Preparation for release of skorch v0.12.0

CHANGES.md

@@ -7,12 +7,21 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Added
+
+### Changed
+
+### Fixed
+
+## [0.12.0] - 2022-10-06
+
 ### Added
 - Added `load_best` attribute to `EarlyStopping` callback to automatically load module weights of the best result at the end of training
 - Added a method, `trim_for_prediction`, on the net classes, which trims the net from everything not required for using it for prediction; call this after fitting to reduce the size of the net
 - Added experimental support for [huggingface accelerate](https://github.com/huggingface/accelerate); use the provided mixin class to add advanced training capabilities provided by the accelerate library to skorch
 - Add integration for Huggingface tokenizers; use `skorch.hf.HuggingfaceTokenizer` to train a Huggingface tokenizer on your custom data; use `skorch.hf.HuggingfacePretrainedTokenizer` to load a pre-trained Huggingface tokenizer
 - Added support for creating model checkpoints on Hugging Face Hub using [`HfHubStorage`](https://skorch.readthedocs.io/en/latest/hf.html#skorch.hf.HfHubStorage)
+- Added a [notebook](https://nbviewer.org/github/skorch-dev/skorch/blob/master/notebooks/CORA-geometric.ipynb) that shows how to use skorch with PyTorch Geometric (#863)
 
 ### Changed
 - The minimum required scikit-learn version has been bumped to 0.22.0
@@ -23,6 +32,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fix a bug in `SliceDataset` that prevented it to be used with `to_numpy` (#858)
 - Fix a bug that occurred when loading a net that has device set to None (#876)
 - Fix a bug that in some cases could prevent loading a net that was trained with CUDA without CUDA
+- Enable skorch to work on M1/M2 Apple MacBooks (#884)
 
 ## [0.11.0] - 2021-10-11
 
@@ -273,3 +283,4 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 [0.9.0]: https://github.com/skorch-dev/skorch/compare/v0.8.0...v0.9.0
 [0.10.0]: https://github.com/skorch-dev/skorch/compare/v0.9.0...v0.10.0
 [0.11.0]: https://github.com/skorch-dev/skorch/compare/v0.10.0...v0.11.0
+[0.12.0]: https://github.com/skorch-dev/skorch/compare/v0.11.0...v0.12.0

VERSION

@@ -1 +1 @@
-0.11.1dev
+0.12.0

skorch/callbacks/base.py

@@ -63,84 +63,3 @@ def get_params(self, deep=True):
 
     def set_params(self, **params):
         BaseEstimator.set_params(self, **params)
-
-
-# TODO: remove after some deprecation period, e.g. skorch 0.12
-def _on_batch_overridden(callback):
-    """Check if on_batch_begin or on_batch_end were overridden
-
-    If the method does not exist at all, it's not considered overridden. This is
-    mostly for callbacks that are mocked.
-
-    """
-    try:
-        base_skorch_cls = next(cls for cls in callback.__class__.__mro__
-                               if cls.__module__.startswith('skorch'))
-    except StopIteration:
-        # does not derive from skorch callback, possibly a mock
-        return False
-
-    obb = base_skorch_cls.on_batch_begin
-    obe = base_skorch_cls.on_batch_end
-    return (
-        getattr(callback.__class__, 'on_batch_begin', obb) is not obb
-        or getattr(callback.__class__, 'on_batch_end', obe) is not obe
-    )
-
-
-# TODO: remove after some deprecation period, e.g. skorch 0.12
-def _issue_warning_if_on_batch_override(callback_list):
-    """Check callbacks for overridden on_batch method and issue warning
-
-    We introduced a breaking change by changing the signature of on_batch_begin
-    and on_batch_end. To help users, we try to detect if they use any custom
-    callback that overrides on of these methods and issue a warning if they do.
-    The warning states how to adjust the method signature and how it can be
-    filtered.
-
-    After some transition period, the checking and the warning should be
-    removed again.
-
-    Parameters
-    ----------
-    callback_list : list of (str, callback) tuples
-      List of initialized callbacks.
-
-    Warns
-    -----
-    Issues a ``SkorchWarning`` if any of the callbacks fits the conditions.
-
-    """
-    if not callback_list:
-        return
-
-    callbacks = [callback for _, callback in callback_list]
-
-    # first detect if there are any user defined callbacks
-    user_defined_callbacks = [
-        callback for callback in callbacks
-        if not callback.__module__.startswith('skorch')
-    ]
-    if not user_defined_callbacks:
-        return
-
-    # check if any of these callbacks overrides on_batch_begin or on_batch_end
-    overriding_callbacks = [
-        callback for callback in user_defined_callbacks
-        if _on_batch_overridden(callback)
-    ]
-
-    if not overriding_callbacks:
-        return
-
-    warning_msg = (
-        "You are using an callback that overrides on_batch_begin "
-        "or on_batch_end. As of skorch 0.10, the signature was changed "
-        "from 'on_batch_{begin,end}(self, X, y, ...)' to "
-        "'on_batch_{begin,end}(self, batch, ...)'. To recover, change "
-        "the signature accordingly and add 'X, y = batch' on the first "
-        "line of the method body. To suppress this warning, add:\n"
-        "'import warnings; from skorch.exceptions import SkorchWarning\n"
-        "warnings.filterwarnings('ignore', category=SkorchWarning)'.")
-
-    warnings.warn(warning_msg, SkorchWarning)

skorch/net.py

@@ -23,7 +23,6 @@
 from skorch.callbacks import EpochTimer
 from skorch.callbacks import PrintLog
 from skorch.callbacks import PassthroughScoring
-from skorch.callbacks.base import _issue_warning_if_on_batch_override
 from skorch.dataset import Dataset
 from skorch.dataset import ValidSplit
 from skorch.dataset import get_len
@@ -352,10 +351,6 @@ def notify(self, method_name, **cb_kwargs):
         * on_batch_end
 
         """
-        # TODO: remove after some deprecation period, e.g. skorch 0.12
-        if not self.history:  # perform check only at the start
-            _issue_warning_if_on_batch_override(self.callbacks_)
-
         getattr(self, method_name)(self, **cb_kwargs)
         for _, cb in self.callbacks_:
             getattr(cb, method_name)(self, **cb_kwargs)