspecting the inferred states\n", + "\n", + "The tables below contain only selected reactions and the internal or measured proteins needed to interpret the example. Reaction activity and protein activity vary by condition; neither table contains additional experimental measurements. `Y` and `Z` are observed readouts, whereas `B`, `X`, and `W` are model predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "39c46bc9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:20.762368Z", + "iopub.status.busy": "2026-09-21T23:05:20.762277Z", + "iopub.status.idle": "2026-09-21T23:05:20.767654Z", + "shell.execute_reply": "2026-09-21T23:05:20.767341Z" + }, + "papermill": { + "duration": 0.009138, + "end_time": "2026-09-21T23:05:20.768160", + "exception": false, + "start_time": "2026-09-21T23:05:20.759022", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " C1 C2 C3 C4 C5 C6\n", + "B 0 1 1 0 0 0\n", + "X 1 0 0 1 0 1\n", + "W 1 0 0 1 0 0\n", + "Y 1 0 1 1 1 1\n", + "Z 0 0 0 1 0 0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "condition_names = list(inputs)\n", + "selected_mask = selected.astype(bool)\n", + "reaction_activity = pd.DataFrame(\n", + " np.rint(problem.expr.reaction_active.value).astype(int),\n", + " index=reaction_labels,\n", + " columns=condition_names,\n", + ").loc[selected_mask]\n", + "species_states = pd.DataFrame(\n", + " np.rint(problem.expr.vertex_value.value).astype(int),\n", + " index=method.processed_graph.V,\n", + " columns=condition_names,\n", + ").loc[[\"B\", \"X\", \"W\", \"Y\", \"Z\"]]\n", + "\n", + "display(reaction_activity, species_states)" + ] + }, + { + "cell_type": "markdown", + "id": "362e287c", + "metadata": { + "papermill": { + "duration": 0.002854, + "end_time": "2026-09-21T23:05:20.773812", + "exception": false, + "start_time": "2026-09-21T23:05:20.770958", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Structural connectivity diagnostic\n", + "\n", + "The following plot visualizes the artificial structural flow by edge width. It is useful for checking that selected reactions are connected to experimental inputs and measured outputs, and for finding an incorrectly mapped, disconnected, or dangling reaction.\n", + "\n", + "This is a debugging view. Flow is shared across all conditions and records neither whether a reaction is active in one condition nor how strongly a biochemical signal passes through it." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "fb593712", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-21T23:05:20.779981Z", + "iopub.status.busy": "2026-09-21T23:05:20.779900Z", + "iopub.status.idle": "2026-09-21T23:05:20.972365Z", + "shell.execute_reply": "2026-09-21T23:05:20.971855Z" + }, + "papermill": { + "duration": 0.196308, + "end_time": "2026-09-21T23:05:20.972923", + "exception": false, + "start_time": "2026-09-21T23:05:20.776615", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D->B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Y\n", + "\n", + "\n", + "Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_10\n", + "\n", + "\n", + "AND\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->__cellnopt_and_10\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_11\n", + "\n", + "\n", + "AND\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "W->__cellnopt_and_11\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->__cellnopt_and_11\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->__cellnopt_and_10\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_10->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Z\n", + "\n", + "\n", + "Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "__cellnopt_and_11->Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_cellnopt_model(method, width_by=\"flow\")" + ] + }, + { + "cell_type": "markdown", + "id": "96461460", + "metadata": { + "papermill": { + "duration": 0.002882, + "end_time": "2026-09-21T23:05:20.978942", + "exception": false, + "start_time": "2026-09-21T23:05:20.976060", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "All selected dependency legs happen to carry structural flow `1` in this solution, so they receive the same width. That equality does **not** mean the reactions have equal biological strength or importance. It only means that this particular bookkeeping flow can certify every selected dependency with the minimum required amount.\n", + "\n", + "Structural-flow values are generally non-unique and solver-dependent; a different feasible certificate can assign different widths without changing the selected signaling model. The values are unrelated to protein activity, signal strength, reaction rate, confidence, or causal importance. Use the plot only to verify selected-model connectivity." + ] + }, + { + "cell_type": "markdown", + "id": "7c0e516e", + "metadata": { + "papermill": { + "duration": 0.011294, + "end_time": "2026-09-21T23:05:20.993122", + "exception": false, + "start_time": "2026-09-21T23:05:20.981828", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Biological interpretation and limitations\n", + "\n", + "This method is designed for intracellular signaling experiments in which cells are stimulated or inhibited and measured once at a chosen endpoint. The result is one compact pathway structure whose predicted activity changes across intervention conditions.\n", + "\n", + "The fitted model answers the question posed at the beginning: it is a connected feed-forward signaling logic sufficient to explain these observed snapshots. “Sufficient” does not mean unique, dynamically complete, or necessarily identical to the pathway operating in the cell.\n", + "\n", + "Feedback remains biologically plausible. A snapshot with both `Y` and `Z` active usually cannot reveal whether `Y` activated `Z`, `Z` activated `Y`, an upstream protein activated both, or a loop maintained them. The acyclic restriction therefore reflects what this experiment can identify; it is not evidence that the biological network contains no feedback. Feedback candidates remain visible in the PKN as hypotheses that were available but not established by this endpoint analysis.\n", + "\n", + "A dynamic model is more appropriate when measurements cover activation and relaxation over time, proteins inside a proposed loop are directly perturbed, or the data show adaptation, oscillation, or dependence on initial state. Such analyses must state how time and updates are represented.\n", + "\n", + "Other limitations remain: Boolean states simplify graded and stochastic signaling; inference is restricted to the supplied PKN; alternative models may fit equally well; and unmeasured internal activities remain predictions. Structural flow certifies connectivity only—it does not quantify signaling strength." + ] + }, + { + "cell_type": "markdown", + "id": "causal-structure-bridge", + "metadata": { + "papermill": { + "duration": 0.00288, + "end_time": "2026-09-21T23:05:20.999060", + "exception": false, + "start_time": "2026-09-21T23:05:20.996180", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Connection to causal structure learning\n", + "\n", + "For readers coming from mathematics or causal inference, `CellNOptDAG` can be viewed as **interventional Boolean causal structure learning over a restricted hypothesis space**.\n", + "\n", + "The variables are the binary protein activities $X_v$. The PKN acts as a causal *superstructure*: it specifies the allowed variables, directions, signs, and multi-input clauses. The optimization does not invent interactions outside that superstructure. Instead, it chooses which candidate reactions are needed to explain all intervention conditions.\n", + "\n", + "Let $Y_r$ indicate whether candidate reaction $r$ is selected. If $P_r$ and $N_r$ are its positive and negative inputs, its condition-specific truth value is\n", + "\n", + "$$\n", + "Z_{rc}=Y_r\\land\\bigwedge_{u\\in P_r}X_{uc}\\land\\bigwedge_{w\\in N_r}\\neg X_{wc}.\n", + "$$\n", + "\n", + "For a protein that is not experimentally fixed, alternative selected reactions producing that protein act as OR alternatives:\n", + "\n", + "$$\n", + "X_{vc}=\\bigvee_{r:\\,\\operatorname{product}(r)=v} Z_{rc}.\n", + "$$\n", + "\n", + "The inferred equation for each protein is therefore a signed Boolean expression in disjunctive normal form. For example, selecting `A AND NOT B → X` and `C → X` gives\n", + "\n", + "$$\n", + "X=(A\\land\\neg B)\\lor C.\n", + "$$\n", + "\n", + "The selection variables $Y_r$ are shared across conditions: this is the assumption that the underlying causal mechanisms remain invariant throughout the experiment. The state variables $X_{vc}$ and reaction activities $Z_{rc}$ vary because each condition applies different experimental manipulations.\n", + "\n", + "A stimulus or inhibitor overrides the usual equation for its target and fixes that variable to `1` or `0`. In structural-causal notation, this plays the role of a hard intervention such as $\\operatorname{do}(X_v=1)$ or $\\operatorname{do}(X_v=0)$. CellNOptDAG then searches for a shared model that predicts the measured outcomes of these interventions:\n", + "\n", + "$$\n", + "\\min_Y\\;\\sum_{(v,c)\\,\\mathrm{observed}}|X_{vc}-m_{vc}|+\\lambda\\sum_rY_r.\n", + "$$\n", + "\n", + "The first term is interventional prediction error and the second is a sparsity penalty on structural mechanisms. Because CORNETO also requires the selected dependencies to be acyclic, the fitted Boolean structural equations can be evaluated in topological order without specifying feedback dynamics.\n", + "\n", + "| CellNOpt term | Causal or mathematical interpretation |\n", + "|---|---|\n", + "| Protein activity | Binary endogenous variable |\n", + "| PKN | Causal superstructure or hypothesis space |\n", + "| Selected reaction | Selected structural mechanism or Boolean clause |\n", + "| Stimulus or inhibitor | Hard intervention |\n", + "| Shared reaction selection | Invariant causal structure |\n", + "| Readout mismatch | Interventional prediction loss |\n", + "| Model-size penalty | Sparsity regularization |\n", + "| Acyclicity | Topological evaluation of the structural equations; feedback is excluded |\n", + "| Structural flow | Connectivity certificate, not a causal-effect estimate |\n", + "\n", + "The phrase **causal discovery** needs one qualification here. This is not unrestricted discovery of a causal graph from data alone: the PKN already supplies the possible mechanisms, including the available AND clauses. The method selects among those mechanisms, while OR relationships arise from selecting several reactions with the same product. It is therefore more precise to call the task **PKN-constrained interventional Boolean causal structure learning**.\n", + "\n", + "As in other causal-learning problems, the fitted structure need not be identifiable. Two candidate subnetworks may make exactly the same predictions under the available interventions. The size penalty can prefer a compact representative, but it cannot turn experimentally indistinguishable models into a uniquely established causal explanation." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + }, + "papermill": { + "default_parameters": {}, + "duration": 3.781041, + "end_time": "2026-09-21T23:05:21.220714", + "environment_variables": {}, + "exception": null, + "input_path": "docs/guide/signaling/cellnopt_dag.ipynb", + "output_path": "/private/tmp/cellnopt-dag-executed.ipynb", + "parameters": {}, + "start_time": "2026-09-21T23:05:17.439673", + "version": "2.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/docs/guide/signaling/index.md b/docs/guide/signaling/index.md index 8b67a5cc9..314fbac9d 100644 --- a/docs/guide/signaling/index.md +++ b/docs/guide/signaling/index.md @@ -2,6 +2,10 @@ Inferring signaling networks from omics data is a challenging task due to the complexity of the underlying biological processes. CORNETO provides a set of tools to infer and contextualize signaling networks from omics data and prior knowledge. +`CellNOptDAG` additionally exposes fixed-model prediction and evaluation after +solving. The other signaling methods primarily build explanatory optimization +problems and return their inferred solutions. + ```{toctree} :maxdepth: 3 @@ -9,5 +13,5 @@ carnival.ipynb multisample-carnival.ipynb phonemes.ipynb bidirectional-phonemes.ipynb -cellnopt_ILP.ipynb +cellnopt_dag.ipynb ``` diff --git a/docs/install.md b/docs/install.md index defa01b62..deed8eccc 100644 --- a/docs/install.md +++ b/docs/install.md @@ -38,6 +38,7 @@ pip install corneto CORNETO provides several optional dependency groups: +- **`plot`**: Graphviz, NetworkX, and Matplotlib visualization support - **`research`**: Full research stack with Gurobi, PICOS, visualization, and network tools - **`os`**: Open-source solvers (SCIP, HiGHS) with visualization and network tools - **`ml`**: Machine learning dependencies (JAX, Keras, scikit-learn) @@ -45,6 +46,7 @@ CORNETO provides several optional dependency groups: Install any combination with: ```bash +pip install corneto[plot] # Visualization without the full research 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+ +
+ + +
+

Network optimization, for biology.

+ +

+ An open-source Python framework for expressing and solving biological network optimization problems, + from network inference to general-purpose graph-based modeling. +

+
+ + Read the documentation + + + Read the paper +
+
+ —GitHub stars + Nat. Mach. Intell.2025 + BSD-3-Clause open source +
+
+
+ +
+
+

§ 01 Unified framework

+ +
+
+

For computational biology

+

Infer mechanistic networks from your omics data.

+

+ Move from gene expression, phosphoproteomics, or + metabolomics to condition-specific signaling and + metabolic networks, using biological knowledge. +

+
    +
  • Intracellular signaling from transcriptomics
    • +
  • Context-specific metabolic networks from omics
    • +
  • Multi-condition network inference
    • +
  • Compatible with Decoupler, Omnipath, COBRApy, and more.
    • +
+
+
+

For optimization & modeling

+

A composable API for optimization on graphs.

+

+ Build network optimization problems with modular formulations + for flows, paths, trees, and acyclic networks, then solve them + with the backend and solver of your choice. +

+
    +
  • Reusable constraint & objective building blocks
    • +
  • Backend-agnostic (CVXPY / PICOS; +20 solvers)
    • +
  • Supports directed, undirected and mixed (hyper)graphs
    • +
  • Graphviz and WebAssembly plotting support
    • +
+
+
+
+
+ +
+

§ 02 Applications

+ +
+ +
+ CARNIVAL signaling network +
+
+

Signaling

+

Intracellular Signaling Networks

+

Causal reasoning over prior knowledge signaling networks from + perturbation data.

+
+ + + +
+ Flux balance analysis map +
+
+

Metabolism

+

Flux Balance Analysis

+

Genome-scale metabolic modeling with multi-sample joint + formulations.

+
+ + + +
+ Network optimization feasible space +
+
+

Graph optimization

+

Paths, Trees, DAGs and more

+

Build formulations for finding optimal network structures. +

+
+ +
+
+ +
+
+

§ 03 Part of a broader ecosystem

+ +
+
+

NetworkCommons

+

CORNETO is part of NetworkCommons.

+

+ NetworkCommons helps you build context-specific biological networks by combining omics data, prior + knowledge, and network inference methods in one platform. It makes it easier to access data, run + different methods, and compare results across workflows. Many of its optimization-based methods are + powered by CORNETO, the underlying engine for flexible network inference. +

+

+ Paton V, Türei D, Ivanova O, et al. + NetworkCommons: bridging data, knowledge, and methods to + build and evaluate context-specific biological networks. + Bioinformatics, 2025. +

+
+ + Explore NetworkCommons + + + View on GitHub +
+
+
+
+ NetworkCommons graphical abstract +
+
+
+
+
+ +
+

§ 04 Peer-reviewed research

+ +
+
+

Nature Machine Intelligence  ·  2025

+

Unifying multi-sample network inference from prior knowledge and omics data with CORNETO.

+

+ Rodriguez-Mier, P., Garrido-Rodriguez, M., Gabor, A., & Saez-Rodriguez, J. +

+ + Read on nature.com → + +
+ +
+
+ BibTeX + +
+ 
@article{rodriguez-mier2025,
+  title   = {Unifying multi-sample network inference from prior knowledge
+             and omics data with CORNETO},
+  author  = {Rodriguez-Mier, Pablo and Garrido-Rodriguez, Martin
+             and Gabor, Attila and Saez-Rodriguez, Julio},
+  journal = {Nature Machine Intelligence},
+  year    = {2025},
+  doi     = {10.1038/s42256-025-01069-9},
+  url     = {https://www.nature.com/articles/s42256-025-01069-9}
+}
+
+
+
+ + + + + + + + + + + diff --git a/docs/releases/index.md b/docs/releases/index.md index c573b582d..758934fa3 100644 --- a/docs/releases/index.md +++ b/docs/releases/index.md @@ -7,6 +7,9 @@ This section contains detailed release notes for CORNETO versions, documenting n ```{toctree} :maxdepth: 1 +v1.0.0-rc.5 +v1.0.0-rc.4 +v1.0.0-rc.3 v1.0.0-rc.1 migration-1.0 v1.0.0-beta.2 @@ -41,7 +44,7 @@ CORNETO follows [semantic versioning](https://semver.org/) with the following re - **GitHub Releases**: Follow releases on the [GitHub repository](https://github.com/saezlab/corneto/releases) - **PyPI**: Install the latest version with `pip install --upgrade corneto` -- **Development**: Track development progress on the `dev` branch +- **Development**: Track development through pull requests into `main` ## Contributing to Releases diff --git a/docs/releases/migration-1.0.md b/docs/releases/migration-1.0.md index 8e6f219b4..5cad33060 100644 --- a/docs/releases/migration-1.0.md +++ b/docs/releases/migration-1.0.md @@ -14,13 +14,12 @@ were removed instead of being carried as permanent compatibility aliases. | `corneto.methods.future.MultiSampleIMAT` | `corneto.methods.MultiSampleIMAT` | | `corneto.methods.future.PrizeCollectingSteinerTree` | `corneto.methods.PrizeCollectingSteinerTree` | | `corneto.methods.future.SteinerTreeFlow` | `corneto.methods.SteinerTreeFlow` | -| `corneto.methods.signal.cellnopt_ilp` | `corneto.methods.signaling.cellnopt_ilp` | | `corneto.K` or `corneto.ops` | `corneto.opt` | | `corneto._ml.build_dagnn` | `corneto.ml.build_dagnn` | | `corneto.methods.shortest_path.create_multisample_shortest_path` | `corneto.methods.create_multisample_shortest_path` | | module-level graph serialization | `Graph.save` and `Graph.load` | -The old `future`, `signal`, `K`, and `ops` paths in this table remain warning +The old `future`, `K`, and `ops` paths in this table remain warning compatibility paths throughout 1.x. The private `_ml` path and duplicate graph serialization functions do not have shims. @@ -34,8 +33,9 @@ serialization functions do not have shims. modules (`corneto._graph`, `corneto._data`, `corneto._io`, `corneto._nx`, `corneto._core`, and `corneto._legacy`). - The British-spelling `corneto.methods.signalling` implementation and the old - module-shaped `corneto.methods.signaling`; signaling methods now live in the - `corneto.methods.signaling` package. + module-shaped `corneto.methods.signaling`, as well as the singular + `corneto.methods.signal` compatibility namespace; signaling methods now live + in the `corneto.methods.signaling` package. - Duplicate module-level graph serialization helpers from `corneto.io`. ## Multi-condition convention diff --git a/docs/releases/v1.0.0-rc.1.md b/docs/releases/v1.0.0-rc.1.md index 758a8336a..90e83984c 100644 --- a/docs/releases/v1.0.0-rc.1.md +++ b/docs/releases/v1.0.0-rc.1.md @@ -20,9 +20,11 @@ It requires Python 3.11 or newer and is tested on Python 3.11, 3.12, and 3.13. ## Compatibility window -Imports through `corneto.methods.future`, `corneto.methods.signal`, and the root -`corneto.K` and `corneto.ops` aliases continue to work with `FutureWarning` -through the CORNETO 1.x line. They are scheduled for removal in CORNETO 2.0. +Imports through `corneto.methods.future` and the root `corneto.K` and +`corneto.ops` aliases continue to work with `FutureWarning` through the CORNETO +1.x line. They are scheduled for removal in CORNETO 2.0. The experimental +`corneto.methods.signal` namespace and `cellnoptILP` function are outside this +compatibility window and are removed before the final 1.0 release. See the [1.0 migration guide](migration-1.0.md) for canonical mappings and intentional removals. diff --git a/docs/releases/v1.0.0-rc.3.md b/docs/releases/v1.0.0-rc.3.md new file mode 100644 index 000000000..fbd7b7bc6 --- /dev/null +++ b/docs/releases/v1.0.0-rc.3.md @@ -0,0 +1,27 @@ +# CORNETO 1.0.0 RC3 + +CORNETO 1.0.0 RC3 expands the candidate 1.0 signaling and metabolism workflows +and moves publication to a public-`main` release gate. + +## Highlights + +- `CellNOptDAG` provides class-based Boolean signaling-model inference, + including dedicated model and fit visualizations. +- The FBA and iMAT guides have been overhauled, including a new standalone iMAT + guide and refreshed multi-condition examples. +- Public releases now use one pull request directly into public `main`; that PR + runs the Python 3.11–3.13 tests, quality checks, warning-free documentation + build, and package smoke test before a release tag is created. + +## Experimental API cleanup + +The experimental `cellnoptILP` function and `corneto.methods.signal` namespace +have been removed without compatibility shims. Use the supported class-based +API instead: + +```python +from corneto.methods.signaling import CellNOptDAG +``` + +See the [1.0 migration guide](migration-1.0.md) for the complete supported +surface and intentional removals. diff --git a/docs/releases/v1.0.0-rc.4.md b/docs/releases/v1.0.0-rc.4.md new file mode 100644 index 000000000..0c880a406 --- /dev/null +++ b/docs/releases/v1.0.0-rc.4.md @@ -0,0 +1,31 @@ +# Release v1.0.0-rc.4 {bdg-warning}`Pre-release` + +CORNETO 1.0.0 RC4 adds an AnnNet-centered signaling workflow and clarifies how +experimental data are represented across CORNETO methods. + +## Highlights + +- New signaling helpers read signed interactions and multi-condition + perturbation data from AnnNet, build a `CellNOptDAG` optimization problem, + and add the fitted model and condition-specific predictions back to the same + AnnNet object. +- A new tutorial uses proteins from the LiverDREAM signaling network to show + how AnnNet can keep the candidate pathway, experimental conditions, selected + interactions, predictions, and reaction activities together. +- The tutorial uses normalized synthetic measurements with experimental + variation, making the difference between observed values and Boolean model + predictions visible. +- The Getting Started guide now presents `Data` as CORNETO's general + representation of samples and measured features, while method-specific + `build` and `build_many` functions are explained as convenience interfaces. + +## AnnNet and CellNOptDAG + +Install the optional AnnNet integration with: + +```bash +pip install "corneto[annnet]" +``` + +The general `build_from_data` method interface remains available for annotated +or reusable CORNETO datasets. diff --git a/docs/releases/v1.0.0-rc.5.md b/docs/releases/v1.0.0-rc.5.md new file mode 100644 index 000000000..9cc24dc1b --- /dev/null +++ b/docs/releases/v1.0.0-rc.5.md @@ -0,0 +1,23 @@ +# Release v1.0.0-rc.5 {bdg-warning}`Pre-release` + +CORNETO 1.0.0 RC5 refreshes the project website and strengthens the release and +documentation publication safeguards ahead of the final 1.0 release. + +## Highlights + +- A new self-contained landing page presents CORNETO's biological network + inference and graph optimization capabilities without depending on assets + from a particular documentation version. +- Documentation publishing now prepares and validates the complete Pages tree + before making a single serialized update, preserving historical versions and + preventing release tags from overwriting the homepage. +- Every generated documentation build is checked for the canonical + `corneto.org/switcher.json` URL and the correct version match before it can be + deployed. +- The release helper and package publication workflow now require canonical, + committed release notes before creating or publishing a tag. + +## Compatibility + +This release does not change the CORNETO Python API. The landing-page language +and links target the supported 1.0 documentation and interfaces. diff --git a/docs/tutorials/README.md b/docs/tutorials/README.md index d6444d0cb..e878c13f9 100644 --- a/docs/tutorials/README.md +++ b/docs/tutorials/README.md @@ -72,9 +72,9 @@ git clone https://github.com/saezlab/corneto cd corneto ``` -### 2. Switch to the `dev` Branch +### 2. Start from the `main` Branch ```bash -git checkout dev +git checkout main ``` ### 3. Create a New Branch @@ -121,7 +121,7 @@ git push origin contrib/my-tutorial-name ### 8. Open a Pull Request -Open a **PR against `dev`** with title: +Open a **PR against `main`** with title: ``` docs(tutorial): add my_tutorial_name example ``` diff --git a/docs/tutorials/annnet-signaling/annnet-cellnopt-dag.ipynb b/docs/tutorials/annnet-signaling/annnet-cellnopt-dag.ipynb new file mode 100644 index 000000000..af0ffb265 --- /dev/null +++ b/docs/tutorials/annnet-signaling/annnet-cellnopt-dag.ipynb @@ -0,0 +1,2088 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "intro", + "metadata": { + "tags": [] + }, + "source": [ + "# Fitting a multi-condition signaling network stored in AnnNet\n", + "\n", + "A signaling analysis contains more than a pathway diagram. We also need to know which proteins were stimulated or inhibited, what was measured in each condition, which interactions were selected by the model, and what the model predicts.\n", + "\n", + "In this tutorial, one AnnNet object keeps all of this information together. CORNETO provides the optimization method that fits the network.\n", + "\n", + "We will use AnnNet to hold:\n", + "\n", + "- a signed prior-knowledge network;\n", + "- nine perturbation experiments;\n", + "- the network selected by `CellNOptDAG`; and\n", + "- predicted protein and reaction activities for every condition.\n", + "\n", + "The biological question is:\n", + "\n", + "> Which routes through the candidate network are needed to explain the responses to TGFA, IGF1, and several kinase inhibitors?\n", + "\n", + "The complete analysis can be saved as one `.annnet` file and explored later without rerunning the optimization." + ] + }, + { + "cell_type": "markdown", + "id": "data-scope", + "metadata": { + "tags": [] + }, + "source": [ + "## What data are used?\n", + "\n", + "The protein names and candidate interactions come from a small part of the prior-knowledge network distributed with the [DREAM4 Predictive Signaling Network Challenge](https://pmc.ncbi.nlm.nih.gov/articles/PMC3465072/). The original challenge studied signaling responses in HepG2 cells after stimulation with ligands and treatment with kinase inhibitors.\n", + "\n", + "To keep the model easy to understand, we use a small **synthetic perturbation experiment**. Stimuli and inhibitors are binary, while the responses are normalized values between `0` and `1`. We deliberately include experimental variation, so a Boolean model is not expected to reproduce every value exactly. The example uses real pathway names, but the measurements are illustrative and are not taken from the original DREAM dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "imports", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import annnet as an\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from annnet.utils import plot as plot_annnet\n", + "from IPython.display import display\n", + "\n", + "from corneto.methods.signaling import (\n", + " CellNOptDAG,\n", + " add_cellnopt_conditions,\n", + " add_cellnopt_results,\n", + " build_cellnopt_from_annnet,\n", + " plot_cellnopt_fit,\n", + " plot_cellnopt_model,\n", + ")\n", + "\n", + "plt.rcParams.update({\"figure.dpi\": 120, \"axes.spines.top\": False, \"axes.spines.right\": False})" + ] + }, + { + "cell_type": "markdown", + "id": "network-intro", + "metadata": { + "tags": [] + }, + "source": [ + "## 1. Create the signaling network in AnnNet\n", + "\n", + "We begin with the biological network that will remain with us throughout the analysis. Each interaction has a sign: `+1` for activation and `−1` for inhibition.\n", + "\n", + "The network contains two familiar branches:\n", + "\n", + "- TGFA activates EGFR and can signal toward ERK1/2 and HSP27;\n", + "- IGF1 activates IGFR and can signal toward PI3K and AKT.\n", + "\n", + "We also include several possible shortcuts and alternative routes. They are candidates that CORNETO may select if they help explain the experiments.\n", + "\n", + "The interactions are placed in an AnnNet slice called `prior`. A slice is a named selection within a network. Later, another slice will identify the interactions selected by CellNOptDAG, while the original candidate network remains available." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "create-network", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AnnNet contains 21 proteins and 25 signed interactions\n" + ] + } + ], + "source": [ + "display_name = {\n", + " \"tgfa\": \"TGFA\", \"egfr\": \"EGFR\", \"grb2\": \"GRB2\", \"shc\": \"SHC\",\n", + " \"sos\": \"SOS\", \"ras\": \"RAS\", \"raf1\": \"RAF1\", \"mek12\": \"MEK1/2\",\n", + " \"erk12\": \"ERK1/2\", \"prak\": \"PRAK\", \"hsp27\": \"HSP27\", \"pi3k\": \"PI3K\",\n", + " \"pip3\": \"PIP3\", \"pdk1\": \"PDK1\", \"akt\": \"AKT\", \"igf1\": \"IGF1\",\n", + " \"igfr\": \"IGFR\", \"irs1\": \"IRS1\", \"map3k1\": \"MAP3K1\", \"mkk4\": \"MKK4\",\n", + " \"p38\": \"p38\",\n", + "}\n", + "\n", + "pkn_edges = [\n", + " (\"tgfa\", 1, \"egfr\"), (\"egfr\", 1, \"grb2\"), (\"egfr\", 1, \"shc\"),\n", + " (\"shc\", 1, \"grb2\"), (\"grb2\", 1, \"sos\"), (\"sos\", 1, \"ras\"),\n", + " (\"ras\", 1, \"raf1\"), (\"raf1\", 1, \"mek12\"), (\"mek12\", 1, \"erk12\"),\n", + " (\"erk12\", 1, \"prak\"), (\"prak\", 1, \"hsp27\"), (\"egfr\", 1, \"pi3k\"),\n", + " (\"ras\", 1, \"pi3k\"), (\"pi3k\", 1, \"pip3\"), (\"pip3\", 1, \"pdk1\"),\n", + " (\"pdk1\", 1, \"akt\"), (\"igf1\", 1, \"igfr\"), (\"igfr\", 1, \"irs1\"),\n", + " (\"irs1\", 1, \"pi3k\"), (\"igfr\", 1, \"shc\"), (\"ras\", 1, \"map3k1\"),\n", + " (\"map3k1\", 1, \"mkk4\"), (\"mkk4\", 1, \"p38\"), (\"p38\", 1, \"prak\"),\n", + " (\"akt\", -1, \"raf1\"),\n", + "]\n", + "\n", + "signaling = an.AnnNet(directed=True)\n", + "signaling.history.enable(True)\n", + "signaling.slices.add(\"prior\", role=\"prior_knowledge\")\n", + "signaling.add_vertices(sorted(display_name))\n", + "\n", + "for protein, label in display_name.items():\n", + " signaling.attrs.set_vertex_attrs(protein, label=label, entity_type=\"protein\")\n", + "\n", + "prior_edge_ids = []\n", + "for index, (source, sign, target) in enumerate(pkn_edges):\n", + " edge_id = f\"liverdream_{index:02d}\"\n", + " signaling.add_edges(\n", + " source,\n", + " target,\n", + " edge_id=edge_id,\n", + " slice=\"prior\",\n", + " directed=True,\n", + " parallel=\"parallel\",\n", + " )\n", + " signaling.attrs.set_edge_attrs(\n", + " edge_id,\n", + " interaction=sign,\n", + " source_dataset=\"LiverDREAM\",\n", + " )\n", + " prior_edge_ids.append(edge_id)\n", + "\n", + "signaling.uns.update(\n", + " {\n", + " \"title\": \"LiverDREAM-derived signaling example\",\n", + " \"pkn_source\": \"DREAM4 Predictive Signaling Network Challenge\",\n", + " \"data_scope\": \"Synthetic normalized signaling responses for software demonstration\",\n", + " }\n", + ")\n", + "signaling.history.snapshot(\"prior_loaded\")\n", + "\n", + "print(f\"AnnNet contains {signaling.nv} proteins and {signaling.ne} signed interactions\")" + ] + }, + { + "cell_type": "markdown", + "id": "network-plot-text", + "metadata": { + "tags": [] + }, + "source": [ + "### Inspect the candidate network\n", + "\n", + "AnnNet draws the signed network directly. Arrowheads show activation, while a bar indicates inhibition." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "network-plot", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "akt\n", + "\n", + "AKT\n", + "\n", + "\n", + "\n", + "raf1\n", + "\n", + "RAF1\n", + "\n", + "\n", + "\n", + "akt->raf1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "egfr\n", + "\n", + "EGFR\n", + "\n", + "\n", + "\n", + "grb2\n", + "\n", + "GRB2\n", + "\n", + "\n", + "\n", + "egfr->grb2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "pi3k\n", + "\n", + "PI3K\n", + "\n", + "\n", + "\n", + "egfr->pi3k\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "shc\n", + "\n", + "SHC\n", + "\n", + "\n", + "\n", + "egfr->shc\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "erk12\n", + "\n", + "ERK1/2\n", + "\n", + "\n", + "\n", + "prak\n", + "\n", + "PRAK\n", + "\n", + "\n", + "\n", + "erk12->prak\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sos\n", + "\n", + "SOS\n", + "\n", + "\n", + "\n", + "grb2->sos\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "hsp27\n", + "\n", + "HSP27\n", + "\n", + "\n", + "\n", + "igf1\n", + "\n", + "IGF1\n", + "\n", + "\n", + "\n", + "igfr\n", + "\n", + "IGFR\n", + "\n", + "\n", + "\n", + "igf1->igfr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "irs1\n", + "\n", + "IRS1\n", + "\n", + "\n", + "\n", + "igfr->irs1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "igfr->shc\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "irs1->pi3k\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "map3k1\n", + "\n", + "MAP3K1\n", + "\n", + "\n", + "\n", + "mkk4\n", + "\n", + "MKK4\n", + "\n", + "\n", + "\n", + "map3k1->mkk4\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "mek12\n", + "\n", + "MEK1/2\n", + "\n", + "\n", + "\n", + "mek12->erk12\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "p38\n", + "\n", + "p38\n", + "\n", + "\n", + "\n", + "mkk4->p38\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "p38->prak\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "pdk1\n", + "\n", + "PDK1\n", + "\n", + "\n", + "\n", + "pdk1->akt\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "pip3\n", + "\n", + "PIP3\n", + "\n", + "\n", + "\n", + "pi3k->pip3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "pip3->pdk1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "prak->hsp27\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "raf1->mek12\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ras\n", + "\n", + "RAS\n", + "\n", + "\n", + "\n", + "ras->map3k1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ras->pi3k\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ras->raf1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "shc->grb2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sos->ras\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "tgfa\n", + "\n", + "TGFA\n", + "\n", + "\n", + "\n", + "tgfa->egfr\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_annnet(\n", + " signaling,\n", + " backend=\"graphviz\",\n", + " layout=\"dot\",\n", + " vertex_label_key=\"label\",\n", + " use_weight_style=False,\n", + " graph_attr={\"rankdir\": \"LR\", \"size\": \"12,7\"},\n", + " node_attr={\"shape\": \"ellipse\", \"style\": \"filled\", \"fillcolor\": \"#eef4fb\"},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "conditions-intro", + "metadata": { + "tags": [] + }, + "source": [ + "## 2. Add the perturbation experiments to AnnNet\n", + "\n", + "Each condition records which ligands are present, which internal proteins are inhibited, and the measured activity of ERK1/2, AKT, and HSP27.\n", + "\n", + "The conditions were chosen to distinguish alternative explanations:\n", + "\n", + "- **TGFA with RAS inhibition** tests whether TGFA reaches PI3K through RAS or through the shorter candidate edge from EGFR to PI3K.\n", + "- **TGFA with MEK inhibition** blocks the ERK1/2–HSP27 branch while leaving the route to AKT available.\n", + "- **IGF1 alone** supports the route from IGFR through IRS1 to PI3K and AKT.\n", + "- **IGF1 with PI3K inhibition** tests whether the AKT response depends on PI3K, PIP3, and PDK1.\n", + "\n", + "We store every condition as an AnnNet layer. Inputs, inhibitors, and measurements become attributes of the corresponding protein in that layer." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "condition-data", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Condition layers: ['Basal', 'TGFA', 'TGFA + EGFRi', 'TGFA + RASi', 'TGFA + MEKi', 'IGF1', 'IGF1 + PI3Ki', 'TGFA + IGF1', 'Dual + MEKi']\n" + ] + } + ], + "source": [ + "inputs = {\n", + " \"Basal\": {\"tgfa\": 0, \"igf1\": 0},\n", + " \"TGFA\": {\"tgfa\": 1, \"igf1\": 0},\n", + " \"TGFA + EGFRi\": {\"tgfa\": 1, \"igf1\": 0},\n", + " \"TGFA + RASi\": {\"tgfa\": 1, \"igf1\": 0},\n", + " \"TGFA + MEKi\": {\"tgfa\": 1, \"igf1\": 0},\n", + " \"IGF1\": {\"tgfa\": 0, \"igf1\": 1},\n", + " \"IGF1 + PI3Ki\": {\"tgfa\": 0, \"igf1\": 1},\n", + " \"TGFA + IGF1\": {\"tgfa\": 1, \"igf1\": 1},\n", + " \"Dual + MEKi\": {\"tgfa\": 1, \"igf1\": 1},\n", + "}\n", + "measurements = {\n", + " \"Basal\": {\"erk12\": 0.05, \"akt\": 0.08, \"hsp27\": 0.04},\n", + " \"TGFA\": {\"erk12\": 0.90, \"akt\": 0.85, \"hsp27\": 0.80},\n", + " \"TGFA + EGFRi\": {\"erk12\": 0.10, \"akt\": 0.15, \"hsp27\": 0.08},\n", + " \"TGFA + RASi\": {\"erk12\": 0.15, \"akt\": 0.25, \"hsp27\": 0.12},\n", + " \"TGFA + MEKi\": {\"erk12\": 0.20, \"akt\": 0.75, \"hsp27\": 0.18},\n", + " \"IGF1\": {\"erk12\": 0.12, \"akt\": 0.88, \"hsp27\": 0.10},\n", + " \"IGF1 + PI3Ki\": {\"erk12\": 0.10, \"akt\": 0.20, \"hsp27\": 0.10},\n", + " \"TGFA + IGF1\": {\"erk12\": 0.92, \"akt\": 0.90, \"hsp27\": 0.85},\n", + " \"Dual + MEKi\": {\"erk12\": 0.20, \"akt\": 0.82, \"hsp27\": 0.15},\n", + "}\n", + "inhibitors = {condition: {} for condition in inputs}\n", + "inhibitors[\"TGFA + EGFRi\"] = {\"egfr\": 1}\n", + "inhibitors[\"TGFA + RASi\"] = {\"ras\": 1}\n", + "inhibitors[\"TGFA + MEKi\"] = {\"mek12\": 1}\n", + "inhibitors[\"IGF1 + PI3Ki\"] = {\"pi3k\": 1}\n", + "inhibitors[\"Dual + MEKi\"] = {\"mek12\": 1}\n", + "\n", + "condition_layers = add_cellnopt_conditions(\n", + " signaling,\n", + " inputs=inputs,\n", + " inhibitors=inhibitors,\n", + " measurements=measurements,\n", + ")\n", + "signaling.history.snapshot(\"conditions_added\")\n", + "\n", + "print(\"Condition layers:\", list(condition_layers))" + ] + }, + { + "cell_type": "markdown", + "id": "condition-inspect", + "metadata": { + "tags": [] + }, + "source": [ + "The AnnNet object now contains both the pathway and the experiment. The following small display helper reads selected protein attributes across its condition layers. It is independent of CellNOptDAG and is used only to make compact tables in this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "layer-table-helper", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def vertex_layer_table(network, layers, columns):\n", + " \"\"\"Return selected vertex-layer attributes as a pandas table.\"\"\"\n", + " rows = {}\n", + " for condition, layer in layers.items():\n", + " rows[condition] = {\n", + " label: network.layers.get_vertex_layer_attrs(protein, layer).get(attribute, np.nan)\n", + " for label, (protein, attribute) in columns.items()\n", + " }\n", + " return pd.DataFrame.from_dict(rows, orient=\"index\").rename_axis(\"condition\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "experiment-table", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " TGFA IGF1 EGFR inhibited RAS inhibited MEK inhibited \\\n", + "condition \n", + "Basal 0 0 0.0 0.0 0.0 \n", + "TGFA 1 0 0.0 0.0 0.0 \n", + "TGFA + EGFRi 1 0 1.0 0.0 0.0 \n", + "TGFA + RASi 1 0 0.0 1.0 0.0 \n", + "TGFA + MEKi 1 0 0.0 0.0 1.0 \n", + "IGF1 0 1 0.0 0.0 0.0 \n", + "IGF1 + PI3Ki 0 1 0.0 0.0 0.0 \n", + "TGFA + IGF1 1 1 0.0 0.0 0.0 \n", + "Dual + MEKi 1 1 0.0 0.0 1.0 \n", + "\n", + " PI3K inhibited ERK1/2 observed AKT observed HSP27 observed \n", + "condition \n", + "Basal 0.0 0.05 0.08 0.04 \n", + "TGFA 0.0 0.90 0.85 0.80 \n", + "TGFA + EGFRi 0.0 0.10 0.15 0.08 \n", + "TGFA + RASi 0.0 0.15 0.25 0.12 \n", + "TGFA + MEKi 0.0 0.20 0.75 0.18 \n", + "IGF1 0.0 0.12 0.88 0.10 \n", + "IGF1 + PI3Ki 1.0 0.10 0.20 0.10 \n", + "TGFA + IGF1 0.0 0.92 0.90 0.85 \n", + "Dual + MEKi 0.0 0.20 0.82 0.15 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "experiment_columns = {\n", + " \"TGFA\": (\"tgfa\", \"input\"),\n", + " \"IGF1\": (\"igf1\", \"input\"),\n", + " \"EGFR inhibited\": (\"egfr\", \"inhibited\"),\n", + " \"RAS inhibited\": (\"ras\", \"inhibited\"),\n", + " \"MEK inhibited\": (\"mek12\", \"inhibited\"),\n", + " \"PI3K inhibited\": (\"pi3k\", \"inhibited\"),\n", + " \"ERK1/2 observed\": (\"erk12\", \"observed\"),\n", + " \"AKT observed\": (\"akt\", \"observed\"),\n", + " \"HSP27 observed\": (\"hsp27\", \"observed\"),\n", + "}\n", + "experiment = vertex_layer_table(signaling, condition_layers, experiment_columns).fillna(0)\n", + "experiment" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "experiment-plot", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(11, 4.8))\n", + "matrix = experiment.to_numpy(dtype=float)\n", + "ax.imshow(matrix, cmap=\"Blues\", vmin=0, vmax=1, aspect=\"auto\")\n", + "ax.set_xticks(range(experiment.shape[1]), experiment.columns, rotation=45, ha=\"right\")\n", + "ax.set_yticks(range(experiment.shape[0]), experiment.index)\n", + "for row, column in np.ndindex(matrix.shape):\n", + " ax.text(column, row, f\"{matrix[row, column]:.2g}\", ha=\"center\", va=\"center\", fontsize=8)\n", + "ax.set_title(\"Perturbations and measurements stored in the AnnNet condition layers\")\n", + "ax.set_xlabel(\"Experimental variable\")\n", + "ax.set_ylabel(\"Condition\")\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fit-intro", + "metadata": { + "tags": [] + }, + "source": [ + "## 3. Fit the network with CORNETO\n", + "\n", + "CORNETO reads the signed interactions and experimental conditions stored in AnnNet. CellNOptDAG searches for one set of reactions that explains all nine experiments. The selected reactions are shared across the experiment, although their activity can change from one condition to another.\n", + "\n", + "CellNOptDAG predicts an inactive or active state for each protein, whereas the synthetic measurements vary continuously between `0` and `1`. It therefore searches for the binary response pattern that is closest to the observations rather than expecting a perfect numerical match.\n", + "\n", + "We use a small value of `lambda_reg`. Reducing disagreement with the measurements is the first priority; when two models perform equally well, this value favors the model with fewer reactions." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fit-model", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solver status: optimal\n", + "Total absolute error: 3.70\n", + "Mean absolute error per measurement: 0.14\n" + ] + } + ], + "source": [ + "method = CellNOptDAG(lambda_reg=1e-3)\n", + "problem = build_cellnopt_from_annnet(\n", + " method,\n", + " signaling,\n", + " network_slice=\"prior\",\n", + " condition_layers=condition_layers,\n", + ")\n", + "solution = problem.solve(solver=\"SCIPY\")\n", + "\n", + "total_error = float(problem.objectives[0].value)\n", + "number_of_measurements = sum(len(values) for values in measurements.values())\n", + "\n", + "print(\"Solver status:\", solution.status)\n", + "print(f\"Total absolute error: {total_error:.2f}\")\n", + "print(f\"Mean absolute error per measurement: {total_error / number_of_measurements:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "fit-result-text", + "metadata": { + "tags": [] + }, + "source": [ + "### Which routes were selected?\n", + "\n", + "The fitted network keeps both stimulus-to-readout branches. It also keeps `RAS → PI3K`, which helps explain why TGFA produces a strong AKT response while RAS inhibition strongly reduces it.\n", + "\n", + "The direct shortcut `EGFR → PI3K` is not selected. If it were present, PI3K could remain active after RAS inhibition, making the low AKT measurement harder to explain. The candidate routes from IGFR to ERK1/2 and from RAS through p38 to HSP27 are also unnecessary for this fit.\n", + "\n", + "An interaction that is not selected should not be interpreted as biologically false. It was available in the candidate network, but this experiment can be explained without it." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fit-plots", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "tgfa\n", + "\n", + "\n", + "tgfa\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "egfr\n", + "\n", + "\n", + "egfr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "tgfa->egfr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "grb2\n", + "\n", + "\n", + "grb2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "egfr->grb2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "shc\n", + "\n", + "\n", + "shc\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "egfr->shc\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "pi3k\n", + "\n", + "\n", + "pi3k\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "egfr->pi3k\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sos\n", + "\n", + "\n", + "sos\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "grb2->sos\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "shc->grb2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ras\n", + "\n", + "\n", + "ras\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sos->ras\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "raf1\n", + "\n", + "\n", + "raf1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ras->raf1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ras->pi3k\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "map3k1\n", + "\n", + "\n", + "map3k1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ras->map3k1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "mek12\n", + "\n", + "\n", + "mek12\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "raf1->mek12\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "erk12\n", + "\n", + "\n", + "erk12\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "mek12->erk12\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "prak\n", + "\n", + "\n", + "prak\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "erk12->prak\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "hsp27\n", + "\n", + "\n", + "hsp27\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "prak->hsp27\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "pip3\n", + "\n", + "\n", + "pip3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "pi3k->pip3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "pdk1\n", + "\n", + "\n", + "pdk1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "pip3->pdk1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "akt\n", + "\n", + "\n", + "akt\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "pdk1->akt\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "akt->raf1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "igf1\n", + "\n", + "\n", + "igf1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "igfr\n", + "\n", + "\n", + "igfr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "igf1->igfr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "igfr->shc\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "irs1\n", + "\n", + "\n", + "irs1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "igfr->irs1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "irs1->pi3k\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "mkk4\n", + "\n", + "\n", + "mkk4\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "map3k1->mkk4\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "p38\n", + "\n", + "\n", + "p38\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "mkk4->p38\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "p38->prak\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(plot_cellnopt_model(method, show_unselected=True))\n", + "plot_cellnopt_fit(method, view=\"heatmap\")" + ] + }, + { + "cell_type": "markdown", + "id": "annotate-intro", + "metadata": { + "tags": [] + }, + "source": [ + "## 4. Add the fitted model to AnnNet\n", + "\n", + "The optimization is complete, but the AnnNet object does not yet contain the inferred results. One signaling-specific CORNETO helper adds them:\n", + "\n", + "- selected interactions are added to a slice called `cellnopt_selected`;\n", + "- predicted protein activities are added to each condition layer;\n", + "- selected reactions are marked as active or inactive in each condition; and\n", + "- fitting summaries are attached to the corresponding layers.\n", + "\n", + "This helper works from CellNOptDAG variables and standard AnnNet attributes. It does not contain any LiverDREAM-specific proteins or conditions." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "annotate-results", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "selected reactions 16.0\n", + "selected prior interactions 16.0\n", + "condition-specific interaction records 144.0\n", + "total absolute error 3.7\n", + "Name: stored CellNOptDAG results, dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_summary = add_cellnopt_results(\n", + " signaling,\n", + " method,\n", + " problem,\n", + " solution=solution,\n", + ")\n", + "\n", + "pd.Series(\n", + " {\n", + " \"selected reactions\": result_summary[\"selected_reactions\"],\n", + " \"selected prior interactions\": result_summary[\"selected_prior_edges\"],\n", + " \"condition-specific interaction records\": result_summary[\"condition_edges\"],\n", + " \"total absolute error\": sum(result_summary[\"condition_errors\"].values()),\n", + " },\n", + " name=\"stored CellNOptDAG results\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For new conditions, use the fixed `CellNOptDAG` model directly: call `predict(inputs=..., inhibitors=...)` without measurements, then call `evaluate(...)` when held-out measurements are available. This notebook stores in-sample fitted results in AnnNet; the reusable prediction lifecycle and result metadata are demonstrated in the [CellNOptDAG guide](../../guide/signaling/cellnopt_dag.ipynb)." + ] + }, + { + "cell_type": "markdown", + "id": "questions-intro", + "metadata": { + "tags": [] + }, + "source": [ + "## 5. Explore the complete analysis in AnnNet\n", + "\n", + "AnnNet now contains the candidate pathway, all experimental conditions, and the fitted results. We can answer biological questions from this object instead of combining solver variables with separate dictionaries.\n", + "\n", + "### Which candidate interactions belong to the fitted model?\n", + "\n", + "The `prior` and `cellnopt_selected` slices can be compared without losing either version of the pathway." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "selected-query", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sourcesigntarget
0TGFAactivatesEGFR
1EGFRactivatesGRB2
2GRB2activatesSOS
3SOSactivatesRAS
4RASactivatesRAF1
5RAF1activatesMEK1/2
6MEK1/2activatesERK1/2
7ERK1/2activatesPRAK
8PRAKactivatesHSP27
9RASactivatesPI3K
10PI3KactivatesPIP3
11PIP3activatesPDK1
12PDK1activatesAKT
13IGF1activatesIGFR
14IGFRactivatesIRS1
15IRS1activatesPI3K
\n", + "
" + ], + "text/plain": [ + " source sign target\n", + "0 TGFA activates EGFR\n", + "1 EGFR activates GRB2\n", + "2 GRB2 activates SOS\n", + "3 SOS activates RAS\n", + "4 RAS activates RAF1\n", + "5 RAF1 activates MEK1/2\n", + "6 MEK1/2 activates ERK1/2\n", + "7 ERK1/2 activates PRAK\n", + "8 PRAK activates HSP27\n", + "9 RAS activates PI3K\n", + "10 PI3K activates PIP3\n", + "11 PIP3 activates PDK1\n", + "12 PDK1 activates AKT\n", + "13 IGF1 activates IGFR\n", + "14 IGFR activates IRS1\n", + "15 IRS1 activates PI3K" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selected_prior_ids = set(prior_edge_ids) & set(signaling.slices.edges(\"cellnopt_selected\"))\n", + "selected_rows = []\n", + "for edge_id in prior_edge_ids:\n", + " if edge_id not in selected_prior_ids:\n", + " continue\n", + " sources, targets = signaling.get_edge(edge_id)\n", + " source_node = next(iter(sources))\n", + " target_node = next(iter(targets))\n", + " source = source_node[0] if isinstance(source_node, tuple) else source_node\n", + " target = target_node[0] if isinstance(target_node, tuple) else target_node\n", + " sign = signaling.attrs.get_edge_attrs(edge_id)[\"interaction\"]\n", + " selected_rows.append(\n", + " {\n", + " \"source\": display_name[source],\n", + " \"sign\": \"activates\" if sign == 1 else \"inhibits\",\n", + " \"target\": display_name[target],\n", + " }\n", + " )\n", + "\n", + "pd.DataFrame(selected_rows)" + ] + }, + { + "cell_type": "markdown", + "id": "akt-question", + "metadata": { + "tags": [] + }, + "source": [ + "### How does AKT respond across the experiment?\n", + "\n", + "Observed and predicted AKT values are attributes of the same protein in different AnnNet layers. Reading them side by side shows both the perturbation response and the quality of the fit." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "akt-query", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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Observed AKTPredicted AKT
condition
Basal0.080.0
TGFA0.851.0
TGFA + EGFRi0.150.0
TGFA + RASi0.250.0
TGFA + MEKi0.751.0
IGF10.881.0
IGF1 + PI3Ki0.200.0
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" + ], + "text/plain": [ + " Observed AKT Predicted AKT\n", + "condition \n", + "Basal 0.08 0.0\n", + "TGFA 0.85 1.0\n", + "TGFA + EGFRi 0.15 0.0\n", + "TGFA + RASi 0.25 0.0\n", + "TGFA + MEKi 0.75 1.0\n", + "IGF1 0.88 1.0\n", + "IGF1 + PI3Ki 0.20 0.0\n", + "TGFA + IGF1 0.90 1.0\n", + "Dual + MEKi 0.82 1.0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "akt_response = vertex_layer_table(\n", + " signaling,\n", + " condition_layers,\n", + " {\n", + " \"Observed AKT\": (\"akt\", \"observed\"),\n", + " \"Predicted AKT\": (\"akt\", \"predicted\"),\n", + " },\n", + ")\n", + "\n", + "ax = akt_response.plot.bar(figsize=(10, 3.8), color=[\"#9ecae1\", \"#2166ac\"])\n", + "ax.set_ylim(0, 1.15)\n", + "ax.set_ylabel(\"Boolean activity\")\n", + "ax.set_title(\"AKT measurements and predictions read from AnnNet\")\n", + "ax.legend(frameon=False, ncol=2)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "akt_response" + ] + }, + { + "cell_type": "markdown", + "id": "reaction-question", + "metadata": { + "tags": [] + }, + "source": [ + "### Which reactions are active after a perturbation?\n", + "\n", + "A reaction may belong to the fitted network but be inactive in a particular condition. The result layers preserve this distinction. For example, MEK inhibition removes activity from the ERK1/2 branch while leaving the route to AKT available." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "reaction-query", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 egfr -> grb2\n", + "1 grb2 -> sos\n", + "2 pdk1 -> akt\n", + "3 pi3k -> pip3\n", + "4 pip3 -> pdk1\n", + "5 raf1 -> mek12\n", + "6 ras -> pi3k\n", + "7 ras -> raf1\n", + "8 sos -> ras\n", + "9 tgfa -> egfr\n", + "Name: TGFA + MEKi, dtype: object" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def active_reactions_in(network, layer):\n", + " \"\"\"Return the distinct CellNOpt reactions active in one AnnNet layer.\"\"\"\n", + " reactions = set()\n", + " for edge_id in network.layers.layer_edge_set(layer):\n", + " attributes = network.attrs.get_edge_attrs(edge_id)\n", + " if attributes.get(\"selected\") and attributes.get(\"active\"):\n", + " reactions.add(attributes[\"reaction\"])\n", + " return sorted(reactions)\n", + "\n", + "\n", + "condition = \"TGFA + MEKi\"\n", + "pd.Series(active_reactions_in(signaling, condition_layers[condition]), name=condition)" + ] + }, + { + "cell_type": "markdown", + "id": "save-intro", + "metadata": { + "tags": [] + }, + "source": [ + "## 6. Save the analysis and use it again\n", + "\n", + "AnnNet's native format preserves the network, annotations, slices, condition layers, and fitting results. To demonstrate that the saved object is useful on its own, we reopen it and read the AKT result again without using the CORNETO model or its solver variables." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "save-reload", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved analysis: build/annnet-cellnopt-liverdream.annnet\n" + ] + }, + { + "data": { + "text/html": [ + "
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Observed AKTPredicted AKT
condition
Basal0.080.0
TGFA0.851.0
TGFA + EGFRi0.150.0
TGFA + RASi0.250.0
TGFA + MEKi0.751.0
IGF10.881.0
IGF1 + PI3Ki0.200.0
TGFA + IGF10.901.0
Dual + MEKi0.821.0
\n", + "
" + ], + "text/plain": [ + " Observed AKT Predicted AKT\n", + "condition \n", + "Basal 0.08 0.0\n", + "TGFA 0.85 1.0\n", + "TGFA + EGFRi 0.15 0.0\n", + "TGFA + RASi 0.25 0.0\n", + "TGFA + MEKi 0.75 1.0\n", + "IGF1 0.88 1.0\n", + "IGF1 + PI3Ki 0.20 0.0\n", + "TGFA + IGF1 0.90 1.0\n", + "Dual + MEKi 0.82 1.0" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output_dir = Path(\"build\")\n", + "output_dir.mkdir(exist_ok=True)\n", + "analysis_path = output_dir / \"annnet-cellnopt-liverdream.annnet\"\n", + "\n", + "an.io.write(signaling, analysis_path, overwrite=True)\n", + "restored = an.io.read(analysis_path)\n", + "\n", + "restored_akt = vertex_layer_table(\n", + " restored,\n", + " condition_layers,\n", + " {\n", + " \"Observed AKT\": (\"akt\", \"observed\"),\n", + " \"Predicted AKT\": (\"akt\", \"predicted\"),\n", + " },\n", + ")\n", + "\n", + "assert restored_akt.equals(akt_response)\n", + "print(\"Saved analysis:\", analysis_path)\n", + "restored_akt" + ] + }, + { + "cell_type": "markdown", + "id": "why-annnet", + "metadata": { + "tags": [] + }, + "source": [ + "## What does AnnNet add?\n", + "\n", + "AnnNet is the central graph object for the analysis. It provides a common place for the biological network, experimental data, and inferred results:\n", + "\n", + "- **Proteins keep the same identity across conditions.** AKT is one protein with different measurements and predictions in different layers.\n", + "- **The candidate and fitted networks coexist.** Slices make them easy to compare without overwriting the original pathway.\n", + "- **Results remain connected to the biology.** Predictions and reaction activities are attached to the proteins, interactions, and conditions they describe.\n", + "- **The analysis can be exchanged and extended.** Another researcher can reopen the file, inspect a condition, or add annotations without reconstructing the work from notebook variables.\n", + "- **CORNETO methods can share the same graph object.** CORNETO supplies optimization methods; AnnNet supplies the annotated network that those methods read and enrich.\n", + "\n", + "For a small one-off calculation, separate edge lists and tables may be sufficient. AnnNet becomes particularly useful when an analysis contains many conditions, annotations, or results from several methods and needs to remain reusable beyond the notebook that created it." + ] + }, + { + "cell_type": "markdown", + "id": "limitations", + "metadata": { + "tags": [] + }, + "source": [ + "## Limitations of this example\n", + "\n", + "The normalized measurements are synthetic, so the selected pathway is not a new biological result. Their variation illustrates an imperfect fit, but it is not an estimate of experimental noise in HepG2 cells. Different perturbations, measurements, or regularization choices could support a different model.\n", + "\n", + "CellNOptDAG uses the signed protein interactions and condition data needed for this analysis. AnnNet can contain additional annotations and layers that are not used by this particular optimization method; they remain available for other analyses.\n", + "\n", + "CellNOptDAG requires the selected model to have no feedback loops. A feedback interaction can still be biologically plausible even when it is not included in this fitted model. CORNETO also checks that selected reactions connect experimental inputs to measured outputs. This is a mathematical connectivity requirement, not a biochemical flux or a measure of interaction strength.\n", + "\n", + "### Further reading\n", + "\n", + "- Rodriguez-Mier *et al.* [Unifying multi-sample network inference from prior knowledge and omics data with CORNETO](https://doi.org/10.1038/s42256-025-01069-9), *Nature Machine Intelligence* (2025).\n", + "- Prill *et al.* [Crowdsourcing Network Inference: The DREAM Predictive Signaling Network Challenge](https://pmc.ncbi.nlm.nih.gov/articles/PMC3465072/).\n", + "- AnnNet documentation: [layers](https://saezlab.github.io/annnet/reference/core/layers/), [slices](https://saezlab.github.io/annnet/reference/core/slices/), and [native storage](https://saezlab.github.io/annnet/explanations/io-annnet/)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/annnet-signaling/pixi.toml b/docs/tutorials/annnet-signaling/pixi.toml new file mode 100644 index 000000000..5de94efe9 --- /dev/null +++ b/docs/tutorials/annnet-signaling/pixi.toml @@ -0,0 +1,16 @@ +[workspace] +name = "annnet-signaling" +channels = ["conda-forge"] +platforms = ["linux-64", "osx-64", "osx-arm64", "win-64"] + +[dependencies] +python = "3.11.*" +matplotlib = "==3.10.3" +networkx = "==3.5" +pandas = "==2.3.3" +graphviz = "*" +pip = "*" + +[pypi-dependencies] +corneto = { path = "../../..", editable = true, extras = ["plot"] } +annnet = { git = "https://github.com/saezlab/annnet.git", rev = "e37ab0069c2882154582354ed0ba2b01dd9b1c37", extras = ["polars", "networkx", "zarr_io"] } diff --git a/docs/tutorials/causal/README.md b/docs/tutorials/causal/README.md new file mode 100644 index 000000000..b23ab887f --- /dev/null +++ b/docs/tutorials/causal/README.md @@ -0,0 +1,56 @@ +# Linear DAG discovery on the Sachs dataset + +This tutorial uses `LinearDAGDiscovery` to find a network that helps explain +single-cell signaling measurements from the Sachs study. It includes two +general-stimulation conditions and six conditions with an annotated treatment +target. The notebook selects 100 cells from each condition for a practical run. + +The notebook demonstrates: + +- inspect the measured signals and treatment conditions; +- map treatments to their measured targets; +- fit a network using hard-intervention annotations; +- read the selected arrows and their coefficients; and +- fit an alternative model with estimated treatment offsets. + +The [`LinearDAGDiscovery` guide](../../guide/causal/linear-dag-discovery.ipynb) +covers the method's other options and held-out prediction. The notebook uses +GUROBI, so running its fits requires a working Gurobi license. + +## Run the tutorial + +From the CORNETO repository root, install the tutorial environment and the +checked-out CORNETO source: + +```bash +cd docs/tutorials/causal +pixi install +pixi run python -m pip install -e ../../.. +mkdir -p build +pixi run python -m papermill linear-dag-discovery-sachs.ipynb build/linear-dag-discovery-sachs.ipynb +``` + +From the repository's notebook runner, the equivalent command is: + +```bash +python docs/tutorials/run_notebooks.py causal \ + --editable-corneto --corneto-root . +``` + +The runner writes executed notebooks to `build/` unless `--rewrite` is used. + +## Dataset provenance + +The `v1` dataset contains 7,466 observations of 11 signaling proteins and +phospholipids, together with an `intervention_label` column. It is a +natural-log-transformed, tutorial-oriented representation of the nine measured +conditions in the [Zenodo Sachs dataset](https://doi.org/10.5281/zenodo.7681811) +(version 2); the simulated conditions and ground-truth file are not included. +The source record identifies the data as [CC BY +4.0](https://creativecommons.org/licenses/by/4.0/). The versioned dataset +files are under `datasets/sachs/v1/`; installed users can resolve the same +files with `corneto.datasets.fetch_dataset("sachs")`. + +See `datasets/sachs/v1/README.md` for the transformation, citation, and +attribution, and `datasets/sachs/v1/condition_manifest.csv` for the condition +to target mapping. diff --git a/docs/tutorials/causal/linear-dag-discovery-sachs.ipynb b/docs/tutorials/causal/linear-dag-discovery-sachs.ipynb new file mode 100644 index 000000000..7d71e29e4 --- /dev/null +++ b/docs/tutorials/causal/linear-dag-discovery-sachs.ipynb @@ -0,0 +1,2071 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "00000000", + "metadata": { + "papermill": { + "duration": 0.005965, + "end_time": "2026-09-21T19:12:48.674066", + "exception": false, + "start_time": "2026-09-21T19:12:48.668101", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Find an explanatory signaling network in the Sachs data\n", + "\n", + "How might the measured signals in a cell be related, and how do they respond\n", + "when a signaling molecule is perturbed? In this tutorial, we use\n", + "`LinearDAGDiscovery` to fit a small network to single-cell measurements from\n", + "the Sachs study. Sachs and colleagues used Bayesian networks; here we use a\n", + "linear model. An arrow from one signal to another means that the first\n", + "signal helps explain the second in the fitted linear model.\n", + "\n", + "The study used human primary CD4+ T cells. Across experimental conditions,\n", + "researchers stimulated the cells through CD3 and CD28, sometimes added\n", + "ICAM-2, or applied compounds that perturb signaling. After 15 minutes,\n", + "they measured 11 protein and phospholipid signals in individual cells by\n", + "flow cytometry, a technique that records several signals per cell.\n", + "\n", + "The original study called CD3/CD28 and ICAM-2 **general perturbations**.\n", + "In this tutorial, their two conditions are **observational with respect\n", + "to the 11 measured signals**: the cells were stimulated, but no measured\n", + "signal was directly targeted. The **intervention conditions** use more\n", + "specific treatments with an annotated target. Together, these conditions\n", + "show how the signals vary and respond to targeted treatments.\n", + "\n", + "We will load the cell measurements, mark the intervention targets, fit a\n", + "network, and read its selected arrows. We will then try a second way to model\n", + "the interventions. The\n", + "[LinearDAGDiscovery guide](../../guide/causal/linear-dag-discovery.ipynb)\n", + "explains the method and additional modeling options.\n", + "\n", + "Data: [Sachs Zenodo record](https://zenodo.org/records/7681811).\n", + "Original study: [Sachs and colleagues (2005)](https://doi.org/10.1126/science.1105809)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "00000002", + "metadata": { + "papermill": { + "duration": 1.310467, + "end_time": "2026-09-21T19:12:49.993781", + "exception": false, + "start_time": "2026-09-21T19:12:48.683314", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "from IPython.display import display\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import corneto as cn\n", + "from corneto.backend import CvxpyBackend\n", + "from corneto.data import Data\n", + "from corneto.datasets import fetch_dataset\n", + "from corneto.methods import LinearDAGDiscovery\n", + "\n", + "DATASET_DIR = fetch_dataset(\"sachs\")\n", + "DATASET_PATH = DATASET_DIR / \"measurements.tsv\"\n", + "CONDITION_MANIFEST_PATH = DATASET_DIR / \"condition_manifest.csv\"\n", + "NODE_COLUMNS = [\n", + " \"raf\", \"mek\", \"plc\", \"pip2\", \"pip3\", \"erk\",\n", + " \"akt\", \"pka\", \"pkc\", \"p38\", \"jnk\",\n", + "]\n", + "# Use 100 cells per condition for a quicker run; use None for all cells.\n", + "SAMPLES_PER_CONDITION: int | None = 100\n", + "MIN_ABS_COEFFICIENT = 0.25\n", + "SEED = 7\n", + "SOLVE_SECONDS = 300\n", + "LAMBDA_UNEXPLAINED = 1.0\n" + ] + }, + { + "cell_type": "markdown", + "id": "00000001", + "metadata": { + "papermill": { + "duration": 0.002061, + "end_time": "2026-09-21T19:12:48.680803", + "exception": false, + "start_time": "2026-09-21T19:12:48.678742", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## 1. Load and inspect the measurements\n", + "\n", + "Each row in the Sachs data represents one cell. The columns below record the\n", + "signals measured in that cell. Most protein measurements indicate\n", + "phosphorylation, a chemical change commonly used to track signaling\n", + "activity. PIP2 and PIP3 are signaling lipids. The values have already\n", + "been transformed with the natural logarithm.\n", + "\n", + "| Column | Biological measurement |\n", + "| --- | --- |\n", + "| `raf` | phosphorylated Raf |\n", + "| `mek` | phosphorylated MEK1/2 |\n", + "| `plc` | phosphorylated PLCγ |\n", + "| `pip2` | PIP2 abundance |\n", + "| `pip3` | PIP3 abundance |\n", + "| `erk` | phosphorylated ERK1/2 |\n", + "| `akt` | phosphorylated Akt |\n", + "| `pka` | PKA-substrate phosphorylation signal |\n", + "| `pkc` | phosphorylated PKC |\n", + "| `p38` | phosphorylated p38 |\n", + "| `jnk` | phosphorylated JNK |\n", + "\n", + "The measurement file contains these 11 signals for each cell. A separate\n", + "condition manifest names the treatment and, when applicable, the measured\n", + "signal treated as its direct target. The next cell joins the two files and\n", + "shows the first few rows of the resulting data frame." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "00000004", + "metadata": { + "papermill": { + "duration": 0.053164, + "end_time": "2026-09-21T19:12:50.050544", + "exception": false, + "start_time": "2026-09-21T19:12:49.997380", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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conditionintervention_labelintervention_targetrafmekakt
0cd3cd28observationalNone3.272.582.83
1cd3cd28observationalNone3.582.803.48
2cd3cd28observationalNone4.083.793.48
3cd3cd28observationalNone4.294.422.47
4cd3cd28observationalNone3.522.993.83
5cd3cd28observationalNone2.931.323.25
6cd3cd28observationalNone3.803.602.88
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" + ], + "text/plain": [ + " condition intervention_label intervention_target raf mek akt\n", + "0 cd3cd28 observational None 3.27 2.58 2.83\n", + "1 cd3cd28 observational None 3.58 2.80 3.48\n", + "2 cd3cd28 observational None 4.08 3.79 3.48\n", + "3 cd3cd28 observational None 4.29 4.42 2.47\n", + "4 cd3cd28 observational None 3.52 2.99 3.83\n", + "5 cd3cd28 observational None 2.93 1.32 3.25\n", + "6 cd3cd28 observational None 3.80 3.60 2.88\n", + "7 cd3cd28 observational None 3.86 2.71 3.81" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def load_sachs_dataframe(path: Path, manifest_path: Path) -> pd.DataFrame:\n", + " measurements = pd.read_csv(path, sep=\"\\t\")\n", + " manifest = pd.read_csv(manifest_path)\n", + " blocks = []\n", + " cursor = 0\n", + " for row in manifest.itertuples(index=False):\n", + " if row.start_row != cursor or row.stop_row <= cursor:\n", + " raise ValueError(\"Sachs condition rows must be contiguous.\")\n", + " block = measurements.iloc[row.start_row:row.stop_row].copy()\n", + " if not block[\"intervention_label\"].eq(row.archive_label).all():\n", + " raise ValueError(f\"Unexpected archive label for {row.condition}.\")\n", + " block.insert(1, \"condition\", row.condition)\n", + " block.insert(\n", + " 2,\n", + " \"intervention_target\",\n", + " row.intervention_target if pd.notna(row.intervention_target) else None,\n", + " )\n", + " blocks.append(block)\n", + " cursor = row.stop_row\n", + " if cursor != len(measurements):\n", + " raise ValueError(\"The condition manifest does not cover every cell.\")\n", + " return pd.concat(blocks, ignore_index=True)\n", + "\n", + "\n", + "raw = load_sachs_dataframe(DATASET_PATH, CONDITION_MANIFEST_PATH)\n", + "raw[[\"condition\", \"intervention_label\", \"intervention_target\", \"raf\", \"mek\", \"akt\"]].head(8).round(2)" + ] + }, + { + "cell_type": "markdown", + "id": "00000005", + "metadata": { + "papermill": { + "duration": 0.001192, + "end_time": "2026-09-21T19:12:50.053209", + "exception": false, + "start_time": "2026-09-21T19:12:50.052017", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The first rows come from the CD3/CD28 condition, which has no annotated\n", + "direct target among the measured signals. `condition` names the treatment,\n", + "and `intervention_target` gives its measured target when there is one.\n", + "The archive's `intervention_label` sometimes names a different signal, so\n", + "the model uses the target supplied by the condition manifest." + ] + }, + { + "cell_type": "markdown", + "id": "sachs-select-cells", + "metadata": {}, + "source": [ + "## 2. Choose cells for the analysis\n", + "\n", + "The full file has 7,466 cells across nine conditions. We leave out the\n", + "LY294002 condition because it targets PI3K, which is absent from the\n", + "11 measured signals. This leaves 6,618 cells in eight conditions.\n", + "\n", + "To keep the fitting time manageable, we select 100 cells from each\n", + "remaining condition. The next table shows the direct target, number\n", + "of available cells, and number selected for each condition." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "00000006", + "metadata": { + "papermill": { + "duration": 0.013789, + "end_time": "2026-09-21T19:12:50.068510", + "exception": false, + "start_time": "2026-09-21T19:12:50.054721", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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targetavailable_cellsselected_cells
condition
cd3cd28—853100
cd3cd28_icam2—902100
cd3cd28_aktinhibakt911100
cd3cd28_g0076pkc723100
cd3cd28_psitectpip2810100
cd3cd28_u0126mek799100
cd3cd28_ly—8480
pmapkc913100
b2camppka707100
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" + ], + "text/plain": [ + " target available_cells selected_cells\n", + "condition \n", + "cd3cd28 — 853 100\n", + "cd3cd28_icam2 — 902 100\n", + "cd3cd28_aktinhib akt 911 100\n", + "cd3cd28_g0076 pkc 723 100\n", + "cd3cd28_psitect pip2 810 100\n", + "cd3cd28_u0126 mek 799 100\n", + "cd3cd28_ly — 848 0\n", + "pma pkc 913 100\n", + "b2camp pka 707 100" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analysis_raw = raw.loc[raw[\"condition\"] != \"cd3cd28_ly\"].copy()\n", + "\n", + "\n", + "def stratified_subsample(\n", + " frame: pd.DataFrame,\n", + " samples_per_condition: int | None,\n", + " seed: int,\n", + ") -> pd.DataFrame:\n", + " \"\"\"Keep all rows or take a reproducible sample from each condition.\"\"\"\n", + " if samples_per_condition is None:\n", + " return frame.copy().reset_index(drop=True)\n", + " if samples_per_condition <= 0:\n", + " raise ValueError(\"samples_per_condition must be positive or None\")\n", + " rng = np.random.default_rng(seed)\n", + " selected = []\n", + " for condition, group in frame.groupby(\"condition\", sort=True):\n", + " if len(group) > samples_per_condition:\n", + " indices = rng.choice(group.index.to_numpy(), size=samples_per_condition, replace=False)\n", + " group = group.loc[np.sort(indices)]\n", + " selected.append(group.copy())\n", + " return pd.concat(selected, ignore_index=True)\n", + "\n", + "\n", + "sampled = stratified_subsample(analysis_raw, samples_per_condition=SAMPLES_PER_CONDITION, seed=SEED)\n", + "condition_summary = raw.groupby(\"condition\", sort=False).agg(\n", + " target=(\"intervention_target\", \"first\"),\n", + " available_cells=(\"condition\", \"size\"),\n", + ")\n", + "condition_summary[\"selected_cells\"] = sampled.groupby(\"condition\").size()\n", + "condition_summary[\"selected_cells\"] = condition_summary[\"selected_cells\"].fillna(0).astype(int)\n", + "condition_summary[\"target\"] = condition_summary[\"target\"].fillna(\"—\")\n", + "condition_summary" + ] + }, + { + "cell_type": "markdown", + "id": "00000007", + "metadata": { + "papermill": { + "duration": 0.001209, + "end_time": "2026-09-21T19:12:50.071359", + "exception": false, + "start_time": "2026-09-21T19:12:50.070150", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "A dash in the target column means the condition has no annotated direct\n", + "target among the measured signals. The two general-stimulation conditions\n", + "contribute 200 cells; the six targeted-treatment conditions contribute\n", + "600. Each selected cell remains a separate observation. Set\n", + "`SAMPLES_PER_CONDITION=None` to use all 6,618 retained cells." + ] + }, + { + "cell_type": "markdown", + "id": "00000008", + "metadata": { + "papermill": { + "duration": 0.00115, + "end_time": "2026-09-21T19:12:50.074016", + "exception": false, + "start_time": "2026-09-21T19:12:50.072866", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## 3. Describe possible connections and interventions\n", + "\n", + "The Sachs pathway diagram also shows CD3, CD28, ZAP70, PI3K, and other\n", + "components absent from these measurements. Here we model the 11 measured\n", + "signals: `LinearDAGDiscovery` requires observations for every graph vertex.\n", + "An inferred arrow can therefore summarize a path through unmeasured\n", + "components.\n", + "\n", + "Before fitting, we give CORNETO a set of possible arrows between the\n", + "measured signals. For this example, we allow an arrow between every\n", + "pair of different signals, in either direction: 110 candidates. The\n", + "optimizer will select a smaller set without loops that lead back to\n", + "a starting signal. An interaction value of `0` leaves each arrow's\n", + "sign open to be learned from the measurements.\n", + "\n", + "We then turn each cell into a CORNETO sample. Its 11 measured values\n", + "map to the 11 graph vertices. For a treated cell, we mark the directly\n", + "targeted signal with `intervention=\"hard\"`. The fit uses its measured\n", + "value when explaining other signals and skips the target's own equation.\n", + "Cells receiving the same treatment share an `intervention_group`, while\n", + "each cell keeps its own measurements. See the guide's\n", + "{ref}`intervention metadata table `\n", + "for the accepted fields and the alternative shift intervention." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "00000009", + "metadata": { + "papermill": { + "duration": 0.308967, + "end_time": "2026-09-21T19:12:50.384197", + "exception": false, + "start_time": "2026-09-21T19:12:50.075230", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "prior_edges = [\n", + " (source, 0, target)\n", + " for source in NODE_COLUMNS\n", + " for target in NODE_COLUMNS\n", + " if source != target\n", + "]\n", + "prior = cn.Graph.from_tuples(prior_edges)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0000000a", + "metadata": { + "papermill": { + "duration": 0.029209, + "end_time": "2026-09-21T19:12:50.415173", + "exception": false, + "start_time": "2026-09-21T19:12:50.385964", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "measured signals 11\n", + "candidate arrows 110\n", + "cells used 800\n", + "targeted conditions 6\n", + "Name: value, dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def to_corneto_data(frame: pd.DataFrame, intervention_mode: str = \"hard\") -> Data:\n", + " if intervention_mode not in {\"hard\", \"shift\"}:\n", + " raise ValueError(\"intervention_mode must be 'hard' or 'shift'\")\n", + " samples = {}\n", + " for sample_index, row in frame.iterrows():\n", + " condition = str(row[\"condition\"])\n", + " target = row[\"intervention_target\"]\n", + " sample_name = f\"sachs_{sample_index:04d}_{condition}\"\n", + " samples[sample_name] = {}\n", + " for node in NODE_COLUMNS:\n", + " feature = {\"mapping\": \"vertex\", \"value\": float(row[node])}\n", + " if pd.notna(target) and node == target:\n", + " feature[\"intervention\"] = intervention_mode\n", + " feature[\"intervention_group\"] = condition\n", + " samples[sample_name][node] = feature\n", + " return Data.from_cdict(samples)\n", + "\n", + "\n", + "data = to_corneto_data(sampled)\n", + "targeted_cells = sampled.loc[sampled[\"intervention_target\"].notna()]\n", + "pd.Series(\n", + " {\n", + " \"measured signals\": prior.num_vertices,\n", + " \"candidate arrows\": prior.num_edges,\n", + " \"cells used\": len(data.samples),\n", + " \"targeted conditions\": targeted_cells[\"condition\"].nunique(),\n", + " },\n", + " name=\"value\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0000000b", + "metadata": { + "papermill": { + "duration": 0.001466, + "end_time": "2026-09-21T19:12:50.418297", + "exception": false, + "start_time": "2026-09-21T19:12:50.416831", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## 4. Fit the network\n", + "\n", + "The model predicts each signal from a weighted sum of signals with\n", + "arrows pointing to it. We favor fewer arrows, allow at most three\n", + "arrows into each signal, and require selected arrows to have a\n", + "normalized coefficient of at least `0.25`. We also favor connections\n", + "from treated targets to measured responses. The guide explains these\n", + "{ref}`model choices ` in detail.\n", + "\n", + "This example uses GUROBI with a 300-second time limit. The result\n", + "shows how many arrows were selected and the solver status. A status of\n", + "`user_limit` means the time limit was reached; the selected network\n", + "may improve with more solve time.\n", + "\n", + "The {ref}`guide's prediction workflow ` shows how\n", + "to assess a fitted model on held-out cells or conditions." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0000000c", + "metadata": { + "papermill": { + "duration": 300.302974, + "end_time": "2026-09-21T19:17:50.723041", + "exception": false, + "start_time": "2026-09-21T19:12:50.420067", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " intervention model solver status selected arrows solve time (s)\n", + "0 hard user_limit 22 300.045893" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def selected_edge_count(problem) -> int | None:\n", + " values = problem.expr.edge_selected.value\n", + " if values is None:\n", + " return None\n", + " return int((np.asarray(values).reshape(-1) > 0.5).sum())\n", + "\n", + "\n", + "def require_usable_solution(problem, solved) -> None:\n", + " if solved.value is None or not np.isfinite(solved.value):\n", + " raise RuntimeError(f\"The solver returned no usable solution ({solved.status}).\")\n", + " if problem.expr.edge_selected.value is None:\n", + " raise RuntimeError(f\"The solver returned no edge-selection values ({solved.status}).\")\n", + "\n", + "\n", + "def fit_summary(label: str, problem, solved) -> dict:\n", + " return {\n", + " \"intervention model\": label,\n", + " \"solver status\": str(solved.status),\n", + " \"selected arrows\": selected_edge_count(problem),\n", + " \"solve time (s)\": getattr(solved.solver_stats, \"solve_time\", None),\n", + " }\n", + "\n", + "\n", + "def fit_model(fit_data: Data, **method_options):\n", + " method = LinearDAGDiscovery(\n", + " lambda_edges=0.03,\n", + " max_parents=3,\n", + " min_abs_coefficient=MIN_ABS_COEFFICIENT,\n", + " lambda_unexplained=LAMBDA_UNEXPLAINED,\n", + " backend=CvxpyBackend(),\n", + " **method_options,\n", + " )\n", + " problem = method.build(prior, fit_data)\n", + " solved = problem.solve(\n", + " solver=\"GUROBI\",\n", + " max_seconds=SOLVE_SECONDS,\n", + " verbosity=0,\n", + " IntegralityFocus=1,\n", + " )\n", + " require_usable_solution(problem, solved)\n", + " return method, problem, solved\n", + "\n", + "\n", + "exact_method, exact_problem, exact_solved = fit_model(data)\n", + "hard_fit_summary = pd.DataFrame([fit_summary(\"hard\", exact_problem, exact_solved)])\n", + "hard_fit_summary" + ] + }, + { + "cell_type": "markdown", + "id": "0000000d", + "metadata": { + "papermill": { + "duration": 0.002175, + "end_time": "2026-09-21T19:17:50.727628", + "exception": false, + "start_time": "2026-09-21T19:17:50.725453", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## 5. Examine the selected arrows\n", + "\n", + "Each row of the table below is an arrow in the fitted network.\n", + "`coefficient` tells us how much the model's predicted target signal\n", + "changes when its source signal increases by one unit, with the other\n", + "selected inputs held fixed. `normalized_coefficient` rescales that\n", + "slope so the `0.25` threshold can be applied across signal pairs.\n", + "The `sign` column summarizes whether the fitted relationship is\n", + "positive or negative. The guide explains the remaining\n", + "{ref}`edge fields `." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0000000e", + "metadata": { + "papermill": { + "duration": 0.01134, + "end_time": "2026-09-21T19:17:50.741123", + "exception": false, + "start_time": "2026-09-21T19:17:50.729783", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sourcetargetcoefficientnormalized_coefficientsign
1mekraf0.5939700.923388positive
11akterk0.8784600.820500positive
14pkcmek0.8075020.638058positive
18pkcjnk0.6908710.608625positive
17pkcp380.5918430.570519positive
19p38pka-0.611769-0.567900negative
2mekerk-0.332300-0.546553negative
6mekjnk0.4287360.477999positive
4mekpka-0.417555-0.472875negative
3mekakt0.2430350.427971positive
7pip2plc0.3084200.406950positive
13pkapip2-0.441460-0.403002negative
8pip3pip20.6881530.374042positive
12pkaplc-0.299091-0.360261negative
5mekp380.2579650.314708positive
16pkcakt0.1941900.270202positive
20jnkpip3-0.146542-0.250000negative
0rafjnk-0.348597-0.250000negative
15pkcerk0.1923630.250000positive
10aktplc0.3227300.250000positive
9erkpip20.3977350.250000positive
21jnkp380.2284700.250000positive
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" + ], + "text/plain": [ + " source target coefficient normalized_coefficient sign\n", + "1 mek raf 0.593970 0.923388 positive\n", + "11 akt erk 0.878460 0.820500 positive\n", + "14 pkc mek 0.807502 0.638058 positive\n", + "18 pkc jnk 0.690871 0.608625 positive\n", + "17 pkc p38 0.591843 0.570519 positive\n", + "19 p38 pka -0.611769 -0.567900 negative\n", + "2 mek erk -0.332300 -0.546553 negative\n", + "6 mek jnk 0.428736 0.477999 positive\n", + "4 mek pka -0.417555 -0.472875 negative\n", + "3 mek akt 0.243035 0.427971 positive\n", + "7 pip2 plc 0.308420 0.406950 positive\n", + "13 pka pip2 -0.441460 -0.403002 negative\n", + "8 pip3 pip2 0.688153 0.374042 positive\n", + "12 pka plc -0.299091 -0.360261 negative\n", + "5 mek p38 0.257965 0.314708 positive\n", + "16 pkc akt 0.194190 0.270202 positive\n", + "20 jnk pip3 -0.146542 -0.250000 negative\n", + "0 raf jnk -0.348597 -0.250000 negative\n", + "15 pkc erk 0.192363 0.250000 positive\n", + "10 akt plc 0.322730 0.250000 positive\n", + "9 erk pip2 0.397735 0.250000 positive\n", + "21 jnk p38 0.228470 0.250000 positive" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def solution_edge_table(method) -> pd.DataFrame:\n", + " graph = method.get_solution_graph()\n", + " rows = []\n", + " for edge_index, (source_set, target_set) in enumerate(graph.E):\n", + " attributes = graph.get_attr_edge(edge_index)\n", + " rows.append(\n", + " {\n", + " \"source\": next(iter(source_set)),\n", + " \"target\": next(iter(target_set)),\n", + " \"coefficient\": attributes[\"coefficient\"],\n", + " \"normalized_coefficient\": attributes[\"normalized_coefficient\"],\n", + " \"sign\": \"positive\" if attributes[\"coefficient\"] > 0 else \"negative\",\n", + " }\n", + " )\n", + " columns = [\"source\", \"target\", \"coefficient\", \"normalized_coefficient\", \"sign\"]\n", + " result = pd.DataFrame(rows, columns=columns)\n", + " if result.empty:\n", + " return result\n", + " return result.sort_values(\"normalized_coefficient\", key=np.abs, ascending=False)\n", + "\n", + "\n", + "exact_edges = solution_edge_table(exact_method)\n", + "exact_edges" + ] + }, + { + "cell_type": "markdown", + "id": "0000000f", + "metadata": { + "papermill": { + "duration": 0.002195, + "end_time": "2026-09-21T19:17:50.745758", + "exception": false, + "start_time": "2026-09-21T19:17:50.743563", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The network plot makes the table easier to scan: blue arrows have positive\n", + "fitted coefficients, orange arrows have negative ones, and labels show their\n", + "coefficient values. Pick an arrow in the plot and find the same source and\n", + "target in the table. A positive coefficient means the model predicts a higher\n", + "target signal when its source is one unit higher on the log scale, with the\n", + "other selected inputs held fixed; a negative coefficient means it predicts\n", + "a lower target signal. These are slopes in the fitted equations. Biological\n", + "interpretation requires the assumptions discussed in the guide." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "00000010", + "metadata": { + "papermill": { + "duration": 0.394283, + "end_time": "2026-09-21T19:17:51.142425", + "exception": false, + "start_time": "2026-09-21T19:17:50.748142", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Selected edges (fitted coefficients)\n", + "\n", + "\n", + "raf\n", + "\n", + "raf\n", + "\n", + "\n", + "\n", + "jnk\n", + "\n", + "jnk\n", + "\n", + "\n", + "\n", + "raf->jnk\n", + "\n", + "\n", + "\n", + "-0.35\n", + "\n", + "\n", + "\n", + "p38\n", + "\n", + "p38\n", + "\n", + "\n", + "\n", + "jnk->p38\n", + "\n", + "\n", + "+0.23\n", + "\n", + "\n", + "\n", + "pip3\n", + "\n", + "pip3\n", + "\n", + "\n", + "\n", + "jnk->pip3\n", + "\n", + "\n", + "\n", + "-0.15\n", + "\n", + "\n", + "\n", + "mek\n", + "\n", + "mek\n", + "\n", + "\n", + "\n", + "mek->raf\n", + "\n", + "\n", + "+0.59\n", + "\n", + "\n", + "\n", + "mek->jnk\n", + "\n", + "\n", + "+0.43\n", + "\n", + "\n", + "\n", + "erk\n", + "\n", + "erk\n", + "\n", + "\n", + "\n", + "mek->erk\n", + "\n", + "\n", + "\n", + "-0.33\n", + "\n", + "\n", + "\n", + "akt\n", + "\n", + "akt\n", + "\n", + "\n", + "\n", + "mek->akt\n", + "\n", + "\n", + "+0.24\n", + "\n", + "\n", + "\n", + "pka\n", + "\n", + "pka\n", + "\n", + "\n", + "\n", + "mek->pka\n", + "\n", + "\n", + "\n", + "-0.42\n", + "\n", + "\n", + "\n", + "mek->p38\n", + "\n", + "\n", + "+0.26\n", + "\n", + "\n", + "\n", + "pip2\n", + "\n", + "pip2\n", + "\n", + "\n", + "\n", + "erk->pip2\n", + "\n", + "\n", + "+0.40\n", + "\n", + "\n", + "\n", + "akt->erk\n", + "\n", + "\n", + "+0.88\n", + "\n", + "\n", + "\n", + "plc\n", + "\n", + "plc\n", + "\n", + "\n", + "\n", + "akt->plc\n", + "\n", + "\n", + "+0.32\n", + "\n", + "\n", + "\n", + "pka->pip2\n", + "\n", + "\n", + "\n", + "-0.44\n", + "\n", + "\n", + "\n", + "pka->plc\n", + "\n", + "\n", + "\n", + "-0.30\n", + "\n", + "\n", + "\n", + "p38->pka\n", + "\n", + "\n", + "\n", + "-0.61\n", + "\n", + "\n", + "\n", + "pip2->plc\n", + "\n", + "\n", + "+0.31\n", + "\n", + "\n", + "\n", + "pip3->pip2\n", + "\n", + "\n", + "+0.69\n", + "\n", + "\n", + "\n", + "pkc\n", + "\n", + "pkc\n", + "\n", + "\n", + "\n", + "pkc->jnk\n", + "\n", + "\n", + "+0.69\n", + "\n", + "\n", + "\n", + "pkc->mek\n", + "\n", + "\n", + "+0.81\n", + "\n", + "\n", + "\n", + "pkc->erk\n", + "\n", + "\n", + "+0.19\n", + "\n", + "\n", + "\n", + "pkc->akt\n", + "\n", + "\n", + "+0.19\n", + "\n", + "\n", + "\n", + "pkc->p38\n", + "\n", + "\n", + "+0.59\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "solution_graph = exact_method.get_solution_graph()\n", + "edge_styles = {}\n", + "for edge_index, row in exact_edges.iterrows():\n", + " edge_styles[int(edge_index)] = {\n", + " \"color\": \"#0072B2\" if row[\"coefficient\"] >= 0 else \"#D55E00\",\n", + " \"fontcolor\": \"#333333\",\n", + " \"label\": f\"{row['coefficient']:+.2f}\",\n", + " \"penwidth\": \"2.5\",\n", + " }\n", + "assert set(edge_styles) == set(range(solution_graph.num_edges))\n", + "display(\n", + " solution_graph.plot(\n", + " renderer=\"graphviz\",\n", + " custom_edge_attr=edge_styles,\n", + " graph_attr={\"rankdir\": \"LR\", \"label\": \"Selected edges (fitted coefficients)\", \"labelloc\": \"t\"},\n", + " )\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "00000011", + "metadata": { + "papermill": { + "duration": 0.00228, + "end_time": "2026-09-21T19:17:51.147063", + "exception": false, + "start_time": "2026-09-21T19:17:51.144783", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## 6. Try another intervention model\n", + "\n", + "So far, we modeled each treated target as externally fixed. Here we\n", + "keep its usual equation and let the treatment add an offset. The next\n", + "cell marks those targets with `intervention=\"shift\"` and estimates\n", + "one offset for each target and treatment condition.\n", + "\n", + "The first output compares the solver status and number of selected\n", + "arrows in the two fits. The second lists the estimated offsets. A\n", + "positive offset raises the target's predicted value; a negative\n", + "offset lowers it. These values are on the log-measurement scale and\n", + "describe the fitted condition effect, not the drug dose.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "00000012", + "metadata": { + "papermill": { + "duration": 300.333981, + "end_time": "2026-09-21T19:22:51.483171", + "exception": false, + "start_time": "2026-09-21T19:17:51.149190", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " condition target estimated log-scale shift\n", + "0 b2camp pka -2.963049\n", + "1 cd3cd28_aktinhib akt -0.396231\n", + "2 cd3cd28_g0076 pkc 2.058811\n", + "3 cd3cd28_psitect pip2 -3.389326\n", + "4 cd3cd28_u0126 mek 3.518794\n", + "5 pma pkc 0.383895" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shift_data = to_corneto_data(sampled, intervention_mode=\"shift\")\n", + "shift_method, shift_problem, shift_solved = fit_model(\n", + " shift_data,\n", + " intervention_shift_bound=4.0,\n", + " lambda_intervention_shifts=0.1,\n", + ")\n", + "shift_fit_summary = pd.DataFrame([fit_summary(\"additive shift\", shift_problem, shift_solved)])\n", + "\n", + "# The fitted offset for each treated cell is stored by vertex and sample.\n", + "shift_values = np.asarray(shift_problem.expr.intervention_shift.value)\n", + "shift_rows = []\n", + "for condition, cells in targeted_cells.groupby(\"condition\", sort=True):\n", + " target = cells[\"intervention_target\"].iloc[0]\n", + " target_index = list(prior.V).index(target)\n", + " offsets = shift_values[target_index, cells.index.to_numpy()]\n", + " if not np.allclose(offsets, offsets[0]):\n", + " raise RuntimeError(f\"Inconsistent fitted shifts for {condition}.\")\n", + " shift_rows.append(\n", + " {\n", + " \"condition\": condition,\n", + " \"target\": target,\n", + " \"estimated log-scale shift\": float(offsets[0]),\n", + " }\n", + " )\n", + "\n", + "display(pd.concat([hard_fit_summary, shift_fit_summary], ignore_index=True))\n", + "pd.DataFrame(shift_rows)" + ] + }, + { + "cell_type": "markdown", + "id": "sachs-shift-plot-intro", + "metadata": {}, + "source": [ + "The plot below shows the network selected by the shift fit. Solid blue and\n", + "orange arrows connect measured signals; their labels are fitted\n", + "coefficients. Dashed purple arrows show the treatments. Each is labeled\n", + "with the treatment name and its learned shift, and points to the measured\n", + "target. Two treatments point to PKC because each has its own estimated\n", + "effect on that signal. The purple **Treatments** box only groups these\n", + "experimental conditions in the picture; it is not a measured signal or\n", + "a variable in the fitted network." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "sachs-shift-network-plot", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Shift fit: selected network and learned treatment effects\n", + "\n", + "\n", + "mek\n", + "\n", + "mek\n", + "\n", + "\n", + "\n", + "raf\n", + "\n", + "raf\n", + "\n", + "\n", + "\n", + "mek->raf\n", + "\n", + "\n", + "+0.54\n", + "\n", + "\n", + "\n", + "erk\n", + "\n", + "erk\n", + "\n", + "\n", + "\n", + "mek->erk\n", + "\n", + "\n", + "-0.33\n", + "\n", + "\n", + "\n", + "akt\n", + "\n", + "akt\n", + "\n", + "\n", + "\n", + "mek->akt\n", + "\n", + "\n", + "+0.24\n", + "\n", + "\n", + "\n", + "pka\n", + "\n", + "pka\n", + "\n", + "\n", + "\n", + "mek->pka\n", + "\n", + "\n", + "-0.56\n", + "\n", + "\n", + "\n", + "p38\n", + "\n", + "p38\n", + "\n", + "\n", + "\n", + "mek->p38\n", + "\n", + "\n", + "+0.26\n", + "\n", + "\n", + "\n", + "plc\n", + "\n", + "plc\n", + "\n", + "\n", + "\n", + "erk->plc\n", + "\n", + "\n", + "+0.30\n", + "\n", + "\n", + "\n", + "pip3\n", + "\n", + "pip3\n", + "\n", + "\n", + "\n", + "erk->pip3\n", + "\n", + "\n", + "-0.22\n", + "\n", + "\n", + "\n", + "akt->erk\n", + "\n", + "\n", + "+0.88\n", + "\n", + "\n", + "\n", + "pka->plc\n", + "\n", + "\n", + "-0.37\n", + "\n", + "\n", + "\n", + "pka->pip3\n", + "\n", + "\n", + "+0.17\n", + "\n", + "\n", + "\n", + "pip2\n", + "\n", + "pip2\n", + "\n", + "\n", + "\n", + "pka->pip2\n", + "\n", + "\n", + "-0.27\n", + "\n", + "\n", + "\n", + "p38->raf\n", + "\n", + "\n", + "+0.20\n", + "\n", + "\n", + "\n", + "p38->pip2\n", + "\n", + "\n", + "+0.30\n", + "\n", + "\n", + "\n", + "pip2->plc\n", + "\n", + "\n", + "+0.31\n", + "\n", + "\n", + "\n", + "pip2->pip3\n", + "\n", + "\n", + "+0.26\n", + "\n", + "\n", + "\n", + "pkc\n", + "\n", + "pkc\n", + "\n", + "\n", + "\n", + "pkc->mek\n", + "\n", + "\n", + "+0.46\n", + "\n", + "\n", + "\n", + "pkc->raf\n", + "\n", + "\n", + "-0.22\n", + "\n", + "\n", + "\n", + "pkc->erk\n", + "\n", + "\n", + "+0.19\n", + "\n", + "\n", + "\n", + "pkc->akt\n", + "\n", + "\n", + "+0.20\n", + "\n", + "\n", + "\n", + "pkc->pka\n", + "\n", + "\n", + "-0.28\n", + "\n", + "\n", + "\n", + "pkc->p38\n", + "\n", + "\n", + "+0.59\n", + "\n", + "\n", + "\n", + "jnk\n", + "\n", + "jnk\n", + "\n", + "\n", + "\n", + "pkc->jnk\n", + "\n", + "\n", + "+0.89\n", + "\n", + "\n", + "\n", + "jnk->mek\n", + "\n", + "\n", + "+0.41\n", + "\n", + "\n", + "\n", + "jnk->pka\n", + "\n", + "\n", + "-0.25\n", + "\n", + "\n", + "\n", + "jnk->p38\n", + "\n", + "\n", + "+0.23\n", + "\n", + "\n", + "\n", + "treatment_conditions\n", + "\n", + "Treatments\n", + "\n", + "\n", + "\n", + "treatment_conditions->mek\n", + "\n", + "\n", + "U0126\n", + "shift +3.52\n", + "\n", + "\n", + "\n", + "treatment_conditions->akt\n", + "\n", + "\n", + "AKT inhibitor\n", + "shift -0.40\n", + "\n", + "\n", + "\n", + "treatment_conditions->pka\n", + "\n", + "\n", + "β2cAMP\n", + "shift -2.96\n", + "\n", + "\n", + "\n", + "treatment_conditions->pip2\n", + "\n", + "\n", + "Psitect\n", + "shift -3.39\n", + "\n", + "\n", + "\n", + "treatment_conditions->pkc\n", + "\n", + "\n", + "Gö6976\n", + "shift +2.06\n", + "\n", + "\n", + "\n", + "treatment_conditions->pkc\n", + "\n", + "\n", + "PMA\n", + "shift +0.38\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "shift_edges = solution_edge_table(shift_method)\n", + "shift_plot = shift_method.get_solution_graph().copy()\n", + "shift_edge_styles = {}\n", + "for edge_index, row in shift_edges.iterrows():\n", + " shift_edge_styles[int(edge_index)] = {\n", + " \"color\": \"#0072B2\" if row[\"coefficient\"] >= 0 else \"#D55E00\",\n", + " \"fontcolor\": \"#333333\",\n", + " \"label\": f\"{row['coefficient']:+.2f}\",\n", + " \"penwidth\": \"2.5\",\n", + " }\n", + "\n", + "treatment_labels = {\n", + " \"b2camp\": \"β2cAMP\",\n", + " \"cd3cd28_aktinhib\": \"AKT inhibitor\",\n", + " \"cd3cd28_g0076\": \"Gö6976\",\n", + " \"cd3cd28_psitect\": \"Psitect\",\n", + " \"cd3cd28_u0126\": \"U0126\",\n", + " \"pma\": \"PMA\",\n", + "}\n", + "treatment_vertex = \"treatment_conditions\"\n", + "for row in shift_rows:\n", + " edge_index = shift_plot.add_edge(treatment_vertex, row[\"target\"])\n", + " shift_edge_styles[edge_index] = {\n", + " \"color\": \"#7B3294\",\n", + " \"fontcolor\": \"#7B3294\",\n", + " \"style\": \"dashed\",\n", + " \"label\": f\"{treatment_labels[row['condition']]}\\nshift {row['estimated log-scale shift']:+.2f}\",\n", + " \"penwidth\": \"2\",\n", + " }\n", + "\n", + "display(\n", + " shift_plot.plot(\n", + " renderer=\"graphviz\",\n", + " custom_edge_attr=shift_edge_styles,\n", + " custom_vertex_attr={\n", + " treatment_vertex: {\n", + " \"label\": \"Treatments\",\n", + " \"shape\": \"box\",\n", + " \"style\": \"rounded,filled\",\n", + " \"fillcolor\": \"#F3E8F8\",\n", + " \"color\": \"#7B3294\",\n", + " \"fixedsize\": \"false\",\n", + " }\n", + " },\n", + " graph_attr={\"rankdir\": \"LR\", \"label\": \"Shift fit: selected network and learned treatment effects\", \"labelloc\": \"t\"},\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "00000013", + "metadata": { + "papermill": { + "duration": 0.002988, + "end_time": "2026-09-21T19:22:51.489895", + "exception": false, + "start_time": "2026-09-21T19:22:51.486907", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## 7. Final comments\n", + "\n", + "We turned single-cell measurements and treatment annotations into two\n", + "fitted networks. Their selected arrows and coefficients describe\n", + "relationships that help explain the measured signals under each model.\n", + "The dashed treatment arrows in the shift plot show estimated offsets,\n", + "not additional signaling pathways. These results provide hypotheses\n", + "about signaling rather than a verified biological pathway.\n", + "\n", + "For a biological analysis, compare predictions on held-out data and repeat\n", + "the fit with different cell samples, candidate interactions, model settings,\n", + "and solve times. Then examine which arrows remain stable." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + }, + "papermill": { + "default_parameters": {}, + "duration": 604.215521, + "end_time": "2026-09-21T19:22:52.016787", + "environment_variables": {}, + "exception": null, + "input_path": "docs/tutorials/causal/linear-dag-discovery-sachs.ipynb", + "output_path": "/private/tmp/sachs_800_gurobi_executed.ipynb", + "parameters": {}, + "start_time": "2026-09-21T19:12:47.801266", + "version": "2.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/causal/pixi.toml b/docs/tutorials/causal/pixi.toml new file mode 100644 index 000000000..4902fe120 --- /dev/null +++ b/docs/tutorials/causal/pixi.toml @@ -0,0 +1,21 @@ +[workspace] +name = "linear_dag_sachs" +channels = ["conda-forge"] +platforms = ["linux-64", "osx-64", "osx-arm64", "win-64"] + +[dependencies] +python = "3.11.*" +matplotlib = "==3.10.3" +pandas = "==2.3.3" +python-graphviz = "*" +graphviz = "*" +pip = "*" +ipykernel = "*" +matplotlib-inline = "*" + +[pypi-dependencies] +cvxpy = "==1.6.6" +highspy = "==1.11.0" +gurobipy = "==13.0.1" +corneto = "==1.0.0rc1" +papermill = "==2.6.0" diff --git a/docs/tutorials/contrib/multi_condition_tutorial/multi_condition_carnival.ipynb b/docs/tutorials/contrib/multi_condition_tutorial/multi_condition_carnival.ipynb index 04d2015bc..8bba7846f 100644 --- a/docs/tutorials/contrib/multi_condition_tutorial/multi_condition_carnival.ipynb +++ b/docs/tutorials/contrib/multi_condition_tutorial/multi_condition_carnival.ipynb @@ -5,10 +5,10 @@ "id": "733ed707", "metadata": { "papermill": { - "duration": 0.007795, - "end_time": "2025-06-27T16:26:31.958477", + "duration": 0.014082, + "end_time": "2026-08-06T17:18:33.995397+00:00", "exception": false, - "start_time": "2025-06-27T16:26:31.950682", + "start_time": "2026-08-06T17:18:33.981315+00:00", "status": "completed" }, "tags": [] @@ -22,10 +22,10 @@ "id": "f6d68759", "metadata": { "papermill": { - "duration": 0.005521, - "end_time": "2025-06-27T16:26:31.970289", + "duration": 0.007242, + "end_time": "2026-08-06T17:18:34.009927+00:00", "exception": false, - "start_time": "2025-06-27T16:26:31.964768", + "start_time": "2026-08-06T17:18:34.002685+00:00", "status": "completed" }, "tags": [] @@ -47,16 +47,16 @@ "id": "898d2193", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:31.981279Z", - "iopub.status.busy": "2025-06-27T16:26:31.981112Z", - "iopub.status.idle": "2025-06-27T16:26:35.690468Z", - "shell.execute_reply": "2025-06-27T16:26:35.690235Z" + "iopub.execute_input": "2026-08-06T17:18:34.022895Z", + "iopub.status.busy": "2026-08-06T17:18:34.022755Z", + "iopub.status.idle": "2026-08-06T17:18:39.293179Z", + "shell.execute_reply": "2026-08-06T17:18:39.292438Z" }, "papermill": { - "duration": 3.715461, - "end_time": "2025-06-27T16:26:35.691165", + "duration": 5.277507, + "end_time": "2026-08-06T17:18:39.293705+00:00", "exception": false, - "start_time": "2025-06-27T16:26:31.975704", + "start_time": "2026-08-06T17:18:34.016198+00:00", "status": "completed" }, "tags": [] @@ -77,6 +77,48 @@ "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-08-06 19:18:37 | [INFO] Downloading data from `https://omnipathdb.org/queries/enzsub?format=json`\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-08-06 19:18:37 | [INFO] Downloading data from `https://omnipathdb.org/queries/interactions?format=json`\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-08-06 19:18:38 | [INFO] Downloading data from `https://omnipathdb.org/queries/complexes?format=json`\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-08-06 19:18:38 | [INFO] Downloading data from `https://omnipathdb.org/queries/annotations?format=json`\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-08-06 19:18:38 | [INFO] Downloading data from `https://omnipathdb.org/queries/intercell?format=json`\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-08-06 19:18:38 | [INFO] Downloading data from `https://omnipathdb.org/about?format=text`\n" + ] + }, { "data": { "text/html": [ @@ -88,17 +130,17 @@ " style=\"width:100%;max-width:100px;\" />\n", " \n", " \n", - "
Installed version:v1.0.0rc1
Available backends:CVXPY v1.9.2
Default backend (corneto.opt):CVXPY
Installed solvers:CLARABEL, SCS, GUROBI, SCIPY, OSQP
Plot backend (default):auto -> graphviz
Available plot backends:graphviz v0.21; graphviz-wasm
Installed path:/docs/tutorials/contrib/multi_condition_tutorial/.pixi/envs/default/lib/python3.11/site-packages/corneto
Repository:https://github.com/saezlab/corneto
\n", + "
Installed version:v1.0.0rc5
Available backends:CVXPY v1.9.1
Default backend (corneto.opt):CVXPY
Installed solvers:CLARABEL, SCS, GUROBI, SCIPY, HIGHS, OSQP
Plot backend (default):auto -> graphviz
Available plot backends:graphviz v0.21; graphviz-wasm
Installed path:/docs/tutorials/contrib/multi_condition_tutorial/.pixi/envs/default/lib/python3.11/site-packages/corneto
Repository:https://github.com/saezlab/corneto
\n", " \n", " \n", " \n", " " ], "text/plain": [ - "Installed version: v1.0.0rc1\n", - "Available backends: CVXPY v1.9.2\n", + "Installed version: v1.0.0rc5\n", + "Available backends: CVXPY v1.9.1\n", "Default backend (corneto.opt): CVXPY\n", - "Installed solvers: CLARABEL, SCS, GUROBI, SCIPY, OSQP\n", + "Installed solvers: CLARABEL, SCS, GUROBI, SCIPY, HIGHS, OSQP\n", "Plot backend (default): auto -> graphviz\n", "Available plot backends: graphviz v0.21; graphviz-wasm\n", "Installed path: /docs/tutorials/contrib/multi_condition_tutorial/.pixi/envs/default/lib/python3.11/site-packages/corneto\n", @@ -145,16 +187,16 @@ "id": "abdaf92c", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:35.699557Z", - "iopub.status.busy": "2025-06-27T16:26:35.699364Z", - "iopub.status.idle": "2025-06-27T16:26:35.701274Z", - "shell.execute_reply": "2025-06-27T16:26:35.701023Z" + "iopub.execute_input": "2026-08-06T17:18:39.302901Z", + "iopub.status.busy": "2026-08-06T17:18:39.302707Z", + "iopub.status.idle": "2026-08-06T17:18:39.304423Z", + "shell.execute_reply": "2026-08-06T17:18:39.304021Z" }, "papermill": { - "duration": 0.006679, - "end_time": "2025-06-27T16:26:35.701978", + "duration": 0.007145, + "end_time": "2026-08-06T17:18:39.305025+00:00", "exception": false, - "start_time": "2025-06-27T16:26:35.695299", + "start_time": "2026-08-06T17:18:39.297880+00:00", "status": "completed" }, "tags": [] @@ -171,16 +213,16 @@ "id": "6e5049a7", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:35.710475Z", - "iopub.status.busy": "2025-06-27T16:26:35.710346Z", - "iopub.status.idle": "2025-06-27T16:26:37.153536Z", - "shell.execute_reply": "2025-06-27T16:26:37.153280Z" + "iopub.execute_input": "2026-08-06T17:18:39.314162Z", + "iopub.status.busy": "2026-08-06T17:18:39.314065Z", + "iopub.status.idle": "2026-08-06T17:18:40.407036Z", + "shell.execute_reply": "2026-08-06T17:18:40.406718Z" }, "papermill": { - "duration": 1.448152, - "end_time": "2025-06-27T16:26:37.154216", + "duration": 1.0984, + "end_time": "2026-08-06T17:18:40.407571+00:00", "exception": false, - "start_time": "2025-06-27T16:26:35.706064", + "start_time": "2026-08-06T17:18:39.309171+00:00", "status": "completed" }, "tags": [] @@ -417,16 +459,16 @@ "id": "6da46b87", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:37.162941Z", - "iopub.status.busy": "2025-06-27T16:26:37.162752Z", - "iopub.status.idle": "2025-06-27T16:26:37.167505Z", - "shell.execute_reply": "2025-06-27T16:26:37.167246Z" + "iopub.execute_input": "2026-08-06T17:18:40.417129Z", + "iopub.status.busy": "2026-08-06T17:18:40.417013Z", + "iopub.status.idle": "2026-08-06T17:18:40.421569Z", + "shell.execute_reply": "2026-08-06T17:18:40.421201Z" }, "papermill": { - "duration": 0.009649, - "end_time": "2025-06-27T16:26:37.168160", + "duration": 0.009982, + "end_time": "2026-08-06T17:18:40.421988+00:00", "exception": false, - "start_time": "2025-06-27T16:26:37.158511", + "start_time": "2026-08-06T17:18:40.412006+00:00", "status": "completed" }, "tags": [] @@ -454,19 +496,19 @@ { "cell_type": "code", "execution_count": 5, - "id": "040bd32e", + "id": "22acccbb", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:37.176455Z", - "iopub.status.busy": "2025-06-27T16:26:37.176347Z", - "iopub.status.idle": "2025-06-27T16:26:37.179943Z", - "shell.execute_reply": "2025-06-27T16:26:37.179699Z" + "iopub.execute_input": "2026-08-06T17:18:40.430816Z", + "iopub.status.busy": "2026-08-06T17:18:40.430710Z", + "iopub.status.idle": "2026-08-06T17:18:40.434972Z", + "shell.execute_reply": "2026-08-06T17:18:40.434628Z" }, "papermill": { - "duration": 0.008389, - "end_time": "2025-06-27T16:26:37.180556", + "duration": 0.009313, + "end_time": "2026-08-06T17:18:40.435425+00:00", "exception": false, - "start_time": "2025-06-27T16:26:37.172167", + "start_time": "2026-08-06T17:18:40.426112+00:00", "status": "completed" }, "tags": [] @@ -488,20 +530,23 @@ } ], "source": [ - "# renaming conditions for clarity\n", + "# Assign conditions explicitly. No sample is silently assigned to the control group.\n", + "sample_ids = adata.obs_names.to_series()\n", + "is_normal = sample_ids.str.match(r\"^(WT|Pax5|Ebf1|PE\\.\\d+$)\")\n", + "is_spontaneous = sample_ids.str.match(r\"^PE\\.Leuk\\.\")\n", + "is_sb = sample_ids.str.match(r\"^SB\")\n", + "\n", + "membership_count = np.column_stack([is_normal, is_spontaneous, is_sb]).sum(axis=1)\n", + "if not np.all(membership_count == 1):\n", + " invalid = sample_ids.index[membership_count != 1].tolist()\n", + " raise ValueError(f\"Samples with ambiguous or unknown condition: {invalid}\")\n", + "\n", "adata.obs[\"condition\"] = np.select(\n", - " [\n", - " adata.obs.index.str.contains(\"WT\"),\n", - " adata.obs.index.str.contains(\"Pax5\"),\n", - " adata.obs.index.str.contains(\"Ebf1\"),\n", - " adata.obs.index.str.contains(\"Leuk\"),\n", - " adata.obs.index.str.contains(\"SB\"),\n", - " ],\n", - " [\"Normal\", \"Normal\", \"Normal\", \"Leukemic_spontaneous\", \"Leukemic_SB\"],\n", - " default=\"Normal\",\n", + " [is_normal, is_spontaneous, is_sb],\n", + " [\"Normal\", \"Leukemic_spontaneous\", \"Leukemic_SB\"],\n", + " default=\"Unknown\",\n", ")\n", - "# Visualize metadata\n", - "adata.obs[\"condition\"].value_counts()" + "adata.obs[\"condition\"].value_counts()\n" ] }, { @@ -509,10 +554,10 @@ "id": "608b1a5d", "metadata": { "papermill": { - "duration": 0.003716, - "end_time": "2025-06-27T16:26:37.188116", + "duration": 0.004012, + "end_time": "2026-08-06T17:18:40.443450+00:00", "exception": false, - "start_time": "2025-06-27T16:26:37.184400", + "start_time": "2026-08-06T17:18:40.439438+00:00", "status": "completed" }, "tags": [] @@ -526,42 +571,139 @@ "id": "e1808e39", "metadata": { "papermill": { - "duration": 0.00373, - "end_time": "2025-06-27T16:26:37.195743", + "duration": 0.004171, + "end_time": "2026-08-06T17:18:40.451484+00:00", "exception": false, - "start_time": "2025-06-27T16:26:37.192013", + "start_time": "2026-08-06T17:18:40.447313+00:00", "status": "completed" }, "tags": [] }, "source": [ - "We will validate the network for the comparison Leukemic_SB vs Normal (enough samples)" + "Both contrasts use a fixed stratified holdout for lambda selection. The normal split is shared so condition scores remain comparable." ] }, { "cell_type": "code", "execution_count": 6, - "id": "cd7dc0f3", + "id": "holdout-split", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:37.203776Z", - "iopub.status.busy": "2025-06-27T16:26:37.203668Z", - "iopub.status.idle": "2025-06-27T16:26:37.208069Z", - "shell.execute_reply": "2025-06-27T16:26:37.207707Z" + "iopub.execute_input": "2026-08-06T17:18:40.460031Z", + "iopub.status.busy": "2026-08-06T17:18:40.459925Z", + "iopub.status.idle": "2026-08-06T17:18:40.468184Z", + "shell.execute_reply": "2026-08-06T17:18:40.467792Z" }, "papermill": { - "duration": 0.009229, - "end_time": "2025-06-27T16:26:37.208705", + "duration": 0.013392, + "end_time": "2026-08-06T17:18:40.468594+00:00", "exception": false, - "start_time": "2025-06-27T16:26:37.199476", + "start_time": "2026-08-06T17:18:40.455202+00:00", "status": "completed" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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contrasttrain_normaltrain_diseasevalidation_normalvalidation_disease
0spontaneous11542
1SB112348
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" + ], + "text/plain": [ + " contrast train_normal train_disease validation_normal \\\n", + "0 spontaneous 11 5 4 \n", + "1 SB 11 23 4 \n", + "\n", + " validation_disease \n", + "0 2 \n", + "1 8 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "adata1 = adata[adata.obs[\"condition\"].isin([\"Normal\", \"Leukemic_spontaneous\"])].copy()\n", - "adata2 = adata[adata.obs[\"condition\"].isin([\"Normal\", \"Leukemic_SB\"])].copy()" + "# Use a fixed, stratified biological holdout for lambda selection.\n", + "# The normal split is shared by both contrasts.\n", + "rng = np.random.default_rng(seed)\n", + "\n", + "\n", + "def train_validation_ids(ids, validation_fraction=0.25):\n", + " ids = np.asarray(sorted(ids), dtype=object)\n", + " ids = ids[rng.permutation(len(ids))]\n", + " n_validation = max(2, int(round(len(ids) * validation_fraction)))\n", + " n_validation = min(n_validation, len(ids) - 2)\n", + " return ids[n_validation:], ids[:n_validation]\n", + "\n", + "\n", + "normal_ids = adata.obs_names[adata.obs[\"condition\"] == \"Normal\"]\n", + "spontaneous_ids = adata.obs_names[adata.obs[\"condition\"] == \"Leukemic_spontaneous\"]\n", + "sb_ids = adata.obs_names[adata.obs[\"condition\"] == \"Leukemic_SB\"]\n", + "\n", + "normal_train, normal_validation = train_validation_ids(normal_ids)\n", + "spontaneous_train, spontaneous_validation = train_validation_ids(spontaneous_ids)\n", + "sb_train, sb_validation = train_validation_ids(sb_ids)\n", + "\n", + "adata1 = adata[list(normal_train) + list(spontaneous_train)].copy()\n", + "adata2 = adata[list(normal_train) + list(sb_train)].copy()\n", + "validation_adata1 = adata[list(normal_validation) + list(spontaneous_validation)].copy()\n", + "validation_adata2 = adata[list(normal_validation) + list(sb_validation)].copy()\n", + "\n", + "pd.DataFrame(\n", + " {\n", + " \"contrast\": [\"spontaneous\", \"SB\"],\n", + " \"train_normal\": [len(normal_train)] * 2,\n", + " \"train_disease\": [len(spontaneous_train), len(sb_train)],\n", + " \"validation_normal\": [len(normal_validation)] * 2,\n", + " \"validation_disease\": [len(spontaneous_validation), len(sb_validation)],\n", + " }\n", + ")\n" ] }, { @@ -569,10 +711,10 @@ "id": "07c7d3d3", "metadata": { "papermill": { - "duration": 0.003725, - "end_time": "2025-06-27T16:26:37.216269", + "duration": 0.004263, + "end_time": "2026-08-06T17:18:40.476851+00:00", "exception": false, - "start_time": "2025-06-27T16:26:37.212544", + "start_time": "2026-08-06T17:18:40.472588+00:00", "status": "completed" }, "tags": [] @@ -586,10 +728,10 @@ "id": "e7c71295", "metadata": { "papermill": { - "duration": 0.003862, - "end_time": "2025-06-27T16:26:37.223929", + "duration": 0.004058, + "end_time": "2026-08-06T17:18:40.484816+00:00", "exception": false, - "start_time": "2025-06-27T16:26:37.220067", + "start_time": "2026-08-06T17:18:40.480758+00:00", "status": "completed" }, "tags": [] @@ -604,16 +746,16 @@ "id": "8d3736fb", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:37.231879Z", - "iopub.status.busy": "2025-06-27T16:26:37.231785Z", - "iopub.status.idle": "2025-06-27T16:26:37.242595Z", - "shell.execute_reply": "2025-06-27T16:26:37.242384Z" + "iopub.execute_input": "2026-08-06T17:18:40.493582Z", + "iopub.status.busy": "2026-08-06T17:18:40.493465Z", + "iopub.status.idle": "2026-08-06T17:18:40.502081Z", + "shell.execute_reply": "2026-08-06T17:18:40.501653Z" }, "papermill": { - "duration": 0.015527, - "end_time": "2025-06-27T16:26:37.243183", + "duration": 0.013804, + "end_time": "2026-08-06T17:18:40.502529+00:00", "exception": false, - "start_time": "2025-06-27T16:26:37.227656", + "start_time": "2026-08-06T17:18:40.488725+00:00", "status": "completed" }, "tags": [] @@ -622,7 +764,7 @@ { "data": { "text/plain": [ - "AnnData object with n_obs × n_vars = 22 × 13585\n", + "AnnData object with n_obs × n_vars = 16 × 13599\n", " obs: 'condition'" ] }, @@ -650,16 +792,16 @@ "id": "3e985dfb", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:37.251601Z", - "iopub.status.busy": "2025-06-27T16:26:37.251494Z", - "iopub.status.idle": "2025-06-27T16:26:40.745733Z", - "shell.execute_reply": "2025-06-27T16:26:40.745361Z" + "iopub.execute_input": "2026-08-06T17:18:40.511385Z", + "iopub.status.busy": "2026-08-06T17:18:40.511278Z", + "iopub.status.idle": "2026-08-06T17:18:44.078771Z", + "shell.execute_reply": "2026-08-06T17:18:44.078375Z" }, "papermill": { - "duration": 3.49943, - "end_time": "2025-06-27T16:26:40.746431", + "duration": 3.572902, + "end_time": "2026-08-06T17:18:44.079527+00:00", "exception": false, - "start_time": "2025-06-27T16:26:37.247001", + "start_time": "2026-08-06T17:18:40.506625+00:00", "status": "completed" }, "tags": [] @@ -676,10 +818,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/b4/gwkwsdb93sv11rtztqbm3l040000gn/T/ipykernel_39621/3807548237.py:3: DeprecationWarning: design_factors is deprecated and will soon be removed.Please consider providing a formulaic formula using the design argumentinstead.\n", + "/var/folders/b4/gwkwsdb93sv11rtztqbm3l040000gn/T/ipykernel_6018/3807548237.py:3: DeprecationWarning: design_factors is deprecated and will soon be removed.Please consider providing a formulaic formula using the design argumentinstead.\n", " dds1 = DeseqDataSet(\n", "Fitting size factors...\n", - "... done in 0.01 seconds.\n", + "... done in 0.00 seconds.\n", "\n" ] }, @@ -694,10 +836,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "... done in 0.49 seconds.\n", + "... done in 0.47 seconds.\n", "\n", "Fitting dispersion trend curve...\n", - "... done in 0.14 seconds.\n", + "... done in 0.12 seconds.\n", "\n", "Fitting MAP dispersions...\n" ] @@ -706,7 +848,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "... done in 0.69 seconds.\n", + "... done in 0.59 seconds.\n", "\n", "Fitting LFCs...\n" ] @@ -715,21 +857,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "... done in 0.42 seconds.\n", + "... done in 0.34 seconds.\n", "\n", "Calculating cook's distance...\n", - "... done in 0.02 seconds.\n", + "... done in 0.01 seconds.\n", "\n", - "Replacing 35 outlier genes.\n", + "Replacing 24 outlier genes.\n", "\n", "Fitting dispersions...\n", "... done in 0.01 seconds.\n", "\n", "Fitting MAP dispersions...\n", - "... done in 0.01 seconds.\n", + "... done in 0.02 seconds.\n", "\n", "Fitting LFCs...\n", - "... done in 0.01 seconds.\n", + "... done in 0.02 seconds.\n", "\n" ] } @@ -752,16 +894,16 @@ "id": "59939567", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:40.755486Z", - "iopub.status.busy": "2025-06-27T16:26:40.755371Z", - "iopub.status.idle": "2025-06-27T16:26:41.102050Z", - "shell.execute_reply": "2025-06-27T16:26:41.101790Z" + "iopub.execute_input": "2026-08-06T17:18:44.089859Z", + "iopub.status.busy": "2026-08-06T17:18:44.089740Z", + "iopub.status.idle": "2026-08-06T17:18:44.360535Z", + "shell.execute_reply": "2026-08-06T17:18:44.360131Z" }, "papermill": { - "duration": 0.351851, - "end_time": "2025-06-27T16:26:41.102751", + "duration": 0.276523, + "end_time": "2026-08-06T17:18:44.361113+00:00", "exception": false, - "start_time": "2025-06-27T16:26:40.750900", + "start_time": "2026-08-06T17:18:44.084590+00:00", "status": "completed" }, "tags": [] @@ -781,40 +923,40 @@ "Log2 fold change & Wald test p-value: condition Leukemic_spontaneous vs Normal\n", " baseMean log2FoldChange lfcSE stat pvalue \\\n", "ID \n", - "Itm2a 319.854773 1.040954 0.173014 6.016588 1.781313e-09 \n", - "Sergef 518.491011 1.165972 0.102390 11.387579 4.822033e-30 \n", - "Fam109a 353.074249 0.270614 0.099379 2.723045 6.468333e-03 \n", - "Dhx9 28622.656109 -0.009588 0.058553 -0.163747 8.699304e-01 \n", - "Ssu72 3007.691599 -0.047053 0.044517 -1.056967 2.905267e-01 \n", + "Itm2a 297.542647 0.893484 0.215371 4.148578 3.345473e-05 \n", + "Sergef 516.318790 1.271089 0.118853 10.694655 1.078196e-26 \n", + "Fam109a 340.369933 0.246593 0.111188 2.217812 2.656763e-02 \n", + "Dhx9 28163.396930 -0.038032 0.069192 -0.549658 5.825541e-01 \n", + "Ssu72 2932.274736 0.002847 0.056434 0.050457 9.597584e-01 \n", "... ... ... ... ... ... \n", - "Ctsw 204.692803 -1.015983 0.513348 -1.979131 4.780130e-02 \n", - "Ryk 82.285327 -2.689443 0.325351 -8.266294 1.381879e-16 \n", - "Rhd 519.107839 -4.933983 0.449260 -10.982454 4.641417e-28 \n", - "Pxmp4 791.774976 -0.367077 0.068277 -5.376307 7.602927e-08 \n", - "Gm25500 9.166514 0.879435 0.429192 2.049048 4.045745e-02 \n", + "Ryk 77.201401 -2.781647 0.394009 -7.059862 1.666677e-12 \n", + "Rhd 378.982439 -4.297269 0.470384 -9.135671 6.500233e-20 \n", + "Pxmp4 787.520768 -0.381142 0.089869 -4.241074 2.224523e-05 \n", + "Gm25500 9.541753 0.665993 0.526790 1.264247 2.061412e-01 \n", + "Prss39 7.693766 0.742798 0.663470 1.119565 2.628993e-01 \n", "\n", " padj \n", "ID \n", - "Itm2a 5.852272e-09 \n", - "Sergef 9.577094e-29 \n", - "Fam109a 1.008288e-02 \n", - "Dhx9 8.900440e-01 \n", - "Ssu72 3.376801e-01 \n", + "Itm2a 7.810706e-05 \n", + "Sergef 2.958178e-25 \n", + "Fam109a 3.984264e-02 \n", + "Dhx9 6.351182e-01 \n", + "Ssu72 9.670747e-01 \n", "... ... \n", - "Ctsw 6.506168e-02 \n", - "Ryk 8.517614e-16 \n", - "Rhd 7.596825e-27 \n", - "Pxmp4 2.123474e-07 \n", - "Gm25500 5.581542e-02 \n", + "Ryk 9.404290e-12 \n", + "Rhd 8.472136e-19 \n", + "Pxmp4 5.345232e-05 \n", + "Gm25500 2.544401e-01 \n", + "Prss39 3.158001e-01 \n", "\n", - "[13585 rows x 6 columns]\n" + "[13599 rows x 6 columns]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "... done in 0.23 seconds.\n", + "... done in 0.22 seconds.\n", "\n" ] } @@ -831,16 +973,16 @@ "id": "f694aac5", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:41.111867Z", - "iopub.status.busy": "2025-06-27T16:26:41.111747Z", - "iopub.status.idle": "2025-06-27T16:26:41.116795Z", - "shell.execute_reply": "2025-06-27T16:26:41.116547Z" + "iopub.execute_input": "2026-08-06T17:18:44.370968Z", + "iopub.status.busy": "2026-08-06T17:18:44.370852Z", + "iopub.status.idle": "2026-08-06T17:18:44.375826Z", + "shell.execute_reply": "2026-08-06T17:18:44.375497Z" }, "papermill": { - "duration": 0.010205, - "end_time": "2025-06-27T16:26:41.117451", + "duration": 0.010821, + "end_time": "2026-08-06T17:18:44.376540+00:00", "exception": false, - "start_time": "2025-06-27T16:26:41.107246", + "start_time": "2026-08-06T17:18:44.365719+00:00", "status": "completed" }, "tags": [] @@ -886,49 +1028,49 @@ " \n", " \n", " \n", - " Polm\n", - " 2883.081884\n", - " -7.255498\n", - " 0.281539\n", - " -25.770836\n", - " 1.883068e-146\n", - " 1.279074e-142\n", + " P2ry13\n", + " 850.345057\n", + " 3.279132\n", + " 0.139851\n", + " 23.447320\n", + " 1.407802e-121\n", + " 9.559678e-118\n", " \n", " \n", - " Egfl6\n", - " 958.998149\n", - " -6.296339\n", - " 0.244241\n", - " -25.779215\n", - " 1.516794e-146\n", - " 1.279074e-142\n", + " Zfp518b\n", + " 587.950374\n", + " -7.670839\n", + " 0.327080\n", + " -23.452452\n", + " 1.247909e-121\n", + " 9.559678e-118\n", " \n", " \n", - " Kdm5b\n", - " 2202.514358\n", - " -5.510307\n", - " 0.220788\n", - " -24.957441\n", - " 1.772980e-137\n", - " 8.028642e-134\n", + " Blvrb\n", + " 1843.921725\n", + " -3.118163\n", + " 0.139554\n", + " -22.343761\n", + " 1.388454e-110\n", + " 6.285531e-107\n", " \n", " \n", - " Milr1\n", - " 1647.365126\n", - " 1.244237\n", - " 0.050208\n", - " 24.781408\n", - " 1.422471e-135\n", - " 4.831069e-132\n", + " Kif19a\n", + " 326.577342\n", + " -5.914527\n", + " 0.269931\n", + " -21.911251\n", + " 2.029295e-106\n", + " 6.889963e-103\n", " \n", " \n", - " Adgre5\n", - " 10346.763067\n", - " -3.102891\n", - " 0.125360\n", - " -24.751915\n", - " 2.956557e-135\n", - " 8.032965e-132\n", + " Zbtb42\n", + " 319.477801\n", + " -2.080728\n", + " 0.099745\n", + " -20.860557\n", + " 1.222398e-96\n", + " 3.320278e-93\n", " \n", " \n", "\n", @@ -937,19 +1079,19 @@ "text/plain": [ " baseMean log2FoldChange lfcSE stat pvalue \\\n", "ID \n", - "Polm 2883.081884 -7.255498 0.281539 -25.770836 1.883068e-146 \n", - "Egfl6 958.998149 -6.296339 0.244241 -25.779215 1.516794e-146 \n", - "Kdm5b 2202.514358 -5.510307 0.220788 -24.957441 1.772980e-137 \n", - "Milr1 1647.365126 1.244237 0.050208 24.781408 1.422471e-135 \n", - "Adgre5 10346.763067 -3.102891 0.125360 -24.751915 2.956557e-135 \n", + "P2ry13 850.345057 3.279132 0.139851 23.447320 1.407802e-121 \n", + "Zfp518b 587.950374 -7.670839 0.327080 -23.452452 1.247909e-121 \n", + "Blvrb 1843.921725 -3.118163 0.139554 -22.343761 1.388454e-110 \n", + "Kif19a 326.577342 -5.914527 0.269931 -21.911251 2.029295e-106 \n", + "Zbtb42 319.477801 -2.080728 0.099745 -20.860557 1.222398e-96 \n", "\n", - " padj \n", - "ID \n", - "Polm 1.279074e-142 \n", - "Egfl6 1.279074e-142 \n", - "Kdm5b 8.028642e-134 \n", - "Milr1 4.831069e-132 \n", - "Adgre5 8.032965e-132 " + " padj \n", + "ID \n", + "P2ry13 9.559678e-118 \n", + "Zfp518b 9.559678e-118 \n", + "Blvrb 6.285531e-107 \n", + "Kif19a 6.889963e-103 \n", + "Zbtb42 3.320278e-93 " ] }, "execution_count": 10, @@ -967,10 +1109,10 @@ "id": "9d225d14", "metadata": { "papermill": { - "duration": 0.004119, - "end_time": "2025-06-27T16:26:41.125903", + "duration": 0.01456, + "end_time": "2026-08-06T17:18:44.395758+00:00", "exception": false, - "start_time": "2025-06-27T16:26:41.121784", + "start_time": "2026-08-06T17:18:44.381198+00:00", "status": "completed" }, "tags": [] @@ -985,16 +1127,16 @@ "id": "db5c29e6", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:41.134833Z", - "iopub.status.busy": "2025-06-27T16:26:41.134709Z", - "iopub.status.idle": "2025-06-27T16:26:41.149591Z", - "shell.execute_reply": "2025-06-27T16:26:41.149323Z" + "iopub.execute_input": "2026-08-06T17:18:44.405462Z", + "iopub.status.busy": "2026-08-06T17:18:44.405354Z", + "iopub.status.idle": "2026-08-06T17:18:44.415403Z", + "shell.execute_reply": "2026-08-06T17:18:44.415055Z" }, "papermill": { - "duration": 0.020297, - "end_time": "2025-06-27T16:26:41.150303", + "duration": 0.015533, + "end_time": "2026-08-06T17:18:44.415921+00:00", "exception": false, - "start_time": "2025-06-27T16:26:41.130006", + "start_time": "2026-08-06T17:18:44.400388+00:00", "status": "completed" }, "tags": [] @@ -1003,7 +1145,7 @@ { "data": { "text/plain": [ - "AnnData object with n_obs × n_vars = 46 × 13404\n", + "AnnData object with n_obs × n_vars = 34 × 13286\n", " obs: 'condition'" ] }, @@ -1031,16 +1173,16 @@ "id": "e89d9909", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:41.159530Z", - "iopub.status.busy": "2025-06-27T16:26:41.159410Z", - "iopub.status.idle": "2025-06-27T16:26:43.521865Z", - "shell.execute_reply": "2025-06-27T16:26:43.521582Z" + "iopub.execute_input": "2026-08-06T17:18:44.425588Z", + "iopub.status.busy": "2026-08-06T17:18:44.425495Z", + "iopub.status.idle": "2026-08-06T17:18:46.519936Z", + "shell.execute_reply": "2026-08-06T17:18:46.519517Z" }, "papermill": { - "duration": 2.367767, - "end_time": "2025-06-27T16:26:43.522638", + "duration": 2.09991, + "end_time": "2026-08-06T17:18:46.520455+00:00", "exception": false, - "start_time": "2025-06-27T16:26:41.154871", + "start_time": "2026-08-06T17:18:44.420545+00:00", "status": "completed" }, "tags": [] @@ -1050,7 +1192,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/b4/gwkwsdb93sv11rtztqbm3l040000gn/T/ipykernel_39621/3739200126.py:3: DeprecationWarning: design_factors is deprecated and will soon be removed.Please consider providing a formulaic formula using the design argumentinstead.\n", + "/var/folders/b4/gwkwsdb93sv11rtztqbm3l040000gn/T/ipykernel_6018/3739200126.py:3: DeprecationWarning: design_factors is deprecated and will soon be removed.Please consider providing a formulaic formula using the design argumentinstead.\n", " dds2 = DeseqDataSet(\n", "Fitting size factors...\n", "... done in 0.01 seconds.\n", @@ -1075,7 +1217,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "... done in 0.55 seconds.\n", + "... done in 0.53 seconds.\n", "\n", "Fitting dispersion trend curve...\n", "... done in 0.12 seconds.\n", @@ -1087,7 +1229,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "... done in 0.75 seconds.\n", + "... done in 0.64 seconds.\n", "\n", "Fitting LFCs...\n" ] @@ -1096,21 +1238,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "... done in 0.42 seconds.\n", + "... done in 0.36 seconds.\n", "\n", "Calculating cook's distance...\n", - "... done in 0.02 seconds.\n", + "... done in 0.01 seconds.\n", "\n", - "Replacing 201 outlier genes.\n", + "Replacing 400 outlier genes.\n", "\n", "Fitting dispersions...\n", "... done in 0.04 seconds.\n", "\n", "Fitting MAP dispersions...\n", - "... done in 0.03 seconds.\n", + "... done in 0.04 seconds.\n", "\n", "Fitting LFCs...\n", - "... done in 0.04 seconds.\n", + "... done in 0.02 seconds.\n", "\n" ] } @@ -1133,16 +1275,16 @@ "id": "59f5a51b", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:43.532100Z", - "iopub.status.busy": "2025-06-27T16:26:43.531996Z", - "iopub.status.idle": "2025-06-27T16:26:43.858701Z", - "shell.execute_reply": "2025-06-27T16:26:43.858453Z" + "iopub.execute_input": "2026-08-06T17:18:46.530648Z", + "iopub.status.busy": "2026-08-06T17:18:46.530544Z", + "iopub.status.idle": "2026-08-06T17:18:46.794080Z", + "shell.execute_reply": "2026-08-06T17:18:46.793765Z" }, "papermill": { - "duration": 0.332112, - "end_time": "2025-06-27T16:26:43.859447", + "duration": 0.269301, + "end_time": "2026-08-06T17:18:46.794635+00:00", "exception": false, - "start_time": "2025-06-27T16:26:43.527335", + "start_time": "2026-08-06T17:18:46.525334+00:00", "status": "completed" }, "tags": [] @@ -1162,40 +1304,40 @@ "Log2 fold change & Wald test p-value: condition Leukemic_SB vs Normal\n", " baseMean log2FoldChange lfcSE stat pvalue \\\n", "ID \n", - "Itm2a 336.316323 0.710476 0.224228 3.168537 1.532084e-03 \n", - "Sergef 692.078769 1.218302 0.070644 17.245745 1.204506e-66 \n", - "Fam109a 382.268647 0.329745 0.097073 3.396873 6.816056e-04 \n", - "Dhx9 27794.304719 -0.031645 0.064081 -0.493828 6.214275e-01 \n", - "Ssu72 2994.457694 0.004410 0.061412 0.071816 9.427485e-01 \n", + "Itm2a 354.819498 0.814702 0.274887 2.963769 3.038960e-03 \n", + "Sergef 690.444712 1.247942 0.089243 13.983566 1.963960e-44 \n", + "Fam109a 372.293652 0.304612 0.105585 2.884977 3.914420e-03 \n", + "Dhx9 28125.836843 -0.017291 0.070394 -0.245630 8.059687e-01 \n", + "Ssu72 2943.414653 0.012841 0.071734 0.179011 8.579288e-01 \n", "... ... ... ... ... ... \n", - "Ctsw 151.303280 -1.150780 0.374313 -3.074381 2.109398e-03 \n", - "Ryk 50.267450 -2.400835 0.309995 -7.744751 9.576951e-15 \n", - "Rhd 259.540718 -4.740190 0.424503 -11.166439 5.951916e-29 \n", - "Pxmp4 783.587183 -0.148554 0.095118 -1.561793 1.183368e-01 \n", - "Gm25500 8.618646 0.386326 0.302098 1.278810 2.009639e-01 \n", + "Ctsw 152.509072 -1.282962 0.455445 -2.816943 4.848307e-03 \n", + "Ryk 48.834169 -2.269091 0.335420 -6.764932 1.333718e-11 \n", + "Rhd 192.158985 -4.350260 0.483113 -9.004647 2.163614e-19 \n", + "Pxmp4 779.412902 -0.184564 0.117464 -1.571242 1.161264e-01 \n", + "Gm25500 8.770752 0.167093 0.366501 0.455914 6.484515e-01 \n", "\n", " padj \n", "ID \n", - "Itm2a 2.906320e-03 \n", - "Sergef 2.409731e-64 \n", - "Fam109a 1.365860e-03 \n", - "Dhx9 6.748999e-01 \n", - "Ssu72 9.529865e-01 \n", + "Itm2a 5.951595e-03 \n", + "Sergef 2.438614e-42 \n", + "Fam109a 7.476564e-03 \n", + "Dhx9 8.450873e-01 \n", + "Ssu72 8.875218e-01 \n", "... ... \n", - "Ctsw 3.916660e-03 \n", - "Ryk 6.703365e-14 \n", - "Rhd 1.323043e-27 \n", - "Pxmp4 1.573129e-01 \n", - "Gm25500 2.530503e-01 \n", + "Ctsw 9.094254e-03 \n", + "Ryk 8.976585e-11 \n", + "Rhd 3.401867e-18 \n", + "Pxmp4 1.596332e-01 \n", + "Gm25500 7.073339e-01 \n", "\n", - "[13404 rows x 6 columns]\n" + "[13286 rows x 6 columns]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "... done in 0.23 seconds.\n", + "... done in 0.21 seconds.\n", "\n" ] } @@ -1212,16 +1354,16 @@ "id": "2b55a0ae", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:43.869016Z", - "iopub.status.busy": "2025-06-27T16:26:43.868905Z", - "iopub.status.idle": "2025-06-27T16:26:43.874053Z", - "shell.execute_reply": "2025-06-27T16:26:43.873820Z" + "iopub.execute_input": "2026-08-06T17:18:46.805070Z", + "iopub.status.busy": "2026-08-06T17:18:46.804956Z", + "iopub.status.idle": "2026-08-06T17:18:46.810179Z", + "shell.execute_reply": "2026-08-06T17:18:46.809805Z" }, "papermill": { - "duration": 0.010542, - "end_time": "2025-06-27T16:26:43.874723", + "duration": 0.011077, + "end_time": "2026-08-06T17:18:46.810700+00:00", "exception": false, - "start_time": "2025-06-27T16:26:43.864181", + "start_time": "2026-08-06T17:18:46.799623+00:00", "status": "completed" }, "tags": [] @@ -1268,48 +1410,48 @@ " \n", " \n", " Mfsd2b\n", - " 2252.744179\n", - " -9.412063\n", - " 0.308793\n", - " -30.480193\n", - " 4.769906e-204\n", - " 6.393582e-200\n", + " 2514.371164\n", + " -9.636851\n", + " 0.378623\n", + " -25.452370\n", + " 6.645556e-143\n", + " 8.829286e-139\n", " \n", " \n", - " Itga2b\n", - " 8434.243135\n", - " -11.914356\n", - " 0.401437\n", - " -29.679285\n", - " 1.421308e-193\n", - " 9.525603e-190\n", + " Pf4\n", + " 3805.666665\n", + " -13.199275\n", + " 0.541930\n", + " -24.356055\n", + " 5.001204e-131\n", + " 3.322300e-127\n", " \n", " \n", - " Adgrl4\n", - " 148.349960\n", - " -9.117488\n", - " 0.322229\n", - " -28.295029\n", - " 3.978579e-176\n", - " 1.777629e-172\n", + " Itga2b\n", + " 9515.339854\n", + " -12.057495\n", + " 0.503300\n", + " -23.956866\n", + " 7.836582e-127\n", + " 3.470561e-123\n", " \n", " \n", - " Gp1ba\n", - " 2396.209744\n", - " -8.226071\n", - " 0.323265\n", - " -25.446856\n", - " 7.648486e-143\n", - " 2.563008e-139\n", + " Ppbp\n", + " 19266.371435\n", + " -16.866204\n", + " 0.706022\n", + " -23.889076\n", + " 3.978005e-126\n", + " 1.321294e-122\n", " \n", " \n", - " Ppbp\n", - " 17321.675345\n", - " -15.613716\n", - " 0.621843\n", - " -25.108767\n", - " 3.989417e-139\n", - " 1.069483e-135\n", + " Adgrl4\n", + " 133.471953\n", + " -9.102316\n", + " 0.387205\n", + " -23.507763\n", + " 3.397368e-122\n", + " 9.027486e-119\n", " \n", " \n", "\n", @@ -1318,19 +1460,19 @@ "text/plain": [ " baseMean log2FoldChange lfcSE stat pvalue \\\n", "ID \n", - "Mfsd2b 2252.744179 -9.412063 0.308793 -30.480193 4.769906e-204 \n", - "Itga2b 8434.243135 -11.914356 0.401437 -29.679285 1.421308e-193 \n", - "Adgrl4 148.349960 -9.117488 0.322229 -28.295029 3.978579e-176 \n", - "Gp1ba 2396.209744 -8.226071 0.323265 -25.446856 7.648486e-143 \n", - "Ppbp 17321.675345 -15.613716 0.621843 -25.108767 3.989417e-139 \n", + "Mfsd2b 2514.371164 -9.636851 0.378623 -25.452370 6.645556e-143 \n", + "Pf4 3805.666665 -13.199275 0.541930 -24.356055 5.001204e-131 \n", + "Itga2b 9515.339854 -12.057495 0.503300 -23.956866 7.836582e-127 \n", + "Ppbp 19266.371435 -16.866204 0.706022 -23.889076 3.978005e-126 \n", + "Adgrl4 133.471953 -9.102316 0.387205 -23.507763 3.397368e-122 \n", "\n", " padj \n", "ID \n", - "Mfsd2b 6.393582e-200 \n", - "Itga2b 9.525603e-190 \n", - "Adgrl4 1.777629e-172 \n", - "Gp1ba 2.563008e-139 \n", - "Ppbp 1.069483e-135 " + "Mfsd2b 8.829286e-139 \n", + "Pf4 3.322300e-127 \n", + "Itga2b 3.470561e-123 \n", + "Ppbp 1.321294e-122 \n", + "Adgrl4 9.027486e-119 " ] }, "execution_count": 14, @@ -1343,15 +1485,130 @@ "results_df2.sort_values(by=\"padj\", ascending=True, inplace=False).head()" ] }, + { + "cell_type": "code", + "execution_count": 15, + "id": "holdout-de", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-06T17:18:46.820876Z", + "iopub.status.busy": "2026-08-06T17:18:46.820773Z", + "iopub.status.idle": "2026-08-06T17:18:50.899155Z", + "shell.execute_reply": "2026-08-06T17:18:50.898672Z" + }, + "papermill": { + "duration": 4.084438, + "end_time": "2026-08-06T17:18:50.899944+00:00", + "exception": false, + "start_time": "2026-08-06T17:18:46.815506+00:00", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
conditionvalidation_genes
0spontaneous13739
1SB13536
\n", + "
" + ], + "text/plain": [ + " condition validation_genes\n", + "0 spontaneous 13739\n", + "1 SB 13536" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def fit_validation_de(adata_subset, disease_condition):\n", + " validation = adata_subset.copy()\n", + " dc.pp.filter_by_expr(\n", + " validation,\n", + " group=\"condition\",\n", + " min_count=10,\n", + " min_total_count=15,\n", + " large_n=10,\n", + " min_prop=0.5,\n", + " )\n", + " inference = DefaultInference()\n", + " dds = DeseqDataSet(\n", + " adata=validation,\n", + " design=\"~condition\",\n", + " refit_cooks=False,\n", + " inference=inference,\n", + " quiet=True,\n", + " )\n", + " dds.deseq2()\n", + " stats = DeseqStats(\n", + " dds,\n", + " contrast=[\"condition\", disease_condition, \"Normal\"],\n", + " inference=inference,\n", + " quiet=True,\n", + " )\n", + " stats.summary()\n", + " return stats.results_df\n", + "\n", + "\n", + "validation_results_df1 = fit_validation_de(\n", + " validation_adata1, \"Leukemic_spontaneous\"\n", + ")\n", + "validation_results_df2 = fit_validation_de(validation_adata2, \"Leukemic_SB\")\n", + "\n", + "pd.DataFrame(\n", + " {\n", + " \"condition\": [\"spontaneous\", \"SB\"],\n", + " \"validation_genes\": [len(validation_results_df1), len(validation_results_df2)],\n", + " }\n", + ")\n" + ] + }, { "cell_type": "markdown", "id": "6e64e7dc", "metadata": { "papermill": { - "duration": 0.004459, - "end_time": "2025-06-27T16:26:43.883740", + "duration": 0.004856, + "end_time": "2026-08-06T17:18:50.909734+00:00", "exception": false, - "start_time": "2025-06-27T16:26:43.879281", + "start_time": "2026-08-06T17:18:50.904878+00:00", "status": "completed" }, "tags": [] @@ -1362,20 +1619,20 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "6a207d41", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:43.893521Z", - "iopub.status.busy": "2025-06-27T16:26:43.893400Z", - "iopub.status.idle": "2025-06-27T16:26:45.713556Z", - "shell.execute_reply": "2025-06-27T16:26:45.713257Z" + "iopub.execute_input": "2026-08-06T17:18:50.919850Z", + "iopub.status.busy": "2026-08-06T17:18:50.919735Z", + "iopub.status.idle": "2026-08-06T17:18:53.183748Z", + "shell.execute_reply": "2026-08-06T17:18:53.183110Z" }, "papermill": { - "duration": 1.825943, - "end_time": "2025-06-27T16:26:45.714293", + "duration": 2.269916, + "end_time": "2026-08-06T17:18:53.184202+00:00", "exception": false, - "start_time": "2025-06-27T16:26:43.888350", + "start_time": "2026-08-06T17:18:50.914286+00:00", "status": "completed" }, "tags": [] @@ -1405,9 +1662,6 @@ " source\n", " target\n", " weight\n", - " resources\n", - " references\n", - " sign_decision\n", " \n", " \n", " \n", @@ -1415,75 +1669,63 @@ " 0\n", " Myc\n", " Tert\n", - " 1.0\n", - " DoRothEA-A;ExTRI;HTRI;NTNU.Curated;Pavlidis202...\n", - " 10022128;10491298;10606235;10637317;10723141;1...\n", - " PMID\n", + " 1\n", " \n", " \n", " 1\n", " Spi1\n", - " Bglap3\n", - " 1.0\n", - " ExTRI\n", - " 10022617\n", - " default activation\n", + " Bglap2\n", + " 1\n", " \n", " \n", " 2\n", " Spi1\n", " Bglap\n", - " 1.0\n", - " ExTRI\n", - " 10022617\n", - " default activation\n", + " 1\n", " \n", " \n", " 3\n", " Spi1\n", - " Bglap2\n", - " 1.0\n", - " ExTRI\n", - " 10022617\n", - " default activation\n", + " Bglap3\n", + " 1\n", " \n", " \n", " 4\n", " Smad3\n", " Jun\n", - " 1.0\n", - " ExTRI;NTNU.Curated;TFactS;TRRUST\n", - " 10022869;12374795\n", - " PMID\n", + " 1\n", " \n", " \n", "\n", "" ], "text/plain": [ - " source target weight resources \\\n", - "0 Myc Tert 1.0 DoRothEA-A;ExTRI;HTRI;NTNU.Curated;Pavlidis202... \n", - "1 Spi1 Bglap3 1.0 ExTRI \n", - "2 Spi1 Bglap 1.0 ExTRI \n", - "3 Spi1 Bglap2 1.0 ExTRI \n", - "4 Smad3 Jun 1.0 ExTRI;NTNU.Curated;TFactS;TRRUST \n", - "\n", - " references sign_decision \n", - "0 10022128;10491298;10606235;10637317;10723141;1... PMID \n", - "1 10022617 default activation \n", - "2 10022617 default activation \n", - "3 10022617 default activation \n", - "4 10022869;12374795 PMID " + " source target weight\n", + "0 Myc Tert 1\n", + "1 Spi1 Bglap2 1\n", + "2 Spi1 Bglap 1\n", + "3 Spi1 Bglap3 1\n", + "4 Smad3 Jun 1" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Retrieve CollecTRI gene regulatory network (through Omnipath)\n", - "collectri = dc.op.collectri(organism=\"mouse\")\n", + "# Retrieve native mouse CollecTRI interactions through OmniPath.\n", + "# This avoids a fragile human-to-mouse translation download.\n", + "collectri_raw = op.interactions.CollecTRI.get(organisms=\"mouse\", genesymbols=True)\n", + "collectri = collectri_raw.assign(\n", + " source=collectri_raw[\"source_genesymbol\"],\n", + " target=collectri_raw[\"target_genesymbol\"],\n", + " weight=(\n", + " collectri_raw[\"consensus_stimulation\"].astype(int)\n", + " - collectri_raw[\"consensus_inhibition\"].astype(int)\n", + " ),\n", + ")[[\"source\", \"target\", \"weight\"]]\n", + "collectri = collectri.loc[collectri[\"weight\"] != 0].drop_duplicates()\n", "collectri.head()" ] }, @@ -1492,10 +1734,10 @@ "id": "31c0ba08", "metadata": { "papermill": { - "duration": 0.012738, - "end_time": "2025-06-27T16:26:45.731856", + "duration": 0.005057, + "end_time": "2026-08-06T17:18:53.194362+00:00", "exception": false, - "start_time": "2025-06-27T16:26:45.719118", + "start_time": "2026-08-06T17:18:53.189305+00:00", "status": "completed" }, "tags": [] @@ -1509,10 +1751,10 @@ "id": "ee1d5eef", "metadata": { "papermill": { - "duration": 0.004369, - "end_time": "2025-06-27T16:26:45.740709", + "duration": 0.00469, + "end_time": "2026-08-06T17:18:53.203817+00:00", "exception": false, - "start_time": "2025-06-27T16:26:45.736340", + "start_time": "2026-08-06T17:18:53.199127+00:00", "status": "completed" }, "tags": [] @@ -1523,20 +1765,20 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "e908dd4d", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:45.750661Z", - "iopub.status.busy": "2025-06-27T16:26:45.750515Z", - "iopub.status.idle": "2025-06-27T16:26:45.756676Z", - "shell.execute_reply": "2025-06-27T16:26:45.756449Z" + "iopub.execute_input": "2026-08-06T17:18:53.213972Z", + "iopub.status.busy": "2026-08-06T17:18:53.213861Z", + "iopub.status.idle": "2026-08-06T17:18:53.219387Z", + "shell.execute_reply": "2026-08-06T17:18:53.219020Z" }, "papermill": { - "duration": 0.012124, - "end_time": "2025-06-27T16:26:45.757362", + "duration": 0.011313, + "end_time": "2026-08-06T17:18:53.219825+00:00", "exception": false, - "start_time": "2025-06-27T16:26:45.745238", + "start_time": "2026-08-06T17:18:53.208512+00:00", "status": "completed" }, "tags": [] @@ -1572,9 +1814,8 @@ " Mks1\n", " Vps28\n", " Setd6\n", - " Psma4\n", + " Gstm2\n", " ...\n", - " Cd37\n", " Rag2\n", " Itgb1bp2\n", " Sec23ip\n", @@ -1584,58 +1825,59 @@ " Rhd\n", " Pxmp4\n", " Gm25500\n", + " Prss39\n", " \n", " \n", " \n", " \n", " Leukemic_spontaneous.vs.Normal\n", - " 6.016588\n", - " 11.387579\n", - " 2.723045\n", - " -0.163747\n", - " -1.056967\n", - " 7.738055\n", - " 3.636604\n", - " 2.671039\n", - " 6.008118\n", - " 3.939383\n", + " 4.148578\n", + " 10.694655\n", + " 2.217812\n", + " -0.549658\n", + " 0.050457\n", + " 5.610092\n", + " 5.178963\n", + " 3.846342\n", + " 8.079712\n", + " -2.671615\n", " ...\n", - " 3.891274\n", - " -2.875231\n", - " -0.371605\n", - " -11.321515\n", - " 2.340763\n", - " -1.979131\n", - " -8.266294\n", - " -10.982454\n", - " -5.376307\n", - " 2.049048\n", + " -4.541439\n", + " -0.473154\n", + " -9.441182\n", + " 1.012695\n", + " -1.222221\n", + " -7.059862\n", + " -9.135671\n", + " -4.241074\n", + " 1.264247\n", + " 1.119565\n", " \n", " \n", "\n", - "

1 rows × 13585 columns

\n", + "

1 rows × 13599 columns

\n", "" ], "text/plain": [ "ID Itm2a Sergef Fam109a Dhx9 \\\n", - "Leukemic_spontaneous.vs.Normal 6.016588 11.387579 2.723045 -0.163747 \n", + "Leukemic_spontaneous.vs.Normal 4.148578 10.694655 2.217812 -0.549658 \n", "\n", "ID Ssu72 Eif2b2 Mks1 Vps28 \\\n", - "Leukemic_spontaneous.vs.Normal -1.056967 7.738055 3.636604 2.671039 \n", + "Leukemic_spontaneous.vs.Normal 0.050457 5.610092 5.178963 3.846342 \n", "\n", - "ID Setd6 Psma4 ... Cd37 Rag2 \\\n", - "Leukemic_spontaneous.vs.Normal 6.008118 3.939383 ... 3.891274 -2.875231 \n", + "ID Setd6 Gstm2 ... Rag2 Itgb1bp2 \\\n", + "Leukemic_spontaneous.vs.Normal 8.079712 -2.671615 ... -4.541439 -0.473154 \n", "\n", - "ID Itgb1bp2 Sec23ip Gosr1 Ctsw \\\n", - "Leukemic_spontaneous.vs.Normal -0.371605 -11.321515 2.340763 -1.979131 \n", + "ID Sec23ip Gosr1 Ctsw Ryk \\\n", + "Leukemic_spontaneous.vs.Normal -9.441182 1.012695 -1.222221 -7.059862 \n", "\n", - "ID Ryk Rhd Pxmp4 Gm25500 \n", - "Leukemic_spontaneous.vs.Normal -8.266294 -10.982454 -5.376307 2.049048 \n", + "ID Rhd Pxmp4 Gm25500 Prss39 \n", + "Leukemic_spontaneous.vs.Normal -9.135671 -4.241074 1.264247 1.119565 \n", "\n", - "[1 rows x 13585 columns]" + "[1 rows x 13599 columns]" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1647,20 +1889,20 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "98b26a75", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:45.766692Z", - "iopub.status.busy": "2025-06-27T16:26:45.766599Z", - "iopub.status.idle": "2025-06-27T16:26:45.847069Z", - "shell.execute_reply": "2025-06-27T16:26:45.846746Z" + "iopub.execute_input": "2026-08-06T17:18:53.230255Z", + "iopub.status.busy": "2026-08-06T17:18:53.230141Z", + "iopub.status.idle": "2026-08-06T17:18:53.303782Z", + "shell.execute_reply": "2026-08-06T17:18:53.303420Z" }, "papermill": { - "duration": 0.085951, - "end_time": "2025-06-27T16:26:45.847784", + "duration": 0.079526, + "end_time": "2026-08-06T17:18:53.304435+00:00", "exception": false, - "start_time": "2025-06-27T16:26:45.761833", + "start_time": "2026-08-06T17:18:53.224909+00:00", "status": "completed" }, "tags": [] @@ -1670,84 +1912,42 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-07-22 16:14:16 | [INFO] ulm - Running ulm\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:decoupler:ulm - Running ulm\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-07-22 16:14:16 | [INFO] Extracted omics mat with 1 rows (observations) and 13585 columns (features)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:decoupler:Extracted omics mat with 1 rows (observations) and 13585 columns (features)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-07-22 16:14:17 | [INFO] Network adjacency matrix has 4442 unique features and 640 unique sources\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:decoupler:Network adjacency matrix has 4442 unique features and 640 unique sources\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-07-22 16:14:17 | [INFO] ulm - fitting 640 univariate models of 13585 observations (targets) with 13583 degrees of freedom\n" + "2026-08-06 19:18:53 | [INFO] ulm - Running ulm\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:decoupler:ulm - fitting 640 univariate models of 13585 observations (targets) with 13583 degrees of freedom\n" + "2026-08-06 19:18:53 | [INFO] Extracted omics mat with 1 rows (observations) and 13599 columns (features)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2026-07-22 16:14:17 | [INFO] ulm - adjusting p-values by FDR\n" + "2026-08-06 19:18:53 | [INFO] Network adjacency matrix has 3416 unique features and 540 unique sources\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:decoupler:ulm - adjusting p-values by FDR\n" + "2026-08-06 19:18:53 | [INFO] ulm - fitting 540 univariate models of 13599 observations (targets) with 13597 degrees of freedom\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2026-07-22 16:14:17 | [INFO] ulm - done\n" + "2026-08-06 19:18:53 | [INFO] ulm - adjusting p-values by FDR\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:decoupler:ulm - done\n" + "2026-08-06 19:18:53 | [INFO] ulm - done\n" ] }, { @@ -1771,79 +1971,82 @@ " \n", " \n", " \n", - " Abl1\n", - " Ahr\n", - " Aire\n", - " Apex1\n", - " Ar\n", - " Arid1a\n", - " Arid1b\n", - " Arid3a\n", - " Arid3b\n", - " Arid4a\n", + " A0A087WPA7\n", + " A0A087WQ36\n", + " A0A087WR05\n", + " A0A087WSP5\n", + " A0A0A6YWE3\n", + " A0A0A6YWQ4\n", + " A0A0A6YXS3\n", + " A0A0G2JEM8\n", + " A0A0J9YTU7\n", + " A0A0J9YTW3\n", " ...\n", - " Zfpm1\n", - " Zfpm2\n", - " Zglp1\n", " Zgpat\n", " Zhx2\n", " Zic1\n", - " Zic2\n", - " Zkscan3\n", - " Zkscan4\n", - " Zkscan7\n", + " Znf143\n", + " Znf148\n", + " Znf263\n", + " Znf354c\n", + " Znf382\n", + " Znf436\n", + " Znf76\n", " \n", " \n", " \n", " \n", " Leukemic_spontaneous.vs.Normal\n", - " 1.452427\n", - " -0.114799\n", - " -2.245622\n", - " 0.882775\n", - " -0.297864\n", - " -0.176459\n", - " -0.632378\n", - " -0.453538\n", - " -1.821644\n", - " 0.260495\n", + " -1.656074\n", + " -1.979076\n", + " -5.328064\n", + " 0.162649\n", + " -1.833279\n", + " 0.779211\n", + " 0.51319\n", + " -1.233113\n", + " -0.126254\n", + " 0.446425\n", " ...\n", - " -1.439184\n", - " 0.110701\n", - " -1.527737\n", - " -0.94774\n", - " 1.098365\n", - " 0.530819\n", - " -0.030262\n", - " 1.943526\n", - " 1.943526\n", - " 1.024536\n", + " -0.910795\n", + " 1.29885\n", + " -0.013044\n", + " -0.382275\n", + " -1.58947\n", + " -0.731727\n", + " 1.448071\n", + " -0.402687\n", + " -1.753189\n", + " 0.140664\n", " \n", " \n", "\n", - "

1 rows × 640 columns

\n", + "

1 rows × 540 columns

\n", "" ], "text/plain": [ - " Abl1 Ahr Aire Apex1 \\\n", - "Leukemic_spontaneous.vs.Normal 1.452427 -0.114799 -2.245622 0.882775 \n", + " A0A087WPA7 A0A087WQ36 A0A087WR05 \\\n", + "Leukemic_spontaneous.vs.Normal -1.656074 -1.979076 -5.328064 \n", + "\n", + " A0A087WSP5 A0A0A6YWE3 A0A0A6YWQ4 \\\n", + "Leukemic_spontaneous.vs.Normal 0.162649 -1.833279 0.779211 \n", "\n", - " Ar Arid1a Arid1b Arid3a \\\n", - "Leukemic_spontaneous.vs.Normal -0.297864 -0.176459 -0.632378 -0.453538 \n", + " A0A0A6YXS3 A0A0G2JEM8 A0A0J9YTU7 \\\n", + "Leukemic_spontaneous.vs.Normal 0.51319 -1.233113 -0.126254 \n", "\n", - " Arid3b Arid4a ... Zfpm1 Zfpm2 \\\n", - "Leukemic_spontaneous.vs.Normal -1.821644 0.260495 ... -1.439184 0.110701 \n", + " A0A0J9YTW3 ... Zgpat Zhx2 Zic1 \\\n", + "Leukemic_spontaneous.vs.Normal 0.446425 ... -0.910795 1.29885 -0.013044 \n", "\n", - " Zglp1 Zgpat Zhx2 Zic1 \\\n", - "Leukemic_spontaneous.vs.Normal -1.527737 -0.94774 1.098365 0.530819 \n", + " Znf143 Znf148 Znf263 Znf354c \\\n", + "Leukemic_spontaneous.vs.Normal -0.382275 -1.58947 -0.731727 1.448071 \n", "\n", - " Zic2 Zkscan3 Zkscan4 Zkscan7 \n", - "Leukemic_spontaneous.vs.Normal -0.030262 1.943526 1.943526 1.024536 \n", + " Znf382 Znf436 Znf76 \n", + "Leukemic_spontaneous.vs.Normal -0.402687 -1.753189 0.140664 \n", "\n", - "[1 rows x 640 columns]" + "[1 rows x 540 columns]" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1855,20 +2058,20 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "8fcafe29", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:45.858454Z", - "iopub.status.busy": "2025-06-27T16:26:45.858302Z", - "iopub.status.idle": "2025-06-27T16:26:46.031323Z", - "shell.execute_reply": "2025-06-27T16:26:46.030982Z" + "iopub.execute_input": "2026-08-06T17:18:53.315182Z", + "iopub.status.busy": "2026-08-06T17:18:53.315078Z", + "iopub.status.idle": "2026-08-06T17:18:53.453952Z", + "shell.execute_reply": "2026-08-06T17:18:53.453588Z" }, "papermill": { - "duration": 0.179107, - "end_time": "2025-06-27T16:26:46.032159", + "duration": 0.14476, + "end_time": "2026-08-06T17:18:53.454427+00:00", "exception": false, - "start_time": "2025-06-27T16:26:45.853052", + "start_time": "2026-08-06T17:18:53.309667+00:00", "status": "completed" }, "tags": [] @@ -1876,7 +2079,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1899,10 +2102,10 @@ "id": "57ce04b7", "metadata": { "papermill": { - "duration": 0.004759, - "end_time": "2025-06-27T16:26:46.041865", + "duration": 0.005101, + "end_time": "2026-08-06T17:18:53.464888+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.037106", + "start_time": "2026-08-06T17:18:53.459787+00:00", "status": "completed" }, "tags": [] @@ -1913,20 +2116,20 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "a0e08969", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:46.052285Z", - "iopub.status.busy": "2025-06-27T16:26:46.052148Z", - "iopub.status.idle": "2025-06-27T16:26:46.058100Z", - "shell.execute_reply": "2025-06-27T16:26:46.057854Z" + "iopub.execute_input": "2026-08-06T17:18:53.476084Z", + "iopub.status.busy": "2026-08-06T17:18:53.475980Z", + "iopub.status.idle": "2026-08-06T17:18:53.481029Z", + "shell.execute_reply": "2026-08-06T17:18:53.480743Z" }, "papermill": { - "duration": 0.011936, - "end_time": "2025-06-27T16:26:46.058798", + "duration": 0.011552, + "end_time": "2026-08-06T17:18:53.481597+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.046862", + "start_time": "2026-08-06T17:18:53.470045+00:00", "status": "completed" }, "tags": [] @@ -1979,50 +2182,50 @@ " \n", " \n", " Leukemic_SB.vs.Normal\n", - " 3.168537\n", - " 17.245745\n", - " 3.396873\n", - " -0.493828\n", - " 0.071816\n", - " 6.812841\n", - " 6.314824\n", - " 3.452622\n", - " 5.558162\n", - " 5.259825\n", + " 2.963769\n", + " 13.983566\n", + " 2.884977\n", + " -0.24563\n", + " 0.179011\n", + " 5.428026\n", + " 6.215017\n", + " 3.262983\n", + " 5.410293\n", + " 4.873833\n", " ...\n", - " 1.184029\n", - " -2.941684\n", - " -2.355503\n", - " -7.619795\n", - " 2.099912\n", - " -3.074381\n", - " -7.744751\n", - " -11.166439\n", - " -1.561793\n", - " 1.27881\n", + " 1.520396\n", + " -2.272855\n", + " -3.153128\n", + " -6.110922\n", + " 1.6341\n", + " -2.816943\n", + " -6.764932\n", + " -9.004647\n", + " -1.571242\n", + " 0.455914\n", " \n", " \n", "\n", - "

1 rows × 13404 columns

\n", + "

1 rows × 13286 columns

\n", "" ], "text/plain": [ - "ID Itm2a Sergef Fam109a Dhx9 Ssu72 \\\n", - "Leukemic_SB.vs.Normal 3.168537 17.245745 3.396873 -0.493828 0.071816 \n", + "ID Itm2a Sergef Fam109a Dhx9 Ssu72 \\\n", + "Leukemic_SB.vs.Normal 2.963769 13.983566 2.884977 -0.24563 0.179011 \n", "\n", "ID Eif2b2 Mks1 Vps28 Setd6 Psma4 ... \\\n", - "Leukemic_SB.vs.Normal 6.812841 6.314824 3.452622 5.558162 5.259825 ... \n", + "Leukemic_SB.vs.Normal 5.428026 6.215017 3.262983 5.410293 4.873833 ... \n", "\n", - "ID Cd37 Rag2 Itgb1bp2 Sec23ip Gosr1 \\\n", - "Leukemic_SB.vs.Normal 1.184029 -2.941684 -2.355503 -7.619795 2.099912 \n", + "ID Cd37 Rag2 Itgb1bp2 Sec23ip Gosr1 \\\n", + "Leukemic_SB.vs.Normal 1.520396 -2.272855 -3.153128 -6.110922 1.6341 \n", "\n", - "ID Ctsw Ryk Rhd Pxmp4 Gm25500 \n", - "Leukemic_SB.vs.Normal -3.074381 -7.744751 -11.166439 -1.561793 1.27881 \n", + "ID Ctsw Ryk Rhd Pxmp4 Gm25500 \n", + "Leukemic_SB.vs.Normal -2.816943 -6.764932 -9.004647 -1.571242 0.455914 \n", "\n", - "[1 rows x 13404 columns]" + "[1 rows x 13286 columns]" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2034,20 +2237,20 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "4b241977", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:46.068952Z", - "iopub.status.busy": "2025-06-27T16:26:46.068759Z", - "iopub.status.idle": "2025-06-27T16:26:46.135301Z", - "shell.execute_reply": "2025-06-27T16:26:46.135063Z" + "iopub.execute_input": "2026-08-06T17:18:53.492841Z", + "iopub.status.busy": "2026-08-06T17:18:53.492741Z", + "iopub.status.idle": "2026-08-06T17:18:53.546079Z", + "shell.execute_reply": "2026-08-06T17:18:53.545698Z" }, "papermill": { - "duration": 0.07234, - "end_time": "2025-06-27T16:26:46.135988", + "duration": 0.059643, + "end_time": "2026-08-06T17:18:53.546578+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.063648", + "start_time": "2026-08-06T17:18:53.486935+00:00", "status": "completed" }, "tags": [] @@ -2057,84 +2260,42 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-07-22 16:14:17 | [INFO] ulm - Running ulm\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:decoupler:ulm - Running ulm\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-07-22 16:14:17 | [INFO] Extracted omics mat with 1 rows (observations) and 13404 columns (features)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:decoupler:Extracted omics mat with 1 rows (observations) and 13404 columns (features)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-07-22 16:14:17 | [INFO] Network adjacency matrix has 4378 unique features and 625 unique sources\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:decoupler:Network adjacency matrix has 4378 unique features and 625 unique sources\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-07-22 16:14:17 | [INFO] ulm - fitting 625 univariate models of 13404 observations (targets) with 13402 degrees of freedom\n" + "2026-08-06 19:18:53 | [INFO] ulm - Running ulm\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:decoupler:ulm - fitting 625 univariate models of 13404 observations (targets) with 13402 degrees of freedom\n" + "2026-08-06 19:18:53 | [INFO] Extracted omics mat with 1 rows (observations) and 13286 columns (features)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2026-07-22 16:14:17 | [INFO] ulm - adjusting p-values by FDR\n" + "2026-08-06 19:18:53 | [INFO] Network adjacency matrix has 3335 unique features and 530 unique sources\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:decoupler:ulm - adjusting p-values by FDR\n" + "2026-08-06 19:18:53 | [INFO] ulm - fitting 530 univariate models of 13286 observations (targets) with 13284 degrees of freedom\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2026-07-22 16:14:17 | [INFO] ulm - done\n" + "2026-08-06 19:18:53 | [INFO] ulm - adjusting p-values by FDR\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:decoupler:ulm - done\n" + "2026-08-06 19:18:53 | [INFO] ulm - done\n" ] }, { @@ -2158,76 +2319,79 @@ " \n", " \n", " \n", - " Abl1\n", - " Ahr\n", - " Aire\n", - " Apex1\n", - " Ar\n", - " Arid1a\n", - " Arid1b\n", - " Arid3a\n", - " Arid3b\n", - " Arid4a\n", + " A0A087WPA7\n", + " A0A087WQ36\n", + " A0A087WR05\n", + " A0A087WSP5\n", + " A0A0A6YWE3\n", + " A0A0A6YWQ4\n", + " A0A0A6YXS3\n", + " A0A0G2JEM8\n", + " A0A0J9YTU7\n", + " A0A0J9YTW3\n", " ...\n", - " Zfpm1\n", - " Zfpm2\n", - " Zglp1\n", " Zgpat\n", " Zhx2\n", " Zic1\n", - " Zic2\n", - " Zkscan3\n", - " Zkscan4\n", - " Zkscan7\n", + " Znf143\n", + " Znf148\n", + " Znf263\n", + " Znf354c\n", + " Znf382\n", + " Znf436\n", + " Znf76\n", " \n", " \n", " \n", " \n", " Leukemic_SB.vs.Normal\n", - " 1.198672\n", - " 0.664946\n", - " -0.932051\n", - " 1.41717\n", - " 0.441607\n", - " 0.676726\n", - " -1.148908\n", - " -0.229291\n", - " -1.489206\n", - " 0.929682\n", + " -1.03892\n", + " -0.881134\n", + " -5.416913\n", + " 0.317954\n", + " -0.179198\n", + " 0.755719\n", + " 0.235632\n", + " -0.68796\n", + " 0.674024\n", + " -0.397479\n", " ...\n", - " -1.618189\n", - " -0.165162\n", - " -0.623841\n", - " -1.382759\n", - " 1.355504\n", - " 0.659013\n", - " -0.049629\n", - " 1.390874\n", - " 1.390874\n", - " 1.316169\n", + " -1.245498\n", + " 1.207684\n", + " 0.267398\n", + " -0.224013\n", + " -2.08245\n", + " -0.238626\n", + " 1.961743\n", + " -1.454379\n", + " -1.668492\n", + " 0.348216\n", " \n", " \n", "\n", - "

1 rows × 625 columns

\n", + "

1 rows × 530 columns

\n", "" ], "text/plain": [ - " Abl1 Ahr Aire Apex1 Ar \\\n", - "Leukemic_SB.vs.Normal 1.198672 0.664946 -0.932051 1.41717 0.441607 \n", + " A0A087WPA7 A0A087WQ36 A0A087WR05 A0A087WSP5 \\\n", + "Leukemic_SB.vs.Normal -1.03892 -0.881134 -5.416913 0.317954 \n", "\n", - " Arid1a Arid1b Arid3a Arid3b Arid4a ... \\\n", - "Leukemic_SB.vs.Normal 0.676726 -1.148908 -0.229291 -1.489206 0.929682 ... \n", + " A0A0A6YWE3 A0A0A6YWQ4 A0A0A6YXS3 A0A0G2JEM8 \\\n", + "Leukemic_SB.vs.Normal -0.179198 0.755719 0.235632 -0.68796 \n", "\n", - " Zfpm1 Zfpm2 Zglp1 Zgpat Zhx2 \\\n", - "Leukemic_SB.vs.Normal -1.618189 -0.165162 -0.623841 -1.382759 1.355504 \n", + " A0A0J9YTU7 A0A0J9YTW3 ... Zgpat Zhx2 \\\n", + "Leukemic_SB.vs.Normal 0.674024 -0.397479 ... -1.245498 1.207684 \n", "\n", - " Zic1 Zic2 Zkscan3 Zkscan4 Zkscan7 \n", - "Leukemic_SB.vs.Normal 0.659013 -0.049629 1.390874 1.390874 1.316169 \n", + " Zic1 Znf143 Znf148 Znf263 Znf354c \\\n", + "Leukemic_SB.vs.Normal 0.267398 -0.224013 -2.08245 -0.238626 1.961743 \n", "\n", - "[1 rows x 625 columns]" + " Znf382 Znf436 Znf76 \n", + "Leukemic_SB.vs.Normal -1.454379 -1.668492 0.348216 \n", + "\n", + "[1 rows x 530 columns]" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -2239,20 +2403,20 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "id": "6eb7b812", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:46.146483Z", - "iopub.status.busy": "2025-06-27T16:26:46.146381Z", - "iopub.status.idle": "2025-06-27T16:26:46.314456Z", - "shell.execute_reply": "2025-06-27T16:26:46.314128Z" + "iopub.execute_input": "2026-08-06T17:18:53.558181Z", + "iopub.status.busy": "2026-08-06T17:18:53.557948Z", + "iopub.status.idle": "2026-08-06T17:18:53.669875Z", + "shell.execute_reply": "2026-08-06T17:18:53.669504Z" }, "papermill": { - "duration": 0.17408, - "end_time": "2025-06-27T16:26:46.315218", + "duration": 0.118305, + "end_time": "2026-08-06T17:18:53.670432+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.141138", + "start_time": "2026-08-06T17:18:53.552127+00:00", "status": "completed" }, "tags": [] @@ -2260,7 +2424,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -2283,10 +2447,10 @@ "id": "1622ab72", "metadata": { "papermill": { - "duration": 0.005062, - "end_time": "2025-06-27T16:26:46.325734", + "duration": 0.005458, + "end_time": "2026-08-06T17:18:53.681656+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.320672", + "start_time": "2026-08-06T17:18:53.676198+00:00", "status": "completed" }, "tags": [] @@ -2297,20 +2461,20 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "id": "876f79a8", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:46.336712Z", - "iopub.status.busy": "2025-06-27T16:26:46.336578Z", - "iopub.status.idle": "2025-06-27T16:26:46.386615Z", - "shell.execute_reply": "2025-06-27T16:26:46.386344Z" + "iopub.execute_input": "2026-08-06T17:18:53.693068Z", + "iopub.status.busy": "2026-08-06T17:18:53.692958Z", + "iopub.status.idle": "2026-08-06T17:18:53.735320Z", + "shell.execute_reply": "2026-08-06T17:18:53.734974Z" }, "papermill": { - "duration": 0.056662, - "end_time": "2025-06-27T16:26:46.387278", + "duration": 0.048985, + "end_time": "2026-08-06T17:18:53.735875+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.330616", + "start_time": "2026-08-06T17:18:53.686890+00:00", "status": "completed" }, "tags": [] @@ -2322,7 +2486,7 @@ "849" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -2341,10 +2505,10 @@ "id": "4dd5caaa", "metadata": { "papermill": { - "duration": 0.004966, - "end_time": "2025-06-27T16:26:46.397472", + "duration": 0.005164, + "end_time": "2026-08-06T17:18:53.746740+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.392506", + "start_time": "2026-08-06T17:18:53.741576+00:00", "status": "completed" }, "tags": [] @@ -2355,166 +2519,53 @@ }, { "cell_type": "code", - "execution_count": 23, - "id": "2b319745", + "execution_count": 24, + "id": "d7432d92", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:46.407894Z", - "iopub.status.busy": "2025-06-27T16:26:46.407775Z", - "iopub.status.idle": "2025-06-27T16:26:46.410503Z", - "shell.execute_reply": "2025-06-27T16:26:46.410179Z" + "iopub.execute_input": "2026-08-06T17:18:53.758484Z", + "iopub.status.busy": "2026-08-06T17:18:53.758373Z", + "iopub.status.idle": "2026-08-06T17:18:53.760454Z", + "shell.execute_reply": "2026-08-06T17:18:53.760114Z" }, "papermill": { - "duration": 0.008711, - "end_time": "2025-06-27T16:26:46.411121", + "duration": 0.008698, + "end_time": "2026-08-06T17:18:53.760899+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.402410", + "start_time": "2026-08-06T17:18:53.752201+00:00", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ - "df_de_receptors1 = results_df1.loc[results_df1.index.intersection(unique_receptors)]\n", - "df_de_receptors1 = df_de_receptors1.sort_values(by=\"stat\", ascending=False)" + "def select_receptors(results_df, receptor_universe, graph, n=30, padj=0.05, min_log2fc=0.5):\n", + " candidates = results_df.loc[results_df.index.intersection(receptor_universe)].copy()\n", + " candidates = candidates.loc[\n", + " (candidates[\"padj\"] < padj) & (candidates[\"log2FoldChange\"] >= min_log2fc)\n", + " ]\n", + " candidates = candidates.loc[candidates.index.intersection(graph.V)]\n", + " return candidates.sort_values([\"stat\", \"log2FoldChange\"], ascending=False).head(n)\n", + "\n", + "\n", + "# The PKN is created below; receptor selection is finalized after that step.\n" ] }, { - "cell_type": "code", - "execution_count": 24, - "id": "4868219e", + "cell_type": "markdown", + "id": "a99812f6", "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:26:46.421541Z", - "iopub.status.busy": "2025-06-27T16:26:46.421443Z", - "iopub.status.idle": "2025-06-27T16:26:46.424966Z", - "shell.execute_reply": "2025-06-27T16:26:46.424729Z" - }, "papermill": { - "duration": 0.009433, - "end_time": "2025-06-27T16:26:46.425544", + "duration": 0.005268, + "end_time": "2026-08-06T17:18:53.771486+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.416111", + "start_time": "2026-08-06T17:18:53.766218+00:00", "status": "completed" }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " baseMean log2FoldChange lfcSE stat pvalue \\\n", - "ID \n", - "Mrc1 314.324326 3.312083 0.179667 18.434602 6.932705e-76 \n", - "St14 1073.819725 1.904158 0.116095 16.401736 1.858495e-60 \n", - "Cd48 5533.470898 0.664655 0.054604 12.172306 4.365699e-34 \n", - "Ifngr1 6551.268902 1.418280 0.120961 11.725146 9.473804e-32 \n", - "Lrrc4c 82.859784 10.019258 0.895652 11.186547 4.745541e-29 \n", - "\n", - " padj \n", - "ID \n", - "Mrc1 2.943150e-73 \n", - "St14 3.078982e-58 \n", - "Cd48 1.212843e-32 \n", - "Ifngr1 2.174014e-30 \n", - "Lrrc4c 8.584311e-28 " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "# We will take the top 20 receptors that increased the expression after treatment\n", - "df_top_receptors1 = df_de_receptors1.head(30)\n", - "df_top_receptors1.head()" + "Receptors are selected after constructing the PKN so that statistical evidence and PKN membership are both enforced.\n" ] }, { @@ -2522,10 +2573,10 @@ "id": "76abebef", "metadata": { "papermill": { - "duration": 0.005078, - "end_time": "2025-06-27T16:26:46.435803", + "duration": 0.005325, + "end_time": "2026-08-06T17:18:53.782177+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.430725", + "start_time": "2026-08-06T17:18:53.776852+00:00", "status": "completed" }, "tags": [] @@ -2535,167 +2586,37 @@ ] }, { - "cell_type": "code", - "execution_count": 25, - "id": "db13b8a9", + "cell_type": "markdown", + "id": "951d061e", "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:26:46.447140Z", - "iopub.status.busy": "2025-06-27T16:26:46.446924Z", - "iopub.status.idle": "2025-06-27T16:26:46.449749Z", - "shell.execute_reply": "2025-06-27T16:26:46.449460Z" - }, "papermill": { - "duration": 0.009265, - "end_time": "2025-06-27T16:26:46.450405", + "duration": 0.005261, + "end_time": "2026-08-06T17:18:53.792917+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.441140", + "start_time": "2026-08-06T17:18:53.787656+00:00", "status": "completed" }, "tags": [] }, - "outputs": [], "source": [ - "df_de_receptors2 = results_df2.loc[results_df2.index.intersection(unique_receptors)]\n", - "df_de_receptors2 = df_de_receptors2.sort_values(by=\"stat\", ascending=False)" + "The same receptor rule is used for both conditions; no fixed top-$n$ list is accepted without an evidence threshold.\n" ] }, { - "cell_type": "code", - "execution_count": 26, - "id": "94f89870", + "cell_type": "markdown", + "id": "7a480889", "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:26:46.461276Z", - "iopub.status.busy": "2025-06-27T16:26:46.461168Z", - "iopub.status.idle": "2025-06-27T16:26:46.464669Z", - "shell.execute_reply": "2025-06-27T16:26:46.464417Z" - }, "papermill": { - "duration": 0.009608, - "end_time": "2025-06-27T16:26:46.465295", + "duration": 0.005371, + "end_time": "2026-08-06T17:18:53.803621+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.455687", + "start_time": "2026-08-06T17:18:53.798250+00:00", "status": "completed" }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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baseMeanlog2FoldChangelfcSEstatpvaluepadj
ID
Lsr770.4171824.3604080.34609712.5988142.143445e-368.425435e-35
Ptprs5164.4245361.3033090.10946311.9063931.096177e-323.257906e-31
Nlgn21309.7016651.9503020.16526311.8012083.847470e-321.106684e-30
Cd2442529.4739982.9928440.25503311.7351438.418380e-322.345945e-30
Lrrc4c267.12584210.6504990.91674411.6177433.348651e-318.801043e-30
\n", - "
" - ], - "text/plain": [ - " baseMean log2FoldChange lfcSE stat pvalue \\\n", - "ID \n", - "Lsr 770.417182 4.360408 0.346097 12.598814 2.143445e-36 \n", - "Ptprs 5164.424536 1.303309 0.109463 11.906393 1.096177e-32 \n", - "Nlgn2 1309.701665 1.950302 0.165263 11.801208 3.847470e-32 \n", - "Cd244 2529.473998 2.992844 0.255033 11.735143 8.418380e-32 \n", - "Lrrc4c 267.125842 10.650499 0.916744 11.617743 3.348651e-31 \n", - "\n", - " padj \n", - "ID \n", - "Lsr 8.425435e-35 \n", - "Ptprs 3.257906e-31 \n", - "Nlgn2 1.106684e-30 \n", - "Cd244 2.345945e-30 \n", - "Lrrc4c 8.801043e-30 " - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "# We will take the top 20 receptors that increased the expression after treatment\n", - "df_top_receptors2 = df_de_receptors2.head(30)\n", - "df_top_receptors2.head()" + "This avoids treating weakly changing receptors as condition-specific inputs merely because they occupy a high rank.\n" ] }, { @@ -2703,10 +2624,10 @@ "id": "db4b045f", "metadata": { "papermill": { - "duration": 0.00524, - "end_time": "2025-06-27T16:26:46.475629", + "duration": 0.005175, + "end_time": "2026-08-06T17:18:53.814172+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.470389", + "start_time": "2026-08-06T17:18:53.808997+00:00", "status": "completed" }, "tags": [] @@ -2717,20 +2638,20 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "id": "2f4dcaf5", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:46.486523Z", - "iopub.status.busy": "2025-06-27T16:26:46.486406Z", - "iopub.status.idle": "2025-06-27T16:26:46.492465Z", - "shell.execute_reply": "2025-06-27T16:26:46.492181Z" + "iopub.execute_input": "2026-08-06T17:18:53.825714Z", + "iopub.status.busy": "2026-08-06T17:18:53.825605Z", + "iopub.status.idle": "2026-08-06T17:18:53.830722Z", + "shell.execute_reply": "2026-08-06T17:18:53.830391Z" }, "papermill": { - "duration": 0.012545, - "end_time": "2025-06-27T16:26:46.493260", + "duration": 0.011718, + "end_time": "2026-08-06T17:18:53.831205+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.480715", + "start_time": "2026-08-06T17:18:53.819487+00:00", "status": "completed" }, "tags": [] @@ -2747,24 +2668,24 @@ " style=\"width:100%;max-width:100px;\" />\n", " \n", " \n", - "
Installed version:v1.0.0rc1
Available backends:CVXPY v1.9.2
Default backend (corneto.opt):CVXPY
Installed solvers:CLARABEL, SCS, GUROBI, SCIPY, OSQP
Plot backend (default):auto -> graphviz
Available plot backends:graphviz v0.21; graphviz-wasm
Installed path:/docs/tutorials/contrib/multi_condition_tutorial/.pixi/envs/default/lib/python3.11/site-packages/corneto
Repository:https://github.com/saezlab/corneto
\n", + "
Installed version:v1.0.0rc5
Available backends:CVXPY v1.9.1
Default backend (corneto.opt):CVXPY
Installed solvers:CLARABEL, SCS, GUROBI, SCIPY, HIGHS, OSQP
Plot backend (default):auto -> graphviz
Available plot backends:graphviz v0.21; graphviz-wasm
Installed path:/docs/tutorials/contrib/multi_condition_tutorial/.pixi/envs/default/lib/python3.11/site-packages/corneto
Repository:https://github.com/saezlab/corneto
\n", " \n", " \n", " \n", " " ], "text/plain": [ - "Installed version: v1.0.0rc1\n", - "Available backends: CVXPY v1.9.2\n", + "Installed version: v1.0.0rc5\n", + "Available backends: CVXPY v1.9.1\n", "Default backend (corneto.opt): CVXPY\n", - "Installed solvers: CLARABEL, SCS, GUROBI, SCIPY, OSQP\n", + "Installed solvers: CLARABEL, SCS, GUROBI, SCIPY, HIGHS, OSQP\n", "Plot backend (default): auto -> graphviz\n", "Available plot backends: graphviz v0.21; graphviz-wasm\n", "Installed path: /docs/tutorials/contrib/multi_condition_tutorial/.pixi/envs/default/lib/python3.11/site-packages/corneto\n", "Repository: https://github.com/saezlab/corneto" ] }, - "execution_count": 27, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2775,20 +2696,20 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "id": "905f837a", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:46.505512Z", - "iopub.status.busy": "2025-06-27T16:26:46.505345Z", - "iopub.status.idle": "2025-06-27T16:26:46.509701Z", - "shell.execute_reply": "2025-06-27T16:26:46.509361Z" + "iopub.execute_input": "2026-08-06T17:18:53.843520Z", + "iopub.status.busy": "2026-08-06T17:18:53.843415Z", + "iopub.status.idle": "2026-08-06T17:18:53.856223Z", + "shell.execute_reply": "2026-08-06T17:18:53.855733Z" }, "papermill": { - "duration": 0.011658, - "end_time": "2025-06-27T16:26:46.510626", + "duration": 0.019815, + "end_time": "2026-08-06T17:18:53.856786+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.498968", + "start_time": "2026-08-06T17:18:53.836971+00:00", "status": "completed" }, "tags": [] @@ -2805,10 +2726,10 @@ "id": "e622292d", "metadata": { "papermill": { - "duration": 0.005725, - "end_time": "2025-06-27T16:26:46.522102", + "duration": 0.005808, + "end_time": "2026-08-06T17:18:53.868419+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.516377", + "start_time": "2026-08-06T17:18:53.862611+00:00", "status": "completed" }, "tags": [] @@ -2819,20 +2740,20 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "id": "26362a71", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:46.534112Z", - "iopub.status.busy": "2025-06-27T16:26:46.533965Z", - "iopub.status.idle": "2025-06-27T16:26:46.772513Z", - "shell.execute_reply": "2025-06-27T16:26:46.772222Z" + "iopub.execute_input": "2026-08-06T17:18:53.880170Z", + "iopub.status.busy": "2026-08-06T17:18:53.880063Z", + "iopub.status.idle": "2026-08-06T17:18:54.022017Z", + "shell.execute_reply": "2026-08-06T17:18:54.021603Z" }, "papermill": { - "duration": 0.245546, - "end_time": "2025-06-27T16:26:46.773234", + "duration": 0.148604, + "end_time": "2026-08-06T17:18:54.022540+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.527688", + "start_time": "2026-08-06T17:18:53.873936+00:00", "status": "completed" }, "tags": [] @@ -3027,7 +2948,7 @@ "4 2 1 18258429 " ] }, - "execution_count": 29, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -3040,20 +2961,20 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "id": "5d99c8b4", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:46.784967Z", - "iopub.status.busy": "2025-06-27T16:26:46.784841Z", - "iopub.status.idle": "2025-06-27T16:26:46.788828Z", - "shell.execute_reply": "2025-06-27T16:26:46.788587Z" + "iopub.execute_input": "2026-08-06T17:18:54.036271Z", + "iopub.status.busy": "2026-08-06T17:18:54.036165Z", + "iopub.status.idle": "2026-08-06T17:18:54.039977Z", + "shell.execute_reply": "2026-08-06T17:18:54.039505Z" }, "papermill": { - "duration": 0.010398, - "end_time": "2025-06-27T16:26:46.789425", + "duration": 0.010785, + "end_time": "2026-08-06T17:18:54.040439+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.779027", + "start_time": "2026-08-06T17:18:54.029654+00:00", "status": "completed" }, "tags": [] @@ -3129,7 +3050,7 @@ "4 Rack1 -1 Trpm6" ] }, - "execution_count": 30, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -3142,20 +3063,20 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "id": "2321fe29", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:46.801142Z", - "iopub.status.busy": "2025-06-27T16:26:46.800943Z", - "iopub.status.idle": "2025-06-27T16:26:47.118733Z", - "shell.execute_reply": "2025-06-27T16:26:47.118462Z" + "iopub.execute_input": "2026-08-06T17:18:54.052695Z", + "iopub.status.busy": "2026-08-06T17:18:54.052600Z", + "iopub.status.idle": "2026-08-06T17:18:54.546516Z", + "shell.execute_reply": "2026-08-06T17:18:54.546168Z" }, "papermill": { - "duration": 0.324346, - "end_time": "2025-06-27T16:26:47.119472", + "duration": 0.500912, + "end_time": "2026-08-06T17:18:54.547085+00:00", "exception": false, - "start_time": "2025-06-27T16:26:46.795126", + "start_time": "2026-08-06T17:18:54.046173+00:00", "status": "completed" }, "tags": [] @@ -3167,7 +3088,7 @@ "(4304, 9505)" ] }, - "execution_count": 31, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -3180,99 +3101,198 @@ }, { "cell_type": "markdown", - "id": "6e9dc57b", + "id": "heldout-pathway-resource", "metadata": { "papermill": { - "duration": 0.005477, - "end_time": "2025-06-27T16:26:47.130597", + "duration": 0.005926, + "end_time": "2026-08-06T17:18:54.559107+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.125120", + "start_time": "2026-08-06T17:18:54.553181+00:00", "status": "completed" }, "tags": [] }, "source": [ - "## Identifying target TFs per condition" + "## Independent pathway view for held-out validation\n", + "\n", + "Individual protein activity need not follow expression of the corresponding gene. We therefore use two unsigned validation views: whether selected internal-node genes are disproportionately deregulated, and whether the selected internal nodes concentrate in pathways that are deregulated as groups. SignaLink pathway annotations are used for the latter. They are not included in the CARNIVAL objective.\n", + "\n", + "The installed resource currently exposes human symbols. Conserved symbols are mapped case-insensitively to mouse genes present in this dataset and PKN, and mapping coverage is reported explicitly." ] }, { "cell_type": "code", - "execution_count": 32, - "id": "f889a11a-6288-453e-8208-1dbb34c50450", + "execution_count": 30, + "id": "heldout-pathway-resource-code", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:47.141998Z", - "iopub.status.busy": "2025-06-27T16:26:47.141795Z", - "iopub.status.idle": "2025-06-27T16:26:47.143488Z", - "shell.execute_reply": "2025-06-27T16:26:47.143272Z" + "iopub.execute_input": "2026-08-06T17:18:54.571330Z", + "iopub.status.busy": "2026-08-06T17:18:54.571217Z", + "iopub.status.idle": "2026-08-06T17:18:55.242079Z", + "shell.execute_reply": "2026-08-06T17:18:55.241655Z" }, "papermill": { - "duration": 0.008109, - "end_time": "2025-06-27T16:26:47.144139", + "duration": 0.678076, + "end_time": "2026-08-06T17:18:55.242885+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.136030", + "start_time": "2026-08-06T17:18:54.564809+00:00", "status": "completed" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "human_annotation_genes 1938\n", + "mapped_mouse_genes 816\n", + "mapped_pathways 13\n", + "Name: pathway_resource, dtype: int64" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "max_pval = 0.01" + "signalink_human = dc.op.resource(\"SignaLink_pathway\", organism=\"human\")\n", + "mouse_symbol_lookup = {str(symbol).upper(): str(symbol) for symbol in G.V}\n", + "mouse_symbol_lookup.update(\n", + " {str(symbol).upper(): str(symbol) for symbol in adata.var_names}\n", + ")\n", + "\n", + "signalink = signalink_human.assign(\n", + " genesymbol=signalink_human[\"genesymbol\"].str.upper().map(mouse_symbol_lookup)\n", + ").dropna(subset=[\"genesymbol\"])[[\"genesymbol\", \"pathway\"]]\n", + "signalink = signalink.drop_duplicates()\n", + "pathway_net = signalink.rename(\n", + " columns={\"pathway\": \"source\", \"genesymbol\": \"target\"}\n", + ").assign(weight=1.0)\n", + "\n", + "pathway_members = {\n", + " pathway: set(frame[\"genesymbol\"])\n", + " for pathway, frame in signalink.groupby(\"pathway\")\n", + "}\n", + "\n", + "def heldout_pathway_activity(results_df, contrast_name):\n", + " matrix = results_df[[\"stat\"]].T.rename(index={\"stat\": contrast_name})\n", + " activities, _ = dc.mt.ulm(data=matrix, net=pathway_net, tmin=5, verbose=False)\n", + " return activities.iloc[0].abs()\n", + "\n", + "\n", + "validation_pathways1 = heldout_pathway_activity(\n", + " validation_results_df1, \"Leukemic_spontaneous.vs.Normal\"\n", + ")\n", + "validation_pathways2 = heldout_pathway_activity(\n", + " validation_results_df2, \"Leukemic_SB.vs.Normal\"\n", + ")\n", + "\n", + "pd.Series(\n", + " {\n", + " \"human_annotation_genes\": signalink_human[\"genesymbol\"].nunique(),\n", + " \"mapped_mouse_genes\": signalink[\"genesymbol\"].nunique(),\n", + " \"mapped_pathways\": signalink[\"pathway\"].nunique(),\n", + " },\n", + " name=\"pathway_resource\",\n", + ")" ] }, { "cell_type": "markdown", - "id": "9152ec15", + "id": "6e9dc57b", "metadata": { "papermill": { - "duration": 0.005574, - "end_time": "2025-06-27T16:26:47.155112", + "duration": 0.006546, + "end_time": "2026-08-06T17:18:55.256428+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.149538", + "start_time": "2026-08-06T17:18:55.249882+00:00", "status": "completed" }, "tags": [] }, "source": [ - "### Contrast 1: Leukemic_spontaneous vs Normal" + "## Identifying target TFs per condition" ] }, { "cell_type": "code", - "execution_count": 33, - "id": "71f94374", + "execution_count": 31, + "id": "952fb95c", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:47.166548Z", - "iopub.status.busy": "2025-06-27T16:26:47.166329Z", - "iopub.status.idle": "2025-06-27T16:26:47.170610Z", - "shell.execute_reply": "2025-06-27T16:26:47.170379Z" + "iopub.execute_input": "2026-08-06T17:18:55.269700Z", + "iopub.status.busy": "2026-08-06T17:18:55.269591Z", + "iopub.status.idle": "2026-08-06T17:18:55.271095Z", + "shell.execute_reply": "2026-08-06T17:18:55.270761Z" }, "papermill": { - "duration": 0.010873, - "end_time": "2025-06-27T16:26:47.171316", + "duration": 0.008995, + "end_time": "2026-08-06T17:18:55.271583+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.160443", + "start_time": "2026-08-06T17:18:55.262588+00:00", "status": "completed" }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Pknox1-3.854072A0A979HLR9-4.223345
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" ], "text/plain": [ - " Leukemic_spontaneous.vs.Normal\n", - "Myc 5.927294\n", - "Nr3c1 -3.807611\n", - "Klf1 -3.827762\n", - "Pax2 -3.833372\n", - "Pknox1 -3.854072" + " Leukemic_spontaneous.vs.Normal\n", + "Myc 6.919333\n", + "B5B2N2 -3.745155\n", + "Ppard -3.772645\n", + "Pknox1 -3.841270\n", + "A0A979HLR9 -4.223345" ] }, - "execution_count": 33, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# As measurements, we take the estimated TFs, we will filter out TFs with p-val > 0.001\n", + "# As measurements, we take the estimated TFs, we will filter out TFs with p-val > 0.01\n", "significant_tfs1 = (\n", " tf_acts1[tf_pvals1 <= max_pval].T.dropna().sort_values(by=\"Leukemic_spontaneous.vs.Normal\", ascending=False)\n", ")\n", @@ -3331,20 +3351,20 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "id": "416634be", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:47.182677Z", - "iopub.status.busy": "2025-06-27T16:26:47.182554Z", - "iopub.status.idle": "2025-06-27T16:26:47.185810Z", - "shell.execute_reply": "2025-06-27T16:26:47.185569Z" + "iopub.execute_input": "2026-08-06T17:18:55.313016Z", + "iopub.status.busy": "2026-08-06T17:18:55.312902Z", + "iopub.status.idle": "2026-08-06T17:18:55.316113Z", + "shell.execute_reply": "2026-08-06T17:18:55.315712Z" }, "papermill": { - "duration": 0.009613, - "end_time": "2025-06-27T16:26:47.186414", + "duration": 0.010417, + "end_time": "2026-08-06T17:18:55.316726+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.176801", + "start_time": "2026-08-06T17:18:55.306309+00:00", "status": "completed" }, "tags": [] @@ -3353,23 +3373,16 @@ { "data": { "text/plain": [ - "{'Myc': 5.9272938726510445,\n", - " 'Nr3c1': -3.8076109957400055,\n", - " 'Pax2': -3.833372272814152,\n", - " 'Pknox1': -3.8540720496124936,\n", - " 'Spi1': -3.870956617433766,\n", - " 'Cebpb': -4.041315700038792,\n", - " 'Srf': -4.189456236895589,\n", - " 'Ets1': -4.531838015984738,\n", - " 'Stat6': -4.762013996738547,\n", - " 'Fli1': -5.337254315751466,\n", - " 'Sp3': -5.344906237393298,\n", - " 'Runx1': -5.966766274845339,\n", - " 'Gata1': -6.145405587377315,\n", - " 'Sp1': -6.180554655220554}" + "{'Myc': 6.919332774298009,\n", + " 'Ppard': -3.7726452616087784,\n", + " 'Pknox1': -3.841269863918438,\n", + " 'Foxo3': -4.4365678956436305,\n", + " 'Fli1': -4.488422107569552,\n", + " 'Sp3': -4.919151910048148,\n", + " 'Gata1': -4.936540966143217}" ] }, - "execution_count": 34, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -3387,10 +3400,10 @@ "id": "0108fd04", "metadata": { "papermill": { - "duration": 0.005361, - "end_time": "2025-06-27T16:26:47.197273", + "duration": 0.006281, + "end_time": "2026-08-06T17:18:55.328822+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.191912", + "start_time": "2026-08-06T17:18:55.322541+00:00", "status": "completed" }, "tags": [] @@ -3401,20 +3414,20 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "id": "06cb9872", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:47.208512Z", - "iopub.status.busy": "2025-06-27T16:26:47.208404Z", - "iopub.status.idle": "2025-06-27T16:26:47.212441Z", - "shell.execute_reply": "2025-06-27T16:26:47.212245Z" + "iopub.execute_input": "2026-08-06T17:18:55.340974Z", + "iopub.status.busy": "2026-08-06T17:18:55.340859Z", + "iopub.status.idle": "2026-08-06T17:18:55.344384Z", + "shell.execute_reply": "2026-08-06T17:18:55.344080Z" }, "papermill": { - "duration": 0.010442, - "end_time": "2025-06-27T16:26:47.213053", + "duration": 0.010212, + "end_time": "2026-08-06T17:18:55.344848+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.202611", + "start_time": "2026-08-06T17:18:55.334636+00:00", "status": "completed" }, "tags": [] @@ -3447,64 +3460,64 @@ " \n", " \n", " Myc\n", - " 5.456814\n", + " 6.785773\n", " \n", " \n", - " Phox2a\n", - " -4.102671\n", + " A0A6I8MWY6\n", + " -3.894287\n", " \n", " \n", - " Runx1\n", - " -5.481601\n", + " Foxo3\n", + " -3.979161\n", " \n", " \n", - " Klf1\n", - " -5.749658\n", + " A0A087WR05\n", + " -5.416913\n", " \n", " \n", " Pknox1\n", - " -5.823616\n", + " -6.016046\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Leukemic_SB.vs.Normal\n", - "Myc 5.456814\n", - "Phox2a -4.102671\n", - "Runx1 -5.481601\n", - "Klf1 -5.749658\n", - "Pknox1 -5.823616" + " Leukemic_SB.vs.Normal\n", + "Myc 6.785773\n", + "A0A6I8MWY6 -3.894287\n", + "Foxo3 -3.979161\n", + "A0A087WR05 -5.416913\n", + "Pknox1 -6.016046" ] }, - "execution_count": 35, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# As measurements, we take the estimated TFs, we will filter out TFs with p-val > 0.001\n", + "# As measurements, we take the estimated TFs, we will filter out TFs with p-val > 0.01\n", "significant_tfs2 = tf_acts2[tf_pvals2 <= max_pval].T.dropna().sort_values(by=\"Leukemic_SB.vs.Normal\", ascending=False)\n", "significant_tfs2.head()" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "id": "5b92adb0", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:47.224584Z", - "iopub.status.busy": "2025-06-27T16:26:47.224484Z", - "iopub.status.idle": "2025-06-27T16:26:47.227351Z", - "shell.execute_reply": "2025-06-27T16:26:47.227122Z" + "iopub.execute_input": "2026-08-06T17:18:55.357621Z", + "iopub.status.busy": "2026-08-06T17:18:55.357515Z", + "iopub.status.idle": "2026-08-06T17:18:55.360585Z", + "shell.execute_reply": "2026-08-06T17:18:55.360318Z" }, "papermill": { - "duration": 0.009245, - "end_time": "2025-06-27T16:26:47.227923", + "duration": 0.00981, + "end_time": "2026-08-06T17:18:55.361083+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.218678", + "start_time": "2026-08-06T17:18:55.351273+00:00", "status": "completed" }, "tags": [] @@ -3513,15 +3526,14 @@ { "data": { "text/plain": [ - "{'Myc': 5.4568137702067805,\n", - " 'Phox2a': -4.102671408127372,\n", - " 'Runx1': -5.481600720105362,\n", - " 'Pknox1': -5.823615535106708,\n", - " 'Fli1': -7.269774022313037,\n", - " 'Gata1': -8.17857460262512}" + "{'Myc': 6.785773233182686,\n", + " 'Foxo3': -3.9791614216864715,\n", + " 'Pknox1': -6.01604625567456,\n", + " 'Fli1': -6.153183562598498,\n", + " 'Gata1': -6.283017534496937}" ] }, - "execution_count": 36, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -3533,86 +3545,40 @@ ] }, { - "cell_type": "code", - "execution_count": 37, - "id": "fc108c48-c2b8-4c57-a202-e271dca017a4", + "cell_type": "markdown", + "id": "f42a2f65", "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:26:47.239358Z", - "iopub.status.busy": "2025-06-27T16:26:47.239263Z", - "iopub.status.idle": "2025-06-27T16:26:47.242510Z", - "shell.execute_reply": "2025-06-27T16:26:47.242279Z" - }, "papermill": { - "duration": 0.009694, - "end_time": "2025-06-27T16:26:47.243144", + "duration": 0.006136, + "end_time": "2026-08-06T17:18:55.373256+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.233450", + "start_time": "2026-08-06T17:18:55.367120+00:00", "status": "completed" }, "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Myc': np.float64(1.0),\n", - " 'Phox2a': np.float64(-1.0),\n", - " 'Runx1': np.float64(-1.0),\n", - " 'Pknox1': np.float64(-1.0),\n", - " 'Fli1': np.float64(-1.0),\n", - " 'Gata1': np.float64(-1.0)}" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "def balance_dicts_by_top_abs(dict1, dict2, discretize=False):\n", - " # Decide which is smaller and which is larger\n", - " if len(dict1) <= len(dict2):\n", - " small, large = dict1, dict2\n", - " else:\n", - " small, large = dict2, dict1\n", - "\n", - " n = len(small)\n", - " # Take the n keys from large with the largest abs(values)\n", - " top_large_items = sorted(large.items(), key=lambda kv: abs(kv[1]), reverse=True)[:n]\n", - "\n", - " # Prepare output dicts\n", - " small_balanced = dict(small) # already of size n\n", - " large_balanced = dict(top_large_items)\n", + "## Comparable condition evidence\n", "\n", - " if discretize:\n", - " small_balanced = {k: np.sign(v) for k, v in small_balanced.items()}\n", - " large_balanced = {k: np.sign(v) for k, v in large_balanced.items()}\n", - "\n", - " return small_balanced, large_balanced\n", - "\n", - "\n", - "# Discretize not needed, but simplifies multi-cond analysis since both conditions have the same cost\n", - "d_measurements1, d_measurements2 = balance_dicts_by_top_abs(measurements1, measurements2, discretize=True)\n", - "d_measurements1" + "The conditions contain different numbers of significant TFs. Truncating the larger dictionary loses information, and the previous helper also changed the order of the conditions when the first dictionary was larger. Instead, all TFs are retained and their absolute weights are normalized to sum to one within each condition. This makes the maximum inactive-output cost comparable without changing CORNETO's regularizer.\n" ] }, { "cell_type": "code", - "execution_count": 38, - "id": "75be182c-259d-456d-a96b-7a1abedc447c", + "execution_count": 36, + "id": "70a12a3d", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:47.255207Z", - "iopub.status.busy": "2025-06-27T16:26:47.255077Z", - "iopub.status.idle": "2025-06-27T16:26:47.257100Z", - "shell.execute_reply": "2025-06-27T16:26:47.256883Z" + "iopub.execute_input": "2026-08-06T17:18:55.385779Z", + "iopub.status.busy": "2026-08-06T17:18:55.385670Z", + "iopub.status.idle": "2026-08-06T17:18:55.389771Z", + "shell.execute_reply": "2026-08-06T17:18:55.389315Z" }, "papermill": { - "duration": 0.008938, - "end_time": "2025-06-27T16:26:47.257655", + "duration": 0.011306, + "end_time": "2026-08-06T17:18:55.390240+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.248717", + "start_time": "2026-08-06T17:18:55.378934+00:00", "status": "completed" }, "tags": [] @@ -3620,74 +3586,123 @@ "outputs": [ { "data": { + "text/html": [ + "
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conditionn_tfstotal_weight
0Leukemic_spontaneous71.0
1Leukemic_SB51.0
\n", + "
" + ], "text/plain": [ - "{'Sp1': np.float64(-1.0),\n", - " 'Gata1': np.float64(-1.0),\n", - " 'Runx1': np.float64(-1.0),\n", - " 'Myc': np.float64(1.0),\n", - " 'Sp3': np.float64(-1.0),\n", - " 'Fli1': np.float64(-1.0)}" + " condition n_tfs total_weight\n", + "0 Leukemic_spontaneous 7 1.0\n", + "1 Leukemic_SB 5 1.0" ] }, - "execution_count": 38, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "d_measurements2" - ] - }, - { - "cell_type": "markdown", - "id": "2e89212e", - "metadata": { - "papermill": { - "duration": 0.005472, - "end_time": "2025-06-27T16:26:47.268665", - "exception": false, - "start_time": "2025-06-27T16:26:47.263193", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Creating a CARNIVAL problem per condition" + "def normalized_signed_measurements(measurements):\n", + " clean = {k: float(v) for k, v in measurements.items() if np.isfinite(v) and v != 0}\n", + " total_weight = sum(abs(v) for v in clean.values())\n", + " if total_weight == 0:\n", + " raise ValueError(\"A condition has no non-zero TF measurements\")\n", + " return {k: v / total_weight for k, v in clean.items()}\n", + "\n", + "\n", + "d_measurements1 = normalized_signed_measurements(measurements1)\n", + "d_measurements2 = normalized_signed_measurements(measurements2)\n", + "\n", + "assert set(d_measurements1) == set(measurements1)\n", + "assert set(d_measurements2) == set(measurements2)\n", + "assert np.isclose(sum(abs(v) for v in d_measurements1.values()), 1)\n", + "assert np.isclose(sum(abs(v) for v in d_measurements2.values()), 1)\n", + "\n", + "measurement_summary = pd.DataFrame(\n", + " {\n", + " \"condition\": [\"Leukemic_spontaneous\", \"Leukemic_SB\"],\n", + " \"n_tfs\": [len(d_measurements1), len(d_measurements2)],\n", + " \"total_weight\": [\n", + " sum(abs(v) for v in d_measurements1.values()),\n", + " sum(abs(v) for v in d_measurements2.values()),\n", + " ],\n", + " }\n", + ")\n", + "measurement_summary\n" ] }, { "cell_type": "markdown", - "id": "7b0f2d23", + "id": "9adb38a4", "metadata": { "papermill": { - "duration": 0.005589, - "end_time": "2025-06-27T16:26:47.279979", + "duration": 0.005953, + "end_time": "2026-08-06T17:18:55.402460+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.274390", + "start_time": "2026-08-06T17:18:55.396507+00:00", "status": "completed" }, "tags": [] }, "source": [ - "### Contrast 1: Leukemic_spontaneous vs Normal" + "## Condition-specific receptor inputs\n", + "\n", + "Receptor expression is only a proxy for receptor activity, so candidates must show positive differential expression, pass the adjusted-$p$ threshold, and occur in the PKN. Input signs remain unknown (`0`), as CORNETO infers their direction.\n" ] }, { "cell_type": "code", - "execution_count": 39, - "id": "8302ae22", + "execution_count": 37, + "id": "41faf212", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:47.291800Z", - "iopub.status.busy": "2025-06-27T16:26:47.291562Z", - "iopub.status.idle": "2025-06-27T16:26:47.294624Z", - "shell.execute_reply": "2025-06-27T16:26:47.294407Z" + "iopub.execute_input": "2026-08-06T17:18:55.414843Z", + "iopub.status.busy": "2026-08-06T17:18:55.414730Z", + "iopub.status.idle": "2026-08-06T17:18:55.553524Z", + "shell.execute_reply": "2026-08-06T17:18:55.553171Z" }, "papermill": { - "duration": 0.009775, - "end_time": "2025-06-27T16:26:47.295237", + "duration": 0.145837, + "end_time": "2026-08-06T17:18:55.554040+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.285462", + "start_time": "2026-08-06T17:18:55.408203+00:00", "status": "completed" }, "tags": [] @@ -3695,51 +3710,124 @@ "outputs": [ { "data": { + "text/html": [ + "
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conditionn_receptorsn_reachable_tfsreachable_tf_weightreceptors
0Leukemic_spontaneous1461.0[Acvr2b, Bambi, Cdon, Flt3, Galr3, Ifngr1, Ifn...
1Leukemic_SB1241.0[Acvr2b, Cdon, Fgfr1, Flt3, Galr3, Ifngr1, Ifn...
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" + ], "text/plain": [ - "{'Ifngr1': 0,\n", - " 'Lrrc4c': 0,\n", - " 'Tnfrsf11a': 0,\n", - " 'Cdon': 0,\n", - " 'Notch1': 0,\n", - " 'Ptprs': 0,\n", - " 'Nlgn2': 0,\n", - " 'Flt3': 0,\n", - " 'Ephb4': 0,\n", - " 'Il2rg': 0,\n", - " 'Marco': 0,\n", - " 'Axl': 0,\n", - " 'Tlr4': 0,\n", - " 'Ifngr2': 0,\n", - " 'Znrf3': 0}" + " condition n_receptors n_reachable_tfs reachable_tf_weight \\\n", + "0 Leukemic_spontaneous 14 6 1.0 \n", + "1 Leukemic_SB 12 4 1.0 \n", + "\n", + " receptors \n", + "0 [Acvr2b, Bambi, Cdon, Flt3, Galr3, Ifngr1, Ifn... \n", + "1 [Acvr2b, Cdon, Fgfr1, Flt3, Galr3, Ifngr1, Ifn... " ] }, - "execution_count": 39, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# We will infer the direction, so for the inputs, we use a value of 0 (=unknown direction)\n", - "inputs1 = {k: 0 for k in df_top_receptors1.index.intersection(G.V).values}\n", - "inputs1" + "df_top_receptors1 = select_receptors(results_df1, unique_receptors, G)\n", + "df_top_receptors2 = select_receptors(results_df2, unique_receptors, G)\n", + "\n", + "inputs1 = {k: 0 for k in df_top_receptors1.index}\n", + "inputs2 = {k: 0 for k in df_top_receptors2.index}\n", + "\n", + "if not inputs1 or not inputs2:\n", + " raise ValueError(\"Receptor filtering left a condition without candidate inputs\")\n", + "\n", + "\n", + "def restrict_to_reachable_evidence(graph, inputs, measurements):\n", + " condition_graph = graph.prune(list(inputs), list(measurements))\n", + " retained_vertices = set(condition_graph.V)\n", + " reachable_inputs = {node: value for node, value in inputs.items() if node in retained_vertices}\n", + " reachable_measurements = {\n", + " node: value for node, value in measurements.items() if node in retained_vertices\n", + " }\n", + " if not reachable_inputs or not reachable_measurements:\n", + " raise ValueError(\"A condition has no receptor-to-TF path in the PKN\")\n", + " return reachable_inputs, normalized_signed_measurements(reachable_measurements)\n", + "\n", + "\n", + "inputs1, d_measurements1 = restrict_to_reachable_evidence(G, inputs1, d_measurements1)\n", + "inputs2, d_measurements2 = restrict_to_reachable_evidence(G, inputs2, d_measurements2)\n", + "\n", + "pd.DataFrame(\n", + " {\n", + " \"condition\": [\"Leukemic_spontaneous\", \"Leukemic_SB\"],\n", + " \"n_receptors\": [len(inputs1), len(inputs2)],\n", + " \"n_reachable_tfs\": [len(d_measurements1), len(d_measurements2)],\n", + " \"reachable_tf_weight\": [\n", + " sum(abs(value) for value in d_measurements1.values()),\n", + " sum(abs(value) for value in d_measurements2.values()),\n", + " ],\n", + " \"receptors\": [sorted(inputs1), sorted(inputs2)],\n", + " }\n", + ")\n" ] }, { "cell_type": "code", - "execution_count": 40, - "id": "300ae94a", + "execution_count": 38, + "id": "d1d78253", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:47.306756Z", - "iopub.status.busy": "2025-06-27T16:26:47.306657Z", - "iopub.status.idle": "2025-06-27T16:26:47.309038Z", - "shell.execute_reply": "2025-06-27T16:26:47.308826Z" + "iopub.execute_input": "2026-08-06T17:18:55.567323Z", + "iopub.status.busy": "2026-08-06T17:18:55.567214Z", + "iopub.status.idle": "2026-08-06T17:18:55.570077Z", + "shell.execute_reply": "2026-08-06T17:18:55.569734Z" }, "papermill": { - "duration": 0.008826, - "end_time": "2025-06-27T16:26:47.309605", + "duration": 0.010303, + "end_time": "2026-08-06T17:18:55.570703+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.300779", + "start_time": "2026-08-06T17:18:55.560400+00:00", "status": "completed" }, "tags": [] @@ -3748,3488 +3836,3660 @@ { "data": { "text/plain": [ - "Data(n_samples=1, n_feats=[21])" + "Data(n_samples=2, n_feats=[20 16])" ] }, - "execution_count": 40, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Create the dataset in standard format\n", - "carnival_data1 = dict()\n", - "for inp, v in inputs1.items():\n", - " carnival_data1[inp] = dict(value=v, role=\"input\", mapping=\"vertex\")\n", - "for out, v in d_measurements1.items():\n", - " carnival_data1[out] = dict(value=v, role=\"output\", mapping=\"vertex\")\n", - "data1 = cn.Data.from_cdict({\"sample1\": carnival_data1})\n", - "data1" + "def make_carnival_sample(inputs, measurements):\n", + " sample = {\n", + " node: dict(value=value, role=\"input\", mapping=\"vertex\")\n", + " for node, value in inputs.items()\n", + " }\n", + " sample.update(\n", + " {\n", + " node: dict(value=value, role=\"output\", mapping=\"vertex\")\n", + " for node, value in measurements.items()\n", + " }\n", + " )\n", + " return sample\n", + "\n", + "\n", + "carnival_data1 = make_carnival_sample(inputs1, d_measurements1)\n", + "carnival_data2 = make_carnival_sample(inputs2, d_measurements2)\n", + "\n", + "data1 = cn.Data.from_cdict({\"spontaneous\": carnival_data1})\n", + "data2 = cn.Data.from_cdict({\"SB\": carnival_data2})\n", + "data = cn.Data.from_cdict(\n", + " {\"spontaneous\": carnival_data1, \"SB\": carnival_data2}\n", + ")\n", + "data\n" ] }, { "cell_type": "markdown", - "id": "61f9a43f", + "id": "438174df", "metadata": { "papermill": { - "duration": 0.005616, - "end_time": "2025-06-27T16:26:47.320834", + "duration": 0.006353, + "end_time": "2026-08-06T17:18:55.583940+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.315218", + "start_time": "2026-08-06T17:18:55.577587+00:00", "status": "completed" }, "tags": [] }, "source": [ - "### Contrast 2: Leukemic_SB vs Normal" + "## CARNIVAL solution and validation helpers\n", + "\n", + "The same solver settings and diagnostics are used for the direct and multi-condition\n", + "models. Keeping these definitions shared makes their fits and complexities comparable." ] }, { "cell_type": "code", - "execution_count": 41, - "id": "f9f5e971", + "execution_count": 39, + "id": "4c1f873d", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:47.332306Z", - "iopub.status.busy": "2025-06-27T16:26:47.332211Z", - "iopub.status.idle": "2025-06-27T16:26:47.335040Z", - "shell.execute_reply": "2025-06-27T16:26:47.334820Z" + "iopub.execute_input": "2026-08-06T17:18:55.596774Z", + "iopub.status.busy": "2026-08-06T17:18:55.596664Z", + "iopub.status.idle": "2026-08-06T17:18:55.603591Z", + "shell.execute_reply": "2026-08-06T17:18:55.603191Z" }, "papermill": { - "duration": 0.009323, - "end_time": "2025-06-27T16:26:47.335639", + "duration": 0.01425, + "end_time": "2026-08-06T17:18:55.604063+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.326316", + "start_time": "2026-08-06T17:18:55.589813+00:00", "status": "completed" }, "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Ptprs': 0,\n", - " 'Nlgn2': 0,\n", - " 'Lrrc4c': 0,\n", - " 'Tnfrsf11a': 0,\n", - " 'Ifngr1': 0,\n", - " 'Marco': 0,\n", - " 'Ifngr2': 0,\n", - " 'Ctla4': 0,\n", - " 'Znrf3': 0,\n", - " 'Flt3': 0,\n", - " 'Ephb4': 0,\n", - " 'Il9r': 0,\n", - " 'Icam1': 0,\n", - " 'Axl': 0}" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# We will infer the direction, so for the inputs, we use a value of 0 (=unknown direction)\n", - "inputs2 = {k: 0 for k in df_top_receptors2.index.intersection(G.V).values}\n", - "inputs2" + "from corneto.methods import CarnivalFlow\n", + "\n", + "\n", + "def as_matrix(values):\n", + " values = np.asarray(values)\n", + " return values[:, None] if values.ndim == 1 else values\n", + "\n", + "\n", + "def solve_carnival(graph, dataset, lambda_reg):\n", + " method = CarnivalFlow(lambda_reg=lambda_reg, indirect_rule_penalty=1)\n", + " problem = method.build_from_data(graph, dataset)\n", + " problem.solve(\n", + " solver=\"GUROBI\",\n", + " verbosity=0,\n", + " max_seconds=max_time,\n", + " mip_gap=0.001,\n", + " seed=seed,\n", + " Threads=1,\n", + " )\n", + " return {\n", + " \"method\": method,\n", + " \"problem\": problem,\n", + " \"vertices\": list(method.processed_graph.V),\n", + " \"vertex_value\": as_matrix(problem.expr.vertex_value.value),\n", + " \"edge_value\": as_matrix(problem.expr.edge_value.value),\n", + " \"objectives\": {\n", + " objective.name: float(np.asarray(objective.value).sum())\n", + " for objective in problem.objectives\n", + " },\n", + " }\n", + "\n", + "\n", + "def tf_fit_metrics(solution, condition_index, measurements):\n", + " positions = {node: i for i, node in enumerate(solution[\"vertices\"])}\n", + " reachable = {node: value for node, value in measurements.items() if node in positions}\n", + " total = sum(abs(value) for value in reachable.values())\n", + " if total == 0:\n", + " return dict(reachable_tfs=0, reachable_weight=0, tf_match=0, tf_inactive=1, tf_opposite=0)\n", + "\n", + " matched = inactive = opposite = 0.0\n", + " for node, expected in reachable.items():\n", + " weight = abs(expected)\n", + " observed = np.sign(solution[\"vertex_value\"][positions[node], condition_index])\n", + " if observed == np.sign(expected):\n", + " matched += weight\n", + " elif observed == 0:\n", + " inactive += weight\n", + " else:\n", + " opposite += weight\n", + "\n", + " return dict(\n", + " reachable_tfs=len(reachable),\n", + " reachable_weight=total,\n", + " tf_match=matched / total,\n", + " tf_inactive=inactive / total,\n", + " tf_opposite=opposite / total,\n", + " )\n", + "\n", + "\n", + "def hypergeometric_z(population, positives, draws, hits):\n", + " if population <= 1 or positives <= 0 or draws <= 0 or positives >= population:\n", + " return 0.0\n", + " rate = positives / population\n", + " variance = draws * rate * (1 - rate) * (population - draws) / (population - 1)\n", + " return (hits - draws * rate) / np.sqrt(variance) if variance > 0 else 0.0\n", + "\n", + "\n", + "def internal_validation_metrics(\n", + " solution,\n", + " condition_index,\n", + " inputs,\n", + " measurements,\n", + " results_df,\n", + " pathway_activity,\n", + " min_abs_log2fc=0.5,\n", + " max_padj=0.1,\n", + "):\n", + " excluded = set(inputs) | set(measurements)\n", + " values = pd.Series(\n", + " solution[\"vertex_value\"][:, condition_index], index=solution[\"vertices\"]\n", + " )\n", + " active = values.loc[values.abs() > 0.5].drop(index=excluded, errors=\"ignore\")\n", + "\n", + " background = results_df.index.intersection(solution[\"vertices\"]).difference(excluded)\n", + " evidence = results_df.loc[background, [\"log2FoldChange\", \"padj\"]].dropna()\n", + " background = evidence.index\n", + " active = active.loc[active.index.intersection(background)]\n", + " deregulated = evidence.index[\n", + " (evidence[\"log2FoldChange\"].abs() >= min_abs_log2fc)\n", + " & (evidence[\"padj\"] <= max_padj)\n", + " ]\n", + " hits = active.index.intersection(deregulated)\n", + "\n", + " population = len(background)\n", + " positives = len(deregulated)\n", + " draws = len(active)\n", + " n_hits = len(hits)\n", + " selected_rate = n_hits / draws if draws else 0.0\n", + " background_rate = positives / population if population else 0.0\n", + " fold_enrichment = selected_rate / background_rate if background_rate else 0.0\n", + "\n", + " network_pathway_profile = {}\n", + " annotated_active = set()\n", + " background_set = set(background)\n", + " active_set = set(active.index)\n", + " for pathway, members in pathway_members.items():\n", + " members = members & background_set\n", + " if len(members) < 5:\n", + " continue\n", + " overlap = active_set & members\n", + " annotated_active.update(overlap)\n", + " network_pathway_profile[pathway] = max(\n", + " 0.0,\n", + " hypergeometric_z(population, len(members), draws, len(overlap)),\n", + " )\n", + "\n", + " network_pathway_profile = pd.Series(network_pathway_profile, dtype=float)\n", + " common_pathways = network_pathway_profile.index.intersection(pathway_activity.index)\n", + " network_scores = network_pathway_profile.loc[common_pathways].to_numpy()\n", + " expression_scores = pathway_activity.loc[common_pathways].to_numpy()\n", + " denominator = np.linalg.norm(network_scores) * np.linalg.norm(expression_scores)\n", + " pathway_concordance = (\n", + " float(network_scores @ expression_scores / denominator) if denominator else 0.0\n", + " )\n", + "\n", + " return dict(\n", + " active_internal_nodes=draws,\n", + " deregulated_internal_nodes=n_hits,\n", + " node_deregulated_fraction=selected_rate,\n", + " background_deregulated_fraction=background_rate,\n", + " node_deregulation_enrichment=fold_enrichment,\n", + " node_deregulation_z=hypergeometric_z(\n", + " population, positives, draws, n_hits\n", + " ),\n", + " annotated_internal_nodes=len(annotated_active),\n", + " pathway_concordance=pathway_concordance,\n", + " )\n", + "\n", + "\n", + "def structural_metrics(solution, condition_index, inputs):\n", + " edge_values = solution[\"edge_value\"][:, condition_index]\n", + " vertex_values = solution[\"vertex_value\"][:, condition_index]\n", + " positions = {node: i for i, node in enumerate(solution[\"vertices\"])}\n", + " active_inputs = sum(\n", + " node in positions and abs(vertex_values[positions[node]]) > 0.5 for node in inputs\n", + " )\n", + " return dict(\n", + " active_edges=int(np.sum(np.abs(edge_values) > 0.5)),\n", + " active_nodes=int(np.sum(np.abs(vertex_values) > 0.5)),\n", + " active_inputs=int(active_inputs),\n", + " )\n" ] }, { - "cell_type": "code", - "execution_count": 42, - "id": "9e0a46c3", + "cell_type": "markdown", + "id": "a6d64c29", "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:26:47.348022Z", - "iopub.status.busy": "2025-06-27T16:26:47.347844Z", - "iopub.status.idle": "2025-06-27T16:26:47.350597Z", - "shell.execute_reply": "2025-06-27T16:26:47.350352Z" - }, "papermill": { - "duration": 0.009687, - "end_time": "2025-06-27T16:26:47.351192", + "duration": 0.005904, + "end_time": "2026-08-06T17:18:55.616115+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.341505", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Data(n_samples=1, n_feats=[20])" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create the dataset in standard format\n", - "carnival_data2 = dict()\n", - "for inp, v in inputs2.items():\n", - " carnival_data2[inp] = dict(value=v, role=\"input\", mapping=\"vertex\")\n", - "for out, v in d_measurements2.items():\n", - " carnival_data2[out] = dict(value=v, role=\"output\", mapping=\"vertex\")\n", - "data2 = cn.Data.from_cdict({\"sample2\": carnival_data2})\n", - "data2" - ] - }, - { - "cell_type": "markdown", - "id": "4a4ee6c6", - "metadata": { - "papermill": { - "duration": 0.005624, - "end_time": "2025-06-27T16:26:47.362636", - "exception": false, - "start_time": "2025-06-27T16:26:47.357012", + "start_time": "2026-08-06T17:18:55.610211+00:00", "status": "completed" }, "tags": [] }, "source": [ - "## Solving multi-condition CARNIVAL problem with CORNETO" + "## Direct differential network: spontaneous leukemia vs SB leukemia\n", + "\n", + "The two disease-versus-normal contrasts describe large shared leukemia effects. To ask\n", + "what differs between leukemias, we must fit the direct contrast from the counts. We use\n", + "`Leukemic_spontaneous - Leukemic_SB`, so positive values mean relatively higher in\n", + "spontaneous leukemia and negative values mean relatively higher in SB leukemia. They do\n", + "**not** mean absolute activation or inhibition relative to normal.\n", + "\n", + "The training samples define genes, receptor candidates, TF activities, and the CARNIVAL\n", + "network. The held-out leukemia samples are used only to select the differential-network\n", + "lambda. The full data are used once at the end to refit the displayed network." ] }, { "cell_type": "code", - "execution_count": 43, - "id": "d0775abd", + "execution_count": 40, + "id": "2d7bb319", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:47.375185Z", - "iopub.status.busy": "2025-06-27T16:26:47.374975Z", - "iopub.status.idle": "2025-06-27T16:26:47.377227Z", - "shell.execute_reply": "2025-06-27T16:26:47.377017Z" + "iopub.execute_input": "2026-08-06T17:18:55.629105Z", + "iopub.status.busy": "2026-08-06T17:18:55.628994Z", + "iopub.status.idle": "2026-08-06T17:19:00.105035Z", + "shell.execute_reply": "2026-08-06T17:19:00.104608Z" }, "papermill": { - "duration": 0.009499, - "end_time": "2025-06-27T16:26:47.377820", + "duration": 4.483296, + "end_time": "2026-08-06T17:19:00.105636+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.368321", + "start_time": "2026-08-06T17:18:55.622340+00:00", "status": "completed" }, "tags": [] }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting dispersions...\n", + "... done in 0.05 seconds.\n", + "\n", + "Fitting MAP dispersions...\n", + "... done in 0.05 seconds.\n", + "\n", + "Fitting LFCs...\n", + "... done in 0.04 seconds.\n", + "\n" + ] + }, { "data": { + "text/html": [ + "
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baseMeanlog2FoldChangelfcSEstatpvaluepadj
ID
Nov6726.372248-10.1710701.226824-8.2905691.127082e-161.509500e-12
Tuba1c6612.117489-1.1009400.182744-6.0244951.696380e-091.135981e-05
Zfp874a494.5737580.7365570.1334215.5205323.379756e-081.131627e-04
Nlrp1b152.2865062.7144490.4912725.5253433.288427e-081.131627e-04
AI5068161284.533981-1.3039510.244834-5.3258561.004785e-072.691416e-04
\n", + "
" + ], "text/plain": [ - "Data(n_samples=2, n_feats=[21 20])" + " baseMean log2FoldChange lfcSE stat pvalue \\\n", + "ID \n", + "Nov 6726.372248 -10.171070 1.226824 -8.290569 1.127082e-16 \n", + "Tuba1c 6612.117489 -1.100940 0.182744 -6.024495 1.696380e-09 \n", + "Zfp874a 494.573758 0.736557 0.133421 5.520532 3.379756e-08 \n", + "Nlrp1b 152.286506 2.714449 0.491272 5.525343 3.288427e-08 \n", + "AI506816 1284.533981 -1.303951 0.244834 -5.325856 1.004785e-07 \n", + "\n", + " padj \n", + "ID \n", + "Nov 1.509500e-12 \n", + "Tuba1c 1.135981e-05 \n", + "Zfp874a 1.131627e-04 \n", + "Nlrp1b 1.131627e-04 \n", + "AI506816 2.691416e-04 " ] }, - "execution_count": 43, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data = cn.Data.from_cdict({\"sample1\": carnival_data1, \"sample2\": carnival_data2})\n", - "data" + "def fit_direct_de(adata_subset, min_prop=0.8, refit_cooks=True):\n", + " direct = adata_subset.copy()\n", + " dc.pp.filter_by_expr(\n", + " direct,\n", + " group=\"condition\",\n", + " min_count=10,\n", + " min_total_count=15,\n", + " large_n=10,\n", + " min_prop=min_prop,\n", + " )\n", + " inference = DefaultInference()\n", + " dds = DeseqDataSet(\n", + " adata=direct,\n", + " design=\"~condition\",\n", + " refit_cooks=refit_cooks,\n", + " inference=inference,\n", + " quiet=True,\n", + " )\n", + " dds.deseq2()\n", + " stats = DeseqStats(\n", + " dds,\n", + " contrast=[\"condition\", \"Leukemic_spontaneous\", \"Leukemic_SB\"],\n", + " inference=inference,\n", + " quiet=True,\n", + " )\n", + " stats.summary()\n", + " return stats.results_df\n", + "\n", + "\n", + "direct_train_adata = adata[list(spontaneous_train) + list(sb_train)].copy()\n", + "direct_validation_adata = adata[\n", + " list(spontaneous_validation) + list(sb_validation)\n", + "].copy()\n", + "\n", + "direct_results = fit_direct_de(direct_train_adata)\n", + "direct_validation_results = fit_direct_de(\n", + " direct_validation_adata, min_prop=0.5, refit_cooks=False\n", + ")\n", + "direct_results.sort_values(\"padj\").head()" + ] + }, + { + "cell_type": "markdown", + "id": "1d30a976", + "metadata": { + "papermill": { + "duration": 0.006716, + "end_time": "2026-08-06T17:19:00.118904+00:00", + "exception": false, + "start_time": "2026-08-06T17:19:00.112188+00:00", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Gene-expression specificity\n", + "\n", + "The left panel shows why the direct contrast is needed: most disease-versus-normal fold\n", + "changes lie near the diagonal. Threshold-only categories are useful descriptions, but\n", + "failure to cross a threshold in one contrast is not evidence that two leukemias differ.\n", + "The right panel therefore uses the direct statistical test for specificity." ] }, { "cell_type": "code", - "execution_count": 44, - "id": "d2f0e5f0", + "execution_count": 41, + "id": "48a8e257", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:47.389675Z", - "iopub.status.busy": "2025-06-27T16:26:47.389575Z", - "iopub.status.idle": "2025-06-27T16:26:47.399564Z", - "shell.execute_reply": "2025-06-27T16:26:47.399358Z" + "iopub.execute_input": "2026-08-06T17:19:00.132202Z", + "iopub.status.busy": "2026-08-06T17:19:00.132080Z", + "iopub.status.idle": "2026-08-06T17:19:00.333687Z", + "shell.execute_reply": "2026-08-06T17:19:00.333347Z" }, "papermill": { - "duration": 0.016607, - "end_time": "2025-06-27T16:26:47.400134", + "duration": 0.209167, + "end_time": "2026-08-06T17:19:00.334316+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.383527", + "start_time": "2026-08-06T17:19:00.125149+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gurobipy successfully imported.\n", - "Gurobi environment started successfully.\n", - "Starting optimization of the test model...\n", - "Test optimization was successful.\n", - "Gurobi environment disposed.\n", - "Gurobi is correctly installed and working.\n" - ] + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { "data": { "text/plain": [ - "True" + "control-relative log2FC Pearson r 0.905318\n", + "spontaneous vs normal DE genes 5830.000000\n", + "SB vs normal DE genes 5229.000000\n", + "shared DE genes 4421.000000\n", + "shared DE genes with same sign (%) 99.638091\n", + "direct DE: spontaneous higher 14.000000\n", + "direct DE: SB higher 27.000000\n", + "Name: training_split, dtype: float64" ] }, - "execution_count": 44, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from corneto.utils import check_gurobi\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "gene_threshold = 0.5\n", + "gene_fdr = 0.05\n", + "common_genes = results_df1.index.intersection(results_df2.index)\n", + "control_comparison = pd.DataFrame(\n", + " {\n", + " \"spontaneous_log2fc\": results_df1.loc[common_genes, \"log2FoldChange\"],\n", + " \"spontaneous_padj\": results_df1.loc[common_genes, \"padj\"],\n", + " \"SB_log2fc\": results_df2.loc[common_genes, \"log2FoldChange\"],\n", + " \"SB_padj\": results_df2.loc[common_genes, \"padj\"],\n", + " }\n", + ").dropna()\n", + "\n", + "spontaneous_sig = (\n", + " (control_comparison[\"spontaneous_padj\"] <= gene_fdr)\n", + " & (control_comparison[\"spontaneous_log2fc\"].abs() >= gene_threshold)\n", + ")\n", + "sb_sig = (\n", + " (control_comparison[\"SB_padj\"] <= gene_fdr)\n", + " & (control_comparison[\"SB_log2fc\"].abs() >= gene_threshold)\n", + ")\n", + "same_sign = np.sign(control_comparison[\"spontaneous_log2fc\"]) == np.sign(\n", + " control_comparison[\"SB_log2fc\"]\n", + ")\n", + "control_comparison[\"category\"] = \"below threshold\"\n", + "control_comparison.loc[spontaneous_sig & ~sb_sig, \"category\"] = \"spontaneous only\"\n", + "control_comparison.loc[~spontaneous_sig & sb_sig, \"category\"] = \"SB only\"\n", + "control_comparison.loc[spontaneous_sig & sb_sig & same_sign, \"category\"] = \"shared, same sign\"\n", + "control_comparison.loc[spontaneous_sig & sb_sig & ~same_sign, \"category\"] = \"shared, opposite sign\"\n", + "\n", + "direct_gene_table = direct_results[[\"log2FoldChange\", \"padj\"]].dropna().copy()\n", + "direct_gene_table[\"specificity\"] = \"not direct-DE\"\n", + "direct_gene_sig = (\n", + " (direct_gene_table[\"padj\"] <= gene_fdr)\n", + " & (direct_gene_table[\"log2FoldChange\"].abs() >= gene_threshold)\n", + ")\n", + "direct_gene_table.loc[\n", + " direct_gene_sig & (direct_gene_table[\"log2FoldChange\"] > 0), \"specificity\"\n", + "] = \"spontaneous higher\"\n", + "direct_gene_table.loc[\n", + " direct_gene_sig & (direct_gene_table[\"log2FoldChange\"] < 0), \"specificity\"\n", + "] = \"SB higher\"\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4.5))\n", + "category_colors = {\n", + " \"below threshold\": \"#d0d0d0\",\n", + " \"shared, same sign\": \"#6f6f6f\",\n", + " \"shared, opposite sign\": \"#8e44ad\",\n", + " \"spontaneous only\": \"#d95f02\",\n", + " \"SB only\": \"#1b9e77\",\n", + "}\n", + "for category in category_colors:\n", + " frame = control_comparison.loc[control_comparison[\"category\"] == category]\n", + " axes[0].scatter(\n", + " frame[\"spontaneous_log2fc\"],\n", + " frame[\"SB_log2fc\"],\n", + " s=7,\n", + " alpha=0.45,\n", + " color=category_colors[category],\n", + " label=f\"{category} ({len(frame)})\",\n", + " )\n", + "limits = np.nanmax(\n", + " np.abs(control_comparison[[\"spontaneous_log2fc\", \"SB_log2fc\"]].to_numpy())\n", + ")\n", + "axes[0].plot([-limits, limits], [-limits, limits], \"k--\", linewidth=1)\n", + "axes[0].set(\n", + " xlabel=\"Spontaneous vs normal log2FC\",\n", + " ylabel=\"SB vs normal log2FC\",\n", + " title=\"Control-relative effects\",\n", + ")\n", + "axes[0].legend(fontsize=7, frameon=False)\n", + "\n", + "volcano_colors = {\n", + " \"not direct-DE\": \"#d0d0d0\",\n", + " \"spontaneous higher\": \"#d95f02\",\n", + " \"SB higher\": \"#1b9e77\",\n", + "}\n", + "volcano_y = -np.log10(direct_gene_table[\"padj\"].clip(lower=1e-300))\n", + "for category in volcano_colors:\n", + " mask = direct_gene_table[\"specificity\"] == category\n", + " axes[1].scatter(\n", + " direct_gene_table.loc[mask, \"log2FoldChange\"],\n", + " volcano_y.loc[mask],\n", + " s=8,\n", + " alpha=0.55,\n", + " color=volcano_colors[category],\n", + " label=f\"{category} ({mask.sum()})\",\n", + " )\n", + "axes[1].axvline(-gene_threshold, color=\"black\", linestyle=\"--\", linewidth=0.8)\n", + "axes[1].axvline(gene_threshold, color=\"black\", linestyle=\"--\", linewidth=0.8)\n", + "axes[1].axhline(-np.log10(gene_fdr), color=\"black\", linestyle=\"--\", linewidth=0.8)\n", + "axes[1].set(\n", + " xlabel=\"Spontaneous vs SB log2FC\",\n", + " ylabel=\"-log10 adjusted p-value\",\n", + " title=\"Direct leukemia contrast\",\n", + ")\n", + "axes[1].legend(fontsize=8, frameon=False)\n", + "plt.tight_layout()\n", + "plt.show()\n", "\n", - "check_gurobi()" + "shared_de = spontaneous_sig & sb_sig\n", + "gene_specificity_metrics = pd.Series(\n", + " {\n", + " \"control-relative log2FC Pearson r\": control_comparison[\n", + " [\"spontaneous_log2fc\", \"SB_log2fc\"]\n", + " ].corr().iloc[0, 1],\n", + " \"spontaneous vs normal DE genes\": int(spontaneous_sig.sum()),\n", + " \"SB vs normal DE genes\": int(sb_sig.sum()),\n", + " \"shared DE genes\": int(shared_de.sum()),\n", + " \"shared DE genes with same sign (%)\": 100\n", + " * float(same_sign.loc[shared_de].mean()),\n", + " \"direct DE: spontaneous higher\": int(\n", + " (direct_gene_table[\"specificity\"] == \"spontaneous higher\").sum()\n", + " ),\n", + " \"direct DE: SB higher\": int(\n", + " (direct_gene_table[\"specificity\"] == \"SB higher\").sum()\n", + " ),\n", + " },\n", + " name=\"training_split\",\n", + ")\n", + "gene_specificity_metrics" ] }, { - "cell_type": "code", - "execution_count": 45, - "id": "661e0d70-b917-4d9a-aa97-75482fe34c8d", + "cell_type": "markdown", + "id": "bfd42d61", "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:26:47.412352Z", - "iopub.status.busy": "2025-06-27T16:26:47.412244Z", - "iopub.status.idle": "2025-06-27T16:26:47.664552Z", - "shell.execute_reply": "2025-06-27T16:26:47.664284Z" - }, "papermill": { - "duration": 0.25918, - "end_time": "2025-06-27T16:26:47.665249", + "duration": 0.006641, + "end_time": "2026-08-06T17:19:00.347856+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.406069", + "start_time": "2026-08-06T17:19:00.341215+00:00", "status": "completed" }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Inputs: (7/30), Outputs: (5/6)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Inputs: (4/30), Outputs: (6/6)\n" - ] - } - ], "source": [ - "def subgraph(G, inp, out):\n", - " inp_s = set(G.V).intersection(inp)\n", - " out_s = set(G.V).intersection(out)\n", - " Gs = G.prune(inp_s, out_s)\n", - " tot_inp = set(Gs.V).intersection(inp)\n", - " tot_out = set(Gs.V).intersection(out)\n", - " print(f\"Inputs: ({len(tot_inp)}/{len(inp)}), Outputs: ({len(tot_out)}/{len(out)})\")\n", - " return Gs\n", - "\n", - "\n", - "Gs1 = subgraph(G, df_top_receptors1.index.tolist(), list(d_measurements1.keys()))\n", - "Gs2 = subgraph(G, df_top_receptors2.index.tolist(), list(d_measurements2.keys()))" + "### Receptor-expression and TF-activity specificity\n", + "\n", + "Receptor expression is used only to nominate possible differential inputs; CARNIVAL is\n", + "left to infer their signs. TF activities are inferred directly from the spontaneous-vs-SB\n", + "gene-level statistic. The bars below retain direction: orange is relatively higher in\n", + "spontaneous leukemia and green is relatively higher in SB leukemia." ] }, { "cell_type": "code", - "execution_count": 46, - "id": "2d12fdab-2924-4a42-b4b0-70a15081f026", + "execution_count": 42, + "id": "5971453e", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:47.677853Z", - "iopub.status.busy": "2025-06-27T16:26:47.677739Z", - "iopub.status.idle": "2025-06-27T16:26:48.189740Z", - "shell.execute_reply": "2025-06-27T16:26:48.189437Z" + "iopub.execute_input": "2026-08-06T17:19:00.362159Z", + "iopub.status.busy": "2026-08-06T17:19:00.362055Z", + "iopub.status.idle": "2026-08-06T17:19:00.477923Z", + "shell.execute_reply": "2026-08-06T17:19:00.477630Z" }, "papermill": { - "duration": 0.519222, - "end_time": "2025-06-27T16:26:48.190570", + "duration": 0.124336, + "end_time": "2026-08-06T17:19:00.478631+00:00", "exception": false, - "start_time": "2025-06-27T16:26:47.671348", + "start_time": "2026-08-06T17:19:00.354295+00:00", "status": "completed" }, "tags": [] }, "outputs": [ + { + "data": { + "image/png": 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YDCOLtokhVfGhHsN9ZlVEuqFtET+4o+1jCOLJJ59c53Ljy66q+NEd7Vh12Nyc7p8DDjggd2+OpEIkeiIxF0PwInlTdbjYKaecki/DHMFl1BmIK9/EsK/yukIMt6q5vggAqq4vtr3m66rttc5MJBBqdtmv7zFdrvM0syKt5dcTw+1qLiuGtUWAXh461tB9VZuYp7Z9WH7P12d/1yZeawRoM2vbGPIWw1djmGEMp4jgJt5H8f6q7f0ZbR8/HKqK90fV91xsb9WkY0P2cfm11tWeVUWNukjsxvF6++235wRSbHskDuvzuRLrisAuks8193MkpUL5uIm6ZzFcMtYRgWhsSyRXY1goQFPVkBh0TmKR+L6KdVQdBjWn5uS7qKHE1XMu9n3UK606RR3HmYmhnRHDRAImajzG8M+IA8onmMuxQH1iv4aKuCRi6CgbUB7KH2Up4n0QCaPGPLZeffXVGd5DkXCNeLT8HorSAhGnR1wTSbl4rZHMqq1eLpSpKUW9xfj3yPY35qXM47L08aMqaq3UJj7oqqrtrEIsI86KRa+f2jRmEq02kYiK8eNRpLnq9tUsuNwQ8QP3ggsuyEma+JEdH+xRI2hWGtIWsS8jKbXYYovlH/PRS+rJJ5/MyZSaIpkSSbey2J4Ismr+OJ/T/RPFwGOK4DPqRkQdnRgrH2eYevbsmetCxJj5mOLLsdxrKnrMRA+pWFf5h3vUHqhNObCIba+tyGht99Wlrtdbn2O6XEsoxuZHke7alF9PJCvqumJKzUC3vvuqNjFP9CarKc62Vd2esvqe5YvXGsdbbFtdiazohRXriWRUuXdUmJNaSfF6aitMXp99XG6vutqz5mdAfC5Gof6qataeqku8ByNpF0FlXb3J4jOmHHxGbbWYom2ihkPUdojAc/To0c326o9A09aQGHROYpH4vnriiSdyj+TGSkzNyXdRQ4mr541IwkTsGifSIl4te/nll6vNVzX2a0wRO1988cWVGqpxEZmoiTarOlgNPbaiF1nV+lg1Hy+LOqgxxW+hOIkWtdSiZ2DUKo2i9VCTpBT1EmcAovdJFBGOXjyNJRIO8QEagUb0MpjdZdx///25aGX8uGsMVXtPzeoywhH8RCKnahAUgcacXCUkfrjvueeeubB0FHiMpEsUQG7MtoiET5zJeeihh3KyJ85sRG+p2q7iFcXp119//crt6LkVP87rExzOzv6J7YleIPHaoydUFFKs+iVfTsbEsLIYyhRDEKMAYyScoth23Hf++efPdB3R0yq+tCO5VU7sxHEYycUijum4ylwEC5HIqOsLOoZYxlDAKIJ99NFH12v99dlXcXzX1oMnetxE4BBFYmMoWs0rP0abzY7Yl7HceK1R5Lw25fdPzd6AVa+01FCxvfH6Yz9XHTZRn2L25faqqz1rbnvN7Y6zidFrsmrCsa5emZFIim2Nrv/xo6m2xHBt4tiIXnRxZjaCzyjWG8MvAZqSxohB6xuLxPdV9OiIniYzG/pU1/doY38XNZS4et6oLYaJ3kO//e1vq80XhfLjOI4h/3vvvXedJ/ca8jskxNX7IuaMIXwRf0YyKBJVs9KQ9cSxFbF1JFnLJ8rqs/w40RjxSgwpjDhHUoraSEoxg7j6Q3mc8CeffJITF/EhFz+go2toOcvfGOIDOX70xRCVY489NtdLia6gcQYhxuBHlj3qrMxM/MiNIV7xQR9XqorERPQuih9oEYDEB39Du8nGZVxDDJGKACVee10/FsuXHo7hYfEDMXorxFXAIrFUHlY2O6I9ykmNmlcQnNO2iHkiSRBX3ihfvS1uR9AXP8Zrtnm8vuihEVcgK1/Rbe21187DFhtrm+JKJPGFGImYaLtI7MU2xZd3eXx6tEe0d+yLCCqjXk9cjS++4Mo9RCKJEfsseo5E0ip6ucQQt5g3ki1R/yvEVXkiQIwr6MSVQ+L50c26Zo2muXVMx9mi6OESx0oEA1FLK15rJKCiC3T0BosaZdFLKmpKxWuI4yuGNUb370i8xf81e+fUZ1/F8R2Js0jAxWV6Yxhc3Hf88cfnBFTUXYv9FonAODsdydHovVezRkd9RZf26AUUw/IiCRj7MAKVCE6i3eOKi3F8xD6NHoGRMI02i3aM1zm7otv4ZZddlv+Ps+ORsIxjLgKj+gR4UWctEp6xLXHlpPhsLF9psKp4PbEfY7sj+Iq6ZdF+EbRVTWBFL7lo00hYx/suhp/GmcU4FiIZvOmmm+a2iraO+6KnVdRJiyEn5XpekaCObvAxnCA+i2O4a2xjLHdmVz8EaM4xaH1jkfgujvXFd1F8lkdCKXpNxdDy+F6I7/gQ35nRszc+nyNmic/3uoZ4zcl3UUOJq+eNiLniN0IcP3Hl5Ti2Ij77/PPPq80XcV1cYTdKckRcEbFvnBiN7/qId+JK2w39HVIWSdRI2kav8zjOZzXksKHriZNfUaIgTmJHvBjzxXsjksZxgjuuhBxxesTUEfNGnBPvqejVHTFO1TqhMINqZc9p1spXPilPbdq0KS299NL5Kl3nn39+6ZNPPpnhOXN6lZAQV6eIKzXE1cTiKlNxdZS4AlpcKeWtt96qdhWIHXbYodZtj6taHXPMMaXllluu1Lp163zFsvXXXz9f7eurr76a5RU3al4Ra8qUKaXDDjustNRSS+WrjlW9GlttV/q44IILSr169Sq1bds2XxkmrtpS11VhZnX1vapimVWvNFMfs2qLsWPH5v26xRZbVK5EF+IqIHGVt7gCXvm1ll/DCy+8kB+LfdOhQ4fSPvvsU/r4449neG1zsn/iyoL9+/fPV9eJYy+uGrLXXnuVXn311WpXh9tggw1Kiy22WG7r5ZdfPl8d7bPPPqu2vrhSWzw3Xmesr0uXLvn1Xn311dXme/LJJ0sbb7xxXlbMc/LJJ5d+85vf1Pvqe7Ud5w05pkNcmWXDDTeszBdXlql5PMQV6KJto93i9Sy77LL59m233VaZpyH76v333y9ts802+fF4Tuy7srgy0b777ltaYokl8rrianbxnql6rMzq6jW1iedfdtll+QqKsX87depU6tOnT+nee++tzPPUU0/l+xZddNH83ov34IsvvjjDe6Sutq/tPTdmzJh89Zlye8TfsZ76XPEoPgdOPPHEfBzF/olj5emnn57hfRzznXTSSXm/xHzrrbde6a677srzVG3b8Pe//z3v4zjmal7FL9p14MCBeTnR9tEGffv2LZ177rmVeS699NJ835JLLpnbsUePHvmKRLFPAZp7DDqnsUj4+uuvS6effnpppZVWytsR34cRQ8R3R9nLL7+cr8gW31exDbVd0baxvovE1fWPqxvj6nt1xXazEvFMOe6L7/GIKf/617/OcMW7EFeqi2Mm1hXH0Oqrr1668MILZ/t3SIir98ZVpeu6emRtV99r6HrifXLaaafl2LAcy8VVzSMWL1/N+r777ittt912uQ3K7+O4OvLjjz8+W+1K89Ai/pkxVQXMazH8J3q4RO+dcpHuop155pm5x070yKlZT4j5i30FAAAsaAzfg/nMO++8k4fkxNCu6BIeQ9AAAACgqWm8650CjSJq08TY9K+++irXP3I1LQAAAJoiw/cAAAAAKJyeUgAAAAAUTlIKAAAAgMJJSgEAAABQOFffq2H69Olp7NixqUOHDqlFixbF7xEAoF5KpVL68ssvU9euXdNCCznP1tSIyQCg6cdlklI1REKqe/fujb1/AIC5ZPTo0albt27at4kRkwFA04/LJKVqiB5S5Ybs2LFj4+4dAKDRfPjhh2n11VevfHfTtIjJAKDpx2WSUjWUh+xFQkpSCgDmX5MmTcr/G27fNInJAKDpx2UKMAAAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcJJSAAAAABROUgoAAACAwrUqfpUAzVe34YPn9SZAkzFtwqR5vQkACwTxBzC/xmV6SgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcJJSAADzsdGjR6dDDz00de3aNbVp0yb17NkzHXvssWn8+PGVeb766qt09NFHp27duqVFFlkkrbbaamnYsGHVltOrV6/UokWLPLVs2TIvL5b7+eefV+YZMWJEZZ6YllhiibTFFlukJ598stqyzjzzzGrzlae///3v+fE33ngj7b777pV1Xn755XO9nQCABY+kFADAfOrdd99NG2ywQfr3v/+dbrrppvT222+nq6++Oj388MOpT58+acKECXm+448/Pj3wwAPpj3/8Y3rzzTfz7Z/85Cfp7rvvrra8s88+O40bNy598MEH6cYbb0yPPfZYOuaYY2ZY76hRo/J8kaRaaqml0g477JA++eSTavOsscYaeZ6q02abbZYf++9//5uWX375dMEFF6QuXbrM1TYCABZcklIAAPOpo446KveOeuihh9Lmm2+eevTokbbbbrvcI+nDDz9MP/vZz/J8Tz/9dDrooINSv379cu+kH/3oR2nttddOzz//fLXldejQISeJll122dS/f/904IEHphdffHGG9S699NJ5vjXXXDOddtppaeLEienZZ5+tNk+rVq3yPFWn2Naw4YYbposvvjjtvffeqW3btnO1jQCABZekFADAfCh6QT344INp0KBBeUheVZEA2m+//dItt9ySSqVS2nTTTdM999yTE1Vx+5FHHsm9qwYMGFDn8mPe++67L/Xu3bvOeaLH0/Dhw/PfrVu3TnPTlClT0qRJk6pNAEDTJikFADAfeuutt3KCKepD1Sbuj3pQn376afrVr36VVl999VxTKnorbbvttumqq67KyaqqTjnllNS+ffuc5Ip5o97TL37xixmWHY/FfDFddtllaf31109bbrlltXlee+21yjwxbbTRRnP0eocOHZo6depUmbp37z5HywMA5n+SUgAAC6BIWIVIQkVS6plnnsm9pV544YV06aWX5h5W5cLjZSeffHJ6+eWX06uvvprrUoWoF/Xdd99Vm+/xxx/Pw/qijlUUVr/uuutm6Cm1yiqr5GWVp9tvv32OXs+QIUPyMMHyFAXeAYCmrdW83gAAAGa04oor5p5M//znP9Ouu+46w+P/+te/chHyqNl06qmnpjvvvDMnmMJaa62VE0WXXHJJ2mqrrSrPWXLJJfNyw0orrZSvihcF02O4X9X5lltuudS5c+e08sorp2+++Sbttttu6fXXX69WHyqSYeVlNYZYtvpTANC86CkFADAfWmKJJdLWW2+dh+F9/fXX1R776KOP8tXzDj744DR16tQ8LbRQ9bCuZcuWafr06TNdR8wTai6/qgMOOCAvJ7YDAKAxSUoBAMynfv3rX+cC4FGw/LHHHstD2h544IGcrIpeTKeffnrq2LFjvjJfDM0bMWJEeu+99/Jwuz/84Q+5h1NVX375ZU5ojRs3Lo0cOTI/J3pP9e3bt85tiGTXcccdly644IJc+Lw+vv3228qwvvg7iqrH32+//fYctwkA0HRISgEAzKdiiN1zzz2Xll9++bTXXnvl+k7bbbddTkg9+eSTucB4uPnmm9OGG26Yr8gXBc8jgXTeeeelI488stryIom1zDLLpK5du6Ydd9wxtWvXLv3tb3/LvbJmZuDAgbk3ViTJ6mPs2LFp3XXXzVMkwGIYYfx92GGHzUFrAABNTYtSuUomWVx+OK74EgU248wjQGPqNnywBoVGMm3CpPTxScOa3Xf2GWecka+Y99BDD+V6UE2VmAwaj/gDmF/jMoXOAQAWIGeddVbq1atXevbZZ1Pv3r1nqCUFALCgkJQCAFjAHHLIIfN6EwAA5phTawAAAAAUTlIKAAAAgMJJSgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCtSp+lQAAABRlzCEXaGxgrhozZkzqftKwBj9PTykAAAAACicpBQAAAEDhJKUAAAAAKJykFAAAAACFk5QCAAAAoHCSUgAAAAAUTlIKAAAAgMJJSgEAAABQuFZpPjRixIjUv3//9Pnnn6fOnTvXOs91112XjjvuuPTFF18Uvn0AAADzo27DB8/rTQCaoWkTJi1YPaUOPvjg1KJFizy1bt06Lb/88umkk05KkydPnq3l3XHHHWnrrbdOSy21VOrYsWPq06dPevDBBxt9uwEAAABYwIfvbbvttmncuHHp3XffTeeee2666qqrcmJqdjz22GM5KXX//fenF154Ife02mmnndJLL73U6NsNAAAAwAKclGrbtm3q0qVL6t69e9p3333Tfvvtl+666646h+v16NEjLbroomm33XZL48ePr/b45Zdfnn7605+mDTfcMK200krp/PPPz//fe++9Bb0aAAAAABbIQueLLLJImjp16gz3P/vss2ngwIFp0KBB6eWXX869oKJn1cxMnz49ffnll2nxxRefi1sMAAAAwAJd6HzkyJHpT3/6U9pyyy1neOyXv/xlGjBgQBo8+P8X7Vt55ZXTU089lR544IE6l3fppZfm+lR77bXXTNc7ZcqUPJVNmjR7xbkAAAAAWEB6St13332pffv2aeGFF86FyTfbbLN0xRVXzDDfm2++mR+vqubtqm666aZ05plnpltuuSUtvfTSM92GoUOHpk6dOlWmGEoIAAAAQBNOSsUwvBiON2rUqPTNN9/kK+jVlkQqlUr1XmYkog499NB06623pq222mqW8w8ZMiRNnDixMo0ePbrBrwMAAACABWj4Xrt27dKKK644y/lWX3319Mwzz1S7r+btcg+pqD0V/++www71LrYeEwAAAADNsKbUzBxzzDGpb9++6aKLLkq77rpreuihh2aoJxWJqAMPPDDXn9p4443TRx99VCmeHsPyAAAAAJh/zFdX36tLJJl+97vf5XpT66yzTk5KnXbaadXmueaaa9K0adPSUUcdlZZZZpnKdOyxx86z7QYAAACgdi1KDSnY1AzE1feiZ1XUl+rYseO83hygiek2/P9fRRSYc9MmTEofnzTMd3YTJSaD2SPWABakuGyB6CkFAAAAQNMiKQUAAABA4SSlAACYbf369UvHHXfcLOe766678lWXW7ZsWa/5AYCmT1IKAIBqDj744NSiRYt05JFHztAygwYNyo/FPOGOO+5I55xzTuXxXr16pcsvv3yG5x1xxBFpjz32SKNHj642PwDQfElKAQAwg+7du6ebb745ff3115X7vvnmm3TTTTelHj16VO5bfPHFU4cOHWbagl999VX65JNP0oABA1LXrl1nOT8A0DxISgEAMIP11lsvJ5+iJ1RZ/B3JqnXXXbfW4Xvx93/+8590/PHH595UMY0YMaKShNpiiy0q9wEASEoBAFCrQw45JA0fPrxy+/e//30aOHBgna0VSatu3bqls88+O40bNy5Pffv2TaNGjcqP33777ZX7apoyZUqaNGlStQkAaNokpQAAqNUBBxyQnnjiifT+++/nHlBPPvlk2n///etsrRjKF4XMo2dUly5d8tSmTZu09NJLVx4v31fT0KFDU6dOnSpT9MgCAJq2VvN6AwAAmD8tueSSaYcddkjXX399KpVK+e+4b24YMmRIOuGEEyq3o6eUxBQANG2SUgAA1CmG6x199NH57yuvvHKutVTbtm3zBAA0H5JSAADUadttt03ffvtt/juunjcrMTTvu+++06IAwCypKQUAQJ2iRtSbb76Zp/h7Vnr16pUee+yx9OGHH6bPPvtMywIAdZKUAgBgpjp27Jin+ogr70Vh9BVWWCEttdRSWhYAqJPhewAAVHPdddfNtEXuuuuuyt8jRoyo9tjGG2+cXnnllWr3de7cORdKBwCoSk8pAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIVrVfwqAQAAmBvGHHKBhgUKN2bMmNT9pGENfp6eUgAAAAAUTlIKAAAAgMJJSgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFK5V8asEAACgsXUbPlijAvPEtAmTZut5ekoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFC4VsWvEqD5GnPIBfN6E6DJGDNmTOp+0rB5vRkAAMwmPaUAAAAAKJykFAAAAACFk5QCAKBOLVq0SHfddddMW+hf//pX2njjjdPCCy+c1llnHa0JANSLpBQAQBPwySefpCOOOCL16NEjtW3bNnXp0iUNGDAgPf3003N93WeccUZq165dGjVqVHr44Yfzfeedd17q27dvWnTRRVPnzp3n+jYAAAsehc4BAJqA3XffPU2dOjVdf/31afnll08ff/xxThBNmDCh1vlj3tatWzfKut955520ww47pJ49e1bu+/bbb9Oee+6Z+vTpk6699tpGWQ8A0LToKQUAsID74osv0hNPPJEuvPDC1L9//5wc2mijjdKQIUNysqg8DO/qq69Ou+yyS+7VdO655+b777333rT++uvnoXeRzDrrrLPStGnTqi1/3LhxabvttkuLLLJIWm655dJtt91WeSyW+8ILL6Szzz47/33mmWfm+2M5xx9/fFpzzTULbQsAYMEhKQUAsIBr3759nqL205QpU2Y6zC6SUq+99loaOHBgevDBB9P++++fjjnmmPTPf/4zXXPNNem6667LQ++q+vnPf557Yr3yyit5/n322Se9+eablYTVGmuskU488cT890knnTRbryG2e9KkSdUmAKBpk5QCAFjAtWrVKieTYuhe1G/aZJNN0qmnnppeffXVavPtu+++ORkVPaKiN1UknwYPHpwOOuigfN/WW2+dzjnnnJycqiqG4R122GFp5ZVXzo9vsMEG6YorrsiPRe2qWH8kxeLv+H92DB06NHXq1Kkyde/efQ5aBABYEEhKAQA0AdGTaezYsemee+7JBc5HjBiR1ltvvZysKotkUlXlYXflnlYxHX744bnH03//+9/KfFEXqqq4Xe4p1VhiqOHEiRMr0+jRoxt1+QDA/EehcwCAJiLqQkVvp5hOP/303LsphuwdfPDB+fGoJVXV9OnTc+2nH/zgB7Uua2aiflRjiisGxgQANB96SgEANFGrr756mjx5cp2PR0+qUaNGpRVXXHGGaaGF/i9MfOaZZ6o9L26vuuqqc3XbAYCmT08pAIAF3Pjx43Pdp6gXtdZaa6UOHTqk559/Pl100UW5sHldojfVjjvumOs3xfMjERV1qKIQevnqfCGuthdD/zbddNN04403ppEjR6Zrr712ptv0wQcfpAkTJuT/v/vuu/Tyyy/n+yPhNbt1pwCApkVSCgBgARdJnt69e6fLLrssvfPOO2nq1Kk50RT1oaLgeV2i9tR9992X60pFAqt169a5B1QM+6sqhvjdfPPNadCgQbmYeSSmohfWzETCKwqvl6277rr5/0ceeST169dvjl8zALDga1EqlUrzeiPmJ3H54bjiSxTY7Nix47zeHACgDmPGjMmJF9/ZTZOYDBqu2/DBmg2YJ6ZNmJQ+PmlYg+MyNaUAAAAAKJykFAAAAACFk5QCAAAAoHCSUgAAAAAUTlIKAAAAgMJJSgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFK5V8asEmHPdhg/WjNDMTZswaV5vAsB8ZcwhF8zrTQCaqTFjxqTuJw1r8PP0lAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFA4SSkAAAAACicpBQAAAEDhJKUAAAAAKJykFAAAAACFk5QCAAAAoHCtil8lAAAAodvwwRoCWOBNmzBptp6npxQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFA4SSkAAGbq4IMPTrvuuqtWAgAalaQUAAAAAIWTlAIAoN569eqVLr/88mr3rbPOOunMM8+s3G7RokX63e9+l3bbbbe06KKLppVWWindc889WhkAqEZSCgCARnfWWWelvfbaK7366qtp++23T/vtt1+aMGGClgYAKiSlAACYK3Wo9tlnn7Tiiium888/P02ePDmNHDmyzvmnTJmSJk2aVG0CAJo2SSkAABrdWmutVfm7Xbt2qUOHDumTTz6pc/6hQ4emTp06Vabu3bvbKwDQxElKAQBQ/+BxoYVSqVSqdt/UqVNnmK9169bVbkedqenTp9e53CFDhqSJEydWptGjR9srANDEtZrXGwAAwIJjqaWWSuPGjavcjmF277333hwvt23btnkCAJoPPaUAAKi3LbbYIt1www3p8ccfT6+//no66KCDUsuWLbUgANBgekoBADBTMeyuVatWlWF27777btpxxx1z7adzzjmnUXpKAQDNj6QUAAAzFQXK4yp6oWPHjumWW26p9nj0lqqqZs2p8MUXX2hlAKAaw/cAAKjV559/nv7yl7+kESNGpK222korAQCNSk8pAABqNXDgwPTcc8+lE088Me2yyy5aCQBoVJJSAADU6s4779QyAMBcY/geAAAAAIWTlAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFA4SSkAAAAACicpBQAAAEDhJKUAAAAAKJykFAAAAACFk5QCAAAAoHCtZudJH330UTrvvPPSX/7yl/Thhx+mpZdeOq2zzjrpuOOOS1tuueUsnz9s2LA8vf/++/n2GmuskU4//fS03XbbpTkxbty4dOKJJ6YXXnghvfXWW+mYY45Jl19++RwtEwAAYG4Zc8gFGhdY4I0ZMyZ1P2nY3O8pFYmk9ddfP/3jH/9IF110UXrttdfSAw88kPr375+OOuqoei2jW7du6YILLkjPP/98nrbYYou0yy67pDfeeCPNjlKplKZNm5amTJmSllpqqfSzn/0srb322rO1LAAAAADmvgYnpQYNGpRatGiRRo4cmfbYY4+08sor555OJ5xwQnrmmWfSiBEjUps2bdLjjz9eec6ll16allxyydyTKey0005p++23z8+NKXpdtW/fPj+/LBJMP/3pT1P37t1T27Zt00orrZSuvfba/FisI7bhwQcfTBtssEF+PNbXq1ev9Mtf/jIdeOCBqVOnTo3TQgAAAADM2+F7EyZMyL2iIonUrl27GR7v3Llz6tevXx7Gd8ABB6RXXnkl96yKnks33XRTWmaZZWZ4znfffZduu+22NHny5NSnT5/K/ZFYevrpp9OvfvWr3OvpvffeS5999lm150bS6pJLLknLL798XjcAAAAATTAp9fbbb+ehcquuuupM5zv33HPT3//+9/SjH/0oD8mLBNVuu+1WbZ4Y9hdJqG+++Sb3krrzzjvT6quvnh/797//nW699db0t7/9LW211Vb5vkg81XT22WenrbfeOs2J6JEVU9mkSZPmaHkAAAAANPLwvUhIhRg6NzMxfO+Pf/xjuv3229PXX39da7HxVVZZJb388st5yN6Pf/zjdNBBB6V//vOf+bG4v2XLlmnzzTef6Xpi6N6cGjp0aB7qV55iuCAAAAAA81FSKuo6RULqzTffnOW8Tz31VGXIX0y1Ja5WXHHFnFiKxFAM0Yt6UGGRRRap1/bUNoSwoYYMGZImTpxYmUaPHj3HywQAAACgEZNSiy++eBowYEC68sorcw2omr744ov8/zvvvJOOP/749Nvf/jZtvPHGuT7U9OnTZ9kLqzyMbs0118zzP/roo2luiyLpHTt2rDYBAAAAMJ9dfe+qq67Kxck32mijPDzvrbfeyj2noiB51IiKx6KG1DbbbJMOOeSQNHz48PT666/nK/CVnXrqqflqeVEEPWpLRSH0uKLefvvtlx+Pq+jFcL6BAwemu+66Kxc5j8ejztSsxNC/mL766qv06aef5r/LwwIBAAAAWAALnYflllsuvfjii/kKfCeeeGIaN25cWmqppdL666+fhg0blu+PZNO9996b5+/SpUv63e9+l/baa69clHydddZJH3/8cU5cxXOjjtNaa62Vr+pXtWh5LCuSV4MGDUrjx49PPXr0yLdnZd111638/cILL6Q//elPqWfPnnmbAAAA5hfdhg+e15sA0CimTZi9i8a1KJWrl1O5+l4kyqK+lKF8MP8SxAER/Hx80jDf2U2UmIzmQDwDNPe4rMHD9wAAAABgTklKAQAAAFA4SSkAAAAACicpBQAAAEDhJKUAAAAAKJykFAAAAACFk5QCAAAAoHCSUgAAAAAUTlIKAAAAgMJJSgEAAABQOEkpAAAAAAonKQUAAABA4SSlAACYI++//35q0aJFevnll7UkAFBvklIAAM3cJ598ko444ojUo0eP1LZt29SlS5c0YMCA9PTTT9fr+d27d0/jxo1L3//+9yv3HXvssWn99dfPy1tnnXXm4tYDAAuqVvN6AwAAmLd23333NHXq1HT99den5ZdfPn388cfp4YcfThMmTKjX81u2bJkTWVWVSqU0cODA9Oyzz6ZXX311Lm05ALAgk5QCAGjGvvjii/TEE0+kESNGpM033zzf17Nnz7TRRhtV5omheVdddVW655578nyRgLrooovSnnvuWRm+t9xyy6WXXnqp0ivqV7/6Vf7/008/lZQCAGpl+B4AQDPWvn37PN11111pypQpdc7385//PPeoeuWVV9L++++f9tlnn/Tmm2822nbEuidNmlRtAgCaNkkpAIBmrFWrVum6667LQ/c6d+6cNtlkk3TqqafO0LspekUddthhaeWVV07nnHNO2mCDDdIVV1zRaNsxdOjQ1KlTp8oUdaoAgKZNUgoAoJmLHlBjx47Nw/OiwHkM0VtvvfVysqqsT58+1Z4Ttxuzp9SQIUPSxIkTK9Po0aMbbdkAwPxJUgoAgLTwwgunrbfeOp1++unpqaeeSgcffHA644wzZtoyUWuqscRV+jp27FhtAgCaNkkpAABmsPrqq6fJkydXbj/zzDPVHo/bq666qpYDAGabq+8BADRj48ePz/WiBg4cmNZaa63UoUOH9Pzzz+er6+2yyy6V+W677bZcR2rTTTdNN954Yxo5cmS69tpr61zu22+/nb766qv00Ucfpa+//jq9/PLLlWRXmzZtCnltAMD8TVIKAKAZiyvv9e7dO1122WXpnXfeSVOnTs1Fxg8//PBc8LzsrLPOSjfffHMaNGhQ6tKlS05MRYKpLlEU/dFHH63cXnfddfP/7733XurVq9dcflUAwIJAUgoAoBmLWk5x5buYZqZr167poYceqvWxSDKVSqVq90WxdACAmVFTCgAAAIDCSUoBAAAAUDjD9wAAmKmaQ/MAABqDnlIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcJJSAAAAABSuVfGrBAAAYMwhF2gEoEkYM2ZM6n7SsAY/T08pAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAK16r4VQLMuTGHXKAZoZkbM2ZM6n7SsHm9GUAz0W344Hm9CQDzrWkTJs3W8/SUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFA4SSkAAAAACicpBQDQzB188MGpRYsWM0zbbrvtbC/zvPPOS3379k2LLrpo6ty5c6NuLwDQNLSa1xsAAMC8Fwmo4cOHV7uvbdu2DV7Ot99+m9q0aZP/33PPPVOfPn3Stdde24hbCgA0FXpKAQCQE1BdunSpNi222GK5Zc4888zUo0ePPE/Xrl3TMcccU2mxXr16pXPPPTf3turUqVM6/PDD8/1nnXVWOv7449Oaa66pdQGAWukpBQBAnf785z+nyy67LN18881pjTXWSB999FF65ZVXqs1z8cUXp5///OfptNNO05IAQL1JSgEAkO67777Uvn37ai1xyimnpHbt2uVeU1tttVVq3bp17jG10UYbVZtviy22SCeddNIcteKUKVPyVDZp0iR7BQCaOEkpAABS//7907Bhw6q1xOKLL54mT56cLr/88rT88svnulPbb7992mmnnVKrVv8XRm6wwQZz3IJDhw7NQ/4AgOZDTSkAAHKPqBVXXLHaFEmp7t27p1GjRqUrr7wyLbLIImnQoEFps802S1OnTq323Dk1ZMiQNHHixMo0evRoewUAmjg9pQAAmKlIRu288855Ouqoo9Kqq66aXnvttbTeeus1WstFEfXZudofALDgkpQCACDXc4oi5tUCxVatcq2p7777LvXu3Tstuuii6YYbbshJqp49e8601T744IM0YcKE/H88/+WXX873Rw+smrWrAIDmSVIKAID0wAMPpGWWWaZaS6yyyirpggsuyNMJJ5yQk0trrrlmuvfee9MSSywx01Y7/fTT0/XXX1+5ve666+b/H3nkkdSvXz8tDgCkFqVSqaQdUrUrvXTq1CnXMujYsaOmAYD51JgxY3K9I9/ZTZOYjPlNt+GD5/UmAMy3pk2YlD4+aViD4zKFzgEAAAAonKQUAAAAAIVTU6pguv0CQON1EwcAYMGlpxQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcK2KXyUAAMCCZcwhF8zrTQCYb40ZMyZ1P2lYg5+npxQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAoXKviVwkAAFC3bsMHax6ABci0CZNm63l6SgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAoJpPPvkkHXHEEalHjx6pbdu2qUuXLmnAgAHp6aefrndL7bzzzvn5Cy+8cFpmmWXSAQcckMaOHaulAYAKSSkAAKrZfffd0yuvvJKuv/769O9//zvdc889qV+/fmnChAn1bqn+/funW2+9NY0aNSrdfvvt6Z133kl77LGHlgYAKlr9358AADR3X3zxRXriiSfSiBEj0uabb57v69mzZ9poo40q87Ro0SJdddVVOVkV80VPqosuuijtueeelXmOP/74yt/x/MGDB6ddd901TZ06NbVu3brgVwUAzI/0lAIAoKJ9+/Z5uuuuu9KUKVPqbJmf//znlR5V+++/f9pnn33Sm2++Weu80cPqxhtvTH379pWQAgAqJKUAAKho1apVuu666/LQvc6dO6dNNtkknXrqqenVV1+t1krRK+qwww5LK6+8cjrnnHPSBhtskK644opq85xyyimpXbt2aYkllkgffPBBuvvuu+ts6UiATZo0qdoEADRtklIAAFQTPaCiKHkMz4sC5zFEb7311svJqrI+ffpUe07crtlT6uSTT04vvfRSeuihh1LLli3TgQcemEqlUq2tPXTo0NSpU6fK1L17d3sFAJo4SSkAAGYQV83beuut0+mnn56eeuqpdPDBB6czzjhjpi0VtaaqWnLJJXNPqljOzTffnO6///70zDPP1PrcIUOGpIkTJ1am0aNH2ysA0MRJSgEAMEurr756mjx5cuV2zeRS3F511VXrfH65h1Rddaratm2bOnbsWG0CAJo2V98DAKBi/PjxuV7UwIED01prrZU6dOiQnn/++Xx1vV122aUy32233ZbrSG266aa5iPnIkSPTtddemx+Lv2OKxxZbbLH07rvv5h5XK6ywwgzD/gCA5ktSCgCAirjyXu/evdNll12W3nnnnTR16tRc3+nwww/PBc/LzjrrrDwkb9CgQalLly45MRW9qcIiiyyS7rjjjjzcL3pXLbPMMmnbbbfN80ePKAAASSkAAKqJpFEUHY9pZrp27ZoLmNdmzTXXTP/4xz+0LAAwU2pKAQAAAFA4SSkAAAAACqemFAAADVK+kh4AwALZU+qTTz5JRxxxROrRo0euXRAFMgcMGJCefvrpebVJAAAAADT1nlK77757vprL9ddfn5Zffvn08ccfp4cffjhNmDBhXm0SAAAAAE25p9QXX3yRnnjiiXThhRem/v37p549e6aNNtooDRkyJO2www55nhYtWqRrrrkm7bjjjmnRRRdNq622Wu5F9fbbb6d+/fqldu3apT59+uRLFVc1bNiwtMIKK6Q2bdqkVVZZJd1www3z4iUCAAAAML8lpdq3b5+nu+66K02ZMqXO+c4555x04IEHppdffjmtuuqqad99981D/iJ59fzzz+d5jj766Mr8d955Zzr22GPTiSeemF5//fU87yGHHJIeeeSROtcR6580aVK1CQAAAIAmmJRq1apVuu666/LQvc6dO6dNNtkknXrqqenVV1+tNl8klPbaa6+08sorp1NOOSW9//77ab/99su1p6LnVCSgRowYUZn/kksuSQcffHAaNGhQfs4JJ5yQfvCDH+T76zJ06NDUqVOnytS9e/e5+toBAAAAmIeFzqOm1NixY9M999yTk0yRXFpvvfVysqpsrbXWqvz9ve99L/+/5pprVrvvm2++qfRuevPNN3OCq6q4HffXJXpdTZw4sTKNHj26UV8nAAAAAPNRUiosvPDCaeutt06nn356euqpp3IvpzPOOKPyeOvWrSt/R42puu6bPn36DPdVvWRxzfuqiiv/dezYsdoEAAAAQBNOStW0+uqrp8mTJ8/282NIXxRQryqSXXE/AAAAAPOPVvNipePHj0977rlnGjhwYB6i16FDh1y4/KKLLkq77LLLbC/35JNPzjWoYhjglltume699950xx13pL///e+Nuv0AAAAALIBJqbjyXu/evdNll12W3nnnnTR16tRcYPzwww/PBc9n16677pp++ctfposvvjgdc8wxabnllkvDhw9P/fr1a9TtBwAAAGDOtChF0SUqomh6XIUvip7PjfpS3YYP1toA0AimTZiUPj5p2Fz7zqZpx2QAQOMZM2ZM7mzU0O/t+aqmFAAAAADNg6QUAAAAAIWTlAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFA4SSkAAAAACicpBQAAAEDhWhW/SgAAmPe6DR88rzcBAJqEaRMmzdbz9JQCAAAAoHCSUgAAAAAUTlIKAAAAgMJJSgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAavHUU0+lli1bpm233XaGxz744IO00047pXbt2qUll1wyHXPMMenbb7+ttR1XWWWV1KZNm/Thhx/O8NjHH3+cDj744NS1a9e06KKL5nW99dZblcfff//91KJFi1qn2267rTLfiy++mLbeeuvUuXPntMQSS6Qf/ehH6auvvpphOS+//PIM29CvX7903HHH1Xm7pqrb0L59+7T22mun6667bob5br311rTOOuvk19WzZ8908cUX17lMAKB5kpQCAKjF73//+/STn/wkPfHEEzkJVfbdd9+lHXbYIU2ePDk/dvPNN6fbb789nXjiiTMsIx7/5ptv0p577jlD4qZUKqVdd901vfvuu+nuu+9OL730Uk7ebLXVVnnZoXv37mncuHHVprPOOisnw7bbbrs8z9ixY/NzVlxxxfTss8+mBx54IL3xxhs52TW3DB8+PG/LK6+8kn74wx+mQw45JD344IOVx//617+m/fbbLx155JHp9ddfT1dddVX6xS9+kX7961/PtW0CABY8klIAADVEUih6+vz4xz9OO+64Y7WE0kMPPZT++c9/pj/+8Y9p3XXXzQmhSy+9NP32t79NkyZNqraca6+9Nu27777pgAMOyEmuSESVRY+oZ555Jg0bNixtuOGGuUdVJG+ih9NNN92U54meWl26dKk23XnnnTkRFL2Uwn333Zdat26drrzyyryMWFb8HYmyt99+e67s2+iRFduywgorpFNPPTUtvvjiuV3Kbrjhhpxwi6TU8ssvn5N4p5xySrrwwgurtQEA0LxJSgEA1HDLLbfkBE9M+++/f+4ZVE6mPP300+n73/9+HnJXNmDAgDRlypT0wgsvVO778ssv8xC7eH4MrYtE14gRIyqPx/xh4YUXrtwXSagY6hc9rGoTy48heIceemi15cRzFlro/8K6RRZZJP9f13IaS/Qai+TdhAkTcmKs6jZVfV3lbRozZkz6z3/+U+uy4jmR1Ks6AQBNm6QUAEAN0cMpkkkh6jxF76WHH3443/7oo4/S9773vWrzL7bYYjkxFI+VxbC+lVZaKa2xxho52bT33nvn5ZatuuqqebjekCFD0ueff55rUl1wwQV5GTE0rjbx/NVWWy317du3ct8WW2yRnxM1m2IZsazovRRqLieeFz2sqk6PP/54g/f/Pvvsk5/btm3b3Gsrekoddthh1ZJ0d9xxR26z6dOnp3//+9/p8ssvr3WbyoYOHZo6depUmWLoIgDQtElKAQBUMWrUqDRy5MicRAqtWrXKiZcYflcWRb5rip5UVe+vmtgK8Xckar744ot8O3oWxRC7SNhEUicKgkdPqqgVFUmsmr7++uv0pz/9qVovqRBJr+uvvz4PIYxlxLC6GDIXibOay4keYNHTquq0wQYbNHj/X3bZZfm5f/vb33Ix87gdNa3KDj/88HT00UfnoY+RrNt4440r7VnbawuRnJs4cWJlGj16dIO3CwBYsLSa1xsAADA/iWTStGnT0rLLLlst4RRJpOiFFEmfKCheVdw/derUSg+qqDkV8zz33HO5llLV4W5RLypqVYX1118/J3ciCRO9nJZaaqnUu3fvWhNFf/7zn9N///vfdOCBB87wWNStiimu5hdF0CM5FoXFl1tuuWrzRe+jqsmjqkP9GiLaIJYTUwxRjNpasc2rr756fjzWH/Wjzj///NyLK15XuadZr169al1m9LqKCQBoPvSUAgD4X5GM+sMf/pB7HVXtTRRXmYuhdjfeeGPq06dPvqJc1WFoUeQ7EiqRZContjbbbLP8vKrL+elPf1ptCF9ZDFeLxE0UP3/++efTLrvsMsM88bydd945z1eXSIrFsLroERU1naKW1dwWiandd98993SqKXpFRXIvektFMi7abumll57r2wQALBj0lAIA+F9xJbvo9RRD5CJRVNUee+yRE0ORNIoeQXFFvajjFEW+TzrppDxkrWPHjrnHVFx97uyzz84F0auKuksXXXRRTlatvfbauZdRJJl69OiRXnvttXTsscfmq9Zts8021Z4XV9F77LHH0v3331/rvvr1r39dqRcVQ+pOPvnkXJ8qrpI3Oz799NOcRKuqfPW/2px44on59UTbRI+pzz77LPfs6tevX/rmm29yofh4rY8++uhsbQ8A0DTpKQUA8L8i6bTVVlvNkJAK0Ruo3GvqL3/5S+6JtMkmm6S99torJ5IuueSSPN8999yTxo8fn3bbbbcZlhGFz9dcc81Kb6nobRXJrSh6fswxx+S/o0dRTVHPKnoc1UxWlUUNrOgVFcv+zW9+k6655pq8vNkVtatiSF7V6eqrr65z/lhvtNvpp59euS/qXEWCKtrojTfeyPWyNtpoo9neJgCg6WlRKl/fmCwuPxyBaNR2iLOdja3b8MFaGgAawbQJk9LHJw2ba9/ZNO2YLIjLAGDexmV6SgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAoXItSqVQqfrXzr0mTJqVOnTqliRMnpo4dO87rzQEA6jBmzJjUvXt339lNlJgMAJp+XKanFAAAAACFk5QCAAAAoHCSUgAAAAAUTlIKAAAAgMJJSgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIVrVfwq52+lUin/P2nSpHm9KQDATHz55ZfVvrtpWsRkAND04zJJqToasnv37o21bwCAuWj8+PGpU6dO2riJEZMBQNOPy1qUnF6sZvr06Wns2LGpQ4cOqUWLFjM0WPSgioTV6NGjU8eOHRtjny3wtIl2cax4//hM8Vk7L0ycODH16NEjff7556lz587zZBuYdzFZcyHO0jaOG+8rnznzB5/Hcycu01OqhoUWWih169Ztlg0XCSlJKW1SH44VbeI4mT3eO9qlId/dNN+YrLnwmahtHDfeVz5z5g8+jxs3LhPFAQAAAFA4SSkAAAAACicp1UBt27ZNZ5xxRv4fbeJY8f6ZUz5TtIljxfsHfE/4Dm1s4gvt49hpfN5Xc6d9FDoHAAAAoHB6SgEAAABQOEkpAAAAAAonKQUAAABA4SSlZtOIESNSixYtap2ee+651Nz95S9/Sb17906LLLJIWnLJJdMPfvCD1Jz16tVrhuNk8ODB83qz5gtTpkxJ66yzTm6Tl19+OTVnO++8c+rRo0daeOGF0zLLLJMOOOCANHbs2NScvf/+++nQQw9Nyy23XP48WWGFFXIBxW+//TY1Z+edd17q27dvWnTRRVPnzp1Tc3TVVVfl4yLeL+uvv356/PHH5/UmQSHEWLUTa9WPuGtG4q/aicFmTTzWOHGZpNRsih8D48aNqzYddthh+Qtxgw02SM3Z7bffnn9MH3LIIemVV15JTz75ZNp3331Tc3f22WdXO15OO+20eb1J84Wf/vSnqWvXrvN6M+YL/fv3T7feemsaNWpUfh+98847aY899kjN2b/+9a80ffr0dM0116Q33ngjXXbZZenqq69Op556amrOIim35557ph//+MepObrlllvScccdl372s5+ll156Kf3P//xP2m677dIHH3wwrzcN5iox1syJtWZN3DUj8VftxGCz1tzjsUaLy0o0im+//ba09NJLl84+++xm3aJTp04tLbvssqXf/e5383pT5is9e/YsXXbZZfN6M+Y7999/f2nVVVctvfHGG6X4OHrppZfm9SbNV+6+++5SixYt8ucL/+eiiy4qLbfccpqkVCoNHz681KlTp2bXFhtttFHpyCOPrHZffJYMHjx4nm0TzG1irJkTa82auKt+xF91E4PVrrnGY40Vl+kp1Ujuueee9Nlnn6WDDz44NWcvvvhi+vDDD9NCCy2U1l133TwEKbKk0cOhubvwwgvTEksskYeqRVfP5j786OOPP06HH354uuGGG/IQJKqbMGFCuvHGG3OvzNatW2ueKiZOnJgWX3xxbdJMxWfnCy+8kLbZZptq98ftp556ap5tF8xtYqxZE2vVTdxVP+KvmRODMTfiMkmpRnLttdemAQMGpO7du6fm7N13383/n3nmmXl42n333ZcWW2yxtPnmm+cP+ebq2GOPTTfffHN65JFH0tFHH50uv/zyNGjQoNRclUqlnMA98sgjm/1w15pOOeWU1K5du5zAjC6vd9999zzZR/OrGNJ4xRVX5GOH5ilOAH333Xfpe9/7XrX74/ZHH300z7YL5jYx1syJteom7po18desicGYW3GZpFQNkUypq4B5eXr++eerPWfMmDHpwQcfzMV4m3u7RO2XEONJd99991zkbPjw4fnx2267LTXXY+X444/Pibm11lor1x6LmjiRyBw/fnxqjm0SSYVJkyalIUOGpKauoZ8pJ598ch6L/dBDD6WWLVumAw88MAeTTc3sfNZG0fdtt902j92P91FTMztt0pxFe1QV75Oa98GCQIw1523TnGKtqsRdc942zS3+CmKwxm8f0hzFZS1iDJ9GrJ7pi2lmoph5VJUvO+ecc/KP7Bi21lSH2dS3XZ5++um0xRZb5Gr7m266aeWxuBLfVlttlYetNedjpSyOlW7duqVnnnkmt01za5O999473XvvvdU+qCLDHkHAfvvtl66//vrUVMzJcRIJ7+h9GV1f+/Tpk5qShrZLJKSiEGm8X6677ro8RLipmZ1jJdoiCkt+8cUXqTl1E48hv3GiY7fddqvWSyKu4Pnoo4/O0+2DhhJjzXnbNKdYqypx15y3TXOLv4IYrHHbp7nGY40Zl7Wa5RzNzJJLLpmn+oqcXvQEimx6U01INaRdomdU27Zt89XDykmpqVOn5kuK9uzZMzXnY6WqOBMTouZWc2yTX/3qV+ncc8+t3I6EQwx/jSs3NLXAcU6Ok/I5g7h8c1PTkHaJHxaRkCr3vGyKCak5PVaakzZt2uRj4W9/+1u14Cdu77LLLvN022B2iLHmvG2aU6xVlbhrztumucVfQQzWeO1DapS4TFJqDv3jH/9I7733XpMeutcQHTt2zLVezjjjjHyGIRJRF198cX4shtw0R9F7LM7SxY/qTp06peeeey53Md95551Tjx49UnNU83W3b98+/7/CCivks5rN0ciRI/MUydyowxa1Q04//fTcJk3xLF19RcKyX79++Zi55JJL0qefflp5rEuXLqm5inpjUacv/o9ehnEmKqy44oqV91NTdsIJJ6QDDjgg16SL98dvfvOb3BZqjdGUibHqJtaaOXFX3cRfdRODzVpzj8caLS6r1zX6qNM+++xT6tu3rxaqIi5ff+KJJ5aWXnrpUocOHUpbbbVV6fXXX2+2bfTCCy+UevfunS8TuvDCC5dWWWWV0hlnnFGaPHnyvN60+cZ7770Xp6RKL730Uqm5evXVV0v9+/cvLb744qW2bduWevXqlS+tOmbMmFJzv8RuHBu1Tc3ZQQcdVGubPPLII6Xm4sorr8yXgG/Tpk1pvfXWKz366KPzepNgrhNj1U6s1TDirv8j/qqbGGzWxGONE5epKQUAAABA4ZpmYQ4AAAAA5muSUgAAAAAUTlIKAAAAgMJJSgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUgmagRYsW6a677prXm9Gs9erVK11++eXzejMAAApx8MEHp1133bXe85955plpnXXWmavbBMx/JKVgAf6ij2RTTK1bt07f+9730tZbb51+//vfp+nTp1ebd9y4cWm77babq9szPwUS/fr1S8cdd1yTW1dNv/3tb9P//M//pMUWWyxPW221VRo5cmSdx0nV6e23367M89FHH6Wf/OQnafnll09t27ZN3bt3TzvttFN6+OGH58GrAoDmoa4YIk4kxnd12XXXXZc6d+5c53LK3/VHHnnkDI8NGjQoPxbzzC3vv/9+XsfLL79c7f5f/vKXedvr66STTqoWezQ0qQUsmCSlYAG27bbb5oRTBAN//etfU//+/dOxxx6bdtxxxzRt2rTKfF26dMnJhrpMnTo1NTelUqlaGy2IRowYkfbZZ5/0yCOPpKeffjr16NEjbbPNNunDDz+s9TipOi233HL5sTh21l9//fSPf/wjXXTRRem1115LDzzwQD6WjjrqqHn0ygCAhogTSjfffHP6+uuvK/d988036aabbsrxwbzQqVOnmSbTamrfvn1aYokl0oKsKcSXUDRJKViARaIpEk7LLrtsWm+99dKpp56a7r777pygqnpmqurwvfLZrFtvvTWfoVt44YXTH//4x/zY8OHD02qrrZbvW3XVVdNVV11VbX1jxoxJe++9d1p88cVTu3bt0gYbbJCeffbZvK6zzjorvfLKK5WeODM7Mxa9udZYY428/csss0w6+uijK4998MEHaZdddsmBSceOHdNee+2VPv744xl6ZN1www15SFwEPLFNX375ZeWs2qOPPprPzpW3JV5zJHDi7wcffDBvd6z78ccfT++8805eX/Q0i3VuuOGG6e9//3u17Y12WGmllXK7xHx77LHHTNdVH7N6neHcc89NSy+9dOrQoUM67LDD0uDBg6v1RrvxxhvzGdC4L/ZX9JyKXnI1eziVj5OqU8uWLaudQY0eVvG6Vl555bxvTjjhhPTMM8/U67UAAPNWxIGRfLrjjjsq98Xfkaxad911Z/rc8ePH55Nc3bp1S4suumhac801czKrqogvLrzwwrTiiivmuCLWdd555+XHyie6Yj0RU0R8WbOn0zXXXJPj1Zq9+Xfeeed00EEHzdDrPv6+/vrrc1xbjrEilttiiy2qxY3l7Y9tihNstYn4NE62RTwVMVecjHv++ecrjz/55JNp8803z689ep4PGDAgff755/mxKVOmpGOOOSbHYxEHbrrppum5556rPLeu+DKSU3GyL3qhL7LIImnttddOf/7zn2e6H6C5kpSCJia+rOOLr2pQUptTTjklf8m++eab+cs3Eho/+9nPcoAR951//vnp5z//eQ4IwldffZW/sMeOHZvuueee/AX/05/+NAcXP/zhD9OJJ56YkxnlnjhxX22GDRuWe+D86Ec/yr1yYlkR4IT4Ao/gZcKECTnZ87e//S0njWouK+6LJNt9992Xp5j3ggsuyI9FgqhPnz7p8MMPr2xLBGRlsc1Dhw7Nr3GttdbKr2v77bfPiaiXXnopt0UMXYukUYigJdrp7LP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" + ], + "text/plain": [ + " spontaneous vs normal SB vs normal shared \\\n", + "feature \n", + "receptor candidates 30 30 20 \n", + "significant TF activities 10 7 6 \n", + "\n", + " control-relative Jaccard \\\n", + "feature \n", + "receptor candidates 0.500000 \n", + "significant TF activities 0.545455 \n", + "\n", + " direct: spontaneous higher direct: SB higher \n", + "feature \n", + "receptor candidates 0 3 \n", + "significant TF activities 0 8 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", "text": [ - "Unreachable vertices for sample: 12\n", - "Unreachable vertices for sample: 7\n" + "Control-relative TF activity Pearson r: 0.854\n" ] } ], "source": [ - "c = CarnivalFlow(lambda_reg=0, indirect_rule_penalty=1)\n", - "P = c.build(G, data)" + "def select_direct_receptors(\n", + " results_df, receptor_universe, graph, n=30, padj=0.05, min_abs_log2fc=0.5\n", + "):\n", + " candidates = results_df.loc[results_df.index.intersection(receptor_universe)].copy()\n", + " candidates = candidates.loc[\n", + " (candidates[\"padj\"] <= padj)\n", + " & (candidates[\"log2FoldChange\"].abs() >= min_abs_log2fc)\n", + " ]\n", + " candidates = candidates.loc[candidates.index.intersection(graph.V)]\n", + " return candidates.assign(abs_stat=candidates[\"stat\"].abs()).sort_values(\n", + " [\"abs_stat\", \"padj\"], ascending=[False, True]\n", + " ).head(n)\n", + "\n", + "\n", + "direct_receptors = select_direct_receptors(direct_results, unique_receptors, G)\n", + "direct_mat = direct_results[[\"stat\"]].T.rename(\n", + " index={\"stat\": \"Leukemic_spontaneous.vs.Leukemic_SB\"}\n", + ")\n", + "direct_tf_acts, direct_tf_pvals = dc.mt.ulm(\n", + " data=direct_mat, net=collectri, verbose=False\n", + ")\n", + "direct_significant_tfs = (\n", + " direct_tf_acts[direct_tf_pvals <= max_pval]\n", + " .T.dropna()\n", + " .sort_values(\"Leukemic_spontaneous.vs.Leukemic_SB\")\n", + ")\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", + "receptor_plot = direct_receptors.sort_values(\"log2FoldChange\")\n", + "axes[0].barh(\n", + " receptor_plot.index,\n", + " receptor_plot[\"log2FoldChange\"],\n", + " color=np.where(receptor_plot[\"log2FoldChange\"] > 0, \"#d95f02\", \"#1b9e77\"),\n", + ")\n", + "axes[0].axvline(0, color=\"black\", linewidth=0.8)\n", + "axes[0].set(\n", + " xlabel=\"Direct contrast log2FC\",\n", + " title=\"Differentially expressed receptor candidates\",\n", + ")\n", + "\n", + "tf_plot = direct_significant_tfs.iloc[\n", + " np.argsort(\n", + " direct_significant_tfs[\n", + " \"Leukemic_spontaneous.vs.Leukemic_SB\"\n", + " ].abs().to_numpy()\n", + " )[-20:]\n", + "].sort_values(\"Leukemic_spontaneous.vs.Leukemic_SB\")\n", + "tf_values = tf_plot[\"Leukemic_spontaneous.vs.Leukemic_SB\"]\n", + "axes[1].barh(\n", + " tf_plot.index,\n", + " tf_values,\n", + " color=np.where(tf_values > 0, \"#d95f02\", \"#1b9e77\"),\n", + ")\n", + "axes[1].axvline(0, color=\"black\", linewidth=0.8)\n", + "axes[1].set(xlabel=\"ULM activity score\", title=\"Direct differential TF activities\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "control_tf_common = tf_acts1.columns.intersection(tf_acts2.columns)\n", + "tf_profile_correlation = tf_acts1.loc[\n", + " \"Leukemic_spontaneous.vs.Normal\", control_tf_common\n", + "].corr(tf_acts2.loc[\"Leukemic_SB.vs.Normal\", control_tf_common])\n", + "control_tf_set1 = set(significant_tfs1.index)\n", + "control_tf_set2 = set(significant_tfs2.index)\n", + "control_receptor_set1 = set(df_top_receptors1.index)\n", + "control_receptor_set2 = set(df_top_receptors2.index)\n", + "\n", + "receptor_tf_metrics = pd.DataFrame(\n", + " {\n", + " \"feature\": [\"receptor candidates\", \"significant TF activities\"],\n", + " \"spontaneous vs normal\": [len(control_receptor_set1), len(control_tf_set1)],\n", + " \"SB vs normal\": [len(control_receptor_set2), len(control_tf_set2)],\n", + " \"shared\": [\n", + " len(control_receptor_set1 & control_receptor_set2),\n", + " len(control_tf_set1 & control_tf_set2),\n", + " ],\n", + " \"control-relative Jaccard\": [\n", + " len(control_receptor_set1 & control_receptor_set2)\n", + " / len(control_receptor_set1 | control_receptor_set2),\n", + " len(control_tf_set1 & control_tf_set2)\n", + " / len(control_tf_set1 | control_tf_set2),\n", + " ],\n", + " \"direct: spontaneous higher\": [\n", + " int((direct_receptors[\"log2FoldChange\"] > 0).sum()),\n", + " int(\n", + " (\n", + " direct_significant_tfs[\n", + " \"Leukemic_spontaneous.vs.Leukemic_SB\"\n", + " ]\n", + " > 0\n", + " ).sum()\n", + " ),\n", + " ],\n", + " \"direct: SB higher\": [\n", + " int((direct_receptors[\"log2FoldChange\"] < 0).sum()),\n", + " int(\n", + " (\n", + " direct_significant_tfs[\n", + " \"Leukemic_spontaneous.vs.Leukemic_SB\"\n", + " ]\n", + " < 0\n", + " ).sum()\n", + " ),\n", + " ],\n", + " }\n", + ").set_index(\"feature\")\n", + "\n", + "display(receptor_tf_metrics)\n", + "print(f\"Control-relative TF activity Pearson r: {tf_profile_correlation:.3f}\")" ] }, { - "cell_type": "code", - "execution_count": 47, - "id": "8113725f-9fc7-4e6f-aca3-75add7943db6", + "cell_type": "markdown", + "id": "cc156123", "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:26:48.203343Z", - "iopub.status.busy": "2025-06-27T16:26:48.203215Z", - "iopub.status.idle": "2025-06-27T16:26:48.205530Z", - "shell.execute_reply": "2025-06-27T16:26:48.205283Z" - }, "papermill": { - "duration": 0.009226, - "end_time": "2025-06-27T16:26:48.206124", + "duration": 0.007134, + "end_time": "2026-08-06T17:19:00.492750+00:00", "exception": false, - "start_time": "2025-06-27T16:26:48.196898", + "start_time": "2026-08-06T17:19:00.485616+00:00", "status": "completed" }, "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "5" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "len(set(c.processed_graph.V).intersection(d_measurements1.keys()))" + "### Select lambda for the differential network\n", + "\n", + "The direct network uses the same five-percentage-point TF-fit tolerance as the joint\n", + "analysis. Among eligible lambdas, held-out deregulation of internal-node genes and\n", + "held-out pathway concordance are weighted equally. We then choose the sparsest network\n", + "within 0.05 of the best validation score. This selects complexity without using the\n", + "samples that will be used for the final refit." ] }, { "cell_type": "code", - "execution_count": 48, - "id": "c5396ee0-71b4-43ed-ba48-380edcbd0b49", + "execution_count": 43, + "id": "dd4009bc", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:26:48.218731Z", - "iopub.status.busy": "2025-06-27T16:26:48.218536Z", - "iopub.status.idle": "2025-06-27T16:26:48.220736Z", - "shell.execute_reply": "2025-06-27T16:26:48.220515Z" + "iopub.execute_input": "2026-08-06T17:19:00.506964Z", + "iopub.status.busy": "2026-08-06T17:19:00.506860Z", + "iopub.status.idle": "2026-08-06T17:19:07.268001Z", + "shell.execute_reply": "2026-08-06T17:19:07.267622Z" }, "papermill": { - "duration": 0.009153, - "end_time": "2025-06-27T16:26:48.221333", + "duration": 6.769033, + "end_time": "2026-08-06T17:19:07.268555+00:00", "exception": false, - "start_time": "2025-06-27T16:26:48.212180", + "start_time": "2026-08-06T17:19:00.499522+00:00", "status": "completed" }, "tags": [] }, "outputs": [ - { - "data": { - "text/plain": [ - "6" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(set(c.processed_graph.V).intersection(d_measurements2.keys()))" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "f1c27628-84a2-47d3-9025-f9ac0f6dab62", - "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:26:48.233675Z", - "iopub.status.busy": "2025-06-27T16:26:48.233581Z", - "iopub.status.idle": "2025-06-27T16:26:48.235555Z", - "shell.execute_reply": "2025-06-27T16:26:48.235345Z" - }, - "papermill": { - "duration": 0.008893, - "end_time": "2025-06-27T16:26:48.236119", - "exception": false, - "start_time": "2025-06-27T16:26:48.227226", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fli1 -1.0 -1.0\n", - "Runx1 -1.0 -1.0\n", - "Gata1 -1.0 -1.0\n", - "Myc 1.0 1.0\n" - ] - } - ], - "source": [ - "# How many TFs are in common between conditions?\n", - "s1 = set(c.processed_graph.V).intersection(d_measurements1.keys())\n", - "s2 = set(c.processed_graph.V).intersection(d_measurements2.keys())\n", - "for g in s1.intersection(s2):\n", - " print(g, d_measurements1[g], d_measurements2[g])" - ] - }, - { - "cell_type": "markdown", - "id": "446add0f", - "metadata": { - "papermill": { - "duration": 0.005884, - "end_time": "2025-06-27T16:26:48.247901", - "exception": false, - "start_time": "2025-06-27T16:26:48.242017", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Which is the best lambda to choose?" - ] - }, - { - "cell_type": "markdown", - "id": "3f60c6ff", - "metadata": { - "papermill": { - "duration": 0.005862, - "end_time": "2025-06-27T16:26:48.259563", - "exception": false, - "start_time": "2025-06-27T16:26:48.253701", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "To choose a robust value of $\\lambda$ we sample multiple solutions, as in the tutorial https://saezlab.github.io/corneto/dev/tutorials/network-sampler.html#vertex-based-perturbation. \\\n", - "We define 3 metrics to choose $\\lambda$: \\\n", - "\\\n", - "$sign\\_agreement\\_ratio = \\frac{n.\\:of\\:nodes\\:matching\\:sign\\:with\\:stat\\:value}{total\\:n.\\:of\\:nodes}$ \\\n", - "\\\n", - "$de\\_overlap\\_ratio = \\frac{n.\\:of\\:nodes\\:also\\:differentially\\:expressed}{total\\:n.\\:of\\:nodes}$ \\\n", - "\\\n", - "$overall\\_agreement = \\frac{sign\\_agreement\\_ratio\\:+\\:de\\_overlap\\_ratio}{2}$ \n" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "7f4e1ef5", - "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:26:48.271683Z", - "iopub.status.busy": "2025-06-27T16:26:48.271571Z" - }, - "papermill": { - "duration": 76.785688, - "end_time": "2025-06-27T16:28:05.051045", - "exception": false, - "start_time": "2025-06-27T16:26:48.265357", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "lambda: 0\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Unreachable vertices for sample: 12\n", - "Unreachable vertices for sample: 7\n" + "Solving direct contrast, lambda=0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Unreachable vertices for sample: 0\n", "Set parameter Username\n" ] }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set parameter LicenseID to value 2775543\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Academic license - for non-commercial use only - expires 2027-02-05\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[1/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=1) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=99, rel.err=3.9500 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[2/30] rejected (tol=0.05 violated by 'error_sample2_1' with rel.err=0.5000) (total rejected=2) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=92, rel.err=3.6000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[3/30] accepted (total accepted=1) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=69, rel.err=2.4500 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[4/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=3) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=93, rel.err=3.6500 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[5/30] rejected (tol=0.05 violated by 'error_sample2_1' with rel.err=0.5000) (total rejected=4) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=91, rel.err=3.5500 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[6/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=5) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=111, rel.err=4.5500 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[7/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=6) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=100, rel.err=4.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[8/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=7) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=90, rel.err=3.5000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[9/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=8) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=80, rel.err=3.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[10/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=9) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=90, rel.err=3.5000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[11/30] accepted (total accepted=2) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=67, rel.err=2.3500 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[12/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=10) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=96, rel.err=3.8000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[13/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=11) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=96, rel.err=3.8000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[14/30] accepted (total accepted=3) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=100, rel.err=4.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[15/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=12) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=107, rel.err=4.3500 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[16/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=13) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=93, rel.err=3.6500 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[17/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=14) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=102, rel.err=4.1000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[18/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=15) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=110, rel.err=4.5000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[19/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=16) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=81, rel.err=3.0500 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[20/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=17) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=95, rel.err=3.7500 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[21/30] rejected (tol=0.05 violated by 'error_sample2_1' with rel.err=0.5000) (total rejected=18) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=85, rel.err=3.2500 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[22/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=19) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=82, rel.err=3.1000 (excluded from tol. check)\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[23/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=20) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=116, rel.err=4.8000 (excluded from tol. check)\n" + "2026-08-06 19:19:00 | [INFO] Set parameter Username\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[24/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.6667) (total rejected=21) -> error_sample1_0: val=1, rel.err=0.6667, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=2, rel.err=2000000000.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=96, rel.err=3.8000 (excluded from tol. check)\n" + "Set parameter LicenseID to value 2775543\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[25/30] rejected (tol=0.05 violated by 'error_sample2_1' with rel.err=0.5000) (total rejected=22) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=99, rel.err=3.9500 (excluded from tol. check)\n" + "2026-08-06 19:19:00 | [INFO] Set parameter LicenseID to value 2775543\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[26/30] rejected (tol=0.05 violated by 'error_sample2_1' with rel.err=0.5000) (total rejected=23) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=94, rel.err=3.7000 (excluded from tol. check)\n" + "Academic license - for non-commercial use only - expires 2027-02-05\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[27/30] rejected (tol=0.05 violated by 'error_sample2_1' with rel.err=0.5000) (total rejected=24) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=104, rel.err=4.2000 (excluded from tol. check)\n" + "2026-08-06 19:19:00 | [INFO] Academic license - for non-commercial use only - expires 2027-02-05\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[28/30] rejected (tol=0.05 violated by 'error_sample2_1' with rel.err=0.5000) (total rejected=25) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=103, rel.err=4.1500 (excluded from tol. check)\n" + "Solving direct contrast, lambda=0.001\n", + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[29/30] rejected (tol=0.05 violated by 'error_sample2_1' with rel.err=0.5000) (total rejected=26) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=104, rel.err=4.2000 (excluded from tol. check)\n" + "Solving direct contrast, lambda=0.003\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[30/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=27) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=1, rel.err=0.5000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=1, rel.err=1000000000.0000, regularization_edge_has_signal_OR: val=85, rel.err=3.2500 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:Done. accepted=3, rejected=27, solutions returned=4\n" + "Solving direct contrast, lambda=0.01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "lambda: 0.01\n" + "Unreachable vertices for sample: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Unreachable vertices for sample: 12\n", - "Unreachable vertices for sample: 7\n" + "Solving direct contrast, lambda=0.03\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[1/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=1) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=51, rel.err=1.8333 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[2/30] accepted (total accepted=1) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=38, rel.err=1.1111 (excluded from tol. check)\n" + "Solving direct contrast, lambda=0.1\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[3/30] accepted (total accepted=2) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=29, rel.err=0.6111 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[4/30] accepted (total accepted=3) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=19, rel.err=0.0556 (excluded from tol. check)\n" + "Solving direct contrast, lambda=0.3\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[5/30] accepted (total accepted=4) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=40, rel.err=1.2222 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[6/30] accepted (total accepted=5) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=34, rel.err=0.8889 (excluded from tol. check)\n" - ] + "data": { + "image/png": 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0.30061.00.000001.000000.0000.00.0067910.00.00000000.000000000False0.00.0000000.000000
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" + ], + "text/plain": [ + " reachable_tfs reachable_weight tf_match tf_inactive tf_opposite \\\n", + "lambda \n", + "0.000 6 1.0 0.84753 0.15247 0.0 \n", + "0.001 6 1.0 0.84753 0.15247 0.0 \n", + "0.003 6 1.0 0.84753 0.15247 0.0 \n", + "0.010 6 1.0 0.84753 0.15247 0.0 \n", + "0.030 6 1.0 0.84753 0.15247 0.0 \n", + "0.100 6 1.0 0.00000 1.00000 0.0 \n", + "0.300 6 1.0 0.00000 1.00000 0.0 \n", + "\n", + " active_internal_nodes deregulated_internal_nodes \\\n", + "lambda \n", + "0.000 13 0 \n", + "0.001 11 0 \n", + "0.003 11 0 \n", + "0.010 11 0 \n", + "0.030 11 0 \n", + "0.100 0 0 \n", + "0.300 0 0 \n", + "\n", + " node_deregulated_fraction background_deregulated_fraction \\\n", + "lambda \n", + "0.000 0.0 0.006791 \n", + "0.001 0.0 0.006791 \n", + "0.003 0.0 0.006791 \n", + "0.010 0.0 0.006791 \n", + "0.030 0.0 0.006791 \n", + "0.100 0.0 0.006791 \n", + "0.300 0.0 0.006791 \n", + "\n", + " node_deregulation_enrichment node_deregulation_z \\\n", + "lambda \n", + "0.000 0.0 -0.301232 \n", + "0.001 0.0 -0.276613 \n", + "0.003 0.0 -0.276613 \n", + "0.010 0.0 -0.276613 \n", + "0.030 0.0 -0.276613 \n", + "0.100 0.0 0.000000 \n", + "0.300 0.0 0.000000 \n", + "\n", + " annotated_internal_nodes pathway_concordance active_edges \\\n", + "lambda \n", + "0.000 10 0.744590 19 \n", + "0.001 10 0.634779 17 \n", + "0.003 10 0.634779 17 \n", + "0.010 9 0.599674 17 \n", + "0.030 9 0.707311 17 \n", + "0.100 0 0.000000 0 \n", + "0.300 0 0.000000 0 \n", + "\n", + " active_nodes active_inputs eligible relative_node_validation \\\n", + "lambda \n", + "0.000 19 1 True 0.0 \n", + "0.001 17 1 True 0.0 \n", + "0.003 17 1 True 0.0 \n", + "0.010 17 1 True 0.0 \n", + "0.030 17 1 True 0.0 \n", + "0.100 0 0 False 0.0 \n", + "0.300 0 0 False 0.0 \n", + "\n", + " relative_pathway_validation validation_score \n", + "lambda \n", + "0.000 1.000000 0.500000 \n", + "0.001 0.852522 0.426261 \n", + "0.003 0.852522 0.426261 \n", + "0.010 0.805375 0.402688 \n", + "0.030 0.949933 0.474966 \n", + "0.100 0.000000 0.000000 \n", + "0.300 0.000000 0.000000 " + ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[12/30] accepted (total accepted=11) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=55, rel.err=2.0556 (excluded from tol. check)\n" + "Selected differential-network lambda: 0.03\n" ] + } + ], + "source": [ + "direct_measurements_raw = direct_significant_tfs.loc[\n", + " direct_significant_tfs.index.intersection(G.V)\n", + "].to_dict()[\"Leukemic_spontaneous.vs.Leukemic_SB\"]\n", + "direct_measurements = normalized_signed_measurements(direct_measurements_raw)\n", + "direct_inputs = {node: 0 for node in direct_receptors.index}\n", + "direct_inputs, direct_measurements = restrict_to_reachable_evidence(\n", + " G, direct_inputs, direct_measurements\n", + ")\n", + "direct_sample = make_carnival_sample(direct_inputs, direct_measurements)\n", + "direct_data = cn.Data.from_cdict({\"spontaneous_vs_SB\": direct_sample})\n", + "direct_validation_pathways = heldout_pathway_activity(\n", + " direct_validation_results, \"Leukemic_spontaneous.vs.Leukemic_SB\"\n", + ")\n", + "\n", + "lambda_values = [0.0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3]\n", + "direct_solutions = {}\n", + "direct_rows = []\n", + "for lambda_reg in lambda_values:\n", + " print(f\"Solving direct contrast, lambda={lambda_reg:g}\")\n", + " solution = solve_carnival(G, direct_data, lambda_reg)\n", + " direct_solutions[lambda_reg] = solution\n", + " direct_rows.append(\n", + " {\n", + " \"lambda\": lambda_reg,\n", + " **tf_fit_metrics(solution, 0, direct_measurements),\n", + " **internal_validation_metrics(\n", + " solution,\n", + " 0,\n", + " direct_inputs,\n", + " direct_measurements,\n", + " direct_validation_results,\n", + " direct_validation_pathways,\n", + " ),\n", + " **structural_metrics(solution, 0, direct_inputs),\n", + " }\n", + " )\n", + "\n", + "direct_diagnostics = pd.DataFrame(direct_rows).set_index(\"lambda\")\n", + "direct_baseline_match = direct_diagnostics.loc[0.0, \"tf_match\"]\n", + "direct_diagnostics[\"eligible\"] = (\n", + " (direct_diagnostics[\"tf_match\"] >= direct_baseline_match - 0.05)\n", + " & (direct_diagnostics[\"active_edges\"] > 0)\n", + " & (direct_diagnostics[\"active_inputs\"] > 0)\n", + ")\n", + "positive_direct_node_z = direct_diagnostics[\"node_deregulation_z\"].clip(lower=0)\n", + "best_direct_node_z = positive_direct_node_z.max()\n", + "best_direct_pathway = direct_diagnostics[\"pathway_concordance\"].max()\n", + "direct_diagnostics[\"relative_node_validation\"] = (\n", + " positive_direct_node_z / best_direct_node_z if best_direct_node_z > 0 else 0.0\n", + ")\n", + "direct_diagnostics[\"relative_pathway_validation\"] = (\n", + " direct_diagnostics[\"pathway_concordance\"] / best_direct_pathway\n", + " if best_direct_pathway > 0\n", + " else 0.0\n", + ")\n", + "direct_diagnostics[\"validation_score\"] = direct_diagnostics[\n", + " [\"relative_node_validation\", \"relative_pathway_validation\"]\n", + "].mean(axis=1)\n", + "\n", + "eligible_direct = direct_diagnostics.loc[direct_diagnostics[\"eligible\"]]\n", + "if eligible_direct.empty:\n", + " raise RuntimeError(\"No lambda preserves a fitted, non-empty differential network\")\n", + "best_direct_validation = eligible_direct[\"validation_score\"].max()\n", + "near_best_direct = eligible_direct.loc[\n", + " eligible_direct[\"validation_score\"] >= best_direct_validation - 0.05\n", + "]\n", + "selected_direct_lambda = (\n", + " near_best_direct.reset_index()\n", + " .sort_values(\n", + " [\"active_edges\", \"validation_score\", \"lambda\"],\n", + " ascending=[True, False, False],\n", + " )\n", + " .iloc[0][\"lambda\"]\n", + ")\n", + "\n", + "fig, axis = plt.subplots(figsize=(7, 4))\n", + "axis.plot(\n", + " direct_diagnostics.index,\n", + " direct_diagnostics[\"tf_match\"],\n", + " marker=\"o\",\n", + " label=\"TF matched weight\",\n", + ")\n", + "axis.plot(\n", + " direct_diagnostics.index,\n", + " direct_diagnostics[\"validation_score\"],\n", + " marker=\"o\",\n", + " label=\"held-out validation\",\n", + ")\n", + "axis.axvline(selected_direct_lambda, color=\"black\", linestyle=\"--\", label=\"selected lambda\")\n", + "axis.set(xscale=\"symlog\", xlabel=\"lambda\", ylabel=\"score\", ylim=(-0.03, 1.03))\n", + "axis.legend(frameon=False)\n", + "axis.set_title(\"Differential-network model selection\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "display(direct_diagnostics)\n", + "print(f\"Selected differential-network lambda: {selected_direct_lambda:g}\")" + ] + }, + { + "cell_type": "markdown", + "id": "872dc98d", + "metadata": { + "papermill": { + "duration": 0.008242, + "end_time": "2026-08-06T17:19:07.284996+00:00", + "exception": false, + "start_time": "2026-08-06T17:19:07.276754+00:00", + "status": "completed" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[13/30] accepted (total accepted=12) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=33, rel.err=0.8333 (excluded from tol. check)\n" - ] + "tags": [] + }, + "source": [ + "### Selected differential network and full-data corroboration\n", + "\n", + "The selected training network remains the inferential result: it was frozen before the\n", + "held-out samples were used for validation. Rebuilding the topology after adding those\n", + "samples would consume the holdout and, because CARNIVAL's edge penalty is absolute, the\n", + "same lambda is not automatically comparable when many more significant TF outputs enter\n", + "the full-data contrast.\n", + "\n", + "We therefore use all leukemia samples only for a sensitivity summary of gene, receptor,\n", + "and TF evidence. The network shown below is the held-out-tested model. Its node signs are\n", + "relative: `+1` means higher inferred activity in spontaneous leukemia and `-1` means\n", + "higher inferred activity in SB leukemia." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "bb845857", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-06T17:19:07.301031Z", + "iopub.status.busy": "2026-08-06T17:19:07.300892Z", + "iopub.status.idle": "2026-08-06T17:19:09.822002Z", + "shell.execute_reply": "2026-08-06T17:19:09.821613Z" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[14/30] accepted (total accepted=13) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=26, rel.err=0.4444 (excluded from tol. check)\n" - ] + "papermill": { + "duration": 2.529911, + "end_time": "2026-08-06T17:19:09.822491+00:00", + "exception": false, + "start_time": "2026-08-06T17:19:07.292580+00:00", + "status": "completed" }, + "tags": [] + }, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[15/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=2) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=41, rel.err=1.2778 (excluded from tol. check)\n" + "Fitting dispersions...\n", + "... done in 0.03 seconds.\n", + "\n", + "Fitting MAP dispersions...\n", + "... done in 0.03 seconds.\n", + "\n", + "Fitting LFCs...\n", + "... done in 0.04 seconds.\n", + "\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[16/30] accepted (total accepted=14) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=46, rel.err=1.5556 (excluded from tol. check)\n" - ] + "data": { + "text/plain": [ + "reachable_tfs 6.000000\n", + "reachable_weight 1.000000\n", + "tf_match 0.847530\n", + "tf_inactive 0.152470\n", + "tf_opposite 0.000000\n", + "active_edges 17.000000\n", + "active_nodes 17.000000\n", + "active_inputs 1.000000\n", + "heldout_validation_score 0.474966\n", + "heldout_pathway_concordance 0.707311\n", + "Name: held-out-tested differential network, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[17/30] accepted (total accepted=15) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=29, rel.err=0.6111 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[18/30] accepted (total accepted=16) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=34, rel.err=0.8889 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[19/30] accepted (total accepted=17) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=18, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[20/30] accepted (total accepted=18) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=26, rel.err=0.4444 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[21/30] accepted (total accepted=19) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=39, rel.err=1.1667 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[22/30] accepted (total accepted=20) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=39, rel.err=1.1667 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[23/30] accepted (total accepted=21) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=58, rel.err=2.2222 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[24/30] accepted (total accepted=22) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=37, rel.err=1.0556 (excluded from tol. check)\n" - ] + "data": { + "text/plain": [ + "direct_DE_genes 537.0\n", + "spontaneous_higher_genes 58.0\n", + "SB_higher_genes 479.0\n", + "differential_receptor_candidates 30.0\n", + "significant_differential_TFs 35.0\n", + "training_DE_genes_replicated 39.0\n", + "replicated_gene_direction_agreement 1.0\n", + "Name: all-sample evidence sensitivity, dtype: float64" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "full_direct_adata = adata[\n", + " adata.obs[\"condition\"].isin([\"Leukemic_spontaneous\", \"Leukemic_SB\"])\n", + "].copy()\n", + "full_direct_results = fit_direct_de(full_direct_adata)\n", + "full_direct_mat = full_direct_results[[\"stat\"]].T.rename(\n", + " index={\"stat\": \"Leukemic_spontaneous.vs.Leukemic_SB\"}\n", + ")\n", + "full_direct_tf_acts, full_direct_tf_pvals = dc.mt.ulm(\n", + " data=full_direct_mat, net=collectri, verbose=False\n", + ")\n", + "full_direct_significant_tfs = (\n", + " full_direct_tf_acts[full_direct_tf_pvals <= max_pval].T.dropna()\n", + ")\n", + "full_direct_receptors = select_direct_receptors(\n", + " full_direct_results, unique_receptors, G\n", + ")\n", + "\n", + "full_direct_gene_mask = (\n", + " (full_direct_results[\"padj\"] <= gene_fdr)\n", + " & (full_direct_results[\"log2FoldChange\"].abs() >= gene_threshold)\n", + ")\n", + "full_direct_gene_sign = np.sign(\n", + " full_direct_results.loc[full_direct_gene_mask, \"log2FoldChange\"]\n", + ")\n", + "training_specific_genes = direct_gene_table.index[direct_gene_sig]\n", + "replicated_genes = training_specific_genes.intersection(full_direct_gene_sign.index)\n", + "training_full_sign_agreement = (\n", + " np.sign(direct_results.loc[replicated_genes, \"log2FoldChange\"])\n", + " == full_direct_gene_sign.loc[replicated_genes]\n", + ").mean()\n", + "\n", + "final_direct_solution = direct_solutions[selected_direct_lambda]\n", + "selected_direct_metrics = pd.Series(\n", + " {\n", + " **tf_fit_metrics(final_direct_solution, 0, direct_measurements),\n", + " **structural_metrics(final_direct_solution, 0, direct_inputs),\n", + " \"heldout_validation_score\": direct_diagnostics.loc[\n", + " selected_direct_lambda, \"validation_score\"\n", + " ],\n", + " \"heldout_pathway_concordance\": direct_diagnostics.loc[\n", + " selected_direct_lambda, \"pathway_concordance\"\n", + " ],\n", + " },\n", + " name=\"held-out-tested differential network\",\n", + ")\n", + "full_evidence_metrics = pd.Series(\n", + " {\n", + " \"direct_DE_genes\": int(full_direct_gene_mask.sum()),\n", + " \"spontaneous_higher_genes\": int((full_direct_gene_sign > 0).sum()),\n", + " \"SB_higher_genes\": int((full_direct_gene_sign < 0).sum()),\n", + " \"differential_receptor_candidates\": len(full_direct_receptors),\n", + " \"significant_differential_TFs\": len(full_direct_significant_tfs),\n", + " \"training_DE_genes_replicated\": len(replicated_genes),\n", + " \"replicated_gene_direction_agreement\": training_full_sign_agreement,\n", + " },\n", + " name=\"all-sample evidence sensitivity\",\n", + ")\n", + "\n", + "display(selected_direct_metrics)\n", + "full_evidence_metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "886fbd8e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-06T17:19:09.839091Z", + "iopub.status.busy": "2026-08-06T17:19:09.838981Z", + "iopub.status.idle": "2026-08-06T17:19:09.875654Z", + "shell.execute_reply": "2026-08-06T17:19:09.875241Z" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[25/30] accepted (total accepted=23) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=39, rel.err=1.1667 (excluded from tol. check)\n" - ] + "papermill": { + "duration": 0.044804, + "end_time": "2026-08-06T17:19:09.876101+00:00", + "exception": false, + "start_time": "2026-08-06T17:19:09.831297+00:00", + "status": "completed" }, + "tags": [] + }, + "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[26/30] accepted (total accepted=24) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=36, rel.err=1.0000 (excluded from tol. check)\n" - ] + "data": { + "text/html": [], + "text/plain": [] + }, + "metadata": {}, + "output_type": "display_data" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[27/30] rejected (tol=0.05 violated by 'error_sample1_0' with rel.err=0.3333) (total rejected=3) -> error_sample1_0: val=2, rel.err=0.3333, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=1, rel.err=1000000000.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=43, rel.err=1.3889 (excluded from tol. check)\n" - ] + "data": { + "text/html": [], + "text/plain": [] + }, + "metadata": {}, + "output_type": "display_data" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[28/30] accepted (total accepted=25) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=36, rel.err=1.0000 (excluded from tol. check)\n" - 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{ - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[29/30] accepted (total accepted=26) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=36, rel.err=1.0000 (excluded from tol. check)\n" - ] + "tags": [] + }, + "source": [ + "The direct network captures relative leukemia differences that the shared-control models\n", + "are not forced to fit. It should therefore be interpreted alongside—not instead of—the\n", + "multi-condition analysis below: the former emphasizes differences, while the latter\n", + "retains mechanisms shared by both leukemias relative to normal." + ] + }, + { + "cell_type": "markdown", + "id": "560db3a1", + "metadata": { + "papermill": { + "duration": 0.007733, + "end_time": "2026-08-06T17:19:09.906384+00:00", + "exception": false, + "start_time": "2026-08-06T17:19:09.898651+00:00", + "status": "completed" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[30/30] accepted (total accepted=27) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=40, rel.err=1.2222 (excluded from tol. check)\n" - ] + "tags": [] + }, + "source": [ + "## Multi-condition network inference and deterministic $\\lambda$ analysis\n", + "\n", + "We now return to the two disease-versus-normal conditions. These models retain shared\n", + "leukemia biology and estimate absolute control-relative signs; their union regularizer is\n", + "not intended to replace the direct differential network above." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "de6c8d47", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-06T17:19:09.922457Z", + "iopub.status.busy": "2026-08-06T17:19:09.922336Z", + "iopub.status.idle": "2026-08-06T17:19:50.211055Z", + "shell.execute_reply": "2026-08-06T17:19:50.210685Z" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:Done. accepted=27, rejected=3, solutions returned=28\n" - ] + "papermill": { + "duration": 40.297779, + "end_time": "2026-08-06T17:19:50.211625+00:00", + "exception": false, + "start_time": "2026-08-06T17:19:09.913846+00:00", + "status": "completed" }, + "tags": [] + }, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "lambda: 0.1\n" + "Solving lambda=0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Unreachable vertices for sample: 12\n", - "Unreachable vertices for sample: 7\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[1/30] accepted (total accepted=1) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[2/30] accepted (total accepted=2) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[3/30] accepted (total accepted=3) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[4/30] accepted (total accepted=4) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[5/30] accepted (total accepted=5) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[6/30] accepted (total accepted=6) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[7/30] accepted (total accepted=7) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[8/30] accepted (total accepted=8) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 2\n", + "Unreachable vertices for sample: 10\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[9/30] accepted (total accepted=9) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Solving lambda=0.001\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[10/30] accepted (total accepted=10) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[11/30] accepted (total accepted=11) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[12/30] accepted (total accepted=12) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 2\n", + "Unreachable vertices for sample: 10\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[13/30] accepted (total accepted=13) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Solving lambda=0.003\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[14/30] accepted (total accepted=14) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[15/30] accepted (total accepted=15) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[16/30] accepted (total accepted=16) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 2\n", + "Unreachable vertices for sample: 10\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[17/30] accepted (total accepted=17) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Solving lambda=0.01\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[18/30] accepted (total accepted=18) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[19/30] accepted (total accepted=19) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[20/30] accepted (total accepted=20) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 2\n", + "Unreachable vertices for sample: 10\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[21/30] accepted (total accepted=21) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Solving lambda=0.03\n", + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[22/30] accepted (total accepted=22) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[23/30] accepted (total accepted=23) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 2\n", + "Unreachable vertices for sample: 10\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[24/30] accepted (total accepted=24) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Solving lambda=0.1\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[25/30] accepted (total accepted=25) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[26/30] accepted (total accepted=26) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[27/30] accepted (total accepted=27) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 2\n", + "Unreachable vertices for sample: 10\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[28/30] accepted (total accepted=28) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Solving lambda=0.3\n", + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[29/30] accepted (total accepted=29) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 0\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:corneto.methods.sampler:[30/30] accepted (total accepted=30) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" + "Unreachable vertices for sample: 2\n", + "Unreachable vertices for sample: 10\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:Done. accepted=30, rejected=0, solutions returned=31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "lambda: 0.2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unreachable vertices for sample: 12\n", - "Unreachable vertices for sample: 7\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[1/30] accepted (total accepted=1) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[2/30] accepted (total accepted=2) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[3/30] accepted (total accepted=3) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[4/30] accepted (total accepted=4) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[5/30] accepted (total accepted=5) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[6/30] accepted (total accepted=6) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[7/30] accepted (total accepted=7) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[8/30] accepted (total accepted=8) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[9/30] accepted (total accepted=9) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[10/30] accepted (total accepted=10) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[11/30] accepted (total accepted=11) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/docs/tutorials/contrib/multi_condition_tutorial/.pixi/envs/default/lib/python3.11/site-packages/numpy/_core/fromnumeric.py:83: RuntimeWarning: invalid value encountered in reduce\n", - " return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[12/30] accepted (total accepted=12) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[13/30] accepted (total accepted=13) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[14/30] accepted (total accepted=14) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[15/30] accepted (total accepted=15) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[16/30] accepted (total accepted=16) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[17/30] accepted (total accepted=17) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[18/30] accepted (total accepted=18) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[19/30] accepted (total accepted=19) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[20/30] accepted (total accepted=20) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[21/30] accepted (total accepted=21) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[22/30] accepted (total accepted=22) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[23/30] accepted (total accepted=23) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[24/30] accepted (total accepted=24) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[25/30] accepted (total accepted=25) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[26/30] accepted (total accepted=26) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[27/30] accepted (total accepted=27) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[28/30] accepted (total accepted=28) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[29/30] accepted (total accepted=29) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[30/30] accepted (total accepted=30) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:Done. accepted=30, rejected=0, solutions returned=31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "lambda: 0.3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unreachable vertices for sample: 12\n", - "Unreachable vertices for sample: 7\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[1/30] accepted (total accepted=1) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[2/30] accepted (total accepted=2) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[3/30] accepted (total accepted=3) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[4/30] accepted (total accepted=4) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[5/30] accepted (total accepted=5) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[6/30] accepted (total accepted=6) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[7/30] accepted (total accepted=7) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[8/30] accepted (total accepted=8) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[9/30] accepted (total accepted=9) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[10/30] accepted (total accepted=10) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[11/30] accepted (total accepted=11) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[12/30] accepted (total accepted=12) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[13/30] accepted (total accepted=13) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[14/30] accepted (total accepted=14) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[15/30] accepted (total accepted=15) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[16/30] accepted (total accepted=16) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[17/30] accepted (total accepted=17) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[18/30] accepted (total accepted=18) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[19/30] accepted (total accepted=19) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[20/30] accepted (total accepted=20) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[21/30] accepted (total accepted=21) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[22/30] accepted (total accepted=22) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[23/30] accepted (total accepted=23) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[24/30] accepted (total accepted=24) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[25/30] accepted (total accepted=25) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[26/30] accepted (total accepted=26) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[27/30] accepted (total accepted=27) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[28/30] accepted (total accepted=28) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[29/30] accepted (total accepted=29) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[30/30] accepted (total accepted=30) -> error_sample1_0: val=3, rel.err=0.0000, error_sample2_1: val=2, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:Done. accepted=30, rejected=0, solutions returned=31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "lambda: 0.5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unreachable vertices for sample: 12\n", - "Unreachable vertices for sample: 7\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[1/30] accepted (total accepted=1) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[2/30] accepted (total accepted=2) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[3/30] accepted (total accepted=3) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[4/30] accepted (total accepted=4) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[5/30] accepted (total accepted=5) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[6/30] accepted (total accepted=6) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[7/30] accepted (total accepted=7) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[8/30] accepted (total accepted=8) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[9/30] accepted (total accepted=9) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[10/30] accepted (total accepted=10) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[11/30] accepted (total accepted=11) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[12/30] accepted (total accepted=12) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[13/30] accepted (total accepted=13) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[14/30] accepted (total accepted=14) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[15/30] accepted (total accepted=15) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[16/30] accepted (total accepted=16) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[17/30] accepted (total accepted=17) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[18/30] accepted (total accepted=18) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[19/30] accepted (total accepted=19) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[20/30] accepted (total accepted=20) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[21/30] accepted (total accepted=21) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[22/30] accepted (total accepted=22) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[23/30] accepted (total accepted=23) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[24/30] accepted (total accepted=24) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[25/30] accepted (total accepted=25) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[26/30] accepted (total accepted=26) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[27/30] accepted (total accepted=27) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[28/30] accepted (total accepted=28) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[29/30] accepted (total accepted=29) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[30/30] accepted (total accepted=30) -> error_sample1_0: val=4, rel.err=0.0000, error_sample2_1: val=5, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=3, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:Done. accepted=30, rejected=0, solutions returned=31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "lambda: 0.7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unreachable vertices for sample: 12\n", - "Unreachable vertices for sample: 7\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[1/30] accepted (total accepted=1) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[2/30] accepted (total accepted=2) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[3/30] accepted (total accepted=3) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[4/30] accepted (total accepted=4) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[5/30] accepted (total accepted=5) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[6/30] accepted (total accepted=6) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[7/30] accepted (total accepted=7) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[8/30] accepted (total accepted=8) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[9/30] accepted (total accepted=9) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[10/30] accepted (total accepted=10) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[11/30] accepted (total accepted=11) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[12/30] accepted (total accepted=12) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[13/30] accepted (total accepted=13) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[14/30] accepted (total accepted=14) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[15/30] accepted (total accepted=15) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[16/30] accepted (total accepted=16) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[17/30] accepted (total accepted=17) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[18/30] accepted (total accepted=18) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[19/30] accepted (total accepted=19) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[20/30] accepted (total accepted=20) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[21/30] accepted (total accepted=21) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[22/30] accepted (total accepted=22) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[23/30] accepted (total accepted=23) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[24/30] accepted (total accepted=24) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[25/30] accepted (total accepted=25) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[26/30] accepted (total accepted=26) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[27/30] accepted (total accepted=27) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[28/30] accepted (total accepted=28) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[29/30] accepted (total accepted=29) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[30/30] accepted (total accepted=30) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:Done. accepted=30, rejected=0, solutions returned=31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "lambda: 0.9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unreachable vertices for sample: 12\n", - "Unreachable vertices for sample: 7\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[1/30] accepted (total accepted=1) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[2/30] accepted (total accepted=2) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[3/30] accepted (total accepted=3) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[4/30] accepted (total accepted=4) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[5/30] accepted (total accepted=5) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[6/30] accepted (total accepted=6) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[7/30] accepted (total accepted=7) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[8/30] accepted (total accepted=8) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[9/30] accepted (total accepted=9) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[10/30] accepted (total accepted=10) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[11/30] accepted (total accepted=11) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[12/30] accepted (total accepted=12) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[13/30] accepted (total accepted=13) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[14/30] accepted (total accepted=14) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[15/30] accepted (total accepted=15) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[16/30] accepted (total accepted=16) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[17/30] accepted (total accepted=17) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[18/30] accepted (total accepted=18) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[19/30] accepted (total accepted=19) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[20/30] accepted (total accepted=20) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[21/30] accepted (total accepted=21) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[22/30] accepted (total accepted=22) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[23/30] accepted (total accepted=23) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[24/30] accepted (total accepted=24) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[25/30] accepted (total accepted=25) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[26/30] accepted (total accepted=26) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[27/30] accepted (total accepted=27) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[28/30] accepted (total accepted=28) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[29/30] accepted (total accepted=29) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[30/30] accepted (total accepted=30) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:Done. accepted=30, rejected=0, solutions returned=31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "lambda: 0.95\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unreachable vertices for sample: 12\n", - "Unreachable vertices for sample: 7\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[1/30] accepted (total accepted=1) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[2/30] accepted (total accepted=2) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[3/30] accepted (total accepted=3) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[4/30] accepted (total accepted=4) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[5/30] accepted (total accepted=5) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[6/30] accepted (total accepted=6) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[7/30] accepted (total accepted=7) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[8/30] accepted (total accepted=8) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[9/30] accepted (total accepted=9) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[10/30] accepted (total accepted=10) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[11/30] accepted (total accepted=11) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[12/30] accepted (total accepted=12) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[13/30] accepted (total accepted=13) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[14/30] accepted (total accepted=14) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[15/30] accepted (total accepted=15) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[16/30] accepted (total accepted=16) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[17/30] accepted (total accepted=17) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[18/30] accepted (total accepted=18) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[19/30] accepted (total accepted=19) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[20/30] accepted (total accepted=20) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[21/30] accepted (total accepted=21) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[22/30] accepted (total accepted=22) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[23/30] accepted (total accepted=23) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[24/30] accepted (total accepted=24) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[25/30] accepted (total accepted=25) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[26/30] accepted (total accepted=26) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[27/30] accepted (total accepted=27) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[28/30] accepted (total accepted=28) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[29/30] accepted (total accepted=29) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:[30/30] accepted (total accepted=30) -> error_sample1_0: val=5, rel.err=0.0000, error_sample2_1: val=6, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=0, rel.err=0.0000 (excluded from tol. check)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:corneto.methods.sampler:Done. accepted=30, rejected=0, solutions returned=31\n" - ] - } - ], - "source": [ - "# retrieving alternative network solutions. This snippet will take a while to run\n", - "from corneto.methods.sampler import sample_alternative_solutions\n", - "\n", - "lambda_val = [0, 0.01, 0.1, 0.2, 0.3, 0.5, 0.7, 0.9, 0.95]\n", - "other_optimal_v = dict() # collecting the vertex sampled solutions for each lambda\n", - "index = dict()\n", - "for i in lambda_val:\n", - " print(\"lambda: \", i)\n", - " c = CarnivalFlow(lambda_reg=i, indirect_rule_penalty=1)\n", - " P = c.build(G, data)\n", - " vertex_results = sample_alternative_solutions(\n", - " P,\n", - " \"vertex_value\",\n", - " percentage=0.03,\n", - " scale=0.03,\n", - " rel_opt_tol=0.05,\n", - " max_samples=30, # number of alternative solutions to sample\n", - " solver_kwargs=dict(solver=\"gurobi\", max_seconds=max_time, mip_gap=0.01, seed=seed),\n", - " )\n", - " other_optimal_v[\"lambda\" + str(i)] = vertex_results[\"vertex_value\"]\n", - " index[\"lambda\" + str(i)] = c.processed_graph.V" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "433c2052", - "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:24:46.333452Z", - "iopub.status.busy": "2025-06-27T16:24:46.333299Z", - "iopub.status.idle": "2025-06-27T16:24:46.336876Z", - "shell.execute_reply": "2025-06-27T16:24:46.336578Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def compute_nodes_metrics(nodes, inputs, measurements, results_df, column):\n", - " \"\"\"Computes the metrics for the nodes in the network based on the provided inputs, measurements, and results dataframe.\n", - "\n", - " Args:\n", - " nodes (pd.DataFrame): data frame containing the node scores per condition.\n", - " inputs (dict): Dictionary of input nodes with their values.\n", - " measurements (dict): Dictionary of output nodes with their values.\n", - " results_df (pd.DataFrame): DataFrame containing the results of differential expression analysis.\n", - " column (str): Column name to be used for filtering conditions.\n", - "\n", - " Returns:\n", - " sign_agreement_ratio (float): Ratio of nodes with the same sign as the DE analysis.\n", - " de_overlap_ratio (float): Ratio of DE genes that overlap with the nodes.\n", - " overall_agreement (float): Overall agreement between DE genes and nodes.\n", - " \"\"\"\n", - " nodes = nodes.loc[(nodes[column] != 0.0)][column] # nodes with a score different from 0\n", - " de = results_df.loc[results_df[\"padj\"] < 0.05].index.tolist() # de genes\n", - " in_out_nodes = set(measurements.keys()).union(\n", - " set(nodes.index[nodes.index.isin(inputs.keys())])\n", - " ) # collecting input and output nodes which we don´t want to evaluate\n", - " middle_nodes = set(nodes.index).difference(in_out_nodes)\n", - " commmon_genes = list(\n", - " set(results_df.index).intersection(middle_nodes)\n", - " ) # common genes between network and DE analysis genes\n", - " stat = results_df.loc[commmon_genes, \"stat\"]\n", - " stat_sign = np.sign(stat)\n", - " middle_nodes_sign = np.sign(nodes.loc[commmon_genes])\n", - " sign_agreement_ratio = 0\n", - " if len(middle_nodes_sign) > 0:\n", - " sign_agreement_ratio = (middle_nodes_sign == stat_sign).sum() / len(middle_nodes_sign)\n", - " de_overlap_ratio = 0\n", - " if len(middle_nodes) > 0:\n", - " de_overlap_ratio = len(set(de).intersection(middle_nodes)) / len(middle_nodes)\n", - " overall_agreement = (np.array(de_overlap_ratio) + np.array(sign_agreement_ratio)) / 2\n", - "\n", - " return sign_agreement_ratio, de_overlap_ratio, overall_agreement" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "5cbd529a", - "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:24:46.359822Z", - "iopub.status.busy": "2025-06-27T16:24:46.359691Z", - "iopub.status.idle": "2025-06-27T16:24:47.176230Z", - "shell.execute_reply": "2025-06-27T16:24:47.175953Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Initialize metric containers\n", - "metrics_avg_1_per_lambda = []\n", - "metrics_avg_2_per_lambda = []\n", - "metrics_sd_1_per_lambda = []\n", - "metrics_sd_2_per_lambda = []\n", - "\n", - "for k in other_optimal_v.keys():\n", - " metrics1_per_sample = []\n", - " metrics2_per_sample = []\n", - "\n", - " for i in range(len(other_optimal_v[k])):\n", - " n = pd.DataFrame(\n", - " other_optimal_v[k][i],\n", - " index=index[k],\n", - " columns=[\"vertex_activity1\", \"vertex_activity2\"],\n", - " ) # retrieving node scores\n", - "\n", - " sign_agreement1, de_overlap1, overall_agreement1 = compute_nodes_metrics(\n", - " n, inputs1, d_measurements1, results_df1, \"vertex_activity1\"\n", - " )\n", - " sign_agreement2, de_overlap2, overall_agreement2 = compute_nodes_metrics(\n", - " n, inputs2, d_measurements2, results_df2, \"vertex_activity2\"\n", - " )\n", - "\n", - " metrics1_per_sample.append(\n", - " {\n", - " \"sign_agreement\": sign_agreement1,\n", - " \"de_overlap\": de_overlap1,\n", - " \"overall\": overall_agreement1,\n", - " }\n", - " )\n", - " metrics2_per_sample.append(\n", - " {\n", - " \"sign_agreement\": sign_agreement2,\n", - " \"de_overlap\": de_overlap2,\n", - " \"overall\": overall_agreement2,\n", - " }\n", - " )\n", - "\n", - " metrics1_per_sample_df = pd.DataFrame(metrics1_per_sample)\n", - " metrics2_per_sample_df = pd.DataFrame(metrics2_per_sample)\n", - " metrics_avg_1_per_lambda.append(metrics1_per_sample_df.mean().to_dict())\n", - " metrics_avg_2_per_lambda.append(metrics2_per_sample_df.mean().to_dict())\n", - " metrics_sd_1_per_lambda.append(metrics1_per_sample_df.std().to_dict())\n", - " metrics_sd_2_per_lambda.append(metrics2_per_sample_df.std().to_dict())\n", - "\n", - "# Convert lists of dictionaries to DataFrames\n", - "m_avg_df1 = pd.DataFrame(metrics_avg_1_per_lambda)\n", - "m_avg_df2 = pd.DataFrame(metrics_avg_2_per_lambda)\n", - "m_sd_df1 = pd.DataFrame(metrics_sd_1_per_lambda)\n", - "m_sd_df2 = pd.DataFrame(metrics_sd_2_per_lambda)\n", - "\n", - "##plotting metrics\n", - "import matplotlib.pyplot as plt\n", - "\n", - "metric_name = [\"sign_agreement\", \"de_overlap\", \"overall\"]\n", - "metric_label = [\"Sign agreement ratio\", \"DE overlap ratio\", \"Overall agreement\"]\n", - "\n", - "fig, axes = plt.subplots(1, 3, figsize=(25, 5), sharex=True)\n", - "for i in range(len(metric_name)):\n", - " axes[i].errorbar(\n", - " lambda_val,\n", - " m_avg_df1[metric_name[i]],\n", - " yerr=m_sd_df1[metric_name[i]],\n", - " label=\"Leukemic_spontaneous\",\n", - " marker=\"o\",\n", - " )\n", - " axes[i].errorbar(\n", - " lambda_val,\n", - " m_avg_df2[metric_name[i]],\n", - " yerr=m_sd_df2[metric_name[i]],\n", - " label=\"Leukemic_SB\",\n", - " marker=\"o\",\n", - " )\n", - " axes[i].grid()\n", - " axes[i].set_xscale(\"linear\") # adjust scale according to your results\n", - " axes[i].set_xlabel(\"Lambda\")\n", - " axes[i].set_ylabel(metric_label[i])\n", - " axes[i].set_title(metric_label[i] + \" vs Lambda\")\n", - " if i == 0:\n", - " axes[i].legend(loc=\"upper left\")" - ] - }, - { - "cell_type": "markdown", - "id": "8c64a16f", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "source": [ - "We choose the lambda value basing on the Overall agreement metric. In this case a reasonable value of $\\lambda$ is 2" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "6be6c716", - "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:24:47.222512Z", - "iopub.status.busy": "2025-06-27T16:24:47.222360Z", - "iopub.status.idle": "2025-06-27T16:24:48.755251Z", - "shell.execute_reply": "2025-06-27T16:24:48.755020Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: Your problem has 15944 variables, 45059 constraints, and 1 parameters.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: It is compliant with the following grammars: DCP, DQCP\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: DCP verification time: 0.0006 seconds.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: Expression tree has 11 nodes.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: Your problem is compiled with the CPP canonicalization backend.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: Compiling problem (target solver=GUROBI).\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: Reduction chain: Dcp2Cone -> CvxAttr2Constr -> EliminateZeroSized -> ConeMatrixStuffing -> GUROBI\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: Applying reduction Dcp2Cone\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: Applying reduction CvxAttr2Constr\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: Applying reduction EliminateZeroSized\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: Applying reduction ConeMatrixStuffing\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: Applying reduction GUROBI\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: Finished problem compilation (took 8.338e-02 seconds).\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: (Subsequent compilations of this problem, using the same arguments, should take less time.)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "(CVXPY) Jul 22 04:23:17 PM: Invoking solver GUROBI to obtain a solution.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unreachable vertices for sample: 12\n", - "Unreachable vertices for sample: 7\n", - "===============================================================================\n", - " CVXPY \n", - " v1.9.2 \n", - "===============================================================================\n", - "-------------------------------------------------------------------------------\n", - " Compilation \n", - "-------------------------------------------------------------------------------\n", - "-------------------------------------------------------------------------------\n", - " Numerical solver \n", - "-------------------------------------------------------------------------------\n", - "Set parameter OutputFlag to value 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set parameter QCPDual to value 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gurobi Optimizer version 12.0.2 build v12.0.2rc0 (mac64[arm] - Darwin 25.5.0 25F84)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU model: Apple M4 Pro\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Thread count: 12 physical cores, 12 logical processors, using up to 12 threads\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Non-default parameters:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "QCPDual 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimize a model with 45059 rows, 15944 columns and 159976 nonzeros\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model fingerprint: 0xad65e9b6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Variable types: 3844 continuous, 12100 integer (12100 binary)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Coefficient statistics:\n" - ] + "data": { + "text/html": [ + "
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70.010SB41.00.7291940.2708060.0510.200000...-0.33929850.727538101010.7291940.01410
80.030spontaneous61.00.7044990.2955010.0930.333333...-0.49307790.818096141410.7044990.01410
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14 rows × 22 columns

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" + ], + "text/plain": [ + " lambda condition reachable_tfs reachable_weight tf_match \\\n", + "0 0.000 spontaneous 6 1.0 0.704499 \n", + "1 0.000 SB 4 1.0 0.729194 \n", + "2 0.001 spontaneous 6 1.0 0.704499 \n", + "3 0.001 SB 4 1.0 0.729194 \n", + "4 0.003 spontaneous 6 1.0 0.704499 \n", + "5 0.003 SB 4 1.0 0.729194 \n", + "6 0.010 spontaneous 6 1.0 0.704499 \n", + "7 0.010 SB 4 1.0 0.729194 \n", + "8 0.030 spontaneous 6 1.0 0.704499 \n", + "9 0.030 SB 4 1.0 0.729194 \n", + "10 0.100 spontaneous 6 1.0 0.537594 \n", + "11 0.100 SB 4 1.0 0.729194 \n", + "12 0.300 spontaneous 6 1.0 0.000000 \n", + "13 0.300 SB 4 1.0 0.000000 \n", + "\n", + " tf_inactive tf_opposite active_internal_nodes \\\n", + "0 0.295501 0.0 21 \n", + "1 0.270806 0.0 4 \n", + "2 0.295501 0.0 9 \n", + "3 0.270806 0.0 5 \n", + "4 0.295501 0.0 8 \n", + "5 0.270806 0.0 7 \n", + "6 0.295501 0.0 9 \n", + "7 0.270806 0.0 5 \n", + "8 0.295501 0.0 9 \n", + "9 0.270806 0.0 5 \n", + "10 0.462406 0.0 6 \n", + "11 0.270806 0.0 5 \n", + "12 1.000000 0.0 0 \n", + "13 1.000000 0.0 0 \n", + "\n", + " deregulated_internal_nodes node_deregulated_fraction ... \\\n", + "0 7 0.333333 ... \n", + "1 1 0.250000 ... \n", + "2 3 0.333333 ... \n", + "3 1 0.200000 ... \n", + "4 2 0.250000 ... \n", + "5 1 0.142857 ... \n", + "6 3 0.333333 ... \n", + "7 1 0.200000 ... \n", + "8 3 0.333333 ... \n", + "9 1 0.200000 ... \n", + "10 3 0.500000 ... \n", + "11 1 0.200000 ... \n", + "12 0 0.000000 ... \n", + "13 0 0.000000 ... \n", + "\n", + " node_deregulation_z annotated_internal_nodes pathway_concordance \\\n", + "0 -0.760992 16 0.657710 \n", + "1 -0.076571 3 0.552921 \n", + "2 -0.493077 9 0.788685 \n", + "3 -0.339298 5 0.727538 \n", + "4 -0.945899 8 0.802111 \n", + "5 -0.745687 7 0.772395 \n", + "6 -0.493077 9 0.788685 \n", + "7 -0.339298 5 0.727538 \n", + "8 -0.493077 9 0.818096 \n", + "9 -0.339298 5 0.730120 \n", + "10 0.430854 6 0.850635 \n", + "11 -0.339298 5 0.748767 \n", + "12 0.000000 0 0.000000 \n", + "13 0.000000 0 0.000000 \n", + "\n", + " active_edges active_nodes active_inputs single_tf_match \\\n", + "0 30 30 3 0.704499 \n", + "1 11 11 2 0.729194 \n", + "2 14 14 1 0.704499 \n", + "3 10 10 1 0.729194 \n", + "4 14 14 1 0.704499 \n", + "5 12 12 1 0.729194 \n", + "6 14 14 1 0.704499 \n", + "7 10 10 1 0.729194 \n", + "8 14 14 1 0.704499 \n", + "9 10 10 1 0.729194 \n", + "10 10 10 1 0.000000 \n", + "11 10 10 1 0.000000 \n", + "12 0 0 0 0.000000 \n", + "13 0 0 0 0.000000 \n", + "\n", + " joint_fit_regret union_edges shared_edges \n", + "0 0.0 35 6 \n", + "1 0.0 35 6 \n", + "2 0.0 14 10 \n", + "3 0.0 14 10 \n", + "4 0.0 14 12 \n", + "5 0.0 14 12 \n", + "6 0.0 14 10 \n", + "7 0.0 14 10 \n", + "8 0.0 14 10 \n", + "9 0.0 14 10 \n", + "10 0.0 10 10 \n", + "11 0.0 10 10 \n", + "12 0.0 0 0 \n", + "13 0.0 0 0 \n", + "\n", + "[14 rows x 22 columns]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lambda_values = [0.0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3]\n", + "condition_specs = {\n", + " \"spontaneous\": dict(\n", + " data=data1,\n", + " inputs=inputs1,\n", + " measurements=d_measurements1,\n", + " results=validation_results_df1,\n", + " pathway_activity=validation_pathways1,\n", + " ),\n", + " \"SB\": dict(\n", + " data=data2,\n", + " inputs=inputs2,\n", + " measurements=d_measurements2,\n", + " results=validation_results_df2,\n", + " pathway_activity=validation_pathways2,\n", + " ),\n", + "}\n", + "\n", + "single_solutions = {}\n", + "joint_solutions = {}\n", + "diagnostic_rows = []\n", + "\n", + "for lambda_reg in lambda_values:\n", + " print(f\"Solving lambda={lambda_reg:g}\")\n", + " single_solutions[lambda_reg] = {\n", + " name: solve_carnival(G, spec[\"data\"], lambda_reg)\n", + " for name, spec in condition_specs.items()\n", + " }\n", + " joint_solutions[lambda_reg] = solve_carnival(G, data, lambda_reg)\n", + " joint_edge_activity = np.abs(joint_solutions[lambda_reg][\"edge_value\"]) > 0.5\n", + " union_edges = int(np.any(joint_edge_activity, axis=1).sum())\n", + " shared_edges = int(np.all(joint_edge_activity, axis=1).sum())\n", + "\n", + " for condition_index, (name, spec) in enumerate(condition_specs.items()):\n", + " single = single_solutions[lambda_reg][name]\n", + " joint = joint_solutions[lambda_reg]\n", + " single_fit = tf_fit_metrics(single, 0, spec[\"measurements\"])\n", + " joint_fit = tf_fit_metrics(joint, condition_index, spec[\"measurements\"])\n", + " row = {\n", + " \"lambda\": lambda_reg,\n", + " \"condition\": name,\n", + " **joint_fit,\n", + " **internal_validation_metrics(\n", + " joint,\n", + " condition_index,\n", + " spec[\"inputs\"],\n", + " spec[\"measurements\"],\n", + " spec[\"results\"],\n", + " spec[\"pathway_activity\"],\n", + " ),\n", + " **structural_metrics(joint, condition_index, spec[\"inputs\"]),\n", + " \"single_tf_match\": single_fit[\"tf_match\"],\n", + " \"joint_fit_regret\": max(0.0, single_fit[\"tf_match\"] - joint_fit[\"tf_match\"]),\n", + " \"union_edges\": union_edges,\n", + " \"shared_edges\": shared_edges,\n", + " }\n", + " diagnostic_rows.append(row)\n", + "\n", + "diagnostics = pd.DataFrame(diagnostic_rows)\n", + "diagnostics\n" + ] + }, + { + "cell_type": "markdown", + "id": "587afafa", + "metadata": { + "papermill": { + "duration": 0.008361, + "end_time": "2026-08-06T17:19:50.228040+00:00", + "exception": false, + "start_time": "2026-08-06T17:19:50.219679+00:00", + "status": "completed" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Matrix range [5e-01, 7e+02]\n" - ] + "tags": [] + }, + "source": [ + "### Eligibility and fair scoring\n", + "\n", + "Because TF evidence is normalized to total weight one per condition, a five-percentage-point loss in matched TF weight is used as the admissibility tolerance. A candidate is rejected if either condition has no active input, edge, or matched TF, or if joint fitting loses more TF support than allowed relative to the joint $\\lambda=0$ and single-condition references. This keeps internal-network validation from rewarding sparsity obtained by discarding important outputs.\n", + "\n", + "Eligible networks are scored using only held-out expression. The node score asks whether selected internal-node genes are more often deregulated than the reachable PKN background, without requiring expression and inferred protein activity to have the same sign. The pathway score compares SignaLink enrichment among selected internal nodes with absolute pathway-level deregulation in held-out expression. Both components are normalized within condition and weighted equally; the worst-condition score prevents the larger SB group from masking failure in the spontaneous condition.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "a14bea8b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-06T17:19:50.244800Z", + "iopub.status.busy": "2026-08-06T17:19:50.244685Z", + "iopub.status.idle": "2026-08-06T17:19:50.258706Z", + "shell.execute_reply": "2026-08-06T17:19:50.258206Z" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Objective range [5e-01, 2e+00]\n" - ] + "papermill": { + "duration": 0.023198, + "end_time": "2026-08-06T17:19:50.259222+00:00", + "exception": false, + "start_time": "2026-08-06T17:19:50.236024+00:00", + "status": "completed" }, + "tags": [] + }, + "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " Bounds range [1e+00, 1e+00]\n" - ] + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " eligible fair_score mean_score union_edges\n", + "lambda \n", + "0.000 True 0.357926 0.372263 35\n", + "0.001 True 0.463586 0.467274 14\n", + "0.003 True 0.471478 0.485739 14\n", + "0.010 True 0.463586 0.467274 14\n", + "0.030 True 0.472634 0.476754 14\n", + "0.100 False 0.484705 0.742352 10\n", + "0.300 False 0.000000 0.000000 0" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - " RHS range [1e+00, 1e+03]\n" + "Selected lambda: 0.03\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found heuristic solution: objective 0.0000000\n" - ] + "data": { + "text/html": [ + "
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lambdaconditionreachable_tfsreachable_weighttf_matchtf_inactivetf_oppositeactive_internal_nodesderegulated_internal_nodesnode_deregulated_fraction...joint_fit_regretunion_edgesshared_edgesfit_gatesingle_fit_gatenonempty_gatecondition_eligiblerelative_node_validationrelative_pathway_validationvalidation_score
80.03spontaneous61.00.7044990.2955010.0930.333333...0.01410TrueTrueTrueTrue0.00.9617470.480874
90.03SB41.00.7291940.2708060.0510.200000...0.01410TrueTrueTrueTrue0.00.9452670.472634
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2 rows × 29 columns

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" + ], + "text/plain": [ + " lambda condition reachable_tfs reachable_weight tf_match \\\n", + "8 0.03 spontaneous 6 1.0 0.704499 \n", + "9 0.03 SB 4 1.0 0.729194 \n", + "\n", + " tf_inactive tf_opposite active_internal_nodes \\\n", + "8 0.295501 0.0 9 \n", + "9 0.270806 0.0 5 \n", + "\n", + " deregulated_internal_nodes node_deregulated_fraction ... \\\n", + "8 3 0.333333 ... \n", + "9 1 0.200000 ... \n", + "\n", + " joint_fit_regret union_edges shared_edges fit_gate single_fit_gate \\\n", + "8 0.0 14 10 True True \n", + "9 0.0 14 10 True True \n", + "\n", + " nonempty_gate condition_eligible relative_node_validation \\\n", + "8 True True 0.0 \n", + "9 True True 0.0 \n", + "\n", + " relative_pathway_validation validation_score \n", + "8 0.961747 0.480874 \n", + "9 0.945267 0.472634 \n", + "\n", + "[2 rows x 29 columns]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "baseline_match = (\n", + " diagnostics.loc[diagnostics[\"lambda\"] == 0]\n", + " .set_index(\"condition\")[\"tf_match\"]\n", + " .to_dict()\n", + ")\n", + "tf_tolerance = {\"spontaneous\": 0.05, \"SB\": 0.05}\n", + "\n", + "diagnostics[\"fit_gate\"] = diagnostics.apply(\n", + " lambda row: row.tf_match >= baseline_match[row.condition] - tf_tolerance[row.condition],\n", + " axis=1,\n", + ")\n", + "diagnostics[\"single_fit_gate\"] = diagnostics.apply(\n", + " lambda row: row.joint_fit_regret <= tf_tolerance[row.condition], axis=1\n", + ")\n", + "diagnostics[\"nonempty_gate\"] = (\n", + " (diagnostics[\"active_edges\"] > 0)\n", + " & (diagnostics[\"active_inputs\"] > 0)\n", + " & (diagnostics[\"tf_match\"] > 0)\n", + ")\n", + "diagnostics[\"condition_eligible\"] = (\n", + " diagnostics[\"fit_gate\"]\n", + " & diagnostics[\"single_fit_gate\"]\n", + " & diagnostics[\"nonempty_gate\"]\n", + ")\n", + "\n", + "positive_node_z = diagnostics[\"node_deregulation_z\"].clip(lower=0)\n", + "best_node_z = positive_node_z.groupby(diagnostics[\"condition\"]).transform(\"max\")\n", + "best_pathway = diagnostics.groupby(\"condition\")[\"pathway_concordance\"].transform(\"max\")\n", + "diagnostics[\"relative_node_validation\"] = np.divide(\n", + " positive_node_z,\n", + " best_node_z,\n", + " out=np.zeros(len(diagnostics), dtype=float),\n", + " where=best_node_z > 0,\n", + ")\n", + "diagnostics[\"relative_pathway_validation\"] = np.divide(\n", + " diagnostics[\"pathway_concordance\"],\n", + " best_pathway,\n", + " out=np.zeros(len(diagnostics), dtype=float),\n", + " where=best_pathway > 0,\n", + ")\n", + "diagnostics[\"validation_score\"] = diagnostics[\n", + " [\"relative_node_validation\", \"relative_pathway_validation\"]\n", + "].mean(axis=1)\n", + "\n", + "selection = diagnostics.groupby(\"lambda\").agg(\n", + " eligible=(\"condition_eligible\", \"all\"),\n", + " fair_score=(\"validation_score\", \"min\"),\n", + " mean_score=(\"validation_score\", \"mean\"),\n", + " union_edges=(\"union_edges\", \"first\"),\n", + ")\n", + "\n", + "eligible = selection.loc[selection[\"eligible\"]].copy()\n", + "if eligible.empty:\n", + " raise RuntimeError(\"No lambda preserves a non-empty, adequately fitted network for both conditions\")\n", + "\n", + "best_fair = eligible[\"fair_score\"].max()\n", + "near_best = eligible.loc[eligible[\"fair_score\"] >= best_fair - 0.05]\n", + "selected_lambda = (\n", + " near_best.reset_index()\n", + " .sort_values(\n", + " [\"union_edges\", \"fair_score\", \"lambda\"],\n", + " ascending=[True, False, False],\n", + " )\n", + " .iloc[0][\"lambda\"]\n", + ")\n", + "\n", + "display(selection)\n", + "print(f\"Selected lambda: {selected_lambda:g}\")\n", + "diagnostics.loc[diagnostics[\"lambda\"] == selected_lambda]\n" + ] + }, + { + "cell_type": "markdown", + "id": "85cbf8d6", + "metadata": { + "papermill": { + "duration": 0.008374, + "end_time": "2026-08-06T17:19:50.275962+00:00", + "exception": false, + "start_time": "2026-08-06T17:19:50.267588+00:00", + "status": "completed" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Presolve removed 21164 rows and 1239 columns\n" - ] + "tags": [] + }, + "source": [ + "## Held-out validation interpretation\n", + "\n", + "Lambda selection above uses a fixed stratified biological holdout. TFs, receptor inputs, and networks are inferred only from training samples. Held-out expression is used only to test whether selected internal-node genes are disproportionately deregulated and whether their pathway composition agrees with held-out pathway-level deregulation. Neither criterion assumes that a protein's activity sign must equal the expression sign of its gene.\n", + "\n", + "These criteria support transcriptional responsiveness of network regions, not direct validation of receptor, kinase, or protein activity. Because the spontaneous holdout is necessarily small, the scores are tutorial-scale model-selection evidence rather than precise generalization estimates. A production analysis should repeat the stratified split, use sample-level pathway activities, and aggregate paired condition scores and label-permutation controls.\n", + "\n", + "This data-level holdout is distinct from alternative-network sampling: it informs model selection, whereas the solution sampler below is run only after lambda has been fixed.\n" + ] + }, + { + "cell_type": "markdown", + "id": "202f7e40", + "metadata": { + "papermill": { + "duration": 0.008177, + "end_time": "2026-08-06T17:19:50.291933+00:00", + "exception": false, + "start_time": "2026-08-06T17:19:50.283756+00:00", + "status": "completed" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Presolve time: 0.36s\n" - ] + "tags": [] + }, + "source": [ + "## Final model at the selected $\\lambda$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "full-data-refit", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-06T17:19:50.308915Z", + "iopub.status.busy": "2026-08-06T17:19:50.308803Z", + "iopub.status.idle": "2026-08-06T17:20:02.511734Z", + "shell.execute_reply": "2026-08-06T17:20:02.511191Z" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Presolved: 23895 rows, 14705 columns, 137721 nonzeros\n" - ] + "papermill": { + "duration": 12.212701, + "end_time": "2026-08-06T17:20:02.512545+00:00", + "exception": false, + "start_time": "2026-08-06T17:19:50.299844+00:00", + "status": "completed" }, + "tags": [] + }, + "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Variable types: 3431 continuous, 11274 integer (11261 binary)\n" + "Fitting dispersions...\n", + "... done in 0.01 seconds.\n", + "\n", + "Fitting MAP dispersions...\n", + "... done in 0.01 seconds.\n", + "\n", + "Fitting LFCs...\n", + "... done in 0.01 seconds.\n", + "\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Deterministic concurrent LP optimizer: primal and dual simplex\n" + "Fitting dispersions...\n", + "... done in 0.04 seconds.\n", + "\n", + "Fitting MAP dispersions...\n", + "... done in 0.04 seconds.\n", + "\n", + "Fitting LFCs...\n", + "... done in 0.03 seconds.\n", + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Showing primal log only...\n" + "Unreachable vertices for sample: 3\n", + "Unreachable vertices for sample: 11\n" ] + } + ], + "source": [ + "def fit_full_de(adata_subset, disease_condition):\n", + " full = adata_subset.copy()\n", + " dc.pp.filter_by_expr(\n", + " full,\n", + " group=\"condition\",\n", + " min_count=10,\n", + " min_total_count=15,\n", + " large_n=10,\n", + " min_prop=0.8,\n", + " )\n", + " inference = DefaultInference()\n", + " dds = DeseqDataSet(\n", + " adata=full,\n", + " design=\"~condition\",\n", + " refit_cooks=True,\n", + " inference=inference,\n", + " quiet=True,\n", + " )\n", + " dds.deseq2()\n", + " stats = DeseqStats(\n", + " dds,\n", + " contrast=[\"condition\", disease_condition, \"Normal\"],\n", + " inference=inference,\n", + " quiet=True,\n", + " )\n", + " stats.summary()\n", + " return stats.results_df\n", + "\n", + "\n", + "def infer_measurements(results_df, contrast_name):\n", + " matrix = results_df[[\"stat\"]].T.rename(index={\"stat\": contrast_name})\n", + " activities, pvalues = dc.mt.ulm(data=matrix, net=collectri, verbose=False)\n", + " selected = activities[pvalues <= max_pval].T.dropna()\n", + " raw = selected.loc[selected.index.intersection(G.V)].to_dict()[contrast_name]\n", + " return normalized_signed_measurements(raw)\n", + "\n", + "\n", + "full_adata1 = adata[adata.obs[\"condition\"].isin([\"Normal\", \"Leukemic_spontaneous\"])].copy()\n", + "full_adata2 = adata[adata.obs[\"condition\"].isin([\"Normal\", \"Leukemic_SB\"])].copy()\n", + "full_results_df1 = fit_full_de(full_adata1, \"Leukemic_spontaneous\")\n", + "full_results_df2 = fit_full_de(full_adata2, \"Leukemic_SB\")\n", + "\n", + "final_measurements1 = infer_measurements(\n", + " full_results_df1, \"Leukemic_spontaneous.vs.Normal\"\n", + ")\n", + "final_measurements2 = infer_measurements(full_results_df2, \"Leukemic_SB.vs.Normal\")\n", + "final_receptors1 = select_receptors(full_results_df1, unique_receptors, G)\n", + "final_receptors2 = select_receptors(full_results_df2, unique_receptors, G)\n", + "final_inputs1 = {node: 0 for node in final_receptors1.index}\n", + "final_inputs2 = {node: 0 for node in final_receptors2.index}\n", + "final_inputs1, final_measurements1 = restrict_to_reachable_evidence(\n", + " G, final_inputs1, final_measurements1\n", + ")\n", + "final_inputs2, final_measurements2 = restrict_to_reachable_evidence(\n", + " G, final_inputs2, final_measurements2\n", + ")\n", + "\n", + "final_data = cn.Data.from_cdict(\n", + " {\n", + " \"spontaneous\": make_carnival_sample(final_inputs1, final_measurements1),\n", + " \"SB\": make_carnival_sample(final_inputs2, final_measurements2),\n", + " }\n", + ")\n", + "final_solution = solve_carnival(G, final_data, selected_lambda)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "ed968900", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-06T17:20:02.530651Z", + "iopub.status.busy": "2026-08-06T17:20:02.530538Z", + "iopub.status.idle": "2026-08-06T17:20:02.533628Z", + "shell.execute_reply": "2026-08-06T17:20:02.533093Z" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "papermill": { + "duration": 0.012748, + "end_time": "2026-08-06T17:20:02.534292+00:00", + "exception": false, + "start_time": "2026-08-06T17:20:02.521544+00:00", + "status": "completed" }, + "tags": [] + }, + "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Concurrent spin time: 0.01s\n" - ] + "data": { + "text/plain": [ + "error_spontaneous_0 0.204626\n", + "penalty_indirect_rules_0 0.000000\n", + "error_SB_1 0.343275\n", + "penalty_indirect_rules_1 0.000000\n", + "regularization_edge_has_signal_OR 14.000000\n", + "Name: value, dtype: float64" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_method = final_solution[\"method\"]\n", + "final_problem = final_solution[\"problem\"]\n", + "\n", + "final_objectives = pd.Series(final_solution[\"objectives\"], name=\"value\")\n", + "final_objectives\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "final-diagnostics", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-06T17:20:02.552038Z", + "iopub.status.busy": "2026-08-06T17:20:02.551931Z", + "iopub.status.idle": "2026-08-06T17:20:02.556360Z", + "shell.execute_reply": "2026-08-06T17:20:02.555982Z" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "papermill": { + "duration": 0.014482, + "end_time": "2026-08-06T17:20:02.556982+00:00", + "exception": false, + "start_time": "2026-08-06T17:20:02.542500+00:00", + "status": "completed" }, + "tags": [] + }, + "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Solved with dual simplex\n" - ] + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " reachable_tfs reachable_weight tf_match tf_inactive \\\n", + "condition \n", + "spontaneous 5 1.0 0.795374 0.204626 \n", + "SB 3 1.0 0.656725 0.343275 \n", + "\n", + " tf_opposite active_edges active_nodes active_inputs \n", + "condition \n", + "spontaneous 0.0 14 14 1 \n", + "SB 0.0 9 9 1 " + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_rows = []\n", + "for condition_index, (name, inputs, measurements) in enumerate(\n", + " [\n", + " (\"spontaneous\", final_inputs1, final_measurements1),\n", + " (\"SB\", final_inputs2, final_measurements2),\n", + " ]\n", + "):\n", + " final_rows.append(\n", + " {\n", + " \"condition\": name,\n", + " **tf_fit_metrics(final_solution, condition_index, measurements),\n", + " **structural_metrics(final_solution, condition_index, inputs),\n", + " }\n", + " )\n", + "\n", + "final_diagnostics = pd.DataFrame(final_rows).set_index(\"condition\")\n", + "final_diagnostics\n" + ] + }, + { + "cell_type": "markdown", + "id": "2049db1e", + "metadata": { + "papermill": { + "duration": 0.008143, + "end_time": "2026-08-06T17:20:02.573551+00:00", + "exception": false, + "start_time": "2026-08-06T17:20:02.565408+00:00", + "status": "completed" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "tags": [] + }, + "source": [ + "## Alternative-solution sampling for the selected $\\lambda$\n", + "\n", + "Sampling is now a final robustness analysis and cannot influence $\\lambda$. Regularization is included in the near-optimality checks, the random generator is seeded, and acceptance counts are reported because `max_samples` is the number of attempts rather than a guaranteed number of returned networks.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "d86688d8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-06T17:20:02.591257Z", + "iopub.status.busy": "2026-08-06T17:20:02.591150Z", + "iopub.status.idle": "2026-08-06T17:22:02.129970Z", + "shell.execute_reply": "2026-08-06T17:22:02.129581Z" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Root relaxation: objective -1.434375e+00, 2089 iterations, 0.14 seconds (0.31 work units)\n" - ] + "papermill": { + "duration": 119.548228, + "end_time": "2026-08-06T17:22:02.130519+00:00", + "exception": false, + "start_time": "2026-08-06T17:20:02.582291+00:00", + "status": "completed" }, + "tags": [] + }, + "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "\n" + "2026-08-06 19:20:12 | [INFO] [1/30] accepted (total accepted=1) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " Nodes | Current Node | Objective Bounds | Work\n" + "2026-08-06 19:20:15 | [INFO] [2/30] accepted (total accepted=2) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n" + "2026-08-06 19:20:18 | [INFO] [3/30] accepted (total accepted=3) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "\n" + "2026-08-06 19:20:23 | [INFO] [4/30] accepted (total accepted=4) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " 0 0 -1.43438 0 93 0.00000 -1.43438 - - 0s\n" + "2026-08-06 19:20:27 | [INFO] [5/30] accepted (total accepted=5) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " 0 0 -0.93977 0 113 0.00000 -0.93977 - - 0s\n" + "2026-08-06 19:20:30 | [INFO] [6/30] accepted (total accepted=6) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " 0 0 -0.93977 0 78 0.00000 -0.93977 - - 0s\n" + "2026-08-06 19:20:34 | [INFO] [7/30] accepted (total accepted=7) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " 0 0 -0.92708 0 82 0.00000 -0.92708 - - 0s\n" + "2026-08-06 19:20:37 | [INFO] [8/30] accepted (total accepted=8) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " 0 0 -0.92045 0 76 0.00000 -0.92045 - - 0s\n" + "2026-08-06 19:20:40 | [INFO] [9/30] accepted (total accepted=9) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "H 0 0 -0.5000000 -0.89063 78.1% - 0s\n" + "2026-08-06 19:20:44 | [INFO] [10/30] accepted (total accepted=10) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " 0 0 -0.89063 0 76 -0.50000 -0.89063 78.1% - 0s\n" + "2026-08-06 19:20:48 | [INFO] [11/30] accepted (total accepted=11) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "\n" + "2026-08-06 19:20:53 | [INFO] [12/30] accepted (total accepted=12) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Cutting planes:\n" + "2026-08-06 19:20:56 | [INFO] [13/30] accepted (total accepted=13) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " Gomory: 3\n" + "2026-08-06 19:21:01 | [INFO] [14/30] accepted (total accepted=14) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " Implied bound: 16\n" + "2026-08-06 19:21:05 | [INFO] [15/30] accepted (total accepted=15) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " Zero half: 2\n" + "2026-08-06 19:21:10 | [INFO] [16/30] accepted (total accepted=16) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " RLT: 2\n" + "2026-08-06 19:21:14 | [INFO] [17/30] accepted (total accepted=17) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " BQP: 1\n" + "2026-08-06 19:21:18 | [INFO] [18/30] accepted (total accepted=18) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "\n" + "2026-08-06 19:21:22 | [INFO] [19/30] accepted (total accepted=19) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Explored 1 nodes (5098 simplex iterations) in 0.88 seconds (1.64 work units)\n" + "2026-08-06 19:21:25 | [INFO] [20/30] accepted (total accepted=20) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Thread count was 12 (of 12 available processors)\n" + "2026-08-06 19:21:29 | [INFO] [21/30] accepted (total accepted=21) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "\n" + "2026-08-06 19:21:33 | [INFO] [22/30] accepted (total accepted=22) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Solution count 2: -0.5 0 \n" + "2026-08-06 19:21:36 | [INFO] [23/30] accepted (total accepted=23) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "\n" + "2026-08-06 19:21:40 | [INFO] [24/30] accepted (total accepted=24) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Optimal solution found (tolerance 1.00e-04)\n" + "2026-08-06 19:21:43 | [INFO] [25/30] accepted (total accepted=25) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Best objective -5.000000000000e-01, best bound -5.000000000000e-01, gap 0.0000%\n" + "2026-08-06 19:21:46 | [INFO] [26/30] accepted (total accepted=26) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "(CVXPY) Jul 22 04:23:18 PM: Problem status: optimal\n" + "2026-08-06 19:21:50 | [INFO] [27/30] accepted (total accepted=27) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "(CVXPY) Jul 22 04:23:18 PM: Optimal value: 1.050e+01\n" + "2026-08-06 19:21:54 | [INFO] [28/30] accepted (total accepted=28) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "(CVXPY) Jul 22 04:23:18 PM: Compilation took 8.338e-02 seconds\n" + "2026-08-06 19:21:58 | [INFO] [29/30] accepted (total accepted=29) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "(CVXPY) Jul 22 04:23:18 PM: Solver (including time spent in interface) took 1.425e+00 seconds\n" + "2026-08-06 19:22:02 | [INFO] [30/30] accepted (total accepted=30) -> error_SB_1: val=0.343275, rel.err=0.0000, error_spontaneous_0: val=0.204626, rel.err=0.0000, penalty_indirect_rules_0: val=0, rel.err=0.0000, penalty_indirect_rules_1: val=0, rel.err=0.0000, regularization_edge_has_signal_OR: val=14, rel.err=0.0000\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "-------------------------------------------------------------------------------\n", - " Summary \n", - "-------------------------------------------------------------------------------\n" + "2026-08-06 19:22:02 | [INFO] Done. accepted=30, rejected=0, solutions returned=31\n" ] }, - { - "data": { - "text/plain": [ - "Problem(Minimize(Expression(AFFINE, UNKNOWN, (1,))), [Inequality(Constant(CONSTANT, ZERO, (2420,))), Inequality(Variable((2420,), _flow)), Inequality(Constant(CONSTANT, ZERO, (712, 2))), Inequality(Variable((712, 2), _dag_layer)), Equality(Expression(AFFINE, UNKNOWN, (712,)), Constant(CONSTANT, ZERO, ())), Inequality(Expression(AFFINE, NONNEGATIVE, (2420, 2))), Equality(Expression(AFFINE, UNKNOWN, (12,)), Constant(CONSTANT, ZERO, ())), Equality(Expression(AFFINE, NONNEGATIVE, (12,)), Constant(CONSTANT, ZERO, ())), Equality(Expression(AFFINE, NONNEGATIVE, (12,)), Constant(CONSTANT, ZERO, ())), Equality(Expression(AFFINE, UNKNOWN, (7,)), Constant(CONSTANT, ZERO, ())), Equality(Expression(AFFINE, NONNEGATIVE, (7,)), Constant(CONSTANT, ZERO, ())), Equality(Expression(AFFINE, NONNEGATIVE, (7,)), Constant(CONSTANT, ZERO, ())), Inequality(Expression(AFFINE, NONNEGATIVE, (712, 2))), Inequality(Expression(AFFINE, UNKNOWN, (2406,))), Inequality(Expression(AFFINE, UNKNOWN, (2406,))), Inequality(Constant(CONSTANT, NONNEGATIVE, (688,))), Inequality(Expression(AFFINE, UNKNOWN, (2406,))), Inequality(Expression(AFFINE, UNKNOWN, (2406,))), Inequality(Constant(CONSTANT, NONNEGATIVE, (688,))), Inequality(Expression(AFFINE, NONNEGATIVE, (2420, 2))), Inequality(Expression(AFFINE, NONNEGATIVE, (2413, 2))), Inequality(Expression(AFFINE, NONNEGATIVE, (2413, 2))), Equality(Expression(AFFINE, NONNEGATIVE, (3,)), Constant(CONSTANT, ZERO, ())), Equality(Expression(AFFINE, NONNEGATIVE, (3,)), Constant(CONSTANT, ZERO, ())), Inequality(Expression(AFFINE, NONNEGATIVE, (2420,))), Inequality(Variable((2420,), edge_has_signal_OR, boolean=True))])" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c = CarnivalFlow(lambda_reg=0.5, indirect_rule_penalty=1)\n", - "P = c.build(G, data)\n", - "\n", - "# Optimal solutions are found when Gap is close to 0\n", - "P.solve(solver=\"GUROBI\", verbosity=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "2e53f8f2", - "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:24:48.781780Z", - "iopub.status.busy": "2025-06-27T16:24:48.781673Z", - "iopub.status.idle": "2025-06-27T16:24:48.784104Z", - "shell.execute_reply": "2025-06-27T16:24:48.783838Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "error_sample1_0 4.0\n", - "penalty_indirect_rules_0 [0.]\n", - "error_sample2_1 5.0\n", - "penalty_indirect_rules_1 [0.]\n", - "regularization_edge_has_signal_OR 3.0\n" + "Returned 31 solutions: one incumbent plus 30 accepted perturbations\n" ] } ], "source": [ - "# optimization metrics\n", - "for o in P.objectives:\n", - " print(o.name, o.value)" + "from corneto.methods.sampler import sample_alternative_solutions\n", + "\n", + "\n", + "sampled = sample_alternative_solutions(\n", + " final_problem,\n", + " \"edge_value\",\n", + " percentage=0.03,\n", + " scale=max(float(selected_lambda) / 10, 1e-6),\n", + " rel_opt_tol=0.05,\n", + " max_samples=30,\n", + " solver_kwargs=dict(\n", + " solver=\"GUROBI\", max_seconds=max_time, mip_gap=0.01, seed=seed, Threads=1\n", + " ),\n", + " rng=seed,\n", + " collect_vars=[\"vertex_value\", \"edge_value\"],\n", + " exclude_objectives_pattern=None,\n", + ")\n", + "\n", + "n_returned = len(sampled[\"vertex_value\"])\n", + "print(f\"Returned {n_returned} solutions: one incumbent plus {n_returned - 1} accepted perturbations\")\n" ] }, { "cell_type": "code", - "execution_count": 55, - "id": "5e2e6dba", + "execution_count": 52, + "id": "75b918bf", "metadata": { "execution": { - "iopub.execute_input": "2025-06-27T16:24:48.811523Z", - "iopub.status.busy": "2025-06-27T16:24:48.811386Z", - "iopub.status.idle": "2025-06-27T16:24:48.817855Z", - "shell.execute_reply": "2025-06-27T16:24:48.817610Z" + "iopub.execute_input": "2026-08-06T17:22:02.149078Z", + "iopub.status.busy": "2026-08-06T17:22:02.148966Z", + "iopub.status.idle": "2026-08-06T17:22:02.154924Z", + "shell.execute_reply": "2026-08-06T17:22:02.154531Z" }, "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, + "duration": 0.016095, + "end_time": "2026-08-06T17:22:02.155364+00:00", + "exception": false, + "start_time": "2026-08-06T17:22:02.139269+00:00", "status": "completed" }, "tags": [] @@ -7256,410 +7516,92 @@ " \n", " \n", " \n", - " \n", - " edge_activity1\n", - " edge_activity2\n", + " condition\n", + " nodes_selected_at_least_50pct\n", + " nodes_selected_at_least_80pct\n", + " edges_selected_at_least_50pct\n", + " edges_selected_at_least_80pct\n", " \n", " \n", " \n", " \n", - " (Mapk14)\n", - " (Mapkapk2)\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " (Map2k3)\n", - " (Dyrk1b)\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " (Akt1)\n", - " (Chuk)\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " (Cdkn1a)\n", - " (Cdk1)\n", - " 0.0\n", - " 0.0\n", + " 0\n", + " spontaneous\n", + " 14\n", + " 12\n", + " 14\n", + " 9\n", " \n", " \n", - " (Mdc1)\n", - " (Rnf8)\n", - " 0.0\n", - " 0.0\n", + " 1\n", + " SB\n", + " 9\n", + " 7\n", + " 9\n", + " 4\n", " \n", " \n", "\n", "" ], "text/plain": [ - " edge_activity1 edge_activity2\n", - "(Mapk14) (Mapkapk2) 0.0 0.0\n", - "(Map2k3) (Dyrk1b) 0.0 0.0\n", - "(Akt1) (Chuk) 0.0 0.0\n", - "(Cdkn1a) (Cdk1) 0.0 0.0\n", - "(Mdc1) (Rnf8) 0.0 0.0" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# having a look to edge values per condition\n", - "pd.DataFrame(\n", - " P.expr.edge_value.value,\n", - " index=c.processed_graph.E,\n", - " columns=[\"edge_activity1\", \"edge_activity2\"],\n", - ").head(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "15d239a6", - "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:24:48.845257Z", - "iopub.status.busy": "2025-06-27T16:24:48.845135Z", - "iopub.status.idle": "2025-06-27T16:24:48.848716Z", - "shell.execute_reply": "2025-06-27T16:24:48.848474Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
vertex_activity1vertex_activity2
Fus0.00.0
Pstpip10.00.0
Ppp1r1b0.00.0
Arhgap330.00.0
Lrrk20.00.0
\n", - "
" - ], - "text/plain": [ - " vertex_activity1 vertex_activity2\n", - "Fus 0.0 0.0\n", - "Pstpip1 0.0 0.0\n", - "Ppp1r1b 0.0 0.0\n", - "Arhgap33 0.0 0.0\n", - "Lrrk2 0.0 0.0" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# having a look to node values per condition\n", - "pd.DataFrame(\n", - " P.expr.vertex_value.value,\n", - " index=c.processed_graph.V,\n", - " columns=[\"vertex_activity1\", \"vertex_activity2\"],\n", - ").head(5)" - ] - }, - { - "cell_type": "markdown", - "id": "1341b7e7", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Inferred network for contrast 1: Leukemic_spontaneous vs Normal" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "6843e1d2", - "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:24:48.902335Z", - "iopub.status.busy": "2025-06-27T16:24:48.902182Z", - "iopub.status.idle": "2025-06-27T16:24:49.348978Z", - "shell.execute_reply": "2025-06-27T16:24:49.348555Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Ifngr1\n", - "\n", - "Ifngr1\n", - "\n", - "\n", - "\n", - "Jak2\n", - "\n", - "Jak2\n", - "\n", - "\n", - "\n", - "Ifngr1->Jak2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Myc\n", - "\n", - "Myc\n", - "\n", - "\n", - "\n", - "Jak2->Myc\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_2416_source\n", - "\n", - "\n", - "\n", - "\n", - "e_2416_source->Ifngr1\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" + " edges_selected_at_least_50pct edges_selected_at_least_80pct \n", + "0 14 9 \n", + "1 9 4 " ] }, - "execution_count": 57, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "sol_edges1 = np.flatnonzero(np.abs(P.expr.edge_value.value[:, 0]) > 0.5)\n", - "c.processed_graph.plot_values(\n", - " vertex_values=P.expr.vertex_value.value[:, 0],\n", - " edge_values=P.expr.edge_value.value[:, 0],\n", - " edge_indexes=sol_edges1,\n", - ")" + "vertex_frequency = np.mean(np.abs(sampled[\"vertex_value\"]) > 0.5, axis=0)\n", + "edge_frequency = np.mean(np.abs(sampled[\"edge_value\"]) > 0.5, axis=0)\n", + "\n", + "vertex_frequency_df = pd.DataFrame(\n", + " vertex_frequency,\n", + " index=final_solution[\"vertices\"],\n", + " columns=[\"spontaneous\", \"SB\"],\n", + ")\n", + "edge_frequency_df = pd.DataFrame(\n", + " edge_frequency,\n", + " index=final_method.processed_graph.E,\n", + " columns=[\"spontaneous\", \"SB\"],\n", + ")\n", + "\n", + "sampling_summary = pd.DataFrame(\n", + " {\n", + " \"condition\": [\"spontaneous\", \"SB\"],\n", + " \"nodes_selected_at_least_50pct\": (vertex_frequency_df >= 0.5).sum().to_numpy(),\n", + " \"nodes_selected_at_least_80pct\": (vertex_frequency_df >= 0.8).sum().to_numpy(),\n", + " \"edges_selected_at_least_50pct\": (edge_frequency_df >= 0.5).sum().to_numpy(),\n", + " \"edges_selected_at_least_80pct\": (edge_frequency_df >= 0.8).sum().to_numpy(),\n", + " }\n", + ")\n", + "sampling_summary\n" ] }, { "cell_type": "markdown", - "id": "44d37e6e", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Inferred network for contrast 2: Leukemic_SB vs Normal" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "f913c744", + "id": "5c07afab", "metadata": { - "execution": { - "iopub.execute_input": "2025-06-27T16:24:49.404554Z", - "iopub.status.busy": "2025-06-27T16:24:49.404391Z", - "iopub.status.idle": "2025-06-27T16:24:49.500869Z", - "shell.execute_reply": "2025-06-27T16:24:49.500548Z" - }, "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, + "duration": 0.008756, + "end_time": "2026-08-06T17:22:02.172938+00:00", + "exception": false, + "start_time": "2026-08-06T17:22:02.164182+00:00", "status": "completed" }, "tags": [] }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Ifngr1\n", - "\n", - "Ifngr1\n", - "\n", - "\n", - "\n", - "Jak2\n", - "\n", - "Jak2\n", - "\n", - "\n", - "\n", - "Ifngr1->Jak2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Myc\n", - "\n", - "Myc\n", - "\n", - "\n", - "\n", - "Jak2->Myc\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "e_2416_source\n", - "\n", - "\n", - "\n", - "\n", - "e_2416_source->Ifngr1\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "sol_edges2 = np.flatnonzero(np.abs(P.expr.edge_value.value[:, 1]) > 0.5)\n", - "c.processed_graph.plot_values(\n", - " vertex_values=P.expr.vertex_value.value[:, 1],\n", - " edge_values=P.expr.edge_value.value[:, 1],\n", - " edge_indexes=sol_edges2,\n", - ")" + "The frequency tables distinguish stable conclusions from topology chosen arbitrarily among near-optimal solutions. If few edges recur, the result should be reported as structurally uncertain rather than using the samples to revisit $\\lambda$.\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aafa14e6-6c2a-4f6a-8850-968fc4b362a7", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [] } ], "metadata": { @@ -7678,18 +7620,18 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.18" + "version": "3.11.15" }, "papermill": { "default_parameters": {}, - "duration": 94.139096, - "end_time": "2025-06-27T16:28:05.209251", + "duration": 211.814088, + "end_time": "2026-08-06T17:22:04.905354+00:00", "environment_variables": {}, "exception": null, "input_path": "multi_condition_carnival.ipynb", "output_path": "multi_condition_carnival.ipynb", "parameters": {}, - "start_time": 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+ - coptpy>=8.0.3 ; extra == 'copt' - cvxopt ; extra == 'cvxopt' - diffcp ; extra == 'diffcp' - ecos ; extra == 'ecos' - ecos ; extra == 'ecos-bb' - - ortools>=9.7,<9.12 ; extra == 'glop' + - ortools>=9.7,<9.15 ; extra == 'glop' - cvxopt ; extra == 'glpk' - cvxopt ; extra == 'glpk-mi' - gurobipy ; extra == 'gurobi' - highspy ; extra == 'highs' - mosek ; extra == 'mosek' - - ortools>=9.7,<9.12 ; extra == 'pdlp' + - ortools>=9.7,<9.15 ; extra == 'pdlp' - piqp ; extra == 'piqp' - proxsuite ; extra == 'proxqp' - qoco ; extra == 'qoco' + - qpalm~=1.2.5 ; extra == 'qpalm' - pyscipopt ; extra == 'scip' - scipy ; extra == 'scipy' - - xpress ; extra == 'xpress' + - xpress>=9.5 ; extra == 'xpress' - daqp ; extra == 'daqp' + - knitro ; extra == 'knitro' + - unopy ; extra == 'uno' - pytest ; extra == 'testing' - hypothesis ; extra == 'testing' - sphinx ; extra == 'doc' @@ -10166,14 +12178,7 @@ packages: - sphinx-inline-tabs ; extra == 'doc' - sphinx-design ; extra == 'doc' - sphinx-immaterial>=0.11.7 ; extra == 'doc' - requires_python: '>=3.9' -- pypi: https://files.pythonhosted.org/packages/ea/e1/33203651c73e126bba82788e3d7813330e987df1e3c31ab36f47dd5878d6/qdldl-0.1.9.post1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl - name: qdldl - version: 0.1.9.post1 - sha256: e6d333d1de8f39ca3c34d0d88ee00a3d1b0d90f54dafad8d554b646c6110c7dd - requires_dist: - - numpy>=1.7 - - scipy>=0.13.2 + requires_python: '>=3.11' - pypi: https://files.pythonhosted.org/packages/f1/a4/aefb044a2cd8d7334c8a47d3fb2c9f328ac48cb349468cc31c20b539305f/MarkupSafe-3.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl name: markupsafe version: 3.0.2 @@ -10247,22 +12252,6 @@ packages: requires_dist: - numpy>=1.7 - scipy>=0.13.2 -- pypi: https://files.pythonhosted.org/packages/fe/39/979e8e21520d4e47a0bbe349e2713c0aac6f3d853d0e5b34d76206c439aa/platformdirs-4.3.8-py3-none-any.whl - name: platformdirs - version: 4.3.8 - sha256: ff7059bb7eb1179e2685604f4aaf157cfd9535242bd23742eadc3c13542139b4 - requires_dist: - - furo>=2024.8.6 ; extra == 'docs' - - proselint>=0.14 ; extra == 'docs' - - sphinx-autodoc-typehints>=3 ; extra == 'docs' - - sphinx>=8.1.3 ; extra == 'docs' - - appdirs==1.4.4 ; extra == 'test' - - covdefaults>=2.3 ; extra == 'test' - - pytest-cov>=6 ; extra == 'test' - - pytest-mock>=3.14 ; extra == 'test' - - pytest>=8.3.4 ; extra == 'test' - - mypy>=1.14.1 ; extra == 'type' - requires_python: '>=3.9' - pypi: https://files.pythonhosted.org/packages/ff/6b/d45873c5e0242196f042d555526f92aa9e0c32355a1be1ff8c27f077fd37/cffi-1.17.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl name: cffi version: 1.17.1 diff --git a/docs/tutorials/contrib/multi_condition_tutorial/pixi.toml b/docs/tutorials/contrib/multi_condition_tutorial/pixi.toml index a9260b60c..14bacfb32 100644 --- a/docs/tutorials/contrib/multi_condition_tutorial/pixi.toml +++ b/docs/tutorials/contrib/multi_condition_tutorial/pixi.toml @@ -10,13 +10,23 @@ matplotlib = "==3.10.3" seaborn = "==0.13.2" dcor = "==0.6" python-graphviz = "*" -pandas = ">=2.2,<3" +graphviz = "*" +numpy = "*" +pandas = "==2.3.3" +ipykernel = "*" +papermill = "*" +nbclient = "*" +charset-normalizer = "*" pip = "*" [pypi-dependencies] gurobipy = ">=12.0.0" -decoupler = "==2.2.0" -omnipath = "==1.0.9" +decoupler = "==2.1.4" +omnipath = "==1.0.12" pydeseq2 = "==0.5.1" -cvxpy = "==1.6.6" -corneto = "==1.0.0rc1" +cvxpy = "==1.9.1" +cvxpy-base = "==1.9.1" +corneto = "==1.0.0rc5" + +[tasks] +run-notebook = "python -m papermill multi_condition_carnival.ipynb multi_condition_carnival.ipynb --cwd ." diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index e6083add0..83f359db2 100755 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -10,11 +10,13 @@ :maxdepth: 1 fba/context-specific-metabolic-omics.ipynb +annnet-signaling/annnet-cellnopt-dag.ipynb carnival/single-sample-carnival-transcriptomics.ipynb carnival/multi-receptor-integration.ipynb carnival/network-sampler.ipynb carnival/network-sampler-example.ipynb ml/kpnn-with-sc.ipynb +causal/linear-dag-discovery-sachs.ipynb ``` ## Additional Tutorials diff --git a/pyproject.toml b/pyproject.toml index 8202eb7de..441233dec 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,11 @@ repository = "https://github.com/saezlab/corneto/" release = "corneto.release:main" [project.optional-dependencies] +plot = [ + "networkx>=3.2.1", + "matplotlib>=3.5.2", + "graphviz>=0.20.1", +] annnet = [ "annnet[polars]>=0.2.0,<0.3" ] diff --git a/scripts/build_all_docs_local.sh b/scripts/build_all_docs_local.sh index 39c246270..d8a2899a0 100755 --- a/scripts/build_all_docs_local.sh +++ b/scripts/build_all_docs_local.sh @@ -133,7 +133,9 @@ for tag in $(git tag -l "v*" --sort=v:refname); do done echo "==> Updating root index and switcher" -cp "${repo_root}/docs/custom-index.html" "${pages_wt}/index.html" +cp "${repo_root}/docs/landing/index.html" "${pages_wt}/index.html" +mkdir -p "${pages_wt}/assets" +rsync -a --delete "${repo_root}/docs/landing/assets/" "${pages_wt}/assets/" touch "${pages_wt}/.nojekyll" "${poetry_bin}" run python "${repo_root}/scripts/generate_switcher.py" \ --output "${pages_wt}/switcher.json" \ diff --git a/scripts/generate_switcher.py b/scripts/generate_switcher.py index 38efa5ad8..468a7edd6 100644 --- a/scripts/generate_switcher.py +++ b/scripts/generate_switcher.py @@ -37,12 +37,7 @@ def build_entries(base_url: str) -> list[dict[str, object]]: "version": "stable", "url": f"{base_url}/stable/", "preferred": True, - }, - { - "name": "latest", - "version": "latest", - "url": f"{base_url}/latest/", - }, + } ] for tag in _get_tags(): entries.append( diff --git a/scripts/prepare_docs_pages.py b/scripts/prepare_docs_pages.py new file mode 100644 index 000000000..5fac3e389 --- /dev/null +++ b/scripts/prepare_docs_pages.py @@ -0,0 +1,209 @@ +#!/usr/bin/env python3 +"""Prepare a checked-out gh-pages tree for one documentation deployment.""" + +from __future__ import annotations + +import argparse +import json +import re +import shutil +from html.parser import HTMLParser +from pathlib import Path +from urllib.parse import unquote, urlparse + +try: + from .generate_switcher import build_entries +except ImportError: # pragma: no cover - direct script execution + from generate_switcher import build_entries + + +VERSION_FOLDER_RE = re.compile(r"^(?:stable|v[0-9A-Za-z][0-9A-Za-z._-]*)$") +CSS_URL_RE = re.compile(r"url\(\s*(['\"]?)(.*?)\1\s*\)", re.IGNORECASE) +SWITCHER_URL_RE = re.compile(r"DOCUMENTATION_OPTIONS\.theme_switcher_json_url\s*=\s*(['\"])(?P.*?)\1") +SWITCHER_VERSION_RE = re.compile(r"DOCUMENTATION_OPTIONS\.theme_switcher_version_match\s*=\s*(['\"])(?P.*?)\1") +ASSET_ATTRIBUTES = {"src", "poster"} +LANDING_HOST = "corneto.org" + + +class _LandingReferenceParser(HTMLParser): + def __init__(self) -> None: + super().__init__() + self.asset_references: list[str] = [] + + def handle_starttag(self, tag: str, attrs: list[tuple[str, str | None]]) -> None: + attributes = dict(attrs) + if tag == "meta" and attributes.get("property") == "og:image" and attributes.get("content"): + self.asset_references.append(attributes["content"]) + if tag == "meta" and attributes.get("name") == "twitter:image" and attributes.get("content"): + self.asset_references.append(attributes["content"]) + for name, value in attrs: + if not value: + continue + if name in ASSET_ATTRIBUTES: + self.asset_references.append(value) + elif name == "srcset": + self.asset_references.extend(item.strip().split()[0] for item in value.split(",") if item.strip()) + elif name == "href" and _is_landing_asset_reference(value): + self.asset_references.append(value) + + +def _asset_relative_path(reference: str) -> Path | None: + parsed = urlparse(reference) + if parsed.scheme in {"data", "mailto", "javascript"}: + return None + if parsed.scheme or parsed.netloc: + if parsed.scheme not in {"http", "https"} or parsed.netloc != LANDING_HOST: + return None + path = unquote(parsed.path).lstrip("/") + else: + path = unquote(parsed.path).lstrip("/") + if not path.startswith("assets/"): + return None + return Path(path) + + +def _is_landing_asset_reference(reference: str) -> bool: + return _asset_relative_path(reference) is not None + + +def validate_landing_bundle(landing_dir: Path) -> list[Path]: + """Validate local asset references and return their bundle-relative paths.""" + landing_dir = landing_dir.resolve() + index_path = landing_dir / "index.html" + assets_dir = landing_dir / "assets" + if not index_path.is_file(): + raise ValueError(f"Landing page is missing: {index_path}") + if not assets_dir.is_dir(): + raise ValueError(f"Landing assets directory is missing: {assets_dir}") + + html = index_path.read_text(encoding="utf-8") + parser = _LandingReferenceParser() + parser.feed(html) + parser.asset_references.extend(match.group(2) for match in CSS_URL_RE.finditer(html)) + + resolved_references: list[Path] = [] + missing: list[str] = [] + invalid: list[str] = [] + for reference in parser.asset_references: + relative_path = _asset_relative_path(reference) + if relative_path is None: + parsed = urlparse(reference) + if not (parsed.scheme or parsed.netloc) and reference not in {"", "/"}: + invalid.append(reference) + continue + candidate = (landing_dir / relative_path).resolve() + try: + candidate.relative_to(assets_dir.resolve()) + except ValueError: + invalid.append(reference) + continue + if not candidate.is_file(): + missing.append(reference) + continue + resolved_references.append(relative_path) + + if invalid: + raise ValueError(f"Landing asset references must use assets/: {sorted(set(invalid))}") + if missing: + raise ValueError(f"Landing assets are missing: {sorted(set(missing))}") + if not resolved_references: + raise ValueError("Landing page does not reference any local assets") + return sorted(set(resolved_references)) + + +def _validate_version_folder(version_folder: str) -> None: + if not VERSION_FOLDER_RE.fullmatch(version_folder): + raise ValueError(f"Unsafe documentation destination: {version_folder!r}") + + +def _validate_nonempty_directory(path: Path, label: str) -> None: + if not path.is_dir() or not any(item.is_file() for item in path.rglob("*")): + raise ValueError(f"{label} is missing or empty: {path}") + + +def validate_built_switcher(build_dir: Path, version_folder: str, base_url: str) -> None: + """Require the generated docs to target the global switcher correctly.""" + index_path = build_dir / "index.html" + if not index_path.is_file(): + raise ValueError(f"Generated documentation index is missing: {index_path}") + html = index_path.read_text(encoding="utf-8") + url_match = SWITCHER_URL_RE.search(html) + version_match = SWITCHER_VERSION_RE.search(html) + if not url_match or not version_match: + raise ValueError("Generated documentation is missing the theme switcher configuration") + + expected_url = f"{base_url.rstrip('/')}/switcher.json" + actual_url = url_match.group("value") + if actual_url != expected_url and not actual_url.startswith(f"{expected_url}?"): + raise ValueError(f"Unexpected switcher URL: expected {expected_url!r}, found {actual_url!r}") + actual_version = version_match.group("value") + if actual_version != version_folder: + raise ValueError(f"Unexpected switcher version match: expected {version_folder!r}, found {actual_version!r}") + + +def _replace_tree(source: Path, destination: Path) -> None: + if destination.is_symlink(): + raise ValueError(f"Refusing to replace symlinked destination: {destination}") + if destination.exists(): + shutil.rmtree(destination) + shutil.copytree(source, destination) + + +def prepare_pages_tree( + *, + pages_dir: Path, + build_dir: Path, + landing_dir: Path, + version_folder: str, + base_url: str, + update_root: bool, +) -> None: + """Update one version and the global Pages files in an existing checkout.""" + _validate_version_folder(version_folder) + if update_root and version_folder != "stable": + raise ValueError("Only the stable deployment may update the root landing page") + pages_dir = pages_dir.resolve() + build_dir = build_dir.resolve() + landing_dir = landing_dir.resolve() + if not pages_dir.is_dir(): + raise ValueError(f"Pages checkout is missing: {pages_dir}") + _validate_nonempty_directory(build_dir, "Sphinx build") + validate_built_switcher(build_dir, version_folder, base_url) + if update_root: + validate_landing_bundle(landing_dir) + + _replace_tree(build_dir, pages_dir / version_folder) + + if update_root: + shutil.copy2(landing_dir / "index.html", pages_dir / "index.html") + _replace_tree(landing_dir / "assets", pages_dir / "assets") + (pages_dir / ".nojekyll").touch() + + switcher_path = pages_dir / "switcher.json" + entries = build_entries(base_url.rstrip("/")) + switcher_path.write_text(json.dumps(entries, indent=2) + "\n", encoding="utf-8") + + +def main() -> int: + """Run the deployment preparation command.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--pages-dir", required=True, type=Path) + parser.add_argument("--build-dir", required=True, type=Path) + parser.add_argument("--landing-dir", required=True, type=Path) + parser.add_argument("--version-folder", required=True) + parser.add_argument("--base-url", required=True) + parser.add_argument("--update-root", action="store_true") + args = parser.parse_args() + prepare_pages_tree( + pages_dir=args.pages_dir, + build_dir=args.build_dir, + landing_dir=args.landing_dir, + version_folder=args.version_folder, + base_url=args.base_url, + update_root=args.update_root, + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/publish_docs_tag.sh b/scripts/publish_docs_tag.sh index ed199e60d..39f418a6e 100755 --- a/scripts/publish_docs_tag.sh +++ b/scripts/publish_docs_tag.sh @@ -4,14 +4,13 @@ set -euo pipefail usage() { cat <<'EOF' Usage: - scripts/publish_docs_tag.sh --tag vX.Y.Z[-suffix] [--base-url URL] [--remote origin] [--python /path/to/python] [--poetry /path/to/poetry] [--update-root] [--keep-temp] [--dry-run] + scripts/publish_docs_tag.sh --tag vX.Y.Z[-suffix] [--base-url URL] [--remote origin] [--python /path/to/python] [--poetry /path/to/poetry] [--keep-temp] [--dry-run] Examples: scripts/publish_docs_tag.sh --tag v1.0.0-beta.3 scripts/publish_docs_tag.sh --tag v1.0.0-beta.3 --base-url https://corneto.org scripts/publish_docs_tag.sh --tag v1.0.0-beta.3 --python /opt/homebrew/bin/python3.11 scripts/publish_docs_tag.sh --tag v1.0.0-beta.3 --poetry /opt/homebrew/bin/poetry - scripts/publish_docs_tag.sh --tag v1.0.0-beta.3 --update-root EOF } @@ -20,7 +19,6 @@ base_url="https://corneto.org" remote="origin" python_bin="" poetry_bin="" -update_root=0 keep_temp=0 dry_run=0 @@ -46,10 +44,6 @@ while [[ $# -gt 0 ]]; do poetry_bin="${2:-}" shift 2 ;; - --update-root) - update_root=1 - shift - ;; --keep-temp) keep_temp=1 shift @@ -153,20 +147,13 @@ fi mkdir -p "${pages_wt}/${tag}" rsync -a --delete "${tag_wt}/docs/_build/html/" "${pages_wt}/${tag}/" -if [[ "${update_root}" -eq 1 ]]; then - echo "==> Updating root index and switcher" - mkdir -p "${pages_wt}" - cp "${tag_wt}/docs/custom-index.html" "${pages_wt}/index.html" - touch "${pages_wt}/.nojekyll" - "${poetry_bin}" run python "${repo_root}/scripts/generate_switcher.py" \ - --output "${pages_wt}/switcher.json" \ - --base-url "${base_url}" - "${poetry_bin}" run python "${repo_root}/scripts/patch_switcher_urls.py" \ - --root "${pages_wt}" \ - --new-url "${base_url}/switcher.json" -else - echo "==> Skipping root index/switcher update (use --update-root to enable)" -fi +echo "==> Updating switcher" +"${poetry_bin}" run python "${repo_root}/scripts/generate_switcher.py" \ + --output "${pages_wt}/switcher.json" \ + --base-url "${base_url}" +"${poetry_bin}" run python "${repo_root}/scripts/patch_switcher_urls.py" \ + --root "${pages_wt}" \ + --new-url "${base_url}/switcher.json" echo "==> Committing and pushing gh-pages changes" cd "${pages_wt}" @@ -179,7 +166,7 @@ if [[ -n "$(git status --porcelain)" ]]; then fi git fetch "${remote}" gh-pages >/dev/null 2>&1 || true if ! git merge --ff-only "${remote}/gh-pages" >/dev/null 2>&1; then - echo "Remote gh-pages moved. Refusing to overwrite. Re-run after updating, or use --update-root with a manual merge." >&2 + echo "Remote gh-pages moved. Refusing to overwrite; re-run after updating." >&2 exit 1 fi git push "${remote}" gh-pages diff --git a/tests/graph/test_plotting.py b/tests/graph/test_plotting.py index 764d66ece..a663b91f9 100644 --- a/tests/graph/test_plotting.py +++ b/tests/graph/test_plotting.py @@ -1,9 +1,10 @@ import sys from types import ModuleType +import numpy as np import pytest -from corneto._plotting import to_dot_source +from corneto._plotting import _scaled_magnitudes, _select_solution_sample, to_dot_source from corneto._util import supports_html from corneto.contrib._util import ( DEFAULT_WASM_GRAPHVIZ_JS_URL, @@ -55,6 +56,64 @@ def test_to_dot_source_honors_global_biological_node_shape(): assert '"e_0_source" [shape="point"]' in src +def test_to_dot_source_includes_isolated_vertices(): + g = Graph() + g.add_vertex("isolated") + + src = to_dot_source( + g, + custom_vertex_attr={"isolated": {"shape": "box", "label": "Cue"}}, + ) + + assert '"isolated" [shape="box", label="Cue"]' in src + + +def test_scaled_magnitudes_preserve_zero_and_handle_constant_values(): + assert np.allclose(_scaled_magnitudes(np.zeros(3)), np.zeros(3)) + assert np.allclose( + _scaled_magnitudes(np.array([2.0, 2.0, 0.0]), scale="log", clip_quantile=0.05), + [1.0, 1.0, 0.0], + ) + scaled = _scaled_magnitudes( + np.array([0.0, 1.0, 10.0, 1e6]), + scale="log", + clip_quantile=0.25, + ) + assert np.all(np.isfinite(scaled)) + assert scaled[0] == 0 + assert np.all(np.diff(scaled) >= 0) + assert scaled[-1] == 1 + + +def test_solution_sample_selection_supports_named_samples(): + data = Data.from_cdict( + { + "first": {}, + "second": {}, + } + ) + selected = _select_solution_sample( + { + "vertex_values": np.array([[1.0, -1.0], [0.0, 2.0]]), + "edge_values": np.array([[3.0, 4.0]]), + }, + sample="second", + feature_data=data, + ) + + assert np.allclose(selected["vertex_values"], [-1.0, 2.0]) + assert np.allclose(selected["edge_values"], [4.0]) + + +def test_solution_sample_selection_requires_sample_for_matrix(): + with pytest.raises(ValueError, match="select one with sample"): + _select_solution_sample( + {"vertex_values": np.ones((2, 2))}, + sample=None, + feature_data=None, + ) + + def test_dot_wasm_html_accepts_raw_dot_source(): html = dot_wasm_html('digraph {"A" -> "B";}') assert DEFAULT_WASM_GRAPHVIZ_JS_URL in html @@ -178,6 +237,31 @@ def _fake_plot_with_networkx(graph, **kwargs): assert obj is sentinel_fig +def test_networkx_renderer_uses_filtered_shared_plot_model(): + pytest.importorskip("matplotlib") + pytest.importorskip("networkx") + g = Graph() + g.add_edge("A", "B") + g.add_edge("B", "C") + + figure = g.plot( + renderer="networkx", + edge_indexes=[1], + custom_vertex_attr={ + "B": {"shape": "box", "fillcolor": "#9ACD32", "style": "filled"}, + "C": {"shape": "diamond", "label": "Output"}, + }, + custom_edge_attr={1: {"color": "#C43C39", "penwidth": "3", "arrowhead": "tee"}}, + ) + + axis = figure.axes[0] + labels = {text.get_text() for text in axis.texts} + assert "A" not in labels + assert "B" in labels + assert "Output" in labels + assert len(axis.patches) == 1 + + def test_plot_values_uses_plot_renderer_path(monkeypatch): g = Graph() g.add_edge("A", "B") diff --git a/tests/methods/causal/test_linear_dag.py b/tests/methods/causal/test_linear_dag.py new file mode 100644 index 000000000..03d53ea50 --- /dev/null +++ b/tests/methods/causal/test_linear_dag.py @@ -0,0 +1,757 @@ +"""Tests for flow-supported linear DAG discovery.""" + +from pathlib import Path + +import nbformat +import numpy as np +import pytest +from nbclient import NotebookClient + +from corneto.backend import CvxpyBackend +from corneto.data import Data +from corneto.graph import Graph +from corneto.methods.causal import LinearDAGDiscovery + + +def _single_edge_data(effect: float) -> Data: + return Data.from_cdict( + { + "obs": { + "A": {"mapping": "vertex", "value": 0.0}, + "B": {"mapping": "vertex", "value": 0.0}, + }, + "do_A": { + "A": {"mapping": "vertex", "value": 2.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 2.0 * effect}, + }, + } + ) + + +def _four_node_interventional_data() -> Data: + """Identifiable noiseless SEM data with interventions on every parent.""" + samples = {} + + def add_sample(name, a, b=None, c=None, *, intervened=()): + if b is None: + b = 1.2 * a + if c is None: + c = -0.8 * a + d = b + 0.7 * c + values = {"A": a, "B": b, "C": c, "D": d} + samples[name] = { + vertex: { + "mapping": "vertex", + "value": value, + **({"intervened": True} if vertex in intervened else {}), + } + for vertex, value in values.items() + } + + for index, a in enumerate((-1.5, -0.4, 0.8, 1.7)): + add_sample(f"obs_{index}", a) + add_sample("do_A_low", -2.0, intervened={"A"}) + add_sample("do_A_high", 2.2, intervened={"A"}) + add_sample("do_B_high", -0.7, b=2.1, intervened={"B"}) + add_sample("do_B_low", 1.1, b=-1.4, intervened={"B"}) + add_sample("do_C_high", -1.0, c=1.8, intervened={"C"}) + add_sample("do_C_low", 0.6, c=-1.7, intervened={"C"}) + return Data.from_cdict(samples) + + +def test_recovers_supported_linear_edge(backend): + graph = Graph.from_tuples([("A", 1, "B")]) + method = LinearDAGDiscovery( + lambda_edges=0.01, + fit_intercept=False, + enforce_signs=True, + backend=backend, + ) + problem = method.build(graph, _single_edge_data(effect=1.5)) + problem.solve() + + assert np.array_equal(method.get_selected_edge_indices(), np.array([0])) + coefficient = np.asarray(problem.expr.edge_coefficient.value).reshape(-1) + assert np.isclose(coefficient[0], 1.5, atol=1e-7) + assert np.array_equal(method.get_edge_usage(), np.array([[True]])) + assert np.isclose(problem.expr.flow.value[0, 0], 1.0, atol=1e-7) + + +def test_known_shift_keeps_soft_target_equation_and_flow_source(backend): + """Known additive shifts retain the target mechanism and its flow source.""" + graph = Graph.from_tuples([("A", 1, "B"), ("B", 1, "C")]) + data = Data.from_cdict( + { + "obs_0": { + "A": {"mapping": "vertex", "value": 0.0}, + "B": {"mapping": "vertex", "value": 0.0}, + "C": {"mapping": "vertex", "value": 0.0}, + }, + "obs_1": { + "A": {"mapping": "vertex", "value": 1.0}, + "B": {"mapping": "vertex", "value": 2.0}, + "C": {"mapping": "vertex", "value": 2.0}, + }, + "do_A": { + "A": {"mapping": "vertex", "value": 2.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 4.0}, + "C": {"mapping": "vertex", "value": 4.0}, + }, + "shift_1": { + "A": {"mapping": "vertex", "value": 1.0}, + "B": { + "mapping": "vertex", + "value": 5.0, + "intervention": "shift", + "shift": 3.0, + }, + "C": {"mapping": "vertex", "value": 5.0}, + }, + "shift_2": { + "A": {"mapping": "vertex", "value": 2.0}, + "B": { + "mapping": "vertex", + "value": 7.0, + "intervention": "shift", + "shift": 3.0, + }, + "C": {"mapping": "vertex", "value": 7.0}, + }, + } + ) + method = LinearDAGDiscovery( + fit_intercept=False, + min_commodity_coverage=1.0, + backend=backend, + ) + problem = method.build(graph, data) + problem.solve() + + assert np.array_equal(method.get_selected_edge_indices(), np.array([0, 1])) + assert method._soft_intervened[1].tolist() == [False, False, False, True, True] + assert method._valid_residual[1].tolist() == [True, True, True, True, True] + assert method._commodity_sources == ["A", "B"] + assert np.all(method._flow_ub[0, 1:] > 0) + commodity_info = method.get_commodity_info() + assert commodity_info[1]["group_count"] == 1 + assert commodity_info[1]["evidence_units"][0]["sample_names"] == ("shift_1", "shift_2") + assert np.allclose(problem.expr.prediction.value[1, :], [0.0, 2.0, 4.0, 5.0, 7.0], atol=1e-7) + + coefficients = np.asarray(problem.expr.edge_coefficient.value).reshape(-1) + assert np.allclose(coefficients, [2.0, 1.0], atol=1e-7) + + +def test_estimated_shift_is_shared_by_target_and_intervention_group(backend): + """Replicates in one group use one bounded continuous shift parameter.""" + graph = Graph.from_tuples([("A", 1, "B")]) + data = Data.from_cdict( + { + "obs_0": { + "A": {"mapping": "vertex", "value": 0.0}, + "B": {"mapping": "vertex", "value": 0.0}, + }, + "obs_1": { + "A": {"mapping": "vertex", "value": 1.0}, + "B": {"mapping": "vertex", "value": 2.0}, + }, + "do_A": { + "A": {"mapping": "vertex", "value": 1.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 2.0}, + }, + "replicate_1": { + "A": {"mapping": "vertex", "value": 1.0}, + "B": { + "mapping": "vertex", + "value": 5.0, + "intervention": "shift", + "intervention_group": "drug", + }, + }, + "replicate_2": { + "A": {"mapping": "vertex", "value": 2.0}, + "B": { + "mapping": "vertex", + "value": 7.0, + "intervention": "shift", + "intervention_group": "drug", + }, + }, + } + ) + method = LinearDAGDiscovery( + fit_intercept=False, + intervention_shift_bound=4.0, + lambda_intervention_shifts=0.01, + backend=backend, + ) + problem = method.build(graph, data) + + assert method._intervention_design is not None + assert method._intervention_design.effect_keys == (("B", "drug"),) + assert method._intervention_design.mapping.shape == (10, 1) + assert problem.expr.intervention_shift_parameters.shape == (1, 1) + + problem.solve() + + assert np.isclose(np.asarray(problem.expr.intervention_shift_parameters.value).reshape(-1)[0], 3.0, atol=1e-7) + assert np.isclose(np.asarray(problem.expr.edge_coefficient.value).reshape(-1)[0], 2.0, atol=1e-7) + + +def test_explicit_intervention_groups_share_one_structural_flow(backend): + """Replicate cells count once per group while equal signatures share a flow.""" + graph = Graph.from_tuples([("A", 1, "B")]) + data = Data.from_cdict( + { + "obs": { + "A": {"mapping": "vertex", "value": 0.0}, + "B": {"mapping": "vertex", "value": 0.0}, + }, + "drug_1_cell_1": { + "A": { + "mapping": "vertex", + "value": 1.0, + "intervened": True, + "intervention_group": "drug_1", + }, + "B": {"mapping": "vertex", "value": 1.0}, + }, + "drug_1_cell_2": { + "A": { + "mapping": "vertex", + "value": 2.0, + "intervened": True, + "intervention_group": "drug_1", + }, + "B": {"mapping": "vertex", "value": 2.0}, + }, + "drug_2_cell_1": { + "A": { + "mapping": "vertex", + "value": 3.0, + "intervened": True, + "intervention_group": "drug_2", + }, + "B": {"mapping": "vertex", "value": 3.0}, + }, + } + ) + + method = LinearDAGDiscovery(backend=backend) + method.build(graph, data) + + assert method._values.shape[1] == 4 + info = method.get_commodity_info() + assert len(info) == 1 + assert info[0]["source"] == "A" + assert info[0]["group_count"] == 2 + assert tuple(unit["intervention_group"] for unit in info[0]["evidence_units"]) == ("drug_1", "drug_2") + assert info[0]["evidence_units"][0]["sample_names"] == ("drug_1_cell_1", "drug_1_cell_2") + + +def test_ungrouped_replicates_share_one_implicit_flow(backend): + """Absent group metadata creates one implicit unit per signature, not per cell.""" + graph = Graph.from_tuples([("A", 1, "B")]) + data = Data.from_cdict( + { + "do_A_1": { + "A": {"mapping": "vertex", "value": 1.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 1.0}, + }, + "do_A_2": { + "A": {"mapping": "vertex", "value": 2.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 2.0}, + }, + } + ) + + method = LinearDAGDiscovery(backend=backend) + method.build(graph, data) + + info = method.get_commodity_info() + assert len(info) == 1 + assert info[0]["group_count"] == 1 + assert info[0]["evidence_units"][0]["explicit_group"] is False + assert info[0]["evidence_units"][0]["sample_names"] == ("do_A_1", "do_A_2") + + +def test_explicit_intervention_group_requires_one_structural_signature(backend): + """Explicit replicate groups cannot hide different measurement masks.""" + graph = Graph.from_tuples([("A", 1, "B"), ("A", 1, "C")]) + data = Data.from_cdict( + { + "obs": { + "A": {"mapping": "vertex", "value": 0.0}, + "B": {"mapping": "vertex", "value": 0.0}, + "C": {"mapping": "vertex", "value": 0.0}, + }, + "do_A_complete": { + "A": { + "mapping": "vertex", + "value": 1.0, + "intervened": True, + "intervention_group": "drug", + }, + "B": {"mapping": "vertex", "value": 1.0}, + "C": {"mapping": "vertex", "value": 1.0}, + }, + "do_A_missing_C": { + "A": { + "mapping": "vertex", + "value": 2.0, + "intervened": True, + "intervention_group": "drug", + }, + "B": {"mapping": "vertex", "value": 2.0}, + "C": {"mapping": "vertex", "value": None}, + }, + } + ) + + with pytest.raises(ValueError, match="inconsistent structural signatures"): + LinearDAGDiscovery(backend=backend).build(graph, data) + + +def test_different_implicit_signatures_remain_separate(backend): + """Ungrouped cells with different eligible sinks require distinct flows.""" + graph = Graph.from_tuples([("A", 1, "B"), ("A", 1, "C")]) + data = Data.from_cdict( + { + "obs": { + "A": {"mapping": "vertex", "value": 0.0}, + "B": {"mapping": "vertex", "value": 0.0}, + "C": {"mapping": "vertex", "value": 0.0}, + }, + "do_A_complete": { + "A": {"mapping": "vertex", "value": 1.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 1.0}, + "C": {"mapping": "vertex", "value": 1.0}, + }, + "do_A_missing_C": { + "A": {"mapping": "vertex", "value": 2.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 2.0}, + "C": {"mapping": "vertex", "value": None}, + }, + } + ) + + method = LinearDAGDiscovery(backend=backend) + method.build(graph, data) + + info = method.get_commodity_info() + assert len(info) == 2 + assert {record["sink_vertices"] for record in info} == {("B", "C"), ("B",)} + + +def test_coverage_counts_intervention_groups_instead_of_cells(backend): + """Two explicit groups sharing a reachable flow satisfy two coverage units.""" + graph = Graph.from_tuples([("A", 1, "C"), ("D", 1, "B")]) + data = Data.from_cdict( + { + "obs": { + "A": {"mapping": "vertex", "value": 0.0}, + "B": {"mapping": "vertex", "value": 0.0}, + "C": {"mapping": "vertex", "value": 0.0}, + "D": {"mapping": "vertex", "value": 0.0}, + }, + "do_A_1": { + "A": { + "mapping": "vertex", + "value": 1.0, + "intervened": True, + "intervention_group": "drug_1", + }, + "B": {"mapping": "vertex", "value": 0.0}, + "C": {"mapping": "vertex", "value": 1.0}, + "D": {"mapping": "vertex", "value": 0.0}, + }, + "do_A_2": { + "A": { + "mapping": "vertex", + "value": 2.0, + "intervened": True, + "intervention_group": "drug_2", + }, + "B": {"mapping": "vertex", "value": 0.0}, + "C": {"mapping": "vertex", "value": 2.0}, + "D": {"mapping": "vertex", "value": 0.0}, + }, + "do_B": { + "A": {"mapping": "vertex", "value": 0.0}, + "B": { + "mapping": "vertex", + "value": 1.0, + "intervened": True, + "intervention_group": "drug_B", + }, + "C": {"mapping": "vertex", "value": 0.0}, + "D": {"mapping": "vertex", "value": 0.0}, + }, + } + ) + method = LinearDAGDiscovery( + fit_intercept=False, + min_commodity_coverage=2 / 3, + backend=backend, + ) + problem = method.build(graph, data) + problem.solve() + + assert np.array_equal( + np.asarray(problem.expr.commodity_group_count.value).reshape(-1), + np.array([2.0, 1.0]), + ) + active = np.asarray(problem.expr.commodity_active.value).reshape(-1) > 0.5 + assert active.tolist() == [True, False] + + strict_problem = LinearDAGDiscovery( + fit_intercept=False, + min_commodity_coverage=1.0, + backend=backend, + ).build(graph, data) + solve_options = {"primals": None} if str(backend) == "PICOS" else {} + result = strict_problem.solve(**solve_options) + assert str(result.status).lower() in {"infeasible", "infeasible_or_unbounded"} + + +def test_estimated_shift_requires_a_group(backend): + graph = Graph.from_tuples([("A", 1, "B")]) + data = Data.from_cdict( + { + "obs": { + "A": {"mapping": "vertex", "value": 0.0}, + "B": {"mapping": "vertex", "value": 0.0}, + }, + "shift": { + "A": {"mapping": "vertex", "value": 1.0}, + "B": {"mapping": "vertex", "value": 3.0, "intervention": "shift"}, + }, + } + ) + + with pytest.raises(ValueError, match="intervention group"): + LinearDAGDiscovery(backend=backend).build(graph, data) + + +def test_existing_positional_flow_arguments_remain_compatible(): + """New shift options must not change legacy positional argument bindings.""" + method = LinearDAGDiscovery( + 0.01, + 5.0, + True, + 10.0, + None, + "absolute", + True, + None, + None, + False, + "interaction", + "intervened", + 7.0, + ) + + assert method.flow_capacity == 7.0 + assert method.intervention_type_key == "intervention" + + +def test_nan_shift_groups_are_rejected(backend): + """Missing numeric group labels cannot silently create separate effects.""" + graph = Graph.from_tuples([("A", 1, "B")]) + data = Data.from_cdict( + { + "obs": { + "A": {"mapping": "vertex", "value": 0.0}, + "B": {"mapping": "vertex", "value": 0.0}, + }, + "shift_1": { + "A": {"mapping": "vertex", "value": 1.0}, + "B": { + "mapping": "vertex", + "value": 3.0, + "intervention": "shift", + "intervention_group": float("nan"), + }, + }, + "shift_2": { + "A": {"mapping": "vertex", "value": 2.0}, + "B": { + "mapping": "vertex", + "value": 5.0, + "intervention": "shift", + "intervention_group": float("nan"), + }, + }, + } + ) + + with pytest.raises(ValueError, match="finite"): + LinearDAGDiscovery(backend=backend).build(graph, data) + + +def test_recovers_identifiable_dag_from_cyclic_prior(backend): + """Recover a multi-level SEM despite reverse and cross-edge distractors.""" + edges = [ + ("A", 1, "B"), + ("A", -1, "C"), + ("B", 1, "D"), + ("C", 1, "D"), + ("B", 1, "A"), + ("C", 1, "A"), + ("D", 1, "B"), + ("D", 1, "C"), + ("B", 1, "C"), + ("C", 1, "B"), + ] + graph = Graph.from_tuples(edges) + method = LinearDAGDiscovery( + lambda_edges=0.01, + coefficient_bound=3, + fit_intercept=False, + max_parents=2, + enforce_signs=True, + backend=backend, + ) + problem = method.build(graph, _four_node_interventional_data()) + problem.solve() + + selected = set(method.get_selected_edge_indices()) + assert selected == {0, 1, 2, 3} + method.get_solution_graph().toposort() + + coefficient = np.asarray(problem.expr.edge_coefficient.value).reshape(-1) + assert np.allclose(coefficient[:4], [1.2, -0.8, 1.0, 0.7], atol=1e-7) + assert np.allclose(coefficient[4:], 0, atol=1e-8) + + usage = method.get_edge_usage() + selected_values = np.asarray(problem.expr.edge_selected.value).reshape(-1) > 0.5 + assert np.array_equal(usage.any(axis=1), selected_values) + assert np.all(usage[selected_values].sum(axis=1) >= 1) + + +def test_optional_coverage_does_not_force_no_effect_edge(backend): + graph = Graph.from_tuples([("A", 1, "B")]) + method = LinearDAGDiscovery( + lambda_edges=0.1, + fit_intercept=False, + backend=backend, + ) + problem = method.build(graph, _single_edge_data(effect=0.0)) + problem.solve() + + assert method.get_selected_edge_indices().size == 0 + coefficient = np.asarray(problem.expr.edge_coefficient.value).reshape(-1) + assert np.isclose(coefficient[0], 0.0, atol=1e-8) + assert np.allclose(problem.expr.flow.value, 0.0, atol=1e-8) + assert "commodity_active" not in problem.expr + + +def test_one_commodity_per_intervened_vertex(backend): + graph = Graph.from_tuples([("A", 1, "C"), ("B", 1, "C")]) + data = Data.from_cdict( + { + "condition": { + "A": {"mapping": "vertex", "value": 1.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 2.0, "intervened": True}, + "C": {"mapping": "vertex", "value": 3.0}, + } + } + ) + method = LinearDAGDiscovery( + lambda_edges=0.01, + fit_intercept=False, + min_commodity_coverage=1.0, + backend=backend, + ) + problem = method.build(graph, data) + problem.solve() + + assert method._commodity_sources == ["A", "B"] + assert method.get_edge_usage().shape == (2, 2) + assert np.all(np.asarray(problem.expr.commodity_active.value) > 0.5) + + +def test_coverage_distinguishes_unreachable_intervention(backend): + """Partial coverage drops an isolated commodity; strict coverage is infeasible.""" + graph = Graph.from_tuples([("A", 1, "C"), ("D", 1, "B")]) + data = Data.from_cdict( + { + "condition": { + "A": {"mapping": "vertex", "value": 2.0, "intervened": True}, + "B": {"mapping": "vertex", "value": -1.0, "intervened": True}, + "C": {"mapping": "vertex", "value": 4.0}, + "D": {"mapping": "vertex", "value": 0.0}, + } + } + ) + method = LinearDAGDiscovery( + lambda_edges=0.01, + fit_intercept=False, + min_commodity_coverage=0.5, + backend=backend, + ) + problem = method.build(graph, data) + problem.solve() + + active = np.asarray(problem.expr.commodity_active.value).reshape(-1) > 0.5 + active_by_source = dict(zip(method._commodity_sources, active, strict=True)) + assert active_by_source == {"A": True, "B": False} + assert np.array_equal(method.get_selected_edge_indices(), np.array([0])) + + strict_problem = LinearDAGDiscovery( + fit_intercept=False, + min_commodity_coverage=1, + backend=backend, + ).build(graph, data) + solve_options = {"primals": None} if str(backend) == "PICOS" else {} + result = strict_problem.solve(**solve_options) + assert str(result.status).lower() in {"infeasible", "infeasible_or_unbounded"} + + +def test_recovers_chain_with_missing_response(backend): + """A missing sink response is omitted without losing other equations.""" + graph = Graph.from_tuples([("A", 1, "B"), ("B", 1, "C")]) + data = Data.from_cdict( + { + "obs": { + "A": {"mapping": "vertex", "value": 0.0}, + "B": {"mapping": "vertex", "value": 0.0}, + "C": {"mapping": "vertex", "value": 0.0}, + }, + "do_A_1": { + "A": {"mapping": "vertex", "value": 1.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 2.0}, + "C": {"mapping": "vertex", "value": 6.0}, + }, + "do_A_missing_C": { + "A": {"mapping": "vertex", "value": 2.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 4.0}, + "C": {"mapping": "vertex", "value": None}, + }, + "do_A_missing_B": { + "A": {"mapping": "vertex", "value": 2.5, "intervened": True}, + "B": {"mapping": "vertex", "value": None}, + "C": {"mapping": "vertex", "value": 999.0}, + }, + "do_A_3": { + "A": {"mapping": "vertex", "value": 3.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 6.0}, + "C": {"mapping": "vertex", "value": 18.0}, + }, + } + ) + method = LinearDAGDiscovery( + fit_intercept=False, + coefficient_bound=4, + backend=backend, + ) + problem = method.build(graph, data) + problem.solve() + + assert np.array_equal(method.get_selected_edge_indices(), np.array([0, 1])) + coefficient = np.asarray(problem.expr.edge_coefficient.value).reshape(-1) + assert np.allclose(coefficient, [2, 3], atol=1e-7) + c_index = method._vertex_index["C"] + missing_sample = method._sample_names.index("do_A_missing_C") + assert not method._valid_residual[c_index, missing_sample] + missing_parent_sample = method._sample_names.index("do_A_missing_B") + b_index = method._vertex_index["B"] + assert not method._valid_residual[b_index, missing_parent_sample] + assert not method._valid_residual[c_index, missing_parent_sample] + + +def test_standardized_weighted_loss_is_unit_invariant(backend): + """Response units must not change discovery under standardized loss.""" + + def scaled_data(scale): + data = _single_edge_data(effect=1.5) + for sample in data.samples.values(): + for feature in sample.features: + if feature.id == "B": + feature.data["value"] *= scale + return data + + selected = [] + scales = [] + for unit_scale in (1.0, 1000.0): + method = LinearDAGDiscovery( + lambda_edges=0.5, + coefficient_bound=2000, + fit_intercept=False, + standardize_loss=True, + sample_weights={"do_A": 2.0}, + backend=backend, + ) + problem = method.build( + Graph.from_tuples([("A", 1, "B")]), + scaled_data(unit_scale), + ) + problem.solve() + selected.append(method.get_selected_edge_indices()) + scales.append(np.asarray(problem.expr.loss_scale.value).reshape(-1)[1]) + + assert np.array_equal(selected[0], np.array([0])) + assert np.array_equal(selected[0], selected[1]) + assert np.isclose(scales[1] / scales[0], 1000) + + +def test_rejects_fully_latent_vertex(backend): + graph = Graph.from_tuples([("A", 1, "B")]) + data = Data.from_cdict( + { + "condition": { + "A": {"mapping": "vertex", "value": 1.0, "intervened": True}, + } + } + ) + with pytest.raises(ValueError, match="latent vertices"): + LinearDAGDiscovery(backend=backend).build(graph, data) + + +def test_cvxpy_formulation_uses_only_required_integer_variables(): + """Boundary edges and commodity unions must not introduce extra binaries.""" + graph = Graph.from_tuples([("A", 1, "C"), ("B", 1, "C")]) + data = Data.from_cdict( + { + "condition": { + "A": {"mapping": "vertex", "value": 1.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 2.0, "intervened": True}, + "C": {"mapping": "vertex", "value": 3.0}, + } + } + ) + problem = LinearDAGDiscovery( + fit_intercept=False, + backend=CvxpyBackend(), + ).build(graph, data) + cvxpy_problem = problem.solve(solver="SCIPY") + + boolean_count = sum(variable.size for variable in cvxpy_problem.variables() if variable.attributes["boolean"]) + integer_count = sum(variable.size for variable in cvxpy_problem.variables() if variable.attributes["integer"]) + continuous_count = cvxpy_problem.size_metrics.num_scalar_variables - boolean_count - integer_count + + # E shared selectors + E*K edge/commodity selectors + two coefficient-sign + # binaries per edge from the default exact coefficient support. Optional + # commodity activity binaries are omitted when no coverage term is requested. + assert boolean_count == 2 + 2 * 2 + 2 * 2 + assert integer_count == 0 + # (E + 3 boundary edges)*K flows + V DAG layers + E coefficients + # + one L1 residual for the sole non-intervened equation. + assert continuous_count == 5 * 2 + 3 + 2 + 1 + assert cvxpy_problem.size_metrics.num_scalar_variables == 26 + + +def test_linear_dag_guide_notebook_executes_every_cell(): + notebook_path = Path(__file__).parents[3] / "docs/guide/signaling/linear-dag-discovery.ipynb" + notebook = nbformat.read(notebook_path, as_version=4) + + executed = NotebookClient( + notebook, + timeout=300, + kernel_name="python3", + resources={"metadata": {"path": str(notebook_path.parent)}}, + ).execute() + + errors = [ + output for cell in executed.cells for output in cell.get("outputs", []) if output.get("output_type") == "error" + ] + assert errors == [] diff --git a/tests/methods/causal/test_linear_dag_audit.py b/tests/methods/causal/test_linear_dag_audit.py new file mode 100644 index 000000000..3f8634764 --- /dev/null +++ b/tests/methods/causal/test_linear_dag_audit.py @@ -0,0 +1,560 @@ +"""Regression tests for findings from the linear DAG discovery audit.""" + +import numpy as np +import pytest + +from corneto.backend import PicosBackend +from corneto.data import Data +from corneto.graph import Graph +from corneto.methods.causal import LinearDAGDiscovery + + +def _data(rows): + return Data.from_cdict( + { + sample_name: { + vertex: { + "mapping": "vertex", + "value": value, + **({"intervened": True} if vertex in intervened else {}), + } + for vertex, value in values.items() + } + for sample_name, values, intervened in rows + } + ) + + +def _solve(problem, backend): + if isinstance(backend, PicosBackend): + return problem.solve(solver="glpk", primals=None) + return problem.solve() + + +def _zero_connector_data(scale_b=1.0, scale_c=1.0): + data = _data( + [ + ("obs", {"A": 0.0, "B": 1.0, "C": 2.0}, ()), + ("do_A", {"A": 1.0, "B": 0.0, "C": 0.0}, ("A",)), + ] + ) + for sample in data.samples.values(): + for feature in sample.features: + if feature.id == "B": + feature.data["value"] *= scale_b + elif feature.id == "C": + feature.data["value"] *= scale_c + return data + + +def test_cointervention_blocks_flow_through_intervened_target(backend): + """A commodity cannot use an incoming mechanism removed by intervention.""" + graph = Graph.from_tuples([("A", 1, "B"), ("B", 1, "C")]) + data = _data( + [ + ("obs", {"A": 0.0, "B": 0.0, "C": 0.0}, ()), + ("do_AB", {"A": 1.0, "B": 2.0, "C": 4.0}, ("A", "B")), + ] + ) + method = LinearDAGDiscovery( + fit_intercept=False, + min_commodity_coverage=1.0, + backend=backend, + ) + + problem = method.build(graph, data) + result = _solve(problem, backend) + + assert str(result.status).lower() in {"infeasible", "infeasible_or_unbounded"} + + +def test_partial_coverage_keeps_nonblocked_cointervention_commodity(backend): + """A co-intervened source remains usable when its route avoids the other target.""" + graph = Graph.from_tuples([("A", 1, "B"), ("B", 1, "C")]) + data = _data( + [ + ("obs", {"A": 0.0, "B": 0.0, "C": 0.0}, ()), + ("do_AB", {"A": 1.0, "B": 2.0, "C": 4.0}, ("A", "B")), + ] + ) + method = LinearDAGDiscovery( + fit_intercept=False, + min_commodity_coverage=0.5, + backend=backend, + ) + + problem = method.build(graph, data) + _solve(problem, backend) + + active = np.asarray(problem.expr.commodity_active.value).reshape(-1) > 0.5 + active_by_source = dict(zip(method._commodity_sources, active, strict=True)) + usage = method.get_edge_usage() + usage_by_source = {source: usage[:, index] for index, source in enumerate(method._commodity_sources)} + + assert active_by_source == {"A": False, "B": True} + assert not usage_by_source["A"].any() + assert np.array_equal(np.flatnonzero(usage_by_source["B"]), np.array([1])) + + +def test_squared_loss_builds_with_multiple_residuals_and_weights(backend): + """Squared loss uses a backend-portable vector operation.""" + graph = Graph.from_tuples([("A", 1, "B"), ("A", 1, "C")]) + data = _data( + [ + ("obs", {"A": 0.0, "B": 0.0, "C": 0.0}, ()), + ("do_A", {"A": 2.0, "B": 3.0, "C": 4.0}, ("A",)), + ] + ) + method = LinearDAGDiscovery( + fit_intercept=False, + loss="squared", + vertex_weights={"B": 2.0, "C": 3.0}, + sample_weights={"do_A": 4.0}, + backend=backend, + ) + + problem = method.build(graph, data) + + assert "squared_fit" in {objective.name for objective in problem.objectives} + + +def test_default_flow_capacity_does_not_change_recovery_with_epsilon(backend): + """Increasing flow epsilon must not impose an artificial branch limit.""" + graph = Graph.from_tuples([("A", 1, "B"), ("A", 1, "C")]) + data = _data( + [ + ("obs", {"A": 0.0, "B": 0.0, "C": 0.0}, ()), + ("do_A", {"A": 1.0, "B": 1.0, "C": 1.0}, ("A",)), + ] + ) + + selected = [] + for epsilon in (1.0, 2.0): + method = LinearDAGDiscovery( + fit_intercept=False, + flow_epsilon=epsilon, + backend=backend, + ) + problem = method.build(graph, data) + _solve(problem, backend) + selected.append(method.get_selected_edge_indices()) + + assert np.array_equal(selected[0], np.array([0, 1])) + assert np.array_equal(selected[1], np.array([0, 1])) + + +def test_explicit_flow_capacity_is_not_scaled_with_epsilon(backend): + """An explicit capacity remains a genuine user-specified constraint.""" + graph = Graph.from_tuples([("A", 1, "B")]) + data = _data( + [ + ("obs", {"A": 0.0, "B": 0.0}, ()), + ("do_A", {"A": 1.0, "B": 1.0}, ("A",)), + ] + ) + method = LinearDAGDiscovery( + fit_intercept=False, + flow_capacity=3.0, + flow_epsilon=2.0, + backend=backend, + ) + + method.build(graph, data) + + assert method._resolved_flow_capacity == 3.0 + + +def test_optional_coverage_allows_intervention_sample_without_sinks(backend): + """A sample with only intervened measurements need not abort inference.""" + graph = Graph.from_tuples([("A", 1, "B")]) + data = _data( + [ + ("obs", {"A": 0.0, "B": 0.0}, ()), + ("do_AB", {"A": 1.0, "B": 2.0}, ("A", "B")), + ] + ) + method = LinearDAGDiscovery( + fit_intercept=False, + min_commodity_coverage=0.0, + lambda_unexplained=0.0, + backend=backend, + ) + + problem = method.build(graph, data) + result = _solve(problem, backend) + + assert str(result.status).lower() == "optimal" + assert method._commodity_sink_edges == [[], []] + + +def test_full_coverage_is_infeasible_with_intervention_without_sinks(backend): + """A sinkless intervention remains represented but cannot satisfy full coverage.""" + graph = Graph.from_tuples([("A", 1, "B")]) + data = _data( + [ + ("obs", {"A": 0.0, "B": 0.0}, ()), + ("do_AB", {"A": 1.0, "B": 2.0}, ("A", "B")), + ] + ) + method = LinearDAGDiscovery( + fit_intercept=False, + min_commodity_coverage=1.0, + backend=backend, + ) + + problem = method.build(graph, data) + result = _solve(problem, backend) + + assert str(result.status).lower() in {"infeasible", "infeasible_or_unbounded"} + + +def test_exact_coefficient_support_has_nonzero_fitted_coefficients(backend): + """Exact support keeps selected edges in the fitted coefficient support.""" + graph = Graph.from_tuples([("A", 1, "B"), ("B", 1, "C")]) + data = _zero_connector_data() + method = LinearDAGDiscovery( + fit_intercept=False, + min_abs_coefficient=0.25, + backend=backend, + ) + assert method.coefficient_support == "exact" + problem = method.build(graph, data) + _solve(problem, backend) + + selected = method.get_selected_edge_indices() + normalized = np.asarray(problem.expr.edge_coefficient_normalized.value).reshape(-1) + + assert np.array_equal(selected, np.array([0, 1])) + assert np.all(np.abs(normalized[selected]) >= 0.25 - 1e-8) + + +def test_default_exact_support_enforces_its_threshold(backend): + """The default exact-support threshold must survive solver tolerances.""" + graph = Graph.from_tuples([("A", 1, "B"), ("B", 1, "C")]) + data = _zero_connector_data() + method = LinearDAGDiscovery(fit_intercept=False, backend=backend) + problem = method.build(graph, data) + _solve(problem, backend) + + selected = method.get_selected_edge_indices() + normalized = np.asarray(problem.expr.edge_coefficient_normalized.value).reshape(-1) + + assert np.array_equal(selected, np.array([0, 1])) + assert np.all(np.abs(normalized[selected]) >= method.min_abs_coefficient) + + +@pytest.mark.parametrize( + ("interaction", "effect"), + [(1, 1.5), (-1, -1.5), (None, -1.5)], +) +def test_exact_support_handles_positive_negative_and_unknown_signs(backend, interaction, effect): + """Exact support preserves fitted sign information for every prior sign case.""" + graph = Graph.from_tuples([("A", interaction, "B")]) + data = _data( + [ + ("obs", {"A": 0.0, "B": 0.0}, ()), + ("do_A", {"A": 2.0, "B": 2.0 * effect}, ("A",)), + ] + ) + method = LinearDAGDiscovery( + fit_intercept=False, + enforce_signs=True, + backend=backend, + ) + problem = method.build(graph, data) + _solve(problem, backend) + + selected = method.get_selected_edge_indices() + coefficient = np.asarray(problem.expr.edge_coefficient.value).reshape(-1)[0] + normalized = np.asarray(problem.expr.edge_coefficient_normalized.value).reshape(-1)[0] + + assert np.array_equal(selected, np.array([0])) + assert np.sign(coefficient) == np.sign(effect) + assert abs(normalized) >= method.min_abs_coefficient + + +@pytest.mark.parametrize("coefficient_support", ["exact", "structural"]) +def test_forced_coverage_distinguishes_zero_response_support(backend, coefficient_support): + """Exact support rejects zero effects while structural support permits connectors.""" + graph = Graph.from_tuples([("A", 1, "B")]) + data = _data( + [ + ("obs", {"A": 0.0, "B": 0.0}, ()), + ("do_A", {"A": 1.0, "B": 0.0}, ("A",)), + ] + ) + method = LinearDAGDiscovery( + fit_intercept=False, + coefficient_support=coefficient_support, + min_commodity_coverage=1.0, + backend=backend, + ) + problem = method.build(graph, data) + _solve(problem, backend) + + assert np.array_equal(method.get_selected_edge_indices(), np.array([0])) + normalized = np.asarray(problem.expr.edge_coefficient_normalized.value).reshape(-1)[0] + if coefficient_support == "exact": + assert abs(normalized) >= method.min_abs_coefficient + else: + assert np.isclose(normalized, 0.0, atol=1e-8) + + +@pytest.mark.parametrize("coefficient_support", ["exact", "structural"]) +def test_unselected_edges_have_zero_fitted_coefficients(backend, coefficient_support): + """Both support modes keep coefficients zero on unselected prior edges.""" + graph = Graph.from_tuples([("A", 1, "B"), ("A", 1, "C")]) + data = _data( + [ + ("obs", {"A": 0.0, "B": 0.0, "C": 0.0}, ()), + ("do_A", {"A": 1.0, "B": 2.0, "C": 0.0}, ("A",)), + ] + ) + method = LinearDAGDiscovery( + fit_intercept=False, + coefficient_support=coefficient_support, + backend=backend, + ) + problem = method.build(graph, data) + _solve(problem, backend) + + selected = method.get_selected_edge_indices() + normalized = np.asarray(problem.expr.edge_coefficient_normalized.value).reshape(-1) + + assert np.array_equal(selected, np.array([0])) + assert np.isclose(normalized[1], 0.0, atol=1e-8) + + +def test_exact_support_accepts_threshold_equal_to_coefficient_bound(backend): + """The exact-support threshold may equal the configured coefficient bound.""" + graph = Graph.from_tuples([("A", 1, "B")]) + data = _data( + [ + ("obs", {"A": 0.0, "B": 0.0}, ()), + ("do_A", {"A": 2.0, "B": 2.0}, ("A",)), + ] + ) + method = LinearDAGDiscovery( + coefficient_bound=0.25, + min_abs_coefficient=0.25, + fit_intercept=False, + min_commodity_coverage=1.0, + backend=backend, + ) + problem = method.build(graph, data) + _solve(problem, backend) + + selected = method.get_selected_edge_indices() + coefficient = np.asarray(problem.expr.edge_coefficient.value).reshape(-1)[0] + normalized = np.asarray(problem.expr.edge_coefficient_normalized.value).reshape(-1)[0] + + assert np.array_equal(selected, np.array([0])) + assert np.isclose(abs(coefficient), 0.25, atol=1e-8) + assert np.isclose(abs(normalized), 0.25, atol=1e-8) + + +def test_default_exact_support_is_invariant_to_measurement_rescaling(backend): + """Normalized coefficient support is unchanged when one variable is rescaled.""" + graph = Graph.from_tuples([("A", 1, "B"), ("B", 1, "C")]) + selected = [] + normalized = [] + for scale_b in (1.0, 100.0): + method = LinearDAGDiscovery( + fit_intercept=True, + coefficient_bound=100.0, + intercept_bound=200.0, + backend=backend, + ) + problem = method.build(graph, _zero_connector_data(scale_b=scale_b)) + _solve(problem, backend) + selected.append(method.get_selected_edge_indices()) + normalized.append(np.asarray(problem.expr.edge_coefficient_normalized.value).reshape(-1)) + + assert np.array_equal(selected[0], np.array([0, 1])) + assert np.array_equal(selected[0], selected[1]) + assert np.all(np.abs(normalized[0][selected[0]]) >= 0.25) + assert np.all(np.abs(normalized[1][selected[1]]) >= 0.25) + + +def test_exact_support_is_invariant_to_intervention_source_rescaling(backend): + """A source's intervention measurements determine its predictor scale.""" + graph = Graph.from_tuples([("A", 1, "B")]) + selected = [] + normalized = [] + coefficients = [] + for source_scale in (1.0, 100.0): + data = _data( + [ + ("obs", {"A": 0.0, "B": 0.0}, ()), + ("do_A", {"A": source_scale, "B": 1.0}, ("A",)), + ] + ) + method = LinearDAGDiscovery( + fit_intercept=False, + standardize_loss=False, + backend=backend, + ) + problem = method.build(graph, data) + _solve(problem, backend) + selected.append(method.get_selected_edge_indices()) + normalized.append(np.asarray(problem.expr.edge_coefficient_normalized.value).reshape(-1)) + coefficients.append(np.asarray(problem.expr.edge_coefficient.value).reshape(-1)) + + assert np.array_equal(selected[0], np.array([0])) + assert np.array_equal(selected[0], selected[1]) + assert np.allclose(normalized[0], normalized[1], atol=1e-8) + assert np.isclose(coefficients[0][0], 1.0, atol=1e-8) + assert np.isclose(coefficients[1][0], 0.01, atol=1e-8) + + +def test_exact_support_is_invariant_to_leaf_response_rescaling(backend): + """A terminal response's units must not change normalized support.""" + graph = Graph.from_tuples([("A", 1, "B")]) + selected = [] + coefficients = [] + for response_scale in (1.0, 0.01): + data = _data( + [ + ("obs", {"A": 0.0, "B": 0.0}, ()), + ("do_A", {"A": 2.0, "B": 3.0 * response_scale}, ("A",)), + ] + ) + method = LinearDAGDiscovery(fit_intercept=False, backend=backend) + problem = method.build(graph, data) + _solve(problem, backend) + selected.append(method.get_selected_edge_indices()) + coefficients.append(np.asarray(problem.expr.edge_coefficient.value).reshape(-1)[0]) + + assert np.array_equal(selected[0], np.array([0])) + assert np.array_equal(selected[1], selected[0]) + assert np.allclose(coefficients, [1.5, 0.015], atol=1e-8) + + +def test_tiny_exact_support_threshold_cannot_validate_zero(backend): + """Validation must not erase a positive threshold through its tolerance.""" + graph = Graph.from_tuples([("A", 1, "B"), ("B", 1, "C")]) + method = LinearDAGDiscovery( + fit_intercept=False, + min_abs_coefficient=1e-12, + backend=backend, + ) + problem = method.build(graph, _zero_connector_data()) + _solve(problem, backend) + + with pytest.raises(ValueError, match="invalid"): + method.get_selected_edge_indices() + with pytest.raises(ValueError, match="invalid"): + method.get_edge_usage() + + +def test_exact_coefficient_support_works_with_different_bounds(backend): + """Exact support remains meaningful across raw coefficient bounds.""" + graph = Graph.from_tuples([("A", 1, "B"), ("B", 1, "C")]) + for coefficient_bound in (0.6, 4.0): + method = LinearDAGDiscovery( + fit_intercept=False, + coefficient_bound=coefficient_bound, + min_abs_coefficient=0.25, + backend=backend, + ) + problem = method.build(graph, _zero_connector_data()) + _solve(problem, backend) + + selected = method.get_selected_edge_indices() + coefficients = np.asarray(problem.expr.edge_coefficient.value).reshape(-1) + normalized = np.asarray(problem.expr.edge_coefficient_normalized.value).reshape(-1) + + assert np.array_equal(selected, np.array([0, 1])) + assert np.all(np.abs(normalized[selected]) >= 0.25 - 1e-8) + assert np.all(np.abs(coefficients) <= coefficient_bound + 1e-8) + + +def test_normalized_threshold_is_not_compared_to_raw_bound(backend): + """A normalized threshold may exceed the raw bound when predictor units differ.""" + graph = Graph.from_tuples([("A", 1, "B")]) + data = _data( + [ + ("obs", {"A": 0.0, "B": 0.0}, ()), + ("do_A", {"A": 10.0, "B": 5.0}, ("A",)), + ] + ) + method = LinearDAGDiscovery( + coefficient_bound=0.5, + min_abs_coefficient=1.0, + fit_intercept=False, + standardize_loss=False, + backend=backend, + ) + problem = method.build(graph, data) + _solve(problem, backend) + + selected = method.get_selected_edge_indices() + coefficient = np.asarray(problem.expr.edge_coefficient.value).reshape(-1)[0] + normalized = np.asarray(problem.expr.edge_coefficient_normalized.value).reshape(-1)[0] + + assert np.array_equal(selected, np.array([0])) + assert np.isclose(coefficient, 0.5, atol=1e-8) + assert np.isclose(normalized, 1.0, atol=1e-8) + + +@pytest.mark.parametrize("value", [0.0, -1.0, np.nan, np.inf, True]) +def test_rejects_invalid_min_abs_coefficient(value): + """The exact-support threshold must be finite, positive, and non-boolean.""" + with pytest.raises((TypeError, ValueError)): + LinearDAGDiscovery(coefficient_bound=5.0, min_abs_coefficient=value) + + +def test_structural_coefficient_support_allows_zero_connector(backend): + """Structural support preserves the connector-edge interpretation.""" + graph = Graph.from_tuples([("A", 1, "B"), ("B", 1, "C")]) + data = _data( + [ + ("obs", {"A": 0.0, "B": 1.0, "C": 2.0}, ()), + ("do_A", {"A": 1.0, "B": 0.0, "C": 0.0}, ("A",)), + ] + ) + method = LinearDAGDiscovery( + fit_intercept=False, + coefficient_support="structural", + backend=backend, + ) + problem = method.build(graph, data) + _solve(problem, backend) + + selected = method.get_selected_edge_indices() + coefficients = np.asarray(problem.expr.edge_coefficient.value).reshape(-1) + + assert np.array_equal(selected, np.array([0, 1])) + assert np.isclose(coefficients[0], 0.0, atol=1e-8) + + solution_edges = method.get_solution_graph().get_attr_edges() + assert solution_edges[0]["connector"] is True + assert solution_edges[0]["interaction"] == 0 + assert solution_edges[0]["inferred_interaction"] == 0 + assert solution_edges[0]["prior_interaction"] == 1 + assert solution_edges[1]["connector"] is False + + +def test_solution_graph_does_not_retain_conflicting_prior_sign(backend): + """Solution metadata must expose fitted effects separately from prior signs.""" + graph = Graph.from_tuples([("A", 1, "B")]) + data = _data( + [ + ("obs", {"A": 0.0, "B": 0.0}, ()), + ("do_A", {"A": 2.0, "B": -3.0}, ("A",)), + ] + ) + method = LinearDAGDiscovery(fit_intercept=False, backend=backend) + problem = method.build(graph, data) + _solve(problem, backend) + + coefficient = np.asarray(problem.expr.edge_coefficient.value).reshape(-1)[0] + normalized = np.asarray(problem.expr.edge_coefficient_normalized.value).reshape(-1)[0] + solution_edge = method.get_solution_graph().get_attr_edge(0) + + assert coefficient < 0 + assert solution_edge["interaction"] < 0 + assert solution_edge["inferred_interaction"] == -1 + assert np.isclose(solution_edge["coefficient"], coefficient, atol=1e-8) + assert np.isclose(solution_edge["normalized_coefficient"], normalized, atol=1e-8) + assert solution_edge["prior_interaction"] == 1 diff --git a/tests/methods/causal/test_linear_dag_sparse.py b/tests/methods/causal/test_linear_dag_sparse.py new file mode 100644 index 000000000..3faa3f92d --- /dev/null +++ b/tests/methods/causal/test_linear_dag_sparse.py @@ -0,0 +1,175 @@ +"""Sparse affine-operator checks for linear DAG discovery.""" + +import numpy as np +from scipy.sparse import eye, issparse + +from corneto._constants import VarType +from corneto.backend import PicosBackend +from corneto.backend._base import _sparse_vector_entry_repetition, _sparse_vector_replication +from corneto.data import Data +from corneto.graph import Graph +from corneto.methods.causal import LinearDAGDiscovery + + +def _is_sparse_constant(symbol) -> bool: + """Recognize sparse constants in both backend expression implementations.""" + value = getattr(symbol.e, "value", None) + if issparse(value): + return True + picos_constant = getattr(symbol.e, "_cached__constant_coef", None) + return picos_constant is not None and hasattr(picos_constant, "CCS") + + +def _single_edge_data() -> Data: + return Data.from_cdict( + { + "obs": { + "A": {"mapping": "vertex", "value": 0.0}, + "B": {"mapping": "vertex", "value": 0.0}, + }, + "do_A": { + "A": {"mapping": "vertex", "value": 2.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 3.0}, + }, + } + ) + + +def test_sparse_vector_maps_preserve_column_major_layout(): + values = np.array([1.0, 2.0, 3.0]) + + repeated_columns = _sparse_vector_replication(3, 2) @ values + repeated_entries = _sparse_vector_entry_repetition(3, 2) @ values + + np.testing.assert_allclose(repeated_columns, [1, 2, 3, 1, 2, 3]) + np.testing.assert_allclose(repeated_entries, [1, 1, 2, 2, 3, 3]) + + +def test_sparse_constants_do_not_allocate_dense_bound_metadata(backend): + constant = backend.Constant(eye(1000, format="csr")) + + assert constant.lb is None + assert constant.ub is None + assert constant._lb is None + assert constant._ub is None + + +def test_selected_flow_matrix_operators_are_sparse(backend): + graph = Graph.from_tuples([("A", 1, "B")]) + problem = backend.SelectedFlow( + graph, + lb=np.zeros((1, 3)), + ub=np.full((1, 3), 5.0), + n_flows=3, + edge_indices=[0], + selector_groups=[0, 0, 1], + ) + + matrix_constants = [ + symbol + for symbol in problem.symbols.values() + if len(getattr(symbol.e, "shape", ())) == 2 and not symbol.is_variable + ] + + assert matrix_constants + assert all(_is_sparse_constant(symbol) for symbol in matrix_constants) + + +def test_linear_dag_prediction_uses_sparse_affine_operators(backend): + graph = Graph.from_tuples([("A", 1, "B")]) + problem = LinearDAGDiscovery(fit_intercept=True, backend=backend).build(graph, _single_edge_data()) + + prediction_constants = [symbol for symbol in problem.expr.prediction._proxy_symbols if not symbol.is_variable] + + assert prediction_constants + assert all(_is_sparse_constant(symbol) for symbol in prediction_constants) + + +def test_linear_dag_coverage_uses_sparse_affine_operators(backend): + graph = Graph.from_tuples([("A", 1, "B")]) + problem = LinearDAGDiscovery( + fit_intercept=False, + min_commodity_coverage=1.0, + backend=backend, + ).build(graph, _single_edge_data()) + + matrix_constants = [ + symbol + for symbol in problem.symbols.values() + if not symbol.is_variable and len(getattr(symbol.e, "shape", ())) == 2 + ] + + assert matrix_constants + assert all(_is_sparse_constant(symbol) for symbol in matrix_constants) + + +def test_picos_flow_conservation_keeps_sparse_incidence(): + graph = Graph.from_tuples([("A", 1, "B")]) + problem = PicosBackend().Flow(graph, n_flows=2) + + matrix_constants = [ + symbol + for symbol in problem.constraints[0]._proxy_symbols + if not symbol.is_variable and len(getattr(symbol.e, "shape", ())) == 2 + ] + + assert matrix_constants + assert all(_is_sparse_constant(symbol) for symbol in matrix_constants) + + +def test_picos_axis_sums_keep_sparse_affine_coefficients(): + backend = PicosBackend() + expression = backend.Variable("sum_value", (1000, 12)) + values = np.arange(12000, dtype=float).reshape((1000, 12), order="F") + expression.e.value = values + expected = { + 0: values.sum(axis=0, keepdims=True), + 1: values.sum(axis=1, keepdims=True), + None: values.sum(), + } + shapes = {0: (1, 12), 1: (1000, 1), None: (1, 1)} + + for axis in (0, 1, None): + reduced = expression.sum(axis=axis) + coefficients = list(reduced.e._coefs.values()) + + assert coefficients + assert all(hasattr(coefficient, "CCS") for coefficient in coefficients) + assert reduced.shape == shapes[axis] + np.testing.assert_allclose(np.asarray(reduced.value), expected[axis]) + + +def test_picos_axis_sums_preserve_explicit_vector_orientation(): + backend = PicosBackend() + expression = backend.Variable("orientation_value", (3,)) + problem = backend.Problem() + problem += expression == np.array([1.0, 2.0, 4.0]) + + row_sum = expression.reshape((1, 3)).sum(axis=0) + column_sum = expression.reshape((3, 1)).sum(axis=1) + transposed_sum = expression.T.sum(axis=0) + problem.solve(solver="glpk") + + assert row_sum.shape == (1, 3) + assert column_sum.shape == (3, 1) + assert transposed_sum.shape == (1, 3) + np.testing.assert_allclose(np.asarray(row_sum.value), [[1.0, 2.0, 4.0]]) + np.testing.assert_allclose(np.asarray(column_sum.value), [[1.0], [2.0], [4.0]]) + np.testing.assert_allclose(np.asarray(transposed_sum.value), [[1.0, 2.0, 4.0]]) + + +def test_acyclic_parent_limits_are_applied_vertexwise(backend): + graph = Graph.from_tuples([("A", 1, "B")]) + problem = backend.Problem() + positive = backend.Variable("positive", (1,), vartype=VarType.BINARY) + problem.register("positive", positive) + problem += positive == np.array([1]) + backend.Acyclic( + graph, + problem, + indicator_positive_var_name="positive", + max_parents={"A": 0, "B": 1}, + ) + + solved = problem.solve() + assert str(solved.status).lower() == "optimal" diff --git a/tests/methods/signaling/test_cellnopt_annnet.py b/tests/methods/signaling/test_cellnopt_annnet.py new file mode 100644 index 000000000..0dd81f0b2 --- /dev/null +++ b/tests/methods/signaling/test_cellnopt_annnet.py @@ -0,0 +1,48 @@ +import pytest + +from corneto.methods.signaling import CellNOptDAG +from corneto.methods.signaling.annnet import ( + add_cellnopt_conditions, + add_cellnopt_results, + build_cellnopt_from_annnet, +) + +annnet = pytest.importorskip("annnet") + + +def test_cellnopt_reads_conditions_and_adds_results_to_annnet(backend): + graph = annnet.AnnNet(directed=True) + graph.history.enable(True) + graph.slices.add("prior") + graph.add_edges( + [ + {"source": "L", "target": "A", "edge_id": "e0"}, + {"source": "A", "target": "Y", "edge_id": "e1"}, + {"source": "L", "target": "Y", "edge_id": "shortcut"}, + ], + slice="prior", + default_edge_directed=True, + ) + for edge_id in graph.edges(): + graph.attrs.set_edge_attrs(edge_id, interaction=1) + layers = add_cellnopt_conditions( + graph, + inputs={"off": {"L": 0}, "on": {"L": 1}, "blocked": {"L": 1}}, + inhibitors={"off": {}, "on": {}, "blocked": {"A": 1}}, + measurements={"off": {"Y": 0}, "on": {"Y": 1}, "blocked": {"Y": 0}}, + ) + + method = CellNOptDAG(lambda_reg=1e-3, backend=backend) + problem = build_cellnopt_from_annnet(method, graph, network_slice="prior") + solution = problem.solve() + summary = add_cellnopt_results(graph, method, problem, solution=solution) + + assert solution.status == "optimal" + assert layers == {"off": ("off",), "on": ("on",), "blocked": ("blocked",)} + assert graph.layers.get_vertex_layer_attrs("L", ("on",))["input"] == 1 + assert graph.layers.get_vertex_layer_attrs("A", ("blocked",))["inhibited"] == 1 + assert graph.layers.get_vertex_layer_attrs("Y", ("on",))["predicted"] == 1 + assert graph.layers.get_layer_attrs(("blocked",))["endpoint_absolute_error"] == 0 + assert graph.slices.exists("cellnopt_selected") + assert summary["selected_reactions"] == 2 + assert summary["condition_errors"] == {"off": 0.0, "on": 0.0, "blocked": 0.0} diff --git a/tests/methods/signaling/test_cellnopt_dag.py b/tests/methods/signaling/test_cellnopt_dag.py new file mode 100644 index 000000000..15e2bb6f6 --- /dev/null +++ b/tests/methods/signaling/test_cellnopt_dag.py @@ -0,0 +1,266 @@ +import numpy as np + +from corneto.backend import PicosBackend +from corneto.graph import Graph +from corneto.methods.signaling.cellnopt_dag import CellNOptDAG + + +def _vertex_values(method, problem, vertex): + index = method.processed_graph.V.index(vertex) + values = np.asarray(problem.expr.vertex_value.value, dtype=float) + return values.reshape(problem.expr.vertex_value.shape)[index].reshape(-1) + + +def _flat_values(expression): + return np.asarray(expression.value, dtype=float).reshape(expression.shape).reshape(-1) + + +def _assert_infeasible(problem, backend): + if isinstance(backend, PicosBackend): + result = problem.solve(primals=None) + else: + result = problem.solve() + assert result.status == "infeasible" + + +def _solve(method, graph, *, inputs, measurements, inhibitors=None): + problem = method.build_many( + graph, + inputs=inputs, + measurements=measurements, + inhibitors=inhibitors, + ) + result = problem.solve() + assert result.status == "optimal" + return problem + + +def test_and_gate_is_one_reaction_with_conjunctive_truth(backend): + graph = Graph.from_tuples( + [ + ("A", 1, "AND1"), + ("B", 1, "AND1"), + ("AND1", 1, "Y"), + ] + ) + inputs = { + "neither": {"A": 0, "B": 0}, + "a_only": {"A": 1, "B": 0}, + "b_only": {"A": 0, "B": 1}, + "both": {"A": 1, "B": 1}, + } + measurements = { + "neither": {"Y": 0}, + "a_only": {"Y": 0}, + "b_only": {"Y": 0}, + "both": {"Y": 1}, + } + + method = CellNOptDAG(lambda_reg=1e-3, backend=backend) + problem = _solve( + method, + graph, + inputs=inputs, + measurements=measurements, + ) + + assert len(method.reactions) == 1 + assert method.reactions[0].positive_literals == ("A", "B") + assert np.allclose(_flat_values(problem.expr.reaction_selected), [1]) + assert np.allclose( + np.asarray(problem.expr.reaction_active.value).reshape(1, -1), + [[0, 0, 0, 1]], + ) + assert np.allclose(_vertex_values(method, problem, "Y"), [0, 0, 0, 1]) + assert np.all(np.asarray(problem.expr.flow.value)[:2] >= method.epsilon) + + +def test_or_is_induced_by_multiple_active_producing_reactions(backend): + graph = Graph.from_tuples( + [ + ("A", 1, "Y"), + ("B", 1, "Y"), + ] + ) + inputs = { + "neither": {"A": 0, "B": 0}, + "a_only": {"A": 1, "B": 0}, + "b_only": {"A": 0, "B": 1}, + "both": {"A": 1, "B": 1}, + } + measurements = { + "neither": {"Y": 0}, + "a_only": {"Y": 1}, + "b_only": {"Y": 1}, + "both": {"Y": 1}, + } + + method = CellNOptDAG(lambda_reg=1e-3, backend=backend) + problem = _solve( + method, + graph, + inputs=inputs, + measurements=measurements, + ) + + assert np.allclose(_flat_values(problem.expr.reaction_selected), [1, 1]) + assert np.allclose( + np.asarray(problem.expr.reaction_active.value), + [[0, 1, 0, 1], [0, 0, 1, 1]], + ) + assert np.allclose(_vertex_values(method, problem, "Y"), [0, 1, 1, 1]) + + +def test_positive_and_negative_alternatives_fit_complementary_conditions(backend): + graph = Graph.from_tuples( + [ + ("A", 1, "Y"), + ("A", -1, "Y"), + ] + ) + method = CellNOptDAG(lambda_reg=1e-3, backend=backend) + problem = _solve( + method, + graph, + inputs={"off": {"A": 0}, "on": {"A": 1}}, + measurements={"off": {"Y": 1}, "on": {"Y": 1}}, + ) + + assert np.allclose(_flat_values(problem.expr.reaction_selected), [1, 1]) + assert np.allclose( + np.asarray(problem.expr.reaction_active.value), + [[0, 1], [1, 0]], + ) + assert np.allclose(_vertex_values(method, problem, "Y"), [1, 1]) + + +def test_same_inputs_with_conflicting_measurements_cannot_both_be_fit(backend): + graph = Graph.from_tuples([("A", 1, "Y")]) + method = CellNOptDAG(lambda_reg=0, backend=backend) + problem = _solve( + method, + graph, + inputs={"first": {"A": 1}, "second": {"A": 1}}, + measurements={"first": {"Y": 0}, "second": {"Y": 1}}, + ) + + assert np.isclose(problem.objectives[0].value, 1) + predictions = _vertex_values(method, problem, "Y") + assert np.allclose(predictions, [0, 0]) or np.allclose(predictions, [1, 1]) + + +def test_irrelevant_active_input_does_not_force_a_disconnected_branch(backend): + graph = Graph.from_tuples( + [ + ("A", 1, "B"), + ("C", 1, "D"), + ] + ) + method = CellNOptDAG(lambda_reg=1e-3, backend=backend) + problem = _solve( + method, + graph, + inputs={"condition": {"A": 1, "C": 1}}, + measurements={"condition": {"B": 1}}, + ) + + assert np.allclose(_flat_values(problem.expr.reaction_selected), [1, 0]) + assert np.allclose(_vertex_values(method, problem, "C"), [1]) + assert np.allclose(_vertex_values(method, problem, "D"), [0]) + + +def test_flow_rejects_a_selected_component_without_experimental_boundaries(backend): + graph = Graph.from_tuples( + [ + ("A", 1, "B"), + ("C", 1, "D"), + ] + ) + method = CellNOptDAG(lambda_reg=0, backend=backend) + problem = method.build( + graph, + inputs={"A": 1}, + measurements={"B": 1}, + ) + problem += problem.expr.reaction_selected[1] == 1 + + _assert_infeasible(problem, backend) + + +def test_flow_rejects_a_selected_dangling_branch(backend): + graph = Graph.from_tuples( + [ + ("A", 1, "B"), + ("A", 1, "C"), + ] + ) + method = CellNOptDAG(lambda_reg=0, backend=backend) + problem = method.build( + graph, + inputs={"A": 1}, + measurements={"B": 1}, + ) + problem += problem.expr.reaction_selected[1] == 1 + + _assert_infeasible(problem, backend) + + +def test_inhibition_overrides_product_without_disabling_reaction_truth(backend): + graph = Graph.from_tuples([("A", 1, "B")]) + method = CellNOptDAG(lambda_reg=0, backend=backend) + problem = method.build( + graph, + inputs={"A": 1}, + inhibitors={"B": 1}, + measurements={"B": 0}, + ) + problem += problem.expr.reaction_selected[0] == 1 + + result = problem.solve() + + assert result.status == "optimal" + assert np.allclose(problem.expr.reaction_active.value, [[1]]) + assert np.allclose(_vertex_values(method, problem, "B"), [0]) + + +def test_acyclicity_rejects_a_selected_feedback_cycle(backend): + graph = Graph.from_tuples( + [ + ("A", 1, "B"), + ("B", 1, "A"), + ] + ) + method = CellNOptDAG(lambda_reg=0, backend=backend) + problem = method.build( + graph, + inputs={"A": 1}, + measurements={"B": 1}, + ) + problem += problem.expr.reaction_selected == 1 + + _assert_infeasible(problem, backend) + + +def test_constraint_blocks_are_vectorized_over_reactions_and_conditions(backend): + small_graph = Graph.from_tuples([("A", 1, "B")]) + large_graph = Graph.from_tuples([(f"v{i}", 1, f"v{i + 1}") for i in range(30)]) + small = CellNOptDAG(lambda_reg=0, backend=backend).build( + small_graph, + inputs={"A": 1}, + measurements={"B": 1}, + ) + large = CellNOptDAG(lambda_reg=0, backend=backend).build_many( + large_graph, + inputs={ + "one": {"v0": 1}, + "two": {"v0": 1}, + "three": {"v0": 1}, + }, + measurements={ + "one": {"v30": 1}, + "two": {"v30": 1}, + "three": {"v30": 1}, + }, + ) + + assert len(small.constraints) == len(large.constraints) diff --git a/tests/methods/signaling/test_cellnopt_ilp.py b/tests/methods/signaling/test_cellnopt_ilp.py deleted file mode 100644 index 32e11ffaa..000000000 --- a/tests/methods/signaling/test_cellnopt_ilp.py +++ /dev/null @@ -1,116 +0,0 @@ -import numpy as np -import pytest - -import corneto as cn -from corneto.backend import Backend, CvxpyBackend, PicosBackend -from corneto.methods.signaling.cellnopt_ilp import cellnoptILP - - -# PICOS does not work -@pytest.fixture(params=[CvxpyBackend, PicosBackend]) -def backend(request): - opt: Backend = request.param() - if isinstance(opt, CvxpyBackend): - opt._default_solver = "SCIPY" - elif isinstance(opt, PicosBackend): - opt._default_solver = "glpk" - return opt - - -def get_test_graph_1(): - G1 = cn.Graph.from_tuples( - [ - ("EGF", 1, "AND1"), - ("TNFa", 1, "AND1"), - ("AND1", 1, "Ras"), - ("EGF", 1, "Ras"), - ("TNFa", 1, "Ras"), - ] - ) - G1.add_edge((), "EGF") - G1.add_edge((), "TNFa") - G1.add_edge("Ras", ()) - - return G1 - - -@pytest.mark.skip(reason="Error with PICOS solvers") -def test_cellnoptILP_AND(backend): - G1 = get_test_graph_1() - - # RAS is only active iff both EGF and TNFa are active -> we need to identify the AND gate - exp_list_G1_and = { - "exp0": {"input": {"EGF": 0, "TNFa": 0}, "output": {"Ras": 0}}, - "exp1": {"input": {"EGF": 1, "TNFa": 0}, "output": {"Ras": 0}}, - "exp2": {"input": {"EGF": 0, "TNFa": 1}, "output": {"Ras": 0}}, - "exp3": {"input": {"EGF": 1, "TNFa": 1}, "output": {"Ras": 1}}, - } - - P = cellnoptILP(G1, exp_list_G1_and, verbose=True, alpha_flow=0.001, backend=backend) - expected_edge_values = np.array( - [ - [0.0, 1.0, 0.0, 1.0], - [0.0, 0.0, 1.0, 1.0], - [-0.0, -0.0, -0.0, 1.0], - [0.0, -0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, -0.0], - [0.0, 1.0, 0.0, 1.0], - [0.0, 0.0, 1.0, 1.0], - [0.0, 0.0, 0.0, 1.0], - ] - ) - - # vertices do not have a specific order (set of vertices is a list, but the order is not fixed) - expected_vertex_values = np.array( - [ - [-0.0, -0.0, -0.0, 1.0], - [-0.0, 1.0, -0.0, 1.0], - [-0.0, -0.0, 1.0, 1.0], - [0.0, -0.0, -0.0, 1.0], - ] - ) - sum([o.value for o in P.objectives]) - assert np.isclose(sum([o.value for o in P.objectives]), 0.006) - assert np.isclose(P.expr.edge_activates.value, expected_edge_values).all() - assert np.isclose(np.sum(P.expr.vertex_value.value, axis=0), expected_vertex_values.sum(axis=0)).all() - - -# @pytest.mark.skip(reason="not compatible with picos") -def test_cellnoptILP_OR(backend): - G1 = get_test_graph_1() - - # RAS is only active iff both EGF and TNFa are active -> we need to identify the AND gate - exp_list_G1_or = { - "exp0": {"input": {"EGF": 0, "TNFa": 0}, "output": {"Ras": 0}}, - "exp1": {"input": {"EGF": 1, "TNFa": 0}, "output": {"Ras": 1}}, - "exp2": {"input": {"EGF": 0, "TNFa": 1}, "output": {"Ras": 1}}, - "exp3": {"input": {"EGF": 1, "TNFa": 1}, "output": {"Ras": 1}}, - } - - P = cellnoptILP(G1, exp_list_G1_or, verbose=True, alpha_flow=0.001, backend=backend) - expected_edge_values = np.array( - [ - [0.0, -0.0, 0.0, -0.0], - [0.0, 0.0, -0.0, -0.0], - [-0.0, -0.0, -0.0, -0.0], - [0.0, 1.0, 0.0, 1.0], - [0.0, 0.0, 1.0, 1.0], - [0.0, 1.0, 0.0, 1.0], - [0.0, 0.0, 1.0, 1.0], - [0.0, 1.0, 1.0, 1.0], - ] - ) - - # vertices do not have a specific order (set of vertices is a list, but the order is not fixed) - expected_vertex_values = np.array( - [ - [-0.0, -0.0, -0.0, -0.0], - [-0.0, 1.0, -0.0, 1.0], - [-0.0, -0.0, 1.0, 1.0], - [0.0, 1.0, 1.0, 1.0], - ] - ) - - assert np.isclose(sum([o.value for o in P.objectives]), 0.005) - assert np.isclose(P.expr.edge_activates.value, expected_edge_values).all() - assert np.isclose(np.sum(P.expr.vertex_value.value, axis=0), expected_vertex_values.sum(axis=0)).all() diff --git a/tests/methods/signaling/test_cellnopt_plotting.py b/tests/methods/signaling/test_cellnopt_plotting.py new file mode 100644 index 000000000..ecfe0004a --- /dev/null +++ b/tests/methods/signaling/test_cellnopt_plotting.py @@ -0,0 +1,269 @@ +from pathlib import Path + +import nbformat +import numpy as np +import pytest +from nbclient import NotebookClient + +from corneto._plotting import to_dot_source +from corneto.graph import Graph +from corneto.methods.signaling import ( + CellNOptDAG, + plot_cellnopt_fit, + plot_cellnopt_model, +) +from corneto.methods.signaling.cellnopt_plotting import ( + _build_cellnopt_model_plot, +) + + +def _solve(backend, graph, *, inputs, measurements, inhibitors=None, lambda_reg=1e-3): + method = CellNOptDAG(lambda_reg=lambda_reg, backend=backend) + problem = method.build_many( + graph, + inputs=inputs, + measurements=measurements, + inhibitors=inhibitors, + ) + result = problem.solve() + assert result.status == "optimal" + return method, problem + + +def test_model_plot_expands_and_preserves_or_and_inhibition(backend): + graph = Graph.from_tuples( + [ + ("A", 1, "AND1"), + ("B", -1, "AND1"), + ("AND1", 1, "Y"), + ("C", 1, "Y"), + ] + ) + method, problem = _solve( + backend, + graph, + inputs={ + "and_route": {"A": 1, "B": 0, "C": 0}, + "or_route": {"A": 0, "B": 0, "C": 1}, + }, + measurements={ + "and_route": {"Y": 1}, + "or_route": {"Y": 1}, + }, + ) + + spec = _build_cellnopt_model_plot(method, problem, width_by="flow") + and_nodes = [vertex for vertex in spec.graph.V if str(vertex).startswith("__cellnopt_and_")] + assert len(and_nodes) == 1 + and_node = and_nodes[0] + assert any(source == frozenset({"C"}) and target == frozenset({"Y"}) for source, target in spec.graph.E) + assert any(source == frozenset({"A"}) and target == frozenset({and_node}) for source, target in spec.graph.E) + assert any(source == frozenset({and_node}) and target == frozenset({"Y"}) for source, target in spec.graph.E) + + negative_edges = [ + index for index, attrs in enumerate(spec.graph.get_attr_edges()) if attrs.get("interaction") == -1 + ] + assert len(negative_edges) == 1 + assert spec.edge_attributes[negative_edges[0]]["arrowhead"] == "tee" + assert spec.edge_attributes[negative_edges[0]]["color"] == "#C43C39" + aggregate_edges = [ + attrs for attrs in spec.edge_attributes.values() if "aggregate structural flow" in attrs["tooltip"] + ] + assert len(aggregate_edges) == 1 + assert np.isfinite(float(aggregate_edges[0]["penwidth"])) + + dot = to_dot_source( + spec.graph, + graph_attr=spec.graph_attributes, + node_attr=spec.node_attributes, + custom_edge_attr=spec.edge_attributes, + custom_vertex_attr=spec.vertex_attributes, + ) + assert 'label="AND"' in dot + assert 'arrowhead="tee"' in dot + assert dot.count('-> "Y"') == 2 + + +def test_condition_plot_distinguishes_active_and_selected_inactive_reactions(backend): + graph = Graph.from_tuples([("A", 1, "Y")]) + method, problem = _solve( + backend, + graph, + inputs={"off": {"A": 0}, "on": {"A": 1}}, + measurements={"off": {"Y": 0}, "on": {"Y": 1}}, + ) + + off = _build_cellnopt_model_plot(method, problem, condition="off") + on = _build_cellnopt_model_plot(method, problem, condition="on") + hidden = _build_cellnopt_model_plot( + method, + problem, + condition="off", + show_inactive=False, + ) + + assert off.edge_attributes[0]["style"] == "dashed" + assert "active=0" in off.edge_attributes[0]["tooltip"] + assert on.edge_attributes[0]["style"] == "solid" + assert "active=1" in on.edge_attributes[0]["tooltip"] + assert hidden.graph.num_edges == 0 + assert set(hidden.graph.V) == {"A", "Y"} + + +def test_model_plot_can_reveal_unselected_reactions(backend): + graph = Graph.from_tuples( + [ + ("A", 1, "Y"), + ("C", 1, "D"), + ] + ) + method, problem = _solve( + backend, + graph, + inputs={"condition": {"A": 1, "C": 1}}, + measurements={"condition": {"Y": 1}}, + ) + + selected = _build_cellnopt_model_plot(method, problem) + complete = _build_cellnopt_model_plot(method, problem, show_unselected=True) + + assert selected.graph.num_edges == 1 + assert complete.graph.num_edges == 2 + unselected = [attrs for attrs in complete.edge_attributes.values() if "selected=0" in attrs["tooltip"]] + assert len(unselected) == 1 + assert unselected[0]["style"] == "dotted" + + +def test_fit_views_support_uneven_measurements_and_named_subsets(backend): + pytest.importorskip("matplotlib") + graph = Graph.from_tuples( + [ + ("A", 1, "Y"), + ("A", 1, "Z"), + ] + ) + method, problem = _solve( + backend, + graph, + inputs={ + "very_long_enabled_condition": {"A": 1}, + "blocked": {"A": 1}, + }, + inhibitors={ + "very_long_enabled_condition": {}, + "blocked": {"Y": 1}, + }, + measurements={ + "very_long_enabled_condition": {"Y": 0.8}, + "blocked": {"Z": 1}, + }, + ) + + grid_figure, grid_axes = plot_cellnopt_fit(method, problem) + inferred_figure, inferred_axes = plot_cellnopt_fit( + method, + problem, + signals=["A"], + ) + heatmap_figure, heatmap_axes = plot_cellnopt_fit( + method, + problem, + view="heatmap", + conditions=["blocked"], + signals=["Z"], + ) + + assert grid_axes.shape == (2, 3) + assert inferred_axes.shape == (2, 2) + assert heatmap_axes.shape == (4,) + assert grid_figure is grid_axes[0, 0].figure + assert inferred_figure is inferred_axes[0, 0].figure + assert heatmap_figure is heatmap_axes[0].figure + + measured_axis = grid_axes[0, 0] + assert [line.get_label() for line in measured_axis.lines] == [ + "_nolegend_", + "Model", + "Observed", + ] + assert all(np.allclose(line.get_xdata(), 0) for line in measured_axis.lines) + assert np.allclose(measured_axis.lines[-2].get_ydata(), [1]) + assert np.allclose(measured_axis.lines[-1].get_ydata(), [0.8]) + assert [tick.get_text() for tick in grid_axes[-1, 0].get_xticklabels()] == ["Endpoint"] + + prediction_only_axis = grid_axes[0, 1] + assert [line.get_label() for line in prediction_only_axis.lines] == ["Model"] + assert all([line.get_label() for line in inferred_axes[row, 0].lines] == ["Model"] for row in range(2)) + assert grid_axes[1, 0].patch.get_hatch() == "//" + assert prediction_only_axis.patch.get_hatch() == "//" + + +def test_fit_grid_handles_one_condition_and_one_signal(backend): + pytest.importorskip("matplotlib") + method, problem = _solve( + backend, + Graph.from_tuples([("A", 1, "Y")]), + inputs={"single_endpoint": {"A": 1}}, + measurements={"single_endpoint": {"Y": 0.2}}, + ) + + figure, axes = plot_cellnopt_fit(method, problem) + + assert axes.shape == (1, 2) + assert figure is axes[0, 0].figure + assert [tick.get_text() for tick in axes[0, 0].get_xticklabels()] == ["Endpoint"] + assert [line.get_label() for line in axes[0, 0].lines] == [ + "_nolegend_", + "Model", + "Observed", + ] + assert np.allclose(axes[0, 0].lines[-2].get_ydata(), [0]) + assert np.allclose(axes[0, 0].lines[-1].get_ydata(), [0.2]) + + +def test_networkx_model_plot_returns_real_figure(backend): + pytest.importorskip("matplotlib") + pytest.importorskip("networkx") + method, problem = _solve( + backend, + Graph.from_tuples([("A", -1, "Y")]), + inputs={"condition": {"A": 0}}, + measurements={"condition": {"Y": 1}}, + ) + + figure = plot_cellnopt_model(method, problem, renderer="networkx") + + assert len(figure.axes) == 1 + assert {"A", "Y"} <= {text.get_text() for text in figure.axes[0].texts} + assert len(figure.axes[0].patches) == 1 + + +def test_plotting_requires_a_solved_problem(): + method = CellNOptDAG(lambda_reg=0) + method.build( + Graph.from_tuples([("A", 1, "Y")]), + inputs={"A": 1}, + measurements={"Y": 1}, + ) + + with pytest.raises(ValueError, match="Solve a feasible problem"): + plot_cellnopt_model(method, renderer="networkx") + with pytest.raises(ValueError, match="Solve a feasible problem"): + plot_cellnopt_fit(method) + + +def test_cellnopt_worked_notebook_executes_every_cell(): + notebook_path = Path(__file__).parents[3] / "docs/guide/signaling/cellnopt_dag.ipynb" + notebook = nbformat.read(notebook_path, as_version=4) + + executed = NotebookClient( + notebook, + timeout=300, + kernel_name="python3", + resources={"metadata": {"path": str(notebook_path.parent)}}, + ).execute() + + errors = [ + output for cell in executed.cells for output in cell.get("outputs", []) if output.get("output_type") == "error" + ] + assert errors == [] diff --git a/tests/methods/test_bidirectional_phonemes.py b/tests/methods/test_bidirectional_phonemes.py index a4403c682..c1f1ce65c 100644 --- a/tests/methods/test_bidirectional_phonemes.py +++ b/tests/methods/test_bidirectional_phonemes.py @@ -4,6 +4,7 @@ import pytest from corneto import Data, Graph +from corneto.backend import CvxpyBackend from corneto.methods import BidirectionalPHONEMeS, PHONEMeS @@ -332,6 +333,40 @@ def test_exposed_shapes_and_auxiliary_edges(backend): assert method.processed_graph.num_edges == 7 +def test_cvxpy_formulation_selects_only_mapped_biological_blocks(): + """Forward/reverse mapping should avoid indicators on unused flow entries.""" + graph = Graph() + graph.add_edges([("A", "K"), ("K", "B")]) + problem = BidirectionalPHONEMeS( + default_edge_cost=0, + anchor_policy="both", + backend=CvxpyBackend(), + ).build( + graph, + regulated_kinases=["K"], + phosphosite_scores={"A": -2, "B": -2}, + ) + cvxpy_problem = problem.solve(solver="SCIPY") + + boolean_count = sum(variable.size for variable in cvxpy_problem.variables() if variable.attributes["boolean"]) + # 4 directional biological supports + 2 edge/condition unions + + # 6 anchor selectors + 6 directional vertex selectors + 3 vertex unions + + # 2 auxiliary selectors for the supplied sliced expressions. + assert boolean_count == 23 + assert cvxpy_problem.size_metrics.num_scalar_variables == 40 + assert "_flow_ipos" not in problem.expr + assert "_flow_ineg" not in problem.expr + assert problem.expr.edge_selected_directional.shape == (2, 2) + assert np.allclose( + np.asarray(problem.expr.anchor_flow_downstream.value), + np.asarray(problem.expr.anchor_active_downstream.value)[[1], :], + ) + assert np.allclose( + np.asarray(problem.expr.anchor_flow_upstream.value), + np.asarray(problem.expr.anchor_active_upstream.value)[[1], :], + ) + + def test_constraint_blocks_are_vectorized(backend): def build_problem(length, conditions): graph = Graph() diff --git a/tests/methods/test_pcst_edge_cases.py b/tests/methods/test_pcst_edge_cases.py index f885551c2..f77c6f3e5 100644 --- a/tests/methods/test_pcst_edge_cases.py +++ b/tests/methods/test_pcst_edge_cases.py @@ -1,7 +1,7 @@ import numpy as np import pytest -from corneto.backend import PicosBackend +from corneto.backend import CvxpyBackend, PicosBackend from corneto.data import Data from corneto.graph import EdgeType, Graph from corneto.methods.pcst import PrizeCollectingSteinerTree @@ -84,6 +84,29 @@ def test_steiner_different_fixed_roots_per_sample_is_feasible(backend, request): assert np.isclose(sum(edge_cost_values), 4.0, atol=1e-6) +def test_steiner_per_sample_max_flow_bounds_are_edge_by_sample(backend): + """Distinct sample maxima should bound every biological and boundary flow.""" + graph = _small_undirected_path_graph() + method = SteinerTreeFlow( + root_vertex=["A", "C"], + max_flow=[2, 3], + strict_acyclic=True, + backend=backend, + ) + problem = method.build_many( + graph, + terminals={"s1": ["A", "C"], "s2": ["A", "C"]}, + ) + problem.solve() + + flow = np.asarray(problem.expr.flow.value) + assert flow.shape == (method.processed_graph.num_edges, 2) + assert np.max(np.abs(flow[:, 0])) <= 2 + 1e-8 + assert np.max(np.abs(flow[:, 1])) <= 3 + 1e-8 + assert np.asarray(problem.expr.flow.lb).shape == flow.shape + assert np.asarray(problem.expr.flow.ub).shape == flow.shape + + def test_steiner_lambda_reg_promotes_edge_sharing_across_samples(backend, request): if isinstance(backend, PicosBackend) and not request.config.getoption("--run-optional"): pytest.skip("PicosBackend is optional (use --run-optional)") @@ -298,3 +321,84 @@ def test_steiner_multisample_fixed_roots_isolate_in_edges_per_sample(backend, re assert in_c not in sample0_edges assert in_c in sample1_edges assert in_a not in sample1_edges + + +@pytest.mark.parametrize( + ("edge_type", "expected_binaries", "expected_variables"), + [ + (EdgeType.DIRECTED, 2, 9), + (EdgeType.UNDIRECTED, 4, 11), + ], +) +def test_cvxpy_strict_steiner_supports_only_biological_edges( + edge_type, + expected_binaries, + expected_variables, +): + """Strict Steiner support should exclude root and terminal boundaries.""" + graph = Graph() + graph.add_edge("A", "B", type=edge_type) + graph.add_edge("B", "C", type=edge_type) + problem = SteinerTreeFlow( + root_vertex="A", + strict_acyclic=True, + backend=CvxpyBackend(), + ).build( + graph, + terminals=["A", "C"], + ) + cvxpy_problem = problem.solve(solver="SCIPY") + + boolean_count = sum(variable.size for variable in cvxpy_problem.variables() if variable.attributes["boolean"]) + assert boolean_count == expected_binaries + assert cvxpy_problem.size_metrics.num_scalar_variables == expected_variables + assert problem.expr.with_flow.shape == (graph.num_edges, 1) + assert "_flow_ipos" not in problem.expr + assert "_flow_ineg" not in problem.expr + + +def test_cvxpy_non_strict_undirected_steiner_uses_one_selector_per_edge(): + """Non-strict Steiner should not pay for unused direction binaries.""" + graph = _small_undirected_path_graph() + problem = SteinerTreeFlow( + root_vertex="A", + strict_acyclic=False, + backend=CvxpyBackend(), + ).build( + graph, + terminals=["A", "C"], + ) + cvxpy_problem = problem.solve(solver="SCIPY") + + boolean_count = sum(variable.size for variable in cvxpy_problem.variables() if variable.attributes["boolean"]) + assert boolean_count == graph.num_edges + flow_variables = int(np.prod(problem.expr.flow.shape)) + assert cvxpy_problem.size_metrics.num_scalar_variables == flow_variables + graph.num_edges + assert "with_flow_positive" not in problem.expr + assert "with_flow_negative" not in problem.expr + + +def test_cvxpy_best_root_reuses_candidate_support_for_prizes(): + """Prize collection should reuse best-root boundary direction selectors.""" + graph = Graph() + graph.add_edge("A", "B", type=EdgeType.DIRECTED) + graph.add_edge("B", "C", type=EdgeType.DIRECTED) + problem = PrizeCollectingSteinerTree( + root_selection_strategy="best", + strict_acyclic=False, + backend=CvxpyBackend(), + ).build( + graph, + prizes={"B": 2, "C": 3}, + terminals=["B", "C"], + ) + cvxpy_problem = problem.solve(solver="SCIPY") + + boolean_count = sum(variable.size for variable in cvxpy_problem.variables() if variable.attributes["boolean"]) + # Two biological selectors and positive/negative selectors for two root + # candidates. Prize support aliases those root-candidate selectors. + assert boolean_count == 6 + assert cvxpy_problem.size_metrics.num_scalar_variables == 10 + assert "_flow_prize_pos_0" not in problem.expr + assert "_flow_prize_neg_0" not in problem.expr + assert problem.expr.selected_prized_flow_edges_0.shape == (2,) diff --git a/tests/methods/test_phonemes.py b/tests/methods/test_phonemes.py index 67013736c..ae0848683 100644 --- a/tests/methods/test_phonemes.py +++ b/tests/methods/test_phonemes.py @@ -6,6 +6,7 @@ import pytest from corneto import Data, Graph +from corneto.backend import CvxpyBackend from corneto.graph import EdgeType from corneto.methods import PHONEMeS @@ -236,6 +237,26 @@ def test_auxiliary_edges_are_excluded_and_shapes_are_stable(backend): assert method.processed_graph.num_edges == graph.num_edges + 2 +def test_cvxpy_formulation_selects_only_biological_edges(): + """Boundary flows should not receive positive/negative indicator binaries.""" + graph = Graph() + graph.add_edges([("r", "a"), ("a", "m")]) + problem = PHONEMeS(backend=CvxpyBackend()).build( + graph, + perturbations=["r"], + phosphosite_scores={"m": -1}, + ) + cvxpy_problem = problem.solve(solver="SCIPY") + + boolean_count = sum(variable.size for variable in cvxpy_problem.variables() if variable.attributes["boolean"]) + # Two biological edge selectors plus three vertex selectors. The two + # auxiliary boundary flows do not introduce binaries. + assert boolean_count == 5 + assert cvxpy_problem.size_metrics.num_scalar_variables == 12 + assert "_flow_ipos" not in problem.expr + assert "_flow_ineg" not in problem.expr + + def test_constraint_blocks_do_not_scale_with_graph_or_conditions(backend): def build_problem(num_internal): graph = Graph() diff --git a/tests/methods/test_predictive_api.py b/tests/methods/test_predictive_api.py new file mode 100644 index 000000000..837d12bf9 --- /dev/null +++ b/tests/methods/test_predictive_api.py @@ -0,0 +1,382 @@ +"""Focused tests for fixed-model mechanistic prediction APIs.""" + +import numpy as np +import pytest + +from corneto.data import Data +from corneto.graph import Graph +from corneto.methods.causal import LinearDAGDiscovery +from corneto.methods.signaling import CellNOptDAG + + +def _feature_value(data, sample, vertex): + return next(feature.value for feature in data.samples[sample].features if feature.id == vertex) + + +def _feature(data, sample, vertex): + return next(feature for feature in data.samples[sample].features if feature.id == vertex) + + +def _linear_training_data(): + return Data.from_cdict( + { + "baseline": { + "A": {"mapping": "vertex", "value": 0.0}, + "B": {"mapping": "vertex", "value": 0.0}, + }, + "intervention": { + "A": {"mapping": "vertex", "value": 2.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 6.0}, + }, + } + ) + + +def test_linear_fit_predicts_forward_without_refitting(backend): + graph = Graph.from_tuples([("A", 1, "B")]) + model = LinearDAGDiscovery(fit_intercept=False, backend=backend).fit( + graph, + _linear_training_data(), + solve_options={"verbosity": 0}, + ) + + def no_solve(*args, **kwargs): + raise AssertionError("predict must not solve") + + model.problem.solve = no_solve + held_out = Data.from_cdict( + { + "test": { + "A": {"mapping": "vertex", "value": 3.0}, + # This endogenous observation must not be used as a predictor. + "B": {"mapping": "vertex", "value": 999.0}, + } + } + ) + prediction = model.predict(held_out) + assert np.isclose(_feature_value(prediction, "test", "A"), 0.0) + assert np.isclose(_feature_value(prediction, "test", "B"), 0.0) + assert _feature(prediction, "test", "A").data["predicted"] + assert not _feature(prediction, "test", "A").data["clamped"] + metrics = model.evaluate(held_out) + assert metrics["forward_count"] == 2 + assert metrics["local_count"] == 2 + changed_root = Data.from_cdict( + { + "test": { + "A": {"mapping": "vertex", "value": 4.0}, + "B": {"mapping": "vertex", "value": 999.0}, + } + } + ) + changed_prediction = model.predict(changed_root) + assert changed_prediction.to_dict() == prediction.to_dict() + assert model.evaluate(changed_root)["forward_loss"] != metrics["forward_loss"] + + +def test_linear_manual_build_and_solve_supports_prediction(backend): + graph = Graph.from_tuples([("A", 1, "B")]) + model = LinearDAGDiscovery(fit_intercept=False, backend=backend) + problem = model.build(graph, _linear_training_data()) + problem.solve() + prediction = model.predict( + Data.from_cdict({"test": {"A": {"mapping": "vertex", "value": 1.0, "intervened": True}}}) + ) + assert np.isclose(_feature_value(prediction, "test", "B"), 3.0) + + +def test_linear_soft_target_can_be_missing_and_estimated_group_is_reused(backend): + graph = Graph.from_tuples([("A", 1, "B")]) + training = Data.from_cdict( + { + "obs": { + "A": {"mapping": "vertex", "value": 0.0}, + "B": {"mapping": "vertex", "value": 0.0}, + }, + "do": { + "A": {"mapping": "vertex", "value": 1.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 2.0}, + }, + "shift_1": { + "A": {"mapping": "vertex", "value": 1.0}, + "B": { + "mapping": "vertex", + "value": 5.0, + "intervention": "shift", + "intervention_group": "drug", + }, + }, + "shift_2": { + "A": {"mapping": "vertex", "value": 2.0}, + "B": { + "mapping": "vertex", + "value": 7.0, + "intervention": "shift", + "intervention_group": "drug", + }, + }, + } + ) + model = LinearDAGDiscovery(fit_intercept=False, backend=backend).fit(graph, training) + prediction = model.predict( + Data.from_cdict( + { + "held_out": { + "A": {"mapping": "vertex", "value": 3.0, "intervened": True}, + "B": { + "mapping": "vertex", + "value": None, + "intervention": "shift", + "intervention_group": "drug", + }, + } + } + ) + ) + assert np.isclose(_feature_value(prediction, "held_out", "B"), 9.0) + with pytest.raises(ValueError, match="No fitted soft-intervention shift"): + model.predict( + Data.from_cdict( + { + "held_out": { + "A": {"mapping": "vertex", "value": 3.0, "intervened": True}, + "B": { + "mapping": "vertex", + "value": None, + "intervention": "shift", + "intervention_group": "new_drug", + }, + } + } + ) + ) + + +def test_linear_known_shift_group_is_not_reused_without_test_shift(backend): + graph = Graph.from_tuples([("A", 1, "B")]) + training = Data.from_cdict( + { + "obs": { + "A": {"mapping": "vertex", "value": 0.0}, + "B": {"mapping": "vertex", "value": 0.0}, + }, + "do": { + "A": {"mapping": "vertex", "value": 1.0, "intervened": True}, + "B": {"mapping": "vertex", "value": 2.0}, + }, + "shift": { + "A": {"mapping": "vertex", "value": 1.0}, + "B": { + "mapping": "vertex", + "value": 5.0, + "intervention": "shift", + "shift": 3.0, + "intervention_group": "known_drug", + }, + }, + } + ) + model = LinearDAGDiscovery(fit_intercept=False, backend=backend).fit(graph, training) + with pytest.raises(ValueError, match="No fitted soft-intervention shift"): + model.predict( + Data.from_cdict( + { + "held_out": { + "A": {"mapping": "vertex", "value": 1.0, "intervened": True}, + "B": { + "mapping": "vertex", + "value": None, + "intervention": "shift", + "intervention_group": "known_drug", + }, + } + } + ) + ) + explicit = model.predict( + Data.from_cdict( + { + "held_out": { + "A": {"mapping": "vertex", "value": 1.0, "intervened": True}, + "B": { + "mapping": "vertex", + "value": None, + "intervention": "shift", + "shift": 3.0, + "intervention_group": "known_drug", + }, + } + } + ) + ) + assert np.isclose(_feature_value(explicit, "held_out", "B"), 5.0) + + +def test_linear_rejects_prediction_before_build_or_with_malformed_data(backend): + model = LinearDAGDiscovery(backend=backend) + with pytest.raises(ValueError, match="not been built"): + model.predict(Data.from_cdict({"test": {}})) + graph = Graph.from_tuples([("A", 1, "B")]) + model.build(graph, _linear_training_data()).solve() + with pytest.raises(TypeError, match="Data object"): + model.predict({"test": {}}) + + +def test_linear_evaluation_honors_vertex_and_held_out_sample_weights(backend): + graph = Graph.from_tuples([("A", 1, "B")]) + model = LinearDAGDiscovery( + fit_intercept=False, + vertex_weights={"A": 5.0, "B": 1.0}, + backend=backend, + ).fit(graph, _linear_training_data()) + held_out = Data.from_cdict( + { + "s1": { + "A": {"mapping": "vertex", "value": 1.0}, + "B": {"mapping": "vertex", "value": 2.0}, + }, + "s2": { + "A": {"mapping": "vertex", "value": 2.0}, + "B": {"mapping": "vertex", "value": 4.0}, + }, + } + ) + scales = model.loss_scales + expected_vertex_weighted = (5.0 * (1.0 / scales[0] + 2.0 / scales[0]) + (2.0 / scales[1] + 4.0 / scales[1])) / 12.0 + metrics = model.evaluate(held_out) + assert np.isclose(metrics["forward_loss"], expected_vertex_weighted) + expected_sample_weighted = ( + 5.0 * (4.0 * 1.0 / scales[0] + 1.0 * 2.0 / scales[0]) + + (4.0 * 2.0 / scales[1] + 1.0 * 4.0 / scales[1]) + ) / 30.0 + weighted = model.evaluate(held_out, sample_weights={"s1": 4.0, "s2": 1.0}) + assert np.isclose(weighted["forward_loss"], expected_sample_weighted) + with pytest.raises(ValueError, match="unknown keys"): + model.evaluate(held_out, sample_weights={"unseen": 1.0}) + with pytest.raises(ValueError, match="positive"): + model.evaluate(held_out, sample_weights={"s1": 0.0}) + with pytest.raises(TypeError, match="mapping"): + model.evaluate(held_out, sample_weights=[1.0, 1.0]) + + +def test_cellnopt_fit_prediction_and_inhibitor_clamping(backend): + graph = Graph.from_tuples([("A", 1, "B"), ("B", 1, "Y")]) + model = CellNOptDAG(lambda_reg=0, backend=backend).fit( + graph, + inputs={"train": {"A": 1}}, + measurements={"train": {"Y": 1}}, + solve_options={"verbosity": 0}, + ) + prediction = model.predict( + inputs={"active": {"A": 1}, "blocked": {"A": 1}}, + inhibitors={"active": {}, "blocked": {"B": 1}}, + ) + assert _feature_value(prediction, "active", "Y") == 1.0 + assert _feature_value(prediction, "blocked", "Y") == 0.0 + metrics = model.evaluate( + inputs={"active": {"A": 1}, "blocked": {"A": 1}}, + inhibitors={"active": {}, "blocked": {"B": 1}}, + measurements={"blocked": {"Y": 0}, "active": {"Y": 1}}, + ) + assert metrics["loss"] == 0.0 + assert metrics["by_condition"] == {"active": 0.0, "blocked": 0.0} + + +def test_fit_solver_options_validate_mapping_and_mutual_exclusion(backend): + graph = Graph.from_tuples([("A", 1, "Y")]) + model = CellNOptDAG(lambda_reg=0, backend=backend) + kwargs = { + "inputs": {"train": {"A": 1}}, + "measurements": {"train": {"Y": 1}}, + } + with pytest.raises(TypeError, match="mapping"): + model.fit(graph, **kwargs, solve_options=[]) + with pytest.raises(TypeError, match="either in solve_options"): + model.fit(graph, **kwargs, solve_options={"verbosity": 0}, verbosity=0) + + +def test_cellnopt_manual_build_predicts_without_solver(backend): + graph = Graph.from_tuples([("A", 1, "Y")]) + model = CellNOptDAG(lambda_reg=0, backend=backend) + problem = model.build(graph, inputs={"A": 1}, measurements={"Y": 1}) + with pytest.raises(ValueError, match="not been solved"): + model.predict(inputs={"test": {"A": 1}}) + problem.solve() + problem.solve = lambda *args, **kwargs: (_ for _ in ()).throw(AssertionError("predict must not solve")) + prediction = model.predict(inputs={"test": {"A": 1}}) + assert _feature_value(prediction, "test", "Y") == 1.0 + assert not _feature(prediction, "test", "A").data["predicted"] + assert _feature(prediction, "test", "A").data["forced"] + assert _feature(prediction, "test", "Y").data["predicted"] + + +def test_rebuilding_linear_model_invalidates_the_previous_solution(backend): + graph = Graph.from_tuples([("A", 1, "B")]) + model = LinearDAGDiscovery(fit_intercept=False, backend=backend).fit(graph, _linear_training_data()) + model.build(graph, _linear_training_data()) + assert model.solve_result is None + with pytest.raises(ValueError, match="not been solved"): + model.predict( + Data.from_cdict({"test": {"A": {"mapping": "vertex", "value": 1.0, "intervened": True}}}) + ) + + +def test_rebuilding_cellnopt_model_invalidates_the_previous_solution(backend): + graph = Graph.from_tuples([("A", 1, "Y")]) + model = CellNOptDAG(lambda_reg=0, backend=backend).fit( + graph, + inputs={"train": {"A": 1}}, + measurements={"train": {"Y": 1}}, + ) + model.build(graph, inputs={"A": 1}, measurements={"Y": 1}) + assert model.solve_result is None + with pytest.raises(ValueError, match="not been solved"): + model.predict(inputs={"test": {"A": 1}}) + + +def test_cellnopt_fixed_prediction_matches_solved_training_values(backend): + graph = Graph.from_tuples( + [ + ("A", 1, "Y"), + ("B", 1, "Y"), + ("C", -1, "Y"), + ("D", 1, "AND1"), + ("E", 1, "AND1"), + ("AND1", 1, "Z"), + ] + ) + inputs = { + "a": {"A": 1, "C": 1}, + "b": {"B": 1, "C": 1}, + "c_off": {"C": 0}, + "c_on": {"C": 1}, + "and": {"D": 1, "E": 1, "C": 1}, + "d_only": {"D": 1, "C": 1}, + "e_only": {"E": 1, "C": 1}, + "inhibited": {"A": 1, "C": 1}, + } + inhibitors = { + condition: ({"Y": 1} if condition == "inhibited" else {}) + for condition in inputs + } + measurements = { + "a": {"Y": 1, "Z": 0}, + "b": {"Y": 1, "Z": 0}, + "c_off": {"Y": 1, "Z": 0}, + "c_on": {"Y": 0, "Z": 0}, + "and": {"Y": 0, "Z": 1}, + "d_only": {"Y": 0, "Z": 0}, + "e_only": {"Y": 0, "Z": 0}, + "inhibited": {"Y": 0, "Z": 0}, + } + model = CellNOptDAG(lambda_reg=0, backend=backend) + problem = model.build_many(graph, inputs=inputs, measurements=measurements, inhibitors=inhibitors) + problem += problem.expr.reaction_selected == 1 + problem.solve() + assert np.allclose(np.asarray(problem.expr.reaction_selected.value).reshape(-1), [1, 1, 1, 1]) + solved = np.asarray(problem.expr.vertex_value.value, dtype=float).reshape(problem.expr.vertex_value.shape) + model.problem.solve = lambda *args, **kwargs: (_ for _ in ()).throw(AssertionError("predict must not solve")) + predicted = model.predict(inputs=inputs, inhibitors=inhibitors) + for condition_index, condition in enumerate(inputs): + for vertex_index, vertex in enumerate(model.processed_graph.V): + assert np.isclose(_feature_value(predicted, condition, vertex), solved[vertex_index, condition_index]) diff --git a/tests/methods/test_user_inputs.py b/tests/methods/test_user_inputs.py index 4093c3717..10edfde9b 100644 --- a/tests/methods/test_user_inputs.py +++ b/tests/methods/test_user_inputs.py @@ -102,13 +102,32 @@ def test_imat_separates_objectives_and_expression(backend): } gene_method = MultiSampleIMAT(backend=backend) - gene_method.build(model, gene_expression={"G1": 2}, objectives={"R1": -1}) + gene_method.build( + model, + gene_expression={"G1": 2}, + objectives={"R1": -1}, + reaction_bounds={"R1": (1, 8)}, + ) gene_feature = next( feature for feature in gene_method.processed_data.samples["condition"].features if feature.id == "R1" ) assert gene_feature.data["role"] == "objective" assert gene_feature.data["imat_score"] == 2.0 + bounded_gene_method = MultiSampleIMAT(backend=backend) + bounded_gene_method.build( + model, + gene_expression={"G1": 2}, + reaction_bounds={"R1": (1, 8)}, + ) + bounded_gene_feature = next( + feature for feature in bounded_gene_method.processed_data.samples["condition"].features if feature.id == "R1" + ) + assert bounded_gene_feature.data["role"] == "expression" + assert bounded_gene_feature.data["value"] == 2.0 + assert bounded_gene_feature.data["lower_bound"] == 1.0 + assert bounded_gene_feature.data["upper_bound"] == 8.0 + many = MultiSampleIMAT(backend=backend) many.build_many( model, diff --git a/tests/test_api_contract.py b/tests/test_api_contract.py index 21fc32036..3426de278 100644 --- a/tests/test_api_contract.py +++ b/tests/test_api_contract.py @@ -58,6 +58,7 @@ def test_methods_api_surface_and_identity(): "BidirectionalPHONEMeS", "CarnivalFlow", "CarnivalILP", + "LinearDAGDiscovery", "MultiSampleFBA", "MultiSampleIMAT", "PHONEMeS", @@ -79,6 +80,12 @@ def test_methods_api_surface_and_identity(): assert methods.create_multisample_shortest_path is create_multisample_shortest_path +def test_experimental_cellnopt_ilp_surface_is_removed(): + """The superseded experimental CellNOpt ILP modules have no shims.""" + assert importlib.util.find_spec("corneto.methods.signal") is None + assert importlib.util.find_spec("corneto.methods.signaling.cellnopt_ilp") is None + + def test_ml_api_replaces_private_module(): """KPNN helpers live in the public ML module without a private-path shim.""" assert ml.__all__ == [ diff --git a/tests/test_backend.py b/tests/test_backend.py index 96ca84708..6950273c8 100644 --- a/tests/test_backend.py +++ b/tests/test_backend.py @@ -1045,3 +1045,219 @@ def test_nonzero_indicator_matrix_bounds_block_and_sign_per_entry(backend): # Free entries should have exactly one active sign indicator. assert np.isclose(tot_val[0, 1], 1.0, atol=1e-9) assert np.isclose(tot_val[2, 0], 1.0, atol=1e-9) + + +def test_exact_support_reuses_nonnegative_selector(backend): + """ExactSupport should couple a supplied selector without another binary.""" + value = backend.Variable("supported_value", (2,), lb=0, ub=5) + selected = backend.Variable("supplied_selector", (2,), vartype=VarType.BINARY) + problem = backend.Problem() + problem += backend.ExactSupport( + value, + selected=selected, + epsilon=1, + nonnegative=True, + name="exact_support", + ) + problem += selected == np.array([0, 1]) + problem.add_objective(value.sum()) + problem.solve() + + assert np.allclose(np.asarray(selected.value).reshape(-1), [0, 1], atol=1e-8) + assert np.allclose(np.asarray(value.value).reshape(-1), [0, 1], atol=1e-8) + binary_symbols = [symbol for symbol in problem.symbols.values() if symbol._vartype == VarType.BINARY] + assert [symbol.name for symbol in binary_symbols] == ["supplied_selector"] + + +def test_exact_support_broadcasts_per_entry_epsilon(backend): + """ExactSupport should apply explicitly broadcastable gaps elementwise.""" + epsilon = np.array([[0.25, 0.5]]) + value = backend.Variable("array_supported_value", (2, 2), lb=0, ub=5) + problem = backend.Problem() + problem += backend.ExactSupport( + value, + epsilon=epsilon, + nonnegative=True, + name="array_support", + ) + problem += problem.expr.array_support == np.ones((2, 2)) + problem += value == np.broadcast_to(epsilon, value.shape) + problem.solve() + + assert np.allclose(np.asarray(value.value), np.broadcast_to(epsilon, value.shape), atol=1e-8) + + +def test_exact_support_rejects_nonbroadcastable_epsilon_shape(backend): + """ExactSupport should not reinterpret same-sized epsilon arrays by reshaping.""" + value = backend.Variable("shape_checked_value", (3, 2), lb=-5, ub=5) + + with pytest.raises(ValueError, match="broadcast"): + backend.ExactSupport(value, epsilon=np.ones((2, 3))) + + +@pytest.mark.parametrize("lower, upper", [(-np.inf, 5), (-5, np.inf)]) +def test_exact_support_requires_finite_bounds(backend, lower, upper): + """ExactSupport should reject infinite bounds even when they were supplied.""" + value = backend.Variable("finite_bound_value", (2,), lb=lower, ub=upper) + + with pytest.raises(ValueError, match="finite"): + backend.ExactSupport(value, epsilon=1) + + +@pytest.mark.parametrize("nonnegative", [False, True]) +def test_exact_support_requires_binary_supplied_selector(backend, nonnegative): + """A supplied selector must not relax ExactSupport into a continuous formulation.""" + value = backend.Variable("selector_value", (2,), lb=0 if nonnegative else -5, ub=5) + selector = backend.Variable("continuous_selector", (2,), lb=0, ub=1) + + with pytest.raises(TypeError, match="binary"): + backend.ExactSupport(value, selected=selector, epsilon=1, nonnegative=nonnegative) + + +def test_exact_support_rejects_fractional_selector_expression(backend): + """A fractional selector expression must be linked to a binary selector.""" + value = backend.Variable("fractional_selector_value", (1,), lb=0, ub=5) + binary = backend.Variable("selector_binary", (1,), vartype=VarType.BINARY) + + problem = backend.Problem() + problem += backend.ExactSupport(value, selected=0.5 * binary, epsilon=1, nonnegative=True) + problem += binary == 1 + if isinstance(backend, PicosBackend): + result = problem.solve(solver="glpk", primals=None) + else: + result = problem.solve() + + assert str(result.status).lower() in {"infeasible", "infeasible_or_unbounded"} + + +def test_exact_support_signed_value(backend): + """Signed exact support should expose mutually exclusive directions.""" + value = backend.Variable("signed_value", (2,), lb=-4, ub=4) + problem = backend.Problem() + problem += backend.ExactSupport( + value, + epsilon=0.5, + name="signed_support", + ) + problem += problem.expr.signed_support == np.array([1, 1]) + problem += value[0] >= 0 + problem += value[1] <= 0 + problem.add_objective(value[0] - value[1]) + problem.solve() + + assert np.allclose(np.asarray(value.value).reshape(-1), [0.5, -0.5], atol=1e-7) + assert np.allclose(np.asarray(problem.expr.signed_support.value).reshape(-1), [1, 1], atol=1e-8) + + +def test_selected_flow_uses_negative_direction_when_required(backend): + """Signed SelectedFlow should expose the actual traversal direction.""" + graph = Graph() + graph.add_edge((), "B") + graph.add_edge("A", "B") + graph.add_edge("A", ()) + + problem = backend.SelectedFlow( + graph, + lb=np.array([0, -5, 0]), + ub=np.array([5, 5, 5]), + edge_indices=[1], + epsilon=1, + ) + problem += problem.expr.selected_any == 1 + problem.solve() + + assert -5 - 1e-7 <= problem.expr.flow.value[1, 0] <= -1 + 1e-7 + assert np.isclose(np.asarray(problem.expr.selected_by_flow.value).reshape(-1)[0], 1, atol=1e-8) + assert np.isclose(np.asarray(problem.expr.selected_by_flow_positive.value).reshape(-1)[0], 0, atol=1e-8) + assert np.isclose(np.asarray(problem.expr.selected_by_flow_negative.value).reshape(-1)[0], 1, atol=1e-8) + + +def test_selected_flow_mixed_bounds_uses_minimal_binary_types(): + """Only edge rows that permit negative flow should get direction binaries.""" + graph = Graph.from_tuples([("A", 1, "B"), ("B", 1, "A")]) + problem = CvxpyBackend().SelectedFlow( + graph, + lb=np.array([[0, 0], [-5, -5]]), + ub=np.full((2, 2), 5), + n_flows=2, + edge_indices=[0, 1], + epsilon=1, + ) + cvxpy_problem = problem.solve(solver="SCIPY") + + boolean_count = sum(variable.size for variable in cvxpy_problem.variables() if variable.attributes["boolean"]) + # Nonnegative edge: 1*2 support binaries. Signed edge: 2*2 direction + # binaries. Shared structural union: 2 binaries. + assert boolean_count == 2 + 4 + 2 + assert problem.expr.selected_by_flow.shape == (2, 2) + assert problem.expr.selected_by_flow_positive.shape == (2, 2) + assert problem.expr.selected_by_flow_negative.shape == (2, 2) + + +def test_selected_flow_signed_shared_dag_rejects_opposite_directions(): + """Different flows cannot traverse one edge oppositely in a shared DAG.""" + flow_graph = Graph() + flow_graph.add_edge((), "A") + flow_graph.add_edge((), "B") + flow_graph.add_edge("A", "B") + flow_graph.add_edge("A", ()) + flow_graph.add_edge("B", ()) + acyclic_graph = Graph.from_tuples([("A", 1, "B")]) + + problem = CvxpyBackend().SelectedFlow( + flow_graph, + lb=np.array( + [ + [0, 0], + [0, 0], + [-5, -5], + [0, 0], + [0, 0], + ] + ), + ub=np.array( + [ + [5, 0], + [0, 5], + [5, 5], + [0, 5], + [5, 0], + ] + ), + n_flows=2, + edge_indices=[2], + epsilon=1, + acyclic_graph=acyclic_graph, + ) + problem += problem.expr.flow[2, 0] >= 1 + problem += problem.expr.flow[2, 1] <= -1 + cvxpy_problem = problem.solve(solver="SCIPY") + + assert cvxpy_problem.status == cp.INFEASIBLE + boolean_count = sum(variable.size for variable in cvxpy_problem.variables() if variable.attributes["boolean"]) + # Two directions per flow plus two direction-union binaries. There is no + # redundant structural-union binary. + assert boolean_count == 2 * 2 + 2 + + +def test_acyclic_signed_max_parents_counts_reversed_edges(): + """A negative edge contributes a parent at its structural source.""" + graph = Graph.from_tuples([("A", 1, "C"), ("C", 1, "B")]) + backend = CvxpyBackend() + problem = backend.Problem() + positive = backend.Variable("positive", (2,), vartype=VarType.BINARY) + negative = backend.Variable("negative", (2,), vartype=VarType.BINARY) + problem.register("positive", positive) + problem.register("negative", negative) + problem += positive == np.array([1, 0]) + problem += negative == np.array([0, 1]) + backend.Acyclic( + graph, + problem, + indicator_positive_var_name="positive", + indicator_negative_var_name="negative", + max_parents={"C": 1}, + ) + + cvxpy_problem = problem.solve(solver="SCIPY") + assert cvxpy_problem.status == cp.INFEASIBLE diff --git a/tests/test_datasets.py b/tests/test_datasets.py new file mode 100644 index 000000000..ffa727647 --- /dev/null +++ b/tests/test_datasets.py @@ -0,0 +1,194 @@ +import hashlib +import io +import tarfile +from pathlib import Path + +import pytest + +import corneto.datasets._catalog as catalog +import corneto.datasets._fetch as fetch + + +def _sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def _write_dataset(root: Path, name: str, version: str, files: dict[str, str]) -> Path: + directory = root / name / version + directory.mkdir(parents=True) + for filename, content in files.items(): + (directory / filename).write_text(content, encoding="utf-8") + return directory + + +def _make_remote_spec(tmp_path: Path, monkeypatch, *, unsafe_member: str | None = None): + files = { + "measurements.tsv": "value\n1\n", + "condition_manifest.csv": "condition\nexample\n", + "README.md": "example dataset\n", + } + payload = tmp_path / "payload" + payload.mkdir() + for filename, content in files.items(): + (payload / filename).write_text(content, encoding="utf-8") + + archive = tmp_path / "dataset.tar.gz" + with tarfile.open(archive, "w:gz") as handle: + if unsafe_member is None: + for filename in files: + handle.add(payload / filename, arcname=filename) + else: + info = tarfile.TarInfo(unsafe_member) + content = b"unsafe\n" + info.size = len(content) + handle.addfile(info, fileobj=io.BytesIO(content)) + + spec = catalog.DatasetSpec( + name="example", + version="v1", + remote_url="https://data.example.org/example-v1.tar.gz", + archive_sha256=_sha256(archive), + file_sha256={ + filename: hashlib.sha256(content.encode("utf-8")).hexdigest() for filename, content in files.items() + }, + ) + monkeypatch.setitem(catalog.DATASETS, (spec.name, spec.version), spec) + + def open_archive(request, timeout): + assert request.full_url == spec.remote_url + return archive.open("rb") + + monkeypatch.setattr(fetch, "urlopen", open_archive) + return spec + + +def test_default_version_uses_local_repository_dataset(tmp_path): + repository_root = Path(__file__).parents[1] + + result = fetch.fetch_dataset("sachs", data_home=tmp_path, auto_download=False) + + assert result == (repository_root / "datasets" / "sachs" / "v1").resolve() + assert (result / "measurements.tsv").is_file() + assert (result / "condition_manifest.csv").is_file() + + +def test_explicit_future_version_can_use_local_source(tmp_path, monkeypatch): + files = { + "measurements.tsv": "value\n2\n", + "condition_manifest.csv": "condition\nfuture\n", + "README.md": "future dataset\n", + } + directory = _write_dataset(tmp_path / "datasets", "example", "v2", files) + spec = catalog.DatasetSpec( + name="example", + version="v2", + remote_url=None, + archive_sha256="unused", + file_sha256={filename: hashlib.sha256(content.encode()).hexdigest() for filename, content in files.items()}, + ) + monkeypatch.setitem(catalog.DATASETS, (spec.name, spec.version), spec) + + result = fetch.fetch_dataset("example", version="v2", source_dir=tmp_path / "datasets", auto_download=False) + + assert result == directory.resolve() + + +def test_local_source_wins_over_remote(tmp_path, monkeypatch): + spec = _make_remote_spec(tmp_path, monkeypatch) + local = _write_dataset( + tmp_path / "checkout" / "datasets", + spec.name, + spec.version, + { + "measurements.tsv": "value\nlocal\n", + "condition_manifest.csv": "condition\nlocal\n", + "README.md": "local dataset\n", + }, + ) + local_spec = catalog.DatasetSpec( + name=spec.name, + version=spec.version, + remote_url=spec.remote_url, + archive_sha256=spec.archive_sha256, + file_sha256={filename: _sha256(local / filename) for filename in spec.file_sha256}, + ) + monkeypatch.setitem(catalog.DATASETS, (spec.name, spec.version), local_spec) + + def fail_download(*args, **kwargs): + raise AssertionError("remote source should not be used") + + monkeypatch.setattr(fetch, "urlopen", fail_download) + result = fetch.fetch_dataset(spec.name, source_dir=tmp_path / "checkout", data_home=tmp_path / "cache") + + assert result == local.resolve() + + +def test_remote_download_is_cached_and_reused(tmp_path, monkeypatch): + spec = _make_remote_spec(tmp_path, monkeypatch) + source_dir = tmp_path / "missing-source" + data_home = tmp_path / "cache" + + result = fetch.fetch_dataset(spec.name, source_dir=source_dir, data_home=data_home) + assert result == (data_home / spec.name / spec.version).resolve() + assert (result / ".complete.json").is_file() + + def fail_download(*args, **kwargs): + raise AssertionError("cached source should be used") + + monkeypatch.setattr(fetch, "urlopen", fail_download) + cached = fetch.fetch_dataset(spec.name, source_dir=source_dir, data_home=data_home) + + assert cached == result + + +def test_auto_download_false_does_not_use_remote(tmp_path, monkeypatch): + spec = _make_remote_spec(tmp_path, monkeypatch) + + with pytest.raises(FileNotFoundError, match="auto_download is false"): + fetch.fetch_dataset( + spec.name, + source_dir=tmp_path / "missing", + data_home=tmp_path / "cache", + auto_download=False, + ) + + +def test_checksum_mismatch_is_rejected(tmp_path, monkeypatch): + spec = _make_remote_spec(tmp_path, monkeypatch) + broken = catalog.DatasetSpec( + name=spec.name, + version=spec.version, + remote_url=spec.remote_url, + archive_sha256="0" * 64, + file_sha256=spec.file_sha256, + ) + monkeypatch.setitem(catalog.DATASETS, (broken.name, broken.version), broken) + + with pytest.raises(fetch.DatasetIntegrityError, match="Checksum mismatch"): + fetch.fetch_dataset(spec.name, source_dir=tmp_path / "missing", data_home=tmp_path / "cache") + + +def test_unsafe_archive_member_is_rejected(tmp_path, monkeypatch): + spec = _make_remote_spec(tmp_path, monkeypatch, unsafe_member="../measurements.tsv") + + with pytest.raises(fetch.DatasetIntegrityError, match="unsafe member"): + fetch.fetch_dataset(spec.name, source_dir=tmp_path / "missing", data_home=tmp_path / "cache") + + assert not (tmp_path / "measurements.tsv").exists() + + +def test_incomplete_cache_is_not_accepted(tmp_path, monkeypatch): + spec = _make_remote_spec(tmp_path, monkeypatch) + incomplete = tmp_path / "cache" / spec.name / spec.version + incomplete.mkdir(parents=True) + (incomplete / "measurements.tsv").write_text("partial\n", encoding="utf-8") + + result = fetch.fetch_dataset( + spec.name, + source_dir=tmp_path / "missing", + data_home=tmp_path / "cache", + auto_download=True, + ) + + assert (result / ".complete.json").is_file() + assert (result / "measurements.tsv").read_text(encoding="utf-8") == "value\n1\n" diff --git a/tests/test_deprecations.py b/tests/test_deprecations.py index d41ab62e8..b9cf74472 100644 --- a/tests/test_deprecations.py +++ b/tests/test_deprecations.py @@ -1,7 +1,5 @@ """Tests for compatibility APIs retained during the CORNETO 1.x line.""" -import importlib - import pytest import corneto as cn @@ -24,14 +22,3 @@ def test_undocumented_backend_compatibility_apis_are_removed(): with pytest.raises(TypeError, match="unexpected keyword argument 'graph'"): ProblemDef(graph=object()) - - -def test_signal_package_warns_and_reexports_canonical_cellnopt(): - """The old CellNOpt path warns and preserves function identity.""" - canonical = importlib.import_module("corneto.methods.signaling.cellnopt_ilp") - - with pytest.warns(FutureWarning, match=r"corneto\.methods\.signal is deprecated"): - legacy = importlib.import_module("corneto.methods.signal.cellnopt_ilp") - - assert legacy.cellnoptILP is canonical.cellnoptILP - assert legacy.expand_graph_for_flows is canonical.expand_graph_for_flows diff --git a/tests/test_generate_switcher.py b/tests/test_generate_switcher.py new file mode 100644 index 000000000..0df9b6794 --- /dev/null +++ b/tests/test_generate_switcher.py @@ -0,0 +1,24 @@ +"""Tests for the public documentation version switcher.""" + +from scripts.generate_switcher import build_entries + + +def test_switcher_lists_stable_and_release_tags_without_dev_docs(monkeypatch): + """The public trunk has no separate latest/development documentation site.""" + monkeypatch.setattr("scripts.generate_switcher._get_tags", lambda: ["v1.0.0-rc.2"]) + + entries = build_entries("https://corneto.org") + + assert entries == [ + { + "name": "stable", + "version": "stable", + "url": "https://corneto.org/stable/", + "preferred": True, + }, + { + "name": "v1.0.0-rc.2", + "version": "v1.0.0-rc.2", + "url": "https://corneto.org/v1.0.0-rc.2/", + }, + ] diff --git a/tests/test_prepare_docs_pages.py b/tests/test_prepare_docs_pages.py new file mode 100644 index 000000000..2a2c41b1e --- /dev/null +++ b/tests/test_prepare_docs_pages.py @@ -0,0 +1,173 @@ +"""Tests for assembling the multi-version GitHub Pages tree.""" + +from pathlib import Path + +import pytest + +from scripts.prepare_docs_pages import prepare_pages_tree, validate_built_switcher, validate_landing_bundle + + +def _write(path: Path, content: str = "content") -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(content, encoding="utf-8") + + +def _landing_bundle(root: Path) -> Path: + landing = root / "landing" + _write( + landing / "index.html", + """ + + + +Docs +""", + ) + _write(landing / "assets" / "favicon.ico") + _write(landing / "assets" / "social.jpg") + _write(landing / "assets" / "logo.png") + return landing + + +def _sphinx_build(root: Path, version: str) -> Path: + build = root / f"build-{version}" + _write( + build / "index.html", + f""" +""", + ) + return build + + +def test_main_deployment_updates_root_and_exactly_replaces_stable(tmp_path): + pages = tmp_path / "pages" + build = _sphinx_build(tmp_path, "stable") + landing = _landing_bundle(tmp_path) + _write(pages / "stable" / "stale.html") + _write(pages / "v0.9.0" / "keep.html") + _write(pages / "assets" / "stale.png") + _write(pages / "index.html", "old landing") + _write(pages / "CNAME", "corneto.org") + + prepare_pages_tree( + pages_dir=pages, + build_dir=build, + landing_dir=landing, + version_folder="stable", + base_url="https://corneto.org", + update_root=True, + ) + + assert (pages / "stable" / "index.html").read_text(encoding="utf-8") == (build / "index.html").read_text( + encoding="utf-8" + ) + assert not (pages / "stable" / "stale.html").exists() + assert (pages / "v0.9.0" / "keep.html").is_file() + assert (pages / "CNAME").read_text(encoding="utf-8") == "corneto.org" + assert (pages / "index.html").read_text(encoding="utf-8") == (landing / "index.html").read_text(encoding="utf-8") + assert sorted(path.name for path in (pages / "assets").iterdir()) == ["favicon.ico", "logo.png", "social.jpg"] + assert (pages / ".nojekyll").is_file() + assert (pages / "switcher.json").is_file() + + +def test_tag_deployment_preserves_root_landing_and_assets(tmp_path): + pages = tmp_path / "pages" + build = _sphinx_build(tmp_path, "v1.0.0") + _write(pages / "index.html", "current landing") + _write(pages / "assets" / "current.png") + _write(pages / "v1.0.0" / "stale.html") + + prepare_pages_tree( + pages_dir=pages, + build_dir=build, + landing_dir=tmp_path / "unused-landing", + version_folder="v1.0.0", + base_url="https://corneto.org", + update_root=False, + ) + + assert (pages / "index.html").read_text(encoding="utf-8") == "current landing" + assert (pages / "assets" / "current.png").is_file() + assert (pages / "v1.0.0" / "index.html").read_text(encoding="utf-8") == (build / "index.html").read_text( + encoding="utf-8" + ) + assert not (pages / "v1.0.0" / "stale.html").exists() + assert (pages / "switcher.json").is_file() + + +def test_landing_validation_reports_missing_asset(tmp_path): + landing = _landing_bundle(tmp_path) + (landing / "assets" / "logo.png").unlink() + + with pytest.raises(ValueError, match="Landing assets are missing"): + validate_landing_bundle(landing) + + +def test_landing_validation_checks_same_site_social_image(tmp_path): + landing = _landing_bundle(tmp_path) + (landing / "assets" / "social.jpg").unlink() + + with pytest.raises(ValueError, match=r"https://corneto\.org/assets/social\.jpg"): + validate_landing_bundle(landing) + + +def test_built_switcher_rejects_wrong_url(tmp_path): + build = _sphinx_build(tmp_path, "stable") + index = build / "index.html" + index.write_text(index.read_text(encoding="utf-8").replace("corneto.org", "example.org"), encoding="utf-8") + + with pytest.raises(ValueError, match="Unexpected switcher URL"): + validate_built_switcher(build, "stable", "https://corneto.org") + + +def test_built_switcher_rejects_wrong_version(tmp_path): + build = _sphinx_build(tmp_path, "latest") + + with pytest.raises(ValueError, match="Unexpected switcher version match"): + validate_built_switcher(build, "stable", "https://corneto.org") + + +def test_built_switcher_rejects_missing_configuration(tmp_path): + build = tmp_path / "build" + _write(build / "index.html", "") + + with pytest.raises(ValueError, match="missing the theme switcher configuration"): + validate_built_switcher(build, "stable", "https://corneto.org") + + +@pytest.mark.parametrize("version_folder", ["latest", "../stable", "v1/escape", "/stable", ""]) +def test_unsafe_version_folder_is_rejected(tmp_path, version_folder): + pages = tmp_path / "pages" + build = tmp_path / "build" + pages.mkdir() + _write(build / "index.html") + + with pytest.raises(ValueError, match="Unsafe documentation destination"): + prepare_pages_tree( + pages_dir=pages, + build_dir=build, + landing_dir=tmp_path / "unused-landing", + version_folder=version_folder, + base_url="https://corneto.org", + update_root=False, + ) + + +def test_tag_cannot_update_root_landing(tmp_path): + pages = tmp_path / "pages" + build = tmp_path / "build" + pages.mkdir() + _write(build / "index.html") + + with pytest.raises(ValueError, match="Only the stable deployment"): + prepare_pages_tree( + pages_dir=pages, + build_dir=build, + landing_dir=_landing_bundle(tmp_path), + version_folder="v1.0.0", + base_url="https://corneto.org", + update_root=True, + ) diff --git a/tests/test_release.py b/tests/test_release.py index 072020055..136429240 100644 --- a/tests/test_release.py +++ b/tests/test_release.py @@ -17,21 +17,25 @@ def test_dry_run_uses_selected_remote(monkeypatch, capsys): monkeypatch.setattr(release, "_ensure_clean_tree", lambda: None) monkeypatch.setattr(release, "_ensure_on_main", lambda: None) monkeypatch.setattr(release, "_ensure_up_to_date_with_remote_main", calls.append) - monkeypatch.setattr(release, "_ensure_dev_is_merged", calls.append) monkeypatch.setattr( release, "_ensure_tag_does_not_exist", lambda version, remote: calls.append((version, remote)), ) + monkeypatch.setattr( + release, + "_ensure_release_notes", + lambda version: calls.append(("notes", version)), + ) result = release.main(["v1.0.0-beta.8", "--remote", "public", "--dry-run"]) assert result == 0 assert calls == [ - "public", "public", "public", ("v1.0.0-beta.8", "public"), + ("notes", "v1.0.0-beta.8"), ] assert "Would create and push tag: v1.0.0-beta.8" in capsys.readouterr().out @@ -61,3 +65,51 @@ def test_remote_name_cannot_be_an_option(): assert "Invalid remote name" in str(exc) else: raise AssertionError("Expected an invalid remote name to be rejected") + + +def test_release_notes_require_canonical_prerelease_heading_and_highlights(tmp_path): + """Pre-release notes use the same title and badge as generated documentation.""" + notes_dir = tmp_path / "docs" / "releases" + notes_dir.mkdir(parents=True) + notes = notes_dir / "v1.2.3-rc.4.md" + notes.write_text( + "# Release v1.2.3-rc.4 {bdg-warning}`Pre-release`\n\n## Highlights\n\n- A release highlight.\n", + encoding="utf-8", + ) + + release._ensure_release_notes("v1.2.3-rc.4", root=tmp_path) + + +def test_release_notes_reject_inconsistent_title(tmp_path): + """A manually reformatted release name cannot pass the release guard.""" + notes_dir = tmp_path / "docs" / "releases" + notes_dir.mkdir(parents=True) + (notes_dir / "v1.2.3-rc.4.md").write_text( + "# CORNETO 1.2.3 RC4\n\n## Highlights\n", + encoding="utf-8", + ) + + try: + release._ensure_release_notes("v1.2.3-rc.4", root=tmp_path) + except release.ReleaseError as exc: + assert "must start with" in str(exc) + assert "Pre-release" in str(exc) + else: + raise AssertionError("Expected an inconsistent release-note title to be rejected") + + +def test_release_notes_require_highlights(tmp_path): + """Every manually prepared release page retains the shared highlights section.""" + notes_dir = tmp_path / "docs" / "releases" + notes_dir.mkdir(parents=True) + (notes_dir / "v1.2.3.md").write_text( + "# Release v1.2.3\n\n## Details\n", + encoding="utf-8", + ) + + try: + release._ensure_release_notes("v1.2.3", root=tmp_path) + except release.ReleaseError as exc: + assert "## Highlights" in str(exc) + else: + raise AssertionError("Expected missing highlights to be rejected") From b51f917bbcb34df219ef1f45ca3145646e19a68d Mon Sep 17 00:00:00 2001 From: Pablo Rodriguez Mier Date: Wed, 23 Sep 2026 15:22:18 +0200 Subject: [PATCH 2/5] fix(annnet): carry PR #60 node API into dev --- corneto/methods/signaling/annnet.py | 22 +++++++++---------- .../annnet-cellnopt-dag.ipynb | 18 +++++++-------- .../methods/signaling/test_cellnopt_annnet.py | 8 +++---- 3 files changed, 24 insertions(+), 24 deletions(-) diff --git a/corneto/methods/signaling/annnet.py b/corneto/methods/signaling/annnet.py index 0f209a5ca..2eb7db91e 100644 --- a/corneto/methods/signaling/annnet.py +++ b/corneto/methods/signaling/annnet.py @@ -106,11 +106,11 @@ def add_cellnopt_conditions( {condition_aspect: list(condition_names)}, ) - known_vertices = set(graph.vertices()) - base_vertices = list(graph.vertices()) + known_nodes = set(graph.nodes()) + base_nodes = list(graph.nodes()) layers = {condition: (condition,) for condition in condition_names} for condition, layer in layers.items(): - graph.add_vertices(base_vertices, layer=layer) + graph.add_nodes(base_nodes, layer=layer) values_by_role = ( (inputs[condition], input_attr), (inhibitors[condition], inhibitor_attr), @@ -119,12 +119,12 @@ def add_cellnopt_conditions( for values, attribute in values_by_role: if not isinstance(values, Mapping): raise TypeError(f"Values for condition {condition!r} must be mappings keyed by protein.") - unknown = set(values) - known_vertices + unknown = set(values) - known_nodes if unknown: protein = sorted(unknown, key=str)[0] raise ValueError(f"Unknown protein {protein!r} in condition {condition!r}.") for protein, value in values.items(): - graph.layers.set_vertex_layer_attrs(str(protein), layer, **{attribute: value}) + graph.layers.set_node_attrs(str(protein), layer, **{attribute: value}) return layers @@ -201,9 +201,9 @@ def build_cellnopt_from_annnet( inputs[condition] = {} inhibitors[condition] = {} measurements[condition] = {} - for protein in graph.layers.layer_vertex_set(layer): + for protein in graph.layers.layer_node_set(layer): protein_id = str(protein[0]) if isinstance(protein, tuple) else str(protein) - attributes = graph.layers.get_vertex_layer_attrs(protein_id, layer) + attributes = graph.layers.node_attrs(protein_id, layer) if input_attr in attributes: inputs[condition][protein_id] = attributes[input_attr] if attributes.get(inhibitor_attr): @@ -292,17 +292,17 @@ def add_cellnopt_results( condition_errors = {} for condition_index, condition in enumerate(condition_names): layer = context.condition_layers[condition] - graph.add_vertices(vertices, layer=layer) + graph.add_nodes(vertices, layer=layer) endpoint_error = 0.0 for vertex_index, protein in enumerate(vertices): predicted = float(predictions[vertex_index, condition_index]) attributes = {prediction_attr: predicted} - existing = graph.layers.get_vertex_layer_attrs(protein, layer) + existing = graph.layers.node_attrs(protein, layer) if measurement_attr in existing: error = abs(predicted - float(existing[measurement_attr])) attributes[error_attr] = error endpoint_error += error - graph.layers.set_vertex_layer_attrs(protein, layer, **attributes) + graph.layers.set_node_attrs(protein, layer, **attributes) condition_edges = [] for reaction_index in selected_indices: @@ -344,7 +344,7 @@ def add_cellnopt_results( } if solution is not None and getattr(solution, "status", None) is not None: layer_attributes["solver_status"] = str(solution.status) - graph.layers.set_layer_attrs(layer, **layer_attributes) + graph.layers.set_attrs(layer, **layer_attributes) condition_errors[condition] = endpoint_error graph.history.snapshot("cellnopt_results_added") diff --git a/docs/tutorials/annnet-signaling/annnet-cellnopt-dag.ipynb b/docs/tutorials/annnet-signaling/annnet-cellnopt-dag.ipynb index af0ffb265..208fb7bea 100644 --- a/docs/tutorials/annnet-signaling/annnet-cellnopt-dag.ipynb +++ b/docs/tutorials/annnet-signaling/annnet-cellnopt-dag.ipynb @@ -133,10 +133,10 @@ "signaling = an.AnnNet(directed=True)\n", "signaling.history.enable(True)\n", "signaling.slices.add(\"prior\", role=\"prior_knowledge\")\n", - "signaling.add_vertices(sorted(display_name))\n", + "signaling.add_nodes(sorted(display_name))\n", "\n", "for protein, label in display_name.items():\n", - " signaling.attrs.set_vertex_attrs(protein, label=label, entity_type=\"protein\")\n", + " signaling.attrs.set_node_attrs(protein, label=label, entity_type=\"protein\")\n", "\n", "prior_edge_ids = []\n", "for index, (source, sign, target) in enumerate(pkn_edges):\n", @@ -495,7 +495,7 @@ " signaling,\n", " backend=\"graphviz\",\n", " layout=\"dot\",\n", - " vertex_label_key=\"label\",\n", + " node_label_key=\"label\",\n", " use_weight_style=False,\n", " graph_attr={\"rankdir\": \"LR\", \"size\": \"12,7\"},\n", " node_attr={\"shape\": \"ellipse\", \"style\": \"filled\", \"fillcolor\": \"#eef4fb\"},\n", @@ -599,12 +599,12 @@ }, "outputs": [], "source": [ - "def vertex_layer_table(network, layers, columns):\n", - " \"\"\"Return selected vertex-layer attributes as a pandas table.\"\"\"\n", + "def node_layer_table(network, layers, columns):\n", + " \"\"\"Return selected node-layer attributes as a pandas table.\"\"\"\n", " rows = {}\n", " for condition, layer in layers.items():\n", " rows[condition] = {\n", - " label: network.layers.get_vertex_layer_attrs(protein, layer).get(attribute, np.nan)\n", + " label: network.layers.node_attrs(protein, layer).get(attribute, np.nan)\n", " for label, (protein, attribute) in columns.items()\n", " }\n", " return pd.DataFrame.from_dict(rows, orient=\"index\").rename_axis(\"condition\")" @@ -818,7 +818,7 @@ " \"AKT observed\": (\"akt\", \"observed\"),\n", " \"HSP27 observed\": (\"hsp27\", \"observed\"),\n", "}\n", - "experiment = vertex_layer_table(signaling, condition_layers, experiment_columns).fillna(0)\n", + "experiment = node_layer_table(signaling, condition_layers, experiment_columns).fillna(0)\n", "experiment" ] }, @@ -1797,7 +1797,7 @@ } ], "source": [ - "akt_response = vertex_layer_table(\n", + "akt_response = node_layer_table(\n", " signaling,\n", " condition_layers,\n", " {\n", @@ -2007,7 +2007,7 @@ "an.io.write(signaling, analysis_path, overwrite=True)\n", "restored = an.io.read(analysis_path)\n", "\n", - "restored_akt = vertex_layer_table(\n", + "restored_akt = node_layer_table(\n", " restored,\n", " condition_layers,\n", " {\n", diff --git a/tests/methods/signaling/test_cellnopt_annnet.py b/tests/methods/signaling/test_cellnopt_annnet.py index 0dd81f0b2..d3f246e57 100644 --- a/tests/methods/signaling/test_cellnopt_annnet.py +++ b/tests/methods/signaling/test_cellnopt_annnet.py @@ -39,10 +39,10 @@ def test_cellnopt_reads_conditions_and_adds_results_to_annnet(backend): assert solution.status == "optimal" assert layers == {"off": ("off",), "on": ("on",), "blocked": ("blocked",)} - assert graph.layers.get_vertex_layer_attrs("L", ("on",))["input"] == 1 - assert graph.layers.get_vertex_layer_attrs("A", ("blocked",))["inhibited"] == 1 - assert graph.layers.get_vertex_layer_attrs("Y", ("on",))["predicted"] == 1 - assert graph.layers.get_layer_attrs(("blocked",))["endpoint_absolute_error"] == 0 + assert graph.layers.node_attrs("L", ("on",))["input"] == 1 + assert graph.layers.node_attrs("A", ("blocked",))["inhibited"] == 1 + assert graph.layers.node_attrs("Y", ("on",))["predicted"] == 1 + assert graph.layers.attrs(("blocked",))["endpoint_absolute_error"] == 0 assert graph.slices.exists("cellnopt_selected") assert summary["selected_reactions"] == 2 assert summary["condition_errors"] == {"off": 0.0, "on": 0.0, "blocked": 0.0} From 0642f4630d00557175e8249c24b888ee06fab16b Mon Sep 17 00:00:00 2001 From: Pablo Rodriguez Mier Date: Wed, 23 Sep 2026 17:07:51 +0200 Subject: [PATCH 3/5] docs(release): prepare CORNETO 1.0.0-rc.6 --- corneto/methods/_predictive.py | 1 - docs/releases/index.md | 1 + docs/releases/v1.0.0-rc.6.md | 21 +++++++++++++++++++++ tests/methods/causal/test_linear_dag.py | 2 +- 4 files changed, 23 insertions(+), 2 deletions(-) create mode 100644 docs/releases/v1.0.0-rc.6.md diff --git a/corneto/methods/_predictive.py b/corneto/methods/_predictive.py index 80e79e766..0d78f808f 100644 --- a/corneto/methods/_predictive.py +++ b/corneto/methods/_predictive.py @@ -62,4 +62,3 @@ def require_expression_value(problem: Any, name: str, method: str) -> np.ndarray if not np.all(np.isfinite(array)): raise ValueError(f"{method} has no usable finite solution for {name!r}.") return array - diff --git a/docs/releases/index.md b/docs/releases/index.md index 758934fa3..604e806d5 100644 --- a/docs/releases/index.md +++ b/docs/releases/index.md @@ -7,6 +7,7 @@ This section contains detailed release notes for CORNETO versions, documenting n ```{toctree} :maxdepth: 1 +v1.0.0-rc.6 v1.0.0-rc.5 v1.0.0-rc.4 v1.0.0-rc.3 diff --git a/docs/releases/v1.0.0-rc.6.md b/docs/releases/v1.0.0-rc.6.md new file mode 100644 index 000000000..fc51f195f --- /dev/null +++ b/docs/releases/v1.0.0-rc.6.md @@ -0,0 +1,21 @@ +# Release v1.0.0-rc.6 {bdg-warning}`Pre-release` + +CORNETO 1.0.0 RC6 adds causal structure discovery from perturbation data and +extends reusable signaling-model workflows. + +## Highlights + +- Added `LinearDAGDiscovery` for sparse, acyclic linear causal models constrained + by a directed prior-knowledge network. It supports hard and shift interventions, + model fitting, prediction, evaluation, and residual diagnostics. +- Extended `CellNOptDAG` with a reusable prediction and evaluation lifecycle and + expanded its multi-condition workflow and documentation. +- Added a dataset catalog and fetch helpers, with a versioned Sachs signaling + dataset and tutorial for linear causal discovery. +- Updated the AnnNet interoperability examples and bridge for the current node + API, and refined shared sparse-flow and backend support used by other methods. + +## Compatibility + +This release adds public causal-model and dataset APIs. The AnnNet integration +and examples target the node API introduced in PR #60. diff --git a/tests/methods/causal/test_linear_dag.py b/tests/methods/causal/test_linear_dag.py index 03d53ea50..d863581a3 100644 --- a/tests/methods/causal/test_linear_dag.py +++ b/tests/methods/causal/test_linear_dag.py @@ -741,7 +741,7 @@ def test_cvxpy_formulation_uses_only_required_integer_variables(): def test_linear_dag_guide_notebook_executes_every_cell(): - notebook_path = Path(__file__).parents[3] / "docs/guide/signaling/linear-dag-discovery.ipynb" + notebook_path = Path(__file__).parents[3] / "docs/guide/causal/linear-dag-discovery.ipynb" notebook = nbformat.read(notebook_path, as_version=4) executed = NotebookClient( From 19c79d68023296bf287cf269194e7eee9930a979 Mon Sep 17 00:00:00 2001 From: Pablo Rodriguez Mier Date: Wed, 23 Sep 2026 17:17:08 +0200 Subject: [PATCH 4/5] style: format predictive API tests --- tests/methods/test_predictive_api.py | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/tests/methods/test_predictive_api.py b/tests/methods/test_predictive_api.py index 837d12bf9..1541258b2 100644 --- a/tests/methods/test_predictive_api.py +++ b/tests/methods/test_predictive_api.py @@ -246,8 +246,7 @@ def test_linear_evaluation_honors_vertex_and_held_out_sample_weights(backend): metrics = model.evaluate(held_out) assert np.isclose(metrics["forward_loss"], expected_vertex_weighted) expected_sample_weighted = ( - 5.0 * (4.0 * 1.0 / scales[0] + 1.0 * 2.0 / scales[0]) - + (4.0 * 2.0 / scales[1] + 1.0 * 4.0 / scales[1]) + 5.0 * (4.0 * 1.0 / scales[0] + 1.0 * 2.0 / scales[0]) + (4.0 * 2.0 / scales[1] + 1.0 * 4.0 / scales[1]) ) / 30.0 weighted = model.evaluate(held_out, sample_weights={"s1": 4.0, "s2": 1.0}) assert np.isclose(weighted["forward_loss"], expected_sample_weighted) @@ -316,9 +315,7 @@ def test_rebuilding_linear_model_invalidates_the_previous_solution(backend): model.build(graph, _linear_training_data()) assert model.solve_result is None with pytest.raises(ValueError, match="not been solved"): - model.predict( - Data.from_cdict({"test": {"A": {"mapping": "vertex", "value": 1.0, "intervened": True}}}) - ) + model.predict(Data.from_cdict({"test": {"A": {"mapping": "vertex", "value": 1.0, "intervened": True}}})) def test_rebuilding_cellnopt_model_invalidates_the_previous_solution(backend): @@ -355,10 +352,7 @@ def test_cellnopt_fixed_prediction_matches_solved_training_values(backend): "e_only": {"E": 1, "C": 1}, "inhibited": {"A": 1, "C": 1}, } - inhibitors = { - condition: ({"Y": 1} if condition == "inhibited" else {}) - for condition in inputs - } + inhibitors = {condition: ({"Y": 1} if condition == "inhibited" else {}) for condition in inputs} measurements = { "a": {"Y": 1, "Z": 0}, "b": {"Y": 1, "Z": 0}, From 0c263df23437d663cbf65f689faee52c5a370fef Mon Sep 17 00:00:00 2001 From: Pablo Rodriguez Mier Date: Wed, 23 Sep 2026 17:28:50 +0200 Subject: [PATCH 5/5] docs: include causal tutorial setup page in toctree --- docs/tutorials/causal/README.md | 2 +- docs/tutorials/index.md | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/tutorials/causal/README.md b/docs/tutorials/causal/README.md index b23ab887f..f4e5ac266 100644 --- a/docs/tutorials/causal/README.md +++ b/docs/tutorials/causal/README.md @@ -1,4 +1,4 @@ -# Linear DAG discovery on the Sachs dataset +# Running the LinearDAG Sachs tutorial This tutorial uses `LinearDAGDiscovery` to find a network that helps explain single-cell signaling measurements from the Sachs study. It includes two diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index 83f359db2..3baac65f8 100755 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -16,6 +16,7 @@ carnival/multi-receptor-integration.ipynb carnival/network-sampler.ipynb carnival/network-sampler-example.ipynb ml/kpnn-with-sc.ipynb +causal/README causal/linear-dag-discovery-sachs.ipynb ```