a LC-NL model

[Dexue kong]

Dear Professor , 

      I have written a LC-NL model using the " swissmetro" data, the model calculation results are shown in the following text，the estimated value of each unknown parameter is 0 . I don't know what's wrong with my code?

————

Traceback (most recent call last):
  File "D:/python/Workspace/PycharmProjects/test/LCMbiogeme/nestchuli/LCNL_Try.py", line 145, in <module>
    results = biogeme.estimate()
  File "D:\python\Anaconda3\lib\site-packages\biogeme\biogeme.py", line 701, in estimate
    self.calculateInitLikelihood()
  File "D:\python\Anaconda3\lib\site-packages\biogeme\biogeme.py", line 409, in calculateInitLikelihood
    scaled=False)
  File "D:\python\Anaconda3\lib\site-packages\biogeme\biogeme.py", line 445, in calculateLikelihood
    f = self.theC.calculateLikelihood(x, self.fixedBetaValues)
  File "src\cbiogeme.pyx", line 97, in biogeme.cbiogeme.pyBiogeme.calculateLikelihood
RuntimeError: src/bioExprLog.cc:61: Biogeme exception: Current values of the literals:
ASC_CAR_class0 = 0
ASC_CAR_class1 = 0
ASC_CAR_class2 = 0
ASC_SM_class0 = 0
ASC_SM_class1 = 0
ASC_SM_class2 = 0
ASC_TRAIN_class0 = 0
ASC_TRAIN_class1 = 0
ASC_TRAIN_class2 = 0
B_COST_class0 = 0
B_COST_class1 = 0
B_COST_class2 = 0
B_TIME_class0 = 0
B_TIME_class1 = 0
B_TIME_class2 = 0
CAR_AV_SP = 0
CAR_CO_SCALED = 0
CAR_TT_SCALED = 0
CHOICE = 1
MU_class0 = 1
MU_class1 = 1
MU_class2 = 1
PROB_class0 = 0.5
PROB_class1 = 0.5
SM_AV = 1
SM_COST_SCALED = 0
SM_TT_SCALED = 0.56
TRAIN_AV_SP = 1
TRAIN_COST_SCALED = 0
TRAIN_TT_SCALED = 1.32
Cannot take the log of a non positive number [-0.693147]

——————

The code is as follows ：

import pandas as pd
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('swissmetro.dat', '\t')
database = db.Database('swissmetro', df)

# They are organized as panel data. The variable ID identifies each individual.
database.panel("ID")

# The following statement allows you to use the names of the variable
# as Python variable.
globals().update(database.variables)

# 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 = 3

B_COST = [Beta(f'B_COST_class{i}', 0, None, None, 0) for i in range(numberOfClasses)]
B_TIME = [Beta(f'B_TIME_class{i}', 0, None, None, 0) for i in range(numberOfClasses)]
ASC_CAR = [Beta(f'ASC_CAR_class{i}', 0, None, None, 0) for i in range(numberOfClasses)]
ASC_TRAIN = [Beta(f'ASC_TRAIN_class{i}', 0, None, None, 0) for i in range(numberOfClasses)]
ASC_SM = [Beta(f'ASC_SM_class{i}', 0, None, None, 1) for i in range(numberOfClasses)]

# 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)

# Utility functions
V1 = [ASC_TRAIN[i] + B_TIME[i] * TRAIN_TT_SCALED + B_COST[i] * TRAIN_COST_SCALED
for i in range(numberOfClasses)]
V2 = [ASC_SM[i] + B_TIME[i] * SM_TT_SCALED + B_COST[i] * SM_COST_SCALED
for i in range(numberOfClasses)]
V3 = [ASC_CAR[i] + B_TIME[i] * CAR_TT_SCALED + B_COST[i] * CAR_CO_SCALED
for i in range(numberOfClasses)]
V = [{1: V1[i],
2: V2[i],
3: V3[i]} for i in range(numberOfClasses)]

# Associate the availability conditions with the alternatives
av = {1: TRAIN_AV_SP,
2: SM_AV,
3: CAR_AV_SP}

# Definition of nests:
# 1: nests parameter
# 2: list of alternatives
MU = [Beta(f'MU_class{i}', 1, 1, 10, 0) for i in range(numberOfClasses)]

existing = [[MU[i], [1, 3] for i in range(numberOfClasses)]
future = 1.0, [2]
nests = [[existing[i], future] for i in range(numberOfClasses)]

# The choice model is a discrete mixture of logit, with availability conditions
# We calculate the conditional probability for each class
prob = [models.lognested(V[i], av, nests[i], CHOICE) for i in range(numberOfClasses)]

# Class membership model
# Parameters for the class membership model
CLASS_CTE = [Beta(f'CLASS_CTE_class{i}', 0, None, None, 0) for i in range(numberOfClasses)]
CLASS_INC = [Beta(f'CLASS_INC_class{i}', 0, None, None, 0) for i in range(numberOfClasses)]

CLASS_CTE[2] = 0
CLASS_INC[2] = 0
W = [CLASS_CTE[i] + CLASS_INC[i] * INCOME for i in range(numberOfClasses)]
PROB_class = [models.logit({0: W[0], 1: W[1], 2: W[2]}, None, 0) for i in range(numberOfClasses)]

# Conditional to the random variables, likelihood for the individual.
probIndiv = PROB_class[0] * prob[0] + PROB_class[1] * prob[1] + PROB_class[2] * prob[2]

# We integrate over the random variables using Monte-Carlo
logprob = log(probIndiv)

# 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 = '2_LCNL_kdx'

# Estimate the parameters
results = biogeme.estimate()
pandasResults = results.getEstimatedParameters()
print(pandasResults)

[Bierlaire Michel]

The error message is clear: you are trying to take the log of a negative number. 

In order to discover where the problem comes from, use the simulate function of biogeme to report the various quantities involved in your expression.

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