fixed imports

main.py

@@ -1,12 +1,11 @@
 from argparse import ArgumentParser
 
-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 view_generators import *
-from time import time
+from src.data.dataset_builder import MultilingualDataset
+from src.funnelling import *
+from src.util.common import MultilingualIndex, get_params, get_method_name
+from src.util.evaluation import evaluate
+from src.util.results_csv import CSVlog
+from src.view_generators import *
 
 
 def main(args):
@@ -60,18 +59,17 @@ def main(args):
 
     # Training ---------------------------------------
     print('\n[Training Generalized Funnelling]')
-    time_init = time()
-    time_tr = time()
+    time_init = time.time()
     gfun.fit(lX, ly)
-    time_tr = round(time() - time_tr, 3)
+    time_tr = round(time.time() - time_init, 3)
     print(f'Training completed in {time_tr} seconds!')
 
     # Testing ----------------------------------------
     print('\n[Testing Generalized Funnelling]')
-    time_te = time()
+    time_te = time.time()
     ly_ = gfun.predict(lXte)
     l_eval = evaluate(ly_true=lyte, ly_pred=ly_)
-    time_te = round(time() - time_te, 3)
+    time_te = round(time.time() - time_te, 3)
     print(f'Testing completed in {time_te} seconds!')
 
     # Logging ---------------------------------------
@@ -101,7 +99,7 @@ def main(args):
                             notes='')
     print('Averages: MF1, mF1, MK, mK', np.round(np.mean(np.array(metrics), axis=0), 3))
 
-    overall_time = round(time() - time_init, 3)
+    overall_time = round(time.time() - time_init, 3)
     exit(f'\nExecuted in: {overall_time} seconds!')
 
 
@@ -112,7 +110,7 @@ if __name__ == '__main__':
 
     parser.add_argument('-o', '--output', dest='csv_dir',
                         help='Result file (default ../csv_log/gfun_results.csv)', type=str,
-                        default='csv_logs/gfun/gfun_results.csv')
+                        default='../csv_logs/gfun/gfun_results.csv')
 
     parser.add_argument('-x', '--post_embedder', dest='post_embedder', action='store_true',
                         help='deploy posterior probabilities embedder to compute document embeddings',
@@ -138,7 +136,7 @@ if __name__ == '__main__':
                         help='Optimize SVMs C hyperparameter',
                         default=False)
 
-    parser.add_argument('-n', '--nepochs', dest='nepochs', type=str,
+    parser.add_argument('-n', '--nepochs', dest='nepochs', type=int,
                         help='Number of max epochs to train Recurrent embedder (i.e., -g)')
 
     parser.add_argument('-j', '--n_jobs', dest='n_jobs', type=int,

src/data/datamodule.py

@@ -135,15 +135,15 @@ class RecurrentDataModule(pl.LightningDataModule):
                                                  lPad_index=self.multilingualIndex.l_pad())
 
     def train_dataloader(self):
-        return DataLoader(self.training_dataset, batch_size=self.batchsize, num_workers=self.n_jobs,
+        return DataLoader(self.training_dataset, batch_size=self.batchsize, num_workers=N_WORKERS,
                           collate_fn=self.training_dataset.collate_fn)
 
     def val_dataloader(self):
-        return DataLoader(self.val_dataset, batch_size=self.batchsize, num_workers=self.n_jobs,
+        return DataLoader(self.val_dataset, batch_size=self.batchsize, num_workers=N_WORKERS,
                           collate_fn=self.val_dataset.collate_fn)
 
     def test_dataloader(self):
-        return DataLoader(self.test_dataset, batch_size=self.batchsize, num_workers=self.n_jobs,
+        return DataLoader(self.test_dataset, batch_size=self.batchsize, num_workers=N_WORKERS,
                           collate_fn=self.test_dataset.collate_fn)
 
 

src/data/dataset_builder.py

@@ -1,5 +1,4 @@
 import itertools
-import pickle
 import re
 from os.path import exists
 
@@ -12,10 +11,10 @@ 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
+from src.data.languages import NLTK_LANGMAP, RCV2_LANGS_WITH_NLTK_STEMMING
+from src.data.reader.jrcacquis_reader import *
+from src.data.reader.rcv_reader import fetch_RCV1, fetch_RCV2
+from src.data.text_preprocessor import NLTKStemTokenizer, preprocess_documents
 
 
 class MultilingualDataset:

src/data/reader/jrcacquis_reader.py

@@ -14,9 +14,9 @@ import rdflib
 from rdflib.namespace import RDF, SKOS
 from sklearn.datasets import get_data_home
 
-from data.languages import JRC_LANGS
-from data.languages import lang_set
-from util.file import download_file, list_dirs, list_files
+from src.data.languages import JRC_LANGS
+from src.data.languages import lang_set
+from src.util.file import download_file, list_dirs, list_files
 
 """
 JRC Acquis' Nomenclature:

src/data/reader/rcv_reader.py

@@ -5,8 +5,8 @@ from zipfile import ZipFile
 
 import numpy as np
 
-from util.file import download_file_if_not_exists
-from util.file import list_files
+from src.util.file import download_file_if_not_exists
+from src.util.file import list_files
 
 """
 RCV2's Nomenclature:

src/data/reader/wikipedia_tools.py

@@ -11,7 +11,6 @@ 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"]

src/data/text_preprocessor.py

@@ -2,7 +2,7 @@ from nltk import word_tokenize
 from nltk.corpus import stopwords
 from nltk.stem import SnowballStemmer
 
-from data.languages import NLTK_LANGMAP
+from src.data.languages import NLTK_LANGMAP
 
 
 def preprocess_documents(documents, lang):

src/funnelling.py

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

src/models/learners.py

@@ -7,7 +7,7 @@ from sklearn.model_selection import GridSearchCV
 from sklearn.multiclass import OneVsRestClassifier
 from sklearn.svm import SVC
 
-from util.standardizer import StandardizeTransformer
+from src.util.standardizer import StandardizeTransformer
 
 
 def get_learner(calibrate=False, kernel='linear', C=1):

src/models/lstm_class.py

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

src/models/pl_bert.py

@@ -3,8 +3,8 @@ import torch
 from torch.optim.lr_scheduler import StepLR
 from transformers import BertForSequenceClassification, AdamW
 
-from util.common import define_pad_length, pad
-from util.pl_metrics import CustomF1, CustomK
+from src.util.common import define_pad_length, pad
+from src.util.pl_metrics import CustomF1, CustomK
 
 
 class BertModel(pl.LightningModule):

src/models/pl_gru.py

@@ -7,9 +7,9 @@ from torch.autograd import Variable
 from torch.optim.lr_scheduler import StepLR
 from transformers import AdamW
 
-from models.helpers import init_embeddings
-from util.common import define_pad_length, pad
-from util.pl_metrics import CustomF1, CustomK
+from src.models.helpers import init_embeddings
+from src.util.common import define_pad_length, pad
+from src.util.pl_metrics import CustomF1, CustomK
 
 
 class RecurrentModel(pl.LightningModule):

src/util/common.py

@@ -4,7 +4,7 @@ from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import normalize
 
-from util.embeddings_manager import supervised_embeddings_tfidf
+from src.util.embeddings_manager import supervised_embeddings_tfidf
 
 
 class TfidfVectorizerMultilingual:

src/util/embeddings_manager.py

@@ -4,7 +4,7 @@ import numpy as np
 import torch
 from torchtext.vocab import Vectors
 
-from util.SIF_embed import remove_pc
+from src.util.SIF_embed import remove_pc
 
 
 class PretrainedEmbeddings(ABC):

src/util/evaluation.py

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

src/util/pl_metrics.py

@@ -1,7 +1,7 @@
 import torch
 from pytorch_lightning.metrics import Metric
 
-from util.common import is_false, is_true
+from src.util.common import is_false, is_true
 
 
 def _update(pred, target, device):

src/view_generators.py

@@ -21,12 +21,12 @@ 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
+from src.data.datamodule import RecurrentDataModule, BertDataModule, tokenize
+from src.models.learners import *
+from src.models.pl_bert import BertModel
+from src.models.pl_gru import RecurrentModel
+from src.util.common import TfidfVectorizerMultilingual, _normalize
+from src.util.embeddings_manager import MuseLoader, XdotM, wce_matrix
 
 
 class ViewGen(ABC):
@@ -232,7 +232,7 @@ class RecurrentGen(ViewGen):
         self.multilingualIndex.train_val_split(val_prop=0.2, max_val=2000, seed=1)
         self.multilingualIndex.embedding_matrices(self.pretrained, supervised=self.wce)
         self.model = self._init_model()
-        self.logger = TensorBoardLogger(save_dir='tb_logs', name='rnn', default_hp_metric=False)
+        self.logger = TensorBoardLogger(save_dir='../tb_logs', name='rnn', default_hp_metric=False)
         # self.logger = CSVLogger(save_dir='csv_logs', name='rnn_dev')
 
     def _init_model(self):
@@ -293,9 +293,9 @@ class RecurrentGen(ViewGen):
         data = self.multilingualIndex.l_devel_index()
         self.model.to('cuda' if self.gpus else 'cpu')
         self.model.eval()
-        time_init = time()
+        time_init = time.time()
         l_embeds = self.model.encode(data, l_pad, batch_size=256)
-        transform_time = round(time() - time_init, 3)
+        transform_time = round(time.time() - time_init, 3)
         print(f'Executed! Transform took: {transform_time}')
         return l_embeds
 
@@ -328,7 +328,7 @@ class BertGen(ViewGen):
         self.n_jobs = n_jobs
         self.stored_path = stored_path
         self.model = self._init_model()
-        self.logger = TensorBoardLogger(save_dir='tb_logs', name='bert', default_hp_metric=False)
+        self.logger = TensorBoardLogger(save_dir='../tb_logs', name='bert', default_hp_metric=False)
 
     def _init_model(self):
         output_size = self.multilingualIndex.get_target_dim()
@@ -362,14 +362,12 @@ class BertGen(ViewGen):
         data = tokenize(data, max_len=512)
         self.model.to('cuda' if self.gpus else 'cpu')
         self.model.eval()
-        time_init = time()
+        time_init = time.time()
         l_emebds = self.model.encode(data, batch_size=64)
-        transform_time = round(time() - time_init, 3)
+        transform_time = round(time.time() - time_init, 3)
         print(f'Executed! Transform took: {transform_time}')
         return l_emebds
 
     def fit_transform(self, lX, ly):
         # we can assume that we have already indexed data for transform() since we are first calling fit()
         return self.fit(lX, ly).transform(lX)
-
-