public void CART_maketree_check_all_validation_data_set()
{
initData_Jason();
BuildCART cart =
new BuildCART();
cart.SetMissingValue(999);
cart.SetParameters(1);
ModelCART mb =
(ModelCART)cart.BuildModel(_trainingData,
_attributeHeaders,
_indexTargetAttribute);
int count = 0;
for (int row = 0; row < _validationData[0].Length; row++)
{
double[] data = new double[] { _validationData[0][row], _validationData[1][row] };
double value = mb.RunModelForSingleData(data);
if (value == _validationData[2][row])
{
count++;
}
}
Assert.AreEqual(count, 9);
}
public override Common.MLCore.ModelBase BuildModel(
double[][] trainingData,
string[] attributeHeaders,
int indexTargetAttribute)
{
//Verify data and set variables
VerifyData(trainingData, attributeHeaders, indexTargetAttribute);
ModelBaggedDecisionTree model =
new ModelBaggedDecisionTree(_missingValue,
_indexTargetAttribute,
_trainingData.Length - 1,
_numberOfTrees);
//By default samples/tree is same as original samples
if (_numberOfSamplesPerTree == long.MaxValue)
{
_numberOfSamplesPerTree = _trainingData[0].Length;
}
//Split the data for each tree
//Parallelize this
Parallel.For(0, _numberOfTrees, new ParallelOptions {
MaxDegreeOfParallelism = _maxParallelThreads
}, ii =>
//for (int ii=0;ii<_numberOfTrees;ii++)
{
Random rnd = new Random();
ConcurrentBag <long> trainingDataRowIndices =
new ConcurrentBag <long>();
//Initialize the rows
for (long idx = 0;
idx < _numberOfSamplesPerTree; idx++)
{
//Random Sampling with Replacement
long rowIdx = rnd.Next(0, _trainingData[0].Length - 1);
//rowIdx = idx + startIdx;
//rowIdx = rowIdx > _noOfDataSamples - 1 ?
// rowIdx - _noOfDataSamples : rowIdx;
trainingDataRowIndices.Add(rowIdx);
}
//For test only
BuildCART buildCart = new BuildCART();
ModelCART modelCart = (ModelCART)buildCart.BuildModel(trainingData,
attributeHeaders,
indexTargetAttribute,
trainingDataRowIndices);
model.AddTree(ii, modelCart);
}); //Number of trees
return(model);
}
public void CART_maketree_check_root_node_value()
{
initData_Jason();
BuildCART cart =
new BuildCART();
cart.SetMissingValue(999);
cart.SetParameters(1);
ModelCART mb =
(ModelCART)cart.BuildModel(_trainingData,
_attributeHeaders,
_indexTargetAttribute);
Assert.AreEqual(mb.Root.AttributeIndex, 0);
Assert.AreEqual(mb.Root.Value, 6.642287351);
}
public void CART_maketree_special_no_splitting_possible_true()
{
initData_special_no_splitting_possible();
BuildCART cart =
new BuildCART();
ModelCART model =
(ModelCART)cart.BuildModel(_trainingData,
_attributeHeaders,
_indexTargetAttribute);
int row = 1;
double[] data = GetSingleTrainingRowDataForTest(row);
double value = model.RunModelForSingleData(data);
Assert.AreEqual(value,
_trainingData[_indexTargetAttribute][row]);
}
public void CART_maketree_check_root_node_structure()
{
initData_Jason();
BuildCART cart =
new BuildCART();
cart.SetMissingValue(999);
cart.SetParameters(1);
ModelBase mb =
(ModelBase)cart.BuildModel(_trainingData,
_attributeHeaders,
_indexTargetAttribute);
Assert.AreEqual(mb.Root.Children.Count, 2);
Assert.AreEqual(mb.Root.Children[0].Children, null);
Assert.AreEqual(mb.Root.Children[1].Children, null);
}
public void CART_gini_index_test()
{
initData_Jason();
BuildCART cart =
new BuildCART();
setPrivateVariablesInBuildObject(cart);
PrivateObject obj = new PrivateObject(cart);
double gini = (double)obj.Invoke("GetGiniImpurity",
new object[] {
0,
2.771244718
});
Assert.IsTrue(
SupportFunctions.DoubleCompare(gini, 0.49382716));
}
public void CART_maketree_validate_single_training_data()
{
initData_Jason();
BuildCART cart =
new BuildCART();
cart.SetMissingValue(999);
cart.SetParameters(1);
ModelCART mb =
(ModelCART)cart.BuildModel(_trainingData,
_attributeHeaders,
_indexTargetAttribute);
double[] data = new double[] { _trainingData[0][0], _trainingData[1][0] };
double value = mb.RunModelForSingleData(data);
Assert.AreEqual(value, _trainingData[2][0]);
}
public override Common.MLCore.ModelBase BuildModel(
double[][] trainingData,
string[] attributeHeaders,
int indexTargetAttribute)
{
//Verify data and set variables
VerifyData(trainingData, attributeHeaders, indexTargetAttribute);
ModelRandomForest model =
new ModelRandomForest(_missingValue,
_indexTargetAttribute,
_trainingData.Length - 1,
_numberOfTrees);
//By default samples/tree is same as original samples
if (_numberOfFeaturesPerTree == int.MaxValue)
{
_numberOfFeaturesPerTree = (int)System.Math.Ceiling(
System.Math.Sqrt((double)_trainingData.Length));
}
//Create for each
//Parallelize this
Parallel.For(0, _numberOfTrees,
new ParallelOptions {
MaxDegreeOfParallelism = _maxParallelThreads
}, ii =>
//for (int ii = 0; ii < _numberOfTrees; ii++)
{
//For test only
BuildCART buildCart = new BuildCART();
buildCart.SetParametersForRandomForest(_numberOfFeaturesPerTree);
ModelCART modelCart = (ModelCART)buildCart.BuildModel(trainingData,
attributeHeaders,
indexTargetAttribute);
model.AddTree(ii, modelCart);
});//Number of trees
return(model);
}