Added some runnable applications of this model

2018-04-27 11:15:37 +02:00
parent 4175c45ceb
commit 048825648a
16 changed files with 329 additions and 1389 deletions
@@ -0,0 +1,79 @@
+# -*- coding: utf-8 -*-
+from __future__ import unicode_literals
+
+import sys
+
+sys.path.insert(0, '../../../')
+from prepare_data import *
+
+import pickle
+
+# from keras import backend as Input
+np.random.seed(7)
+
+# obtain data from parameters
+if len(sys.argv) < 3:
+    print('Please provide arguments for this script to work. First argument should be location of file with unaccented words and morphological data, '
+          'second the name of file where you would like to save results to and third location of ReLDI tagger. Example: python accentuate.py '
+          '\'test_data/original_connected_text\' \'test_data/accented_connected_text\' \'../reldi_tagger\'')
+    raise Exception
+read_location = sys.argv[1]
+write_location = sys.argv[2]
+reldi_location = sys.argv[3]
+
+# get environment variables necessary for calculations
+pickle_input = open('preprocessed_data/environment.pkl', 'rb')
+environment = pickle.load(pickle_input)
+dictionary = environment['dictionary']
+max_word = environment['max_word']
+max_num_vowels = environment['max_num_vowels']
+vowels = environment['vowels']
+accented_vowels = environment['accented_vowels']
+feature_dictionary = environment['feature_dictionary']
+syllable_dictionary = environment['syllable_dictionary']
+
+# get models
+data = Data('l', shuffle_all_inputs=False)
+letter_location_model, syllable_location_model, syllabled_letters_location_model = data.load_location_models(
+    'cnn/word_accetuation/cnn_dictionary/v5_3/20_final_epoch.h5',
+    'cnn/word_accetuation/syllables/v3_3/20_final_epoch.h5',
+    'cnn/word_accetuation/syllabled_letters/v3_3/20_final_epoch.h5')
+
+letter_location_co_model, syllable_location_co_model, syllabled_letters_location_co_model = data.load_location_models(
+    'cnn/word_accetuation/cnn_dictionary/v5_2/20_final_epoch.h5',
+    'cnn/word_accetuation/syllables/v3_2/20_final_epoch.h5',
+    'cnn/word_accetuation/syllabled_letters/v3_2/20_final_epoch.h5')
+
+letter_type_model, syllable_type_model, syllabled_letter_type_model = data.load_type_models(
+    'cnn/accent_classification/letters/v3_1/20_final_epoch.h5',
+    'cnn/accent_classification/syllables/v2_1/20_final_epoch.h5',
+    'cnn/accent_classification/syllabled_letters/v2_1/20_final_epoch.h5')
+
+letter_type_co_model, syllable_type_co_model, syllabled_letter_type_co_model = data.load_type_models(
+    'cnn/accent_classification/letters/v3_0/20_final_epoch.h5',
+    'cnn/accent_classification/syllables/v2_0/20_final_epoch.h5',
+    'cnn/accent_classification/syllabled_letters/v2_0/20_final_epoch.h5')
+
+# get word tags
+tagged_words, original_text = data.tag_words(reldi_location, read_location)
+
+# find accentuation locations
+predictions = data.get_ensemble_location_predictions(tagged_words, letter_location_model, syllable_location_model, syllabled_letters_location_model,
+                                                     letter_location_co_model, syllable_location_co_model, syllabled_letters_location_co_model,
+                                                     dictionary, max_word, max_num_vowels, vowels, accented_vowels, feature_dictionary,
+                                                     syllable_dictionary)
+
+location_accented_text = data.create_connected_text_locations(tagged_words, original_text, predictions, vowels)
+
+# accentuate text
+location_y = np.around(predictions)
+type_predictions = data.get_ensemble_type_predictions(tagged_words, location_y, letter_type_model, syllable_type_model, syllabled_letter_type_model,
+                                                      letter_type_co_model, syllable_type_co_model, syllabled_letter_type_co_model,
+                                                      dictionary, max_word, max_num_vowels, vowels, accented_vowels, feature_dictionary,
+                                                      syllable_dictionary)
+
+accented_text = data.create_connected_text_accented(tagged_words, original_text, type_predictions, location_y, vowels, accented_vowels)
+
+# save accentuated text
+with open(write_location, 'w') as f:
+    f.write(accented_text)