private static ITransformer BuildTrainEvaluateAndSaveModel(MLContext mlContext)
{
// STEP 1: Common data loading configuration
//IDataView baseTrainingDataView = mlContext.Data.LoadFromTextFile<TaxiTrip>(TrainingDataFile, hasHeader: true, separatorChar: ',');
//IDataView testDataView = mlContext.Data.LoadFromTextFile<TaxiTrip>(TestingDataFile, hasHeader: true, separatorChar: ',');
var baseTrainingDataView = mlContext.Data.LoadFromEnumerable(TaxiTripCsvReader.GetDataFromCsv(TrainingDataFile));
var testDataView = mlContext.Data.LoadFromEnumerable(TaxiTripCsvReader.GetDataFromCsv(TestingDataFile));
//Sample code of removing extreme data like "outliers" for FareAmounts higher than $150 and lower than $1 which can be error-data
var trainingDataView = mlContext.Data.FilterRowsByColumn(baseTrainingDataView, nameof(TaxiTrip.FareAmount), lowerBound: 1, upperBound: 150);
var a = trainingDataView.GetColumn <float>(nameof(TaxiTrip.FareAmount));
// STEP 2: Common data process configuration with pipeline data transformations
var dataProcessPipeline = mlContext.Transforms.CopyColumns(outputColumnName: "Label", inputColumnName: nameof(TaxiTrip.FareAmount))
.Append(mlContext.Transforms.Categorical.OneHotEncoding(outputColumnName: "VendorIdEncoded", inputColumnName: nameof(TaxiTrip.VendorId)))
.Append(mlContext.Transforms.Categorical.OneHotEncoding(outputColumnName: "RateCodeEncoded", inputColumnName: nameof(TaxiTrip.RateCode)))
.Append(mlContext.Transforms.Categorical.OneHotEncoding(outputColumnName: "PaymentTypeEncoded", inputColumnName: nameof(TaxiTrip.PaymentType)))
.Append(mlContext.Transforms.NormalizeMeanVariance(outputColumnName: nameof(TaxiTrip.PassengerCount)))
.Append(mlContext.Transforms.NormalizeMeanVariance(outputColumnName: nameof(TaxiTrip.TripTime)))
.Append(mlContext.Transforms.NormalizeMeanVariance(outputColumnName: nameof(TaxiTrip.TripDistance)))
.Append(mlContext.Transforms.Concatenate("Features", "VendorIdEncoded", "RateCodeEncoded", "PaymentTypeEncoded", nameof(TaxiTrip.PassengerCount)
, nameof(TaxiTrip.TripTime), nameof(TaxiTrip.TripDistance)));
// (OPTIONAL) Peek data (such as 5 records) in training DataView after applying the ProcessPipeline's transformations into "Features"
ConsoleHelper.PeekDataViewInConsole(mlContext, trainingDataView, dataProcessPipeline, 5);
ConsoleHelper.PeekVectorColumnDataInConsole(mlContext, "Features", trainingDataView, dataProcessPipeline, 5);
// STEP 3: Set the training algorithm, then create and config the modelBuilder - Selected Trainer (SDCA Regression algorithm)
var trainer = mlContext.Regression.Trainers.Sdca(labelColumnName: "Label", featureColumnName: "Features");
var trainingPipeline = dataProcessPipeline.Append(trainer);
// STEP 4: Train the model fitting to the DataSet
//The pipeline is trained on the dataset that has been loaded and transformed.
Console.WriteLine("=============== Training the model ===============");
ConsoleHelper.PeekDataViewInConsole(mlContext, trainingDataView, trainingPipeline, 5);
var trainedModel = trainingPipeline.Fit(trainingDataView);
// STEP 5: Evaluate the model and show accuracy stats
Console.WriteLine("===== Evaluating Model's accuracy with Test data =====");
IDataView predictions = trainedModel.Transform(testDataView);
var metrics = mlContext.Regression.Evaluate(predictions, labelColumnName: "Label", scoreColumnName: "Score");
ConsoleHelper.PrintRegressionMetrics(trainer.ToString(), metrics);
// STEP 6: Save/persist the trained model to a .ZIP file
mlContext.Model.Save(trainedModel, trainingDataView.Schema, ModelFile);
Console.WriteLine("The model is saved to {0}", ModelFile);
return(trainedModel);
}
private static void PlotRegressionChart(MLContext mlContext,
string testDataSetPath,
int numberOfRecordsToRead,
string[] args)
{
ITransformer trainedModel;
using (var stream = new FileStream(ModelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
trainedModel = mlContext.Model.Load(stream, out var modelInputSchema);
}
// Create prediction engine related to the loaded trained model
var predFunction = mlContext.Model.CreatePredictionEngine <TaxiTrip, TaxiTripFarePrediction>(trainedModel);
string chartFileName = string.Empty;
using (var pl = new PLStream())
{
// use SVG backend and write to SineWaves.svg in current directory
if (args.Length == 1 && args[0] == "svg")
{
pl.sdev("svg");
chartFileName = "TaxiRegressionDistribution.svg";
pl.sfnam(chartFileName);
}
else
{
pl.sdev("pngcairo");
chartFileName = "TaxiRegressionDistribution.png";
pl.sfnam(chartFileName);
}
// use white background with black foreground
pl.spal0("cmap0_alternate.pal");
// Initialize plplot
pl.init();
// set axis limits
const int xMinLimit = 0;
const int xMaxLimit = 35; //Rides larger than $35 are not shown in the chart
const int yMinLimit = 0;
const int yMaxLimit = 35; //Rides larger than $35 are not shown in the chart
pl.env(xMinLimit, xMaxLimit, yMinLimit, yMaxLimit, AxesScale.Independent, AxisBox.BoxTicksLabelsAxes);
// Set scaling for mail title text 125% size of default
pl.schr(0, 1.25);
// The main title
pl.lab("Measured", "Predicted", "Distribution of Taxi Fare Prediction");
pl.col0(1);
int totalNumber = numberOfRecordsToRead;
var testData = TaxiTripCsvReader.GetDataFromCsv(testDataSetPath, totalNumber).ToList();
//This code is the symbol to paint
char code = (char)9;
// plot using other color
//pl.col0(9); //Light Green
//pl.col0(4); //Red
pl.col0(2); //Blue
double yTotal = 0;
double xTotal = 0;
double xyMultiTotal = 0;
double xSquareTotal = 0;
for (int i = 0; i < testData.Count; i++)
{
var x = new double[1];
var y = new double[1];
//Make Prediction
var FarePrediction = predFunction.Predict(testData[i]);
x[0] = testData[i].FareAmount;
y[0] = FarePrediction.FareAmount;
//Paint a dot
pl.poin(x, y, code);
xTotal += x[0];
yTotal += y[0];
double multi = x[0] * y[0];
xyMultiTotal += multi;
double xSquare = x[0] * x[0];
xSquareTotal += xSquare;
double ySquare = y[0] * y[0];
Console.WriteLine($"-------------------------------------------------");
Console.WriteLine($"Predicted : {FarePrediction.FareAmount}");
Console.WriteLine($"Actual: {testData[i].FareAmount}");
Console.WriteLine($"-------------------------------------------------");
}
// Regression Line calculation explanation:
// https://www.khanacademy.org/math/statistics-probability/describing-relationships-quantitative-data/more-on-regression/v/regression-line-example
double minY = yTotal / totalNumber;
double minX = xTotal / totalNumber;
double minXY = xyMultiTotal / totalNumber;
double minXsquare = xSquareTotal / totalNumber;
double m = ((minX * minY) - minXY) / ((minX * minX) - minXsquare);
double b = minY - (m * minX);
//Generic function for Y for the regression line
// y = (m * x) + b;
double x1 = 1;
//Function for Y1 in the line
double y1 = (m * x1) + b;
double x2 = 39;
//Function for Y2 in the line
double y2 = (m * x2) + b;
var xArray = new double[2];
var yArray = new double[2];
xArray[0] = x1;
yArray[0] = y1;
xArray[1] = x2;
yArray[1] = y2;
pl.col0(4);
pl.line(xArray, yArray);
// end page (writes output to disk)
pl.eop();
}
// Open Chart File In Microsoft Photos App (Or default app, like browser for .svg)
Console.WriteLine("Showing chart...");
var p = new Process();
var chartFileNamePath = @".\" + chartFileName;
p.StartInfo = new ProcessStartInfo(chartFileNamePath)
{
UseShellExecute = true
};
p.Start();
}
