Parameter Fitter: Replace variadic argument in fitted_parameters with 'problem_type'

Author: janezd

Orange/base.py

@@ -190,7 +190,8 @@ def active_preprocessors(self):
                 self.preprocessors is not type(self).preprocessors):
             yield from type(self).preprocessors
 
-    def fitted_parameters(self, *args, **kwargs) -> list:
+    # declared for derived classes, pylint: disable=unused-argument
+    def fitted_parameters(self, problem_type: Union[str, Table, Domain]) -> list:
         return []
 
     # pylint: disable=no-self-use

Orange/classification/random_forest.py

@@ -59,6 +59,6 @@ def __init__(self,
         super().__init__(preprocessors=preprocessors)
         self.params = vars()
 
-    def fitted_parameters(self) -> list[Learner.FittedParameter]:
+    def fitted_parameters(self, _) -> list[Learner.FittedParameter]:
         return [self.FittedParameter("n_estimators", "Number of trees",
                                      "Trees", int, 1, None)]

Orange/modelling/randomforest.py

@@ -31,4 +31,4 @@ def fitted_parameters(
             self,
             problem_type: Union[str, Table, Domain]
     ) -> list[Learner.FittedParameter]:
-        return self.get_learner(problem_type).fitted_parameters()
+        return self.get_learner(problem_type).fitted_parameters(problem_type)

Orange/regression/pls.py

@@ -255,7 +255,7 @@ def incompatibility_reason(self, domain):
                     reason = "Only numeric target variables expected."
         return reason
 
-    def fitted_parameters(self) -> list[Learner.FittedParameter]:
+    def fitted_parameters(self, _) -> list[Learner.FittedParameter]:
         return [self.FittedParameter("n_components", "Number of components",
                                      "Comp", int, 1, None)]
 

Orange/regression/random_forest.py

@@ -58,6 +58,6 @@ def __init__(self,
         super().__init__(preprocessors=preprocessors)
         self.params = vars()
 
-    def fitted_parameters(self) -> list[Learner.FittedParameter]:
+    def fitted_parameters(self, _) -> list[Learner.FittedParameter]:
         return [self.FittedParameter("n_estimators", "Number of trees",
                                      "Trees", int, 1, None)]

Orange/regression/tests/test_pls.py

@@ -22,7 +22,7 @@ def table(rows, attr, variables):
 
 class TestPLSRegressionLearner(unittest.TestCase):
     def test_fitted_parameters(self):
-        fitted_parameters = PLSRegressionLearner().fitted_parameters()
+        fitted_parameters = PLSRegressionLearner().fitted_parameters(None)
         self.assertIsInstance(fitted_parameters, list)
         self.assertEqual(len(fitted_parameters), 1)
 

Orange/widgets/evaluate/owparameterfitter.py

@@ -439,14 +439,10 @@ def _settings_changed(self):
 
     @property
     def fitted_parameters(self) -> list:
-        if not self._learner:
+        if not self._learner \
+                or isinstance(self._learner, Fitter) and not self._data:
             return []
-        if isinstance(self._learner, Fitter):
-            if not self._data:
-                return []
-            return self._learner.fitted_parameters(self._data)
-        else:
-            return self._learner.fitted_parameters()
+        return self._learner.fitted_parameters(self._data)
 
     @property
     def initial_parameters(self) -> dict:

Orange/widgets/evaluate/tests/test_owparameterfitter.py

@@ -19,7 +19,7 @@
 
 
 class DummyLearner(PLSRegressionLearner):
-    def fitted_parameters(self):
+    def fitted_parameters(self, _):
         return [
             self.FittedParameter("n_components", "Foo", "foo", int, 5, None),
             self.FittedParameter("n_components", "Bar", "bar", int, 5, 10),