The classes in this module are usually not used directly, but instead through the :pyalpenglow.Getter class. For more info, read
/general/implement_your_model/general/cpp_api/general/simulation_attributes/general/python_api
Note that there are some C++ classes that have no python interface. These are not documented here.
The function of the filter interface is limiting the available set of items. Current filters are whitelist-type filters, implementing :pyalpenglow.cpp.WhitelistFilter.
To use a filter in an experiment, wrap the model into the filter using :pyalpenglow.cpp.WhitelistFilter2ModelAdapter.
Example:
class LabelExperiment(prs.OnlineExperiment):
'''Sample experiment illustrating the usage of LabelFilter. The
experiment contains a PopularityModel and a LabelFilter.'''
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)alpenglow.cpp.WhitelistFilter
alpenglow.cpp.WhitelistFilter2ModelAdapter
alpenglow.cpp.LabelFilterParameters
alpenglow.cpp.LabelFilter
alpenglow.cpp.AvailabilityFilter
Use offline evaluators in traditional, fixed train/test split style learning. Check the code of :pyalpenglow.offline.OfflineModel.OfflineModel descendants for usage examples.
alpenglow.cpp.PrecisionRecallEvaluatorParameters
alpenglow.cpp.PrecisionRecallEvaluator
alpenglow.cpp.OfflineRankingComputerParameters
alpenglow.cpp.OfflinePredictions
alpenglow.cpp.OfflineRankingComputer
alpenglow.cpp.OfflineEvaluator
This module contains the classes that are responsible for reading in the dataset and serving it to other classes of the experiment.
Interface :pyalpenglow.cpp.RecommenderData is the anchestor for classes that read in the dataset. The two most frequently used implementations are :pyalpenglow.cpp.DataframeData and :pyalpenglow.cpp.LegacyRecommenderData.
Interface :pyalpenglow.cpp.RecommenderDataIterator is the anchestor for classes that serve the data to the classes in the online experiment. See /general/anatomy_of_experiment for general information. The most frequently used implementations are :pyalpenglow.cpp.ShuffleIterator and :pyalpenglow.cpp.SimpleIterator.
alpenglow.cpp.RandomOnlineIteratorParameters
alpenglow.cpp.RandomOnlineIterator
alpenglow.cpp.ShuffleIteratorParameters
alpenglow.cpp.ShuffleIterator
alpenglow.cpp.DataframeData
alpenglow.cpp.SimpleIterator
alpenglow.cpp.RecommenderDataIterator
alpenglow.cpp.FactorRepr
alpenglow.cpp.UserItemFactors
alpenglow.cpp.FactorModelReader
alpenglow.cpp.EigenFactorModelReader
alpenglow.cpp.RandomIteratorParameters
alpenglow.cpp.RandomIterator
alpenglow.cpp.InlineAttributeReader
alpenglow.cpp.RecDat
alpenglow.cpp.RecPred
alpenglow.cpp.RecommenderData
alpenglow.cpp.LegacyRecommenderDataParameters
alpenglow.cpp.LegacyRecommenderData
This module contains miscellaneous helper classes.
alpenglow.cpp.PeriodComputerParameters
alpenglow.cpp.PeriodComputer
alpenglow.cpp.SpMatrix
alpenglow.cpp.Random
alpenglow.cpp.RankComputerParameters
alpenglow.cpp.RankComputer
alpenglow.cpp.Bias
alpenglow.cpp.SparseAttributeContainerParameters
alpenglow.cpp.SparseAttributeContainer
alpenglow.cpp.FileSparseAttributeContainer
alpenglow.cpp.Recency
alpenglow.cpp.PowerLawRecencyParameters
alpenglow.cpp.PowerLawRecency
alpenglow.cpp.PopContainer
alpenglow.cpp.TopPopContainer
alpenglow.cpp.ToplistCreatorParameters
alpenglow.cpp.ToplistCreator
alpenglow.cpp.ToplistCreatorGlobalParameters
alpenglow.cpp.ToplistCreatorGlobal
alpenglow.cpp.ToplistCreatorPersonalizedParameters
alpenglow.cpp.ToplistCreatorPersonalized
This module contains the gradient computer classes that implement gradient computation necessary in gradient methods. See :pyalpenglow.experiments.FactorExperiment for an example.
alpenglow.cpp.GradientComputer
alpenglow.cpp.GradientComputerPointWise
This module contains the implementation of objective functions that are necessary for gradient computation in gradient learning methods. See :pyalpenglow.experiments.FactorExperiment for a usage example.
alpenglow.cpp.ObjectiveMSE
alpenglow.cpp.ObjectivePointWise
alpenglow.cpp.ObjectivePairWise
alpenglow.cpp.ObjectiveListWise
This module contains the general interfaces that are implemented by classes belonging to different modules.
alpenglow.cpp.Initializable
alpenglow.cpp.Updater
alpenglow.cpp.NeedsExperimentEnvironment
All the samples in an implicit dataset are positive samples. To make gradient methods work, we need to provide negative samples too. This module contains classes that implement different negative sample generation algorithms. These classes implement :pyalpenglow.cpp.NegativeSampleGenerator. The most frequently used implementation is :pyalpenglow.cpp.UniformNegativeSampleGenerator.
alpenglow.cpp.UniformNegativeSampleGeneratorParameters
alpenglow.cpp.UniformNegativeSampleGenerator
alpenglow.cpp.PooledPositiveSampleGeneratorParameters
alpenglow.cpp.PooledPositiveSampleGenerator
alpenglow.cpp.NegativeSampleGenerator
Use offline learners in traditional, fixed train/test split style learning. Check the code of :pyalpenglow.offline.OfflineModel.OfflineModel descendants for usage examples.
alpenglow.cpp.OfflineIteratingOnlineLearnerWrapperParameters
alpenglow.cpp.OfflineIteratingOnlineLearnerWrapper
alpenglow.cpp.OfflineEigenFactorModelALSLearnerParameters
alpenglow.cpp.OfflineEigenFactorModelALSLearner
alpenglow.cpp.OfflineLearner
alpenglow.cpp.OfflineExternalModelLearnerParameters
alpenglow.cpp.OfflineExternalModelLearner
Loggers implement evaluators, statistics etc. in the online experiment. These classes implement interface :pyalpenglow.cpp.Logger. See /general/anatomy_of_experiment for a general view.
alpenglow.cpp.OnlinePredictions
alpenglow.cpp.PredictionLogger
alpenglow.cpp.RankingLog
alpenglow.cpp.RankingLogs
alpenglow.cpp.MemoryRankingLoggerParameters
alpenglow.cpp.MemoryRankingLogger
alpenglow.cpp.ProceedingLogger
alpenglow.cpp.TransitionModelLoggerParameters
alpenglow.cpp.TransitionModelLogger
alpenglow.cpp.Logger
alpenglow.cpp.OnlinePredictorParameters
alpenglow.cpp.OnlinePredictor
alpenglow.cpp.MemoryUsageLogger
alpenglow.cpp.InterruptLogger
alpenglow.cpp.ConditionalMetaLogger
alpenglow.cpp.ListConditionalMetaLoggerParameters
alpenglow.cpp.ListConditionalMetaLogger
alpenglow.cpp.InputLoggerParameters
alpenglow.cpp.InputLogger
The central classes of the online experiments.
alpenglow.cpp.OnlineExperimentParameters
alpenglow.cpp.OnlineExperiment
alpenglow.cpp.ExperimentEnvironment
The prediction models in the experiments. The model interface is :pyalpenglow.cpp.Model. See /general/rank_computing_optimization about different evaluation methods.
This module contains the matrix factorization based models.
alpenglow.cpp.EigenFactorModelParameters
alpenglow.cpp.EigenFactorModel
alpenglow.cpp.FactorModelUpdater
alpenglow.cpp.FmModelUpdaterParameters
alpenglow.cpp.FmModelUpdater
alpenglow.cpp.FmModelParameters
alpenglow.cpp.FmModel
alpenglow.cpp.AsymmetricFactorModelGradientUpdaterParameters
alpenglow.cpp.AsymmetricFactorModelGradientUpdater
alpenglow.cpp.SvdppModelUpdater
alpenglow.cpp.FactorModelParameters
alpenglow.cpp.FactorModel
alpenglow.cpp.AsymmetricFactorModelUpdater
alpenglow.cpp.SvdppModelGradientUpdaterParameters
alpenglow.cpp.SvdppModelGradientUpdater
alpenglow.cpp.FactorModelGlobalRankingScoreIterator
alpenglow.cpp.SvdppModelParameters
alpenglow.cpp.SvdppModel
alpenglow.cpp.FactorModelGradientUpdaterParameters
alpenglow.cpp.FactorModelGradientUpdater
alpenglow.cpp.AsymmetricFactorModelParameters
alpenglow.cpp.AsymmetricFactorModel
This submodule contains the simple baseline models like nearest neighbor or most popular.
alpenglow.cpp.TransitionProbabilityModel
alpenglow.cpp.NearestNeighborModelParameters
alpenglow.cpp.NearestNeighborModel
alpenglow.cpp.PopularityTimeFrameModelUpdaterParameters
alpenglow.cpp.PopularityTimeFrameModelUpdater
alpenglow.cpp.PersonalPopularityModel
alpenglow.cpp.PersonalPopularityModelUpdater
alpenglow.cpp.PopularityModel
alpenglow.cpp.NearestNeighborModelUpdaterParameters
alpenglow.cpp.NearestNeighborModelUpdater
alpenglow.cpp.TransitionProbabilityModelUpdaterParameters
alpenglow.cpp.TransitionProbabilityModelUpdater
alpenglow.cpp.PopularityModelUpdater
This module contains the models that combine other models. The most frequently used class is :pyalpenglow.cpp.CombinedModel. See /general/combination for a usage example.
alpenglow.cpp.ToplistCombinationModelParameters
alpenglow.cpp.ToplistCombinationModel
alpenglow.cpp.WeightedModelStructure
alpenglow.cpp.WMSUpdater
alpenglow.cpp.RandomChoosingCombinedModelParameters
alpenglow.cpp.RandomChoosingCombinedModel
alpenglow.cpp.CombinedModelParameters
alpenglow.cpp.CombinedModel
alpenglow.cpp.RandomChoosingCombinedModelExpertUpdaterParameters
alpenglow.cpp.RandomChoosingCombinedModelExpertUpdater
alpenglow.cpp.Evaluator
alpenglow.cpp.CombinedDoubleLayerModelGradientUpdaterParameters
alpenglow.cpp.CombinedDoubleLayerModelGradientUpdater
alpenglow.cpp.TopListRecommender
alpenglow.cpp.MassPredictor
alpenglow.cpp.Model
alpenglow.cpp.ExternalModelParameters
alpenglow.cpp.ExternalModel
alpenglow.cpp.PythonModel
alpenglow.cpp.PythonToplistModel
alpenglow.cpp.PythonRankingIteratorModel
alpenglow.cpp.SimilarityModel
alpenglow.cpp.ModelGradientUpdater
alpenglow.cpp.ModelMultiUpdater
alpenglow.cpp.RankingScoreIteratorProvider
alpenglow.cpp.GlobalRankingScoreIterator
The classes in this module are responsible for generating data subsets from the past. This is necessary for embedding offline models into the online framework, that needs to be updated in a batch. See :pyalpenglow.experiments.BatchFactorExperiment for a usage example.
alpenglow.cpp.DataGenerator
alpenglow.cpp.SamplingDataGeneratorParameters
alpenglow.cpp.SamplingDataGenerator
alpenglow.cpp.CompletePastDataGenerator
alpenglow.cpp.TimeframeDataGeneratorParameters
alpenglow.cpp.TimeframeDataGenerator
This module contains classes that modifiy the learning process, e.g. delay the samples or feed them in a batch into offline learning methods.
alpenglow.cpp.LearnerPeriodicDelayedWrapperParameters
alpenglow.cpp.LearnerPeriodicDelayedWrapper
alpenglow.cpp.PeriodicOfflineLearnerWrapperParameters
alpenglow.cpp.PeriodicOfflineLearnerWrapper