Use Flake8

.flake8

@@ -0,0 +1,9 @@
+[flake8]
+ignore = E501,E402,E731,E211,F403,F405
+exclude =
+    ./venv
+    ./.idea
+    ./notebooks
+    *conf*.py
+     src/*
+

.travis.yml

@@ -33,7 +33,7 @@ install:
   - travis_wait 30 pip install .
 
   # Install packages to run tests.
-  - pip install flaky pytest pytest-cache pytest-cov pytest-forked pytest-pep8 pytest-xdist pyux
+  - pip install flaky pytest pytest-cache pytest-cov pytest-forked pytest-xdist pyux flake8
 
 
 # command to run tests
@@ -43,7 +43,7 @@ script:
   - sed -i -e 's/"backend":[[:space:]]*"[^"]*/"backend":\ "'$KERAS_BACKEND'/g' ~/.keras/keras.json;
   - echo -e "Running tests with the following config:\n$(cat ~/.keras/keras.json)"
   - if [[ "$TEST_MODE" == "PEP8" ]]; then
-      PYTHONPATH=$PWD:$PYTHONPATH python -m pytest --pep8 -m pep8 -n0;
+      PYTHONPATH=$PWD:$PYTHONPATH python -m flake8 --config=./.flake8 .;
     elif [[ "$TEST_MODE" == "GENERAL" ]]; then
       PYTHONPATH=$PWD:$PYTHONPATH pytest -s tests/test_load_params.py tests/utils/ tests/data_engine/ 2>&1 ;
     elif [[ "$TEST_MODE" == "ENCODINGS" ]]; then

data_engine/prepare_data.py

@@ -226,9 +226,9 @@ def build_dataset(params):
 
     else:
         # We can easily recover it with a single line
-        ds = loadDataset(os.path.join(params['DATASET_STORE_PATH'],
-                                      'Dataset_' + params['DATASET_NAME'] +
-                                      '_' + params['SRC_LAN'] + params['TRG_LAN'] + '.pkl'))
+        ds = loadDataset(
+            os.path.join(params['DATASET_STORE_PATH'],
+                         'Dataset_' + params['DATASET_NAME'] + '_' + params['SRC_LAN'] + params['TRG_LAN'] + '.pkl'))
 
         # Prepare references
         prepare_references(ds,
@@ -304,5 +304,6 @@ def prepare_references(ds, repeat, n=1, set_names=None):
 
         logger.info('Samples reduced to ' + str(new_len) + ' in ' + s + ' set.')
 
+
 # Backwards compatibility:
 keep_n_captions = prepare_references

demo-web/sample_server.py

@@ -3,10 +3,6 @@
 
 from __future__ import print_function
 
-try:
-    import itertools.imap as map
-except ImportError:
-    pass
 import argparse
 import ast
 import logging
@@ -22,9 +18,9 @@
 from keras_wrapper.cnn_model import loadModel, updateModel
 from keras_wrapper.dataset import loadDataset
 from keras_wrapper.extra.isles_utils import *
-from keras_wrapper.extra.read_write import pkl2dict, list2file
+from keras_wrapper.extra.read_write import pkl2dict
 from keras_wrapper.online_trainer import OnlineTrainer
-from keras_wrapper.utils import decode_predictions_beam_search, flatten_list_of_lists
+from keras_wrapper.utils import decode_predictions_beam_search
 from nmt_keras.model_zoo import TranslationModel
 # from online_models import build_online_models
 from utils.utils import update_parameters
@@ -547,7 +543,6 @@ def main():
     # Get word2index and index2word dictionaries
     index2word_y = dataset.vocabulary[parameters['OUTPUTS_IDS_DATASET'][0]]['idx2words']
     word2index_y = dataset.vocabulary[parameters['OUTPUTS_IDS_DATASET'][0]]['words2idx']
-    index2word_x = dataset.vocabulary[parameters['INPUTS_IDS_DATASET'][0]]['idx2words']
     word2index_x = dataset.vocabulary[parameters['INPUTS_IDS_DATASET'][0]]['words2idx']
 
     excluded_words = None

nmt_keras/model_zoo.py

@@ -444,10 +444,10 @@ def AttentionRNNEncoderDecoder(self, params):
             * Possibly deep encoder/decoder
         See:
             * `Neural Machine Translation by Jointly Learning to Align and Translate`_.
-            * `Nematus\: a Toolkit for Neural Machine Translation`_.
+            * `Nematus: a Toolkit for Neural Machine Translation`_.
 
         .. _Neural Machine Translation by Jointly Learning to Align and Translate: https://arxiv.org/abs/1409.0473
-        .. _Nematus\: a Toolkit for Neural Machine Translation: https://arxiv.org/abs/1703.04357
+        .. _Nematus: a Toolkit for Neural Machine Translation: https://arxiv.org/abs/1703.04357
 
         :param int params: Dictionary of hyper-params (see config.py)
         :return: None

nmt_keras/training.py

@@ -42,9 +42,7 @@ def train_model(params, load_dataset=None):
                 dataset = loadDataset(
                     os.path.join(
                         params['DATASET_STORE_PATH'],
-                        'Dataset_' +
-                        params['DATASET_NAME'] + '_' +
-                        params['SRC_LAN'] + params['TRG_LAN'] + '.pkl')
+                        'Dataset_' + params['DATASET_NAME'] + '_' + params['SRC_LAN'] + params['TRG_LAN'] + '.pkl')
                 )
 
                 epoch_offset = 0 if dataset.len_train == 0 else int(

tests/NMT_architectures/unidir_deep_GRU_ConditionalLSTM.py

@@ -67,5 +67,6 @@ def test_NMT_Unidir_deep_GRU_ConditionalLSTM():
     print("Done")
     clean_dirs(params)
 
+
 if __name__ == '__main__':
     pytest.main([__file__])

tests/data_engine/test_prepare_data.py

@@ -94,5 +94,6 @@ def test_keep_n_captions():
                 assert len(eval('ds.X_' + split + str([params['INPUTS_IDS_DATASET'][0]]))) == len_split
                 assert len(eval('ds.Y_' + split + str([params['OUTPUTS_IDS_DATASET'][0]]))) == len_split
 
+
 if __name__ == '__main__':
     pytest.main([__file__])

tests/utils/test_process_word_vectors.py

@@ -11,8 +11,7 @@ def test_text_word2vec2npy():
     vectors_name = 'wiki.fiu_vro.vec'
     path = os.path.dirname(inspect.getfile(inspect.currentframe()))
     if not os.path.exists(path + '/' + vectors_name):
-        call(["wget https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/" + vectors_name + " -O " +
-              path + "/" + vectors_name],
+        call(["wget https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/" + vectors_name + " -O " + path + "/" + vectors_name],
              shell=True)
     txtvec2npy(path + '/' + vectors_name, './', vectors_name[:-4])
     vectors = np.load('./' + vectors_name[:-4] + '.npy', allow_pickle=True).item()