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import argparse
import json
import numpy as np
import tqdm
from pathlib import Path
from pprint import pprint
from collections import defaultdict, Counter
from transformers import AutoTokenizer
import sys
sys.path.append("/home/hdd/lijinyi/CompressionInAvalon/promptcompressor/SCRL_new")
print(sys.path)
import scrl.utils as utils
from scrl.model import load_checkpoint, load_model
from scrl.eval_metrics import compute_token_f1, rouge_scorer, ROUGE_TYPES
from nltk import word_tokenize
import nltk
nltk.download('punkt')
print("punkt done!")
def main(args):
if args.model_dir is not None and args.checkpoint is None:
model = load_model(
Path(args.model_dir), device=args.device, prefix="best"
)
elif args.model_dir is None and args.checkpoint is not None:
model = load_checkpoint(Path(args.checkpoint), device=args.device)
else:
raise Exception("Provide either a model directory or checkpoint.")
model = load_model(Path(args.model_dir), device=args.device)
tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
dataset = list(utils.read_jsonl(args.dataset))
all_scores = defaultdict(list)
for item in tqdm.tqdm(dataset):
src = item["text"]
if args.lower_src:
src = src.lower()
tgts = item["summaries"]
pred = model.predict([src], tokenizer, args.device)[0]
if args.max_chars > 0:
pred = pred[:args.max_chars]
src_tokens = word_tokenize(src)
pred_tokens = word_tokenize(pred)
if args.lower_summary:
pred_tokens = [t.lower() for t in pred_tokens]
if args.pretokenized:
src_tokens = src.split()
else:
src_tokens = word_tokenize(src)
item_scores = defaultdict(list)
for tgt in tgts:
if args.pretokenized:
tgt_tokens = tgt.split()
else:
tgt_tokens = word_tokenize(tgt)
if args.lower_summary:
tgt_tokens = [t.lower() for t in tgt_tokens]
token_fscore = compute_token_f1(tgt_tokens, pred_tokens, use_counts=True)
rouge_scores = rouge_scorer.score(tgt, pred)
for rouge_type, rouge_type_scores in rouge_scores.items():
item_scores[f"{rouge_type}-p"].append(rouge_type_scores.precision)
item_scores[f"{rouge_type}-r"].append(rouge_type_scores.recall)
item_scores[f"{rouge_type}-f"].append(rouge_type_scores.fmeasure)
item_scores["token-f1"].append(token_fscore)
item_scores["tgt-len"].append(len(tgt_tokens))
item_scores["tgt-cr"].append(len(tgt_tokens) / len(src_tokens))
for k, values in item_scores.items():
item_mean = np.mean(values)
all_scores[k].append(item_mean)
all_scores["pred-len"].append(len(pred_tokens))
all_scores["src-len"].append(len(src_tokens))
all_scores["pred-cr"].append(len(pred_tokens) / len(src_tokens))
if args.verbose:
print("SRC:", src)
print("TGT:", tgts[0])
print("PRED:", pred)
print("=" * 100)
print("="*100)
print("RESULTS:")
print("="*20, "Length (#tokens):", "="*20)
for metric in ("src-len", "tgt-len", "pred-len"):
mean = np.mean(all_scores[metric])
print(f"{metric}: {mean:.2f}")
print()
print("="*20, "Compression ratio:", "="*20)
for metric in ("tgt-cr", "pred-cr"):
mean = np.mean(all_scores[metric])
print(f"{metric}: {mean:.2f}")
print()
print("="*20, "Token F1-Score:", "="*20)
mean = np.mean(all_scores["token-f1"])
print(f"f1-score: {mean:.3f}")
print()
print("="*20, "ROUGE F1-Scores:", "="*20)
for rouge_type in ROUGE_TYPES:
mean = np.mean(all_scores[f"{rouge_type}-f"])
print(f"{rouge_type}: {mean:.4f}")
print()
print("="*20, "ROUGE Recall:", "="*20)
for rouge_type in ROUGE_TYPES:
mean = np.mean(all_scores[f"{rouge_type}-r"])
print(f"{rouge_type}: {mean:.4f}")
print()
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', required=True)
parser.add_argument('--model-dir', required=False)
parser.add_argument('--checkpoint', required=False)
parser.add_argument('--device', default="cpu")
parser.add_argument('--pretokenized', action="store_true")
parser.add_argument('--max-chars', type=int, default=-1)
parser.add_argument('--verbose', action="store_true")
parser.add_argument('--lower-src', action="store_true")
parser.add_argument('--lower-summary', action="store_true")
return parser.parse_args()
if __name__ == '__main__':
main(parse_args())
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