def _config(self, top_k, seed):
model = rs.SvdppModel(
**self.parameter_defaults(begin_min=-0.01,
begin_max=0.01,
dimension=10,
use_sigmoid=False,
norm_type="exponential",
gamma=0.8,
user_vector_weight=0.5,
history_weight=0.5))
gradient_updater = rs.SvdppModelGradientUpdater(
**self.parameter_defaults(
learning_rate=0.05,
cumulative_item_updates=False,
))
gradient_updater.set_model(model)
simple_updater = rs.SvdppModelUpdater()
simple_updater.set_model(model)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
gradient_computer.add_gradient_updater(gradient_updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=20,
initialize_all=False,
seed=928357823,
))
negative_sample_generator.add_updater(gradient_computer)
return (model, [negative_sample_generator, simple_updater], [])
def config(self, elems):
config = self.parameter_defaults(
top_k=100,
min_time=0,
seed=0,
out_file=None,
filters=[],
loggers=[],
)
model = rs.SvdppModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
use_sigmoid=False,
norm_type="exponential",
gamma=0.8,
user_vector_weight=0.5,
history_weight=0.5
))
gradient_updater = rs.SvdppModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05,
cumulative_item_updates=False,
))
gradient_updater.set_model(model)
simple_updater = rs.SvdppModelUpdater()
simple_updater.set_model(model)
learner = rs.ImplicitGradientLearner()
learner.add_gradient_updater(gradient_updater)
learner.add_simple_updater(simple_updater)
learner.set_model(model)
negative_sample_generator = rs.UniformNegativeSampleGenerator(**self.parameter_defaults(
negative_rate=20,
initialize_all=False,
seed=928357823,
))
learner.set_negative_sample_generator(negative_sample_generator)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
learner.set_gradient_computer(gradient_computer)
return {
'config': config,
'model': model,
'learner': learner
}
def _fit(self, recommender_data, users, items, matrix):
model = rs.SvdppModel(**self.parameter_defaults(
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="constant",
gamma=1,
user_vector_weight=0.5,
history_weight=0.5))
gradient_updater = rs.SvdppModelGradientUpdater(
**self.parameter_defaults(
learning_rate=0.05,
cumulative_item_updates=False,
))
gradient_updater.set_model(model)
simple_updater = rs.SvdppModelUpdater()
simple_updater.set_model(model)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
gradient_computer.add_gradient_updater(gradient_updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=9, initialize_all=False, max_item=-1))
negative_sample_generator.set_train_matrix(matrix)
negative_sample_generator.set_items(items)
negative_sample_generator.add_updater(gradient_computer)
learner = rs.OfflineIteratingOnlineLearnerWrapper(
**self.parameter_defaults(
seed=254938879,
number_of_iterations=20,
shuffle=True,
))
learner.add_early_updater(simple_updater)
learner.add_iterate_updater(negative_sample_generator)
return (model, learner)