def test_knn_interpolate():
G = build_graph(data, decay=None)
assert_raises(ValueError, G.interpolate, data)
pca_data = PCA(2).fit_transform(data)
transitions = G.extend_to_data(data)
assert(np.all(G.interpolate(pca_data, Y=data) ==
G.interpolate(pca_data, transitions=transitions)))
def test_set_params():
G = graphtools.base.Data(data, n_pca=20)
assert G.get_params() == {"n_pca": 20, "random_state": None}
G.set_params(random_state=13)
assert G.random_state == 13
assert_raises(ValueError, G.set_params, n_pca=10)
G.set_params(n_pca=G.n_pca)
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_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_set_params():
X, sample_idx = generate_swiss_roll()
G = build_graph(
X, sample_idx=sample_idx, kernel_symm="mnn", theta=0.5, n_pca=None, thresh=1e-4
)
assert G.get_params() == {
"n_pca": None,
"random_state": 42,
"kernel_symm": "mnn",
"theta": 0.5,
"anisotropy": 0,
"beta": 1,
"knn": 3,
"decay": 10,
"bandwidth": None,
"distance": "euclidean",
"thresh": 1e-4,
"n_jobs": 1,
}
G.set_params(n_jobs=4)
assert G.n_jobs == 4
for graph in G.subgraphs:
assert graph.n_jobs == 4
assert graph.knn_tree.n_jobs == 4
G.set_params(random_state=13)
assert G.random_state == 13
for graph in G.subgraphs:
assert graph.random_state == 13
G.set_params(verbose=2)
assert G.verbose == 2
for graph in G.subgraphs:
assert graph.verbose == 2
G.set_params(verbose=0)
assert_raises(ValueError, G.set_params, knn=15)
assert_raises(ValueError, G.set_params, decay=15)
assert_raises(ValueError, G.set_params, distance="manhattan")
assert_raises(ValueError, G.set_params, thresh=1e-3)
assert_raises(ValueError, G.set_params, beta=0.2)
G.set_params(
knn=G.knn, decay=G.decay, thresh=G.thresh, distance=G.distance, beta=G.beta
)
def test_inverse_transform_dense_no_pca():
G = build_graph(data, n_pca=None)
np.testing.assert_allclose(data[:, 5:7],
G.inverse_transform(G.data_nu, columns=[5, 6]),
atol=1e-12)
assert np.all(G.data == G.inverse_transform(G.data_nu))
assert_raises(ValueError, G.inverse_transform, G.data[:, 0])
assert_raises(ValueError, G.inverse_transform, G.data[:, None, :15])
assert_raises(ValueError, G.inverse_transform, G.data[:, :15])
def test_inverse_transform_dense_pca():
G = build_graph(data, n_pca=data.shape[1] - 1)
np.testing.assert_allclose(G.data,
G.inverse_transform(G.data_nu),
atol=1e-12)
np.testing.assert_allclose(G.data[:, -1, None],
G.inverse_transform(G.data_nu, columns=-1),
atol=1e-12)
np.testing.assert_allclose(G.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(ValueError, G.inverse_transform, G.data[:, 0])
assert_raises(ValueError, G.inverse_transform, G.data[:, None, :15])
assert_raises(ValueError, G.inverse_transform, G.data[:, :15])
def test_set_params():
G = build_graph(data, decay=10, thresh=0)
assert G.get_params() == {
'n_pca': 20,
'random_state': 42,
'kernel_symm': '+',
'gamma': None,
'knn': 3,
'decay': 10,
'distance': 'euclidean',
'precomputed': None
}
assert_raises(ValueError, G.set_params, knn=15)
assert_raises(ValueError, G.set_params, decay=15)
assert_raises(ValueError, G.set_params, distance='manhattan')
assert_raises(ValueError, G.set_params, precomputed='distance')
G.set_params(knn=G.knn,
decay=G.decay,
distance=G.distance,
precomputed=G.precomputed)
def test_set_params():
G = build_graph(data, decay=None)
assert G.get_params() == {
'n_pca': 20,
'random_state': 42,
'kernel_symm': '+',
'gamma': None,
'knn': 3,
'decay': None,
'distance': 'euclidean',
'thresh': 0,
'n_jobs': -1,
'verbose': 0
}
G.set_params(n_jobs=4)
assert G.n_jobs == 4
assert G.knn_tree.n_jobs == 4
G.set_params(random_state=13)
assert G.random_state == 13
G.set_params(verbose=2)
assert G.verbose == 2
G.set_params(verbose=0)
assert_raises(ValueError, G.set_params, knn=15)
assert_raises(ValueError, G.set_params, decay=10)
assert_raises(ValueError, G.set_params, distance='manhattan')
assert_raises(ValueError, G.set_params, thresh=1e-3)
assert_raises(ValueError, G.set_params, gamma=0.99)
assert_raises(ValueError, G.set_params, kernel_symm='*')
G.set_params(knn=G.knn,
decay=G.decay,
thresh=G.thresh,
distance=G.distance,
gamma=G.gamma,
kernel_symm=G.kernel_symm)
def test_set_params():
X, sample_idx = generate_swiss_roll()
G = build_graph(X,
sample_idx=sample_idx,
kernel_symm='gamma',
gamma=0.5,
n_pca=None,
thresh=1e-4)
assert G.get_params() == {
'n_pca': None,
'random_state': 42,
'kernel_symm': 'gamma',
'gamma': 0.5,
'beta': 1,
'adaptive_k': 'sqrt',
'knn': 3,
'decay': 10,
'distance': 'euclidean',
'thresh': 1e-4,
'n_jobs': 1
}
G.set_params(n_jobs=4)
assert G.n_jobs == 4
for graph in G.subgraphs:
assert graph.n_jobs == 4
assert graph.knn_tree.n_jobs == 4
G.set_params(random_state=13)
assert G.random_state == 13
for graph in G.subgraphs:
assert graph.random_state == 13
G.set_params(verbose=2)
assert G.verbose == 2
for graph in G.subgraphs:
assert graph.verbose == 2
G.set_params(verbose=0)
assert_raises(ValueError, G.set_params, knn=15)
assert_raises(ValueError, G.set_params, decay=15)
assert_raises(ValueError, G.set_params, distance='manhattan')
assert_raises(ValueError, G.set_params, thresh=1e-3)
assert_raises(ValueError, G.set_params, beta=0.2)
assert_raises(ValueError, G.set_params, adaptive_k='min')
G.set_params(knn=G.knn,
decay=G.decay,
thresh=G.thresh,
distance=G.distance,
beta=G.beta,
adaptive_k=G.adaptive_k)
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])
def test_transform_dense_no_pca():
G = build_graph(data, n_pca=None)
assert np.all(G.data_nu == G.transform(G.data))
assert_raises(ValueError, G.transform, G.data[:, 0])
assert_raises(ValueError, G.transform, G.data[:, None, :15])
assert_raises(ValueError, G.transform, G.data[:, :15])
def test_set_params():
G = build_graph(data, decay=None)
assert G.get_params() == {
"n_pca": 20,
"random_state": 42,
"kernel_symm": "+",
"theta": None,
"anisotropy": 0,
"knn": 3,
"knn_max": None,
"decay": None,
"bandwidth": None,
"bandwidth_scale": 1,
"distance": "euclidean",
"thresh": 0,
"n_jobs": -1,
"verbose": 0,
}, G.get_params()
G.set_params(n_jobs=4)
assert G.n_jobs == 4
assert G.knn_tree.n_jobs == 4
G.set_params(random_state=13)
assert G.random_state == 13
G.set_params(verbose=2)
assert G.verbose == 2
G.set_params(verbose=0)
assert_raises(ValueError, G.set_params, knn=15)
assert_raises(ValueError, G.set_params, knn_max=15)
assert_raises(ValueError, G.set_params, decay=10)
assert_raises(ValueError, G.set_params, distance="manhattan")
assert_raises(ValueError, G.set_params, thresh=1e-3)
assert_raises(ValueError, G.set_params, theta=0.99)
assert_raises(ValueError, G.set_params, kernel_symm="*")
assert_raises(ValueError, G.set_params, anisotropy=0.7)
assert_raises(ValueError, G.set_params, bandwidth=5)
assert_raises(ValueError, G.set_params, bandwidth_scale=5)
G.set_params(
knn=G.knn,
decay=G.decay,
thresh=G.thresh,
distance=G.distance,
theta=G.theta,
anisotropy=G.anisotropy,
kernel_symm=G.kernel_symm,
)
def test_set_params():
G = build_graph(data, decay=10, thresh=0)
assert G.get_params() == {
"n_pca": 20,
"random_state": 42,
"kernel_symm": "+",
"theta": None,
"knn": 3,
"anisotropy": 0,
"decay": 10,
"bandwidth": None,
"bandwidth_scale": 1,
"distance": "euclidean",
"precomputed": None,
}
assert_raises(ValueError, G.set_params, knn=15)
assert_raises(ValueError, G.set_params, decay=15)
assert_raises(ValueError, G.set_params, distance="manhattan")
assert_raises(ValueError, G.set_params, precomputed="distance")
assert_raises(ValueError, G.set_params, bandwidth=5)
assert_raises(ValueError, G.set_params, bandwidth_scale=5)
G.set_params(knn=G.knn,
decay=G.decay,
distance=G.distance,
precomputed=G.precomputed)
def test_exact_graph_anisotropy():
k = 3
a = 13
n_pca = 20
anisotropy = 0.9
data_small = data[np.random.choice(len(data),
len(data) // 2,
replace=False)]
pca = PCA(n_pca, svd_solver="randomized", random_state=42).fit(data_small)
data_small_nu = pca.transform(data_small)
pdx = squareform(pdist(data_small_nu, 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 + K.T
K = np.divide(K, 2)
d = K.sum(1)
W = K / (np.outer(d, d)**anisotropy)
np.fill_diagonal(W, 0)
G = pygsp.graphs.Graph(W)
G2 = build_graph(
data_small,
thresh=0,
n_pca=n_pca,
decay=a,
knn=k - 1,
random_state=42,
use_pygsp=True,
anisotropy=anisotropy,
)
assert isinstance(G2, graphtools.graphs.TraditionalGraph)
assert G.N == G2.N
np.testing.assert_equal(G.dw, G2.dw)
assert (G2.W != G.W).sum() == 0
assert (G.W != G2.W).nnz == 0
assert_raises(
ValueError,
build_graph,
data_small,
thresh=0,
n_pca=n_pca,
decay=a,
knn=k - 1,
random_state=42,
use_pygsp=True,
anisotropy=-1,
)
assert_raises(
ValueError,
build_graph,
data_small,
thresh=0,
n_pca=n_pca,
decay=a,
knn=k - 1,
random_state=42,
use_pygsp=True,
anisotropy=2,
)
assert_raises(
ValueError,
build_graph,
data_small,
thresh=0,
n_pca=n_pca,
decay=a,
knn=k - 1,
random_state=42,
use_pygsp=True,
anisotropy="invalid",
)
