"""File 26triangularPanelMixture.py

:author: Michel Bierlaire, EPFL
:date: Mon Sep  9 10:27:40 2019

 Example of a mixture of logit models, using Monte-Carlo integration.
 THe micing distribution is user-defined (triangular, here).
 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
from biogeme.expressions import Beta, DefineVariable, bioDraws, MonteCarlo, PanelLikelihoodTrajectory, log

# 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.
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)
SIGMA_CAR = Beta('SIGMA_CAR',0,None,None,0)
SIGMA_SM = Beta('SIGMA_SM',0,None,None,0)
SIGMA_TRAIN = Beta('SIGMA_TRAIN',0,None,None,0)

# Provide my own random number generator to the database.
# See the numpy.random documentation to obtain a list of other distributions.
def theTriangularGenerator(sampleSize, numberOfDraws):
    return np.random.triangular(-1,0,1,(sampleSize,numberOfDraws))

myRandomNumberGenerators = {'TRIANGULAR':(theTriangularGenerator,'Draws from a triangular distribution')}
database.setRandomNumberGenerators(myRandomNumberGenerators)

# Define random parameters, with a triangular distribution, designed
# to be used for Monte-Carlo simulation
EC_CAR = SIGMA_CAR * bioDraws('EC_CAR','TRIANGULAR')
EC_TRAIN = SIGMA_TRAIN * bioDraws('EC_TRAIN','TRIANGULAR')

# 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)

# Definition of the utility functions
V1 = ASC_TRAIN + B_TIME * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED + EC_TRAIN
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 + EC_CAR

# 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 =  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}

# Conditional to the random parameters, the likelihood of one observation is
# given by the logit model (called the kernel)
obsprob = models.logit(V,av,CHOICE)

# Conditional to the random parameters, the likelihood of all observations for
# one individual (the trajectory) is the product of the likelihood of
# each observation.
condprobIndiv = PanelLikelihoodTrajectory(obsprob)

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

# 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=500)
biogeme.modelName = "26triangularPanelMixture"

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