ylabel='Accuracy %',
title='Accuracy with bagging(Base={})'.format(100 *
(1 - base_accuracy)))
ax.legend()
for xy in zip(num_samples, error_rates):
ax.annotate('(%s, %s)' % xy, xy=xy, textcoords='data')
fig.savefig('../data/bagging_accuracy_result.png')
if __name__ == '__main__':
training_data = data_loader.load_diabetes_train_from_file()
testing_data = data_loader.load_diabetes_test_from_file()
# Testing 1 DTree
tree = DTree(sample=False)
tree.train(training_data)
tree.test(testing_data)
print('Error rate for single learner is {}'.format(
100 * (1 - tree.last_error_rate)))
# Testing ensemble
error_rates = []
for num_sample in [1, 3, 5, 10, 20]:
for max_depth in [None, 1, 2, 3, 6]:
bagger = Bagging(num_samples=num_sample, max_depth=max_depth)
bagger.train_ensemble(training_data)
error_rate = bagger.test(testing_data)[0]
if max_depth is None:
error_rates.append(error_rate)
print('Num Samples: {} Depth: {} Accuracy: {}'.format(
num_sample, max_depth, 100 * (1 - error_rate)))
def train_ensemble(self, training_data):
for i in range(self._num_samples):
curr_tree = DTree(sample=True, max_depth=self._max_depth)
curr_tree.train(training_data)
self._learners.append(curr_tree)