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from sklearn.metrics import accuracy_score, f1_score, classification_report
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
from pathlib import Path
from fingpt.FinGPT_Benchmark.utils import *
import sys
sys.path.append('../')
def binary2multi(dataset):
pred, label = [], []
tmp_pred, tmp_label = [], []
for i, row in dataset.iterrows():
tmp_pred.append(row['pred'])
tmp_label.append(row['label'])
if (i + 1) % 9 == 0:
pred.append(tmp_pred)
label.append(tmp_label)
tmp_pred, tmp_label = [], []
return pred, label
def map_output(feature):
pred = 1 if 'yes' in feature['out_text'].lower() else 0
label = 1 if 'yes' in feature['output'].lower() else 0
return {'label': label, 'pred': pred}
def test_headline(args, model, tokenizer):
# dataset = load_from_disk('../data/fingpt-headline')['test']
dataset = load_from_disk(Path(__file__).parent.parent / 'data/fingpt-headline-instruct')['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]
tqdm.write(f'{idx}: {res_sentences[0]}')
if (idx + 1) % log_interval == 0:
tqdm.write(f'{idx}: {res_sentences[0]}')
out_text = [o.split("Answer: ")[1] 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.to_pandas()
print(dataset)
dataset.to_csv('tmp.csv')
# binary
acc = accuracy_score(dataset["label"], dataset["pred"])
f1 = f1_score(dataset["label"], dataset["pred"], average="binary")
# multi-class
pred, label = binary2multi(dataset)
print(f"\n|| Acc: {acc} || F1 binary: {f1} ||\n")
print(classification_report(label, pred, digits=4, target_names=['price or not', 'price up', 'price stable',
'price down', 'price past', 'price future',
'event past', 'event future', 'asset comp']))
return dataset |