8000 TST Extend tests for `scipy.sparse/*array` in `sklearn/linear_model/tests/test_sparse_coordinate_descent` by Charlie-XIAO · Pull Request #27237 · scikit-learn/scikit-learn · GitHub
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TST Extend tests for scipy.sparse/*array in sklearn/linear_model/tests/test_sparse_coordinate_descent #27237

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merged 4 commits into from
Sep 13, 2023
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TST Extend tests for scipy.sparse/*array in sklearn/linear_model/tests/test_sparse_coordinate_descent #27237

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TST Extend tests for scipy.sparse/*array in sklearn/linear_model/test…
Charlie-XIAO Aug 30, 2023
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Merge remote-tracking branch 'upstream/main' into tst_sp_sparse_coord…
Charlie-XIAO Sep 13, 2023
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Charlie-XIAO Sep 13, 2023
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glemaitre Sep 13, 2023
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78 changes: 46 additions & 32 deletions sklearn/linear_model/tests/test_sparse_coordinate_descent.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
create_memmap_backed_data,
ignore_warnings,
)
from sklearn.utils.fixes import COO_CONTAINERS, CSC_CONTAINERS, LIL_CONTAINERS


def test_sparse_coef():
Expand All @@ -23,9 +24,10 @@ def test_sparse_coef():
assert clf.sparse_coef_.toarray().tolist()[0] == clf.coef_


def test_lasso_zero():
@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_lasso_zero(csc_container):
# Check that the sparse lasso can handle zero data without crashing
X = sp.csc_matrix((3, 1))
X = csc_container((3, 1))
y = [0, 0, 0]
T = np.array([[1], [2], [3]])
clf = Lasso().fit(X, y)
Expand All @@ -36,11 +38,12 @@ def test_lasso_zero():


@pytest.mark.parametrize("with_sample_weight", [True, False])
def test_enet_toy_list_input(with_sample_weight):
@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_enet_toy_list_input(with_sample_weight, csc_container):
# Test ElasticNet for various values of alpha and l1_ratio with list X

X = np.array([[-1], [0], [1]])
X = sp.csc_matrix(X)
X = csc_container(X)
Y = [-1, 0, 1] # just a straight line
T = np.array([[2], [3], [4]]) # test sample
if with_sample_weight:
Expand Down Expand Up @@ -73,18 +76,19 @@ def test_enet_toy_list_input(with_sample_weight):
assert_almost_equal(clf.dual_gap_, 0)


def test_enet_toy_explicit_sparse_input():
< 10000 span class='blob-code-inner blob-code-marker ' data-code-marker="+">@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
def test_enet_toy_explicit_sparse_input(lil_container):
# Test ElasticNet for various values of alpha and l1_ratio with sparse X
f = ignore_warnings
# training samples
X = sp.lil_matrix((3, 1))
X = lil_container((3, 1))
X[0, 0] = -1
# X[1, 0] = 0
X[2, 0] = 1
Y = [-1, 0, 1] # just a straight line (the identity function)

# test samples
T = sp.lil_matrix((3, 1))
T = lil_container((3, 1))
T[0, 0] = 2
T[1, 0] = 3
T[2, 0] = 4
Expand Down Expand Up @@ -113,6 +117,7 @@ def test_enet_toy_explicit_sparse_input():


def make_sparse_data(
sparse_container,
n_samples=100,
n_features=100,
n_informative=10,
Expand All @@ -137,17 +142,24 @@ def make_sparse_data(

# generate training ground truth labels
y = np.dot(X, w)
X = sp.csc_matrix(X)
X = sparse_container(X)
if n_targets == 1:
y = np.ravel(y)
return X, y


def _test_sparse_enet_not_as_toy_dataset(alpha, fit_intercept, positive):
@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
@pytest.mark.parametrize(
"alpha, fit_intercept, positive",
[(0.1, False, False), (0.1, True, False), (1e-3, False, True), (1e-3, True, True)],
)
def test_sparse_enet_not_as_toy_dataset(csc_container, alpha, fit_intercept, positive):
n_samples, n_features, max_iter = 100, 100, 1000
n_informative = 10

X, y = make_sparse_data(n_samples, n_features, n_informative, positive=positive)
X, y = make_sparse_data(
csc_container, n_samples, n_features, n_informative, positive=positive
)

X_train, X_test = X[n_samples // 2 :], X[: n_samples // 2]
y_train, y_test = y[n_samples // 2 :], y[: n_samples // 2]
Expand Down Expand Up @@ -188,18 +200,14 @@ def _test_sparse_enet_not_as_toy_dataset(alpha, fit_intercept, positive):
assert np.sum(s_clf.coef_ != 0.0) < 2 * n_informative


def test_sparse_enet_not_as_toy_dataset():
_test_sparse_enet_not_as_toy_dataset(alpha=0.1, fit_intercept=False, positive=False)
_test_sparse_enet_not_as_toy_dataset(alpha=0.1, fit_intercept=True, positive=False)
_test_sparse_enet_not_as_toy_dataset(alpha=1e-3, fit_intercept=False, positive=True)
_test_sparse_enet_not_as_toy_dataset(alpha=1e-3, fit_intercept=True, positive=True)


def test_sparse_lasso_not_as_toy_dataset():
@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_sparse_lasso_not_as_toy_dataset(csc_container):
n_samples = 100
max_iter = 1000
n_informative = 10
X, y = make_sparse_data(n_samples=n_samples, n_informative=n_informative)
X, y = make_sparse_data(
csc_container, n_samples=n_samples, n_informative=n_informative
)

X_train, X_test = X[n_samples // 2 :], X[: n_samples // 2]
y_train, y_test = y[n_samples // 2 :], y[: n_samples // 2]
Expand All @@ -219,9 +227,10 @@ def test_sparse_lasso_not_as_toy_dataset():
assert np.sum(s_clf.coef_ != 0.0) == n_informative


def test_enet_multitarget():
@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_enet_multitarget(csc_container):
n_targets = 3
X, y = make_sparse_data(n_targets=n_targets)
X, y = make_sparse_data(csc_container, n_targets=n_targets)

estimator = ElasticNet(alpha=0.01, precompute=False)
# XXX: There is a bug when precompute is not False!
Expand All @@ -239,8 +248,9 @@ def test_enet_multitarget():
assert_array_almost_equal(dual_gap[k], estimator.dual_gap_)


def test_path_parameters():
X, y = make_sparse_data()
@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_path_parameters(csc_container):
X, y = make_sparse_data(csc_container)
max_iter = 50
n_alphas = 10
clf = ElasticNetCV(
Expand All @@ -263,8 +273,9 @@ def test_path_parameters():
@pytest.mark.parametrize("fit_intercept", [False, True])
@pytest.mark.parametrize("n_samples, n_features", [(24, 6), (6, 24)])
@pytest.mark.parametrize("with_sample_weight", [True, False])
@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_sparse_dense_equality(
Model, fit_intercept, n_samples, n_features, with_sample_weight
Model, fit_intercept, n_samples, n_features, with_sample_weight, csc_container
):
X, y = make_regression(
n_samples=n_samples,
Expand All @@ -279,7 +290,7 @@ def test_sparse_dense_equality(
sw = np.abs(np.random.RandomState(42).normal(scale=10, size=y.shape))
else:
sw = None
Xs = sp.csc_matrix(X)
Xs = csc_container(X)
params = {"fit_intercept": fit_intercept}
reg_dense = Model(**params).fit(X, y, sample_weight=sw)
reg_sparse = Model(**params).fit(Xs, y, sample_weight=sw)
Expand All @@ -292,8 +303,9 @@ def test_sparse_dense_equality(
assert_allclose(reg_sparse.coef_, reg_dense.coef_)


def test_same_output_sparse_dense_lasso_and_enet_cv():
X, y = make_sparse_data(n_samples=40, n_features=10)
@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_same_output_sparse_dense_lasso_and_enet_cv(csc_container):
X, y = make_sparse_data(csc_container, n_samples=40, n_features=10)
clfs = ElasticNetCV(max_iter=100)
clfs.fit(X, y)
clfd = ElasticNetCV(max_iter=100)
Expand All @@ -313,7 +325,8 @@ def test_same_output_sparse_dense_lasso_and_enet_cv():
assert_array_almost_equal(clfs.alphas_, clfd.alphas_)


def test_same_multiple_output_sparse_dense():
@pytest.mark.parametrize("coo_container", COO_CONTAINERS)
def test_same_multiple_output_sparse_dense(coo_container):
l = ElasticNet()
X = [
[0, 1, 2, 3, 4],
Expand All @@ -332,20 +345,21 @@ def test_same_multiple_output_sparse_dense():
predict_dense = l.predict(sample)

l_sp = ElasticNet()
X_sp = sp.coo_matrix(X)
X_sp = coo_container(X)
l_sp.fit(X_sp, y)
sample_sparse = sp.coo_matrix(sample)
sample_sparse = coo_container(sample)
predict_sparse = l_sp.predict(sample_sparse)

assert_array_almost_equal(predict_sparse, predict_dense)


def test_sparse_enet_coordinate_descent():
@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_sparse_enet_coordinate_descent(csc_container):
"""Test that a warning is issued if model does not converge"""
clf = Lasso(max_iter=2)
n_samples = 5
n_features = 2
X = sp.csc_matrix((n_samples, n_features)) * 1e50
X = csc_container((n_samples, n_features)) * 1e50
y = np.ones(n_samples)
warning_message = (
"Objective did not converge. You might want "
Expand Down
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