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a1.py
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a1.py
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########################################
#
# @file 01logit.py
# @author: Michel Bierlaire, EPFL
# @date: Thu Sep 6 15:14:39 2018
#
# Logit model
# Three alternatives: Train, Car and Swissmetro
# SP data
#
#######################################
import pandas as pd
import biogeme.database as db
import biogeme.biogeme as bio
pandas = pd.read_table("tripdata.dat")
database = db.Database("tripdata",pandas)
# 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_AUTO_DRIVE = Beta('ASC_AUTO_DRIVE',0,None,None,0)
ASC_AUTO_PASS = Beta('ASC_AUTO_PASS',0,None,None,0)
ASC_METRO = Beta('ASC_METRO',0,None,None,0)
ASC_TRAIN = Beta('ASC_TRAIN',0,None,None,0)
ASC_WALK = Beta('ASC_WALK',0,None,None,0)
B_IVTT = Beta('B_IVTT',0,None,None,0)
#SM_COST = SM_CO * ( GA == 0 )
#TRAIN_COST = TRAIN_CO * ( GA == 0 )
#CAR_AV_SP = DefineVariable('CAR_AV_SP',CAR_AV * ( SP != 0 ),database)
#TRAIN_AV_SP = DefineVariable('TRAIN_AV_SP',TRAIN_AV * ( SP != 0 ),database)
#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_AUTO_DRIVE + \
B_IVTT * ivtt1
V2 = ASC_AUTO_PASS + \
B_IVTT * ivtt2
V3 = ASC_METRO + \
B_IVTT * ivtt3
V4 = ASC_TRAIN + \
B_IVTT * ivtt4
V5 = ASC_WALK + \
B_IVTT * ivtt5
# Associate utility functions with the numbering of alternatives
V = {1: V1,
2: V2,
3: V3,
4: V4,
5: V5}
# Associate the availability conditions with the alternatives
av = {1: avail1,
2: avail2,
3: avail3,
4: avail4,
5: avail5}
logprob = bioLogLogit(V,av,choice)
biogeme = bio.BIOGEME(database,logprob) # This is my log likelihood function
biogeme.modelName = "A1Practice"
results = biogeme.estimate()
# Print the estimated values
betas = results.getBetaValues()
for k,v in betas.items():
print(f"{k}=\t{v:.3g}")
# Get the results in a pandas table
pandasResults = results.getEstimatedParameters()
print(pandasResults)