Merge pull request #2127 from pavlin-policar/fitter-change-kwargs-def…

…ault-params

[FIX] Fitter: Change params uses default if None

Orange/modelling/base.py

@@ -30,7 +30,7 @@ class Fitter(Learner, metaclass=FitterMeta):
     learners.
 
     """
-    __fits__ = None
+    __fits__ = {}
     __returns__ = Model
 
     # Constants to indicate what kind of problem we're dealing with
@@ -83,7 +83,9 @@ def __kwargs(self, problem_type):
         learner_kwargs = set(
             self.__fits__[problem_type].__init__.__code__.co_varnames[1:])
         changed_kwargs = self._change_kwargs(self.kwargs, problem_type)
-        return {k: v for k, v in changed_kwargs.items() if k in learner_kwargs}
+        # Make sure to remove any params that are set to None and use defaults
+        filtered_kwargs = {k: v for k, v in changed_kwargs.items() if v is not None}
+        return {k: v for k, v in filtered_kwargs.items() if k in learner_kwargs}
 
     def _change_kwargs(self, kwargs, problem_type):
         """Handle the kwargs to be passed to the learner before they are used.
@@ -104,10 +106,9 @@ def supports_weights(self):
         """The fitter supports weights if both the classification and
         regression learners support weights."""
         return (
-            hasattr(self.get_learner(self.CLASSIFICATION), 'supports_weights')
-            and self.get_learner(self.CLASSIFICATION).supports_weights) and (
-            hasattr(self.get_learner(self.REGRESSION), 'supports_weights')
-            and self.get_learner(self.REGRESSION).supports_weights)
+            getattr(self.get_learner(self.CLASSIFICATION), 'supports_weights', False) and
+            getattr(self.get_learner(self.REGRESSION), 'supports_weights', False)
+        )
 
     @property
     def params(self):

Orange/modelling/linear.py

@@ -13,9 +13,9 @@ class SGDLearner(Fitter):
 
     def _change_kwargs(self, kwargs, problem_type):
         if problem_type is self.CLASSIFICATION:
-            kwargs['loss'] = kwargs['classification_loss']
-            kwargs['epsilon'] = kwargs['classification_epsilon']
+            kwargs['loss'] = kwargs.get('classification_loss')
+            kwargs['epsilon'] = kwargs.get('classification_epsilon')
         elif problem_type is self.REGRESSION:
-            kwargs['loss'] = kwargs['regression_loss']
-            kwargs['epsilon'] = kwargs['regression_epsilon']
+            kwargs['loss'] = kwargs.get('regression_loss')
+            kwargs['epsilon'] = kwargs.get('regression_epsilon')
         return kwargs

Orange/tests/test_fitter.py

@@ -152,3 +152,43 @@ class DummyFitter(Fitter):
         pp_data = fitter.preprocess(self.heart_disease)
         self.assertTrue(not any(
             isinstance(v, ContinuousVariable) for v in pp_data.domain.variables))
+
+    def test_default_kwargs_with_change_kwargs(self):
+        """Fallback to default args in case specialized params not specified.
+        """
+        class DummyClassificationLearner(LearnerClassification):
+            def __init__(self, param='classification_default', **_):
+                super().__init__()
+                self.param = param
+
+            def fit_storage(self, data):
+                return DummyModel(self.param)
+
+        class DummyRegressionLearner(LearnerRegression):
+            def __init__(self, param='regression_default', **_):
+                super().__init__()
+                self.param = param
+
+            def fit_storage(self, data):
+                return DummyModel(self.param)
+
+        class DummyModel:
+            def __init__(self, param):
+                self.param = param
+
+        class DummyFitter(Fitter):
+            __fits__ = {'classification': DummyClassificationLearner,
+                        'regression': DummyRegressionLearner}
+
+            def _change_kwargs(self, kwargs, problem_type):
+                if problem_type == self.CLASSIFICATION:
+                    kwargs['param'] = kwargs.get('classification_param')
+                else:
+                    kwargs['param'] = kwargs.get('regression_param')
+                return kwargs
+
+        learner = DummyFitter()
+        iris, housing = Table('iris')[:5], Table('housing')[:5]
+        self.assertEqual(learner(iris).param, 'classification_default')
+        self.assertEqual(learner(housing).param, 'regression_default')
+