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TST Changes assert to pytest style in tests/test_random_projection.py #19846

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Merged
merged 9 commits into from
Apr 9, 2021
Merged

TST Changes assert to pytest style in tests/test_random_projection.py #19846

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69e0456
refactor errors in test_random_projection
LSturtew Apr 8, 2021
160c81a
added pytest parameterize
LSturtew Apr 8, 2021
3ffbfe7
Delete tryingstuff.py
LSturtew Apr 8, 2021
5814b3a
use for loop instead of parameterize
LSturtew Apr 8, 2021
db8db3f
Merge branch 'assert_raises_test_random_projection' of github.com:LSt…
LSturtew Apr 8, 2021
2ac678b
linting
LSturtew Apr 8, 2021
16b3c02
linting
LSturtew Apr 8, 2021
034725f
linting
LSturtew Apr 8, 2021
426ce15
removed double test
LSturtew Apr 9, 2021
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92 changes: 44 additions & 48 deletions sklearn/tests/test_random_projection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,12 +14,9 @@
from sklearn.random_projection import SparseRandomProjection
from sklearn.random_projection import GaussianRandomProjection

from sklearn.utils._testing import assert_raises
from sklearn.utils._testing import assert_raise_message
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_warns
from sklearn.exceptions import DataDimensionalityWarning

all_sparse_random_matrix: List[Any] = [_sparse_random_matrix]
Expand Down Expand Up @@ -59,19 +56,21 @@ def densify(matrix):
###############################################################################
# test on JL lemma
###############################################################################
def test_invalid_jl_domain():
assert_raises(ValueError, johnson_lindenstrauss_min_dim, 100, eps=1.1)
assert_raises(ValueError, johnson_lindenstrauss_min_dim, 100, eps=0.0)
assert_raises(ValueError, johnson_lindenstrauss_min_dim, 100, eps=-0.1)
assert_raises(ValueError, johnson_lindenstrauss_min_dim, 0, eps=0.5)

@pytest.mark.parametrize("n_samples, eps", [
(100, 1.1),
(100, 0.0),
(100, -0.1),
(0, 0.5)
])
def test_invalid_jl_domain(n_samples, eps):
with pytest.raises(ValueError):
johnson_lindenstrauss_min_dim(n_samples, eps=eps)

def test_input_size_jl_min_dim():
assert_raises(ValueError, johnson_lindenstrauss_min_dim,
3 * [100], eps=2 * [0.9])

assert_raises(ValueError, johnson_lindenstrauss_min_dim, 3 * [100],
eps=2 * [0.9])
def test_input_size_jl_min_dim():
with pytest.raises(ValueError):
johnson_lindenstrauss_min_dim(3 * [100], eps=2 * [0.9])

johnson_lindenstrauss_min_dim(np.random.randint(1, 10, size=(10, 10)),
eps=np.full((10, 10), 0.5))
Expand All @@ -81,18 +80,17 @@ def test_input_size_jl_min_dim():
# tests random matrix generation
###############################################################################
def check_input_size_random_matrix(random_matrix):
assert_raises(ValueError, random_matrix, 0, 0)
assert_raises(ValueError, random_matrix, -1, 1)
assert_raises(ValueError, random_matrix, 1, -1)
assert_raises(ValueError, random_matrix, 1, 0)
assert_raises(ValueError, random_matrix, -1, 0)
inputs = [(0, 0), (-1, 1), (1, -1), (1, 0), (-1, 0)]
for n_components, n_features in inputs:
with pytest.raises(ValueError):
random_matrix(n_components, n_features)


def check_size_generated(random_matrix):
assert random_matrix(1, 5).shape == (1, 5)
assert random_matrix(5, 1).shape == (5, 1)
assert random_matrix(5, 5).shape == (5, 5)
assert random_matrix(1, 1).shape == (1, 1)
inputs = [(1, 5), (5, 1), (5, 5), (1, 1)]
for n_components, n_features in inputs:
assert random_matrix(n_components, n_features).shape == (
n_components, n_features)


def check_zero_mean_and_unit_norm(random_matrix):
Expand All @@ -109,8 +107,8 @@ def check_input_with_sparse_random_matrix(random_matrix):
n_components, n_features = 5, 10

for density in [-1., 0.0, 1.1]:
assert_raises(ValueError,
random_matrix, n_components, n_features, density=density)
with pytest.raises(ValueError):
random_matrix(n_components, n_features, density=density)


@pytest.mark.parametrize("random_matrix", all_random_matrix)
Expand Down Expand Up @@ -153,9 +151,9 @@ def test_sparse_random_matrix():
s = 1 / density

A = _sparse_random_matrix(n_components,
n_features,
density=density,
random_state=0)
n_features,
density=density,
random_state=0)
A = densify(A)

# Check possible values
Expand Down Expand Up @@ -196,31 +194,27 @@ def test_sparse_random_matrix():
###############################################################################
# tests on random projection transformer
###############################################################################
def test_sparse_random_projection_transformer_invalid_density():
for RandomProjection in all_SparseRandomProjection:
assert_raises(ValueError,
RandomProjection(density=1.1).fit, data)

assert_raises(ValueError,
RandomProjection(density=0).fit, data)

assert_raises(ValueError,
RandomProjection(density=-0.1).fit, data)
@pytest.mark.parametrize("density", [1.1, 0, -0.1])
def test_sparse_random_projection_transformer_invalid_density(density):
for RandomProjection in all_SparseRandomProjection:
with pytest.raises(ValueError):
RandomProjection(density=density).fit(data)


def test_random_projection_transformer_invalid_input():
@pytest.mark.parametrize("n_components, fit_data", [
('auto', [[0, 1, 2]]), (-10, data)]
)
def test_random_projection_transformer_invalid_input(n_components, fit_data):
for RandomProjection in all_RandomProjection:
assert_raises(ValueError,
RandomProjection(n_components='auto').fit, [[0, 1, 2]])

assert_raises(ValueError,
RandomProjection(n_components=-10).fit, data)
with pytest.raises(ValueError):
RandomProjection(n_components=n_components).fit(fit_data)


def test_try_to_transform_before_fit():
for RandomProjection in all_RandomProjection:
assert_raises(ValueError,
RandomProjection(n_components='auto').transform, data)
with pytest.raises(ValueError):
RandomProjection(n_components='auto').transform(data)


def test_too_many_samples_to_find_a_safe_embedding():
Expand All @@ -232,7 +226,8 @@ def test_too_many_samples_to_find_a_safe_embedding():
'eps=0.100000 and n_samples=1000 lead to a target dimension'
' of 5920 which is larger than the original space with'
' n_features=100')
assert_raise_message(ValueError, expected_msg, rp.fit, data)
with pytest.raises(ValueError, match=expected_msg):
rp.fit(data)


def test_random_projection_embedding_quality():
Expand Down Expand Up @@ -318,7 +313,8 @@ def test_correct_RandomProjection_dimensions_embedding():
assert_array_equal(projected_1, projected_3)

# Try to transform with an input X of size different from fitted.
assert_raises(ValueError, rp.transform, data[:, 1:5])
with pytest.raises(ValueError):
rp.transform(data[:, 1:5])

# it is also possible to fix the number of components and the density
# level
Expand All @@ -337,8 +333,8 @@ def test_warning_n_components_greater_than_n_features():
data, _ = make_sparse_random_data(5, n_features, int(n_features / 4))

for RandomProjection in all_RandomProjection:
assert_warns(DataDimensionalityWarning,
RandomProjection(n_components=n_features + 1).fit, data)
with pytest.warns(DataDimensionalityWarning):
RandomProjection(n_components=n_features + 1).fit(data)


def test_works_with_sparse_data():
Expand Down