Address #925 (#926)

Changing the `_get_param_names` method to return a list instead of a
generator to fix the exception error message when passing unknown
parameters to `set_params`. Before the error message just included
the generator `repr`-string as the list of possible parameters.
Now the string contains the possible parameter names instead.

CHANGES.md

@@ -18,6 +18,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed install command to work with recent changes in Google Colab (#928)
 - Fixed a couple of bugs related to using non-default modules and criteria (#927)
 - Fixed a bug when using `AccelerateMixin` in a multi-GPU setup (#947)
+- `_get_param_names` returns a list instead of a generator so that subsequent
+  error messages return useful information instead of a generator `repr`
+  string (#925)
 
 ## [0.12.1] - 2022-11-18
 

skorch/callbacks/base.py

@@ -56,7 +56,7 @@ def on_grad_computed(
         """
 
     def _get_param_names(self):
-        return (key for key in self.__dict__ if not key.endswith('_'))
+        return [key for key in self.__dict__ if not key.endswith('_')]
 
     def get_params(self, deep=True):
         return BaseEstimator.get_params(self, deep=deep)

skorch/net.py

@@ -1934,7 +1934,7 @@ def get_params_for_optimizer(self, prefix, named_parameters):
         return args, kwargs
 
     def _get_param_names(self):
-        return (k for k in self.__dict__ if not k.endswith('_'))
+        return [k for k in self.__dict__ if not k.endswith('_')]
 
     def _get_params_callbacks(self, deep=True):
         """sklearn's .get_params checks for `hasattr(value,

skorch/tests/test_net.py

@@ -1102,6 +1102,27 @@ def test_set_params_works(self, net, data):
         assert net.module_.sequential[3].in_features == 20
         assert np.isclose(net.lr, 0.2)
 
+    def test_unknown_set_params_gives_helpful_message(self, net_fit):
+        # test that the error message of set_params includes helpful
+        # information instead of, e.g., generator expressions.
+        # sklearn 0.2x does not output the parameter names so we can
+        # skip detailled checks of the error message there.
+
+        sklearn_0_2x_string = "Check the list of available parameters with `estimator.get_params().keys()`"
+
+        with pytest.raises(ValueError) as e:
+            net_fit.set_params(invalid_parameter_xyz=42)
+
+        exception_str = str(e.value)
+
+        if sklearn_0_2x_string in exception_str:
+            return
+
+        expected_keys = ["module", "criterion"]
+
+        for key in expected_keys:
+            assert key in exception_str[exception_str.find("Valid parameters are: ") :]
+
     def test_set_params_then_initialize_remembers_param(
             self, net_cls, module_cls):
         net = net_cls(module_cls)