Ejemplo n.º 1
0
        /// <summary>
        /// Predict a target using a tree regression model trained with the <see cref="LightGbmRegressorTrainer"/>.
        /// </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="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 Score output column indicating the predicted value.</returns>
        /// <example>
        /// <format type="text/markdown">
        /// <![CDATA[
        ///  [!code-csharp[LightGBM](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Static/LightGBMRegression.cs)]
        /// ]]></format>
        /// </example>
        public static Scalar <float> LightGbm(this RegressionContext.RegressionTrainers ctx,
                                              Scalar <float> label, Vector <float> features, Scalar <float> weights = null,
                                              int?numLeaves       = null,
                                              int?minDataPerLeaf  = null,
                                              double?learningRate = null,
                                              int numBoostRound   = LightGbmArguments.Defaults.NumBoostRound,
                                              Action <LightGbmArguments> advancedSettings      = null,
                                              Action <LightGbmRegressionModelParameters> onFit = null)
        {
            CheckUserValues(label, features, weights, numLeaves, minDataPerLeaf, learningRate, numBoostRound, advancedSettings, onFit);

            var rec = new TrainerEstimatorReconciler.Regression(
                (env, labelName, featuresName, weightsName) =>
            {
                var trainer = new LightGbmRegressorTrainer(env, labelName, featuresName, weightsName, numLeaves,
                                                           minDataPerLeaf, learningRate, numBoostRound, advancedSettings);
                if (onFit != null)
                {
                    return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
                }
                return(trainer);
            }, label, features, weights);

            return(rec.Score);
        }
Ejemplo n.º 2
0
        public void LightGBMRegressorEstimator()
        {
            // Pipeline.
            var pipeline = new LightGbmRegressorTrainer(Env, "Label", "Features", advancedSettings: s => {
                s.NThread           = 1;
                s.NormalizeFeatures = NormalizeOption.Warn;
                s.CatL2             = 5;
            });

            TestEstimatorCore(pipeline, GetRegressionPipeline());
            Done();
        }
Ejemplo n.º 3
0
        [ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // LightGBM is 64-bit only
        public void LightGBMRegressorEstimator()
        {
            var dataView = GetRegressionPipeline();
            var trainer  = new LightGbmRegressorTrainer(Env, "Label", "Features", advancedSettings: s =>
            {
                s.NThread           = 1;
                s.NormalizeFeatures = NormalizeOption.Warn;
                s.CatL2             = 5;
            });

            TestEstimatorCore(trainer, dataView);
            var model = trainer.Train(dataView, dataView);

            Done();
        }
Ejemplo n.º 4
0
        /// <summary>
        /// Predict a target using a tree regression model trained with the <see cref="LightGbmRegressorTrainer"/>.
        /// </summary>
        /// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
        /// <param name="label">The label column.</param>
        /// <param name="features">The features column.</param>
        /// <param name="weights">The 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> LightGbm(this RegressionCatalog.RegressionTrainers catalog,
                                              Scalar <float> label, Vector <float> features, Scalar <float> weights,
                                              Options options,
                                              Action <LightGbmRegressionModelParameters> onFit = null)
        {
            CheckUserValues(label, features, weights, options, onFit);

            var rec = new TrainerEstimatorReconciler.Regression(
                (env, labelName, featuresName, weightsName) =>
            {
                options.LabelColumnName         = labelName;
                options.FeatureColumnName       = featuresName;
                options.ExampleWeightColumnName = weightsName;

                var trainer = new LightGbmRegressorTrainer(env, options);
                if (onFit != null)
                {
                    return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
                }
                return(trainer);
            }, label, features, weights);

            return(rec.Score);
        }
Ejemplo n.º 5
0
        /// <summary>
        /// Predict a target using a tree regression model trained with the <see cref="LightGbmRegressorTrainer"/>.
        /// </summary>
        /// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
        /// <param name="label">The label column.</param>
        /// <param name="features">The features column.</param>
        /// <param name="weights">The weights column.</param>
        /// <param name="numberOfLeaves">The number of leaves to use.</param>
        /// <param name="minimumExampleCountPerLeaf">The minimal number of data points allowed in a leaf of the tree, out of the subsampled data.</param>
        /// <param name="learningRate">The learning rate.</param>
        /// <param name="numberOfIterations">Number of iterations.</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>
        /// <example>
        /// <format type="text/markdown">
        /// <![CDATA[
        ///  [!code-csharp[LightGBM](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Static/LightGBMRegression.cs)]
        /// ]]></format>
        /// </example>
        public static Scalar <float> LightGbm(this RegressionCatalog.RegressionTrainers catalog,
                                              Scalar <float> label, Vector <float> features, Scalar <float> weights = null,
                                              int?numberOfLeaves             = null,
                                              int?minimumExampleCountPerLeaf = null,
                                              double?learningRate            = null,
                                              int numberOfIterations         = Options.Defaults.NumberOfIterations,
                                              Action <LightGbmRegressionModelParameters> onFit = null)
        {
            CheckUserValues(label, features, weights, numberOfLeaves, minimumExampleCountPerLeaf, learningRate, numberOfIterations, onFit);

            var rec = new TrainerEstimatorReconciler.Regression(
                (env, labelName, featuresName, weightsName) =>
            {
                var trainer = new LightGbmRegressorTrainer(env, labelName, featuresName, weightsName, numberOfLeaves,
                                                           minimumExampleCountPerLeaf, learningRate, numberOfIterations);
                if (onFit != null)
                {
                    return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
                }
                return(trainer);
            }, label, features, weights);

            return(rec.Score);
        }