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07estimationMonteCarlo_mlhs_anti_500.py
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07estimationMonteCarlo_mlhs_anti_500.py
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"""File 07estimationMonteCarlo_mlhs_anti_500.py
:author: Michel Bierlaire, EPFL
:date: Wed Dec 11 17:21:52 2019
Estimation of a mixtures of logit models where the integral is
approximated using MonteCarlo integration, with antithetic Modified
Latin Hypercube Sampling draws.
"""
# pylint: disable=invalid-name, undefined-variable
import pandas as pd
import biogeme.database as db
import biogeme.biogeme as bio
import biogeme.models as models
from biogeme.expressions import Beta, bioDraws, MonteCarlo, log
pandas = pd.read_csv('swissmetro.dat', sep='\t')
database = db.Database('swissmetro', pandas)
# The following statement allows you to use the names of the variable
# as Python variable.
globals().update(database.variables)
# Removing some observations
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_TIME_S = Beta('B_TIME_S', 1, None, None, 0)
B_COST = Beta('B_COST', 0, None, None, 0)
# Define a random parameter, normally distributed, designed to be used
# for Monte-Carlo simulation
B_TIME_RND = B_TIME + B_TIME_S * bioDraws('B_TIME_RND', 'NORMAL_MLHS_ANTI')
# Definition of new variables
SM_COST = SM_CO * (GA == 0)
TRAIN_COST = TRAIN_CO * (GA == 0)
CAR_AV_SP = CAR_AV * (SP != 0)
TRAIN_AV_SP = TRAIN_AV * (SP != 0)
TRAIN_TT_SCALED = TRAIN_TT / 100.0
TRAIN_COST_SCALED = TRAIN_COST / 100
SM_TT_SCALED = SM_TT / 100.0
SM_COST_SCALED = SM_COST / 100
CAR_TT_SCALED = CAR_TT / 100
CAR_CO_SCALED = CAR_CO / 100
# Definition of the utility functions
V1 = ASC_TRAIN + \
B_TIME_RND * TRAIN_TT_SCALED + \
B_COST * TRAIN_COST_SCALED
V2 = ASC_SM + \
B_TIME_RND * SM_TT_SCALED + \
B_COST * SM_COST_SCALED
V3 = ASC_CAR + \
B_TIME_RND * CAR_TT_SCALED + \
B_COST * CAR_CO_SCALED
# Associate utility functions with the numbering of alternatives
V = {1: V1,
2: V2,
3: V3}
# Associate the availability conditions with the alternatives
av = {1: TRAIN_AV_SP,
2: SM_AV,
3: CAR_AV_SP}
# The choice model is a logit, with availability conditions
prob = models.logit(V, av, CHOICE)
logprob = log(MonteCarlo(prob))
R = 500
biogeme = bio.BIOGEME(database, logprob, numberOfDraws=R)
biogeme.modelName = '07estimationMonteCarlo_mlhs_anti_500'
results = biogeme.estimate()
# Get the results in a pandas table
pandasResults = results.getEstimatedParameters()
print(pandasResults)