PcaCatalog.RandomizedPca Yöntem

Tanım

Ad Alanı:

Microsoft.ML

Bütünleştirilmiş Kod:

Microsoft.ML.PCA.dll

Paket:

Microsoft.ML v3.0.1

Aşırı Yüklemeler

RandomizedPca(AnomalyDetectionCatalog+AnomalyDetectionTrainers, RandomizedPcaTrainer+Options)	Rastgele tekil değer ayrıştırma (SVD) algoritmasını kullanarak yaklaşık bir asıl bileşen analizi (PCA) modelini eğiten gelişmiş seçeneklerle oluşturun RandomizedPcaTrainer .
RandomizedPca(AnomalyDetectionCatalog+AnomalyDetectionTrainers, String, String, Int32, Int32, Boolean, Nullable<Int32>)	Rastgele tekil değer ayrıştırma (SVD) algoritması kullanarak yaklaşık bir asıl bileşen analizi (PCA) modeli eğiten oluşturma RandomizedPcaTrainer.

RandomizedPca(AnomalyDetectionCatalog+AnomalyDetectionTrainers, RandomizedPcaTrainer+Options)

Rastgele tekil değer ayrıştırma (SVD) algoritmasını kullanarak yaklaşık bir asıl bileşen analizi (PCA) modelini eğiten gelişmiş seçeneklerle oluşturun RandomizedPcaTrainer .

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

Parametreler

catalog

AnomalyDetectionCatalog.AnomalyDetectionTrainers

Anomali algılama kataloğu eğitmen nesnesi.

options

RandomizedPcaTrainer.Options

Algoritmaya gelişmiş seçenekler.

Döndürülenler

RandomizedPcaTrainer

Örnekler

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; }
        }
    }
}

Açıklamalar

Varsayılan olarak, tahmin edilen puana göre bir veri noktasının etiketini belirlemek için kullanılan eşik 0,5'tir. Puanlar 0 ile 1 arasında değişir. Tahmini puanı 0,5'ten yüksek olan bir veri noktası aykırı değer olarak kabul edilir. Bu eşiği değiştirmek için kullanın ChangeModelThreshold<TModel>(AnomalyPredictionTransformer<TModel>, Single) .

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

Rastgele tekil değer ayrıştırma (SVD) algoritması kullanarak yaklaşık bir asıl bileşen analizi (PCA) modeli eğiten oluşturma RandomizedPcaTrainer.

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

Parametreler

catalog

AnomalyDetectionCatalog.AnomalyDetectionTrainers

Anomali algılama kataloğu eğitmen nesnesi.

featureColumnName

String

Özellik sütununun adı. Sütun verileri bilinen boyutlu bir vektör Singleolmalıdır.

exampleWeightColumnName

String

Örnek ağırlık sütununun adı (isteğe bağlı). Ağırlık sütununu kullanmak için sütun verilerinin türünde Singleolması gerekir.

rank

Int32

PCA'daki bileşen sayısı.

oversampling

Int32

Rastgele PCA eğitimi için fazla örnekleme parametresi.

ensureZeroMean

Boolean

Etkinleştirilirse, veriler sıfır ortalama olacak şekilde ortalanır.

seed

Nullable<Int32>

Rastgele sayı oluşturma için çekirdek.

Döndürülenler

RandomizedPcaTrainer

Örnekler

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; }
        }
    }
}

Açıklamalar

Varsayılan olarak, tahmin edilen puana göre bir veri noktasının etiketini belirlemek için kullanılan eşik 0,5'tir. Puanlar 0 ile 1 arasında değişir. Tahmini puanı 0,5'ten yüksek olan bir veri noktası aykırı değer olarak kabul edilir. Bu eşiği değiştirmek için kullanın ChangeModelThreshold<TModel>(AnomalyPredictionTransformer<TModel>, Single) .