style: 💄 lint (#200)

* style: 💄 lint

fix lint matched pylint

* style: 💄  fix lint & add isort config

fix lint problems and add isort config

.isort.cfg

@@ -0,0 +1,3 @@
+[settings]
+src_paths=basicts,tests
+skip_glob=baselines/*,assets/*,examples/*

.pylintrc

@@ -8,11 +8,11 @@
 [MASTER]
 
 # Files or directories to be skipped. They should be base names, not paths.
-ignore=third_party
+ignore=baselines,assets
 
 # Files or directories matching the regex patterns are skipped. The regex
 # matches against base names, not paths.
-ignore-patterns=
+ignore-patterns=^\.|^_|^.*\.md|^.*\.txt|^.*\.CFF|^LICENSE
 
 # Pickle collected data for later comparisons.
 persistent=no
@@ -85,6 +85,7 @@ disable=abstract-method,
         input-builtin,
         intern-builtin,
         invalid-str-codec,
+        invalid-name,
         locally-disabled,
         logging-format-interpolation,
         logging-fstring-interpolation,
@@ -433,3 +434,7 @@ valid-metaclass-classmethod-first-arg=mcs
 overgeneral-exceptions=builtins.StandardError,
                        builtins.Exception,
                        builtins.BaseException
+
+[DESIGN]
+# https://pylint.pycqa.org/en/latest/user_guide/messages/refactor/too-many-positional-arguments.html
+max-positional-arguments=10

basicts/__init__.py

@@ -1,4 +1,4 @@
-from .launcher import launch_training, launch_evaluation
+from .launcher import launch_evaluation, launch_training
 from .runners import BaseEpochRunner
 
 __version__ = '0.4.6.2'

basicts/data/base_dataset.py

@@ -1,5 +1,5 @@
-from typing import List
 from dataclasses import dataclass
+from typing import List
 
 import numpy as np
 from torch.utils.data import Dataset

basicts/data/simple_tsf_dataset.py

@@ -1,5 +1,5 @@
-import json
 import inspect
+import json
 import logging
 from typing import List
 

basicts/launcher.py

@@ -1,10 +1,12 @@
 import os
-from typing import Dict, Union, Optional
 import traceback
+from typing import Dict, Optional, Union
+
 import easytorch
-from easytorch.utils import get_logger, set_visible_devices
 from easytorch.config import init_cfg
 from easytorch.device import set_device_type
+from easytorch.utils import get_logger, set_visible_devices
+
 
 def evaluation_func(cfg: Dict,
                     ckpt_path: str = None,

basicts/metrics/__init__.py

@@ -1,7 +1,7 @@
 from .mae import masked_mae
+from .mape import masked_mape
 from .mse import masked_mse
 from .rmse import masked_rmse
-from .mape import masked_mape
 from .wape import masked_wape
 
 ALL_METRICS = {

basicts/metrics/mae.py

@@ -1,5 +1,6 @@
-import torch
 import numpy as np
+import torch
+
 
 def masked_mae(prediction: torch.Tensor, target: torch.Tensor, null_val: float = np.nan) -> torch.Tensor:
     """

basicts/metrics/mape.py

@@ -1,5 +1,6 @@
-import torch
 import numpy as np
+import torch
+
 
 def masked_mape(prediction: torch.Tensor, target: torch.Tensor, null_val: float = np.nan) -> torch.Tensor:
     """

basicts/metrics/mse.py

@@ -1,5 +1,6 @@
-import torch
 import numpy as np
+import torch
+
 
 def masked_mse(prediction: torch.Tensor, target: torch.Tensor, null_val: float = np.nan) -> torch.Tensor:
     """

basicts/metrics/rmse.py

@@ -1,8 +1,9 @@
-import torch
 import numpy as np
+import torch
 
 from .mse import masked_mse
 
+
 def masked_rmse(prediction: torch.Tensor, target: torch.Tensor, null_val: float = np.nan) -> torch.Tensor:
     """
     Calculate the Masked Root Mean Squared Error (RMSE) between predicted and target values,

basicts/metrics/wape.py

@@ -1,5 +1,6 @@
-import torch
 import numpy as np
+import torch
+
 
 def masked_wape(prediction: torch.Tensor, target: torch.Tensor, null_val: float = np.nan) -> torch.Tensor:
     """

basicts/runners/__init__.py

@@ -1,7 +1,7 @@
 from .base_epoch_runner import BaseEpochRunner
 from .base_tsf_runner import BaseTimeSeriesForecastingRunner
-from .runner_zoo.simple_tsf_runner import SimpleTimeSeriesForecastingRunner
 from .runner_zoo.no_bp_runner import NoBPRunner
+from .runner_zoo.simple_tsf_runner import SimpleTimeSeriesForecastingRunner
 
 __all__ = ['BaseEpochRunner', 'BaseTimeSeriesForecastingRunner',
            'SimpleTimeSeriesForecastingRunner', 'NoBPRunner']

basicts/runners/base_epoch_runner.py

@@ -1,28 +1,30 @@
+import logging
 import os
 import time
-import logging
 from abc import ABCMeta, abstractmethod
-from typing import Tuple, Union, Optional, Dict
+from typing import Dict, Optional, Tuple, Union
 
 import setproctitle
-from tqdm import tqdm
-from packaging import version
 import torch
+from easytorch.config import get_ckpt_save_dir
+from easytorch.core.checkpoint import (backup_last_ckpt, clear_ckpt, load_ckpt,
+                                       save_ckpt)
+from easytorch.core.data_loader import build_data_loader, build_data_loader_ddp
+from easytorch.core.meter_pool import MeterPool
+from easytorch.device import to_device
+from easytorch.utils import (TimePredictor, get_local_rank, get_logger,
+                             is_master, master_only, set_env)
+from packaging import version
 from torch import nn
-from torch.utils.data import Dataset, DataLoader
 from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.utils.tensorboard import SummaryWriter
+from torch.utils.data import DataLoader, Dataset
 from torch.utils.data.distributed import DistributedSampler
+from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm
 
-from easytorch.core.meter_pool import MeterPool
-from easytorch.core.checkpoint import load_ckpt, save_ckpt, backup_last_ckpt, clear_ckpt
-from easytorch.core.data_loader import build_data_loader, build_data_loader_ddp
-from easytorch.config import get_ckpt_save_dir
-from easytorch.utils import TimePredictor, get_logger, get_local_rank, is_master, master_only, set_env
-from easytorch.device import to_device
-
-from . import optim
 from ..utils import get_dataset_name
+from . import optim
+
 
 class BaseEpochRunner(metaclass=ABCMeta):
     """

basicts/runners/base_tsf_runner.py

@@ -1,18 +1,19 @@
-import os
+import functools
+import inspect
 import json
 import math
-import inspect
-import functools
-from typing import Tuple, Union, Optional, Dict
+import os
+from typing import Dict, Optional, Tuple, Union
 
-import torch
 import numpy as np
-from tqdm import tqdm
+import torch
 from easydict import EasyDict
 from easytorch.utils import master_only
+from tqdm import tqdm
 
+from ..metrics import (masked_mae, masked_mape, masked_mse, masked_rmse,
+                       masked_wape)
 from .base_epoch_runner import BaseEpochRunner
-from ..metrics import masked_mae, masked_mape, masked_rmse, masked_wape, masked_mse
 
 
 class BaseTimeSeriesForecastingRunner(BaseEpochRunner):
@@ -343,7 +344,7 @@ def train_iters(self, epoch: int, iter_index: int, data: Union[torch.Tensor, Tup
             forward_return['prediction'] = forward_return['prediction'][:, :cl_length, :, :]
             forward_return['target'] = forward_return['target'][:, :cl_length, :, :]
         loss = self.metric_forward(self.loss, forward_return)
-        self.update_epoch_meter(f'train/loss', loss.item())
+        self.update_epoch_meter('train/loss', loss.item())
 
         for metric_name, metric_func in self.metrics.items():
             metric_item = self.metric_forward(metric_func, forward_return)

basicts/runners/optim/__init__.py

@@ -1,2 +1,3 @@
 from torch.optim import Optimizer
+
 from .builder import build_lr_scheduler, build_optim

basicts/runners/optim/optimizers.py

@@ -1,16 +1,13 @@
 # define more optimizers here
-import os
 import inspect
-from typing import Union, Tuple, Optional
+from typing import Optional, Tuple, Union
 
 import torch
+from easytorch.device import _DEVICE_TYPE
+from easytorch.utils import get_local_rank, get_world_size
 from torch import Tensor
 from torch.optim import AdamW
 
-from easytorch.device import _DEVICE_TYPE
-from easytorch.utils import get_world_size, get_local_rank
-
-
 __all__ = ['AdamWnanoGPT', 'Muon']
 
 class AdamWnanoGPT(AdamW):

basicts/runners/runner_zoo/no_bp_runner.py

@@ -7,4 +7,3 @@ class NoBPRunner(SimpleTimeSeriesForecastingRunner):
 
     def backward(self, loss: torch.Tensor):
         pass
-        return

basicts/runners/runner_zoo/simple_tsf_runner.py

@@ -1,8 +1,10 @@
 from typing import Dict
 
 import torch
+
 from ..base_tsf_runner import BaseTimeSeriesForecastingRunner
 
+
 class SimpleTimeSeriesForecastingRunner(BaseTimeSeriesForecastingRunner):
     """
     A Simple Runner for Time Series Forecasting: 
@@ -90,13 +92,13 @@ def forward(self, data: Dict, epoch: int = None, iter_num: int = None, train: bo
         # Select input features
         history_data = self.select_input_features(history_data)
         future_data_4_dec = self.select_input_features(future_data)
-        
+
         if not train:
             # For non-training phases, use only temporal features
             future_data_4_dec[..., 0] = torch.empty_like(future_data_4_dec[..., 0])
 
         # Forward pass through the model
-        model_return = self.model(history_data=history_data, future_data=future_data_4_dec, 
+        model_return = self.model(history_data=history_data, future_data=future_data_4_dec,
                                   batch_seen=iter_num, epoch=epoch, train=train)
 
         # Parse model return

basicts/scaler/__init__.py

@@ -1,6 +1,6 @@
 from .base_scaler import BaseScaler
-from .z_score_scaler import ZScoreScaler
 from .min_max_scaler import MinMaxScaler
+from .z_score_scaler import ZScoreScaler
 
 __all__ = [
     'BaseScaler',

basicts/scaler/min_max_scaler.py

@@ -1,7 +1,7 @@
 import json
 
-import torch
 import numpy as np
+import torch
 
 from .base_scaler import BaseScaler
 
@@ -72,7 +72,7 @@ def transform(self, input_data: torch.Tensor) -> torch.Tensor:
         Returns:
             torch.Tensor: The normalized data with the same shape as the input.
         """
-        
+
         _min = self.min.to(input_data.device)
         _max = self.max.to(input_data.device)
         input_data[..., self.target_channel] = (input_data[..., self.target_channel] - _min) / (_max - _min)

basicts/scaler/z_score_scaler.py

@@ -1,7 +1,7 @@
 import json
 
-import torch
 import numpy as np
+import torch
 
 from .base_scaler import BaseScaler
 

basicts/utils/__init__.py

@@ -1,9 +1,10 @@
-from .xformer import data_transformation_4_xformer
-from .serialization import load_adj, load_pkl, dump_pkl, \
-        load_dataset_data, get_regular_settings, load_dataset_desc
-from .misc import clock, check_nan_inf, remove_nan_inf, \
-        partial_func as partial
 from .config import get_dataset_name
+from .misc import check_nan_inf, clock
+from .misc import partial_func as partial
+from .misc import remove_nan_inf
+from .serialization import (dump_pkl, get_regular_settings, load_adj,
+                            load_dataset_data, load_dataset_desc, load_pkl)
+from .xformer import data_transformation_4_xformer
 
 __all__ = ['load_adj', 'load_pkl', 'dump_pkl',
            'clock', 'check_nan_inf',

basicts/utils/config.py

@@ -1,5 +1,6 @@
 from typing import Dict
 
+
 def get_dataset_name(cfg: Dict) -> str:
     """
     Extract the dataset name from the configuration dictionary.

basicts/utils/misc.py

@@ -1,5 +1,4 @@
 import time
-from typing import Dict
 from functools import partial
 
 import torch

basicts/utils/serialization.py

@@ -1,12 +1,12 @@
 import json
 import pickle
+
 import numpy as np
-from .adjacent_matrix_norm import (
-    calculate_scaled_laplacian,
-    calculate_symmetric_normalized_laplacian,
-    calculate_symmetric_message_passing_adj,
-    calculate_transition_matrix
-)
+
+from .adjacent_matrix_norm import (calculate_scaled_laplacian,
+                                   calculate_symmetric_message_passing_adj,
+                                   calculate_symmetric_normalized_laplacian,
+                                   calculate_transition_matrix)
 
 
 def get_regular_settings(dataset_name: str) -> dict:

examples/arch.py

@@ -1,6 +1,8 @@
+# pylint: disable=unused-argument
 import torch
 from torch import nn
 
+
 class MultiLayerPerceptron(nn.Module):
     """
     A simple Multi-Layer Perceptron (MLP) model with two fully connected layers.