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test_06integral.py
102 lines (79 loc) · 3.36 KB
/
test_06integral.py
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import os
import unittest
import pandas as pd
import biogeme.database as db
import biogeme.biogeme as bio
from biogeme import models
from biogeme.expressions import (
Beta,
RandomVariable,
Integrate,
log,
exp,
)
myPath = os.path.dirname(os.path.abspath(__file__))
df = pd.read_csv(f'{myPath}/swissmetro.dat', sep='\t')
database = db.Database('swissmetro', df)
# The Pandas data structure is available as database.data. Use all the
# Pandas functions to invesigate the database
# print(database.data.describe())
globals().update(database.variables)
# 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_TIME_S = Beta('B_TIME_S', 1, None, None, 0)
B_COST = Beta('B_COST', 0, None, None, 0)
# Define a random parameter, normally distirbuted, designed to be used
# for Monte-Carlo simulation
omega = RandomVariable('omega')
a = -1
b = 1
x = a + (b - a) / (1 + exp(-omega))
dx = (b - a) * exp(-omega) * (1 + exp(-omega)) ** (-2)
B_TIME_RND = B_TIME + B_TIME_S * x
# Utility functions
# If the person has a GA (season ticket) her incremental cost is actually 0
# rather than the cost value gathered from the
# network data.
SM_COST = SM_CO * (GA == 0)
TRAIN_COST = TRAIN_CO * (GA == 0)
# For numerical reasons, it is good practice to scale the data to
# that the values of the parameters are around 1.0.
# A previous estimation with the unscaled data has generated
# parameters around -0.01 for both cost and time. Therefore, time and
# cost are multipled my 0.01.
TRAIN_TT_SCALED = database.DefineVariable('TRAIN_TT_SCALED', TRAIN_TT / 100.0)
TRAIN_COST_SCALED = database.DefineVariable(
'TRAIN_COST_SCALED', TRAIN_COST / 100
)
SM_TT_SCALED = database.DefineVariable('SM_TT_SCALED', SM_TT / 100.0)
SM_COST_SCALED = database.DefineVariable('SM_COST_SCALED', SM_COST / 100)
CAR_TT_SCALED = database.DefineVariable('CAR_TT_SCALED', CAR_TT / 100)
CAR_CO_SCALED = database.DefineVariable('CAR_CO_SCALED', CAR_CO / 100)
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
CAR_AV_SP = database.DefineVariable('CAR_AV_SP', CAR_AV * (SP != 0))
TRAIN_AV_SP = database.DefineVariable('TRAIN_AV_SP', TRAIN_AV * (SP != 0))
av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}
# The choice model is a logit, with availability conditions
condprob = models.logit(V, av, CHOICE)
prob = Integrate(condprob * dx / (b - a), 'omega')
logprob = log(prob)
class test_06integral(unittest.TestCase):
def testEstimation(self):
biogeme = bio.BIOGEME(database, logprob)
biogeme.saveIterations = False
biogeme.generateHtml = False
biogeme.generatePickle = False
results = biogeme.estimate()
self.assertAlmostEqual(results.data.logLike, -5215.072, 2)
if __name__ == '__main__':
unittest.main()