TextCatalog.FeaturizeText 메서드

정의

네임스페이스:

Microsoft.ML

어셈블리:

Microsoft.ML.Transforms.dll

패키지:

Microsoft.ML v2.0.0

오버로드

FeaturizeText(TransformsCatalog+TextTransforms, String, String)	Create a TextFeaturizingEstimator, which transforms a text column into a featurized vector of Single that represents normalized counts of n-grams and char-grams.
FeaturizeText(TransformsCatalog+TextTransforms, String, TextFeaturizingEstimator+Options, String[])	Create a TextFeaturizingEstimator, which transforms a text column into featurized vector of Single that represents normalized counts of n-grams and char-grams.

FeaturizeText(TransformsCatalog+TextTransforms, String, String)

Create a TextFeaturizingEstimator, which transforms a text column into a featurized vector of Single that represents normalized counts of n-grams and char-grams.

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

매개 변수

catalog

TransformsCatalog.TextTransforms

텍스트 관련 변환의 카탈로그입니다.

outputColumnName

String

의 변환에서 생성된 열의 inputColumnName이름입니다. 이 열의 데이터 형식은 벡터가 Single됩니다.

inputColumnName

String

변환할 열의 이름입니다. 이 값으로 null설정하면 해당 값이 outputColumnName 원본으로 사용됩니다. 이 추정기는 텍스트 데이터에 대해 작동합니다.

반환

TextFeaturizingEstimator

예제

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[])

Create a TextFeaturizingEstimator, which transforms a text column into featurized vector of Single that represents normalized counts of n-grams and char-grams.

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

매개 변수

catalog

TransformsCatalog.TextTransforms

텍스트 관련 변환의 카탈로그입니다.

outputColumnName

String

의 변환에서 생성된 열의 inputColumnNames이름입니다. 이 열의 데이터 형식은 벡터가 Single됩니다.

options

TextFeaturizingEstimator.Options

알고리즘에 대한 고급 옵션입니다.

inputColumnNames

String[]

변환할 열의 이름입니다. 이 값으로 null설정하면 해당 값이 outputColumnName 원본으로 사용됩니다. 이 추정기는 텍스트 데이터에 대해 작동하며 한 번에 여러 열을 변환하여 모든 열에 Single 대한 결과 기능으로 하나의 벡터를 생성할 수 있습니다.

반환

TextFeaturizingEstimator

예제

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; }
        }
    }
}

설명

이 변환은 여러 열에서 작동할 수 있습니다.