I am following the process shown on Wine Quality Prediction End-to-End ML Project on Krish Naik's YouTube channel to do a Flight Fare Prediction Project.
I run this cell of model trainer pipeline on 04_model_trainer.ipynb:
try:
config = ConfigurationManager()
model_trainer_config = config.get_model_trainer_config()
model_trainer_config = ModelTrainer(model_trainer_config)
# model_trainer_config.train()
model_trainer_config.initiate_model_training()
except Exception as e:
raise e
I get this error:
TypeError: initiate_model_training() missing 4 required positional arguments: 'X_train', 'X_test', 'y_train', and 'y_test'
Here is the full traceback:
[2023-12-16 21:58:22,484: INFO: common: yaml file: config\config.yaml loaded successfully]
[2023-12-16 21:58:22,493: INFO: common: yaml file: params.yaml loaded successfully]
[2023-12-16 21:58:22,493: INFO: common: yaml file: schema.yaml loaded successfully]
[2023-12-16 21:58:22,493: INFO: common: created directory at: artifacts]
[2023-12-16 21:58:22,493: INFO: common: created directory at: artifacts/model_trainer]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[25], line 8
6 model_trainer_config.initiate_model_training()
7 except Exception as e:
----> 8 raise e
Cell In[25], line 6
4 model_trainer_config = ModelTrainer(model_trainer_config)
5 # model_trainer_config.train()
----> 6 model_trainer_config.initiate_model_training()
7 except Exception as e:
8 raise e
TypeError: initiate_model_training() missing 4 required positional arguments: 'X_train', 'X_test', 'y_train', and 'y_test'
Here is the code of ModelTrainer class:
class ModelTrainer:
def __init__(self, model_trainer_config):
self.model_trainer_config = model_trainer_config
# def __init__(self):
# self.model_trainer_config = ModelTrainerConfig()
def save_obj(file_path, obj):
try:
dir_path = os.path.dirname(file_path)
os.makedirs(dir_path, exist_ok=True)
with open(file_path, 'wb') as file_obj:
joblib.dump(obj, file_obj, compress= ('gzip'))
except Exception as e:
logger.info('Error occured in utils save_obj')
raise e
def evaluate_model(X_train, y_train, X_test, y_test, models):
try:
report = {}
for i in range(len(models)):
model = list(models.values())[i]
# Train model
model.fit(X_train,y_train)
# Predict Testing data
y_test_pred = model.predict(X_test)
# Get R2 scores for train and test data
test_model_score = r2_score(y_test,y_test_pred)
report[list(models.keys())[i]] = test_model_score
return report
except Exception as e:
logger.info('Exception occured during model training')
raise e
def initiate_model_training(self, X_train, X_test, y_train, y_test):
try:
logger.info('Splitting ')
models={
'LinearRegression':LinearRegression(),
'Lasso':Lasso(),
'Ridge':Ridge(),
'Elasticnet':ElasticNet(),
'RandomForestRegressor': RandomForestRegressor(),
'GradientBoostRegressor()' : GradientBoostingRegressor(),
"AdaBoost" : AdaBoostRegressor(),
'DecisionTreeRegressor' : DecisionTreeRegressor(),
"SupportVectorRegressor" : SVR(),
"KNN" : KNeighborsRegressor()
}
model_report:dict = ModelTrainer.evaluate_model(X_train,y_train, X_test, y_test, models)
print(model_report)
print("\n====================================================================================")
logger.info(f'Model Report : {model_report}')
# to get best model score from dictionary
best_model_score = max(sorted(model_report.values()))
best_model_name = list(model_report.keys())[
list(model_report.values()).index(best_model_score)
]
best_model = models[best_model_name]
print(f"Best Model Found, Model Name :{best_model_name}, R2-score: {best_model_score}")
print("\n====================================================================================")
logger.info(f"Best Model Found, Model name: {best_model_name}, R2-score: {best_model_score}")
logger.info(f"{best_model.feature_names_in_}")
ModelTrainer.save_obj(
file_path = self.model_trainer_config.trained_model_file_path,
obj = best_model
)
except Exception as e:
logger.info('Exception occured at model trianing')
raise e
Here is my file in GitHub.
My file encoding is UTF-8
Would you please help me to fix this issue?
So based on the logic that you have in your jupyter notebook, you should likely have initial_model_training() modified to look like the below function:
def initiate_model_training(self): # removing the required variables to be passed into the function because those variables are created below (assuming they were correctly generated in train() )
# lines below taken from your commented out train() function
train_data = pd.read_csv(self.config.train_data_path)
test_data = pd.read_csv(self.config.test_data_path)
X_train = train_data.drop([self.config.target_column], axis=1)
X_test = test_data.drop([self.config.target_column], axis=1)
y_train = train_data[[self.config.target_column]]
y_test = test_data[[self.config.target_column]]
# lines above taken from your commented out train() function
try:
logger.info('Splitting ')
models={
'LinearRegression':LinearRegression(),
'Lasso':Lasso(),
'Ridge':Ridge(),
'Elasticnet':ElasticNet(),
'RandomForestRegressor': RandomForestRegressor(),
'GradientBoostRegressor()' : GradientBoostingRegressor(),
"AdaBoost" : AdaBoostRegressor(),
'DecisionTreeRegressor' : DecisionTreeRegressor(),
"SupportVectorRegressor" : SVR(),
"KNN" : KNeighborsRegressor()
}
model_report:dict = ModelTrainer.evaluate_model(X_train,y_train, X_test, y_test, models)
print(model_report)
print("\n====================================================================================")
logger.info(f'Model Report : {model_report}')
# to get best model score from dictionary
best_model_score = max(sorted(model_report.values()))
best_model_name = list(model_report.keys())[
list(model_report.values()).index(best_model_score)
]
best_model = models[best_model_name]
print(f"Best Model Found, Model Name :{best_model_name}, R2-score: {best_model_score}")
print("\n====================================================================================")
logger.info(f"Best Model Found, Model name: {best_model_name}, R2-score: {best_model_score}")
logger.info(f"{best_model.feature_names_in_}")
ModelTrainer.save_obj(
file_path = self.model_trainer_config.trained_model_file_path,
obj = best_model
)
except Exception as e:
logger.info('Exception occured at model trianing')
raise e
you should look through and understand how these https://drive.google.com/file/d/1c7k8i1l2X_r9i4yWAkQzxiP1Nu8_wqap/view these files work (from the tutorial that you are following), before you start to modify the code.