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evaluate_llm.py
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evaluate_llm.py
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import json
import argparse
class LLMEvaluator:
def __init__(self):
self.results = {}
def reset(self):
self.results = {}
def get_results(self):
self.final_results = {}
for k in self.results.keys():
self.final_results[k] = {}
for v in self.results[k].keys():
if v != "num":
self.final_results[k][v] = 0
for task, stats in self.results.items():
for stat in stats.keys():
if stat == "num":
continue
if stats["num"] == 0:
self.final_results[task][stat] = 0
else:
self.final_results[task][stat] = stats[stat] / stats["num"]
return self.final_results
def evaluate(self, question, generation, answer, question_type, question_step, show_opd, show_pc, show_pss, show_pom, show_question):
if question_type not in self.results.keys():
if question_type == "eval_object_property_description":
self.results[question_type] = {
"num": 0,
"hardness_accuracy": 0,
"roughness_accuracy": 0,
"texture_accuracy": 0,
"combined_accuracy": 0
}
elif "eval_property_superlative_selection" in question_type:
if "eval_property_superlative_selection" not in self.results.keys():
self.results["eval_property_superlative_selection"] = {
"num": 0,
"accuracy": 0
}
else:
self.results[question_type] = {
"num": 0,
"accuracy": 0
}
result = None
if question_type == "eval_object_property_description":
if show_opd and show_question:
print("\n\n" + question)
result = self.evaluate_opd(generation, answer, show_opd)
elif question_type == "eval_property_comparison":
if show_pc and show_question:
print("\n\n" + question)
result = self.evaluate_pc(generation, answer, show_pc)
elif "eval_property_superlative_selection" in question_type:
if show_pss and show_question:
print("\n\n" + question)
question_type = "eval_property_superlative_selection"
result = self.evaluate_pss(generation, answer, show_pss)
elif question_type == "eval_property_object_match":
if show_pom and show_question:
print("\n\n" + question)
result = self.evaluate_pom(generation, answer, show_pom)
if result is not None:
if question_type == "eval_object_property_description":
self.results[question_type]["hardness_accuracy"] += result[0]
self.results[question_type]["roughness_accuracy"] += result[1]
self.results[question_type]["texture_accuracy"] += result[2]
self.results[question_type]["combined_accuracy"] += result[3]
else:
self.results[question_type]["accuracy"] += result
self.results[question_type]["num"] += 1
def evaluate_opd(self, generation, answer, show):
# evaluate each property separately
hard_answer = answer.split("presents")[-1].strip().split("and")[0].strip()
rough_answer = answer.split("presents")[-1].strip().split("and")[1].strip().split("with")[0].strip()
texture_answer = answer.split("presents")[-1].strip().split("and")[1].strip().split("with")[1].strip()
if show:
print("\nOPD:", generation, "||", answer)
try:
hard_generation = generation.split("presents")[-1].strip().split("and")[0].strip()
rough_generation = generation.split("presents")[-1].strip().split("and")[1].strip().split("with")[0].strip()
texture_generation = generation.split("presents")[-1].strip().split("and")[1].strip().split("with")[1].strip()
except IndexError:
return [0, 0, 0, 0]
correct = [0, 0, 0, 0]
if hard_answer == hard_generation:
correct[0] = 1
if rough_answer == rough_generation:
correct[1] = 1
if texture_generation[:len(texture_answer)] == texture_answer:
correct[2] = 1
final_answer = answer.split("presents")[-1]
final_gen = generation.split("presents")[-1]
if final_gen[:len(final_answer)] == final_answer:
correct[3] = 1
return correct
def evaluate_pc(self, generation, answer, show):
answer = answer.split("Conclusion: ")[-1]
answer_len = len(answer)
if show:
print("\nPC:", generation, "||", answer)
if generation.split("Conclusion: ")[-1][:answer_len] == answer:
return 1
else:
return 0
def evaluate_pss(self, generation, answer, show):
answer = answer.split("Conclusion: ")[-1]
generation = generation.split("Conclusion: ")[-1]
answer_len = len(answer)
if show:
print("\nPSS:", generation, "||", answer)
if generation[:answer_len] == answer:
return 1
else:
return 0
def evaluate_pom(self, generation, answer, show):
answer = answer.split("Conclusion: ")[-1]
answer_len = len(answer)
if show:
print("\nPOM:", generation, "||", answer)
if generation.split("Conclusion: ")[-1][:answer_len] == answer:
return 1
else:
return 0
random_scores = {
"eval_property_comparison": {
"accuracy": 0.333
},
"eval_property_object_match": {
"accuracy": 0.167
},
"eval_property_superlative_selection": {
"accuracy": 0.333
},
"eval_object_property_description": {
"hardness_accuracy": 0.33,
"roughness_accuracy": 0.33,
"texture_accuracy": 0.33,
"combined_accuracy": 0.037
}
}
def print_stats(json_path, show_opd, show_pc, show_pss, show_pom, show_question):
with open(json_path, "r") as f:
data = json.load(f)
f.close()
evaluator = LLMEvaluator()
for d in data:
evaluator.evaluate(d["question"], d["generation"], d["answer"], d["question_type"], d["question_step"], show_opd, show_pc, show_pss, show_pom, show_question)
results = evaluator.get_results()
print("\n")
for t in results.keys():
if t not in random_scores.keys():
continue
print(t)
for k, v in results[t].items():
print(f"\t{k} -----> {v} ({random_scores[t][k]})")
print("\n")
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if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--test_preds_path', help='predictions file to evaluate')
args = parser.parse_args()
# print generations/answers or not
show_opd = False
show_pc = False
show_pss = False
show_pom = False
show_question = False
print_stats(args.test_preds_path, show_opd, show_pc, show_pss, show_pom, show_question)