Data preparation for Swissmetro (binary choice)

Data preparation for Swissmetro, and definition of the variables. The data is designed to estimate binary logit models. All observations such that Swissmetro was chosen are removed.

author:

Michel Bierlaire, EPFL

date:

Mon Mar 6 15:17:03 2023

import pandas as pd
import biogeme.database as db
from biogeme.expressions import Variable

Read the data.

df = pd.read_csv('swissmetro.dat', sep='\t')
database = db.Database('swissmetro', df)

Definition of the variables.

PURPOSE = Variable('PURPOSE')
CHOICE = Variable('CHOICE')
GA = Variable('GA')
LUGGAGE = Variable('LUGGAGE')
TRAIN_CO = Variable('TRAIN_CO')
CAR_AV = Variable('CAR_AV')
SP = Variable('SP')
TRAIN_AV = Variable('TRAIN_AV')
TRAIN_TT = Variable('TRAIN_TT')
SM_TT = Variable('SM_TT')
CAR_TT = Variable('CAR_TT')
CAR_CO = Variable('CAR_CO')
SM_CO = Variable('SM_CO')
SM_AV = Variable('SM_AV')
MALE = Variable('MALE')
GROUP = Variable('GROUP')
TRAIN_HE = Variable('TRAIN_HE')
SM_HE = Variable('SM_HE')
INCOME = Variable('INCOME')

# Excluding observations.
exclude = ((PURPOSE != 1) * (PURPOSE != 3) + (CHOICE == 0) + (CHOICE == 2)) > 0
database.remove(exclude)

Definition of new variables.

SM_COST = database.DefineVariable('SM_COST', SM_CO * (GA == 0))
TRAIN_COST = database.DefineVariable('TRAIN_COST', TRAIN_CO * (GA == 0))
CAR_AV_SP = database.DefineVariable('CAR_AV_SP', CAR_AV * (SP != 0))
TRAIN_AV_SP = database.DefineVariable('TRAIN_AV_SP', TRAIN_AV * (SP != 0))
TRAIN_TT_SCALED = database.DefineVariable('TRAIN_TT_SCALED', TRAIN_TT / 100)
TRAIN_COST_SCALED = database.DefineVariable('TRAIN_COST_SCALED', TRAIN_COST / 100)
SM_TT_SCALED = database.DefineVariable('SM_TT_SCALED', SM_TT / 100)
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)