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from seqeval.metrics import accuracy_score
from datasets import load_dataset, load_from_disk
from tqdm import tqdm
import datasets
import torch
from torch.utils.data import DataLoader
from functools import partial
import re
import sys
import numpy as np
from fingpt.FinGPT_Benchmark.utils import *
from pathlib import Path
sys.path.append('../')
def cvt_text_to_pred(text):
if not text:
return 'nan'
pred_match = re.search(r'\d+(.\d+)', text)
if pred_match is not None:
pred = pred_match.group()
else:
print(text)
pred = '0.0'
return pred
def map_output(feature):
label = cvt_text_to_pred(feature['output'])
pred = cvt_text_to_pred(feature['out_text'])
return {'label': label, 'pred': pred}
dataset = load_from_disk(Path(__file__).parent.parent / 'data/fingpt-convfinqa')['test']
dataset = dataset.map(partial(test_mapping, args), load_from_cache_file=False)
def collate_fn(batch):
inputs = tokenizer(
[f["prompt"] for f in batch], return_tensors='pt',
padding=True, max_length=args.max_length,
return_token_type_ids=False
)
return inputs
dataloader = DataLoader(dataset, batch_size=args.batch_size, collate_fn=collate_fn, shuffle=False)
out_text_list = []
log_interval = len(dataloader) // 5
for idx, inputs in enumerate(tqdm(dataloader)):
inputs = {key: value.to(model.device) for key, value in inputs.items()}
res = model.generate(**inputs, max_length=args.max_length, eos_token_id=tokenizer.eos_token_id)
res_sentences = [tokenizer.decode(i, skip_special_tokens=True) for i in res]
if (idx + 1) % log_interval == 0:
tqdm.write(f'{idx}: {res_sentences[0]}')
out_text = [o.split("Answer: ")[1] if "Answer: " in o else "" for o in res_sentences]
out_text_list += out_text
torch.cuda.empty_cache()
dataset = dataset.add_column("out_text", out_text_list)
dataset = dataset.map(map_output, load_from_cache_file=False)
dataset = dataset.filter(lambda x: x['pred'] != 'nan')
dataset = dataset.to_pandas()
print(dataset)
dataset.to_csv('tmp.csv')
label = [float(d) for d in dataset['label']]
pred = [float(d) for d in dataset['pred']]
print('Accuracy: ', accuracy_score(label, pred))
return dataset |