static void Main(string[] args)
{
// cargamos los datos
loadTrainigData();
//Clase de ML.NET, la necesitamos para acceder a las operaciones del machine learning
var mlContext = new MLContext();
//Convertimos nuestros datos de trainingData en IDataView (Interfaz de ML.NET)
IDataView dataView = mlContext.Data.LoadFromEnumerable <FeedbackTrainingData>(trainingData);
// Creamos el pipeline
//definimos nuestro algoritmo
var pipeline = mlContext.Transforms.Text.FeaturizeText("Features", "feedbackText").Append(mlContext.BinaryClassification.Trainers.FastTree(numberOfLeaves: 50, numberOfTrees: 50, minimumExampleCountPerLeaf: 1));
// Entrenamos el algoritmo
var model = pipeline.Fit(dataView);
//Test data para ver accuracy
loadTestData();
IDataView dataViewTest = mlContext.Data.LoadFromEnumerable <FeedbackTrainingData>(testData);
var predictions = model.Transform(dataViewTest);
var metrics = mlContext.BinaryClassification.Evaluate(predictions, "Label");
Console.WriteLine(metrics.Accuracy);
//
Console.WriteLine("Feedback:");
string feedbackString = Console.ReadLine();
var predictionFunction = mlContext.Model.CreatePredictionEngine <FeedbackTrainingData, FeedbackPrediction>(model);
var feedbackInput = new FeedbackTrainingData();
feedbackInput.feedbackText = feedbackString;
var feedbackPredicted = predictionFunction.Predict(feedbackInput);
Console.WriteLine("Se predice:" + feedbackPredicted.isGood);
Console.Read();
}
static void Main(string[] args)
{
// load the training data
LoadTrainingData();
var mlContext = new MLContext();
IDataView dataView = mlContext.CreateStreamingDataView <FeedbackTrainingData>(trainingData);
var pipeline = mlContext.Transforms.Text.FeaturizeText("FeedbackText", "Features").Append(mlContext.BinaryClassification.Trainers.FastTree(numLeaves: 50, numTrees: 50, minDatapointsInLeaves: 1));
var model = pipeline.Fit(dataView);
LoadTestData();
IDataView dataView1 = mlContext.CreateStreamingDataView <FeedbackTrainingData>(testData);
var predictions = model.Transform(dataView1);
var metrics = mlContext.BinaryClassification.Evaluate(predictions, "Label");
Console.WriteLine("Feedback Prediction Model Accuracy : " + metrics.Accuracy);
while (true)
{
Console.WriteLine("\nEnter Feedback String:");
string feedbackString = Console.ReadLine().ToString();
var predictionFunction = model.MakePredictionFunction <FeedbackTrainingData, FeedbackPrediction>(mlContext);
var feedbackInput = new FeedbackTrainingData();
feedbackInput.FeedbackText = feedbackString;
var feedbackPredicted = predictionFunction.Predict(feedbackInput);
if (feedbackPredicted.IsGood)
{
Console.WriteLine("+ve feedback");
}
else
{
Console.WriteLine("-ve feedback");
}
}
}