CIV1538BiogemeTutorial/26triangularPanelMixture.py at master · jfhawkin/CIV1538BiogemeTutorial · GitHub

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import pandas as pd
import biogeme.database as db
import biogeme.biogeme as bio
import biogeme.models as models

pandas = pd.read_table("swissmetro.dat")
database = db.Database("swissmetro",pandas)

database.panel("ID")

# The Pandas data structure is available as database.data. Use all the
# Pandas functions to invesigate the database
#print(database.data.describe())

from headers import *

# Removing some observations can be done directly using pandas.
#remove = (((database.data.PURPOSE != 1) & (database.data.PURPOSE != 3)) | (database.data.CHOICE == 0))
#database.data.drop(database.data[remove].index,inplace=True)

# Here we use the "biogeme" way for backward compatibility
exclude = (( PURPOSE != 1 ) * (  PURPOSE   !=  3  ) +  ( CHOICE == 0 )) > 0
database.remove(exclude)

ASC_CAR = Beta('ASC_CAR',0,None,None,0)
ASC_TRAIN = Beta('ASC_TRAIN',0,None,None,0)
ASC_SM = Beta('ASC_SM',0,None,None,1)
B_TIME = Beta('B_TIME',0,None,None,0)
B_COST = Beta('B_COST',0,None,None,0)

SIGMA_CAR = Beta('SIGMA_CAR',0,None,None,0)
SIGMA_SM = Beta('SIGMA_SM',0,None,None,0)
SIGMA_TRAIN = Beta('SIGMA_TRAIN',0,None,None,0)


# Provide my own random number generator to the database.
# See the numpy.random documentation to obtain a list of other distributions.
def theTriangularGenerator(size):
    return np.random.triangular(-1,0,1,size=size)

myRandomNumberGenerators = {'TRIANGULAR':theTriangularGenerator}
database.setRandomNumberGenerators(myRandomNumberGenerators)

# Define a random parameter,  with a triangular distribution, designed to be used
# for Monte-Carlo simulation
EC_CAR = SIGMA_CAR * bioDraws('EC_CAR','TRIANGULAR')
EC_SM = SIGMA_SM * bioDraws('EC_SM','TRIANGULAR')
EC_TRAIN = SIGMA_TRAIN * bioDraws('EC_TRAIN','TRIANGULAR')


SM_COST =  SM_CO   * (  GA   ==  0  )
TRAIN_COST =  TRAIN_CO   * (  GA   ==  0  )

TRAIN_TT_SCALED = DefineVariable('TRAIN_TT_SCALED',\
                                 TRAIN_TT / 100.0,database)
TRAIN_COST_SCALED = DefineVariable('TRAIN_COST_SCALED',\
                                   TRAIN_COST / 100,database)
SM_TT_SCALED = DefineVariable('SM_TT_SCALED', SM_TT / 100.0,database)
SM_COST_SCALED = DefineVariable('SM_COST_SCALED', SM_COST / 100,database)
CAR_TT_SCALED = DefineVariable('CAR_TT_SCALED', CAR_TT / 100,database)
CAR_CO_SCALED = DefineVariable('CAR_CO_SCALED', CAR_CO / 100,database)

V1 = ASC_TRAIN + B_TIME * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED + EC_TRAIN
V2 = ASC_SM + B_TIME * SM_TT_SCALED + B_COST * SM_COST_SCALED + EC_SM
V3 = ASC_CAR + B_TIME * CAR_TT_SCALED + B_COST * CAR_CO_SCALED + EC_CAR

# Associate utility functions with the numbering of alternatives
V = {1: V1,
     2: V2,
     3: V3}


# Associate the availability conditions with the alternatives
CAR_AV_SP =  DefineVariable('CAR_AV_SP',CAR_AV  * (  SP   !=  0  ),database)
TRAIN_AV_SP =  DefineVariable('TRAIN_AV_SP',TRAIN_AV  * (  SP   !=  0  ),database)

av = {1: TRAIN_AV_SP,
      2: SM_AV,
      3: CAR_AV_SP}

obsprob = models.logit(V,av,CHOICE)
condprobIndiv = PanelLikelihoodTrajectory(obsprob)
logprob = log(MonteCarlo(condprobIndiv))

biogeme  = bio.BIOGEME(database,logprob,numberOfDraws=500)
biogeme.modelName = "26triangularPanelMixture"
results = biogeme.estimate()
print("Results=",results)