Re-org baselines

exps/trading/baselines.py

@@ -15,9 +15,11 @@
 # python exps/trading/baselines.py --alg TabNet     #
 #                                                   #
 # python exps/trading/baselines.py --alg Transformer#
-# python exps/trading/baselines.py --alg TSF-A      #
+# python exps/trading/baselines.py --alg TSF        #
+# python exps/trading/baselines.py --alg TSF-4x64-d0
 #####################################################
 import sys
+import copy
 import argparse
 from collections import OrderedDict
 from pathlib import Path
@@ -38,6 +40,33 @@ from qlib.workflow import R
 from qlib.utils import flatten_dict
 
 
+def to_pos_drop(config, value):
+    config = copy.deepcopy(config)
+    net = config["task"]["model"]["kwargs"]["net_config"]
+    net["pos_drop"] = value
+    return config
+
+
+def to_layer(config, embed_dim, depth):
+    config = copy.deepcopy(config)
+    net = config["task"]["model"]["kwargs"]["net_config"]
+    net["embed_dim"] = embed_dim
+    net["num_heads"] = [4] * depth
+    net["mlp_hidden_multipliers"] = [4] * depth
+    return config
+
+
+def extend_transformer_settings(alg2configs, name):
+    config = copy.deepcopy(alg2configs[name])
+    for i in range(6):
+        for j in [24, 32, 48, 64]:
+            for k in [0, 0.1]:
+                alg2configs[name + "-{:}x{:}-d{:}".format(i, j, k)] = to_layer(
+                    to_pos_drop(config, k), j, i
+                )
+    return alg2configs
+
+
 def retrieve_configs():
     # https://github.com/microsoft/qlib/blob/main/examples/benchmarks/
     config_dir = (lib_dir / ".." / "configs" / "qlib").resolve()
@@ -60,29 +89,28 @@ def retrieve_configs():
     alg2names["NAIVE-V1"] = "workflow_config_naive_v1_Alpha360.yaml"
     alg2names["NAIVE-V2"] = "workflow_config_naive_v2_Alpha360.yaml"
     alg2names["Transformer"] = "workflow_config_transformer_Alpha360.yaml"
-    alg2names["TSF-A"] = "workflow_config_transformer_basic_Alpha360.yaml"
+    alg2names["TSF"] = "workflow_config_transformer_basic_Alpha360.yaml"
 
     # find the yaml paths
-    alg2paths = OrderedDict()
+    alg2configs = OrderedDict()
     print("Start retrieving the algorithm configurations")
     for idx, (alg, name) in enumerate(alg2names.items()):
         path = config_dir / name
         assert path.exists(), "{:} does not exist.".format(path)
-        alg2paths[alg] = str(path)
+        with open(path) as fp:
+            alg2configs[alg] = yaml.safe_load(fp)
         print(
             "The {:02d}/{:02d}-th baseline algorithm is {:9s} ({:}).".format(
-                idx, len(alg2names), alg, path
+                idx, len(alg2configs), alg, path
             )
         )
-    return alg2paths
+    alg2configs = extend_transformer_settings(alg2configs, "TSF-A")
+    return alg2configs
 
 
-def main(xargs, exp_yaml):
+def main(xargs, config):
-    assert Path(exp_yaml).exists(), "{:} does not exist.".format(exp_yaml)
 
     pprint("Run {:}".format(xargs.alg))
-    with open(exp_yaml) as fp:
-        config = yaml.safe_load(fp)
     config = update_market(config, xargs.market)
     config = update_gpu(config, xargs.gpu)
 
@@ -105,7 +133,7 @@ def main(xargs, exp_yaml):
 
 if __name__ == "__main__":
 
-    alg2paths = retrieve_configs()
+    alg2configs = retrieve_configs()
 
     parser = argparse.ArgumentParser("Baselines")
     parser.add_argument(
@@ -121,7 +149,7 @@ if __name__ == "__main__":
         choices=["csi100", "csi300", "all"],
         help="The market indicator.",
     )
-    parser.add_argument("--times", type=int, default=10, help="The repeated run times.")
+    parser.add_argument("--times", type=int, default=5, help="The repeated run times.")
     parser.add_argument(
         "--gpu", type=int, default=0, help="The GPU ID used for train / test."
     )
@@ -134,4 +162,4 @@ if __name__ == "__main__":
     )
     args = parser.parse_args()
 
-    main(args, alg2paths[args.alg])
+    main(args, alg2configs[args.alg])

exps/trading/organize_results.py

@@ -126,7 +126,7 @@ def query_info(save_dir, verbose):
     experiments = R.list_experiments()
 
     key_map = {
-        "RMSE": "RMSE",
+        # "RMSE": "RMSE",
         "IC": "IC",
         "ICIR": "ICIR",
         "Rank IC": "Rank_IC",

lib/xlayers/super_norm.py

@@ -18,7 +18,7 @@ class SuperLayerNorm1D(SuperModule):
     """Super Layer Norm."""
 
     def __init__(
-        self, dim: IntSpaceType, eps: float = 1e-5, elementwise_affine: bool = True
+        self, dim: IntSpaceType, eps: float = 1e-6, elementwise_affine: bool = True
     ) -> None:
         super(SuperLayerNorm1D, self).__init__()
         self._in_dim = dim