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2_bert_abbrev_expansion.py
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2_bert_abbrev_expansion.py
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from enum import Enum
from tokenize import String
from typing import List, Dict, Tuple
from collections import defaultdict, Counter
from numpy import full
from bert_utils import get_torch_device
import logging, sys, json
import pandas as pd
from abbr_utils import ExpansionInstance, BestFitPolicy, choose_best_fit, get_lang_model_predictions
def unify_predictions(predictions: List[ExpansionInstance]) -> List[Dict]:
sentences_unified_substitutions = defaultdict(list)
indexed_predictions: Dict[str, ExpansionInstance] = {}
for pred in predictions:
doc_id, parag_id, s_id, sent_mask_part = pred.sent_id.split('.') # sbl00756.p1.s147.0
sent_id = f"{doc_id}.{parag_id}.{s_id}"
sentences_unified_substitutions[sent_id].append((pred.mask_index, pred.prediction, pred.gold, pred.abbrev))
indexed_predictions[sent_id] = pred # Just save one per sent_id, the masks will be overriden anyway on the next step...
unified_predictions: List[ExpansionInstance] = []
for sent_id, substitutes in sentences_unified_substitutions.items():
template_obj = indexed_predictions[sent_id]
template = template_obj.query.split()
prd_lst, gld_lst, ix_lst, abbr_lst = [], [], [], []
for s_ix, s_pred_tok, s_gold_tok, s_abbrev in substitutes:
template[s_ix] = s_pred_tok
ix_lst.append(s_ix)
prd_lst.append(s_pred_tok)
gld_lst.append(s_gold_tok)
abbr_lst.append(s_abbrev)
unified_predictions.append({'doc_id': template_obj.doc_id, "sent_id": sent_id, 'predicted_sentence': template, 'pred':prd_lst, 'gold': gld_lst, 'indices': ix_lst, 'abbrs': abbr_lst})
return unified_predictions
def mask_token_replacer(dataset: List, current_mask: str, new_mask: str) -> List:
for example in dataset:
sentence = example['masked_tokens']
mask_ix = sentence.index(current_mask)
sentence[mask_ix] = new_mask
example['masked_tokens'] = sentence
return dataset
def load_abbreviations_dataset(filepath: str) -> List[Dict]:
data = []
with open(filepath) as f:
for line in f.readlines():
data.append(json.loads(line))
return data
def load_abbreviations_prompt_dataset(filepath: str, mask_token: str = '<mask>', is_word: str = 'je') -> List[Dict]:
data = []
window_size = 20
with open(filepath) as f:
for line in f.readlines():
example = json.loads(line)
sentence = example['masked_tokens']
mask_ix = sentence.index(mask_token)
sentence[mask_ix] = example['candidate']
prev_ctx_ix = mask_ix - window_size if mask_ix - window_size > 0 else 0
post_ctx_ix = mask_ix - window_size if mask_ix + window_size < len(sentence) else len(sentence)
sentence[mask_ix] = example['candidate']
full_prompt = sentence[prev_ctx_ix:post_ctx_ix] + ['[SEP]'] + [example['candidate'], is_word, mask_token]
assert full_prompt.count(mask_token) == 1, 'The full prompt is not properly masked!'
example['masked_tokens'] = full_prompt
data.append(example)
return data
def evaluate_expansion_predictions(predictions: List[ExpansionInstance]):
matches, errors = 0, 0
soft_matches, soft_errors = 0, 0
error_data, soft_error_list = [], []
word_not_found = 0
for pred in predictions:
if pred.prediction.lower() == pred.gold.lower():
matches += 1
else:
errors += 1
if pred.prediction == '<NO-FIT>':
word_not_found += 1
error_data.append(pred._asdict())
# Soft analysis, we take it as a match if the candidate is IN The Prediciton List (we can figure out later some heuristics to try to get it form the list)
# YET WE COULD GO SOFTER! Levenshtein ditance to the abbr candidate? partial character match? Just to pick something...
if pred.gold.lower() in [p.text.lower() for p in pred.pred_candidates]:
soft_matches += 1
else:
soft_errors += 1
top_prob_candidates = [p.text for p in pred.pred_candidates[:5]]
levenshtein_candidates = choose_best_fit(pred.pred_candidates, BestFitPolicy.LEVENSHTEIN, topk=5)
jaccard_n1_candidates = choose_best_fit(pred.pred_candidates, BestFitPolicy.JACCARD_N1, topk=5)
jaccard_n2_candidates = choose_best_fit(pred.pred_candidates, BestFitPolicy.JACCARD_N2, topk=5)
soft_error_list.append({'sent_id': pred.sent_id, 'masked': pred.abbrev, 'gold_expansion': pred.gold,
'model_candidates': top_prob_candidates,
'lev_candidates': levenshtein_candidates,
'jacc_n1': jaccard_n1_candidates,
'jacc_n2': jaccard_n2_candidates
})
logging.info(f"Correct = {matches} || Errors: {errors} (from which {word_not_found} where <NO-FIT>) || Accuracy = {matches*100/(errors+matches):.2f}%")
logging.info(f"Possibly Correct = {soft_matches} || Definitely Errors (not found in the Top-N): {soft_errors} || Accuracy = {soft_matches*100/(soft_errors+soft_matches):.2f}%")
err_ignore_no_found = errors - word_not_found
logging.info(f"Correct = {matches} || Errors (ignoring <NO-FIT>): {err_ignore_no_found}|| Accuracy (ignoring <NO-FIT>) = {matches*100/(err_ignore_no_found+matches):.2f}%")
pd.DataFrame(error_data).to_csv('abbreviations/data/ERRORS.csv', columns=['gold', 'prediction', 'query'])
pd.DataFrame(soft_error_list).to_csv('abbreviations/data/ERRORS_NO-TOPN.csv')
if __name__ == '__main__':
TOP_K=5
DATA_PARTITION='dev'
MODEL_OUT_DIR = 'data'
BERT_MODEL_NAME= "EMBEDDIA/sloberta"
BERT_TOKENIZER_NAME='EMBEDDIA/sloberta'
gpu_wanted = 0
# Get GPU (if available)
GPU_DEV, USE_CUDA = get_torch_device(verbose=True, gpu_ix=gpu_wanted)
if USE_CUDA:
GPU_IX = gpu_wanted
else:
GPU_IX = -1
# Initialize Logger
console_hdlr = logging.StreamHandler(sys.stdout)
file_hdlr = logging.FileHandler(filename=f"{MODEL_OUT_DIR}/bert_abbrev_expansion.{DATA_PARTITION}.log")
logging.basicConfig(level=logging.INFO, handlers=[console_hdlr, file_hdlr])
# Load Dataset
test_data = load_abbreviations_dataset(f"data/sbl-51abbr.masked.upperbound.preexp.{DATA_PARTITION}.json")
#### Fill-in Masked abbreviation with BERT predictions ...
## [logging.info(x['Sentence']) for x in dataset]
predictions, pred_dict = get_lang_model_predictions(test_data, BERT_MODEL_NAME, BERT_TOKENIZER_NAME, TOP_K, GPU_IX)
pd.DataFrame([p._asdict() for p in predictions]).to_json(f'{MODEL_OUT_DIR}/model_raw_predictions.{DATA_PARTITION}.jsonl', orient='records')
# Save Predictions
unified_predictions = unify_predictions(predictions)
pd.DataFrame(unified_predictions).to_json(f'{MODEL_OUT_DIR}/model_unified_predictions.{DATA_PARTITION}.jsonl')
# Save Accumulated Per-Candidate Predictions
unique_vals_preds = {k: Counter(v).most_common(10) for k,v in pred_dict.items()}
json.dump(unique_vals_preds, open(f"{MODEL_OUT_DIR}/bert_prediction_mapping.{DATA_PARTITION}.json", "w"), indent=2)
evaluate_expansion_predictions(predictions)