Add trained pipelines for orders 1~5 (개별 파라미터) + training script

models_by_order/order1/pipeline.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34f774ecce18b5ee2983c3ae76d42096a726ea447ea9f12883960925db19d933
+size 178070

models_by_order/order2/pipeline.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:017e7eb292066baca4286b2a17cd6e02a799ea7bc5c68ec0165e3ba2232e99ee
+size 266967

models_by_order/order3/pipeline.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9a9717526ffcf56a25a03a7a8e16bd022e55d3bf50d552b755c23ad61ab65129
+size 353015

models_by_order/order4/pipeline.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fed8400a0d7dce5c7ae690f59b87e92c424afa7e8f8fd49b2b0714d182838508
+size 490677

models_by_order/order5/pipeline.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf1a8a1fc6ba59d599ddab9615ae343f9a9ed224451257633b4c1303123b3c66
+size 921883

onbid-map-carp-train.py

@@ -0,0 +1,193 @@
+# onbid-map-carp-train.py
+
+import os
+import shutil
+import stat
+import pandas as pd
+import joblib
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from xgboost import XGBRegressor
+from huggingface_hub import HfApi, Repository
+
+# 차수별 하이퍼파라미터 설정
+ORDER_PARAMS = {
+    1: {
+        "objective": "reg:squarederror",
+        "max_depth": 4,
+        "learning_rate": 0.10,
+        "n_estimators": 100,
+        "subsample": 0.8,
+        "colsample_bytree": 0.8,
+        "random_state": 42,
+    },
+    2: {
+        "objective": "reg:squarederror",
+        "max_depth": 5,
+        "learning_rate": 0.05,
+        "n_estimators": 120,
+        "subsample": 0.85,
+        "colsample_bytree": 0.9,
+        "random_state": 42,
+    },
+    3: {
+        "objective": "reg:squarederror",
+        "max_depth": 6,
+        "learning_rate": 0.03,
+        "n_estimators": 150,
+        "subsample": 0.9,
+        "colsample_bytree": 0.9,
+        "random_state": 42,
+    },
+    4: {
+        "objective": "reg:squarederror",
+        "max_depth": 7,
+        "learning_rate": 0.02,
+        "n_estimators": 180,
+        "subsample": 0.9,
+        "colsample_bytree": 0.95,
+        "random_state": 42,
+    },
+    5: {
+        "objective": "reg:squarederror",
+        "max_depth": 8,
+        "learning_rate": 0.01,
+        "n_estimators": 200,
+        "subsample": 0.95,
+        "colsample_bytree": 0.95,
+        "random_state": 42,
+    },
+}
+
+# 환경 변수에서 Hugging Face 토큰 읽기
+HF_REPO_NAME = "asteroidddd/onbid-map-carp"
+HF_TOKEN     = os.getenv("HF_TOKEN")
+if HF_TOKEN is None:
+    raise ValueError("환경 변수 HF_TOKEN이 설정되어 있지 않습니다.")
+
+# 이 스크립트의 경로
+SCRIPT_PATH = os.path.abspath(__file__)
+
+def rm_readonly(func, path, exc_info):
+    os.chmod(path, stat.S_IWRITE)
+    func(path)
+
+def main():
+
+    # 데이터 불러오기
+    df = pd.read_pickle(r'C:\Users\hwang\Desktop\OSSP\car_data.pkl')
+
+    # 자동차만 필터링 (이미 되어 있음)
+    if "대분류" in df.columns:
+        df = df[(df["대분류"] == "자동차")].reset_index(drop=True)
+
+    # '낙찰차수' 컬럼을 정수형으로 변환하고, 5 이상은 5로 통일
+    df["낙찰차수"] = df["낙찰차수"].astype(int).apply(lambda x: x if x < 5 else 5)
+
+    # 차수별 모델 학습 & 저장
+    for order in [1, 2, 3, 4, 5]:
+
+        # 해당 차수 데이터만 필터링
+        subset = df[df["낙찰차수"] == order].copy().reset_index(drop=True)
+        if subset.empty:
+            print(f"차수 {order} 데이터가 없습니다. 건너뜁니다.")
+            continue
+
+        # 날짜 컬럼(datetime) 파생변수 생성
+        if "최초입찰시기" in subset.columns:
+            subset["최초입찰시기"] = pd.to_datetime(subset["최초입찰시기"])
+            subset["최초입찰_연도"] = subset["최초입찰시기"].dt.year
+            subset["최초입찰_월"] = subset["최초입찰시기"].dt.month
+            subset["최초입찰_일"] = subset["최초입찰시기"].dt.day
+            subset["최초입찰_요일"] = subset["최초입찰시기"].dt.weekday
+            subset = subset.drop(columns=["최초입찰시기"])
+        else:
+            raise KeyError("최초입찰시기 컬럼이 데이터프레임에 없습니다.")
+
+        # 사용할 피처 컬럼 결정
+        base_cols = ["대분류", "중분류", "소분류", "제조사", "차종", "기관"]
+        date_cols = ["최초입찰_연도", "최초입찰_월", "최초입찰_일", "최초입찰_요일"]
+        if order == 1:
+            bid_cols = ["1차최저입찰가"]
+        elif order == 2:
+            bid_cols = ["1차최저입찰가", "2차최저입찰가"]
+        elif order == 3:
+            bid_cols = ["1차최저입찰가", "2차최저입찰가", "3차최저입찰가"]
+        elif order == 4:
+            bid_cols = ["1차최저입찰가", "2차최저입찰가", "3차최저입찰가", "4차최저입찰가"]
+        else:  # order == 5
+            bid_cols = ["1차최저입찰가", "2차최저입찰가", "3차최저입찰가", "4차최저입찰가", "5차최저입찰가"]
+
+        feature_cols = base_cols + date_cols + bid_cols
+        X = subset[feature_cols].copy()
+        y = subset["낙찰가율_최초최저가기준"].copy()
+
+        # 전처리 + 모델 파이프라인 정의
+        preprocessor = ColumnTransformer(
+            transformers=[("ohe", OneHotEncoder(handle_unknown="ignore"), base_cols)],
+            remainder="passthrough"
+        )
+
+        # 차수별 파라미터를 꺼내서 XGBRegressor 생성
+        params = ORDER_PARAMS.get(order)
+        model = XGBRegressor(**params)
+        pipeline = Pipeline([
+            ("preprocessor", preprocessor),
+            ("regressor", model)
+        ])
+
+        # 전체 데이터로 학습
+        pipeline.fit(X, y)
+        print(f"차수 {order} 모델 학습 완료 (params: {params})")
+
+        # 모델 저장
+        output_dir = f"output/order{order}"
+        os.makedirs(output_dir, exist_ok=True)
+        joblib.dump(pipeline, os.path.join(output_dir, "pipeline.pkl"))
+        print(f"  → pipeline.pkl 저장: {output_dir}/pipeline.pkl")
+
+    # requirements.txt 작성
+    deps = ["pandas", "scikit-learn", "xgboost", "joblib", "huggingface_hub"]
+    with open("requirements.txt", "w", encoding="utf-8") as f:
+        f.write("\n".join(deps))
+
+    # 레포 생성 시도
+    api = HfApi()
+    try:
+        api.create_repo(repo_id=HF_REPO_NAME, token=HF_TOKEN)
+    except Exception:
+        pass
+
+    # 로컬에 레포 클론 (기존 디렉토리 삭제 포함)
+    local_dir = "hf_repo"
+    if os.path.isdir(local_dir):
+        shutil.rmtree(local_dir, onerror=rm_readonly)
+    repo = Repository(local_dir=local_dir, clone_from=HF_REPO_NAME, use_auth_token=HF_TOKEN)
+
+    # output/order{차수} 내 파일을 hf_repo/models_by_order/order{차수} 폴더로 복사
+    for order in [1, 2, 3, 4, 5]:
+        src_dir = f"output/order{order}"
+        if not os.path.isdir(src_dir):
+            continue
+
+        dst_dir = os.path.join(local_dir, "models_by_order", f"order{order}")
+        os.makedirs(dst_dir, exist_ok=True)
+
+        src_file = os.path.join(src_dir, "pipeline.pkl")
+        if os.path.isfile(src_file):
+            shutil.copy(src_file, os.path.join(dst_dir, "pipeline.pkl"))
+
+    # 스크립트 파일 및 requirements.txt도 함께 복사
+    for src in [SCRIPT_PATH, "requirements.txt"]:
+        dst = os.path.join(local_dir, os.path.basename(src))
+        shutil.copy(src, dst)
+
+    # 커밋 및 푸시
+    repo.git_add(auto_lfs_track=True)
+    repo.git_commit("Add trained pipelines for orders 1~5 (개별 파라미터) + training script")
+    repo.git_push()
+    print("Hugging Face Hub에 모델 업로드 완료")
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,5 @@
+pandas
+scikit-learn
+xgboost
+joblib
+huggingface_hub