def run(args):
# 데이터 파일 읽기
data = pd.read_csv(data_dir + "/train.csv", parse_dates=["order_date"])
# 피처 엔지니어링 실행
train, test, y, features = feature_engineering(data, args.YEAR_MONTH)
if args.MODEL == "lgbm":
# Cross Validation Out Of Fold로 LightGBM 모델 훈련 및 예측
y_oof, test_preds = lgbm(train, y, test, features, lgbm_params(), WANDB_USE=True)
if args.MODEL == "cat":
y_oof, test_preds = cat(train, y, test, features, cat_params(), WANDB_USE=True)
if args.MODEL == "xgb":
y_oof, test_preds = xgboost(train, y, test, features, xgb_params(), WANDB_USE=True)
# 테스트 결과 제출 파일 읽기
sub = pd.read_csv(data_dir + "/sample_submission.csv")
# 테스트 예측 결과 저장
sub["probability"] = test_preds
# 제출 파일 쓰기
os.makedirs(output_dir, exist_ok=True)
sub.to_csv(
os.path.join(output_dir, f"{args.MODEL}_{args.FILE_NAME}.csv"), index=False
)
def main():
df_train = pd.read_csv("data/training-set.csv")
df_test = pd.read_csv("data/testing-set.csv").drop("Next_Premium", axis=1)
df_features = feature_engineering(df_train, df_test)
# print("\nFeatures:")
# print(df_features.sample(10))
model_files = [
".".join(x.split(".")[:-1]) for x in glob.glob(MODEL_PATH)
if x.endswith(".pd")
]
print(model_files)
val_tmp, test_tmp = [], []
print("Validation")
for filename in model_files:
val_tmp.append(
np.clip(pd.read_pickle(filename + ".pd").values[:, 0], 0, 2e8))
print("%.2f %.2f %.2f %.2f %.2f" %
(np.min(val_tmp), np.percentile(val_tmp, 25), np.median(val_tmp),
np.percentile(val_tmp, 75), np.max(val_tmp)))
df_val_ens = pd.DataFrame(np.stack(val_tmp, axis=1), columns=model_files)
# print(df_val_ens.head())
print("=" * 20)
print("Test")
for filename in model_files:
test_tmp.append(np.clip(joblib.load(filename + ".pkl"), 0, 2e8))
print("%.2f %.2f %.2f %.2f %.2f" %
(np.min(test_tmp), np.percentile(test_tmp, 25),
np.median(test_tmp), np.percentile(test_tmp,
75), np.max(test_tmp)))
df_test_ens = pd.DataFrame(np.stack(test_tmp, axis=1), columns=model_files)
print("=" * 20)
# print(df_test_ens.head())
df_train = df_train.set_index("Policy_Number").join(df_features)
df_test = df_test.set_index("Policy_Number").join(df_features)
df_val_ens.set_index(df_train.index, inplace=True)
df_test_ens.set_index(df_test.index, inplace=True)
df_train = pd.concat([df_train, df_val_ens], axis=1, ignore_index=False)
df_test = pd.concat([df_test, df_test_ens], axis=1, ignore_index=False)
print(df_train.head())
# df_train["ratio_pred_nom"] = np.clip(
# df_train["ratio_pred"], 0, 2) * df_train["total_premium"]
# df_test["ratio_pred_nom"] = np.clip(
# df_test["ratio_pred"], 0, 2) * df_test["total_premium"]
del df_train["index"]
del df_test["index"]
df_test["Next_Premium"], loss = fit_and_predict(df_train,
df_test,
n_best_features=int(
sys.argv[1]),
ens_features=model_files)
df_test["Next_Premium"] = np.clip(df_test["Next_Premium"], 0, 5e5)
df_test[["Next_Premium"]].to_csv("sub_ens_{}_{:.0f}.csv".format(
date.today().strftime("%m%d"), loss * 100),
float_format="%.2f")
def main():
df_train = pd.read_csv("data/training-set.csv")
df_test = pd.read_csv("data/testing-set.csv")
df_features = feature_engineering(df_train, df_test)
model_files = [
".".join(x.split(".")[:-1]) for x in glob.glob(MODEL_PATH)
if x.endswith(".pd")
]
val_tmp, test_tmp = [], []
print("Validation")
for filename in model_files:
val_tmp.append(
np.clip(pd.read_pickle(filename + ".pd").values[:, 0], 0, 2e8))
print("%.2f %.2f %.2f %.2f %.2f" %
(np.min(val_tmp), np.percentile(val_tmp, 25), np.median(val_tmp),
np.percentile(val_tmp, 75), np.max(val_tmp)))
df_val_ens = pd.DataFrame(np.stack(val_tmp, axis=1), columns=model_files)
# print(df_val_ens.head())
print("=" * 20)
print("Test")
for filename in model_files:
test_tmp.append(np.clip(joblib.load(filename + ".pkl"), 0, 2e8))
print("%.2f %.2f %.2f %.2f %.2f" %
(np.min(test_tmp), np.percentile(test_tmp, 25),
np.median(test_tmp), np.percentile(test_tmp,
75), np.max(test_tmp)))
df_test_ens = pd.DataFrame(np.stack(test_tmp, axis=1), columns=model_files)
print("=" * 20)
# print(df_test_ens.head())
df_train = df_train.set_index("Policy_Number").join(df_features)
df_test = df_test.set_index("Policy_Number").join(df_features)
df_train, df_test = preprocess_features(df_train, df_test)
df_val_ens.set_index(df_train.index, inplace=True)
df_test_ens.set_index(df_test.index, inplace=True)
df_train = pd.concat([df_train, df_val_ens], axis=1)
df_test = pd.concat([df_test, df_test_ens], axis=1)
scaler = StandardScaler(copy=True)
columns = model_files
scaler.fit(
pd.concat([df_test_ens[columns], df_val_ens[columns]], axis=0).values)
df_train[columns] = scaler.transform(df_train[columns].values)
df_test[columns] = scaler.transform(df_test[columns].values)
print("train:\n", df_train[columns].describe())
print("test:\n", df_test[columns].describe())
df_test["Next_Premium"], loss = fit_and_predict(
df_train[columns + ["Next_Premium"]],
df_test[columns + ["Next_Premium"]])
df_test["Next_Premium"] = np.clip(df_test["Next_Premium"], 0, 5e5)
df_test[["Next_Premium"]].to_csv("sub_ens_dnn_{}_{:.0f}.csv".format(
date.today().strftime("%m%d"), loss * 10000),
float_format="%.2f")
def main():
df_train = pd.read_csv("data/training-set.csv")
df_test = pd.read_csv("data/testing-set.csv").drop("Next_Premium", axis=1)
df_features = feature_engineering(df_train, df_test)
# print("\nFeatures:")
# print(df_features.sample(10))
df_train = df_train.set_index("Policy_Number").join(df_features)
df_test = df_test.set_index("Policy_Number").join(df_features)
del df_train["index"]
del df_test["index"]
fit_and_predict(df_train, df_test, n_best_features=int(sys.argv[1]))
def main():
df_train = pd.read_csv("data/training-set.csv")
df_test = pd.read_csv("data/testing-set.csv")
df_features = feature_engineering(df_train, df_test)
df_train = df_train.set_index("Policy_Number").join(df_features)
df_test = df_test.set_index("Policy_Number").join(df_features)
del df_features
del df_train["index"]
del df_test["index"]
# del df_train["overlap_test"]
# del df_test["overlap_test"]
df_train, df_test = preprocess_features(df_train, df_test)
fit_and_predict(df_train, df_test)
