TextCatalog.FeaturizeText Metodo

Definizione

Spazio dei nomi:

Microsoft.ML

Assembly:

Microsoft.ML.Transforms.dll

Pacchetto:

Microsoft.ML v3.0.1

Overload

FeaturizeText(TransformsCatalog+TextTransforms, String, String)	Creare un TextFeaturizingEstimatoroggetto , che trasforma una colonna di testo in un vettore con caratteristiche di che rappresenta i conteggi normalizzati di Single n-grammi e char-grammi.
FeaturizeText(TransformsCatalog+TextTransforms, String, TextFeaturizingEstimator+Options, String[])	Creare un TextFeaturizingEstimatoroggetto , che trasforma una colonna di testo in vettore con caratteristiche di che rappresenta i conteggi normalizzati di Single n-grammi e char-grammi.

FeaturizeText(TransformsCatalog+TextTransforms, String, String)

Creare un TextFeaturizingEstimatoroggetto , che trasforma una colonna di testo in un vettore con caratteristiche di che rappresenta i conteggi normalizzati di Single n-grammi e char-grammi.

public static Microsoft.ML.Transforms.Text.TextFeaturizingEstimator FeaturizeText (this Microsoft.ML.TransformsCatalog.TextTransforms catalog, string outputColumnName, string inputColumnName = default);

static member FeaturizeText : Microsoft.ML.TransformsCatalog.TextTransforms * string * string -> Microsoft.ML.Transforms.Text.TextFeaturizingEstimator

<Extension()>
Public Function FeaturizeText (catalog As TransformsCatalog.TextTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing) As TextFeaturizingEstimator

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 dati di testo.

Restituisce

TextFeaturizingEstimator

Esempio

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

namespace Samples.Dynamic
{
    public static class FeaturizeText
    {
        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 = "ML.NET's FeaturizeText API uses a " +
                    "composition of several basic transforms to convert text " +
                    "into numeric features." },

                new TextData(){ Text = "This API can be used as a featurizer to " +
                    "perform text classification." },

                new TextData(){ Text = "There are a number of approaches to text " +
                    "classification." },

                new TextData(){ Text = "One of the simplest and most common " +
                    "approaches is called “Bag of Words”." },

                new TextData(){ Text = "Text classification can be used for a " +
                    "wide variety of tasks" },

                new TextData(){ Text = "such as sentiment analysis, topic " +
                    "detection, intent identification etc." },
            };

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

            // A pipeline for converting text into numeric features.
            // The following call to 'FeaturizeText' instantiates 
            // 'TextFeaturizingEstimator' with default parameters.
            // The default settings for the TextFeaturizingEstimator are
            //      * StopWordsRemover: None
            //      * CaseMode: Lowercase
            //      * OutputTokensColumnName: None
            //      * KeepDiacritics: false, KeepPunctuations: true, KeepNumbers:
            //          true
            //      * WordFeatureExtractor: NgramLength = 1
            //      * CharFeatureExtractor: NgramLength = 3, UseAllLengths = false
            // The length of the output feature vector depends on these settings.
            var textPipeline = mlContext.Transforms.Text.FeaturizeText("Features",
                "Text");

            // Fit to data.
            var textTransformer = textPipeline.Fit(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.Features.Length}");

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

            //  Expected output:
            //   Number of Features: 332
            //   Features: 0.0857  0.0857  0.0857  0.0857  0.0857  0.0857  0.0857  0.0857  0.0857  0.1715 ...
        }

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

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

FeaturizeText(TransformsCatalog+TextTransforms, String, TextFeaturizingEstimator+Options, String[])

Creare un TextFeaturizingEstimatoroggetto , che trasforma una colonna di testo in vettore con caratteristiche di che rappresenta i conteggi normalizzati di Single n-grammi e char-grammi.

public static Microsoft.ML.Transforms.Text.TextFeaturizingEstimator FeaturizeText (this Microsoft.ML.TransformsCatalog.TextTransforms catalog, string outputColumnName, Microsoft.ML.Transforms.Text.TextFeaturizingEstimator.Options options, params string[] inputColumnNames);

static member FeaturizeText : Microsoft.ML.TransformsCatalog.TextTransforms * string * Microsoft.ML.Transforms.Text.TextFeaturizingEstimator.Options * string[] -> Microsoft.ML.Transforms.Text.TextFeaturizingEstimator

<Extension()>
Public Function FeaturizeText (catalog As TransformsCatalog.TextTransforms, outputColumnName As String, options As TextFeaturizingEstimator.Options, ParamArray inputColumnNames As String()) As TextFeaturizingEstimator

Parametri

catalog

TransformsCatalog.TextTransforms

Catalogo della trasformazione correlata al testo.

outputColumnName

String

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

options

TextFeaturizingEstimator.Options

Opzioni avanzate per l'algoritmo.

inputColumnNames

String[]

Nome delle colonne da trasformare. Se impostato su null, il valore dell'oggetto outputColumnName verrà usato come origine. Questo strumento di stima opera sui dati di testo e può trasformare diverse colonne contemporaneamente, generando un vettore di Single come le funzionalità risultanti per tutte le colonne.

Restituisce

TextFeaturizingEstimator

Esempio

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

namespace Samples.Dynamic
{
    public static class FeaturizeTextWithOptions
    {
        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 = "ML.NET's FeaturizeText API uses a " +
                "composition of several basic transforms to convert text into " +
                "numeric features." },

                new TextData(){ Text = "This API can be used as a featurizer to " +
                "perform text classification." },

                new TextData(){ Text = "There are a number of approaches to text " +
                "classification." },

                new TextData(){ Text = "One of the simplest and most common " +
                "approaches is called “Bag of Words”." },

                new TextData(){ Text = "Text classification can be used for a " +
                "wide variety of tasks" },

                new TextData(){ Text = "such as sentiment analysis, topic " +
                "detection, intent identification etc." },
            };

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

            // A pipeline for converting text into numeric features.
            // The following call to 'FeaturizeText' instantiates
            // 'TextFeaturizingEstimator' with given parameters. The length of the
            // output feature vector depends on these settings.
            var options = new TextFeaturizingEstimator.Options()
            {
                // Also output tokenized words
                OutputTokensColumnName = "OutputTokens",
                CaseMode = TextNormalizingEstimator.CaseMode.Lower,
                // Use ML.NET's built-in stop word remover
                StopWordsRemoverOptions = new StopWordsRemovingEstimator.Options()
                {
                    Language = TextFeaturizingEstimator.Language.English
                },

                WordFeatureExtractor = new WordBagEstimator.Options()
                {
                    NgramLength
                    = 2,
                    UseAllLengths = true
                },

                CharFeatureExtractor = new WordBagEstimator.Options()
                {
                    NgramLength
                    = 3,
                    UseAllLengths = false
                },
            };
            var textPipeline = mlContext.Transforms.Text.FeaturizeText("Features",
                options, "Text");

            // Fit to data.
            var textTransformer = textPipeline.Fit(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.Features.Length}");

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

            Console.WriteLine("\nTokens: " + string.Join(",", prediction
                .OutputTokens));

            //  Expected output:
            //   Number of Features: 282
            //   Features: 0.0941  0.0941  0.0941  0.0941  0.0941  0.0941  0.0941  0.0941  0.0941  0.1881 ...
            //   Tokens: ml.net's,featurizetext,api,uses,composition,basic,transforms,convert,text,numeric,features.
        }

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

        private class TransformedTextData : TextData
        {
            public float[] Features { get; set; }
            public string[] OutputTokens { get; set; }
        }
    }
}

Commenti

Questa trasformazione può funzionare su diverse colonne.