private void PredictParagraphs()
{
Receive <PredictParagraphsMsg>((x) =>
{
using (Py.GIL())
{
var path = System.IO.Path.GetFullPath("...\\...\\...\\Nlp\\");
dynamic pythonDataLoader = Py.Import("data_loader");
var paragraphs = pythonDataLoader.split_into_paragraphs(pythonDataLoader.read_pdf_to_text(path + x.FileName));
dynamic pythonDataClassification = Py.Import("data_classifiers");
var converter = new PyConverter();
converter.AddListType();
converter.Add(new Int32Type());
converter.Add(new Int64Type());
converter.Add(new DoubleType());
converter.Add(new FloatType());
converter.Add(new StringType());
var predicted_paragraphs = pythonDataClassification.predict_arguments(trainedModel, paragraphs);
var testLabels = pythonDataLoader.read_labels_of_paragraphs(path + x.TrainingDataName);
var predictedLabels = pythonDataLoader.get_labels(predicted_paragraphs);
pythonDataClassification.display_metrics(testLabels, predictedLabels);
List <object> positive_paragraphs = converter.ToClr(pythonDataClassification.get_positive_paragraphs(predicted_paragraphs));
List <object> negative_paragraphs = converter.ToClr(pythonDataClassification.get_negative_paragraphs(predicted_paragraphs));
judge.Tell(new ReturnParagraphsMsg(positive_paragraphs, negative_paragraphs));
}
});
}
public PyConverter NewMyListConverter()
{
using (Py.GIL())
{
var converter = new PyConverter(); //create a instance of PyConverter
converter.AddListType();
converter.Add(new StringType());
converter.Add(new Int64Type());
converter.Add(new Int32Type());
converter.Add(new FloatType());
converter.Add(new DoubleType());
return(converter);
}
}
private void TellRelatedParagraphs()
{
Receive <RelatedArgumentsQueryMsg>((x) =>
{
using (Py.GIL())
{
threshold = 0.05;
dynamic pythonDataProcessing = Py.Import("data_processing");
var converter = new PyConverter();
converter.AddListType();
converter.Add(new DoubleType());
var argument = x.BlacklistedArguments.First();
List <String> paragraphsLeft = positiveParagraphs.Except(x.BlacklistedArguments).ToList();
var valueMap = new Dictionary <int, Double>();
var resultList = new List <string>();
foreach (var paragraph in paragraphsLeft)
{
var ngramEvaluation = pythonDataProcessing.evaluate_sentence(argument, paragraph, 2);
List <object> ngramScores = converter.ToClr(ngramEvaluation);
var value = 0.0;
foreach (var score in ngramScores)
{
value += (double)score;
}
valueMap.Add(valueMap.Count, value);
}
Dictionary <int, Double> sortedValueMap = (valueMap.OrderByDescending(y => y.Value)).ToDictionary(y => y.Key, y => y.Value);
foreach (var val in sortedValueMap)
{
if (val.Value > threshold)
{
resultList.Add(paragraphsLeft.ElementAt(val.Key));
}
else
{
break;
}
if (resultList.Count > 1)
{
break;
}
}
x.QuerySender.Tell(new RelatedArgumentsDefenderResponseMsg(resultList));
}
});
}
public PyConverter GetConverter()
{
//using (Py.GIL())
//{
var converter = new PyConverter();
converter.AddListType();
converter.Add(new StringType());
converter.Add(new Int64Type());
converter.Add(new Int32Type());
converter.Add(new FloatType());
converter.Add(new DoubleType());
converter.AddDictType <object, object>();
return(converter);
//}
}