def setUp(self):
np.random.seed(90267)
rnd.seed(90267)
Choice = Variable('Choice')
Variable1 = Variable('Variable1')
Variable2 = Variable('Variable2')
beta1 = Beta('beta1', 0, None, None, 0)
beta2 = Beta('beta2', 0, None, None, 0)
V1 = beta1 * Variable1
V2 = beta2 * Variable2
V3 = 0
V = {1: V1, 2: V2, 3: V3}
likelihood = models.loglogit(V, av=None, i=Choice)
self.myBiogeme = bio.BIOGEME(myData1, likelihood)
self.myBiogeme.modelName = 'simpleExample'
self.theFunction = rosenbrock()
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 testEstimation(self):
AutoTime = Variable('AutoTime')
TransitTime = Variable('TransitTime')
Choice = Variable('Choice')
ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)
B_TIME = Beta('B_TIME', 0, None, None, 0)
V = {0: ASC_CAR + B_TIME * AutoTime, 1: B_TIME * TransitTime}
av = {0: 1, 1: 1}
logprob = models.loglogit(V, av, Choice)
biogeme = bio.BIOGEME(self.database, logprob)
biogeme.modelName = 'test'
biogeme.generateHtml = False
results = biogeme.estimate()
self.assertAlmostEqual(results.data.logLike, -6.166042, 2)
def run_simulation(data_file_directory_for_simulation, data_file_name_for_simulation, output_directory_for_simulation,
betas, household_income_limit):
"""
:author: Antonin Danalet, based on the example '01logit_simul.py' by Michel Bierlaire, EPFL, on biogeme.epfl.ch
Simulation with a binary logit model. Two alternatives: work from home at least some times, or not."""
# Read the data
df_persons = pd.read_csv(data_file_directory_for_simulation / data_file_name_for_simulation, ';')
database = db.Database('persons', df_persons)
# The following statement allows you to use the names of the variable as Python variable.
globals().update(database.variables)
# Parameters to be estimated
alternative_specific_constant = Beta('alternative_specific_constant', 0, None, None, 0)
b_no_post_school_education = Beta('b_no_post_school_education', 0, None, None, 0)
b_secondary_education = Beta('b_secondary_education', 0, None, None, 0)
b_tertiary_education = Beta('b_tertiary_education', 0, None, None, 0)
b_university = Beta('b_university', 0, None, None, 1)
b_male = Beta('b_male', 0, None, None, 0)
b_public_transport_connection_quality_na_home = Beta('b_public_transport_connection_quality_na_home',
0, None, None, 0)
b_public_transport_connection_quality_a_work = Beta('b_public_transport_connection_quality_are_a_work',
0, None, None, 1)
b_rural_work = Beta('b_rural_work', 0, None, None, 0)
b_home_work_distance = Beta('b_home_work_distance', 0, None, None, 0)
b_business_sector_agriculture = Beta('b_business_sector_agriculture', 0, None, None, 0)
b_business_sector_production = Beta('b_business_sector_production', 0, None, None, 0)
b_business_sector_wholesale = Beta('b_business_sector_wholesale', 0, None, None, 1)
b_business_sector_retail = Beta('b_business_sector_retail', 0, None, None, 0)
b_business_sector_gastronomy = Beta('b_business_sector_gastronomy', 0, None, None, 0)
b_business_sector_finance = Beta('b_business_sector_finance', 0, None, None, 1)
b_business_sector_services_fc = Beta('b_business_sector_services_fc', 0, None, None, 0)
b_business_sector_other_services = Beta('b_business_sector_other_services', 0, None, None, 1)
b_business_sector_others = Beta('b_business_sector_others', 0, None, None, 1)
b_business_sector_non_movers = Beta('b_business_sector_non_movers', 0, None, None, 0)
b_executives = Beta('b_executives', 0, None, None, 0)
b_german = Beta('b_german', 0, None, None, 0)
b_hh_income_8000_or_less = Beta('b_hh_income_8000_or_less', 0, None, None, 0)
# Definition of new variables
no_post_school_educ = education == 1
secondary_education = education == 2
tertiary_education = education == 3
university = education == 4
male = (sex == 1)
public_transport_quality_NA_home = (public_transport_connection_quality_ARE_home == 5)
public_transport_quality_A_work = (public_transport_connection_quality_ARE_work == 1)
home_work_distance = (home_work_crow_fly_distance * (home_work_crow_fly_distance >= 0.0) / 100000.0)
business_sector_agriculture = type_1 == 1
business_sector_retail = type_1 == 4
business_sector_gastronomy = type_1 == 5
business_sector_finance = type_1 == 6
business_sector_production = type_1 == 2
business_sector_wholesale = type_1 == 3
business_sector_services_fC = type_1 == 7
business_sector_other_services = type_1 == 8
business_sector_others = type_1 == 9
business_sector_non_movers = type_1 == 10
german = language == 1
nationality_switzerland = nation == 0
nationality_germany_austria = nation == 1
nationality_italy_vatican = nation == 2
nationality_france_monaco_s_marino = nation == 3
nationality_northwestern_europe = nation == 4
nationality_eastern_europe = nation == 7
hh_income_8000_or_less = hh_income < household_income_limit
executives = (0 < position_in_bus) * (position_in_bus < 19)
rural_work = urban_rural_typology_work == 3
# Utility
utility_function_telecommuting = alternative_specific_constant + \
b_executives * executives + \
b_no_post_school_education * no_post_school_educ + \
b_secondary_education * secondary_education + \
b_tertiary_education * tertiary_education + \
b_university * university + \
b_male * male + \
b_public_transport_connection_quality_na_home * public_transport_quality_NA_home + \
b_public_transport_connection_quality_a_work * public_transport_quality_A_work + \
b_rural_work * rural_work + \
b_home_work_distance * home_work_distance + \
models.piecewiseFormula(age, [0, 20, 35, 75, 200]) + \
b_business_sector_agriculture * business_sector_agriculture + \
b_business_sector_retail * business_sector_retail + \
b_business_sector_gastronomy * business_sector_gastronomy + \
b_business_sector_finance * business_sector_finance + \
b_business_sector_production * business_sector_production + \
b_business_sector_wholesale * business_sector_wholesale + \
b_business_sector_services_fc * business_sector_services_fC + \
b_business_sector_other_services * business_sector_other_services + \
b_business_sector_others * business_sector_others + \
b_business_sector_non_movers * business_sector_non_movers + \
b_german * german + \
b_nationality_ch_germany_france_italy_nw_e * nationality_switzerland + \
b_nationality_ch_germany_france_italy_nw_e * nationality_germany_austria + \
b_nationality_ch_germany_france_italy_nw_e * nationality_italy_vatican + \
b_nationality_ch_germany_france_italy_nw_e * nationality_france_monaco_s_marino + \
b_nationality_ch_germany_france_italy_nw_e * nationality_northwestern_europe + \
b_nationality_ch_germany_france_italy_nw_e * nationality_eastern_europe + \
models.piecewiseFormula(work_percentage, [0, 90, 101]) + \
b_hh_income_8000_or_less * hh_income_8000_or_less
utility_function_no_telecommuting = 0
# Associate utility functions with the numbering of alternatives
utility_functions_with_numbering_of_alternatives = {1: utility_function_telecommuting, # Yes or sometimes
3: utility_function_no_telecommuting} # No
availability_conditions = {1: 1, # Always available
3: 1} # Always available
# The choice model is a logit, with availability conditions
prob_telecommuting = models.logit(utility_functions_with_numbering_of_alternatives, availability_conditions, 1)
prob_no_telecommuting = models.logit(utility_functions_with_numbering_of_alternatives, availability_conditions, 3)
simulate = {'Prob. telecommuting': prob_telecommuting,
'Prob. no telecommuting': prob_no_telecommuting}
# Create the Biogeme object
biogeme = bio.BIOGEME(database, simulate)
biogeme.modelName = 'logit_telecommuting_simul'
# Define level of verbosity
logger = msg.bioMessage()
# logger.setSilent()
logger.setWarning()
# logger.setGeneral()
# logger.setDetailed()
# Get the betas from the estimation (without corrections)
# path_to_estimation_folder = Path('../data/output/models/estimation/')
# if os.path.isfile(path_to_estimation_folder / 'logit_telecommuting~00.pickle'):
# raise Exception('There are several model outputs! Careful.')
# results = res.bioResults(pickleFile=path_to_estimation_folder / 'logit_telecommuting.pickle')
# betas_without_correction = results.getBetaValues()
# Change the working directory, so that biogeme writes in the correct folder, i.e., where this file is
standard_directory = os.getcwd()
os.chdir(output_directory_for_simulation)
results = biogeme.simulate(theBetaValues=betas)
# print(results.describe())
df_persons = pd.concat([df_persons, results], axis=1)
# Go back to the normal working directory
os.chdir(standard_directory)
# For unemployed people, fix probability of doing some home office to 0 (and probability of not doing to 1).
df_persons.loc[df_persons.employed == 0, 'Prob. telecommuting'] = 0.0 # Unemployed people
df_persons.loc[df_persons.employed == 0, 'Prob. no telecommuting'] = 1.0 # Unemployed people
df_persons.loc[df_persons.employed == -99, 'Prob. telecommuting'] = 0.0 # Other people
df_persons.loc[df_persons.employed == -99, 'Prob. no telecommuting'] = 1.0 # Other people
# By definition, apprentices don't work from home (because they were not asked in the MTMC)
df_persons.loc[df_persons.position_in_bus == 3, 'Prob. telecommuting'] = 0.0
df_persons.loc[df_persons.position_in_bus == 3, 'Prob. no telecommuting'] = 1.0
# Add a realisation of the probability
df_persons['random 0/1'] = np.random.rand(len(df_persons))
df_persons['telecommuting_model'] = np.where(df_persons['random 0/1'] < df_persons['Prob. telecommuting'], 1, 0)
del df_persons['random 0/1']
''' Save the file '''
data_file_name = 'persons_from_SynPop_with_probability_telecommuting.csv'
df_persons.to_csv(output_directory_for_simulation / data_file_name, sep=',', index=False)
# 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)
# 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 = DefineVariable('CAR_AV_SP', CAR_AV * (SP != 0), database)
# The Pandas data structure is available as database.data. Use all the
# Pandas functions to invesigate the database
#print(database.data.describe())
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)
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)
B_TIME_S = Beta('B_TIME_S', 0, None, None, 0)
# Define a random parameter, normally distirbuted, designed to be used
# for Monte-Carlo simulation
B_TIME_RND = B_TIME + B_TIME_S * bioDraws('B_TIME_RND', 'NORMAL')
SM_COST = SM_CO * (GA == 0)
TRAIN_COST = TRAIN_CO * (GA == 0)
TRAIN_TT_SCALED = DefineVariable('TRAIN_TT_SCALED',\
age_65_more = DefineVariable('age_65_more', age >= Numeric(65), database)
moreThanOneCar = DefineVariable('moreThanOneCar', NbCar > 1, database)
moreThanOneBike = DefineVariable('moreThanOneBike', NbBicy > 1, database)
individualHouse = DefineVariable('individualHouse',\
HouseType == 1,database)
male = DefineVariable('male', Gender == 1, database)
haveChildren = DefineVariable('haveChildren',\
((FamilSitu == 3)+(FamilSitu == 4)) > 0,database)
haveGA = DefineVariable('haveGA', GenAbST == 1, database)
highEducation = DefineVariable('highEducation', Education >= 6, database)
### Coefficients
# Read the estimates from the structural equation estimation, and use
# them as starting values
coef_age_65_more = Beta('coef_age_65_more', 0.03623576587917444, None, None, 0)
coef_haveChildren = Beta('coef_haveChildren', -0.027979246306050195, None,
None, 0)
coef_haveGA = Beta('coef_haveGA', -0.749193972835575, None, None, 0)
coef_highEducation = Beta('coef_highEducation', -0.25961437893330336, None,
None, 0)
coef_individualHouse = Beta('coef_individualHouse', -0.1162986643952338, None,
None, 0)
coef_intercept = Beta('coef_intercept', 0.3534782074218524, None, None, 0)
coef_male = Beta('coef_male', 0.07950619870640736, None, None, 0)
coef_moreThanOneBike = Beta('coef_moreThanOneBike', -0.36347727873174407, None,
None, 0)
coef_moreThanOneCar = Beta('coef_moreThanOneCar', 0.714788103177542, None,
None, 0)
### Latent variable: structural equation
coef_highEducation = -0.24726576867313482
coef_individualHouse = -0.08887159771570047
coef_intercept = 0.40149819890908217
coef_male = 0.0661412838697794
coef_moreThanOneBike = -0.2776091744681671
coef_moreThanOneCar = 0.5335541575826122
### Latent variable: structural equation
# Note that the expression must be on a single line. In order to
# write it across several lines, each line must terminate with
# the \ symbol
omega = RandomVariable('omega')
density = dist.normalpdf(omega)
sigma_s = Beta('sigma_s', 0.8625193422179722, None, None, 0)
CARLOVERS = \
coef_intercept +\
coef_age_65_more * age_65_more +\
formulaIncome+\
coef_moreThanOneCar * moreThanOneCar +\
coef_moreThanOneBike * moreThanOneBike +\
coef_individualHouse * individualHouse +\
coef_male * male +\
coef_haveChildren * haveChildren +\
coef_haveGA * haveGA +\
coef_highEducation * highEducation +\
sigma_s * omega
from biogeme.expressions import Beta, DefineVariable, bioLinearUtility
from statistics import *
# Read the data
df = pd.read_csv('../data/optima.dat', sep='\t')
database = db.Database('optima', df)
# The following statement allows you to use the names of the variable
# as Python variable.
globals().update(database.variables)
# Exclude observations such that the chosen alternative is -1
database.remove(Choice == -1.0)
# List of parameters to be estimated
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.
##Method 1 from python
"""
TimePT_scaled = TimePT / 200
formulaIncome = models.piecewiseFormula(ScaledIncome, thresholds,
[0.0, 0.0, 0.0, 0.0, 0.0])
# Definition of other variables
age_65_more = DefineVariable('age_65_more', age >= 65, database)
moreThanOneCar = DefineVariable('moreThanOneCar', NbCar > 1, database)
moreThanOneBike = DefineVariable('moreThanOneBike', NbBicy > 1, database)
individualHouse = DefineVariable('individualHouse', HouseType == 1, database)
male = DefineVariable('male', Gender == 1, database)
haveChildren = DefineVariable('haveChildren', \
((FamilSitu == 3) + (FamilSitu == 4)) > 0, database)
haveGA = DefineVariable('haveGA', GenAbST == 1, database)
highEducation = DefineVariable('highEducation', Education >= 6, database)
# Parameters to be estimated
coef_intercept = Beta('coef_intercept', 0.0, None, None, 1)
coef_age_65_more = Beta('coef_age_65_more', 0.0, None, None, 0)
coef_age_unknown = Beta('coef_age_unknown', 0.0, None, None, 0)
coef_haveGA = Beta('coef_haveGA', 0.0, None, None, 0)
coef_moreThanOneCar = Beta('coef_moreThanOneCar', 0.0, None, None, 0)
coef_moreThanOneBike = Beta('coef_moreThanOneBike', 0.0, None, None, 0)
coef_individualHouse = Beta('coef_individualHouse', 0.0, None, None, 0)
coef_male = Beta('coef_male', 0.0, None, None, 0)
coef_haveChildren = Beta('coef_haveChildren', 0.0, None, None, 0)
coef_highEducation = Beta('coef_highEducation', 0.0, None, None, 0)
### Latent variable: structural equation
# Note that the expression must be on a single line. In order to
# write it across several lines, each line must terminate with
# the \ symbol
thresholds,
[0.0, 0.0, 0.0, 0.0, 0.0])
# Definition of other variables
age_65_more = DefineVariable('age_65_more', age >= 65, database)
moreThanOneCar = DefineVariable('moreThanOneCar', NbCar > 1, database)
moreThanOneBike = DefineVariable('moreThanOneBike', NbBicy > 1, database)
individualHouse = DefineVariable('individualHouse', HouseType == 1, database)
male = DefineVariable('male', Gender == 1, database)
haveChildren = DefineVariable('haveChildren', \
((FamilSitu == 3) + (FamilSitu == 4)) > 0, database)
haveGA = DefineVariable('haveGA', GenAbST == 1, database)
highEducation = DefineVariable('highEducation', Education >= 6, database)
# Parameters to be estimated
coef_intercept = Beta('coef_intercept', 0.0, None, None, 0)
coef_age_65_more = Beta('coef_age_65_more', 0.0, None, None, 0)
coef_age_unknown = Beta('coef_age_unknown', 0.0, None, None, 0)
coef_haveGA = Beta('coef_haveGA', 0.0, None, None, 0)
coef_moreThanOneCar = Beta('coef_moreThanOneCar', 0.0, None, None, 0)
coef_moreThanOneBike = Beta('coef_moreThanOneBike', 0.0, None, None, 0)
coef_individualHouse = Beta('coef_individualHouse', 0.0, None, None, 0)
coef_male = Beta('coef_male', 0.0, None, None, 0)
coef_haveChildren = Beta('coef_haveChildren', 0.0, None, None, 0)
coef_highEducation = Beta('coef_highEducation', 0.0, None, None, 0)
### Latent variable: structural equation
# Note that the expression must be on a single line. In order to
# write it across several lines, each line must terminate with
# the \ symbol
# 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)
Scale_group3 = Beta('Scale_group3', 1, 0.001, 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,
# Definition of other variables
age_65_more = DefineVariable('age_65_more', age >= Numeric(65), database)
moreThanOneCar = DefineVariable('moreThanOneCar', NbCar > 1, database)
moreThanOneBike = DefineVariable('moreThanOneBike', NbBicy > 1, database)
individualHouse = DefineVariable('individualHouse',\
HouseType == 1,database)
male = DefineVariable('male', Gender == 1, database)
haveChildren = DefineVariable('haveChildren',\
((FamilSitu == 3)+(FamilSitu == 4)) > 0,database)
haveGA = DefineVariable('haveGA', GenAbST == 1, database)
highEducation = DefineVariable('highEducation', Education >= 6, database)
### Coefficients
coef_intercept = Beta('coef_intercept', 0, None, None, 0)
coef_age_65_more = Beta('coef_age_65_more', 0, None, None, 0)
coef_haveGA = Beta('coef_haveGA', 0, None, None, 0)
coef_ContIncome_0_4000 = Beta('coef_ContIncome_0_4000', 0, None, None, 0)
coef_ContIncome_4000_6000 = Beta('coef_ContIncome_4000_6000', 0, None, None, 0)
coef_ContIncome_6000_8000 = Beta('coef_ContIncome_6000_8000', 0, None, None, 0)
coef_ContIncome_8000_10000 = Beta('coef_ContIncome_8000_10000', 0, None, None,
0)
coef_ContIncome_10000_more = Beta('coef_ContIncome_10000_more', 0, None, None,
0)
coef_moreThanOneCar = Beta('coef_moreThanOneCar', 0, None, None, 0)
coef_moreThanOneBike = Beta('coef_moreThanOneBike', 0, None, None, 0)
coef_individualHouse = Beta('coef_individualHouse', 0, None, None, 0)
coef_male = Beta('coef_male', 0, None, None, 0)
coef_haveChildren = Beta('coef_haveChildren', 0, None, None, 0)
coef_highEducation = Beta('coef_highEducation', 0, None, None, 0)
# The Pandas data structure is available as database.data. Use all the
# Pandas functions to invesigate the database
#print(database.data.describe())
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)
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)
MU = Beta('MU', 1, 0, 1, 0)
MU_EXISTING = Beta('MU_EXISTING', 1, 1, None, 1)
MU_PUBLIC = Beta('MU_PUBLIC', 1, 1, None, 0)
ALPHA_EXISTING = Beta('ALPHA_EXISTING', 0.5, 0, 1, 0)
ALPHA_PUBLIC = 1 - ALPHA_EXISTING
SM_COST = SM_CO * (GA == 0)
TRAIN_COST = TRAIN_CO * (GA == 0)
TRAIN_TT_SCALED = TRAIN_TT / 100.0
import biogeme.models as models
import biogeme.messaging as msg
from biogeme.expressions import Beta, DefineVariable, bioDraws, \
PanelLikelihoodTrajectory, MonteCarlo, log
# Read the data
df = pd.read_csv('comboall.dat', '\t')
database = db.Database('comboall', df)
# They are organized as panel data. The variable ID identifies each individual.
database.panel("ID")
globals().update(database.variables)
# Parameters to be estimated. One version for each latent class.
numberOfClasses = 2
ASC_1 = [Beta(f'ASC_1{i}', 0.1, None, None, 0) for i in range(numberOfClasses)]
ASC_11 = [
Beta(f'ASC_11{i}', 0.1, None, None, 0) for i in range(numberOfClasses)
]
ASC_2 = [Beta(f'ASC_2{i}', 0.1, None, None, 0) for i in range(numberOfClasses)]
ASC_21 = [
Beta(f'ASC_21{i}', 0.1, None, None, 0) for i in range(numberOfClasses)
]
ASC_3 = [Beta(f'ASC_3{i}', 0.1, None, None, 0) for i in range(numberOfClasses)]
ASC_4 = [Beta(f'ASC_4{i}', 0.1, None, None, 1) for i in range(numberOfClasses)]
beta_shcosttaxi1 = [
Beta(f'beta_shcosttaxi1{i}', 0.1, None, None, 0)
for i in range(numberOfClasses)
]
beta_shcostsharedcar = [
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. One version for each latent class.
numberOfClasses = 2
B_COST = [
Beta(f'B_COST_class{i}', 0, None, None, 0) for i in range(numberOfClasses)
]
# Define a random parameter, normally distributed across individuals,
# designed to be used for Monte-Carlo simulation
B_TIME = [
Beta(f'B_TIME_class{i}', 0, None, None, 0) for i in range(numberOfClasses)
]
# It is advised not to use 0 as starting value for the following parameter.
B_TIME_S = [
Beta(f'B_TIME_S_class{i}', 1, None, None, 0)
for i in range(numberOfClasses)
]
B_TIME_RND = [
B_TIME[i] + B_TIME_S[i] * bioDraws(f'B_TIME_RND_class{i}', 'NORMAL_ANTI')
coef_moreThanOneCar = structBetas['coef_moreThanOneCar']
coef_moreThanOneBike = structBetas['coef_moreThanOneBike']
coef_individualHouse = structBetas['coef_individualHouse']
coef_male = structBetas['coef_male']
coef_haveChildren = structBetas['coef_haveChildren']
coef_highEducation = structBetas['coef_highEducation']
### Latent variable: structural equation
# Note that the expression must be on a single line. In order to
# write it across several lines, each line must terminate with
# the \ symbol
# Define a random parameter, normally distributed, designed to be used
# for numerical integration
sigma_s = Beta('sigma_s', 1, None, None, 0)
CARLOVERS = coef_intercept + \
coef_age_65_more * age_65_more + \
formulaIncome + \
coef_moreThanOneCar * moreThanOneCar + \
coef_moreThanOneBike * moreThanOneBike + \
coef_individualHouse * individualHouse + \
coef_male * male + \
coef_haveChildren * haveChildren + \
coef_haveGA * haveGA + \
coef_highEducation * highEducation + \
sigma_s * bioDraws('EC', 'NORMAL_MLHS')
# Choice model
ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)
import biogeme.models as models
import biogeme.messaging as msg
from biogeme.expressions import Beta, DefineVariable, bioDraws, \
PanelLikelihoodTrajectory, MonteCarlo, log
# Read the data
df = pd.read_csv('outside.dat', '\t')
database = db.Database('outside', df)
# They are organized as panel data. The variable ID identifies each individual.
database.panel("ID")
globals().update(database.variables)
# Parameters to be estimated. One version for each latent class.
numberOfClasses = 2
ASC_1 = [Beta(f'ASC_1{i}', 0.1, None, None, 1) for i in range(numberOfClasses)]
ASC_2 = [Beta(f'ASC_2{i}', 0.1, None, None, 0) for i in range(numberOfClasses)]
ASC_3 = [Beta(f'ASC_3{i}', 0.1, None, None, 0) for i in range(numberOfClasses)]
beta_shcostperdist1 = [Beta(f'beta_shcostperdist1{i}', 0.1, None, None, 0) for i in range(numberOfClasses)]
beta_shtime = [Beta(f'beta_shtime{i}', 0.1, None, None, 0) for i in range(numberOfClasses)]
beta_sharedist = [Beta(f'beta_sharedist{i}', 0.1, None, None, 0) for i in range(numberOfClasses)]
beta_maascostperdist2 = [Beta(f'beta_maascostperdist2{i}', 0.1, None, None, 0) for i in range(numberOfClasses)]
beta_maastime1 = [Beta(f'beta_maastime1{i}', 0.1, None, None, 0) for i in range(numberOfClasses)]
beta_maastime2 = [Beta(f'beta_maastime2{i}', 0.1, None, None, 0) for i in range(numberOfClasses)]
beta_extra = [Beta(f'beta_extra{i}', 0.1, None, None, 0) for i in range(numberOfClasses)]
beta_maasdist = [Beta(f'beta_maasdist{i}', 0.1, None, None, 0) for i in range(numberOfClasses)]
beta_maasdisbike = [Beta(f'beta_maasdisbike{i}', 0.1, None, None, 0) for i in range(numberOfClasses)]
beta_totcostperdist = [Beta(f'beta_totcostperdist{i}', 0.1, None, None, 0) for i in range(numberOfClasses)]
def run_estimation_2015_2020():
"""
:author: Antonin Danalet, based on the example '01logit.py' by Michel Bierlaire, EPFL, on biogeme.epfl.ch
A binary logit model on the possibility to work from home at least some times."""
# Read the data
data_file_directory = Path('../data/output/data/estimation/2015_2020/')
df = pd.read_csv(data_file_directory / 'persons.csv', ';')
database = db.Database('persons', df)
# The following statement allows you to use the names of the variable as Python variable.
globals().update(database.variables)
# Parameters to be estimated
alternative_specific_constant = Beta('alternative_specific_constant', 0,
None, None, 0)
b_no_post_school_education = Beta('b_no_post_school_education', 0, None,
None, 0)
b_secondary_education = Beta('b_secondary_education', 0, None, None, 0)
b_tertiary_education = Beta('b_tertiary_education', 0, None, None, 0)
b_male_2020 = Beta('b_male_2020', 0, None, None, 1)
b_single_household_2020 = Beta('b_single_household_2020', 0, None, None, 1)
b_couple_without_children_2015 = Beta('b_couple_without_children_2015', 0,
None, None, 0)
b_couple_without_children_2020 = Beta('b_couple_without_children_2020', 0,
None, None, 0)
b_couple_with_children_2020 = Beta('b_couple_with_children_2020', 0, None,
None, 1)
b_single_parent_with_children_2020 = Beta(
'b_single_parent_with_children_2020', 0, None, None, 1)
b_not_family_household_2020 = Beta('b_not_family_household_2020', 0, None,
None, 1)
b_public_transport_connection_quality_abc_home_2020 = Beta(
'b_public_transport_connection_quality_abc_home_2020', 0, None, None,
1)
b_public_transport_connection_quality_na_home_2015 = Beta(
'b_public_transport_connection_quality_na_home_2015', 0, None, None, 0)
b_public_transport_connection_quality_na_home_2020 = Beta(
'b_public_transport_connection_quality_na_home_2020', 0, None, None, 1)
b_public_transport_connection_quality_abcd_work_2020 = Beta(
'b_public_transport_connection_quality_abcd_work_2020', 0, None, None,
1)
b_urban_home_2020 = Beta('b_urban_home_2020', 0, None, None, 1)
b_rural_home_2020 = Beta('b_rural_home_2020', 0, None, None, 1)
b_intermediate_home_2020 = Beta('b_intermediate_home_2020', 0, None, None,
1)
b_urban_work_2020 = Beta('b_urban_work_2020', 0, None, None, 1)
b_rural_work_2020 = Beta('b_rural_work_2020', 0, None, None, 1)
b_intermediate_work_2020 = Beta('b_intermediate_work_2020', 0, None, None,
0)
b_home_work_distance = Beta('b_home_work_distance', 0, None, None, 0)
b_home_work_distance_zero = Beta('b_home_work_distance_zero', 0, None,
None, 0)
b_home_work_distance_na = Beta('b_home_work_distance_na', 0, None, None, 0)
b_business_sector_agriculture_2020 = Beta(
'b_business_sector_agriculture_2020', 0, None, None, 1)
b_business_sector_production = Beta('b_business_sector_production', 0,
None, None, 0)
b_business_sector_wholesale = Beta('b_business_sector_wholesale', 0, None,
None, 0)
b_business_sector_retail = Beta('b_business_sector_retail', 0, None, None,
0)
b_business_sector_gastronomy = Beta('b_business_sector_gastronomy', 0,
None, None, 0)
b_business_sector_finance = Beta('b_business_sector_finance', 0, None,
None, 0)
b_business_sector_services_fc_2020 = Beta(
'b_business_sector_services_fc_2020', 0, None, None, 1)
b_business_sector_other_services = Beta('b_business_sector_other_services',
0, None, None, 0)
b_business_sector_others = Beta('b_business_sector_others', 0, None, None,
0)
b_business_sector_non_movers = Beta('b_business_sector_non_movers', 0,
None, None, 0)
b_executives = Beta('b_executives', 0, None, None, 0)
b_german = Beta('b_german', 0, None, None, 0)
b_hh_income_na = Beta('b_hh_income_na', 0, None, None, 0)
b_hh_income_8000_or_less = Beta('b_hh_income_8000_or_less', 0, None, None,
0)
b_owning_a_general_abo = Beta('b_owning_a_general_abo', 0, None, None, 0)
b_regional_abo_2020 = Beta('b_regional_abo_2020', 0, None, None, 1)
b_regional_abo_na_2020 = Beta('b_regional_abo_na_2020', 0, None, None, 1)
b_half_fare_abo_2020 = Beta('b_half_fare_abo_2020', 0, None, None, 1)
b_half_fare_abo_na_2020 = Beta('b_half_fare_abo_na_2020', 0, None, None, 1)
b_car_avail_2020 = Beta('b_car_avail_2020', 0, None, None, 1)
b_car_avail_na_2020 = Beta('b_car_avail_na_2020', 0, None, None, 1)
b_mobility_resource_na = Beta('b_mobility_resource_na', 0, None, None, 0)
b_mobility_resource_car_general_abo_2020 = Beta(
'b_mobility_resource_car_general_abo_2020', 0, None, None, 1)
b_mobility_resource_car_half_fare_abo = Beta(
'b_mobility_resource_car_half_fare_abo', 0, None, None, 0)
b_mobility_resource_car_2020 = Beta('b_mobility_resource_car_2020', 0,
None, None, 1)
b_mobility_resource_general_abo_no_car_2020 = Beta(
'b_mobility_resource_general_no_car_abo_2020', 0, None, None, 0)
b_mobility_resource_half_fare_abo_2020 = Beta(
'b_mobility_resource_half_fare_abo_2020', 0, None, None, 1)
b_mobility_resource_none_2020 = Beta('b_mobility_resource_none_2020', 0,
None, None, 1)
b_mobility_resource_car_half_fare_regional_abo_2020 = Beta(
'b_mobility_resource_car_half_fare_regional_abo_2020', 0, None, None,
1)
b_mobility_resource_car_regional_abo_2020 = Beta(
'b_mobility_resource_car_regional_abo_2020', 0, None, None, 1)
b_mobility_resource_half_fare_regional_abo_2020 = Beta(
'b_mobility_resource_half_fare_regional_abo_2020', 0, None, None, 1)
b_mobility_resource_regional_abo_2020 = Beta(
'b_mobility_resource_regional_abo_2020', 0, None, None, 1)
scale_2020 = Beta('scale_2020', 1, 0.001, None, 0)
''' Definition of new variables '''
male_2020 = DefineVariable('male', (sex == 1) * (year == 2020), database)
single_household_2020 = DefineVariable('single_household_2020',
(hh_type == 10) * (year == 2020),
database)
couple_without_children_2015 = DefineVariable(
'couple_without_children_2015', (hh_type == 210) * (year == 2015),
database)
couple_without_children_2020 = DefineVariable(
'couple_without_children_2020', (hh_type == 210) * (year == 2020),
database)
couple_with_children_2020 = DefineVariable(
'couple_with_children_2020', (hh_type == 220) * (year == 2020),
database)
single_parent_with_children_2020 = DefineVariable(
'single_parent_with_children_2020', (hh_type == 230) * (year == 2020),
database)
not_family_household_2020 = DefineVariable(
'not_family_household_2020', (hh_type == 30) * (year == 2020),
database)
public_transport_connection_quality_abc_home_2020 = \
DefineVariable('public_transport_connection_quality_abc_home_2020',
((public_transport_connection_quality_ARE_home == 1) +
(public_transport_connection_quality_ARE_home == 2) +
(public_transport_connection_quality_ARE_home == 3)) * (year == 2020), database)
public_transport_connection_quality_na_home_2015 = \
DefineVariable('public_transport_connection_quality_NA_home_2015',
(public_transport_connection_quality_ARE_home == 5) * (year == 2015), database)
public_transport_connection_quality_na_home_2020 = \
DefineVariable('public_transport_connection_quality_NA_home_2020',
(public_transport_connection_quality_ARE_home == 5) * (year == 2020), database)
public_transport_connection_quality_abcd_work_2020 = \
DefineVariable('public_transport_connection_quality_abc_work_2020',
((public_transport_connection_quality_ARE_work == 1) +
(public_transport_connection_quality_ARE_work == 2) +
(public_transport_connection_quality_ARE_work == 3) +
(public_transport_connection_quality_ARE_work == 4)) * (year == 2020), database)
urban_home_2020 = DefineVariable(
'urban_home_2020', (urban_typology_home == 1) * (year == 2020),
database)
rural_home_2020 = DefineVariable(
'rural_home_2020', (urban_typology_home == 3) * (year == 2020),
database)
intermediate_home_2020 = DefineVariable(
'intermediate_home_2020', (urban_typology_home == 2) * (year == 2020),
database)
urban_work_2020 = DefineVariable(
'urban_work_2020', (urban_typology_work == 1) * (year == 2020),
database)
rural_work_2020 = DefineVariable(
'rural_work_2020', (urban_typology_work == 3) * (year == 2020),
database)
intermediate_work_2020 = DefineVariable(
'intermediate_work_2020', (urban_typology_work == 2) * (year == 2020),
database)
home_work_distance = DefineVariable(
'home_work_distance',
home_work_crow_fly_distance * (home_work_crow_fly_distance >= 0.0) /
100000.0, database)
home_work_distance_zero = DefineVariable(
'home_work_distance_zero', home_work_crow_fly_distance == 0.0,
database)
home_work_distance_na = DefineVariable('home_work_distance_na',
home_work_crow_fly_distance == -999,
database)
executives = DefineVariable('executives', work_position == 1, database)
german = DefineVariable('german', language == 1, database)
hh_income_na = DefineVariable('hh_income_na', hh_income < 0, database)
hh_income_8000_or_less = DefineVariable(
'hh_income_8000_or_less', (hh_income == 1) + (hh_income == 2) +
(hh_income == 3) + (hh_income == 4), database)
owning_a_general_abo = DefineVariable('owning_a_general_abo',
GA_ticket == 1, database)
regional_abo_2020 = DefineVariable('regional_abo_2020',
(Verbund_Abo == 1) * (year == 2020),
database)
half_fare_abo_2020 = DefineVariable('half_fare_abo_2020',
(halbtax_ticket == 1) * (year == 2020),
database)
car_avail_always_or_on_demand_2020 = DefineVariable(
'car_avail_always_or_on_demand_2020',
((car_avail == 1) + (car_avail == 2)) * (year == 2020), database)
regional_abo_na_2020 = DefineVariable('regional_abo_na_2020',
(Verbund_Abo < 0) * (year == 2020),
database)
half_fare_abo_na_2020 = DefineVariable(
'half_fare_abo_na_2020', (halbtax_ticket < 0) * (year == 2020),
database)
car_avail_na_2020 = DefineVariable('car_avail_na_2020',
(car_avail < 0) * (year == 2020),
database)
mobility_resource_na = DefineVariable('mobility_resource_na',
mobility_resources == -98, database)
mobility_resource_car_general_abo_2020 = DefineVariable(
'mobility_resource_car_general_abo_2020',
(mobility_resources == 1) * (year == 2020), database)
mobility_resource_car_half_fare_abo = DefineVariable(
'mobility_resource_car_half_fare_abo', mobility_resources == 2,
database)
mobility_resource_car_2020 = DefineVariable('mobility_resource_car_2020',
(mobility_resources == 3) *
(year == 2020), database)
mobility_resource_general_abo_no_car_2020 = DefineVariable(
'mobility_resource_general_abo_no_car_2020',
(mobility_resources == 4) * (year == 2020), database)
mobility_resource_half_fare_abo_2020 = DefineVariable(
'mobility_resource_half_fare_abo_2020',
(mobility_resources == 5) * (year == 2020), database)
mobility_resource_none_2020 = DefineVariable('mobility_resource_none_2020',
(mobility_resources == 6) *
(year == 2020), database)
mobility_resource_car_half_fare_regional_abo_2020 = \
DefineVariable('mobility_resource_car_half_fare_regional_abo_2020',
(mobility_resources == 20) * (year == 2020), database)
mobility_resource_car_regional_abo_2020 = DefineVariable(
'mobility_resource_car_regional_abo_2020',
(mobility_resources == 30) * (year == 2020), database)
mobility_resource_half_fare_regional_abo_2020 = DefineVariable(
'mobility_resource_half_fare_regional_abo_2020',
(mobility_resources == 50) * (year == 2020), database)
mobility_resource_regional_abo_2020 = DefineVariable(
'mobility_resource_regional_abo_2020',
(mobility_resources == 60) * (year == 2020), database)
business_sector_agriculture_2020 = DefineVariable(
'business_sector_agriculture_2020',
business_sector_agriculture * (year == 2020), database)
business_sector_services_fc_2020 = DefineVariable(
'business_sector_services_fc_2020',
business_sector_services_fc * (year == 2020), database)
# Utility
U = alternative_specific_constant + \
b_executives * executives + \
b_no_post_school_education * no_post_school_educ + \
b_secondary_education * secondary_education + \
b_tertiary_education * tertiary_education + \
b_couple_without_children_2015 * couple_without_children_2015 + \
b_couple_without_children_2020 * couple_without_children_2020 + \
b_public_transport_connection_quality_na_home_2015 * public_transport_connection_quality_na_home_2015 + \
b_public_transport_connection_quality_na_home_2020 * public_transport_connection_quality_na_home_2020 + \
b_home_work_distance * home_work_distance + \
b_home_work_distance_zero * home_work_distance_zero + \
b_home_work_distance_na * home_work_distance_na + \
models.piecewiseFormula(age, [15, 19, 31, 79, 85]) + \
b_business_sector_retail * business_sector_retail + \
b_business_sector_gastronomy * business_sector_gastronomy + \
b_business_sector_finance * business_sector_finance + \
b_business_sector_production * business_sector_production + \
b_business_sector_wholesale * business_sector_wholesale + \
b_business_sector_other_services * business_sector_other_services + \
b_business_sector_others * business_sector_others + \
b_business_sector_non_movers * business_sector_non_movers + \
b_german * german + \
models.piecewiseFormula(work_percentage, [0, 90, 101]) + \
b_hh_income_na * hh_income_na + \
b_hh_income_8000_or_less * hh_income_8000_or_less + \
b_owning_a_general_abo * owning_a_general_abo + \
b_mobility_resource_na * mobility_resource_na + \
b_mobility_resource_car_half_fare_abo * mobility_resource_car_half_fare_abo + \
b_male_2020 * male_2020 + \
b_single_household_2020 * single_household_2020 + \
b_couple_with_children_2020 * couple_with_children_2020 + \
b_single_parent_with_children_2020 * single_parent_with_children_2020 + \
b_not_family_household_2020 * not_family_household_2020 + \
b_public_transport_connection_quality_abc_home_2020 * public_transport_connection_quality_abc_home_2020 + \
b_public_transport_connection_quality_abcd_work_2020 * public_transport_connection_quality_abcd_work_2020 + \
b_urban_home_2020 * urban_home_2020 + \
b_rural_home_2020 * rural_home_2020 + \
b_intermediate_home_2020 * intermediate_home_2020 + \
b_urban_work_2020 * urban_work_2020 + \
b_rural_work_2020 * rural_work_2020 + \
b_intermediate_work_2020 * intermediate_work_2020 + \
b_business_sector_agriculture_2020 * business_sector_agriculture_2020 + \
b_business_sector_services_fc_2020 * business_sector_services_fc_2020 + \
b_regional_abo_2020 * regional_abo_2020 + \
b_regional_abo_na_2020 * regional_abo_na_2020 + \
b_half_fare_abo_2020 * half_fare_abo_2020 + \
b_half_fare_abo_na_2020 * half_fare_abo_na_2020 + \
b_car_avail_2020 * car_avail_always_or_on_demand_2020 + \
b_car_avail_na_2020 * car_avail_na_2020 + \
b_mobility_resource_car_general_abo_2020 * mobility_resource_car_general_abo_2020 + \
b_mobility_resource_car_2020 * mobility_resource_car_2020 + \
b_mobility_resource_general_abo_no_car_2020 * mobility_resource_general_abo_no_car_2020 + \
b_mobility_resource_half_fare_abo_2020 * mobility_resource_half_fare_abo_2020 + \
b_mobility_resource_none_2020 * mobility_resource_none_2020 + \
b_mobility_resource_car_half_fare_regional_abo_2020 * mobility_resource_car_half_fare_regional_abo_2020 + \
b_mobility_resource_car_regional_abo_2020 * mobility_resource_car_regional_abo_2020 + \
b_mobility_resource_half_fare_regional_abo_2020 * mobility_resource_half_fare_regional_abo_2020 + \
b_mobility_resource_regional_abo_2020 * mobility_resource_regional_abo_2020
U_no_telecommuting = 0
# Scale associated with 2020 is estimated
scale = (year == 2015) + (year == 2020) * scale_2020
# Associate utility functions with the numbering of alternatives
V = {
1: scale * U, # Yes or sometimes
0: U_no_telecommuting
} # No
av = {1: 1, 0: 1}
# Definition of the model. This is the contribution of each
# observation to the log likelihood function.
logprob = models.loglogit(
V,
av, # All alternatives are supposed to be always available
telecommuting) # Choice variable
# Change the working directory, so that biogeme writes in the correct folder
standard_directory = os.getcwd()
output_directory = '../data/output/models/estimation/2015_2020/'
os.chdir(output_directory)
# Create the Biogeme object
biogeme = bio.BIOGEME(database, logprob)
output_file_name = 'logit_telecommuting_2015_2020'
biogeme.modelName = output_file_name
# Estimate the parameters
results = biogeme.estimate()
# Get the results in LaTeX
results.writeLaTeX()
# Go back to the normal working directory
os.chdir(standard_directory)
import biogeme.database as db
import biogeme.biogeme as bio
import biogeme.models as models
import biogeme.messaging as msg
from biogeme.expressions import Beta, DefineVariable, bioDraws, \
PanelLikelihoodTrajectory, MonteCarlo, log
# Read the data
df = pd.read_csv('outside.dat', '\t')
database = db.Database('outside', df)
database.panel("ID")
# They are organized as panel data. The variable ID identifies each individual.
globals().update(database.variables)
# Parameters to be estimated
ASC_1 = Beta('ASC_1', 0, None, None, 1)
ASC_11 = Beta('ASC_11', 0, None, None, 0)
ASC_2 = Beta('ASC_2', 0, None, None, 0)
ASC_21 = Beta('ASC_21', 0, None, None, 0)
ASC_3 = Beta('ASC_3', 0, None, None, 0)
ASC_31 = Beta('ASC_31', 0, None, None, 0)
ASC_4 = Beta('ASC_4', 0, None, None, 0)
# Shared error parameters, fix the mean-parameter to 0
SIGMA_SH_MAAS_M = Beta('SIGMA_SH_MAAS_M', 0, None, None, 1)
SIGMA_SH_MAAS_STD = Beta('SIGMA_SH_MAAS_STD', 0, None, None, 0)
SIGMA_SH_MAASRND = SIGMA_SH_MAAS_M + SIGMA_SH_MAAS_STD * bioDraws(
'SIGMA_SH_MAASRND', 'NORMAL')
beta_fam_package = Beta('beta_fam_package', 0, None, None, 0)
beta_fam_private = Beta('beta_fam_private', 0, None, None, 0)
beta_age_package = Beta('beta_age_package', 0, None, None, 0)
numberOfRows = database.data.shape[0]
normalizedWeight = Weight * numberOfRows / sumWeight
# 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, we use 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)
# Define new variables. Must be consistent with estimation results.
TimePT_scaled = TimePT / 200
TimeCar_scaled = TimeCar / 200
MarginalCostPT_scaled = MarginalCostPT / 10
CostCarCHF_scaled = CostCarCHF / 10
distance_km_scaled = distance_km / 5
# The Pandas data structure is available as database.data. Use all the
# Pandas functions to invesigate the database
#print(database.data.describe())
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)
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)
SIGMA_CAR = Beta('SIGMA_CAR', 0, None, None, 0)
SIGMA_SM = Beta('SIGMA_SM', 0, None, None, 1)
SIGMA_TRAIN = Beta('SIGMA_TRAIN', 0, None, None, 0)
# Define a random parameter, normally distirbuted, designed to be used
# for Monte-Carlo simulation
EC_CAR = SIGMA_CAR * bioDraws('EC_CAR', 'NORMAL')
EC_SM = SIGMA_SM * bioDraws('EC_SM', 'NORMAL')
EC_TRAIN = SIGMA_TRAIN * bioDraws('EC_TRAIN', 'NORMAL')
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
-0.33669944071374086, 0.2177481824894475, -0.6224104823788724,
1.1592372281254595, -0.35530173883216076
])
age_65_more = DefineVariable('age_65_more', age >= Numeric(65), database)
moreThanOneCar = DefineVariable('moreThanOneCar', NbCar > 1, database)
moreThanOneBike = DefineVariable('moreThanOneBike', NbBicy > 1, database)
individualHouse = DefineVariable('individualHouse',\
HouseType == 1,database)
male = DefineVariable('male', Gender == 1, database)
haveChildren = DefineVariable('haveChildren',\
((FamilSitu == 3)+(FamilSitu == 4)) > 0,database)
haveGA = DefineVariable('haveGA', GenAbST == 1, database)
highEducation = DefineVariable('highEducation', Education >= 6, database)
ASC_CAR = Beta('ASC_CAR', 0.4118057751313065, None, None, 0)
ASC_SM = Beta('ASC_SM', 1.0134144912346523, None, None, 0)
BETA_COST_HWH = Beta('BETA_COST_HWH', -1.7754249429871691, None, None, 0)
BETA_COST_OTHER = Beta('BETA_COST_OTHER', -1.5427606901901116, None, None, 0)
BETA_DIST = Beta('BETA_DIST', -4.952750818319787, None, None, 0)
BETA_TIME_CAR_CL = Beta('BETA_TIME_CAR_CL', -0.1412781305192878, None, None, 0)
BETA_TIME_CAR_REF = Beta('BETA_TIME_CAR_REF', -26.647254885078237, None, None,
0)
BETA_TIME_PT_CL = Beta('BETA_TIME_PT_CL', -0.5048865862522101, None, None, 0)
BETA_TIME_PT_REF = Beta('BETA_TIME_PT_REF', -4.883762313381118, None, None, 0)
BETA_WAITING_TIME = Beta('BETA_WAITING_TIME', -0.052434936015492456, None,
None, 0)
coef_age_65_more = Beta('coef_age_65_more', 1.1187240773542817, None, None, 0)
coef_haveChildren = Beta('coef_haveChildren', 0.18090193116052988, None, None,
0)
coef_haveGA = Beta('coef_haveGA', 4.657846388649241, None, None, 0)
# Exclude
exclude = (ArrivalTimeHours_1 == -1)
database.remove(exclude)
# Choice
chosenAlternative = (BestAlternative_1 * 1) + (BestAlternative_2 *
2) + (BestAlternative_3 * 3)
# Parameters to be estimated
# Arguments:
# 1 Name for report. Typically, the same as the variable
# 2 Starting value
# 3 Lower bound
# 4 Upper bound
# 5 0: estimate the parameter, 1: keep it fixed
Constant1 = Beta('Constant1', 0, None, None, 1)
Constant2 = Beta('Constant2', 0, None, None, 0)
Constant3 = Beta('Constant3', 0, None, None, 0)
LogFare = Beta('LogFare', 0, None, None, 0)
Legroom = Beta('Legroom', 0, None, None, 0)
SchedDE = Beta('SchedDE', 0, None, None, 0)
SchedDL = Beta('SchedDL', 0, None, None, 0)
Total_TT1 = Beta('Total_TT1', 0, None, None, 0)
Total_TT2 = Beta('Total_TT2', 0, None, None, 0)
Total_TT3 = Beta('Total_TT3', 0, None, None, 0)
# Define here arithmetic expressions for variables that are not directly available from the data
DepTimeSensitive = DefineVariable('DepTimeSensitive',
q11_DepartureOrArrivalIsImportant == 1,
database)
ArrTimeSensitive = DefineVariable('ArrTimeSensitive',
# 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.
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)
# Parameters to be estimated
ASC_1 = Beta('ASC_1', 0, None, None, 0)
ASC_11 = Beta('ASC_11', 0, None, None, 0)
ASC_2 = Beta('ASC_2', 0, None, None, 0)
ASC_21 = Beta('ASC_21', 0, None, None, 0)
ASC_3 = Beta('ASC_3', 0, None, None, 0)
ASC_31 = Beta('ASC_31', 0, None, None, 0)
ASC_4 = Beta('ASC_4', 0, None, None, 1)
ASC_41 = Beta('ASC_41', 0, None, None, 1)
beta_shcosttaxi1 = Beta('beta_shcosttaxi1', 0, None, None, 0)
beta_shcosttaxi2 = Beta('beta_shcosttaxi2', 0, None, None, 0)
beta_shcostsharedcar = Beta('beta_shcostsharedcar', 0, None, None, 0)
beta_shcostsharedcar2 = Beta('beta_shcostsharedcar2', 0, None, None, 0)
beta_shtime_taxi = Beta('beta_shtimetaxi', 0, None, None, 0)
beta_shtime_taxi1 = Beta('beta_shtimetaxi1', 0, None, None, 0)
beta_shtime_share = Beta('beta_shtimeshare', 0, None, None, 0)
])
# Definition of other variables
age_65_more = DefineVariable('age_65_more', age >= 65, database)
moreThanOneCar = DefineVariable('moreThanOneCar', NbCar > 1, database)
moreThanOneBike = DefineVariable('moreThanOneBike', NbBicy > 1, database)
individualHouse = DefineVariable('individualHouse', HouseType == 1, database)
male = DefineVariable('male', Gender == 1, database)
haveChildren = DefineVariable('haveChildren', \
((FamilSitu == 3) + (FamilSitu == 4)) > 0, database)
haveGA = DefineVariable('haveGA', GenAbST == 1, database)
highEducation = DefineVariable('highEducation', Education >= 6, database)
### Coefficients
coef_intercept = Beta('coef_intercept', betas['coef_intercept'], None, None, 0)
coef_age_65_more = Beta('coef_age_65_more', betas['coef_age_65_more'], None,
None, 0)
coef_haveGA = Beta('coef_haveGA', betas['coef_haveGA'], None, None, 0)
coef_moreThanOneCar = Beta('coef_moreThanOneCar', betas['coef_moreThanOneCar'],
None, None, 0)
coef_moreThanOneBike = Beta('coef_moreThanOneBike',
betas['coef_moreThanOneBike'], None, None, 0)
coef_individualHouse = Beta('coef_individualHouse',
betas['coef_individualHouse'], None, None, 0)
coef_male = Beta('coef_male', betas['coef_male'], None, None, 0)
coef_haveChildren = Beta('coef_haveChildren', betas['coef_haveChildren'], None,
None, 0)
coef_highEducation = Beta('coef_highEducation', betas['coef_highEducation'],
None, None, 0)
# 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
B_TIME = Beta('B_TIME', 0, None, None, 0)
B_COST = Beta('B_COST', 0, None, None, 0)
# Parameters for the ordered logit.
# tau1 <= 0
tau1 = Beta('tau1', -1, None, 0, 0)
# delta2 >= 0
delta2 = Beta('delta2', 2, 0, None, 0)
tau2 = tau1 + delta2
# Definition of new variables
TRAIN_COST = TRAIN_CO * (GA == 0)
TRAIN_TT_SCALED = DefineVariable('TRAIN_TT_SCALED', TRAIN_TT / 100.0, database)
TRAIN_COST_SCALED = DefineVariable('TRAIN_COST_SCALED', TRAIN_COST / 100,
database)
# 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)
LAMBDA = Beta('LAMBDA', 1.5, 0.0001, 5, 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,
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. One version for each latent class.
numberOfClasses = 2
B_COST = [
Beta(f'B_COST_class{i}', 0, None, None, 0) for i in range(numberOfClasses)
]
# Define a random parameter, normally distributed across individuals,
# designed to be used for Monte-Carlo simulation
B_TIME = [
Beta(f'B_TIME_class{i}', 0, None, None, 0) for i in range(numberOfClasses)
]
# It is advised not to use 0 as starting value for the following parameter.
B_TIME_S = [
Beta(f'B_TIME_S_class{i}', 1, None, None, 0)
for i in range(numberOfClasses)
]
B_TIME_RND = [
B_TIME[i] + B_TIME_S[i] * bioDraws(f'B_TIME_RND_class{i}', 'NORMAL_ANTI')