Create utils.py

src/utils/main_utils/utils.py

@@ -0,0 +1,102 @@
+import yaml 
+from src.exception.exception import DeliveryTimeException
+from src.logging.logger import logging
+import os, sys 
+import numpy as np 
+import pickle 
+
+from sklearn.metrics import r2_score
+from sklearn.model_selection import GridSearchCV
+
+def read_yaml_file(file_path:str) -> dict:
+    try:
+        with open(file_path, 'rb') as yaml_file:
+            return yaml.safe_load(yaml_file)
+
+    except Exception as e:
+        DeliveryTimeException(e, sys)
+
+def write_yaml_file(file_path:str, content:object, replace:bool=False) -> None:
+    try:
+        if replace:
+            if os.path.exists(file_path):
+                os.remove(file_path)
+        os.makedirs(os.path.dirname(file_path), exist_ok=True)
+        with open(file_path, 'w') as file:
+            yaml.dump(content, file)
+
+    except Exception as e:
+        raise DeliveryTimeException(e, sys)
+    
+def save_numpy_array_data(file_path:str, array:np.array):
+    """
+    Save numpy array data to file 
+    file_path: str location of file to save 
+    array:np.array data to save
+    """
+    try:
+        dir_path=os.path.dirname(file_path)
+        os.makedirs(dir_path, exist_ok=True)
+        with open(file_path, 'wb') as file_obj:
+            np.save(file_obj, array)
+    except Exception as e:
+        raise DeliveryTimeException(e, sys)
+    
+def save_object(file_path:str, obj:object) -> None:
+    try:
+        logging.info("Entered the save_object method of MainUtils class")
+        os.makedirs(os.path.dirname(file_path), exist_ok=True)
+        with open(file_path, "wb") as file_obj:
+            pickle.dump(obj, file_obj)
+        logging.info("Exited the save_object method of MainUtils class")
+    except Exception as e:
+        raise DeliveryTimeException(e, sys)
+    
+def load_object(file_path:str) ->object:
+    try:
+        if not os.path.exists(file_path):
+            raise Exception(f"The file: {file_path} does not exist") 
+        with open(file_path, 'rb') as file_obj:
+            print(file_obj)
+            return pickle.load(file_obj)
+    except Exception as e:
+        raise DeliveryTimeException(e, sys)
+    
+def load_numpy_array_data(file_path:str) -> np.array:
+    """
+    Load numpy array data from file
+    file_path: str location of file to load
+    return: np.array data loaded
+    """
+    try:
+        with open(file_path, 'rb') as file_obj:
+            return np.load(file_obj)
+    except Exception as e:
+        raise DeliveryTimeException(e, sys)
+    
+def evaluate_models(X_train, y_train, X_test, y_test, models, param):
+    try:
+        report = {}
+
+        for i in range(len(list(models))):
+            model = list(models.values())[i]
+            para = param[list(models.keys())[i]]
+
+            gs = GridSearchCV(model, para, cv=3)
+            gs.fit(X_train, y_train)
+
+            model.set_params(**gs.best_params_)
+            model.fit(X_train, y_train)
+
+            y_train_pred = model.predict(X_train)
+            y_test_pred = model.predict(X_test)
+            
+            test_model_score = r2_score(y_test, y_test_pred)
+            report[list(models.keys())[i]] = test_model_score
+
+            
+        return report
+            
+    except Exception as e:
+        raise DeliveryTimeException(e, sys)
+