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| import warnings | |
| warnings.filterwarnings("ignore") | |
| from sklearn.metrics import accuracy_score,f1_score | |
| from datasets import load_dataset, load_from_disk, Dataset | |
| from tqdm import tqdm | |
| import datasets | |
| import torch | |
| from torch.utils.data import DataLoader | |
| from functools import partial | |
| from pathlib import Path | |
| dic = { | |
| 0:"negative", | |
| 1:'neutral', | |
| 2:'positive', | |
| } | |
| with open(Path(__file__).parent / 'sentiment_templates.txt') as f: | |
| templates = [l.strip() for l in f.readlines()] | |
| def format_example(example: dict) -> dict: | |
| context = f"Instruction: {example['instruction']}\n" | |
| if example.get("input"): | |
| context += f"Input: {example['input']}\n" | |
| context += "Answer: " | |
| target = example["output"] | |
| return {"context": context, "target": target} | |
| def change_target(x): | |
| if 'positive' in x or 'Positive' in x: | |
| return 'positive' | |
| elif 'negative' in x or 'Negative' in x: | |
| return 'negative' | |
| else: | |
| return 'neutral' | |
| def vote_output(x): | |
| output_dict = {'positive': 0, 'negative': 0, 'neutral': 0} | |
| for i in range(len(templates)): | |
| pred = change_target(x[f'out_text_{i}'].lower()) | |
| output_dict[pred] += 1 | |
| if output_dict['positive'] > output_dict['negative']: | |
| return 'positive' | |
| elif output_dict['negative'] > output_dict['positive']: | |
| return 'negative' | |
| else: | |
| return 'neutral' | |
| def test_fpb(args, model, tokenizer, prompt_fun=None): | |
| batch_size = args.batch_size | |
| # instructions = load_dataset("financial_phrasebank", "sentences_50agree") | |
| instructions = load_from_disk(Path(__file__).parent.parent / "data/financial_phrasebank-sentences_50agree/") | |
| instructions = instructions["train"] | |
| instructions = instructions.train_test_split(seed = 42)['test'] | |
| instructions = instructions.to_pandas() | |
| instructions.columns = ["input", "output"] | |
| instructions["output"] = instructions["output"].apply(lambda x:dic[x]) | |
| if prompt_fun is None: | |
| instructions["instruction"] = "What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}." | |
| else: | |
| instructions["instruction"] = instructions.apply(prompt_fun, axis = 1) | |
| instructions[["context","target"]] = instructions.apply(format_example, axis = 1, result_type="expand") | |
| # print example | |
| print(f"\n\nPrompt example:\n{instructions['context'][0]}\n\n") | |
| context = instructions['context'].tolist() | |
| total_steps = instructions.shape[0]//batch_size + 1 | |
| print(f"Total len: {len(context)}. Batchsize: {batch_size}. Total steps: {total_steps}") | |
| out_text_list = [] | |
| for i in tqdm(range(total_steps)): | |
| tmp_context = context[i* batch_size:(i+1)* batch_size] | |
| tokens = tokenizer(tmp_context, return_tensors='pt', padding=True, max_length=512, return_token_type_ids=False) | |
| for k in tokens.keys(): | |
| tokens[k] = tokens[k].cuda() | |
| res = model.generate(**tokens, max_length=512, eos_token_id=tokenizer.eos_token_id) | |
| res_sentences = [tokenizer.decode(i, skip_special_tokens=True) for i in res] | |
| # print(f'{i}: {res_sentences[0]}') | |
| out_text = [o.split("Answer: ")[1] for o in res_sentences] | |
| out_text_list += out_text | |
| torch.cuda.empty_cache() | |
| instructions["out_text"] = out_text_list | |
| instructions["new_target"] = instructions["target"].apply(change_target) | |
| instructions["new_out"] = instructions["out_text"].apply(change_target) | |
| acc = accuracy_score(instructions["new_target"], instructions["new_out"]) | |
| f1_macro = f1_score(instructions["new_target"], instructions["new_out"], average = "macro") | |
| f1_micro = f1_score(instructions["new_target"], instructions["new_out"], average = "micro") | |
| f1_weighted = f1_score(instructions["new_target"], instructions["new_out"], average = "weighted") | |
| print(f"Acc: {acc}. F1 macro: {f1_macro}. F1 micro: {f1_micro}. F1 weighted (BloombergGPT): {f1_weighted}. ") | |
| return instructions | |
| def test_fpb_mlt(args, model, tokenizer): | |
| batch_size = args.batch_size | |
| # dataset = load_dataset("financial_phrasebank", "sentences_50agree") | |
| dataset = load_from_disk(Path(__file__).parent.parent / 'data/financial_phrasebank-sentences_50agree/') | |
| dataset = dataset["train"]#.select(range(300)) | |
| dataset = dataset.train_test_split(seed=42)['test'] | |
| dataset = dataset.to_pandas() | |
| dataset.columns = ["input", "output"] | |
| dataset["output"] = dataset["output"].apply(lambda x: dic[x]) | |
| dataset["text_type"] = dataset.apply(lambda x: 'news', axis=1) | |
| dataset["output"] = dataset["output"].apply(change_target) | |
| dataset = dataset[dataset["output"] != 'neutral'] | |
| out_texts_list = [[] for _ in range(len(templates))] | |
| def collate_fn(batch): | |
| inputs = tokenizer( | |
| [f["context"] for f in batch], return_tensors='pt', | |
| padding=True, max_length=args.max_length, | |
| return_token_type_ids=False | |
| ) | |
| return inputs | |
| for i, template in enumerate(templates): | |
| dataset = dataset[['input', 'output', "text_type"]] | |
| dataset["instruction"] = dataset['text_type'].apply(lambda x: template.format(type=x) + "\nOptions: positive, negative") | |
| # dataset["instruction"] = dataset['text_type'].apply(lambda x: template.format(type=x) + "\nOptions: negative, positive") | |
| dataset[["context", "target"]] = dataset.apply(format_example, axis=1, result_type="expand") | |
| dataloader = DataLoader(Dataset.from_pandas(dataset), batch_size=args.batch_size, collate_fn=collate_fn, shuffle=False) | |
| 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, do_sample=False, max_length=args.max_length, eos_token_id=tokenizer.eos_token_id, max_new_tokens=10) | |
| 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_texts_list[i] += out_text | |
| torch.cuda.empty_cache() | |
| for i in range(len(templates)): | |
| dataset[f"out_text_{i}"] = out_texts_list[i] | |
| dataset[f"out_text_{i}"] = dataset[f"out_text_{i}"].apply(change_target) | |
| dataset["new_out"] = dataset.apply(vote_output, axis=1, result_type="expand") | |
| dataset.to_csv('tmp.csv') | |
| for k in [f"out_text_{i}" for i in range(len(templates))] + ["new_out"]: | |
| acc = accuracy_score(dataset["target"], dataset[k]) | |
| f1_macro = f1_score(dataset["target"], dataset[k], average="macro") | |
| f1_micro = f1_score(dataset["target"], dataset[k], average="micro") | |
| f1_weighted = f1_score(dataset["target"], dataset[k], average="weighted") | |
| print(f"Acc: {acc}. F1 macro: {f1_macro}. F1 micro: {f1_micro}. F1 weighted (BloombergGPT): {f1_weighted}. ") | |
| return dataset |