Sort imports

docs/source/conf.py

@@ -25,9 +25,7 @@
 )
 author = "Diego Baptista Theuerkauf"
 
-from probinet.version import (
-    __version__,
-)  # noqa: E402 # pylint: disable=wrong-import-position
+from probinet.version import __version__  # noqa: E402 # pylint: disable=wrong-import-position
 
 version = __version__
 

probinet/evaluation/likelihood.py

@@ -8,10 +8,8 @@
 import pandas as pd
 
 from probinet.evaluation.expectation_computation import (
-    compute_expected_adjacency_tensor,
-    compute_expected_adjacency_tensor_multilayer,
-    compute_mean_lambda0,
-)
+    compute_expected_adjacency_tensor, compute_expected_adjacency_tensor_multilayer,
+    compute_mean_lambda0)
 from probinet.utils.tools import log_and_raise_error
 
 

probinet/evaluation/link_prediction.py

@@ -8,9 +8,7 @@
 import numpy as np
 from sklearn import metrics
 
-from probinet.evaluation.expectation_computation import (
-    compute_expected_adjacency_tensor_multilayer,
-)
+from probinet.evaluation.expectation_computation import compute_expected_adjacency_tensor_multilayer
 
 
 @singledispatch

probinet/input/loader.py

@@ -2,25 +2,22 @@
 Functions for handling the data.
 """
 
-import logging
+import csv
 from importlib.resources import files
+import logging
 from os import PathLike
 from pathlib import Path
 from typing import Any, Optional, Union
 
 import networkx as nx
 import numpy as np
 import pandas as pd
-import csv
-
 
-from .preprocessing import (
-    create_adjacency_tensor_from_graph_list,
-    create_sparse_adjacency_tensor_from_graph_list,
-)
-from .stats import print_graph_stats
 from ..models.classes import GraphData
 from ..utils.tools import log_and_raise_error
+from .preprocessing import (
+    create_adjacency_tensor_from_graph_list, create_sparse_adjacency_tensor_from_graph_list)
+from .stats import print_graph_stats
 
 
 def build_adjacency_from_networkx(

probinet/model_selection/acd_cross_validation.py

@@ -7,10 +7,7 @@
 
 import numpy as np
 
-from probinet.evaluation.expectation_computation import (
-    calculate_expectation_acd,
-    calculate_Q_dense,
-)
+from probinet.evaluation.expectation_computation import calculate_expectation_acd, calculate_Q_dense
 
 from ..evaluation.expectation_computation import compute_mean_lambda0
 from ..evaluation.link_prediction import compute_link_prediction_AUC

probinet/model_selection/cross_validation.py

@@ -18,16 +18,11 @@
 import numpy as np
 
 from probinet.evaluation.expectation_computation import (
-    calculate_conditional_expectation,
-    calculate_expectation,
-)
+    calculate_conditional_expectation, calculate_expectation)
 from probinet.evaluation.likelihood import calculate_opt_func
 from probinet.evaluation.link_prediction import compute_link_prediction_AUC
 from probinet.input.loader import build_adjacency_from_file
-from probinet.model_selection.masking import (
-    extract_mask_kfold,
-    shuffle_indices_all_matrix,
-)
+from probinet.model_selection.masking import extract_mask_kfold, shuffle_indices_all_matrix
 from probinet.models.classes import GraphData
 
 

probinet/model_selection/dyncrep_cross_validation.py

@@ -8,9 +8,7 @@
 
 import numpy as np
 
-from probinet.evaluation.expectation_computation import (
-    calculate_conditional_expectation_dyncrep,
-)
+from probinet.evaluation.expectation_computation import calculate_conditional_expectation_dyncrep
 from probinet.evaluation.likelihood import likelihood_conditional
 from probinet.evaluation.link_prediction import compute_link_prediction_AUC
 from probinet.model_selection.cross_validation import CrossValidation

probinet/model_selection/masking.py

@@ -2,7 +2,7 @@
 This module provides functions for shuffling indices and extracting masks for selecting the held-out set in the adjacency tensor and design matrix.
 """
 
-from typing import List, Tuple, Sequence
+from typing import List, Sequence, Tuple
 
 import numpy as np
 

probinet/model_selection/mtcov_cross_validation.py

@@ -8,17 +8,11 @@
 
 import numpy as np
 
-from probinet.evaluation.expectation_computation import (
-    compute_covariate_prediction_accuracy,
-)
+from probinet.evaluation.expectation_computation import compute_covariate_prediction_accuracy
 from probinet.evaluation.likelihood import loglikelihood
 from probinet.evaluation.link_prediction import compute_multilayer_link_prediction_AUC
 from probinet.model_selection.cross_validation import CrossValidation
-from probinet.model_selection.masking import (
-    extract_masks,
-    shuffle_indicesG,
-    shuffle_indicesX,
-)
+from probinet.model_selection.masking import extract_masks, shuffle_indicesG, shuffle_indicesX
 from probinet.models.classes import GraphData
 from probinet.models.mtcov import MTCOV
 

probinet/models/acd.py

@@ -5,38 +5,26 @@
 """
 
 import logging
-import time
 from os import PathLike
 from pathlib import Path
-from typing import Any, List, Tuple, Optional
+import time
+from typing import Any, List, Optional, Tuple
 
 import numpy as np
 import pandas as pd
 from scipy.stats import poisson
 from sparse import COO
 
 from probinet.evaluation.expectation_computation import (
-    compute_mean_lambda0,
-    compute_mean_lambda0_nonzero,
-)
-from .base import ModelBase, ModelUpdateMixin
-from .classes import GraphData
-from .constants import EPS_, OUTPUT_FOLDER, BG_DEFAULT, K_DEFAULT, AG_DEFAULT
+    compute_mean_lambda0, compute_mean_lambda0_nonzero)
+
 from ..input.preprocessing import preprocess_adjacency_tensor
-from ..types import (
-    GraphDataType,
-    MaskType,
-    EndFileType,
-    FilesType,
-    ArraySequence,
-    SubsNzType,
-)
-from ..utils.matrix_operations import (
-    sp_uttkrp,
-    sp_uttkrp_assortative,
-    transpose_tensor,
-)
+from ..types import ArraySequence, EndFileType, FilesType, GraphDataType, MaskType, SubsNzType
+from ..utils.matrix_operations import sp_uttkrp, sp_uttkrp_assortative, transpose_tensor
 from ..utils.tools import get_item_array_from_subs, log_and_raise_error
+from .base import ModelBase, ModelUpdateMixin
+from .classes import GraphData
+from .constants import AG_DEFAULT, BG_DEFAULT, EPS_, K_DEFAULT, OUTPUT_FOLDER
 
 
 class AnomalyDetection(ModelBase, ModelUpdateMixin):

probinet/models/base.py

@@ -2,14 +2,14 @@
 Base classes for the models classes.
 """
 
-import dataclasses
-import logging
-import os
-import time
 from abc import ABC, abstractmethod
 from argparse import Namespace
+import dataclasses
 from functools import singledispatchmethod
+import logging
+import os
 from pathlib import Path
+import time
 from typing import Any, Dict, Optional, Tuple
 
 import numpy as np

probinet/models/crep.py

@@ -5,28 +5,22 @@
 """
 
 import logging
-import time
 from pathlib import Path
+import time
 from typing import Any, List, Optional, Tuple, Union
 
-import numpy as np
 from numpy import dtype, ndarray
+import numpy as np
 from sparse import COO
 
-from .base import ModelBase, ModelUpdateMixin
-from .classes import GraphData
-from .constants import OUTPUT_FOLDER
 from ..evaluation.expectation_computation import compute_mean_lambda0
 from ..input.preprocessing import preprocess_adjacency_tensor
-from ..types import (
-    GraphDataType,
-    MaskType,
-    EndFileType,
-    FilesType,
-    ArraySequence,
-)
+from ..types import ArraySequence, EndFileType, FilesType, GraphDataType, MaskType
 from ..utils.matrix_operations import sp_uttkrp, sp_uttkrp_assortative
 from ..utils.tools import get_item_array_from_subs, log_and_raise_error
+from .base import ModelBase, ModelUpdateMixin
+from .classes import GraphData
+from .constants import OUTPUT_FOLDER
 
 
 class CRep(ModelBase, ModelUpdateMixin):

probinet/models/dyncrep.py

@@ -2,34 +2,25 @@
 Class definition of DynCRep, the algorithm to perform inference in temporal networks :cite:`safdari2022reciprocity`.
 """
 
-import logging
-import time
 from argparse import Namespace
+import logging
 from pathlib import Path
+import time
 from typing import Any, Dict, Optional, Tuple
 
 import numpy as np
 from scipy.optimize import brentq, root
 from sparse import COO
 
-from .base import ModelBase, ModelUpdateMixin
-from .classes import GraphData
-from .constants import EPS_, OUTPUT_FOLDER
 from ..evaluation.expectation_computation import (
-    compute_lagrange_multiplier,
-    compute_mean_lambda0,
-    u_with_lagrange_multiplier,
-)
+    compute_lagrange_multiplier, compute_mean_lambda0, u_with_lagrange_multiplier)
 from ..input.preprocessing import preprocess_adjacency_tensor
-from ..types import (
-    GraphDataType,
-    MaskType,
-    EndFileType,
-    FilesType,
-    ArraySequence,
-)
+from ..types import ArraySequence, EndFileType, FilesType, GraphDataType, MaskType
 from ..utils.matrix_operations import sp_uttkrp, sp_uttkrp_assortative
 from ..utils.tools import get_item_array_from_subs, log_and_raise_error
+from .base import ModelBase, ModelUpdateMixin
+from .classes import GraphData
+from .constants import EPS_, OUTPUT_FOLDER
 
 
 class DynCRep(ModelBase, ModelUpdateMixin):

probinet/models/jointcrep.py

@@ -5,20 +5,20 @@
 """
 
 import logging
-import time
 from pathlib import Path
-from typing import Any, Tuple, Union, Optional
+import time
+from typing import Any, Optional, Tuple, Union
 
 import numpy as np
 
-from .base import ModelBase, ModelUpdateMixin
-from .classes import GraphData
-from .constants import OUTPUT_FOLDER, K_DEFAULT
 from ..evaluation.expectation_computation import compute_mean_lambda0
 from ..input.preprocessing import preprocess_adjacency_tensor
-from ..types import GraphDataType, EndFileType, FilesType
+from ..types import EndFileType, FilesType, GraphDataType
 from ..utils.matrix_operations import sp_uttkrp, sp_uttkrp_assortative, transpose_tensor
 from ..utils.tools import check_symmetric, get_item_array_from_subs, log_and_raise_error
+from .base import ModelBase, ModelUpdateMixin
+from .classes import GraphData
+from .constants import K_DEFAULT, OUTPUT_FOLDER
 
 
 class JointCRep(ModelBase, ModelUpdateMixin):

probinet/models/mtcov.py

@@ -5,25 +5,25 @@
 :cite:`contisciani2020community`.
 """
 
+from argparse import Namespace
 import logging
+from pathlib import Path
 import sys
 import time
-from argparse import Namespace
-from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple
 
 import numpy as np
 import scipy.sparse
 from sparse import COO
 
-from .base import ModelBase, ModelUpdateMixin
-from .classes import GraphData
-from .constants import OUTPUT_FOLDER
 from ..evaluation.expectation_computation import compute_mean_lambda0
 from ..input.loader import build_adjacency_and_design_from_file
 from ..input.preprocessing import preprocess_adjacency_tensor, preprocess_data_matrix
-from ..types import GraphDataType, EndFileType, FilesType, ArraySequence
+from ..types import ArraySequence, EndFileType, FilesType, GraphDataType
 from ..utils.matrix_operations import sp_uttkrp, sp_uttkrp_assortative
+from .base import ModelBase, ModelUpdateMixin
+from .classes import GraphData
+from .constants import OUTPUT_FOLDER
 
 MAX_BATCH_SIZE = 5000
 RANDOM_SEED = 10

probinet/synthetic/anomaly.py

@@ -13,7 +13,7 @@
 from scipy.optimize import brentq
 
 from ..models.constants import OUTPUT_FOLDER
-from ..synthetic.base import GraphProcessingMixin, affinity_matrix
+from ..synthetic.base import affinity_matrix, GraphProcessingMixin
 from ..synthetic.dynamic import eq_c, membership_vectors
 from ..utils.tools import flt
 from ..visualization.plot import plot_M

probinet/synthetic/base.py

@@ -3,17 +3,17 @@
 """
 
 from abc import ABCMeta
+from enum import Enum
 import logging
 import math
 from os import PathLike
-from enum import Enum
 from pathlib import Path
-from typing import Tuple, Optional, Union
+from typing import Optional, Tuple, Union
 
-import pandas as pd
 from matplotlib import pyplot as plt
 import networkx as nx
 import numpy as np
+import pandas as pd
 
 from probinet.visualization.plot import plot_A
 

probinet/synthetic/dynamic.py

@@ -16,10 +16,10 @@
 from scipy.sparse import coo_matrix
 
 from probinet.visualization.plot import plot_M
-from .base import GraphProcessingMixin, affinity_matrix
 
 from ..models.constants import EPS_, OUTPUT_FOLDER
 from ..utils.matrix_operations import normalize_nonzero_membership
+from .base import affinity_matrix, GraphProcessingMixin
 
 
 class SyntheticDynCRep(GraphProcessingMixin):

probinet/synthetic/multilayer.py

@@ -3,12 +3,12 @@
 with one categorical attribute. Self-loops are removed and only the largest connected component is considered.
 """
 
-import logging
 from abc import ABCMeta
+import logging
 import os
-import warnings
 from pathlib import Path
 from typing import Optional
+import warnings
 
 import matplotlib.pyplot as plt
 import networkx as nx