def test_sample_toy_fit_nonsparse_transform_sparse():
# Fit with non-sparse, test with sparse
n = SubsampledNeighborsTransformer(0.3, random_state=3)
expected_result = csr_matrix(
([3.464102, 1.732051, 1.732051], ([0, 1, 2], [2, 0, 1])), shape=(4, 4))
assert_array_almost_equal(
n.fit(X).transform(X_csr).toarray(), expected_result.toarray())
expected_result = csr_matrix(([2.0, 1.0, 1.0], ([0, 1, 2], [2, 0, 1])),
shape=(4, 4))
assert_array_equal(
n.fit(X2).transform(X2_csr).toarray(), expected_result.toarray())
def test_iris_euclidean():
n = SubsampledNeighborsTransformer(0.4,
metric='euclidean',
random_state=42)
assert_almost_equal(np.mean(n.fit(iris.data).transform(iris.data)),
0.836,
decimal=2)
n = SubsampledNeighborsTransformer(0.4,
eps=2.0,
metric='euclidean',
random_state=42)
assert_almost_equal(np.mean(n.fit(iris.data).transform(iris.data)),
0.147,
decimal=2)
def test_sample_toy_fit_sparse_transform_nonsparse():
# Fit with sparse, test with non-sparse
n = SubsampledNeighborsTransformer(0.9, random_state=2)
expected_result = csr_matrix(([
1.732051, 8.660254, 1.732051, 1.732051, 6.928203, 3.464102, 5.196152,
5.196152, 6.928203
], ([0, 0, 1, 1, 1, 2, 2, 3, 3], [1, 3, 0, 2, 3, 0, 3, 2, 1])),
shape=(4, 4))
assert_array_almost_equal(
n.fit(X_csr).transform(X).toarray(), expected_result.toarray())
expected_result = csr_matrix(
([1.0, 3.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0],
([0, 0, 1, 1, 1, 2, 2, 3, 3], [1, 3, 0, 2, 3, 3, 0, 2, 1])),
shape=(4, 4))
assert_array_equal(
n.fit(X2_csr).transform(X2).toarray(), expected_result.toarray())
def test_iris_cosine():
n = SubsampledNeighborsTransformer(0.6,
eps=0.1,
metric='cosine',
random_state=42)
assert_almost_equal(np.mean(n.fit(iris.data).transform(iris.data)),
0.009,
decimal=2)
def test_sample_toy_different():
# Fit and transform with different matrices
n = SubsampledNeighborsTransformer(0.5, random_state=5)
expected_result = csr_matrix(([5.055689, 8.833459], ([0, 1], [2, 1])),
shape=(2, 3))
assert_array_almost_equal(
n.fit(X_fit).transform(X_transform).toarray(),
expected_result.toarray())
def test_sample_toy_noncsr():
# Fit and transform with non-CSR sparse matrices
n = SubsampledNeighborsTransformer(0.8, random_state=4)
expected_result = csr_matrix(([
3.464102, 8.660254, 1.732051, 3.464102, 5.196152, 5.196152, 6.928203,
8.660254
], ([0, 0, 1, 2, 2, 3, 3, 3], [2, 3, 0, 0, 3, 2, 1, 0])),
shape=(4, 4))
assert_array_almost_equal(
n.fit(X_csr.tocoo()).transform(X_csr.tolil()).toarray(),
expected_result.toarray())
expected_result = csr_matrix(
([2.0, 3.0, 1.0, 1.0, 2.0, 1.0, 2.0, 3.0],
([0, 0, 1, 2, 2, 3, 3, 3], [2, 3, 0, 3, 0, 2, 1, 0])),
shape=(4, 4))
assert_array_equal(
n.fit(X2_csr.todok()).transform(X2_csr.tocsc()).toarray(),
expected_result.toarray())