def train_MNL(data):
for mode in modes_list:
# availability
data[mode+'_avail'] = 1
database = db.Database("MNL_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 = {}
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')
logprob = bioexp.bioLogLogit(V, AV, CHOICE)
formulas = {'loglike': logprob}
biogeme = bio.BIOGEME(database, formulas,numberOfThreads = 4)
biogeme.modelName = "MNL_SGP"
results = biogeme.estimate()
os.remove("MNL_SGP.html")
os.remove("MNL_SGP.pickle")
# Print the estimated values
betas = results.getBetaValues()
beta={}
for k, v in betas.items():
beta[k] = v
return beta
def train_MNL(data):
for mode in modes_list:
# availability
data[mode+'_avail'] = 1
database = db.Database("MNL_Train", data)
beta_dic = {}
variables = {}
ASC_1 = bioexp.Beta('B___ASC___choice1',0,None,None,1) #fixed
ASC_2 = bioexp.Beta('B___ASC___choice2',0,None,None,0)
for key in att:
beta_dic[key] = {}
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['choice1']:ASC_1, key_choice_index['choice2']:ASC_2}
AV = {}
for key in att:
AV[key_choice_index[key]] = bioexp.Variable(key+'_avail')
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')
logprob = bioexp.bioLogLogit(V, AV, CHOICE)
formulas = {'loglike': logprob}
biogeme = bio.BIOGEME(database, formulas,numberOfThreads = 4)
biogeme.modelName = "MNL_Train"
results = biogeme.estimate()
os.remove("MNL_Train.html")
os.remove("MNL_Train.pickle")
# Print the estimated values
betas = results.getBetaValues()
beta={}
for k, v in betas.items():
beta[k] = v
return beta
def setUp(self):
self.myData = myData2
self.Person = ex.Variable('Person')
self.Variable1 = ex.Variable('Variable1')
self.Variable2 = ex.Variable('Variable2')
self.Choice = ex.Variable('Choice')
self.Av1 = ex.Variable('Av1')
self.Av2 = ex.Variable('Av2')
self.Av3 = ex.Variable('Av3')
self.beta1 = ex.Beta('beta1', 1, None, None, 0)
self.beta2 = ex.Beta('beta2', 2, None, None, 0)
self.beta3 = ex.Beta('beta3', 3, None, None, 1)
self.beta4 = ex.Beta('beta4', 2, None, None, 1)
df = pd.read_csv("swissmetro.dat", '\t')
database = db.Database("swissmetro", df)
# 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_CAR = be.Beta('ASC_CAR', 0, None, None, 0)
ASC_TRAIN = be.Beta('ASC_TRAIN', 0, None, None, 0)
ASC_SM = be.Beta('ASC_SM', 0, None, None, 1)
B_TIME = be.Beta('B_TIME', 0, None, None, 0)
B_COST = be.Beta('B_COST', 0, None, None, 0)
# Definition of new variables
SM_COST = be.Variable('SM_CO') * (be.Variable('GA') == 0)
TRAIN_COST = be.Variable('TRAIN_CO') * (be.Variable('GA') == 0)
# Definition of new variables: adding columns to the database
CAR_AV_SP = be.DefineVariable('CAR_AV_SP', be.Variable('CAR_AV') * (be.Variable('SP') != 0), database)
TRAIN_AV_SP = be.DefineVariable('TRAIN_AV_SP', be.Variable('TRAIN_AV') * (be.Variable('SP') != 0), database)
TRAIN_TT_SCALED = be.DefineVariable('TRAIN_TT_SCALED', be.Variable('TRAIN_TT') / 100.0, database)
TRAIN_COST_SCALED = be.DefineVariable('TRAIN_COST_SCALED', TRAIN_COST / 100, database)
SM_TT_SCALED = be.DefineVariable('SM_TT_SCALED', be.Variable('SM_TT') / 100.0, database)
SM_COST_SCALED = be.DefineVariable('SM_COST_SCALED', SM_COST / 100, database)
CAR_TT_SCALED = be.DefineVariable('CAR_TT_SCALED', be.Variable('CAR_TT') / 100, database)
CAR_CO_SCALED = be.DefineVariable('CAR_CO_SCALED', be.Variable('CAR_CO') / 100, database)
# Definition of the utility functions
V1 = ASC_TRAIN + \
B_TIME * TRAIN_TT_SCALED + \
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