Add TabNet config

qlib/contrib/model/tabnet.py

@@ -9,19 +9,24 @@ from ...model.base import Model
 from ...data.dataset import DatasetH
 from ...data.dataset.handler import DataHandlerLP
 
+
 class TabNetModel(Model):
     """TabNetModel Model"""
 
-    def __init__(self, n_d, n_a, 
-                        n_steps, 
-                        gamma, 
-                        n_independent, 
-                        n_shared, 
-                        seed, 
-                        momentum, 
-                        lambda_sparse, 
-                        optimizer_params, 
-                        **kwargs):
+    def __init__(
+        self,
+        n_d,
+        n_a,
+        n_steps,
+        gamma,
+        n_independent,
+        n_shared,
+        seed,
+        momentum,
+        lambda_sparse,
+        optimizer_params,
+        **kwargs
+    ):
         self.model = None
 
         self.n_d = n_d
@@ -47,28 +52,28 @@ class TabNetModel(Model):
         seed=0,
         momentum=0.02,
         lambda_sparse=1e-3,
-        optimizer_params={'lr':2e-3},
+        optimizer_params={"lr": 2e-3},
         **kwargs
     ):
 
         df_train, df_valid = dataset.prepare(
             ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
         )
-        x_train, y_train = df_train["feature"].values, df_train["label"].values*100
-        x_valid, y_valid = df_valid["feature"].values, df_valid["label"].values*100
+        x_train, y_train = df_train["feature"].values, df_train["label"].values * 100
+        x_valid, y_valid = df_valid["feature"].values, df_valid["label"].values * 100
 
         self.model = TabNetRegressor(
-                    n_d=self.n_d,
-                    n_a=self.n_a,
-                    n_steps=self.n_steps,
-                    gamma=self.gamma,
-                    n_independent=self.n_independent,
-                    n_shared=self.n_shared,
-                    seed=self.seed,
-                    momentum=self.momentum,
-                    lambda_sparse=self.lambda_sparse,
-                    optimizer_params=self.optimizer_params,
-                    **kwargs
+            n_d=self.n_d,
+            n_a=self.n_a,
+            n_steps=self.n_steps,
+            gamma=self.gamma,
+            n_independent=self.n_independent,
+            n_shared=self.n_shared,
+            seed=self.seed,
+            momentum=self.momentum,
+            lambda_sparse=self.lambda_sparse,
+            optimizer_params=self.optimizer_params,
+            **kwargs
         )
         self.model.fit(x_train, y_train, eval_set=[(x_valid, y_valid)])
 

qlib/contrib/strategy/strategy.py

@@ -25,7 +25,9 @@ class BaseStrategy:
         return 0.95
 
     def generate_order_list(self, score_series, current, trade_exchange, pred_date, trade_date):
-        """Parameter
+        """
+        Parameters:
+        -----------
         score_series : pd.Seires
             stock_id , score
         current : Position()
@@ -44,8 +46,8 @@ class BaseStrategy:
 
     def update(self, score_series, pred_date, trade_date):
         """User can use this method to update strategy state each trade date.
-        Parameter
-        ---------
+        Parameters:
+        -----------
         score_series : pd.Series
             stock_id , score
         pred_date : pd.Timestamp
@@ -97,7 +99,7 @@ class AdjustTimer:
     Responsible for timing of position adjusting
 
     This is designed as multiple inheritance mechanism due to
-    - the is_adjust may need access to the internel state of a strategyw
+    - the is_adjust may need access to the internel state of a strategy
     - it can be reguard as a enhancement to the existing strategy
     """
 
@@ -139,7 +141,7 @@ class WeightStrategyBase(BaseStrategy, AdjustTimer):
     def generate_target_weight_position(self, score, current, trade_date):
         """
         Parameters:
-        ---------
+        -----------
         score : pred score for this trade date, pd.Series, index is stock_id, contain 'score' column
         current : current position, use Position() class
         trade_exchange : Exchange()
@@ -228,7 +230,7 @@ class TopkDropoutStrategy(BaseStrategy, ListAdjustTimer):
         Gnererate order list according to score_series at trade_date, will not change current.
 
         Parameters:
-        ----------
+        -----------
         score_series : pd.Series
             stock_id , score
         current : Position()