def _fit(self, recommender_data, users, items, matrix):
model = rs.EigenFactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
seed=67439852,
))
learner = rs.OfflineEigenFactorModelALSLearner(
**self.parameter_defaults(
number_of_iterations=3,
regularization_lambda=0.0001,
alpha=40,
implicit=1,
clear_before_fit=1,
))
learner.set_model(model)
return (model, learner)
def _config(self, top_k, seed):
model = rs.EigenFactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
seed=67439852,
))
offline_learner = rs.OfflineEigenFactorModelALSLearner(
**self.parameter_defaults(
number_of_iterations=15,
regularization_lambda=1e-3,
alpha=40,
implicit=1,
clear_before_fit=1,
))
offline_learner.set_model(model)
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, [])