def test_perceptron(test_path):
stream = SEAGenerator(random_state=1)
learner = PerceptronMask(random_state=1)
cnt = 0
max_samples = 5000
y_pred = array('i')
X_batch = []
y_batch = []
y_proba = []
wait_samples = 100
while cnt < max_samples:
X, y = stream.next_sample()
X_batch.append(X[0])
y_batch.append(y[0])
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
y_pred.append(learner.predict(X)[0])
y_proba.append(learner.predict_proba(X)[0])
learner.partial_fit(X, y, classes=stream.target_values)
cnt += 1
expected_predictions = array('i', [
1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0,
1
])
assert np.alltrue(y_pred == expected_predictions)
test_file = os.path.join(test_path, 'data_perceptron_proba.npy')
y_proba_expected = np.load(test_file)
assert np.allclose(y_proba, y_proba_expected)
expected_info = "PerceptronMask(alpha=0.0001, class_weight=None, early_stopping=False, " \
"eta0=1.0, fit_intercept=True, max_iter=1000, n_iter_no_change=5, " \
"n_jobs=None, penalty=None, random_state=1, shuffle=True, tol=0.001, " \
"validation_fraction=0.1, verbose=0, warm_start=False)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
# Coverage tests
learner.reset()
if not sklearn_version.startswith("0.21"):
learner.fit(X=np.asarray(X_batch[:4500]),
y=np.asarray(y_batch[:4500], dtype=int))
else:
# Root cause of failure (TypeError: an integer is required) is in the fit() method
# in sklearn 0.21.0. This is a workaround until a fix is made available in sklearn
learner.partial_fit(X=np.asarray(X_batch[:4500]),
y=np.asarray(y_batch[:4500]),
classes=stream.target_values)
learner.predict(X=X_batch[4501:]) # Run for coverage
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
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
class TestPCA(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.ionosphere = Table('ionosphere')
cls.iris = Table('iris')
cls.zoo = Table('zoo')
def test_pca(self):
data = self.ionosphere
self.__pca_test_helper(data, n_com=3, min_xpl_var=0.5)
self.__pca_test_helper(data, n_com=10, min_xpl_var=0.7)
self.__pca_test_helper(data, n_com=32, min_xpl_var=1)
def __pca_test_helper(self, data, n_com, min_xpl_var):
pca = PCA(n_components=n_com)
pca_model = pca(data)
pca_xpl_var = np.sum(pca_model.explained_variance_ratio_)
self.assertGreaterEqual(pca_xpl_var + 1e-6, min_xpl_var)
self.assertEqual(n_com, pca_model.n_components)
self.assertEqual((n_com, data.X.shape[1]), pca_model.components_.shape)
proj = np.dot(data.X - pca_model.mean_, pca_model.components_.T)
np.testing.assert_almost_equal(pca_model(data).X, proj)
def test_sparse_pca(self):
data = self.ionosphere[:100]
self.__sparse_pca_test_helper(data, n_com=3, max_err=1500)
self.__sparse_pca_test_helper(data, n_com=10, max_err=1000)
self.__sparse_pca_test_helper(data, n_com=32, max_err=500)
def __sparse_pca_test_helper(self, data, n_com, max_err):
sparse_pca = SparsePCA(n_components=n_com,
ridge_alpha=0.001,
random_state=0)
pca_model = sparse_pca(data)
self.assertEqual(n_com, pca_model.n_components)
self.assertEqual((n_com, data.X.shape[1]), pca_model.components_.shape)
self.assertLessEqual(pca_model.error_[-1], max_err)
def test_randomized_pca(self):
data = self.ionosphere
self.__rnd_pca_test_helper(data, n_com=3, min_xpl_var=0.5)
self.__rnd_pca_test_helper(data, n_com=10, min_xpl_var=0.7)
self.__rnd_pca_test_helper(data, n_com=32, min_xpl_var=0.98)
def __rnd_pca_test_helper(self, data, n_com, min_xpl_var):
rnd_pca = PCA(n_components=n_com, svd_solver='randomized')
pca_model = rnd_pca(data)
pca_xpl_var = np.sum(pca_model.explained_variance_ratio_)
self.assertGreaterEqual(pca_xpl_var, min_xpl_var)
self.assertEqual(n_com, pca_model.n_components)
self.assertEqual((n_com, data.X.shape[1]), pca_model.components_.shape)
proj = np.dot(data.X - pca_model.mean_, pca_model.components_.T)
np.testing.assert_almost_equal(pca_model(data).X, proj)
@unittest.skipIf(
sklearn_version.startswith('0.20'),
"https://github.com/scikit-learn/scikit-learn/issues/12234")
def test_incremental_pca(self):
data = self.ionosphere
self.__ipca_test_helper(data, n_com=3, min_xpl_var=0.49)
self.__ipca_test_helper(data, n_com=32, min_xpl_var=1)
def __ipca_test_helper(self, data, n_com, min_xpl_var):
pca = IncrementalPCA(n_components=n_com)
pca_model = pca(data[::2])
pca_xpl_var = np.sum(pca_model.explained_variance_ratio_)
self.assertGreaterEqual(pca_xpl_var + 1e-6, min_xpl_var)
self.assertEqual(n_com, pca_model.n_components)
self.assertEqual((n_com, data.X.shape[1]), pca_model.components_.shape)
proj = np.dot(data.X - pca_model.mean_, pca_model.components_.T)
np.testing.assert_almost_equal(pca_model(data).X, proj)
pc1_ipca = pca_model.components_[0]
self.assertAlmostEqual(np.linalg.norm(pc1_ipca), 1)
pc1_pca = PCA(n_components=n_com)(data).components_[0]
self.assertAlmostEqual(np.linalg.norm(pc1_pca), 1)
self.assertNotAlmostEqual(abs(pc1_ipca.dot(pc1_pca)), 1, 2)
pc1_ipca = pca_model.partial_fit(data[1::2]).components_[0]
self.assertAlmostEqual(abs(pc1_ipca.dot(pc1_pca)), 1, 4)
def test_truncated_svd(self):
data = self.ionosphere
self.__truncated_svd_test_helper(data,
n_components=3,
min_variance=0.5)
self.__truncated_svd_test_helper(data,
n_components=10,
min_variance=0.7)
self.__truncated_svd_test_helper(data,
n_components=31,
min_variance=0.99)
def __truncated_svd_test_helper(self, data, n_components, min_variance):
model = TruncatedSVD(n_components=n_components)(data)
svd_variance = np.sum(model.explained_variance_ratio_)
self.assertGreaterEqual(svd_variance + 1e-6, min_variance)
self.assertEqual(n_components, model.n_components)
self.assertEqual((n_components, data.X.shape[1]),
model.components_.shape)
proj = np.dot(data.X, model.components_.T)
np.testing.assert_almost_equal(model(data).X, proj)
def test_compute_value(self):
iris = self.iris
pca = PCA(n_components=2)(iris)
pca_iris = pca(iris)
pca_iris2 = Table(pca_iris.domain, iris)
np.testing.assert_almost_equal(pca_iris.X, pca_iris2.X)
np.testing.assert_equal(pca_iris.Y, pca_iris2.Y)
pca_iris3 = pickle.loads(pickle.dumps(pca_iris))
np.testing.assert_almost_equal(pca_iris.X, pca_iris3.X)
np.testing.assert_equal(pca_iris.Y, pca_iris3.Y)
def test_transformed_domain_does_not_pickle_data(self):
iris = self.iris
pca = PCA(n_components=2)(iris)
pca_iris = pca(iris)
pca_iris2 = Table(pca_iris.domain, iris)
pca_iris2 = pickle.loads(pickle.dumps(pca_iris))
self.assertIsNone(pca_iris2.domain[0].compute_value.transformed)
def test_chain(self):
zoo_c = Continuize()(self.zoo)
pca = PCA(n_components=3)(zoo_c)(self.zoo)
pca2 = PCA(n_components=3)(zoo_c)(zoo_c)
pp = [Continuize()]
pca3 = PCA(n_components=3, preprocessors=pp)(self.zoo)(self.zoo)
np.testing.assert_almost_equal(pca.X, pca2.X)
np.testing.assert_almost_equal(pca.X, pca3.X)
def test_PCA_scorer(self):
data = self.iris
pca = PCA(preprocessors=[Normalize()])
pca.component = 1
scores = pca.score_data(data)
self.assertEqual(scores.shape[1], len(data.domain.attributes))
self.assertEqual(['petal length', 'petal width'],
sorted([
data.domain.attributes[i].name
for i in np.argsort(scores[0])[-2:]
]))
self.assertEqual([round(s, 4) for s in scores[0]],
[0.5224, 0.2634, 0.5813, 0.5656])
def test_PCA_scorer_component(self):
pca = PCA()
for i in range(1, len(self.zoo.domain.attributes) + 1):
pca.component = i
scores = pca.score_data(self.zoo)
self.assertEqual(scores.shape,
(pca.component, len(self.zoo.domain.attributes)))
def test_PCA_scorer_all_components(self):
n_attr = len(self.iris.domain.attributes)
pca = PCA()
scores = pca.score_data(self.iris)
self.assertEqual(scores.shape, (n_attr, n_attr))
def test_max_components(self):
d = np.random.RandomState(0).rand(20, 20)
data = Table(d)
pca = PCA()(data)
self.assertEqual(len(pca.explained_variance_ratio_), 20)
pca = PCA(n_components=10)(data)
self.assertEqual(len(pca.explained_variance_ratio_), 10)
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
def test_multi_output_learner():
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 not sklearn_version.startswith("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
class TestPCA(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.ionosphere = Table(test_filename('datasets/ionosphere.tab'))
cls.iris = Table('iris')
cls.zoo = Table('zoo')
def test_pca(self):
data = self.ionosphere
self.__pca_test_helper(data, n_com=3, min_xpl_var=0.5)
self.__pca_test_helper(data, n_com=10, min_xpl_var=0.7)
self.__pca_test_helper(data, n_com=32, min_xpl_var=1)
def __pca_test_helper(self, data, n_com, min_xpl_var):
pca = PCA(n_components=n_com)
pca_model = pca(data)
pca_xpl_var = np.sum(pca_model.explained_variance_ratio_)
self.assertGreaterEqual(pca_xpl_var + 1e-6, min_xpl_var)
self.assertEqual(n_com, pca_model.n_components)
self.assertEqual((n_com, data.X.shape[1]), pca_model.components_.shape)
proj = np.dot(data.X - pca_model.mean_, pca_model.components_.T)
np.testing.assert_almost_equal(pca_model(data).X, proj)
def test_sparse_pca(self):
data = self.ionosphere[:100]
self.__sparse_pca_test_helper(data, n_com=3, max_err=1500)
self.__sparse_pca_test_helper(data, n_com=10, max_err=1000)
self.__sparse_pca_test_helper(data, n_com=32, max_err=500)
def __sparse_pca_test_helper(self, data, n_com, max_err):
sparse_pca = SparsePCA(n_components=n_com, ridge_alpha=0.001, random_state=0)
pca_model = sparse_pca(data)
self.assertEqual(n_com, pca_model.n_components)
self.assertEqual((n_com, data.X.shape[1]), pca_model.components_.shape)
self.assertLessEqual(pca_model.error_[-1], max_err)
def test_randomized_pca(self):
data = self.ionosphere
self.__rnd_pca_test_helper(data, n_com=3, min_xpl_var=0.5)
self.__rnd_pca_test_helper(data, n_com=10, min_xpl_var=0.7)
self.__rnd_pca_test_helper(data, n_com=32, min_xpl_var=0.98)
def __rnd_pca_test_helper(self, data, n_com, min_xpl_var):
rnd_pca = PCA(n_components=n_com, svd_solver='randomized')
pca_model = rnd_pca(data)
pca_xpl_var = np.sum(pca_model.explained_variance_ratio_)
self.assertGreaterEqual(pca_xpl_var, min_xpl_var)
self.assertEqual(n_com, pca_model.n_components)
self.assertEqual((n_com, data.X.shape[1]), pca_model.components_.shape)
proj = np.dot(data.X - pca_model.mean_, pca_model.components_.T)
np.testing.assert_almost_equal(pca_model(data).X, proj)
def test_improved_randomized_pca_properly_called(self):
# It doesn't matter what we put into the matrix
x_ = np.random.normal(0, 1, (100, 20))
x = Table.from_numpy(Domain.from_numpy(x_), x_)
pca.randomized_pca = MagicMock(wraps=pca.randomized_pca)
PCA(10, svd_solver="randomized", random_state=42)(x)
pca.randomized_pca.assert_called_once()
pca.randomized_pca.reset_mock()
PCA(10, svd_solver="arpack", random_state=42)(x)
pca.randomized_pca.assert_not_called()
def test_improved_randomized_pca_dense_data(self):
"""Randomized PCA should work well on dense data."""
random_state = check_random_state(42)
# Let's take a tall, skinny matrix
x_ = random_state.normal(0, 1, (100, 20))
x = Table.from_numpy(Domain.from_numpy(x_), x_)
pca = PCA(10, svd_solver="full", random_state=random_state)(x)
rpca = PCA(10, svd_solver="randomized", random_state=random_state)(x)
np.testing.assert_almost_equal(
pca.components_, rpca.components_, decimal=8
)
np.testing.assert_almost_equal(
pca.explained_variance_, rpca.explained_variance_, decimal=8
)
np.testing.assert_almost_equal(
pca.singular_values_, rpca.singular_values_, decimal=8
)
# And take a short, fat matrix
x_ = random_state.normal(0, 1, (20, 100))
x = Table.from_numpy(Domain.from_numpy(x_), x_)
pca = PCA(10, svd_solver="full", random_state=random_state)(x)
rpca = PCA(10, svd_solver="randomized", random_state=random_state)(x)
np.testing.assert_almost_equal(
pca.components_, rpca.components_, decimal=8
)
np.testing.assert_almost_equal(
pca.explained_variance_, rpca.explained_variance_, decimal=8
)
np.testing.assert_almost_equal(
pca.singular_values_, rpca.singular_values_, decimal=8
)
def test_improved_randomized_pca_sparse_data(self):
"""Randomized PCA should work well on dense data."""
random_state = check_random_state(42)
# Let's take a tall, skinny matrix
x_ = random_state.negative_binomial(1, 0.5, (100, 20))
x = Table.from_numpy(Domain.from_numpy(x_), x_).to_sparse()
pca = PCA(10, svd_solver="full", random_state=random_state)(x.to_dense())
rpca = PCA(10, svd_solver="randomized", random_state=random_state)(x)
np.testing.assert_almost_equal(
pca.components_, rpca.components_, decimal=8
)
np.testing.assert_almost_equal(
pca.explained_variance_, rpca.explained_variance_, decimal=8
)
np.testing.assert_almost_equal(
pca.singular_values_, rpca.singular_values_, decimal=8
)
# And take a short, fat matrix
x_ = random_state.negative_binomial(1, 0.5, (20, 100))
x = Table.from_numpy(Domain.from_numpy(x_), x_).to_sparse()
pca = PCA(10, svd_solver="full", random_state=random_state)(x.to_dense())
rpca = PCA(10, svd_solver="randomized", random_state=random_state)(x)
np.testing.assert_almost_equal(
pca.components_, rpca.components_, decimal=8
)
np.testing.assert_almost_equal(
pca.explained_variance_, rpca.explained_variance_, decimal=8
)
np.testing.assert_almost_equal(
pca.singular_values_, rpca.singular_values_, decimal=8
)
@unittest.skipIf(sklearn_version.startswith('0.20'),
"https://github.com/scikit-learn/scikit-learn/issues/12234")
def test_incremental_pca(self):
data = self.ionosphere
self.__ipca_test_helper(data, n_com=3, min_xpl_var=0.49)
self.__ipca_test_helper(data, n_com=32, min_xpl_var=1)
def __ipca_test_helper(self, data, n_com, min_xpl_var):
pca = IncrementalPCA(n_components=n_com)
pca_model = pca(data[::2])
pca_xpl_var = np.sum(pca_model.explained_variance_ratio_)
self.assertGreaterEqual(pca_xpl_var + 1e-6, min_xpl_var)
self.assertEqual(n_com, pca_model.n_components)
self.assertEqual((n_com, data.X.shape[1]), pca_model.components_.shape)
proj = np.dot(data.X - pca_model.mean_, pca_model.components_.T)
np.testing.assert_almost_equal(pca_model(data).X, proj)
pc1_ipca = pca_model.components_[0]
self.assertAlmostEqual(np.linalg.norm(pc1_ipca), 1)
pc1_pca = PCA(n_components=n_com)(data).components_[0]
self.assertAlmostEqual(np.linalg.norm(pc1_pca), 1)
self.assertNotAlmostEqual(abs(pc1_ipca.dot(pc1_pca)), 1, 2)
pc1_ipca = pca_model.partial_fit(data[1::2]).components_[0]
self.assertAlmostEqual(abs(pc1_ipca.dot(pc1_pca)), 1, 4)
def test_truncated_svd(self):
data = self.ionosphere
self.__truncated_svd_test_helper(data, n_components=3, min_variance=0.5)
self.__truncated_svd_test_helper(data, n_components=10, min_variance=0.7)
self.__truncated_svd_test_helper(data, n_components=31, min_variance=0.99)
def __truncated_svd_test_helper(self, data, n_components, min_variance):
model = TruncatedSVD(n_components=n_components)(data)
svd_variance = np.sum(model.explained_variance_ratio_)
self.assertGreaterEqual(svd_variance + 1e-6, min_variance)
self.assertEqual(n_components, model.n_components)
self.assertEqual((n_components, data.X.shape[1]), model.components_.shape)
proj = np.dot(data.X, model.components_.T)
np.testing.assert_almost_equal(model(data).X, proj)
def test_compute_value(self):
iris = self.iris
pca = PCA(n_components=2)(iris)
pca_iris = pca(iris)
pca_iris2 = iris.transform(pca_iris.domain)
np.testing.assert_almost_equal(pca_iris.X, pca_iris2.X)
np.testing.assert_equal(pca_iris.Y, pca_iris2.Y)
pca_iris3 = pickle.loads(pickle.dumps(pca_iris))
np.testing.assert_almost_equal(pca_iris.X, pca_iris3.X)
np.testing.assert_equal(pca_iris.Y, pca_iris3.Y)
def test_transformed_domain_does_not_pickle_data(self):
iris = self.iris
pca = PCA(n_components=2)(iris)
pca_iris = pca(iris)
pca_iris2 = iris.transform(pca_iris.domain)
pca_iris2 = pickle.loads(pickle.dumps(pca_iris))
self.assertIsNone(pca_iris2.domain[0].compute_value.transformed)
def test_chain(self):
zoo_c = Continuize()(self.zoo)
pca = PCA(n_components=3)(zoo_c)(self.zoo)
pca2 = PCA(n_components=3)(zoo_c)(zoo_c)
pp = [Continuize()]
pca3 = PCA(n_components=3, preprocessors=pp)(self.zoo)(self.zoo)
np.testing.assert_almost_equal(pca.X, pca2.X)
np.testing.assert_almost_equal(pca.X, pca3.X)
def test_PCA_scorer(self):
data = self.iris
pca = PCA(preprocessors=[Normalize()])
pca.component = 1
scores = pca.score_data(data)
self.assertEqual(scores.shape[1], len(data.domain.attributes))
self.assertEqual(['petal length', 'petal width'],
sorted([data.domain.attributes[i].name
for i in np.argsort(scores[0])[-2:]]))
self.assertEqual([round(s, 4) for s in scores[0]],
[0.5224, 0.2634, 0.5813, 0.5656])
def test_PCA_scorer_component(self):
pca = PCA()
for i in range(1, len(self.zoo.domain.attributes) + 1):
pca.component = i
scores = pca.score_data(self.zoo)
self.assertEqual(scores.shape,
(pca.component, len(self.zoo.domain.attributes)))
def test_PCA_scorer_all_components(self):
n_attr = len(self.iris.domain.attributes)
pca = PCA()
scores = pca.score_data(self.iris)
self.assertEqual(scores.shape, (n_attr, n_attr))
def test_max_components(self):
d = np.random.RandomState(0).rand(20, 20)
data = Table.from_numpy(None, d)
pca = PCA()(data)
self.assertEqual(len(pca.explained_variance_ratio_), 20)
pca = PCA(n_components=10)(data)
self.assertEqual(len(pca.explained_variance_ratio_), 10)