#!/usr/bin/env python
# coding: utf-8

# In[ ]:


#%%
"""File 02weight.py

:author: Michel Bierlaire, EPFL
:date: Sat Sep  7 18:11:18 2019

 Example of a logit model with Weighted Exogenous Sample Maximum
 Likelihood (WESML).  Three alternatives: Train, Car and Swissmetro 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 investigate the database. For example:
# 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)

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

# Definition of the weight
weight = 8.890991e-01 * (1.0 * (GROUP == 2) + 1.2 * (GROUP == 3))

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

# These notes will be included as such in the report file.
userNotes = (
    'Example of a logit model with three alternatives: '
    'Train, Car and Swissmetro.'
    ' Weighted Exogenous Sample Maximum Likelihood estimator (WESML)'
)


# Create the Biogeme object
formulas = {'loglike': logprob, 'weight': weight}
biogeme = bio.BIOGEME(database, formulas, userNotes=userNotes)
biogeme.modelName = '02weight'

## It is possible to control the generation of the HTML and the pickle files
biogeme.generateHtml = True
biogeme.generatePickle = False
results = biogeme.estimate()

# Get the results in a pandas table
pandasResults = results.getEstimatedParameters()
print(pandasResults)