次の方法で共有


TextCatalog.FeaturizeText メソッド

定義

オーバーロード

FeaturizeText(TransformsCatalog+TextTransforms, String, String)

テキスト列を TextFeaturizingEstimator、n-gram と char-gram の正規化されたカウントを表す特徴付きベクター Single に変換する作成します。

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

テキスト列を TextFeaturizingEstimator特徴付けされたベクター Single に変換し、n-gram と char-gram の正規化されたカウントを表す文字列を作成します。

FeaturizeText(TransformsCatalog+TextTransforms, String, String)

テキスト列を TextFeaturizingEstimator、n-gram と char-gram の正規化されたカウントを表す特徴付きベクター Single に変換する作成します。

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

パラメーター

catalog
TransformsCatalog.TextTransforms

テキスト関連の変換のカタログ。

outputColumnName
String

の変換によって生成される列の inputColumnName名前。 この列のデータ型は、次の Singleベクターになります。

inputColumnName
String

変換する列の名前。 に null設定すると、その値が outputColumnName ソースとして使用されます。 この推定機能は、テキスト データに対して動作します。

戻り値

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; }
        }
    }
}

適用対象

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

テキスト列を TextFeaturizingEstimator特徴付けされたベクター Single に変換し、n-gram と char-gram の正規化されたカウントを表す文字列を作成します。

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

パラメーター

catalog
TransformsCatalog.TextTransforms

テキスト関連の変換のカタログ。

outputColumnName
String

の変換によって生成される列の inputColumnNames名前。 この列のデータ型は、次の Singleベクターになります。

options
TextFeaturizingEstimator.Options

アルゴリズムの詳細オプション。

inputColumnNames
String[]

変換する列の名前。 に null設定すると、その値が outputColumnName ソースとして使用されます。 このエスティメーターはテキスト データに対して動作し、複数の列を一度に変換して、すべての列の Single 結果の特徴として 1 つのベクトルを生成できます。

戻り値

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; }
        }
    }
}

注釈

この変換は、複数の列に対して動作できます。

適用対象