ConversionsExtensionsCatalog.ConvertType Methode

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Paket:

Microsoft.ML v2.0.0

Überlädt

ConvertType(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[], DataKind)	Erstellen Sie einen TypeConvertingEstimator, der den Typ der Daten in den angegebenen outputKindTyp konvertiert.
ConvertType(TransformsCatalog+ConversionTransforms, String, String, DataKind)	Erstellen Sie einen TypeConvertingEstimator, der den Typ der Daten in den angegebenen outputKindTyp konvertiert.

ConvertType(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[], DataKind)

Erstellen Sie einen TypeConvertingEstimator, der den Typ der Daten in den angegebenen outputKindTyp konvertiert.

public static Microsoft.ML.Transforms.TypeConvertingEstimator ConvertType (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, Microsoft.ML.InputOutputColumnPair[] columns, Microsoft.ML.Data.DataKind outputKind = Microsoft.ML.Data.DataKind.Single);

static member ConvertType : Microsoft.ML.TransformsCatalog.ConversionTransforms * Microsoft.ML.InputOutputColumnPair[] * Microsoft.ML.Data.DataKind -> Microsoft.ML.Transforms.TypeConvertingEstimator

<Extension()>
Public Function ConvertType (catalog As TransformsCatalog.ConversionTransforms, columns As InputOutputColumnPair(), Optional outputKind As DataKind = Microsoft.ML.Data.DataKind.Single) As TypeConvertingEstimator

Parameter

catalog

TransformsCatalog.ConversionTransforms

Der Katalog der Konvertierungstransformation.

columns

InputOutputColumnPair[]

Die Eingabe- und Ausgabespalten. Diese Transformation funktioniert über numerische, boolesche, Text- DateTime und Schlüsseldatentypen.

outputKind

DataKind

Die erwartete Art der Ausgabespalte.

Gibt zurück

TypeConvertingEstimator

Beispiele

using System;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
    // This example illustrates how to convert multiple columns of different types
    // to one type, in this case System.Single. 
    // This is often a useful data transformation before concatenating the features
    // together and passing them to a particular estimator.
    public static class ConvertTypeMultiColumn
    {
        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(seed: 1);

            var rawData = new[] {
                new InputData() { Feature1 = true, Feature2 = "0.4",
                    Feature3 = DateTime.Now, Feature4 = 0.145},

                new InputData() { Feature1 = false, Feature2 = "0.5",
                    Feature3 = DateTime.Today, Feature4 = 3.14},

                new InputData() { Feature1 = false, Feature2 = "14",
                    Feature3 = DateTime.Today, Feature4 = 0.2046},

                new InputData() { Feature1 = false, Feature2 = "23",
                    Feature3 = DateTime.Now, Feature4 = 0.1206},

                new InputData() { Feature1 = true, Feature2 = "8904",
                    Feature3 = DateTime.UtcNow, Feature4 = 8.09},
            };

            // Convert the data to an IDataView.
            var data = mlContext.Data.LoadFromEnumerable(rawData);

            // Construct the pipeline.
            var pipeline = mlContext.Transforms.Conversion.ConvertType(new[]
            {
                    new InputOutputColumnPair("Converted1", "Feature1"),
                    new InputOutputColumnPair("Converted2", "Feature2"),
                    new InputOutputColumnPair("Converted3", "Feature3"),
                    new InputOutputColumnPair("Converted4", "Feature4"),
             },
             DataKind.Single);

            // Let's fit our pipeline to the data.
            var transformer = pipeline.Fit(data);
            // Transforming the same data. This will add the 4 columns defined in
            // the pipeline, containing the converted
            // values of the initial columns. 
            var transformedData = transformer.Transform(data);

            // Shape the transformed data as a strongly typed IEnumerable.
            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(
                transformedData, true);

            // Printing the results.
            Console.WriteLine("Converted1\t Converted2\t Converted3\t Converted4");
            foreach (var item in convertedData)
                Console.WriteLine($"\t{item.Converted1}\t {item.Converted2}\t\t  " +
                    $"{item.Converted3}\t {item.Converted4}");

            // Transformed data.
            //
            // Converted1   Converted2    Converted3     Converted4
            //      1        0.4        6.368921E+17        0.145
            //      0        0.5        6.368916E+17        3.14
            //      0        14         6.368916E+17        0.2046
            //      0        23         6.368921E+17        0.1206
            //      1       8904        6.368924E+17        8.09

        }

        // The initial data type
        private class InputData
        {
            public bool Feature1;
            public string Feature2;
            public DateTime Feature3;
            public double Feature4;
        }

        // The resulting data type after the transformation
        private class TransformedData : InputData
        {
            public float Converted1 { get; set; }
            public float Converted2 { get; set; }
            public float Converted3 { get; set; }
            public float Converted4 { get; set; }
        }
    }
}

Hinweise

Diese Transformation kann über mehrere Spalten ausgeführt werden.

ConvertType(TransformsCatalog+ConversionTransforms, String, String, DataKind)

Erstellen Sie einen TypeConvertingEstimator, der den Typ der Daten in den angegebenen outputKindTyp konvertiert.

public static Microsoft.ML.Transforms.TypeConvertingEstimator ConvertType (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, string outputColumnName, string inputColumnName = default, Microsoft.ML.Data.DataKind outputKind = Microsoft.ML.Data.DataKind.Single);

static member ConvertType : Microsoft.ML.TransformsCatalog.ConversionTransforms * string * string * Microsoft.ML.Data.DataKind -> Microsoft.ML.Transforms.TypeConvertingEstimator

<Extension()>
Public Function ConvertType (catalog As TransformsCatalog.ConversionTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional outputKind As DataKind = Microsoft.ML.Data.DataKind.Single) As TypeConvertingEstimator

Parameter

catalog

TransformsCatalog.ConversionTransforms

Der Katalog der Konvertierungstransformation.

outputColumnName

String

Name der Spalte, die aus der Transformation von inputColumnName.

inputColumnName

String

Name der zu transformierenden Spalte. Wenn dieser Wert als nullQuelle festgelegt ist, wird der Wert des Werts outputColumnName als Quelle verwendet. Diese Transformation funktioniert über numerische, boolesche, Text- DateTime und Schlüsseldatentypen.

outputKind

DataKind

Die erwartete Art der Ausgabespalte.

Gibt zurück

TypeConvertingEstimator

Beispiele

using System;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
    public static class ConvertType
    {
        public static void Example()
        {
            var mlContext = new MLContext(seed: 1);
            var rawData = new[] {
                new InputData() { Survived = true },
                new InputData() { Survived = false },
                new InputData() { Survived = true },
                new InputData() { Survived = false },
                new InputData() { Survived = false },
            };

            var data = mlContext.Data.LoadFromEnumerable(rawData);

            // Construct the pipeline.
            var pipeline = mlContext.Transforms.Conversion.ConvertType(
                "SurvivedInt32", "Survived", DataKind.Int32);

            // Let's train our pipeline, and then apply it to the same data.
            var transformer = pipeline.Fit(data);
            var transformedData = transformer.Transform(data);

            // Display original column 'Survived' (boolean) and converted column 
            // SurvivedInt32' (Int32)
            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(
                transformedData, true);

            foreach (var item in convertedData)
            {
                Console.WriteLine("A:{0,-10}  Aconv:{1}", item.Survived,
                    item.SurvivedInt32);
            }

            // Output
            // A: True     Aconv:1
            // A: False    Aconv:0
            // A: True     Aconv:1
            // A: False    Aconv:0
            // A: False    Aconv:0
        }

        private class InputData
        {
            public bool Survived;
        }

        private sealed class TransformedData : InputData
        {
            public Int32 SurvivedInt32 { get; set; }
        }
    }
}