style: 💄 lint

fix lint matched pylint

.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/runners/base_tsf_runner.py

@@ -343,7 +343,7 @@ 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/optimizers.py

@@ -1,5 +1,4 @@
 # define more optimizers here
-import os
 import inspect
 from typing import Union, Tuple, Optional
 

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

@@ -90,13 +90,13 @@ 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/min_max_scaler.py

@@ -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/utils/misc.py

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

examples/arch.py

@@ -1,3 +1,4 @@
+# pylint: disable=unused-argument
 import torch
 from torch import nn
 

examples/complete_config.py

@@ -1,12 +1,4 @@
 ############################## Import Dependencies ##############################
-
-import os
-import sys
-from easydict import EasyDict
-
-# TODO: Remove this when basicts can be installed via pip
-sys.path.append(os.path.abspath(__file__ + '/../../..'))
-
 # Import metrics & loss functions
 from basicts.metrics import masked_mae, masked_mape, masked_rmse
 # Import dataset class
@@ -15,10 +7,14 @@
 from basicts.runners import SimpleTimeSeriesForecastingRunner
 # Import scaler class
 from basicts.scaler import ZScoreScaler
+# Import dataset settings
+from basicts.utils import get_regular_settings
 # Import model architecture
 from .arch import MultiLayerPerceptron as MLP
 
-from basicts.utils import get_regular_settings
+
+import os
+from easydict import EasyDict
 
 ############################## Hot Parameters ##############################
 
@@ -82,7 +78,9 @@
 
 ############################## Scaler Configuration ##############################
 
-CFG.SCALER = EasyDict() # Scaler settings. Default: None. If not specified, the data will not be normalized, i.e., the data will be used directly for training, validation, and test.
+# Scaler settings. Default: None.
+# If not specified, the data will not be normalized, i.e., the data will be used directly for training, validation, and test.
+CFG.SCALER = EasyDict()
 
 # Scaler settings
 CFG.SCALER.TYPE = ZScoreScaler # Scaler class
@@ -101,11 +99,22 @@
 CFG.MODEL.NAME = MODEL_ARCH.__name__ # Model name, must be specified, used for saving checkpoints and set the process title.
 CFG.MODEL.ARCH = MODEL_ARCH # Model architecture, must be specified.
 CFG.MODEL.PARAM = MODEL_PARAM # Model parameters
-CFG.MODEL.FORWARD_FEATURES = [0, 1, 2] # Features used as input. The size of input data `history_data` is usually [B, L, N, C], this parameter specifies the index of the last dimension, i.e., history_data[:, :, :, CFG.MODEL.FORWARD_FEATURES].
-CFG.MODEL.TARGET_FEATURES = [0] # Features used as output. The size of target data `future_data` is usually [B, L, N, C], this parameter specifies the index of the last dimension, i.e., future_data[:, :, :, CFG.MODEL.TARGET_FEATURES].
-CFG.MODEL.TARGET_TIME_SERIES = [5, 6] # The index of the time series to be predicted, default is None. This setting is particularly useful in a Multivariate-to-Univariate setup. For example, if 7 time series are input and the last two need to be predicted, you can set `CFG.MODEL.TARGET_TIME_SERIES=[5, 6]` to achieve this.
-CFG.MODEL.SETUP_GRAPH = False # Whether to set up the computation graph. Default: False. Implementation of many works (e.g., DCRNN, GTS) acts like TensorFlow, which creates parameters in the first feedforward process.
-CFG.MODEL.DDP_FIND_UNUSED_PARAMETERS = False # Controls the `find_unused_parameters parameter` of `torch.nn.parallel.DistributedDataParallel`. In distributed computing, if there are unused parameters in the forward process, PyTorch usually raises a RuntimeError. In such cases, this parameter should be set to True.
+# Features used as input. The size of input data `history_data` is usually [B, L, N, C],
+# this parameter specifies the index of the last dimension, i.e., history_data[:, :, :, CFG.MODEL.FORWARD_FEATURES].
+CFG.MODEL.FORWARD_FEATURES = [0, 1, 2]
+# Features used as output. The size of target data `future_data` is usually [B, L, N, C],
+# this parameter specifies the index of the last dimension, i.e., future_data[:, :, :, CFG.MODEL.TARGET_FEATURES].
+CFG.MODEL.TARGET_FEATURES = [0]
+# The index of the time series to be predicted, default is None. This setting is particularly useful in a Multivariate-to-Univariate setup.
+# For example, if 7 time series are input and the last two need to be predicted, you can set `CFG.MODEL.TARGET_TIME_SERIES=[5, 6]` to achieve this.
+CFG.MODEL.TARGET_TIME_SERIES = [5, 6]
+# Whether to set up the computation graph. Default: False.
+# Implementation of many works (e.g., DCRNN, GTS) acts like TensorFlow, which creates parameters in the first feedforward process.
+CFG.MODEL.SETUP_GRAPH = False
+# Controls the `find_unused_parameters parameter` of `torch.nn.parallel.DistributedDataParallel`.
+# In distributed computing, if there are unused parameters in the forward process, PyTorch usually raises a RuntimeError.
+# In such cases, this parameter should be set to True.
+CFG.MODEL.DDP_FIND_UNUSED_PARAMETERS = False
 
 ############################## Metrics Configuration ##############################
 
@@ -132,7 +141,12 @@
     MODEL_ARCH.__name__,
     '_'.join([DATA_NAME, str(CFG.TRAIN.NUM_EPOCHS), str(INPUT_LEN), str(OUTPUT_LEN)])
 ) # Directory to save checkpoints. Default: 'checkpoints/{MODEL_NAME}/{DATASET_NAME}_{NUM_EPOCHS}_{INPUT_LEN}_{OUTPUT_LEN}'
-CFG.TRAIN.CKPT_SAVE_STRATEGY = None # Checkpoint save strategy. `CFG.TRAIN.CKPT_SAVE_STRATEGY` should be None, an int value, a list or a tuple. None: remove last checkpoint file every epoch. Default: None. Int: save checkpoint every `CFG.TRAIN.CKPT_SAVE_STRATEGY` epoch.  List or Tuple: save checkpoint when epoch in `CFG.TRAIN.CKPT_SAVE_STRATEGY, remove last checkpoint file when last_epoch not in ckpt_save_strategy
+# Checkpoint save strategy. `CFG.TRAIN.CKPT_SAVE_STRATEGY` should be None, an int value, a list or a tuple.
+# Default: None.
+# None: remove last checkpoint file every epoch.
+# Int: save checkpoint every `CFG.TRAIN.CKPT_SAVE_STRATEGY` epoch.
+# List or Tuple: save checkpoint when epoch in `CFG.TRAIN.CKPT_SAVE_STRATEGY, remove last checkpoint file when last_epoch not in ckpt_save_strategy
+CFG.TRAIN.CKPT_SAVE_STRATEGY = None
 CFG.TRAIN.FINETUNE_FROM = None # Checkpoint path for fine-tuning. Default: None. If not specified, the model will be trained from scratch.
 CFG.TRAIN.STRICT_LOAD = True # Whether to strictly load the checkpoint. Default: True.
 
@@ -205,10 +219,12 @@
 CFG.TEST.DATA.NUM_WORKERS = 0
 CFG.TEST.DATA.PIN_MEMORY = False
 
-############################## Evaluation Configuration ##############################
-
+########################### Evaluation Configuration ##########################
 CFG.EVAL = EasyDict()
 
 # Evaluation parameters
-CFG.EVAL.HORIZONS = [3, 6, 12]  # The prediction horizons for evaluation. Default value: []. NOTE: HORIZONS[i] refers to testing **on the i-th** time step, representing the loss for that specific time step. This is a common setting in spatiotemporal forecasting. For long-sequence predictions, it is recommended to keep HORIZONS set to the default value [] to avoid confusion.
+# The prediction horizons for evaluation. Default value: [].
+# NOTE: HORIZONS[i] refers to testing **on the i-th** time step, representing the loss for that specific time step.
+# This is a common setting in spatiotemporal forecasting. For long-sequence predictions, it is recommended to keep HORIZONS set to the default value [] to avoid confusion.
+CFG.EVAL.HORIZONS = []
 CFG.EVAL.USE_GPU = True # Whether to use GPU for evaluation. Default: True

examples/complete_config_cn.py

@@ -1,13 +1,4 @@
-
 ############################## 导入依赖 ##############################
-
-import os
-import sys
-from easydict import EasyDict
-
-# TODO: 当 basicts 可以通过 pip 安装时，移除这行代码
-sys.path.append(os.path.abspath(__file__ + '/../../..'))
-
 # 导入指标和损失函数
 from basicts.metrics import masked_mae, masked_mape, masked_rmse
 # 导入数据集类
@@ -16,10 +7,14 @@
 from basicts.runners import SimpleTimeSeriesForecastingRunner
 # 导入缩放器类
 from basicts.scaler import ZScoreScaler
+# 导入数据集配置
+from basicts.utils import get_regular_settings
 # 导入模型架构
 from .arch import MultiLayerPerceptron as MLP
 
-from basicts.utils import get_regular_settings
+
+import os
+from easydict import EasyDict
 
 ############################## 热门参数 ##############################
 
@@ -102,11 +97,19 @@
 CFG.MODEL.NAME = MODEL_ARCH.__name__ # 模型名称，必须指定，用于保存检查点和设置进程标题。
 CFG.MODEL.ARCH = MODEL_ARCH # 模型架构，必须指定。
 CFG.MODEL.PARAM = MODEL_PARAM # 模型参数，必须指定。
-CFG.MODEL.FORWARD_FEATURES = [0, 1, 2] # 作为输入使用的特征。输入数据的大小通常为 [B, L, N, C]，此参数指定最后一个维度的索引，即 history_data[:, :, :, CFG.MODEL.FORWARD_FEATURES]。
-CFG.MODEL.TARGET_FEATURES = [0] # 作为输出使用的特征。目标数据的大小通常为 [B, L, N, C]，此参数指定最后一个维度的索引，即 future_data[:, :, :, CFG.MODEL.TARGET_FEATURES]。
-CFG.MODEL.TARGET_TIME_SERIES = None # 待预测的时间序列索引，默认为None。该参数在多变量到单变量预测（Multivariate-to-Univariate）的场景下特别有用。例如，当输入7条时间序列时，若需要预测最后两条序列，可以通过设置`CFG.MODEL.TARGET_TIME_SERIES=[5, 6]`来实现。
-CFG.MODEL.SETUP_GRAPH = False # 是否设置计算图。默认值：False。许多论文的实现（如 DCRNN，GTS）类似于 TensorFlow，需要第一次前向传播时建立计算图并创建参数。
-CFG.MODEL.DDP_FIND_UNUSED_PARAMETERS = False # 控制 torch.nn.parallel.DistributedDataParallel 的 `find_unused_parameters` 参数。在分布式计算中，如果前向传播过程中存在未使用的参数，PyTorch 通常会抛出 RuntimeError。在这种情况下，应将此参数设置为 True。
+# 作为输入使用的特征。输入数据的大小通常为 [B, L, N, C]，
+# 此参数指定最后一个维度的索引，即 history_data[:, :, :, CFG.MODEL.FORWARD_FEATURES]。
+CFG.MODEL.FORWARD_FEATURES = [0, 1, 2]
+# 作为输出使用的特征。目标数据的大小通常为 [B, L, N, C]，此参数指定最后一个维度的索引，即 future_data[:, :, :, CFG.MODEL.TARGET_FEATURES]。
+CFG.MODEL.TARGET_FEATURES = [0]
+# 待预测的时间序列索引，默认为None。该参数在多变量到单变量预测（Multivariate-to-Univariate）的场景下特别有用。
+# 例如，当输入7条时间序列时，若需要预测最后两条序列，可以通过设置`CFG.MODEL.TARGET_TIME_SERIES=[5, 6]`来实现。
+CFG.MODEL.TARGET_TIME_SERIES = None
+# 是否设置计算图。默认值：False。许多论文的实现（如 DCRNN，GTS）类似于 TensorFlow，需要第一次前向传播时建立计算图并创建参数。
+CFG.MODEL.SETUP_GRAPH = False
+# 控制 torch.nn.parallel.DistributedDataParallel 的 `find_unused_parameters` 参数。
+# 在分布式计算中，如果前向传播过程中存在未使用的参数，PyTorch 通常会抛出 RuntimeError。在这种情况下，应将此参数设置为 True。
+CFG.MODEL.DDP_FIND_UNUSED_PARAMETERS = False
 
 ############################## 指标配置 ##############################
 
@@ -128,12 +131,17 @@
 
 # 训练参数
 CFG.TRAIN.NUM_EPOCHS = NUM_EPOCHS
+ # 保存检查点的目录。默认值：'checkpoints/{MODEL_NAME}/{DATASET_NAME}_{NUM_EPOCHS}_{INPUT_LEN}_{OUTPUT_LEN}'
 CFG.TRAIN.CKPT_SAVE_DIR = os.path.join(
     'checkpoints',
     MODEL_ARCH.__name__,
     '_'.join([DATA_NAME, str(CFG.TRAIN.NUM_EPOCHS), str(INPUT_LEN), str(OUTPUT_LEN)])
-) # 保存检查点的目录。默认值：'checkpoints/{MODEL_NAME}/{DATASET_NAME}_{NUM_EPOCHS}_{INPUT_LEN}_{OUTPUT_LEN}'
-CFG.TRAIN.CKPT_SAVE_STRATEGY = None # 检查点保存策略。`CFG.TRAIN.CKPT_SAVE_STRATEGY` 可以是 None、整数值、列表或元组。默认值：None。None：每个 epoch 移除最后一个检查点文件。整数：每隔 `CFG.TRAIN.CKPT_SAVE_STRATEGY` 个 epoch 保存一次检查点。列表或元组：当 epoch 在 `CFG.TRAIN.CKPT_SAVE_STRATEGY` 中时保存检查点，当 last_epoch 不在 ckpt_save_strategy 中时移除最后一个检查点文件。“移除”操作是将最后一个检查点文件重命名为 .bak 文件，BasicTS会每个10个epoch清空一次.bak文件。
+)
+# 检查点保存策略。`CFG.TRAIN.CKPT_SAVE_STRATEGY` 可以是 None、整数值、列表或元组。默认值：None。
+# None：每个 epoch 移除最后一个检查点文件。整数：每隔 `CFG.TRAIN.CKPT_SAVE_STRATEGY` 个 epoch 保存一次检查点。
+# 列表或元组：当 epoch 在 `CFG.TRAIN.CKPT_SAVE_STRATEGY` 中时保存检查点，当 last_epoch 不在 ckpt_save_strategy 中时移除最后一个检查点文件。
+# “移除”操作是将最后一个检查点文件重命名为 .bak 文件，BasicTS会每个10个epoch清空一次.bak文件。
+CFG.TRAIN.CKPT_SAVE_STRATEGY = None
 CFG.TRAIN.FINETUNE_FROM = None # 微调的检查点路径。默认值：None。如果未指定，模型将从头开始训练。
 CFG.TRAIN.STRICT_LOAD = True # 是否严格加载检查点。默认值：True。
 
@@ -213,5 +221,7 @@
 CFG.EVAL = EasyDict()
 
 # 评估参数
-CFG.EVAL.HORIZONS = [3, 6, 12]  # 评估时的预测时间范围。默认值为 []。注意：HORIZONS[i] 指的是在 ”第 i 个时间片“ 上进行测试，表示该时间片的损失（Loss）。这是时空预测中的常见配置。对于长序列预测，建议将 HORIZONS 保持为默认值 []，以避免引发误解。
+# 评估时的预测时间范围。默认值为 []。注意：HORIZONS[i] 指的是在 ”第 i 个时间片“ 上进行测试，表示该时间片的损失（Loss）。
+# 这是时空预测中的常见配置。对于长序列预测，建议将 HORIZONS 保持为默认值 []，以避免引发误解。
+CFG.EVAL.HORIZONS = []
 CFG.EVAL.USE_GPU = True # 是否在评估时使用 GPU。默认值：True

examples/regular_config.py

@@ -1,15 +1,13 @@
-import os
-import sys
-from easydict import EasyDict
-sys.path.append(os.path.abspath(__file__ + '/../../..'))
-
 from basicts.metrics import masked_mae, masked_mape, masked_rmse
 from basicts.data import TimeSeriesForecastingDataset
 from basicts.runners import SimpleTimeSeriesForecastingRunner
 from basicts.scaler import ZScoreScaler
 from basicts.utils import get_regular_settings
 from .arch import MultiLayerPerceptron as MLP
 
+import os
+from easydict import EasyDict
+
 ############################## Hot Parameters ##############################
 # Dataset & Metrics configuration
 DATA_NAME = 'PEMS08'  # Dataset name

experiments/evaluate.py

@@ -1,3 +1,4 @@
+# pylint: disable=wrong-import-position
 import os
 import sys
 from argparse import ArgumentParser

experiments/train.py

@@ -1,4 +1,5 @@
 # Run a baseline model in BasicTS framework.
+# pylint: disable=wrong-import-position
 import os
 import sys
 from argparse import ArgumentParser
@@ -23,5 +24,5 @@ def main():
     basicts.launch_training(args.cfg, args.gpus, node_rank=0)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()