DataOperationsCatalog.BootstrapSample Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.Data.dll

Paket:

Microsoft.ML v3.0.1

Ambil perkiraan sampel bootstrap .input

public Microsoft.ML.IDataView BootstrapSample (Microsoft.ML.IDataView input, int? seed = default, bool complement = false);

member this.BootstrapSample : Microsoft.ML.IDataView * Nullable<int> * bool -> Microsoft.ML.IDataView

Public Function BootstrapSample (input As IDataView, Optional seed As Nullable(Of Integer) = Nothing, Optional complement As Boolean = false) As IDataView

Parameter

input

IDataView

Data input.

seed

Nullable<Int32>

Benih acak. Jika tidak ditentukan, status acak akan berasal dari MLContext.

complement

Boolean

Apakah ini adalah sampel di luar tas, yaitu, semua baris yang tidak dipilih oleh transformasi. Dapat digunakan untuk membuat sepasang sampel pelengkap dengan menggunakan seed yang sama.

Mengembalikan

IDataView

Contoh

using System;
using Microsoft.ML;

namespace Samples.Dynamic
{
    public static class BootstrapSample
    {
        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.
            var mlContext = new MLContext();

            // Get a small dataset as an IEnumerable.
            var rawData = new[] {
                new DataPoint() { Label = true, Feature = 1.017325f},
                new DataPoint() { Label = false, Feature = 0.6326591f},
                new DataPoint() { Label = false, Feature = 0.0326252f},
                new DataPoint() { Label = false, Feature = 0.8426974f},
                new DataPoint() { Label = true, Feature = 0.9947656f},
                new DataPoint() { Label = true, Feature = 1.017325f},
            };

            var data = mlContext.Data.LoadFromEnumerable(rawData);

            // Now take a bootstrap sample of this dataset to create a new dataset. 
            // The bootstrap is a resampling technique that creates a training set
            // of the same size by picking with replacement from the original
            // dataset. With the bootstrap, we expect that the resampled dataset
            // will have about 63% of the rows of the original dataset
            // (i.e. 1-e^-1), with some rows represented more than once.
            // BootstrapSample is a streaming implementation of the boostrap that
            // enables sampling from a dataset too large to hold in memory. To
            // enable streaming, BootstrapSample approximates the bootstrap by 
            // sampling each row according to a Poisson(1) distribution. Note that
            // this streaming approximation treats each row independently, thus the
            // resampled dataset is not guaranteed to be the same length as the 
            // input dataset. Let's take a look at the behavior of the
            // BootstrapSample by examining a few draws:
            for (int i = 0; i < 3; i++)
            {
                var resample = mlContext.Data.BootstrapSample(data, seed: i);

                var enumerable = mlContext.Data
                    .CreateEnumerable<DataPoint>(resample, reuseRowObject: false);

                Console.WriteLine($"Label\tFeature");
                foreach (var row in enumerable)
                {
                    Console.WriteLine($"{row.Label}\t{row.Feature}");
                }
                Console.WriteLine();
            }
            // Expected output:
            //  Label Feature
            //  True    1.017325
            //  False   0.6326591
            //  False   0.6326591
            //  False   0.6326591
            //  False   0.0326252
            //  False   0.0326252
            //  True    0.8426974
            //  True    0.8426974

            //  Label Feature
            //  True    1.017325
            //  True    1.017325
            //  False   0.6326591
            //  False   0.6326591
            //  False   0.0326252
            //  False   0.0326252
            //  False   0.0326252
            //  True    0.9947656

            //  Label Feature
            //  False   0.6326591
            //  False   0.0326252
            //  True    0.8426974
            //  True    0.8426974
            //  True    0.8426974
        }

        private class DataPoint
        {
            public bool Label { get; set; }

            public float Feature { get; set; }
        }
    }
}

Keterangan

Sampler ini adalah versi streaming dari pengambilan sampel ulang bootstrap. Alih-alih mengambil seluruh himpunan data ke dalam memori dan pengambilan sampel ulang, BootstrapSample(IDataView, Nullable<Int32>, Boolean) streaming melalui himpunan data dan menggunakan distribusi Poisson(1) untuk memilih berapa kali baris tertentu akan ditambahkan ke sampel. Parameter complement memungkinkan pembuatan sampel bootstap dan sampel out-of-bag yang melengkapi dengan menggunakan yang sama seed.