Fix errors when SignalRecord is not called before SigAna/PortAna

qlib/workflow/record_temp.py

@@ -110,7 +110,7 @@ class SignalRecord(RecordTemp):
     This is the Signal Record class that generates the signal prediction. This class inherits the ``RecordTemp`` class.
     """
 
-    def __init__(self, model=None, dataset=None, recorder=None, **kwargs):
+    def __init__(self, model=None, dataset=None, recorder=None):
         super().__init__(recorder=recorder)
         self.model = model
         self.dataset = dataset
@@ -163,14 +163,16 @@ class SigAnaRecord(SignalRecord):
 
     artifact_path = "sig_analysis"
 
-    def __init__(self, recorder, ana_long_short=False, ann_scaler=252, **kwargs):
+    def __init__(self, recorder, ana_long_short=False, ann_scaler=252):
+        super().__init__(recorder=recorder)
         self.ana_long_short = ana_long_short
         self.ann_scaler = ann_scaler
-        super().__init__(recorder=recorder, **kwargs)
-        # The name must be unique. Otherwise it will be overridden
 
-    def generate(self):
-        self.check(parent=True)
+    def generate(self, **kwargs):
+        try:
+            self.check(parent=True)
+        except:
+            super().generate()
 
         pred = self.load("pred.pkl")
         label = self.load("label.pkl")
@@ -228,7 +230,7 @@ class PortAnaRecord(SignalRecord):
         config["backtest"] : dict
             define the backtest kwargs.
         """
-        super().__init__(recorder=recorder)
+        super().__init__(recorder=recorder, **kwargs)
 
         self.strategy_config = config["strategy"]
         self.backtest_config = config["backtest"]
@@ -236,10 +238,13 @@ class PortAnaRecord(SignalRecord):
 
     def generate(self, **kwargs):
         # check previously stored prediction results
-        self.check(parent=True)  # "Make sure the parent process is completed and store the data properly."
+        try:
+            self.check(parent=True)  # "Make sure the parent process is completed and store the data properly."
+        except:
+            super().generate()
 
         # custom strategy and get backtest
-        pred_score = super().load()
+        pred_score = super().load("pred.pkl")
         report_dict = normal_backtest(pred_score, strategy=self.strategy, **self.backtest_config)
         report_normal = report_dict.get("report_df")
         positions_normal = report_dict.get("positions")

tests/test_all_pipeline.py

@@ -139,6 +139,33 @@ def train():
     return pred_score, {"ic": ic, "ric": ric}, rid
 
 
+def train_with_sigana():
+    """train model followed by SigAnaRecord
+
+    Returns
+    -------
+        pred_score: pandas.DataFrame
+            predict scores
+        performance: dict
+            model performance
+    """
+    model = init_instance_by_config(task["model"])
+    dataset = init_instance_by_config(task["dataset"])
+
+    # start exp
+    with R.start(experiment_name="workflow"):
+        R.log_params(**flatten_dict(task))
+        model.fit(dataset)
+
+        # predict and calculate ic and ric
+        recorder = R.get_recorder()
+        sar = SigAnaRecord(recorder, model=model, dataset=dataset)
+        sar.generate()
+        ic = sar.load(sar.get_path("ic.pkl"))
+        ric = sar.load(sar.get_path("ric.pkl"))
+    return pred_score, {"ic": ic, "ric": ric}, rid
+
+
 def fake_experiment():
     """A fake experiment workflow to test uri
 
@@ -214,6 +241,11 @@ class TestAllFlow(TestAutoData):
         self.assertTrue(pass_current, msg="current uri is incorrect")
         shutil.rmtree(str(Path(uri_path.strip("file:")).resolve()))
 
+    def test_3_train_with_sigana(self):
+        TestAllFlow.PRED_SCORE, ic_ric, TestAllFlow.RID = train_with_sigana()
+        self.assertGreaterEqual(ic_ric["ic"].all(), 0, "train failed")
+        self.assertGreaterEqual(ic_ric["ric"].all(), 0, "train failed")
+
 
 def suite():
     _suite = unittest.TestSuite()