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| # -*- coding: utf-8 -*- | |
| """ Use torchMoji to score texts for emoji distribution. | |
| The resulting emoji ids (0-63) correspond to the mapping | |
| in emoji_overview.png file at the root of the torchMoji repo. | |
| Writes the result to a csv file. | |
| """ | |
| from __future__ import print_function, division, unicode_literals | |
| import sys | |
| from os.path import abspath, dirname | |
| import json | |
| import csv | |
| import numpy as np | |
| from torchmoji.sentence_tokenizer import SentenceTokenizer | |
| from torchmoji.model_def import torchmoji_emojis | |
| from torchmoji.global_variables import PRETRAINED_PATH, VOCAB_PATH | |
| sys.path.insert(0, dirname(dirname(abspath(__file__)))) | |
| OUTPUT_PATH = 'test_sentences.csv' | |
| TEST_SENTENCES = ['I love mom\'s cooking', | |
| 'I love how you never reply back..', | |
| 'I love cruising with my homies', | |
| 'I love messing with yo mind!!', | |
| 'I love you and now you\'re just gone..', | |
| 'This is shit', | |
| 'This is the shit'] | |
| def top_elements(array, k): | |
| ind = np.argpartition(array, -k)[-k:] | |
| return ind[np.argsort(array[ind])][::-1] | |
| maxlen = 30 | |
| print('Tokenizing using dictionary from {}'.format(VOCAB_PATH)) | |
| with open(VOCAB_PATH, 'r') as f: | |
| vocabulary = json.load(f) | |
| st = SentenceTokenizer(vocabulary, maxlen) | |
| print('Loading model from {}.'.format(PRETRAINED_PATH)) | |
| model = torchmoji_emojis(PRETRAINED_PATH) | |
| print(model) | |
| def doImportableFunction(): | |
| print('Running predictions.') | |
| tokenized, _, _ = st.tokenize_sentences(TEST_SENTENCES) | |
| prob = model(tokenized) | |
| for prob in [prob]: | |
| # Find top emojis for each sentence. Emoji ids (0-63) | |
| # correspond to the mapping in emoji_overview.png | |
| # at the root of the torchMoji repo. | |
| print('Writing results to {}'.format(OUTPUT_PATH)) | |
| scores = [] | |
| for i, t in enumerate(TEST_SENTENCES): | |
| t_tokens = tokenized[i] | |
| t_score = [t] | |
| t_prob = prob[i] | |
| ind_top = top_elements(t_prob, 5) | |
| t_score.append(sum(t_prob[ind_top])) | |
| t_score.extend(ind_top) | |
| t_score.extend([t_prob[ind] for ind in ind_top]) | |
| scores.append(t_score) | |
| print(t_score) | |
| with open(OUTPUT_PATH, 'w') as csvfile: | |
| writer = csv.writer(csvfile, delimiter=str(','), lineterminator='\n') | |
| writer.writerow(['Text', 'Top5%', | |
| 'Emoji_1', 'Emoji_2', 'Emoji_3', 'Emoji_4', 'Emoji_5', | |
| 'Pct_1', 'Pct_2', 'Pct_3', 'Pct_4', 'Pct_5']) | |
| for i, row in enumerate(scores): | |
| try: | |
| writer.writerow(row) | |
| except: | |
| print("Exception at row {}!".format(i)) | |
| return |