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, top_k, seed):
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)
return (model, updater, [])
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)