ConversionsExtensionsCatalog.Hash Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.Data.dll

Paket:

Microsoft.ML v2.0.0

Overload

Hash(TransformsCatalog+ConversionTransforms, HashingEstimator+ColumnOptions[])	Buat HashingEstimator, yang hash jenis InputColumnName data kolom input ke kolom baru: Name.
Hash(TransformsCatalog+ConversionTransforms, String, String, Int32, Int32)	Buat HashingEstimator, yang hash data dari kolom yang ditentukan ke inputColumnName kolom baru: outputColumnName.

Hash(TransformsCatalog+ConversionTransforms, HashingEstimator+ColumnOptions[])

Buat HashingEstimator, yang hash jenis InputColumnName data kolom input ke kolom baru: Name.

public static Microsoft.ML.Transforms.HashingEstimator Hash (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, params Microsoft.ML.Transforms.HashingEstimator.ColumnOptions[] columns);

static member Hash : Microsoft.ML.TransformsCatalog.ConversionTransforms * Microsoft.ML.Transforms.HashingEstimator.ColumnOptions[] -> Microsoft.ML.Transforms.HashingEstimator

<Extension()>
Public Function Hash (catalog As TransformsCatalog.ConversionTransforms, ParamArray columns As HashingEstimator.ColumnOptions()) As HashingEstimator

Parameter

catalog

TransformsCatalog.ConversionTransforms

Katalog transformasi.

columns

HashingEstimator.ColumnOptions[]

Opsi tingkat lanjut untuk estimator yang juga berisi nama kolom input dan output. Estimator ini beroperasi melalui jenis teks, numerik, boolean, kunci, dan DataViewRowId data. Jenis data kolom baru akan menjadi vektor UInt32, atau UInt32 berdasarkan apakah jenis data kolom input adalah vektor atau skalar.

Mengembalikan

HashingEstimator

Contoh

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

namespace Samples.Dynamic
{
    // This example demonstrates hashing of categorical string and integer data types by using Hash transform's 
    // advanced options API.
    public static class HashWithOptions
    {
        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(seed: 1);

            // Get a small dataset as an IEnumerable.
            var rawData = new[] {
                new DataPoint() { Category = "MLB" , Age = 18 },
                new DataPoint() { Category = "NFL" , Age = 14 },
                new DataPoint() { Category = "NFL" , Age = 15 },
                new DataPoint() { Category = "MLB" , Age = 18 },
                new DataPoint() { Category = "MLS" , Age = 14 },
            };

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

            // Construct the pipeline that would hash the two columns and store the
            // results in new columns. The first transform hashes the string column
            // and the second transform hashes the integer column.
            //
            // Hashing is not a reversible operation, so there is no way to retrieve
            // the original value from the hashed value. Sometimes, for debugging,
            // or model explainability, users will need to know what values in the
            // original columns generated the values in the hashed columns, since
            // the algorithms will mostly use the hashed values for further
            // computations. The Hash method will preserve the mapping from the
            // original values to the hashed values in the Annotations of the newly
            // created column (column populated with the hashed values). 
            //
            // Setting the maximumNumberOfInverts parameters to -1 will preserve the
            // full map. If that parameter is left to the default 0 value, the
            // mapping is not preserved.
            var pipeline = mlContext.Transforms.Conversion.Hash(
                    new[]
                    {
                            new HashingEstimator.ColumnOptions(
                                "CategoryHashed",
                                "Category",
                                16,
                                useOrderedHashing: false,
                                maximumNumberOfInverts: -1),

                            new HashingEstimator.ColumnOptions(
                                "AgeHashed",
                                "Age",
                                8,
                                useOrderedHashing: false)
                    });

            // Let's fit our pipeline, and then apply it to the same data.
            var transformer = pipeline.Fit(data);
            var transformedData = transformer.Transform(data);

            // Convert the post transformation from the IDataView format to an
            // IEnumerable <TransformedData> for easy consumption.
            var convertedData = mlContext.Data.CreateEnumerable<
                TransformedDataPoint>(transformedData, true);

            Console.WriteLine("Category CategoryHashed\t Age\t AgeHashed");
            foreach (var item in convertedData)
                Console.WriteLine($"{item.Category}\t {item.CategoryHashed}\t\t  " +
                    $"{item.Age}\t {item.AgeHashed}");

            // Expected data after the transformation.
            //
            // Category CategoryHashed   Age     AgeHashed
            // MLB      36206            18      127
            // NFL      19015            14      62
            // NFL      19015            15      43
            // MLB      36206            18      127
            // MLS      6013             14      62

            // For the Category column, where we set the maximumNumberOfInverts
            // parameter, the names of the original categories, and their
            // correspondence with the generated hash values is preserved in the
            // Annotations in the format of indices and values.the indices array
            // will have the hashed values, and the corresponding element,
            // position -wise, in the values array will contain the original value. 
            //
            // See below for an example on how to retrieve the mapping. 
            var slotNames = new VBuffer<ReadOnlyMemory<char>>();
            transformedData.Schema["CategoryHashed"].Annotations.GetValue(
                "KeyValues", ref slotNames);

            var indices = slotNames.GetIndices();
            var categoryNames = slotNames.GetValues();

            for (int i = 0; i < indices.Length; i++)
                Console.WriteLine($"The original value of the {indices[i]} " +
                    $"category is {categoryNames[i]}");

            // Output Data
            // 
            // The original value of the 6012 category is MLS
            // The original value of the 19014 category is NFL
            // The original value of the 36205 category is MLB
        }

        public class DataPoint
        {
            public string Category { get; set; }
            public uint Age { get; set; }
        }

        public class TransformedDataPoint : DataPoint
        {
            public uint CategoryHashed { get; set; }
            public uint AgeHashed { get; set; }
        }

    }
}

Keterangan

Transformasi ini dapat beroperasi di beberapa kolom.

Hash(TransformsCatalog+ConversionTransforms, String, String, Int32, Int32)

Buat HashingEstimator, yang hash data dari kolom yang ditentukan ke inputColumnName kolom baru: outputColumnName.

public static Microsoft.ML.Transforms.HashingEstimator Hash (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, string outputColumnName, string inputColumnName = default, int numberOfBits = 31, int maximumNumberOfInverts = 0);

static member Hash : Microsoft.ML.TransformsCatalog.ConversionTransforms * string * string * int * int -> Microsoft.ML.Transforms.HashingEstimator

<Extension()>
Public Function Hash (catalog As TransformsCatalog.ConversionTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional numberOfBits As Integer = 31, Optional maximumNumberOfInverts As Integer = 0) As HashingEstimator

Parameter

catalog

TransformsCatalog.ConversionTransforms

Katalog transformasi konversi.

outputColumnName

String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Jenis data kolom ini akan menjadi vektor kunci, atau skalar kunci berdasarkan apakah jenis data kolom input adalah vektor atau skalar.

inputColumnName

String

Nama kolom yang datanya akan di-hash. Jika diatur ke null, nilai outputColumnName akan digunakan sebagai sumber. Estimator ini beroperasi di atas vektor atau skalar teks, numerik, boolean, kunci atau DataViewRowId jenis data.

numberOfBits

Int32

Jumlah bit yang akan di-hash. Harus antara 1 dan 31, inklusif.

maximumNumberOfInverts

Int32

Selama hashing, kami membangun pemetaan antara nilai asli dan nilai hash yang dihasilkan. Representasi teks nilai asli disimpan dalam nama slot anotasi untuk kolom baru. Hashing, dengan demikian, dapat memetakan banyak nilai awal menjadi satu. maximumNumberOfInvertsMenentukan batas atas jumlah pemetaan nilai input yang berbeda ke hash yang harus dipertahankan. 0 tidak mempertahankan nilai input apa pun. -1 mempertahankan semua pemetaan nilai input ke setiap hash.

Mengembalikan

HashingEstimator

Contoh

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

namespace Samples.Dynamic
{
    // This example demonstrates hashing of categorical string and integer data types.
    public static class Hash
    {
        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(seed: 1);

            // Get a small dataset as an IEnumerable.
            var rawData = new[] {
                new DataPoint() { Category = "MLB" , Age = 18 },
                new DataPoint() { Category = "NFL" , Age = 14 },
                new DataPoint() { Category = "NFL" , Age = 15 },
                new DataPoint() { Category = "MLB" , Age = 18 },
                new DataPoint() { Category = "MLS" , Age = 14 },
            };

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

            // Construct the pipeline that would hash the two columns and store the
            // results in new columns. The first transform hashes the string column
            // and the second transform hashes the integer column.
            //
            // Hashing is not a reversible operation, so there is no way to retrieve
            // the original value from the hashed value. Sometimes, for debugging,
            // or model explainability, users will need to know what values in the
            // original columns generated the values in the hashed columns, since
            // the algorithms will mostly use the hashed values for further
            // computations. The Hash method will preserve the mapping from the
            // original values to the hashed values in the Annotations of the newly
            // created column (column populated with the hashed values). 
            //
            // Setting the maximumNumberOfInverts parameters to -1 will preserve the
            // full map. If that parameter is left to the default 0 value, the
            // mapping is not preserved.
            var pipeline = mlContext.Transforms.Conversion.Hash("CategoryHashed",
                "Category", numberOfBits: 16, maximumNumberOfInverts: -1)
                .Append(mlContext.Transforms.Conversion.Hash("AgeHashed", "Age",
                numberOfBits: 8));

            // Let's fit our pipeline, and then apply it to the same data.
            var transformer = pipeline.Fit(data);
            var transformedData = transformer.Transform(data);

            // Convert the post transformation from the IDataView format to an
            // IEnumerable <TransformedData> for easy consumption.
            var convertedData = mlContext.Data.CreateEnumerable<
                TransformedDataPoint>(transformedData, true);

            Console.WriteLine("Category CategoryHashed\t Age\t AgeHashed");
            foreach (var item in convertedData)
                Console.WriteLine($"{item.Category}\t {item.CategoryHashed}\t\t  " +
                    $"{item.Age}\t {item.AgeHashed}");

            // Expected data after the transformation.
            //
            // Category CategoryHashed   Age     AgeHashed
            // MLB      36206            18      127
            // NFL      19015            14      62
            // NFL      19015            15      43
            // MLB      36206            18      127
            // MLS      6013             14      62

            // For the Category column, where we set the maximumNumberOfInverts
            // parameter, the names of the original categories, and their
            // correspondence with the generated hash values is preserved in the
            // Annotations in the format of indices and values.the indices array
            // will have the hashed values, and the corresponding element,
            // position -wise, in the values array will contain the original value. 
            //
            // See below for an example on how to retrieve the mapping. 
            var slotNames = new VBuffer<ReadOnlyMemory<char>>();
            transformedData.Schema["CategoryHashed"].Annotations.GetValue(
                "KeyValues", ref slotNames);

            var indices = slotNames.GetIndices();
            var categoryNames = slotNames.GetValues();

            for (int i = 0; i < indices.Length; i++)
                Console.WriteLine($"The original value of the {indices[i]} " +
                    $"category is {categoryNames[i]}");

            // Output Data
            // 
            // The original value of the 6012 category is MLS
            // The original value of the 19014 category is NFL
            // The original value of the 36205 category is MLB
        }

        public class DataPoint
        {
            public string Category { get; set; }
            public uint Age { get; set; }
        }

        public class TransformedDataPoint : DataPoint
        {
            public uint CategoryHashed { get; set; }
            public uint AgeHashed { get; set; }
        }

    }
}