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import os
from openai import OpenAI
if "OPENAI" in os.environ:
print('Loaded OPENAI Key.')
else:
print('Can not load the OPENAI key.')
client = OpenAI(api_key = os.environ['OPENAI'])
import pandas as pd
from huggingface_hub import hf_hub_download
def compute(params):
public_score = 0
private_score = 0
solution_file = hf_hub_download(
repo_id=params.competition_id,
filename="solution.csv",
token=params.token,
repo_type="dataset",
)
solution_df = pd.read_csv(solution_file)
submission_filename = f"submissions/{params.team_id}-{params.submission_id}.csv"
submission_file = hf_hub_download(
repo_id=params.competition_id,
filename=submission_filename,
token=params.token,
repo_type="dataset",
)
submission_df = pd.read_csv(submission_file)
public_ids = solution_df[solution_df.split == "public"][params.submission_id_col].values
private_ids = solution_df[solution_df.split == "private"][params.submission_id_col].values
public_solution_df = solution_df[solution_df[params.submission_id_col].isin(public_ids)]
public_submission_df = submission_df[submission_df[params.submission_id_col].isin(public_ids)]
private_solution_df = solution_df[solution_df[params.submission_id_col].isin(private_ids)]
private_submission_df = submission_df[submission_df[params.submission_id_col].isin(private_ids)]
public_solution_df = public_solution_df.sort_values(params.submission_id_col).reset_index(drop=True)
public_submission_df = public_submission_df.sort_values(params.submission_id_col).reset_index(drop=True)
private_solution_df = private_solution_df.sort_values(params.submission_id_col).reset_index(drop=True)
private_submission_df = private_submission_df.sort_values(params.submission_id_col).reset_index(drop=True)
# # METRICS Calculation Evaluation
# # _metric = SOME METRIC FUNCTION
# def _metric(outputs, targets):
# # input example: public_solution_df[target_cols], public_submission_df[target_cols]
# score = 0.5
# return score
print('public_solution_df', public_solution_df)
print('private_solution_df', private_solution_df)
## LLM Scoring Evaluation
def _metric(outputs, targets):
# inputs: public_solution_df[target_cols], public_submission_df[target_cols]
# output: score
for row, output in outputs.iterrows():
print('outputs type', type(outputs), 'targets type', type(outputs))
answer = str(output['pred'])
label = str(targets.iloc[row]['pred'])
print('answer:', answer)
print('label:', label)
prompt=f"Give me a score from 1 to 10 (higher is better) judging how similar these two captions are. Caption one: {answer}. Caption two: {label}\nScore:"
response = client.completions.create(
model="gpt-3.5-turbo-instruct",
prompt=prompt,
temperature=0,
max_tokens=1,
)
eval_result = response.choices[0].text.strip()
print('eval_result', eval_result)
score = int(eval_result)
# try:
# except:
# print("Error: API Calling")
# return
return score
target_cols = [col for col in solution_df.columns if col not in [params.submission_id_col, "split"]]
public_score = _metric(public_solution_df[target_cols], public_submission_df[target_cols])
private_score = _metric(private_solution_df[target_cols], private_submission_df[target_cols])
metric_name = "metric1"
metric_dict = {"public_score": {metric_name: public_score},
"private_score": {metric_name: private_score}
}
return metric_dict |