private int GetClass(int[] instance, Id3Node tree)
{
if (tree.IsLeaf)
{
return(tree.Class);
}
int valueIndex = instance[tree.AttributeIndex];
if (!tree.Children.ContainsKey(valueIndex))
{
Id3Node maxChild = null;
int maxCount = int.MinValue;
foreach (Id3Node child in tree.Children.Values)
{
int count = child.ValueClassCounts.Values.SelectMany(kvp => kvp.Values).Sum();
if (count > maxCount)
{
maxCount = count;
maxChild = child;
}
}
return(GetClass(instance, maxChild));
}
return(GetClass(instance, tree.Children[valueIndex]));
}
public static Id3Node BuildTree(List <int[]> instances, int classAttributeIndex, double confidence, int maxDepth)
{
bool[] visitedAttributes = new bool[instances.First().Length];
Id3Node node = BuildTree(instances, classAttributeIndex, confidence, visitedAttributes, maxDepth);
return(node);
}
public Id3Classifier(List <int[]> instances, int classIndex, double confidence)
{
Instances = instances;
Confidence = confidence;
Tree = Id3Node.BuildTree(instances, classIndex, confidence);
}
/// <summary>
/// Builds a tree based on the instances received.
/// </summary>
/// <param name="instances">The list of instances to train on.</param>
/// <param name="classAttributeIndex">The index of the class attribute.</param>
/// <param name="confidence">The confidence threshold for split stop.</param>
/// <param name="visitedAttributes">Flag array to keep track of which attributes have already been visited so far.</param>
/// <returns>The trained tree for the given list of instances and confidence threshold</returns>
public static Id3Node BuildTree(List <int[]> instances, int classAttributeIndex, double confidence, bool[] visitedAttributes)
{
// If all instances are of the same class.
int classType = instances[0][classAttributeIndex];
if (instances.All(i => i[classAttributeIndex] == classType))
{
return(new Id3Node
{
Class = classType
});
}
// If all attributes except the class attribute has been visited already
bool allAttributesVisited = true;
for (int i = 0; i < visitedAttributes.Length; i++)
{
if (i == classAttributeIndex)
{
continue;
}
if (!visitedAttributes[i])
{
allAttributesVisited = false;
break;
}
}
// If all attributes have been visited, return the class with higher occurrance.
if (allAttributesVisited)
{
return(GetClassNodeForMax(instances, classAttributeIndex));
}
Id3Node bestNode = BestAttributeNode(instances, visitedAttributes, classAttributeIndex);
if (!IsValidChiSquared(bestNode, confidence))
{
return(GetClassNodeForMax(instances, classAttributeIndex));
}
bool[] localVisitedAttributes = new bool[visitedAttributes.Length];
visitedAttributes.CopyTo(localVisitedAttributes, 0);
localVisitedAttributes[bestNode.AttributeIndex] = true;
foreach (int attributeValue in bestNode.ValueClassCounts.Keys)
{
bestNode.Children[attributeValue] = BuildTree(instances.Where(i => i[bestNode.AttributeIndex] == attributeValue).ToList(), classAttributeIndex, confidence, localVisitedAttributes);
bestNode.Children[attributeValue].Parent = bestNode;
bestNode.Children[attributeValue].ParentValue = attributeValue;
}
return(bestNode);
}
private static int GetCount(Id3Node node)
{
if (node == null)
{
return(0);
}
if (node.IsLeaf)
{
return(1);
}
return(1 + node.Children.Values.Select(n => GetCount(n)).Sum());
}
private int GetClass(int[] instance, Id3Node tree)
{
if (tree.IsLeaf)
{
return(tree.Class);
}
int valueIndex = instance[tree.AttributeIndex];
Id3Node child = tree.Children.ContainsKey(valueIndex)
? tree.Children[valueIndex]
: tree.MaxChild;
return(GetClass(instance, child));
}
private static void PrintTreeAsRules(StringBuilder sb, ref StringBuilder sbMaxPositive, ref StringBuilder sbMaxNegative, ref int maxPositive, ref int maxNegative, Id3Node tree, ArffHeader header)
{
if (tree.IsLeaf)
{
Id3Node leaf = tree;
StringBuilder localSB = new StringBuilder();
localSB.AppendLine("Rule is:");
localSB.AppendLine();
int count = tree.Parent.ValueClassCounts[tree.ParentValue].Values.Sum();
while (tree.Parent != null)
{
string value = tree.ParentValue == -1 ? "?" : ((ArffNominalAttribute)header.Attributes.ElementAt(tree.Parent.AttributeIndex).Type).Values[tree.ParentValue];
localSB.Append($"<{header.Attributes.ElementAt(tree.Parent.AttributeIndex).Name}> equals to <{value}>");
localSB.Append(" and ");
tree = tree.Parent;
}
localSB.AppendLine();
localSB.AppendLine("---------------------------------------");
if (leaf.Class == 0)
{
sb.Append(localSB.ToString());
if (count > maxPositive)
{
maxPositive = count;
sbMaxPositive = localSB;
sbMaxPositive.AppendLine($"COUNT: {count}");
}
}
else
{
if (count > maxNegative)
{
maxNegative = count;
sbMaxNegative = localSB;
sbMaxNegative.AppendLine($"COUNT: {count}");
}
}
}
else
{
foreach (KeyValuePair <int, Id3Node> kvp in tree.Children)
{
PrintTreeAsRules(sb, ref sbMaxPositive, ref sbMaxNegative, ref maxPositive, ref maxNegative, kvp.Value, header);
}
}
}
private Id3Node()
{
_maxChild = new Lazy <Id3Node>(() =>
{
Id3Node maxChild = null;
int maxCount = int.MinValue;
foreach (Id3Node child in Children.Values)
{
int count = child.ValueClassCounts.Values.SelectMany(kvp => kvp.Values).Sum();
if (count > maxCount)
{
maxCount = count;
maxChild = child;
}
}
return(maxChild);
});
_count = new Lazy <int>(() =>
{
return(GetCount(this));
});
}
private static Id3Node BestAttributeNode(IEnumerable <int[]> instances, bool[] visitedAttributes, int classAttributeIndex)
{
// Each node will have the information on how many times a given value is present for a given class.
Dictionary <int, Id3Node> attributeNodes = GetAttributeNodes(instances, visitedAttributes, classAttributeIndex);
// Calculate the conditional entropy and pick the min, as this will maximize mutual information.
int minEntropyAttributeIndex = -1;
double minEntropy = double.MaxValue;
foreach (KeyValuePair <int, Id3Node> kvp in attributeNodes)
{
int attributeIndex = kvp.Key;
Id3Node currentNode = kvp.Value;
double entropy = GetEntropy(currentNode.ValueClassCounts);
if (entropy < minEntropy)
{
minEntropy = entropy;
minEntropyAttributeIndex = attributeIndex;
}
}
return(attributeNodes[minEntropyAttributeIndex]);
}
public Id3Classifier(List <int[]> instances, int classIndex, double confidence, int maxDepth)
{
Confidence = confidence;
Tree = Id3Node.BuildTree(instances, classIndex, confidence, maxDepth);
}
private static bool IsValidChiSquared(Id3Node node, double confidence)
{
// Get total counts for all classes
double total = 0;
Dictionary <int, double> allClassCounts = new Dictionary <int, double>();
foreach (KeyValuePair <int, Dictionary <int, int> > kvp in node.ValueClassCounts)
{
Dictionary <int, int> classCounts = kvp.Value;
foreach (KeyValuePair <int, int> kvpClassCount in classCounts)
{
int classIndex = kvpClassCount.Key;
int classCount = kvpClassCount.Value;
total += classCount;
if (!allClassCounts.ContainsKey(classIndex))
{
allClassCounts[classIndex] = 0;
}
allClassCounts[classIndex] += classCount;
}
}
// Get expected counts for all possible values of the attribute
Dictionary <int, Dictionary <int, double> > expectedCountPerValuePerClass = new Dictionary <int, Dictionary <int, double> >();
foreach (KeyValuePair <int, Dictionary <int, int> > kvpValueClassCounts in node.ValueClassCounts)
{
int valueIndex = kvpValueClassCounts.Key;
Dictionary <int, int> classCounts = kvpValueClassCounts.Value;
if (!expectedCountPerValuePerClass.ContainsKey(valueIndex))
{
expectedCountPerValuePerClass[valueIndex] = new Dictionary <int, double>();
}
double totalCountForValue = classCounts.Select(kvp => kvp.Value).Sum();
foreach (KeyValuePair <int, int> kvpClassCount in classCounts)
{
int classIndex = kvpClassCount.Key;
int classCount = kvpClassCount.Value;
expectedCountPerValuePerClass[valueIndex][classIndex] = allClassCounts[classIndex] * (totalCountForValue / total);
}
}
double distribution = 0;
foreach (KeyValuePair <int, Dictionary <int, int> > kvpValueClassCounts in node.ValueClassCounts)
{
int valueIndex = kvpValueClassCounts.Key;
Dictionary <int, int> classCounts = kvpValueClassCounts.Value;
foreach (KeyValuePair <int, int> kvpClassCount in classCounts)
{
int classIndex = kvpClassCount.Key;
int classCount = kvpClassCount.Value;
double expectedCount = expectedCountPerValuePerClass[valueIndex][classIndex];
distribution += Math.Pow((classCount - expectedCount), 2) / expectedCount;
}
}
int possibleValues = node.ValueClassCounts.Keys.Count;
if (possibleValues <= 0)
{
possibleValues = 1;
}
ChiSquared chiSquared = new ChiSquared(possibleValues);
return(confidence <= chiSquared.CumulativeDistribution(distribution));
}
static void Main(string[] args)
{
// Training
ArffHeader header = null;
List <object[]> instances = new List <object[]>();
using (ArffReader arffReader = new ArffReader(_arffFile))
{
header = arffReader.ReadHeader();
object[] instance;
while ((instance = arffReader.ReadInstance()) != null)
{
instances.Add(instance);
}
}
List <int[]> trainingData = new List <int[]>(instances.Select(objectArray => objectArray.Select(o => o == null ? -1 : (int)o).ToArray()));
// Test
instances = new List <object[]>();
using (ArffReader arffReader = new ArffReader(_testArffFile))
{
header = arffReader.ReadHeader();
object[] instance;
while ((instance = arffReader.ReadInstance()) != null)
{
instances.Add(instance);
}
}
List <int[]> testData = new List <int[]>(instances.Select(objectArray => objectArray.Select(o => o == null ? -1 : (int)o).ToArray()));
double[] confidences = new double[]
{
0.0,
0.1,
0.2,
0.4,
0.6,
0.8,
0.9,
0.95,
0.99,
0.9999
};
PrintAsCsv(header, trainingData, @"c:\users\andresz\desktop\data.csv");
Parallel.ForEach(confidences, confidence =>
{
Id3Node tree = Id3Node.BuildTree(trainingData, trainingData[0].Length - 1, confidence);
Console.WriteLine($"Confidence {confidence}: Num of nodes {GetCount(tree)}");
// Test accuracy on training
Console.WriteLine($"Confidence {confidence}: Accuracy on train = { trainingData.Where(instance => GetClass(instance, tree) == instance[trainingData[0].Length - 1]).Count() / (double)trainingData.Count}");
// Test accuracy on test
Console.WriteLine($"Confidence {confidence}: Accuracy on test = { testData.Where(instance => GetClass(instance, tree) == instance[testData[0].Length - 1]).Count() / (double)testData.Count}");
StringBuilder sb = new StringBuilder();
StringBuilder sbMaxPositive = new StringBuilder();
StringBuilder sbMaxNegative = new StringBuilder();
int maxPositive = int.MinValue;
int maxNegative = int.MinValue;
// Only print small trees.
if (confidence > 0.5)
{
PrintTreeAsRules(sb, ref sbMaxPositive, ref sbMaxNegative, ref maxPositive, ref maxNegative, tree, header);
sb.AppendLine("The most max positive rule is:");
sb.AppendLine(sbMaxPositive.ToString());
sb.AppendLine();
sb.AppendLine("The most max negative rule is:");
sb.AppendLine(sbMaxNegative.ToString());
Directory.CreateDirectory(_outputFolder);
File.WriteAllText(Path.Combine(_outputFolder, $"Tree{confidence}.txt"), sb.ToString());
}
});
}
