def test_kneighbors_regressor():
# Test chaining KNeighborsTransformer and classifiers/regressors
rng = np.random.RandomState(0)
X = 2 * rng.rand(40, 5) - 1
X2 = 2 * rng.rand(40, 5) - 1
y = rng.rand(40, 1)
n_neighbors = 12
radius = 1.5
# We precompute more neighbors than necessary, to have equivalence between
# k-neighbors estimator after radius-neighbors transformer, and vice-versa.
factor = 2
k_trans = KNeighborsTransformer(n_neighbors=n_neighbors, mode='distance')
k_trans_factor = KNeighborsTransformer(n_neighbors=int(n_neighbors *
factor),
mode='distance')
r_trans = RadiusNeighborsTransformer(radius=radius, mode='distance')
r_trans_factor = RadiusNeighborsTransformer(radius=int(radius * factor),
mode='distance')
k_reg = KNeighborsRegressor(n_neighbors=n_neighbors)
r_reg = RadiusNeighborsRegressor(radius=radius)
test_list = [
(k_trans, k_reg),
(k_trans_factor, r_reg),
(r_trans, r_reg),
(r_trans_factor, k_reg),
]
for trans, reg in test_list:
# compare the chained version and the compact version
reg_compact = clone(reg)
reg_precomp = clone(reg)
reg_precomp.set_params(metric='precomputed')
reg_chain = make_pipeline(clone(trans), reg_precomp)
y_pred_chain = reg_chain.fit(X, y).predict(X2)
y_pred_compact = reg_compact.fit(X, y).predict(X2)
assert_array_almost_equal(y_pred_chain, y_pred_compact)
def test_dbscan():
# Test chaining RadiusNeighborsTransformer and DBSCAN
radius = 0.3
n_clusters = 3
X = generate_clustered_data(n_clusters=n_clusters)
# compare the chained version and the compact version
est_chain = make_pipeline(
RadiusNeighborsTransformer(radius=radius, mode='distance'),
DBSCAN(metric='precomputed', eps=radius))
est_compact = DBSCAN(eps=radius)
labels_chain = est_chain.fit_predict(X)
labels_compact = est_compact.fit_predict(X)
assert_array_almost_equal(labels_chain, labels_compact)
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)
