ConversionsExtensionsCatalog.MapKeyToVector 方法

参考

定义

命名空间:: Microsoft.ML

程序集:: Microsoft.ML.Data.dll

包:: Microsoft.ML v3.0.1

包:: Microsoft.ML v1.0.0

包:: Microsoft.ML v1.1.0

包:: Microsoft.ML v1.2.0

包:: Microsoft.ML v1.3.1

包:: Microsoft.ML v1.4.0

包:: Microsoft.ML v1.5.5

包:: Microsoft.ML v1.6.0

包:: Microsoft.ML v1.7.0

包:: Microsoft.ML v2.0.0

重要

一些信息与预发行产品相关，相应产品在发行之前可能会进行重大修改。对于此处提供的信息，Microsoft 不作任何明示或暗示的担保。

重载

MapKeyToVector(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[], Boolean)	创建一个 KeyToVectorMappingEstimator，它将键的值映射到表示值的浮点向量。
MapKeyToVector(TransformsCatalog+ConversionTransforms, String, String, Boolean)	创建一个 KeyToVectorMappingEstimator，它将键的值映射到表示值的浮点向量。

MapKeyToVector(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[], Boolean)

创建一个 KeyToVectorMappingEstimator，它将键的值映射到表示值的浮点向量。

public static Microsoft.ML.Transforms.KeyToVectorMappingEstimator MapKeyToVector (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, Microsoft.ML.InputOutputColumnPair[] columns, bool outputCountVector = false);

static member MapKeyToVector : Microsoft.ML.TransformsCatalog.ConversionTransforms * Microsoft.ML.InputOutputColumnPair[] * bool -> Microsoft.ML.Transforms.KeyToVectorMappingEstimator

<Extension()>
Public Function MapKeyToVector (catalog As TransformsCatalog.ConversionTransforms, columns As InputOutputColumnPair(), Optional outputCountVector As Boolean = false) As KeyToVectorMappingEstimator

参数

catalog: TransformsCatalog.ConversionTransforms

转换转换的目录。

columns: InputOutputColumnPair[]

输入和输出列。新列的数据类型是表示原始值的向量 Single 。

outputCountVector: Boolean

是否将多个指示器向量合并为一个计数向量，而不是将它们串联在一起。仅当输入列是键的向量时，才相关。

KeyToVectorMappingEstimator

示例

using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
    public class MapKeyToVectorMultiColumn
    {
        /// This example demonstrates the use of MapKeyToVector by mapping keys to
        /// floats[] for multiple columns at once. Because the ML.NET KeyType maps
        /// the missing value to zero, counting starts at 1, so the uint values
        /// converted to KeyTypes will appear skewed by one.
        /// See https://github.com/dotnet/machinelearning/blob/main/docs/code/IDataViewTypeSystem.md#key-types
        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.
            var rawData = new[] {
                new DataPoint() { Timeframe = 9, Category = 5 },
                new DataPoint() { Timeframe = 8, Category = 4 },
                new DataPoint() { Timeframe = 8, Category = 4 },
                new DataPoint() { Timeframe = 9, Category = 3 },
                new DataPoint() { Timeframe = 2, Category = 3 },
                new DataPoint() { Timeframe = 3, Category = 5 }
            };

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

            // Constructs the ML.net pipeline
            var pipeline = mlContext.Transforms.Conversion.MapKeyToVector(new[]{
                    new InputOutputColumnPair ("TimeframeVector", "Timeframe"),
                    new InputOutputColumnPair ("CategoryVector", "Category")
            });

            // Fits the pipeline to the data.
            IDataView transformedData = pipeline.Fit(data).Transform(data);

            // Getting the resulting data as an IEnumerable.
            // This will contain the newly created columns.
            IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<
                TransformedData>(transformedData, reuseRowObject: false);

            Console.WriteLine($" Timeframe           TimeframeVector         " +
                $"Category    CategoryVector");

            foreach (var featureRow in features)
                Console.WriteLine(featureRow.Timeframe + "     " +
                    string.Join(',', featureRow.TimeframeVector) + "   " +
                    featureRow.Category + "      " +
                    string.Join(',', featureRow.CategoryVector));

            // TransformedData obtained post-transformation.
            //
            // Timeframe          TimeframeVector    Category    CategoryVector
            //  10              0,0,0,0,0,0,0,0,0,1       6          0,0,0,0,0
            //  9               0,0,0,0,0,0,0,0,1,0       5          0,0,0,0,1
            //  9               0,0,0,0,0,0,0,0,1,0       5          0,0,0,0,1
            //  10              0,0,0,0,0,0,0,0,0,1       4          0,0,0,1,0
            //  3               0,0,1,0,0,0,0,0,0,0       4          0,0,0,1,0
            //  4               0,0,0,1,0,0,0,0,0,0       6          0,0,0,0,0
        }

        private class DataPoint
        {
            // The maximal value used is 9; but since 0 is reserved for missing
            // value, we set the count to 10.
            [KeyType(10)]
            public uint Timeframe { get; set; }

            [KeyType(6)]
            public uint Category { get; set; }

        }

        private class TransformedData : DataPoint
        {
            public float[] TimeframeVector { get; set; }
            public float[] CategoryVector { get; set; }
        }
    }
}

注解

此转换可以针对多个键列进行操作。

适用于

MapKeyToVector(TransformsCatalog+ConversionTransforms, String, String, Boolean)