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DEP deprecate loss_function_ attribute in SGDClassifier and SGDOneClassSVM #27979

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merged 4 commits into from
Dec 19, 2023
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DEP deprecate loss_function_ attribute in SGDClassifier and SGDOneClassSVM #27979

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1d3b6a9
DEP loss_function_ of SGDClassifier and SGDOneClassSVM
lorentzenchr Dec 18, 2023
8350c86
TST avoid warning in SGD test
lorentzenchr Dec 18, 2023
b56e9d4
DOC add whatsnew
lorentzenchr Dec 18, 2023
dd9e0cc
nitpicks
jeremiedbb Dec 19, 2023
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5 changes: 5 additions & 0 deletions doc/whats_new/v1.4.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -547,6 +547,11 @@ Changelog
type promotion rules of NumPy 2.
:pr:`27899` by :user:`Olivier Grisel <ogrisel>`.

- |API| The attribute `loss_function_` of :class:`linear_model.SGDClassifier` and
:class:`linear_model.SGDOneClassSVM` has been deprecated and will be removed in
version 1.6.
:pr:`27979` by :user:`Christian Lorentzen <lorentzenchr>`.

:mod:`sklearn.metrics`
......................

Expand Down
28 changes: 23 additions & 5 deletions sklearn/linear_model/_stochastic_gradient.py
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Expand Up @@ -22,7 +22,7 @@
)
from ..exceptions import ConvergenceWarning
from ..model_selection import ShuffleSplit, StratifiedShuffleSplit
from ..utils import check_random_state, compute_class_weight
from ..utils import check_random_state, compute_class_weight, deprecated
from ..utils._param_validation import Hidden, Interval, StrOptions
from ..utils.extmath import safe_sparse_dot
from ..utils.metaestimators import available_if
Expand Down Expand Up @@ -323,6 +323,16 @@ def _make_validation_score_cb(
classes=classes,
)

# TODO(1.6): Remove
# mypy error: Decorated property not supported
@deprecated( # type: ignore
"Attribute `loss_function_` was deprecated in version 1.4 and will be removed "
"in 1.6."
)
@property
def loss_function_(self):
return self._loss_function_


def _prepare_fit_binary(est, y, i, input_dtye):
"""Initialization for fit_binary.
Expand Down Expand Up @@ -455,7 +465,7 @@ def fit_binary(
intercept,
average_coef,
average_intercept,
est.loss_function_,
est._loss_function_,
penalty_type,
alpha,
C,
Expand Down Expand Up @@ -619,7 +629,7 @@ def _partial_fit(
% (n_features, self.coef_.shape[-1])
)

self.loss_function_ = self._get_loss_function(loss)
self._loss_function_ = self._get_loss_function(loss)
if not hasattr(self, "t_"):
self.t_ = 1.0

Expand Down Expand Up @@ -1132,6 +1142,10 @@ class SGDClassifier(BaseSGDClassifier):

loss_function_ : concrete ``LossFunction``

.. deprecated:: 1.4
Attribute `loss_function_` was deprecated in version 1.4 and will be
removed in 1.6.

classes_ : array of shape (n_classes,)

t_ : int
Expand Down Expand Up @@ -2122,6 +2136,10 @@ class SGDOneClassSVM(BaseSGD, OutlierMixin):

loss_function_ : concrete ``LossFunction``

.. deprecated:: 1.4
``loss_function_`` was deprecated in version 1.4 and will be removed in
1.6.

n_features_in_ : int
Number of features seen during :term:`fit`.

Expand Down Expand Up @@ -2260,7 +2278,7 @@ def _fit_one_class(self, X, alpha, C, sample_weight, learning_rate, max_iter):
intercept[0],
average_coef,
average_intercept[0],
self.loss_function_,
self._loss_function_,
penalty_type,
alpha,
C,
Expand Down Expand Up @@ -2354,7 +2372,7 @@ def _partial_fit(
self._average_coef = np.zeros(n_features, dtype=X.dtype, order="C")
self._average_intercept = np.zeros(1, dtype=X.dtype, order="C")

self.loss_function_ = self._get_loss_function(loss)
self._loss_function_ = self._get_loss_function(loss)
if not hasattr(self, "t_"):
self.t_ = 1.0

Expand Down
24 changes: 20 additions & 4 deletions sklearn/linear_model/tests/test_sgd.py
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Expand Up @@ -756,10 +756,13 @@ def test_sgd_proba(klass):
p = clf.predict_proba([[-1, -1]])
assert p[0, 1] < 0.5

p = clf.predict_log_proba([[3, 2]])
assert p[0, 1] > p[0, 0]
p = clf.predict_log_proba([[-1, -1]])
assert p[0, 1] < p[0, 0]
# If predict_proba is 0, we get "RuntimeWarning: divide by zero encountered
# in log". We avoid it here.
with np.errstate(divide="ignore"):
p = clf.predict_log_proba([[3, 2]])
assert p[0, 1] > p[0, 0]
p = clf.predict_log_proba([[-1, -1]])
assert p[0, 1] < p[0, 0]

# log loss multiclass probability estimates
clf = klass(loss="log_loss", alpha=0.01, max_iter=10).fit(X2, Y2)
Expand Down Expand Up @@ -2196,3 +2199,16 @@ def test_sgd_numerical_consistency(SGDEstimator):
sgd_32.fit(X_32, Y_32)

assert_allclose(sgd_64.coef_, sgd_32.coef_)


# TODO(1.6): remove
@pytest.mark.parametrize("Estimator", [SGDClassifier, SGDOneClassSVM])
def test_loss_attribute_deprecation(Estimator):
# Check that we raise the proper deprecation warning if accessing
# `loss_function_`.
X = np.array([[1, 2], [3, 4]])
y = np.array([1, 0])
est = Estimator().fit(X, y)

with pytest.warns(FutureWarning, match="`loss_function_` was deprecated"):
est.loss_function_