如何访问FeaturizeText in Microsoft.ML生成的n-克？

Question

我设法在Microsoft.ML中运行了第一个文本分析器，我想要找到由模型确定的ngram列表，但是我只能得到数字向量“计数”事件，而不知道它们所指的是什么。

到目前为止，以下是我工作代码的核心：

var mlContext = new MLContext();
var articles = SampleData.Articles.Select(a => new TextData{ Text=a }).ToArray();
var dataview = mlContext.Data.LoadFromEnumerable(articles);
var options = new TextFeaturizingEstimator.Options() {
  OutputTokensColumnName = "OutputTokens",
  CaseMode = TextNormalizingEstimator.CaseMode.Lower,
  KeepDiacritics = false,
  KeepPunctuations = false,
  KeepNumbers = false,
  Norm = TextFeaturizingEstimator.NormFunction.L2,
  StopWordsRemoverOptions = new StopWordsRemovingEstimator.Options() {
    Language = TextFeaturizingEstimator.Language.Dutch,
  },
  WordFeatureExtractor = new WordBagEstimator.Options() {
    NgramLength = 4,
    SkipLength = 1,
    UseAllLengths = true,
    MaximumNgramsCount = new int[] { 20, 10, 10, 10 },
    Weighting = NgramExtractingEstimator.WeightingCriteria.TfIdf,
  },
  CharFeatureExtractor = null,
};
var textPipeline = mlContext.Transforms.Text   
  .FeaturizeText("Features", options, "Text");
var textTransformer = textPipeline.Fit(dataview);
var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
foreach (var article in articles)
{
  var prediction = predictionEngine.Predict(article);
  Console.WriteLine($"Article: {article.Text.Substring(0, 30)}...");
  Console.WriteLine($"Number of Features: {prediction.Features.Length}");
  Console.WriteLine($"Features: {string.Join(",", prediction.Features.Take(50).Select(f => f.ToString("0.00")))}\n");
}

Answer 1

嗯，我想明白了，如果有人碰到同样的问题，我想在这里分享。首先，像往常一样创建模型。注意放置Ngram步骤输出的列的名称(在我们的例子中是"ProduceNgrams")。

然后，"Schema.GetSlotNames“和"slotNames.GetValues”的组合完成了获取所需ngram的任务：

var textPipeline =
    mlContext.Transforms.Text.NormalizeText("Tokens", "Text", TextNormalizingEstimator.CaseMode.Lower, false, false, false)
    .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens"))
    .Append(mlContext.Transforms.Text.RemoveDefaultStopWords("Tokens", language: StopWordsRemovingEstimator.Language.Dutch))
    .Append(mlContext.Transforms.Conversion.MapValueToKey("Tokens"))
    .Append(mlContext.Transforms.Text.ProduceNgrams("NgramFeatures", "Tokens"))
    .Append(mlContext.Transforms.Text.LatentDirichletAllocation("LDAFeatures", "NgramFeatures", 
      numberOfTopics: 10
    ))
    .Append(mlContext.Transforms.NormalizeLpNorm("Features", "LDAFeatures"));

var textTransformer = textPipeline.Fit(dataview);
var transformedDataView = textTransformer.Transform(dataview);

VBuffer<ReadOnlyMemory<char>> slotNames = default;
transformedDataView.Schema["NgramFeatures"].GetSlotNames(ref slotNames);
var ngrams = slotNames.GetValues().ToArray().Select(x => x.Span.ToString()); //.Replace('|',' '));
Console.WriteLine($"Ngrams: {string.Join(", ", ngrams)}\n");

var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
var articlesWithFeatures = new List<(TextData, TransformedTextData)>();
foreach (var article in articles)
{
  var articleWithFeatures = predictionEngine.Predict(article);
  Console.WriteLine($"Article: {article.Text.Substring(0, 30)}...");
  Console.WriteLine($"Number of Features: {articleWithFeatures.Features.Length}");
  Console.WriteLine($"Features: {string.Join(",", articleWithFeatures.Features.Take(50).Select(f => f.ToString("0.00")))}\n");

  articlesWithFeatures.Add((article, articleWithFeatures));
}