def test_classifier_chains():
seed = 112
stream = MultilabelGenerator(random_state=seed, n_targets=3, n_samples=5150)
stream.prepare_for_use()
estimator = SGDClassifier(random_state=seed, tol=1e-3, max_iter=10)
learner = ClassifierChain(base_estimator=estimator, random_state=seed)
X, y = stream.next_sample(150)
learner.partial_fit(X, y)
cnt = 0
max_samples = 5000
predictions = []
true_labels = []
wait_samples = 100
correct_predictions = 0
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])
true_labels.append(y[0])
if np.array_equal(y[0], predictions[-1]):
correct_predictions += 1
learner.partial_fit(X, y)
cnt += 1
if not sklearn_version.startswith("0.21"):
expected_predictions = [[0.0, 0.0, 1.0], [1.0, 0.0, 0.0], [1.0, 0.0, 1.0], [1.0, 1.0, 1.0], [1.0, 0.0, 1.0],
[1.0, 0.0, 0.0], [1.0, 0.0, 1.0], [1.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0],
[1.0, 0.0, 0.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0],
[0.0, 0.0, 1.0], [1.0, 1.0, 1.0], [1.0, 0.0, 1.0], [1.0, 1.0, 1.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 1.0], [1.0, 1.0, 0.0],
[0.0, 1.0, 0.0], [1.0, 0.0, 1.0], [1.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0],
[1.0, 0.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 0.0], [1.0, 0.0, 1.0],
[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [1.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0],
[0.0, 0.0, 0.0], [1.0, 1.0, 1.0], [0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]
assert np.alltrue(np.array_equal(predictions, expected_predictions))
expected_correct_predictions = 21
assert correct_predictions == expected_correct_predictions
expected_info = "ClassifierChain(base_estimator=SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n" \
" early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n" \
" l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=10,\n" \
" n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\n" \
" power_t=0.5, random_state=112, shuffle=True, tol=0.001,\n" \
" validation_fraction=0.1, verbose=0, warm_start=False),\n" \
" order=None, random_state=112)"
assert learner.get_info() == expected_info
else:
expected_predictions = [[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [1.0, 0.0, 1.0], [1.0, 0.0, 1.0], [0.0, 0.0, 1.0],
[1.0, 0.0, 0.0], [1.0, 0.0, 1.0], [1.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0],
[1.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0],
[0.0, 0.0, 1.0], [1.0, 0.0, 1.0], [0.0, 0.0, 0.0], [1.0, 0.0, 1.0], [0.0, 0.0, 0.0],
[0.0, 1.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[0.0, 1.0, 1.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 0.0],
[0.0, 1.0, 1.0], [1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0],
[1.0, 0.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 0.0], [1.0, 0.0, 1.0],
[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0],
[0.0, 0.0, 0.0], [1.0, 1.0, 1.0], [0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]
assert np.alltrue(np.array_equal(predictions, expected_predictions))
expected_correct_predictions = 26
assert correct_predictions == expected_correct_predictions
expected_info = "ClassifierChain(base_estimator=SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n" \
" early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n" \
" l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=10,\n" \
" n_iter_no_change=5, n_jobs=None, penalty='l2', power_t=0.5,\n" \
" random_state=112, shuffle=True, tol=0.001,\n" \
" validation_fraction=0.1, verbose=0, warm_start=False),\n" \
" order=None, random_state=112)"
assert learner.get_info() == expected_info
assert type(learner.predict(X)) == np.ndarray
def test_classifier_chains():
seed = 112
stream = MultilabelGenerator(random_state=seed,
n_targets=3,
n_samples=5150)
estimator = SGDClassifier(random_state=seed, max_iter=10)
learner = ClassifierChain(base_estimator=estimator, random_state=seed)
X, y = get_next_n_samples(stream, 150)
learner.partial_fit(X, y)
cnt = 0
max_samples = 5000
predictions = []
true_labels = []
wait_samples = 100
correct_predictions = 0
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])
true_labels.append(y[0])
if np.array_equal(y[0], predictions[-1]):
correct_predictions += 1
learner.partial_fit(X, y)
cnt += 1
if not sklearn_version.startswith("0.21"):
expected_predictions = [[0., 0., 1.], [0., 0., 0.], [1., 0., 1.],
[1., 0., 1.], [0., 0., 1.], [1., 0., 0.],
[1., 0., 1.], [1., 0., 1.], [0., 0., 1.],
[0., 0., 0.], [1., 0., 1.], [0., 0., 1.],
[0., 0., 1.], [0., 0., 1.], [0., 0., 1.],
[0., 0., 1.], [1., 0., 1.], [0., 0., 0.],
[1., 0., 1.], [0., 0., 0.], [0., 1., 1.],
[0., 1., 1.], [0., 0., 1.], [0., 1., 1.],
[0., 1., 1.], [0., 1., 1.], [0., 1., 0.],
[0., 1., 0.], [1., 1., 1.], [0., 1., 0.],
[0., 1., 1.], [1., 0., 1.], [0., 1., 1.],
[0., 0., 0.], [0., 0., 0.], [1., 0., 0.],
[1., 1., 1.], [0., 1., 1.], [0., 0., 0.],
[1., 0., 1.], [0., 0., 1.], [0., 0., 0.],
[0., 0., 0.], [0., 0., 1.], [0., 1., 0.],
[0., 0., 0.], [1., 1., 1.], [0., 0., 0.],
[1., 1., 1.]]
assert np.alltrue(np.array_equal(predictions, expected_predictions))
expected_correct_predictions = 26
assert correct_predictions == expected_correct_predictions
expected_info = "ClassifierChain(base_estimator=SGDClassifier(max_iter=10, " \
"random_state=112), order=None, random_state=112)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
else:
expected_predictions = [[0.0, 0.0, 1.0], [0.0, 0.0, 0.0],
[1.0, 0.0, 1.0], [1.0, 0.0, 1.0],
[0.0, 0.0, 1.0], [1.0, 0.0, 0.0],
[1.0, 0.0, 1.0], [1.0, 0.0, 1.0],
[0.0, 0.0, 1.0], [0.0, 0.0, 0.0],
[1.0, 0.0, 1.0], [0.0, 0.0, 1.0],
[0.0, 0.0, 1.0], [0.0, 0.0, 1.0],
[0.0, 0.0, 1.0], [0.0, 0.0, 1.0],
[1.0, 0.0, 1.0], [0.0, 0.0, 0.0],
[1.0, 0.0, 1.0], [0.0, 0.0, 0.0],
[0.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[0.0, 0.0, 1.0], [0.0, 1.0, 1.0],
[0.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[0.0, 1.0, 0.0], [0.0, 1.0, 0.0],
[1.0, 1.0, 1.0], [0.0, 1.0, 0.0],
[0.0, 1.0, 1.0], [1.0, 0.0, 1.0],
[0.0, 1.0, 1.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 0.0], [1.0, 0.0, 0.0],
[1.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[0.0, 0.0, 0.0], [1.0, 0.0, 1.0],
[0.0, 0.0, 1.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 0.0], [0.0, 0.0, 1.0],
[0.0, 1.0, 0.0], [0.0, 0.0, 0.0],
[1.0, 1.0, 1.0], [0.0, 0.0, 0.0],
[1.0, 1.0, 1.0]]
assert np.alltrue(np.array_equal(predictions, expected_predictions))
expected_correct_predictions = 26
assert correct_predictions == expected_correct_predictions
expected_info = "ClassifierChain(base_estimator=SGDClassifier(max_iter=10, " \
"random_state=112), order=None, random_state=112)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
assert type(learner.predict(X)) == np.ndarray