[ENH] sort import

abl/__init__.py

@@ -1,2 +1,2 @@
 from .learning import abl_model, basic_nn
-from .reasoning import reasoner, kb
+from .reasoning import kb, reasoner

abl/bridge/base_bridge.py

@@ -1,11 +1,10 @@
 from abc import ABCMeta, abstractmethod
-from typing import Any, List, Tuple, Optional, Union
+from typing import Any, List, Optional, Tuple, Union
 
 from ..learning import ABLModel
 from ..reasoning import ReasonerBase
 from ..structures import ListData
 
-
 DataSet = Tuple[List[List[Any]], Optional[List[List[Any]]], List[List[Any]]]
 
 
@@ -37,6 +36,7 @@ class BaseBridge(metaclass=ABCMeta):
     @abstractmethod
     def abduce_pseudo_label(self, data_samples: ListData) -> List[List[Any]]:
         """Placeholder for abduce pseudo labels."""
+        pass
 
     @abstractmethod
     def idx_to_pseudo_label(self, data_samples: ListData) -> List[List[Any]]:

abl/dataset/bridge_dataset.py

@@ -1,5 +1,6 @@
+from typing import Any, List, Tuple
+
 from torch.utils.data import Dataset
-from typing import List, Any, Tuple
 
 
 class BridgeDataset(Dataset):

abl/dataset/classification_dataset.py

@@ -1,6 +1,7 @@
+from typing import Any, Callable, List, Tuple
+
 import torch
 from torch.utils.data import Dataset
-from typing import List, Any, Tuple, Callable
 
 
 class ClassificationDataset(Dataset):

abl/dataset/regression_dataset.py

@@ -1,6 +1,7 @@
+from typing import Any, List, Tuple
+
 import torch
 from torch.utils.data import Dataset
-from typing import List, Any, Tuple
 
 
 class RegressionDataset(Dataset):

abl/evaluation/__init__.py

@@ -1,3 +1,3 @@
 from .base_metric import BaseMetric
-from .symbol_metric import SymbolMetric
 from .semantics_metric import SemanticsMetric
+from .symbol_metric import SymbolMetric

abl/evaluation/base_metric.py

@@ -1,8 +1,8 @@
+import logging
 from abc import ABCMeta, abstractmethod
 from typing import Any, List, Optional, Sequence
-from ..utils import print_log
 
-import logging
+from ..utils import print_log
 
 
 class BaseMetric(metaclass=ABCMeta):

abl/evaluation/semantics_metric.py

@@ -1,4 +1,5 @@
 from typing import Optional, Sequence
+
 from .base_metric import BaseMetric
 
 

abl/evaluation/symbol_metric.py

@@ -1,4 +1,5 @@
-from typing import Optional, Sequence, Callable
+from typing import Callable, Optional, Sequence
+
 from .base_metric import BaseMetric
 
 

abl/learning/basic_nn.py

@@ -10,14 +10,15 @@
 #
 # ================================================================#
 
-import torch
+import os
+from typing import Any, Callable, List, Optional, T, Tuple
+
 import numpy
+import torch
 from torch.utils.data import DataLoader
-from ..utils.logger import print_log
-from ..dataset import ClassificationDataset
 
-import os
-from typing import List, Any, T, Optional, Callable, Tuple
+from ..dataset import ClassificationDataset
+from ..utils.logger import print_log
 
 
 class BasicNN:

abl/reasoning/__init__.py

@@ -1,2 +1,2 @@
+from .kb import KBBase, ground_KB, prolog_KB
 from .reasoner import ReasonerBase
-from .kb import KBBase, prolog_KB

abl/reasoning/kb.py

@@ -1,24 +1,16 @@
-from abc import ABC, abstractmethod
 import bisect
-import numpy as np
-
+from abc import ABC, abstractmethod
 from collections import defaultdict
-from itertools import product, combinations
-
-from ..utils.utils import (
-    flatten,
-    reform_idx,
-    hamming_dist,
-    check_equal,
-    to_hashable,
-    hashable_to_list,
-)
-
+from functools import lru_cache
+from itertools import combinations, product
 from multiprocessing import Pool
 
-from functools import lru_cache
+import numpy as np
 import pyswip
 
+from ..utils.utils import (check_equal, flatten, hamming_dist,
+                           hashable_to_list, reform_idx, to_hashable)
+
 
 class KBBase(ABC):
     def __init__(self, pseudo_label_list, max_err=0, use_cache=True):

abl/reasoning/reasoner.py

@@ -1,12 +1,8 @@
 import numpy as np
-from zoopt import Dimension, Objective, Parameter, Opt
-from ..utils.utils import (
-    confidence_dist,
-    flatten,
-    reform_idx,
-    hamming_dist,
-    calculate_revision_num,
-)
+from zoopt import Dimension, Objective, Opt, Parameter
+
+from ..utils.utils import (calculate_revision_num, confidence_dist, flatten,
+                           hamming_dist, reform_idx)
 
 
 class ReasonerBase:

abl/utils/utils.py

@@ -1,6 +1,7 @@
-import numpy as np
 from itertools import chain
 
+import numpy as np
+
 
 def flatten(nested_list):
     """

docs/conf.py

@@ -1,8 +1,8 @@
 # -*- coding: utf-8 -*-
 
-import sys
 import os
 import re
+import sys
 
 if not 'READTHEDOCS' in os.environ:
     sys.path.insert(0, os.path.abspath('..'))
@@ -11,7 +11,6 @@ sys.path.append(os.path.abspath('./ABL/'))
 # from sphinx.locale import _
 from sphinx_rtd_theme import __version__
 
-
 project = u'ABL'
 slug = re.sub(r'\W+', '-', project.lower())
 author = u'Yu-Xuan Huang, Wen-Chao Hu, En-Hao Gao'

examples/hed/hed_bridge.py

@@ -1,18 +1,18 @@
 import os
 from collections import defaultdict
+
 import torch
 from torch.utils.data import DataLoader
 
-from abl.reasoning import ReasonerBase
-from abl.learning import ABLModel, BasicNN
 from abl.bridge import SimpleBridge
-from abl.evaluation import BaseMetric
 from abl.dataset import BridgeDataset, RegressionDataset
+from abl.evaluation import BaseMetric
+from abl.learning import ABLModel, BasicNN
+from abl.reasoning import ReasonerBase
 from abl.utils import print_log
-
-from examples.hed.utils import gen_mappings, InfiniteSampler
-from examples.models.nn import SymbolNetAutoencoder
 from examples.hed.datasets.get_hed import get_pretrain_data
+from examples.hed.utils import InfiniteSampler, gen_mappings
+from examples.models.nn import SymbolNetAutoencoder
 
 
 class HEDBridge(SimpleBridge):

examples/hed/utils.py

@@ -1,6 +1,6 @@
+import numpy as np
 import torch
 import torch.nn as nn
-import numpy as np
 import torch.utils.data.sampler as sampler
 
 

examples/mnist_add/datasets/get_mnist_add.py

@@ -1,6 +1,7 @@
 import torchvision
 from torchvision.transforms import transforms
 
+
 def get_data(file, img_dataset, get_pseudo_label):
     X = []
     if get_pseudo_label:

examples/models/nn.py

@@ -11,8 +11,8 @@
 # ================================================================#
 
 
-import torch
 import numpy as np
+import torch
 from torch import nn
 
 

setup.py

@@ -1,4 +1,5 @@
 import os
+
 from setuptools import find_packages, setup