Ejemplo n.º 1
0
        /// <summary>
        ///  Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Trainers.LogisticRegressionBinaryTrainer"/> 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="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="options">Advanced arguments to the algorithm.</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,
                                                                                                                                          Options options,
                                                                                                                                          Action <CalibratedModelParametersBase <LinearBinaryModelParameters, PlattCalibrator> > onFit = null)
        {
            Contracts.CheckValue(label, nameof(label));
            Contracts.CheckValue(features, nameof(features));
            Contracts.CheckValue(options, nameof(options));
            Contracts.CheckValueOrNull(onFit);

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

                var trainer = new LogisticRegressionBinaryTrainer(env, options);

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

            return(rec.Output);
        }
Ejemplo n.º 2
0
        /// <summary>
        ///  Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Trainers.LogisticRegressionBinaryTrainer"/> 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.LogisticRegressionBinaryTrainer"/>. 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 LogisticRegressionBinaryTrainer(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);
        }