def setUp(self):
data_ = zeros((5,8))
random.seed(1)
data_[:,:4] = random.rand(5,4)
data_ = DataArray(data_, ['Const', 'Var1', 'Var2', 'Var3', 'choice2_ind',
'choice1', 'choice2', 'choice3'])
var_list = [('table1', 'Var1'),
('table1', 'Var2'),
('table1', 'Var3'),
('table1', 'Const')]
variance = array([[1]])
choice1 = ['choice1']
choice2 = ['choice2']
choice3 = ['SOV', 'HOV']
coefficients1 = [{'const':2, 'Var1':2.11}]
coefficients2 = [{'const':1.5, 'var2':-.2, 'var3':16.4}]
coefficients3 = [{'Const':2, 'Var1':2.11}, {'Const':1.2}]
specification1 = Specification(choice1, coefficients1)
specification2 = Specification(choice2, coefficients2)
specification3 = Specification(choice3, coefficients3)
errorspecification = LinearRegErrorSpecification(variance)
model1 = LinearRegressionModel(specification1, errorspecification)
model2 = LinearRegressionModel(specification2, errorspecification)
model3 = LogitChoiceModel(specification3)
data_filter2 = DataFilter('choice2_ind', 'less than', 25,
{'choice2_ind':1, 'choice2':1}) #Run Until Condition
#Subset for the model condition
data_filter1 = DataFilter('Const', 'less than', 0.3)
model_seq1 = SubModel(model1, 'regression', 'choice1')
model_seq2 = SubModel(model2, 'regression', 'choice2',
data_filter=data_filter1, run_until_condition=data_filter2)
model_seq3 = SubModel(model3, 'choice', 'choice3',
run_until_condition=data_filter2)
model_list = [model_seq1, model_seq2, model_seq3]
# SPECIFY SEED TO REPLICATE RESULTS, DATA FILTER AND
# RUN UNTIL CONDITION
component = AbstractComponent('DummyComponent', model_list, var_list)
component.run(data_)
print component.data.data
def __init__(self, choices, coefficients, logsumparameter=None):
Specification.__init__(self, choices, coefficients)
if self.number_choices > 1:
raise SpecificationError, """not a valid inout - more than 1 """\
"""equations specified for the branch in the tree; only one """\
""" choice expected"""
if logsumparameter is not None:
if logsumparameter > 1 or logsumparameter < 0:
raise SpecificationError, """not a valid input - logsumparameter """\
"""out of range; valid range [0-1]"""
self.logsumparameter = logsumparameter
def __init__(self, choices, coefficients, thresholds, distribution=None):
self.thresholds = thresholds
Specification.__init__(self, choices, coefficients)
if distribution is None:
self.distribution = "logit"
else:
self.distribution = distribution
checkVal, checkText = self.check_text_only([self.distribution])
if not checkVal:
raise SpecificationError, """the distribution specification is not """ """a valid string"""
if self.distribution not in ["logit", "probit"]:
raise SpecificationError, """the ordered model formulations """ """supported are probit and logit specification"""
def __init__(self, choices, coefficients, distribution=None):
Specification.__init__(self, choices, coefficients)
if distribution is None:
self.distribution = 'poisson'
else:
self.distribution = distribution
checkVal, checkText = self.check_text_only([self.distribution])
if not checkVal:
raise SpecificationError, """the distribution specification is not """\
"""a valid string"""
if self.distribution not in ['poisson', 'negativebinomial']:
raise SpecificationError, """the ordered model formulations """\
""" supported are poisson and negativebinomial specification"""
def __init__(self, choices, coefficients, distribution=None):
Specification.__init__(self, choices, coefficients)
if distribution is None:
self.distribution = 'poisson'
else:
self.distribution = distribution
checkVal, checkText = self.check_text_only([self.distribution])
if not checkVal:
raise SpecificationError, """the distribution specification is not """\
"""a valid string"""
if self.distribution not in ['poisson', 'negativebinomial']:
raise SpecificationError, """the ordered model formulations """\
""" supported are poisson and negativebinomial specification"""
def __init__(self, choices, coefficients, thresholds, distribution=None):
self.thresholds = thresholds
Specification.__init__(self, choices, coefficients)
if distribution is None:
self.distribution = 'logit'
else:
self.distribution = distribution
checkVal, checkText = self.check_text_only([self.distribution])
if not checkVal:
raise SpecificationError, """the distribution specification is not """\
"""a valid string"""
if self.distribution not in ['logit', 'probit']:
raise SpecificationError, """the ordered model formulations """\
"""supported are probit and logit specification"""
def setUp(self):
choices = ['SOV', 'HOV']
coefficients = [{'Constant': 2, 'Var1': 2.11}, {'Constant': 1.2}]
data = array([[1, 1.1], [1, -0.25], [1, 3.13], [1, -0.11]])
self.choiceset1 = DataArray(ma.array([[0, 1], [0, 1], [1, 1], [1, 1]]),
['SOV', 'HOV'])
self.data = DataArray(data, ['Constant', 'Var1'])
self.specification = Specification(choices, coefficients)
self.utils_array_act = zeros(
(self.data.rows, self.specification.number_choices))
self.utils_array_act[:,
0] = self.data.data[:,
0] * 2 + self.data.data[:,
1] * 2.11
self.utils_array_act[:, 1] = self.data.data[:, 0] * 1.2
self.exp_utils_array_act = exp(self.utils_array_act)
self.prob_array_act = (
self.exp_utils_array_act.transpose() /
self.exp_utils_array_act.cumsum(-1)[:, -1]).transpose()
# for the selected data, and seed = 1, chosen alternatives are
self.selected_act = array([['sov'], ['hov'], ['sov'], ['sov']])
self.selected_act1 = array([['hov'], ['hov'], ['sov'], ['sov']])
def setUp(self):
choice = ['SOV']
coefficients = [{'constant': 2, 'Var1': 2.11}]
data = array([[1, 1.1], [1, -0.25], [1, 3.13], [1, -0.11]])
self.data = DataArray(data, ['Constant', 'VaR1'])
self.specification = Specification(choice, coefficients)
def setUp(self):
choice = ['age_tt_product']
coefficients = [{'age': 1, 'tt': 1}]
data = array([[1, 15], [2, 10]])
self.data = DataArray(data, ['Age', 'TT'])
self.specification = Specification(choice, coefficients)
def setUp(self):
choice = ['DURATION']
coefficients = [{'constant':2, 'Var1':2.11}]
data = array([[1, 1.1], [1, -0.25], [1, 3.13], [1, -0.11]])
variance = array([[1]])
self.data = DataArray(data, ['Constant', 'VaR1'])
self.specification = Specification(choice, coefficients)
self.errorspecification = LinearRegErrorSpecification(variance)
def setUp(self):
choices = ['SOV', 'HOV']
variables = [['Constant', 'Var1'], ['Constant']]
coefficients = [{'Constant': 2, 'Var1': 2.11}, {'Constant': 1.2}]
self.data = DataArray(array([[1, 1.1], [1, -0.25]]),
['Constant', 'Var1'])
self.specification = Specification(choices, coefficients)
self.model = Model(self.specification)
def setUp(self):
choice = ['Frontier']
coefficients = [{'constant': 2, 'Var1': 2.11}]
data = array([[1, 1.1], [1, -0.25], [1, 3.13], [1, -0.11]])
variance = array([[1., 0], [0, 1.1]])
self.data = DataArray(data, ['Constant', 'VaR1'])
self.specification = Specification(choice, coefficients)
self.errorspecification = StochasticRegErrorSpecification(
variance, 'start')
def setUp(self):
choices = ['SOV', 'HOV']
coefficients = [{'Constant': 2, 'Var1': 2.11}, {'Constant': 1.2}]
data = array([[1, 1.1], [1, -0.25], [1, 3.13], [1, -0.11]])
variance = array([[1.1]])
variance1 = array([[1.1, 1.2], [2.1, 2.2]])
self.data = DataArray(data, ['Constant', 'VAR1'])
self.specification = Specification(choices, coefficients)
self.errorspecification = ErrorSpecification(variance, 'normal')
self.errorspecification1 = ErrorSpecification(variance1, 'normal')
