Release 1.2.0 (#1115)

# Version 1.2.0

This is a smaller release, most changes concern examples and development and thus don't affect users of skorch.

## Changed

- Loading of skorch nets using pickle: When unpickling a skorch net, you may come across a PyTorch warning that goes: "FutureWarning: You are using torch.load with weights_only=False [...]"; to avoid this warning, pickle the net again and use the new pickle file (#1092)

## Added

- Add Contributing Guidelines for skorch. (#1097)
- Add an example of hyper-parameter optimization using [Optuna](https://optuna.org/) [here](https://github.com/skorch-dev/skorch/tree/master/examples/optuna) (#1098)
- Add Example for Streaming Dataset(#1105)
- Add pyproject.toml to Improve CI/CD and Tooling (#1108)

Thanks @raphaelrubrice, @omahs, and @ParagEkbote for their contributions.

**Full Changelog**: v1.1.0...v1.2.0

Release commit specific:

* Bump verison to 1.2.0
* Update CHANGES.md
* Remove workarounds that have been fixed in sklearn
Only affects tests

Author: BenjaminBossan

CHANGES.md

@@ -9,6 +9,14 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+### Changed
+
+### Fixed
+
+## [1.2.0]
+
+### Added
+
 - Add Contributing Guidelines for skorch. (#1097)
 - Add an example of hyper-parameter optimization using [Optuna](https://optuna.org/) [here](https://github.com/skorch-dev/skorch/tree/master/examples/optuna) (#1098)
 - Add Example for Streaming Dataset(#1105)

VERSION

@@ -1 +1 @@
-1.1.1dev
+1.2.0

skorch/tests/test_classifier.py

@@ -172,12 +172,7 @@ def test_pass_empty_classes_raises(
         expected = "NeuralNetClassifier has no attribute 'classes_'"
         assert msg == expected
 
-    @pytest.mark.xfail
     def test_with_calibrated_classifier_cv(self, net_fit, data):
-        # TODO: This fails with sklearn 1.4.0 because CCCV does not work when
-        # y_proba is float32. This will be fixed in
-        # https://github.com/scikit-learn/scikit-learn/pull/28247, at which
-        # point the test should pass again and the xfail can be removed.
         from sklearn.calibration import CalibratedClassifierCV
         cccv = CalibratedClassifierCV(net_fit, cv=2)
         cccv.fit(*data)
@@ -381,12 +376,7 @@ def test_default_loss_does_call_sigmoid(
         net.predict_proba(X)
         assert mock.call_count > 0
 
-    @pytest.mark.xfail
     def test_with_calibrated_classifier_cv(self, net_fit, data):
-        # TODO: This fails with sklearn 1.4.0 because CCCV does not work when
-        # y_proba is float32. This will be fixed in
-        # https://github.com/scikit-learn/scikit-learn/pull/28247, at which
-        # point the test should pass again and the xfail can be removed.
         from sklearn.calibration import CalibratedClassifierCV
         cccv = CalibratedClassifierCV(net_fit, cv=2)
         cccv.fit(*data)

skorch/tests/test_helper.py

@@ -437,7 +437,6 @@ def test_grid_search_with_slds_works(
             self, slds, y, classifier_module):
         from sklearn.model_selection import GridSearchCV
         from skorch import NeuralNetClassifier
-        from skorch.utils import to_numpy
 
         net = NeuralNetClassifier(
             classifier_module,
@@ -451,16 +450,12 @@ def test_grid_search_with_slds_works(
         gs = GridSearchCV(
             net, params, refit=False, cv=3, scoring='accuracy', error_score='raise'
         )
-        # TODO: after sklearn > 1.6 is released, the to_numpy call should no longer be
-        # required and be removed, see:
-        # https://github.com/skorch-dev/skorch/pull/1078#discussion_r1887197261
-        gs.fit(slds, to_numpy(y))  # does not raise
+        gs.fit(slds, y)  # does not raise
 
     def test_grid_search_with_slds_and_internal_split_works(
             self, slds, y, classifier_module):
         from sklearn.model_selection import GridSearchCV
         from skorch import NeuralNetClassifier
-        from skorch.utils import to_numpy
 
         net = NeuralNetClassifier(classifier_module)
         params = {
@@ -470,10 +465,7 @@ def test_grid_search_with_slds_and_internal_split_works(
         gs = GridSearchCV(
             net, params, refit=True, cv=3, scoring='accuracy', error_score='raise'
         )
-        # TODO: after sklearn > 1.6 is released, the to_numpy call should no longer be
-        # required and be removed, see:
-        # https://github.com/skorch-dev/skorch/pull/1078#discussion_r1887197261
-        gs.fit(slds, to_numpy(y))  # does not raise
+        gs.fit(slds, y)  # does not raise
 
     def test_grid_search_with_slds_X_and_slds_y(
             self, slds, slds_y, classifier_module):