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)); }