ConversionsExtensionsCatalog.ConvertType 方法

定义

命名空间:

Microsoft.ML

程序集:

Microsoft.ML.Data.dll

包:

Microsoft.ML v3.0.1

重载

ConvertType(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[], DataKind)	创建一个 TypeConvertingEstimator，它将数据类型转换为在中指定的 outputKind类型。
ConvertType(TransformsCatalog+ConversionTransforms, String, String, DataKind)	创建一个 TypeConvertingEstimator，它将数据类型转换为在中指定的 outputKind类型。

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

创建一个 TypeConvertingEstimator，它将数据类型转换为在中指定的 outputKind类型。

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

参数

catalog

TransformsCatalog.ConversionTransforms

转换转换的目录。

columns

InputOutputColumnPair[]

输入和输出列。 此转换对数值、布尔值、文本 DateTime 和键数据类型进行操作。

outputKind

DataKind

输出列的预期类型。

返回

TypeConvertingEstimator

示例

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; }
        }
    }
}

注解

此转换可以对多个列进行操作。

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

创建一个 TypeConvertingEstimator，它将数据类型转换为在中指定的 outputKind类型。

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

参数

catalog

TransformsCatalog.ConversionTransforms

转换转换的目录。

outputColumnName

String

由转换 inputColumnName生成的列的名称。

inputColumnName

String

要转换的列的名称。 If set to null, the value of the outputColumnName will be used as source. 此转换对数值、布尔值、文本 DateTime 和键数据类型进行操作。

outputKind

DataKind

输出列的预期类型。

返回

TypeConvertingEstimator

示例

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; }
        }
    }
}