PcaCatalog.RandomizedPca Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.PCA.dll

Paket:

Microsoft.ML v2.0.0

Overload

RandomizedPca(AnomalyDetectionCatalog+AnomalyDetectionTrainers, RandomizedPcaTrainer+Options)	Buat RandomizedPcaTrainer dengan opsi tingkat lanjut, yang melatih model perkiraan analisis komponen utama (PCA) menggunakan algoritma dekomposisi nilai tunggal (SVD) acak.
RandomizedPca(AnomalyDetectionCatalog+AnomalyDetectionTrainers, String, String, Int32, Int32, Boolean, Nullable<Int32>)	Buat RandomizedPcaTrainer, yang melatih model perkiraan analisis komponen utama (PCA) menggunakan algoritma dekomposisi nilai tunggal (SVD) acak.

RandomizedPca(AnomalyDetectionCatalog+AnomalyDetectionTrainers, RandomizedPcaTrainer+Options)

Buat RandomizedPcaTrainer dengan opsi tingkat lanjut, yang melatih model perkiraan analisis komponen utama (PCA) menggunakan algoritma dekomposisi nilai tunggal (SVD) acak.

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

Parameter

catalog

AnomalyDetectionCatalog.AnomalyDetectionTrainers

Objek pelatih katalog deteksi anomali.

options

RandomizedPcaTrainer.Options

Opsi tingkat lanjut untuk algoritma.

Mengembalikan

RandomizedPcaTrainer

Contoh

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; }
        }
    }
}

Keterangan

Secara default ambang batas yang digunakan untuk menentukan label titik data berdasarkan skor yang diprediksi adalah 0,5. Skor berkisar dari 0 hingga 1. Poin data dengan skor yang diprediksi lebih tinggi dari 0,5 dianggap sebagai outlier. Gunakan ChangeModelThreshold<TModel>(AnomalyPredictionTransformer<TModel>, Single) untuk mengubah ambang ini.

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

Buat RandomizedPcaTrainer, yang melatih model perkiraan analisis komponen utama (PCA) menggunakan algoritma dekomposisi nilai tunggal (SVD) acak.

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

Parameter

catalog

AnomalyDetectionCatalog.AnomalyDetectionTrainers

Objek pelatih katalog deteksi anomali.

featureColumnName

String

Nama kolom fitur. Data kolom harus merupakan vektor berukuran besar yang diketahui dari Single.

exampleWeightColumnName

String

Nama kolom berat contoh (opsional). Untuk menggunakan kolom bobot, data kolom harus berjenis Single.

rank

Int32

Jumlah komponen dalam PCA.

oversampling

Int32

Parameter pengambilan sampel berlebih untuk pelatihan PCA acak.

ensureZeroMean

Boolean

Jika diaktifkan, data berpusat menjadi rata-rata nol.

seed

Nullable<Int32>

Benih untuk pembuatan angka acak.

Mengembalikan

RandomizedPcaTrainer

Contoh

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; }
        }
    }
}

Keterangan

Secara default ambang batas yang digunakan untuk menentukan label titik data berdasarkan skor yang diprediksi adalah 0,5. Skor berkisar dari 0 hingga 1. Poin data dengan skor yang diprediksi lebih tinggi dari 0,5 dianggap sebagai outlier. Gunakan ChangeModelThreshold<TModel>(AnomalyPredictionTransformer<TModel>, Single) untuk mengubah ambang ini.