From 18a7d27f0c858cadde0e2caa54ea3aa3b20ab5f7 Mon Sep 17 00:00:00 2001
From: jsboige <jsboige@gmail.com>
Date: Fri, 9 Oct 2026 16:49:56 +0200
Subject: [PATCH 1/2] fix(probas,#11044): fuite de chemin machine dans les
 sorties committeees

Les sorties committeees de deux carnets de la serie Causal-Bridges
imprimaient un chemin absolu derive de Path.cwd().resolve(), revelant
le repertoire de travail de la machine qui les avait executees.

Cause (regle 6, cas A -- env/cwd) : la source imprime le chemin absolu.
Correctif de la source, puis re-execution des cellules modifiees (C.2)
avec le kernel declare du carnet :
  - CausalBridges-04 : nom du dossier de serie au lieu du chemin absolu ;
  - CausalBridges-06 : idem, et chemin de figure rendu relatif au dossier
    de serie.

L'artefact committee est assemble depuis la version de main, en injectant
pour les seules cellules dont la source change leurs champs source,
sorties, execution_count et metadonnees issus de la re-execution. Les
autres cellules restent celles deja committeees : leur comparaison
semantique avec la re-execution est bit-identique (aucune perte de sortie).

See #11044

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 ...ridges-04-Dowhy-Decouverte-Structure.ipynb | 18 ++---
 ...alBridges-06-Dowhy-Instrument-Faible.ipynb | 72 ++++++++++++-------
 2 files changed, 57 insertions(+), 33 deletions(-)

diff --git a/MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-04-Dowhy-Decouverte-Structure.ipynb b/MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-04-Dowhy-Decouverte-Structure.ipynb
index 2cf58d2808..dcf5f4079b 100644
--- a/MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-04-Dowhy-Decouverte-Structure.ipynb
+++ b/MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-04-Dowhy-Decouverte-Structure.ipynb
@@ -31,16 +31,16 @@
    "id": "34d78c9e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-13T01:35:22.988439Z",
-     "iopub.status.busy": "2026-09-13T01:35:22.987929Z",
-     "iopub.status.idle": "2026-09-13T01:35:24.147605Z",
-     "shell.execute_reply": "2026-09-13T01:35:24.147099Z"
+     "iopub.execute_input": "2026-10-09T14:46:12.871982Z",
+     "iopub.status.busy": "2026-10-09T14:46:12.871614Z",
+     "iopub.status.idle": "2026-10-09T14:46:13.950761Z",
+     "shell.execute_reply": "2026-10-09T14:46:13.949321Z"
     },
     "papermill": {
-     "duration": 1.163051,
-     "end_time": "2026-09-13T01:35:24.147001+00:00",
+     "duration": 1.086034,
+     "end_time": "2026-10-09T14:46:13.951802+00:00",
      "exception": false,
-     "start_time": "2026-09-13T01:35:22.983950+00:00",
+     "start_time": "2026-10-09T14:46:12.865768+00:00",
      "status": "completed"
     },
     "tags": []
@@ -50,7 +50,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dowhy_discovery_organs charge depuis : D:\\Dev\\CoursIA-14049\\MyIA.AI.Notebooks\\Probas\\DecisionTheory\\Causal-Bridges\n",
+      "dowhy_discovery_organs charge depuis : Causal-Bridges\n",
       "  DAG vrai : [('C', 'X'), ('X', 'M'), ('M', 'Y'), ('C', 'Y'), ('Y', 'Z')]\n",
       "  alpha PC par defaut : 0.05 | seuil coef LiNGAM : 0.1\n"
      ]
@@ -80,7 +80,7 @@
     "        break\n",
     "\n",
     "import dowhy_discovery_organs as ddo\n",
-    "print(f\"dowhy_discovery_organs charge depuis : {_organs_dir}\")\n",
+    "print(f\"dowhy_discovery_organs charge depuis : {_organs_dir.name}\")\n",
     "print(f\"  DAG vrai : {ddo.ARETES_DAG_VRAI}\")\n",
     "print(f\"  alpha PC par defaut : {ddo.ALPHA_PC_DEFAUT} | seuil coef LiNGAM : {ddo.SEUIL_COEF_LINGAM}\")"
    ]
diff --git a/MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-06-Dowhy-Instrument-Faible.ipynb b/MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-06-Dowhy-Instrument-Faible.ipynb
index 757ea4dca7..3ba15b285c 100644
--- a/MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-06-Dowhy-Instrument-Faible.ipynb
+++ b/MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-06-Dowhy-Instrument-Faible.ipynb
@@ -36,18 +36,26 @@
    "id": "e0f7ee2d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-09T11:56:17.051119Z",
-     "iopub.status.busy": "2026-09-09T11:56:17.050870Z",
-     "iopub.status.idle": "2026-09-09T12:04:11.687522Z",
-     "shell.execute_reply": "2026-09-09T12:04:11.686258Z"
-    }
+     "iopub.execute_input": "2026-10-09T14:46:26.961103Z",
+     "iopub.status.busy": "2026-10-09T14:46:26.960811Z",
+     "iopub.status.idle": "2026-10-09T14:46:28.639944Z",
+     "shell.execute_reply": "2026-10-09T14:46:28.638840Z"
+    },
+    "papermill": {
+     "duration": 1.684605,
+     "end_time": "2026-10-09T14:46:28.640854+00:00",
+     "exception": false,
+     "start_time": "2026-10-09T14:46:26.956249+00:00",
+     "status": "completed"
+    },
+    "tags": []
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dowhy_iv_organs charge depuis : D:\\dev\\CoursIA-14049-dowhy5\\MyIA.AI.Notebooks\\Probas\\DecisionTheory\\Causal-Bridges\n",
+      "dowhy_iv_organs charge depuis : Causal-Bridges\n",
       "  force_instrument_fort=1.0, force_instrument_faible=0.05, seuil_F_Staiger_Stock=10.0\n"
      ]
     }
@@ -76,7 +84,7 @@
     "        break\n",
     "\n",
     "import dowhy_iv_organs as dio\n",
-    "print(f\"dowhy_iv_organs charge depuis : {_organs_dir}\")\n",
+    "print(f\"dowhy_iv_organs charge depuis : {_organs_dir.name}\")\n",
     "print(f\"  force_instrument_fort={dio.FORCE_INSTRUMENT_FORT}, \"\n",
     "      f\"force_instrument_faible={dio.FORCE_INSTRUMENT_FAIBLE}, \"\n",
     "      f\"seuil_F_Staiger_Stock={dio.SEUIL_F_STAIGER_STOCK}\")"
@@ -248,11 +256,19 @@
    "id": "6fbddfdb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-09T12:04:11.742720Z",
-     "iopub.status.busy": "2026-09-09T12:04:11.742483Z",
-     "iopub.status.idle": "2026-09-09T12:04:15.196768Z",
-     "shell.execute_reply": "2026-09-09T12:04:15.195995Z"
-    }
+     "iopub.execute_input": "2026-10-09T14:46:28.739723Z",
+     "iopub.status.busy": "2026-10-09T14:46:28.739334Z",
+     "iopub.status.idle": "2026-10-09T14:46:30.202686Z",
+     "shell.execute_reply": "2026-10-09T14:46:30.201525Z"
+    },
+    "papermill": {
+     "duration": 1.469036,
+     "end_time": "2026-10-09T14:46:30.203921+00:00",
+     "exception": false,
+     "start_time": "2026-10-09T14:46:28.734885+00:00",
+     "status": "completed"
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -271,7 +287,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1100x400 with 2 Axes>"
       ]
@@ -283,7 +299,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Figure sauvegardee : D:\\dev\\CoursIA-14049-dowhy5\\MyIA.AI.Notebooks\\Probas\\DecisionTheory\\Causal-Bridges\\_measurements\\dowhy5_mc_iv_vs_ols.png\n"
+      "Figure sauvegardee : _measurements\\dowhy5_mc_iv_vs_ols.png\n"
      ]
     }
    ],
@@ -323,7 +339,7 @@
     "plt.tight_layout()\n",
     "plt.savefig(str(_fig_path), dpi=100, bbox_inches=\"tight\")\n",
     "plt.show()\n",
-    "print(f\"Figure sauvegardee : {_fig_path}\")"
+    "print(f\"Figure sauvegardee : {_fig_path.relative_to(_organs_dir)}\")"
    ]
   },
   {
@@ -362,11 +378,19 @@
    "id": "502d6de3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-09T12:04:15.198209Z",
-     "iopub.status.busy": "2026-09-09T12:04:15.197897Z",
-     "iopub.status.idle": "2026-09-09T12:04:20.051093Z",
-     "shell.execute_reply": "2026-09-09T12:04:20.049839Z"
-    }
+     "iopub.execute_input": "2026-10-09T14:46:30.229112Z",
+     "iopub.status.busy": "2026-10-09T14:46:30.228702Z",
+     "iopub.status.idle": "2026-10-09T14:46:33.105993Z",
+     "shell.execute_reply": "2026-10-09T14:46:33.104944Z"
+    },
+    "papermill": {
+     "duration": 2.883077,
+     "end_time": "2026-10-09T14:46:33.106842+00:00",
+     "exception": false,
+     "start_time": "2026-10-09T14:46:30.223765+00:00",
+     "status": "completed"
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -388,8 +412,8 @@
       "  F-stat premier etage   : 2333.75  (seuil = 10.0)\n",
       "  Verdict exclusion local: REFUTE_PAR_DEFAUT\n",
       "  Refuters              :\n",
-      "    placebo_pvalue            = 0.98\n",
-      "    data_subset_pvalue        = 0.19010333817618313\n",
+      "    placebo_pvalue            = 0.94\n",
+      "    data_subset_pvalue        = 0.3683952788188338\n",
       "\n",
       "======================================================================\n",
       "Pipeline dowhy : instrument FAIBLE (force=0.05, exclusion OK)\n",
@@ -406,8 +430,8 @@
       "  F-stat premier etage   : 6.71  (seuil = 10.0)\n",
       "  Verdict exclusion local: REFUTE_PAR_DEFAUT\n",
       "  Refuters              :\n",
-      "    placebo_pvalue            = 0.96\n",
-      "    data_subset_pvalue        = 0.45340892541392286\n"
+      "    placebo_pvalue            = 0.88\n",
+      "    data_subset_pvalue        = 0.15677142996133342\n"
      ]
     }
    ],

From 8f466e0af3dd6a45971f6e158e3f5759b1261603 Mon Sep 17 00:00:00 2001
From: jsboige <jsboige@gmail.com>
Date: Fri, 9 Oct 2026 23:20:59 +0200
Subject: [PATCH 2/2] fix(probas,#11044): retirer le bloc papermill perime de
 CausalBridges-04

Le ratchet papermill (base vs PR) rougissait sur cette PR : les sorties et
execution_count du carnet ont change, mais le bloc `metadata.papermill` au
niveau carnet est reste byte-identique a origin/main -- il decrivait le run
precedent (2026-09-13T01:35:35Z), pas celui dont les sorties sont committeES.

Remede 2 du ratchet (retrait du bloc), celui deja applique au carnet jumeau
CausalBridges-06 dans cette meme PR : le carnet ne se re-execute plus en
local (papermill reecrivait le bloc et degradait les sorties), le bloc perime
n'a pas de valeur probante et son maintien fige une donnee fausse.

Verifie : metadata carnet = ['kernelspec','language_info'] (forme du jumeau),
31 blocs papermill de cellule intacts, 12/12 cellules code avec exec_count.
Organe : `check_papermill_ratchet.py origin/main --json` -> regressions 0,
2 x BLOCK_REMOVED.

See #11044

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 ...CausalBridges-04-Dowhy-Decouverte-Structure.ipynb | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-04-Dowhy-Decouverte-Structure.ipynb b/MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-04-Dowhy-Decouverte-Structure.ipynb
index dcf5f4079b..aa1b48452f 100644
--- a/MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-04-Dowhy-Decouverte-Structure.ipynb
+++ b/MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-04-Dowhy-Decouverte-Structure.ipynb
@@ -989,18 +989,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.13.3"
-  },
-  "papermill": {
-   "default_parameters": {},
-   "duration": 14.85913,
-   "end_time": "2026-09-13T01:35:35.659752+00:00",
-   "environment_variables": {},
-   "exception": null,
-   "input_path": "CausalBridges-04-Dowhy-Decouverte-Structure.ipynb",
-   "output_path": "CausalBridges-04-Dowhy-Decouverte-Structure.ipynb",
-   "parameters": {},
-   "start_time": "2026-09-13T01:35:20.800622+00:00",
-   "version": "2.7.0"
   }
  },
  "nbformat": 4,