0

我设法获得了第一个在 Microsoft.ML 中运行的文本分析器。我想获得模型确定的 ngram 列表，但我只能在不知道它们指的是什么的情况下获得“计数”出现次数的数值向量。

到目前为止，这是我的工作代码的核心：

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");
}
4

1 回答 1

0

好吧，我想通了，如果有人遇到同样的问题，我想在这里分享。首先，您像往常一样创建模型。注意放置 Ngrams 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”的组合可以获取所需的 ngram：

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));
}
于 2022-02-10T13:35:56.887 回答