Run isort to sort imports

experiments/ClassificationUtil.py

@@ -5,22 +5,20 @@
 ==============================================================
 """
 
-import os
 import copy
 import math
+import os
 import pickle
 
-import pandas as pd
 import numpy as np
-
+import pandas as pd
+from ConstantsUtil import ConstantsUtil
 from ete3 import NCBITaxa
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import classification_report, precision_recall_fscore_support
-from sklearn.ensemble import RandomForestClassifier
 from joblib import dump
-
 from RBPPredictionUtil import RBPPredictionUtil
-from ConstantsUtil import ConstantsUtil
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report, precision_recall_fscore_support
+from sklearn.model_selection import train_test_split
 
 
 class ClassificationUtil(object):

experiments/RBPPredictionUtil.py

@@ -7,11 +7,9 @@
 
 import os
 
-from SequenceParsingUtil import SequenceParsingUtil
-
-import pandas as pd
 import numpy as np
-
+import pandas as pd
+from SequenceParsingUtil import SequenceParsingUtil
 from xgboost import XGBClassifier
 
 

experiments/SequenceParsingUtil.py

@@ -5,12 +5,12 @@
 ======================================================================
 """
 
-import regex as re
 import os
 from collections import defaultdict
 
-import numpy as np
 import nltk
+import numpy as np
+import regex as re
 from Bio import SeqIO
 
 

experiments/boeckaerts.py

@@ -12,29 +12,31 @@
 
 # IMPORT LIBRARIES
 # --------------------------------------------------
-import os
+import datetime as dt
 import math
+import os
 import warnings
+
+import matplotlib.pyplot as plt
 import numpy as np
 import scipy as sp
-import datetime as dt
-from numba import jit
-import matplotlib.pyplot as plt
+from Bio import Entrez, SeqIO, pairwise2
 from Bio.Blast import NCBIWWW, NCBIXML
-from Bio import SeqIO, Entrez, pairwise2
+from Bio.Blast.Applications import NcbiblastpCommandline
 from Bio.SubsMat import MatrixInfo as matlist
-from sklearn.preprocessing import label_binarize
+from numba import jit
 from sklearn.exceptions import UndefinedMetricWarning
-from Bio.Blast.Applications import NcbiblastpCommandline
-from sklearn.model_selection import GridSearchCV, GroupKFold
 from sklearn.metrics import (
  accuracy_score,
- make_scorer,
+ auc,
  f1_score,
+ make_scorer,
+ precision_recall_curve,
  precision_score,
  recall_score,
 )
-from sklearn.metrics import precision_recall_curve, auc
+from sklearn.model_selection import GridSearchCV, GroupKFold
+from sklearn.preprocessing import label_binarize
 
 
 # DNA FEATURES

phiembed.py

@@ -12,10 +12,10 @@
 import os
 import re
 
-from Bio import SeqIO
 import joblib
 import torch
-from transformers import T5Tokenizer, T5EncoderModel
+from Bio import SeqIO
+from transformers import T5EncoderModel, T5Tokenizer
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 tokenizer = T5Tokenizer.from_pretrained(