In [ ]:
#%%
"""File 07discreteMixture.py

:author: Michel Bierlaire, EPFL
:date: Sun Sep  8 00:06:20 2019

 Example of a discrete mixture of logit (or latent class model)
 Three alternatives: Train, Car and Swissmetro
 SP data
"""

import pandas as pd
import biogeme.database as db
import biogeme.biogeme as bio
import biogeme.models as models
import biogeme.messaging as msg
from biogeme.expressions import Beta, DefineVariable, log

# Read the data
df = pd.read_csv('swissmetro.dat', '\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())

# The following statement allows you to use the names of the variable
# as Python variable.
globals().update(database.variables)

# 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)

# Parameters to be estimated
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_COST = Beta('B_COST', 0, None, None, 0)

PROB_CLASS1 = Beta('PROB_CLASS1', 0.5, 0, 1, 0)
PROB_CLASS2 = 1 - PROB_CLASS1

# Definition of new variables
SM_COST = SM_CO * (GA == 0)
TRAIN_COST = TRAIN_CO * (GA == 0)

# Definition of new variables: adding columns to the database
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)

# Definition of the utility functions for latent class 1, where the
# time coefficient is zero
V11 = ASC_TRAIN + B_COST * TRAIN_COST_SCALED
V12 = ASC_SM + B_COST * SM_COST_SCALED
V13 = ASC_CAR + B_COST * CAR_CO_SCALED

# Associate utility functions with the numbering of alternatives
V1 = {1: V11,
      2: V12,
      3: V13}

# Definition of the utility functions for latent class 2, whete the
# time coefficient is estimated
V21 = ASC_TRAIN + B_TIME * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED
V22 = ASC_SM + B_TIME * SM_TT_SCALED + B_COST * SM_COST_SCALED
V23 = ASC_CAR + B_TIME * CAR_TT_SCALED + B_COST * CAR_CO_SCALED

# Associate utility functions with the numbering of alternatives
V2 = {1: V21,
      2: V22,
      3: V23}

# Associate the availability conditions with the alternatives
av = {1: TRAIN_AV_SP,
      2: SM_AV,
      3: CAR_AV_SP}


# The choice model is a discrete mixture of logit, with availability conditions
prob1 = models.logit(V1, av, CHOICE)
prob2 = models.logit(V2, av, CHOICE)
prob = PROB_CLASS1 * prob1 + PROB_CLASS2 * prob2
logprob = log(prob)

# Define level of verbosity
logger = msg.bioMessage()
logger.setSilent()
#logger.setWarning()
#logger.setGeneral()
#logger.setDetailed()

# Create the Biogeme object
biogeme = bio.BIOGEME(database, logprob)
biogeme.modelName = '07discreteMixture'

# Estimate the parameters
results = biogeme.estimate()
pandasResults = results.getEstimatedParameters()
print(pandasResults)