private static void Predict(MLContext mlContext, ITransformer mlModel, SampleObservation sampleData)
{
// Create prediction engine related to the loaded ML model
var predEngine = mlModel.CreatePredictionEngine <SampleObservation, SamplePrediction>(mlContext);
// Try a single prediction
var predictionResult = predEngine.Predict(sampleData);
Console.WriteLine($"Single Prediction --> Actual value: {sampleData.Fare_amount} | Predicted value: {predictionResult.Score}");
}
public float Predict(string sentimentText)
{
var sampleData = new SampleObservation()
{
Col0 = sentimentText
};
var prediction = _predictionEnginePool.Predict(sampleData);
return(CalculatePercentage(prediction.Score));
}
private static void PredictSingle(SampleRegressionModelScorer modelScorer)
{
// Create sample data to do a single prediction with it
SampleObservation sampleData = CreateSingleDataSample();
// Try a single prediction
var predictionResult = modelScorer.PredictionEngine.Predict(sampleData);
Console.WriteLine($"=============== Single Prediction ===============");
Console.WriteLine($"Actual value: {sampleData.Fare_amount} | Predicted value: {predictionResult.Score}");
Console.WriteLine($"==================================================");
}
private static void PredictSingle(IMLModelEngine <SampleObservation, SamplePrediction> mlModelScorer)
{
// Create sample data to do a single prediction with it
SampleObservation sampleData = CreateSingleDataSample();
// Make a single prediction
var resultprediction = mlModelScorer.Predict(sampleData);
Console.WriteLine($"=============== Single Prediction ===============");
Console.WriteLine($"Actual value: {sampleData.Fare_amount} | Predicted value: {resultprediction.Score}");
Console.WriteLine($"==================================================");
}
public ActionResult <float> PredictSentiment([FromQuery] string sentimentText)
{
// Predict sentiment using ML.NET model
SampleObservation sampleData = new SampleObservation {
Col0 = sentimentText
};
// Predict sentiment
SamplePrediction prediction = _predictionEnginePool.Predict(sampleData);
float percentage = CalculatePercentage(prediction.Score);
return(percentage);
}
// Method to load single row of data to try a single prediction
// You can change this code and create your own sample data here (Hardcoded or from any source)
private static SampleObservation CreateSingleDataSample(MLContext mlContext, string dataFilePath)
{
// Read dataset to get a single row for trying a prediction
IDataView dataView = mlContext.Data.LoadFromTextFile <SampleObservation>(
path: dataFilePath,
hasHeader: true,
separatorChar: ',');
// Here (SampleObservation object) you could provide new test data, hardcoded or from the end-user application, instead of the row from the file.
SampleObservation sampleForPrediction = mlContext.Data.CreateEnumerable <SampleObservation>(dataView, false)
.First();
return(sampleForPrediction);
}
public ActionResult <string> PredictSentiment([FromQuery] string sentimentText)
{
SampleObservation sampleData = new SampleObservation()
{
SentimentText = sentimentText
};
//Predict sentiment
SamplePrediction prediction = _modelEngine.Predict(sampleData);
bool isToxic = prediction.IsToxic;
float probability = CalculatePercentage(prediction.Score);
string retVal = $"Prediction: Is Toxic?: '{isToxic.ToString()}' with {probability.ToString()}% probability of toxicity for the text '{sentimentText}'";
return(retVal);
}
static void Main(string[] args)
{
MLContext mlContext = new MLContext();
//Load ML Model from .zip file
ITransformer mlModel = LoadModelFromFile(mlContext, MODEL_FILEPATH);
// Create sample data to do a single prediction with it
SampleObservation sampleData = CreateSingleDataSample(mlContext, DATA_FILEPATH);
// Test a single prediction
Predict(mlContext, mlModel, sampleData);
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
public ActionResult <SamplePrediction> GetSentiment([FromQuery] SampleObservation input)
{
return(_predictionEnginePool.Predict(input));
}
