def test_transformer_result():
# Test the number of neighbors returned
n_neighbors = 5
n_samples_fit = 20
n_queries = 18
n_features = 10
rng = np.random.RandomState(42)
X = rng.randn(n_samples_fit, n_features)
X2 = rng.randn(n_queries, n_features)
radius = np.percentile(euclidean_distances(X), 10)
# with n_neighbors
for mode in ["distance", "connectivity"]:
add_one = mode == "distance"
nnt = KNeighborsTransformer(n_neighbors=n_neighbors, mode=mode)
Xt = nnt.fit_transform(X)
assert Xt.shape == (n_samples_fit, n_samples_fit)
assert Xt.data.shape == (n_samples_fit * (n_neighbors + add_one), )
assert Xt.format == "csr"
assert _is_sorted_by_data(Xt)
X2t = nnt.transform(X2)
assert X2t.shape == (n_queries, n_samples_fit)
assert X2t.data.shape == (n_queries * (n_neighbors + add_one), )
assert X2t.format == "csr"
assert _is_sorted_by_data(X2t)
# with radius
for mode in ["distance", "connectivity"]:
add_one = mode == "distance"
nnt = RadiusNeighborsTransformer(radius=radius, mode=mode)
Xt = nnt.fit_transform(X)
assert Xt.shape == (n_samples_fit, n_samples_fit)
assert not Xt.data.shape == (n_samples_fit * (n_neighbors + add_one), )
assert Xt.format == "csr"
assert _is_sorted_by_data(Xt)
X2t = nnt.transform(X2)
assert X2t.shape == (n_queries, n_samples_fit)
assert not X2t.data.shape == (n_queries * (n_neighbors + add_one), )
assert X2t.format == "csr"
assert _is_sorted_by_data(X2t)
def test_explicit_diagonal():
# Test that the diagonal is explicitly stored in the sparse graph
n_neighbors = 5
n_samples_fit, n_samples_transform, n_features = 20, 18, 10
rng = np.random.RandomState(42)
X = rng.randn(n_samples_fit, n_features)
X2 = rng.randn(n_samples_transform, n_features)
nnt = KNeighborsTransformer(n_neighbors=n_neighbors)
Xt = nnt.fit_transform(X)
assert _has_explicit_diagonal(Xt)
assert np.all(Xt.data.reshape(n_samples_fit, n_neighbors + 1)[:, 0] == 0)
Xt = nnt.transform(X)
assert _has_explicit_diagonal(Xt)
assert np.all(Xt.data.reshape(n_samples_fit, n_neighbors + 1)[:, 0] == 0)
# Using transform on new data should not always have zero diagonal
X2t = nnt.transform(X2)
assert not _has_explicit_diagonal(X2t)
