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
        }
Пример #4
0
    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}