def config(self, elems):
config = self.parameter_defaults(
top_k=100,
min_time=0,
seed=0,
out_file=None,
filters=[],
loggers=[],
)
model = rs.TransitionProbabilityModel()
updater = rs.TransitionProbabilityModelUpdater(
**self.parameter_defaults(filter_freq_updates=False,
mode_="normal",
label_transition_mode_=False,
label_file_name_=""))
updater.set_model(model)
learner = rs.SimpleLearner()
learner.add_simple_updater(updater)
learner.set_model(model)
model = model
learner = learner
filters = [model]
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.NearestNeighborModel(
**self.parameter_defaults(gamma=0.8,
norm="num",
direction="forward",
gamma_threshold=0,
num_of_neighbors=10))
updater = rs.NearestNeighborModelUpdater(**self.parameter_defaults(
compute_similarity_period=86400, period_mode="time-based"))
updater.set_model(model)
learner = rs.SimpleLearner()
learner.add_simple_updater(updater)
learner.set_model(model)
model = model
learner = learner
filters = [model]
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.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.PersonalPopularityModel()
updater = rs.PersonalPopularityModelUpdater()
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}