public void Test_GetColumnVector_ByIndex_ObjectVarsion()
{
// Given
var expectedVector = new DataVector <object>(new object[] { "a1.1", "a2.1", "a3.1" }, "C1");
// When
var actualVector = _subject.GetColumnVector <object>(0);
// Then
Assert.AreEqual(expectedVector, actualVector);
}
protected virtual IDecisionTreeNode BuildDecisionNode(
IDataFrame dataFrame,
string dependentFeatureName,
IDecisionTreeModelBuilderParams additionalParams,
IAlredyUsedAttributesInfo alreadyUsedAttributesInfo,
int treeDepth,
bool isFirstSplit = false)
{
if (dataFrame.GetColumnVector <object>(dependentFeatureName).DataItems.Distinct().Count() == 1 || MaximalTreeDepthHasBeenReached(additionalParams, treeDepth))
{
return(BuildLeaf(dataFrame, dependentFeatureName));
}
// TODO: later on add additional params indicating which features were already used
ISplittingResult splitResult = BestSplitSelector.SelectBestSplit(
dataFrame,
dependentFeatureName,
SplitQualityChecker,
alreadyUsedAttributesInfo);
if (SplitIsEmpty(splitResult))
{
return(BuildLeaf(dataFrame, dependentFeatureName));
}
if (additionalParams.UsePrunningHeuristicDuringTreeBuild && this.StatisticalSignificanceChecker != null)
{
var isSplitSignificant = StatisticalSignificanceChecker.IsSplitStatisticallySignificant(
dataFrame,
splitResult,
dependentFeatureName);
if (!isSplitSignificant)
{
return(BuildLeaf(dataFrame, dependentFeatureName));
}
}
var children = new ConcurrentDictionary <IDecisionTreeLink, IDecisionTreeNode>();
if (isFirstSplit)
{
Parallel.ForEach(
splitResult.SplittedDataSets,
splitData =>
{
this.AddChildFromSplit(dependentFeatureName, additionalParams, splitData, children, alreadyUsedAttributesInfo, treeDepth + 1);
});
}
else
{
foreach (var splitData in splitResult.SplittedDataSets)
{
this.AddChildFromSplit(dependentFeatureName, additionalParams, splitData, children, alreadyUsedAttributesInfo, treeDepth + 1);
}
}
return(BuildConcreteDecisionTreeNode(splitResult, children));
}
protected virtual Tuple <Matrix <double>, IList <TPredictionResult>, IList <string> > PrepareTrainingData(
IDataFrame dataFrame,
string dependentFeatureName)
{
var dataColumns = dataFrame.ColumnNames.Where(col => col != dependentFeatureName).ToList();
var trainingData = dataFrame.GetSubsetByColumns(dataColumns).GetAsMatrix();
IDataVector <TPredictionResult> expectedOutcomes = dataFrame.GetColumnVector <TPredictionResult>(dependentFeatureName);
return(new Tuple <Matrix <double>, IList <TPredictionResult>, IList <string> >(trainingData, expectedOutcomes, dataColumns));
}
public bool IsSplitStatisticallySignificant(
IDataFrame initialDataFrame,
ISplittingResult splittingResults,
string dependentFeatureName)
{
var uniqueDependentValuesCounts = initialDataFrame
.GetColumnVector(dependentFeatureName)
.Values
.GroupBy(elem => elem)
.ToDictionary(grp => grp.Key, grp => grp.Count() / (double)initialDataFrame.RowCount);
var chisquareStatisticSum = 0.0;
if (splittingResults.IsSplitNumeric)
{
return(true);
}
var degreesOfFreedom = (uniqueDependentValuesCounts.Keys.Count - 1)
+ (splittingResults.SplittedDataSets.Count - 1);
foreach (var splittingResult in splittingResults.SplittedDataSets)
{
var splitSize = splittingResult.SplittedDataFrame.RowCount;
var actualDependentFeatureValues =
splittingResult.SplittedDataFrame.GetColumnVector(dependentFeatureName)
.Values.GroupBy(elem => elem)
.ToDictionary(grp => grp.Key, grp => grp.Count());
foreach (var uniqueDependentValueCount in uniqueDependentValuesCounts)
{
var expectedCount = uniqueDependentValueCount.Value * splitSize;
var actualCount = 0;
if (actualDependentFeatureValues.ContainsKey(uniqueDependentValueCount.Key))
{
actualCount = actualDependentFeatureValues[uniqueDependentValueCount.Key];
}
var actualChisquareValue = Math.Pow(actualCount - expectedCount, 2) / expectedCount;
chisquareStatisticSum += actualChisquareValue;
}
}
if (ChiSquared.IsValidParameterSet(degreesOfFreedom))
{
var statisticValue = 1 - ChiSquared.CDF(degreesOfFreedom, chisquareStatisticSum);
if (statisticValue < significanceLevel)
{
return(true);
}
}
return(false);
}
public IDecisionTreeLeaf BuildLeaf(IDataFrame finalData, string dependentFeatureName)
{
var counts = new Dictionary<object, int>();
var finalValues = finalData.GetColumnVector(dependentFeatureName);
foreach (var val in finalValues)
{
if (!counts.ContainsKey(val))
{
counts.Add(val, 0);
}
counts[val] += 1;
}
return new DecisionTreeLeaf(dependentFeatureName, counts.OrderBy(kvp => kvp.Value).Reverse().First().Key);
}
public bool IsSplitStatisticallySignificant(
IDataFrame initialDataFrame,
ISplittingResult splittingResults,
string dependentFeatureName)
{
var uniqueDependentValuesCounts = initialDataFrame
.GetColumnVector(dependentFeatureName)
.Values
.GroupBy(elem => elem)
.ToDictionary(grp => grp.Key, grp => grp.Count() / (double)initialDataFrame.RowCount);
var chisquareStatisticSum = 0.0;
if (splittingResults.IsSplitNumeric)
{
return true;
}
var degreesOfFreedom = (uniqueDependentValuesCounts.Keys.Count - 1)
+ (splittingResults.SplittedDataSets.Count - 1);
foreach (var splittingResult in splittingResults.SplittedDataSets)
{
var splitSize = splittingResult.SplittedDataFrame.RowCount;
var actualDependentFeatureValues =
splittingResult.SplittedDataFrame.GetColumnVector(dependentFeatureName)
.Values.GroupBy(elem => elem)
.ToDictionary(grp => grp.Key, grp => grp.Count());
foreach (var uniqueDependentValueCount in uniqueDependentValuesCounts)
{
var expectedCount = uniqueDependentValueCount.Value * splitSize;
var actualCount = 0;
if (actualDependentFeatureValues.ContainsKey(uniqueDependentValueCount.Key))
{
actualCount = actualDependentFeatureValues[uniqueDependentValueCount.Key];
}
var actualChisquareValue = Math.Pow(actualCount - expectedCount, 2) / expectedCount;
chisquareStatisticSum += actualChisquareValue;
}
}
if (ChiSquared.IsValidParameterSet(degreesOfFreedom))
{
var statisticValue = 1 - ChiSquared.CDF(degreesOfFreedom, chisquareStatisticSum);
if (statisticValue < significanceLevel)
{
return true;
}
}
return false;
}
public IDecisionTreeLeaf BuildLeaf(IDataFrame finalData, string dependentFeatureName)
{
var counts = new Dictionary <object, int>();
var finalValues = finalData.GetColumnVector(dependentFeatureName);
foreach (var val in finalValues)
{
if (!counts.ContainsKey(val))
{
counts.Add(val, 0);
}
counts[val] += 1;
}
return(new DecisionTreeLeaf(dependentFeatureName, counts.OrderBy(kvp => kvp.Value).Reverse().First().Key));
}
protected override Tuple <IList <ISplittedData>, ISplittingParams, double> EvaluateCategoricalSplit(
IDataFrame dataToSplit,
string dependentFeatureName,
string splittingFeatureName,
double bestSplitQualitySoFar,
double initialEntropy,
ISplitQualityChecker splitQualityChecker,
IAlredyUsedAttributesInfo alredyUsedAttributesInfo)
{
var totalRowsCount = dataToSplit.RowCount;
var uniqueFeatureValues = dataToSplit.GetColumnVector(splittingFeatureName).Distinct();
double locallyBestSplitQuality = double.NegativeInfinity;
IBinarySplittingParams localBestSplitParams = null;
IList <ISplittedData> locallyBestSplitData = null;
foreach (var featureValue in uniqueFeatureValues)
{
if (!alredyUsedAttributesInfo.WasAttributeAlreadyUsedWithValue(splittingFeatureName, featureValue))
{
var binarySplitParams = new BinarySplittingParams(splittingFeatureName, featureValue, dependentFeatureName);
var splittedData = CategoricalDataSplitter.SplitData(dataToSplit, binarySplitParams);
if (splittedData.Count == 1)
{
return(new Tuple <IList <ISplittedData>, ISplittingParams, double>(
new List <ISplittedData>(),
binarySplitParams,
double.NegativeInfinity));
}
var splitQuality = splitQualityChecker.CalculateSplitQuality(
initialEntropy,
totalRowsCount,
splittedData,
dependentFeatureName);
if (splitQuality > locallyBestSplitQuality)
{
locallyBestSplitQuality = splitQuality;
locallyBestSplitData = splittedData;
localBestSplitParams = binarySplitParams;
}
}
}
return(new Tuple <IList <ISplittedData>, ISplittingParams, double>(
locallyBestSplitData,
localBestSplitParams,
locallyBestSplitQuality));
}
protected override Tuple<IList<ISplittedData>, ISplittingParams, double> EvaluateCategoricalSplit(
IDataFrame dataToSplit,
string dependentFeatureName,
string splittingFeatureName,
double bestSplitQualitySoFar,
double initialEntropy,
ISplitQualityChecker splitQualityChecker,
IAlredyUsedAttributesInfo alredyUsedAttributesInfo)
{
var totalRowsCount = dataToSplit.RowCount;
var uniqueFeatureValues = dataToSplit.GetColumnVector(splittingFeatureName).Distinct();
double locallyBestSplitQuality = double.NegativeInfinity;
IBinarySplittingParams localBestSplitParams = null;
IList<ISplittedData> locallyBestSplitData = null;
foreach (var featureValue in uniqueFeatureValues)
{
if (!alredyUsedAttributesInfo.WasAttributeAlreadyUsedWithValue(splittingFeatureName, featureValue))
{
var binarySplitParams = new BinarySplittingParams(splittingFeatureName, featureValue, dependentFeatureName);
var splittedData = CategoricalDataSplitter.SplitData(dataToSplit, binarySplitParams);
if (splittedData.Count == 1)
{
return new Tuple<IList<ISplittedData>, ISplittingParams, double>(
new List<ISplittedData>(),
binarySplitParams,
double.NegativeInfinity);
}
var splitQuality = splitQualityChecker.CalculateSplitQuality(
initialEntropy,
totalRowsCount,
splittedData,
dependentFeatureName);
if (splitQuality > locallyBestSplitQuality)
{
locallyBestSplitQuality = splitQuality;
locallyBestSplitData = splittedData;
localBestSplitParams = binarySplitParams;
}
}
}
return new Tuple<IList<ISplittedData>, ISplittingParams, double>(
locallyBestSplitData,
localBestSplitParams,
locallyBestSplitQuality);
}
public IList<ISplittedData> SplitData(IDataFrame dataToSplit, ISplittingParams splttingParams)
{
var splitFeature = splttingParams.SplitOnFeature;
var totalRowsCount = dataToSplit.RowCount;
var uniqueValues = dataToSplit.GetColumnVector(splitFeature).Distinct();
var splittedData = new List<ISplittedData>();
//TODO: AAA emarassingly parallel - test it for performance
foreach (var uniqueValue in uniqueValues)
{
var query = BuildQuery(splitFeature, uniqueValue);
var splitResult = dataToSplit.GetSubsetByQuery(query);
var subsetCount = splitResult.RowCount;
var link = new DecisionLink(
CalcInstancesPercentage(totalRowsCount, subsetCount),
subsetCount,
uniqueValue);
splittedData.Add(new SplittedData(link, splitResult));
}
return splittedData;
}
public IList <ISplittedData> SplitData(IDataFrame dataToSplit, ISplittingParams splttingParams)
{
var splitFeature = splttingParams.SplitOnFeature;
var totalRowsCount = dataToSplit.RowCount;
var uniqueValues = dataToSplit.GetColumnVector(splitFeature).Distinct();
var splittedData = new List <ISplittedData>();
//TODO: AAA emarassingly parallel - test it for performance
foreach (var uniqueValue in uniqueValues)
{
var query = BuildQuery(splitFeature, uniqueValue);
var splitResult = dataToSplit.GetSubsetByQuery(query);
var subsetCount = splitResult.RowCount;
var link = new DecisionLink(
CalcInstancesPercentage(totalRowsCount, subsetCount),
subsetCount,
uniqueValue);
splittedData.Add(new SplittedData(link, splitResult));
}
return(splittedData);
}
public double GetInitialEntropy(IDataFrame baseData, string dependentFeatureName) => this.ImpuryMeasure.ImpurityValue(baseData.GetColumnVector <TDecisionType>(dependentFeatureName));
public Tuple <ISplittingResult, double> FindBestSplitPoint(
IDataFrame baseData,
string dependentFeatureName,
string numericFeatureToProcess,
ICategoricalSplitQualityChecker splitQualityChecker,
IBinaryNumericDataSplitter binaryNumericDataSplitter,
double initialEntropy)
{
var uniqueDependentValues = baseData.GetColumnVector(dependentFeatureName).Values.Distinct().ToList();
var dependentValuesSortedByNumericFeature = OrderColumn(baseData, dependentFeatureName, numericFeatureToProcess);
var dependentValsCounts = new List <Vector <double> >();
var breakPoints = new List <int>();
var lastKnowDependentValue = dependentValuesSortedByNumericFeature.First().DependentVal;
for (int elemIdx = 0; elemIdx < dependentValuesSortedByNumericFeature.Count; elemIdx++)
{
var currentElem = dependentValuesSortedByNumericFeature[elemIdx];
var currentDependentValue = currentElem.DependentVal;
var indexOfCurrentDependentValue = uniqueDependentValues.IndexOf(currentDependentValue);
var dependentValsCountAllocation = DenseVector.OfArray(Enumerable.Repeat(0.0, uniqueDependentValues.Count).ToArray());
if (elemIdx != 0)
{
dependentValsCounts[elemIdx - 1].CopyTo(dependentValsCountAllocation);
}
dependentValsCountAllocation[indexOfCurrentDependentValue]++;
if (!currentDependentValue.Equals(lastKnowDependentValue))
{
lastKnowDependentValue = currentDependentValue;
breakPoints.Add(elemIdx - 1);
}
dependentValsCounts.Add(dependentValsCountAllocation);
}
var bestSplitQualitySoFar = double.NegativeInfinity;
int bestBreakpointSoFar = -1;
foreach (var breakpointIdx in breakPoints)
{
var dependentValsCountUpToBreakpoint = dependentValsCounts[breakpointIdx];
var dependentValsCountAboveBreakpoint =
dependentValsCounts.Last().Subtract(dependentValsCountUpToBreakpoint);
var splitEntropy = splitQualityChecker.CalculateSplitQuality(
initialEntropy,
baseData.RowCount,
new List <IList <int> >
{
VectorToIntArray(dependentValsCountUpToBreakpoint), VectorToIntArray(dependentValsCountAboveBreakpoint)
});
if (splitEntropy > bestSplitQualitySoFar)
{
bestSplitQualitySoFar = splitEntropy;
bestBreakpointSoFar = breakpointIdx;
}
}
var splitVal = CalculateSplitPoint(
dependentValuesSortedByNumericFeature[bestBreakpointSoFar + 1].FeatureVal,
dependentValuesSortedByNumericFeature[bestBreakpointSoFar].FeatureVal);
var split = binaryNumericDataSplitter.SplitData(
baseData,
new BinarySplittingParams(numericFeatureToProcess, splitVal, dependentFeatureName));
var splitResult = new BinarySplittingResult(true, numericFeatureToProcess, split, splitVal);
return(new Tuple <ISplittingResult, double>(splitResult, bestSplitQualitySoFar));
}
protected static bool ShouldStopRecusrsiveBuilding(IDataFrame dataFrame, string dependentFeatureName)
{
return !dataFrame.GetColumnType(dependentFeatureName).IsNumericType() && dataFrame.GetColumnVector<object>(dependentFeatureName).DataItems.Distinct().Count() == 1;
}
protected virtual IDecisionTreeNode BuildDecisionNode(
IDataFrame dataFrame,
string dependentFeatureName,
IDecisionTreeModelBuilderParams additionalParams,
IAlredyUsedAttributesInfo alreadyUsedAttributesInfo,
int treeDepth,
bool isFirstSplit = false)
{
if (dataFrame.GetColumnVector<object>(dependentFeatureName).DataItems.Distinct().Count() == 1 || MaximalTreeDepthHasBeenReached(additionalParams, treeDepth))
{
return BuildLeaf(dataFrame, dependentFeatureName);
}
// TODO: later on add additional params indicating which features were already used
ISplittingResult splitResult = BestSplitSelector.SelectBestSplit(
dataFrame,
dependentFeatureName,
SplitQualityChecker,
alreadyUsedAttributesInfo);
if (SplitIsEmpty(splitResult))
{
return BuildLeaf(dataFrame, dependentFeatureName);
}
if (additionalParams.UsePrunningHeuristicDuringTreeBuild && this.StatisticalSignificanceChecker != null)
{
var isSplitSignificant = StatisticalSignificanceChecker.IsSplitStatisticallySignificant(
dataFrame,
splitResult,
dependentFeatureName);
if (!isSplitSignificant)
{
return BuildLeaf(dataFrame, dependentFeatureName);
}
}
var children = new ConcurrentDictionary<IDecisionTreeLink, IDecisionTreeNode>();
if (isFirstSplit)
{
Parallel.ForEach(
splitResult.SplittedDataSets,
splitData =>
{
this.AddChildFromSplit(dependentFeatureName, additionalParams, splitData, children, alreadyUsedAttributesInfo, treeDepth + 1);
});
}
else
{
foreach (var splitData in splitResult.SplittedDataSets)
{
this.AddChildFromSplit(dependentFeatureName, additionalParams, splitData, children, alreadyUsedAttributesInfo, treeDepth + 1);
}
}
return BuildConcreteDecisionTreeNode(splitResult, children);
}
protected static bool ShouldStopRecusrsiveBuilding(IDataFrame dataFrame, string dependentFeatureName)
{
return(!dataFrame.GetColumnType(dependentFeatureName).IsNumericType() && dataFrame.GetColumnVector <object>(dependentFeatureName).DataItems.Distinct().Count() == 1);
}
public Tuple<ISplittingResult, double> FindBestSplitPoint(
IDataFrame baseData,
string dependentFeatureName,
string numericFeatureToProcess,
ICategoricalSplitQualityChecker splitQualityChecker,
IBinaryNumericDataSplitter binaryNumericDataSplitter,
double initialEntropy)
{
var uniqueDependentValues = baseData.GetColumnVector(dependentFeatureName).Values.Distinct().ToList();
var dependentValuesSortedByNumericFeature = OrderColumn(baseData, dependentFeatureName, numericFeatureToProcess);
var dependentValsCounts = new List<Vector<double>>();
var breakPoints = new List<int>();
var lastKnowDependentValue = dependentValuesSortedByNumericFeature.First().DependentVal;
for (int elemIdx = 0; elemIdx < dependentValuesSortedByNumericFeature.Count; elemIdx++)
{
var currentElem = dependentValuesSortedByNumericFeature[elemIdx];
var currentDependentValue = currentElem.DependentVal;
var indexOfCurrentDependentValue = uniqueDependentValues.IndexOf(currentDependentValue);
var dependentValsCountAllocation = DenseVector.OfArray(Enumerable.Repeat(0.0, uniqueDependentValues.Count).ToArray());
if (elemIdx != 0)
{
dependentValsCounts[elemIdx - 1].CopyTo(dependentValsCountAllocation);
}
dependentValsCountAllocation[indexOfCurrentDependentValue]++;
if (!currentDependentValue.Equals(lastKnowDependentValue))
{
lastKnowDependentValue = currentDependentValue;
breakPoints.Add(elemIdx - 1);
}
dependentValsCounts.Add(dependentValsCountAllocation);
}
var bestSplitQualitySoFar = double.NegativeInfinity;
int bestBreakpointSoFar = -1;
foreach (var breakpointIdx in breakPoints)
{
var dependentValsCountUpToBreakpoint = dependentValsCounts[breakpointIdx];
var dependentValsCountAboveBreakpoint =
dependentValsCounts.Last().Subtract(dependentValsCountUpToBreakpoint);
var splitEntropy = splitQualityChecker.CalculateSplitQuality(
initialEntropy,
baseData.RowCount,
new List<IList<int>>
{
VectorToIntArray(dependentValsCountUpToBreakpoint), VectorToIntArray(dependentValsCountAboveBreakpoint)
});
if (splitEntropy > bestSplitQualitySoFar)
{
bestSplitQualitySoFar = splitEntropy;
bestBreakpointSoFar = breakpointIdx;
}
}
var splitVal = CalculateSplitPoint(
dependentValuesSortedByNumericFeature[bestBreakpointSoFar + 1].FeatureVal,
dependentValuesSortedByNumericFeature[bestBreakpointSoFar].FeatureVal);
var split = binaryNumericDataSplitter.SplitData(
baseData,
new BinarySplittingParams(numericFeatureToProcess, splitVal, dependentFeatureName));
var splitResult = new BinarySplittingResult(true, numericFeatureToProcess, split, splitVal);
return new Tuple<ISplittingResult, double>(splitResult, bestSplitQualitySoFar);
}
