public List <DataStructures.TaxiFarePrediction> RunMultiplePredictions(int numberOfPredictions)
{
// Load data as input for predictions.
IDataView inputDataForPredictions = context.Data.LoadFromTextFile <TaxiTrip>(_datasetFile, hasHeader: true, separatorChar: ',');
Console.WriteLine("Predictions from saved model:");
Console.WriteLine($"\n \n Test {numberOfPredictions} transactions, from the test datasource, that should be predicted as fraud (true):");
var transactionList = new List <DataStructures.TaxiFarePrediction>();
TaxiTripFarePredictionWithContribution prediction;
DataStructures.TaxiFarePrediction explainedPrediction;
context.Data.CreateEnumerable <TaxiTrip>(inputDataForPredictions, reuseRowObject: false)
.Take(numberOfPredictions)
.Select(testData => testData)
.ToList()
.ForEach(testData =>
{
testData.PrintToConsole();
prediction = predictionEngine.Predict(testData);
explainedPrediction = new DataStructures.TaxiFarePrediction(prediction.FareAmount, prediction.GetFeatureContributions(model.GetOutputSchema(inputDataForPredictions.Schema)));
transactionList.Add(explainedPrediction);
});
return(transactionList);
}
public static PlotModel GetPlotModel(DataStructures.TaxiFarePrediction prediction)
{
var model = new PlotModel {
Title = "Taxi Fare Prediction Impact per Feature"
};
var barSeries = new BarSeries
{
ItemsSource = new List <BarItem>(new[]
{
new BarItem {
Value = (prediction.Features[0].Value)
},
new BarItem {
Value = (prediction.Features[1].Value)
},
new BarItem {
Value = (prediction.Features[2].Value)
}
}),
LabelPlacement = LabelPlacement.Inside,
LabelFormatString = "{0:.00}"
};
model.Series.Add(barSeries);
model.Axes.Add(new CategoryAxis
{
Position = AxisPosition.Left,
Key = "FeatureAxis",
ItemsSource = new[]
{
prediction.Features[0].Name,
prediction.Features[1].Name,
prediction.Features[2].Name
}
});
return(model);
}
