/// <summary> /// FastTree <see cref="RankingContext"/>. /// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="FastTreeRankingTrainer"/>. /// </summary> /// <param name="ctx">The <see cref="RegressionContext"/>.</param> /// <param name="label">The label column.</param> /// <param name="features">The features column.</param> /// <param name="groupId">The groupId column.</param> /// <param name="weights">The optional weights column.</param> /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param> /// <param name="numLeaves">The maximum number of leaves per decision tree.</param> /// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of a regression tree, out of the subsampled data.</param> /// <param name="learningRate">The learning rate.</param> /// <param name="advancedSettings">Algorithm advanced settings.</param> /// <param name="onFit">A delegate that is called every time the /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the /// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive /// the linear model that was trained. Note that this action cannot change the result in any way; /// it is only a way for the caller to be informed about what was learnt.</param> /// <returns>The Score output column indicating the predicted value.</returns> public static Scalar <float> FastTree <TVal>(this RankingContext.RankingTrainers ctx, Scalar <float> label, Vector <float> features, Key <uint, TVal> groupId, Scalar <float> weights = null, int numLeaves = Defaults.NumLeaves, int numTrees = Defaults.NumTrees, int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves, double learningRate = Defaults.LearningRates, Action <FastTreeRankingTrainer.Arguments> advancedSettings = null, Action <FastTreeRankingModelParameters> onFit = null) { CheckUserValues(label, features, weights, numLeaves, numTrees, minDatapointsInLeaves, learningRate, advancedSettings, onFit); var rec = new TrainerEstimatorReconciler.Ranker <TVal>( (env, labelName, featuresName, groupIdName, weightsName) => { var trainer = new FastTreeRankingTrainer(env, labelName, featuresName, groupIdName, weightsName, numLeaves, numTrees, minDatapointsInLeaves, learningRate, advancedSettings); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } return(trainer); }, label, features, groupId, weights); return(rec.Score); }
/// <summary> /// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="LightGbmRankingTrainer"/>. /// </summary> /// <param name="ctx">The <see cref="RankingContext"/>.</param> /// <param name="label">The label column.</param> /// <param name="features">The features column.</param> /// <param name="groupId">The groupId column.</param> /// <param name="weights">The weights column.</param> /// <param name="numLeaves">The number of leaves to use.</param> /// <param name="numBoostRound">Number of iterations.</param> /// <param name="minDataPerLeaf">The minimal number of documents allowed in a leaf of the tree, out of the subsampled data.</param> /// <param name="learningRate">The learning rate.</param> /// <param name="advancedSettings">Algorithm advanced settings.</param> /// <param name="onFit">A delegate that is called every time the /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the /// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive /// the linear model that was trained. Note that this action cannot change the result in any way; /// it is only a way for the caller to be informed about what was learnt.</param> /// <returns>The set of output columns including in order the predicted binary classification score (which will range /// from negative to positive infinity), the calibrated prediction (from 0 to 1), and the predicted label.</returns> public static Scalar <float> LightGbm <TVal>(this RankingContext.RankingTrainers ctx, Scalar <float> label, Vector <float> features, Key <uint, TVal> groupId, Scalar <float> weights = null, int?numLeaves = null, int?minDataPerLeaf = null, double?learningRate = null, int numBoostRound = LightGbmArguments.Defaults.NumBoostRound, Action <LightGbmArguments> advancedSettings = null, Action <LightGbmRankingModelParameters> onFit = null) { CheckUserValues(label, features, weights, numLeaves, minDataPerLeaf, learningRate, numBoostRound, advancedSettings, onFit); Contracts.CheckValue(groupId, nameof(groupId)); var rec = new TrainerEstimatorReconciler.Ranker <TVal>( (env, labelName, featuresName, groupIdName, weightsName) => { var trainer = new LightGbmRankingTrainer(env, labelName, featuresName, groupIdName, weightsName, numLeaves, minDataPerLeaf, learningRate, numBoostRound, advancedSettings); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } return(trainer); }, label, features, groupId, weights); return(rec.Score); }
/// <summary> /// FastTree <see cref="RankingContext"/>. /// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="FastTreeRankingTrainer"/>. /// </summary> /// <param name="ctx">The <see cref="RegressionContext"/>.</param> /// <param name="label">The label column.</param> /// <param name="features">The features column.</param> /// <param name="groupId">The groupId column.</param> /// <param name="weights">The optional weights column.</param> /// <param name="options">Algorithm advanced settings.</param> /// <param name="onFit">A delegate that is called every time the /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the /// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive /// the linear model that was trained. Note that this action cannot change the result in any way; /// it is only a way for the caller to be informed about what was learnt.</param> /// <returns>The Score output column indicating the predicted value.</returns> public static Scalar <float> FastTree <TVal>(this RankingContext.RankingTrainers ctx, Scalar <float> label, Vector <float> features, Key <uint, TVal> groupId, Scalar <float> weights, FastTreeRankingTrainer.Options options, Action <FastTreeRankingModelParameters> onFit = null) { Contracts.CheckValueOrNull(options); CheckUserValues(label, features, weights, onFit); var rec = new TrainerEstimatorReconciler.Ranker <TVal>( (env, labelName, featuresName, groupIdName, weightsName) => { options.LabelColumn = labelName; options.FeatureColumn = featuresName; options.GroupIdColumn = groupIdName; options.WeightColumn = weightsName != null ? Optional <string> .Explicit(weightsName) : Optional <string> .Implicit(DefaultColumnNames.Weight); var trainer = new FastTreeRankingTrainer(env, options); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } return(trainer); }, label, features, groupId, weights); return(rec.Score); }
/// <summary> /// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="FastTreeRankingTrainer"/>. /// </summary> /// <param name="ctx">The <see cref="RankingContext"/>.</param> /// <param name="options">Algorithm advanced settings.</param> public static FastTreeRankingTrainer FastTree(this RankingContext.RankingTrainers ctx, FastTreeRankingTrainer.Options options) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); return(new FastTreeRankingTrainer(env, options)); }
/// <summary> /// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="FastTreeRankingTrainer"/>. /// </summary> /// <param name="ctx">The <see cref="RegressionContext"/>.</param> /// <param name="label">The label column.</param> /// <param name="features">The features column.</param> /// <param name="groupId">The groupId column.</param> /// <param name="weights">The optional weights column.</param> /// <param name="advancedSettings">Algorithm advanced settings.</param> public static FastTreeRankingTrainer FastTree(this RankingContext.RankingTrainers ctx, string label = DefaultColumnNames.Label, string groupId = DefaultColumnNames.GroupId, string features = DefaultColumnNames.Features, string weights = null, Action<FastTreeRankingTrainer.Arguments> advancedSettings = null) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); return new FastTreeRankingTrainer(env, label, features, groupId, weights, advancedSettings); }
/// <summary> /// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="FastTreeRankingTrainer"/>. /// </summary> /// <param name="ctx">The <see cref="RankingContext"/>.</param> /// <param name="labelColumn">The labelColumn column.</param> /// <param name="featureColumn">The featureColumn column.</param> /// <param name="groupId">The groupId column.</param> /// <param name="weights">The optional weights column.</param> /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param> /// <param name="numLeaves">The maximum number of leaves per decision tree.</param> /// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param> /// <param name="learningRate">The learning rate.</param> public static FastTreeRankingTrainer FastTree(this RankingContext.RankingTrainers ctx, string labelColumn = DefaultColumnNames.Label, string featureColumn = DefaultColumnNames.Features, string groupId = DefaultColumnNames.GroupId, string weights = null, int numLeaves = Defaults.NumLeaves, int numTrees = Defaults.NumTrees, int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves, double learningRate = Defaults.LearningRates) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); return(new FastTreeRankingTrainer(env, labelColumn, featureColumn, groupId, weights, numLeaves, numTrees, minDatapointsInLeaves, learningRate)); }
/// <summary> /// Predict a target using a decision tree binary classification model trained with the <see cref="LightGbmRankingTrainer"/>. /// </summary> /// <param name="ctx">The <see cref="RankingContext"/>.</param> /// <param name="labelColumn">The labelColumn column.</param> /// <param name="featureColumn">The features column.</param> /// <param name="weights">The weights column.</param> /// <param name="groupIdColumn">The groupId column.</param> /// <param name="numLeaves">The number of leaves to use.</param> /// <param name="numBoostRound">Number of iterations.</param> /// <param name="minDataPerLeaf">The minimal number of documents allowed in a leaf of the tree, out of the subsampled data.</param> /// <param name="learningRate">The learning rate.</param> /// <param name="advancedSettings">A delegate to set more settings. /// The settings here will override the ones provided in the direct signature, /// if both are present and have different values. /// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param> public static LightGbmRankingTrainer LightGbm(this RankingContext.RankingTrainers ctx, string labelColumn = DefaultColumnNames.Label, string featureColumn = DefaultColumnNames.Features, string groupIdColumn = DefaultColumnNames.GroupId, string weights = null, int?numLeaves = null, int?minDataPerLeaf = null, double?learningRate = null, int numBoostRound = LightGbmArguments.Defaults.NumBoostRound, Action <LightGbmArguments> advancedSettings = null) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); return(new LightGbmRankingTrainer(env, labelColumn, featureColumn, groupIdColumn, weights, numLeaves, minDataPerLeaf, learningRate, numBoostRound, advancedSettings)); }