added list of integers to set of accepted parameter types for arff traces

Author: janvanrijn

openml/runs/functions.py

@@ -594,11 +594,16 @@ def _extract_arfftrace_attributes(model):
     for key in model.cv_results_:
         if key.startswith('param_'):
             # supported types should include all types, including bool, int float
-            supported_types = (bool, int, float, six.string_types, tuple)
-            if all(isinstance(i, supported_types) or i is None for i in model.cv_results_[key]):
-                type = 'STRING'
-            else:
-                raise TypeError('Unsupported param type in param grid')
+            supported_basic_types = (bool, int, float, six.string_types)
+            for param_value in model.cv_results_[key]:
+                if isinstance(param_value, supported_basic_types) or param_value is None:
+                    # basic string values
+                    type = 'STRING'
+                elif isinstance(param_value, list) and all(isinstance(i, int) for i in param_value):
+                    # list of integers
+                    type = 'STRING'
+                else:
+                    raise TypeError('Unsupported param type in param grid: %s' %key)
 
             # we renamed the attribute param to parameter, as this is a required
             # OpenML convention

tests/test_runs/test_run_functions.py

@@ -615,7 +615,7 @@ def test__get_seeded_model_raises(self):
             self.assertRaises(ValueError, _get_seeded_model, model=clf, seed=42)
 
     def test__extract_arfftrace(self):
-        param_grid = {"hidden_layer_sizes": [(5, 5), (10, 10), (20, 20)],
+        param_grid = {"hidden_layer_sizes": [[5, 5], [10, 10], [20, 20]],
                       "activation" : ['identity', 'logistic', 'tanh', 'relu'],
                       "learning_rate_init": [0.1, 0.01, 0.001, 0.0001],
                       "max_iter": [10, 20, 40, 80]}
@@ -627,6 +627,9 @@ def test__extract_arfftrace(self):
         X, y = task.get_X_and_y()
         clf.fit(X[train], y[train])
 
+        # check num layers of MLP
+        self.assertIn(clf.best_estimator_.hidden_layer_sizes, param_grid['hidden_layer_sizes'])
+
         trace_attribute_list = _extract_arfftrace_attributes(clf)
         trace_list = _extract_arfftrace(clf, 0, 0)
         self.assertIsInstance(trace_attribute_list, list)
@@ -660,7 +663,6 @@ def test__extract_arfftrace(self):
                     else: # att_type = real
                         self.assertIsInstance(trace_list[line_idx][att_idx], float)
 
-
         self.assertEqual(set(param_grid.keys()), optimized_params)
 
     def test__prediction_to_row(self):