static void Main(string[] args)
{
Console.WriteLine("SentimentAnalysis Start!");
//1. Create ML.NET context/environment
MLContext mLContext = new MLContext();
//2. Create DataReader with data schema mapped to file's columns
string baseDataPath = @"Data/base.tsv";
var reader = new TextLoader.Arguments()
{
Separator = "tab",
HasHeader = true,
Column = new TextLoader.Column[] {
new TextLoader.Column("Label", DataKind.Bool, 0),
new TextLoader.Column("Text", DataKind.Text, 1)
}
};
//Load training data
IDataView trainingDataView = mLContext.Data.TextReader(reader).Read(new MultiFileSource(baseDataPath));
//3.Create a flexible pipeline (composed by a chain of estimators) for creating/traing the model.
var pipeline = mLContext.Transforms.Text.FeaturizeText("Text", "Features")
.Append(mLContext.BinaryClassification.Trainers.FastTree(numLeaves: 50, numTrees: 50, minDatapointsInLeafs: 20));
//Train model
var model = pipeline.Fit(trainingDataView);
//Evaluate model
var testDataPath = @"Data/test.tsv";
IDataView testDataView = mLContext.Data.TextReader(reader).Read(new MultiFileSource(testDataPath));
var predictions = model.Transform(testDataView);
var metrics = mLContext.BinaryClassification.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($"F1Score: {metrics.F1Score:P2}");
Console.WriteLine("=============== End of model evaluation ===============");
Console.ReadLine();
//Save Model
using (var stream = new FileStream(@"Data/model.zip", FileMode.Create, FileAccess.Write, FileShare.Write))
{
mLContext.Model.Save(model, stream);
}
//Consume model
var predictionFunct = model.MakePredictionFunction <SentimentIssue, SentimentPrediction>(mLContext);
var sampleStatement = new SentimentIssue
{
Text = "This is a very rude movie"
};
var resultprediction = predictionFunct.Predict(sampleStatement);
Console.WriteLine($"Text: {sampleStatement.Text} | Prediction: {(resultprediction.Prediction ? "Negative" : "Positive")} sentiment");
Console.ReadLine();
}
// (選用) 使用儲存的定型模型進行預測
private static void TestSinglePrediction(MLContext mlContext)
{
// 要預測的資料來源
var sampleStatement = new SentimentIssue {
Text = "This is a very rude movie"
};
// 載入定型模型
using (var stream = new FileStream(ModelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
var trainedModel = mlContext.Model.Load(stream);
// 建立預測引擎
var predictionEngine = trainedModel.CreatePredictionEngine <SentimentIssue, SentimentPrediction>(mlContext);
// 計算預測值
var predict = predictionEngine.Predict(sampleStatement);
Console.WriteLine($"=============== Single Prediction ===============");
Console.WriteLine($"Text: {sampleStatement.Text} | Prediction: {(Convert.ToBoolean(predict.Prediction) ? "Toxic" : "Nice")} sentiment | Probability: {predict.Probability} ");
Console.WriteLine($"==================================================");
}
}
// (OPTIONAL) Try/test a single prediction by loading the model from the file, first.
private static void TestSinglePrediction(MLContext mlContext)
{
ConsoleHelper.ConsoleWriteHeader("=============== Testing prediction engine ===============");
SentimentIssue sampleStatement = new SentimentIssue {
Text = "This is a very rude movie"
};
ITransformer trainedModel = mlContext.Model.Load(ModelPath, out var modelInputSchema);
Console.WriteLine($"=============== Loaded Model OK ===============");
// Create prediction engine related to the loaded trained model
var predEngine = mlContext.Model.CreatePredictionEngine <SentimentIssue, SentimentPrediction>(trainedModel);
Console.WriteLine($"=============== Created Prediction Engine OK ===============");
// Score
var predictedResult = predEngine.Predict(sampleStatement);
Console.WriteLine($"=============== Single Prediction ===============");
Console.WriteLine($"Text: {sampleStatement.Text} | Prediction: {(Convert.ToBoolean(predictedResult.Prediction) ? "Toxic" : "Non Toxic")} sentiment");
Console.WriteLine($"==================================================");
}
// (OPTIONAL) Try/test a single prediction by loding the model from the file, first.
private static void TestSinglePrediction(MLContext mlContext)
{
SentimentIssue sampleStatement = new SentimentIssue {
Text = "This is a very rude movie"
};
ITransformer trainedModel;
using (var stream = new FileStream(ModelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
trainedModel = mlContext.Model.Load(stream);
}
// Create prediction engine related to the loaded trained model
var predEngine = trainedModel.CreatePredictionEngine <SentimentIssue, SentimentPrediction>(mlContext);
//Score
var resultprediction = predEngine.Predict(sampleStatement);
Console.WriteLine($"=============== Single Prediction ===============");
Console.WriteLine($"Text: {sampleStatement.Text} | Prediction: {(Convert.ToBoolean(resultprediction.Prediction) ? "Toxic" : "Nice")} sentiment | Probability: {resultprediction.Probability} ");
Console.WriteLine($"==================================================");
}
