def _fit(self, recommender_data, users, items, matrix):
model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
seed=self.parameter_default('factor_seed', 67439852),
))
updater = rs.FactorModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05, regularization_rate=0.0))
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(updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=0, 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=self.parameter_default('learner_seed', 254938879),
number_of_iterations=9,
shuffle=True,
))
learner.add_iterate_updater(negative_sample_generator)
return (model, learner)
def _fit(self, recommender_data, users, items, matrix):
model = rs.PopularityModel()
updater = rs.PopularityModelUpdater()
updater.set_model(model)
learner = rs.OfflineIteratingOnlineLearnerWrapper(
seed=0,
number_of_iterations=0,
shuffle=False,
)
learner.add_updater(updater)
return (model, 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)
def _fit(self, recommender_data, users, items, matrix):
model = rs.NearestNeighborModel(
gamma=1,
norm="off",
direction="both",
gamma_threshold=0,
num_of_neighbors=self.parameter_default("num_of_neighbors", 10),
)
updater = rs.NearestNeighborModelUpdater(
period_mode="off",
)
updater.set_model(model)
learner = rs.OfflineIteratingOnlineLearnerWrapper(
seed=254938879,
number_of_iterations=0,
shuffle=False,
)
learner.add_updater(updater)
return (model, 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)
# objective
point_wise = rs.ObjectiveMSE()
batch_gradient_computer = rs.GradientComputerPointWise()
batch_gradient_computer.set_objective(point_wise)
batch_gradient_computer.set_model(model)
batch_gradient_computer.add_gradient_updater(batch_updater)
# 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,
))
batch_negative_sample_generator.add_updater(batch_gradient_computer)
batch_offline_learner = rs.OfflineIteratingOnlineLearnerWrapper(
**self.parameter_defaults(
seed=254938879,
number_of_iterations=3,
shuffle=True,
))
batch_offline_learner.add_iterate_updater(
batch_negative_sample_generator)
batch_online_learner = rs.PeriodicOfflineLearnerWrapper(
**self.parameter_defaults(
write_model=False,
read_model=False,
clear_model=False,
learn=True,
base_out_file_name="",
base_in_file_name="",
))
batch_online_learner.set_model(model)
batch_online_learner.add_offline_learner(batch_offline_learner)
batch_data_generator_parameters = self.parameter_defaults(
timeframe_length=0, )
if (batch_data_generator_parameters['timeframe_length'] == 0):
print("Full experiment")
batch_data_generator = rs.CompletePastDataGenerator()
else:
print("Timeframe experiment")
batch_data_generator = rs.TimeframeDataGenerator(
**batch_data_generator_parameters)
batch_online_learner.set_data_generator(batch_data_generator)
batch_period_computer = rs.PeriodComputer(**self.parameter_defaults(
period_length=86400,
start_time=-1,
period_mode="time",
))
batch_online_learner.set_period_computer(batch_period_computer)
#
# 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)
# objective
point_wise = rs.ObjectiveMSE()
online_gradient_computer = rs.GradientComputerPointWise()
online_gradient_computer.set_objective(point_wise)
online_gradient_computer.set_model(model)
online_gradient_computer.add_gradient_updater(online_updater)
# 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,
))
online_negative_sample_generator.add_updater(online_gradient_computer)
learner = [batch_online_learner, online_negative_sample_generator]
return (model, 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,
))
updater = rs.FactorModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05, regularization_rate=0.0))
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(updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=0,
initialize_all=False,
seed=67439852,
filter_repeats=False,
))
negative_sample_generator.add_updater(gradient_computer)
offline_learner = rs.OfflineIteratingOnlineLearnerWrapper(
**self.parameter_defaults(
seed=254938879,
number_of_iterations=3,
shuffle=True,
))
offline_learner.add_iterate_updater(negative_sample_generator)
online_learner = rs.PeriodicOfflineLearnerWrapper(
**self.parameter_defaults(
write_model=False,
read_model=False,
clear_model=False,
learn=True,
base_out_file_name="",
base_in_file_name="",
))
online_learner.set_model(model)
online_learner.add_offline_learner(offline_learner)
data_generator_parameters = self.parameter_defaults(
timeframe_length=0, )
if (data_generator_parameters['timeframe_length'] == 0):
data_generator = rs.CompletePastDataGenerator()
else:
data_generator = rs.TimeframeDataGenerator(
**data_generator_parameters)
online_learner.set_data_generator(data_generator)
period_computer = rs.PeriodComputer(**self.parameter_defaults(
period_length=86400,
start_time=-1,
period_mode="time",
))
online_learner.set_period_computer(period_computer)
return (model, online_learner, [])
