| | |
| |
|
| | """ 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 example_helper |
| | 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 |
| |
|
| | 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) |
| | print('Running predictions.') |
| | tokenized, _, _ = st.tokenize_sentences(TEST_SENTENCES) |
| | prob = model(tokenized) |
| |
|
| | for prob in [prob]: |
| | |
| | |
| | |
| | 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)) |
| |
|
