Ejemplo n.º 1
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 /// <summary>
 /// Classification model selecting EnsembleLearner.
 /// Trains several models and selects the best subset of models for the ensemble.
 /// The selection of the best set of models is based on cross validation.
 /// http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="ensembleStrategy">Strategy on how to combine the models</param>
 /// <param name="ensembleSelection">Ensemble selection method used to find the beset subset of models</param>
 public ClassificationModelSelectingEnsembleLearner(
     IIndexedLearner <ProbabilityPrediction>[] learners,
     ICrossValidation <ProbabilityPrediction> crossValidation,
     IClassificationEnsembleStrategy ensembleStrategy,
     IClassificationEnsembleSelection ensembleSelection)
     : this(learners, crossValidation, () => ensembleStrategy, ensembleSelection)
 {
 }
Ejemplo n.º 2
0
 /// <summary>
 /// Regression model selecting EnsembleLearner.
 /// Trains several models and selects the best subset of models for the ensemble.
 /// The selection of the best set of models is based on cross validation.
 /// http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="ensembleStrategy">Strategy on how to combine the models</param>
 /// <param name="ensembleSelection">Ensemble selection method used to find the beset subset of models</param>
 public RegressionModelSelectingEnsembleLearner(
     IIndexedLearner <double>[] learners,
     ICrossValidation <double> crossValidation,
     IRegressionEnsembleStrategy ensembleStrategy,
     IRegressionEnsembleSelection ensembleSelection)
     : this(learners, crossValidation, () => ensembleStrategy, ensembleSelection)
 {
 }
 /// <summary>
 /// Stacking Classification Ensemble Learner.
 /// Combines several models into a single ensemble model using a top or meta level model to combine the models.
 /// The bottom level models generates output for the top level model using cross validation.
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="metaLearner">Meta learner or top level model for combining the ensemble models</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="includeOriginalFeaturesForMetaLearner">True; the meta learner also receives the original features.
 /// False; the meta learner only receives the output of the ensemble models as features. Default is true</param>
 public ClassificationStackingEnsembleLearner(
     IIndexedLearner <ProbabilityPrediction>[] learners,
     ILearner <ProbabilityPrediction> metaLearner,
     ICrossValidation <ProbabilityPrediction> crossValidation,
     bool includeOriginalFeaturesForMetaLearner = true)
     : this(learners, (obs, targets) => metaLearner.Learn(obs, targets),
            crossValidation, includeOriginalFeaturesForMetaLearner)
 {
 }
 /// <summary>
 /// Stacking Regression Ensemble Learner.
 /// Combines several models into a single ensemble model using a top or meta level model to combine the models.
 /// The bottom level models generates output for the top level model using cross validation.
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="metaLearner">Meta learner or top level model for combining the ensemble models</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="includeOriginalFeaturesForMetaLearner">True; the meta learner also receives the original features.
 /// False; the meta learner only receives the output of the ensemble models as features. Default is true</param>
 public RegressionStackingEnsembleLearner(
     IIndexedLearner <double>[] learners,
     ILearner <double> metaLearner,
     ICrossValidation <double> crossValidation,
     bool includeOriginalFeaturesForMetaLearner = true)
     : this(learners, (obs, targets) => metaLearner.Learn(obs, targets),
            crossValidation, includeOriginalFeaturesForMetaLearner)
 {
 }
 /// <summary>
 /// Classification model selecting EnsembleLearner.
 /// Trains several models and selects the best subset of models for the ensemble using greedy backward elimination.
 /// The selection of the best set of models is based on cross validation.
 /// http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="numberOfModelsToSelect">Number of models to select</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="ensembleStrategy">Strategy for ensembling models</param>
 /// <param name="metric">Metric to minimize</param>
 public ClassificationBackwardEliminationModelSelectingEnsembleLearner(
     IIndexedLearner <ProbabilityPrediction>[] learners,
     int numberOfModelsToSelect,
     ICrossValidation <ProbabilityPrediction> crossValidation,
     IClassificationEnsembleStrategy ensembleStrategy,
     IMetric <double, ProbabilityPrediction> metric)
     : base(learners, crossValidation, ensembleStrategy,
            new BackwardEliminationClassificationEnsembleSelection(
                metric, ensembleStrategy, numberOfModelsToSelect))
 {
 }
 /// <summary>
 /// Stacking Classification Ensemble Learner.
 /// Combines several models into a single ensemble model using a top or meta level model to combine the models.
 /// Combines several models into a single ensemble model using a top or meta level model to combine the models.
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="metaLearner">Meta learner or top level model for combining the ensemble models</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="includeOriginalFeaturesForMetaLearner">True; the meta learner also receives the original features.
 /// False; the meta learner only receives the output of the ensemble models as features</param>
 public ClassificationStackingEnsembleLearner(
     IIndexedLearner <ProbabilityPrediction>[] learners,
     Func <F64Matrix, double[], IPredictorModel <ProbabilityPrediction> > metaLearner,
     ICrossValidation <ProbabilityPrediction> crossValidation,
     bool includeOriginalFeaturesForMetaLearner = true)
 {
     m_learners        = learners ?? throw new ArgumentException(nameof(learners));
     m_crossValidation = crossValidation ?? throw new ArgumentException(nameof(crossValidation));
     m_metaLearner     = metaLearner ?? throw new ArgumentException(nameof(metaLearner));
     m_includeOriginalFeaturesForMetaLearner = includeOriginalFeaturesForMetaLearner;
 }
Ejemplo n.º 7
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 /// <summary>
 /// Regression model selecting EnsembleLearner.
 /// Trains several models and selects the best subset of models for the ensemble using backwards elimination.
 /// The selection of the best set of models is based on cross validation.
 /// http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="numberOfModelsToSelect">Number of models to select</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="ensembleStrategy">Strategy for ensembling models</param>
 /// <param name="metric">Metric to minimize</param>
 public RegressionBackwardEliminationModelSelectingEnsembleLearner(
     IIndexedLearner <double>[] learners,
     int numberOfModelsToSelect,
     ICrossValidation <double> crossValidation,
     IRegressionEnsembleStrategy ensembleStrategy,
     IMetric <double, double> metric)
     : base(learners, crossValidation, ensembleStrategy,
            new BackwardEliminationRegressionEnsembleSelection(metric,
                                                               ensembleStrategy, numberOfModelsToSelect))
 {
 }
Ejemplo n.º 8
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 /// <summary>
 /// Classification model selecting EnsembleLearner.
 /// Trains several models and selects the best subset of models for the ensemble.
 /// The selection of the best set of models is based on cross validation.
 /// http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="ensembleStrategy">Strategy on how to combine the models</param>
 /// <param name="ensembleSelection">Ensemble selection method used to find the beset subset of models</param>
 public ClassificationModelSelectingEnsembleLearner(
     IIndexedLearner <ProbabilityPrediction>[] learners,
     ICrossValidation <ProbabilityPrediction> crossValidation,
     Func <IClassificationEnsembleStrategy> ensembleStrategy,
     IClassificationEnsembleSelection ensembleSelection)
 {
     m_learners          = learners ?? throw new ArgumentNullException(nameof(learners));
     m_crossValidation   = crossValidation ?? throw new ArgumentNullException(nameof(crossValidation));
     m_ensembleStrategy  = ensembleStrategy ?? throw new ArgumentNullException(nameof(ensembleStrategy));
     m_ensembleSelection = ensembleSelection ?? throw new ArgumentNullException(nameof(ensembleSelection));
 }
Ejemplo n.º 9
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 /// <summary>
 /// Regression model selecting EnsembleLearner.
 /// Trains several models and selects the best subset of models for the ensemble.
 /// The selection of the best set of models is based on cross validation.
 /// Trains several models and selects the best subset of models for the ensemble.
 /// http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="ensembleStrategy">Strategy on how to combine the models</param>
 /// <param name="ensembleSelection">Ensemble selection method used to find the beset subset of models</param>
 public RegressionModelSelectingEnsembleLearner(
     IIndexedLearner <double>[] learners,
     ICrossValidation <double> crossValidation,
     Func <IRegressionEnsembleStrategy> ensembleStrategy,
     IRegressionEnsembleSelection ensembleSelection)
 {
     m_learners          = learners ?? throw new ArgumentNullException(nameof(learners));
     m_crossValidation   = crossValidation ?? throw new ArgumentNullException(nameof(crossValidation));
     m_ensembleStrategy  = ensembleStrategy ?? throw new ArgumentNullException(nameof(ensembleStrategy));
     m_ensembleSelection = ensembleSelection ?? throw new ArgumentNullException(nameof(ensembleSelection));
 }
Ejemplo n.º 10
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 /// <summary>
 /// Regression model selecting EnsembleLearner.
 /// Trains several models and selects the best subset of models for the ensemble using greedy forward selection.
 /// The selection of the best set of models is based on cross validation.
 /// http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="numberOfModelsToSelect">Number of models to select</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="ensembleStrategy">Strategy for ensembling models</param>
 /// <param name="metric">Metric to minimize</param>
 /// <param name="numberOfModelsFromStart">Number of models from start of the search.
 /// The top n models will be selected based in their solo performance</param>
 /// <param name="selectWithReplacement">If true the same model can be selected multiple times.
 /// This will correspond to weighting the models. If false each model can only be selected once. Default is true</param>
 public RegressionForwardSearchModelSelectingEnsembleLearner(
     IIndexedLearner <double>[] learners,
     int numberOfModelsToSelect,
     ICrossValidation <double> crossValidation,
     IRegressionEnsembleStrategy ensembleStrategy,
     IMetric <double, double> metric,
     int numberOfModelsFromStart = 1,
     bool selectWithReplacement  = true)
     : base(learners, crossValidation, ensembleStrategy,
            new ForwardSearchRegressionEnsembleSelection(metric, ensembleStrategy, numberOfModelsToSelect,
                                                         numberOfModelsFromStart, selectWithReplacement))
 {
 }
Ejemplo n.º 11
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 /// <summary>
 /// Classification model selecting EnsembleLearner.
 /// Trains several models and selects the best subset of models for the ensemble using greedy forward selection.
 /// The selection of the best set of models is based on cross validation.
 /// http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="numberOfModelsToSelect">Number of models to select</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="ensembleStrategy">Strategy for ensembling models</param>
 /// <param name="metric">Metric to minimize</param>
 /// <param name="numberOfModelsFromStart">Number of models from start of the search.
 /// The top n models will be selected based in their solo performance</param>
 /// <param name="selectWithReplacement">If true the same model can be selected multiple times.
 /// This will correspond to weighting the models. If false each model can only be selected once. Default is true</param>
 public ClassificationForwardSearchModelSelectingEnsembleLearner(
     IIndexedLearner <ProbabilityPrediction>[] learners,
     int numberOfModelsToSelect,
     ICrossValidation <ProbabilityPrediction> crossValidation,
     IClassificationEnsembleStrategy ensembleStrategy,
     IMetric <double, ProbabilityPrediction> metric,
     int numberOfModelsFromStart = 1,
     bool selectWithReplacement  = true)
     : base(learners, crossValidation, ensembleStrategy,
            new ForwardSearchClassificationEnsembleSelection(
                metric, ensembleStrategy, numberOfModelsToSelect,
                numberOfModelsFromStart, selectWithReplacement))
 {
 }
 /// <summary>
 /// Regression model selecting EnsembleLearner.
 /// Trains several models and selects the best subset of models for the ensemble using iterative random selection.
 /// The selection of the best set of models is based on cross validation.
 /// http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="numberOfModelsToSelect">Number of models to select</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="ensembleStrategy">Strategy for ensembling models</param>
 /// <param name="metric">Metric to minimize</param>
 /// <param name="iterations">Number of iterations to random select model combinations.</param>
 /// <param name="selectWithReplacement">If true the same model can be selected multiple times.</param>
 /// <param name="seed"></param>
 public RegressionRandomModelSelectingEnsembleLearner(
     IIndexedLearner <double>[] learners,
     int numberOfModelsToSelect,
     ICrossValidation <double> crossValidation,
     IRegressionEnsembleStrategy ensembleStrategy,
     IMetric <double, double> metric,
     int iterations             = 50,
     bool selectWithReplacement = true,
     int seed = 42)
     : base(learners, crossValidation, ensembleStrategy,
            new RandomRegressionEnsembleSelection(metric, ensembleStrategy, numberOfModelsToSelect,
                                                  iterations, selectWithReplacement, seed))
 {
 }
Ejemplo n.º 13
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 /// <summary>
 /// Classification model selecting EnsembleLearner.
 /// Trains several models and selects the best subset of models for the ensemble using iterative random selection.
 /// The selection of the best set of models is based on cross validation.
 /// http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="numberOfModelsToSelect">Number of models to select</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="ensembleStrategy">Strategy for ensembling models</param>
 /// <param name="metric">Metric to minimize</param>
 /// <param name="iterations">Number of iterations to random select model combinations.</param>
 /// <param name="selectWithReplacement">If true the same model can be selected multiple times.</param>
 /// <param name="seed"></param>
 public ClassificationRandomModelSelectingEnsembleLearner(
     IIndexedLearner <ProbabilityPrediction>[] learners,
     int numberOfModelsToSelect,
     ICrossValidation <ProbabilityPrediction> crossValidation,
     IClassificationEnsembleStrategy ensembleStrategy,
     IMetric <double, ProbabilityPrediction> metric,
     int iterations             = 50,
     bool selectWithReplacement = true,
     int seed = 42)
     : base(learners, crossValidation, ensembleStrategy,
            new RandomClassificationEnsembleSelection(
                metric, ensembleStrategy, numberOfModelsToSelect,
                iterations, selectWithReplacement, seed))
 {
 }
Ejemplo n.º 14
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 /// <summary>
 /// Stacking Regression Ensemble Learner.
 /// Combines several models into a single ensemble model using a top or meta level model to combine the models.
 /// The bottom level models generates output for the top level model using cross validation.
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="metaLearner">Meta learner or top level model for combining the ensemble models</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="includeOriginalFeaturesForMetaLearner">True; the meta learner also recieves the original features.
 /// False; the meta learner only recieves the output of the ensemble models as features</param>
 public RegressionStackingEnsembleLearner(IIndexedLearner <double>[] learners, Func <F64Matrix, double[], IPredictorModel <double> > metaLearner,
                                          ICrossValidation <double> crossValidation, bool includeOriginalFeaturesForMetaLearner = true)
 {
     if (learners == null)
     {
         throw new ArgumentException("learners");
     }
     if (crossValidation == null)
     {
         throw new ArgumentException("crossValidation");
     }
     if (metaLearner == null)
     {
         throw new ArgumentException("metaLearner");
     }
     m_learners        = learners;
     m_crossValidation = crossValidation;
     m_metaLearner     = metaLearner;
     m_includeOriginalFeaturesForMetaLearner = includeOriginalFeaturesForMetaLearner;
 }
 /// <summary>
 /// Regression model selecting EnsembleLearner.
 /// Trains several models and selects the best subset of models for the ensemble.
 /// The selection of the best set of models is based on cross validation.
 /// Trains several models and selects the best subset of models for the ensemble.
 /// http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="ensembleStrategy">Strategy on how to combine the models</param>
 /// <param name="ensembleSelection">Enemble selection method used to find the beset subset of models</param>
 public RegressionModelSelectingEnsembleLearner(IIndexedLearner <double>[] learners, ICrossValidation <double> crossValidation,
                                                Func <IRegressionEnsembleStrategy> ensembleStrategy, IRegressionEnsembleSelection ensembleSelection)
 {
     if (learners == null)
     {
         throw new ArgumentNullException("learners");
     }
     if (crossValidation == null)
     {
         throw new ArgumentNullException("crossValidation");
     }
     if (ensembleStrategy == null)
     {
         throw new ArgumentNullException("ensembleStrategy");
     }
     if (ensembleSelection == null)
     {
         throw new ArgumentNullException("ensembleSelection");
     }
     m_learners          = learners;
     m_crossValidation   = crossValidation;
     m_ensembleStrategy  = ensembleStrategy;
     m_ensembleSelection = ensembleSelection;
 }
 /// <summary>
 /// Classification model selecting EnsembleLearner.
 /// Trains several models and selects the best subset of models for the ensemble.
 /// The selection of the best set of models is based on cross validation.
 /// http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
 /// </summary>
 /// <param name="learners">Learners in the ensemble</param>
 /// <param name="crossValidation">Cross validation method</param>
 /// <param name="ensembleStrategy">Strategy on how to combine the models</param>
 /// <param name="ensembleSelection">Ensemble selection method used to find the beset subset of models</param>
 public ClassificationModelSelectingEnsembleLearner(IIndexedLearner <ProbabilityPrediction>[] learners, ICrossValidation <ProbabilityPrediction> crossValidation,
                                                    Func <IClassificationEnsembleStrategy> ensembleStrategy, IClassificationEnsembleSelection ensembleSelection)
 {
     if (learners == null)
     {
         throw new ArgumentNullException("learners");
     }
     if (crossValidation == null)
     {
         throw new ArgumentNullException("crossValidation");
     }
     if (ensembleStrategy == null)
     {
         throw new ArgumentNullException("ensembleStrategy");
     }
     if (ensembleSelection == null)
     {
         throw new ArgumentNullException("ensembleSelection");
     }
     m_learners          = learners;
     m_crossValidation   = crossValidation;
     m_ensembleStrategy  = ensembleStrategy;
     m_ensembleSelection = ensembleSelection;
 }