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import os
from time import sleep
import pandas as pd
import openai
from datasets import load_dataset
data = load_dataset("cardiffnlp/relentless", split="test")
openai.api_key = os.getenv("OPENAI_API_KEY", None)
pretty_name = {"competitor/rival of": "Rival", "friend/ally of": "Ally", "influenced by": "Inf", "known for": "Know", "similar to": "Sim"}
pretty_model = {"gpt-3.5-turbo": "GPT-3.5", "gpt-4": "GPT-4"}
def get_reply(model, text):
while True:
try:
reply = openai.ChatCompletion.create(model=model, messages=[{"role": "user", "content": text}])
break
except Exception:
print('Rate limit exceeded. Waiting for 10 seconds.')
sleep(10)
return reply['choices'][0]['message']['content']
prompt_dict = {
"friend/ally of": "entities that are friends or allies",
"competitor/rival of": "entities that are competitors or rivals",
"known for": "what entities are known for",
"influenced by": "what has influenced different entities",
"similar to": "entities that are similar"
}
def get_prompt(_data):
ref = "\n".join([str(_i) for _i in _data["prototypical_examples"]])
prefix = f'Consider the following reference list of {prompt_dict[_data["relation_type"]]}, \n{ref}\n' \
f'Now sort the entity pairs from the following list based on the extent to which they also represent ' \
f'{prompt_dict[_data["relation_type"]]} in descending order. Do not include the pairs from the reference list. ' \
f'The output should contain all the entity pairs from the following list and no duplicates:\n'
x = "\n".join([f'{str(_i)}' for _i in _data["pairs"]])
return f'{prefix}\n\n{x}'
if __name__ == '__main__':
os.makedirs('results/chat', exist_ok=True)
full_result = []
valid_count = []
for target_model in ['gpt-3.5-turbo', 'gpt-4']:
for d in data:
output_file = f"results/chat/{target_model}.{d['relation_type'].replace(' ', '_').replace('/', '-')}.json"
if not os.path.exists(output_file):
print(target_model, d['relation_type'])
i = get_prompt(d)
out = get_reply(target_model, i)
with open(output_file, 'w') as f:
f.write(out)
with open(output_file) as f:
string_pairs = [f'{str(_i)}' for _i in d["pairs"]]
out = [i for i in f.read().split("\n") if len(i) > 0]
# out = [str(eval(i)) for i in out]
new_out = []
for i in out:
try:
i = "[" + i.replace("],", "]").split("[")[1]
i = i.split("]")[0] + "]"
i = str(eval(i))
if i not in new_out:
new_out.append(i)
except Exception:
continue
ex = [i for i in string_pairs if i not in new_out]
valid_n = len(d['pairs']) - len(ex)
# valid_count.append({"model": target_model, "relation_type": d['relation_type'], "valid": f"{valid_n} ({round(100 * valid_n/len(d['pairs']))}%)"})
valid_count.append({"model": target_model, "relation_type": d['relation_type'], "valid": 100 * valid_n / len(d['pairs'])})
new_out = new_out + ex
maps = {x: n + 1 for n, x in enumerate(new_out)}
prediction = [maps[i] for i in string_pairs]
true_rank = d['ranks']
tmp = pd.DataFrame([true_rank, prediction], index=['true', 'pred']).T
cor = tmp.corr("spearman").values[0, 1]
full_result.append({"model": target_model, "relation_type": d['relation_type'], "correlation": cor})
df = pd.DataFrame(full_result)
df = df.pivot(columns="relation_type", index="model", values="correlation")
df['Avg'] = df.mean(1)
df = (df * 100).round(1)
df_cnt = pd.DataFrame(valid_count)
df_cnt = df_cnt.pivot(index='model', columns='relation_type')
df_cnt['Avg'] = df_cnt.mean(1)
df_cnt = df_cnt.round(1)
df = pd.DataFrame(df.astype(str).values + " (" + df_cnt.astype(str).values + "%)", columns=[pretty_name[c] if c in pretty_name else c for c in df.columns], index=df.index)
df.index = [pretty_model[m] for m in df.index]
df = df.T
print(df.to_latex())
# df.to_csv("results/chat/chat.csv")
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