Add TabNet config

README.md

@@ -197,7 +197,8 @@ Here is a list of models built on `Qlib`.
 - [GRU based on pytorch](qlib/contrib/model/pytorch_gru.py)
 - [LSTM based on pytorcn](qlib/contrib/model/pytorch_lstm.py)
 - [GATs based on pytorch](qlib/contrib/model/pytorch_gats.py)
-- [TFT based on tensorflow-1.15.0](examples/benchmarks/TFT/tft.py)
+- [TabNet based on pytorch](qlib/contrib/model/tabnet.py)
+<!-- - [TFT based on tensorflow](examples/benchmarks/TFT/tft.py) -->
 
 Your PR of new Quant models is highly welcomed.
 

examples/benchmarks/CatBoost/workflow_config_catboost.yaml

@@ -37,7 +37,7 @@ task:
         module_path: qlib.data.dataset
         kwargs:
             handler:
-                class: ALPHA360_Denoise
+                class: Alpha158
                 module_path: qlib.contrib.data.handler
                 kwargs: *data_handler_config
             segments:

examples/benchmarks/DNN/workflow_config_dnn.yaml

@@ -44,7 +44,7 @@ task:
         module_path: qlib.data.dataset
         kwargs:
             handler:
-                class: ALPHA360_Denoise
+                class: Alpha158
                 module_path: qlib.contrib.data.handler
                 kwargs: *data_handler_config
             segments:

examples/benchmarks/TabNet/requirements.txt

@@ -0,0 +1,5 @@
+pandas==1.1.2
+numpy==1.17.4
+scikit_learn==0.23.2
+torch==1.7.0
+pytorch-tabnet==2.0.1

examples/benchmarks/TabNet/workflow_config_tabnet.yaml

@@ -0,0 +1,66 @@
+provider_uri: "~/.qlib/qlib_data/cn_data"
+region: cn
+market: &market csi300
+benchmark: &benchmark SH000300
+data_handler_config: &data_handler_config
+    start_time: 2008-01-01
+    end_time: 2020-08-01
+    fit_start_time: 2008-01-01
+    fit_end_time: 2014-12-31
+    instruments: *market
+port_analysis_config: &port_analysis_config
+    strategy:
+        class: TopkDropoutStrategy
+        module_path: qlib.contrib.strategy.strategy
+        kwargs:
+            topk: 50
+            n_drop: 5
+    backtest:
+        verbose: False
+        limit_threshold: 0.095
+        account: 100000000
+        benchmark: *benchmark
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
+task:
+    model:
+        class: TabNetModel
+        module_path: qlib.contrib.model.tabnet
+        kwargs:
+            n_d: 8
+            n_a: 8
+            n_steps: 3
+            gamma: 1.3
+            n_independent: 2
+            n_shared: 2
+            seed: 0
+            momentum: 0.02
+            lambda_sparse: 1e-3
+            optimizer_params: {lr: 2e-3}
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: Alpha158
+                module_path: qlib.contrib.data.handler
+                kwargs: *data_handler_config
+            segments:
+                train: [2008-01-01, 2014-12-31]
+                valid: [2015-01-01, 2016-12-31]
+                test: [2017-01-01, 2020-08-01]
+    record: 
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: 
+            ana_long_short: False
+            ann_scaler: 252
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: 
+            config: *port_analysis_config

examples/workflow_by_code_tabnet.py

@@ -71,7 +71,7 @@ if __name__ == "__main__":
                 "seed": 0,
                 "momentum": 0.02,
                 "lambda_sparse": 1e-3,
-                "optimizer_params": {'lr':2e-3}
+                "optimizer_params": {"lr": 2e-3},
             },
         },
         "dataset": {

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, 
+    def __init__(
-                        n_steps, 
+        self,
-                        gamma, 
+        n_d,
-                        n_independent, 
+        n_a,
-                        n_shared, 
+        n_steps,
-                        seed, 
+        gamma,
-                        momentum, 
+        n_independent,
-                        lambda_sparse, 
+        n_shared,
-                        optimizer_params, 
+        seed,
-                        **kwargs):
+        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_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_valid, y_valid = df_valid["feature"].values, df_valid["label"].values * 100
 
         self.model = TabNetRegressor(
-                    n_d=self.n_d,
+            n_d=self.n_d,
-                    n_a=self.n_a,
+            n_a=self.n_a,
-                    n_steps=self.n_steps,
+            n_steps=self.n_steps,
-                    gamma=self.gamma,
+            gamma=self.gamma,
-                    n_independent=self.n_independent,
+            n_independent=self.n_independent,
-                    n_shared=self.n_shared,
+            n_shared=self.n_shared,
-                    seed=self.seed,
+            seed=self.seed,
-                    momentum=self.momentum,
+            momentum=self.momentum,
-                    lambda_sparse=self.lambda_sparse,
+            lambda_sparse=self.lambda_sparse,
-                    optimizer_params=self.optimizer_params,
+            optimizer_params=self.optimizer_params,
-                    **kwargs
+            **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()

setup.py

@@ -58,6 +58,7 @@ REQUIRED = [
     "joblib>=0.17.0",
     "fire>=0.3.1",
     "ruamel.yaml>=0.16.12",
+    "pytorch-tabnet>=2.0.1",
 ]
 
 # Numpy include