Add early stopping to double ensemble model, add example (#1375)

* Add early stopping to double ensemble model, add example

* Fix lint error

examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_early_stop_Alpha158.yaml

@@ -0,0 +1,95 @@
+qlib_init:
+    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
+        kwargs:
+            signal:
+                - <MODEL> 
+                - <DATASET>
+            topk: 50
+            n_drop: 5
+    backtest:
+        start_time: 2017-01-01
+        end_time: 2020-08-01
+        account: 100000000
+        benchmark: *benchmark
+        exchange_kwargs:
+            limit_threshold: 0.095
+            deal_price: close
+            open_cost: 0.0005
+            close_cost: 0.0015
+            min_cost: 5
+task:
+    model:
+        class: DEnsembleModel
+        module_path: qlib.contrib.model.double_ensemble
+        kwargs:
+            base_model: "gbm"
+            loss: mse
+            num_models: 3
+            enable_sr: True
+            enable_fs: True
+            alpha1: 1
+            alpha2: 1
+            bins_sr: 10
+            bins_fs: 5
+            decay: 0.5
+            sample_ratios:
+                - 0.8
+                - 0.7
+                - 0.6
+                - 0.5
+                - 0.4
+            sub_weights:
+                - 1
+                - 1
+                - 1
+            epochs: 1000
+            early_stopping_rounds: 50
+            colsample_bytree: 0.8879
+            learning_rate: 0.2
+            subsample: 0.8789
+            lambda_l1: 205.6999
+            lambda_l2: 580.9768
+            max_depth: 8
+            num_leaves: 210
+            num_threads: 20
+            verbosity: -1
+    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: 
+            model: <MODEL>
+            dataset: <DATASET>
+        - 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

qlib/contrib/model/double_ensemble.py

@@ -30,6 +30,7 @@ class DEnsembleModel(Model, FeatureInt):
         sample_ratios=None,
         sub_weights=None,
         epochs=100,
+        early_stopping_rounds=None,
         **kwargs
     ):
         self.base_model = base_model  # "gbm" or "mlp", specifically, we use lgbm for "gbm"
@@ -59,6 +60,7 @@ class DEnsembleModel(Model, FeatureInt):
         self.params = {"objective": loss}
         self.params.update(kwargs)
         self.loss = loss
+        self.early_stopping_rounds = early_stopping_rounds
 
     def fit(self, dataset: DatasetH):
         df_train, df_valid = dataset.prepare(
@@ -103,14 +105,19 @@ class DEnsembleModel(Model, FeatureInt):
     def train_submodel(self, df_train, df_valid, weights, features):
         dtrain, dvalid = self._prepare_data_gbm(df_train, df_valid, weights, features)
         evals_result = dict()
+
+        callbacks = [lgb.log_evaluation(20), lgb.record_evaluation(evals_result)]
+        if self.early_stopping_rounds:
+            callbacks.append(lgb.early_stopping(self.early_stopping_rounds))
+            self.logger.info("Training with early_stopping...")
+
         model = lgb.train(
             self.params,
             dtrain,
             num_boost_round=self.epochs,
             valid_sets=[dtrain, dvalid],
             valid_names=["train", "valid"],
-            verbose_eval=20,
-            evals_result=evals_result,
+            callbacks=callbacks,
         )
         evals_result["train"] = list(evals_result["train"].values())[0]
         evals_result["valid"] = list(evals_result["valid"].values())[0]