public void testPredictionForSingleIssue(IntentData intentData)
{
// Predict labels and scores for single hard-coded issue.
var prediction = this._predEngine.Predict(intentData);
var fullPredictions = this.getBestThreePredictions(prediction);
Console.WriteLine($"==== Displaying prediction of Issue with Title = {intentData.text} ====");
Console.WriteLine("1st Label: " + fullPredictions[0].PredictedLabel + " with score: " + fullPredictions[0].Score);
Console.WriteLine("2nd Label: " + fullPredictions[1].PredictedLabel + " with score: " + fullPredictions[1].Score);
Console.WriteLine("3rd Label: " + fullPredictions[2].PredictedLabel + " with score: " + fullPredictions[2].Score);
Console.WriteLine($"=============== Single Prediction - Result: {prediction.PredictedLabel} ===============");
}
private static void testSingleLabelPrediction()
{
var input = Console.ReadLine();
while (input != "exit")
{
var intenter = new Intenter(modelPath: modelPath, new MLContext());
var intent = new IntentData {
text = input, label = string.Empty
};
intenter.testPredictionForSingleIssue(intent);
input = Console.ReadLine();
}
}
private static void buildAndTrainModel(string dataSetLocation, string testSetLocation, string modelPath, MyTrainerStrategy selectedStrategy)
{
// Create MLContext to be shared across the model creation workflow objects
// Set a random seed for repeatable/deterministic results across multiple trainings.
var mlContext = new MLContext(seed: 1);
// STEP 1: Common data loading configuration
var trainingDataView = mlContext.Data.LoadFromTextFile <IntentData>(dataSetLocation, hasHeader: true, separatorChar: '\t', allowSparse: false);
var testingDataView = mlContext.Data.LoadFromTextFile <IntentData>(testSetLocation, hasHeader: true, separatorChar: '\t', allowSparse: false);
// STEP 2: Common data process configuration with pipeline data transformations
var dataProcessPipeline = mlContext.Transforms.Conversion
.MapValueToKey(outputColumnName: "label", inputColumnName: nameof(IntentData.label))
.Append(mlContext.Transforms.Text.FeaturizeText(outputColumnName: "text", inputColumnName: nameof(IntentData.text)))
.Append(mlContext.Transforms.Concatenate(outputColumnName: "Features", "text"))
.AppendCacheCheckpoint(mlContext)
;
// Use in-memory cache for small/medium datasets to lower training time.
// Do NOT use it (remove .AppendCacheCheckpoint()) when handling very large datasets.
// (OPTIONAL) Peek data (such as 2 records) in training DataView after applying the ProcessPipeline's transformations into "Features"
ConsoleHelper.peekDataViewInConsole(mlContext, trainingDataView, dataProcessPipeline, 2);
// STEP 3: Create the selected training algorithm/trainer
IEstimator <ITransformer> trainer = null;
switch (selectedStrategy)
{
case MyTrainerStrategy.SdcaMultiClassTrainer:
trainer = mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("label", "Features");
break;
case MyTrainerStrategy.OvaAveragedPerceptronTrainer:
{
// Create a binary classification trainer.
var averagedPerceptronBinaryTrainer = mlContext.BinaryClassification.Trainers.AveragedPerceptron("label", "Features", numberOfIterations: 10);
// Compose an OVA (One-Versus-All) trainer with the BinaryTrainer.
// In this strategy, a binary classification algorithm is used to train one classifier for each class, "
// which distinguishes that class from all other classes. Prediction is then performed by running these binary classifiers, "
// and choosing the prediction with the highest confidence score.
trainer = mlContext.MulticlassClassification.Trainers.OneVersusAll(averagedPerceptronBinaryTrainer, labelColumnName: "label");
break;
}
}
//Set the trainer/algorithm and map label to value (original readable state)
var trainingPipeline = dataProcessPipeline.Append(trainer).Append(mlContext.Transforms.Conversion
.MapKeyToValue("PredictedLabel"))
;
// STEP 4: Cross-Validate with single dataset (since we don't have two datasets, one for training and for evaluate)
// in order to evaluate and get the model's accuracy metrics
Console.WriteLine("=============== Cross-validating to get model's accuracy metrics ===============");
var crossValidationResults = mlContext.MulticlassClassification.CrossValidate(data: testingDataView, estimator: trainingPipeline, numberOfFolds: 6, labelColumnName: "label");
ConsoleHelper.printMulticlassClassificationFoldsAverageMetrics(trainer?.ToString(), crossValidationResults);
// STEP 5: Train the model fitting to the DataSet
Console.WriteLine("=============== Training the model ===============");
var trainedModel = trainingPipeline.Fit(trainingDataView);
// (OPTIONAL) Try/test a single prediction with the "just-trained model" (Before saving the model)
var issue = new IntentData
{
text = "food"
};
// Create prediction engine related to the loaded trained model
var predEngine = mlContext.Model.CreatePredictionEngine <IntentData, IntentPrediction>(trainedModel);
//Score
var prediction = predEngine.Predict(issue);
Console.WriteLine($"=============== Single Prediction just-trained-model - Result: {prediction.PredictedLabel} ===============");
//
// STEP 6: Save/persist the trained model to a .ZIP file
Console.WriteLine("=============== Saving the model to a file ===============");
mlContext.Model.Save(trainedModel, trainingDataView.Schema, modelPath);
ConsoleHelper.consoleWriteHeader("Training process finalized");
}