TextCatalog.ProduceNgrams Metodo

Definizione

Spazio dei nomi:

Microsoft.ML

Assembly:

Microsoft.ML.Transforms.dll

Pacchetto:

Microsoft.ML v2.0.0

Crea un oggetto NgramExtractingEstimator che produce un vettore di conteggi di n-grammi (sequenze di parole consecutive) rilevate nel testo di 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

Parametri

catalog

TransformsCatalog.TextTransforms

Catalogo della trasformazione correlata al testo.

outputColumnName

String

Nome della colonna risultante dalla trasformazione di inputColumnName. Il tipo di dati di questa colonna sarà un vettore di Single.

inputColumnName

String

Nome della colonna da trasformare. Se impostato su null, il valore dell'oggetto outputColumnName verrà usato come origine. Questo strumento di stima opera sui vettori del tipo di dati delle chiavi.

ngramLength

Int32

Lunghezza Ngram.

skipLength

Int32

Numero di token da ignorare tra ogni n-gram. Per impostazione predefinita, nessun token viene ignorato.

useAllLengths

Boolean

Se includere tutte le lunghezze n-gram fino a ngramLength o solo ngramLength.

maximumNgramsCount

Int32

Numero massimo di n grammi da archiviare nel dizionario.

weighting

NgramExtractingEstimator.WeightingCriteria

Misura statistica usata per valutare l'importanza di una parola o n-gram per un documento in un corpus. Quando maximumNgramsCount è minore del numero totale di n-grammi rilevati questa misura viene usata per determinare quale n-grammi mantenere.

Restituisce

NgramExtractingEstimator

Esempio

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; }
        }
    }
}