static void Main(string[] args)
{
//Step 1 :- We need to load the training data
LoadTrainingData();
// Step 2 :- Create object of MLCOntext
var mlContext = new MLContext();
// Step 3 :- Convert your data in to IDataView
IDataView dataView = mlContext.CreateStreamingDataView
<FeedBackTrainingData>(trainingdata);
// Step 4 :- We need to create the pipe line
// define the work flows in it.
var pipeline = mlContext.Transforms.
Text.FeaturizeText("FeedBackText", "Features")
.Append(mlContext.BinaryClassification.Trainers.FastTree
(numLeaves: 50, numTrees: 50, minDatapointsInLeaves: 1));
// Step 5 :- Traing the algorithm and we want the model out
var model = pipeline.Fit(dataView);
// Step 6 :- Load the test data and run the test data
// to check our models accuracy
LoadTestData();
IDataView dataView1 = mlContext.
CreateStreamingDataView <FeedBackTrainingData>(testData);
var predictions = model.Transform(dataView1);
var metrics = mlContext.BinaryClassification.Evaluate(predictions, "Label");
Console.WriteLine(metrics.Accuracy);
// Step 7 :- use the model
string strcont = "Y";
while (strcont == "Y")
{
Console.WriteLine("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("Predicted :- " + feedbackpredicted.IsGood);
}
Console.ReadLine();
}
static void Main(string[] args)
{
Console.WriteLine("Very first machine learning algorithm \nTo determine is a sentence is good or bad\n.");
//step one load training data
LoadTraningData();
var mlContext = new MLContext();
IDataView dataview = mlContext.Data.LoadFromEnumerable <FeedBackTrainingData>(trainingData);
var pipline = mlContext.Transforms
.Text.FeaturizeText("Features", "FeedbackText")
.Append(mlContext.BinaryClassification.Trainers
.FastTree(numberOfLeaves: 50, numberOfTrees: 50, minimumExampleCountPerLeaf: 1));
var model = pipline.Fit(dataview);
LoadTestData();
IDataView testdataview = mlContext.Data.LoadFromEnumerable <FeedBackTrainingData>(testData);
var predictions = model.Transform(testdataview);
var metrics = mlContext.BinaryClassification.Evaluate(predictions, "Label");
var predictionFunction = mlContext.Model.CreatePredictionEngine
<FeedBackTrainingData, FeedBackPrediction>
(model);
while (true)
{
Console.WriteLine("Enter a feed back string: ");
string feedBackString = Console.ReadLine().ToString();
var feedbackInput = new FeedBackTrainingData();
feedbackInput.FeedbackText = feedBackString;
var feedbackPredicted = predictionFunction.Predict(feedbackInput);
Console.WriteLine("Predicted Text :- " + feedbackPredicted.IsGood);
Console.WriteLine("Accuracy of Prediction:- " + metrics.Accuracy);
Console.ReadLine();
}
}
