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기존 baseline model에 상승 하락 예측 binary model을 ensemble함.
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README에 프로젝트에 대한 설명, 결과 등이 자세하게 적혀 있어서 너무 좋네요 👍
| import pandas as pd | ||
| from typing import List | ||
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| def data_loader(dataset_name: str, drop_column=None) -> pd.DataFrame: |
| y_train_list = [] | ||
| x_valid_list = [] | ||
| y_valid_list = [] | ||
| from sklearn.model_selection import StratifiedKFold |
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import 함수는 처음에 다 적어주시면 좋을 것 같아요!
| test_df_filled = pd.DataFrame(final_test, columns=self.test_df.columns) | ||
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| # 원래 데이터에서 ID, target, _type 컬럼을 가져와서 결합 | ||
| df2 = pd.concat([train_df_filled, test_df_filled], axis=0).reset_index(drop=True) |
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df2 보다는 명확히 어떤 상황에서 쓰이는 df인지 알 수 있는 변수명을 쓰면 좋을 것 같아요!
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EDA 과정은 따로 프로젝트에 포함하지 않아도 될 것 같아요! 프로젝트 코드에는 .ipynb 파일은 없는 게 깔끔해보입니다
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중복되는 코드가 조금 있어보이는데, 모듈화를 하면 더 깔끔할 것 같네요!
| model = BinaryEnsemble() | ||
| else: | ||
| print("Invalid model name") | ||
| exit(1) |
| accuracy, auroc = get_accuracy_and_auroc(args.valid_type, model, train_df) | ||
| model = train_model(model, train_df) | ||
| save_submission(model, test_df, submission_df) | ||
| save_log(args, accuracy, auroc, params) |
| from sklearn.model_selection import train_test_split | ||
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| def get_accuracy_and_auroc(valid_type, model, train_df): |
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함수마다 I/O 명시되면 좋을 것 같습니다. 어떤 용도로 사용되는지 잘 보이게 하는 게 좋습니다.
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