"""File 03scale.py

:author: Michel Bierlaire, EPFL
:date: Thu Sep  6 15:14:39 2018

 Illustrates heteroscedastic specification. A different scale is
 associated with different segments of the sample.
 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

# Read the data
df = pd.read_csv("swissmetro.dat",sep='\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)
Scale_group3 = Beta('Scale_group3',1,0.001,None,0)

# Scale associated with group 3 is estimated
scale = (GROUP != 3) + (GROUP == 3) * Scale_group3

# 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
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

# Scale the utility functions, and associate them with the numbering
# of alternatives
V = {1: scale * V1,
     2: scale * V2,
     3: scale * 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}

# Definition of the model. This is the contribution of each
# observation to the log likelihood function.
logprob = models.loglogit(V,av,CHOICE)

# 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)
biogeme.modelName = "03scale"

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