"""File 03nestedElasticities.py

:author: Michel Bierlaire, EPFL
:date: Wed Sep 11 10:28:11 2019

 We use a previously estimated nested logit model.
 Three alternatives: public transporation, car and slow modes.
 RP data.
 We calculate disaggregate and aggregate direct point elasticities.
"""
import sys
import pandas as pd
import biogeme.database as db
import biogeme.biogeme as bio
import biogeme.models as models
import biogeme.results as res

print("Running 03nestedElasticities.py...")

# Read the data
df = pd.read_csv("optima.dat",sep='\t')
database = db.Database("optima",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.
from headers import *

# Exclude observations such that the chosen alternative is -1
exclude = (Choice == -1.0)
database.remove(exclude)

# Normalize the weights
sumWeight = database.data['Weight'].sum()
normalizedWeight = Weight * 1906 / 0.814484

# Calculate the number of accurences of a value in the database
numberOfMales = database.count("Gender",1)
print(f"Number of males:   {numberOfMales}")
numberOfFemales = database.count("Gender",2)
print(f"Number of females: {numberOfFemales}")
# For more complex conditions, using directly Pandas
unreportedGender = \
                   database.data[(database.data["Gender"] != 1)
                    & (database.data["Gender"] != 2)].count()["Gender"]
print(f"Unreported gender: {unreportedGender}")

# List of parameters. Their value will be set later.
ASC_CAR = Beta('ASC_CAR',0,None,None,0)
ASC_PT = Beta('ASC_PT',0,None,None,1)
ASC_SM = Beta('ASC_SM',0,None,None,0)
BETA_TIME_FULLTIME = Beta('BETA_TIME_FULLTIME',0,None,None,0)
BETA_TIME_OTHER = Beta('BETA_TIME_OTHER',0,None,None,0)
BETA_DIST_MALE = Beta('BETA_DIST_MALE',0,None,None,0)
BETA_DIST_FEMALE = Beta('BETA_DIST_FEMALE',0,None,None,0)
BETA_DIST_UNREPORTED = Beta('BETA_DIST_UNREPORTED',0,None,None,0)
BETA_COST = Beta('BETA_COST',0,None,None,0)

# Definition of variables:
# For numerical reasons, it is good practice to scale the data to
# that the values of the parameters are around 1.0.

# The following statements are designed to preprocess the data.
# It is like creating a new columns in the data file. This
# should be preferred to the statement like
# TimePT_scaled = Time_PT / 200.0
# which will cause the division to be reevaluated again and again,
# throuh the iterations. For models taking a long time to
# estimate, it may make a significant difference.

TimePT_scaled = TimePT / 200
TimeCar_scaled = TimeCar / 200
MarginalCostPT_scaled = MarginalCostPT / 10
CostCarCHF_scaled = CostCarCHF / 10
distance_km_scaled = distance_km / 5

male = (Gender == 1)
female = (Gender == 2)
unreportedGender = (Gender == -1)

fulltime = (OccupStat == 1)
notfulltime = (OccupStat != 1)

# Definition of utility functions:
V_PT = ASC_PT + BETA_TIME_FULLTIME * TimePT_scaled * fulltime + \
       BETA_TIME_OTHER * TimePT_scaled * notfulltime + \
       BETA_COST * MarginalCostPT_scaled
V_CAR = ASC_CAR + \
        BETA_TIME_FULLTIME * TimeCar_scaled * fulltime + \
        BETA_TIME_OTHER * TimeCar_scaled * notfulltime + \
        BETA_COST * CostCarCHF_scaled
V_SM = ASC_SM + \
       BETA_DIST_MALE * distance_km_scaled * male + \
       BETA_DIST_FEMALE * distance_km_scaled * female + \
       BETA_DIST_UNREPORTED * distance_km_scaled * unreportedGender

# Associate utility functions with the numbering of alternatives
V = {0: V_PT,
     1: V_CAR,
     2: V_SM}

# Associate the availability conditions with the alternatives.
# In this example all alternatives are available for each individual.
av = {0: 1,
      1: 1,
      2: 1}

# Definition of the nests:
# 1: nests parameter
# 2: list of alternatives

MU_NOCAR = Beta('MU_NOCAR',1.0,1.0,None,0)

CAR_NEST = 1.0 , [ 1]
NO_CAR_NEST = MU_NOCAR , [ 0, 2]
nests = CAR_NEST, NO_CAR_NEST

# The choice model is a nested logit
prob_pt = models.nested(V,av,nests,0)
prob_car = models.nested(V,av,nests,1)
prob_sm = models.nested(V,av,nests,2)

# Calculation of the direct elasticities. 
# We use the "Derive" operator to calculate the derivatives. 
direct_elas_pt_time = \
  Derive(prob_pt,'TimePT') * TimePT / prob_pt 
direct_elas_pt_cost = \
  Derive(prob_pt,'MarginalCostPT') * MarginalCostPT / prob_pt 
direct_elas_car_time = \
  Derive(prob_car,'TimeCar') * TimeCar / prob_car 
direct_elas_car_cost = \
  Derive(prob_car,'CostCarCHF') * CostCarCHF / prob_car 
direct_elas_sm_dist = \
  Derive(prob_sm,'distance_km') * distance_km / prob_sm

simulate = {'weight': normalizedWeight,
            'Prob. car': prob_car,
            'Prob. public transportation': prob_pt,
            'Prob. slow modes':prob_sm,
            'direct_elas_pt_time':direct_elas_pt_time,
            'direct_elas_pt_cost':direct_elas_pt_cost,
            'direct_elas_car_time':direct_elas_car_time,
            'direct_elas_car_cost':direct_elas_car_cost,
            'direct_elas_sm_dist':direct_elas_sm_dist}

biogeme  = bio.BIOGEME(database,simulate)
biogeme.modelName = "03nestedElasticties"

# Retrieve the values of the parameters
# First, extract the names of parameters needed for the simulation
betas = biogeme.freeBetaNames

# Read the estimation results from the file
results = res.bioResults(pickleFile='01nestedEstimation.pickle')

# Extract the values that are necessary
betaValues = results.getBetaValues(betas)

# simulatedValues is a Panda dataframe with the same number of rows as
# the database, and as many columns as formulas to simulate.
simulatedValues = biogeme.simulate(betaValues)

# We calculate the elasticities
simulatedValues['Weighted prob. car'] = \
  simulatedValues['weight'] * simulatedValues['Prob. car']
simulatedValues['Weighted prob. PT'] = \
  simulatedValues['weight'] * simulatedValues['Prob. public transportation']
simulatedValues['Weighted prob. SM'] = \
  simulatedValues['weight'] * simulatedValues['Prob. slow modes']

denominator_car = simulatedValues['Weighted prob. car'].sum()
denominator_pt = simulatedValues['Weighted prob. PT'].sum()
denominator_sm = simulatedValues['Weighted prob. SM'].sum()

direct_elas_term_car_time = (simulatedValues['Weighted prob. car']
  * simulatedValues['direct_elas_car_time'] / denominator_car).sum()
print(f"Aggregate direct elasticity of car wrt time: {direct_elas_term_car_time:.3g}")

direct_elas_term_car_cost = (simulatedValues['Weighted prob. car']
  * simulatedValues['direct_elas_car_cost'] / denominator_car).sum()
print(f"Aggregate direct elasticity of car wrt cost: {direct_elas_term_car_cost:.3g}")

direct_elas_term_pt_time = (simulatedValues['Weighted prob. PT']
  * simulatedValues['direct_elas_pt_time'] / denominator_pt).sum()
print(f"Aggregate direct elasticity of PT wrt time: {direct_elas_term_pt_time:.3g}")

direct_elas_term_pt_cost = (simulatedValues['Weighted prob. PT']
  * simulatedValues['direct_elas_pt_cost'] / denominator_pt).sum()
print(f"Aggregate direct elasticity of PT wrt cost: {direct_elas_term_pt_cost:.3g}")

direct_elas_term_sm_dist = (simulatedValues['Weighted prob. SM']
  * simulatedValues['direct_elas_sm_dist'] / denominator_sm).sum()
print(f"Aggregate direct elasticity of SM wrt distance: {direct_elas_term_sm_dist:.3g}")







