def test_transform_sparse_adaptive_pca():
G = build_graph(data, sparse=True, n_pca=True, random_state=42)
assert np.all(G.data_nu == G.transform(G.data))
with assert_raises_message(
ValueError,
"data of shape ({0}, 1) cannot be transformed to graph built on data of shape ({0}, {1}). Expected shape ({0}, {1})".format(
G.data.shape[0], G.data.shape[1]
),
):
G.transform(sp.csr_matrix(G.data)[:, 0])
with assert_raises_message(
ValueError,
"data of shape ({0}, 15) cannot be transformed to graph built on data of shape ({0}, {1}). Expected shape ({0}, {1})".format(
G.data.shape[0], G.data.shape[1]
),
):
G.transform(sp.csr_matrix(G.data)[:, :15])
G2 = build_graph(
data, sparse=True, n_pca=True, rank_threshold=G.rank_threshold, random_state=42
)
assert np.allclose(G2.data_nu, G2.transform(G2.data))
assert np.allclose(G2.data_nu, G.transform(G.data))
G3 = build_graph(data, sparse=True, n_pca=G2.n_pca, random_state=42)
assert np.allclose(G3.data_nu, G3.transform(G3.data))
assert np.allclose(G3.data_nu, G2.transform(G2.data))
def test_transform_sparse_pca():
G = build_graph(data, sparse=True, n_pca=20)
assert np.all(G.data_nu == G.transform(G.data))
with assert_raises_message(
ValueError,
"data of shape ({0}, 1) cannot be transformed to graph built on data of shape ({0}, {1}). Expected shape ({0}, {1})".format(
G.data.shape[0], G.data.shape[1]
),
):
G.transform(sp.csr_matrix(G.data)[:, 0])
with assert_raises_message(
ValueError,
"data of shape ({0}, 15) cannot be transformed to graph built on data of shape ({0}, {1}). Expected shape ({0}, {1})".format(
G.data.shape[0], G.data.shape[1]
),
):
G.transform(sp.csr_matrix(G.data)[:, :15])
def test_inverse_transform_sparse_no_pca():
G = build_graph(data, sparse=True, n_pca=None)
assert np.sum(G.data != G.inverse_transform(G.data_nu)) == 0
with assert_raises_message(
ValueError,
"data of shape ({0}, 1) cannot be inverse transformed from graph built on reduced data of shape ({0}, {1})".format(
G.data.shape[0], G.data.shape[1]
),
):
G.inverse_transform(sp.csr_matrix(G.data)[:, 0])
with assert_raises_message(
ValueError,
"data of shape ({0}, 15) cannot be inverse transformed from graph built on reduced data of shape ({0}, {1})".format(
G.data.shape[0], G.data.shape[1]
),
):
G.inverse_transform(sp.csr_matrix(G.data)[:, :15])
def test_transform_sparse_no_pca():
G = build_graph(data, sparse=True, n_pca=None)
assert np.sum(G.data_nu != G.transform(G.data)) == 0
with assert_raises_message(
ValueError,
"data of shape {} cannot be transformed to graph built on data of shape {}".format(
G.data.tocsr()[:, 0].shape, G.data.shape
),
):
G.transform(sp.csr_matrix(G.data)[:, 0])
with assert_raises_message(
ValueError,
"data of shape {} cannot be transformed to graph built on data of shape {}".format(
G.data.tocsr()[:, :15].shape, G.data.shape
),
):
G.transform(sp.csr_matrix(G.data)[:, :15])
def test_transform_sparse_adaptive_pca():
G = build_graph(data, sparse=True, n_pca=True, random_state=42)
assert np.all(G.data_nu == G.transform(G.data))
assert_raises(ValueError, G.transform, sp.csr_matrix(G.data)[:, 0])
assert_raises(ValueError, G.transform, sp.csr_matrix(G.data)[:, :15])
G2 = build_graph(data,
sparse=True,
n_pca=True,
rank_threshold=G.rank_threshold,
random_state=42)
assert np.allclose(G2.data_nu, G2.transform(G2.data))
assert np.allclose(G2.data_nu, G.transform(G.data))
G3 = build_graph(data, sparse=True, n_pca=G2.n_pca, random_state=42)
assert np.allclose(G3.data_nu, G3.transform(G3.data))
assert np.allclose(G3.data_nu, G2.transform(G2.data))
def test_anndata_sparse():
try:
anndata
except NameError:
# not installed
return
G = build_graph(anndata.AnnData(sp.csr_matrix(data)))
assert isinstance(G, graphtools.base.BaseGraph)
assert isinstance(G.data, sp.csr_matrix)
def test_inverse_transform_sparse_svd():
G = build_graph(data, sparse=True, n_pca=data.shape[1] - 1)
np.testing.assert_allclose(data,
G.inverse_transform(G.data_nu),
atol=1e-12)
np.testing.assert_allclose(data[:, -1, None],
G.inverse_transform(G.data_nu, columns=-1),
atol=1e-12)
np.testing.assert_allclose(data[:, 5:7],
G.inverse_transform(G.data_nu, columns=[5, 6]),
atol=1e-12)
assert_raises(IndexError,
G.inverse_transform,
G.data_nu,
columns=data.shape[1])
assert_raises(TypeError, G.inverse_transform, sp.csr_matrix(G.data)[:, 0])
assert_raises(TypeError, G.inverse_transform,
sp.csr_matrix(G.data)[:, :15])
assert_raises(ValueError, G.inverse_transform, data[:, 0])
assert_raises(ValueError, G.inverse_transform, data[:, :15])
def test_inverse_transform_sparse_svd():
G = build_graph(data, sparse=True, n_pca=data.shape[1] - 1)
np.testing.assert_allclose(data, G.inverse_transform(G.data_nu), atol=1e-12)
np.testing.assert_allclose(
data[:, -1, None], G.inverse_transform(G.data_nu, columns=-1), atol=1e-12
)
np.testing.assert_allclose(
data[:, 5:7], G.inverse_transform(G.data_nu, columns=[5, 6]), atol=1e-12
)
with assert_raises_message(
IndexError, "index 64 is out of bounds for axis 1 with size 64"
):
G.inverse_transform(G.data_nu, columns=data.shape[1])
with assert_raises_message(
TypeError,
"A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array.",
):
G.inverse_transform(sp.csr_matrix(G.data)[:, 0])
with assert_raises_message(
TypeError,
"A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array.",
):
G.inverse_transform(sp.csr_matrix(G.data)[:, :15])
with assert_raises_message(
ValueError,
"data of shape ({0},) cannot be inverse transformed from graph built on reduced data of shape ({0}, {1}). Expected shape ({0}, {1})".format(
data.shape[0], G.n_pca
),
):
G.inverse_transform(data[:, 0])
with assert_raises_message(
ValueError,
"data of shape ({0}, 15) cannot be inverse transformed from graph built on reduced data of shape ({0}, {1}). Expected shape ({0}, {1})".format(
data.shape[0], G.n_pca
),
):
G.inverse_transform(data[:, :15])
def test_truncated_exact_graph_no_pca():
k = 3
a = 13
n_pca = None
thresh = 1e-4
data_small = data[np.random.choice(len(data),
len(data) // 10,
replace=False)]
pdx = squareform(pdist(data_small, metric="euclidean"))
knn_dist = np.partition(pdx, k, axis=1)[:, :k]
epsilon = np.max(knn_dist, axis=1)
weighted_pdx = (pdx.T / epsilon).T
K = np.exp(-1 * weighted_pdx**a)
K[K < thresh] = 0
W = K + K.T
W = np.divide(W, 2)
np.fill_diagonal(W, 0)
G = pygsp.graphs.Graph(W)
G2 = build_graph(
data_small,
thresh=thresh,
graphtype="exact",
n_pca=n_pca,
decay=a,
knn=k - 1,
random_state=42,
use_pygsp=True,
)
assert G.N == G2.N
np.testing.assert_equal(G.dw, G2.dw)
assert (G.W != G2.W).nnz == 0
assert (G2.W != G.W).sum() == 0
assert isinstance(G2, graphtools.graphs.TraditionalGraph)
G2 = build_graph(
sp.csr_matrix(data_small),
thresh=thresh,
graphtype="exact",
n_pca=n_pca,
decay=a,
knn=k - 1,
random_state=42,
use_pygsp=True,
)
assert G.N == G2.N
np.testing.assert_equal(G.dw, G2.dw)
assert (G.W != G2.W).nnz == 0
assert (G2.W != G.W).sum() == 0
assert isinstance(G2, graphtools.graphs.TraditionalGraph)
def test_inverse_transform_sparse_no_pca():
G = build_graph(data, sparse=True, n_pca=None)
assert np.sum(G.data != G.inverse_transform(G.data_nu)) == 0
assert_raises(ValueError, G.inverse_transform, sp.csr_matrix(G.data)[:, 0])
assert_raises(ValueError, G.inverse_transform,
sp.csr_matrix(G.data)[:, :15])
def test_transform_sparse_pca():
G = build_graph(data, sparse=True, n_pca=20)
assert np.all(G.data_nu == G.transform(G.data))
assert_raises(ValueError, G.transform, sp.csr_matrix(G.data)[:, 0])
assert_raises(ValueError, G.transform, sp.csr_matrix(G.data)[:, :15])