Source code for pml.supervised.decision_trees.interface

# Copyright (C) 2012 David Rusk
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"""
Decision trees classification algorithm.

@author: drusk
"""

from pml.supervised.classifiers import AbstractClassifier
from pml.supervised.decision_trees import id3
from pml.supervised.decision_trees.tree_plotting import MatplotlibAnnotationTreePlotter

class DecisionTree(AbstractClassifier):
    """
    Decision tree classifier.
    
    Builds a tree which is like a flow chart.  It allows a decision to be 
    reached by checking the values for various features and following the 
    appropriate branches until a destination is reached.
        
    In addition to being useful as a classifier, the structure of the 
    decision tree can lend insight into the data. 
    """
    
    def __init__(self, training_set):
        """
        Constructs a new decision tree.
        
        Args:
          training_set: model.DataSet
            The training data to use when building the decision tree.
        """
        self.training_set = training_set
        self._tree = id3.build_tree(training_set)
        self._plotter = MatplotlibAnnotationTreePlotter(self._tree)
    
    def _classify(self, sample):
        """
        Predicts a sample's classification based on the decision tree that 
        was built from the training data.
        
        Args:
          sample: 
            The sample or observation to be classified.
          
        Returns:
          The sample's classification.
        """
        node = self._tree.get_root_node()
        while not node.is_leaf():
            feature = node.get_value()
            branch = sample[feature]
            node = node.get_child(branch)
        
        return node.get_value()

    def plot(self):
        """
        Generates a plot of the decision tree to visualize its structure.
        
        Returns:
          void
        """
        self._plotter.plot()