def test_fit(nrows, ncols, n_components, sparse_input, density,
sparse_format, batch_size_divider):
if sparse_format == 'csc':
pytest.skip("cupyx.scipy.sparse.csc.csc_matrix does not support"
" indexing as of cupy 7.6.0")
if sparse_input:
X = cupyx.scipy.sparse.random(nrows, ncols, density=density,
random_state=10, format=sparse_format)
else:
X, _ = make_blobs(n_samples=nrows, n_features=ncols, random_state=10)
cu_ipca = cuIPCA(n_components=n_components,
batch_size=int(nrows / batch_size_divider))
cu_ipca.fit(X)
cu_t = cu_ipca.transform(X)
cu_inv = cu_ipca.inverse_transform(cu_t)
sk_ipca = skIPCA(n_components=n_components,
batch_size=int(nrows / batch_size_divider))
if sparse_input:
X = X.get()
else:
X = cp.asnumpy(X)
sk_ipca.fit(X)
sk_t = sk_ipca.transform(X)
sk_inv = sk_ipca.inverse_transform(sk_t)
assert array_equal(cu_inv, sk_inv,
5e-5, with_sign=True)
def test_partial_fit(nrows, ncols, n_components, density,
batch_size_divider):
X, _ = make_blobs(n_samples=nrows, n_features=ncols, random_state=10)
cu_ipca = cuIPCA(n_components=n_components)
sample_size = int(nrows / batch_size_divider)
for i in range(0, nrows, sample_size):
cu_ipca.partial_fit(X[i:i + sample_size].copy())
cu_t = cu_ipca.transform(X)
cu_inv = cu_ipca.inverse_transform(cu_t)
sk_ipca = skIPCA(n_components=n_components)
X = cp.asnumpy(X)
for i in range(0, nrows, sample_size):
sk_ipca.partial_fit(X[i:i + sample_size].copy())
sk_t = sk_ipca.transform(X)
sk_inv = sk_ipca.inverse_transform(sk_t)
assert array_equal(cu_inv, sk_inv,
5e-5, with_sign=True)
def test_exceptions():
X = cupyx.scipy.sparse.eye(10)
ipca = cuIPCA()
with pytest.raises(TypeError):
ipca.partial_fit(X)
X = X.toarray()
with pytest.raises(NotFittedError):
ipca.transform(X)
with pytest.raises(NotFittedError):
ipca.inverse_transform(X)
with pytest.raises(ValueError):
cuIPCA(n_components=8).fit(X[:5])
with pytest.raises(ValueError):
cuIPCA(n_components=8).fit(X[:, :5])