/// <summary>
/// Loads the saved model
/// Creates a single issue of test data.
/// Predicts Area based on test data.
/// Combines test data and predictions for reporting.
/// Displays the predicted results.
/// </summary>
private static void PredictIssue()
{
var loadedModel = _mlContext.Model.Load(_modelPath, out var modelInputSchema);
var singleIssue = new GitHubIssue()
{
Title = "Entity Framework crashes", Description = "When connecting to the database, EF is crashing"
};
// PredictionEngine is not thread-safe. Use in prototype
// In production use PredictionEnginePool instead
_predEngine = _mlContext.Model.CreatePredictionEngine <GitHubIssue, IssuePrediction>(loadedModel);
var prediction = _predEngine.Predict(singleIssue);
Console.WriteLine($"=============== Single Prediction - Result: {prediction.Area} ===============");
}
/// <summary>
/// Creates the training algorithm class.
/// Trains the model.
/// Predicts area based on training data.
/// Returns the model.
/// </summary>
/// <param name="trainingDataView"></param>
/// <param name="pipeline"></param>
/// <returns></returns>
public static async Task <IEstimator <ITransformer> > BuildAndTrainModelAsync(IDataView trainingDataView, IEstimator <ITransformer> pipeline)
{
var trainingPipeline = pipeline.Append(_mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features"))
.Append(_mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
_trainedModel = await Task.Run(() => trainingPipeline.Fit(trainingDataView));
_predEngine = _mlContext.Model.CreatePredictionEngine <GitHubIssue, IssuePrediction>(_trainedModel);
var issue = new GitHubIssue()
{
Title = "WebSockets communication is slow on my machine",
Description = "The WebSockets communication used under the covers by SignalR looks like it is going slow in my development machine.."
};
var prediction = _predEngine.Predict(issue);
Console.WriteLine($"=============== Single Prediction just-trained-model - Result: {prediction.Area} ===============");
return(trainingPipeline);
}