public IPredictionModel BuildModel(IDataFrame dataFrame, string dependentFeatureName, IModelBuilderParams additionalParams)
{
if (!(additionalParams is IRandomForestModelBuilderParams))
{
throw new ArgumentException("Invalid parameters passed to Random Forest Model builder!");
}
var randomForestParams = additionalParams as IRandomForestModelBuilderParams;
var trees = new IDecisionTreeNode[randomForestParams.TreesCount];
var oobErrors = new double[randomForestParams.TreesCount];
var columnsCountToTake = featuresToUseCountCalculator(dataFrame.ColumnsCount - 1);
var featureColumns = dataFrame.ColumnNames.Except(new[] { dependentFeatureName }).ToList();
var randomizer = new Random();
var locker = new object();
Parallel.For(
0,
randomForestParams.TreesCount,
i =>
{
var localRandomizer = new Random(i.GetHashCode());
var randomlySelectedIndices =
Enumerable.Range(0, dataFrame.RowCount)
.Select(_ => localRandomizer.Next(0, dataFrame.RowCount))
.ToList();
var outOfBagIndices =
Enumerable.Range(0, dataFrame.RowCount).Except(randomlySelectedIndices).ToList();
var columnsToTake = new List <string>();
columnsToTake = featureColumns.Shuffle(localRandomizer).Take(columnsCountToTake).ToList();
columnsToTake.Add(dependentFeatureName);
var baggedTestData = dataFrame.Slice(randomlySelectedIndices, columnsToTake);
var oobTestData = dataFrame.Slice(outOfBagIndices, columnsToTake);
var oobExpected = oobTestData.GetColumnVector <TPredictionVal>(dependentFeatureName).Values;
var decisionTree = decisionTreeModelBuilder.BuildModel(
baggedTestData,
dependentFeatureName,
decisionTreeModelBuilderParamsFactory());
var prediction = decisionTreePredictor.Predict(oobTestData, decisionTree, dependentFeatureName);
//TODO: AAA !!! Later on add support for calculating variable importance!!!
var oobError = dataQualityMeasure.CalculateError(oobExpected, prediction);
trees[i] = decisionTree as IDecisionTreeNode;
oobErrors[i] = oobError;
});
return(new RandomForestModel(trees, oobErrors));
}
