def _config(self, top_k, seed):
model = rs.RandomChoosingCombinedModel()
updater = rs.RandomChoosingCombinedModelExpertUpdater(
**self.parameter_defaults(
eta=0.1,
top_k=top_k,
loss_type="abs",
))
updater.set_model(model)
pop_model = rs.PopularityModel()
model.add_model(pop_model)
pop_updater = rs.PopularityModelUpdater()
pop_updater.set_model(pop_model)
pop_model2 = rs.PopularityModel(
) #not updated popularity model, predicts 0 for all items and users
model.add_model(pop_model2)
pop_model3 = rs.PopularityModel(
) #not updated popularity model, predicts 0 for all items and users
model.add_model(pop_model3)
factor_model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
))
model.add_model(factor_model)
factor_updater = rs.FactorModelGradientUpdater(
**self.parameter_defaults(learning_rate=0.05,
regularization_rate=0.0))
factor_updater.set_model(factor_model)
objective = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(objective)
gradient_computer.set_model(factor_model)
gradient_computer.add_gradient_updater(factor_updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=10,
initialize_all=False,
seed=67439852,
filter_repeats=False,
))
negative_sample_generator.add_updater(gradient_computer)
return (model, [updater, pop_updater,
negative_sample_generator], [], [])
def config(self, elems):
config = self.parameter_defaults(
top_k=100,
min_time=0,
seed=0,
out_file=None,
filters=[],
loggers=[],
)
model = rs.PopularityModel()
updater = rs.PopularityTimeFrameModelUpdater(**self.parameter_defaults(
tau=86400
))
updater.set_model(model)
learner = rs.SimpleLearner()
learner.add_simple_updater(updater)
learner.set_model(model)
model = model
learner = learner
return {
'config': config,
'model': model,
'learner': learner
}
def config(self, elems):
config = self.parameter_defaults(
top_k=100,
min_time=0,
seed=0,
out_file=None,
filters=[],
loggers=[],
)
model = rs.PopularityModel()
updater = rs.PopularityModelUpdater()
updater.set_model(model)
simple_learner = rs.SimpleLearner()
simple_learner.add_simple_updater(updater)
simple_learner.set_model(model)
learner = rs.LearnerPeriodicDelayedWrapper(**self.parameter_defaults(
period=86400,
delay=86400
))
learner.set_wrapped_learner(simple_learner)
model = model
learner = learner
return {
'config': config,
'model': model,
'learner': learner
}
def _config(self, top_k, seed):
model = rs.PopularityModel()
updater = rs.PopularityTimeFrameModelUpdater(**self.parameter_defaults(
tau=86400
))
updater.set_model(model)
return (model, updater, [], [])
def _config(self, top_k, seed):
model = ag.PopularityModel()
updater = ag.PopularityModelUpdater()
updater.set_model(model)
label_filter = ag.LabelFilter(**self.parameter_defaults(
label_file_name=""))
adapter = ag.WhitelistFilter2ModelAdapter()
adapter.set_model(model)
adapter.set_whitelist_filter(label_filter)
return (adapter, [updater, label_filter], []
) #note: do not forget to add filter as an updater
def _fit(self, recommender_data, users, items, matrix):
model = rs.PopularityModel()
updater = rs.PopularityModelUpdater()
updater.set_model(model)
learner = rs.OfflineIteratingLearner(
**self.parameter_defaults(seed=67439852, ))
learner.set_model(model)
learner.add_simple_updater(updater)
learner.set_recommender_data(recommender_data)
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 _config(self, top_k, seed):
model = rs.CombinedModel(**self.parameter_defaults(
los_file_name="my_log_file",
log_frequency=100000,
use_user_weights=False,
))
pop_model = rs.PopularityModel()
model.add_model(pop_model)
pop_updater = rs.PopularityModelUpdater()
pop_updater.set_model(pop_model)
factor_model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
))
model.add_model(factor_model)
factor_updater = rs.FactorModelGradientUpdater(
**self.parameter_defaults(learning_rate=0.05,
regularization_rate=0.0))
factor_updater.set_model(factor_model)
objective = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(objective)
gradient_computer.set_model(factor_model)
gradient_computer.add_gradient_updater(factor_updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=10,
initialize_all=False,
seed=67439852,
filter_repeats=False,
))
negative_sample_generator.add_updater(gradient_computer)
return (model, [pop_updater, negative_sample_generator], [], [])
def _config(self, top_k, seed):
model = rs.PopularityModel()
updater = rs.PopularityModelUpdater()
updater.set_model(model)
return (model, updater, [], [])
