import alpenglow.Getter as rs
import alpenglow as prs
[docs]class BatchFactorExperiment(prs.OnlineExperiment):
"""BatchFactorExperiment(dimension=10,begin_min=-0.01,begin_max=0.01,learning_rate=0.05,regularization_rate=0.0,negative_rate=0.0,number_of_iterations=3,period_length=86400)
Batch version of :py:class:`alpenglow.experiments.FactorExperiment.FactorExperiment`,
meaning it retrains its model periodically nd evaluates the latest model between two
training points in an online fashion.
Parameters
----------
dimension : int
The latent factor dimension of the factormodel.
begin_min : double
The factors are initialized randomly, sampling each element uniformly from the interval (begin_min, begin_max).
begin_max : double
See begin_min.
learning_rate : double
The learning rate used in the stochastic gradient descent updates.
regularization_rate : double
The coefficient for the L2 regularization term.
negative_rate : int
The number of negative samples generated after each update. Useful for implicit recommendation.
number_of_iterations : int
The number of iterations over the data in model retrain.
period_length : int
The amount of time between model retrains (seconds).
"""
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)
learner = rs.OfflineImplicitGradientLearner(**self.parameter_defaults(
number_of_iterations=3,
start_time=-1,
period_length=86400,
write_model=False,
read_model=False,
clear_model=False,
learn=True,
base_out_file_name="",
base_in_file_name=""
))
learner.set_model(model)
learner.add_gradient_updater(updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(**self.parameter_defaults(
negative_rate=0,
initialize_all=False,
seed=67439852,
filter_repeats=False,
))
learner.set_negative_sample_generator(negative_sample_generator)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
learner.set_gradient_computer(gradient_computer)
return (model, learner, [], [])