Delete data_analysis/compute_answer.py

data_analysis/compute_answer.py

@@ -1,120 +0,0 @@
-import json
-
-from tqdm import tqdm
-import os
-from openai import OpenAI
-
-client = OpenAI(api_key="")
-
-samples = []
-with open("./data.json", "r") as f:
-    for line in f:
-        samples.append(eval(line.strip()))
-
-
-def evaluate_prediction(client, question, answer, prediction):
-    prompt = (f"Please judge whether the generated answer is right or wrong. We require that the correct answer "
-              f"to the prediction gives a clear answer, not just a calculation process or a disassembly of ideas. "
-              f"The question is {question}. The true answer is \n {answer}. \n The predicted answer is \n {prediction}.\n "
-              f"If the predicted answer is right, please output True. Otherwise output Flase. "
-              f"Don't output any other text content. You only can output True or False.")
-    response = client.chat.completions.create(
-      model="gpt-4o-2024-05-13",
-      messages=[
-        {
-          "role": "user",
-          "content": [
-            {
-              "type": "text",
-              "text": prompt
-            }
-          ]
-        }
-      ],
-      temperature=0,
-      max_tokens=256,
-      top_p=1,
-      frequency_penalty=0,
-      presence_penalty=0
-    )
-    # print(prompt)
-    # print(response.choices[0].message.content)
-    # exit()
-    return response.choices[0].message.content
-
-def read_txt(path):
-    with open(path, "r") as f:
-        return f.read()
-
-save_path = "./save_process"
-model = "gpt-3.5-turbo-0125"
-# model = 'gpt-4o-2024-05-13'
-# model = 'llama-3-8b-instruct'
-# model = 'gpt-3.5-turbo-0125-autoagent'
-# model = 'gpt-4o-2024-05-13-autoagent'
-# model = 'llava-v1.5-13b'
-# model = 'llama3-autoagent'
-
-results = []
-save_f = open(os.path.join(save_path, model, "results.json"), "w")
-save_process = open(os.path.join(save_path, model, "results_process.json"), "w")
-
-for sample in tqdm(samples):
-    result = []
-    if len(sample["questions"]) > 0:
-        # print(sample['id'])
-        predicts = []
-        with open(os.path.join(save_path, model, sample['id']+".json"), "r") as f:
-            for line in f:
-                predicts.append(eval(line.strip()))
-
-        questions = []
-        for id, question_name in enumerate(tqdm(sample["questions"])):
-            question = read_txt(os.path.join("./data", sample["id"], question_name + ".txt"))
-            pre = predicts[id]
-            try:
-                if not model.endswith('autoagent'):
-                    ans = evaluate_prediction(client, question, str(sample["answers"][id]), pre['response'])
-                else:
-                    ans = evaluate_prediction(client, question, str(sample["answers"][id]), pre['summary'])
-            except Exception as e:
-                print(e)
-                ans = "False"
-            # print(result)
-            if not model.endswith('autoagent'):
-                process = [sample["id"], ans, str(sample["answers"][id]), pre['response'][:]]
-            else:
-                process = [sample["id"], ans, str(sample["answers"][id]), pre['summary'][:]]
-
-            result.append(ans)
-            json.dump(process, save_process)
-            save_process.write("\n")
-            save_process.flush()
-    json.dump(result, save_f)
-    save_f.write("\n")
-    save_f.flush()
-    results += result
-
-save_f.close()
-save_process.close()
-
-results_c = []
-for i, result in enumerate(results):
-    if "true" in result.lower():
-        results_c.append(True)
-    else:
-        results_c.append(False)
-
-idx = 0
-score4cha = []
-for sample in tqdm(samples):
-    if len(sample["questions"]) > 0:
-        score_ = sum(results_c[idx:idx+len(sample["questions"])]) / len(sample["questions"])
-        idx += len(sample["questions"])
-        score4cha.append(score_)
-print(f"Accuracy for each challenge is {score4cha}")
-
-
-acc = sum(results_c) / len(results_c)
-print(f"Accuracy for all the {len(results_c)} questions is {acc}")
-