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MAINT Clean deprecation for 1.2: leftovers #24647

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Merged
merged 1 commit into from
Oct 13, 2022
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MAINT Clean deprecation for 1.2: leftovers #24647

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23 changes: 0 additions & 23 deletions sklearn/covariance/_graph_lasso.py
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Expand Up @@ -737,29 +737,6 @@ class GraphicalLassoCV(BaseGraphicalLasso):

.. versionadded:: 1.0

split(k)_score : ndarray of shape (n_alphas,)
Log-likelihood score on left-out data across (k)th fold.

.. deprecated:: 1.0
`split(k)_score` is deprecated in 1.0 and will be removed in 1.2.
Use `split(k)_test_score` instead.

mean_score : ndarray of shape (n_alphas,)
Mean of scores over the folds.

.. deprecated:: 1.0
`mean_score` is deprecated in 1.0 and will be removed in 1.2.
Use `mean_test_score` instead.

std_score : ndarray of shape (n_alphas,)
Standard deviation of scores over the folds.

.. deprecated:: 1.0
`std_score` is deprecated in 1.0 and will be removed in 1.2.
Use `std_test_score` instead.

.. versionadded:: 0.24

n_iter_ : int
Number of iterations run for the optimal alpha.

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9 changes: 0 additions & 9 deletions sklearn/linear_model/_base.py
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Expand Up @@ -76,15 +76,6 @@ def _deprecate_normalize(normalize, estimator_name):

Notes
-----
This function should be updated in 1.2 depending on the value of
`normalize`:
- True, warning: `normalize` was deprecated in 1.2 and will be removed in
1.4. Suggest to use pipeline instead.
- False, `normalize` was deprecated in 1.2 and it will be removed in 1.4.
Leave normalize to its default value.
- `deprecated` - this should only be possible with default == False as from
1.2 `normalize` in all the linear models should be either removed or the
default should be set to False.
This function should be completely removed in 1.4.
"""

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2 changes: 0 additions & 2 deletions sklearn/linear_model/tests/test_sgd.py
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Expand Up @@ -707,8 +707,6 @@ def test_set_coef_multiclass(klass):
clf = klass().fit(X2, Y2, intercept_init=np.zeros((3,)))


# TODO: Remove filterwarnings in v1.2.
@pytest.mark.filterwarnings("ignore:.*squared_loss.*:FutureWarning")
@pytest.mark.parametrize("klass", [SGDClassifier, SparseSGDClassifier])
def test_sgd_predict_proba_method_access(klass):
# Checks that SGDClassifier predict_proba and predict_log_proba methods
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5 changes: 2 additions & 3 deletions sklearn/tests/test_base.py
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Expand Up @@ -647,8 +647,7 @@ def transform(self, X):
warnings.simplefilter("error", UserWarning)
trans.transform(X)

# TODO: Convert to a error in 1.2
# fit on dataframe with feature names that are mixed warns:
# fit on dataframe with feature names that are mixed raises an error:
df_mixed = pd.DataFrame(X_np, columns=["a", "b", 1, 2])
trans = NoOpTransformer()
msg = re.escape(
Expand All @@ -658,7 +657,7 @@ def transform(self, X):
with pytest.raises(TypeError, match=msg):
trans.fit(df_mixed)

# transform on feature names that are mixed also warns:
# transform on feature names that are mixed also raises:
with pytest.raises(TypeError, match=msg):
trans.transform(df_mixed)

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2 changes: 0 additions & 2 deletions sklearn/tests/test_common.py
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Expand Up @@ -463,8 +463,6 @@ def test_check_param_validation(estimator):
check_param_validation(name, estimator)


# TODO: remove this filter in 1.2
@pytest.mark.filterwarnings("ignore::FutureWarning:sklearn")
@pytest.mark.parametrize(
"Estimator",
[
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7 changes: 3 additions & 4 deletions sklearn/tests/test_docstring_parameters.py
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Expand Up @@ -234,10 +234,9 @@ def test_fit_docstring_attributes(name, Estimator):
):
# default="auto" raises an error with the shape of `X`
est.set_params(n_components=2)

# FIXME: TO BE REMOVED for 1.2 (avoid FutureWarning)
if Estimator.__name__ == "TSNE":
est.set_params(learning_rate=200.0, init="random", perplexity=2)
elif Estimator.__name__ == "TSNE":
# default raises an error, perplexity must be less than n_samples
est.set_params(perplexity=2)

# FIXME: TO BE REMOVED for 1.3 (avoid FutureWarning)
if Estimator.__name__ == "SequentialFeatureSelector":
Expand Down