Bugfix: sklearn 0.22

CHANGES.md

@@ -13,6 +13,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
+- Make skorch compatible with sklearn 0.22
 
 ## [0.7.0] - 2019-11-29
 

pylintrc

@@ -56,6 +56,7 @@ disable =
     no-self-use,
     super-on-old-class,
     too-many-function-args,
+    import-outside-toplevel,
 
 
 [REPORTS]

skorch/callbacks/lr_scheduler.py

@@ -1,7 +1,6 @@
 """Contains learning rate scheduler callbacks"""
 
 import sys
-import warnings
 
 # pylint: disable=unused-import
 import numpy as np

skorch/callbacks/scoring.py

@@ -46,11 +46,16 @@ def convert_sklearn_metric_function(scoring):
     sklearn scorer and return it. Otherwise, return ``scoring`` unchanged."""
     if callable(scoring):
         module = getattr(scoring, '__module__', None)
+
+        # those are scoring objects returned by make_scorer starting
+        # from sklearn 0.22
+        scorer_names = ('_PredictScorer', '_ProbaScorer', '_ThresholdScorer')
         if (
                 hasattr(module, 'startswith') and
                 module.startswith('sklearn.metrics.') and
                 not module.startswith('sklearn.metrics.scorer') and
-                not module.startswith('sklearn.metrics.tests.')
+                not module.startswith('sklearn.metrics.tests.') and
+                not scoring.__class__.__name__ in scorer_names
         ):
             return make_scorer(scoring)
     return scoring
@@ -89,7 +94,12 @@ def _get_name(self):
         if isinstance(self.scoring_, partial):
             return self.scoring_.func.__name__
         if isinstance(self.scoring_, _BaseScorer):
-            return self.scoring_._score_func.__name__
+            if hasattr(self.scoring_._score_func, '__name__'):
+                # sklearn < 0.22
+                return self.scoring_._score_func.__name__
+            else:
+                # sklearn >= 0.22
+                return self.scoring_._score_func._score_func.__name__
         if isinstance(self.scoring_, dict):
             raise ValueError("Dict not supported as scorer for multi-metric scoring."
                              " Register multiple scoring callbacks instead.")

skorch/tests/test_utils.py

@@ -463,13 +463,20 @@ def test_index_torch_tensor_with_numpy_bool_array(self, multi_indexing):
         assert (result == X[:100]).all()
 
     def test_index_with_float_array_raises(self, multi_indexing):
+        # sklearn < 0.22 raises IndexError with msg0
+        # sklearn >= 0.22 raises ValueError with msg1
         X = np.zeros(10)
         i = np.arange(3, 0.5)
-        with pytest.raises(IndexError) as exc:
+
+        with pytest.raises((IndexError, ValueError)) as exc:
             multi_indexing(X, i)
 
-        assert exc.value.args[0] == (
-            "arrays used as indices must be of integer (or boolean) type")
+        msg0 = "arrays used as indices must be of integer (or boolean) type"
+        msg1 = ("No valid specification of the columns. Only a scalar, list or "
+                "slice of all integers or all strings, or boolean mask is allowed")
+
+        result = exc.value.args[0]
+        assert result in (msg0, msg1)
 
     def test_boolean_index_2d(self, multi_indexing):
         X = np.arange(9).reshape(3, 3)

skorch/utils.py

@@ -9,21 +9,25 @@
 from enum import Enum
 from functools import partial
 from itertools import tee
+from packaging import version
 import pathlib
 import warnings
 
 import numpy as np
 from scipy import sparse
-from sklearn.utils import safe_indexing
-from sklearn.exceptions import NotFittedError
-from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted
+import sklearn
 import torch
 from torch.nn.utils.rnn import PackedSequence
 from torch.utils.data.dataset import Subset
 
 from skorch.exceptions import DeviceWarning
 from skorch.exceptions import NotInitializedError
 
+if version.parse(sklearn.__version__) >= version.parse('0.22.0'):
+    from sklearn.utils import _safe_indexing as safe_indexing
+else:
+    from sklearn.utils import safe_indexing
+
 
 class Ansi(Enum):
     BLUE = '\033[94m'
@@ -178,7 +182,8 @@ def _indexing_ndframe(data, i):
 
 
 def _indexing_other(data, i):
-    if isinstance(i, (int, np.integer, slice)):
+    # sklearn's safe_indexing doesn't work with tuples since 0.22
+    if isinstance(i, (int, np.integer, slice, tuple)):
         return data[i]
     return safe_indexing(data, i)
 
@@ -494,15 +499,13 @@ def check_is_fitted(estimator, attributes, msg=None, all_or_any=all):
         msg = ("This %(name)s instance is not initialized yet. Call "
                "'initialize' or 'fit' with appropriate arguments "
                "before using this method.")
-    try:
-        sklearn_check_is_fitted(
-            estimator=estimator,
-            attributes=attributes,
-            msg=msg,
-            all_or_any=all_or_any,
-        )
-    except NotFittedError as e:
-        raise NotInitializedError(str(e))
+
+
+    if not isinstance(attributes, (list, tuple)):
+        attributes = [attributes]
+
+    if not all_or_any([hasattr(estimator, attr) for attr in attributes]):
+        raise NotInitializedError(msg % {'name': type(estimator).__name__})
 
 
 class TeeGenerator: