ConversionsExtensionsCatalog.MapKeyToVector Método

Definición

Espacio de nombres:

Microsoft.ML

Ensamblado:

Microsoft.ML.Data.dll

Paquete:

Microsoft.ML v2.0.0

Sobrecargas

MapKeyToVector(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[], Boolean)	Cree un KeyToVectorMappingEstimatorobjeto , que asigna el valor de una clave a un vector de punto flotante que representa el valor.
MapKeyToVector(TransformsCatalog+ConversionTransforms, String, String, Boolean)	Cree un KeyToVectorMappingEstimatorobjeto , que asigna el valor de una clave a un vector de punto flotante que representa el valor.

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

Cree un KeyToVectorMappingEstimatorobjeto , que asigna el valor de una clave a un vector de punto flotante que representa el valor.

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

Parámetros

catalog

TransformsCatalog.ConversionTransforms

Catálogo de la transformación de conversión.

columns

InputOutputColumnPair[]

Columnas de entrada y salida. El tipo de datos de la nueva columna es un vector de Single representar el valor original.

outputCountVector

Boolean

Si se van a combinar varios vectores de indicador en un único vector de recuentos en lugar de concatenarlos. Esto solo es relevante cuando la columna de entrada es un vector de claves.

Devoluciones

KeyToVectorMappingEstimator

Ejemplos

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; }
        }
    }
}

Comentarios

Esta transformación puede funcionar en varias columnas de claves.

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

Cree un KeyToVectorMappingEstimatorobjeto , que asigna el valor de una clave a un vector de punto flotante que representa el valor.

public static Microsoft.ML.Transforms.KeyToVectorMappingEstimator MapKeyToVector (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, string outputColumnName, string inputColumnName = default, bool outputCountVector = false);

static member MapKeyToVector : Microsoft.ML.TransformsCatalog.ConversionTransforms * string * string * bool -> Microsoft.ML.Transforms.KeyToVectorMappingEstimator

<Extension()>
Public Function MapKeyToVector (catalog As TransformsCatalog.ConversionTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional outputCountVector As Boolean = false) As KeyToVectorMappingEstimator

Parámetros

catalog

TransformsCatalog.ConversionTransforms

Catálogo de la transformación de conversión.

outputColumnName

String

Nombre de la columna resultante de la transformación de inputColumnName. El tipo de datos es un vector de representar el valor de Single entrada.

inputColumnName

String

Nombre de la columna que se va a transformar. Si se establece en null, el valor de outputColumnName se usará como origen. Esta transformación funciona sobre las claves.

outputCountVector

Boolean

Si se van a combinar varios vectores de indicador en un único vector de recuentos en lugar de concatenarlos. Esto solo es relevante cuando la columna de entrada es un vector de claves.

Devoluciones

KeyToVectorMappingEstimator

Ejemplos

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

namespace Samples.Dynamic
{
    class MapKeyToVector
    {
        /// This example demonstrates the use of MapKeyToVector by mapping keys to
        /// floats[]. 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 = 8, PartA=1, PartB=2},
                new DataPoint() { Timeframe = 7, PartA=2, PartB=1},
                new DataPoint() { Timeframe = 8, PartA=3, PartB=2},
                new DataPoint() { Timeframe = 3, PartA=3, PartB=3}
            };

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

            // First transform just maps key type to indicator vector. i.e. it's
            // produces vector filled with zeros with size of key cardinality and
            // set 1 to corresponding key's value index in that array. After that we
            // concatenate two columns with single int values into vector of ints.
            // Third transform will create vector of keys, where key type is shared
            // across whole vector. Forth transform output data as count vector and
            // that vector would have size equal to shared key type cardinality and
            // put key counts to corresponding indexes in array. Fifth transform
            // output indicator vector for each key and concatenate them together.
            // Result vector would be size of key cardinality multiplied by size of
            // original vector.
            var pipeline = mlContext.Transforms.Conversion.MapKeyToVector(
                "TimeframeVector", "Timeframe")
                .Append(mlContext.Transforms.Concatenate("Parts", "PartA", "PartB"))
                .Append(mlContext.Transforms.Conversion.MapValueToKey("Parts"))
                .Append(mlContext.Transforms.Conversion.MapKeyToVector(
                    "PartsCount", "Parts", outputCountVector: true))
                .Append(mlContext.Transforms.Conversion.MapKeyToVector(
                    "PartsNoCount", "Parts"));

            // 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    PartsCount  " +
                "PartsNoCount");

            foreach (var featureRow in features)
                Console.WriteLine(featureRow.Timeframe + "          " +
                    string.Join(',', featureRow.TimeframeVector.Select(x => x)) + "  "
                    + string.Join(',', featureRow.PartsCount.Select(x => x)) +
                    "       " + string.Join(',', featureRow.PartsNoCount.Select(
                    x => x)));

            // Expected output:
            //  Timeframe  TimeframeVector    PartsCount  PartsNoCount
            //  9          0,0,0,0,0,0,0,0,1  1,1,0       1,0,0,0,1,0
            //  8          0,0,0,0,0,0,0,1,0  1,1,0       0,1,0,1,0,0
            //  9          0,0,0,0,0,0,0,0,1  0,1,1       0,0,1,0,1,0
            //  4          0,0,0,1,0,0,0,0,0  0,0,2       0,0,1,0,0,1
        }

        private class DataPoint
        {
            [KeyType(9)]
            public uint Timeframe { get; set; }
            public int PartA { get; set; }
            public int PartB { get; set; }

        }

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