Add name filters for exp-org

exps/trading/organize_results.py

@@ -3,7 +3,7 @@
 #####################################################
 # python exps/trading/organize_results.py           #
 #####################################################
-import sys, argparse
+import re, sys, argparse
 import numpy as np
 from typing import List, Text
 from collections import defaultdict, OrderedDict
@@ -121,7 +121,7 @@ def filter_finished(recorders):
     return returned_recorders, not_finished
 
 
-def query_info(save_dir, verbose):
+def query_info(save_dir, verbose, name_filter):
     R.set_uri(save_dir)
     experiments = R.list_experiments()
 
@@ -143,6 +143,8 @@ def query_info(save_dir, verbose):
     for idx, (key, experiment) in enumerate(experiments.items()):
         if experiment.id == "0":
             continue
+        if name_filter is not None and re.match(name_filter, experiment.name) is None:
+            continue
         recorders = experiment.list_recorders()
         recorders, not_finished = filter_finished(recorders)
         if verbose:
@@ -205,6 +207,9 @@ if __name__ == "__main__":
         default=False,
         help="Print detailed log information or not.",
     )
+    parser.add_argument(
+        "--name_filter", type=str, default=".*", help="Filter experiment names."
+    )
     args = parser.parse_args()
 
     print("Show results of {:}".format(args.save_dir))
@@ -216,7 +221,7 @@ if __name__ == "__main__":
 
     all_info_dict = []
     for save_dir in args.save_dir:
-        _, info_dict = query_info(save_dir, args.verbose)
+        _, info_dict = query_info(save_dir, args.verbose, args.name_filter)
         all_info_dict.append(info_dict)
     info_dict = QResult.merge_dict(all_info_dict)
     compare_results(

lib/procedures/q_exps.py

@@ -100,9 +100,9 @@ def run_exp(
         # Train model
         try:
             if hasattr(model, "to"):  # Recoverable model
-                device = model.device
+                ori_device = model.device
                 model = R.load_object(model_obj_name)
-                model.to(device)
+                model.to(ori_device)
             else:
                 model = R.load_object(model_obj_name)
             logger.info("[Find existing object from {:}]".format(model_obj_name))
@@ -119,9 +119,10 @@ def run_exp(
             model.fit(**model_fit_kwargs)
             # remove model to CPU for saving
             if hasattr(model, "to"):
+                old_device = model.device
                 model.to("cpu")
                 R.save_objects(**{model_obj_name: model})
-                model.to()
+                model.to(old_device)
             else:
                 R.save_objects(**{model_obj_name: model})
         except Exception as e:

lib/trade_models/quant_transformer.py

@@ -114,9 +114,9 @@ class QuantTransformer(Model):
 
     def to(self, device):
         if device is None:
-            self.model.to(self.device)
-        else:
-            self.model.to("cpu")
+            device = "cpu"
+        self.device = device
+        self.model.to(self.device)
 
     def loss_fn(self, pred, label):
         mask = ~torch.isnan(label)
@@ -227,7 +227,7 @@ class QuantTransformer(Model):
         # Pre-fetch the potential checkpoints
         ckp_path = os.path.join(save_dir, "{:}.pth".format(self.__class__.__name__))
         if os.path.exists(ckp_path):
-            ckp_data = torch.load(ckp_path)
+            ckp_data = torch.load(ckp_path, map_location=self.device)
             stop_steps, best_score, best_epoch = (
                 ckp_data["stop_steps"],
                 ckp_data["best_score"],
@@ -298,7 +298,7 @@ class QuantTransformer(Model):
                 results_dict=results_dict,
                 start_epoch=iepoch + 1,
             )
-            torch.save(save_info, ckp_path)
+            torch.save(save_info, ckp_path, map_location="cpu")
         self.logger.info(
             "The best score: {:.6f} @ {:02d}-th epoch".format(best_score, best_epoch)
         )