ExtensionsCatalog.IndicateMissingValues Metoda

Definicja

Przestrzeń nazw:

Microsoft.ML

Zestaw:

Microsoft.ML.Transforms.dll

Pakiet:

Microsoft.ML v3.0.1

Przeciążenia

IndicateMissingValues(TransformsCatalog, InputOutputColumnPair[])	Utwórz obiekt MissingValueIndicatorEstimator, który kopiuje dane z kolumny określonej w pliku InputColumnName do nowej kolumny: OutputColumnName.
IndicateMissingValues(TransformsCatalog, String, String)	Utwórz obiekt MissingValueIndicatorEstimator, który skanuje dane z kolumny określonej w inputColumnName kolumnie i wypełnia nową kolumnę określoną w wektorem outputColumnName wartości logicznej, gdzie wartość logiczna i-th ma wartość true , jeśli element i-th w danych kolumny ma brak wartości i false w przeciwnym razie.

IndicateMissingValues(TransformsCatalog, InputOutputColumnPair[])

Utwórz obiekt MissingValueIndicatorEstimator, który kopiuje dane z kolumny określonej w pliku InputColumnName do nowej kolumny: 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

Parametry

catalog

TransformsCatalog

Wykaz przekształcenia.

columns

InputOutputColumnPair[]

Pary kolumn wejściowych i wyjściowych. Ten narzędzie do szacowania działa na danych, które są skalarne lub wektorowe lub SingleDouble.

Zwraca

MissingValueIndicatorEstimator

Przykłady

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; }

        }
    }
}

Uwagi

Ta transformacja może działać w kilku kolumnach.

IndicateMissingValues(TransformsCatalog, String, String)

Utwórz obiekt MissingValueIndicatorEstimator, który skanuje dane z kolumny określonej w inputColumnName kolumnie i wypełnia nową kolumnę określoną w wektorem outputColumnName wartości logicznej, gdzie wartość logiczna i-th ma wartość true , jeśli element i-th w danych kolumny ma brak wartości i false w przeciwnym razie.

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

Parametry

catalog

TransformsCatalog

Wykaz przekształcenia.

outputColumnName

String

Nazwa kolumny wynikającej z przekształcenia inputColumnNameelementu . Typ danych tej kolumny będzie wektorem Boolean.

inputColumnName

String

Nazwa kolumny do skopiowania danych z. Ten narzędzie do szacowania działa nad skalarną lub wektorem Single lub Double.

Zwraca

MissingValueIndicatorEstimator

Przykłady

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; }
        }
    }
}