static void Main(string[] args)
{
if (args.Length < 2) // NOTE: Check argument length
{
PrintHelp();
return;
}
Parser parser = new Parser();
DecisionTree dt = new DecisionTree();
string trainingFileName = args[0];
string testingFileName = args[1];
ID3Data id3Data = parser.ParseID3InformationFile(trainingFileName);
Node learnedTree = dt.ID3(id3Data.TestData, id3Data.GetKeyAttributes(), id3Data);
// Part One
Console.WriteLine("Learned Tree:\n-----------");
Console.WriteLine(learnedTree.ToString());
// Part Two
ID3Data testingData = parser.ParseID3InformationFile(testingFileName);
ArrayList treeClassification = learnedTree.GetTreeClassifications(testingData);
Console.WriteLine("Classifications:\n-------------");
Console.WriteLine(testingData.CompareClassifications(treeClassification));
Console.WriteLine("\n\nPress any key to exit...");
Console.ReadKey();
}
/// <summary>
/// Translate the file data to ID3 data.
/// </summary>
/// <param name="filename">Filename to read from</param>
/// <returns>ID3Data object</returns>
public ID3Data ParseID3InformationFile(string filename)
{
/**
* !File Format!
* !Do not read lines starting with '//'!
* \SOF
* class labels (could be any number; assume 2 for testing purposes)
* # of features (nF) (how many to read in)
* feature1 (list starts)
* ... -> nF (list ends)
* # of examles (nE) (how many to read in)
* example1 (list starts)
* ... -> nE (list ends)
* \EOF
*/
string rawFileContents = GetFileAsString(filename);
ArrayList cleanFileContents = RemoveCommentsAndEmptyLines(rawFileContents);
int i = 0;
ID3Data id3Data = new ID3Data();
id3Data.Categories = new ArrayList()
{
cleanFileContents[i++],
cleanFileContents[i++] // NOTE: This gets line 0 and 1 (the class labels)
};
int attributeCount = Int32.Parse(cleanFileContents[i++].ToString());
for (int f = 0; f < attributeCount; f++, i++) // NOTE: This line is the number of features
{
string[] line = cleanFileContents[i].ToString().Split(' ');
id3Data.Attributes.Add(line[0], new ArrayList()
{
line[1],
line[2]
});
}
int dataItemCount = Int32.Parse(cleanFileContents[i++].ToString());
for (int e = 0; e < dataItemCount; e++, i++)
{
string[] line = System.Text.RegularExpressions.Regex.Split(cleanFileContents[i].ToString(), @"\s+");
id3Data.TestData.Add(
new Data(
line[0],
line[1],
new ArrayList()
{
line[2],
line[3],
line[4],
line[5]
}
)
);
}
return(id3Data);
}
/// <summary>
/// Gets all the tree classification for the given test data object
/// </summary>
/// <param name="testingData">Data object to test with</param>
/// <returns>ArrayList of Tuples[string, string] (name, classification)</returns>
public ArrayList GetTreeClassifications(ID3Data testingData)
{
ArrayList output = new ArrayList();
foreach (Data example in testingData.TestData)
{
string exmapleClassification = Traverse(example);
output.Add(Tuple.Create <string, string>(example.Name, exmapleClassification));
}
return(output);
}
/// <summary>
/// Entropy
/// </summary>
/// <param name="examples"></param>
/// <param name="targetAttribute"></param>
/// <param name="data"></param>
/// <returns></returns>
double Entropy(ArrayList examples, string targetAttribute, ID3Data data)
{
double result = 0;
Dictionary <string, int> dictionary = SummarizeExamplesAttribute(examples, targetAttribute, data);
foreach (KeyValuePair <string, int> kvp in dictionary)
{
double proportion = (float)dictionary[kvp.Key] / (float)examples.Count;
result -= proportion * Math.Log(proportion, 2);
}
return(result);
}
/// <summary>
/// Gets the best attribute for the examples.
/// </summary>
/// <param name="examples">Examples list (as data objects)</param>
/// <param name="attributes">Attributes in the current example list</param>
/// <param name="data">Data object</param>
/// <returns>Best attribute for the current examples</returns>
string GetBestAttribute(ArrayList examples, ArrayList attributes, ID3Data data)
{
string output = "";
double best = double.MinValue;
foreach (string value in attributes)
{
double temp = InformationGain(examples, value, Entropy(examples, value, data), data);
if (temp > best) // REVIEW: Depends on how you do this
{
output = value;
best = temp;
}
}
return(output);
}
/// <summary>
/// ID3 Algorithm
/// *Notes inline comments*
/// </summary>
/// <param name="examples">Example set to be used</param>
/// <param name="attributes">All attributes in the training data</param>
/// <param name="data">ID3 Data object (from the parser)</param>
/// <returns>Roote node of the tree</returns>
public Node ID3(ArrayList examples, ArrayList attributes, ID3Data data)
{
/**
* A <- best attribute
* Assign A as decision attribute for node
* foreach value of A
* create a descendent of node
* sort training examples to leaves
* if examples perfectly classified STOP
* else iterate over leaves
*/
// if all example same category (pure)
// return leaf with that category
// if attributes.empty
// return a leaf with most common category in examples
if (attributes.Count == 0)
{
// string mostCommon = GetMostCommonValue(examples);
string mostCommon = GetMostCommonCategory(examples);
return(new Node(Label: mostCommon, Decision: null));
}
string bestAttribute = GetBestAttribute(examples, attributes, data); // Gets the best attribute for the current examples
Node tree = new Node(Label: bestAttribute, Decision: null); // This nodes decision category
foreach (string value in data.Attributes[bestAttribute])
{
ArrayList subset = SubSet(examples, value); // Generates a subset of examples
Dictionary <string, int> dictionary = SummarizeExamplesValue(examples, value, data); // Checks if the examples are empty
foreach (KeyValuePair <string, int> kvp in dictionary)
{
if (kvp.Value == examples.Count)
{
return(new Node(Label: kvp.Key, Decision: null));
}
}
ArrayList newAttributes = attributes;
newAttributes.Remove(bestAttribute); // Removes current best attribute from list
Node subtree = ID3(subset, newAttributes, data); // Gets the subtree from a new tree being created by the algorithm
subtree.Decision = value;
tree.AddBranch(subtree); // Adds a child to the tree (or a branch)
}
return(tree);
}
/// <summary>
/// Summarizes how many of each attribute are in the current examples.
/// </summary>
/// <param name="examples">Examples to check</param>
/// <param name="targetAttribute">Target attribute to iterate over it's values</param>
/// <param name="data">Data object</param>
/// <returns>Dictionary of summarized examples</returns>
Dictionary <string, int> SummarizeExamplesAttribute(ArrayList examples, string targetAttribute, ID3Data data)
{
Dictionary <string, int> dictionary = new Dictionary <string, int>();
foreach (string value in data.Attributes[targetAttribute])
{
foreach (Data example in examples)
{
if (example.Attributes.Contains(value))
{
if (dictionary.ContainsKey(value))
{
dictionary[value] += 1;
}
else
{
dictionary.Add(value, 1);
}
}
}
}
return(dictionary);
}
/// <summary>
/// Information gain
/// </summary>
/// <param name="examples"></param>
/// <param name="attribute"></param>
/// <param name="entropyOfSet"></param>
/// <param name="data"></param>
/// <returns></returns>
double InformationGain(ArrayList examples, string attribute, double entropyOfSet, ID3Data data)
{
double gain = entropyOfSet; // The current gain
foreach (string value in data.Attributes[attribute]) // For each value the attribute can be
{
ArrayList subset = SubSet(examples, value);
gain -= (float)subset.Count / (float)examples.Count * (float)Entropy(subset, attribute, data);
}
return(gain);
}
