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show_count_stats.py
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show_count_stats.py
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from dataset import JSONLDataset, TabularDataset, PickleDataset
#import models.openai as openai
#from util import parse_example, parse_tsv_example, score_sets
import numpy as np
# from dotenv import load_dotenv
# from prompt import PromptGenerator
import argparse
from tqdm.auto import tqdm
import os
import json
import shutil
#import logging
from datetime import datetime
import signal
import sys, pdb
#logger = logging.getLogger('main')
running = True
def signal_handler(sig, frame):
print('Exiting...')
global running
running = False
def parse_args():
parser = argparse.ArgumentParser(
prog='promptbench',
description='Prompt benchmarking utility'
)
parser.add_argument('-l', '--lang', type=str)
parser.add_argument('-d', '--dataset', type=str)
parser.add_argument('-p', '--prompt', type=str, default='ner')
parser.add_argument('-td', '--target-dataset', type=str)
parser.add_argument('-sd', '--source-dataset', type=str)
#parser.add_argument('-l', '--llama-url', type=str, help="LLaMa API URL")
parser.add_argument('-m', '--model', type=str, help="model", default='gpt-3.5-turbo')
parser.add_argument('-tr', '--target-retrieve', type=int, help="no. examples to retrieve from target", default=0)
parser.add_argument('-sr', '--source-retrieve', type=int, help="no. examples to retrieve from source", default=8)
parser.add_argument('-y', '--yes', action="store_true", help="Say yes to any conditionals")
parser.add_argument('-r', '--result-file', type=str, default=f"{datetime.now().strftime('%Y%m%dT%H%M%S')}")
parser.add_argument('-ssim', '--source-sim', type=str, help="Source similarity matrix")
parser.add_argument('-tsim', '--target-sim', type=str, help="Target similarity matrix")
parser.add_argument('-s', '--split-start', type=int, default=0)
parser.add_argument('-e', '--split-end', type=int, default=100000)
parser.add_argument('-i', '--interm', type=int, default=10)
parser.add_argument('-t', '--temperature', type=float, default=0.5)
return parser.parse_args()
def create_save_dir(save_dir, overwrite):
if os.path.exists(save_dir):
if overwrite:
print('Output folder already exists, overwriting')
shutil.rmtree(save_dir)
else:
print('Overwrite preexisting output folder? (y/N): ', end='')
ch = input()
if (ch == 'y'):
shutil.rmtree(save_dir)
else:
save_dir += '_1'
os.makedirs(save_dir)
return save_dir
def setup_logger(save_dir):
logging.basicConfig(
filename=os.path.join(save_dir, 'logfile.log'),
filemode='a',
format='[%(asctime)s.%(msecs)d](%(name)s:%(levelname)s) %(message)s',
datefmt='%H:%M:%S',
level=logging.INFO
)
def gold_tags_to_tsv_output(sentence):
temp = sentence.strip().split(' ')
temp = [w.rsplit('_', 1) for w in temp]
return '\n'.join([f'{x[0]}\t{x[1]}' for x in temp])
def sentence_to_input(sentence):
temp = sentence.split(' ')
return "[" + ", ".join([f'"{a}"' for a in temp]) + "]"
def gold_tags_to_output(sentence):
temp = [a.rsplit('_', 1) for a in sentence.strip().split(' ')]
return "[" + ", ".join([f'(``{a[0]}", ``{a[1]}")' for a in temp]) + "]"
def construct_prompt(idx, tgt_ds, src_ds):
#ind_tgt = tgt_sim_mat[idx]
#pdb.set_trace()
try:
preds_ = [word.split("_")[1] for word in tgt_ds[idx]['output'].strip().split(" ")]
golds_ = [word.split("_")[1] for word in src_ds[idx]['output'].strip().split(" ")]
except:
pdb.set_trace()
try:
assert(len(preds_) == len(golds_))
except:
pdb.set_trace()
#pdb.set_trace()
#preds_golds = [pred_+"\t"+gold_ for pred_, gold_ in zip(tgt_ds[idx].copy()['pred_labels'], src_ds[idx].copy()['pred_labels'])]
return preds_, golds_
def get_response_from_gpt(example, task, prompt, model):
# confidence scores via sampling multiple times...
# not now.
completion = model.complete(prompt)
if completion is None:
logger.error(f"Did not obtain response for input {example['input']}, setting everything to O")
model.cleanup()
return {
'gold_labels': [a.split('_') for a in example['output'].strip().split(' ')],
'pred_labels': [(a, 'O') for a in example['input'].strip().split(' ')]
}
logger.info(f'Obtained completion: {completion}')
response = parse_tsv_example(task, example, completion)
model.cleanup()
return response
def save_data(data, save_dir):
with open(os.path.join(save_dir, f'responses.json'), 'w+') as outfile:
for response in data['responses']:
outfile.write(f"{json.dumps(response)}\n")
with open(os.path.join(save_dir, f'accuracies.csv'), 'w+') as accfile:
accfile.write(f"precision,recall,f1,total\n")
accfile.write(f"{data['precision']},{data['recall']},{data['f1']},{data['total']}\n")
def main():
signal.signal(signal.SIGINT, signal_handler)
args = parse_args()
#load_dotenv(os.path.join(os.path.dirname(__file__), '../.env'))
#openai.setup_api_key(os.environ.get('OPENAI_API_KEY'))
#save_dir = create_save_dir(args.result_dir, args.yes)
#setup_logger(save_dir)
# pg = PromptGenerator('prompts')
# model_args = openai.ChatGPT.DEFAULT_ARGS
# model_args['engine'] = args.model
# model_args['request_timeout'] = 100
# model = openai.ChatGPT(model_args)
# #ssim = np.load(args.source_sim)
# #tsim = None
# #if args.target_sim:
tsim = np.load(args.target_sim)
#model.default_args['temperature'] = args.temperature
# if args.dataset.endswith('.pkl'):
# ds = PickleDataset(args.dataset)[args.split_start:args.split_end]
# elif args.dataset.endswith('.tsv'):
# ds = TabularDataset(args.dataset, delimiter='\t')[args.split_start:args.split_end]
# else:
# pdb.set_trace()
if args.source_dataset.endswith('.json'):
pdb.set_trace()
sds = JSONLDataset(args.source_dataset)
elif args.source_dataset.endswith('.tsv'):
sds = TabularDataset(args.source_dataset, delimiter='\t')
else:
pdb.set_trace()
tds = None
if args.target_dataset.endswith('.json'):
#pdb.set_trace()
tds = json.load(open(args.target_dataset))
elif args.target_dataset.endswith('.tsv'):
tds = TabularDataset(args.target_dataset, delimiter='\t')
else:
pdb.set_trace()
if tsim.shape == (99,99):
#pdb.set_trace()
selections = []
for i in range(tsim.shape[0]):
selections.append(np.argsort(tsim[i])[::-1][1:][:8].tolist())
assert i not in np.argsort(tsim[i])[::-1][1:][:8].tolist()
tsim = np.array(selections)
#pdb.set_trace()
else:
selections = [j for it in tsim.tolist() for j in it]
#pdb.set_trace()
preds = []
gold = []
#print(selections)
#pdb.set_trace()
count_dict = {"neutral":0, "entailment":0, "contradiction":0}
LABELS = list(count_dict.keys())
for it in tsim.tolist():
curr_pred = [tds[idx] for idx in it]
for label in LABELS:
count_dict[label] += curr_pred.count(label)
# pred_, gold_ = tds[idx], sds[idx].copy()['output']
# preds.append(pred_)
# gold.append(gold_)
print("count stats: ", count_dict)
print("Avg stats:")
for key in count_dict:
print(key, count_dict[key]/len(tsim.tolist()))
if __name__ == "__main__":
main()