ConversionsExtensionsCatalog.Hash Metodo

Definizione

Spazio dei nomi:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Pacchetto:

Microsoft.ML v2.0.0

Overload

Hash(TransformsCatalog+ConversionTransforms, HashingEstimator+ColumnOptions[])	Creare un HashingEstimatoroggetto , che esegue l'hash del tipo di InputColumnName dati della colonna di input in una nuova colonna: Name.
Hash(TransformsCatalog+ConversionTransforms, String, String, Int32, Int32)	Creare un HashingEstimatoroggetto , che esegue l'hash dei dati dalla colonna specificata in inputColumnName a una nuova colonna: outputColumnName.

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

Creare un HashingEstimatoroggetto , che esegue l'hash del tipo di InputColumnName dati della colonna di input in una nuova colonna: 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

Parametri

catalog

TransformsCatalog.ConversionTransforms

Catalogo della trasformazione.

columns

HashingEstimator.ColumnOptions[]

Opzioni avanzate per lo strumento di stima che contengono anche i nomi di colonna di input e output. Questo strumento di stima opera su tipi di testo, numerici, booleani, chiave e DataViewRowId dati. Il tipo di dati della nuova colonna sarà un vettore di UInt32o in UInt32 base al fatto che i tipi di dati della colonna di input siano vettori o scalari.

Restituisce

HashingEstimator

Esempio

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; }
        }

    }
}

Commenti

Questa trasformazione può funzionare su diverse colonne.

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

Creare un HashingEstimatoroggetto , che esegue l'hash dei dati dalla colonna specificata in inputColumnName a una nuova colonna: 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

Parametri

catalog

TransformsCatalog.ConversionTransforms

Catalogo della trasformazione di conversione.

outputColumnName

String

Nome della colonna risultante dalla trasformazione di inputColumnName. Questo tipo di dati della colonna sarà un vettore di chiavi o una scalare di chiave in base al fatto che i tipi di dati della colonna di input siano vettori o scalari.

inputColumnName

String

Nome della colonna i cui dati verranno hashati. Se impostato su null, il valore dell'oggetto outputColumnName verrà usato come origine. Questo strumento di stima opera su vettori o scalari di tipi di testo, numerico, booleano, chiave o DataViewRowId tipi di dati.

numberOfBits

Int32

Numero di bit in cui eseguire l'hash. Deve essere compreso tra 1 e 31, inclusivo.

maximumNumberOfInverts

Int32

Durante l'hashing vengono creati mapping tra i valori originali e i valori hash prodotti. La rappresentazione testuale dei valori originali viene archiviata nei nomi degli slot delle annotazioni per la nuova colonna. L'hashing, ad esempio, può eseguire il mapping di molti valori iniziali a uno. maximumNumberOfInvertsSpecifica il limite superiore del numero di valori di input distinti mappati a un hash che deve essere mantenuto. 0 non mantiene valori di input. -1 mantiene il mapping di tutti i valori di input a ogni hash.

Restituisce

HashingEstimator

Esempio

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; }
        }

    }
}