pre-commits

.pre-commit-config.yaml

@@ -1,19 +1,11 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.6.0
     hooks:
       - id: check-yaml
       - id: end-of-file-fixer
       - id: trailing-whitespace
 
-  - repo: https://github.com/pycqa/flake8
-    rev: 7.1.1
-    hooks:
-      - id: flake8
-        additional_dependencies:
-          - flake8-isort
-        args: [--max-line-length=88]
-
   - repo: https://github.com/pycqa/isort
     rev: 5.13.2
     hooks:
@@ -25,12 +17,5 @@ repos:
     rev: 24.8.0
     hooks:
       - id: black
+        args: [--line-length=88]
         language_version: python3.8
-
-  - repo: https://github.com/pycqa/autoflake
-    rev: v1.6.0
-    hooks:
-      - id: autoflake
-        name: autoflake (remove unused imports and variables)
-        args: ['--in-place', '--remove-all-unused-imports', '--remove-unused-variables']
-        types: [python]

datasets/openpower.py

@@ -1,12 +1,14 @@
 import os
 import warnings
-from typing import Dict, List, Tuple
+from typing import Dict
+from typing import List
+from typing import Tuple
 
 import numpy as np
 import pandas as pd
 import torch
 import yaml
-from torch.utils.data import DataLoader, Dataset
+from torch.utils.data import Dataset
 
 warnings.filterwarnings("ignore", category=pd.errors.SettingWithCopyWarning)
 ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))

eval/discriminative_metric.py

@@ -18,7 +18,8 @@
 Output: discriminative score (np.abs(classification accuracy - 0.5))
 """
 
-from typing import List, Tuple
+from typing import List
+from typing import Tuple
 
 import numpy as np
 import torch
@@ -109,7 +110,7 @@ def discriminative_score_metrics(
     # Extract time information
     ori_time, ori_max_seq_len = extract_time(ori_data)
     generated_time, generated_max_seq_len = extract_time(generated_data)
-    max_seq_len = max(ori_max_seq_len, generated_max_seq_len)
+    max(ori_max_seq_len, generated_max_seq_len)
 
     # Network parameters
     hidden_dim = int(dim * 2)

eval/evaluator.py

@@ -1,19 +1,27 @@
-from typing import Any, Dict, List, Tuple
+from typing import Any
+from typing import Dict
+from typing import List
+from typing import Tuple
 
 import numpy as np
 import pandas as pd
 import torch
 from torch.utils.tensorboard import SummaryWriter
 
 from eval.discriminative_metric import discriminative_score_metrics
-from eval.metrics import (
-    Context_FID, calculate_mmd, calculate_period_bound_mse, dynamic_time_warping_dist,
-    plot_range_with_syn_values, plot_syn_with_closest_real_ts, visualization,)
+from eval.metrics import Context_FID
+from eval.metrics import calculate_mmd
+from eval.metrics import calculate_period_bound_mse
+from eval.metrics import dynamic_time_warping_dist
+from eval.metrics import plot_range_with_syn_values
+from eval.metrics import plot_syn_with_closest_real_ts
+from eval.metrics import visualization
 from eval.predictive_metric import predictive_score_metrics
 from generator.diffcharge.diffusion import DDPM
 from generator.diffusion_ts.gaussian_diffusion import Diffusion_TS
 from generator.gan.acgan import ACGAN
-from generator.llm.llm import GPT, HF
+from generator.llm.llm import GPT
+from generator.llm.llm import HF
 from generator.options import Options
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -458,9 +466,14 @@ def _log_final_results(self):
         """
         Log the final evaluation results.
         """
-        dtw_mean, mmd_mean, mse_mean, fid_mean, discr_mean, pred_mean = (
-            self.get_summary_metrics()
-        )
+        (
+            dtw_mean,
+            mmd_mean,
+            mse_mean,
+            fid_mean,
+            discr_mean,
+            pred_mean,
+        ) = self.get_summary_metrics()
 
         self.writer.add_scalar("Final_Results/DTW", dtw_mean)
         self.writer.add_scalar("Final_Results/MMD", mmd_mean)

eval/loss.py

@@ -3,7 +3,6 @@
 import numpy as np
 import torch
 import torch.nn.functional as F
-from torch import nn
 
 
 def compute_pairwise_distances(x, y):

eval/metrics.py

@@ -1,5 +1,5 @@
 from functools import partial
-from typing import Callable, List, Tuple
+from typing import Tuple
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -10,7 +10,8 @@
 from sklearn.decomposition import PCA
 from sklearn.manifold import TSNE
 
-from eval.loss import gaussian_kernel_matrix, maximum_mean_discrepancy
+from eval.loss import gaussian_kernel_matrix
+from eval.loss import maximum_mean_discrepancy
 from eval.t2vec.t2vec import TS2Vec
 
 

eval/predictive_metric.py

@@ -47,7 +47,7 @@ def predictive_score_metrics(ori_data, generated_data):
 
     ori_time, ori_max_seq_len = extract_time(ori_data)
     generated_time, generated_max_seq_len = extract_time(generated_data)
-    max_seq_len = max([ori_max_seq_len, generated_max_seq_len])
+    max([ori_max_seq_len, generated_max_seq_len])
 
     hidden_dim = max(int(dim / 2), 1)
     iterations = 5000

eval/t2vec/dilated_conv.py

@@ -11,8 +11,6 @@
 Note: Please ensure compliance with the repository's license and credit the original authors when using or distributing this code.
 """
 
-import numpy as np
-import torch
 import torch.nn.functional as F
 from torch import nn
 

eval/t2vec/encoder.py

@@ -13,7 +13,6 @@
 
 import numpy as np
 import torch
-import torch.nn.functional as F
 from torch import nn
 
 from eval.t2vec.dilated_conv import DilatedConvEncoder

eval/t2vec/t2vec.py

@@ -14,12 +14,15 @@
 import numpy as np
 import torch
 import torch.nn.functional as F
-from torch.utils.data import DataLoader, TensorDataset
+from torch.utils.data import DataLoader
+from torch.utils.data import TensorDataset
 
 from eval.loss import hierarchical_contrastive_loss
 from eval.t2vec.encoder import TSEncoder
-from eval.t2vec.utils import (
-    centerize_vary_length_series, split_with_nan, take_per_row, torch_pad_nan,)
+from eval.t2vec.utils import centerize_vary_length_series
+from eval.t2vec.utils import split_with_nan
+from eval.t2vec.utils import take_per_row
+from eval.t2vec.utils import torch_pad_nan
 
 
 class TS2Vec:

eval/t2vec/utils.py

@@ -11,7 +11,6 @@
 Note: Please ensure compliance with the repository's license and credit the original authors when using or distributing this code.
 """
 
-import os
 import pickle
 import random
 from datetime import datetime

generator/conditioning.py

@@ -1,6 +1,5 @@
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 
 
 class ConditioningModule(nn.Module):

generator/diffusion_ts/model_utils.py

@@ -12,14 +12,10 @@
 """
 
 import math
-from functools import partial
 
-import scipy
 import torch
 import torch.nn.functional as F
-from einops import rearrange, reduce
-from scipy.fftpack import next_fast_len
-from torch import einsum, nn
+from torch import nn
 
 
 def exists(x):

generator/diffusion_ts/transformer.py

@@ -16,11 +16,17 @@
 import numpy as np
 import torch
 import torch.nn.functional as F
-from einops import rearrange, reduce, repeat
+from einops import rearrange
+from einops import reduce
+from einops import repeat
 from torch import nn
 
-from generator.diffusion_ts.model_utils import (
-    GELU2, AdaLayerNorm, Conv_MLP, LearnablePositionalEncoding, Transpose, series_decomp,)
+from generator.diffusion_ts.model_utils import GELU2
+from generator.diffusion_ts.model_utils import AdaLayerNorm
+from generator.diffusion_ts.model_utils import Conv_MLP
+from generator.diffusion_ts.model_utils import LearnablePositionalEncoding
+from generator.diffusion_ts.model_utils import Transpose
+from generator.diffusion_ts.model_utils import series_decomp
 
 
 class TrendBlock(nn.Module):

main.py

@@ -1,4 +1,3 @@
-from datasets.openpower import OpenPowerDataset
 from datasets.pecanstreet import PecanStreetDataManager
 from eval.evaluator import Evaluator
 

setup.cfg

@@ -36,11 +36,10 @@ exclude = docs, .tox, .git, __pycache__, .ipynb_checkpoints
 ignore = W503
 
 [isort]
-include_trailing_comma = True
-line_length=99
-lines_between_types = 0
-multi_line_output = 4
-use_parentheses = True
+profile = black
+line_length = 88
+combine_as_imports = true
+force_single_line = true
 
 [aliases]
 test = pytest

tests/test_endata.py

@@ -6,17 +6,11 @@
 import os
 import unittest
 from typing import List
-from unittest.mock import MagicMock, patch
 
 import numpy as np
-import pandas as pd
-import torch
 from sklearn.metrics import mean_squared_error
 
 from datasets.pecanstreet import PecanStreetDataManager
-from eval.evaluator import Evaluator
-from generator.gan.acgan import ACGAN
-from generator.options import Options
 
 TEST_CONFIG_PATH = os.path.join(
     os.path.dirname(os.path.abspath(__file__)), "test_data_config.yaml"

tests/testdata/15minute_data_austin.csv

@@ -19986,4 +19986,4 @@ dataid,local_15min,air1,air2,air3,airwindowunit1,aquarium1,bathroom1,bathroom2,b
 1642,2018-04-14 23:15:00-05,0.002,,,,,0.001,,,,,,,,-0.001,,,0.313,,,,0.000,,-0.001,0.000,,0.091,,,,1.333,,,,,,,,,0.000,0.001,,,,,,,0.492,,0.003,,,,0.008,,,,,,,,0.170,,,,,-0.009,,,,-0.007,0.000,,,,,123.188,123.062
 1642,2018-04-14 23:30:00-05,0.002,,,,,0.002,,,,,,,,0.000,,,0.293,,,,0.000,,-0.002,0.000,,0.185,,,,1.071,,,,,,,,,0.000,0.001,,,,,,,0.219,,0.004,,,,0.008,,,,,,,,0.168,,,,,-0.011,,,,-0.018,0.010,,,,,123.381,123.072
 1642,2018-04-14 23:45:00-05,0.001,,,,,0.002,,,,,,,,0.001,,,0.294,,,,0.000,,-0.002,0.000,,0.101,,,,0.787,,,,,,,,,0.000,0.001,,,,,,,0.030,,0.004,,,,0.008,,,,,,,,0.167,,,,,-0.011,,,,0.000,0.000,,,,,123.616,123.212
-1642,2018-04-15 00:00:00-05,0.002,,,,,0.002,,,,,,,,0.001,,,0.296,,,,0.000,,-0.003,0.000,,0.231,,,,0.893,,,,,,,,,0.000,0.002,,,,,,,0.030,,0.004,,,,0.008,,,,,,,,0.166,,,,,-0.011,,,,0.000,0.000,,,,,123.829,123.346
+1642,2018-04-15 00:00:00-05,0.002,,,,,0.002,,,,,,,,0.001,,,0.296,,,,0.000,,-0.003,0.000,,0.231,,,,0.893,,,,,,,,,0.000,0.002,,,,,,,0.030,,0.004,,,,0.008,,,,,,,,0.166,,,,,-0.011,,,,0.000,0.000,,,,,123.829,123.346

tests/testdata/15minute_data_california.csv

@@ -6714,4 +6714,4 @@ dataid,local_15min,air1,air2,air3,airwindowunit1,aquarium1,bathroom1,bathroom2,b
 3687,2014-03-11 23:00:00-05,0.000,,,,,,,,,,,,,,,,,0.004,,,0.000,0.000,,,,0.004,,,,1.049,,,,,,,,,0.005,,,,,,,,0.423,,0.006,,,,0.001,,,,,,,,0.373,,,,,,,,,,,,,,,120.271,121.162
 3687,2014-03-11 23:15:00-05,0.000,,,,,,,,,,,,,,,,,0.004,,,0.000,0.000,,,,0.004,,,,0.893,,,,,,,,,0.004,,,,,,,,0.435,,0.006,,,,0.000,,,,,,,,0.241,,,,,,,,,,,,,,,120.520,121.225
 3687,2014-03-11 23:30:00-05,0.000,,,,,,,,,,,,,,,,,0.004,,,0.000,0.000,,,,0.004,,,,1.145,,,,,,,,,0.307,,,,,,,,0.465,,0.006,,,,0.002,,,,,,,,0.127,,,,,,,,,,,,,,,120.504,121.450
-3687,2014-03-11 23:45:00-05,0.000,,,,,,,,,,,,,,,,,0.004,,,0.000,0.000,,,,0.004,,,,0.883,,,,,,,,,0.009,,,,,,,,0.422,,0.006,,,,0.000,,,,,,,,0.181,,,,,,,,,,,,,,,120.497,121.445
+3687,2014-03-11 23:45:00-05,0.000,,,,,,,,,,,,,,,,,0.004,,,0.000,0.000,,,,0.004,,,,0.883,,,,,,,,,0.009,,,,,,,,0.422,,0.006,,,,0.000,,,,,,,,0.181,,,,,,,,,,,,,,,120.497,121.445