Fix pylint (#888)

* add_pylint_to_workflow

* fix-pylint

* fix_pylinterror

* fix-issue

qlib/contrib/data/dataset.py

@@ -7,8 +7,7 @@ import warnings
 import numpy as np
 import pandas as pd
 
-from qlib.utils import init_instance_by_config
-from qlib.data.dataset import DatasetH, DataHandler
+from qlib.data.dataset import DatasetH
 
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -16,7 +15,7 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 
 def _to_tensor(x):
     if not isinstance(x, torch.Tensor):
-        return torch.tensor(x, dtype=torch.float, device=device)
+        return torch.tensor(x, dtype=torch.float, device=device)  # pylint: disable=E1101
     return x
 
 

qlib/contrib/data/handler.py

@@ -5,9 +5,7 @@ from ...data.dataset.handler import DataHandlerLP
 from ...data.dataset.processor import Processor
 from ...utils import get_callable_kwargs
 from ...data.dataset import processor as processor_module
-from ...log import TimeInspector
 from inspect import getfullargspec
-import copy
 
 
 def check_transform_proc(proc_l, fit_start_time, fit_end_time):

qlib/contrib/data/processor.py

@@ -1,9 +1,6 @@
 import numpy as np
-import pandas as pd
-import copy
 
 from ...log import TimeInspector
-from ...utils.serial import Serializable
 from ...data.dataset.processor import Processor, get_group_columns
 
 

qlib/contrib/evaluate_portfolio.py

@@ -5,12 +5,10 @@
 from __future__ import division
 from __future__ import print_function
 
-import copy
 import numpy as np
 import pandas as pd
 from scipy.stats import spearmanr, pearsonr
 
-
 from ..data import D
 
 from collections import OrderedDict
@@ -243,4 +241,4 @@ def get_rank_ic(a, b):
 
 
 def get_normal_ic(a, b):
-    return pearsonr(a, b).correlation
+    return pearsonr(a, b)[0]

qlib/contrib/meta/data_selection/dataset.py

@@ -1,24 +1,23 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
-from copy import deepcopy
-from qlib.data.dataset.utils import init_task_handler
-from qlib.utils.data import deepcopy_basic_type
-from qlib.contrib.torch import data_to_tensor
-from qlib.workflow.task.utils import TimeAdjuster
-from qlib.model.meta.task import MetaTask
-from typing import Dict, List, Union, Text, Tuple
-from qlib.data.dataset.handler import DataHandler
-from qlib.log import get_module_logger
-from qlib.utils import auto_filter_kwargs, get_date_by_shift, init_instance_by_config
-from qlib.workflow import R
-from qlib.workflow.task.gen import RollingGen, task_generator
-from joblib import Parallel, delayed
-from qlib.model.meta.dataset import MetaTaskDataset
-from qlib.model.trainer import task_train, TrainerR
-from qlib.data.dataset import DatasetH
-from tqdm.auto import tqdm
 import pandas as pd
 import numpy as np
+from copy import deepcopy
+from joblib import Parallel, delayed  # pylint: disable=E0401
+from typing import Dict, List, Union, Text, Tuple
+from qlib.data.dataset.utils import init_task_handler
+from qlib.data.dataset import DatasetH
+from qlib.contrib.torch import data_to_tensor
+from qlib.model.meta.task import MetaTask
+from qlib.model.meta.dataset import MetaTaskDataset
+from qlib.model.trainer import TrainerR
+from qlib.log import get_module_logger
+from qlib.utils import auto_filter_kwargs, get_date_by_shift, init_instance_by_config
+from qlib.utils.data import deepcopy_basic_type
+from qlib.workflow import R
+from qlib.workflow.task.gen import RollingGen, task_generator
+from qlib.workflow.task.utils import TimeAdjuster
+from tqdm.auto import tqdm
 
 
 class InternalData:

qlib/contrib/meta/data_selection/model.py

@@ -1,28 +1,26 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
-from qlib.log import get_module_logger
 import pandas as pd
 import numpy as np
-from qlib.model.meta.task import MetaTask
 import torch
 from torch import nn
 from torch import optim
 from tqdm.auto import tqdm
-import collections
 import copy
-from typing import Union, List, Tuple, Dict
+from typing import Union, List
 
 from ....data.dataset.weight import Reweighter
 from ....model.meta.dataset import MetaTaskDataset
-from ....model.meta.model import MetaModel, MetaTaskModel
+from ....model.meta.model import MetaTaskModel
 from ....workflow import R
-
 from .utils import ICLoss
 from .dataset import MetaDatasetDS
-from qlib.contrib.meta.data_selection.net import PredNet
-from qlib.data.dataset.weight import Reweighter
+
 from qlib.log import get_module_logger
+from qlib.data.dataset.weight import Reweighter
+from qlib.model.meta.task import MetaTask
+from qlib.contrib.meta.data_selection.net import PredNet
 
 logger = get_module_logger("data selection")
 

qlib/contrib/meta/data_selection/net.py

@@ -1,7 +1,6 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
-import pandas as pd
 import numpy as np
 import torch
 from torch import nn

qlib/contrib/meta/data_selection/utils.py

@@ -1,11 +1,9 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
-import pandas as pd
 import numpy as np
 import torch
 from torch import nn
-from qlib.contrib.torch import data_to_tensor
 
 
 class ICLoss(nn.Module):

qlib/contrib/model/gbdt.py

@@ -101,7 +101,7 @@ class LGBModel(ModelFT, LightGBMFInt):
             verbose level
         """
         # Based on existing model and finetune by train more rounds
-        dtrain, _ = self._prepare_data(dataset, reweighter)
+        dtrain, _ = self._prepare_data(dataset, reweighter)  # pylint: disable=W0632
         if dtrain.empty:
             raise ValueError("Empty data from dataset, please check your dataset config.")
         self.model = lgb.train(

qlib/contrib/model/highfreq_gdbt_model.py

@@ -58,7 +58,7 @@ class HFLGBModel(ModelFT, LightGBMFInt):
         """
         Test the signal in high frequency test set
         """
-        if self.model == None:
+        if self.model is None:
             raise ValueError("Model hasn't been trained yet")
         df_test = dataset.prepare("test", col_set=["feature", "label"], data_key=DataHandlerLP.DK_I)
         df_test.dropna(inplace=True)

qlib/contrib/model/pytorch_adarnn.py

@@ -1,12 +1,10 @@
 # Copyright (c) Microsoft Corporation.
 import os
-from pdb import set_trace
 from torch.utils.data import Dataset, DataLoader
 
 import copy
 from typing import Text, Union
 
-import math
 import numpy as np
 import pandas as pd
 import torch
@@ -182,11 +180,11 @@ class ADARNN(Model):
                 continue
 
             total_loss = torch.zeros(1).cuda()
-            for i in range(len(index)):
-                feature_s = list_feat[index[i][0]]
-                feature_t = list_feat[index[i][1]]
-                label_reg_s = list_label[index[i][0]]
-                label_reg_t = list_label[index[i][1]]
+            for i, n in enumerate(index):
+                feature_s = list_feat[n[0]]
+                feature_t = list_feat[n[1]]
+                label_reg_s = list_label[n[0]]
+                label_reg_t = list_label[n[1]]
                 feature_all = torch.cat((feature_s, feature_t), 0)
 
                 if epoch < self.pre_epoch:
@@ -410,7 +408,7 @@ class AdaRNN(nn.Module):
             in_size = hidden
         self.features = nn.Sequential(*features)
 
-        if use_bottleneck == True:  # finance
+        if use_bottleneck is True:  # finance
             self.bottleneck = nn.Sequential(
                 nn.Linear(n_hiddens[-1], bottleneck_width),
                 nn.Linear(bottleneck_width, bottleneck_width),
@@ -449,7 +447,7 @@ class AdaRNN(nn.Module):
     def forward_pre_train(self, x, len_win=0):
         out = self.gru_features(x)
         fea = out[0]  # [2N,L,H]
-        if self.use_bottleneck == True:
+        if self.use_bottleneck is True:
             fea_bottleneck = self.bottleneck(fea[:, -1, :])
             fc_out = self.fc(fea_bottleneck).squeeze()
         else:
@@ -458,8 +456,8 @@ class AdaRNN(nn.Module):
         out_list_all, out_weight_list = out[1], out[2]
         out_list_s, out_list_t = self.get_features(out_list_all)
         loss_transfer = torch.zeros((1,)).cuda()
-        for i in range(len(out_list_s)):
-            criterion_transder = TransferLoss(loss_type=self.trans_loss, input_dim=out_list_s[i].shape[2])
+        for i, n in enumerate(out_list_s):
+            criterion_transder = TransferLoss(loss_type=self.trans_loss, input_dim=n.shape[2])
             h_start = 0
             for j in range(h_start, self.len_seq, 1):
                 i_start = j - len_win if j - len_win >= 0 else 0
@@ -471,7 +469,7 @@ class AdaRNN(nn.Module):
                         else 1 / (self.len_seq - h_start) * (2 * len_win + 1)
                     )
                     loss_transfer = loss_transfer + weight * criterion_transder.compute(
-                        out_list_s[i][:, j, :], out_list_t[i][:, k, :]
+                        n[:, j, :], out_list_t[i][:, k, :]
                     )
         return fc_out, loss_transfer, out_weight_list
 
@@ -484,7 +482,7 @@ class AdaRNN(nn.Module):
             out, _ = self.features[i](x_input.float())
             x_input = out
             out_lis.append(out)
-            if self.model_type == "AdaRNN" and predict == False:
+            if self.model_type == "AdaRNN" and predict is False:
                 out_gate = self.process_gate_weight(x_input, i)
                 out_weight_list.append(out_gate)
         return out, out_lis, out_weight_list
@@ -524,10 +522,10 @@ class AdaRNN(nn.Module):
         else:
             weight = weight_mat
         dist_mat = torch.zeros(self.num_layers, self.len_seq).cuda()
-        for i in range(len(out_list_s)):
-            criterion_transder = TransferLoss(loss_type=self.trans_loss, input_dim=out_list_s[i].shape[2])
+        for i, n in enumerate(out_list_s):
+            criterion_transder = TransferLoss(loss_type=self.trans_loss, input_dim=n.shape[2])
             for j in range(self.len_seq):
-                loss_trans = criterion_transder.compute(out_list_s[i][:, j, :], out_list_t[i][:, j, :])
+                loss_trans = criterion_transder.compute(n[:, j, :], out_list_t[i][:, j, :])
                 loss_transfer = loss_transfer + weight[i, j] * loss_trans
                 dist_mat[i, j] = loss_trans
         return fc_out, loss_transfer, dist_mat, weight
@@ -546,7 +544,7 @@ class AdaRNN(nn.Module):
     def predict(self, x):
         out = self.gru_features(x, predict=True)
         fea = out[0]
-        if self.use_bottleneck == True:
+        if self.use_bottleneck is True:
             fea_bottleneck = self.bottleneck(fea[:, -1, :])
             fc_out = self.fc(fea_bottleneck).squeeze()
         else:
@@ -572,12 +570,12 @@ class TransferLoss:
         Returns:
             [tensor] -- transfer loss
         """
-        if self.loss_type == "mmd_lin" or self.loss_type == "mmd":
+        if self.loss_type in ("mmd_lin", "mmd"):
             mmdloss = MMD_loss(kernel_type="linear")
             loss = mmdloss(X, Y)
         elif self.loss_type == "coral":
             loss = CORAL(X, Y)
-        elif self.loss_type == "cosine" or self.loss_type == "cos":
+        elif self.loss_type in ("cosine", "cos"):
             loss = 1 - cosine(X, Y)
         elif self.loss_type == "kl":
             loss = kl_div(X, Y)

qlib/contrib/model/pytorch_add.py

@@ -20,7 +20,6 @@ from qlib.contrib.model.pytorch_lstm import LSTMModel
 from qlib.contrib.model.pytorch_utils import count_parameters
 from qlib.data.dataset import DatasetH
 from qlib.data.dataset.handler import DataHandlerLP
-from qlib.data.dataset.processor import CSRankNorm
 from qlib.log import get_module_logger
 from qlib.model.base import Model
 from qlib.utils import get_or_create_path

qlib/contrib/model/pytorch_alstm.py

@@ -5,7 +5,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import numpy as np
 import pandas as pd
 from typing import Text, Union
@@ -150,7 +149,7 @@ class ALSTM(Model):
 
         mask = torch.isfinite(label)
 
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)
@@ -312,8 +311,8 @@ class ALSTMModel(nn.Module):
     def _build_model(self):
         try:
             klass = getattr(nn, self.rnn_type.upper())
-        except:
-            raise ValueError("unknown rnn_type `%s`" % self.rnn_type)
+        except Exception as e:
+            raise ValueError("unknown rnn_type `%s`" % self.rnn_type) from e
         self.net = nn.Sequential()
         self.net.add_module("fc_in", nn.Linear(in_features=self.input_size, out_features=self.hid_size))
         self.net.add_module("act", nn.Tanh())

qlib/contrib/model/pytorch_alstm_ts.py

@@ -5,7 +5,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import numpy as np
 import pandas as pd
 from typing import Text, Union
@@ -20,7 +19,7 @@ from torch.utils.data import DataLoader
 
 from .pytorch_utils import count_parameters
 from ...model.base import Model
-from ...data.dataset import DatasetH, TSDatasetH
+from ...data.dataset import DatasetH
 from ...data.dataset.handler import DataHandlerLP
 from ...model.utils import ConcatDataset
 from ...data.dataset.weight import Reweighter
@@ -160,7 +159,7 @@ class ALSTM(Model):
 
         mask = torch.isfinite(label)
 
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)
@@ -320,8 +319,8 @@ class ALSTMModel(nn.Module):
     def _build_model(self):
         try:
             klass = getattr(nn, self.rnn_type.upper())
-        except:
-            raise ValueError("unknown rnn_type `%s`" % self.rnn_type)
+        except Exception as e:
+            raise ValueError("unknown rnn_type `%s`" % self.rnn_type) from e
         self.net = nn.Sequential()
         self.net.add_module("fc_in", nn.Linear(in_features=self.input_size, out_features=self.hid_size))
         self.net.add_module("act", nn.Tanh())

qlib/contrib/model/pytorch_gats.py

@@ -5,7 +5,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import numpy as np
 import pandas as pd
 from typing import Text, Union
@@ -158,7 +157,7 @@ class GATs(Model):
 
         mask = torch.isfinite(label)
 
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)
@@ -263,7 +262,9 @@ class GATs(Model):
             pretrained_model.load_state_dict(torch.load(self.model_path, map_location=self.device))
 
         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}
+        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.GAT_model.load_state_dict(model_dict)
         self.logger.info("Loading pretrained model Done...")

qlib/contrib/model/pytorch_gats_ts.py

@@ -5,7 +5,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import numpy as np
 import pandas as pd
 import copy
@@ -19,7 +18,6 @@ from torch.utils.data import Sampler
 
 from .pytorch_utils import count_parameters
 from ...model.base import Model
-from ...data.dataset import DatasetH
 from ...data.dataset.handler import DataHandlerLP
 from ...contrib.model.pytorch_lstm import LSTMModel
 from ...contrib.model.pytorch_gru import GRUModel
@@ -178,7 +176,7 @@ class GATs(Model):
 
         mask = torch.isfinite(label)
 
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)
@@ -279,7 +277,9 @@ class GATs(Model):
             pretrained_model.load_state_dict(torch.load(self.model_path, map_location=self.device))
 
         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}
+        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.GAT_model.load_state_dict(model_dict)
         self.logger.info("Loading pretrained model Done...")

qlib/contrib/model/pytorch_gru.py

@@ -5,7 +5,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import numpy as np
 import pandas as pd
 from typing import Text, Union
@@ -150,7 +149,7 @@ class GRU(Model):
 
         mask = torch.isfinite(label)
 
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)

qlib/contrib/model/pytorch_gru_ts.py

@@ -5,7 +5,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import numpy as np
 import pandas as pd
 import copy
@@ -19,7 +18,6 @@ from torch.utils.data import DataLoader
 
 from .pytorch_utils import count_parameters
 from ...model.base import Model
-from ...data.dataset import DatasetH, TSDatasetH
 from ...data.dataset.handler import DataHandlerLP
 from ...model.utils import ConcatDataset
 from ...data.dataset.weight import Reweighter
@@ -159,7 +157,7 @@ class GRU(Model):
 
         mask = torch.isfinite(label)
 
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)

qlib/contrib/model/pytorch_localformer.py

@@ -5,7 +5,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import numpy as np
 import pandas as pd
 from typing import Text, Union
@@ -17,11 +16,9 @@ from ...log import get_module_logger
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from torch.utils.data import DataLoader
 
-from .pytorch_utils import count_parameters
 from ...model.base import Model
-from ...data.dataset import DatasetH, TSDatasetH
+from ...data.dataset import DatasetH
 from ...data.dataset.handler import DataHandlerLP
 from torch.nn.modules.container import ModuleList
 
@@ -102,7 +99,7 @@ class LocalformerModel(Model):
 
         mask = torch.isfinite(label)
 
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)

qlib/contrib/model/pytorch_localformer_ts.py

@@ -5,7 +5,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import numpy as np
 import pandas as pd
 import copy
@@ -18,9 +17,8 @@ import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import DataLoader
 
-from .pytorch_utils import count_parameters
 from ...model.base import Model
-from ...data.dataset import DatasetH, TSDatasetH
+from ...data.dataset import DatasetH
 from ...data.dataset.handler import DataHandlerLP
 from torch.nn.modules.container import ModuleList
 
@@ -101,7 +99,7 @@ class LocalformerModel(Model):
 
         mask = torch.isfinite(label)
 
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)

qlib/contrib/model/pytorch_lstm.py

@@ -5,7 +5,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import numpy as np
 import pandas as pd
 from typing import Text, Union
@@ -146,7 +145,7 @@ class LSTM(Model):
 
         mask = torch.isfinite(label)
 
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)

qlib/contrib/model/pytorch_lstm_ts.py

@@ -5,7 +5,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import numpy as np
 import pandas as pd
 import copy
@@ -18,7 +17,6 @@ import torch.optim as optim
 from torch.utils.data import DataLoader
 
 from ...model.base import Model
-from ...data.dataset import DatasetH, TSDatasetH
 from ...data.dataset.handler import DataHandlerLP
 from ...model.utils import ConcatDataset
 from ...data.dataset.weight import Reweighter
@@ -155,7 +153,7 @@ class LSTM(Model):
 
         mask = torch.isfinite(label)
 
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)

qlib/contrib/model/pytorch_nn.py

@@ -328,6 +328,7 @@ class Net(nn.Module):
         dnn_layers = []
         drop_input = nn.Dropout(0.05)
         dnn_layers.append(drop_input)
+        hidden_units = None
         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)
@@ -338,7 +339,7 @@ class Net(nn.Module):
         dnn_layers.append(drop_input)
         fc = nn.Linear(hidden_units, output_dim)
         dnn_layers.append(fc)
-        # optimizer
+        # optimizer  # pylint: disable=W0631
         self.dnn_layers = nn.ModuleList(dnn_layers)
         self._weight_init()
 

qlib/contrib/model/pytorch_sfm.py

@@ -4,7 +4,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import numpy as np
 import pandas as pd
 from typing import Text, Union
@@ -435,7 +434,7 @@ class SFM(Model):
 
         mask = torch.isfinite(label)
 
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)

qlib/contrib/model/pytorch_tabnet.py

@@ -3,7 +3,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import numpy as np
 import pandas as pd
 from typing import Text, Union
@@ -378,7 +377,7 @@ class TabnetModel(Model):
 
     def metric_fn(self, pred, label):
         mask = torch.isfinite(label)
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
         raise ValueError("unknown metric `%s`" % self.metric)
 

qlib/contrib/model/pytorch_tcn.py

@@ -15,7 +15,6 @@ from ...log import get_module_logger
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from torch.nn.utils import weight_norm
 
 from .pytorch_utils import count_parameters
 from ...model.base import Model
@@ -158,7 +157,7 @@ class TCN(Model):
 
         mask = torch.isfinite(label)
 
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)

qlib/contrib/model/pytorch_tcn_ts.py

@@ -158,7 +158,7 @@ class TCN(Model):
 
         mask = torch.isfinite(label)
 
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)

qlib/contrib/model/pytorch_tcts.py

@@ -5,20 +5,12 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import numpy as np
 import pandas as pd
 import copy
 import random
-from sklearn.metrics import roc_auc_score, mean_squared_error
-import logging
-from ...utils import (
-    unpack_archive_with_buffer,
-    save_multiple_parts_file,
-    get_or_create_path,
-    drop_nan_by_y_index,
-)
-from ...log import get_module_logger, TimeInspector
+from ...utils import get_or_create_path
+from ...log import get_module_logger
 
 import torch
 import torch.nn as nn
@@ -263,7 +255,7 @@ class TCTS(Model):
         x_valid, y_valid = df_valid["feature"], df_valid["label"]
         x_test, y_test = df_test["feature"], df_test["label"]
 
-        if save_path == None:
+        if save_path is None:
             save_path = get_or_create_path(save_path)
         best_loss = np.inf
         while best_loss > self.lowest_valid_performance:

qlib/contrib/model/pytorch_tra.py

@@ -6,10 +6,8 @@ import os
 import copy
 import math
 import json
-import collections
 import numpy as np
 import pandas as pd
-import seaborn as sns
 import matplotlib.pyplot as plt
 
 import torch
@@ -24,7 +22,6 @@ except ImportError:
 
 from tqdm import tqdm
 
-from qlib.utils import get_or_create_path
 from qlib.constant import EPS
 from qlib.log import get_module_logger
 from qlib.model.base import Model

qlib/contrib/model/pytorch_transformer.py

@@ -5,7 +5,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import numpy as np
 import pandas as pd
 from typing import Text, Union
@@ -17,11 +16,9 @@ from ...log import get_module_logger
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from torch.utils.data import DataLoader
 
-from .pytorch_utils import count_parameters
 from ...model.base import Model
-from ...data.dataset import DatasetH, TSDatasetH
+from ...data.dataset import DatasetH
 from ...data.dataset.handler import DataHandlerLP
 
 # qrun examples/benchmarks/Transformer/workflow_config_transformer_Alpha360.yaml ”
@@ -101,7 +98,7 @@ class TransformerModel(Model):
 
         mask = torch.isfinite(label)
 
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)

qlib/contrib/model/pytorch_transformer_ts.py

@@ -5,7 +5,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import numpy as np
 import pandas as pd
 import copy
@@ -18,9 +17,8 @@ import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import DataLoader
 
-from .pytorch_utils import count_parameters
 from ...model.base import Model
-from ...data.dataset import DatasetH, TSDatasetH
+from ...data.dataset import DatasetH
 from ...data.dataset.handler import DataHandlerLP
 
 
@@ -98,7 +96,7 @@ class TransformerModel(Model):
 
         mask = torch.isfinite(label)
 
-        if self.metric == "" or self.metric == "loss":
+        if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)

qlib/contrib/model/pytorch_utils.py

@@ -26,11 +26,11 @@ def count_parameters(models_or_parameters, unit="m"):
     else:
         counts = sum(v.numel() for v in models_or_parameters)
     unit = unit.lower()
-    if unit == "kb" or unit == "k":
+    if unit in ("kb", "k"):
         counts /= 2 ** 10
-    elif unit == "mb" or unit == "m":
+    elif unit in ("mb", "m"):
         counts /= 2 ** 20
-    elif unit == "gb" or unit == "g":
+    elif unit in ("gb", "g"):
         counts /= 2 ** 30
     elif unit is not None:
         raise ValueError("Unknown unit: {:}".format(unit))

qlib/contrib/model/tcn.py

@@ -1,6 +1,5 @@
 # MIT License
 # Copyright (c) 2018 CMU Locus Lab
-import torch
 import torch.nn as nn
 from torch.nn.utils import weight_norm
 

qlib/contrib/online/__init__.py

@@ -1,3 +1,5 @@
+# pylint: skip-file
+
 '''
 TODO:
 

qlib/contrib/online/manager.py

@@ -1,6 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
+# pylint: skip-file
+
 import yaml
 import pathlib
 import pandas as pd

qlib/contrib/online/online_model.py

@@ -1,6 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
+# pylint: skip-file
+
 import random
 import pandas as pd
 from ...data import D

qlib/contrib/online/operator.py

@@ -1,6 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
+# pylint: skip-file
+
 import fire
 import pandas as pd
 import pathlib

qlib/contrib/online/user.py

@@ -1,6 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
+# pylint: skip-file
+
 import logging
 
 from ...log import get_module_logger

qlib/contrib/online/utils.py

@@ -1,6 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
+# pylint: skip-file
+
 import pathlib
 import pickle
 import yaml

qlib/contrib/ops/high_freq.py

@@ -1,12 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
-from pathlib import Path
 import numpy as np
-import pandas as pd
 from datetime import datetime
 
-import qlib
-from qlib.data import D
 from qlib.data.cache import H
 from qlib.data.data import Cal
 from qlib.data.ops import ElemOperator

qlib/contrib/report/analysis_model/analysis_model_performance.py

@@ -34,7 +34,7 @@ def _group_return(pred_label: pd.DataFrame = None, reverse: bool = False, N: int
         {
             "Group%d"
             % (i + 1): pred_label_drop.groupby(level="datetime")["label"].apply(
-                lambda x: x[len(x) // N * i : len(x) // N * (i + 1)].mean()
+                lambda x: x[len(x) // N * i : len(x) // N * (i + 1)].mean()  # pylint: disable=W0640
             )
             for i in range(N)
         }

qlib/contrib/report/graph.py

@@ -282,8 +282,10 @@ class SubplotsGraph:
         if self._subplots_kwargs is None:
             self._init_subplots_kwargs()
 
-        self.__cols = self._subplots_kwargs.get("cols", 2)
-        self.__rows = self._subplots_kwargs.get("rows", math.ceil(len(self._df.columns) / self.__cols))
+        self.__cols = self._subplots_kwargs.get("cols", 2)  # pylint: disable=W0238
+        self.__rows = self._subplots_kwargs.get(  # pylint: disable=W0238
+            "rows", math.ceil(len(self._df.columns) / self.__cols)
+        )
 
         self._sub_graph_data = sub_graph_data
         if self._sub_graph_data is None:

qlib/contrib/strategy/optimizer/base.py

@@ -10,4 +10,3 @@ class BaseOptimizer(abc.ABC):
     @abc.abstractmethod
     def __call__(self, *args, **kwargs) -> object:
         """Generate a optimized portfolio allocation"""
-        pass

qlib/contrib/strategy/optimizer/enhanced_indexing.py

@@ -3,7 +3,6 @@
 
 import numpy as np
 import cvxpy as cp
-import pandas as pd
 
 from typing import Union, Optional, Dict, Any, List
 
@@ -156,7 +155,7 @@ class EnhancedIndexingOptimizer(BaseOptimizer):
 
         # factor deviation
         if self.f_dev is not None:
-            cons.extend([v >= -self.f_dev, v <= self.f_dev])
+            cons.extend([v >= -self.f_dev, v <= self.f_dev])  # pylint: disable=E1130
 
         # total turnover constraint
         t_cons = []

qlib/contrib/strategy/order_generator.py

@@ -6,7 +6,6 @@ This order generator is for strategies based on WeightStrategyBase
 """
 from ...backtest.position import Position
 from ...backtest.exchange import Exchange
-from ...backtest.decision import BaseTradeDecision, TradeDecisionWO
 
 import pandas as pd
 import copy

qlib/contrib/strategy/signal_strategy.py

@@ -3,7 +3,6 @@
 import os
 import copy
 import warnings
-import cvxpy as cp
 import numpy as np
 import pandas as pd
 
@@ -15,11 +14,10 @@ from qlib.model.base import BaseModel
 from qlib.strategy.base import BaseStrategy
 from qlib.backtest.position import Position
 from qlib.backtest.signal import Signal, create_signal_from
-from qlib.backtest.decision import Order, BaseTradeDecision, OrderDir, TradeDecisionWO
+from qlib.backtest.decision import Order, OrderDir, TradeDecisionWO
 from qlib.log import get_module_logger
 from qlib.utils import get_pre_trading_date, load_dataset
-from qlib.utils.resam import resam_ts_data
-from qlib.contrib.strategy.order_generator import OrderGenWInteract, OrderGenWOInteract
+from qlib.contrib.strategy.order_generator import OrderGenWOInteract
 from qlib.contrib.strategy.optimizer import EnhancedIndexingOptimizer
 
 

qlib/contrib/tuner/__init__.py

@@ -0,0 +1 @@
+# pylint: skip-file

qlib/contrib/tuner/config.py

@@ -1,6 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
+# pylint: skip-file
+
 import yaml
 import copy
 import os

qlib/contrib/tuner/launcher.py

@@ -1,6 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
+# pylint: skip-file
+
 # coding=utf-8
 
 import argparse

qlib/contrib/tuner/pipeline.py

@@ -1,6 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
+# pylint: skip-file
+
 import os
 import json
 import logging

qlib/contrib/tuner/space.py

@@ -1,6 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
+# pylint: skip-file
+
 from hyperopt import hp
 
 

qlib/contrib/tuner/tuner.py

@@ -1,6 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
+# pylint: skip-file
+
 import os
 import yaml
 import json