TextCatalog.FeaturizeText 方法
定義
重要
部分資訊涉及發行前產品，在發行之前可能會有大幅修改。 Microsoft 對此處提供的資訊，不做任何明確或隱含的瑕疵擔保。
多載
| FeaturizeText(TransformsCatalog+TextTransforms, String, String) |
建立 TextFeaturizingEstimator ，它會將文字資料行轉換成 特徵化向量， Single 代表 n-gram 和 char-gram 的標準化計數。 |
| FeaturizeText(TransformsCatalog+TextTransforms, String, TextFeaturizingEstimator+Options, String[]) |
建立 TextFeaturizingEstimator ，它會將文字資料行轉換成 特徵化向量， Single 代表 n-gram 和 char-gram 的標準化計數。 |
FeaturizeText(TransformsCatalog+TextTransforms, String, String)
建立 TextFeaturizingEstimator ，它會將文字資料行轉換成 特徵化向量， Single 代表 n-gram 和 char-gram 的標準化計數。
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參數
- catalog
- TransformsCatalog.TextTransforms
與文字相關的轉換目錄。
- inputColumnName
- String
要轉換的資料行名稱。 如果設定為 null ，則會將 的值 outputColumnName 當做來源使用。
此估算器會透過文字資料操作。
傳回
範例
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; }
}
}
}
適用於
FeaturizeText(TransformsCatalog+TextTransforms, String, TextFeaturizingEstimator+Options, String[])
建立 TextFeaturizingEstimator ，它會將文字資料行轉換成 特徵化向量， Single 代表 n-gram 和 char-gram 的標準化計數。
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參數
- catalog
- TransformsCatalog.TextTransforms
與文字相關的轉換目錄。
- options
- TextFeaturizingEstimator.Options
演算法的進階選項。
- inputColumnNames
- String[]
要轉換的資料行名稱。 如果設定為 null ，則會將 的值 outputColumnName 當做來源使用。
此估算器會在文字資料上運作，而且可以一次轉換數個數據行，並產生 一個 向量 Single 作為所有資料行的結果特徵。
傳回
範例
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; }
}
}
}
備註
此轉換可以透過數個數據行運作。