def main():
args = sys.argv
if not args_are_valid(args):
sys.exit()
div_funct_name = args[1]
parser = InputParser(args[2], args[3], args[4])
trained_tree = decision_tree_builder.learn_tree(parser.features,
parser.possible_classification_values,
div_funct_name,
parser.train_data)
train_num_correct = classify_all(trained_tree, parser.train_data)
test_num_correct = classify_all(trained_tree, parser.test_data)
report_outcome(trained_tree, div_funct_name, train_num_correct, test_num_correct,
len(parser.train_data), len(parser.test_data))
def main():
args = sys.argv
if not args_are_valid(args):
sys.exit()
div_funct_name = args[1]
parser = InputParser(args[2], args[3], args[4])
trained_tree = decision_tree_builder.learn_tree(
parser.features, parser.possible_classification_values, div_funct_name,
parser.train_data)
train_num_correct = classify_all(trained_tree, parser.train_data)
test_num_correct = classify_all(trained_tree, parser.test_data)
report_outcome(trained_tree, div_funct_name, train_num_correct,
test_num_correct, len(parser.train_data),
len(parser.test_data))
def main():
train_data = diversity_experiment.get_nursery_data()
shuffle(train_data.examples)
outcomes = {'unpruned': [],
'conservative': [],
'liberal': []}
for i in range(0, K):
# Split into training, test, and validation sets
training_set, the_rest = diversity_experiment.split(train_data.examples, i, K)
test_set, validation_set = validation_split(the_rest)
# Learn the original tree
unpruned = decision_tree_builder.learn_tree(train_data.features,
train_data.class_vals,
DIV_FUNCT,
training_set)
# Make pruned versions too
print "Pruning conservatively"
conservative_tree = TreePruner(copy.deepcopy(unpruned), 'conservative', validation_set).root
print "Pruning liberally."
liberal_tree = TreePruner(copy.deepcopy(unpruned), 'liberal', validation_set).root
trees = {
'unpruned': unpruned,
'liberal': liberal_tree,
'conservative': conservative_tree
}
# Test ALL the trees!
for tree in trees:
outcomes[tree].append(evaluate(trees[tree], test_set))
pairwise = [
('unpruned', 'liberal'),
('unpruned', 'conservative'),
('liberal', 'conservative')
]
# Get t-statistics
diversity_experiment.analyze(outcomes, pairwise, [], 'prune.csv', K)
def main():
train_data = diversity_experiment.get_nursery_data()
shuffle(train_data.examples)
outcomes = {'unpruned': [], 'conservative': [], 'liberal': []}
for i in range(0, K):
# Split into training, test, and validation sets
training_set, the_rest = diversity_experiment.split(
train_data.examples, i, K)
test_set, validation_set = validation_split(the_rest)
# Learn the original tree
unpruned = decision_tree_builder.learn_tree(train_data.features,
train_data.class_vals,
DIV_FUNCT, training_set)
# Make pruned versions too
print "Pruning conservatively"
conservative_tree = TreePruner(copy.deepcopy(unpruned), 'conservative',
validation_set).root
print "Pruning liberally."
liberal_tree = TreePruner(copy.deepcopy(unpruned), 'liberal',
validation_set).root
trees = {
'unpruned': unpruned,
'liberal': liberal_tree,
'conservative': conservative_tree
}
# Test ALL the trees!
for tree in trees:
outcomes[tree].append(evaluate(trees[tree], test_set))
pairwise = [('unpruned', 'liberal'), ('unpruned', 'conservative'),
('liberal', 'conservative')]
# Get t-statistics
diversity_experiment.analyze(outcomes, pairwise, [], 'prune.csv', K)
def test_tree(train_examples, test_examples):
tree = learn_tree(train_data.features, train_data.class_vals,
funct, train_examples)
return classify_all(tree, test_examples)
