ConversionsExtensionsCatalog.MapKeyToValue 方法

定義

命名空間:

Microsoft.ML

組件:

Microsoft.ML.Data.dll

套件:

Microsoft.ML v2.0.0

多載

MapKeyToValue(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[])	建立 KeyToValueMappingEstimator ，將索引鍵類型轉換成其原始值。
MapKeyToValue(TransformsCatalog+ConversionTransforms, String, String)	建立 KeyToValueMappingEstimator ，將索引鍵類型轉換成其原始值。

MapKeyToValue(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[])

建立 KeyToValueMappingEstimator ，將索引鍵類型轉換成其原始值。

public static Microsoft.ML.Transforms.KeyToValueMappingEstimator MapKeyToValue (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, Microsoft.ML.InputOutputColumnPair[] columns);

static member MapKeyToValue : Microsoft.ML.TransformsCatalog.ConversionTransforms * Microsoft.ML.InputOutputColumnPair[] -> Microsoft.ML.Transforms.KeyToValueMappingEstimator

<Extension()>
Public Function MapKeyToValue (catalog As TransformsCatalog.ConversionTransforms, columns As InputOutputColumnPair()) As KeyToValueMappingEstimator

參數

catalog

TransformsCatalog.ConversionTransforms

轉換轉換的目錄。

columns

InputOutputColumnPair[]

輸入和輸出資料行。 此轉換會透過索引鍵運作。 新資料行的資料類型將會是原始值的型別。

傳回

KeyToValueMappingEstimator

範例

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

namespace Samples.Dynamic
{
    /// This example demonstrates the use of the ValueToKeyMappingEstimator, by
    /// mapping KeyType values to the original strings. For more on ML.NET KeyTypes
    /// see: https://github.com/dotnet/machinelearning/blob/main/docs/code/IDataViewTypeSystem.md#key-types
    public class MapKeyToValueMultiColumn
    {
        public static void Example()
        {
            // Create a new context for ML.NET operations. It can be used for
            // exception tracking and logging, as a catalog of available operations
            // and as the source of randomness. Setting the seed to a fixed number
            // in this example to make outputs deterministic.
            var mlContext = new MLContext(seed: 0);
            // Get a small dataset as an IEnumerable.

            // Create a list of data examples.
            var examples = GenerateRandomDataPoints(1000, 10);

            // Convert the examples list to an IDataView object, which is consumable
            // by ML.NET API.
            var dataView = mlContext.Data.LoadFromEnumerable(examples);

            // Create a pipeline.
            var pipeline =
                    // Convert the string labels into key types.
                    mlContext.Transforms.Conversion.MapValueToKey("Label")
                    // Apply StochasticDualCoordinateAscent multiclass trainer.
                    .Append(mlContext.MulticlassClassification.Trainers.
                    SdcaMaximumEntropy());

            // Train the model and do predictions on same data set.
            // Typically predictions would be in a different, validation set.
            var dataWithPredictions = pipeline.Fit(dataView).Transform(dataView);

            // At this point, the Label column is transformed from strings, to
            // DataViewKeyType and the transformation has added the PredictedLabel
            // column, with same DataViewKeyType as transformed Label column.
            // MapKeyToValue would take columns with DataViewKeyType and convert
            // them back to their original values.
            var newPipeline = mlContext.Transforms.Conversion.MapKeyToValue(new[]
            {
                new InputOutputColumnPair("LabelOriginalValue","Label"),
                new InputOutputColumnPair("PredictedLabelOriginalValue",
                "PredictedLabel")

            });

            var transformedData = newPipeline.Fit(dataWithPredictions).Transform(
                dataWithPredictions);

            // Let's iterate over first 5 items.
            transformedData = mlContext.Data.TakeRows(transformedData, 5);
            var values = mlContext.Data.CreateEnumerable<TransformedData>(
                transformedData, reuseRowObject: false);

            // Printing the column names of the transformed data.
            Console.WriteLine($"Label   LabelOriginalValue   PredictedLabel   " +
                $"PredictedLabelOriginalValue");

            foreach (var row in values)
                Console.WriteLine($"{row.Label}\t\t{row.LabelOriginalValue}\t\t\t" +
                    $"{row.PredictedLabel}\t\t\t{row.PredictedLabelOriginalValue}");

            // Expected output:
            //  Label   LabelOriginalValue   PredictedLabel   PredictedLabelOriginalValue
            //  1               AA                      1                       AA
            //  2               BB                      2                       BB
            //  3               CC                      4                       DD
            //  4               DD                      4                       DD
            //  1               AA                      1                       AA

        }

        private class DataPoint
        {
            public string Label { get; set; }
            [VectorType(10)]
            public float[] Features { get; set; }
        }

        private static List<DataPoint> GenerateRandomDataPoints(int count,
            int featureVectorLenght)
        {
            var examples = new List<DataPoint>();
            var rnd = new Random(0);
            for (int i = 0; i < count; ++i)
            {
                var example = new DataPoint();
                example.Features = new float[featureVectorLenght];
                var res = i % 4;
                // Generate random float feature values.
                for (int j = 0; j < featureVectorLenght; ++j)
                {
                    var value = (float)rnd.NextDouble() + res * 0.2f;
                    example.Features[j] = value;
                }

                // Generate label based on feature sum.
                if (res == 0)
                    example.Label = "AA";
                else if (res == 1)
                    example.Label = "BB";
                else if (res == 2)
                    example.Label = "CC";
                else
                    example.Label = "DD";
                examples.Add(example);
            }
            return examples;
        }
        private class TransformedData
        {
            public uint Label { get; set; }
            public uint PredictedLabel { get; set; }
            public string LabelOriginalValue { get; set; }
            public string PredictedLabelOriginalValue { get; set; }
        }
    }
}

備註

此轉換可以透過數個數據行運作。 此轉換通常位於其中一個 多載之後的管線中 MapValueToKey(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[], Int32, ValueToKeyMappingEstimator+KeyOrdinality, Boolean, IDataView)

MapKeyToValue(TransformsCatalog+ConversionTransforms, String, String)

建立 KeyToValueMappingEstimator ，將索引鍵類型轉換成其原始值。

public static Microsoft.ML.Transforms.KeyToValueMappingEstimator MapKeyToValue (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, string outputColumnName, string inputColumnName = default);

static member MapKeyToValue : Microsoft.ML.TransformsCatalog.ConversionTransforms * string * string -> Microsoft.ML.Transforms.KeyToValueMappingEstimator

<Extension()>
Public Function MapKeyToValue (catalog As TransformsCatalog.ConversionTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing) As KeyToValueMappingEstimator

參數

catalog

TransformsCatalog.ConversionTransforms

轉換轉換的目錄。

outputColumnName

String

轉換 inputColumnName 所產生的資料行名稱。 其類型會是原始值的型別。

inputColumnName

String

要轉換的資料行名稱。 如果設定為 null ，則會 outputColumnName 將 的值當做來源使用。 此轉換會透過索引鍵運作。

傳回

KeyToValueMappingEstimator

範例

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

namespace Samples.Dynamic
{
    public class KeyToValueToKey
    {
        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() { Review = "animals birds cats dogs fish horse"},
                new DataPoint() { Review = "horse birds house fish duck cats"},
                new DataPoint() { Review = "car truck driver bus pickup"},
                new DataPoint() { Review = "car truck driver bus pickup horse"},
            };

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

            // A pipeline to convert the terms of the 'Review' column in 
            // making use of default settings.
            var defaultPipeline = mlContext.Transforms.Text.TokenizeIntoWords(
                "TokenizedText", nameof(DataPoint.Review)).Append(mlContext
                .Transforms.Conversion.MapValueToKey(nameof(TransformedData.Keys),
                "TokenizedText"));

            // Another pipeline, that customizes the advanced settings of the
            // ValueToKeyMappingEstimator. We can change the maximumNumberOfKeys to
            // limit how many keys will get generated out of the set of words, and
            // condition the order in which they get evaluated by changing
            // keyOrdinality from the default ByOccurence (order in which they get
            // encountered) to value/alphabetically.
            var customizedPipeline = mlContext.Transforms.Text.TokenizeIntoWords(
                "TokenizedText", nameof(DataPoint.Review)).Append(mlContext
                .Transforms.Conversion.MapValueToKey(nameof(TransformedData.Keys),
                "TokenizedText", maximumNumberOfKeys: 10, keyOrdinality:
                ValueToKeyMappingEstimator.KeyOrdinality.ByValue));

            // The transformed data.
            var transformedDataDefault = defaultPipeline.Fit(trainData).Transform(
                trainData);

            var transformedDataCustomized = customizedPipeline.Fit(trainData)
                .Transform(trainData);

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

            IEnumerable<TransformedData> customizedData = mlContext.Data.
                CreateEnumerable<TransformedData>(transformedDataCustomized,
                reuseRowObject: false);

            Console.WriteLine($"Keys");
            foreach (var dataRow in defaultData)
                Console.WriteLine($"{string.Join(',', dataRow.Keys)}");
            // Expected output:
            //  Keys
            //  1,2,3,4,5,6
            //  6,2,7,5,8,3
            //  9,10,11,12,13
            //  9,10,11,12,13,6

            Console.WriteLine($"Keys");
            foreach (var dataRow in customizedData)
                Console.WriteLine($"{string.Join(',', dataRow.Keys)}");
            // Expected output:
            //  Keys
            //  1,2,4,5,7,8
            //  8,2,9,7,6,4
            //  3,10,0,0,0
            //  3,10,0,0,0,8
            // Retrieve the original values, by appending the KeyToValue estimator to
            // the existing pipelines to convert the keys back to the strings.
            var pipeline = defaultPipeline.Append(mlContext.Transforms.Conversion
                .MapKeyToValue(nameof(TransformedData.Keys)));

            transformedDataDefault = pipeline.Fit(trainData).Transform(trainData);

            // Preview of the DefaultColumnName column obtained.
            var originalColumnBack = transformedDataDefault.GetColumn<VBuffer<
                ReadOnlyMemory<char>>>(transformedDataDefault.Schema[nameof(
                TransformedData.Keys)]);

            foreach (var row in originalColumnBack)
            {
                foreach (var value in row.GetValues())
                    Console.Write($"{value} ");
                Console.WriteLine("");
            }

            // Expected output:
            //  animals birds cats dogs fish horse
            //  horse birds house fish duck cats
            //  car truck driver bus pickup
            //  car truck driver bus pickup horse
        }

        private class DataPoint
        {
            public string Review { get; set; }
        }

        private class TransformedData : DataPoint
        {
            public uint[] Keys { get; set; }
        }
    }
}

備註

此轉換通常位於其中一個 多載之後的管線中 MapValueToKey(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[], Int32, ValueToKeyMappingEstimator+KeyOrdinality, Boolean, IDataView)