"""File 11cnl_sparse.py

:author: Michel Bierlaire, EPFL
:date: Thu Apr 30 18:55:02 2020

 Example of a cross-nested logit model.
 Same as 11cnl, where the zero alphas are not included.
 Three alternatives: Train, Car and Swissmetro
 Train and car are in the same nest.
 SP data
"""

import pandas as pd
import biogeme.database as db
import biogeme.biogeme as bio
from biogeme import models
import biogeme.messaging as msg
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)

MU_EXISTING = Beta('MU_EXISTING', 1, 1, None, 0)
MU_PUBLIC = Beta('MU_PUBLIC', 1, 1, None, 0)
ALPHA_EXISTING = Beta('ALPHA_EXISTING', 0.5, 0, 1, 0)
ALPHA_PUBLIC = 1 - ALPHA_EXISTING

# 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
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

# Associate utility functions with the numbering of alternatives
V = {1: V1, 2: V2, 3: V3}

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

# Definition of nests
# Nest membership parameters
alpha_existing = {1: ALPHA_EXISTING, 3: 1.0}

alpha_public = {1: ALPHA_PUBLIC, 2: 1.0}

nest_existing = MU_EXISTING, alpha_existing
nest_public = MU_PUBLIC, alpha_public
nests = nest_existing, nest_public

# The choice model is a cross-nested logit, with availability conditions
logprob = models.logcnl_avail(V, av, nests, CHOICE)

# 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 = '11cnl_sparse'

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