Fix bugs

notebooks/TOT/time-curve.py

@@ -23,10 +23,12 @@ if str(lib_dir) not in sys.path:
 import qlib
 from qlib import config as qconfig
 from qlib.workflow import R
-qlib.init(provider_uri='~/.qlib/qlib_data/cn_data', region=qconfig.REG_CN)
+
+qlib.init(provider_uri="~/.qlib/qlib_data/cn_data", region=qconfig.REG_CN)
 
 from utils.qlib_utils import QResult
 
+
 def filter_finished(recorders):
     returned_recorders = dict()
     not_finished = 0
@@ -41,9 +43,10 @@ def filter_finished(recorders):
 def add_to_dict(xdict, timestamp, value):
     date = timestamp.date().strftime("%Y-%m-%d")
     if date in xdict:
-      raise ValueError("This date [{:}] is already in the dict".format(date))
+        raise ValueError("This date [{:}] is already in the dict".format(date))
     xdict[date] = value
 
+
 def query_info(save_dir, verbose, name_filter, key_map):
     if isinstance(save_dir, list):
         results = []
@@ -61,7 +64,10 @@ def query_info(save_dir, verbose, name_filter, key_map):
     for idx, (key, experiment) in enumerate(experiments.items()):
         if experiment.id == "0":
             continue
-        if name_filter is not None and re.fullmatch(name_filter, experiment.name) is None:
+        if (
+            name_filter is not None
+            and re.fullmatch(name_filter, experiment.name) is None
+        ):
             continue
         recorders = experiment.list_recorders()
         recorders, not_finished = filter_finished(recorders)
@@ -77,10 +83,10 @@ def query_info(save_dir, verbose, name_filter, key_map):
             )
         result = QResult(experiment.name)
         for recorder_id, recorder in recorders.items():
-            file_names = ['results-train.pkl', 'results-valid.pkl', 'results-test.pkl']
+            file_names = ["results-train.pkl", "results-valid.pkl", "results-test.pkl"]
             date2IC = OrderedDict()
             for file_name in file_names:
-                xtemp = recorder.load_object(file_name)['all-IC']
+                xtemp = recorder.load_object(file_name)["all-IC"]
                 timestamps, values = xtemp.index.tolist(), xtemp.tolist()
                 for timestamp, value in zip(timestamps, values):
                     add_to_dict(date2IC, timestamp, value)
@@ -104,7 +110,7 @@ def query_info(save_dir, verbose, name_filter, key_map):
 
 
 ##
-paths = [root_dir / 'outputs' / 'qlib-baselines-csi300']
+paths = [root_dir / "outputs" / "qlib-baselines-csi300"]
 paths = [path.resolve() for path in paths]
 print(paths)
 
@@ -112,12 +118,12 @@ key_map = dict()
 for xset in ("train", "valid", "test"):
     key_map["{:}-mean-IC".format(xset)] = "IC ({:})".format(xset)
     key_map["{:}-mean-ICIR".format(xset)] = "ICIR ({:})".format(xset)
-qresults = query_info(paths, False, 'TSF-2x24-drop0_0s.*-.*-01', key_map)
-print('Find {:} results'.format(len(qresults)))
+qresults = query_info(paths, False, "TSF-2x24-drop0_0s.*-.*-01", key_map)
+print("Find {:} results".format(len(qresults)))
 times = []
 for qresult in qresults:
-    times.append(qresult.name.split('0_0s')[-1])
+    times.append(qresult.name.split("0_0s")[-1])
 print(times)
-save_path = os.path.join(note_dir, 'temp-time-x.pth')
+save_path = os.path.join(note_dir, "temp-time-x.pth")
 torch.save(qresults, save_path)
 print(save_path)

notebooks/spaces/test.py

@@ -24,38 +24,38 @@ from qlib.model.base import Model
 from qlib.data.dataset import DatasetH
 from qlib.data.dataset.handler import DataHandlerLP
 
-qlib.init(provider_uri='~/.qlib/qlib_data/cn_data', region=qconfig.REG_CN)
+qlib.init(provider_uri="~/.qlib/qlib_data/cn_data", region=qconfig.REG_CN)
 
 dataset_config = {
-            "class": "DatasetH",
-            "module_path": "qlib.data.dataset",
+    "class": "DatasetH",
+    "module_path": "qlib.data.dataset",
+    "kwargs": {
+        "handler": {
+            "class": "Alpha360",
+            "module_path": "qlib.contrib.data.handler",
             "kwargs": {
-                "handler": {
-                    "class": "Alpha360",
-                    "module_path": "qlib.contrib.data.handler",
-                    "kwargs": {
-                        "start_time": "2008-01-01",
-                        "end_time": "2020-08-01",
-                        "fit_start_time": "2008-01-01",
-                        "fit_end_time": "2014-12-31",
-                        "instruments": "csi100",
-                    },
-                },
-                "segments": {
-                    "train": ("2008-01-01", "2014-12-31"),
-                    "valid": ("2015-01-01", "2016-12-31"),
-                    "test": ("2017-01-01", "2020-08-01"),
-                },
+                "start_time": "2008-01-01",
+                "end_time": "2020-08-01",
+                "fit_start_time": "2008-01-01",
+                "fit_end_time": "2014-12-31",
+                "instruments": "csi100",
             },
-        }
+        },
+        "segments": {
+            "train": ("2008-01-01", "2014-12-31"),
+            "valid": ("2015-01-01", "2016-12-31"),
+            "test": ("2017-01-01", "2020-08-01"),
+        },
+    },
+}
 pprint.pprint(dataset_config)
 dataset = init_instance_by_config(dataset_config)
 
 df_train, df_valid, df_test = dataset.prepare(
-            ["train", "valid", "test"],
-            col_set=["feature", "label"],
-            data_key=DataHandlerLP.DK_L,
-        )
+    ["train", "valid", "test"],
+    col_set=["feature", "label"],
+    data_key=DataHandlerLP.DK_L,
+)
 model = get_transformer(None)
 print(model)
 
@@ -72,4 +72,5 @@ label = labels[batch][mask]
 loss = torch.nn.functional.mse_loss(pred, label)
 
 from sklearn.metrics import mean_squared_error
+
 mse_loss = mean_squared_error(pred.numpy(), label.numpy())