Fix pylint error

.pylintrc

@@ -0,0 +1,5 @@
+[TYPECHECK]
+# https://stackoverflow.com/a/53572939 
+# List of members which are set dynamically and missed by Pylint inference
+# system, and so shouldn't trigger E1101 when accessed.
+generated-members=numpy.*, torch.*

docs/developer/code_standard.rst

@@ -14,9 +14,19 @@ Continuous Integration (CI) tools help you stick to the quality standards by run
 
 When you submit a PR request, you can check whether your code passes the CI tests in the "check" section at the bottom of the web page.
 
-A common error is the mixed use of space and tab. You can fix the bug by inputing the following code in the command line.
+1. Qlib will check the code format with black. The PR will raise error if your code does not align to the standard of Qlib(e.g. a common error is the mixed use of space and tab).
+ You can fix the bug by inputing the following code in the command line.
 
 .. code-block:: python
 
     pip install black
     python -m black . -l 120
+
+
+2. Qlib will check your code style pylint. The checking command is implemented in [github action workflow](https://github.com/microsoft/qlib/blob/0e8b94a552f1c457cfa6cd2c1bb3b87ebb3fb279/.github/workflows/test.yml#L66). 
+   Sometime pylint's restrictions are not that reasonable. You can ignore specific errors like this
+
+.. code-block:: python
+
+    return -ICLoss()(pred, target, index)  # pylint: disable=E1130
+

qlib/contrib/model/pytorch_gats.py

@@ -263,8 +263,8 @@ def fit(
 
         model_dict = self.GAT_model.state_dict()
         pretrained_dict = {
-            k: v for k, v in pretrained_model.state_dict().items() if k in model_dict
-        }  # pylint: disable=E1135
+            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.GAT_model.load_state_dict(model_dict)
         self.logger.info("Loading pretrained model Done...")

qlib/contrib/model/pytorch_gats_ts.py

@@ -278,8 +278,8 @@ def fit(
 
         model_dict = self.GAT_model.state_dict()
         pretrained_dict = {
-            k: v for k, v in pretrained_model.state_dict().items() if k in model_dict
-        }  # pylint: disable=E1135
+            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.GAT_model.load_state_dict(model_dict)
         self.logger.info("Loading pretrained model Done...")

qlib/contrib/model/pytorch_nn.py

@@ -15,7 +15,6 @@
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from qlib.model.utils import IndexSampler
 
 from .pytorch_utils import count_parameters
 from ...model.base import Model
@@ -344,7 +343,7 @@ def get_loss(self, pred, w, target, loss_type):
 
     def get_metric(self, pred, target, index):
         # NOTE: the order of the index must follow <datetime, instrument> sorted order
-        return -ICLoss()(pred, target, index)
+        return -ICLoss()(pred, target, index)  # pylint: disable=E1130
 
     def _nn_predict(self, data, return_cpu=True):
         """Reusing predicting NN.