Changed names of constant variables

dislib/utils/saving.py

@@ -30,7 +30,7 @@
     cbor2 = None
 
 # Dislib models with saving tested (model: str -> module: str)
-_implemented_models = {
+IMPLEMENTED_MODELS = {
     "KMeans": "cluster",
     "GaussianMixture": "cluster",
     "CascadeSVM": "classification",
@@ -41,7 +41,7 @@
 }
 
 # Classes used by models
-_dislib_classes = {
+DISLIB_CLASSES = {
     "KMeans": dislib.cluster.KMeans,
     "DecisionTreeClassifier": DecisionTreeClassifier,
     "_Node": _Node,
@@ -50,7 +50,7 @@
     "_SkTreeWrapper": _SkTreeWrapper,
 }
 
-_sklearn_classes = {
+SKLEARN_CLASSES = {
     "SVC": SklearnSVC,
     "DecisionTreeClassifier": SklearnDTClassifier,
 }
@@ -99,10 +99,10 @@ def save_model(model, filepath, overwrite=True, save_format="json"):
 
     # Check for dislib model
     model_name = model.__class__.__name__
-    if model_name not in _implemented_models.keys():
+    if model_name not in IMPLEMENTED_MODELS.keys():
         raise NotImplementedError(
             "Saving has only been implemented for the following models:\n%s"
-            % _implemented_models.keys()
+            % IMPLEMENTED_MODELS.keys()
         )
 
     # Synchronize model
@@ -170,15 +170,15 @@ def load_model(filepath, load_format="json"):
 
     # Check for dislib model
     model_name = model_metadata["model_name"]
-    if model_name not in _implemented_models.keys():
+    if model_name not in IMPLEMENTED_MODELS.keys():
         raise NotImplementedError(
             "Saving has only been implemented for the following models:\n%s"
-            % _implemented_models.keys()
+            % IMPLEMENTED_MODELS.keys()
         )
     del model_metadata["model_name"]
 
     # Create model
-    model_module = getattr(ds, _implemented_models[model_name])
+    model_module = getattr(ds, IMPLEMENTED_MODELS[model_name])
     model_class = getattr(model_module, model_name)
     model = model_class()
     model.__dict__.update(model_metadata)
@@ -249,7 +249,7 @@ def _encode_helper(obj):
             "items": obj.__getstate__(),
         }
     elif isinstance(
-        obj, tuple(_dislib_classes.values()) + tuple(_sklearn_classes.values())
+        obj, tuple(DISLIB_CLASSES.values()) + tuple(SKLEARN_CLASSES.values())
     ):
         return {
             "class_name": obj.__class__.__name__,
@@ -302,12 +302,12 @@ def _decode_helper(obj):
             model.__setstate__(dict_)
             return model
         elif (
-            class_name in _dislib_classes.keys()
+            class_name in DISLIB_CLASSES.keys()
             and "dislib" in obj["module_name"]
         ):
             dict_ = _decode_helper(obj["items"])
             if class_name == "DecisionTreeClassifier":
-                model = _dislib_classes[obj["class_name"]](
+                model = DISLIB_CLASSES[obj["class_name"]](
                     try_features=dict_.pop("try_features"),
                     max_depth=dict_.pop("max_depth"),
                     distr_depth=dict_.pop("distr_depth"),
@@ -317,17 +317,17 @@ def _decode_helper(obj):
                 )
             elif class_name == "_SkTreeWrapper":
                 sk_tree = _decode_helper(dict_.pop("sk_tree"))
-                model = _dislib_classes[obj["class_name"]](sk_tree)
+                model = DISLIB_CLASSES[obj["class_name"]](sk_tree)
             else:
-                model = _dislib_classes[obj["class_name"]]()
+                model = DISLIB_CLASSES[obj["class_name"]]()
             model.__dict__.update(dict_)
             return model
         elif (
-            class_name in _sklearn_classes.keys()
+            class_name in SKLEARN_CLASSES.keys()
             and "sklearn" in obj["module_name"]
         ):
             dict_ = _decode_helper(obj["items"])
-            model = _sklearn_classes[obj["class_name"]]()
+            model = SKLEARN_CLASSES[obj["class_name"]]()
             model.__dict__.update(dict_)
             return model
         elif class_name == "callable":