public static IEstimator <ITransformer> BuildAndTrainModel(IDataView trainingDataView, IEstimator <ITransformer> pipeline)
{
Console.WriteLine("hello0");
var trainingPipeline = pipeline.Append(_mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features"))
.Append(_mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
Console.WriteLine("hello1");
_trainedModel = trainingPipeline.Fit(trainingDataView);
Console.WriteLine("hello2");
_predEngine = _mlContext.Model.CreatePredictionEngine <GitHubIssue, IssuePrediction>(_trainedModel);
Console.WriteLine("hello3");
GitHubIssue issue = new GitHubIssue()
{
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.."
};
Console.WriteLine("hello04");
var prediction = _predEngine.Predict(issue);
Console.WriteLine("hello5");
Console.WriteLine($"=============== Single Prediction just-trained-model - Result: {prediction.Area} ===============");
Console.WriteLine("hello6");
return(trainingPipeline);
}
private static void PredictIssue()
{
ITransformer loadedModel = _mlContext.Model.Load(_modelPath, out var modelInputSchema);
GitHubIssue singleIssue = new GitHubIssue()
{
Title = "Entity Framework crashes", Description = "When connecting to the database, EF is crashing"
};
_predEngine = _mlContext.Model.CreatePredictionEngine <GitHubIssue, IssuePrediction>(loadedModel);
var prediction = _predEngine.Predict(singleIssue);
Console.WriteLine($"=============== Single Prediction - Result: {prediction.Area} ===============");
}
