def train(self, training_examples, train_on_subset=True, num_trees=100, features_considered_per_node=2, **kwds):
print "Training a decision forest of %d trees, using %d examples, and %d features considered per node." % (
num_trees, len(training_examples), features_considered_per_node)
self.trees = []
total_test_output_stats = SummaryStats()
binary_classification = all(example["_OUTPUT"] in [0,1] for example in training_examples)
#binary_classification = True
#for example in training_examples:
# output = example["_OUTPUT"]
# if output not in [0,1]:
# binary_classification = False
# break
for tree_i in xrange(1, num_trees+1):
tree = DecisionTree()
self.trees.append(tree)
test_set_ids = set(xrange(len(training_examples)))
for i in xrange(len(training_examples)):
if train_on_subset: # N samples with replacement ("bootstrap")
index = random.randint(0, len(training_examples)-1)
else:
index = i
tree.add_example(training_examples[index])
test_set_ids.discard(index)
print "Growing tree %d/%d ..." % (tree_i, num_trees),
tree.grow_tree(features_considered_per_node=features_considered_per_node)
# Report the in-sample training error
if binary_classification:
print "area-under-curve for %d training examples is %2.2f" % (
len(tree.examples), tree.test(tree.examples, print_level=0))
else:
print "%2.2f avg err^2 on %d training examples" % (
tree.avg_squared_error(), len(tree.examples)),
# Report the out-of-sample testing error, if we have any out-of-sample
# examples to test on.
if train_on_subset:
print "; ",
test_set = [training_examples[i] for i in test_set_ids]
if binary_classification:
# Do a true out-of-sample test just on this one tree
# Temporarily make this a forest-of-one-tree...
save_trees = self.trees
self.trees = [tree]
self.test(test_set)
self.trees = save_trees
else:
avg_squared_error = tree.avg_squared_error(test_set)
total_test_output_stats.add(avg_squared_error)
print "out-of-sample avg err^2 on %d test cases: %.2f [%.2f avg. for all %d trees so far]" % (len(test_set), avg_squared_error, total_test_output_stats.avg(), tree_i),
print
