def _config(self, top_k, seed):
model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
))
updater = rs.FactorModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05, regularization_rate=0.0))
updater.set_model(model)
learner_parameters = self.parameter_defaults(
number_of_iterations=3,
start_time=-1,
period_length=86400,
write_model=False,
read_model=False,
clear_model=False,
learn=True,
base_out_file_name="",
base_in_file_name="",
timeframe_length=0,
)
if (learner_parameters['timeframe_length'] == 0):
learner_parameters.pop('timeframe_length', None)
learner = rs.OfflineImplicitGradientLearner(**learner_parameters)
else:
learner = rs.PeriodicTimeframeImplicitGradientLearner(
**learner_parameters)
learner.set_model(model)
learner.add_gradient_updater(updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=0,
initialize_all=False,
seed=67439852,
filter_repeats=False,
))
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 (model, learner, [], [])
def config(self, elems):
config = self.parameter_defaults(
top_k=100,
min_time=0,
loggers=[],
)
model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
))
updater = rs.FactorModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05, regularization_rate=0.0))
updater.set_model(model)
learner = rs.OfflineImplicitGradientLearner(
**self.parameter_defaults(number_of_iterations=3,
start_time=-1,
period_length=86400,
write_model=False,
read_model=False,
clear_model=False,
learn=True,
base_out_file_name="",
base_in_file_name=""))
learner.set_model(model)
# learner.set_recommender_data_iterator(elems['recommender_data_iterator'])
learner.add_gradient_updater(updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=0,
initialize_all=False,
))
# negative_sample_generator.set_train_matrix(elems['train_matrix'])
# negative_sample_generator.set_items(elems['items'])
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 _config(self, top_k, seed):
model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
))
#
# batch
#
# updater
batch_updater = rs.FactorModelGradientUpdater(
**self.parameter_defaults(learning_rate=self.parameter_default(
'batch_learning_rate', 0.05),
regularization_rate=0.0))
batch_updater.set_model(model)
# negative sample generator
batch_negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=self.parameter_default('batch_negative_rate',
70),
initialize_all=False,
seed=67439852,
filter_repeats=False,
))
# objective
point_wise = rs.ObjectiveMSE()
batch_gradient_computer = rs.GradientComputerPointWise()
batch_gradient_computer.set_objective(point_wise)
batch_gradient_computer.set_model(model)
# learner
batch_learner_parameters = self.parameter_defaults(
number_of_iterations=9,
start_time=-1,
period_length=86400,
write_model=False,
read_model=False,
clear_model=False,
learn=True,
base_out_file_name="",
base_in_file_name="",
timeframe_length=0,
)
if (batch_learner_parameters['timeframe_length'] == 0):
batch_learner_parameters.pop('timeframe_length', None)
batch_learner = rs.OfflineImplicitGradientLearner(
**batch_learner_parameters)
else:
batch_learner = rs.PeriodicTimeframeImplicitGradientLearner(
**batch_learner_parameters)
batch_learner.set_model(model)
batch_learner.add_gradient_updater(batch_updater)
batch_learner.set_gradient_computer(batch_gradient_computer)
batch_learner.set_negative_sample_generator(
batch_negative_sample_generator)
#
# online
#
# updater
online_updater = rs.FactorModelGradientUpdater(
**self.parameter_defaults(learning_rate=self.parameter_default(
'online_learning_rate', 0.2),
regularization_rate=0.0))
online_updater.set_model(model)
# negative sample generator
online_negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=self.parameter_default('online_negative_rate',
100),
initialize_all=False,
seed=67439852,
filter_repeats=False,
))
# objective
point_wise = rs.ObjectiveMSE()
online_gradient_computer = rs.GradientComputerPointWise()
online_gradient_computer.set_objective(point_wise)
online_gradient_computer.set_model(model)
# learner
online_learner = rs.ImplicitGradientLearner()
online_learner.add_gradient_updater(online_updater)
online_learner.set_model(model)
online_learner.set_negative_sample_generator(
online_negative_sample_generator)
online_learner.set_gradient_computer(online_gradient_computer)
learner = [batch_learner, online_learner]
return (model, learner, [], [])
def config(self, elems):
config = self.parameter_defaults(
top_k=100,
min_time=0,
loggers=[],
)
model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
))
#
# batch
#
# updater
batch_updater = rs.FactorModelGradientUpdater(
learning_rate=self.parameter_default('batch_learning_rate', 0.05),
regularization_rate=0.0)
batch_updater.set_model(model)
# negative sample generator
batch_negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=self.parameter_default('batch_negative_rate',
70),
initialize_all=False,
))
# objective
point_wise = rs.ObjectiveMSE()
batch_gradient_computer = rs.GradientComputerPointWise()
batch_gradient_computer.set_objective(point_wise)
batch_gradient_computer.set_model(model)
# learner
batch_learner = rs.OfflineImplicitGradientLearner(
**self.parameter_defaults(number_of_iterations=9,
start_time=-1,
period_length=86400,
write_model=False,
read_model=False,
clear_model=False,
learn=True,
base_out_file_name="",
base_in_file_name=""))
batch_learner.set_model(model)
batch_learner.add_gradient_updater(batch_updater)
batch_learner.set_gradient_computer(batch_gradient_computer)
batch_learner.set_negative_sample_generator(
batch_negative_sample_generator)
#
# online
#
# updater
online_updater = rs.FactorModelGradientUpdater(
learning_rate=self.parameter_default('online_learning_rate', 0.2),
regularization_rate=0.0)
online_updater.set_model(model)
# negative sample generator
online_negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=self.parameter_default('online_negative_rate',
100),
initialize_all=False,
))
# objective
point_wise = rs.ObjectiveMSE()
online_gradient_computer = rs.GradientComputerPointWise()
online_gradient_computer.set_objective(point_wise)
online_gradient_computer.set_model(model)
# learner
online_learner = rs.ImplicitGradientLearner()
online_learner.add_gradient_updater(online_updater)
online_learner.set_model(model)
online_learner.set_negative_sample_generator(
online_negative_sample_generator)
online_learner.set_gradient_computer(online_gradient_computer)
learner = [batch_learner, online_learner]
return {'config': config, 'model': model, 'learner': learner}