def test_hoeffding_tree(test_path): stream = RandomTreeGenerator(tree_random_state=23, sample_random_state=12, n_classes=4, n_cat_features=2, n_num_features=5, n_categories_per_cat_feature=5, max_tree_depth=6, min_leaf_depth=3, fraction_leaves_per_level=0.15) stream.prepare_for_use() nominal_attr_idx = [x for x in range(5, stream.n_features)] learner = HoeffdingTree(nominal_attributes=nominal_attr_idx) cnt = 0 max_samples = 5000 predictions = array('i') proba_predictions = [] wait_samples = 100 while cnt < max_samples: X, y = stream.next_sample() # Test every n samples if (cnt % wait_samples == 0) and (cnt != 0): predictions.append(learner.predict(X)[0]) proba_predictions.append(learner.predict_proba(X)[0]) learner.partial_fit(X, y) cnt += 1 expected_predictions = array('i', [ 0, 1, 3, 0, 0, 3, 0, 1, 1, 2, 0, 2, 1, 1, 2, 1, 3, 0, 1, 1, 1, 1, 0, 3, 1, 2, 1, 1, 3, 2, 1, 2, 2, 2, 1, 1, 1, 0, 1, 2, 0, 2, 0, 0, 0, 0, 1, 3, 2 ]) test_file = os.path.join(test_path, 'test_hoeffding_tree.npy') data = np.load(test_file) assert np.alltrue(predictions == expected_predictions) assert np.allclose(proba_predictions, data) expected_info = 'HoeffdingTree: max_byte_size: 33554432 - memory_estimate_period: 1000000 - grace_period: 200 ' \ '- split_criterion: info_gain - split_confidence: 1e-07 - tie_threshold: 0.05 ' \ '- binary_split: False - stop_mem_management: False - remove_poor_atts: False ' \ '- no_pre_prune: False - leaf_prediction: nba - nb_threshold: 0 - nominal_attributes: [5, 6, 7,' \ ' 8, 9, 10, 11, 12, 13, 14] - ' assert learner.get_info() == expected_info expected_model_1 = 'Leaf = Class 1.0 | {0.0: 1423.0, 1.0: 1745.0, 2.0: 978.0, 3.0: 854.0}\n' expected_model_2 = 'Leaf = Class 1.0 | {1.0: 1745.0, 2.0: 978.0, 0.0: 1423.0, 3.0: 854.0}\n' assert (learner.get_model_description() == expected_model_1) \ or (learner.get_model_description() == expected_model_2) assert type(learner.predict(X)) == np.ndarray assert type(learner.predict_proba(X)) == np.ndarray
def test_hoeffding_tree_nb(test_path): stream = RandomTreeGenerator(tree_random_state=23, sample_random_state=12, n_classes=4, n_cat_features=2, n_num_features=5, n_categories_per_cat_feature=5, max_tree_depth=6, min_leaf_depth=3, fraction_leaves_per_level=0.15) stream.prepare_for_use() nominal_attr_idx = [x for x in range(5, stream.n_features)] learner = HoeffdingTree(nominal_attributes=nominal_attr_idx, leaf_prediction='nb') cnt = 0 max_samples = 5000 predictions = array('i') proba_predictions = [] wait_samples = 100 while cnt < max_samples: X, y = stream.next_sample() # Test every n samples if (cnt % wait_samples == 0) and (cnt != 0): predictions.append(learner.predict(X)[0]) proba_predictions.append(learner.predict_proba(X)[0]) learner.partial_fit(X, y) cnt += 1 expected_predictions = array('i', [ 0, 1, 3, 0, 0, 3, 0, 1, 1, 2, 0, 2, 1, 1, 2, 1, 3, 0, 1, 1, 1, 1, 0, 3, 1, 2, 1, 1, 3, 2, 1, 2, 2, 2, 1, 1, 1, 0, 1, 2, 0, 2, 0, 0, 0, 0, 1, 3, 2 ]) assert np.alltrue(predictions == expected_predictions) expected_info = "HoeffdingTree(binary_split=False, grace_period=200, leaf_prediction='nb',\n" \ " max_byte_size=33554432, memory_estimate_period=1000000,\n" \ " nb_threshold=0, no_preprune=False,\n" \ " nominal_attributes=[5, 6, 7, 8, 9, 10, 11, 12, 13, 14],\n" \ " remove_poor_atts=False, split_confidence=1e-07,\n" \ " split_criterion='info_gain', stop_mem_management=False,\n" \ " tie_threshold=0.05)" assert learner.get_info() == expected_info
y, test_size=0.2, random_state=1) X_train, X_test, y_train, y_test = X_train.to_numpy(), X_test.to_numpy( ), y_train.to_numpy(), y_test.to_numpy() print(type(X_train)) #print(type(X_train.to_numpy())) # ### Entrenar el clasificador (modelo) # In[3]: clasificador = HoeffdingTree() print(clasificador.get_info()) print("start training") clasificador.fit(X_train, y_train, classes=None, sample_weight=None) print("end training") # # Graficas Paper # In[4]: dataset = pd.read_csv('dataset_seleccion_parametros.csv') dataset.shape # In[5]:
def test_hoeffding_tree_nba(test_path): stream = RandomTreeGenerator(tree_random_state=23, sample_random_state=12, n_classes=4, n_cat_features=2, n_num_features=5, n_categories_per_cat_feature=5, max_tree_depth=6, min_leaf_depth=3, fraction_leaves_per_level=0.15) stream.prepare_for_use() nominal_attr_idx = [x for x in range(5, stream.n_features)] learner = HoeffdingTree(nominal_attributes=nominal_attr_idx) cnt = 0 max_samples = 5000 predictions = array('i') proba_predictions = [] wait_samples = 100 while cnt < max_samples: X, y = stream.next_sample() # Test every n samples if (cnt % wait_samples == 0) and (cnt != 0): predictions.append(learner.predict(X)[0]) proba_predictions.append(learner.predict_proba(X)[0]) learner.partial_fit(X, y) cnt += 1 expected_predictions = array('i', [ 0, 1, 3, 0, 0, 3, 0, 1, 1, 2, 0, 2, 1, 1, 2, 1, 3, 0, 1, 1, 1, 1, 0, 3, 1, 2, 1, 1, 3, 2, 1, 2, 2, 2, 1, 1, 1, 0, 1, 2, 0, 2, 0, 0, 0, 0, 1, 3, 2 ]) test_file = os.path.join(test_path, 'test_hoeffding_tree.npy') data = np.load(test_file) assert np.alltrue(predictions == expected_predictions) assert np.allclose(proba_predictions, data) expected_info = "HoeffdingTree(binary_split=False, grace_period=200, leaf_prediction='nba',\n" \ " max_byte_size=33554432, memory_estimate_period=1000000,\n" \ " nb_threshold=0, no_preprune=False,\n" \ " nominal_attributes=[5, 6, 7, 8, 9, 10, 11, 12, 13, 14],\n" \ " remove_poor_atts=False, split_confidence=1e-07,\n" \ " split_criterion='info_gain', stop_mem_management=False,\n" \ " tie_threshold=0.05)" assert learner.get_info() == expected_info expected_model_1 = 'Leaf = Class 1.0 | {0.0: 1423.0, 1.0: 1745.0, 2.0: 978.0, 3.0: 854.0}\n' assert (learner.get_model_description() == expected_model_1) assert type(learner.predict(X)) == np.ndarray assert type(learner.predict_proba(X)) == np.ndarray X, y = stream.next_sample(20000) learner.split_criterion = 'hellinger' learner.partial_fit(X, y) expected_rules = 'Att (5) == 0.000 and Att (12) == 0.000 | class: 1\n' + \ 'Att (5) == 0.000 and Att (12) == 1.000 | class: 1\n' + \ 'Att (5) == 1.000 and Att (13) == 0.000 and Att (1) <= 0.550 and Att (3) <= 0.730 | class: 0\n' +\ 'Att (5) == 1.000 and Att (13) == 0.000 and Att (1) <= 0.550 and Att (3) > 0.730 | class: 2\n' + \ 'Att (5) == 1.000 and Att (13) == 0.000 and Att (1) > 0.550 and Att (1) <= 0.800 | class: 0\n' + \ 'Att (5) == 1.000 and Att (13) == 0.000 and Att (1) > 0.550 and Att (1) > 0.800 and Att (14) == 0.000 | class: 0\n' + \ 'Att (5) == 1.000 and Att (13) == 0.000 and Att (1) > 0.550 and Att (1) > 0.800 and Att (14) == 1.000 | class: 1\n' + \ 'Att (5) == 1.000 and Att (13) == 1.000 and Att (3) <= 0.730 | class: 1\n' + \ 'Att (5) == 1.000 and Att (13) == 1.000 and Att (3) > 0.730 | class: 0\n' assert expected_rules == learner.get_rules_description()