void IAiTest.Evaluate()
{
IDataView dataView = _context.Data.LoadFromTextFile <SentimentData>($"{RootFolder}/{EvaluateDataFile}", hasHeader: true);
Console.WriteLine("=============== Evaluating Model accuracy with Test data===============");
var predictions = _model.Transform(dataView);
var metrics = _context.BinaryClassification.Evaluate(predictions, "Label", "Score");
Common.ConsoleHelper.PrintBinaryClassificationMetrics(_trainer.ToString(), metrics);
Utility.SaveModelAsFile(_context, _model, dataView, $"{RootFolder}/{ModelFileName}");
}
private static ITransformer BuildTrainEvaluateAndSaveModel(MLContext mlContext)
{
// STEP 1: Common data loading configuration
IDataView dataView = mlContext.Data.LoadFromTextFile <SentimentIssue>(DataPath, hasHeader: true);
DataOperationsCatalog.TrainTestData trainTestSplit =
mlContext.Data.TrainTestSplit(dataView, testFraction: 0.2);
IDataView trainingData = trainTestSplit.TrainSet;
IDataView testData = trainTestSplit.TestSet;
// STEP 2: Common data process configuration with pipeline data transformations
TextFeaturizingEstimator dataProcessPipeline = mlContext.Transforms.Text.FeaturizeText(outputColumnName: "Features",
inputColumnName: nameof(SentimentIssue.Text));
// (OPTIONAL) Peek data (such as 2 records) in training DataView after applying the ProcessPipeline's transformations into "Features"
ConsoleHelper.PeekDataViewInConsole(mlContext, dataView, dataProcessPipeline, 2);
//Peak the transformed features column
//ConsoleHelper.PeekVectorColumnDataInConsole(mlContext, "Features", dataView, dataProcessPipeline, 1);
// STEP 3: Set the training algorithm, then create and config the modelBuilder
SdcaLogisticRegressionBinaryTrainer trainer =
mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(labelColumnName: "Label",
featureColumnName: "Features");
EstimatorChain <BinaryPredictionTransformer <CalibratedModelParametersBase <LinearBinaryModelParameters, PlattCalibrator> > > trainingPipeline = dataProcessPipeline.Append(trainer);
//Measure training time
Stopwatch watch = Stopwatch.StartNew();
// STEP 4: Train the model fitting to the DataSet
Console.WriteLine("=============== Training the model ===============");
ITransformer trainedModel = trainingPipeline.Fit(trainingData);
//Stop measuring time
watch.Stop();
long elapsedMs = watch.ElapsedMilliseconds;
Console.WriteLine($"***** Training time: {elapsedMs / 1000} seconds *****");
// STEP 5: Evaluate the model and show accuracy stats
Console.WriteLine("===== Evaluating Model's accuracy with Test data =====");
IDataView predictions = trainedModel.Transform(testData);
CalibratedBinaryClassificationMetrics metrics = mlContext.BinaryClassification.Evaluate(data: predictions, labelColumnName: "Label",
scoreColumnName: "Score");
ConsoleHelper.PrintBinaryClassificationMetrics(trainer.ToString(), metrics);
// STEP 6: Save/persist the trained model to a .ZIP file
mlContext.Model.Save(trainedModel, trainingData.Schema, ModelPath);
Console.WriteLine("The model is saved to {0}", ModelPath);
return(trainedModel);
}