def testPCA(self):
X = self.iris
for n_comp in np.arange(X.shape[1]):
pca = PCA(n_components=n_comp, svd_solver='full')
X_r = pca.fit(X).transform(X).fetch()
np.testing.assert_equal(X_r.shape[1], n_comp)
X_r2 = pca.fit_transform(X).fetch()
assert_array_almost_equal(X_r, X_r2)
X_r = pca.transform(X).fetch()
X_r2 = pca.fit_transform(X).fetch()
assert_array_almost_equal(X_r, X_r2)
# Test get_covariance and get_precision
cov = pca.get_covariance()
precision = pca.get_precision()
assert_array_almost_equal(
mt.dot(cov, precision).execute(), np.eye(X.shape[1]), 12)
# test explained_variance_ratio_ == 1 with all components
pca = PCA(svd_solver='full')
pca.fit(X)
np.testing.assert_allclose(
pca.explained_variance_ratio_.sum().execute(), 1.0, 3)
def testPCARandomizedSolver(self):
# PCA on dense arrays
X = self.iris
# Loop excluding the 0, invalid for randomized
for n_comp in np.arange(1, X.shape[1]):
pca = PCA(n_components=n_comp,
svd_solver='randomized',
random_state=0)
X_r = pca.fit(X).transform(X)
np.testing.assert_equal(X_r.shape[1], n_comp)
X_r2 = pca.fit_transform(X)
assert_array_almost_equal(X_r.fetch(), X_r2.fetch())
X_r = pca.transform(X)
assert_array_almost_equal(X_r.fetch(), X_r2.fetch())
# Test get_covariance and get_precision
cov = pca.get_covariance()
precision = pca.get_precision()
assert_array_almost_equal(
mt.dot(cov, precision).execute(),
mt.eye(X.shape[1]).execute(), 12)
pca = PCA(n_components=0, svd_solver='randomized', random_state=0)
with self.assertRaises(ValueError):
pca.fit(X)
pca = PCA(n_components=0, svd_solver='randomized', random_state=0)
with self.assertRaises(ValueError):
pca.fit(X)
# Check internal state
self.assertEqual(
pca.n_components,
PCA(n_components=0, svd_solver='randomized',
random_state=0).n_components)
self.assertEqual(
pca.svd_solver,
PCA(n_components=0, svd_solver='randomized',
random_state=0).svd_solver)
