ConversionsExtensionsCatalog.MapValueToKey Método

Definición

Espacio de nombres:

Microsoft.ML

Ensamblado:

Microsoft.ML.Data.dll

Paquete:

Microsoft.ML v2.0.0

Sobrecargas

MapValueToKey(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[], Int32, ValueToKeyMappingEstimator+KeyOrdinality, Boolean, IDataView)	Cree un ValueToKeyMappingEstimatorobjeto , que convierte los valores de categorías en claves.
MapValueToKey(TransformsCatalog+ConversionTransforms, String, String, Int32, ValueToKeyMappingEstimator+KeyOrdinality, Boolean, IDataView)	Cree un , ValueToKeyMappingEstimatorque convierte los valores de categorías en claves numéricas.

MapValueToKey(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[], Int32, ValueToKeyMappingEstimator+KeyOrdinality, Boolean, IDataView)

Cree un ValueToKeyMappingEstimatorobjeto , que convierte los valores de categorías en claves.

public static Microsoft.ML.Transforms.ValueToKeyMappingEstimator MapValueToKey (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, Microsoft.ML.InputOutputColumnPair[] columns, int maximumNumberOfKeys = 1000000, Microsoft.ML.Transforms.ValueToKeyMappingEstimator.KeyOrdinality keyOrdinality = Microsoft.ML.Transforms.ValueToKeyMappingEstimator+KeyOrdinality.ByOccurrence, bool addKeyValueAnnotationsAsText = false, Microsoft.ML.IDataView keyData = default);

static member MapValueToKey : Microsoft.ML.TransformsCatalog.ConversionTransforms * Microsoft.ML.InputOutputColumnPair[] * int * Microsoft.ML.Transforms.ValueToKeyMappingEstimator.KeyOrdinality * bool * Microsoft.ML.IDataView -> Microsoft.ML.Transforms.ValueToKeyMappingEstimator

<Extension()>
Public Function MapValueToKey (catalog As TransformsCatalog.ConversionTransforms, columns As InputOutputColumnPair(), Optional maximumNumberOfKeys As Integer = 1000000, Optional keyOrdinality As ValueToKeyMappingEstimator.KeyOrdinality = Microsoft.ML.Transforms.ValueToKeyMappingEstimator+KeyOrdinality.ByOccurrence, Optional addKeyValueAnnotationsAsText As Boolean = false, Optional keyData As IDataView = Nothing) As ValueToKeyMappingEstimator

Parámetros

catalog

TransformsCatalog.ConversionTransforms

Catálogo de la transformación de conversión.

columns

InputOutputColumnPair[]

Columnas de entrada y salida. Los tipos de datos de entrada pueden ser numéricos, de texto, booleanos DateTime o DateTimeOffset.

maximumNumberOfKeys

Int32

Número máximo de claves que se mantendrán por columna al entrenar.

keyOrdinality

ValueToKeyMappingEstimator.KeyOrdinality

Orden en el que se asignan las claves. Si se establece ByOccurrenceen , las claves se asignan en el orden encontrado. Si se establece en ByValue, se ordenan los valores y las claves se asignan según el criterio de ordenación.

addKeyValueAnnotationsAsText

Boolean

Si se establece en true, use el tipo de texto para los valores, independientemente del tipo de entrada real. Al realizar la asignación inversa, los valores son texto en lugar del tipo de entrada original.

keyData

IDataView

Use una asignación predefinida entre valores y claves, en lugar de crear la asignación a partir de los datos de entrada durante el entrenamiento. Si se especifica, debe ser una sola columna IDataView que contenga los valores. Las claves se asignan en función del valor de keyOrdinality.

Devoluciones

ValueToKeyMappingEstimator

Ejemplos

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

namespace Samples.Dynamic
{
    public static class MapValueToKeyMultiColumn
    {
        /// This example demonstrates the use of the ValueToKeyMappingEstimator, by
        /// mapping strings to KeyType values. For more on ML.NET KeyTypes see:
        /// https://github.com/dotnet/machinelearning/blob/main/docs/code/IDataViewTypeSystem.md#key-types
        /// It is possible to have multiple values map to the same category.
        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() { StudyTime = "0-4yrs" , Course = "CS" },
                new DataPoint() { StudyTime = "6-11yrs" , Course = "CS" },
                new DataPoint() { StudyTime = "12-25yrs" , Course = "LA" },
                new DataPoint() { StudyTime = "0-5yrs" , Course = "DS" }
            };

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

            // Constructs the ML.net pipeline
            var pipeline = mlContext.Transforms.Conversion.MapValueToKey(new[] {
                new  InputOutputColumnPair("StudyTimeCategory", "StudyTime"),
                new  InputOutputColumnPair("CourseCategory", "Course")
                },
                keyOrdinality: Microsoft.ML.Transforms.ValueToKeyMappingEstimator
                    .KeyOrdinality.ByValue, addKeyValueAnnotationsAsText: true);

            // 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($" StudyTime   StudyTimeCategory   Course    " +
                $"CourseCategory");

            foreach (var featureRow in features)
                Console.WriteLine($"{featureRow.StudyTime}\t\t" +
                    $"{featureRow.StudyTimeCategory}\t\t\t{featureRow.Course}\t\t" +
                    $"{featureRow.CourseCategory}");

            // TransformedData obtained post-transformation.
            //
            // StudyTime     StudyTimeCategory   Course    CourseCategory
            // 0-4yrs          1                   CS          1
            // 6-11yrs         4                   CS          1
            // 12-25yrs        3                   LA          3
            // 0-5yrs          2                   DS          2

            // If we wanted to provide the mapping, rather than letting the
            // transform create it, we could do so by creating an IDataView one
            // column containing the values to map to. If the values in the dataset
            // are not found in the lookup IDataView they will get mapped to the
            // missing value, 0. The keyData are shared among the columns, therefore
            // the keys are not contiguous for the column. Create the lookup map
            // data IEnumerable.
            var lookupData = new[] {
                new LookupMap { Key = "0-4yrs" },
                new LookupMap { Key = "6-11yrs" },
                new LookupMap { Key = "25+yrs"  },
                new LookupMap { Key = "CS" },
                new LookupMap { Key = "DS" },
                new LookupMap { Key = "LA"  }
            };

            // Convert to IDataView
            var lookupIdvMap = mlContext.Data.LoadFromEnumerable(lookupData);

            // Constructs the ML.net pipeline
            var pipelineWithLookupMap = mlContext.Transforms.Conversion
                .MapValueToKey(new[] {
                    new  InputOutputColumnPair("StudyTimeCategory", "StudyTime"),
                    new  InputOutputColumnPair("CourseCategory", "Course")
                    },
                    keyData: lookupIdvMap);

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

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

            Console.WriteLine($" StudyTime   StudyTimeCategory  " +
                $"Course CourseCategory");

            foreach (var featureRow in features)
                Console.WriteLine($"{featureRow.StudyTime}\t\t" +
                    $"{featureRow.StudyTimeCategory}\t\t\t{featureRow.Course}\t\t" +
                    $"{featureRow.CourseCategory}");

            // StudyTime    StudyTimeCategory  Course     CourseCategory
            // 0 - 4yrs          1              CS              4
            // 6 - 11yrs         2              CS              4
            // 12 - 25yrs        0              LA              6
            // 0 - 5yrs          0              DS              5

        }

        private class DataPoint
        {
            public string StudyTime { get; set; }
            public string Course { get; set; }
        }

        private class TransformedData : DataPoint
        {
            public uint StudyTimeCategory { get; set; }
            public uint CourseCategory { get; set; }
        }

        // Type for the IDataView that will be serving as the map
        private class LookupMap
        {
            public string Key { get; set; }
        }
    }
}

Comentarios

Esta transformación puede funcionar en varios pares de columnas, creando una asignación para cada par.

MapValueToKey(TransformsCatalog+ConversionTransforms, String, String, Int32, ValueToKeyMappingEstimator+KeyOrdinality, Boolean, IDataView)

Cree un , ValueToKeyMappingEstimatorque convierte los valores de categorías en claves numéricas.

public static Microsoft.ML.Transforms.ValueToKeyMappingEstimator MapValueToKey (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, string outputColumnName, string inputColumnName = default, int maximumNumberOfKeys = 1000000, Microsoft.ML.Transforms.ValueToKeyMappingEstimator.KeyOrdinality keyOrdinality = Microsoft.ML.Transforms.ValueToKeyMappingEstimator+KeyOrdinality.ByOccurrence, bool addKeyValueAnnotationsAsText = false, Microsoft.ML.IDataView keyData = default);

static member MapValueToKey : Microsoft.ML.TransformsCatalog.ConversionTransforms * string * string * int * Microsoft.ML.Transforms.ValueToKeyMappingEstimator.KeyOrdinality * bool * Microsoft.ML.IDataView -> Microsoft.ML.Transforms.ValueToKeyMappingEstimator

<Extension()>
Public Function MapValueToKey (catalog As TransformsCatalog.ConversionTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional maximumNumberOfKeys As Integer = 1000000, Optional keyOrdinality As ValueToKeyMappingEstimator.KeyOrdinality = Microsoft.ML.Transforms.ValueToKeyMappingEstimator+KeyOrdinality.ByOccurrence, Optional addKeyValueAnnotationsAsText As Boolean = false, Optional keyData As IDataView = Nothing) As ValueToKeyMappingEstimator

Parámetros

catalog

TransformsCatalog.ConversionTransforms

Catálogo de la transformación de conversión.

outputColumnName

String

Nombre de la columna que contiene las claves.

inputColumnName

String

Nombre de la columna que contiene los valores categóricos. Si se establece en null, se usa el valor de .outputColumnName Los tipos de datos de entrada pueden ser numéricos, de texto, booleanos DateTime o DateTimeOffset.

maximumNumberOfKeys

Int32

Número máximo de claves que se mantendrán por columna al entrenar.

keyOrdinality

ValueToKeyMappingEstimator.KeyOrdinality

Orden en el que se asignan las claves. Si se establece ByOccurrenceen , las claves se asignan en el orden encontrado. Si se establece en ByValue, se ordenan los valores y las claves se asignan según el criterio de ordenación.

addKeyValueAnnotationsAsText

Boolean

Si se establece en true, use el tipo de texto para los valores, independientemente del tipo de entrada real. Al realizar la asignación inversa, los valores son texto en lugar del tipo de entrada original.

keyData

IDataView

Use una asignación predefinida entre valores y claves, en lugar de crear la asignación a partir de los datos de entrada durante el entrenamiento. Si se especifica, debe ser una sola columna IDataView que contenga los valores. Las claves se asignan en función del valor de keyOrdinality.

Devoluciones

ValueToKeyMappingEstimator

Ejemplos

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; }
        }
    }
}