public static double CalcInformationGain <T>(DecisionTreePartition <T> partition, string feature_variable_name, DoAttributeCostGainHandle handle_cost)
where T : DDataRecord
{
Dictionary <string, DecisionTreePartition <T> > sub_partitions_by_feature_variable = SplitPartitionByFeatureVariable(partition, feature_variable_name, handle_cost);
Dictionary <string, DecisionTreePartition <T> > sub_partitions_by_class_variable = SplitPartitionByClassVariable(partition, handle_cost);
double gain = CalcEntropy(partition, sub_partitions_by_class_variable);
foreach (string variable_value in sub_partitions_by_feature_variable.Keys)
{
DecisionTreePartition <T> sub_partition_by_feature = sub_partitions_by_feature_variable[variable_value];
Dictionary <string, DecisionTreePartition <T> > sub_sub_partitions = SplitPartitionByClassVariable(sub_partition_by_feature, handle_cost);
double entropy = CalcEntropy(sub_partition_by_feature, sub_sub_partitions);
gain -= (sub_partition_by_feature.RecordCount * entropy / partition.RecordCount);
}
if (handle_cost != null)
{
gain = handle_cost(gain, feature_variable_name);
}
return(gain);
}
public static void Split <T>(DecisionTreePartition <T> partition, HashSet <string> feature_variable_names, DoAttributeCostGainHandle handle_cost)
where T : DDataRecord
{
if (feature_variable_names.Count == 0)
{
Dictionary <string, DecisionTreePartition <T> > sub_partitions_by_class_variable = SplitPartitionByClassVariable(partition, handle_cost);
partition.UpdateSubPartitions(sub_partitions_by_class_variable, DecisionTree <T> .ClassVariableName);
return;
}
double max_information_gain = 0;
string selected_feature_name = null;
HashSet <string> temp_feature_variable_names = null;
if (partition.Forest == null)
{
temp_feature_variable_names = feature_variable_names.Clone();
}
else // if in random forest, select a random subset of features to split
{
int subset_featire_variable_count = partition.Forest.FeatureSubsetSize;
List <string> temp_names = feature_variable_names.ToList();
temp_names.Shuffle();
temp_feature_variable_names = new HashSet <string>();
for (int i = 0; i < subset_featire_variable_count; ++i)
{
temp_feature_variable_names.Add(temp_names[i]);
}
}
foreach (string variable_name in temp_feature_variable_names)
{
double g = CalcInformationGain(partition, variable_name, handle_cost);
if (max_information_gain < g)
{
max_information_gain = g;
selected_feature_name = variable_name;
}
}
partition.SplitInformationGain = max_information_gain;
if (selected_feature_name == null) // this suggest the children of the nodes are leave nodes
{
Dictionary <string, DecisionTreePartition <T> > sub_partitions_by_class_variable = SplitPartitionByClassVariable(partition, handle_cost);
partition.UpdateSubPartitions(sub_partitions_by_class_variable, DecisionTree <T> .ClassVariableName);
}
else
{
if (partition.Forest == null) // if not in random forest, remove the used feature variable; otherwise, keep all and in subsequent split, select random subset
{
feature_variable_names.Remove(selected_feature_name);
}
Dictionary <string, DecisionTreePartition <T> > sub_partitions_by_feature_name = SplitPartitionByFeatureVariable(partition, selected_feature_name, handle_cost);
partition.UpdateSubPartitions(sub_partitions_by_feature_name, selected_feature_name);
if (feature_variable_names.Count > 0)
{
foreach (DecisionTreePartition <T> sub_partition_by_feature_name in sub_partitions_by_feature_name.Values)
{
Split(sub_partition_by_feature_name, feature_variable_names.Clone(), handle_cost);
}
}
}
}
public static Dictionary <string, DecisionTreePartition <T> > SplitPartitionByClassVariable <T>(DecisionTreePartition <T> partition, DoAttributeCostGainHandle handle_cost)
where T : DDataRecord
{
IEnumerable <T> data_store = partition.DataStore;
DecisionTreeNode <T> tree_node = partition.TreeNode;
IEnumerable <string> class_variable_value_iterator = tree_node.ClassVariableValueIterator;
Dictionary <string, DecisionTreePartition <T> > sub_partitions_by_class_variable = new Dictionary <string, DecisionTreePartition <T> >();
foreach (string variable_value in class_variable_value_iterator)
{
int record_count = tree_node.FindClassVariableValueCount(variable_value);
DecisionTreeNode <T> sub_node = CreateLeaveNode(DecisionTree <T> .ClassVariableName, variable_value, tree_node, record_count);
sub_node.HandleCost += (gain, fname) =>
{
if (handle_cost != null)
{
return(handle_cost(gain, fname));
}
return(gain);
};
sub_partitions_by_class_variable[variable_value] = DecisionTreePartition <T> .Create(sub_node);
}
return(sub_partitions_by_class_variable);
}
public static Dictionary <string, DecisionTreePartition <T> > SplitPartitionByFeatureVariable <T>(DecisionTreePartition <T> partition, string variable_name, DoAttributeCostGainHandle handle_cost)
where T : DDataRecord
{
IEnumerable <T> data_store = partition.DataStore;
DecisionTreeNode <T> tree_node = partition.TreeNode;
Dictionary <string, DecisionTreePartition <T> > sub_partitions = new Dictionary <string, DecisionTreePartition <T> >();
Dictionary <string, List <T> > sub_data_stores = new Dictionary <string, List <T> >();
foreach (T rec in data_store)
{
string variable_value = rec[variable_name];
List <T> sub_data_store = null;
if (sub_data_stores.ContainsKey(variable_value))
{
sub_data_store = sub_data_stores[variable_value];
}
else
{
sub_data_store = new List <T>();
sub_data_stores[variable_value] = sub_data_store;
}
sub_data_store.Add(rec);
}
foreach (string variable_value in sub_data_stores.Keys)
{
List <T> sub_data_store = sub_data_stores[variable_value];
DecisionTreeNode <T> sub_node = DecisionTreeMethods.CreateTreeNode <T>(variable_name, variable_value, tree_node);
sub_node.HandleCost += (gain, fname) =>
{
if (handle_cost != null)
{
return(handle_cost(gain, fname));
}
return(gain);
};
sub_partitions[variable_value] = DecisionTreePartition <T> .Create(sub_node, sub_data_store);
}
return(sub_partitions);
}
