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
@@ -164,13 +164,15 @@ class SigAnaRecord(SignalRecord):
     artifact_path = "sig_analysis"
 
     def __init__(self, recorder, ana_long_short=False, ann_scaler=252, **kwargs):
+        super().__init__(recorder=recorder, **kwargs)
         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")
@@ -228,18 +230,21 @@ def __init__(self, recorder, config, **kwargs):
         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"]
         self.strategy = init_instance_by_config(self.strategy_config, accept_types=BaseStrategy)
 
     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 +139,35 @@ 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_with_sigana"):
+        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"))
+        pred_score = sar.load("pred.pkl")
+        uri_path = R.get_uri()
+    return pred_score, {"ic": ic, "ric": ric}, uri_path
+
+
 def fake_experiment():
     """A fake experiment workflow to test uri
 
@@ -195,20 +224,26 @@ class TestAllFlow(TestAutoData):
     def tearDownClass(cls) -> None:
         shutil.rmtree(str(Path(C["exp_manager"]["kwargs"]["uri"].strip("file:")).resolve()))
 
-    def test_0_train(self):
+    def test_0_train_with_sigana(self):
+        TestAllFlow.PRED_SCORE, ic_ric, uri_path = train_with_sigana()
+        self.assertGreaterEqual(ic_ric["ic"].all(), 0, "train failed")
+        self.assertGreaterEqual(ic_ric["ric"].all(), 0, "train failed")
+        shutil.rmtree(str(Path(uri_path.strip("file:")).resolve()))
+
+    def test_1_train(self):
         TestAllFlow.PRED_SCORE, ic_ric, TestAllFlow.RID = train()
         self.assertGreaterEqual(ic_ric["ic"].all(), 0, "train failed")
         self.assertGreaterEqual(ic_ric["ric"].all(), 0, "train failed")
 
-    def test_1_backtest(self):
+    def test_2_backtest(self):
         analyze_df = backtest_analysis(TestAllFlow.PRED_SCORE, TestAllFlow.RID)
         self.assertGreaterEqual(
             analyze_df.loc(axis=0)["excess_return_with_cost", "annualized_return"].values[0],
             0.10,
             "backtest failed",
         )
 
-    def test_2_expmanager(self):
+    def test_3_expmanager(self):
         pass_default, pass_current, uri_path = fake_experiment()
         self.assertTrue(pass_default, msg="default uri is incorrect")
         self.assertTrue(pass_current, msg="current uri is incorrect")