def test_fit_transform(self):
X = np.random.randn(1000, 5)
X -= np.mean(X, axis=0)
X /= np.linalg.norm(X, axis=1).max()
clf = PCA(2, epsilon=5, centered=True, data_norm=1)
self.assertIsNotNone(clf.fit_transform(X))
def test_mle_components(self):
X = np.random.randn(25000, 21)
X -= np.mean(X, axis=0)
X /= np.linalg.norm(X, axis=1).max()
clf = PCA("mle", epsilon=5, centered=True, data_norm=1)
self.assertIsNotNone(clf.fit(X))
def test_large_norm(self):
X = np.array(
[0.50, 0.75, 1.00, 1.25, 1.50, 1.75, 1.75, 2.00, 2.25, 2.50, 2.75, 3.00, 3.25, 3.50, 4.00, 4.25, 4.50, 4.75,
5.00, 5.50])
X = X[:, np.newaxis]
clf = PCA(data_norm=1.0, centered=True)
self.assertIsNotNone(clf.fit(X))
def test_centered(self):
clf = PCA(data_norm=3, centered=True)
X = np.array(
[0.50, 0.75, 1.00, 1.25, 1.50, 1.75, 1.75, 2.00, 2.25, 2.50, 2.75, 3.00, 3.25, 3.50, 4.00, 4.25, 4.50, 4.75,
5.00, 5.50])
X = X[:, np.newaxis]
X -= X.mean(axis=0)
self.assertIsNotNone(clf.fit(X))
def test_no_range(self):
clf = PCA(data_norm=6)
X = np.array(
[0.50, 0.75, 1.00, 1.25, 1.50, 1.75, 1.75, 2.00, 2.25, 2.50, 2.75, 3.00, 3.25, 3.50, 4.00, 4.25, 4.50, 4.75,
5.00, 5.50])
X = X[:, np.newaxis]
with self.assertWarns(PrivacyLeakWarning):
clf.fit(X)
def test_big_epsilon(self):
X = np.random.randn(25000, 21)
X -= np.mean(X, axis=0)
X /= np.linalg.norm(X, axis=1).max()
for components in range(1, 10):
clf = PCA(components, epsilon=5, centered=True, data_norm=1)
clf.fit(X)
sk_clf = sk_pca.PCA(components, svd_solver='full')
sk_clf.fit(X)
self.assertAlmostEqual(clf.score(X), sk_clf.score(X), places=4)
def test_accountant(self):
from diffprivlib.accountant import BudgetAccountant
acc = BudgetAccountant()
X = np.random.randn(25000, 21)
X -= np.mean(X, axis=0)
X /= np.linalg.norm(X, axis=1).max()
clf = PCA(5, epsilon=5, centered=True, data_norm=1, accountant=acc)
clf.fit(X)
self.assertEqual((5, 0), acc.total())
with BudgetAccountant(8, 0) as acc2:
clf = PCA(5, epsilon=5, centered=True, data_norm=1)
clf.fit(X)
self.assertEqual((5, 0), acc2.total())
with self.assertRaises(BudgetError):
clf.fit(X)
def test_inf_epsilon(self):
X = np.random.randn(250, 21)
X -= np.mean(X, axis=0)
X /= np.linalg.norm(X, axis=1).max()
for components in range(1, 10):
clf = PCA(components, epsilon=float("inf"), centered=True, data_norm=1)
clf.fit(X)
sk_clf = sk_pca.PCA(components, svd_solver='full')
sk_clf.fit(X)
self.assertAlmostEqual(clf.score(X), sk_clf.score(X))
self.assertTrue(np.allclose(clf.get_precision(), sk_clf.get_precision()))
self.assertTrue(np.allclose(clf.get_covariance(), sk_clf.get_covariance()))
self.assertTrue(np.allclose(np.abs((clf.components_ / sk_clf.components_).sum(axis=1)), clf.n_features_))
def test_solver_warning(self):
with self.assertWarns(DiffprivlibCompatibilityWarning):
PCA(svd_solver='full')
def test_not_none(self):
clf = PCA(epsilon=1, centered=True, data_norm=1)
self.assertIsNotNone(clf)
def test_different_results(self):
from sklearn import datasets
from sklearn.model_selection import train_test_split
dataset = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.2)
mean = np.mean(X_train, axis=0)
X_train -= mean
X_test -= mean
clf = PCA(data_norm=12, centered=True)
clf.fit(X_train, y_train)
transform1 = clf.transform(X_test)
clf = PCA(data_norm=12, centered=True)
clf.fit(X_train, y_train)
transform2 = clf.transform(X_test)
clf = sk_pca.PCA(svd_solver='full')
clf.fit(X_train, y_train)
transform3 = clf.transform(X_test)
self.assertFalse(np.all(transform1 == transform2))
self.assertFalse(np.all(transform3 == transform1) and np.all(transform3 == transform2))
