def generate_random_forest(self):
random_selected_training_samples_list = self.bootstrap_aggregating()
for random_selected_training_samples in random_selected_training_samples_list:
test_decision_tree = DecisionTree()
test_decision_tree.set_training_samples_root(random_selected_training_samples)
test_decision_tree.set_attributes_list(self.dataset.feature_category_list)
decision_tree_root = test_decision_tree.generate_decision_tree(test_decision_tree.training_samples_root,test_decision_tree.attributes_list,random_state=1)
test_decision_tree.set_root(decision_tree_root)
self.forest.append(test_decision_tree)
def test_generate_decision_tree_continuous(self):
test_dataset = Dataset(_dataset_name='watermelon_3.0',
_dataset_file_path='./datasets/watermelon2.csv')
test_dataset.load_dataset(verbose=False)
test_decision_tree = DecisionTree()
test_decision_tree.set_training_samples_root(test_dataset.samples)
test_decision_tree.set_attributes_list(
list(range(test_dataset.num_features)))
test_decision_tree.set_attributes_list(
test_dataset.feature_category_list)
decision_tree_root = test_decision_tree.generate_decision_tree(
test_decision_tree.training_samples_root,
test_decision_tree.attributes_list)
def test_vis_tree_1(self):
test_dataset = Dataset(_dataset_name='watermelon_2.0',
_dataset_file_path='./datasets/watermelon.csv')
test_dataset.load_dataset(verbose=False)
test_decision_tree = DecisionTree()
test_decision_tree.set_training_samples_root(test_dataset.samples)
test_decision_tree.set_attributes_list(
test_dataset.feature_category_list)
decision_tree_root = test_decision_tree.generate_decision_tree(
test_decision_tree.training_samples_root,
test_decision_tree.attributes_list)
test_decision_tree.set_root(decision_tree_root)
test_vis_tree = VisTree(test_decision_tree,test_dataset.feature2number_mapping,\
test_dataset.feature_name_list,_tree_name="test_decision_tree")
test_vis_tree.vis_tree(mode=1)
def test_decision_tree_predict(self):
test_dataset = Dataset(_dataset_name='watermelon_3.0',
_dataset_file_path='./datasets/watermelon2.csv')
test_dataset.load_dataset(verbose=False)
test_decision_tree = DecisionTree()
test_decision_tree.set_training_samples_root(test_dataset.samples)
test_decision_tree.set_attributes_list(
test_dataset.feature_category_list)
decision_tree_root = test_decision_tree.generate_decision_tree(
test_decision_tree.training_samples_root,
test_decision_tree.attributes_list)
test_decision_tree.set_root(decision_tree_root)
for i in range((test_dataset.num_samples)):
test_sample = test_dataset.samples[i, :]
test_X = test_sample[0:-1]
test_y = test_sample[-1]
test_predicted_label = test_decision_tree.predict(
test_sample=test_X)
self.assertEqual(test_predicted_label, test_y)
def test_get_all_possible_values_on_attribute(self):
test_dataset = Dataset(_dataset_name='watermelon_2.0',
_dataset_file_path='./datasets/watermelon.csv')
test_dataset.load_dataset(verbose=False)
test_decision_tree = DecisionTree()
test_decision_tree.set_training_samples_root(test_dataset.samples)
test_decision_tree.set_attributes_list(
test_dataset.feature_category_list)
self.assertEqual(
test_decision_tree.get_all_possible_values_on_attribute((0, 0)),
[0, 1, 2])
self.assertEqual(
test_decision_tree.get_all_possible_values_on_attribute((1, 0)),
[0, 1, 2])
self.assertEqual(
test_decision_tree.get_all_possible_values_on_attribute((5, 0)),
[0, 1])
def test_generate_random_decision_tree_2(self):
test_dataset = Dataset(_dataset_name='watermelon_3.0',
_dataset_file_path='./datasets/watermelon2.csv')
test_dataset.load_dataset(verbose=False)
test_decision_tree = DecisionTree()
test_decision_tree.set_training_samples_root(test_dataset.samples)
test_decision_tree.set_attributes_list(
test_dataset.feature_category_list)
decision_tree_root_1 = test_decision_tree.generate_decision_tree(
test_decision_tree.training_samples_root,
test_decision_tree.attributes_list,
random_state=1)
decision_tree_root_2 = test_decision_tree.generate_decision_tree(
test_decision_tree.training_samples_root,
test_decision_tree.attributes_list,
random_state=2)
def test_generate_decision_tree(self):
test_dataset = Dataset(_dataset_name='watermelon_2.0',
_dataset_file_path='./datasets/watermelon.csv')
test_dataset.load_dataset(verbose=False)
test_decision_tree = DecisionTree()
test_decision_tree.set_training_samples_root(test_dataset.samples)
test_decision_tree.set_attributes_list(
test_dataset.feature_category_list)
decision_tree_root = test_decision_tree.generate_decision_tree(
test_decision_tree.training_samples_root,
test_decision_tree.attributes_list)
self.assertEqual(
len(decision_tree_root.child_node_list[0].child_node_list[1].
child_node_list), 3)
def main():
test_dataset = Dataset(_dataset_name='watermelon_2.0',
_dataset_file_path='./datasets/watermelon.csv')
test_dataset.load_dataset()
myDecisionTree = DecisionTree()