"""File 10nestedBottom.py

:author: Michel Bierlaire, EPFL
:date: Sun Sep  8 00:36:04 2019

 Example of a nested logit model where the normalization is done at
 the bottom level.  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, Variable

# 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())

PURPOSE = Variable('PURPOSE')
CHOICE = Variable('CHOICE')
GA = Variable('GA')
TRAIN_CO = Variable('TRAIN_CO')
CAR_AV = Variable('CAR_AV')
SP = Variable('SP')
TRAIN_AV = Variable('TRAIN_AV')
TRAIN_TT = Variable('TRAIN_TT')
SM_TT = Variable('SM_TT')
CAR_TT = Variable('CAR_TT')
CAR_CO = Variable('CAR_CO')
SM_CO = Variable('SM_CO')
SM_AV = Variable('SM_AV')

# 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)

# If the lower bound is set to zero, the model cannot be evaluated.
# Therefore, we set the lower bound to a small number, strictly larger
# than zero.
MU = Beta('MU', 0.5, 0.000001, 1.0, 0)

# 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 = database.DefineVariable('CAR_AV_SP', CAR_AV * (SP != 0))
TRAIN_AV_SP = database.DefineVariable('TRAIN_AV_SP', TRAIN_AV * (SP != 0))
TRAIN_TT_SCALED = database.DefineVariable('TRAIN_TT_SCALED', TRAIN_TT / 100.0)
TRAIN_COST_SCALED = database.DefineVariable(
    'TRAIN_COST_SCALED', TRAIN_COST / 100
)
SM_TT_SCALED = database.DefineVariable('SM_TT_SCALED', SM_TT / 100.0)
SM_COST_SCALED = database.DefineVariable('SM_COST_SCALED', SM_COST / 100)
CAR_TT_SCALED = database.DefineVariable('CAR_TT_SCALED', CAR_TT / 100)
CAR_CO_SCALED = database.DefineVariable('CAR_CO_SCALED', CAR_CO / 100)

# 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:
# 1: nests parameter
# 2: list of alternatives
existing = 1.0, [1, 3]
future = 1.0, [2]
nests = existing, future

# Definition of the model. This is the contribution of each
# observation to the log likelihood function.
# The choice model is a nested logit, with availability conditions,
# where the scale parameter mu is explicitly involved.
logprob = models.lognestedMevMu(V, av, nests, CHOICE, MU)

# 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 = '10nestedBottom'

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