private static void PredictIssue()
{
ITransformer loadedModel = _mLContext.Model.Load(_modelPath, out var modelInputSchema);
Sentiment_Analysis singlesentiment = new Sentiment_Analysis() { Phrase = "" };
_predEngine = _mLContext.Model.CreatePredictionEngine<Sentiment_Analysis, Sentiments>(loadedModel);
var prediction = _predEngine.Predict(singlesentiment);
Console.WriteLine($"{prediction.Sentiment}");
}
public static IEstimator<ITransformer> BuildAndTrainModel(IDataView trainingDataView, IEstimator<ITransformer> pipeline)
{
var trainingPipline = pipeline.Append(_mLContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features"))
.Append(_mLContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
_trainedmodel = trainingPipline.Fit(trainingDataView);
_predEngine = _mLContext.Model.CreatePredictionEngine<Sentiment_Analysis, Sentiments>(_trainedmodel);
Sentiment_Analysis se = new Sentiment_Analysis()
{
Phrase = //"This terms candidates are very normal"
"This website sucks"
};
var prediction = _predEngine.Predict(se);
Console.WriteLine($"single line prediction { prediction.Sentiment}");
return trainingPipline;
}