ExtensionsCatalog.IndicateMissingValues Método

Definición

Espacio de nombres:

Microsoft.ML

Ensamblado:

Microsoft.ML.Transforms.dll

Paquete:

Microsoft.ML v3.0.1

Sobrecargas

IndicateMissingValues(TransformsCatalog, InputOutputColumnPair[])	Cree un MissingValueIndicatorEstimatorobjeto , que copia los datos de la columna especificada en InputColumnName en una nueva columna: OutputColumnName.
IndicateMissingValues(TransformsCatalog, String, String)	Cree un MissingValueIndicatorEstimatorobjeto , que examina los datos de la columna especificada en inputColumnName y rellena la nueva columna especificada en outputColumnName con el vector de booles donde i-th bool tiene el valor de true si el elemento i-th de los datos de columna tiene el valor que falta y false , de lo contrario, .

IndicateMissingValues(TransformsCatalog, InputOutputColumnPair[])

Cree un MissingValueIndicatorEstimatorobjeto , que copia los datos de la columna especificada en InputColumnName en una nueva columna: 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

Parámetros

catalog

TransformsCatalog

Catálogo de la transformación.

columns

InputOutputColumnPair[]

Pares de columnas de entrada y salida. Este estimador opera sobre los datos que son escalares o vectores de Single o Double.

Devoluciones

MissingValueIndicatorEstimator

Ejemplos

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; }

        }
    }
}

Comentarios

Esta transformación puede funcionar en varias columnas.

IndicateMissingValues(TransformsCatalog, String, String)

Cree un MissingValueIndicatorEstimatorobjeto , que examina los datos de la columna especificada en inputColumnName y rellena la nueva columna especificada en outputColumnName con el vector de booles donde i-th bool tiene el valor de true si el elemento i-th de los datos de columna tiene el valor que falta y false , de lo contrario, .

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

Parámetros

catalog

TransformsCatalog

Catálogo de la transformación.

outputColumnName

String

Nombre de la columna resultante de la transformación de inputColumnName. El tipo de datos de esta columna será un vector de Boolean.

inputColumnName

String

Nombre de la columna de la que se van a copiar los datos. Este estimador opera sobre un vector o escalar de Single o Double.

Devoluciones

MissingValueIndicatorEstimator

Ejemplos

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; }
        }
    }
}