コード例 #1
0
        /// <summary>
        /// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Runtime.Learners.LogisticRegression"/> trainer.
        /// </summary>
        /// <param name="ctx">The regression context trainer object.</param>
        /// <param name="label">The label, or dependent variable.</param>
        /// <param name="features">The features, or independent variables.</param>
        /// <param name="weights">The optional example weights.</param>
        /// <param name="enoforceNoNegativity">Enforce non-negative weights.</param>
        /// <param name="l1Weight">Weight of L1 regularization term.</param>
        /// <param name="l2Weight">Weight of L2 regularization term.</param>
        /// <param name="memorySize">Memory size for <see cref="Microsoft.ML.Runtime.Learners.LogisticRegression"/>. Low=faster, less accurate.</param>
        /// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
        /// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</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 predicted output.</returns>
        public static Scalar <float> PoissonRegression(this RegressionContext.RegressionTrainers ctx,
                                                       Scalar <float> label,
                                                       Vector <float> features,
                                                       Scalar <float> weights                          = null,
                                                       float l1Weight                                  = Arguments.Defaults.L1Weight,
                                                       float l2Weight                                  = Arguments.Defaults.L2Weight,
                                                       float optimizationTolerance                     = Arguments.Defaults.OptTol,
                                                       int memorySize                                  = Arguments.Defaults.MemorySize,
                                                       bool enoforceNoNegativity                       = Arguments.Defaults.EnforceNonNegativity,
                                                       Action <Arguments> advancedSettings             = null,
                                                       Action <PoissonRegressionModelParameters> onFit = null)
        {
            LbfgsStaticUtils.ValidateParams(label, features, weights, l1Weight, l2Weight, optimizationTolerance, memorySize, enoforceNoNegativity, advancedSettings, onFit);

            var rec = new TrainerEstimatorReconciler.Regression(
                (env, labelName, featuresName, weightsName) =>
            {
                var trainer = new PoissonRegression(env, labelName, featuresName, weightsName,
                                                    l1Weight, l2Weight, optimizationTolerance, memorySize, enoforceNoNegativity);

                if (onFit != null)
                {
                    return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
                }

                return(trainer);
            }, label, features, weights);

            return(rec.Score);
        }
コード例 #2
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        /// <summary>
        ///  Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Trainers.LogisticRegression"/> trainer.
        /// </summary>
        /// <param name="catalog">The binary classification catalog trainer object.</param>
        /// <param name="label">The label, or dependent variable.</param>
        /// <param name="features">The features, or independent variables.</param>
        /// <param name="weights">The optional example weights.</param>
        /// <param name="enforceNonNegativity">Enforce non-negative weights.</param>
        /// <param name="l1Regularization">Weight of L1 regularization term.</param>
        /// <param name="l2Regularization">Weight of L2 regularization term.</param>
        /// <param name="historySize">Memory size for <see cref="Microsoft.ML.Trainers.LogisticRegression"/>. Low=faster, less accurate.</param>
        /// <param name="optimizationTolerance">Threshold for optimizer convergence.</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 predicted output.</returns>
        public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) LogisticRegressionBinaryClassifier(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
                                                                                                                                          Scalar <bool> label,
                                                                                                                                          Vector <float> features,
                                                                                                                                          Scalar <float> weights      = null,
                                                                                                                                          float l1Regularization      = Options.Defaults.L1Regularization,
                                                                                                                                          float l2Regularization      = Options.Defaults.L2Regularization,
                                                                                                                                          float optimizationTolerance = Options.Defaults.OptimizationTolerance,
                                                                                                                                          int historySize             = Options.Defaults.HistorySize,
                                                                                                                                          bool enforceNonNegativity   = Options.Defaults.EnforceNonNegativity,
                                                                                                                                          Action <CalibratedModelParametersBase <LinearBinaryModelParameters, PlattCalibrator> > onFit = null)
        {
            LbfgsStaticUtils.ValidateParams(label, features, weights, l1Regularization, l2Regularization, optimizationTolerance, historySize, enforceNonNegativity, onFit);

            var rec = new TrainerEstimatorReconciler.BinaryClassifier(
                (env, labelName, featuresName, weightsName) =>
            {
                var trainer = new LogisticRegression(env, labelName, featuresName, weightsName,
                                                     l1Regularization, l2Regularization, optimizationTolerance, historySize, enforceNonNegativity);

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

            return(rec.Output);
        }
コード例 #3
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        /// <summary>
        ///  Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Runtime.Learners.LogisticRegression"/> trainer.
        /// </summary>
        /// <param name="ctx">The binary classificaiton context trainer object.</param>
        /// <param name="label">The label, or dependent variable.</param>
        /// <param name="features">The features, or independent variables.</param>
        /// <param name="weights">The optional example weights.</param>
        /// <param name="enoforceNoNegativity">Enforce non-negative weights.</param>
        /// <param name="l1Weight">Weight of L1 regularization term.</param>
        /// <param name="l2Weight">Weight of L2 regularization term.</param>
        /// <param name="memorySize">Memory size for <see cref="Microsoft.ML.Runtime.Learners.LogisticRegression"/>. Low=faster, less accurate.</param>
        /// <param name="optimizationTolerance">Threshold for optimizer convergence.</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>
        /// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
        /// <returns>The predicted output.</returns>
        public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) LogisticRegressionBinaryClassifier(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
                                                                                                                                          Scalar <bool> label,
                                                                                                                                          Vector <float> features,
                                                                                                                                          Scalar <float> weights              = null,
                                                                                                                                          float l1Weight                      = Arguments.Defaults.L1Weight,
                                                                                                                                          float l2Weight                      = Arguments.Defaults.L2Weight,
                                                                                                                                          float optimizationTolerance         = Arguments.Defaults.OptTol,
                                                                                                                                          int memorySize                      = Arguments.Defaults.MemorySize,
                                                                                                                                          bool enoforceNoNegativity           = Arguments.Defaults.EnforceNonNegativity,
                                                                                                                                          Action <Arguments> advancedSettings = null,
                                                                                                                                          Action <ParameterMixingCalibratedPredictor> onFit = null)
        {
            LbfgsStaticUtils.ValidateParams(label, features, weights, l1Weight, l2Weight, optimizationTolerance, memorySize, enoforceNoNegativity, advancedSettings, onFit);

            var rec = new TrainerEstimatorReconciler.BinaryClassifier(
                (env, labelName, featuresName, weightsName) =>
            {
                var trainer = new LogisticRegression(env, labelName, featuresName, weightsName,
                                                     l1Weight, l2Weight, optimizationTolerance, memorySize, enoforceNoNegativity, advancedSettings);

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

            return(rec.Output);
        }
コード例 #4
0
        /// <summary>
        /// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Trainers.LogisticRegression"/> trainer.
        /// </summary>
        /// <param name="catalog">The regression catalog trainer object.</param>
        /// <param name="label">The label, or dependent variable.</param>
        /// <param name="features">The features, or independent variables.</param>
        /// <param name="weights">The optional example weights.</param>
        /// <param name="enforceNonNegativity">Enforce non-negative weights.</param>
        /// <param name="l1Regularization">Weight of L1 regularization term.</param>
        /// <param name="l2Regularization">Weight of L2 regularization term.</param>
        /// <param name="historySize">Memory size for <see cref="Microsoft.ML.Trainers.LogisticRegression"/>. Low=faster, less accurate.</param>
        /// <param name="optimizationTolerance">Threshold for optimizer convergence.</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 predicted output.</returns>
        public static Scalar <float> PoissonRegression(this RegressionCatalog.RegressionTrainers catalog,
                                                       Scalar <float> label,
                                                       Vector <float> features,
                                                       Scalar <float> weights      = null,
                                                       float l1Regularization      = Options.Defaults.L1Regularization,
                                                       float l2Regularization      = Options.Defaults.L2Regularization,
                                                       float optimizationTolerance = Options.Defaults.OptimizationTolerance,
                                                       int historySize             = Options.Defaults.HistorySize,
                                                       bool enforceNonNegativity   = Options.Defaults.EnforceNonNegativity,
                                                       Action <PoissonRegressionModelParameters> onFit = null)
        {
            LbfgsStaticUtils.ValidateParams(label, features, weights, l1Regularization, l2Regularization, optimizationTolerance, historySize, enforceNonNegativity, onFit);

            var rec = new TrainerEstimatorReconciler.Regression(
                (env, labelName, featuresName, weightsName) =>
            {
                var trainer = new PoissonRegression(env, labelName, featuresName, weightsName,
                                                    l1Regularization, l2Regularization, optimizationTolerance, historySize, enforceNonNegativity);

                if (onFit != null)
                {
                    return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
                }

                return(trainer);
            }, label, features, weights);

            return(rec.Score);
        }