CategoricalCatalog.OneHotHashEncoding Méthode

public static Microsoft.ML.Transforms.OneHotHashEncodingEstimator OneHotHashEncoding (this Microsoft.ML.TransformsCatalog.CategoricalTransforms catalog, Microsoft.ML.InputOutputColumnPair[] columns, Microsoft.ML.Transforms.OneHotEncodingEstimator.OutputKind outputKind = Microsoft.ML.Transforms.OneHotEncodingEstimator+OutputKind.Indicator, int numberOfBits = 16, uint seed = 314489979, bool useOrderedHashing = true, int maximumNumberOfInverts = 0);

static member OneHotHashEncoding : Microsoft.ML.TransformsCatalog.CategoricalTransforms * Microsoft.ML.InputOutputColumnPair[] * Microsoft.ML.Transforms.OneHotEncodingEstimator.OutputKind * int * uint32 * bool * int -> Microsoft.ML.Transforms.OneHotHashEncodingEstimator

<Extension()>
Public Function OneHotHashEncoding (catalog As TransformsCatalog.CategoricalTransforms, columns As InputOutputColumnPair(), Optional outputKind As OneHotEncodingEstimator.OutputKind = Microsoft.ML.Transforms.OneHotEncodingEstimator+OutputKind.Indicator, Optional numberOfBits As Integer = 16, Optional seed As UInteger = 314489979, Optional useOrderedHashing As Boolean = true, Optional maximumNumberOfInverts As Integer = 0) As OneHotHashEncodingEstimator

Paramètres

Retours

Exemples

using System;
using Microsoft.ML;

namespace Samples.Dynamic.Transforms.Categorical
{
    public static class OneHotHashEncodingMultiColumn
    {
        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 samples = new[]
            {
                new DataPoint {Education = "0-5yrs", ZipCode = "98005"},
                new DataPoint {Education = "0-5yrs", ZipCode = "98052"},
                new DataPoint {Education = "6-11yrs", ZipCode = "98005"},
                new DataPoint {Education = "6-11yrs", ZipCode = "98052"},
                new DataPoint {Education = "11-15yrs", ZipCode = "98005"}
            };

            // Convert training data to IDataView.
            IDataView data = mlContext.Data.LoadFromEnumerable(samples);

            // Multi column example: A pipeline for one hot has encoding two
            // columns 'Education' and 'ZipCode'.
            var multiColumnKeyPipeline =
                mlContext.Transforms.Categorical.OneHotHashEncoding(
                    new[]
                    {
                        new InputOutputColumnPair("Education"),
                        new InputOutputColumnPair("ZipCode")
                    },
                    numberOfBits: 3);

            // Fit and Transform the data.
            IDataView transformedData =
                multiColumnKeyPipeline.Fit(data).Transform(data);

            var convertedData =
                mlContext.Data.CreateEnumerable<TransformedData>(transformedData,
                    true);

            Console.WriteLine(
                "One Hot Hash Encoding of two columns 'Education' and 'ZipCode'.");

            // One Hot Hash Encoding of two columns 'Education' and 'ZipCode'.

            foreach (TransformedData item in convertedData)
                Console.WriteLine("{0}\t\t\t{1}", string.Join(" ", item.Education),
                    string.Join(" ", item.ZipCode));

            // We have 8 slots, because we used numberOfBits = 3.

            // 0 0 0 1 0 0 0 0                 0 0 0 0 0 0 0 1
            // 0 0 0 1 0 0 0 0                 1 0 0 0 0 0 0 0
            // 0 0 0 0 1 0 0 0                 0 0 0 0 0 0 0 1
            // 0 0 0 0 1 0 0 0                 1 0 0 0 0 0 0 0
            // 0 0 0 0 0 0 0 1                 0 0 0 0 0 0 0 1
        }

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

            public string ZipCode { get; set; }
        }

        private class TransformedData
        {
            public float[] Education { get; set; }

            public float[] ZipCode { get; set; }
        }
    }
}

Remarques

Si plusieurs colonnes sont passées à l’estimateur, toutes les colonnes seront traitées dans un seul passage sur les données. Par conséquent, il est plus efficace de spécifier un estimateur avec de nombreuses colonnes que de spécifier de nombreux estimateurs chacun avec une seule colonne.

public static Microsoft.ML.Transforms.OneHotHashEncodingEstimator OneHotHashEncoding (this Microsoft.ML.TransformsCatalog.CategoricalTransforms catalog, string outputColumnName, string inputColumnName = default, Microsoft.ML.Transforms.OneHotEncodingEstimator.OutputKind outputKind = Microsoft.ML.Transforms.OneHotEncodingEstimator+OutputKind.Indicator, int numberOfBits = 16, uint seed = 314489979, bool useOrderedHashing = true, int maximumNumberOfInverts = 0);

static member OneHotHashEncoding : Microsoft.ML.TransformsCatalog.CategoricalTransforms * string * string * Microsoft.ML.Transforms.OneHotEncodingEstimator.OutputKind * int * uint32 * bool * int -> Microsoft.ML.Transforms.OneHotHashEncodingEstimator

<Extension()>
Public Function OneHotHashEncoding (catalog As TransformsCatalog.CategoricalTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional outputKind As OneHotEncodingEstimator.OutputKind = Microsoft.ML.Transforms.OneHotEncodingEstimator+OutputKind.Indicator, Optional numberOfBits As Integer = 16, Optional seed As UInteger = 314489979, Optional useOrderedHashing As Boolean = true, Optional maximumNumberOfInverts As Integer = 0) As OneHotHashEncodingEstimator

Paramètres

Retours

Exemples

using System;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms;

namespace Samples.Dynamic.Transforms.Categorical
{
    public static class OneHotHashEncoding
    {
        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();

            // Create a small dataset as an IEnumerable.
            var samples = new[]
            {
                new DataPoint {Education = "0-5yrs"},
                new DataPoint {Education = "0-5yrs"},
                new DataPoint {Education = "6-11yrs"},
                new DataPoint {Education = "6-11yrs"},
                new DataPoint {Education = "11-15yrs"}
            };

            // Convert training data to an IDataView.
            IDataView data = mlContext.Data.LoadFromEnumerable(samples);

            // A pipeline for one hot hash encoding the 'Education' column.
            var pipeline = mlContext.Transforms.Categorical.OneHotHashEncoding(
                "EducationOneHotHashEncoded", "Education", numberOfBits: 3);

            // Fit and transform the data.
            IDataView hashEncodedData = pipeline.Fit(data).Transform(data);

            PrintDataColumn(hashEncodedData, "EducationOneHotHashEncoded");
            // We have 8 slots, because we used numberOfBits = 3.

            // 0 0 0 1 0 0 0 0
            // 0 0 0 1 0 0 0 0
            // 0 0 0 0 1 0 0 0
            // 0 0 0 0 1 0 0 0
            // 0 0 0 0 0 0 0 1

            // A pipeline for one hot hash encoding the 'Education' column
            // (using keying strategy).
            var keyPipeline = mlContext.Transforms.Categorical.OneHotHashEncoding(
                "EducationOneHotHashEncoded", "Education",
                OneHotEncodingEstimator.OutputKind.Key, 3);

            // Fit and transform the data.
            IDataView hashKeyEncodedData = keyPipeline.Fit(data).Transform(data);

            // Get the data of the newly created column for inspecting.
            var keyEncodedColumn =
                hashKeyEncodedData.GetColumn<uint>("EducationOneHotHashEncoded");

            Console.WriteLine(
                "One Hot Hash Encoding of single column 'Education', with key " +
                "type output.");

            // One Hot Hash Encoding of single column 'Education', with key type output.

            foreach (uint element in keyEncodedColumn)
                Console.WriteLine(element);

            // 4
            // 4
            // 5
            // 5
            // 8
        }

        private static void PrintDataColumn(IDataView transformedData,
            string columnName)
        {
            var countSelectColumn = transformedData.GetColumn<float[]>(
                transformedData.Schema[columnName]);

            foreach (var row in countSelectColumn)
            {
                for (var i = 0; i < row.Length; i++)
                    Console.Write($"{row[i]}\t");
                Console.WriteLine();
            }
        }

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