def main(input_file_name, model_file_name, feature_map_file, output_file_name):
start = datetime.now()
clf, vec = FileUtils.read_logistic_regression_model(model_file_name)
classes = clf.classes_.tolist()
sentences = FileUtils.read_lines(input_file_name)
feature_map_lines = FileUtils.read_lines(feature_map_file)
features_map, counters_dict = DictUtils.create_features_dicts(
feature_map_lines)
tagged_text = viterbi(sentences, features_map, counters_dict, clf, classes)
FileUtils.write_tagged_text(output_file_name, tagged_text)
end = datetime.now()
print('Running Time: {0}'.format(end - start))
def main(input_file_name, model_file_name, feature_map_file, output_file_name):
start = datetime.now()
clf, vec = FileUtils.read_logistic_regression_model(model_file_name)
sentences, max_sentence_len = FileUtils.read_sentences(input_file_name)
feature_map_lines = FileUtils.read_lines(feature_map_file)
features_map, counters_dict = DictUtils.create_features_dicts(
feature_map_lines)
sentences_predictions = memm_greedy(sentences, max_sentence_len,
features_map, counters_dict, clf)
FileUtils.write_prediction(output_file_name, sentences,
sentences_predictions)
end = datetime.now()
print('Running Time: {0}'.format(end - start))
