ExtensionsCatalog.IndicateMissingValues Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.Transforms.dll

Paket:

Microsoft.ML v2.0.0

Overload

IndicateMissingValues(TransformsCatalog, InputOutputColumnPair[])	Buat MissingValueIndicatorEstimator, yang menyalin data dari kolom yang ditentukan ke InputColumnName kolom baru: OutputColumnName.
IndicateMissingValues(TransformsCatalog, String, String)	Buat MissingValueIndicatorEstimator, yang memindai data dari kolom yang ditentukan di inputColumnName dan mengisi kolom baru yang ditentukan outputColumnName dengan vektor bool di mana bool i-th memiliki nilai true jika elemen i-th dalam data kolom memiliki nilai yang hilang dan false sebaliknya.

IndicateMissingValues(TransformsCatalog, InputOutputColumnPair[])

Buat MissingValueIndicatorEstimator, yang menyalin data dari kolom yang ditentukan ke InputColumnName kolom baru: OutputColumnName.

public static Microsoft.ML.Transforms.MissingValueIndicatorEstimator IndicateMissingValues (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns);

static member IndicateMissingValues : Microsoft.ML.TransformsCatalog * Microsoft.ML.InputOutputColumnPair[] -> Microsoft.ML.Transforms.MissingValueIndicatorEstimator

<Extension()>
Public Function IndicateMissingValues (catalog As TransformsCatalog, columns As InputOutputColumnPair()) As MissingValueIndicatorEstimator

Parameter

catalog

TransformsCatalog

Katalog transformasi.

columns

InputOutputColumnPair[]

Pasangan kolom input dan output. Estimator ini beroperasi melalui data yang merupakan skalar atau vektor Single atau Double.

Mengembalikan

MissingValueIndicatorEstimator

Contoh

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

namespace Samples.Dynamic
{
    public static class IndicateMissingValuesMultiColumn
    {
        public static void Example()
        {
            // Create a new ML context, for ML.NET operations. It can be used for
            // exception tracking and logging, as well as the source of randomness.
            var mlContext = new MLContext();

            // Get a small dataset as an IEnumerable and convert it to an IDataView.
            var samples = new List<DataPoint>()
            {
                new DataPoint(){ Features1 = new float[3] {1, 1, 0}, Features2 =
                    new float[2] {1, 1} },

                new DataPoint(){ Features1 = new float[3] {0, float.NaN, 1},
                    Features2 = new float[2] {float.NaN, 1} },

                new DataPoint(){ Features1 = new float[3] {-1, float.NaN, -3},
                    Features2 = new float[2] {1, float.PositiveInfinity} },
            };
            var data = mlContext.Data.LoadFromEnumerable(samples);

            // IndicateMissingValues is used to create a boolean containing 'true'
            // where the value in the input column is missing. For floats and
            // doubles, missing values are NaN. We can use an array of
            // InputOutputColumnPair to apply the MissingValueIndicatorEstimator
            // to multiple columns in one pass over the data.
            var pipeline = mlContext.Transforms.IndicateMissingValues(new[] {
                new InputOutputColumnPair("MissingIndicator1", "Features1"),
                new InputOutputColumnPair("MissingIndicator2", "Features2")
            });

            // Now we can transform the data and look at the output to confirm the
            // behavior of the estimator. This operation doesn't actually evaluate
            // data until we read the data below.
            var tansformer = pipeline.Fit(data);
            var transformedData = tansformer.Transform(data);

            // We can extract the newly created column as an IEnumerable of
            // SampleDataTransformed, the class we define below.
            var rowEnumerable = mlContext.Data.CreateEnumerable<
                SampleDataTransformed>(transformedData, reuseRowObject: false);

            // And finally, we can write out the rows of the dataset, looking at the
            // columns of interest.
            foreach (var row in rowEnumerable)
                Console.WriteLine("Features1: [" + string.Join(", ", row
                    .Features1) + "]\t MissingIndicator1: [" + string.Join(", ",
                    row.MissingIndicator1) + "]\t Features2: [" + string.Join(", ",
                    row.Features2) + "]\t MissingIndicator2: [" + string.Join(", ",
                    row.MissingIndicator2) + "]");

            // Expected output:
            // Features1: [1, 1, 0]     MissingIndicator1: [False, False, False]        Features2: [1, 1]       MissingIndicator2: [False, False]
            // Features1: [0, NaN, 1]   MissingIndicator1: [False, True, False]         Features2: [NaN, 1]     MissingIndicator2: [True, False]
            // Features1: [-1, NaN, -3]         MissingIndicator1: [False, True, False]         Features2: [1, ∞]       MissingIndicator2: [False, False]
        }

        private class DataPoint
        {
            [VectorType(3)]
            public float[] Features1 { get; set; }
            [VectorType(2)]
            public float[] Features2 { get; set; }
        }

        private sealed class SampleDataTransformed : DataPoint
        {
            public bool[] MissingIndicator1 { get; set; }
            public bool[] MissingIndicator2 { get; set; }

        }
    }
}

Keterangan

Transformasi ini dapat beroperasi di beberapa kolom.

IndicateMissingValues(TransformsCatalog, String, String)

Buat MissingValueIndicatorEstimator, yang memindai data dari kolom yang ditentukan di inputColumnName dan mengisi kolom baru yang ditentukan outputColumnName dengan vektor bool di mana bool i-th memiliki nilai true jika elemen i-th dalam data kolom memiliki nilai yang hilang dan false sebaliknya.

public static Microsoft.ML.Transforms.MissingValueIndicatorEstimator IndicateMissingValues (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default);

static member IndicateMissingValues : Microsoft.ML.TransformsCatalog * string * string -> Microsoft.ML.Transforms.MissingValueIndicatorEstimator

<Extension()>
Public Function IndicateMissingValues (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing) As MissingValueIndicatorEstimator

Parameter

catalog

TransformsCatalog

Katalog transformasi.

outputColumnName

String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Jenis data kolom ini akan menjadi vektor .Boolean

inputColumnName

String

Nama kolom untuk menyalin data. Estimator ini beroperasi melalui skalar atau vektor Single atau Double.

Mengembalikan

MissingValueIndicatorEstimator

Contoh

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

namespace Samples.Dynamic
{
    public static class IndicateMissingValues
    {
        public static void Example()
        {
            // Create a new ML context, for ML.NET operations. It can be used for
            // exception tracking and logging, as well as the source of randomness.
            var mlContext = new MLContext();

            // Get a small dataset as an IEnumerable and convert it to an IDataView.
            var samples = new List<DataPoint>()
            {
                new DataPoint(){ Features = new float[3] {1, 1, 0} },
                new DataPoint(){ Features = new float[3] {0, float.NaN, 1} },
                new DataPoint(){ Features = new float[3] {-1, float.NaN, -3} },
            };
            var data = mlContext.Data.LoadFromEnumerable(samples);

            // IndicateMissingValues is used to create a boolean containing 'true'
            // where the value in the input column is missing. For floats and
            // doubles, missing values are represented as NaN.
            var pipeline = mlContext.Transforms.IndicateMissingValues(
                "MissingIndicator", "Features");

            // Now we can transform the data and look at the output to confirm the
            // behavior of the estimator. This operation doesn't actually evaluate
            // data until we read the data below.
            var tansformer = pipeline.Fit(data);
            var transformedData = tansformer.Transform(data);

            // We can extract the newly created column as an IEnumerable of
            // SampleDataTransformed, the class we define below.
            var rowEnumerable = mlContext.Data.CreateEnumerable<
                SampleDataTransformed>(transformedData, reuseRowObject: false);

            // And finally, we can write out the rows of the dataset, looking at the
            // columns of interest.
            foreach (var row in rowEnumerable)
                Console.WriteLine("Features: [" + string.Join(", ", row.Features) +
                    "]\t MissingIndicator: [" + string.Join(", ", row
                    .MissingIndicator) + "]");

            // Expected output:
            // Features: [1, 1, 0]      MissingIndicator: [False, False, False]
            // Features: [0, NaN, 1]    MissingIndicator: [False, True, False]
            // Features: [-1, NaN, -3]  MissingIndicator: [False, True, False]
        }

        private class DataPoint
        {
            [VectorType(3)]
            public float[] Features { get; set; }
        }

        private sealed class SampleDataTransformed : DataPoint
        {
            public bool[] MissingIndicator { get; set; }
        }
    }
}