def estimate_wrap(V, av, CHOICE, database, panelEst, ModNameSeqence, Draws):
'''
#
#
#
'''
if panelEst:
#database.panel('ID')
# Conditional to B_TIME_RND, the likelihood of one observation is
# given by the logit model (called the kernel)
obsprob = exp(models.loglogit(V, av, CHOICE))
# Conditional to B_TIME_RND, the likelihood of all observations for
# one individual (the trajectory) is the product of the likelihood of
# each observation.
condprobIndiv = PanelLikelihoodTrajectory(obsprob)
if Draws > 0:
condprobIndiv = MonteCarlo(condprobIndiv)
logprob = log(condprobIndiv)
else:
prob = exp(models.loglogit(V, av, CHOICE))
if Draws > 0:
prob = MonteCarlo(prob)
logprob = log(prob)
if Draws > 0:
biogeme = bio.BIOGEME(database, logprob, numberOfDraws=Draws)
else:
biogeme = bio.BIOGEME(database, logprob)
biogeme.modelName = ModNameSeqence
return biogeme.estimate()
def test_expr4(self):
omega = ex.RandomVariable('omega')
a = 0
b = 1
x = a + (b - a) / (1 + ex.exp(-omega))
dx = (b - a) * ex.exp(-omega) * (1 + ex.exp(-omega))**(-2)
integrand = x * x
expr4 = ex.Integrate(integrand * dx / (b - a), 'omega')
res = expr4.getValue_c(self.myData)
for v in res:
self.assertAlmostEqual(v, 1.0 / 3.0, 2)
def test_expr5(self):
expr1 = 2 * self.beta1 - ex.exp(-self.beta2) / (
self.beta3 * (self.beta2 >= self.beta1))
expr2 = 2 * self.beta1 * self.Variable1 - ex.exp(-self.beta2 * self.Variable2) / \
(self.beta3 * (self.beta2 >= self.beta1))
expr5 = ex.Elem({1: expr1, 2: expr2}, self.Person) / 10
res = expr5.getValue_c(self.myData)
self.assertListEqual(res, [
0.19548882389211292, 0.19548882389211292, 0.19548882389211292, 8.0,
10.0
])
def test_expr12(self):
expr1 = 2 * self.beta1 - ex.exp(-self.beta2) / (
self.beta3 * (self.beta2 >= self.beta1))
expr2 = 2 * self.beta1 * self.Variable1 - ex.exp(-self.beta2 * self.Variable2) / \
(self.beta3 * (self.beta2 >= self.beta1))
expr5 = ex.Elem({1: expr1, 2: expr2}, self.Person) / 10
expr10 = ex.bioNormalCdf(self.Variable1 / 10 - 1)
expr12 = ex.bioMax(expr5, expr10)
res = expr12.getValue_c(self.myData)
for i, j in zip(
res, [0.5, 0.8413447460685283, 0.9772498680518218, 8.0, 10.0]):
self.assertAlmostEqual(i, j, 5)
def test_expr11(self):
expr1 = 2 * self.beta1 - ex.exp(-self.beta2) / (
self.beta3 * (self.beta2 >= self.beta1))
expr2 = 2 * self.beta1 * self.Variable1 - ex.exp(-self.beta2 * self.Variable2) / \
(self.beta3 * (self.beta2 >= self.beta1))
expr5 = ex.Elem({1: expr1, 2: expr2}, self.Person) / 10
expr10 = ex.bioNormalCdf(self.Variable1 / 10 - 1)
expr11 = ex.bioMin(expr5, expr10)
res = expr11.getValue_c(self.myData)
for i, j in zip(res, [
0.19548882389211292, 0.19548882389211292, 0.19548882389211292,
0.99865010196837, 0.9999683287581669
]):
self.assertAlmostEqual(i, j, 5)
def lognormalpdf(x, mu=0.0, s=1.0):
"""
Log normal pdf
Probability density function of a log normal distribution
.. math:: f(x;\\mu, \\sigma) =
\\frac{1}{x\\sigma \\sqrt{2\\pi}}
\\exp{-\\frac{(\\ln x-\\mu)^2}{2\\sigma^2}}
:param x: location parameter :math:`\\mu` of the lognormal distribution. Default: 0.
:type x: float or biogeme.expression
:param s: scale parameter :math:`\\sigma` of the lognormal distribution. Default: 1.
:type s: float or biogeme.expression
:note: It is assumed that :math:`\\sigma > 0`, but it is not verified by the code.
:return: value of the lognormal pdf.
:rtype: float or biogeme.expression
"""
d = -(log(x) - mu) * (log(x) - mu)
n = 2.0 * s * s
a = d / n
num = exp(a)
den = x * s * 2.506628275
p = (x > 0) * num / den
return p
def test_expr1(self):
expr1 = 2 * self.beta1 - ex.exp(-self.beta2) / (
self.beta3 * (self.beta2 >= self.beta1))
self.assertAlmostEqual(expr1.getValue(), 1.954888238921129, 5)
res = expr1.getValue_c(self.myData)
for v in res:
self.assertAlmostEqual(v, 1.954888238921129, 5)
def test_setOfBetas(self):
expr1 = 2 * self.beta1 - ex.exp(-self.beta2) / (
self.beta3 * (self.beta2 >= self.beta1))
s = expr1.setOfBetas()
self.assertSetEqual(s, {'beta1', 'beta2'})
s = expr1.setOfBetas(free=False, fixed=True)
self.assertSetEqual(s, {'beta3'})
s = expr1.setOfBetas(free=True, fixed=True)
self.assertSetEqual(s, {'beta1', 'beta2', 'beta3'})
def test_expr6(self):
expr1 = 2 * self.beta1 - ex.exp(-self.beta2) / (
self.beta3 * (self.beta2 >= self.beta1))
expr2 = 2 * self.beta1 * self.Variable1 - ex.exp(-self.beta2 * self.Variable2) / \
(self.beta3 * (self.beta2 >= self.beta1))
omega = ex.RandomVariable('omega')
a = 0
b = 1
x = a + (b - a) / (1 + ex.exp(-omega))
dx = (b - a) * ex.exp(-omega) * (1 + ex.exp(-omega))**(-2)
integrand = x * x
expr4 = ex.Integrate(integrand * dx / (b - a), 'omega')
expr6 = ex.bioMultSum([expr1, expr2, expr4])
res = expr6.getValue_c(self.myData)
self.assertListEqual(res, [
22.28822055098741, 42.28822055098741, 62.28822055098741,
82.2882205509874, 102.2882205509874
])
def test_dictOfBetas(self):
expr2 = 2 * self.beta1 * self.Variable1 - ex.exp(-self.beta2 * self.Variable2) / \
(self.beta3 * (self.beta2 >= self.beta1))
b = expr2.dictOfBetas(free=True, fixed=True)
# Note that the following checks only the labels. Its probably
# good enough for our purpose.
self.assertDictEqual(b, {
'beta1': 0,
'beta2': self.beta2,
'beta3': self.beta3
})
def setUp(self):
np.random.seed(90267)
rnd.seed(90267)
Variable1 = Variable('Variable1')
Variable2 = Variable('Variable2')
beta1 = Beta('beta1', -1.0, -3, 3, 0)
beta2 = Beta('beta2', 2.0, -3, 10, 0)
likelihood = -beta1**2 * Variable1 - exp(
beta2 * beta1) * Variable2 - beta2**4
simul = beta1 / Variable1 + beta2 / Variable2
dictOfExpressions = {
'loglike': likelihood,
'beta1': beta1,
'simul': simul
}
self.myBiogeme = bio.BIOGEME(myData1, dictOfExpressions)
self.myBiogeme.modelName = 'simpleExample'
def logisticcdf(x, mu=0.0, s=1.0):
"""
Logistic CDF
Cumulative distribution function of a logistic distribution
.. math:: f(x;\\mu, \\sigma) = \\frac{1}{1+\\exp\\left(-\\frac{x-\\mu}{\\sigma} \\right)}
:param x: location parameter :math:`\\mu` of the logistic distribution. Default: 0.
:type x: float or biogeme.expression
:param x: scale parameter :math:`\\sigma` of the logistic distribution. Default: 1.
:type x: float or biogeme.expression
:note: It is assumed that :math:`\\sigma > 0`, but it is not verified by the code.
:return: value of the logistic CDF.
:rtype: float or biogeme.expression
"""
result = 1.0 / (1.0 + exp(-(x - mu) / s))
return result
def likelihoodregression(meas, model, sigma):
""" Computes likelihood function of a regression model.
:param meas: An expression providing the value :math:`y` of the measure
for the current observation.
:type meas: biogeme.expressions.Expression
:param model: An expression providing the output :math:`m` of the model
for the current observation.
:type model: biogeme.expressions.Expression
:param sigma: An expression (typically, a parameter) providing the
standard error :math:`\\sigma` of the error term.
:type sigma: biogeme.expressions.Expression
:return: The likelihood of the regression, assuming a normal distribution,
that is
.. math:: \\frac{1}{\\sigma} \\phi\\left( \\frac{y-m}{\\sigma} \\right)
Where :math:`\\phi(\\cdot)` is the pdf of the normal distribution.
:rtype: biogeme.expressions.Expression
"""
return exp(loglikelihoodregression(meas, model, sigma))
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_COST = Beta('B_COST', 0, None, None, 0)
# Define a random parameter, normally distributed, designed to be used
# for Monte-Carlo simulation
B_TIME = Beta('B_TIME', 0, None, None, 0)
# It is advised not to use 0 as starting value for the following parameter.
B_TIME_S = Beta('B_TIME_S', 1, None, None, 0)
# Define a random parameter, log normally distributed, designed to be used
# for Monte-Carlo simulation.
omega = RandomVariable('omega')
B_TIME_RND = -exp(B_TIME + B_TIME_S * omega)
density = dist.normalpdf(omega)
# 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)
TimePT_scaled = DefineVariable('TimePT_scaled', TimePT / 200, database)
TimeCar_scaled = DefineVariable('TimeCar_scaled', TimeCar / 200, database)
MarginalCostPT_scaled = DefineVariable('MarginalCostPT_scaled',
MarginalCostPT / 10, database)
CostCarCHF_scaled = DefineVariable('CostCarCHF_scaled', CostCarCHF / 10,
database)
distance_km_scaled = DefineVariable('distance_km_scaled', distance_km / 5,
database)
PurpHWH = DefineVariable('PurpHWH', TripPurpose == 1, database)
PurpOther = DefineVariable('PurpOther', TripPurpose != 1, database)
### Definition of utility functions:
BETA_TIME_PT = BETA_TIME_PT_REF * \
exp(BETA_TIME_PT_CL * CARLOVERS)
V0 = ASC_PT + \
BETA_TIME_PT * TimePT_scaled + \
BETA_WAITING_TIME * WaitingTimePT + \
BETA_COST_HWH * MarginalCostPT_scaled * PurpHWH +\
BETA_COST_OTHER * MarginalCostPT_scaled * PurpOther
BETA_TIME_CAR = BETA_TIME_CAR_REF * \
exp(BETA_TIME_CAR_CL * CARLOVERS)
V1 = ASC_CAR + \
BETA_TIME_CAR * TimeCar_scaled + \
BETA_COST_HWH * CostCarCHF_scaled * PurpHWH + \
BETA_COST_OTHER * CostCarCHF_scaled * PurpOther
pandas['FakeColumn'] = [1.0]
database = db.Database('fakeDatabase', pandas)
def halton13(sampleSize, numberOfDraws):
"""
The user can define new draws. For example, Halton draws
with base 13, skipping the first 10 draws.
"""
return draws.getHaltonDraws(sampleSize, numberOfDraws, base=13, skip=10)
mydraws = {'HALTON13': (halton13, 'Halton draws, base 13, skipping 10')}
database.setRandomNumberGenerators(mydraws)
integrand = exp(bioDraws('U', 'UNIFORM'))
simulatedI = MonteCarlo(integrand)
integrand_halton = exp(bioDraws('U_halton', 'UNIFORM_HALTON2'))
simulatedI_halton = MonteCarlo(integrand_halton)
integrand_halton13 = exp(bioDraws('U_halton13', 'HALTON13'))
simulatedI_halton13 = MonteCarlo(integrand_halton13)
integrand_mlhs = exp(bioDraws('U_mlhs', 'UNIFORM_MLHS'))
simulatedI_mlhs = MonteCarlo(integrand_mlhs)
trueI = exp(1.0) - 1.0
R = 20000
#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)
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_TIME_S = Beta('B_TIME_S', 0, None, None, 0)
B_COST = Beta('B_COST', 0, None, None, 0)
# Define a random parameter, log normally distributed, designed to be used
# for Monte-Carlo simulation
B_TIME_RND = -exp(B_TIME + B_TIME_S * bioDraws('B_TIME_RND', 'NORMAL'))
# Utility functions
#If the person has a GA (season ticket) her incremental cost is actually 0
#rather than the cost value gathered from the
# network data.
SM_COST = SM_CO * (GA == 0)
TRAIN_COST = TRAIN_CO * (GA == 0)
# For numerical reasons, it is good practice to scale the data to
# that the values of the parameters are around 1.0.
# A previous estimation with the unscaled data has generated
# parameters around -0.01 for both cost and time. Therefore, time and
# cost are multipled my 0.01.
def test_signature(self):
expr2 = 2 * self.beta1 * self.Variable1 - ex.exp(-self.beta2 * self.Variable2) / \
(self.beta3 * (self.beta2 >= self.beta1))
expr2._prepareFormulaForEvaluation(self.myData)
s = expr2.getSignature()
self.assertEqual(len(s), 17)
"""
# pylint: disable=invalid-name, undefined-variable
import pandas as pd
import biogeme.database as db
import biogeme.biogeme as bio
from biogeme.expressions import exp, bioDraws, MonteCarlo
# We create a fake database with one entry, as it is required
# to store the draws
pandas = pd.DataFrame()
pandas['FakeColumn'] = [1.0]
database = db.Database('fakeDatabase', pandas)
integrand = exp(bioDraws('U', 'UNIFORM'))
simulatedI = MonteCarlo(integrand)
trueI = exp(1.0) - 1.0
R = 2000
sampleVariance = MonteCarlo(integrand * integrand) - simulatedI * simulatedI
stderr = (sampleVariance / R)**0.5
error = simulatedI - trueI
simulate = {
'Analytical Integral': trueI,
'Simulated Integral': simulatedI,
'Sample variance ': sampleVariance,
'Std Error ': stderr,
def cv_test_model(V,
R,
Draws,
ModName,
test,
myRandomNumberGenerators,
COST_SCALE_CAR=100,
COST_SCALE_PUB=100):
db_test = db.Database("swissmetro_test", test)
db_test.setRandomNumberGenerators(myRandomNumberGenerators)
globals().update(db_test.variables)
#locals().update(db_test.variables)
#define variables
#variables
CAR_AV_SP = DefineVariable('CAR_AV_SP', CAR_AV * (SP != 0), db_test)
TRAIN_AV_SP = DefineVariable('TRAIN_AV_SP', TRAIN_AV * (SP != 0), db_test)
SM_COST = SM_CO * (GA == 0)
TRAIN_COST = TRAIN_CO * (GA == 0)
###Parameters to be estimated (Note not all parameters are used in all models!)
##Attributes
#Alternative specific constants
ASC_CAR = Beta('ASC_CAR', 0, None, None, 1)
ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
ASC_SM = Beta('ASC_SM', 0, None, None, 0)
#Cost (Note: Assumed generic)
B_COST = Beta('B_COST', 0, None, None, 0)
B_COST_BUSINESS = Beta('B_COST_BUSINESS', 0, None, None, 0)
B_COST_PRIVATE = Beta('B_COST_PRIVATE', 0, None, None, 0)
#Time
B_TIME = Beta('B_TIME', 0, None, None, 0)
B_TIME_CAR = Beta('B_TIME_CAR', 0, None, None, 0)
B_TIME_TRAIN = Beta('B_TIME_TRAIN', 0, None, None, 0)
B_TIME_SM = Beta('B_TIME_SM', 0, None, None, 0)
B_TIME_PUB = Beta('B_TIME_PUB', 0, None, None, 0)
B_TIME_CAR_BUSINESS = Beta('B_TIME_CAR_BUSINESS', 0, None, None, 0)
B_TIME_TRAIN_BUSINESS = Beta('B_TIME_TRAIN_BUSINESS', 0, None, None, 0)
B_TIME_SM_BUSINESS = Beta('B_TIME_SM_BUSINESS', 0, None, None, 0)
B_TIME_PUB_BUSINESS = Beta('B_TIME_PUB_BUSINESS', 0, None, None, 0)
B_TIME_CAR_PRIVATE = Beta('B_TIME_CAR_PRIVATE', 0, None, None, 0)
B_TIME_TRAIN_PRIVATE = Beta('B_TIME_TRAIN_PRIVATE', 0, None, None, 0)
B_TIME_SM_PRIVATE = Beta('B_TIME_SM_PRIVATE', 0, None, None, 0)
B_TIME_PUB_PRIVATE = Beta('B_TIME_PUB_PRIVATE', 0, None, None, 0)
#HE (Note: Not available for car)
B_HE = Beta('B_HE', 0, None, None, 0)
B_HE_TRAIN = Beta('B_HE_TRAIN', 0, None, None, 0)
B_HE_SM = Beta('B_HE_SM', 0, None, None, 0)
B_HE_BUSINESS = Beta('B_HE_BUSINESS', 0, None, None, 0)
B_HE_TRAIN_BUSINESS = Beta('B_HE_TRAIN_BUSINESS', 0, None, None, 0)
B_HE_SM_BUSINESS = Beta('B_HE_SM_BUSINESS', 0, None, None, 0)
B_HE_PRIVATE = Beta('B_HE_PRIVATE', 0, None, None, 0)
B_HE_TRAIN_PRIVATE = Beta('B_HE_TRAIN_PRIVATE', 0, None, None, 0)
B_HE_SM_PRIVATE = Beta('B_HE_SM_PRIVATE', 0, None, None, 0)
#Seats (Note: Only avaliable for SM)
B_SEATS = Beta('B_SEATS', 0, None, None, 0)
##Characteristics
#Age
B_AGE_1_TRAIN = Beta('B_AGE_1_TRAIN', 0, None, None, 1) #Note: Reference
B_AGE_2_TRAIN = Beta('B_AGE_2_TRAIN', 0, None, None, 0)
B_AGE_3_TRAIN = Beta('B_AGE_3_TRAIN', 0, None, None, 0)
B_AGE_4_TRAIN = Beta('B_AGE_4_TRAIN', 0, None, None, 0)
B_AGE_5_TRAIN = Beta('B_AGE_5_TRAIN', 0, None, None, 0)
B_AGE_6_TRAIN = Beta('B_AGE_6_TRAIN', 0, None, None, 0)
B_AGE_1_SM = Beta('B_AGE_1_SM', 0, None, None, 1) #Note: Reference
B_AGE_2_SM = Beta('B_AGE_2_SM', 0, None, None, 0)
B_AGE_3_SM = Beta('B_AGE_3_SM', 0, None, None, 0)
B_AGE_4_SM = Beta('B_AGE_4_SM', 0, None, None, 0)
B_AGE_5_SM = Beta('B_AGE_5_SM', 0, None, None, 0)
B_AGE_6_SM = Beta('B_AGE_6_SM', 0, None, None, 0)
B_AGE_1_PUB = Beta('B_AGE_1_PUB', 0, None, None, 1) #Note: Reference
B_AGE_2_PUB = Beta('B_AGE_2_PUB', 0, None, None, 0)
B_AGE_3_PUB = Beta('B_AGE_3_PUB', 0, None, None, 0)
B_AGE_4_PUB = Beta('B_AGE_4_PUB', 0, None, None, 0)
B_AGE_5_PUB = Beta('B_AGE_5_PUB', 0, None, None, 0)
B_AGE_6_PUB = Beta('B_AGE_6_PUB', 0, None, None, 0)
B_AGE_ADULTS_TRAIN = Beta('B_AGE_TRAIN_ADULTS', 0, None, None, 0)
B_AGE_ADULTS_SM = Beta('B_AGE_ADULTS_SM', 0, None, None, 0)
B_AGE_ADULTS_PUB = Beta('B_AGE_ADULTS_PUB', 0, None, None, 0)
#Luggage
B_LUGGAGE_TRAIN = Beta('B_LUGGAGE_TRAIN', 0, None, None, 0)
B_LUGGAGE_SM = Beta('B_LUGGAGE_SM', 0, None, None, 0)
B_LUGGAGE_PUB = Beta('B_LUGGAGE_PUB', 0, None, None, 0)
#Gender
B_MALE_TRAIN = Beta('B_MALE_TRAIN', 0, None, None, 0)
B_MALE_SM = Beta('B_MALE_SM', 0, None, None, 0)
B_MALE_PUB = Beta('B_MALE_PUB', 0, None, None, 0)
#Purpose
B_BUSINESS = Beta('B_BUSINESS', 0, None, None, 0)
B_BUSINESS_TRAIN = Beta('B_BUSINESS_TRAIN', 0, None, None, 0)
B_BUSINESS_SM = Beta('B_BUSINESS_SM', 0, None, None, 0)
B_PRIVATE = Beta('B_PRIVATE', 0, None, None, 0)
B_PRIVATE_TRAIN = Beta('B_PRIVATE_TRAIN', 0, None, None, 0)
B_PRIVATE_SM = Beta('B_PRIVATE_SM', 0, None, None, 0)
B_COMMUTER = Beta('B_COMMUTER', 0, None, None, 0)
B_COMMUTER_TRAIN = Beta('B_COMMUTER_TRAIN', 0, None, None, 0)
B_COMMUTER_SM = Beta('B_COMMUTER_SM', 0, None, None, 0)
#GA
B_GA = Beta('B_GA', 0, None, None, 0)
B_GA_TRAIN = Beta('B_GA_TRAIN', 0, None, None, 0)
B_GA_SM = Beta('B_GA_SM', 0, None, None, 0)
#First
B_FIRST_TRAIN = Beta('B_FIRST_TRAIN', 0, None, None, 0)
B_FIRST_SM = Beta('B_FIRST_SM', 0, None, None, 0)
B_FIRST = Beta('B_FIRST', 0, None, None, 0)
##Non linearization
#Cost
q_COST = Beta('q_COST', 1, None, None, 0)
#Time
q_TIME = Beta('q_TIME', 1, None, None, 0)
q_TIME_TRAIN = Beta('q_TIME_TRAIN', 1, None, None, 0)
q_TIME_SM = Beta('q_TIME_SM', 1, None, None, 0)
q_TIME_CAR = Beta('q_TIME_CAR', 1, None, None, 0)
q_TIME_PUB = Beta('q_TIME_PUB', 1, None, None, 0)
#HE
q_HE = Beta('q_HE', 1, None, None, 0)
##Nesting parameter
MU = Beta('MU', 1, 0, 1, 0)
##ML RANDOM GENERIC TIME LOGNORMAL
BETA_TIME_mean = Beta('BETA_TIME_mean', 0, None, None, 0)
BETA_TIME_std = Beta('BETA_TIME_std', 1, None, None, 0)
BETA_TIME_random = -exp(BETA_TIME_mean + BETA_TIME_std *
bioDraws('BETA_TIME_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME TRAIN LOGNORMAL
BETA_TIME_TRAIN_mean = Beta('BETA_TIME_TRAIN_mean', 0, None, None, 0)
BETA_TIME_TRAIN_std = Beta('BETA_TIME_TRAIN_std', 1, None, None, 0)
BETA_TIME_TRAIN_random = -exp(BETA_TIME_TRAIN_mean + BETA_TIME_TRAIN_std *
bioDraws('BETA_TIME_TRAIN_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME SM LOGNORMAL
BETA_TIME_SM_mean = Beta('BETA_TIME_SM_mean', 0, None, None, 0)
BETA_TIME_SM_std = Beta('BETA_TIME_SM_std', 1, None, None, 0)
BETA_TIME_SM_random = -exp(BETA_TIME_SM_mean + BETA_TIME_SM_std *
bioDraws('BETA_TIME_SM_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME CAR LOGNORMAL
BETA_TIME_CAR_mean = Beta('BETA_TIME_CAR_mean', 0, None, None, 0)
BETA_TIME_CAR_std = Beta('BETA_TIME_CAR_std', 1, None, None, 0)
BETA_TIME_CAR_random = -exp(BETA_TIME_CAR_mean + BETA_TIME_CAR_std *
bioDraws('BETA_TIME_CAR_random', 'NORMAL'))
##ML RANDOM GENERIC COST LOGNORMAL
BETA_COST_mean = Beta('BETA_COST_mean', 0, None, None, 0)
BETA_COST_std = Beta('BETA_COST_std', 1, None, None, 0)
BETA_COST_random = -exp(BETA_COST_mean + BETA_COST_std *
bioDraws('BETA_COST_random', 'NORMAL'))
##ML RANDOM GENERIC HE LOGNORMAL
BETA_HE_mean = Beta('BETA_HE_mean', 0, None, None, 0)
BETA_HE_std = Beta('BETA_HE_std', 1, None, None, 0)
BETA_HE_random = -exp(BETA_HE_mean +
BETA_HE_std * bioDraws('BETA_HE_random', 'NORMAL'))
##ML RANDOM GENERIC TIME NORMAL
BETA_TIME_mean_Norm = Beta('BETA_TIME_mean_Norm', 0, None, None, 0)
BETA_TIME_std_Norm = Beta('BETA_TIME_std_Norm', 1, None, None, 0)
BETA_TIME_random_Norm = BETA_TIME_mean_Norm + BETA_TIME_std_Norm * bioDraws(
'BETA_TIME_random_Norm', 'NORMAL')
##ML RANDOM SPECIFIC TIME TRAIN LOGNORMAL
BETA_TIME_TRAIN_mean_Norm = Beta('BETA_TIME_TRAIN_mean_Norm', 0, None,
None, 0)
BETA_TIME_TRAIN_std_Norm = Beta('BETA_TIME_TRAIN_std_Norm', 1, None, None,
0)
BETA_TIME_TRAIN_random_Norm = BETA_TIME_TRAIN_mean_Norm + BETA_TIME_TRAIN_std_Norm * bioDraws(
'BETA_TIME_TRAIN_random_Norm', 'NORMAL')
##ML RANDOM GENERIC COST LOGNORMAL
BETA_COST_PUB_mean = Beta('BBETA_COST_PUB_mean', 0, None, None, 0)
BETA_COST_PUB_std = Beta('BBETA_COST_PUB_std', 1, None, None, 0)
BETA_COST_PUB_random = -exp(BETA_COST_PUB_mean + BETA_COST_PUB_std *
bioDraws('BBETA_COST_PUB_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME SM NORMAL
BETA_TIME_SM_mean_Norm = Beta('BETA_TIME_SM_mean_Norm', 0, None, None, 0)
BETA_TIME_SM_std_Norm = Beta('BETA_TIME_SM_std_Norm', 1, None, None, 0)
BETA_TIME_SM_random_Norm = BETA_TIME_SM_mean_Norm + BETA_TIME_SM_std_Norm * bioDraws(
'BETA_TIME_SM_random_Norm', 'NORMAL')
##ML RANDOM SPECIFIC TIME CAR NORMAL
BETA_TIME_CAR_mean_Norm = Beta('BETA_TIME_CAR_mean_Norm', 0, None, None, 0)
BETA_TIME_CAR_std_Norm = Beta('BETA_TIME_CAR_std_Norm', 1, None, None, 0)
BETA_TIME_CAR_random_Norm = BETA_TIME_CAR_mean_Norm + BETA_TIME_CAR_std_Norm * bioDraws(
'BETA_TIME_CAR_random_Norm', 'NORMAL')
##ML RANDOM GENERIC COST NORMAL
BETA_COST_mean_Norm = Beta('BETA_COST_mean_Norm', 0, None, None, 0)
BETA_COST_std_Norm = Beta('BETA_COST_std_Norm', 1, None, None, 0)
BETA_COST_random_Norm = BETA_COST_mean_Norm + BETA_COST_std_Norm * bioDraws(
'BETA_COST_random_Norm', 'NORMAL')
##ML RANDOM GENERIC HE NORMAL
BETA_HE_mean_Norm = Beta('BETA_HE_mean_Norm', 0, None, None, 0)
BETA_HE_std_Norm = Beta('BETA_HE_std_Norm', 1, None, None, 0)
BETA_HE_random_Norm = BETA_HE_mean_Norm + BETA_HE_std_Norm * bioDraws(
'BETA_HE_random_Norm', 'NORMAL')
##ML RANDOM GENERIC TIME NORMAL
BBETA_TIME_mean_Norm = Beta('BBETA_TIME_mean_Norm', 0, None, None, 0)
BBETA_TIME_std_Norm = Beta('BBETA_TIME_std_Norm', 1, None, None, 0)
BBETA_TIME_random_Norm = BBETA_TIME_mean_Norm + BBETA_TIME_std_Norm * bioDraws(
'BBETA_TIME_random_Norm', 'NORMAL')
##ML RANDOM SPECIFIC TIME TRAIN LOGNORMAL
BBETA_TIME_TRAIN_mean_Norm = Beta('BBETA_TIME_TRAIN_mean_Norm', 0, None,
None, 0)
BBETA_TIME_TRAIN_std_Norm = Beta('BBETA_TIME_TRAIN_std_Norm', 1, None,
None, 0)
BBETA_TIME_TRAIN_random_Norm = BBETA_TIME_TRAIN_mean_Norm + BBETA_TIME_TRAIN_std_Norm * bioDraws(
'BBETA_TIME_TRAIN_random_Norm', 'NORMAL')
##Scaling 'COST', 'TRAVEL-TIME' and 'HE' by a factor of 100 and adding the scaled variables to the database
'''
***********************************************************************************************
'''
#PUBLIC
TRAIN_COST_SCALED = DefineVariable('TRAIN_COST_SCALED',\
TRAIN_COST / COST_SCALE_PUB,db_test)
SM_COST_SCALED = DefineVariable('SM_COST_SCALED', SM_COST / COST_SCALE_PUB,
db_test)
#CAR
CAR_COST_SCALED = DefineVariable('CAR_COST_SCALED',
CAR_CO / COST_SCALE_CAR, db_test)
'''
***********************************************************************************************
'''
TRAIN_TT_SCALED = DefineVariable('TRAIN_TT_SCALED',\
TRAIN_TT / 100.0,db_test)
TRAIN_HE_SCALED = DefineVariable('TRAIN_HE_SCALED',\
TRAIN_HE / 100, db_test)
SM_TT_SCALED = DefineVariable('SM_TT_SCALED', SM_TT / 100.0, db_test)
SM_HE_SCALED = DefineVariable('SM_HE_SCALED',\
SM_HE / 100, db_test)
CAR_TT_SCALED = DefineVariable('CAR_TT_SCALED', CAR_TT / 100, db_test)
###Defining new variables and adding columns to the database
#Age
AGE_1 = DefineVariable('AGE_1', (AGE == 1),
db_test) #don't scale because is cathegorical
AGE_2 = DefineVariable('AGE_2', (AGE == 2), db_test)
AGE_3 = DefineVariable('AGE_3', (AGE == 3), db_test)
AGE_4 = DefineVariable('AGE_4', (AGE == 4), db_test)
AGE_5 = DefineVariable('AGE_5', (AGE == 5), db_test)
AGE_6 = DefineVariable('AGE_6', (AGE == 6), db_test)
#Purpose
PRIVATE = DefineVariable("PRIVATE", (PURPOSE == 1), db_test)
COMMUTER = DefineVariable("COMMUTER", (PURPOSE == 2), db_test)
BUSINESS = DefineVariable("BUSINESS", (PURPOSE == 3), db_test)
av = {3: CAR_AV_SP, 1: TRAIN_AV_SP, 2: SM_AV}
prob1 = biogeme.expressions.MonteCarlo(exp(models.loglogit(V, av, 1)))
prob2 = biogeme.expressions.MonteCarlo(exp(models.loglogit(V, av, 2)))
prob3 = biogeme.expressions.MonteCarlo(exp(models.loglogit(V, av, 3)))
simulate = {'Prob. TRAIN': prob1, 'Prob. SM': prob2, 'Prob. CAR': prob3}
#print(simulate)
biosim = bio.BIOGEME(db_test, simulate, numberOfDraws=Draws)
biosim.modelName = ModName
SR = biosim.simulate(R.data.betaValues)
return SR
def test_expr2(self):
expr2 = 2 * self.beta1 * self.Variable1 - ex.exp(-self.beta2 * self.Variable2) / \
(self.beta3 * (self.beta2 >= self.beta1))
res = expr2.getValue_c(self.myData)
self.assertListEqual(res, [20.0, 40.0, 60.0, 80.0, 100.0])
def test_getElementaryExpression(self):
expr1 = 2 * self.beta1 - ex.exp(-self.beta2) / (
self.beta3 * (self.beta2 >= self.beta1))
ell = expr1.getElementaryExpression('beta2')
self.assertEqual(ell.name, 'beta2')
database.remove(exclude)
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_TIME_S = Beta('B_TIME_S', 1, None, None, 0)
B_COST = Beta('B_COST', 0, None, None, 0)
# Define a random parameter, normally distirbuted, designed to be used
# for Monte-Carlo simulation
omega = RandomVariable('omega')
a = -1
b = 1
x = a + (b - a) / (1 + exp(-omega))
dx = (b - a) * exp(-omega) * (1 + exp(-omega))**(-2)
B_TIME_RND = B_TIME + B_TIME_S * x
# Utility functions
#If the person has a GA (season ticket) her incremental cost is actually 0
#rather than the cost value gathered from the
# network data.
SM_COST = SM_CO * (GA == 0)
TRAIN_COST = TRAIN_CO * (GA == 0)
# For numerical reasons, it is good practice to scale the data to
# that the values of the parameters are around 1.0.
# A previous estimation with the unscaled data has generated
# parameters around -0.01 for both cost and time. Therefore, time and
q_TIME = Beta('q_TIME', 1, None, None, 0)
q_TIME_TRAIN = Beta('q_TIME_TRAIN', 1, None, None, 0)
q_TIME_SM = Beta('q_TIME_SM', 1, None, None, 0)
q_TIME_CAR = Beta('q_TIME_CAR', 1, None, None, 0)
q_TIME_PUB = Beta('q_TIME_PUB', 1, None, None, 0)
#HE
q_HE = Beta('q_HE', 1, None, None, 0)
##Nesting parameter
MU = Beta('MU', 1, 0, 1, 0)
##ML RANDOM GENERIC TIME LOGNORMAL
BETA_TIME_mean = Beta('BETA_TIME_mean', 0, None, None, 0)
BETA_TIME_std = Beta('BETA_TIME_std', 1, None, None, 0)
BETA_TIME_random = -exp(BETA_TIME_mean +
BETA_TIME_std * bioDraws('BETA_TIME_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME TRAIN LOGNORMAL
BETA_TIME_TRAIN_mean = Beta('BETA_TIME_TRAIN_mean', 0, None, None, 0)
BETA_TIME_TRAIN_std = Beta('BETA_TIME_TRAIN_std', 1, None, None, 0)
BETA_TIME_TRAIN_random = -exp(BETA_TIME_TRAIN_mean + BETA_TIME_TRAIN_std *
bioDraws('BETA_TIME_TRAIN_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME SM LOGNORMAL
BETA_TIME_SM_mean = Beta('BETA_TIME_SM_mean', 0, None, None, 0)
BETA_TIME_SM_std = Beta('BETA_TIME_SM_std', 1, None, None, 0)
BETA_TIME_SM_random = -exp(BETA_TIME_SM_mean + BETA_TIME_SM_std *
bioDraws('BETA_TIME_SM_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME CAR LOGNORMAL
BETA_TIME_CAR_mean = Beta('BETA_TIME_CAR_mean', 0, None, None, 0)
B_COST * TRAIN_COST_SCALED
V2 = ASC_SM + \
B_TIME_RND * SM_TT_SCALED + \
B_COST * SM_COST_SCALED
V3 = ASC_CAR + \
B_TIME_RND * 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}
# Conditional to B_TIME_RND, we have a logit model (called the kernel)
prob = exp(models.loglogit(V, av, CHOICE))
# We integrate over B_TIME_RND using Monte-Carlo
logprob = log(MonteCarlo(prob))
# Define level of verbosity
logger = msg.bioMessage()
#logger.setSilent()
#logger.setWarning()
logger.setGeneral()
#logger.setDetailed()
# Create the Biogeme object
biogeme = bio.BIOGEME(database, logprob, numberOfDraws=100000)
biogeme.modelName = '06unifMixtureMHLS'
# Estimate the parameters
def test_getClassName(self):
expr2 = 2 * self.beta1 * self.Variable1 - ex.exp(-self.beta2 * self.Variable2) / \
(self.beta3 * (self.beta2 >= self.beta1))
c = expr2.getClassName()
self.assertEqual(c, 'Minus')
the first 10 draws.
"""
d = draws.getHaltonDraws(sampleSize,
int(numberOfDraws / 2),
base=13,
skip=10)
return np.concatenate((d, 1 - d), axis=1)
mydraws = {
'HALTON13_ANTI':
(halton13_anti, 'Antithetic Halton draws, base 13, skipping 10')
}
database.setRandomNumberGenerators(mydraws)
integrand = exp(bioDraws('U', 'UNIFORM_ANTI'))
simulatedI = MonteCarlo(integrand)
integrand_halton13 = exp(bioDraws('U_halton13', 'HALTON13_ANTI'))
simulatedI_halton13 = MonteCarlo(integrand_halton13)
integrand_mlhs = exp(bioDraws('U_mlhs', 'UNIFORM_MLHS_ANTI'))
simulatedI_mlhs = MonteCarlo(integrand_mlhs)
trueI = exp(1.0) - 1.0
R = 20000
error = simulatedI - trueI
error_halton13 = simulatedI_halton13 - trueI
def cv_estimate_model(V,
Draws,
ModName,
train,
myRandomNumberGenerators,
COST_SCALE_CAR=100,
COST_SCALE_PUB=100):
db_train = db.Database("swissmetro_train", train)
db_train.setRandomNumberGenerators(myRandomNumberGenerators)
globals().update(db_train.variables)
#locals().update(db_train.variables)
db_train.panel('ID')
#variables
CAR_AV_SP = DefineVariable('CAR_AV_SP', CAR_AV * (SP != 0), db_train)
TRAIN_AV_SP = DefineVariable('TRAIN_AV_SP', TRAIN_AV * (SP != 0), db_train)
SM_COST = SM_CO * (GA == 0)
TRAIN_COST = TRAIN_CO * (GA == 0)
###Parameters to be estimated (Note not all parameters are used in all models!)
##Attributes
#Alternative specific constants
ASC_CAR = Beta('ASC_CAR', 0, None, None, 1)
ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
ASC_SM = Beta('ASC_SM', 0, None, None, 0)
#Cost (Note: Assumed generic)
B_COST = Beta('B_COST', 0, None, None, 0)
B_COST_BUSINESS = Beta('B_COST_BUSINESS', 0, None, None, 0)
B_COST_PRIVATE = Beta('B_COST_PRIVATE', 0, None, None, 0)
#Time
B_TIME = Beta('B_TIME', 0, None, None, 0)
B_TIME_CAR = Beta('B_TIME_CAR', 0, None, None, 0)
B_TIME_TRAIN = Beta('B_TIME_TRAIN', 0, None, None, 0)
B_TIME_SM = Beta('B_TIME_SM', 0, None, None, 0)
B_TIME_PUB = Beta('B_TIME_PUB', 0, None, None, 0)
B_TIME_CAR_BUSINESS = Beta('B_TIME_CAR_BUSINESS', 0, None, None, 0)
B_TIME_TRAIN_BUSINESS = Beta('B_TIME_TRAIN_BUSINESS', 0, None, None, 0)
B_TIME_SM_BUSINESS = Beta('B_TIME_SM_BUSINESS', 0, None, None, 0)
B_TIME_PUB_BUSINESS = Beta('B_TIME_PUB_BUSINESS', 0, None, None, 0)
B_TIME_CAR_PRIVATE = Beta('B_TIME_CAR_PRIVATE', 0, None, None, 0)
B_TIME_TRAIN_PRIVATE = Beta('B_TIME_TRAIN_PRIVATE', 0, None, None, 0)
B_TIME_SM_PRIVATE = Beta('B_TIME_SM_PRIVATE', 0, None, None, 0)
B_TIME_PUB_PRIVATE = Beta('B_TIME_PUB_PRIVATE', 0, None, None, 0)
#HE (Note: Not available for car)
B_HE = Beta('B_HE', 0, None, None, 0)
B_HE_TRAIN = Beta('B_HE_TRAIN', 0, None, None, 0)
B_HE_SM = Beta('B_HE_SM', 0, None, None, 0)
B_HE_BUSINESS = Beta('B_HE_BUSINESS', 0, None, None, 0)
B_HE_TRAIN_BUSINESS = Beta('B_HE_TRAIN_BUSINESS', 0, None, None, 0)
B_HE_SM_BUSINESS = Beta('B_HE_SM_BUSINESS', 0, None, None, 0)
B_HE_PRIVATE = Beta('B_HE_PRIVATE', 0, None, None, 0)
B_HE_TRAIN_PRIVATE = Beta('B_HE_TRAIN_PRIVATE', 0, None, None, 0)
B_HE_SM_PRIVATE = Beta('B_HE_SM_PRIVATE', 0, None, None, 0)
#Seats (Note: Only avaliable for SM)
B_SEATS = Beta('B_SEATS', 0, None, None, 0)
##Characteristics
#Age
B_AGE_1_TRAIN = Beta('B_AGE_1_TRAIN', 0, None, None, 1) #Note: Reference
B_AGE_2_TRAIN = Beta('B_AGE_2_TRAIN', 0, None, None, 0)
B_AGE_3_TRAIN = Beta('B_AGE_3_TRAIN', 0, None, None, 0)
B_AGE_4_TRAIN = Beta('B_AGE_4_TRAIN', 0, None, None, 0)
B_AGE_5_TRAIN = Beta('B_AGE_5_TRAIN', 0, None, None, 0)
B_AGE_6_TRAIN = Beta('B_AGE_6_TRAIN', 0, None, None, 0)
B_AGE_1_SM = Beta('B_AGE_1_SM', 0, None, None, 1) #Note: Reference
B_AGE_2_SM = Beta('B_AGE_2_SM', 0, None, None, 0)
B_AGE_3_SM = Beta('B_AGE_3_SM', 0, None, None, 0)
B_AGE_4_SM = Beta('B_AGE_4_SM', 0, None, None, 0)
B_AGE_5_SM = Beta('B_AGE_5_SM', 0, None, None, 0)
B_AGE_6_SM = Beta('B_AGE_6_SM', 0, None, None, 0)
B_AGE_1_PUB = Beta('B_AGE_1_PUB', 0, None, None, 1) #Note: Reference
B_AGE_2_PUB = Beta('B_AGE_2_PUB', 0, None, None, 0)
B_AGE_3_PUB = Beta('B_AGE_3_PUB', 0, None, None, 0)
B_AGE_4_PUB = Beta('B_AGE_4_PUB', 0, None, None, 0)
B_AGE_5_PUB = Beta('B_AGE_5_PUB', 0, None, None, 0)
B_AGE_6_PUB = Beta('B_AGE_6_PUB', 0, None, None, 0)
B_AGE_ADULTS_TRAIN = Beta('B_AGE_TRAIN_ADULTS', 0, None, None, 0)
B_AGE_ADULTS_SM = Beta('B_AGE_ADULTS_SM', 0, None, None, 0)
B_AGE_ADULTS_PUB = Beta('B_AGE_ADULTS_PUB', 0, None, None, 0)
#Luggage
B_LUGGAGE_TRAIN = Beta('B_LUGGAGE_TRAIN', 0, None, None, 0)
B_LUGGAGE_SM = Beta('B_LUGGAGE_SM', 0, None, None, 0)
B_LUGGAGE_PUB = Beta('B_LUGGAGE_PUB', 0, None, None, 0)
#Gender
B_MALE_TRAIN = Beta('B_MALE_TRAIN', 0, None, None, 0)
B_MALE_SM = Beta('B_MALE_SM', 0, None, None, 0)
B_MALE_PUB = Beta('B_MALE_PUB', 0, None, None, 0)
#Purpose
B_BUSINESS = Beta('B_BUSINESS', 0, None, None, 0)
B_BUSINESS_TRAIN = Beta('B_BUSINESS_TRAIN', 0, None, None, 0)
B_BUSINESS_SM = Beta('B_BUSINESS_SM', 0, None, None, 0)
B_PRIVATE = Beta('B_PRIVATE', 0, None, None, 0)
B_PRIVATE_TRAIN = Beta('B_PRIVATE_TRAIN', 0, None, None, 0)
B_PRIVATE_SM = Beta('B_PRIVATE_SM', 0, None, None, 0)
B_COMMUTER = Beta('B_COMMUTER', 0, None, None, 0)
B_COMMUTER_TRAIN = Beta('B_COMMUTER_TRAIN', 0, None, None, 0)
B_COMMUTER_SM = Beta('B_COMMUTER_SM', 0, None, None, 0)
#GA
B_GA = Beta('B_GA', 0, None, None, 0)
B_GA_TRAIN = Beta('B_GA_TRAIN', 0, None, None, 0)
B_GA_SM = Beta('B_GA_SM', 0, None, None, 0)
#First
B_FIRST_TRAIN = Beta('B_FIRST_TRAIN', 0, None, None, 0)
B_FIRST_SM = Beta('B_FIRST_SM', 0, None, None, 0)
B_FIRST = Beta('B_FIRST', 0, None, None, 0)
##Non linearization
#Cost
q_COST = Beta('q_COST', 1, None, None, 0)
#Time
q_TIME = Beta('q_TIME', 1, None, None, 0)
q_TIME_TRAIN = Beta('q_TIME_TRAIN', 1, None, None, 0)
q_TIME_SM = Beta('q_TIME_SM', 1, None, None, 0)
q_TIME_CAR = Beta('q_TIME_CAR', 1, None, None, 0)
q_TIME_PUB = Beta('q_TIME_PUB', 1, None, None, 0)
#HE
q_HE = Beta('q_HE', 1, None, None, 0)
##Nesting parameter
MU = Beta('MU', 1, 0, 1, 0)
##ML RANDOM GENERIC TIME LOGNORMAL
BETA_TIME_mean = Beta('BETA_TIME_mean', 0, None, None, 0)
BETA_TIME_std = Beta('BETA_TIME_std', 1, None, None, 0)
BETA_TIME_random = -exp(BETA_TIME_mean + BETA_TIME_std *
bioDraws('BETA_TIME_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME TRAIN LOGNORMAL
BETA_TIME_TRAIN_mean = Beta('BETA_TIME_TRAIN_mean', 0, None, None, 0)
BETA_TIME_TRAIN_std = Beta('BETA_TIME_TRAIN_std', 1, None, None, 0)
BETA_TIME_TRAIN_random = -exp(BETA_TIME_TRAIN_mean + BETA_TIME_TRAIN_std *
bioDraws('BETA_TIME_TRAIN_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME SM LOGNORMAL
BETA_TIME_SM_mean = Beta('BETA_TIME_SM_mean', 0, None, None, 0)
BETA_TIME_SM_std = Beta('BETA_TIME_SM_std', 1, None, None, 0)
BETA_TIME_SM_random = -exp(BETA_TIME_SM_mean + BETA_TIME_SM_std *
bioDraws('BETA_TIME_SM_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME CAR LOGNORMAL
BETA_TIME_CAR_mean = Beta('BETA_TIME_CAR_mean', 0, None, None, 0)
BETA_TIME_CAR_std = Beta('BETA_TIME_CAR_std', 1, None, None, 0)
BETA_TIME_CAR_random = -exp(BETA_TIME_CAR_mean + BETA_TIME_CAR_std *
bioDraws('BETA_TIME_CAR_random', 'NORMAL'))
##ML RANDOM GENERIC COST LOGNORMAL
BETA_COST_mean = Beta('BETA_COST_mean', 0, None, None, 0)
BETA_COST_std = Beta('BETA_COST_std', 1, None, None, 0)
BETA_COST_random = -exp(BETA_COST_mean + BETA_COST_std *
bioDraws('BETA_COST_random', 'NORMAL'))
##ML RANDOM GENERIC HE LOGNORMAL
BETA_HE_mean = Beta('BETA_HE_mean', 0, None, None, 0)
BETA_HE_std = Beta('BETA_HE_std', 1, None, None, 0)
BETA_HE_random = -exp(BETA_HE_mean +
BETA_HE_std * bioDraws('BETA_HE_random', 'NORMAL'))
##ML RANDOM GENERIC TIME NORMAL
BETA_TIME_mean_Norm = Beta('BETA_TIME_mean_Norm', 0, None, None, 0)
BETA_TIME_std_Norm = Beta('BETA_TIME_std_Norm', 1, None, None, 0)
BETA_TIME_random_Norm = BETA_TIME_mean_Norm + BETA_TIME_std_Norm * bioDraws(
'BETA_TIME_random_Norm', 'NORMAL')
##ML RANDOM SPECIFIC TIME TRAIN LOGNORMAL
BETA_TIME_TRAIN_mean_Norm = Beta('BETA_TIME_TRAIN_mean_Norm', 0, None,
None, 0)
BETA_TIME_TRAIN_std_Norm = Beta('BETA_TIME_TRAIN_std_Norm', 1, None, None,
0)
BETA_TIME_TRAIN_random_Norm = BETA_TIME_TRAIN_mean_Norm + BETA_TIME_TRAIN_std_Norm * bioDraws(
'BETA_TIME_TRAIN_random_Norm', 'NORMAL')
##ML RANDOM SPECIFIC TIME SM NORMAL
BETA_TIME_SM_mean_Norm = Beta('BETA_TIME_SM_mean_Norm', 0, None, None, 0)
BETA_TIME_SM_std_Norm = Beta('BETA_TIME_SM_std_Norm', 1, None, None, 0)
BETA_TIME_SM_random_Norm = BETA_TIME_SM_mean_Norm + BETA_TIME_SM_std_Norm * bioDraws(
'BETA_TIME_SM_random_Norm', 'NORMAL')
##ML RANDOM SPECIFIC TIME CAR NORMAL
BETA_TIME_CAR_mean_Norm = Beta('BETA_TIME_CAR_mean_Norm', 0, None, None, 0)
BETA_TIME_CAR_std_Norm = Beta('BETA_TIME_CAR_std_Norm', 1, None, None, 0)
BETA_TIME_CAR_random_Norm = BETA_TIME_CAR_mean_Norm + BETA_TIME_CAR_std_Norm * bioDraws(
'BETA_TIME_CAR_random_Norm', 'NORMAL')
##ML RANDOM GENERIC COST LOGNORMAL
BETA_COST_PUB_mean = Beta('BBETA_COST_PUB_mean', 0, None, None, 0)
BETA_COST_PUB_std = Beta('BBETA_COST_PUB_std', 1, None, None, 0)
BETA_COST_PUB_random = -exp(BETA_COST_PUB_mean + BETA_COST_PUB_std *
bioDraws('BBETA_COST_PUB_random', 'NORMAL'))
##ML RANDOM GENERIC COST NORMAL
BETA_COST_mean_Norm = Beta('BETA_COST_mean_Norm', 0, None, None, 0)
BETA_COST_std_Norm = Beta('BETA_COST_std_Norm', 1, None, None, 0)
BETA_COST_random_Norm = BETA_COST_mean_Norm + BETA_COST_std_Norm * bioDraws(
'BETA_COST_random_Norm', 'NORMAL')
##ML RANDOM GENERIC HE NORMAL
BETA_HE_mean_Norm = Beta('BETA_HE_mean_Norm', 0, None, None, 0)
BETA_HE_std_Norm = Beta('BETA_HE_std_Norm', 1, None, None, 0)
BETA_HE_random_Norm = BETA_HE_mean_Norm + BETA_HE_std_Norm * bioDraws(
'BETA_HE_random_Norm', 'NORMAL')
'''
***********************************************************************************************
'''
#PUBLIC
TRAIN_COST_SCALED = DefineVariable('TRAIN_COST_SCALED',\
TRAIN_COST / COST_SCALE_PUB,db_train)
SM_COST_SCALED = DefineVariable('SM_COST_SCALED', SM_COST / COST_SCALE_PUB,
db_train)
#CAR
CAR_COST_SCALED = DefineVariable('CAR_COST_SCALED',
CAR_CO / COST_SCALE_CAR, db_train)
'''
***********************************************************************************************
'''
##Scaling 'COST', 'TRAVEL-TIME' and 'HE' by a factor of 100 and adding the scaled variables to the database
TRAIN_TT_SCALED = DefineVariable('TRAIN_TT_SCALED',\
TRAIN_TT / 100.0,db_train)
TRAIN_HE_SCALED = DefineVariable('TRAIN_HE_SCALED',\
TRAIN_HE / 100, db_train)
SM_TT_SCALED = DefineVariable('SM_TT_SCALED', SM_TT / 100.0, db_train)
SM_HE_SCALED = DefineVariable('SM_HE_SCALED',\
SM_HE / 100, db_train)
CAR_TT_SCALED = DefineVariable('CAR_TT_SCALED', CAR_TT / 100, db_train)
###Defining new variables and adding columns to the database
#Age
AGE_1 = DefineVariable('AGE_1', (AGE == 1),
db_train) #don't scale because is cathegorical
AGE_2 = DefineVariable('AGE_2', (AGE == 2), db_train)
AGE_3 = DefineVariable('AGE_3', (AGE == 3), db_train)
AGE_4 = DefineVariable('AGE_4', (AGE == 4), db_train)
AGE_5 = DefineVariable('AGE_5', (AGE == 5), db_train)
AGE_6 = DefineVariable('AGE_6', (AGE == 6), db_train)
#Purpose
PRIVATE = DefineVariable("PRIVATE", (PURPOSE == 1), db_train)
COMMUTER = DefineVariable("COMMUTER", (PURPOSE == 2), db_train)
BUSINESS = DefineVariable("BUSINESS", (PURPOSE == 3), db_train)
#Model Estimation
av = {3: CAR_AV_SP, 1: TRAIN_AV_SP, 2: SM_AV}
obsprob = exp(models.loglogit(V, av, CHOICE))
condprobIndiv = PanelLikelihoodTrajectory(obsprob)
logprob = log(MonteCarlo(condprobIndiv))
bg = bio.BIOGEME(db_train, logprob, numberOfDraws=Draws)
bg.modelName = ModName
result = bg.estimate()
return result
None, 0)
TimePT_scaled = DefineVariable('TimePT_scaled', TimePT / 200, database)
TimeCar_scaled = DefineVariable('TimeCar_scaled', TimeCar / 200, database)
MarginalCostPT_scaled = DefineVariable('MarginalCostPT_scaled',
MarginalCostPT / 10, database)
CostCarCHF_scaled = DefineVariable('CostCarCHF_scaled', CostCarCHF / 10,
database)
distance_km_scaled = DefineVariable('distance_km_scaled', distance_km / 5,
database)
PurpHWH = DefineVariable('PurpHWH', TripPurpose == 1, database)
PurpOther = DefineVariable('PurpOther', TripPurpose != 1, database)
### DEFINITION OF UTILITY FUNCTIONS:
BETA_TIME_PT = BETA_TIME_PT_REF * exp(BETA_TIME_PT_CL * CARLOVERS)
V0 = ASC_PT + \
BETA_TIME_PT * TimePT_scaled + \
BETA_WAITING_TIME * WaitingTimePT + \
BETA_COST_HWH * MarginalCostPT_scaled * PurpHWH + \
BETA_COST_OTHER * MarginalCostPT_scaled * PurpOther + \
ec_sigma * errorComponent
BETA_TIME_CAR = BETA_TIME_CAR_REF * exp(BETA_TIME_CAR_CL * CARLOVERS)
V1 = ASC_CAR + \
BETA_TIME_CAR * TimeCar_scaled + \
BETA_COST_HWH * CostCarCHF_scaled * PurpHWH + \
BETA_COST_OTHER * CostCarCHF_scaled * PurpOther + \
ec_sigma * errorComponent
database = db.Database('fakeDatabase', pandas)
def halton13(sampleSize, numberOfDraws):
"""
The user can define new draws. For example, Halton draws with base 13, skipping
the first 10 draws.
"""
return draws.getHaltonDraws(sampleSize, numberOfDraws, base=13, skip=10)
mydraws = {'HALTON13': (halton13, 'Halton draws, base 13, skipping 10')}
database.setRandomNumberGenerators(mydraws)
U = bioDraws('U', 'UNIFORM')
integrand = exp(U) + exp(1 - U)
simulatedI = MonteCarlo(integrand) / 2.0
U_halton13 = bioDraws('U_halton13', 'HALTON13')
integrand_halton13 = exp(U_halton13) + exp(1 - U_halton13)
simulatedI_halton13 = MonteCarlo(integrand_halton13) / 2.0
U_mlhs = bioDraws('U_mlhs', 'UNIFORM_MLHS')
integrand_mlhs = exp(U_mlhs) + exp(1 - U_mlhs)
simulatedI_mlhs = MonteCarlo(integrand_mlhs) / 2.0
trueI = exp(1.0) - 1.0
R = 10000
error = simulatedI - trueI
