/// <summary>
/// Predicts a single observation using the ensembled probabilities
/// </summary>
/// <param name="observation"></param>
/// <returns></returns>
public ProbabilityPrediction PredictProbability(double[] observation)
{
var ensembleCols = m_ensembleModels.Length;
var ensemblePredictions = new ProbabilityPrediction[ensembleCols];
for (int i = 0; i < m_ensembleModels.Length; i++)
{
ensemblePredictions[i] = m_ensembleModels[i].Predict(observation);
}
return(m_ensembleStrategy.Combine(ensemblePredictions));
}
double SelectNextModelToAdd(ProbabilityPrediction[][] crossValidatedModelPredictions,
double[] targets,
double currentBestError)
{
var rows = crossValidatedModelPredictions.First().Length;
var candidateModelMatrix = new ProbabilityPrediction[m_selectedModelIndices.Count + 1][];
var candidatePredictions = new ProbabilityPrediction[rows];
var candidateModelIndices = new int[m_selectedModelIndices.Count + 1];
var bestError = currentBestError;
var bestIndex = -1;
foreach (var index in m_remainingModelIndices)
{
m_selectedModelIndices.CopyTo(candidateModelIndices);
candidateModelIndices[candidateModelIndices.Length - 1] = index;
for (int i = 0; i < candidateModelIndices.Length; i++)
{
candidateModelMatrix[i] = crossValidatedModelPredictions[candidateModelIndices[i]];
}
m_ensembleStrategy.Combine(candidateModelMatrix, candidatePredictions);
var error = m_metric.Error(targets, candidatePredictions);
if (error < bestError)
{
bestError = error;
bestIndex = index;
}
}
if(bestIndex != -1)
{
m_selectedModelIndices.Add(bestIndex);
if(!m_selectWithReplacement)
{
m_remainingModelIndices.Remove(bestIndex);
}
}
return bestError;
}
double SelectNextModelToRemove(ProbabilityPrediction[][] crossValidatedModelPredictions,
double[] targets,
double currentBestError)
{
var rows = crossValidatedModelPredictions.First().Length;
var candidateModelMatrix = new ProbabilityPrediction[m_remainingModelIndices.Count - 1][];
var candidatePredictions = new ProbabilityPrediction[rows];
var candidateModelIndices = new int[m_remainingModelIndices.Count - 1];
var bestError = currentBestError;
var bestIndex = -1;
foreach (var index in m_remainingModelIndices)
{
var candidateIndex = 0;
for (int i = 0; i < m_remainingModelIndices.Count; i++)
{
var curIndex = m_remainingModelIndices[i];
if (curIndex != index)
{
candidateModelIndices[candidateIndex++] = m_remainingModelIndices[i];
}
}
for (int i = 0; i < candidateModelIndices.Length; i++)
{
candidateModelMatrix[i] = crossValidatedModelPredictions[candidateModelIndices[i]];
}
m_ensembleStrategy.Combine(candidateModelMatrix, candidatePredictions);
var error = m_metric.Error(targets, candidatePredictions);
if (error < bestError)
{
bestError = error;
bestIndex = index;
}
}
m_remainingModelIndices.Remove(bestIndex);
return(bestError);
}
/// <summary>
/// Greedy forward selection of ensemble models.
/// </summary>
/// <param name="crossValidatedModelPredictions">cross validated predictions from multiple models.
/// Each row in the matrix corresponds to predictions from a separate model</param>
/// <param name="targets">Corresponding targets</param>
/// <returns>The indices of the selected model</returns>
public int[] Select(ProbabilityPrediction[][] crossValidatedModelPredictions, double[] targets)
{
if (crossValidatedModelPredictions.Length < m_numberOfModelsToSelect)
{
throw new ArgumentException("Availible models: " + crossValidatedModelPredictions.Length +
" is smaller than number of models to select: " + m_numberOfModelsToSelect);
}
m_allIndices = Enumerable.Range(0, crossValidatedModelPredictions.Length).ToArray();
var rows = crossValidatedModelPredictions.First().Length;
var candidateModelMatrix = new ProbabilityPrediction[m_numberOfModelsToSelect][];
var candidatePredictions = new ProbabilityPrediction[rows];
var candidateModelIndices = new int[m_numberOfModelsToSelect];
var bestModelIndices = new int[m_numberOfModelsToSelect];
var bestError = double.MaxValue;
for (int i = 0; i < m_iterations; i++)
{
SelectNextRandomIndices(candidateModelIndices);
for (int j = 0; j < candidateModelIndices.Length; j++)
{
candidateModelMatrix[j] = crossValidatedModelPredictions[candidateModelIndices[j]];
}
m_ensembleStrategy.Combine(candidateModelMatrix, candidatePredictions);
var error = m_metric.Error(targets, candidatePredictions);
if (error < bestError)
{
bestError = error;
candidateModelIndices.CopyTo(bestModelIndices, 0);
Trace.WriteLine("Models selected: " + bestModelIndices.Length + ": " + error);
}
}
Trace.WriteLine("Selected model indices: " + string.Join(", ", bestModelIndices.ToArray()));
return(bestModelIndices);
}