Renamed the ModelKinds enum to ModelTypes

Author: wtsimple

models_container.py

@@ -7,13 +7,13 @@
 from spark_launcher import SparkLauncher
 
 
-class ModelKinds(Enum):
+class ModelTypes(Enum):
     CLASSIFICATION = 'classification'
     REGRESSION = 'regression'
 
 
 class Model(object):
-    def __init__(self, model, name='', kind=ModelKinds.CLASSIFICATION):
+    def __init__(self, model, name='', kind=ModelTypes.CLASSIFICATION):
         self.model = model
         self.name = name
         self.kind = kind
@@ -39,7 +39,7 @@ def __init__(self):
     @property
     def classification(self):
         """Returns the classification models"""
-        return self._get_models_of_kind(kind=ModelKinds.CLASSIFICATION)
+        return self._get_models_of_kind(kind=ModelTypes.CLASSIFICATION)
 
 
     def fit(self, data: DataFrame, kind="*"):

tests/test_model_evaluator.py

@@ -6,7 +6,7 @@
 
 from data_preprocessor import DataPreprocessor
 from model_evaluator import ModelEvaluator
-from models_container import ModelsContainer, ModelKinds
+from models_container import ModelsContainer, ModelTypes
 
 """Expected results with real data
     model           dataset   AUC_ROC  AUC_PR
@@ -34,7 +34,7 @@ def test_several_classification_models_fitting(preprocessor_train_data):
     preprocessor.prepare_to_model(target_col='income', to_strip=' .')
 
     models = ModelsContainer()
-    models.fit(preprocessor.train_encoded_df, kind=ModelKinds.CLASSIFICATION)
+    models.fit(preprocessor.train_encoded_df, kind=ModelTypes.CLASSIFICATION)
     expected_results = [
         {"model": models.logistic_class.fitted_model,
          "metrics": {"areaUnderROC": 0.770414, "areaUnderPR": 0.646093}, },