ExtensionsCatalog.IndicateMissingValues Metodo

Definizione

Spazio dei nomi:

Microsoft.ML

Assembly:

Microsoft.ML.Transforms.dll

Pacchetto:

Microsoft.ML v2.0.0

Overload

IndicateMissingValues(TransformsCatalog, InputOutputColumnPair[])	Creare un MissingValueIndicatorEstimatoroggetto , che copia i dati dalla colonna specificata in in InputColumnName una nuova colonna: OutputColumnName.
IndicateMissingValues(TransformsCatalog, String, String)	Creare un MissingValueIndicatorEstimatoroggetto , che esegue l'analisi dei dati dalla colonna specificata in inputColumnName e riempie la nuova colonna specificata in outputColumnName con vettore di valori bool in cui i-th bool ha valore true se l'elemento i-th nei dati di colonna contiene un valore mancante e false in caso contrario.

IndicateMissingValues(TransformsCatalog, InputOutputColumnPair[])

Creare un MissingValueIndicatorEstimatoroggetto , che copia i dati dalla colonna specificata in in InputColumnName una nuova colonna: 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

Parametri

catalog

TransformsCatalog

Catalogo della trasformazione.

columns

InputOutputColumnPair[]

Coppie di colonne di input e di output. Questo strumento di stima opera sui dati che sono scalari o vettori di Single o Double.

Restituisce

MissingValueIndicatorEstimator

Esempio

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; }

        }
    }
}

Commenti

Questa trasformazione può operare su più colonne.

IndicateMissingValues(TransformsCatalog, String, String)

Creare un MissingValueIndicatorEstimatoroggetto , che esegue l'analisi dei dati dalla colonna specificata in inputColumnName e riempie la nuova colonna specificata in outputColumnName con vettore di valori bool in cui i-th bool ha valore true se l'elemento i-th nei dati di colonna contiene un valore mancante e false in caso 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

Parametri

catalog

TransformsCatalog

Catalogo della trasformazione.

outputColumnName

String

Nome della colonna risultante dalla trasformazione di inputColumnName. Il tipo di dati di questa colonna sarà un vettore di Boolean.

inputColumnName

String

Nome della colonna da cui copiare i dati. Questo strumento di stima opera su scalare o vettore di Single o Double.

Restituisce

MissingValueIndicatorEstimator

Esempio

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; }
        }
    }
}