fix_pylint_for_CI

.github/workflows/test.yml

@@ -72,7 +72,7 @@ jobs:
       run: |
         pip install --upgrade pip
         pip install pylint
-        pylint --disable=C0104,C0114,C0115,C0116,C0301,C0302,C0411,C0413,C1802,R0201,R0401,R0801,R0902,R0903,R0911,R0912,R0913,R0914,R0915,R1720,W0105,W0123,W0201,W0511,W0613,W1113,W1514,E0401,E1121,C0103,C0209,R0402,R1705,R1710,R1725,R1735,W0102,W0212,W0221,W0223,W0231,W0237,W0612,W0621,W0622,W0703,W1309,E1102,E1136 --const-rgx='[a-z_][a-z0-9_]{2,30}$' qlib --init-hook "import astroid; astroid.context.InferenceContext.max_inferred = 500"
+        pylint --disable=C0104,C0114,C0115,C0116,C0301,C0302,C0411,C0413,C1802,C3001,R0401,R0801,R0902,R0903,R0911,R0912,R0913,R0914,R0915,R1720,W0105,W0123,W0201,W0511,W0613,W1113,W1514,E0401,E1121,C0103,C0209,R0402,R1705,R1710,R1725,R1735,W0102,W0212,W0221,W0223,W0231,W0237,W0612,W0621,W0622,W0703,W1309,E1102,E1136 --const-rgx='[a-z_][a-z0-9_]{2,30}$' qlib --init-hook "import astroid; astroid.context.InferenceContext.max_inferred = 500"
 
     # The following flake8 error codes were ignored:
       # E501 line too long

qlib/contrib/meta/data_selection/dataset.py

@@ -290,7 +290,7 @@ def _prepare_meta_ipt(self, task):
         ic_df = self.internal_data.data_ic_df
 
         segs = task["dataset"]["kwargs"]["segments"]
-        end = max([segs[k][1] for k in ("train", "valid") if k in segs])
+        end = max(segs[k][1] for k in ("train", "valid") if k in segs)
         ic_df_avail = ic_df.loc[:end, pd.IndexSlice[:, :end]]
 
         # meta data set focus on the **information** instead of preprocess

qlib/contrib/model/pytorch_hist.py

@@ -292,7 +292,9 @@ def fit(
             pretrained_model.load_state_dict(torch.load(self.model_path))
 
         model_dict = self.HIST_model.state_dict()
-        pretrained_dict = {k: v for k, v in pretrained_model.state_dict().items() if k in model_dict}
+        pretrained_dict = {
+            k: v for k, v in pretrained_model.state_dict().items() if k in model_dict
+        }  # pylint: disable=E1135
         model_dict.update(pretrained_dict)
         self.HIST_model.load_state_dict(model_dict)
         self.logger.info("Loading pretrained model Done...")

qlib/contrib/model/pytorch_tra.py

@@ -167,8 +167,8 @@ def _init_model(self):
             for param in self.tra.predictors.parameters():
                 param.requires_grad_(False)
 
-        self.logger.info("# model params: %d" % sum([p.numel() for p in self.model.parameters() if p.requires_grad]))
-        self.logger.info("# tra params: %d" % sum([p.numel() for p in self.tra.parameters() if p.requires_grad]))
+        self.logger.info("# model params: %d" % sum(p.numel() for p in self.model.parameters() if p.requires_grad))
+        self.logger.info("# tra params: %d" % sum(p.numel() for p in self.tra.parameters() if p.requires_grad))
 
         self.optimizer = optim.Adam(list(self.model.parameters()) + list(self.tra.parameters()), lr=self.lr)
 

qlib/data/dataset/__init__.py

@@ -438,7 +438,7 @@ def build_index(data: pd.DataFrame) -> Tuple[pd.DataFrame, dict]:
 
     @property
     def empty(self):
-        return self.__len__() == 0
+        return self.end_idx - self.start_idx == 0
 
     def _get_indices(self, row: int, col: int) -> np.array:
         """
@@ -539,9 +539,6 @@ def __getitem__(self, idx: Union[int, Tuple[object, str], List[int]]):
             data = data.reshape(-1, self.step_len, *data.shape[1:])
         return data
 
-    def __len__(self):
-        return self.end_idx - self.start_idx
-
 
 class TSDatasetH(DatasetH):
     """

qlib/rl/utils/data_queue.py

@@ -145,7 +145,7 @@ def __del__(self):
     def __iter__(self):
         if not self._activated:
             raise ValueError(
-                "Need to call activate() to launch a daemon worker " "to produce data into data queue before using it."
+                "Need to call activate() to launch a daemon worker, to produce data into data queue before using it."
             )
         return self._consumer()