Minor edits (#56)

* fix : .keys() removed from chains, exceptions and transporters

* fix : .keys() removed from pymilo_func

* fix : .keys() removed from tests

* fix : dependabot config updated

* fix : minor edit in codecov.yml

.github/dependabot.yml

@@ -7,3 +7,5 @@ updates:
     time: "01:30"
   open-pull-requests-limit: 10
   target-branch: dev
+  assignees:
+      - "AHReccese"

codecov.yml

@@ -1,8 +1,5 @@
 codecov:
   require_ci_to_pass: yes
-notify:
-    after_n_builds: 1
-    wait_for_ci: yes
 
 coverage:
   precision: 2

otherfiles/version_check.py

@@ -28,7 +28,7 @@
     os.path.join("pymilo", "pymilo_param.py"): PARAMS_ITEMS,
 }
 
-TEST_NUMBER = len(FILES.keys())
+TEST_NUMBER = len(FILES)
 
 
 def print_result(failed=False):
@@ -48,7 +48,7 @@ def print_result(failed=False):
 
 
 if __name__ == "__main__":
-    for file_name in FILES.keys():
+    for file_name in FILES:
         try:
             file_content = codecs.open(
                 file_name, "r", "utf-8", 'ignore').read()

pymilo/chains/clustering_chain.py

@@ -25,7 +25,7 @@ def is_clusterer(model):
     :return: check result as bool
     """
     if isinstance(model, str):
-        return model in SKLEARN_CLUSTERING_TABLE.keys()
+        return model in SKLEARN_CLUSTERING_TABLE
     else:
         return type(model) in SKLEARN_CLUSTERING_TABLE.values()
 
@@ -77,7 +77,7 @@ def serialize_clusterer(clusterer_object):
     :type clusterer_object: any sklearn clustering model
     :return: the serialized json string of the given clusterer
     """
-    for transporter in CLUSTERING_CHAIN.keys():
+    for transporter in CLUSTERING_CHAIN:
         CLUSTERING_CHAIN[transporter].transport(
             clusterer_object, Command.SERIALIZE)
     return clusterer_object.__dict__
@@ -94,10 +94,10 @@ def deserialize_clusterer(clusterer):
     raw_model = SKLEARN_CLUSTERING_TABLE[clusterer.type]()
     data = clusterer.data
 
-    for transporter in CLUSTERING_CHAIN.keys():
+    for transporter in CLUSTERING_CHAIN:
         CLUSTERING_CHAIN[transporter].transport(
             clusterer, Command.DESERIALZIE)
-    for item in data.keys():
+    for item in data:
         setattr(raw_model, item, data[item])
     return raw_model
 

pymilo/chains/decision_tree_chain.py

@@ -27,7 +27,7 @@ def is_decision_tree(model):
     :return: check result as bool
     """
     if isinstance(model, str):
-        return model in SKLEARN_DECISION_TREE_TABLE.keys()
+        return model in SKLEARN_DECISION_TREE_TABLE
     else:
         return type(model) in SKLEARN_DECISION_TREE_TABLE.values()
 
@@ -79,7 +79,7 @@ def serialize_decision_tree(decision_tree_object):
     :type decision_tree_object: any sklearn decision tree model
     :return: the serialized json string of the given decision tree model
     """
-    for transporter in DECISION_TREE_CHAIN.keys():
+    for transporter in DECISION_TREE_CHAIN:
         DECISION_TREE_CHAIN[transporter].transport(
             decision_tree_object, Command.SERIALIZE)
     return decision_tree_object.__dict__
@@ -96,10 +96,10 @@ def deserialize_decision_tree(decision_tree):
     raw_model = SKLEARN_DECISION_TREE_TABLE[decision_tree.type]()
     data = decision_tree.data
 
-    for transporter in DECISION_TREE_CHAIN.keys():
+    for transporter in DECISION_TREE_CHAIN:
         DECISION_TREE_CHAIN[transporter].transport(
             decision_tree, Command.DESERIALZIE)
-    for item in data.keys():
+    for item in data:
         setattr(raw_model, item, data[item])
     return raw_model
 

pymilo/chains/linear_model_chain.py

@@ -31,7 +31,7 @@ def is_linear_model(model):
     :return: check result as bool
     """
     if isinstance(model, str):
-        return model in SKLEARN_LINEAR_MODEL_TABLE.keys()
+        return model in SKLEARN_LINEAR_MODEL_TABLE
     else:
         return type(model) in SKLEARN_LINEAR_MODEL_TABLE.values()
 
@@ -98,15 +98,15 @@ def serialize_linear_model(linear_model_object):
     :return: the serialized json string of the given linear model
     """
     # first serializing the inner linear models...
-    for key in linear_model_object.__dict__.keys():
+    for key in linear_model_object.__dict__:
         if is_linear_model(linear_model_object.__dict__[key]):
             linear_model_object.__dict__[key] = {
                 "inner-model-data": transport_linear_model(linear_model_object.__dict__[key], Command.SERIALIZE),
                 "inner-model-type": get_sklearn_type(linear_model_object.__dict__[key]),
                 "by-pass": True
             }
     # now serializing non-linear model fields
-    for transporter in LINEAR_MODEL_CHAIN.keys():
+    for transporter in LINEAR_MODEL_CHAIN:
         LINEAR_MODEL_CHAIN[transporter].transport(
             linear_model_object, Command.SERIALIZE)
     return linear_model_object.__dict__
@@ -132,17 +132,17 @@ def deserialize_linear_model(linear_model, is_inner_model):
         data = linear_model.data
     # first deserializing the inner linear models(one depth inner linear
     # models have been deserialized -> TODO full depth).
-    for key in data.keys():
+    for key in data:
         if is_deserialized_linear_model(data[key]):
             data[key] = transport_linear_model({
                 "data": data[key]["inner-model-data"],
                 "type": data[key]["inner-model-type"]
             }, Command.DESERIALZIE, is_inner_model=True)
     # now deserializing non-linear models fields
-    for transporter in LINEAR_MODEL_CHAIN.keys():
+    for transporter in LINEAR_MODEL_CHAIN:
         LINEAR_MODEL_CHAIN[transporter].transport(
             linear_model, Command.DESERIALZIE, is_inner_model)
-    for item in data.keys():
+    for item in data:
         setattr(raw_model, item, data[item])
     return raw_model
 

pymilo/chains/neural_network_chain.py

@@ -33,7 +33,7 @@ def is_neural_network(model):
     :return: check result as bool
     """
     if isinstance(model, str):
-        return model in SKLEARN_NEURAL_NETWORK_TABLE.keys()
+        return model in SKLEARN_NEURAL_NETWORK_TABLE
     else:
         return type(model) in SKLEARN_NEURAL_NETWORK_TABLE.values()
 
@@ -85,7 +85,7 @@ def serialize_neural_network(neural_network_object):
     :type neural_network_object: any sklearn neural network model
     :return: the serialized json string of the given neural network model
     """
-    for transporter in NEURAL_NETWORK_CHAIN.keys():
+    for transporter in NEURAL_NETWORK_CHAIN:
         NEURAL_NETWORK_CHAIN[transporter].transport(
             neural_network_object, Command.SERIALIZE)
     return neural_network_object.__dict__
@@ -102,10 +102,10 @@ def deserialize_neural_network(neural_network):
     raw_model = SKLEARN_NEURAL_NETWORK_TABLE[neural_network.type]()
     data = neural_network.data
 
-    for transporter in NEURAL_NETWORK_CHAIN.keys():
+    for transporter in NEURAL_NETWORK_CHAIN:
         NEURAL_NETWORK_CHAIN[transporter].transport(
             neural_network, Command.DESERIALZIE)
-    for item in data.keys():
+    for item in data:
         setattr(raw_model, item, data[item])
     return raw_model
 

pymilo/exceptions/deserialize_exception.py

@@ -44,7 +44,7 @@ def __init__(self, meta_data):
             DeSerilaizatoinErrorTypes.CORRUPTED_JSON_FILE: 'the given json file is not a valid .json file.',
             DeSerilaizatoinErrorTypes.INVALID_MODEL: 'the given model is not supported or is not a valid model.',
             DeSerilaizatoinErrorTypes.VALID_MODEL_INVALID_INTERNAL_STRUCTURE: 'the given model has some non-standard customized internal objects or functions.'}
-        if error_type in error_type_to_message.keys():
+        if error_type in error_type_to_message:
             reason = error_type_to_message[error_type]
         else:
             reason = "an Unknown error occurred."

pymilo/exceptions/serialize_exception.py

@@ -38,7 +38,7 @@ def __init__(self, meta_data):
         error_type_to_message = {
             SerilaizatoinErrorTypes.INVALID_MODEL: 'the given model is not supported or is not a valid model.',
             SerilaizatoinErrorTypes.VALID_MODEL_INVALID_INTERNAL_STRUCTURE: 'the given model has some non-standard customized internal objects or functions.'}
-        if error_type in error_type_to_message.keys():
+        if error_type in error_type_to_message:
             message += error_type_to_message[error_type]
         else:
             message += "an Unknown error occurred."

pymilo/pymilo_func.py

@@ -74,11 +74,11 @@ def compare_model_outputs(exported_output,
     :type epsilon_error: float
     :return: check result as bool
     """
-    if len(exported_output.keys()) != len(imported_output.keys()):
+    if len(exported_output) != len(imported_output):
         return False  # TODO: throw exception
     total_error = 0
-    for key in exported_output.keys():
-        if key not in imported_output.keys():
+    for key in exported_output:
+        if key not in imported_output:
             return False  # TODO: throw exception
         total_error += np.abs(imported_output[key] - exported_output[key])
     return np.abs(total_error) < epsilon_error

pymilo/transporters/general_data_structure_transporter.py

@@ -23,7 +23,7 @@ def serialize_dict(self, dictionary):
         :return: fully serializable dictionary
         """
         black_list_key_values = []
-        for key in dictionary.keys():
+        for key in dictionary:
             # check inner field as a np.ndarray
             if isinstance(dictionary[key], np.ndarray):
                 dictionary[key] = self.ndarray_to_list(dictionary[key])
@@ -191,7 +191,7 @@ def get_deserialized_list(self, content):
         for item in content:
             if is_primitive(item):
                 new_list.append(item)
-            elif "np-type" in item.keys():
+            elif "np-type" in item:
                 new_list.append(
                     NUMPY_TYPE_DICT[item["np-type"]](item['value']))
             else:
@@ -286,7 +286,7 @@ def list_to_ndarray(self, list_item):
         else:
             if is_primitive(list_item):
                 return list_item
-            elif isinstance(list_item, dict) and "np-type" in list_item.keys():
+            elif isinstance(list_item, dict) and "np-type" in list_item:
                 return NUMPY_TYPE_DICT[list_item["np-type"]
                                        ](list_item['value'])
             else:

pymilo/transporters/labelbinarizer_transporter.py

@@ -38,7 +38,7 @@ def get_serialized_label_binarizer(self, label_binarizer):
         :return: pymilo serialized output of label_binarizer object
         """
         data = label_binarizer.__dict__
-        for key in data.keys():
+        for key in data:
             if isinstance(data[key], np.ndarray):
                 data[key] = data[key].tolist()
         return data
@@ -76,6 +76,6 @@ def get_deserialized_label_binarizer(self, content):
         """
         raw_lb = KEYS_NEED_PREPROCESSING_BEFORE_DESERIALIZATION["_label_binarizer"](
         )
-        for item in content.keys():
+        for item in content:
             setattr(raw_lb, item, content[item])
         return raw_lb

pymilo/transporters/transporter.py

@@ -109,7 +109,7 @@ def transport(self, request, command, is_inner_model=False):
         if command == Command.SERIALIZE:
             # request is a sklearn model
             data = request.__dict__
-            for key in data.keys():
+            for key in data:
                 if self.bypass(data[key]):
                     continue  # by-pass!!
                 data[key] = self.serialize(
@@ -125,7 +125,7 @@ def transport(self, request, command, is_inner_model=False):
             else:
                 data = request.data
                 model_type = request.type
-            for key in data.keys():
+            for key in data:
                 data[key] = self.deserialize(data, key, model_type)
             return
 

tests/test_clusterings/test_clusterings.py

@@ -35,6 +35,6 @@ def reset_exported_models_directory():
             os.remove(json_file)
 
 def test_full():
-    for category in CLUSTERINGS.keys():
+    for category in CLUSTERINGS:
         for model in CLUSTERINGS[category]:
             model()

tests/test_decision_trees/test_decision_trees.py

@@ -25,6 +25,6 @@ def reset_exported_models_directory():
             os.remove(json_file)
 
 def test_full():
-    for category in DECISION_TREES.keys():
+    for category in DECISION_TREES:
         for model in DECISION_TREES[category]:
             model()

tests/test_exceptions/test_exceptions.py

@@ -16,7 +16,7 @@
 }
 
 def test_full():
-    for category in EXCEPTION_TESTS.keys():
+    for category in EXCEPTION_TESTS:
         category_all_test_pass = True
         for test in EXCEPTION_TESTS[category]:
             category_all_test_pass = category_all_test_pass and test()

tests/test_linear_models/test_linear_models.py

@@ -123,7 +123,7 @@ def reset_exported_models_directory():
             os.remove(json_file)
 
 def test_full():
-    for category in LINEAR_MODELS.keys():
+    for category in LINEAR_MODELS:
         for model in LINEAR_MODELS[category]:
             if isinstance(model, tuple):
                 func, model_name = model

tests/test_neural_networks/test_neural_networks.py

@@ -25,6 +25,6 @@ def reset_exported_models_directory():
             os.remove(json_file)
 
 def test_full():
-    for category in NEURAL_NETWORKS.keys():
+    for category in NEURAL_NETWORKS:
         for model in NEURAL_NETWORKS[category]:
             model()