如何在Microsoft.ML中访问由FeathurizeText生成的n-gram

Question

我设法获得了运行在Microsoft.ML中的第一个文本分析器。我想获得由模型确定的ngrams列表，但我只能获得数字向量"计数"；在不知道它们指的是什么的情况下发生。

这是我迄今为止工作代码的核心：

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

好吧，我弄明白了，如果有人遇到同样的问题，我想在这里分享。首先，像往常一样创建模型。注意放置Ngrams步骤输出的列的名称(在我们的例子中为"ProduceGrams"(。

然后；Schema.GetSlotNames"；以及"；slotNames.GetValues"获取所需ngrams的技巧：

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));
}