ExtensionsCatalog.ReplaceMissingValues Méthode

Définition

Espace de noms:

Microsoft.ML

Assembly:

Microsoft.ML.Transforms.dll

Paquet:

Microsoft.ML v3.0.1

Surcharges

ReplaceMissingValues(TransformsCatalog, InputOutputColumnPair[], MissingValueReplacingEstimator+ReplacementMode, Boolean)	Créez un ColumnCopyingEstimator, qui copie les données de la colonne spécifiée dans InputColumnName une nouvelle colonne : OutputColumnName et remplace les valeurs manquantes dans celle-ci en fonction de replacementMode.
ReplaceMissingValues(TransformsCatalog, String, String, MissingValueReplacingEstimator+ReplacementMode, Boolean)	Créez un MissingValueReplacingEstimator, qui copie les données de la colonne spécifiée dans inputColumnName une nouvelle colonne : outputColumnName et remplace les valeurs manquantes dans celle-ci en fonction de replacementMode.

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

Créez un ColumnCopyingEstimator, qui copie les données de la colonne spécifiée dans InputColumnName une nouvelle colonne : OutputColumnName et remplace les valeurs manquantes dans celle-ci en fonction de replacementMode.

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

Paramètres

catalog

TransformsCatalog

Catalogue de la transformation.

columns

InputOutputColumnPair[]

Paires de colonnes d’entrée et de sortie. Cet estimateur fonctionne sur scalaire ou vecteur de floats ou doubles.

replacementMode

MissingValueReplacingEstimator.ReplacementMode

Type de remplacement à utiliser comme spécifié dans MissingValueReplacingEstimator.ReplacementMode

imputeBySlot

Boolean

Si true, l’imputation par emplacement du remplacement est effectuée. Sinon, la valeur de remplacement est imputable pour la colonne vectorielle entière. Ce paramètre est ignoré pour les vecteurs scalaires et variables, où l’imputation est toujours pour la colonne entière.

Retours

MissingValueReplacingEstimator

Exemples

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; }
        }
    }
}

Remarques

Cette transformation peut fonctionner sur plusieurs colonnes.

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

Créez un MissingValueReplacingEstimator, qui copie les données de la colonne spécifiée dans inputColumnName une nouvelle colonne : outputColumnName et remplace les valeurs manquantes dans celle-ci en fonction de replacementMode.

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

Paramètres

catalog

TransformsCatalog

Catalogue de la transformation.

outputColumnName

String

Nom de la colonne résultant de la transformation de inputColumnName. Le type de données de cette colonne sera identique à celui de la colonne d’entrée.

inputColumnName

String

Nom de la colonne à partir de laquelle copier les données. Cet estimateur fonctionne sur scalaire ou vecteur d’ou SingleDouble.

replacementMode

MissingValueReplacingEstimator.ReplacementMode

Type de remplacement à utiliser comme spécifié dans MissingValueReplacingEstimator.ReplacementMode

imputeBySlot

Boolean

Si la valeur est true, l’imputation par emplacement du remplacement est effectuée. Sinon, la valeur de remplacement est imputable pour la colonne vectorielle entière. Ce paramètre est ignoré pour les vecteurs scalaires et variables, où l’imputation est toujours pour la colonne entière.

Retours

MissingValueReplacingEstimator

Exemples

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; }
        }
    }
}