Delete data_analysis/show_result.py

data_analysis/show_result.py

@@ -1,73 +0,0 @@
-
-from tqdm import tqdm
-import os
-
-
-samples = []
-with open("./data.json", "r") as f:
-    for line in f:
-        samples.append(eval(line.strip()))
-
-def read_txt(path):
-    with open(path, "r") as f:
-        return f.read()
-
-save_path = "./save_process"
-# model = 'llava-v1.5-13b'
-# model = 'llama-3-8b-instruct'
-model = "gpt-3.5-turbo-0125"
-# model = 'gpt-4o-2024-05-13'
-
-
-
-results = []
-with open(os.path.join(save_path, model, "results.json"), "r") as f:
-    for line in f:
-        results += eval(line.strip())
-
-costs = []
-time_cost = []
-
-id = 0
-for sample in tqdm(samples):
-    result = []
-    if len(sample["questions"]) > 0:
-        predicts = []
-        with open(os.path.join(save_path, model, sample['id']+".json"), "r") as f:
-            for line in f:
-                pre = eval(line.strip())
-                predicts.append(pre)
-                costs.append(pre['cost'])
-                time_cost.append(pre['time'])
-    id += 1
-
-
-
-
-results_c = []
-for i, result in enumerate(results):
-    if "true" in result.lower():
-        results_c.append(True)
-    else:
-        results_c.append(False)
-    # if i>=11:
-    #     break
-
-idx = 0
-score4cha = []
-
-for i, sample in enumerate(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_)
-
-acc = sum(results_c) / len(results_c)
-print(f"Accuracy for all the {len(results_c)} questions is {acc}")
-print(f"Cost for all the {len(results_c)} questions is {sum(costs)}")
-print(f"Consume time for all the {len(results_c)} questions is {sum(time_cost)}")
-print()
-
-
-print(f"Accuracy for each challenge is {score4cha}")
-print(f"Average accuracy for {len(score4cha)} challenge is {sum(score4cha)/len(score4cha)}")