Enhance pytorch nn (#917)

* enhance pytorch_nn

* fix dim bug

* Black format

* Fix pylint error

Author: you-n-g

.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
+

examples/benchmarks/MLP/workflow_config_mlp_Alpha158.yaml

@@ -63,8 +63,6 @@ task:
         module_path: qlib.contrib.model.pytorch_nn
         kwargs:
             loss: mse
-            input_dim: 157
-            output_dim: 1
             lr: 0.002
             lr_decay: 0.96
             lr_decay_steps: 100
@@ -73,6 +71,8 @@ task:
             batch_size: 8192
             GPU: 0
             weight_decay: 0.0002
+            pt_model_kwargs:
+              input_dim: 157
     dataset:
         class: DatasetH
         module_path: qlib.data.dataset

examples/benchmarks/MLP/workflow_config_mlp_Alpha360.yaml

@@ -51,15 +51,15 @@ task:
         module_path: qlib.contrib.model.pytorch_nn
         kwargs:
             loss: mse
-            input_dim: 360
-            output_dim: 1
             lr: 0.002
             lr_decay: 0.96
             lr_decay_steps: 100
             optimizer: adam
             max_steps: 8000
             batch_size: 4096
             GPU: 0
+            pt_model_kwargs:
+              input_dim: 360
     dataset:
         class: DatasetH
         module_path: qlib.data.dataset

qlib/contrib/meta/data_selection/utils.py

@@ -9,6 +9,9 @@
 class ICLoss(nn.Module):
     def forward(self, pred, y, idx, skip_size=50):
         """forward.
+        FIXME:
+        - Some times it will be a slightly different from the result from `pandas.corr()`
+        - It may be caused by the precision problem of model;
 
         :param pred:
         :param y:

qlib/contrib/model/gbdt.py

@@ -10,6 +10,7 @@
 from ...data.dataset.handler import DataHandlerLP
 from ...model.interpret.base import LightGBMFInt
 from ...data.dataset.weight import Reweighter
+from qlib.workflow import R
 
 
 class LGBModel(ModelFT, LightGBMFInt):
@@ -59,10 +60,12 @@ def fit(
         num_boost_round=None,
         early_stopping_rounds=None,
         verbose_eval=20,
-        evals_result=dict(),
+        evals_result=None,
         reweighter=None,
-        **kwargs
+        **kwargs,
     ):
+        if evals_result is None:
+            evals_result = {}  # in case of unsafety of Python default values
         ds_l = self._prepare_data(dataset, reweighter)
         ds, names = list(zip(*ds_l))
         self.model = lgb.train(
@@ -76,10 +79,13 @@ def fit(
             ),
             verbose_eval=verbose_eval,
             evals_result=evals_result,
-            **kwargs
+            **kwargs,
         )
         for k in names:
-            evals_result[k] = list(evals_result[k].values())[0]
+            for key, val in evals_result[k].items():
+                name = f"{key}.{k}"
+                for epoch, m in enumerate(val):
+                    R.log_metrics(**{name.replace("@", "_"): m}, step=epoch)
 
     def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
         if self.model is None:

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...")