TextCatalog.ProduceNgrams Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.Transforms.dll

Paket:

Microsoft.ML v2.0.0

NgramExtractingEstimator Membuat yang menghasilkan vektor hitungan n-gram (urutan kata berturut-turut) yang ditemui dalam teks input.

public static Microsoft.ML.Transforms.Text.NgramExtractingEstimator ProduceNgrams (this Microsoft.ML.TransformsCatalog.TextTransforms catalog, string outputColumnName, string inputColumnName = default, int ngramLength = 2, int skipLength = 0, bool useAllLengths = true, int maximumNgramsCount = 10000000, Microsoft.ML.Transforms.Text.NgramExtractingEstimator.WeightingCriteria weighting = Microsoft.ML.Transforms.Text.NgramExtractingEstimator+WeightingCriteria.Tf);

static member ProduceNgrams : Microsoft.ML.TransformsCatalog.TextTransforms * string * string * int * int * bool * int * Microsoft.ML.Transforms.Text.NgramExtractingEstimator.WeightingCriteria -> Microsoft.ML.Transforms.Text.NgramExtractingEstimator

<Extension()>
Public Function ProduceNgrams (catalog As TransformsCatalog.TextTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional ngramLength As Integer = 2, Optional skipLength As Integer = 0, Optional useAllLengths As Boolean = true, Optional maximumNgramsCount As Integer = 10000000, Optional weighting As NgramExtractingEstimator.WeightingCriteria = Microsoft.ML.Transforms.Text.NgramExtractingEstimator+WeightingCriteria.Tf) As NgramExtractingEstimator

Parameter

catalog

TransformsCatalog.TextTransforms

Katalog transformasi terkait teks.

outputColumnName

String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Jenis data kolom ini akan menjadi vektor .Single

inputColumnName

String

Nama kolom yang akan diubah. Jika diatur ke null, nilai outputColumnName akan digunakan sebagai sumber. Estimator ini beroperasi atas vektor jenis data kunci.

ngramLength

Int32

Panjang Ngram.

skipLength

Int32

Jumlah token yang akan dilewati di antara setiap n-gram. Secara default tidak ada token yang dilewati.

useAllLengths

Boolean

Apakah akan menyertakan semua panjang n-gram hingga ngramLength atau hanya ngramLength.

maximumNgramsCount

Int32

Jumlah maksimum n-gram untuk disimpan dalam kamus.

weighting

NgramExtractingEstimator.WeightingCriteria

Ukuran statistik yang digunakan untuk mengevaluasi seberapa penting kata atau n-gram untuk dokumen dalam korpus. Ketika maximumNgramsCount lebih kecil dari jumlah total n-gram yang ditemui, ukuran ini digunakan untuk menentukan n-gram mana yang akan disimpan.

Mengembalikan

NgramExtractingEstimator

Contoh

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

namespace Samples.Dynamic
{
    public static class ProduceNgrams
    {
        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." },
                new TextData(){ Text = "N-gram is a sequence of 'N' consecutive " +
                    "words/tokens." },

                new TextData(){ Text = "ML.NET's ProduceNgrams API produces " +
                    "vector of n-grams." },

                new TextData(){ Text = "Each position in the vector corresponds " +
                    "to a particular n-gram." },

                new TextData(){ Text = "The value at each position corresponds " +
                    "to," },

                new TextData(){ Text = "the number of times n-gram occurred in " +
                    "the data (Tf), or" },

                new TextData(){ Text = "the inverse of the number of documents " +
                    "that contain the n-gram (Idf)," },

                new TextData(){ Text = "or compute both and multiply together " +
                    "(Tf-Idf)." },
            };

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

            // A pipeline for converting text into numeric n-gram features.
            // The following call to 'ProduceNgrams' requires the tokenized
            // text /string as input. This is achieved by calling 
            // 'TokenizeIntoWords' first followed by 'ProduceNgrams'. Please note
            // that the length of the output feature vector depends on the n-gram
            // settings.
            var textPipeline = mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
                "Text")
                // 'ProduceNgrams' takes key type as input. Converting the tokens
                // into key type using 'MapValueToKey'.
                .Append(mlContext.Transforms.Conversion.MapValueToKey("Tokens"))
                .Append(mlContext.Transforms.Text.ProduceNgrams("NgramFeatures",
                    "Tokens",
                    ngramLength: 3,
                    useAllLengths: false,
                    weighting: NgramExtractingEstimator.WeightingCriteria.Tf));

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

            // Create the prediction engine to get the n-gram 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: 52
            //   N-grams:   This|is|an  is|an|example  an|example|to  example|to|compute  to|compute|n-grams.  N-gram|is|a  is|a|sequence  a|sequence|of  sequence|of|'N'  of|'N'|consecutive  ...
            //   Features:     1.0000      1.0000          1.0000           1.0000             1.0000            0.0000      0.0000          0.0000          0.0000          0.0000          ...
        }

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

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