Remove outdated version checks (#888)

Remove version checks that are no longer required because we don't
support the min versions anymore.

None of the version checks we do is actually required anymore. After
removing them, we thus no longer depend on distutils's LooseVersion.

Minimum scikit-learn version has been bumped to 0.22 to make this
change possible.

CHANGES.md

@@ -13,6 +13,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - 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
 
 ### Changed
+- The minimum required scikit-learn version has been bumped to 0.22.0
 - Initialize data loaders for training and validation dataset once per fit call instead of once per epoch ([migration guide](https://skorch.readthedocs.io/en/stable/user/FAQ.html#migration-from-0-11-to-0-12))
 - It is now possible to call `np.asarray` with `SliceDataset`s (#858)
 - 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

requirements.txt

@@ -1,5 +1,5 @@
 numpy>=1.13.3
-scikit-learn>=0.19.1
+scikit-learn>=0.22.0
 scipy>=1.1.0
 tabulate>=0.7.7
 tqdm>=4.14.0

skorch/callbacks/scoring.py

@@ -2,21 +2,16 @@
 
 from contextlib import contextmanager
 from contextlib import suppress
-from distutils.version import LooseVersion
 from functools import partial
 import warnings
 
 import numpy as np
 import sklearn
 from sklearn.metrics import make_scorer, check_scoring
 
-if LooseVersion(sklearn.__version__) >= '0.22':
-    from sklearn.metrics._scorer import _BaseScorer
-else:
-    from sklearn.metrics.scorer import _BaseScorer
-
 from skorch.callbacks import Callback
 from skorch.dataset import unpack_data
+from sklearn.metrics._scorer import _BaseScorer
 from skorch.utils import data_from_dataset
 from skorch.utils import is_skorch_dataset
 from skorch.utils import to_numpy

skorch/tests/callbacks/test_lr_scheduler.py

@@ -1,5 +1,4 @@
 """Tests for lr_scheduler.py"""
-from distutils.version import LooseVersion
 from unittest.mock import Mock
 
 import numpy as np
@@ -191,10 +190,6 @@ def test_lr_scheduler_set_params(self, classifier_module, classifier_data):
     @pytest.mark.parametrize('policy,kwargs', [
         (StepLR, {'gamma': 0.9, 'step_size': 1})
     ])
-    @pytest.mark.skipif(
-        LooseVersion(torch.__version__) < '1.4',
-        reason="Feature isn't supported with this torch version."
-    )
     def test_lr_scheduler_record_epoch_step(self,
                                             classifier_module,
                                             classifier_data,
@@ -212,10 +207,6 @@ def test_lr_scheduler_record_epoch_step(self,
         net.fit(*classifier_data)
         assert np.all(net.history[:, 'event_lr'] == lrs)
 
-    @pytest.mark.skipif(
-        LooseVersion(torch.__version__) < '1.4',
-        reason="Feature isn't supported with this torch version."
-    )
     def test_lr_scheduler_record_batch_step(self, classifier_module, classifier_data):
         X, y = classifier_data
         batch_size = 128

skorch/tests/test_net.py

@@ -6,7 +6,6 @@
 """
 
 import copy
-from distutils.version import LooseVersion
 from functools import partial
 import os
 from pathlib import Path
@@ -1784,23 +1783,19 @@ def test_repr_fitted_works(self, net_cls, module_cls, data):
         expected = """<class 'skorch.classifier.NeuralNetClassifier'>[initialized](
   module_=MLPModule(
     (nonlin): PReLU(num_parameters=1)
-    (output_nonlin): Softmax()
+    (output_nonlin): Softmax(dim=-1)
     (sequential): Sequential(
       (0): Linear(in_features=20, out_features=11, bias=True)
       (1): PReLU(num_parameters=1)
-      (2): Dropout(p=0.5)
+      (2): Dropout(p=0.5, inplace=False)
       (3): Linear(in_features=11, out_features=11, bias=True)
       (4): PReLU(num_parameters=1)
-      (5): Dropout(p=0.5)
+      (5): Dropout(p=0.5, inplace=False)
       (6): Linear(in_features=11, out_features=2, bias=True)
-      (7): Softmax()
+      (7): Softmax(dim=-1)
     )
   ),
 )"""
-        if LooseVersion(torch.__version__) >= '1.2':
-            expected = expected.replace("Softmax()", "Softmax(dim=-1)")
-            expected = expected.replace("Dropout(p=0.5)",
-                                        "Dropout(p=0.5, inplace=False)")
         assert result == expected
 
     def test_fit_params_passed_to_module(self, net_cls, data):

skorch/tests/test_probabilistic.py

@@ -3,7 +3,6 @@
 import copy
 import pickle
 import re
-from distutils.version import LooseVersion
 
 import numpy as np
 import pytest
@@ -19,13 +18,6 @@
 
 gpytorch = pytest.importorskip('gpytorch')
 
-# extract pytorch version without possible '+something' suffix
-pytorch_version, _, _ = torch.__version__.partition('+')
-# Keep up to date with the gpytorch's supported versions:
-# https://github.com/cornellius-gp/gpytorch#installation
-if LooseVersion(pytorch_version) < '1.9':
-    pytest.skip("gpytorch does not support PyTorch versions < 1.9", allow_module_level=True)
-
 
 def get_batch_size(dist):
     """Return the shape of the distribution

skorch/utils.py

@@ -6,7 +6,6 @@
 
 from collections.abc import Mapping, Sequence
 from contextlib import contextmanager
-from distutils.version import LooseVersion
 from enum import Enum
 from functools import partial
 from itertools import tee
@@ -17,6 +16,7 @@
 from scipy import sparse
 import sklearn
 from sklearn.exceptions import NotFittedError
+from sklearn.utils import _safe_indexing as safe_indexing
 from sklearn.utils.validation import check_is_fitted as sk_check_is_fitted
 import torch
 from torch.nn import BCELoss
@@ -28,10 +28,6 @@
 from skorch.exceptions import DeviceWarning
 from skorch.exceptions import NotInitializedError
 
-if LooseVersion(sklearn.__version__) >= '0.22.0':
-    from sklearn.utils import _safe_indexing as safe_indexing
-else:
-    from sklearn.utils import safe_indexing
 
 GPYTORCH_INSTALLED = False
 try: