public static void PredictIssue()
{
// <SnippetLoadModel>
ITransformer loadedModel = _mlContext.Model.Load(_modelPath, out var modelInputSchema);
// </SnippetLoadModel>
// <SnippetAddTestIssue>
Issues singleIssue = new Issues()
{
Title = "Entity Framework crashes", Description = "When connecting to the database, EF is crashing"
};
// </SnippetAddTestIssue>
//Predict label for single hard-coded issue
// <SnippetCreatePredictionEngine>
_predEngine = _mlContext.Model.CreatePredictionEngine <Issues, IssuePrediction>(loadedModel);
// </SnippetCreatePredictionEngine>
// <SnippetPredictIssue>
var prediction = _predEngine.Predict(singleIssue);
// </SnippetPredictIssue>
// <SnippetDisplayResults>
Console.WriteLine($"=============== Single Prediction - Result: {prediction.Area} ===============");
// </SnippetDisplayResults>
}
public static IEstimator <ITransformer> BuildAndTrainModel(IDataView trainingDataView, IEstimator <ITransformer> pipeline)
{
//The SdcaMaximumEntropy is your multiclass classification training algorithm.
// This is appended to the pipeline and accepts the featurized Title and Description (Features) and the Label input parameters to learn from the historic data.
var trainingPipeline = pipeline.Append(_mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features"))
.Append(_mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
Console.WriteLine($"=============== Training the model ===============");
_trainedModel = trainingPipeline.Fit(trainingDataView);
Console.WriteLine($"=============== Finished Training the model Ending time: {DateTime.Now.ToString()} ===============");
// (OPTIONAL) Try/test a single prediction with the "just-trained model" (Before saving the model)
Console.WriteLine($"=============== Single Prediction just-trained-model ===============");
// Create prediction engine related to the loaded trained model
// <SnippetCreatePredictionEngine1>
_predEngine = _mlContext.Model.CreatePredictionEngine <Issues, IssuePrediction>(_trainedModel);
// </SnippetCreatePredictionEngine1>
// <SnippetCreateTestIssue1>
Issues issue = new Issues()
{
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.."
};
// </SnippetCreateTestIssue1>
// <SnippetPredict>
var prediction = _predEngine.Predict(issue);
// </SnippetPredict>
// <SnippetOutputPrediction>
Console.WriteLine($"=============== Single Prediction just-trained-model - Result: {prediction.Area} ===============");
// </SnippetOutputPrediction>
// <SnippetReturnModel>
return(trainingPipeline);
// </SnippetReturnModel>
}
