fixes #373 + unit test

Author: janvanrijn

openml/runs/functions.py

@@ -594,7 +594,7 @@ 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)
+            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:

tests/test_runs/test_run_functions.py

@@ -27,6 +27,7 @@
 from sklearn.feature_selection import VarianceThreshold
 from sklearn.linear_model import LogisticRegression, SGDClassifier, \
     LinearRegression
+from sklearn.neural_network import MLPClassifier
 from sklearn.ensemble import RandomForestClassifier, BaggingClassifier
 from sklearn.svm import SVC, LinearSVC
 from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, \
@@ -614,13 +615,13 @@ def test__get_seeded_model_raises(self):
             self.assertRaises(ValueError, _get_seeded_model, model=clf, seed=42)
 
     def test__extract_arfftrace(self):
-        param_grid = {"max_depth": [3, None],
-                      "max_features": [1, 2, 3, 4],
-                      "bootstrap": [True, False],
-                      "criterion": ["gini", "entropy"]}
+        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]}
         num_iters = 10
         task = openml.tasks.get_task(20)
-        clf = RandomizedSearchCV(RandomForestClassifier(), param_grid, num_iters)
+        clf = RandomizedSearchCV(MLPClassifier(), param_grid, num_iters)
         # just run the task
         train, _ = task.get_train_test_split_indices(0, 0)
         X, y = task.get_X_and_y()