# Associate utility functions with the numbering of alternatives
V = {1: V1,
2: V2,
3: V3}
# Associate the availability conditions with the alternatives
CAR_AV_SP = DefineVariable('CAR_AV_SP',CAR_AV * ( SP != 0 ),database)
TRAIN_AV_SP = DefineVariable('TRAIN_AV_SP',TRAIN_AV * ( SP != 0 ),database)
av = {1: TRAIN_AV_SP,
2: SM_AV,
3: CAR_AV_SP}
#Definition of nests:
# 1: nests parameter
# 2: list of alternatives
existing = MU , [1,3]
future = 1.0 , [2]
nests = existing,future
# The choice model is a nested logit, with availability conditions
logprob = models.lognested(V,av,nests,CHOICE)
biogeme = bio.BIOGEME(database,logprob)
biogeme.modelName = "09nested"
results = biogeme.estimate()
print("Results=",results)
3: availability3,
4: availability4
}
#Definition of nests:
# 1: nests parameter
# 2: list of alternatives
existing = MU, [1, 2]
future1 = 1.0, [3]
future2 = 1.0, [4]
nests = existing, future1, future2
# Definition of the model. This is the contribution of each
# observation to the log likelihood function.
# The choice model is a nested logit, with availability conditions
logprob = models.lognested(choiceset, availability, nests, choice)
# Define level of verbosity
logger = msg.bioMessage()
#logger.setSilent()
#logger.setWarning()
logger.setGeneral()
#logger.setDetailed()
# Create the Biogeme object
biogeme = bio.BIOGEME(database, logprob)
biogeme.modelName = "nested car"
# Estimate the parameters
results = biogeme.estimate()
pandasResults = results.getEstimatedParameters()
def build(self):
exclude = ((self.PURPOSE != 1) * (self.PURPOSE != 3) + (self.CHOICE == 0)) > 0
self.database.remove(exclude)
ASC_CAR = Beta('ASC_CAR', 0, None, None, 0, 'Car cte.')
ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0, 'Train cte.')
ASC_SM = Beta('ASC_SM', 0, None, None, 1, 'Swissmetro cte.')
B_TIME = Beta('B_TIME', 0, None, None, 0, 'Travel time')
B_COST = Beta('B_COST', 0, None, None, 0, 'Travel cost')
MU = Beta('MU', 2.05, 1, 5, 0)
SM_COST = self.SM_CO * (self.GA == 0)
TRAIN_COST = self.TRAIN_CO * (self.GA == 0)
TRAIN_TT_SCALED = DefineVariable('TRAIN_TT_SCALED', \
self.TRAIN_TT / 100.0, self.database)
TRAIN_COST_SCALED = DefineVariable('TRAIN_COST_SCALED', \
TRAIN_COST / 100, self.database)
SM_TT_SCALED = DefineVariable('SM_TT_SCALED', self.SM_TT / 100.0, self.database)
SM_COST_SCALED = DefineVariable('SM_COST_SCALED', SM_COST / 100, self.database)
CAR_TT_SCALED = DefineVariable('CAR_TT_SCALED', self.CAR_TT / 100, self.database)
CAR_CO_SCALED = DefineVariable('CAR_CO_SCALED', self.CAR_CO / 100, self.database)
self.x0 = np.array([0.0, 0.0, 0.0, 0.0, 2.05])
self.bounds = [(-2, 2), (-2, 2), (-2, 2), (-2, 2), (1, 5)]
V1 = ASC_TRAIN + \
B_TIME * TRAIN_TT_SCALED + \
B_COST * TRAIN_COST_SCALED
V2 = ASC_SM + \
B_TIME * SM_TT_SCALED + \
B_COST * SM_COST_SCALED
V3 = ASC_CAR + \
B_TIME * CAR_TT_SCALED + \
B_COST * CAR_CO_SCALED
# Associate utility functions with the numbering of alternatives
V = {1: V1,
2: V2,
3: V3}
# Associate the availability conditions with the alternatives
CAR_AV_SP = DefineVariable('CAR_AV_SP', self.CAR_AV * (self.SP != 0), self.database)
TRAIN_AV_SP = DefineVariable('TRAIN_AV_SP', self.TRAIN_AV * (self.SP != 0), self.database)
av = {1: TRAIN_AV_SP,
2: self.SM_AV,
3: CAR_AV_SP}
# Definition of nests:
# 1: nests parameter
# 2: list of alternatives
existing = MU, [1, 3]
future = 1.0, [2]
nests = existing, future
# The choice model is a nested logit, with availability conditions
logprob = models.lognested(V, av, nests, self.CHOICE)
self.biogeme = bio.BIOGEME(self.database, logprob)
self.biogeme.modelName = "09nested"
self.biogeme.generateHtml = False
self.biogeme.theC.setData(self.database.data)
# Definition of the nests:
# 1: nests parameter
MU_NOCAR = Beta('MU_NOCAR', 1.0, 1.0, None,
0) ###Don't Keep this parameter fixed to the initial value
#(name_param,defaultvalue,downValue,upperValue,bool)
# 2: list of alternatives
CAR_NEST = 1.0, [1]
NO_CAR_NEST = MU_NOCAR, [0, 2]
nests = CAR_NEST, NO_CAR_NEST
# The choice model is a nested logit, with availability conditions
logprob = models.lognested(
V, None, nests,
Choice) ###Non prise en compte de la disponibilité des modes (av)
"""
# Defines an itertor on the data
rowIterator('obsIter')
#Statistics
nullLoglikelihood(av,'obsIter')
choiceSet = [0,1,2]
cteLoglikelihood(choiceSet,Choice,'obsIter')
availabilityStatistics(av,'obsIter')
BIOGEME_OBJECT.STATISTICS['Gender: males'] = \
Sum(male,'obsIter')
BIOGEME_OBJECT.STATISTICS['Gender: females'] = \
Sum(female,'obsIter')
BETA_DIST_MALE * distance_km_scaled * male + \
BETA_DIST_FEMALE * distance_km_scaled * female + \
BETA_DIST_UNREPORTED * distance_km_scaled * unreportedGender
# Associate utility functions with the numbering of alternatives
V = {0: V_PT, 1: V_CAR, 2: V_SM}
# Definition of the nests:
# 1: nests parameter
# 2: list of alternatives
MU_NOCAR = Beta('MU_NOCAR', 1.0, 1.0, None, 0)
CAR_NEST = 1.0, [1]
NO_CAR_NEST = MU_NOCAR, [0, 2]
nests = CAR_NEST, NO_CAR_NEST
# The choice model is a nested logit, with availability conditions
logprob = models.lognested(V, None, nests, Choice)
# Create the Biogeme object
biogeme = bio.BIOGEME(database, logprob)
biogeme.modelName = '01nestedEstimation'
# Estimate the parameters. Calculate also the standard errors using
# bootstrapping.
results = biogeme.estimate(bootstrap=100)
# Get the results in a pandas table
pandasResults = results.getEstimatedParameters()
print(pandasResults)
def train_NL(data):
for mode in modes_list:
# availability
data[mode+'_avail'] = 1
database = db.Database("NL_SGP", data)
beta_dic = {}
variables = {}
ASC_WALK = bioexp.Beta('B___ASC___Walk',0,None,None,1) #fixed
ASC_PT = bioexp.Beta('B___ASC___PT',0,None,None,0)
ASC_RIDEHAIL = bioexp.Beta('B___ASC___RH',0,None,None,0)
ASC_AV = bioexp.Beta('B___ASC___AV',0,None,None,0)
ASC_DRIVE = bioexp.Beta('B___ASC___Drive',0,None,None,0)
for key in att:
beta_dic[key] = {}
if key != 'Walk':
for var in z_vars:
if var not in variables:
variables[var] = bioexp.Variable(var)
beta_name = 'B___' + var + '___' + key
beta_dic[key][beta_name] = bioexp.Beta(beta_name, 0, None, None, 0)
for var in att[key]:
if var not in variables:
variables[var] = bioexp.Variable(var)
beta_name = 'B___' + var + '___' + key
beta_dic[key][beta_name] = bioexp.Beta(beta_name, 0, None, None, 0)
V = {key_choice_index['Walk']:ASC_WALK, key_choice_index['PT']:ASC_PT,
key_choice_index['RH']:ASC_RIDEHAIL,key_choice_index['AV']:ASC_AV,
key_choice_index['Drive']:ASC_DRIVE}
AV = {} # availability
for key in att:
AV[key_choice_index[key]] = bioexp.Variable(key+'_avail')
if key != 'Walk':
for var in z_vars:
beta_name = 'B___' + var + '___' + key
V[key_choice_index[key]] += variables[var] * beta_dic[key][beta_name]
for var in att[key]:
beta_name = 'B___' + var + '___' + key
V[key_choice_index[key]] += variables[var] * beta_dic[key][beta_name]
CHOICE = bioexp.Variable('choice')
MU_car = bioexp.Beta('MU_car', 1, None, None, 0)
MU_pt_walk = bioexp.Beta('MU_pt_walk', 1, None, None, 1)#fixed
N_car = MU_car, [key_choice_index['RH'],key_choice_index['AV'],key_choice_index['Drive']]
N_pt_walk = MU_pt_walk, [key_choice_index['Walk'], key_choice_index['PT']]
nests = N_car, N_pt_walk
logprob = biomodels.lognested(V, AV, nests, CHOICE)
formulas = {'loglike': logprob}
biogeme = bio.BIOGEME(database, formulas,numberOfThreads = 4)
biogeme.modelName = "NL_SGP"
results = biogeme.estimate()
os.remove("NL_SGP.html")
os.remove("NL_SGP.pickle")
# Print the estimated values
betas = results.getBetaValues()
# beta={}
# for k, v in betas.items():
# beta[k] = v
biogeme_file={'V':V, 'av':AV, 'nests':nests,'database':database}
return betas, biogeme_file
