"""File 15panelDiscrete.py

:author: Michel Bierlaire, EPFL
:date: Sun Sep  8 19:30:31 2019

 Example of a discrete mixture of logit models, also called latent class model.
 The datafile is organized as panel data.
 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

# Read the data
df = pd.read_csv("swissmetro.dat",sep='\t')
database = db.Database("swissmetro",df)

# They are organized as panel data. The variable ID identifies each individual.
database.panel("ID")

# 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.
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)

# Parameters to be estimated
ASC_CAR = Beta('ASC_CAR',0.136,None,None,0)
ASC_TRAIN = Beta('ASC_TRAIN',-1,None,None,0)
ASC_SM = Beta('ASC_SM',0,None,None,1)
B_TIME = Beta('B_TIME',-6.3,None,0,0)
B_COST = Beta('B_COST',-3.29,None,0,0)
SIGMA_CAR = Beta('SIGMA_CAR',3.7,None,None,0)
SIGMA_SM = Beta('SIGMA_SM',0.759,None,None,0)
SIGMA_TRAIN = Beta('SIGMA_TRAIN',3.02,None,None,0)

# Define random parameters, normally distributed across individuals,
# designed to be used for Monte-Carlo simulation
EC_CAR = SIGMA_CAR * bioDraws('EC_CAR','NORMAL')
EC_SM = SIGMA_SM * bioDraws('EC_SM','NORMAL')
EC_TRAIN = SIGMA_TRAIN * bioDraws('EC_TRAIN','NORMAL')

# 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 
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)

# Utility functions for latent class 1, where the time coefficient is zero
V11 = ASC_TRAIN + B_COST * TRAIN_COST_SCALED  + EC_TRAIN
V12 = ASC_SM + B_COST * SM_COST_SCALED + EC_SM
V13 = ASC_CAR + B_COST * CAR_CO_SCALED + EC_CAR
V1 = {1: V11,
      2: V12,
      3: V13}

# 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 + EC_TRAIN
V22 = ASC_SM + B_TIME * SM_TT_SCALED + B_COST * SM_COST_SCALED + EC_SM
V23 = ASC_CAR + B_TIME * CAR_TT_SCALED + B_COST * CAR_CO_SCALED + EC_CAR
V2 = {1: V21,
      2: V22,
      3: V23}

# Associate the availability conditions with the alternatives
CAR_AV_SP =  DefineVariable('CAR_AV_SP',CAR_AV  * (  SP   !=  0  ),database)
TRAIN_AV_SP =  DefineVariable('TRAIN_AV_SP',TRAIN_AV  * (  SP   !=  0  ),database)
av = {1: TRAIN_AV_SP,
      2: SM_AV,
      3: CAR_AV_SP}

# Class membership model
W_OTHER = Beta('W_OTHER',0.798,0,1,0)
probClass1 = 1 - W_OTHER
probClass2 = W_OTHER

# The choice model is a discrete mixture of logit, with availability conditions
# Conditional to the random variables, likelihood if the individual is
# in class 1
prob1 = PanelLikelihoodTrajectory(models.logit(V1,av,CHOICE))

# Conditional to the random variables, likelihood if the individual is
# in class 2
prob2 = PanelLikelihoodTrajectory(models.logit(V2,av,CHOICE))

# Conditional to the random variables, likelihood for the individual.
probIndiv = probClass1 * prob1 + probClass2 * prob2

# We integrate over the random variables using Monte-Carlo
logprob = log(MonteCarlo(probIndiv))

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

# Create the Biogeme object
biogeme  = bio.BIOGEME(database,logprob,numberOfDraws=20000)
biogeme.modelName = "15panelDiscrete"

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


