optimized imports

refactor/data/datamodule.py

@@ -1,7 +1,7 @@
-import torch
-from torch.utils.data import Dataset, DataLoader
 import numpy as np
 import pytorch_lightning as pl
+import torch
+from torch.utils.data import Dataset, DataLoader
 from transformers import BertTokenizer
 
 N_WORKERS = 8

refactor/data/dataset_builder.py

@@ -1,19 +1,21 @@
-from os.path import join, exists
-from nltk.corpus import stopwords
-from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
-from sklearn.preprocessing import MultiLabelBinarizer
-from data.reader.jrcacquis_reader import *
-from data.languages import lang_set, NLTK_LANGMAP, RCV2_LANGS_WITH_NLTK_STEMMING
-from data.reader.rcv_reader import fetch_RCV1, fetch_RCV2, fetch_topic_hierarchy
-from data.text_preprocessor import NLTKStemTokenizer, preprocess_documents
-import pickle
-import numpy as np
-from sklearn.model_selection import train_test_split
-from scipy.sparse import issparse
 import itertools
-from tqdm import tqdm
+import pickle
 import re
+from os.path import exists
+
+import numpy as np
+from nltk.corpus import stopwords
 from scipy.sparse import csr_matrix
+from scipy.sparse import issparse
+from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MultiLabelBinarizer
+from tqdm import tqdm
+
+from data.languages import NLTK_LANGMAP, RCV2_LANGS_WITH_NLTK_STEMMING
+from data.reader.jrcacquis_reader import *
+from data.reader.rcv_reader import fetch_RCV1, fetch_RCV2
+from data.text_preprocessor import NLTKStemTokenizer, preprocess_documents
 
 
 class MultilingualDataset:

refactor/data/reader/jrcacquis_reader.py

@@ -1,19 +1,22 @@
 from __future__ import print_function
-import os, sys
+
-from os.path import join
+import os
+import pickle
+import sys
 import tarfile
 import xml.etree.ElementTree as ET
-from sklearn.datasets import get_data_home
+import zipfile
-import pickle
+from collections import Counter
-from util.file import download_file, list_dirs, list_files
+from os.path import join
+from random import shuffle
+
 import rdflib
 from rdflib.namespace import RDF, SKOS
-from rdflib import URIRef
+from sklearn.datasets import get_data_home
-import zipfile
+
 from data.languages import JRC_LANGS
-from collections import Counter
-from random import shuffle
 from data.languages import lang_set
+from util.file import download_file, list_dirs, list_files
 
 """
 JRC Acquis' Nomenclature:

refactor/data/reader/rcv_reader.py

@@ -1,15 +1,12 @@
-from zipfile import ZipFile
-import xml.etree.ElementTree as ET
-from data.languages import RCV2_LANGS_WITH_NLTK_STEMMING, RCV2_LANGS
-from util.file import list_files
-from sklearn.datasets import get_data_home
-import gzip
-from os.path import join, exists
-from util.file import download_file_if_not_exists
 import re
-from collections import Counter
+import xml.etree.ElementTree as ET
+from os.path import join, exists
+from zipfile import ZipFile
+
 import numpy as np
-import sys
+
+from util.file import download_file_if_not_exists
+from util.file import list_files
 
 """
 RCV2's Nomenclature:

refactor/data/reader/wikipedia_tools.py

@@ -1,16 +1,19 @@
 from __future__ import print_function
+
 # import ijson
 # from ijson.common import ObjectBuilder
-import os, sys
+import os
-from os.path import join
-from bz2 import BZ2File
 import pickle
-from util.file import list_dirs, list_files, makedirs_if_not_exist
-from itertools import islice
 import re
+from bz2 import BZ2File
+from itertools import islice
+from os.path import join
 from xml.sax.saxutils import escape
+
 import numpy as np
 
+from util.file import list_dirs, list_files
+
 policies = ["IN_ALL_LANGS", "IN_ANY_LANG"]
 
 """

refactor/data/text_preprocessor.py

@@ -1,8 +1,9 @@
-from nltk.corpus import stopwords
-from data.languages import NLTK_LANGMAP
 from nltk import word_tokenize
+from nltk.corpus import stopwords
 from nltk.stem import SnowballStemmer
 
+from data.languages import NLTK_LANGMAP
+
 
 def preprocess_documents(documents, lang):
     tokens = NLTKStemTokenizer(lang, verbose=True)

refactor/data/tsr_function__.py

@@ -1,8 +1,9 @@
 import math
+
 import numpy as np
-from scipy.stats import t
 from joblib import Parallel, delayed
 from scipy.sparse import csr_matrix, csc_matrix
+from scipy.stats import t
 
 
 def get_probs(tpr, fpr, pc):

refactor/funnelling.py

@@ -1,6 +1,6 @@
 from models.learners import *
-from view_generators import VanillaFunGen
 from util.common import _normalize
+from view_generators import VanillaFunGen
 
 
 class DocEmbedderList:

refactor/main.py

@@ -1,11 +1,11 @@
 from argparse import ArgumentParser
-from funnelling import *
+
-from view_generators import *
 from data.dataset_builder import MultilingualDataset
+from funnelling import *
 from util.common import MultilingualIndex, get_params, get_method_name
 from util.evaluation import evaluate
 from util.results_csv import CSVlog
-from time import time
+from view_generators import *
 
 
 def main(args):

refactor/models/learners.py

@@ -1,10 +1,12 @@
-import numpy as np
 import time
-from scipy.sparse import issparse
+
-from sklearn.multiclass import OneVsRestClassifier
+import numpy as np
-from sklearn.model_selection import GridSearchCV
-from sklearn.svm import SVC
 from joblib import Parallel, delayed
+from scipy.sparse import issparse
+from sklearn.model_selection import GridSearchCV
+from sklearn.multiclass import OneVsRestClassifier
+from sklearn.svm import SVC
+
 from util.standardizer import StandardizeTransformer
 
 

refactor/models/lstm_class.py

@@ -1,7 +1,6 @@
 #taken from https://github.com/prakashpandey9/Text-Classification-Pytorch/blob/master/models/LSTM.py
-import torch
-import torch.nn as nn
 from torch.autograd import Variable
+
 from models.helpers import *
 
 

refactor/models/pl_bert.py

@@ -1,9 +1,10 @@
-import torch
 import pytorch_lightning as pl
+import torch
 from torch.optim.lr_scheduler import StepLR
 from transformers import BertForSequenceClassification, AdamW
-from util.pl_metrics import CustomF1, CustomK
+
 from util.common import define_pad_length, pad
+from util.pl_metrics import CustomF1, CustomK
 
 
 class BertModel(pl.LightningModule):

refactor/models/pl_gru.py

@@ -1,14 +1,15 @@
 # Lightning modules, see https://pytorch-lightning.readthedocs.io/en/latest/lightning_module.html
+import pytorch_lightning as pl
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 from torch.autograd import Variable
 from torch.optim.lr_scheduler import StepLR
 from transformers import AdamW
-import pytorch_lightning as pl
+
 from models.helpers import init_embeddings
-from util.pl_metrics import CustomF1, CustomK
 from util.common import define_pad_length, pad
+from util.pl_metrics import CustomF1, CustomK
 
 
 class RecurrentModel(pl.LightningModule):

refactor/util/common.py

@@ -1,9 +1,9 @@
 import numpy as np
 import torch
-from tqdm import tqdm
 from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.preprocessing import normalize
 from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import normalize
+
 from util.embeddings_manager import supervised_embeddings_tfidf
 
 

refactor/util/embeddings_manager.py

@@ -1,7 +1,9 @@
-from torchtext.vocab import Vectors
-import torch
 from abc import ABC, abstractmethod
+
 import numpy as np
+import torch
+from torchtext.vocab import Vectors
+
 from util.SIF_embed import remove_pc
 
 

refactor/util/evaluation.py

@@ -1,6 +1,7 @@
-from joblib import Parallel, delayed
-from util.metrics import *
 import numpy as np
+from joblib import Parallel, delayed
+
+from util.metrics import *
 
 
 def evaluation_metrics(y, y_):

refactor/util/file.py

@@ -1,6 +1,6 @@
+import urllib
 from os import listdir, makedirs
 from os.path import isdir, isfile, join, exists, dirname
-import urllib
 from pathlib import Path
 
 

refactor/util/pl_metrics.py

@@ -1,5 +1,6 @@
 import torch
 from pytorch_lightning.metrics import Metric
+
 from util.common import is_false, is_true
 
 

refactor/util/results_csv.py

@@ -1,6 +1,7 @@
 import os
-import pandas as pd
+
 import numpy as np
+import pandas as pd
 
 
 class CSVlog:

refactor/view_generators.py

@@ -16,16 +16,18 @@ This module contains the view generators that take care of computing the view sp
 - View generator (-b): generates document embedding via mBERT model.
 """
 from abc import ABC, abstractmethod
-from models.learners import *
-from util.embeddings_manager import MuseLoader, XdotM, wce_matrix
-from util.common import TfidfVectorizerMultilingual, _normalize
-from models.pl_gru import RecurrentModel
-from models.pl_bert import BertModel
-from pytorch_lightning import Trainer
-from data.datamodule import RecurrentDataModule, BertDataModule, tokenize
-from pytorch_lightning.loggers import TensorBoardLogger, CSVLogger
 from time import time
 
+from pytorch_lightning import Trainer
+from pytorch_lightning.loggers import TensorBoardLogger
+
+from data.datamodule import RecurrentDataModule, BertDataModule, tokenize
+from models.learners import *
+from models.pl_bert import BertModel
+from models.pl_gru import RecurrentModel
+from util.common import TfidfVectorizerMultilingual, _normalize
+from util.embeddings_manager import MuseLoader, XdotM, wce_matrix
+
 
 class ViewGen(ABC):
     @abstractmethod