A Python module for constructing a decision tree from multidimensional training data and for using the decision tree for classifying new data
Version 2.2.2 includes scripts in the Examples directory that demonstrate how to carry out bulk classification of all your test data records placed in a CSV file in one fell swoop. Also included are scripts that demonstrate the same for the data records placed in the old-style ‘.dat’ files. The main module code remains unchanged.
Version 2.2.1: The changes made are all in the part of the module that is used for evaluating the quality of training data through a 10-fold cross validation test. The previous version used the default values for the constructor parameters when constructing the decision trees in each iteration of the test. The new version correctly uses the user-supplied values.
Version 2.0 was a major rewrite of the DecisionTree module. This revision was prompted by a number of users wanting to see numeric features incorporated in the construction of decision trees. So here it is! This version allows you to use either purely symbolic features, or purely numeric features, or a mixture of the two. (A feature is numeric if it can take any floating-point value over an interval.)
With regard to the purpose of the module, assuming you have placed your training data in a CSV file, all you have to do is to supply the name of the file to this module and it does the rest for you without much effort on your part for classifying a new data sample. A decision tree classifier consists of feature tests that are arranged in the form of a tree. The feature test associated with the root node is one that can be expected to maximally disambiguate the different possible class labels for a new data record. From the root node hangs a child node for each possible outcome of the feature test at the root. This maximal class-label disambiguation rule is applied at the child nodes recursively until you reach the leaf nodes. A leaf node may correspond either to the maximum depth desired for the decision tree or to the case when there is nothing further to gain by a feature test at the node.
Typical usage syntax:
training_datafile = "stage3cancer.csv" dt = DecisionTree.DecisionTree( training_datafile = training_datafile, csv_class_column_index = 2, csv_columns_for_features = [3,4,5,6,7,8], entropy_threshold = 0.01, max_depth_desired = 8, symbolic_to_numeric_cardinality_threshold = 10, ) dt.get_training_data() dt.calculate_first_order_probabilities() dt.calculate_class_priors() dt.show_training_data() root_node = dt.construct_decision_tree_classifier() root_node.display_decision_tree(" ") test_sample = ['g2 = 4.2', 'grade = 2.3', 'gleason = 4', 'eet = 1.7', 'age = 55.0', 'ploidy = diploid'] classification = dt.classify(root_node, test_sample) print "Classification: ", classification