static void Main(string[] args)
{
//1. carica i dati di training
LoadTrainingData();
//2. crea un oggetto di MLContext
var mlContext = new MLContext();
//3. converte i dato in IData View
//LoadFromEnumerable era ReadFromEnumerable
IDataView dataView = mlContext.Data.LoadFromEnumerable(trainingdata); //<FeedBackTrainingData> fra "LoadFromEnumerable" e "(trainingdata);"
//4. crea la pipeline
var pipeline = mlContext.Transforms.
Text.FeaturizeText("Feedback", "Features")
.Append(mlContext.BinaryClassification.Trainers.FastTree
(numberOfLeaves: 50, numberOfTrees: 50, minimumExampleCountPerLeaf: 1));
//5. train
var model = pipeline.Fit(dataView);
//6. testare con dati appositi, diversi da quelli di training
LoadTestData();
IDataView dataView1 = mlContext.Data.LoadFromEnumerable(trainingdata);
var predictions = model.Transform(dataView1);
var metrics = mlContext.BinaryClassification.Evaluate(predictions, "Label");
Console.WriteLine(metrics.Accuracy);
Console.Read();
//7. utilizzare il modello
Console.WriteLine("Enter a feedback string: ");
string feedbackstring = Console.Read().ToString();
/*var predictionFunction = model.MakePredictionFunction
* <FeedBackTrainingData, FeedBackPrediction>
* (mlContext.Context);*/
var feedbackinput = new FeedBackTrainingData();
feedbackinput.FeedBackText = feedbackstring;
predictionFunction.predict(feedbackinput);
}
static void Main(string[] args)
{
int i = 0;
while (i < 3)
{
i += 1;
//1. carica i dati di training
LoadTrainingData();
//2. crea un oggetto di MLContext
var mlContext = new MLContext();
//3. converte i dato in IData View
IDataView dataView = mlContext.CreateStreamingDataView <FeedBackTrainingData>(trainingdata);
//4. crea la pipeline
var pipeline = mlContext.Transforms.Text.FeaturizeText("FeedBackText", "Features").Append(mlContext.BinaryClassification.Trainers.FastTree(numLeaves: 50, numTrees: 50, minDatapointsInLeaves: 1));
//5. train
var model = pipeline.Fit(dataView);
//6. testare con dati appositi, diversi da quelli di training
LoadTestData();
IDataView dataView1 = mlContext.CreateStreamingDataView <FeedBackTrainingData>(testData);
var predictions = model.Transform(dataView1);
var metrics = mlContext.BinaryClassification.Evaluate(predictions, "Label");
Console.WriteLine(metrics.Accuracy);
//7. utilizzare il modello
Console.Write("Enter a 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);
Console.WriteLine("Is good (predicted): " + feedbackpredicted.IsGood);
Console.ReadLine();
}
}
