294 lines
16 KiB
Python
294 lines
16 KiB
Python
import os
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import re
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from datetime import datetime
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import pandas as pd
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def analyze_info_tool(info_index=1, pattern=r'result_\d+\.\d+_\d+_*', parent_folder='./'):
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for folder in os.listdir(parent_folder):
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# 使用正则表达式匹配文件夹名
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if re.match(pattern, folder):
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folder_path = os.path.join(parent_folder, folder)
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# 对结果遍历 result_0.04_9_r/下文件
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analyze_one_folder(info_index, folder_path)
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def analyze_multiple_folders(folder_paths, info_index=1, restore=True):
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"""
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分析多个指定文件夹中的数据文件，提取并计算指标信息。
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参数：
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folder_paths (list): 要分析的文件夹路径列表
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info_index (int): 要分析的第几个结果，默认为 1，指的是information文件下的第i条记录
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restore (bool): 是否将结果保存为 CSV 文件，默认为 True。如果为 False，则打印结果
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返回：
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pandas.DataFrame: 包含所有分析结果的DataFrame
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"""
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# 结果存储
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all_results = []
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for folder_path in folder_paths:
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# 对每个文件夹进行分析
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results = analyze_one_folder(info_index, folder_path, restore=False)
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all_results.extend(results)
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# 创建DataFrame
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df = pd.DataFrame(all_results, columns=['Dataset', 'Accuracy', 'ANI', 'MSE', 'Queries'])
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if restore:
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# 获取当前的日期和时间
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now = datetime.now()
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# 按照指定的格式生成文件名
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results_filename = f"multiple_folders_analysis_{now.year}-{now.month:02d}-{now.day:02d}-{now.hour:02d}-{now.minute:02d}-{now.second:02d}.csv"
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df.to_csv(results_filename, index=False)
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print(f"Results saved to {results_filename}")
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else:
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for result in all_results:
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dataset_name, acc, ani, mse, queries = result
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print(f'{dataset_name}\n'
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f'\t Acc:{acc}\n'
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f'\t ANI:{ani}\n'
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f'\t MSE:{mse}\n'
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f'\t Que:{queries}\n')
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return df
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def analyze_one_folder(info_index=1, folder_path='./', restore=False):
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"""
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分析指定文件夹中的数据文件，提取并计算指标信息。
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参数：
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info_index (int): 要分析的第几个结果，默认为 1，指的是information文件下的第i条记录。
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folder_path (str): 要分析的文件夹路径，默认为当前文件夹 ('./')。
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restore (bool): 是否将结果保存为 CSV 文件，默认为 False。
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返回：
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list: 包含分析结果的列表，每个元素是一个元组 (dataset_name, acc, ani, mse, queries)
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"""
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# 结果存储
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results = []
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for root, dirs, files in os.walk(folder_path):
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# 跳过所有figures文件夹
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if re.match(r'\*/figures\d+', root):
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continue
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# 存储匹配到的信息
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successful_samples_list = [] # 成功样本数
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ani_list = [] # ANI值
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mse_list = [] # MSE值
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queries_list = [] # queries值
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correctly_classified_samples_list = [] # 总样本数
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for file in files:
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# 筛选子文件夹内所有information*文件
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if re.match(r'information\d', file):
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# 打开文件并读取内容
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with open(os.path.join(root, file), 'r', errors='replace') as f:
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s = f.read()
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# 匹配Successful samples，ANI，MSE，queries的模式
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pattern_successful = r'Successful samples:\s*(\d+)'
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pattern_ani = r'ANI:(\d+\.\d+)'
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pattern_mse = r'MSE:(\d+\.\d+)'
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pattern_queries = r'Mean queries：(\d+\.\d+)'
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pattern_correctly_classified_samples = r'Correctly-classified samples:\s*(\d+)'
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# 使用正则表达式匹配模式
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successful_match = re.finditer(pattern_successful, s)
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ani_match = re.finditer(pattern_ani, s)
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mse_match = re.finditer(pattern_mse, s)
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queries_match = re.finditer(pattern_queries, s)
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correctly_classified_samples_match = re.finditer(pattern_correctly_classified_samples, s)
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try:
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successful_samples = None
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ani = None
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mse = None
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queries = None
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correctly_classified_samples = None
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for _ in range(info_index): # 想要分析的第几个结果
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successful_samples = next(successful_match).group(1)
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ani = next(ani_match).group(1)
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mse = next(mse_match).group(1)
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queries = next(queries_match).group(1)
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correctly_classified_samples = next(correctly_classified_samples_match).group(1)
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successful_samples_list.append(successful_samples)
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ani_list.append(ani)
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mse_list.append(mse)
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queries_list.append(queries)
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correctly_classified_samples_list.append(correctly_classified_samples)
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except StopIteration:
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pass
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ani_value = 0 # 总ani权值
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mse_value = 0 # 总mse权值
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queries_value = 0 # 总queries权值
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successful_samples_counts = 0 # 成功样本数
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correct_samples_counts = 0 # 总样本数
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for i in range(len(successful_samples_list)):
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# 跳过全部失败结果
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if successful_samples_list[i] is None or ani_list[i] is None or mse_list[i] is None:
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continue
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mse_value += float(successful_samples_list[i]) * float(mse_list[i])
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ani_value += float(successful_samples_list[i]) * float(ani_list[i])
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queries_value += float(successful_samples_list[i]) * float(queries_list[i])
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successful_samples_counts += float(successful_samples_list[i])
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correct_samples_counts += float(correctly_classified_samples_list[i])
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# 保存结果
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if successful_samples_counts > 0:
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# 数据集名称是文件夹的最后两个部分
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# dataset_name = os.path.basename(os.path.dirname(root)) + '_' + os.path.basename(root)
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# 数据集名称是文件夹的倒数第两个部分
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# dataset_name = os.path.basename(os.path.dirname(root))
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# 数据集名称是文件夹的最后一个部分
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dataset_name = os.path.basename(root)
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acc = successful_samples_counts / correct_samples_counts
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ani = ani_value / successful_samples_counts
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mse = mse_value / successful_samples_counts
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queries = queries_value / successful_samples_counts
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results.append((dataset_name, acc, ani, mse, queries))
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if restore:
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df = pd.DataFrame(results, columns=['Dataset', 'Accuracy', 'ANI', 'MSE', 'Queries'])
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# 获取当前的日期和时间
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now = datetime.now()
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# 按照指定的格式生成文件名
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results_filename = f"analyze_result_{now.year}-{now.month:02d}-{now.day:02d}-{now.hour:02d}-{now.minute:02d}-{now.second:02d}.csv"
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df.to_csv(results_filename, index=False)
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else:
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for dataset_name, acc, ani, mse, queries in results:
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print(f'{dataset_name}\n'
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f'\t Acc:{acc}\n'
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f'\t ANI:{ani}\n'
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f'\t MSE:{mse}\n'
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f'\t Que:{queries}\n')
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return results
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def count_classes_samples(folder_path, select_colum='Success'):
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"""
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根据给定文件夹路径，分析包含的information*文件，统计文件中第4列和第5列的元组出现个数，
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仅在第9列的值为'Success'时统计。
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:param select_colum: 选择统计的第九列类别
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:param folder_path: 被分析的文件夹根目录
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:return: 元组出现次数的二维数组
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"""
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key_pair_counts = {}
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for root, dirs, files in os.walk(folder_path):
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for file in files:
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# 筛选子文件夹内所有information*文件
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if re.match(r'information\d', file):
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# 打开文件并读取内容
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with open(os.path.join(root, file), 'r') as f:
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data = f.read()
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# 将数据拆分成行并解析成列表
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lines = data.strip().split('\n')
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records = [line.split() for line in lines if len(line.split()) > 8] # 确保至少有9列
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# 统计第4列和第5列的键对值出现次数，仅在第9列为'select_colum'时统计，默认Success
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for record in records:
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if record[8] == select_colum:
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key_pair = (int(record[3]), int(record[4]))
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if key_pair in key_pair_counts:
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key_pair_counts[key_pair] += 1
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else:
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key_pair_counts[key_pair] = 1
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# 将结果转换为二维数组
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result_array = [[key_pair[0], key_pair[1], count] for key_pair, count in key_pair_counts.items()]
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return result_array
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def generate_heat_maps_array(data, default_value=0, classes_size=None):
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"""
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根据给定的数据生成一个二维数组。规则是对于每个元组，例如[6, 1, 1]，
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将生成的二维数组array[6][1]设置为1。
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:param classes_size: 元组维度
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:param data: 包含元组的列表，每个元组包含三个整数（行索引，列索引，值）。
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:param default_value: 生成的二维数组的默认值，默认为0。
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:return: 生成的二维数组。
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"""
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# 找到二维数组的大小
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if classes_size is not None:
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max_row = max_col = classes_size
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else:
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max_row = max(item[0] for item in data) + 1
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max_col = max(item[1] for item in data) + 1
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# 初始化二维数组
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array = [[default_value for _ in range(max_col)] for _ in range(max_row)]
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# 设置数组中的指定值
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for row, col, value in data:
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array[col][row] = value
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return array
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if __name__ == "__main__":
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# 定义要分析的实验结果文件夹路径列表
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experiment_folders = [
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/Car",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/Wafer",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/ItalyPowerDemand",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/AllGestureWiimoteY",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/NonIFECGTho2",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/Trace",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/PigAirwayPressure",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/GesturePebbleZ2",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/AllGestureWiimoteX",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/FordA",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/DodgerLoopDay",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/SonyAIBORobotSurface1",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/PigArtPressure",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/MoteStrain",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/ECGFiveDays",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/PickupGeWZ",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/FreezerSmallTrain",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/TwoLeadECG",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/Lightning7",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/Phoneme",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/DodgerLoopWeekend",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/ECG5000",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/ShakeGestureWiimoteZ",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/GesturePebbleZ1",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/NonIFECGTho1",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/AllGestureWiimoteZ",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/FordB",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/InsectWingbeatSound",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/EOGHorizontalSignal",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/ChlorineConcentration",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/Plane",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/EOGVerticalSignal",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/DodgerLoopGame",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/ECG200",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/Fungi",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/CinCEC",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/SonyAIBORobotSurface2",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/Lightning2",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/FreezerRegularTrain",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/StarLightCurves",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/Earthquakes",
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"/Users/catb/Library/CloudStorage/CloudMounter-B40-4/home/BJTU/project/CPadv/CCPP实验结果/model_f/PigCVP",
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]
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# 分析所有文件夹并将结果保存到单个CSV文件中
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results_df = analyze_multiple_folders(experiment_folders)
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# 如果想查看结果但不保存CSV，可以使用：
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# results_df = analyze_multiple_folders(experiment_folders, restore=False) |