private static void PredictIssue()
{
ITransformer loadedModel;
using (var stream = new FileStream(_modelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
loadedModel = _mlContext.Model.Load(stream);
}
SentimentData singleIssue = new SentimentData()
{
Title = "Entity Framework crashes", Description = "When connecting to the database, EF is crashing"
};
_predEngine = loadedModel.CreatePredictionEngine <SentimentData, SentimentPrediction>(_mlContext);
var prediction = _predEngine.Predict(singleIssue);
Console.WriteLine($"=============== Single Prediction - Result: {prediction.Area} ===============");
}
public static IEstimator <ITransformer> BuildAndTrainModel(IDataView trainingDataView, IEstimator <ITransformer> pipeline)
{
var trainingPipeline = pipeline
.Append(_mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(DefaultColumnNames.Label, DefaultColumnNames.Features))
.Append(_mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
_trainedModel = trainingPipeline.Fit(trainingDataView);
_predEngine = _trainedModel.CreatePredictionEngine <SentimentData, SentimentPrediction>(_mlContext);
SentimentData issue = new SentimentData()
{
Title = "WebSockets communication is slow in my machine",
Description = "The WebSockets communication used under the covers by SignalR looks like is going slow in my development machine.."
};
var prediction = _predEngine.Predict(issue);
Console.WriteLine($"=============== Single Prediction just-trained-model - Result: {prediction.Area} ===============");
return(trainingPipeline);
}