def main():
# Steps to build and prune a decision tree:
# 1. Prepare dataset.
headings, dataset = utils.load_dataset()
random.shuffle(dataset)
# Split the dataset into training data, test data and pruning data if needed.
train_data = dataset[:32000]
test_data = dataset[32000:40000]
# prune_data = dataset[:]
# 2. Grow a decision tree from training data based on entropy or gini.
dt = DecisionTree.build_tree(train_data, DecisionTree.entropy)
# dt = DecisionTree.build_tree(train_data, DecisionTree.gini)
# 3. Visualize the tree.
DecisionTree.plot_tree(dt, headings, conf.org_tree_filepath)
leaves = DecisionTree.count_leaves(dt)
print('Leaves count before pruning: %d' % leaves)
# 4. Run the test data through the tree.
err = DecisionTree.evaluate(dt, test_data)
print('Accuracy before pruning: %d/%d = %f' % \
(len(test_data) - err, len(test_data), (len(test_data) - err) / len(test_data)))
# 5. Prune the tree.
# 5.1 REP: REP requires another dataset for pruning, so we need to split the dataset in a different way.
# 5.2 PP: top-down
DecisionTree.top_down_pessimistic_pruning(dt)
# 5.3 PP: bottom-up.
# DecisionTree.bottom_up_pessimistic_pruning(dt)
# 5.4 MEP
# DecisionTree.minimum_error_pruning(dt)
# 6. Visualize the pruned tree.
DecisionTree.plot_tree(dt, headings, conf.prn_tree_filepath)
leaves = DecisionTree.count_leaves(dt)
print('Leaves count after pruning: %d' % leaves)
# 7. Check if the classification ability is improved after pruning.
err = DecisionTree.evaluate(dt, test_data)
print('Accuracy after pruning: %d/%d = %f' % \
(len(test_data) - err, len(test_data), (len(test_data) - err) / len(test_data)))
from tree import DecisionTree
training_data = [
['Green', 3, 'Apple'],
['Yellow', 3, 'Apple'],
['Red', 1, 'Grape'],
['Red', 1, 'Grape'],
['Yellow', 3, 'Lemon'],
]
decison_tree = DecisionTree()
tree = decison_tree.build_tree(training_data)
decison_tree.print_tree(tree)
def pretty_print_leaf_predictions(counts):
total = sum(counts.values()) * 1.0
probabilities = {}
for label in counts.keys():
probabilities[label] = str(int(counts[label] / total * 100)) + "%"
return probabilities
pretty_print_leaf_predictions(decison_tree.classify(training_data[0], tree))
testing_data = [
['Green', 3, 'Apple'],
['Yellow', 4, 'Apple'],
['Red', 2, 'Grape'],
