#######################################
#
# File: 01logit_stat.py
# Author: Michel Bierlaire, EPFL
# Date: Mon May 23 11:01:28 2011
#
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#
#
# No estimation or simulation is performed here. Only statistics about
# the sample are computed and reported.
#
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from biogeme import *
from headers import *
from loglikelihood import *
from statistics import *
ASC_TRAIN = -0.701188
B_TIME = -1.27786
B_COST = -1.08379
ASC_SM = 0
ASC_CAR = -0.154633
# 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 = 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
V1 = ASC_TRAIN + B_TIME * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED
V2 = ASC_SM + B_TIME * SM_TT_SCALED + B_COST * SM_COST_SCALED
V3 = ASC_CAR + B_TIME * 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 = CAR_AV * ( SP != 0 )
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
prob1 = bioLogit(V,av,1)
prob2 = bioLogit(V,av,2)
prob3 = bioLogit(V,av,3)
# Elasticities can be computed. We illustrate below two
# formulas. Check in the output file that they produce the same
# result.
# First, the general definition of elasticities. This illustrates the
# use of the Derive expression
genelas1 = Derive(prob1,'TRAIN_TT') * TRAIN_TT / prob1
genelas2 = Derive(prob2,'SM_TT') * SM_TT / prob2
genelas3 = Derive(prob3,'CAR_TT') * CAR_TT / prob3
# Second, the elasticity of logit models.
logitelas1 = TRAIN_AV_SP * (1.0 - prob1) * TRAIN_TT_SCALED * B_TIME
logitelas2 = SM_AV * (1.0 - prob2) * SM_TT_SCALED * B_TIME
logitelas3 = CAR_AV_SP * (1.0 - prob3) * CAR_TT_SCALED * B_TIME
# Defines an itertor on the data
rowIterator('obsIter')
# All observations verifying the following expression will not be
# considered for estimation
# The modeler here has developed the model only for work trips.
# Observations such that the dependent variable CHOICE is 0 are also removed.
exclude = (( PURPOSE != 1 ) * ( PURPOSE != 3 ) + ( CHOICE == 0 )) > 0
BIOGEME_OBJECT.EXCLUDE = exclude
# Statistics
nullLoglikelihood(av,'obsIter')
choiceSet = [1,2,3]
cteLoglikelihood(choiceSet,CHOICE,'obsIter')
availabilityStatistics(av,'obsIter')
BIOGEME_OBJECT.STATISTICS['Average prob1'] = Sum(prob1,'obsIter') / Sum(1,'obsIter')
BIOGEME_OBJECT.FORMULAS['Train utility'] = V1
BIOGEME_OBJECT.FORMULAS['Swissmetro utility'] = V2
BIOGEME_OBJECT.FORMULAS['Car utility'] = V3