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generate_predictions.py
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generate_predictions.py
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import argparse
import gc
import multiprocessing as mp
import os
import pickle
import sys
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from scipy.special import softmax
from torch.utils.data import Dataset
from transformers import AutoModelForTokenClassification, AutoTokenizer, TrainingArguments, Trainer
TEST_DIR = 'data/competition_data/test'
#TEST_DIR = 'data/competition_data/mini_test'
print(f"Test data directory: {TEST_DIR}")
ap = argparse.ArgumentParser()
ap.add_argument('--model_paths', nargs='+', required=True)
ap.add_argument("--save_name", type=str, required=True)
ap.add_argument("--max_len", type=int, required=True)
ap.add_argument("--batch_size", type=int, required=True)
ap.add_argument("--reuse", action='store_true', default=False,
help="Whether to reuse tokenized data from previous runs")
args = ap.parse_args()
# Define paths for saving and loading tokenized data
TOKENIZED_TEST_DATA_PATH = f'data/processed/{Path(args.save_name).stem}_tokenized_test_data.pkl'
# Get the path of the current script
script_dir = os.path.dirname(os.path.abspath(__file__))
# Get the parent directory of the script directory
parent_dir = os.path.dirname(script_dir)
# Add the parent directory to the Python path
sys.path.append(parent_dir)
if "longformerwithlstm" in args.save_name:
from models.longformerwithbilstmhead import LongformerForTokenClassificationwithbiLSTM
if "debertawithlstm" in args.save_name:
from models.deberta_lstm import DebertaForTokenClassificationwithbiLSTM
os.environ['TOKENIZERS_PARALLELISM'] = 'false'
# Get the number of available CPU cores
NUM_CORES = mp.cpu_count()
BATCH_SIZE = args.batch_size
MAX_SEQ_LENGTH = args.max_len
PRETRAINED_MODEL_PATHS = args.model_paths
if "debertal_chris" in args.save_name:
print('==> using -1 in offset mapping...')
if ("v3" in args.save_name) | ("v2" in args.save_name):
print('==> using -1 in offset mapping...')
AGG_FUNC = np.mean
print('==> using span token mean...')
MIN_TOKENS = {
"Lead": 32,
"Position": 5,
"Evidence": 35,
"Claim": 7,
"Concluding Statement": 6,
"Counterclaim": 6,
"Rebuttal": 6
}
if "chris" not in args.save_name:
ner_labels = {'O': 0,
'B-Lead': 1,
'I-Lead': 2,
'B-Position': 3,
'I-Position': 4,
'B-Evidence': 5,
'I-Evidence': 6,
'B-Claim': 7,
'I-Claim': 8,
'B-Concluding Statement': 9,
'I-Concluding Statement': 10,
'B-Counterclaim': 11,
'I-Counterclaim': 12,
'B-Rebuttal': 13,
'I-Rebuttal': 14}
else:
print("==> Using Chris BIO")
ner_labels = {'O': 14,
'B-Lead': 0,
'I-Lead': 1,
'B-Position': 2,
'I-Position': 3,
'B-Evidence': 4,
'I-Evidence': 5,
'B-Claim': 6,
'I-Claim': 7,
'B-Concluding Statement': 8,
'I-Concluding Statement': 9,
'B-Counterclaim': 10,
'I-Counterclaim': 11,
'B-Rebuttal': 12,
'I-Rebuttal': 13}
inverted_ner_labels = dict((v, k) for k, v in ner_labels.items())
inverted_ner_labels[-100] = 'Special Token'
test_files = os.listdir(TEST_DIR)
# accepts file path, returns tuple of (file_ID, txt split, NER labels)
def generate_text_for_file(input_filename):
curr_id = input_filename.split('.')[0]
with open(os.path.join(TEST_DIR, input_filename)) as f:
curr_txt = f.read()
return curr_id, curr_txt
with mp.Pool(NUM_CORES) as p:
ner_test_rows = p.map(generate_text_for_file, test_files)
if ("v3" in args.save_name) or ("v2" in args.save_name):
from transformers import DebertaTokenizerFast
tokenizer = DebertaTokenizerFast.from_pretrained(PRETRAINED_MODEL_PATHS[0])
else:
tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_PATHS[0])
ner_test_rows = sorted(ner_test_rows,
key=lambda x: len(tokenizer(x[1], max_length=MAX_SEQ_LENGTH, truncation=True)['input_ids']))
# tokenize and store word ids
def tokenize_with_word_ids(ner_raw_data):
# ner_raw_data is shaped (num_examples, 3) where cols are (ID, words, word-level labels)
tokenized_inputs = tokenizer([x[1] for x in ner_raw_data],
max_length=MAX_SEQ_LENGTH,
return_offsets_mapping=True,
truncation=True, padding=False)
tokenized_inputs['id'] = [x[0] for x in ner_raw_data]
tokenized_inputs['offset_mapping'] = [tokenized_inputs['offset_mapping'][i] for i in range(len(ner_raw_data))]
return tokenized_inputs
# tokenized_all = tokenize_with_word_ids(ner_test_rows)
# Load or tokenize and store word ids
if os.path.exists(TOKENIZED_TEST_DATA_PATH) and args.reuse:
with open(TOKENIZED_TEST_DATA_PATH, 'rb') as f:
tokenized_all = pickle.load(f)
print("Loaded the tokenized test data from previous runs.")
else:
tokenized_all = tokenize_with_word_ids(ner_test_rows)
if args.reuse:
with open(TOKENIZED_TEST_DATA_PATH, 'wb') as f:
pickle.dump(tokenized_all, f)
print("Saved the tokenized test data.")
# import sys
# sys.exit(-100)
class NERDataset(Dataset):
def __init__(self, input_dict):
self.input_dict = input_dict
def __getitem__(self, index):
return {k: self.input_dict[k][index] for k in self.input_dict.keys() if k not in {'id', 'offset_mapping'}}
def get_filename(self, index):
return self.input_dict['id'][index]
def get_offset(self, index):
return self.input_dict['offset_mapping'][index]
def __len__(self):
return len(self.input_dict['input_ids'])
test_dataset = NERDataset(tokenized_all)
soft_predictions = None
hfargs = TrainingArguments(output_dir='tmp/trainer_tmp',
log_level='warning',
per_device_eval_batch_size=BATCH_SIZE)
# Check if GPU is available, if not, use CPU
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
for idx, curr_path in enumerate(PRETRAINED_MODEL_PATHS):
# print(f"Model name: {args.save_name}")
if "longformerwithlstm" in args.save_name:
model = LongformerForTokenClassificationwithbiLSTM.from_pretrained(curr_path)
elif "debertawithlstm" in args.save_name:
model = DebertaForTokenClassificationwithbiLSTM.from_pretrained(curr_path)
elif "lsg" in args.save_name or "debertal_chris" in args.save_name or "debertal" in args.save_name or "debertaxl" in args.save_name:
model = AutoModelForTokenClassification.from_pretrained(curr_path, trust_remote_code=True)
else:
model = AutoModelForTokenClassification.from_pretrained(curr_path, trust_remote_code=True,
torch_dtype=torch.float16)
trainer = Trainer(model.to(device),
hfargs,
tokenizer=tokenizer)
curr_preds, _, _ = trainer.predict(test_dataset)
curr_preds = curr_preds.astype(np.float16)
curr_preds = softmax(curr_preds, -1)
if soft_predictions is not None:
soft_predictions = soft_predictions + curr_preds
else:
soft_predictions = curr_preds
del model, trainer, curr_preds
gc.collect()
soft_predictions = soft_predictions / len(PRETRAINED_MODEL_PATHS)
soft_claim_predictions = soft_predictions[:, :, 8]
predictions = np.argmax(soft_predictions, axis=2)
soft_predictions = np.max(soft_predictions, axis=2)
def generate_token_to_word_mapping(txt, offset):
# GET WORD POSITIONS IN CHARS
w = []
blank = True
for i in range(len(txt)):
if not txt[i].isspace() and blank == True:
w.append(i)
blank = False
elif txt[i].isspace():
blank = True
w.append(1e6)
# MAPPING FROM TOKENS TO WORDS
word_map = -1 * np.ones(len(offset), dtype='int32')
w_i = 0
for i in range(len(offset)):
if offset[i][1] == 0: continue
while offset[i][0] >= (w[w_i + 1] - ("debertal_chris" in args.save_name) - ("v3" in args.save_name) \
- ("v2" in args.save_name)): w_i += 1
word_map[i] = int(w_i)
return word_map
all_preds = []
# Clumsy gathering of predictions at word lvl - only populate with 1st subword pred
for curr_sample_id in range(len(test_dataset)):
curr_preds = []
sample_preds = predictions[curr_sample_id]
sample_offset = test_dataset.get_offset(curr_sample_id)
sample_txt = ner_test_rows[curr_sample_id][1]
sample_word_map = generate_token_to_word_mapping(sample_txt, sample_offset)
word_preds = [''] * (max(sample_word_map) + 1)
word_probs = dict(zip(range((max(sample_word_map) + 1)), [0] * (max(sample_word_map) + 1)))
claim_probs = dict(zip(range((max(sample_word_map) + 1)), [0] * (max(sample_word_map) + 1)))
for i, curr_word_id in enumerate(sample_word_map):
if curr_word_id != -1:
if word_preds[curr_word_id] == '': # only use 1st subword
word_preds[curr_word_id] = inverted_ner_labels[sample_preds[i]]
word_probs[curr_word_id] = soft_predictions[curr_sample_id, i]
claim_probs[curr_word_id] = soft_claim_predictions[curr_sample_id, i]
elif 'B-' in inverted_ner_labels[sample_preds[i]]:
word_preds[curr_word_id] = inverted_ner_labels[sample_preds[i]]
word_probs[curr_word_id] = soft_predictions[curr_sample_id, i]
claim_probs[curr_word_id] = soft_claim_predictions[curr_sample_id, i]
# Dict to hold Lead, Position, Concluding Statement
let_one_dict = dict() # K = Type, V = (Prob of start token, start, end)
# If we see tokens I-X, I-Y, I-X -> change I-Y to I-X
for j in range(1, len(word_preds) - 1):
pred_trio = [word_preds[k] for k in [j - 1, j, j + 1]]
splitted_trio = [x.split('-')[0] for x in pred_trio]
if all([x == 'I' for x in splitted_trio]) and pred_trio[0] == pred_trio[2] and pred_trio[0] != pred_trio[1]:
word_preds[j] = word_preds[j - 1]
# B-X, ? (not B), I-X -> change ? to I-X
for j in range(1, len(word_preds) - 1):
if 'B-' in word_preds[j - 1] and word_preds[j + 1] == f"I-{word_preds[j - 1].split('-')[-1]}" and word_preds[
j] != word_preds[j + 1] and 'B-' not in word_preds[j]:
word_preds[j] = word_preds[j + 1]
# If we see tokens I-X, O, I-X, change center token to the same for stated discourse types
for j in range(1, len(word_preds) - 1):
if word_preds[j - 1] in ['I-Lead', 'I-Position', 'I-Concluding Statement'] and word_preds[j - 1] == word_preds[
j + 1] and word_preds[j] == 'O':
word_preds[j] = word_preds[j - 1]
j = 0 # start of candidate discourse
while j < len(word_preds):
cls = word_preds[j]
cls_splitted = cls.split('-')[-1]
end = j + 1 # try to extend discourse as far as possible
if word_probs[j] > 0.54:
# Must match suffix i.e., I- to I- only; no B- to I-
while end < len(word_preds) and (
word_preds[end].split('-')[-1] == cls_splitted if cls_splitted in ['Lead', 'Position',
'Concluding Statement'] else
word_preds[
end] == f'I-{cls_splitted}'):
end += 1
# if we're here, end is not the same pred as start
if cls != 'O' and (end - j > MIN_TOKENS[cls_splitted] or max(
word_probs[l] for l in range(j, end)) > 0.73): # needs to be longer than class-specified min
if cls_splitted in ['Lead', 'Position', 'Concluding Statement']:
lpc_max_prob = max(word_probs[c] for c in range(j, end))
if cls_splitted in let_one_dict: # Already existing, check contiguous or higher prob
prev_prob, prev_start, prev_end = let_one_dict[cls_splitted]
if cls_splitted in ['Lead',
'Concluding Statement'] and j - prev_end < 49: # If close enough, combine
let_one_dict[cls_splitted] = (max(prev_prob, lpc_max_prob), prev_start, end)
# Delete other preds that lie inside the joined LC discourse
for l in range(len(curr_preds) - 1, 0, -1):
check_span = curr_preds[l][2]
check_start, check_end = int(check_span[0]), int(check_span[-1])
if check_start > prev_start and check_end < end:
del curr_preds[l]
elif lpc_max_prob > prev_prob: # Overwrite if current candidate is more likely
let_one_dict[cls_splitted] = (lpc_max_prob, j, end)
else: # Add to it
let_one_dict[cls_splitted] = (lpc_max_prob, j, end)
else:
# Lookback and add preceding I- tokens
while j - 1 > 0 and word_preds[j - 1] == cls:
j = j - 1
# Try to add the matching B- tag if immediately precedes the current I- sequence
if j - 1 > 0 and word_preds[j - 1] == f'B-{cls_splitted}':
j = j - 1
#############################################################
# Run a bunch of adjustments to discourse predictions based on CV
adj_start, adj_end = j, end + 1
# Run some heuristics against previous discourse
if len(curr_preds) > 0:
prev_span = list(map(int, curr_preds[-1][2].split()))
prev_start, prev_end = prev_span[0], prev_span[-1]
# Join adjacent rebuttals
if cls_splitted in 'Rebuttal':
if curr_preds[-1][1] == cls_splitted and adj_start - prev_end < 32:
del curr_preds[-1]
combined_list = prev_span + list(range(adj_start, adj_end))
curr_preds.append((test_dataset.get_filename(curr_sample_id),
cls_splitted,
' '.join(map(str, combined_list)),
AGG_FUNC([word_probs[i] for i in combined_list if
i in word_probs.keys()])))
j = end
continue
elif cls_splitted in 'Counterclaim':
if curr_preds[-1][1] == cls_splitted and adj_start - prev_end < 24:
del curr_preds[-1]
combined_list = prev_span + list(range(adj_start, adj_end))
curr_preds.append((test_dataset.get_filename(curr_sample_id),
cls_splitted,
' '.join(map(str, combined_list)),
AGG_FUNC([word_probs[i] for i in combined_list if
i in word_probs.keys()])))
j = end
continue
elif cls_splitted in 'Evidence':
if curr_preds[-1][1] == cls_splitted and 8 < adj_start - prev_end < 25:
if max(claim_probs[l] for l in range(prev_end + 1, adj_start)) > 0.35:
claim_tokens = [str(l) for l in range(prev_end + 1, adj_start) if
claim_probs[l] > 0.15]
if len(claim_tokens) > 2:
curr_preds.append((test_dataset.get_filename(curr_sample_id),
'Claim',
' '.join(claim_tokens),
AGG_FUNC([word_probs[int(i)] for i in claim_tokens if
int(i) in word_probs.keys()])))
# If gap with discourse of same type, extend to it
elif curr_preds[-1][1] == cls_splitted and adj_start - prev_end > 2:
adj_start -= 1
# Adjust discourse lengths if too long or short
if cls_splitted == 'Evidence':
if adj_end - adj_start < 45:
adj_start -= 9
else:
adj_end -= 1
elif cls_splitted == 'Claim':
if adj_end - adj_start > 24:
adj_end -= 1
elif cls_splitted == 'Counterclaim':
if adj_end - adj_start > 24:
adj_end -= 1
else:
adj_start -= 1
adj_end += 1
elif cls_splitted == 'Rebuttal':
if adj_end - adj_start > 32:
adj_end -= 1
else:
adj_start -= 1
adj_end += 1
adj_start = max(0, adj_start)
adj_end = min(len(word_preds) - 1, adj_end)
curr_preds.append((test_dataset.get_filename(curr_sample_id),
cls_splitted,
' '.join(map(str, list(range(adj_start, adj_end)))),
AGG_FUNC([word_probs[i] for i in range(adj_start, adj_end) if
i in word_probs.keys()])))
j = end
# Add the Lead, Position, Concluding Statement
for k, v in let_one_dict.items():
pred_start = v[1]
pred_end = v[2]
# Lookback and add preceding I- tokens
while pred_start - 1 > 0 and word_preds[pred_start - 1] == f'I-{k}':
pred_start = pred_start - 1
# Try to add the matching B- tag if immediately precedes the current I- sequence
if pred_start - 1 > 0 and word_preds[pred_start - 1] == f'B-{k}':
pred_start = pred_start - 1
# Extend short Leads and Concluding Statements
if k == 'Lead':
if pred_end - pred_start < 33:
pred_end = min(len(word_preds), pred_end + 5)
else:
pred_end -= 5
elif k == 'Concluding Statement':
if pred_end - pred_start < 23:
pred_start = max(0, pred_start - 1)
pred_end = min(len(word_preds), pred_end + 10)
elif k == 'Position':
if pred_end - pred_start < 18:
pred_end = min(len(word_preds), pred_end + 3)
pred_start = max(0, pred_start)
if pred_end - pred_start > 6:
curr_preds.append((test_dataset.get_filename(curr_sample_id),
k,
' '.join(map(str, list(range(pred_start, pred_end)))),
AGG_FUNC(
[word_probs[i] for i in range(pred_start, pred_end) if i in word_probs.keys()])))
all_preds.extend(curr_preds)
output_df = pd.DataFrame(all_preds)
output_df.columns = ['id', 'class', 'predictionstring', 'scores']
output_df.to_csv(f'{args.save_name}.csv', index=False)