TextCatalog.ProduceHashedNgrams 方法

定義

命名空間:

Microsoft.ML

組件:

Microsoft.ML.Transforms.dll

套件:

Microsoft.ML v2.0.0

多載

ProduceHashedNgrams(TransformsCatalog+TextTransforms, String, String, Int32, Int32, Int32, Boolean, UInt32, Boolean, Int32, Boolean)	建立 NgramHashingEstimator ，將資料從 中指定的 inputColumnName 資料行複製到新的資料行： outputColumnName 並產生雜湊 n-gram 計數的向量。
ProduceHashedNgrams(TransformsCatalog+TextTransforms, String, String[], Int32, Int32, Int32, Boolean, UInt32, Boolean, Int32, Boolean)	建立 NgramHashingEstimator ，它會將資料從 中指定的 inputColumnNames 多個資料行擷取至新的資料行： outputColumnName 並產生雜湊 n-gram 計數的向量。

ProduceHashedNgrams(TransformsCatalog+TextTransforms, String, String, Int32, Int32, Int32, Boolean, UInt32, Boolean, Int32, Boolean)

建立 NgramHashingEstimator ，將資料從 中指定的 inputColumnName 資料行複製到新的資料行： outputColumnName 並產生雜湊 n-gram 計數的向量。

public static Microsoft.ML.Transforms.Text.NgramHashingEstimator ProduceHashedNgrams (this Microsoft.ML.TransformsCatalog.TextTransforms catalog, string outputColumnName, string inputColumnName = default, int numberOfBits = 16, int ngramLength = 2, int skipLength = 0, bool useAllLengths = true, uint seed = 314489979, bool useOrderedHashing = true, int maximumNumberOfInverts = 0, bool rehashUnigrams = false);

static member ProduceHashedNgrams : Microsoft.ML.TransformsCatalog.TextTransforms * string * string * int * int * int * bool * uint32 * bool * int * bool -> Microsoft.ML.Transforms.Text.NgramHashingEstimator

<Extension()>
Public Function ProduceHashedNgrams (catalog As TransformsCatalog.TextTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional numberOfBits As Integer = 16, Optional ngramLength As Integer = 2, Optional skipLength As Integer = 0, Optional useAllLengths As Boolean = true, Optional seed As UInteger = 314489979, Optional useOrderedHashing As Boolean = true, Optional maximumNumberOfInverts As Integer = 0, Optional rehashUnigrams As Boolean = false) As NgramHashingEstimator

參數

catalog

TransformsCatalog.TextTransforms

轉換的目錄。

outputColumnName

String

轉換所產生的 inputColumnName 資料行名稱。 此資料行的資料類型將會是 的 Single 向量。

inputColumnName

String

要從中複製資料的資料行名稱。 此估算器會透過索引鍵類型的向量運作。

numberOfBits

Int32

要雜湊到的位數。 必須介於 1 到 30 之間，包含。

ngramLength

Int32

Ngram 長度。

skipLength

Int32

建構 n-gram 時要略過的權杖數目上限。

useAllLengths

Boolean

是否要包含所有 n-gram 長度，最多 ngramLength 或只 ngramLength 包含 。

seed

UInt32

雜湊種子。

useOrderedHashing

Boolean

當有多個來源資料行) 時，每個來源資料行的位置是否應該包含在雜湊 (中。

maximumNumberOfInverts

Int32

在雜湊處理期間，我們會建構原始值與所產生雜湊值之間的對應。 原始值的文字表示會儲存在新資料行之批註的位置名稱中。因此，雜湊可以將許多初始值對應至一個。 maximumNumberOfInverts 會指定對應至應保留之雜湊的相異輸入值數目上限。 0 不會保留任何輸入值。 -1 會保留與每個雜湊對應的所有輸入值。

rehashUnigrams

Boolean

是否要重新隱藏 Unigram。

傳回

NgramHashingEstimator

備註

NgramHashingEstimator與在內部標記文字時 WordHashBagEstimator 採用標記化文字做為輸入的方式 NgramHashingEstimator 不同 WordHashBagEstimator 。

ProduceHashedNgrams(TransformsCatalog+TextTransforms, String, String[], Int32, Int32, Int32, Boolean, UInt32, Boolean, Int32, Boolean)

建立 NgramHashingEstimator ，它會將資料從 中指定的 inputColumnNames 多個資料行擷取至新的資料行： outputColumnName 並產生雜湊 n-gram 計數的向量。

public static Microsoft.ML.Transforms.Text.NgramHashingEstimator ProduceHashedNgrams (this Microsoft.ML.TransformsCatalog.TextTransforms catalog, string outputColumnName, string[] inputColumnNames = default, int numberOfBits = 16, int ngramLength = 2, int skipLength = 0, bool useAllLengths = true, uint seed = 314489979, bool useOrderedHashing = true, int maximumNumberOfInverts = 0, bool rehashUnigrams = false);

static member ProduceHashedNgrams : Microsoft.ML.TransformsCatalog.TextTransforms * string * string[] * int * int * int * bool * uint32 * bool * int * bool -> Microsoft.ML.Transforms.Text.NgramHashingEstimator

<Extension()>
Public Function ProduceHashedNgrams (catalog As TransformsCatalog.TextTransforms, outputColumnName As String, Optional inputColumnNames As String() = Nothing, Optional numberOfBits As Integer = 16, Optional ngramLength As Integer = 2, Optional skipLength As Integer = 0, Optional useAllLengths As Boolean = true, Optional seed As UInteger = 314489979, Optional useOrderedHashing As Boolean = true, Optional maximumNumberOfInverts As Integer = 0, Optional rehashUnigrams As Boolean = false) As NgramHashingEstimator

參數

catalog

TransformsCatalog.TextTransforms

轉換的目錄。

outputColumnName

String

轉換所產生的 inputColumnNames 資料行名稱。 此資料行的資料類型將會是 已知大小的 Single 向量。

inputColumnNames

String[]

要從中擷取資料的多個資料行名稱。 此估算器會透過索引鍵類型的向量運作。

numberOfBits

Int32

要雜湊到的位數。 必須介於 1 到 30 之間，包含。

ngramLength

Int32

Ngram 長度。

skipLength

Int32

建構 n-gram 時要略過的權杖數目上限。

useAllLengths

Boolean

是否要包含所有 n-gram 長度，最多 ngramLength 或只 ngramLength 包含 。

seed

UInt32

雜湊種子。

useOrderedHashing

Boolean

當有多個來源資料行) 時，每個來源資料行的位置是否應該包含在雜湊 (中。

maximumNumberOfInverts

Int32

在雜湊處理期間，我們會建構原始值與所產生雜湊值之間的對應。 原始值的文字表示會儲存在新資料行之批註的位置名稱中。因此，雜湊可以將許多初始值對應至一個。 maximumNumberOfInverts 會指定對應至應保留之雜湊的相異輸入值數目上限。 0 不會保留任何輸入值。 -1 會保留與每個雜湊對應的所有輸入值。

rehashUnigrams

Boolean

是否要重新隱藏 Unigram。

傳回

NgramHashingEstimator

範例

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

namespace Samples.Dynamic
{
    public static class ProduceHashedNgrams
    {
        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 List<TextData>()
            {
                new TextData(){ Text = "This is an example to compute n-grams " +
                "using hashing." },

                new TextData(){ Text = "N-gram is a sequence of 'N' consecutive" +
                " words/tokens." },

                new TextData(){ Text = "ML.NET's ProduceHashedNgrams API " +
                "produces count of n-grams and hashes it as an index into a " +
                "vector of given bit length." },

                new TextData(){ Text = "The hashing reduces the size of the " +
                "output feature vector" },

                new TextData(){ Text = "which is useful in case when number of " +
                "n-grams is very large." },
            };

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

            // A pipeline for converting text into numeric hashed n-gram features.
            // The following call to 'ProduceHashedNgrams' requires the tokenized
            // text /string as input. This is achieved by calling 
            // 'TokenizeIntoWords' first followed by 'ProduceHashedNgrams'.
            // Please note that the length of the output feature vector depends on
            // the 'numberOfBits' settings.
            var textPipeline = mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
                "Text")
                .Append(mlContext.Transforms.Conversion.MapValueToKey("Tokens"))
                .Append(mlContext.Transforms.Text.ProduceHashedNgrams(
                    "NgramFeatures", "Tokens",
                    numberOfBits: 5,
                    ngramLength: 3,
                    useAllLengths: false,
                    maximumNumberOfInverts: 1));

            // Fit to data.
            var textTransformer = textPipeline.Fit(dataview);
            var transformedDataView = textTransformer.Transform(dataview);

            // Create the prediction engine to get the features extracted from the
            // text.
            var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
                TransformedTextData>(textTransformer);

            // Convert the text into numeric features.
            var prediction = predictionEngine.Predict(samples[0]);

            // Print the length of the feature vector.
            Console.WriteLine("Number of Features: " + prediction.NgramFeatures
                .Length);

            // Preview of the produced n-grams.
            // Get the slot names from the column's metadata.
            // The slot names for a vector column corresponds to the names
            // associated with each position in the vector.
            VBuffer<ReadOnlyMemory<char>> slotNames = default;
            transformedDataView.Schema["NgramFeatures"].GetSlotNames(ref slotNames);
            var NgramFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(
                transformedDataView.Schema["NgramFeatures"]);

            var slots = slotNames.GetValues();
            Console.Write("N-grams: ");
            foreach (var featureRow in NgramFeaturesColumn)
            {
                foreach (var item in featureRow.Items())
                    Console.Write($"{slots[item.Key]}  ");
                Console.WriteLine();
            }

            // Print the first 10 feature values.
            Console.Write("Features: ");
            for (int i = 0; i < 10; i++)
                Console.Write($"{prediction.NgramFeatures[i]:F4}  ");

            //  Expected output:
            //   Number of Features:  32
            //   N-grams:   This|is|an  example|to|compute  compute|n-grams|using  n-grams|using|hashing.  an|example|to  is|an|example  a|sequence|of  of|'N'|consecutive  is|a|sequence  N-gram|is|a  ...
            //   Features:    0.0000          0.0000               2.0000               0.0000               0.0000        1.0000          0.0000        0.0000              1.0000          0.0000  ...
        }

        private class TextData
        {
            public string Text { get; set; }
        }

        private class TransformedTextData : TextData
        {
            public float[] NgramFeatures { get; set; }
        }
    }
}

備註

NgramHashingEstimator與在內部標記文字時 WordHashBagEstimator 採用標記化文字做為輸入的方式 NgramHashingEstimator 不同 WordHashBagEstimator 。