randomforest.py

import os
import math
import numpy as np
from tree import Tree

class Forest(object):
	def __init__(self,
		     num_trees = 10,
		     subsample_flag = True,
		     mode = 'regression',
		     classes = None,
		     comparison = 'axisaligned',
		     selection_count = None,
		     minimum_count = 1,
		     maximum_depth = 10,
		     threshold_count = 100):

		if mode == 'regression':
			self.eval_fn = self.regress
		else:
			self.eval_fn = self.classify

		self.num_trees = num_trees

		self.mode = mode
		self.comparison = comparison
		self.selection_count = selection_count
		self.minimum_count = minimum_count
		self.maximum_depth = maximum_depth
		self.threshold_count = threshold_count

		self.classes = classes
		self.subsample_flag = subsample_flag
		self.trees = []

	def __len__(self):
		return self.num_trees

	def save(self, folder):
		pass

	def load(self, folder):
		pass

	def regress(self, labels):
		return np.mean(labels, axis = 0)

	def classify(self, labels):
		return max(set(labels), key = labels.count)

	def subsample(self, features, labels, ratio = 0.8):
		sampled_features = []
		sampled_labels = []
		size = round(len(features) * ratio)
		while len(sampled_features) < size:
			idx = np.random.randint(len(features))
			sampled_features.append(features[idx])
			sampled_labels.append(labels[idx])

		return (sampled_features, sampled_labels)

	def fit(self, features, labels):
		if self.selection_count is None:
			self.selection_count = int(math.sqrt(len(features[0])))

		for idx in range(self.num_trees):
			sub_tree = Tree(mode = self.mode,
					comparison = self.comparison,
					selection_count = self.selection_count,
					minimum_count = self.minimum_count,
					maximum_depth = self.maximum_depth,
					threshold_count = self.threshold_count)
			self.trees.append(sub_tree)

			if self.subsample_flag == True:
				sampled_features, sampled_labels = self.subsample(features, labels)
				self.trees[idx].fit(sampled_features, sampled_labels)
			else:
				self.trees[idx].fit(features, labels)

	def predict(self, data):
		labels = []
		for feat_row in data:
			prediction = []
			for idx in range(self.num_trees):
				prediction.append(self.trees[idx].predict(feat_row))
			labels.append(self.eval_fn(prediction))
		return labels