pylint code refine & Fix nested example (#848)

* refine code by CI

* fix argument error

* fix nested eample

qlib/contrib/data/dataset.py

@@ -63,9 +63,7 @@ def _get_date_parse_fn(target):
         get_date_parse_fn('20120101')('2017-01-01') => '20170101'
         get_date_parse_fn(20120101)('2017-01-01') => 20170101
     """
-    if isinstance(target, pd.Timestamp):
-        _fn = lambda x: pd.Timestamp(x)  # Timestamp('2020-01-01')
-    elif isinstance(target, int):
+    if isinstance(target, int):
         _fn = lambda x: int(str(x).replace("-", "")[:8])  # 20200201
     elif isinstance(target, str) and len(target) == 8:
         _fn = lambda x: str(x).replace("-", "")[:8]  # '20200201'
@@ -158,7 +156,7 @@ class MTSDatasetH(DatasetH):
         try:
             df = self.handler._learn.copy()  # use copy otherwise recorder will fail
             # FIXME: currently we cannot support switching from `_learn` to `_infer` for inference
-        except:
+        except Exception:
             warnings.warn("cannot access `_learn`, will load raw data")
             df = self.handler._data.copy()
         df.index = df.index.swaplevel()

qlib/contrib/evaluate.py

@@ -371,7 +371,7 @@ def long_short_backtest(
 
 def t_run():
     pred_FN = "./check_pred.csv"
-    pred = pd.read_csv(pred_FN)
+    pred: pd.DataFrame = pd.read_csv(pred_FN)
     pred["datetime"] = pd.to_datetime(pred["datetime"])
     pred = pred.set_index([pred.columns[0], pred.columns[1]])
     pred = pred.iloc[:9000]

qlib/contrib/model/pytorch_adarnn.py

@@ -554,7 +554,7 @@ class AdaRNN(nn.Module):
         return fc_out
 
 
-class TransferLoss(object):
+class TransferLoss:
     def __init__(self, loss_type="cosine", input_dim=512):
         """
         Supported loss_type: mmd(mmd_lin), mmd_rbf, coral, cosine, kl, js, mine, adv

qlib/contrib/model/pytorch_nn.py

@@ -98,7 +98,6 @@ class DNNModelPytorch(Model):
             "\nlr_decay_steps : {}"
             "\noptimizer : {}"
             "\nloss_type : {}"
-            "\neval_steps : {}"
             "\nseed : {}"
             "\ndevice : {}"
             "\nuse_GPU : {}"
@@ -113,7 +112,6 @@ class DNNModelPytorch(Model):
                 lr_decay_steps,
                 optimizer,
                 loss,
-                eval_steps,
                 seed,
                 self.device,
                 self.use_gpu,
@@ -331,8 +329,8 @@ class Net(nn.Module):
         dnn_layers = []
         drop_input = nn.Dropout(0.05)
         dnn_layers.append(drop_input)
-        for i, (input_dim, hidden_units) in enumerate(zip(layers[:-1], layers[1:])):
-            fc = nn.Linear(input_dim, hidden_units)
+        for i, (_input_dim, hidden_units) in enumerate(zip(layers[:-1], layers[1:])):
+            fc = nn.Linear(_input_dim, hidden_units)
             activation = nn.LeakyReLU(negative_slope=0.1, inplace=False)
             bn = nn.BatchNorm1d(hidden_units)
             seq = nn.Sequential(fc, bn, activation)

qlib/contrib/model/pytorch_tra.py

@@ -19,7 +19,7 @@ import torch.nn.functional as F
 
 try:
     from torch.utils.tensorboard import SummaryWriter
-except:
+except ImportError:
     SummaryWriter = None
 
 from tqdm import tqdm
@@ -257,7 +257,7 @@ class TRAModel(Model):
             total_loss += loss.item()
             total_count += 1
 
-        if self.use_daily_transport and len(P_all):
+        if self.use_daily_transport and len(P_all) > 0:
             P_all = pd.concat(P_all, axis=0)
             prob_all = pd.concat(prob_all, axis=0)
             choice_all = pd.concat(choice_all, axis=0)

qlib/contrib/report/graph.py

@@ -15,7 +15,6 @@ from plotly.figure_factory import create_distplot
 
 
 class BaseGraph:
-    """ """
 
     _name = None
 
@@ -297,8 +296,8 @@ class SubplotsGraph:
 
         :return:
         """
-        self._sub_graph_data = list()
-        self._subplot_titles = list()
+        self._sub_graph_data = []
+        self._subplot_titles = []
 
         for i, column_name in enumerate(self._df.columns):
             row = math.ceil((i + 1) / self.__cols)