From 305b9372c7dc24ade46be1dfde759508669e450d Mon Sep 17 00:00:00 2001 From: Hong Zhang Date: Thu, 26 Nov 2020 14:15:54 +0800 Subject: [PATCH] change code style in Catboost and XGboost --- qlib/contrib/model/catboost_model.py | 20 +++++++++---------- qlib/contrib/model/xgboost.py | 30 ++++++++++++++-------------- 2 files changed, 25 insertions(+), 25 deletions(-) diff --git a/qlib/contrib/model/catboost_model.py b/qlib/contrib/model/catboost_model.py index bba006c35..eb97fc75b 100644 --- a/qlib/contrib/model/catboost_model.py +++ b/qlib/contrib/model/catboost_model.py @@ -34,14 +34,14 @@ class CatBoostModel(Model): def fit( self, dataset: DatasetH, - num_boost_round=1000, - early_stopping_rounds=50, - verbose_eval=20, - evals_result=dict(), + num_boost_round = 1000, + early_stopping_rounds = 50, + verbose_eval = 20, + evals_result = dict(), **kwargs ): df_train, df_valid = dataset.prepare( - ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L + ["train", "valid"], col_set = ["feature", "label"], data_key = DataHandlerLP.DK_L ) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] @@ -52,8 +52,8 @@ class CatBoostModel(Model): else: raise ValueError("CatBoost doesn't support multi-label training") - train_pool = Pool(data=x_train, label=y_train_1d) - valid_pool = Pool(data=x_valid, label=y_valid_1d) + train_pool = Pool(data = x_train, label = y_train_1d) + valid_pool = Pool(data = x_valid, label = y_valid_1d) # Initialize the catboost model self._params["iterations"] = num_boost_round @@ -63,7 +63,7 @@ class CatBoostModel(Model): self.model = CatBoost(self._params, **kwargs) # train the model - self.model.fit(train_pool, eval_set=valid_pool, use_best_model=True, **kwargs) + self.model.fit(train_pool, eval_set = valid_pool, use_best_model = True, **kwargs) evals_result = self.model.get_evals_result() evals_result["train"] = list(evals_result["learn"].values())[0] @@ -72,8 +72,8 @@ class CatBoostModel(Model): def predict(self, dataset): if self.model is None: raise ValueError("model is not fitted yet!") - x_test = dataset.prepare("test", col_set="feature") - return pd.Series(self.model.predict(x_test.values), index=x_test.index) + x_test = dataset.prepare("test", col_set = "feature") + return pd.Series(self.model.predict(x_test.values), index = x_test.index) if __name__ == "__main__": diff --git a/qlib/contrib/model/xgboost.py b/qlib/contrib/model/xgboost.py index 039fd2c80..203e71b9a 100755 --- a/qlib/contrib/model/xgboost.py +++ b/qlib/contrib/model/xgboost.py @@ -30,15 +30,15 @@ class XGBModel(Model): def fit( self, dataset: DatasetH, - num_boost_round=1000, - early_stopping_rounds=50, - verbose_eval=20, - evals_result=dict(), + num_boost_round = 1000, + early_stopping_rounds = 50, + verbose_eval = 20, + evals_result = dict(), **kwargs ): df_train, df_valid = dataset.prepare( - ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L + ["train", "valid"], col_set = ["feature", "label"], data_key = DataHandlerLP.DK_L ) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] @@ -49,16 +49,16 @@ class XGBModel(Model): else: raise ValueError("XGBoost doesn't support multi-label training") - dtrain = xgb.DMatrix(x_train.values, label=y_train_1d) - dvalid = xgb.DMatrix(x_valid.values, label=y_valid_1d) + dtrain = xgb.DMatrix(x_train.values, label = y_train_1d) + dvalid = xgb.DMatrix(x_valid.values, label = y_valid_1d) self.model = xgb.train( self._params, - dtrain=dtrain, - num_boost_round=num_boost_round, - evals=[(dtrain, "train"), (dvalid, "valid")], - early_stopping_rounds=early_stopping_rounds, - verbose_eval=verbose_eval, - evals_result=evals_result, + dtrain = dtrain, + num_boost_round = num_boost_round, + evals = [(dtrain, "train"), (dvalid, "valid")], + early_stopping_rounds = early_stopping_rounds, + verbose_eval = verbose_eval, + evals_result = evals_result, **kwargs ) evals_result["train"] = list(evals_result["train"].values())[0] @@ -67,5 +67,5 @@ class XGBModel(Model): def predict(self, dataset): if self.model is None: raise ValueError("model is not fitted yet!") - x_test = dataset.prepare("test", col_set="feature") - return pd.Series(self.model.predict(xgb.DMatrix(x_test.values)), index=x_test.index) + x_test = dataset.prepare("test", col_set = "feature") + return pd.Series(self.model.predict(xgb.DMatrix(x_test.values)), index = x_test.index)