def test_multi_output_learner_classifier():
stream = MultilabelGenerator(n_samples=5150,
n_features=15,
n_targets=3,
n_labels=4,
random_state=112)
estimator = SGDClassifier(random_state=112, max_iter=10, loss='log')
classifier = MultiOutputLearner(base_estimator=estimator)
X, y = get_next_n_samples(stream, 150)
classifier.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(classifier.predict(X)[0])
true_labels.append(y[0])
if np.array_equal(y[0], predictions[-1]):
correct_predictions += 1
classifier.partial_fit(X, y)
cnt += 1
if LooseVersion(sklearn_version) < LooseVersion("0.21"):
expected_predictions = [[1.0, 1.0, 1.0], [1.0, 0.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [0.0, 0.0, 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],
[0.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 0.0, 0.0],
[0.0, 1.0, 1.0], [1.0, 0.0, 0.0],
[1.0, 0.0, 1.0], [0.0, 1.0, 0.0],
[0.0, 0.0, 1.0], [1.0, 0.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[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, 0.0, 1.0], [0.0, 0.0, 1.0],
[0.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[1.0, 0.0, 0.0], [0.0, 1.0, 1.0],
[0.0, 0.0, 1.0], [1.0, 1.0, 0.0],
[1.0, 0.0, 1.0], [1.0, 0.0, 1.0],
[0.0, 0.0, 1.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 0.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 1.0]]
assert np.alltrue(np.array_equal(predictions, expected_predictions))
expected_correct_predictions = 26
assert correct_predictions == expected_correct_predictions
expected_performance = 0.7755102040816326
performance = hamming_score(true_labels, predictions)
assert np.isclose(performance, expected_performance)
expected_info = "MultiOutputLearner(base_estimator=SGDClassifier(loss='log', " \
"random_state=112))"
info = " ".join(
[line.strip() for line in classifier.get_info().split()])
assert info == expected_info
else:
expected_predictions = [[1.0, 1.0, 1.0], [1.0, 0.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 0.0, 1.0],
[1.0, 0.0, 1.0], [1.0, 0.0, 1.0],
[0.0, 1.0, 0.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 0.0],
[1.0, 1.0, 1.0], [1.0, 0.0, 0.0],
[1.0, 0.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 0.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 0.0], [1.0, 0.0, 1.0],
[0.0, 1.0, 1.0], [1.0, 1.0, 0.0],
[1.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 0.0, 1.0],
[0.0, 0.0, 1.0], [0.0, 0.0, 1.0],
[1.0, 0.0, 0.0], [0.0, 1.0, 1.0],
[0.0, 0.0, 1.0], [1.0, 1.0, 0.0],
[1.0, 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, 0.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 1.0]]
np.alltrue(np.array_equal(predictions, expected_predictions))
expected_correct_predictions = 23
assert correct_predictions == expected_correct_predictions
expected_performance = 0.7482993197278911
performance = hamming_score(true_labels, predictions)
assert np.isclose(performance, expected_performance)
expected_info = "MultiOutputLearner(base_estimator=SGDClassifier(loss='log', " \
"max_iter=10, random_state=112))"
info = " ".join(
[line.strip() for line in classifier.get_info().split()])
assert info == expected_info
assert type(classifier.predict(X)) == np.ndarray
assert type(classifier.predict_proba(X)) == np.ndarray
def test_multi_output_learner_classifier():
stream = MultilabelGenerator(n_samples=5150,
n_features=15,
n_targets=3,
n_labels=4,
random_state=112)
stream.prepare_for_use()
estimator = SGDClassifier(random_state=112,
tol=1e-3,
max_iter=10,
loss='log')
classifier = MultiOutputLearner(base_estimator=estimator)
X, y = stream.next_sample(150)
classifier.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(classifier.predict(X)[0])
true_labels.append(y[0])
if np.array_equal(y[0], predictions[-1]):
correct_predictions += 1
classifier.partial_fit(X, y)
cnt += 1
if StrictVersion(sklearn_version) < StrictVersion("0.21"):
expected_predictions = [[1.0, 1.0, 1.0], [1.0, 0.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [0.0, 0.0, 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],
[0.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 0.0, 0.0],
[0.0, 1.0, 1.0], [1.0, 0.0, 0.0],
[1.0, 0.0, 1.0], [0.0, 1.0, 0.0],
[0.0, 0.0, 1.0], [1.0, 0.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[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, 0.0, 1.0], [0.0, 0.0, 1.0],
[0.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[1.0, 0.0, 0.0], [0.0, 1.0, 1.0],
[0.0, 0.0, 1.0], [1.0, 1.0, 0.0],
[1.0, 0.0, 1.0], [1.0, 0.0, 1.0],
[0.0, 0.0, 1.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 0.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 1.0]]
assert np.alltrue(np.array_equal(predictions, expected_predictions))
expected_correct_predictions = 26
assert correct_predictions == expected_correct_predictions
expected_performance = 0.7755102040816326
performance = hamming_score(true_labels, predictions)
assert np.isclose(performance, expected_performance)
expected_info = "MultiOutputLearner(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='log', 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))"
assert classifier.get_info() == expected_info
else:
expected_predictions = [[1.0, 1.0, 1.0], [1.0, 0.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 0.0, 1.0],
[1.0, 0.0, 1.0], [1.0, 0.0, 1.0],
[0.0, 1.0, 0.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 0.0],
[1.0, 1.0, 1.0], [1.0, 0.0, 0.0],
[1.0, 0.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 0.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 0.0], [1.0, 0.0, 1.0],
[0.0, 1.0, 1.0], [1.0, 1.0, 0.0],
[1.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [0.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 0.0, 1.0],
[0.0, 0.0, 1.0], [0.0, 0.0, 1.0],
[1.0, 0.0, 0.0], [0.0, 1.0, 1.0],
[0.0, 0.0, 1.0], [1.0, 1.0, 0.0],
[1.0, 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, 0.0], [1.0, 1.0, 1.0],
[0.0, 1.0, 1.0]]
np.alltrue(np.array_equal(predictions, expected_predictions))
expected_correct_predictions = 23
assert correct_predictions == expected_correct_predictions
expected_performance = 0.7482993197278911
performance = hamming_score(true_labels, predictions)
assert np.isclose(performance, expected_performance)
expected_info = "MultiOutputLearner(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='log', 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))"
assert classifier.get_info() == expected_info
assert type(classifier.predict(X)) == np.ndarray
assert type(classifier.predict_proba(X)) == np.ndarray