public static async Task <Dictionary <string, object> > chooseBestFastTreeRegressor()
{
double bestLoss = Double.MaxValue;
Dictionary <string, object> bestVersion = new Dictionary <string, object>();
foreach (int numLeaves in numLeavesList)
{
foreach (double learningRates in learningRatesList)
{
foreach (int maxBins in maxBinsList)
{
Dictionary <string, object> kwargs = new Dictionary <string, object>()
{
{ "numLeaves", numLeaves }, { "learningRates", learningRates }, { "maxBins", maxBins }
};
var model = await Train("FastTreeRegressor", kwargs);
double loss = Evaluate(model);
WorldCupPrediction predictionForEngland = model.Predict(TestData.TestEngland);
WorldCupPrediction predictionForSweden = model.Predict(TestData.TestSweden);
if (loss < bestLoss)
{
Console.WriteLine($"Best fast tree regressor so far is: NumLeaves = {numLeaves}, LearningRates = {learningRates}, MaxBins = {maxBins}, with loss (RMS) {loss}");
Console.WriteLine($"-- Predicted result is: England VS Sweden = {predictionForEngland.HomeTeamGoals} : {predictionForSweden.HomeTeamGoals}");
bestLoss = loss;
bestVersion["type"] = "FastTreeRegressor";
bestVersion["numLeaves"] = numLeaves;
bestVersion["learningRates"] = learningRates;
bestVersion["maxBins"] = maxBins;
bestVersion["England"] = predictionForEngland.HomeTeamGoals;
bestVersion["Sweden"] = predictionForSweden.HomeTeamGoals;
bestVersion["loss"] = loss;
}
}
}
}
return(bestVersion);
}
public static async Task <Dictionary <string, object> > chooseBestStochasticDualCoordinateAscentRegressor()
{
double bestLoss = Double.MaxValue;
Dictionary <string, object> bestVersion = new Dictionary <string, object>();
foreach (float biasLearningRate in biasLearningRateList)
{
foreach (float l1Threshold in l1ThresholdList)
{
foreach (float l2Const in l2ConstList)
{
Dictionary <string, object> kwargs = new Dictionary <string, object>()
{
{ "biasLearningRate", biasLearningRate }, { "l1Threshold", l1Threshold }, { "l2Const", l2Const }
};
var model = await Train("StochasticDualCoordinateAscentRegressor", kwargs);
double loss = Evaluate(model);
WorldCupPrediction predictionForEngland = model.Predict(TestData.TestEngland);
WorldCupPrediction predictionForSweden = model.Predict(TestData.TestSweden);
if (loss < bestLoss)
{
Console.WriteLine($"Best stochastic dual coordinate ascent regressor so far is: BiasLearningRate = {biasLearningRate}, L1Threshold = {l1Threshold}, L2Const = {l2Const}, with loss (RMS) {loss}");
Console.WriteLine($"-- Predicted result is: England VS Sweden = {predictionForEngland.HomeTeamGoals} : {predictionForSweden.HomeTeamGoals}");
bestLoss = loss;
bestVersion["type"] = "StochasticDualCoordinateAscentRegressor";
bestVersion["biasLearningRate"] = biasLearningRate;
bestVersion["l1Threshold"] = l1Threshold;
bestVersion["l2Const"] = l2Const;
bestVersion["England"] = predictionForEngland.HomeTeamGoals;
bestVersion["Sweden"] = predictionForSweden.HomeTeamGoals;
bestVersion["loss"] = loss;
}
}
}
}
return(bestVersion);
}
