import alpenglow.Getter as rs
import alpenglow.offline
[docs]class AsymmetricFactorModel(alpenglow.offline.OfflineModel):
"""AsymmetricFactorExperiment(dimension=10,begin_min=-0.01,begin_max=0.01,learning_rate=0.05,regularization_rate=0.0,negative_rate=0,number_of_iterations=9)
Implements the recommendation model introduced in [Paterek2007]_.
Parameters
----------
dimension : int
The latent factor dimension of the factormodel.
begin_min : double
The factors are initialized randomly, sampling each element uniformly from the interval (begin_min, begin_max).
begin_max : double
See begin_min.
learning_rate : double
The learning rate used in the stochastic gradient descent updates.
regularization_rate : double
The coefficient for the L2 regularization term.
negative_rate : int
The number of negative samples generated after each update. Useful for implicit recommendation.
number_of_iterations : int
Number of times to iterate over the training data.
"""
def _fit(self, recommender_data, users, items, matrix):
model = rs.AsymmetricFactorModel(
begin_min=self.parameter_default("begin_min", -0.01),
begin_max=self.parameter_default("begin_max", 0.01),
dimension=self.parameter_default("dimension", 10),
use_sigmoid=False,
norm_type="disabled",
gamma=1
)
updater = rs.AsymmetricFactorModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05,
regularization_rate=0.0
))
updater.set_model(model)
simple_updater = rs.AsymmetricFactorModelUpdater()
simple_updater.set_model(model)
negative_sample_generator = rs.UniformNegativeSampleGenerator(**self.parameter_defaults(
negative_rate=0
))
negative_sample_generator.set_train_matrix(matrix)
negative_sample_generator.set_items(items)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
learner = rs.OfflineIteratingImplicitLearner(**self.parameter_defaults(
seed=254938879,
number_of_iterations=9,
))
learner.set_gradient_computer(gradient_computer)
learner.set_negative_sample_generator(negative_sample_generator)
learner.set_model(model)
learner.set_recommender_data(recommender_data)
learner.add_gradient_updater(updater)
learner.add_early_simple_updater(simple_updater)
return (model, learner)