PcaCatalog.RandomizedPca Метод

Определение

Пространство имен:

Microsoft.ML

Сборка:

Microsoft.ML.PCA.dll

Пакет:

Microsoft.ML v5.0.0-preview.1.25125.4

Перегрузки

RandomizedPca(AnomalyDetectionCatalog+AnomalyDetectionTrainers, RandomizedPcaTrainer+Options)	Создавайте RandomizedPcaTrainer с помощью дополнительных параметров, которые обучают модель приблизительного анализа основных компонентов (PCA) с помощью алгоритма распаковки случайных значений (SVD).
RandomizedPca(AnomalyDetectionCatalog+AnomalyDetectionTrainers, String, String, Int32, Int32, Boolean, Nullable<Int32>)	Создание RandomizedPcaTrainer, которое обучает модель приблизительного анализа основных компонентов (PCA) с помощью алгоритма распаковки случайных значений (SVD).

RandomizedPca(AnomalyDetectionCatalog+AnomalyDetectionTrainers, RandomizedPcaTrainer+Options)

Исходный код:

PCACatalog.cs

Создавайте RandomizedPcaTrainer с помощью дополнительных параметров, которые обучают модель приблизительного анализа основных компонентов (PCA) с помощью алгоритма распаковки случайных значений (SVD).

public static Microsoft.ML.Trainers.RandomizedPcaTrainer RandomizedPca(this Microsoft.ML.AnomalyDetectionCatalog.AnomalyDetectionTrainers catalog, Microsoft.ML.Trainers.RandomizedPcaTrainer.Options options);

static member RandomizedPca : Microsoft.ML.AnomalyDetectionCatalog.AnomalyDetectionTrainers * Microsoft.ML.Trainers.RandomizedPcaTrainer.Options -> Microsoft.ML.Trainers.RandomizedPcaTrainer

<Extension()>
Public Function RandomizedPca (catalog As AnomalyDetectionCatalog.AnomalyDetectionTrainers, options As RandomizedPcaTrainer.Options) As RandomizedPcaTrainer

Параметры

catalog

AnomalyDetectionCatalog.AnomalyDetectionTrainers

Объект средства обучения каталога обнаружения аномалий.

options

RandomizedPcaTrainer.Options

Дополнительные параметры алгоритма.

Возвращаемое значение

RandomizedPcaTrainer

Примеры

using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.Trainers.AnomalyDetection
{
    public static class RandomizedPcaSampleWithOptions
    {
        public static void Example()
        {
            // Create a new context for ML.NET operations. It can be used for
            // exception tracking and logging, as a catalog of available operations
            // and as the source of randomness. Setting the seed to a fixed number
            // in this example to make outputs deterministic.
            var mlContext = new MLContext(seed: 0);

            // Training data.
            var samples = new List<DataPoint>()
            {
                new DataPoint(){ Features = new float[3] {0, 2, 1} },
                new DataPoint(){ Features = new float[3] {0, 2, 3} },
                new DataPoint(){ Features = new float[3] {0, 2, 4} },
                new DataPoint(){ Features = new float[3] {0, 2, 1} },
                new DataPoint(){ Features = new float[3] {0, 2, 2} },
                new DataPoint(){ Features = new float[3] {0, 2, 3} },
                new DataPoint(){ Features = new float[3] {0, 2, 4} },
                new DataPoint(){ Features = new float[3] {1, 0, 0} }
            };

            // Convert the List<DataPoint> to IDataView, a consumable format to
            // ML.NET functions.
            var data = mlContext.Data.LoadFromEnumerable(samples);

            var options = new Microsoft.ML.Trainers.RandomizedPcaTrainer.Options()
            {
                FeatureColumnName = nameof(DataPoint.Features),
                Rank = 1,
                Seed = 10,
            };

            // Create an anomaly detector. Its underlying algorithm is randomized
            // PCA.
            var pipeline = mlContext.AnomalyDetection.Trainers.RandomizedPca(
                options);

            // Train the anomaly detector.
            var model = pipeline.Fit(data);

            // Apply the trained model on the training data.
            var transformed = model.Transform(data);

            // Read ML.NET predictions into IEnumerable<Result>.
            var results = mlContext.Data.CreateEnumerable<Result>(transformed,
                reuseRowObject: false).ToList();

            // Let's go through all predictions.
            for (int i = 0; i < samples.Count; ++i)
            {
                // The i-th example's prediction result.
                var result = results[i];

                // The i-th example's feature vector in text format.
                var featuresInText = string.Join(',', samples[i].Features);

                if (result.PredictedLabel)
                    // The i-th sample is predicted as an outlier.
                    Console.WriteLine("The {0}-th example with features [{1}] is" +
                        "an outlier with a score of being outlier {2}", i,
                        featuresInText, result.Score);
                else
                    // The i-th sample is predicted as an inlier.
                    Console.WriteLine("The {0}-th example with features [{1}] is" +
                        "an inlier with a score of being outlier {2}",
                        i, featuresInText, result.Score);
            }
            // Lines printed out should be
            // The 0 - th example with features[0, 2, 1] is an inlier with a score of being outlier 0.2264826
            // The 1 - th example with features[0, 2, 3] is an inlier with a score of being outlier 0.1739471
            // The 2 - th example with features[0, 2, 4] is an inlier with a score of being outlier 0.05711612
            // The 3 - th example with features[0, 2, 1] is an inlier with a score of being outlier 0.2264826
            // The 4 - th example with features[0, 2, 2] is an inlier with a score of being outlier 0.3868995
            // The 5 - th example with features[0, 2, 3] is an inlier with a score of being outlier 0.1739471
            // The 6 - th example with features[0, 2, 4] is an inlier with a score of being outlier 0.05711612
            // The 7 - th example with features[1, 0, 0] is an outlier with a score of being outlier 0.6260795
        }

        // Example with 3 feature values. A training data set is a collection of
        // such examples.
        private class DataPoint
        {
            [VectorType(3)]
            public float[] Features { get; set; }
        }

        // Class used to capture prediction of DataPoint.
        private class Result
        {
            // Outlier gets true while inlier has false.
            public bool PredictedLabel { get; set; }
            // Inlier gets smaller score. Score is between 0 and 1.
            public float Score { get; set; }
        }
    }
}

Комментарии

По умолчанию пороговое значение, используемое для определения метки точки данных на основе прогнозируемой оценки, равно 0,5. Оценки варьируются от 0 до 1. Точка данных с прогнозируемой оценкой выше 0,5 считается выбросом. Используется для ChangeModelThreshold<TModel>(AnomalyPredictionTransformer<TModel>, Single) изменения этого порогового значения.

RandomizedPca(AnomalyDetectionCatalog+AnomalyDetectionTrainers, String, String, Int32, Int32, Boolean, Nullable<Int32>)

Исходный код:

PCACatalog.cs

Создание RandomizedPcaTrainer, которое обучает модель приблизительного анализа основных компонентов (PCA) с помощью алгоритма распаковки случайных значений (SVD).

public static Microsoft.ML.Trainers.RandomizedPcaTrainer RandomizedPca(this Microsoft.ML.AnomalyDetectionCatalog.AnomalyDetectionTrainers catalog, string featureColumnName = "Features", string exampleWeightColumnName = default, int rank = 20, int oversampling = 20, bool ensureZeroMean = true, int? seed = default);

static member RandomizedPca : Microsoft.ML.AnomalyDetectionCatalog.AnomalyDetectionTrainers * string * string * int * int * bool * Nullable<int> -> Microsoft.ML.Trainers.RandomizedPcaTrainer

<Extension()>
Public Function RandomizedPca (catalog As AnomalyDetectionCatalog.AnomalyDetectionTrainers, Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional rank As Integer = 20, Optional oversampling As Integer = 20, Optional ensureZeroMean As Boolean = true, Optional seed As Nullable(Of Integer) = Nothing) As RandomizedPcaTrainer

Параметры

catalog

AnomalyDetectionCatalog.AnomalyDetectionTrainers

Объект средства обучения каталога обнаружения аномалий.

featureColumnName

String

Имя столбца компонента. Данные столбца должны быть вектором известного Singleразмера .

exampleWeightColumnName

String

Имя примера столбца веса (необязательно). Чтобы использовать столбец веса, данные столбца должны иметь тип Single.

rank

Int32

Количество компонентов в PCA.

oversampling

Int32

Параметр oversampling для случайного обучения PCA.

ensureZeroMean

Boolean

Если этот параметр включен, то центрирование данных равно нулю среднего.

seed

Nullable<Int32>

Начальное значение для создания случайного числа.

Возвращаемое значение

RandomizedPcaTrainer

Примеры

using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.Trainers.AnomalyDetection
{
    public static class RandomizedPcaSample
    {
        public static void Example()
        {
            // Create a new context for ML.NET operations. It can be used for except
            // ion tracking and logging, as a catalog of available operations and as
            // the source of randomness. Setting the seed to a fixed number in this
            // example to make outputs deterministic.
            var mlContext = new MLContext(seed: 0);

            // Training data.
            var samples = new List<DataPoint>()
            {
                new DataPoint(){ Features = new float[3] {0, 2, 1} },
                new DataPoint(){ Features = new float[3] {0, 2, 1} },
                new DataPoint(){ Features = new float[3] {0, 2, 1} },
                new DataPoint(){ Features = new float[3] {0, 1, 2} },
                new DataPoint(){ Features = new float[3] {0, 2, 1} },
                new DataPoint(){ Features = new float[3] {2, 0, 0} }
            };

            // Convert the List<DataPoint> to IDataView, a consumable format to
            // ML.NET functions.
            var data = mlContext.Data.LoadFromEnumerable(samples);

            // Create an anomaly detector. Its underlying algorithm is randomized
            // PCA.
            var pipeline = mlContext.AnomalyDetection.Trainers.RandomizedPca(
                featureColumnName: nameof(DataPoint.Features), rank: 1,
                    ensureZeroMean: false);

            // Train the anomaly detector.
            var model = pipeline.Fit(data);

            // Apply the trained model on the training data.
            var transformed = model.Transform(data);

            // Read ML.NET predictions into IEnumerable<Result>.
            var results = mlContext.Data.CreateEnumerable<Result>(transformed,
                reuseRowObject: false).ToList();

            // Let's go through all predictions.
            for (int i = 0; i < samples.Count; ++i)
            {
                // The i-th example's prediction result.
                var result = results[i];

                // The i-th example's feature vector in text format.
                var featuresInText = string.Join(',', samples[i].Features);

                if (result.PredictedLabel)
                    // The i-th sample is predicted as an outlier.
                    Console.WriteLine("The {0}-th example with features [{1}] is " +
                        "an outlier with a score of being inlier {2}", i,
                            featuresInText, result.Score);
                else
                    // The i-th sample is predicted as an inlier.
                    Console.WriteLine("The {0}-th example with features [{1}] is " +
                        "an inlier with a score of being inlier {2}", i,
                        featuresInText, result.Score);
            }
            // Lines printed out should be
            // The 0 - th example with features[0, 2, 1] is an inlier with a score of being outlier 0.1101028
            // The 1 - th example with features[0, 2, 1] is an inlier with a score of being outlier 0.1101028
            // The 2 - th example with features[0, 2, 1] is an inlier with a score of being outlier 0.1101028
            // The 3 - th example with features[0, 1, 2] is an outlier with a score of being outlier 0.5082728
            // The 4 - th example with features[0, 2, 1] is an inlier with a score of being outlier 0.1101028
            // The 5 - th example with features[2, 0, 0] is an outlier with a score of being outlier 1
        }

        // Example with 3 feature values. A training data set is a collection of
        // such examples.
        private class DataPoint
        {
            [VectorType(3)]
            public float[] Features { get; set; }
        }

        // Class used to capture prediction of DataPoint.
        private class Result
        {
            // Outlier gets true while inlier has false.
            public bool PredictedLabel { get; set; }
            // Inlier gets smaller score. Score is between 0 and 1.
            public float Score { get; set; }
        }
    }
}

Комментарии

По умолчанию пороговое значение, используемое для определения метки точки данных на основе прогнозируемой оценки, равно 0,5. Оценки варьируются от 0 до 1. Точка данных с прогнозируемой оценкой выше 0,5 считается выбросом. Используется для ChangeModelThreshold<TModel>(AnomalyPredictionTransformer<TModel>, Single) изменения этого порогового значения.