def test_hoeffding_tree_model_information(): stream = SEAGenerator(random_state=1, noise_percentage=0.05) stream.prepare_for_use() X, y = stream.next_sample(5000) nominal_attr_idx = [x for x in range(5, stream.n_features)] learner = HoeffdingTree(nominal_attributes=nominal_attr_idx) learner.partial_fit(X, y, classes=stream.target_values) expected_info = { 'Tree size (nodes)': 5, 'Tree size (leaves)': 3, 'Active learning nodes': 3, 'Tree depth': 2, 'Active leaf byte size estimate': 0.0, 'Inactive leaf byte size estimate': 0.0, 'Byte size estimate overhead': 1.0 } observed_info = learner.get_model_measurements for k in expected_info: assert k in observed_info assert expected_info[k] == observed_info[k] expected_description = "if Attribute 0 <= 4.549969620513424:\n" \ " if Attribute 1 <= 5.440182925299016:\n" \ " Leaf = Class 0 | {0: 345.54817975126275, 1: 44.43855503614928}\n" \ " if Attribute 1 > 5.440182925299016:\n" \ " Leaf = Class 1 | {0: 54.451820248737235, 1: 268.5614449638507}\n" \ "if Attribute 0 > 4.549969620513424:\n" \ " Leaf = Class 1 | {0: 390.5845685762964, 1: 2372.3747376855454}\n" \ assert expected_description == learner.get_model_description()
def test_hoeffding_tree_categorical_features(test_path): data_path = os.path.join(test_path, 'ht_categorical_features_testcase.npy') stream = np.load(data_path) # Removes the last two columns (regression targets) stream = stream[:, :-2] X, y = stream[:, :-1], stream[:, -1] nominal_attr_idx = np.arange(7).tolist() learner = HoeffdingTree(nominal_attributes=nominal_attr_idx) learner.partial_fit(X, y, classes=np.unique(y)) expected_description = "if Attribute 0 = -15.0:\n" \ " Leaf = Class 2 | {2: 350.0}\n" \ "if Attribute 0 = 0.0:\n" \ " Leaf = Class 0 | {0: 420.0, 1: 252.0}\n" \ "if Attribute 0 = 1.0:\n" \ " Leaf = Class 1 | {0: 312.0, 1: 332.0}\n" \ "if Attribute 0 = 2.0:\n" \ " Leaf = Class 1 | {0: 236.0, 1: 383.0}\n" \ "if Attribute 0 = 3.0:\n" \ " Leaf = Class 1 | {0: 168.0, 1: 459.0}\n" \ "if Attribute 0 = -30.0:\n" \ " Leaf = Class 3.0 | {3.0: 46.0, 4.0: 42.0}\n" assert learner.get_model_description() == expected_description
def test_hoeffding_tree_coverage(): # Cover memory management stream = SEAGenerator(random_state=1, noise_percentage=0.05) stream.prepare_for_use() X, y = stream.next_sample(5000) learner = HoeffdingTree(max_byte_size=30, memory_estimate_period=100, grace_period=10, leaf_prediction='mc') learner.partial_fit(X, y, classes=stream.target_values) learner.reset() # Cover nominal attribute observer stream = RandomTreeGenerator(tree_random_state=1, sample_random_state=1, n_num_features=0, n_categories_per_cat_feature=2) stream.prepare_for_use() X, y = stream.next_sample(1000) learner = HoeffdingTree(leaf_prediction='mc', nominal_attributes=[i for i in range(10)]) learner.partial_fit(X, y, classes=stream.target_values)
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
class VFDT(IncrementalClassifier): def __init__(self, grace_period=200, split_confidence=0.5, leaf_prediction='nba', split_criterion='info_gain'): super().__init__() self.clf = HoeffdingTree(split_confidence=split_confidence, grace_period=grace_period, leaf_prediction=leaf_prediction, split_criterion=split_criterion) def partial_fit(self, one_row): self.clf.partial_fit([one_row[0]], [one_row[1]]) def predict(self, x): return self.clf.predict(x)
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()
probas2 = [] ys = [] # limit = 150 for chunk in tqdm(range(n_chunks[n] - 1)): X, y = stream.get_chunk() proba = clf.predict_proba(X)[:, 1] try: proba2 = clf2.predict_proba(X)[:, 1] except: try: proba2 = np.concatenate(proba2) except: pass clf.partial_fit(X, y) clf2.partial_fit(X, y) probas.append(proba) probas2.append(proba2) ys.append(y) # if chunk == limit: # break probas = np.array(probas) probas = probas.reshape(-1) probas2 = np.array(probas2) probas2 = probas2.reshape(-1) ys = np.array(ys) ys = ys.reshape(-1) ls = (np.linspace(0, 1, probas.shape[0])) # print(ls.shape) # print(probas.shape)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123) # Define a tree for fitting the complete dataset and one for streaming. ht_complete = HoeffdingTree() ht_partial = HoeffdingTree() # Fit the complete dataset. ht_complete.fit(X_train, y_train) ht_complete_score = ht_complete.score(X_test, y_test) print(f'Score when fitting at once: {ht_complete_score}') # Streaming samples one after another. timer = False j = 0 for i in range(len(X_train)): ht_partial.partial_fit(X_train[i].reshape(1, -1), np.array([y_train[i]])) res.append(ht_partial.score(X_test, y_test)) print(f'Score when streaming after {i} samples: {res[-1]}') if res[-1] >= ht_complete_score - 0.01: print(f'(Almost) full score reached! Continue for another {20 - j} samples.') timer = True if timer: j += 1 if j == 20: break # Plot the scores after each sample. plt.figure(figsize=(12, 6)) plt.plot([0, i], [ht_complete_score, ht_complete_score], '--', label='Hoeffding Tree built at once') plt.plot(res, label='Incrementally built Hoeffding Tree') plt.xlabel('Number of Samples', fontsize=15)