def test_label_combination_hoeffding_tree_nb(test_path):
stream = MultilabelGenerator(n_samples=10000,
n_features=15,
n_targets=3,
n_labels=4,
random_state=112)
stream.prepare_for_use()
learner = LabelCombinationHoeffdingTreeClassifier(n_labels=3,
leaf_prediction='nb')
cnt = 0
max_samples = 5000
predictions = []
proba_predictions = []
wait_samples = 100
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
learner.partial_fit(X, y)
if cnt % wait_samples == 0 and (cnt != 0):
predictions.append(learner.predict(X)[0].tolist())
proba_predictions.append(learner.predict_proba(X)[0])
cnt += 1
print(predictions)
expected_predictions = [[0, 0, 1], [1, 1, 1], [0, 1, 1], [0, 1, 1],
[1, 1, 1], [0, 1, 1], [1, 1, 0], [1, 1, 1],
[1, 1, 1], [1, 1, 1], [0, 0, 0], [0, 1, 0],
[1, 0, 0], [1, 0, 1], [1, 1, 1], [1, 1, 1],
[0, 1, 1], [1, 1, 1], [0, 0, 0], [1, 1, 0],
[1, 0, 0], [1, 0, 1], [1, 1, 1], [0, 0, 1],
[1, 0, 1], [1, 1, 1], [1, 0, 0], [1, 1, 1],
[1, 1, 1], [0, 0, 1], [1, 1, 1], [0, 0, 0],
[0, 1, 0], [1, 1, 1], [0, 1, 1], [1, 1, 0],
[0, 0, 1], [0, 0, 0], [1, 1, 1], [1, 1, 1],
[1, 0, 1], [0, 1, 1], [1, 1, 1], [1, 1, 1],
[0, 1, 0], [0, 1, 0], [1, 1, 1], [1, 1, 1],
[1, 1, 1]]
assert np.alltrue(predictions == expected_predictions)
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
expected_info = "LabelCombinationHoeffdingTreeClassifier(binary_split=False, grace_period=200, " \
"leaf_prediction='nb', max_byte_size=33554432, memory_estimate_period=1000000, n_labels=3, " \
"nb_threshold=0, no_preprune=False, nominal_attributes=None, remove_poor_atts=False, " \
"split_confidence=1e-07, split_criterion='info_gain', stop_mem_management=False, " \
"tie_threshold=0.05)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
def test_label_combination_hoeffding_tree_coverage():
# Cover memory management
max_samples = 10000
max_size_kb = 50
stream = MultilabelGenerator(n_samples=10000,
n_features=15,
n_targets=3,
n_labels=4,
random_state=112)
# Unconstrained model has over 62 kB
learner = LabelCombinationHoeffdingTreeClassifier(
n_labels=3,
leaf_prediction='mc',
memory_estimate_period=200,
max_byte_size=max_size_kb * 2**10)
X, y = stream.next_sample(max_samples)
learner.partial_fit(X, y)
assert calculate_object_size(learner, 'kB') <= max_size_kb
# Imports
from skmultiflow.data import MultilabelGenerator
from skmultiflow.meta import MultiOutputLearner
from skmultiflow.trees import LabelCombinationHoeffdingTreeClassifier, HoeffdingTreeClassifier
from skmultiflow.metrics import hamming_score
# Setting up a data stream
stream = MultilabelGenerator(random_state=1, n_samples=200,
n_targets=5, n_features=10)
# Setup Label Combination Hoeffding Tree classifier
lc_ht = LabelCombinationHoeffdingTreeClassifier(n_labels=stream.n_targets)
# Setup variables to control loop and track performance
n_samples = 0
max_samples = 200
true_labels = []
predicts = []
# Train the estimator with the samples provided by the data stream
while n_samples < max_samples and stream.has_more_samples():
X, y = stream.next_sample()
y_pred = lc_ht.predict(X)
lc_ht.partial_fit(X, y, classes=stream.target_values)
predicts.extend(y_pred)
true_labels.extend(y)
n_samples += 1
# Display results
perf = hamming_score(true_labels, predicts)
print('{} samples analyzed.'.format(n_samples))
},
"mcc": {
"name": "Monte Carlo Sampling Classifier Chains",
"model": lambda _: MonteCarloClassifierChain(),
"ensemble": False
},
"pcc": {
"name": "Probabilistic Sampling Classifier Chains",
"model": lambda _: ProbabilisticClassifierChain(SGDClassifier(max_iter=100, loss='log', random_state=1)),
"ensemble": False
},
"lcht": {
"name": "Label Combination Hoeffding Tree",
"model": lambda data_stream: LabelCombinationHoeffdingTreeClassifier(
n_labels=data_stream.n_targets,
stop_mem_management=True,
memory_estimate_period=100,
remove_poor_atts=False # There is a bug when True
),
"ensemble": False
},
"awec": {
"name": "Accuracy Weighted Ensemble Classifier",
"model": lambda data_stream: MultiOutputLearner(
NaiveBayes(),
n_targets=data_stream.n_targets
),
"ensemble": lambda model, _: AccuracyWeightedEnsemble(
base_estimator=model
)
},
"me": {