TextCatalog.FeaturizeText Methode

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Transforms.dll

Paket:

Microsoft.ML v3.0.1

Überlädt

FeaturizeText(TransformsCatalog+TextTransforms, String, String)	Erstellen Sie eine TextFeaturizingEstimator, die eine Textspalte in einen featurisierten Vektor Single umgewandelt, der normalisierte Anzahl von n-Gramm und Char-Gramm darstellt.
FeaturizeText(TransformsCatalog+TextTransforms, String, TextFeaturizingEstimator+Options, String[])	Erstellen Sie eine TextFeaturizingEstimatorTextspalte in einen featurierten Vektor Single , der normalisierte Anzahl von n-Gramm und Char-Gramm darstellt.

FeaturizeText(TransformsCatalog+TextTransforms, String, String)

Erstellen Sie eine TextFeaturizingEstimator, die eine Textspalte in einen featurisierten Vektor Single umgewandelt, der normalisierte Anzahl von n-Gramm und Char-Gramm darstellt.

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

Parameter

catalog

TransformsCatalog.TextTransforms

Der Textbezogene Transformationskatalog.

outputColumnName

String

Name der Spalte, die aus der Transformation von inputColumnName. Der Datentyp dieser Spalte ist ein Vektor von Single.

inputColumnName

String

Name der zu transformierenden Spalte. nullWenn festgelegt auf , wird der Wert des outputColumnName Werts als Quelle verwendet. Diese Stimator arbeitet über Textdaten.

Gibt zurück

TextFeaturizingEstimator

Beispiele

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[])

Erstellen Sie eine TextFeaturizingEstimatorTextspalte in einen featurierten Vektor Single , der normalisierte Anzahl von n-Gramm und Char-Gramm darstellt.

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

Parameter

catalog

TransformsCatalog.TextTransforms

Der Textbezogene Transformationskatalog.

outputColumnName

String

Name der Spalte, die aus der Transformation von inputColumnNames. Der Datentyp dieser Spalte ist ein Vektor von Single.

options

TextFeaturizingEstimator.Options

Erweiterte Optionen für den Algorithmus.

inputColumnNames

String[]

Name der zu transformierenden Spalten. nullWenn festgelegt auf , wird der Wert des outputColumnName Werts als Quelle verwendet. Diese Stimator arbeitet über Textdaten und kann mehrere Spalten gleichzeitig transformieren und einen Vektor Single als die resultierenden Features für alle Spalten liefern.

Gibt zurück

TextFeaturizingEstimator

Beispiele

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; }
        }
    }
}

Hinweise

Diese Transformation kann über mehrere Spalten ausgeführt werden.