work on faillure in tests

summit/multiview_platform/utils/base.py

@@ -68,7 +68,7 @@ class BaseClassifier(BaseEstimator, ):
     def get_base_estimator(self, estimator, estimator_config):
         if estimator_config is None:
             estimator_config = {}
-        if base_estimator is None:
+        if estimator is None:
             return DecisionTreeClassifier(**estimator_config)
         if isinstance(estimator, str):  # pragma: no cover
             if estimator == "DecisionTreeClassifier":
@@ -80,13 +80,13 @@ class BaseClassifier(BaseEstimator, ):
             else:
                 raise ValueError(
                     'Base estimator string {} does not match an available classifier.'.format(
-                        base_estimator))
+                        estimator))
         elif isinstance(estimator, BaseEstimator):
             return estimator.set_params(**estimator_config)
         else:
             raise ValueError(
                 'base_estimator must be either a string or a BaseEstimator child class, it is {}'.format(
-                    type(base_estimator)))
+                    type(estimator)))
 
     def to_str(self, param_name):
         """

summit/multiview_platform/utils/dataset.py

@@ -503,7 +503,7 @@ class HDF5Dataset(Dataset):
         """
         selected_labels = self.get_labels(sample_indices)
         if decode:
-            return [label_name.decode("utf-8")
+            return [label_name
                     for label, label_name in
                     enumerate(self.dataset["Labels"].attrs["names"])
                     if label in selected_labels]

summit/multiview_platform/utils/hyper_parameter_search.py

@@ -153,8 +153,8 @@ class HPSearch:
 class Random(RandomizedSearchCV, HPSearch):
 
     def __init__(self, estimator, param_distributions=None, n_iter=10,
-                 refit=False, n_jobs=1, scoring=None, cv=None,
-                 random_state=None, error_score=np.nan, view_indices=None,
+                 refit=False, n_jobs=1, scoring=None, cv=None,  learning_indices=None,
+                 random_state=None, view_indices=None,
                  framework="monoview",
                  equivalent_draws=True, track_tracebacks=True):
         param_distributions = self.get_param_distribs(estimator, param_distributions)
@@ -165,9 +165,8 @@ class Random(RandomizedSearchCV, HPSearch):
                                     param_distributions=param_distributions,
                                     refit=refit, n_jobs=n_jobs, scoring=scoring,
                                     cv=cv, random_state=random_state)
-
         self.framework = framework
-        self.available_indices = error_score
+        self.available_indices = learning_indices
         self.view_indices = view_indices
         self.equivalent_draws = equivalent_draws
         self.track_tracebacks = track_tracebacks

summit/tests/test_mono_view/test_exec_classif_mono_view.py

@@ -180,7 +180,7 @@ class Test_exec_monoview(unittest.TestCase):
                                                    feature_ids=[str(i) for i in range(test_dataset.get_v(0).shape[1])],
                                                    **{"classifier_name": "decision_tree",
                                                       "view_index": 0,
-                                                      "decision_tree": {}})
+                                                      "decision_tree": {}}, )
         rm_tmp()
 
 # class Test_getKWARGS(unittest.TestCase):

summit/tests/test_utils/test_hyper_parameter_search.py

@@ -66,7 +66,7 @@ class Test_Random(unittest.TestCase):
         cls.scoring = make_scorer(accuracy_score, )
         cls.cv = StratifiedKFold(n_splits=n_splits, )
         cls.random_state = np.random.RandomState(42)
-        cls.learning_indices = np.array([0, 1, 2, 3, 4, ])
+        # cls.learning_indices = np.array([0, 1, 2, 3, 4, ])
         cls.view_indices = None
         cls.framework = "monoview"
         cls.equivalent_draws = False
@@ -78,7 +78,7 @@ class Test_Random(unittest.TestCase):
             self.estimator, self.param_distributions, n_iter=self.n_iter,
             refit=self.refit, n_jobs=self.n_jobs, scoring=self.scoring, cv=self.cv,
             random_state=self.random_state,
-            learning_indices=self.learning_indices, view_indices=self.view_indices,
+             view_indices=self.view_indices,
             framework=self.framework,
             equivalent_draws=self.equivalent_draws
         )
@@ -89,7 +89,6 @@ class Test_Random(unittest.TestCase):
             refit=self.refit, n_jobs=self.n_jobs, scoring=self.scoring,
             cv=self.cv,
             random_state=self.random_state,
-            learning_indices=self.learning_indices,
             view_indices=self.view_indices,
             framework=self.framework,
             equivalent_draws=self.equivalent_draws
@@ -106,7 +105,6 @@ class Test_Random(unittest.TestCase):
             refit=self.refit, n_jobs=self.n_jobs, scoring=self.scoring,
             cv=self.cv,
             random_state=self.random_state,
-            learning_indices=self.learning_indices,
             view_indices=self.view_indices,
             framework="multiview",
             equivalent_draws=self.equivalent_draws
@@ -121,7 +119,6 @@ class Test_Random(unittest.TestCase):
             refit=self.refit, n_jobs=self.n_jobs, scoring=self.scoring,
             cv=self.cv,
             random_state=self.random_state,
-            learning_indices=self.learning_indices,
             view_indices=self.view_indices,
             framework="multiview",
             equivalent_draws=True
@@ -137,7 +134,6 @@ class Test_Random(unittest.TestCase):
             refit=self.refit, n_jobs=self.n_jobs, scoring=self.scoring,
             cv=self.cv,
             random_state=self.random_state,
-            learning_indices=self.learning_indices,
             view_indices=self.view_indices,
             framework="multiview",
             equivalent_draws=False