def construct_model(self, X, Y):
self.logger.info('Creating model_args config {}'.format(
print_dictionary(self.default_configuration)))
with pm.Model() as self.model:
self.Xt = theano.shared(X)
self.Yt = theano.shared(Y)
shapes = {'weights': self.n_object_features}
# shapes = {'weights': (self.n_object_features, 3)}
weights_dict = create_weight_dictionary(self.model_args, shapes)
intercept = pm.Normal('intercept', mu=0, sd=10)
utility = tt.dot(self.Xt, weights_dict['weights']) + intercept
self.p = ttu.sigmoid(utility)
yl = BinaryCrossEntropyLikelihood('yl', p=self.p, observed=self.Yt)
self.logger.info("Model construction completed")
def construct_model(self, X, Y):
"""
Constructs the linear logit model which evaluated the utility score as :math:`U(x) = w \\cdot x`, where
:math:`w` is the weight vector. The probability of choosing the object :math:`x_i` from the query set
:math:`Q = \\{x_1, \\ldots ,x_n\\}` is:
.. math::
P_i = P(x_i \\lvert Q) = \\frac{1}{1+exp(-U(x_i))}
Parameters
----------
X : numpy array
(n_instances, n_objects, n_features)
Feature vectors of the objects
Y : numpy array
(n_instances, n_objects)
Preferences in form of Choices for given objects
Returns
-------
model : pymc3 Model :class:`pm.Model`
"""
logger.info(
"Creating model_args config {}".format(
print_dictionary(self.model_configuration)
)
)
self.trace_ = None
self.trace_vi_ = None
with pm.Model() as self.model:
self.Xt_ = theano.shared(X)
self.Yt_ = theano.shared(Y)
shapes = {"weights": self.n_object_features_fit_}
# shapes = {'weights': (self.n_object_features_fit_, 3)}
weights_dict = create_weight_dictionary(self.model_configuration, shapes)
intercept = pm.Normal("intercept", mu=0, sd=10)
utility = tt.dot(self.Xt_, weights_dict["weights"]) + intercept
self.p_ = ttu.sigmoid(utility)
BinaryCrossEntropyLikelihood("yl", p=self.p_, observed=self.Yt_)
logger.info("Model construction completed")