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