ExtensionsCatalog.ReplaceMissingValues Yöntem

Tanım

Ad Alanı:

Microsoft.ML

Bütünleştirilmiş Kod:

Microsoft.ML.Transforms.dll

Paket:

Microsoft.ML v5.0.0-preview.1.25125.4

Aşırı Yüklemeler

ReplaceMissingValues(TransformsCatalog, InputOutputColumnPair[], MissingValueReplacingEstimator+ReplacementMode, Boolean)	ColumnCopyingEstimatoriçinde belirtilen InputColumnName sütundaki verileri yeni OutputColumnName bir sütuna kopyalayan ve içindeki eksik değerleri değerine göre replacementModedeğiştiren bir oluşturun.
ReplaceMissingValues(TransformsCatalog, String, String, MissingValueReplacingEstimator+ReplacementMode, Boolean)	MissingValueReplacingEstimatoriçinde belirtilen inputColumnName sütundaki verileri yeni outputColumnName bir sütuna kopyalayan ve içindeki eksik değerleri değerine göre replacementModedeğiştiren bir oluşturun.

ReplaceMissingValues(TransformsCatalog, InputOutputColumnPair[], MissingValueReplacingEstimator+ReplacementMode, Boolean)

Kaynak:

ExtensionsCatalog.cs

ColumnCopyingEstimatoriçinde belirtilen InputColumnName sütundaki verileri yeni OutputColumnName bir sütuna kopyalayan ve içindeki eksik değerleri değerine göre replacementModedeğiştiren bir oluşturun.

public static Microsoft.ML.Transforms.MissingValueReplacingEstimator ReplaceMissingValues(this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns, Microsoft.ML.Transforms.MissingValueReplacingEstimator.ReplacementMode replacementMode = Microsoft.ML.Transforms.MissingValueReplacingEstimator+ReplacementMode.DefaultValue, bool imputeBySlot = true);

static member ReplaceMissingValues : Microsoft.ML.TransformsCatalog * Microsoft.ML.InputOutputColumnPair[] * Microsoft.ML.Transforms.MissingValueReplacingEstimator.ReplacementMode * bool -> Microsoft.ML.Transforms.MissingValueReplacingEstimator

<Extension()>
Public Function ReplaceMissingValues (catalog As TransformsCatalog, columns As InputOutputColumnPair(), Optional replacementMode As MissingValueReplacingEstimator.ReplacementMode = Microsoft.ML.Transforms.MissingValueReplacingEstimator+ReplacementMode.DefaultValue, Optional imputeBySlot As Boolean = true) As MissingValueReplacingEstimator

Parametreler

catalog

TransformsCatalog

Dönüşümün kataloğu.

columns

InputOutputColumnPair[]

Giriş ve çıkış sütunları çiftleri. Bu tahmin aracı, şamandıraların veya çiftlerin skaler veya vektörleri üzerinde çalışır.

replacementMode

MissingValueReplacingEstimator.ReplacementMode

'de belirtildiği gibi kullanılacak değiştirme türü MissingValueReplacingEstimator.ReplacementMode

imputeBySlot

Boolean

ise true, değiştirmenin yuva başına kesilmesi gerçekleştirilir. Aksi takdirde, değiştirme değeri vektör sütununun tamamı için engellenmiş olur. Bu ayar skalerler ve değişken vektörler için yoksayılır ve burada noktalama her zaman sütunun tamamı için olur.

Döndürülenler

MissingValueReplacingEstimator

Örnekler

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

namespace Samples.Dynamic
{
    class ReplaceMissingValuesMultiColumn
    {
        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] {0, 1} },

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

                new DataPoint(){ Features1 = new float[3] {-1, 6, -3}, Features2 =
                    new float[2] {0, float.PositiveInfinity} },
            };
            var data = mlContext.Data.LoadFromEnumerable(samples);

            // Here we use the default replacement mode, which replaces the value
            // with the default value for its type.
            var defaultPipeline = mlContext.Transforms.ReplaceMissingValues(new[] {
                new InputOutputColumnPair("MissingReplaced1", "Features1"),
                new InputOutputColumnPair("MissingReplaced2", "Features2")
            },
            MissingValueReplacingEstimator.ReplacementMode.DefaultValue);

            // 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 defaultTransformer = defaultPipeline.Fit(data);
            var defaultTransformedData = defaultTransformer.Transform(data);

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

            // And finally, we can write out the rows of the dataset, looking at the
            // columns of interest.
            foreach (var row in defaultRowEnumerable)
                Console.WriteLine("Features1: [" + string.Join(", ", row
                    .Features1) + "]\t MissingReplaced1: [" + string.Join(", ", row
                    .MissingReplaced1) + "]\t Features2: [" + string.Join(", ", row
                    .Features2) + "]\t MissingReplaced2: [" + string.Join(", ", row
                    .MissingReplaced2) + "]");

            // Expected output:
            // Features1: [1, 1, 0]     MissingReplaced1: [1, 1, 0]     Features2: [1, 1]       MissingReplaced2: [1, 1]
            // Features1: [0, NaN, 1]   MissingReplaced1: [0, 0, 1]     Features2: [0, 1]       MissingReplaced2: [0, 1]
            // Features1: [-1, NaN, -3]         MissingReplaced1: [-1, 0, -3]   Features2: [-1, NaN]    MissingReplaced2: [-1, 0]
            // Features1: [-1, 6, -3]   MissingReplaced1: [-1, 6, -3]   Features2: [0, ∞]       MissingReplaced2: [0, ∞]

            // Here we use the mean replacement mode, which replaces the value with
            // the mean of the non values that were not missing.
            var meanPipeline = mlContext.Transforms.ReplaceMissingValues(new[] {
                new InputOutputColumnPair("MissingReplaced1", "Features1"),
                new InputOutputColumnPair("MissingReplaced2", "Features2")
            },
            MissingValueReplacingEstimator.ReplacementMode.Mean);

            // 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 meanTransformer = meanPipeline.Fit(data);
            var meanTransformedData = meanTransformer.Transform(data);

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

            // And finally, we can write out the rows of the dataset, looking at the
            // columns of interest.
            foreach (var row in meanRowEnumerable)
                Console.WriteLine("Features1: [" + string.Join(", ", row
                    .Features1) + "]\t MissingReplaced1: [" + string.Join(", ", row
                    .MissingReplaced1) + "]\t Features2: [" + string.Join(", ", row
                    .Features2) + "]\t MissingReplaced2: [" + string.Join(", ", row
                    .MissingReplaced2) + "]");

            // Expected output:
            // Features1: [1, 1, 0]     MissingReplaced1: [1, 1, 0]     Features2: [1, 1]       MissingReplaced2: [1, 1]
            // Features1: [0, NaN, 1]   MissingReplaced1: [0, 3.5, 1]   Features2: [0, 1]       MissingReplaced2: [0, 1]
            // Features1: [-1, NaN, -3]         MissingReplaced1: [-1, 3.5, -3]         Features2: [-1, NaN]    MissingReplaced2: [-1, 1]
            // Features1: [-1, 6, -3]   MissingReplaced1: [-1, 6, -3]   Features2: [0, ∞]       MissingReplaced2: [0, ∞]
        }

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

        private sealed class SampleDataTransformed : DataPoint
        {
            [VectorType(3)]
            public float[] MissingReplaced1 { get; set; }
            [VectorType(2)]
            public float[] MissingReplaced2 { get; set; }
        }
    }
}

Açıklamalar

Bu dönüşüm birkaç sütun üzerinde çalışabilir.

ReplaceMissingValues(TransformsCatalog, String, String, MissingValueReplacingEstimator+ReplacementMode, Boolean)

Kaynak:

ExtensionsCatalog.cs

MissingValueReplacingEstimatoriçinde belirtilen inputColumnName sütundaki verileri yeni outputColumnName bir sütuna kopyalayan ve içindeki eksik değerleri değerine göre replacementModedeğiştiren bir oluşturun.

public static Microsoft.ML.Transforms.MissingValueReplacingEstimator ReplaceMissingValues(this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, Microsoft.ML.Transforms.MissingValueReplacingEstimator.ReplacementMode replacementMode = Microsoft.ML.Transforms.MissingValueReplacingEstimator+ReplacementMode.DefaultValue, bool imputeBySlot = true);

static member ReplaceMissingValues : Microsoft.ML.TransformsCatalog * string * string * Microsoft.ML.Transforms.MissingValueReplacingEstimator.ReplacementMode * bool -> Microsoft.ML.Transforms.MissingValueReplacingEstimator

<Extension()>
Public Function ReplaceMissingValues (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional replacementMode As MissingValueReplacingEstimator.ReplacementMode = Microsoft.ML.Transforms.MissingValueReplacingEstimator+ReplacementMode.DefaultValue, Optional imputeBySlot As Boolean = true) As MissingValueReplacingEstimator

Parametreler

catalog

TransformsCatalog

Dönüşümün kataloğu.

outputColumnName

String

dönüştürmesinden kaynaklanan sütunun inputColumnNameadı. Bu sütunun veri türü, giriş sütununun veri türüyle aynı olacaktır.

inputColumnName

String

Verilerin kopyalanması için sütunun adı. Bu tahmin aracı veya skaler veya vektör üzerinde SingleDoubleçalışır.

replacementMode

MissingValueReplacingEstimator.ReplacementMode

'de belirtildiği gibi kullanılacak değiştirme türü MissingValueReplacingEstimator.ReplacementMode

imputeBySlot

Boolean

Doğruysa, yuva başına değiştirme işlemi gerçekleştirilir. Aksi takdirde, değiştirme değeri vektör sütununun tamamı için engellenmiş olur. Bu ayar skalerler ve değişken vektörler için yoksayılır ve burada noktalama her zaman sütunun tamamı için olur.

Döndürülenler

MissingValueReplacingEstimator

Örnekler

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

namespace Samples.Dynamic
{
    class ReplaceMissingValues
    {
        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] {float.PositiveInfinity, 1,
                    0 } },

                new DataPoint(){ Features = new float[3] {0, float.NaN, 1} },
                new DataPoint(){ Features = new float[3] {-1, 2, -3} },
                new DataPoint(){ Features = new float[3] {-1, float.NaN, -3} },
            };
            var data = mlContext.Data.LoadFromEnumerable(samples);

            // Here we use the default replacement mode, which replaces the value
            // with the default value for its type.
            var defaultPipeline = mlContext.Transforms.ReplaceMissingValues(
                "MissingReplaced", "Features", MissingValueReplacingEstimator
                .ReplacementMode.DefaultValue);

            // 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 defaultTransformer = defaultPipeline.Fit(data);
            var defaultTransformedData = defaultTransformer.Transform(data);

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

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

            // Expected output:
            // Features: [∞, 1, 0]      MissingReplaced: [∞, 1, 0]
            // Features: [0, NaN, 1]    MissingReplaced: [0, 0, 1]
            // Features: [-1, 2, -3]    MissingReplaced: [-1, 2, -3]
            // Features: [-1, NaN, -3]  MissingReplaced: [-1, 0, -3]

            // Here we use the mean replacement mode, which replaces the value with
            // the mean of the non values that were not missing.
            var meanPipeline = mlContext.Transforms.ReplaceMissingValues(
                "MissingReplaced", "Features", MissingValueReplacingEstimator
                .ReplacementMode.Mean);

            // 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 meanTransformer = meanPipeline.Fit(data);
            var meanTransformedData = meanTransformer.Transform(data);

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

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

            // Expected output:
            // Features: [∞, 1, 0]      MissingReplaced: [∞, 1, 0]
            // Features: [0, NaN, 1]    MissingReplaced: [0, 1.5, 1]
            // Features: [-1, 2, -3]    MissingReplaced: [-1, 2, -3]
            // Features: [-1, NaN, -3]  MissingReplaced: [-1, 1.5, -3]
        }

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

        private sealed class SampleDataTransformed : DataPoint
        {
            [VectorType(3)]
            public float[] MissingReplaced { get; set; }
        }
    }
}