[MNT] sort import of python files

abl/bridge/base_bridge.py

@@ -1,9 +1,9 @@
 from abc import ABCMeta, abstractmethod
 from typing import Any, List, Optional, Tuple, Union
 
+from ..data.structures import ListData
 from ..learning import ABLModel
 from ..reasoning import Reasoner
-from ..data.structures import ListData
 
 
 class BaseBridge(metaclass=ABCMeta):

abl/bridge/simple_bridge.py

@@ -4,9 +4,9 @@ from typing import Any, List, Optional, Tuple, Union
 from numpy import ndarray
 
 from ..data.evaluation import BaseMetric
+from ..data.structures import ListData
 from ..learning import ABLModel
 from ..reasoning import Reasoner
-from ..data.structures import ListData
 from ..utils import print_log
 from .base_bridge import BaseBridge
 

abl/data/__init__.py

@@ -1,2 +1,4 @@
-from .evaluation import *
-from .structures import *
+from .evaluation import BaseMetric, ReasoningMetric, SymbolAccuracy
+from .structures import ListData
+
+__all__ = ["BaseMetric", "ReasoningMetric", "SymbolAccuracy", "ListData"]

abl/data/evaluation/base_metric.py

@@ -2,8 +2,8 @@ import logging
 from abc import ABCMeta, abstractmethod
 from typing import Any, List, Optional
 
-from ..structures import ListData
 from ...utils import print_log
+from ..structures import ListData
 
 
 class BaseMetric(metaclass=ABCMeta):

abl/learning/__init__.py

@@ -1,5 +1,5 @@
 from .abl_model import ABLModel
 from .basic_nn import BasicNN
-from .torch_dataset import *
+from .torch_dataset import ClassificationDataset, PredictionDataset, RegressionDataset
 
-__all__ = ["ABLModel", "BasicNN"]
+__all__ = ["ABLModel", "BasicNN", "ClassificationDataset", "PredictionDataset", "RegressionDataset"]

abl/learning/basic_nn.py

@@ -8,8 +8,8 @@ import numpy
 import torch
 from torch.utils.data import DataLoader
 
-from .torch_dataset import ClassificationDataset, PredictionDataset
 from ..utils.logger import print_log
+from .torch_dataset import ClassificationDataset, PredictionDataset
 
 
 class BasicNN:

abl/reasoning/kb.py

@@ -1,17 +1,17 @@
 import bisect
-import os
 import inspect
 import logging
+import os
 from abc import ABC, abstractmethod
 from collections import defaultdict
 from itertools import combinations, product
 from multiprocessing import Pool
-from typing import Callable, Any, List, Optional
+from typing import Any, Callable, List, Optional
 
 import numpy as np
 
-from ..utils.logger import print_log
 from ..utils.cache import abl_cache
+from ..utils.logger import print_log
 from ..utils.utils import flatten, hamming_dist, reform_list, to_hashable
 
 

abl/reasoning/reasoner.py

@@ -1,11 +1,11 @@
 import inspect
-from typing import Callable, Any, List, Optional, Union
+from typing import Any, Callable, List, Optional, Union
 
 import numpy as np
 from zoopt import Dimension, Objective, Opt, Parameter, Solution
 
-from ..reasoning import KBBase
 from ..data.structures import ListData
+from ..reasoning import KBBase
 from ..utils.utils import confidence_dist, hamming_dist
 
 

abl/utils/__init__.py

@@ -1,12 +1,6 @@
 from .cache import Cache, abl_cache
 from .logger import ABLLogger, print_log
-from .utils import (
-    confidence_dist,
-    flatten,
-    hamming_dist,
-    reform_list,
-    to_hashable,
-)
+from .utils import confidence_dist, flatten, hamming_dist, reform_list, to_hashable
 
 __all__ = [
     "Cache",

docs/conf.py

@@ -1,9 +1,11 @@
 import os
 import re
 import sys
+
 from docutils import nodes
 from docutils.parsers.rst import roles
 
+
 def colored_text_role(role, rawtext, text, lineno, inliner, options={}, content=[]):
     node = nodes.inline(rawtext, text, classes=[role])
     return [node], []

examples/hed/bridge.py

@@ -5,15 +5,16 @@ from typing import Any, List, Optional, Tuple, Union
 import torch
 
 from abl.bridge import SimpleBridge
-from abl.learning.torch_dataset import RegressionDataset
 from abl.data.evaluation import BaseMetric
+from abl.data.structures import ListData
 from abl.learning import ABLModel, BasicNN
+from abl.learning.torch_dataset import RegressionDataset
 from abl.reasoning import Reasoner
-from abl.data.structures import ListData
 from abl.utils import print_log
+
 from datasets import get_pretrain_data
-from utils import InfiniteSampler, gen_mappings
 from models.nn import SymbolNetAutoencoder
+from utils import InfiniteSampler, gen_mappings
 
 
 class HedBridge(SimpleBridge):

examples/hed/consistency_metric.py

@@ -1,8 +1,8 @@
 from typing import Optional
 
-from abl.reasoning import KBBase
-from abl.data.structures import ListData
 from abl.data.evaluation.base_metric import BaseMetric
+from abl.data.structures import ListData
+from abl.reasoning import KBBase
 
 
 class ConsistencyMetric(BaseMetric):

examples/hed/datasets/__init__.py

@@ -1,4 +1,3 @@
 from .get_dataset import get_dataset, get_pretrain_data, split_equation
 
-
-__all__ = ["get_dataset", "get_pretrain_data", "split_equation"]
+__all__ = ["get_dataset", "get_pretrain_data", "split_equation"]

examples/hed/datasets/get_dataset.py

@@ -2,11 +2,11 @@ import os
 import os.path as osp
 import pickle
 import random
-import gdown
 import zipfile
 from collections import defaultdict
 
 import cv2
+import gdown
 import numpy as np
 from torchvision.transforms import transforms
 

examples/hed/main.py

@@ -1,16 +1,17 @@
-import os.path as osp
 import argparse
+import os.path as osp
 
 import torch
 import torch.nn as nn
 
-from datasets import get_dataset, split_equation
-from models.nn import SymbolNet
 from abl.learning import ABLModel, BasicNN
-from reasoning import HedKB, HedReasoner
-from consistency_metric import ConsistencyMetric
 from abl.utils import ABLLogger, print_log
+
 from bridge import HedBridge
+from consistency_metric import ConsistencyMetric
+from datasets import get_dataset, split_equation
+from models.nn import SymbolNet
+from reasoning import HedKB, HedReasoner
 
 
 def main():

examples/hed/reasoning/reasoning.py

@@ -1,6 +1,8 @@
+import math
 import os
+
 import numpy as np
-import math
+
 from abl.reasoning import PrologKB, Reasoner
 from abl.utils import reform_list
 

examples/hwf/datasets/get_dataset.py

@@ -1,8 +1,8 @@
 import json
 import os
-import gdown
 import zipfile
 
+import gdown
 from PIL import Image
 from torchvision.transforms import transforms
 

examples/hwf/main.py

@@ -5,13 +5,14 @@ import numpy as np
 import torch
 from torch import nn
 
-from datasets import get_dataset
-from models.nn import SymbolNet
-from abl.learning import ABLModel, BasicNN
-from abl.reasoning import KBBase, GroundKB, Reasoner
+from abl.bridge import SimpleBridge
 from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
+from abl.learning import ABLModel, BasicNN
+from abl.reasoning import GroundKB, KBBase, Reasoner
 from abl.utils import ABLLogger, print_log
-from abl.bridge import SimpleBridge
+
+from datasets import get_dataset
+from models.nn import SymbolNet
 
 
 class HwfKB(KBBase):

examples/mnist_add/main.py

@@ -5,13 +5,14 @@ import torch
 from torch import nn
 from torch.optim import RMSprop, lr_scheduler
 
-from datasets import get_dataset
-from models.nn import LeNet5
+from abl.bridge import SimpleBridge
+from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
 from abl.learning import ABLModel, BasicNN
 from abl.reasoning import GroundKB, KBBase, PrologKB, Reasoner
-from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
 from abl.utils import ABLLogger, print_log
-from abl.bridge import SimpleBridge
+
+from datasets import get_dataset
+from models.nn import LeNet5
 
 
 class AddKB(KBBase):

examples/zoo/get_dataset.py

@@ -1,6 +1,7 @@
 import numpy as np
 import openml
 
+
 # Function to load and preprocess the dataset
 def load_and_preprocess_dataset(dataset_id):
     dataset = openml.datasets.get_dataset(dataset_id, download_data=True, download_qualities=False, download_features_meta_data=False)

examples/zoo/kb.py

@@ -1,7 +1,9 @@
-from z3 import Solver, Int, If, Not, Implies, Sum, sat
 import openml
+from z3 import If, Implies, Int, Not, Solver, Sum, sat  # noqa: F401
+
 from abl.reasoning import KBBase
 
+
 class ZooKB(KBBase):
     def __init__(self):
         super().__init__(pseudo_label_list=list(range(7)), use_cache=False)

examples/zoo/main.py

@@ -1,16 +1,18 @@
-import os.path as osp
 import argparse
+import os.path as osp
 
 import numpy as np
 from sklearn.ensemble import RandomForestClassifier
 
-from get_dataset import load_and_preprocess_dataset, split_dataset
+from abl.bridge import SimpleBridge
+from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
 from abl.learning import ABLModel
-from kb import ZooKB
 from abl.reasoning import Reasoner
-from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
-from abl.utils import ABLLogger, print_log, confidence_dist
-from abl.bridge import SimpleBridge
+from abl.utils import ABLLogger, confidence_dist, print_log
+
+from get_dataset import load_and_preprocess_dataset, split_dataset
+from kb import ZooKB
+
 
 def transform_tab_data(X, y):
     return ([[x] for x in X], [[y_item] for y_item in y], [0] * len(y))

tests/conftest.py

@@ -1,12 +1,12 @@
-import pytest
 import numpy as np
+import pytest
 import torch
 import torch.nn as nn
 import torch.optim as optim
 
+from abl.data.structures import ListData
 from abl.learning import BasicNN
 from abl.reasoning import GroundKB, KBBase, PrologKB, Reasoner
-from abl.data.structures import ListData
 
 
 class LeNet5(nn.Module):

tests/test_basic_nn.py

@@ -5,6 +5,7 @@ import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import DataLoader, TensorDataset
 
+
 class TestBasicNN(object):
     @pytest.fixture
     def sample_data(self):

tests/test_reasoning.py

@@ -1,5 +1,5 @@
-import pytest
 import numpy as np
+import pytest
 
 from abl.reasoning import PrologKB, Reasoner