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from src.deepeval.base_task import BaseTask
from collections import defaultdict
from src.deepeval.utils import accuracy, accuracy_standard_error
from typing import Any
import ast
class ComplexReasoningTask(BaseTask):
def __init__(self, model_name):
super().__init__("metunlp/complex-ales", model_name=model_name)
def load_dataset_from_hf(self):
dataset = super().load_dataset_from_hf()
return dataset
def evaluate(self) -> dict[str, Any]:
responses = []
correct_answers = []
total_count = 0
true = 0
for row in self.dataset:
total_count += 1
# Get values from row
choices = ast.literal_eval(row["choices"]) # Convert string to list
narrative = row["narrative"]
question = row["question"]
formatted_choices = "\n".join([f"{chr(65+i)}: {choice}" for i, choice in enumerate(choices)])
correct_answer_letter = row["answer_choice"]
correct_answers.append(correct_answer_letter)
# Prints for debugging
# print(f"Choices: {choices}")
# print("Type of choices:", type(choices))
# Construct the prompt/message
instruction = ""
prompt = f"Soru:\n{narrative}\n{question}\nSeçenekler:\n{formatted_choices}\n{instruction}\n"
message = prompt
# Get/format answer of the model
model_answer = self.generate_response_mcqa_multi_token(message, choices=choices, max_new_tokens=2)
responses.append(model_answer)
model_answer_cleaned = model_answer.strip().replace('\n', '').replace(' ', '').upper().replace(':','')
if correct_answer_letter == model_answer_cleaned:
true += 1
# Print answers
# print(f"Correct Answer: {correct_answer_letter}")
# print(f"Model Answer: {model_answer}")
# print(f"Model Answer Cleaned: {model_answer_cleaned}")
print("Answers:", correct_answers)
print("Results:", responses)
print("Overall Accuracy:", true / total_count)
acc = accuracy(true, total_count)
acc_stderr = accuracy_standard_error(acc, total_count)
return {"acc": acc, "acc_stderr": acc_stderr}
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