MNT: Update CI, add Python 3.11, fix sklearn issues

.github/workflows/testing.yml

@@ -9,28 +9,40 @@ on:
   pull_request:
     branches: [ master ]
 
+# print more columns
+env:
+  COLUMNS: 120
+
 jobs:
   test:
 
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false  # don't cancel all jobs when one fails
       matrix:
-        python_version: ['3.8', '3.9', '3.10']
-        torch_version: ['1.11.0+cpu', '1.12.1+cpu', '1.13.1+cpu', '2.0.0+cpu']
+        python_version: ['3.8', '3.9', '3.10', '3.11']
+        torch_version: ['1.11.0+cpu', '1.12.1+cpu', '1.13.1+cpu', '2.0.1+cpu']
         os: [ubuntu-latest]
+        exclude:
+          - python_version: '3.11'
+            torch_version: '1.11.0+cpu'
+          - python_version: '3.11'
+            torch_version: '1.12.1+cpu'
+          - python_version: '3.11'
+            torch_version:  '2.0.1+cpu'
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
         python-version: ${{ matrix.python_version }}
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
         python -m pip install -r requirements-dev.txt
         python -m pip install -r requirements.txt
+        python -m pip install pytest-pretty
         python -m pip install torch==${{ matrix.torch_version }} -f https://download.pytorch.org/whl/torch_stable.html
         python -m pip list
     - name: Install skorch

README.rst

@@ -247,7 +247,7 @@ minor PyTorch versions, which currently are:
 - 1.11.0
 - 1.12.1
 - 1.13.1
-- 2.0.0
+- 2.0.1
 
 However, that doesn't mean that older versions don't work, just that
 they aren't tested. Since skorch mostly relies on the stable part of

docs/user/installation.rst

@@ -101,7 +101,7 @@ minor PyTorch versions, which currently are:
 - 1.11.0
 - 1.12.1
 - 1.13.1
-- 2.0.0
+- 2.0.1
 
 However, that doesn't mean that older versions don't work, just that
 they aren't tested. Since skorch mostly relies on the stable part of

skorch/classifier.py

@@ -13,8 +13,7 @@
 from skorch.callbacks import EpochScoring
 from skorch.callbacks import PassthroughScoring
 from skorch.dataset import ValidSplit
-from skorch.utils import get_dim, to_numpy
-from skorch.utils import is_dataset
+from skorch.utils import data_from_dataset, is_dataset, get_dim, to_numpy
 
 neural_net_clf_doc_start = """NeuralNet for classification tasks
 
@@ -114,6 +113,15 @@ def check_data(self, X, y):
                    "``iterator_train`` and ``iterator_valid`` parameters "
                    "respectively.")
             raise ValueError(msg)
+
+        if (y is None) and is_dataset(X):
+            try:
+                _, y_ds = data_from_dataset(X)
+                self.classes_inferred_ = np.unique(to_numpy(y_ds))
+            except AttributeError:
+                # If this fails, we might still be good to go, so don't raise
+                pass
+
         if y is not None:
             # pylint: disable=attribute-defined-outside-init
             self.classes_inferred_ = np.unique(to_numpy(y))

skorch/tests/callbacks/test_scoring.py

@@ -220,10 +220,11 @@ def test_with_score_nonexisting_string(
             max_epochs=2,
             train_split=train_split,
         )
-        with pytest.raises(ValueError) as exc:
+        # don't check precise error message, because it stems from sklearn and
+        # may change in the future
+        msg = "'does-not-exist'"
+        with pytest.raises(ValueError, match=msg):
             net.fit(*data)
-        msg = "'does-not-exist' is not a valid scoring value."
-        assert exc.value.args[0].startswith(msg)
 
     def test_with_score_as_custom_func(
             self, net_cls, module_cls, scoring_cls, train_split, data, score55,
@@ -788,10 +789,11 @@ def test_with_score_nonexisting_string(
             max_epochs=2,
             train_split=train_split,
         )
-        with pytest.raises(ValueError) as exc:
+        # don't check precise error message, because it stems from sklearn and
+        # may change in the future
+        msg = "'does-not-exist'"
+        with pytest.raises(ValueError, match=msg):
             net.fit(*data)
-        msg = "'does-not-exist' is not a valid scoring value."
-        assert exc.value.args[0].startswith(msg)
 
     def test_with_score_as_custom_func(
             self, net_cls, module_cls, scoring_cls, train_split, data, score55,

skorch/tests/test_classifier.py

@@ -136,6 +136,30 @@ def test_pass_classes_explicitly_overrides(self, net_cls, module_cls, data):
         net = net_cls(module_cls, max_epochs=0, classes=['foo', 'bar']).fit(*data)
         assert net.classes_ == ['foo', 'bar']
 
+    def test_classes_are_set_with_tensordataset_explicit_y(
+            self, net_cls, module_cls, data
+    ):
+        # see 990
+        X = torch.from_numpy(data[0])
+        y = torch.arange(len(X)) % 10
+        dataset = torch.utils.data.TensorDataset(X, y)
+        net = net_cls(module_cls, max_epochs=0).fit(dataset, y)
+        assert (net.classes_ == [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).all()
+
+    def test_classes_are_set_with_tensordataset_implicit_y(
+            self, net_cls, module_cls, data
+    ):
+        # see 990
+        from skorch.dataset import ValidSplit
+
+        X = torch.from_numpy(data[0])
+        y = torch.arange(len(X)) % 10
+        dataset = torch.utils.data.TensorDataset(X, y)
+        net = net_cls(
+            module_cls, max_epochs=0, train_split=ValidSplit(3, stratified=False)
+        ).fit(dataset, None)
+        assert (net.classes_ == [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).all()
+
     @pytest.mark.parametrize('classes', [[], np.array([])])
     def test_pass_empty_classes_raises(
             self, net_cls, module_cls, data, classes):

skorch/tests/test_utils.py

@@ -440,6 +440,11 @@ def data(self):
         y = np.array([1, 3, 0, 2])
         return X, y
 
+    @pytest.fixture
+    def tensors(self, data):
+        X, y = data
+        return torch.from_numpy(X), torch.from_numpy(y)
+
     @pytest.fixture
     def skorch_ds(self, data):
         from skorch.dataset import Dataset
@@ -501,6 +506,24 @@ def test_subset_with_y_none(self, data_from_dataset, data, subset):
         assert (X == data[0][[1, 3]]).all()
         assert y is None
 
+    def test_with_tensordataset_2_vals(self, data_from_dataset, tensors):
+        dataset = torch.utils.data.dataset.TensorDataset(*tensors)
+        X, y = data_from_dataset(dataset)
+        assert (X == tensors[0]).all()
+        assert (y == tensors[1]).all()
+
+    def test_with_tensordataset_1_val_raises(self, data_from_dataset, tensors):
+        dataset = torch.utils.data.dataset.TensorDataset(tensors[0])
+        msg = "Could not access X and y from dataset."
+        with pytest.raises(AttributeError, match=msg):
+            data_from_dataset(dataset)
+
+    def test_with_tensordataset_3_vals_raises(self, data_from_dataset, tensors):
+        dataset = torch.utils.data.dataset.TensorDataset(*tensors, tensors[0])
+        msg = "Could not access X and y from dataset."
+        with pytest.raises(AttributeError, match=msg):
+            data_from_dataset(dataset)
+
 
 class TestMultiIndexing:
     @pytest.fixture

skorch/utils.py

@@ -440,6 +440,11 @@ def data_from_dataset(dataset, X_indexing=None, y_indexing=None):
       If not None, use this function for indexing into the y data. If
       None, try to automatically determine how to index data.
 
+    Raises
+    ------
+    AttributeError
+      If X and y could not be accessed from the dataset.
+
     """
     X, y = _none, _none
 
@@ -450,6 +455,9 @@ def data_from_dataset(dataset, X_indexing=None, y_indexing=None):
         y = multi_indexing(y, dataset.indices, indexing=y_indexing)
     elif hasattr(dataset, 'X') and hasattr(dataset, 'y'):
         X, y = dataset.X, dataset.y
+    elif isinstance(dataset, torch.utils.data.dataset.TensorDataset):
+        if len(items := dataset.tensors) == 2:
+            X, y = items
 
     if (X is _none) or (y is _none):
         raise AttributeError("Could not access X and y from dataset.")