Merge branch 'main' of https://github.com/you-n-g/qlib into main

examples/benchmarks/SFM/workflow_config_sfm.yaml

@@ -31,21 +31,21 @@ task:
         kwargs:
             d_feat: 6
             hidden_size: 64
-            output_dim: 1
-            freq_dim: 20
+            output_dim: 32
+            freq_dim: 25
             dropout_W: 0.5
             dropout_U: 0.5
-            n_epochs: 10
+            n_epochs: 20
             lr: 1e-3
-            batch_size: 800
+            batch_size: 1600
             early_stop: 20
             eval_steps: 5
             loss: mse
             lr_decay: 0.96
             lr_decay_steps: 100
-            optimizer: gd
+            optimizer: adam
             GPU: 1
-            seed: 0
+            seed: 710
     dataset:
         class: DatasetH
         module_path: qlib.data.dataset

examples/workflow_by_code.ipynb

@@ -31,7 +31,8 @@
     ")\n",
     "from qlib.utils import exists_qlib_data, init_instance_by_config\n",
     "from qlib.workflow import R\n",
-    "from qlib.workflow.record_temp import SignalRecord, PortAnaRecord"
+    "from qlib.workflow.record_temp import SignalRecord, PortAnaRecord\n",
+    "from qlib.utils import flatten_dict"
    ]
   },
   {
@@ -129,7 +130,7 @@
     "\n",
     "# start exp to train model\n",
     "with R.start(experiment_name=\"train_model\"):\n",
-    "    R.log_paramters(**flatten_dict(task))\n",
+    "    R.log_params(**flatten_dict(task))\n",
     "    model.fit(dataset)\n",
     "    R.save_objects(trained_model=model)\n",
     "    rid = R.get_recorder().id\n"
@@ -337,4 +338,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

examples/workflow_by_code_gats.py

@@ -17,7 +17,6 @@ from qlib.utils import exists_qlib_data
 from qlib.utils import init_instance_by_config
 
 
-
 if __name__ == "__main__":
 
     # use default data

examples/workflow_by_code_hats.py

@@ -89,7 +89,6 @@ if __name__ == "__main__":
         # "record": ['SignalRecord', 'SigAnaRecord', 'PortAnaRecord'],
     }
 
-
     model = init_instance_by_config(task["model"])
     dataset = init_instance_by_config(task["dataset"])
     model.fit(dataset, save_path="benchmarks/HATS/model_hat.pkl")

examples/workflow_by_code_sfm.py

@@ -78,7 +78,7 @@ if __name__ == "__main__":
                 "dropout_U": 0.5,
                 "n_epochs": 15,
                 "lr": 1e-3,
-                "metric": "", 
+                "metric": "",
                 "batch_size": 1600,
                 "early_stop": 20,
                 "eval_steps": 5,