Update black formatter

qlib/contrib/model/catboost_model.py

@@ -9,17 +9,17 @@ from ...data.dataset.handler import DataHandlerLP
 
 
 class CatBoostModel(Model):
-	"""CatBoost Model"""
+    """CatBoost Model"""
 
-	def __init__(self, loss="RMSE", **kwargs):
-		# There are more options
-		if loss not in {"RMSE", "Logloss"}:
-			raise NotImplementedError
-		self._params = {"loss_function": loss}
-		self._params.update(kwargs)
-		self.model = None
+    def __init__(self, loss="RMSE", **kwargs):
+        # There are more options
+        if loss not in {"RMSE", "Logloss"}:
+            raise NotImplementedError
+        self._params = {"loss_function": loss}
+        self._params.update(kwargs)
+        self.model = None
 
-	def fit(
+    def fit(
         self,
         dataset: DatasetH,
         num_boost_round=1000,
@@ -27,48 +27,42 @@ class CatBoostModel(Model):
         verbose_eval=20,
         evals_result=dict(),
         **kwargs
-    ):	
-		df_train, df_valid = dataset.prepare(
-    		["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
+    ):
+        df_train, df_valid = dataset.prepare(
+            ["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"]
+        x_train, y_train = df_train["feature"], df_train["label"]
+        x_valid, y_valid = df_valid["feature"], df_valid["label"]
 
-		# CatBoost needs 1D array as its label
-		if y_train.values.ndim == 2 and y_train.values.shape[1] == 1:
-			y_train_1d, y_valid_1d = np.squeeze(y_train.values), np.squeeze(y_valid.values)
-		else:
-			raise ValueError("CatBoost doesn't support multi-label training")
+        # CatBoost needs 1D array as its label
+        if y_train.values.ndim == 2 and y_train.values.shape[1] == 1:
+            y_train_1d, y_valid_1d = np.squeeze(y_train.values), np.squeeze(y_valid.values)
+        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
-		self._params['early_stopping_rounds'] = early_stopping_rounds
-		self._params['verbose_eval'] = verbose_eval
-		self._params['task_type'] =  "GPU" if get_gpu_device_count() > 0 else "CPU"
-		self.model = CatBoost(self._params, **kwargs)
+        # Initialize the catboost model
+        self._params["iterations"] = num_boost_round
+        self._params["early_stopping_rounds"] = early_stopping_rounds
+        self._params["verbose_eval"] = verbose_eval
+        self._params["task_type"] = "GPU" if get_gpu_device_count() > 0 else "CPU"
+        self.model = CatBoost(self._params, **kwargs)
 
-		#train the model
-		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]
-		evals_result["valid"] = list(evals_result["validation"].values())[0]
+        # train the model
+        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]
+        evals_result["valid"] = list(evals_result["validation"].values())[0]
+
+    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(np.squeeze(x_test.values)), index=x_test.index)
 
 
-	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(np.squeeze(x_test.values)), index=x_test.index)
-
-
-if __name__ == '__main__':
-	cat = CatBoostModel()
+if __name__ == "__main__":
+    cat = CatBoostModel()