private static void PredictWithModelLoadedFromFile(SentimentIssue sampleStatement)
{
// Test with Loaded Model from .zip file
using (var env2 = new LocalEnvironment())
{
ITransformer loadedModel;
using (var stream = new FileStream(ModelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
loadedModel = TransformerChain.LoadFrom(env2, stream);
}
// Create prediction engine and make prediction.
var engine = loadedModel.MakePredictionFunction <SentimentIssue, SentimentPrediction>(env2);
var predictionFromLoaded = engine.Predict(sampleStatement);
Console.WriteLine();
Console.WriteLine("=============== Test of model with a sample ===============");
Console.WriteLine($"Text: {sampleStatement.Text} | Prediction: {(Convert.ToBoolean(predictionFromLoaded.Prediction) ? "Toxic" : "Nice")} sentiment | Probability: {predictionFromLoaded.Probability} ");
}
}
static void Main(string[] args)
{
//1. Create ML.NET context/environment
using (var env = new LocalEnvironment())
{
//2. Create DataReader with data schema mapped to file's columns
var reader = new TextLoader(env,
new TextLoader.Arguments()
{
Separator = "tab",
HasHeader = true,
Column = new[]
{
new TextLoader.Column("Label", DataKind.Bool, 0),
new TextLoader.Column("Text", DataKind.Text, 1)
}
});
//Load training data
IDataView trainingDataView = reader.Read(new MultiFileSource(TrainDataPath));
//3.Create a flexible pipeline (composed by a chain of estimators) for creating/traing the model.
var pipeline = new TextTransform(env, "Text", "Features") //Convert the text column to numeric vectors (Features column)
.Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments(), "Features", "Label"));
//.Append(new LinearClassificationTrainer(env, "Features", "Label")); //(Simpler in ML.NET v0.7)
//4. Create and train the model
Console.WriteLine("=============== Create and Train the Model ===============");
var model = pipeline.Fit(trainingDataView);
Console.WriteLine("=============== End of training ===============");
Console.WriteLine();
//5. Evaluate the model and show accuracy stats
//Load evaluation/test data
IDataView testDataView = reader.Read(new MultiFileSource(TestDataPath));
Console.WriteLine("=============== Evaluating Model's accuracy with Test data===============");
var predictions = model.Transform(testDataView);
var binClassificationCtx = new BinaryClassificationContext(env);
var metrics = binClassificationCtx.Evaluate(predictions, "Label");
Console.WriteLine();
Console.WriteLine("Model quality metrics evaluation");
Console.WriteLine("------------------------------------------");
Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
Console.WriteLine($"Auc: {metrics.Auc:P2}");
Console.WriteLine($"F1Score: {metrics.F1Score:P2}");
Console.WriteLine("=============== End of Model's evaluation ===============");
Console.WriteLine();
//6. Test Sentiment Prediction with one sample text
var predictionFunct = model.MakePredictionFunction <SentimentIssue, SentimentPrediction>(env);
Console.Write("Enter a phrase: ");
var phrase = Console.ReadLine();
SentimentIssue sampleStatement = new SentimentIssue
{
//Text = "This is a very rude movie"
Text = phrase
};
var resultprediction = predictionFunct.Predict(sampleStatement);
Console.WriteLine();
Console.WriteLine("=============== Test of model with a sample ===============");
Console.WriteLine($"Text: {sampleStatement.Text} | Prediction: {(Convert.ToBoolean(resultprediction.Prediction) ? "Toxic" : "Nice")} sentiment | Probability: {resultprediction.Probability} ");
// Save model to .ZIP file
SaveModelAsFile(env, model);
// Predict again but now testing the model loading from the .ZIP file
PredictWithModelLoadedFromFile(sampleStatement);
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
}