def test_hoeffding_tree_coverage(test_path):
# Cover nominal attribute observer
test_file = os.path.join(test_path, 'multi_target_regression_data.npz')
data = np.load(test_file)
X = data['X']
Y = data['Y']
learner = MultiTargetRegressionHoeffdingTree(
leaf_prediction='mean', nominal_attributes=[i for i in range(3)])
learner.partial_fit(X, Y)
def test_hoeffding_tree_coverage(test_path):
# Cover nominal attribute observer
test_file = os.path.join(test_path, 'multi_target_regression_data.npz')
data = np.load(test_file)
X = data['X']
Y = data['Y']
# Invalid leaf prediction option
learner = MultiTargetRegressionHoeffdingTree(
leaf_prediction='MEAN', nominal_attributes=[i for i in range(3)])
print(learner.split_criterion)
# Invalid split_criterion
learner.split_criterion = 'ICVR'
learner.partial_fit(X, Y)
def test_evaluate_multi_target_regression_coverage(tmpdir):
from skmultiflow.data import RegressionGenerator
from skmultiflow.trees import MultiTargetRegressionHoeffdingTree
max_samples = 1000
# Stream
stream = RegressionGenerator(n_samples=max_samples,
n_features=20,
n_informative=15,
random_state=1,
n_targets=7)
stream.prepare_for_use()
# Learner
mtrht = MultiTargetRegressionHoeffdingTree(leaf_prediction='adaptive')
output_file = os.path.join(str(tmpdir), "prequential_summary.csv")
metrics = [
'average_mean_square_error', 'average_mean_absolute_error',
'average_root_mean_square_error'
]
evaluator = EvaluatePrequential(max_samples=max_samples,
metrics=metrics,
output_file=output_file)
evaluator.evaluate(stream=stream, model=mtrht, model_names=['MTRHT'])
def test_multi_target_regression_hoeffding_tree_model_description():
stream = RegressionGenerator(n_samples=700,
n_features=20,
n_informative=15,
random_state=1,
n_targets=3)
stream.prepare_for_use()
learner = MultiTargetRegressionHoeffdingTree(leaf_prediction='mean')
max_samples = 700
X, y = stream.next_sample(max_samples)
# Trying to predict without fitting
learner.predict(X[0])
learner.partial_fit(X, y)
expected_description = "if Attribute 11 <= 0.36737233297880056:\n" \
" Leaf = Statistics {0: 450.0000, 1: [-23322.8079, -30257.1616, -18740.9462], " \
"2: [22242706.1751, 29895648.2424, 18855571.7943]}\n" \
"if Attribute 11 > 0.36737233297880056:\n" \
" Leaf = Statistics {0: 250.0000, 1: [33354.8675, 32390.6094, 22886.4176], " \
"2: [15429435.6709, 17908472.4289, 10709746.1079]}\n" \
assert SequenceMatcher(None, expected_description,
learner.get_model_description()).ratio() > 0.9
def test_multi_target_regression_hoeffding_tree_mean(test_path):
stream = RegressionGenerator(n_samples=2000,
n_features=20,
n_informative=15,
random_state=1,
n_targets=3)
stream.prepare_for_use()
learner = MultiTargetRegressionHoeffdingTree(leaf_prediction='mean')
cnt = 0
max_samples = 2000
wait_samples = 200
y_pred = np.zeros((int(max_samples / wait_samples), 3))
y_true = np.zeros((int(max_samples / wait_samples), 3))
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
y_pred[int(cnt / wait_samples), :] = learner.predict(X)
y_true[int(cnt / wait_samples), :] = y
learner.partial_fit(X, y)
cnt += 1
test_file = os.path.join(
test_path, 'expected_preds_multi_target_regression_mean.npy')
expected_predictions = np.load(test_file)
assert np.allclose(y_pred, expected_predictions)
error = mean_absolute_error(y_true, y_pred)
expected_error = 191.2823924547882
assert np.isclose(error, expected_error)
expected_info = "MultiTargetRegressionHoeffdingTree(binary_split=False, grace_period=200,\n" \
" leaf_prediction='mean',\n" \
" learning_ratio_const=True,\n" \
" learning_ratio_decay=0.001,\n" \
" learning_ratio_perceptron=0.02,\n" \
" max_byte_size=33554432,\n" \
" memory_estimate_period=1000000,\n" \
" nb_threshold=0, no_preprune=False,\n" \
" nominal_attributes=None, random_state=None,\n" \
" remove_poor_atts=False,\n" \
" split_confidence=1e-07,\n" \
" stop_mem_management=False,\n" \
" tie_threshold=0.05)"
assert learner.get_info() == expected_info
assert isinstance(learner.get_model_description(), type(''))
assert type(learner.predict(X)) == np.ndarray
def test_multi_target_regression_hoeffding_tree_mean(test_path):
stream = RegressionGenerator(n_samples=500,
n_features=20,
n_informative=15,
random_state=1,
n_targets=3)
stream.prepare_for_use()
learner = MultiTargetRegressionHoeffdingTree(leaf_prediction='mean')
cnt = 0
max_samples = 500
wait_samples = 10
y_pred = np.zeros((int(max_samples / wait_samples), 3))
y_true = np.zeros((int(max_samples / wait_samples), 3))
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
y_pred[int(cnt / wait_samples), :] = learner.predict(X)
y_true[int(cnt / wait_samples), :] = y
learner.partial_fit(X, y)
cnt += 1
test_file = os.path.join(
test_path, 'expected_preds_multi_target_regression_mean.npy')
expected_predictions = np.load(test_file)
# print(expected_predictions.shape)
assert np.allclose(y_pred, expected_predictions)
error = mean_absolute_error(y_true, y_pred)
expected_error = 167.40626294018753
assert np.isclose(error, expected_error)
expected_info = \
'MultiTargetRegressionHoeffdingTree: max_byte_size: 33554432 - ' \
'memory_estimate_period: 1000000 - grace_period: 200 - ' \
'split_criterion: intra cluster variance reduction - ' \
'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: mean - nb_threshold: 0 - ' \
'nominal_attributes: [] - '
assert learner.get_info() == expected_info
assert isinstance(learner.get_model_description(), type(''))
def test_multi_target_regression_hoeffding_tree_categorical_features(
test_path):
data_path = os.path.join(test_path, 'ht_categorical_features_testcase.npy')
stream = np.load(data_path)
X, y = stream[:, :-2], stream[:, -2:]
nominal_attr_idx = np.arange(8)
learner = MultiTargetRegressionHoeffdingTree(
nominal_attributes=nominal_attr_idx, leaf_prediction='perceptron')
learner.partial_fit(X, y)
expected_description = "if Attribute 0 = -15.0:\n" \
" if Attribute 3 = 0.0:\n" \
" Leaf = Statistics {0: 80.0000, 1: [-192.4417, 80.0908], 2: [464.1268, 80.1882]}\n" \
" if Attribute 3 = 1.0:\n" \
" Leaf = Statistics {0: 77.0000, 1: [-184.8333, -7.2503], 2: [444.9068, 42.7423]}\n" \
" if Attribute 3 = 2.0:\n" \
" Leaf = Statistics {0: 56.0000, 1: [-134.1829, -1.0863], 2: [322.1336, 28.1218]}\n" \
" if Attribute 3 = 3.0:\n" \
" Leaf = Statistics {0: 62.0000, 1: [-148.2397, -17.2837], 2: [355.5327, 38.6913]}\n" \
"if Attribute 0 = 0.0:\n" \
" Leaf = Statistics {0: 672.0000, 1: [390.6773, 672.0472], 2: [761.0744, 672.1686]}\n" \
"if Attribute 0 = 1.0:\n" \
" Leaf = Statistics {0: 644.0000, 1: [671.3479, 174.3011], 2: [927.5194, 466.7064]}\n" \
"if Attribute 0 = 2.0:\n" \
" Leaf = Statistics {0: 619.0000, 1: [867.2865, 320.6506], 2: [1262.0992, 435.2835]}\n" \
"if Attribute 0 = 3.0:\n" \
" Leaf = Statistics {0: 627.0000, 1: [987.0864, 331.0822], 2: [1583.8108, 484.0456]}\n" \
"if Attribute 0 = -30.0:\n" \
" Leaf = Statistics {0: 88.0000, 1: [-269.7967, 25.9328], 2: [828.2289, 57.6501]}\n"
assert SequenceMatcher(None, expected_description,
learner.get_model_description()).ratio() > 0.9
new_sample = X[0].copy()
# Adding a new category on the split feature
new_sample[0] = 7
learner.predict([new_sample])
# Let's do the same considering other prediction strategy
learner = MultiTargetRegressionHoeffdingTree(
nominal_attributes=nominal_attr_idx, leaf_prediction='adaptive')
learner.partial_fit(X, y)
learner.predict([new_sample])