Flake8

active_learning_nmt.py

@@ -674,9 +674,7 @@ def parse_args():
                                  'state_below_index': -1,
                                  'output_text_index': 0,
                                  'apply_tokenization': params.get('APPLY_TOKENIZATION', False),
-                                 'tokenize_f': eval('dataset.' +
-                                                    params.get('TOKENIZATION_METHOD', 'tokenize_none')),
-
+                                 'tokenize_f': eval('dataset.' + params.get('TOKENIZATION_METHOD', 'tokenize_none')),
                                  'apply_detokenization': params.get('APPLY_DETOKENIZATION', True),
                                  'detokenize_f': eval('dataset.' + params.get('DETOKENIZATION_METHOD',
                                                                               'detokenize_none')),

interactive_char_nmt_simulation.py

@@ -269,8 +269,6 @@ def interactive_simulation():
     # Get word2index and index2word dictionaries
     index2word_y = dataset.vocabulary[params['OUTPUTS_IDS_DATASET'][0]]['idx2words']
     word2index_y = dataset.vocabulary[params['OUTPUTS_IDS_DATASET'][0]]['words2idx']
-    index2word_x = dataset.vocabulary[params['INPUTS_IDS_DATASET'][0]]['idx2words']
-    word2index_x = dataset.vocabulary[params['INPUTS_IDS_DATASET'][0]]['words2idx']
     unk_id = dataset.extra_words['<unk>']
 
     # Initialize counters
@@ -579,5 +577,6 @@ def interactive_simulation():
         fsrc.close()
         ftrans.close()
 
+
 if __name__ == "__main__":
     interactive_simulation()

interactive_nmt_simulation.py

@@ -1,6 +1,5 @@
 # -*- coding: utf-8 -*-
 from __future__ import print_function
-import time
 import argparse
 import ast
 import codecs
@@ -271,8 +270,6 @@ def interactive_simulation():
     # Get word2index and index2word dictionaries
     index2word_y = dataset.vocabulary[params['OUTPUTS_IDS_DATASET'][0]]['idx2words']
     word2index_y = dataset.vocabulary[params['OUTPUTS_IDS_DATASET'][0]]['words2idx']
-    index2word_x = dataset.vocabulary[params['INPUTS_IDS_DATASET'][0]]['idx2words']
-    word2index_x = dataset.vocabulary[params['INPUTS_IDS_DATASET'][0]]['words2idx']
     unk_id = dataset.extra_words['<unk>']
 
     # Initialize counters
@@ -501,7 +498,6 @@ def interactive_simulation():
                                 mouse_actions_sentence += 1
                                 if checked_index_h - last_checked_index > 1:
                                     mouse_actions_sentence += 1
-                                last_correct_pos = checked_index_h
                                 keystrokes_sentence += new_word_len
                                 # Substitution
                                 new_word_indices = [word2index_y.get(word, unk_id) for word in new_words]
@@ -702,5 +698,6 @@ def interactive_simulation():
         fsrc.close()
         ftrans.close()
 
+
 if __name__ == "__main__":
     interactive_simulation()

main.py

@@ -36,6 +36,7 @@ def parse_args():
 
     return parser.parse_args()
 
+
 if __name__ == "__main__":
     args = parse_args()
     parameters = load_parameters()

nmt_keras/online_models.py

@@ -170,8 +170,8 @@ def build_online_models(models, params):
                 y_true = Input(name="y_true", batch_shape=tuple([None, None, None]))
                 y_pred = nmt_model.model.outputs[0]
                 inputs = [y_true, y_pred, hyp1, preds_h1, weight1, weight2]
-                losses = [Lambda(eval(loss), output_shape=(None,),
-                                 name=loss, supports_masking=False)(inputs) for loss in params['LOSS']]
+                _ = [Lambda(eval(loss), output_shape=(None,),
+                            name=loss, supports_masking=False)(inputs) for loss in params['LOSS']]
 
                 trainer_model = Model(inputs=nmt_model.model.inputs + [state_below_h1] + [y_true, weight1, weight2],
                                       outputs=loss_out)

nmt_keras/training.py

@@ -45,8 +45,7 @@ def train_model(params, load_dataset=None):
             else:
                 logger.info('Updating dataset.')
                 dataset = loadDataset(
-                    params['DATASET_STORE_PATH'] + '/Dataset_' + params['DATASET_NAME'] + '_' + params['SRC_LAN'] +
-                    params['TRG_LAN'] + '.pkl')
+                    params['DATASET_STORE_PATH'] + '/Dataset_' + params['DATASET_NAME'] + '_' + params['SRC_LAN'] + params['TRG_LAN'] + '.pkl')
 
                 epoch_offset = 0 if dataset.len_train == 0 else int(params['RELOAD'] * params['BATCH_SIZE'] / dataset.len_train)
                 params['EPOCH_OFFSET'] = params['RELOAD'] if params['RELOAD_EPOCH'] else epoch_offset
@@ -110,8 +109,8 @@ def train_model(params, load_dataset=None):
         nmt_model.setParams(params)
         nmt_model.setOptimizer()
         if params.get('EPOCH_OFFSET') is None:
-            params['EPOCH_OFFSET'] = params['RELOAD'] if params['RELOAD_EPOCH'] else \
-                int(params['RELOAD'] * params['BATCH_SIZE'] / dataset.len_train)
+            params['EPOCH_OFFSET'] = \
+                params['RELOAD'] if params['RELOAD_EPOCH'] else int(params['RELOAD'] * params['BATCH_SIZE'] / dataset.len_train)
 
     # Store configuration as pkl
     dict2pkl(params, params['STORE_PATH'] + '/config')

utils/compute_cvr_centroid_corpus.py

@@ -5,7 +5,6 @@
 import numpy as np
 import os
 from keras_wrapper.extra.read_write import file2list, numpy2file
-from nltk import ngrams
 
 logging.basicConfig(level=logging.DEBUG, format='[%(asctime)s] %(message)s', datefmt='%d/%m/%Y %H:%M:%S')
 logger = logging.getLogger(__name__)
@@ -54,6 +53,7 @@ def compute_centroid(args):
     print ("Finished. Storing n-gram counts in %s " % args.dest)
     numpy2file(args.dest, centroid)
 
+
 if __name__ == "__main__":
     args = parse_args()
     assert args.sentence_mode in ['average', 'sum'], 'Unknown sentence-mode: "%s"' % args.sentence_mode

utils/compute_ngrams_corpus.py

@@ -41,6 +41,7 @@ def extract_n_grams(args):
     print ("Finished. Storing n-gram counts in %s " % args.dest)
     dict2pkl(n_gram_counts, args.dest)
 
+
 if __name__ == "__main__":
     args = parse_args()
     extract_n_grams(args)