static void Main(string[] args)
{
//1. Create ML.NET context/environment
var env = new ConsoleEnvironment();
//SentimentIssueInput
//2. Create DataReader with data schema mapped to file's columns
//var text = TextLoader.CreateReader(env, ctx => (
// issueInput: new SentimentIssueInput() { Label = ctx.LoadBool(0), Text = ctx.LoadText(1) },
// issueInput2: new SentimentIssueInput() { Label = ctx.LoadBool(0), Text = ctx.LoadText(1) }
// ));
var reader = TextLoader.CreateReader(env, ctx =>
new SentimentIssueInput()
{
Label = ctx.LoadBool(0),
Text = ctx.LoadText(1)
});
//var reader = TextLoader.CreateReader(env, ctx => (label: ctx.LoadBool(0),
// text: ctx.LoadText(1)));
//3. Create an estimator to use afterwards for creating/traing the model.
var bctx = new BinaryClassificationContext(env);
Estimator <SentimentIssueInput, SentimentPred, ITransformer> est = CreateEstimator(reader, bctx);
est.ToString();
//var est = reader.MakeNewEstimator().Append(row =>
//{
// var featurizedText = row.text.FeaturizeText(); //Convert text to numeric vectors
// var prediction = bctx.Trainers.Sdca(row.label, featurizedText); //Specify SDCA trainer based on the label and featurized text columns
// return (row.label, prediction); //Return label and prediction columns. "prediction" holds predictedLabel, score and probability
//});
//Another way to create an Estimator, with the same behaviour, by chaining appends
//var est = reader.MakeNewEstimator().Append(row => (label: row.label,
// featurizedtext: row.text.FeaturizeText())) //Convert text to numeric vectors
// .Append(row => (label: row.label,
// prediction: bctx.Trainers.Sdca(row.label, row.featurizedtext))); //Specify SDCA trainer based on the label and featurized text columns
//4. Build and train the model
//Load training data
//var traindata = reader.Read(new MultiFileSource(TrainDataPath));
//Console.WriteLine("=============== Create and Train the Model ===============");
//var model = est.Fit(traindata);
//Console.WriteLine("=============== End of training ===============");
//Console.WriteLine();
//5. Evaluate the model
//Load test data
//var testdata = reader.Read(new MultiFileSource(TestDataPath));
//Console.WriteLine("=============== Evaluating Model's accuracy with Test data===============");
//var predictions = model.Transform(testdata);
//var metrics = bctx.Evaluate(predictions, row => row.label, row => row.prediction);
//Console.WriteLine();
//Console.WriteLine("PredictionModel quality metrics evaluation");
//Console.WriteLine("------------------------------------------");
//Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
//Console.WriteLine($"Auc: {metrics.Auc:P2}");
//Console.WriteLine($"F1Score: {metrics.F1Score:P2}");
//Console.WriteLine("=============== End of Model's evaluation ===============");
//Console.WriteLine();
//6. Test Sentiment Prediction with one sample text
//var predictionFunct = model.AsDynamic.MakePredictionFunction<SentimentIssue, SentimentPrediction>(env);
//SentimentIssue sampleStatement = new SentimentIssue
// {
// text = "This is a very rude movie"
// };
//var resultprediction = predictionFunct.Predict(sampleStatement);
//Console.WriteLine();
//Console.WriteLine("=============== Test of model with a sample ===============");
//Console.WriteLine($"Text: {sampleStatement.text} | Prediction: {(resultprediction.PredictionLabel ? "Negative" : "Positive")} sentiment | Probability: {resultprediction.Probability} ");
//Console.WriteLine("=============== End of process, hit any key to finish ===============");
//Console.ReadKey();
}