def predict_NL_2(betas, biogeme_file, data):
for mode in modes_list:
# availability
data[mode+'_avail'] = 1
database = db.Database("NL_SGP", data)
# The choice model is a nested logit
prob_Walk = biomodels.nested(biogeme_file['V'], biogeme_file['av'], biogeme_file['nests'], key_choice_index['Walk'])
prob_PT = biomodels.nested(biogeme_file['V'], biogeme_file['av'], biogeme_file['nests'], key_choice_index['PT'])
prob_RH = biomodels.nested(biogeme_file['V'], biogeme_file['av'], biogeme_file['nests'], key_choice_index['RH'])
prob_AV = biomodels.nested(biogeme_file['V'], biogeme_file['av'], biogeme_file['nests'], key_choice_index['AV'])
prob_Drive = biomodels.nested(biogeme_file['V'], biogeme_file['av'], biogeme_file['nests'], key_choice_index['Drive'])
simulate = {'prob_Walk': prob_Walk,
'prob_PT': prob_PT,
'prob_RH': prob_RH,
'prob_AV':prob_AV,
'prob_Drive':prob_Drive}
biogeme = bio.BIOGEME(database, simulate)
# Extract the values that are necessary
betaValues = 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)
prob_list = list(simulatedValues.columns)
data_test = data
for key in prob_list:
data_test[key] = 0
data_test.loc[:,prob_list] = simulatedValues.loc[:, prob_list]
data_test['max_prob'] = data_test[prob_list].max(axis=1)
data_test['CHOOSE'] = 0
for mode in key_choice_index:
col_nameprob = 'prob_' + mode
data_test.loc[data_test[col_nameprob]==data_test['max_prob'],'CHOOSE'] = key_choice_index[mode]
acc = len(data_test.loc[data_test['CHOOSE']==data_test['choice']])/len(data_test)
return acc, data_test
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}
# 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, None, nests, 0)
prob_car = models.nested(V, None, nests, 1)
prob_sm = models.nested(V, None, nests, 2)
# We investigate a scenario where the distance increases by one kilometer.
delta_dist = 1.0
distance_km_scaled_after = (distance_km + delta_dist) / 5
# Utility of the slow mode whem the distance increases by 1 kilometer.
V_SM_after = ASC_SM + \
BETA_DIST_MALE * distance_km_scaled_after * male + \
BETA_DIST_FEMALE * distance_km_scaled_after * female + \
BETA_DIST_UNREPORTED * distance_km_scaled_after * unreportedGender
# Associate utility functions with the numbering of alternatives
V_after = {0: V_PT, 1: V_CAR, 2: V_SM_after}
def scenario(scale):
"""Simulate a scenarios modifying the price of public transportation
:param scale: price multiplier.
:type scale: float
:return: simulated revenues
:rtype: float
"""
# This is the only variable that depends on scale
MarginalCostScenario = MarginalCostPT * scale
MarginalCostPT_scaled = MarginalCostScenario / 10
# The rest of the model is the same for all scenarios
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)
# 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
V = {0: V_PT, 1: V_CAR, 2: V_SM}
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
prob_pt = models.nested(V, None, nests, 0)
simulate = {
'weight': normalizedWeight,
'Revenue public transportation': prob_pt * MarginalCostScenario
}
biogeme = bio.BIOGEME(database, simulate)
biogeme.modelName = '02nestedPlot'
# Read the estimation results from the file
try:
results = res.bioResults(pickleFile='01nestedEstimation.pickle')
except FileNotFoundError:
sys.exit(
'Run first the script 01nestedEstimation.py in order to generate '
'the file 01nestedEstimation.pickle.')
# Simulation
simulatedValues = biogeme.simulate(results.getBetaValues())
# We calculate the sum for all individuals of the generated revenues.
revenues_pt = (simulatedValues['Revenue public transportation'] *
simulatedValues['weight']).sum()
return revenues_pt