/// <summary>
/// Send a request to the process to get the learning/prediction results.
/// The process has to be set up before performing this method.
/// The process will automatically terminate after this method was performed.
/// </summary>
/// <param name="predictedConfigurations">The configurations that were used to predict the nfp values by the learner.</param>
/// <param name="writer">Writer to write the prediction results into a csv File.</param>
public void getLearningResult(List <Configuration> predictedConfigurations, PythonPredictionWriter writer)
{
while (!waitForNextReceivedLine().Equals(FINISHED_LEARNING))
{
}
passLineToApplication(REQUESTING_LEARNING_RESULTS);
printNfpPredictionsPython(waitForNextReceivedLine(), predictedConfigurations, writer);
}
private void printNfpPredictionsPython(string pythonList, List <Configuration> predictedConfigurations, PythonPredictionWriter writer)
{
string[] separators = new String[] { "," };
string[] predictions = pythonList.Split(separators, StringSplitOptions.RemoveEmptyEntries);
if (predictedConfigurations.Count != predictions.Length)
{
GlobalState.logError.log("number of predictions using a python learner does not match with number of configurations");
}
if ((predictions[0].ToLower()).StartsWith("error"))
{
GlobalState.logError.log("There was a error running the Python script.");
StringBuilder errMessage = new StringBuilder();
foreach (string errInfo in predictions)
{
errMessage.Append(errInfo);
}
GlobalState.logError.log("Error message: " + errMessage.ToString());
}
else
{
writer.writePredictions("Configuration;MeasuredValue;PredictedValue\n");
for (int i = 0; i < predictedConfigurations.Count; i++)
{
writer.writePredictions(predictedConfigurations[i].ToString().Replace(";", "_") + ";" + Math.Round(predictedConfigurations[i].GetNFPValue(), 4) + ";" + Math.Round(Convert.ToDouble(predictions[i]), 4) + "\n");
}
}
}
/// <summary>
/// This method prints the predictions in the specified file and returns the error rate.
/// </summary>
/// <param name="pythonList">The predictions from python.</param>
/// <param name="predictedConfigurations">The configurations that were predicted.</param>
/// <param name="writer">The writer object for the file.</param>
/// <returns></returns>
private double printNfpPredictionsPython(string pythonList, List <Configuration> predictedConfigurations, PythonPredictionWriter writer, out List <Configuration> predictedByPython)
{
predictedByPython = new List <Configuration>();
string[] separators = new String[] { "," };
string[] predictions = pythonList.Split(separators, StringSplitOptions.RemoveEmptyEntries);
if (predictedConfigurations.Count != predictions.Length)
{
GlobalState.logError.log("number of predictions using a python learner does not match with number of configurations");
}
if ((predictions[0].ToLower()).StartsWith("error"))
{
GlobalState.logError.log("There was a error running the Python script.");
StringBuilder errMessage = new StringBuilder();
foreach (string errInfo in predictions)
{
errMessage.Append(errInfo);
}
GlobalState.logError.log("Error message: " + errMessage.ToString());
return(Double.NaN);
}
else
{
double error = 0;
writer.writePredictions("Configuration;MeasuredValue;PredictedValue\n");
for (int i = 0; i < predictedConfigurations.Count; i++)
{
writer.writePredictions(predictedConfigurations[i].ToString().Replace(";", "_") + ";" + Math.Round(predictedConfigurations[i].GetNFPValue(), 4) + ";" + Math.Round(Convert.ToDouble(predictions[i]), 4) + "\n");
error += Math.Abs(predictedConfigurations[i].GetNFPValue() - Convert.ToDouble(predictions[i])) / predictedConfigurations[i].GetNFPValue();
var copy = predictedConfigurations[i].Copy();
copy.setMeasuredValue(GlobalState.currentNFP, predictedConfigurations[i].GetNFPValue());
predictedByPython.Add(copy);
}
error /= predictedConfigurations.Count;
return(error);
}
}
