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dividing_into_different_subsets/5/EI/CC_EI.csv

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+Model,CC_subset_1,CC_subset_2,CC_subset_3,CC_subset_4,CC_subset_5
+CodeFuse-DeepSeek-33b,81.63,72.73,33.33,100.0,66.67
+Nxcode-CQ-7B,87.35,88.73,84.17,37.5,98.33
+codegemma-2b,31.63,23.82,5.0,0.0,3.33
+codegemma-7b,46.84,32.18,15.0,20.0,11.67
+codegemma-7b-it,57.35,50.55,38.33,2.5,26.67
+deepseek-coder-1.3b-base,38.57,26.64,9.17,0.0,0.0
+deepseek-coder-6.7b-base,51.68,41.73,18.33,0.0,6.67
+deepseek_coder-6.7b-instruct,73.16,76.36,37.5,0.0,48.33
+deepseek_coder_33b-base,56.58,50.82,36.67,40.0,3.33
+deepseek_coder_33b-instruct,67.86,67.09,56.67,5.0,51.67
+codeqwen1.5-7b,55.92,47.55,16.67,47.5,45.0

dividing_into_different_subsets/5/EI/calculate_humaneval_result.py

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+import json
+import os
+import csv
+# 定义文件所在的目录
+input_dir = 'E:/python-testn/pythonProject3/hh_2/evaluate_result'
+
+# 获取目录中的所有文件
+files = os.listdir(input_dir)
+
+# with open("token_counts_EI.csv","w", newline='') as csvfile:
+#     writer = csv.writer(csvfile)
+#     writer.writerow(["Model", "token_subset_1", "token_subset_2","token_subset_3","token_subset_4","token_subset_5"])
+
+
+# with open("line_counts_EI.csv","w", newline='') as csvfile:
+#     writer = csv.writer(csvfile)
+#     writer.writerow(["Model", "line_subset_1", "line_subset_2","line_subset_3","line_subset_4","line_subset_5"])
+
+with open("CC_EI.csv", "w", newline='') as csvfile:
+    writer = csv.writer(csvfile)
+    writer.writerow(["Model", "CC_subset_1", "CC_subset_2","CC_subset_3","CC_subset_4","CC_subset_5"])
+
+
+
+for file_name in files:
+    # 构建完整的文件路径
+    input_file_path = os.path.join(input_dir, file_name)
+    first_underscore_index = file_name.find('_')
+
+    # 找到最后一个 - 的位置
+    last_dash_index = file_name.rfind('-')
+    model_name = file_name[first_underscore_index + 1:last_dash_index]
+    print(model_name)
+    with open(input_file_path,"r",encoding="utf-8") as file:
+        data1=json.load(file)
+
+    with open("EI.json", "r", encoding="utf-8") as file:
+        data2=json.load(file)
+    sum0=0
+    count0=0
+    sum1=0
+    count1=0
+    sum2=0
+    count2=0
+    sum3 = 0
+    count3 = 0
+    sum4=0
+    count4=0
+
+
+    for (item1,item2) in zip(data1["humaneval"]["pass@1"],data2):
+    #     #按照token个数划分后的评估结果
+    #     if item2["token_diff"] == 0:
+    #         index, value = item1
+    #         print(item2["token_diff"],index,value)
+    #         sum0=sum0+value
+    #         count0=count0+1
+    #     if item2["token_diff"] == 1:
+    #         index, value = item1
+    #         print(item2["token_diff"], index, value)
+    #         sum1=sum1+value
+    #         count1=count1+1
+    #     if item2["token_diff"] == 2:
+    #         index, value = item1
+    #         print(item2["token_diff"], index, value)
+    #         sum2=sum2+value
+    #         count2=count2+1
+    #     if item2["token_diff"] == 3:
+    #         index, value = item1
+    #         print(item2["token_diff"], index, value)
+    #         sum3=sum3+value
+    #         count3=count3+1
+    #     if item2["token_diff"] == 4:
+    #         index, value = item1
+    #         print(item2["token_diff"], index, value)
+    #         sum4 = sum4 + value
+    #         count4 = count4 + 1
+
+
+        #按照行数划分后的评估结果
+        # if item2["line_diff"] == 0:
+        #     index, value = item1
+        #     print(item2["line_diff"],index,value)
+        #     sum0=sum0+value
+        #     count0=count0+1
+        # if item2["line_diff"] == 1:
+        #     index, value = item1
+        #     print(item2["line_diff"], index, value)
+        #     sum1=sum1+value
+        #     count1=count1+1
+        # if item2["line_diff"] == 2:
+        #     index, value = item1
+        #     print(item2["line_diff"], index, value)
+        #     sum2=sum2+value
+        #     count2=count2+1
+        # if item2["line_diff"] == 3:
+        #     index, value = item1
+        #     print(item2["line_diff"], index, value)
+        #     sum3=sum3+value
+        #     count3=count3+1
+        # if item2["line_diff"] == 4:
+        #     index, value = item1
+        #     print(item2["line_diff"], index, value)
+        #     sum4=sum4+value
+        #     count4=count4+1
+
+        #按照圈复杂度划分后的评估结果
+        if item2["CC_diff"] == 0:
+            index, value = item1
+            print(item2["CC_diff"],index,value)
+            sum0=sum0+value
+            count0=count0+1
+        if item2["CC_diff"] == 1:
+            index, value = item1
+            print(item2["CC_diff"], index, value)
+            sum1=sum1+value
+            count1=count1+1
+        if item2["CC_diff"] == 2:
+            index, value = item1
+            print(item2["CC_diff"], index, value)
+            sum2=sum2+value
+            count2=count2+1
+        if item2["CC_diff"] == 3 :
+            index, value = item1
+            print(item2["CC_diff"], index, value)
+            sum3=sum3+value
+            count3=count3+1
+        if item2["CC_diff"] == 4 :
+            index, value = item1
+            print(item2["CC_diff"], index, value)
+            sum4=sum4+value
+            count4=count4+1
+
+
+
+    mean0=round(sum0/count0*100,2)
+
+    mean1=round(sum1/count1*100,2)
+    mean2=round(sum2/count2*100,2)
+    mean3=round(sum3/count3*100,2)
+    mean4=round(sum4/count4*100,2)
+    print("count_result!!")
+    print(count0,count1,count2,count3,count4)
+    print(mean0,mean1,mean2,mean3,count4)
+    # with open("token_counts_EI.csv", mode='a', newline='', encoding='utf-8') as file:
+    #     writer = csv.writer(file)
+    #     writer.writerow([model_name,mean0,mean1,mean2,mean3,mean4])
+
+    # with open("line_counts_EI.csv", mode='a', newline='', encoding='utf-8') as file:
+    #     writer = csv.writer(file)
+    #     writer.writerow([model_name,mean0,mean1,mean2,mean3,mean4])
+    #
+    with open("CC_EI.csv", mode='a', newline='', encoding='utf-8') as file:
+        writer = csv.writer(file)
+        writer.writerow([model_name,mean0,mean1,mean2,mean3,mean4])
+
+

dividing_into_different_subsets/5/EI/even.py

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+import json
+
+# 读取数据
+with open("humaneval_new.json", "r", encoding="utf-8") as f:
+    data = json.load(f)
+
+# 定义划分区间数
+num_intervals = 5
+
+# 计算每个特征的值范围
+line_min = min(item['line'] for item in data)
+line_max = max(item['line'] for item in data)
+line_interval_size = (line_max - line_min) / num_intervals
+
+token_min = min(item['token'] for item in data)
+token_max = max(item['token'] for item in data)
+token_interval_size = (token_max - token_min) / num_intervals
+
+cyclomatic_complexity_min = min(item['cyclomatic_complexity'] for item in data)
+cyclomatic_complexity_max = max(item['cyclomatic_complexity'] for item in data)
+cyclomatic_complexity_interval_size = (cyclomatic_complexity_max - cyclomatic_complexity_min) / num_intervals
+count1=0
+count2=0
+count3=0
+count4=0
+count5=0
+
+# 根据等距划分数据
+for item in data:
+    # 计算 line 特征的区间
+    line_diff = int((item['line'] - line_min) // line_interval_size)
+    item['line_diff'] = min(line_diff,num_intervals-1) # 确保区间索引在范围内
+
+
+    # 计算 token 特征的区间
+    token_diff = int((item['token'] - token_min) // token_interval_size)
+    item['token_diff'] = min(token_diff,num_intervals-1)
+    if item['token_diff'] == 0:
+        count1 = count1 + 1
+    if item['token_diff'] == 1:
+        count2 = count2 + 1
+    if item['token_diff'] == 2:
+        count3 = count3 + 1
+    if item['token_diff'] == 3:
+        count4 = count4 + 1
+    if item['token_diff'] == 4:
+        count5 = count5 + 1
+
+
+    # 计算 cyclomatic_complexity 特征的区间
+    CC_diff = int((item['cyclomatic_complexity'] - cyclomatic_complexity_min) // cyclomatic_complexity_interval_size)
+    item['CC_diff'] = min(CC_diff,num_intervals-1) # 确保区间索引在范围内
+
+# 恢复原始顺序
+data.sort(key=lambda x: x['id'])
+print(count1,count2,count3,count4,count5)
+
+# 将更新后的数据写回JSON文件
+with open('EI.json', 'w', encoding='utf-8') as file:
+    json.dump(data, file, ensure_ascii=False, indent=4)

dividing_into_different_subsets/5/EI/line_counts_EI.csv

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+Model,line_subset_1,line_subset_2,line_subset_3,line_subset_4,line_subset_5
+CodeFuse-DeepSeek-33b,80.0,73.24,73.68,100.0,75.0
+Nxcode-CQ-7B,89.62,88.73,83.16,59.0,78.75
+codegemma-2b,39.46,20.07,21.58,7.0,3.75
+codegemma-7b,48.77,35.85,34.47,15.0,20.0
+codegemma-7b-it,63.23,48.73,44.21,26.0,43.75
+deepseek-coder-1.3b-base,42.69,26.76,24.47,5.0,33.75
+deepseek-coder-6.7b-base,59.85,39.44,38.68,0.0,16.25
+deepseek_coder-6.7b-instruct,81.69,69.72,62.63,24.0,42.5
+deepseek_coder_33b-base,64.62,47.46,48.16,12.0,26.25
+deepseek_coder_33b-instruct,77.92,60.85,68.95,16.0,17.5
+codeqwen1.5-7b,58.85,48.52,48.95,15.0,37.5

dividing_into_different_subsets/5/EI/token_counts_EI.csv

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+Model,token_subset_1,token_subset_2,token_subset_3,token_subset_4,token_subset_5
+CodeFuse-DeepSeek-33b,79.22,78.95,61.9,75.0,100.0
+Nxcode-CQ-7B,90.06,88.25,78.81,84.38,20.0
+codegemma-2b,41.69,21.05,1.19,1.88,0.0
+codegemma-7b,54.48,30.26,22.14,17.5,0.0
+codegemma-7b-it,69.35,40.53,38.1,33.12,0.0
+deepseek-coder-1.3b-base,49.22,21.05,8.33,16.87,0.0
+deepseek-coder-6.7b-base,63.31,35.7,23.1,11.88,0.0
+deepseek_coder-6.7b-instruct,79.81,72.28,55.48,38.75,0.0
+deepseek_coder_33b-base,68.51,45.09,29.76,22.5,0.0
+deepseek_coder_33b-instruct,79.35,62.89,34.52,53.12,0.0
+codeqwen1.5-7b,64.42,42.89,36.43,31.88,0.0