import json import pandas as pd import numpy as np from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction import argparse import os from sklearn.metrics import classification_report from sklearn import metrics from datetime import datetime import boto3 from botocore.exceptions import NoCredentialsError, ClientError from io import TextIOWrapper import zipfile def Levenshtein_Distance(str1, str2): """ 计算并返回两个字符串之间的Levenshtein编辑距离。 参数: - str1: 字符串，第一个比较字符串。 - str2: 字符串，第二个比较字符串。 返回: - int: str1和str2之间的Levenshtein距离。 方法: - 使用动态规划构建一个矩阵(matrix)，其中matrix[i][j]表示str1的前i个字符和str2的前j个字符之间的Levenshtein距离。 - 矩阵的初始值设定为边界情况，即一个字符串与空字符串之间的距离。 - 遍历矩阵填充每个格子的值，根据字符是否相等选择插入、删除或替换操作的最小代价。 """ # 初始化矩阵，大小为(len(str1)+1) x (len(str2)+1)，边界情况下的距离为i和j matrix = [[i + j for j in range(len(str2) + 1)] for i in range(len(str1) + 1)] # 遍历str1和str2的每个字符，更新矩阵中的值 for i in range(1, len(str1) + 1): for j in range(1, len(str2) + 1): # 如果当前字符相等，替换代价为0；否则为1 d = 0 if (str1[i - 1] == str2[j - 1]) else 1 # 更新当前位置的值为从str1[i]转换到str2[j]的最小操作数 matrix[i][j] = min(matrix[i - 1][j] + 1, # 删除操作 matrix[i][j - 1] + 1, # 插入操作 matrix[i - 1][j - 1] + d) # 替换操作 # 返回右下角的值，即str1和str2之间的Levenshtein距离 return matrix[len(str1)][len(str2)] def bbox_offset(b_t, b_s): """ 判断两个边界框（bounding box）之间的重叠程度是否符合给定的标准。 参数: - b_t: 测试文档中的边界框（bbox），格式为(x1, y1, x2, y2)， 其中(x1, y1)是左上角的坐标，(x2, y2)是右下角的坐标。 - b_s: 标准文档中的边界框（bbox），格式同上。 返回: - True: 如果两个边界框的重叠面积与两个边界框合计面积的差的比例超过0.95， 表明它们足够接近。 - False: 否则，表示两个边界框不足够接近。 注意: - 函数首先计算两个bbox的交集区域，如果这个区域的面积相对于两个bbox的面积差非常大， 则认为这两个bbox足够接近。 - 如果交集区域的计算结果导致无效区域（比如宽度或高度为负值），或者分母为0（即两个bbox完全不重叠）， 则函数会返回False。 """ # 分别提取两个bbox的坐标 x1_t, y1_t, x2_t, y2_t = b_t x1_s, y1_s, x2_s, y2_s = b_s # 计算两个bbox交集区域的坐标 x1 = max(x1_t, x1_s) x2 = min(x2_t, x2_s) y1 = max(y1_t, y1_s) y2 = min(y2_t, y2_s) # 如果计算出的交集区域有效，则计算其面积 if x2 > x1 and y2 > y1: area_overlap = (x2 - x1) * (y2 - y1) else: # 交集区域无效，视为无重叠 area_overlap = 0 # 计算两个bbox的总面积，减去重叠部分避免重复计算 area_t = (x2_t - x1_t) * (y2_t - y1_t) + (x2_s - x1_s) * (y2_s - y1_s) - area_overlap # 判断重叠面积是否符合标准 # return area_overlap / total_area if area_t-area_overlap==0 or area_overlap/area_t>0.95: return True else: return False def equations_indicator(test_equations_bboxs, standard_equations_bboxs, test_equations, standard_equations): """ 根据边界框匹配的方程计算编辑距离和BLEU分数。 参数: - test_equations_bboxs: 测试方程的边界框列表。 - standard_equations_bboxs: 标准方程的边界框列表。 - test_equations: 测试方程的列表。 - standard_equations: 标准方程的列表。 返回: - 一个元组，包含匹配方程的平均Levenshtein编辑距离和BLEU分数。 """ # 初始化匹配方程列表 test_match_equations = [] standard_match_equations = [] # 匹配方程基于边界框重叠 for index, (test_bbox, standard_bbox) in enumerate(zip(test_equations_bboxs, standard_equations_bboxs)): if not (test_bbox and standard_bbox): # 跳过任一空列表 continue for i, sb in enumerate(standard_bbox): for j, tb in enumerate(test_bbox): if bbox_offset(sb, tb): standard_match_equations.append(standard_equations[index][i]) test_match_equations.append(test_equations[index][j]) break # 找到第一个匹配后即跳出循环 # 使用Levenshtein距离和BLEU分数计算编辑距离 dis = [Levenshtein_Distance(a, b) for a, b in zip(test_match_equations, standard_match_equations) if a and b] # 应用平滑函数计算BLEU分数 sm_func = SmoothingFunction().method1 bleu = [sentence_bleu([a.split()], b.split(), smoothing_function=sm_func) for a, b in zip(test_match_equations, standard_match_equations) if a and b] # 计算平均编辑距离和BLEU分数，处理空列表情况 equations_edit = np.mean(dis) if dis else float('0.0') equations_bleu = np.mean(bleu) if bleu else float('0.0') return equations_edit, equations_bleu def bbox_match_indicator_general(test_bboxs_list, standard_bboxs_list): """ 计算边界框匹配指标，支持掉落的表格、图像和文本块。 此版本的函数专注于计算基于边界框的匹配指标，而不涉及标签匹配逻辑。 参数: - test_bboxs: 测试集的边界框列表，按页面组织。 - standard_bboxs: 标准集的边界框列表，按页面组织。 返回: - 一个字典，包含准确度、精确度、召回率和F1分数。 """ # 如果两个列表都完全为空，返回0值指标 if all(len(page) == 0 for page in test_bboxs_list) and all(len(page) == 0 for page in standard_bboxs_list): return {'accuracy': 0, 'precision': 0, 'recall': 0, 'f1_score': 0} matched_bbox = [] matched_standard_bbox = [] for test_page, standard_page in zip(test_bboxs_list, standard_bboxs_list): test_page_bbox, standard_page_bbox = [], [] for standard_bbox in standard_page: if len(standard_bbox) != 4: continue matched = False for test_bbox in test_page: if len(test_bbox) == 4 and bbox_offset(standard_bbox, test_bbox): matched = True break test_page_bbox.append(int(matched)) standard_page_bbox.append(1) # 后处理以处理多删情况，保持原逻辑不变 diff_num = len(test_page) + test_page_bbox.count(0) - len(standard_page) if diff_num > 0: test_page_bbox.extend([1] * diff_num) standard_page_bbox.extend([0] * diff_num) matched_bbox.extend(test_page_bbox) matched_standard_bbox.extend(standard_page_bbox) block_report = { 'accuracy': metrics.accuracy_score(matched_standard_bbox, matched_bbox), 'precision': metrics.precision_score(matched_standard_bbox, matched_bbox, zero_division=0), 'recall': metrics.recall_score(matched_standard_bbox, matched_bbox, zero_division=0), 'f1_score': metrics.f1_score(matched_standard_bbox, matched_bbox, zero_division=0) } return block_report def bbox_match_indicator_dropped_text_block(test_dropped_text_bboxs, standard_dropped_text_bboxs, standard_dropped_text_tag, test_dropped_text_tag): """ 计算丢弃文本块的边界框匹配相关指标，包括准确率、精确率、召回率和F1分数， 同时也计算文本块标签的匹配指标。 参数: - test_dropped_text_bboxs: 测试集的丢弃文本块边界框列表 - standard_dropped_text_bboxs: 标准集的丢弃文本块边界框列表 - standard_dropped_text_tag: 标准集的丢弃文本块标签列表 - test_dropped_text_tag: 测试集的丢弃文本块标签列表 返回: - 一个包含边界框匹配指标和文本块标签匹配指标的元组 """ test_text_bbox, standard_text_bbox = [], [] test_tag, standard_tag = [], [] for index, (test_page, standard_page) in enumerate(zip(test_dropped_text_bboxs, standard_dropped_text_bboxs)): # 初始化每个页面的结果列表 test_page_tag, standard_page_tag = [], [] test_page_bbox, standard_page_bbox = [], [] for i, standard_bbox in enumerate(standard_page): matched = False for j, test_bbox in enumerate(test_page): if bbox_offset(standard_bbox, test_bbox): # 匹配成功，记录标签和边界框匹配结果 matched = True test_page_tag.append(test_dropped_text_tag[index][j]) test_page_bbox.append(1) break if not matched: # 未匹配，记录'None'和边界框未匹配结果 test_page_tag.append('None') test_page_bbox.append(0) # 标准边界框和标签总是被视为匹配的 standard_page_tag.append(standard_dropped_text_tag[index][i]) standard_page_bbox.append(1) # 处理可能的多删情况 handle_multi_deletion(test_page, test_page_tag, test_page_bbox, standard_page_tag, standard_page_bbox) # 合并当前页面的结果到整体结果中 test_tag.extend(test_page_tag) standard_tag.extend(standard_page_tag) test_text_bbox.extend(test_page_bbox) standard_text_bbox.extend(standard_page_bbox) # 计算和返回匹配指标 if not standard_text_bbox or not test_text_bbox: # print("警告：边界框列表为空，跳过性能指标的计算。") text_block_report = { 'accuracy': np.nan, 'precision': np.nan, 'recall': np.nan, 'f1_score': np.nan } else: text_block_report = { 'accuracy': metrics.accuracy_score(standard_text_bbox, test_text_bbox), 'precision': metrics.precision_score(standard_text_bbox, test_text_bbox, zero_division=0), 'recall': metrics.recall_score(standard_text_bbox, test_text_bbox, zero_division=0), 'f1_score': metrics.f1_score(standard_text_bbox, test_text_bbox, zero_division=0) } # 对于classification_report，确保至少有一个非'None'标签存在 labels = list(set(standard_tag) - {'None'}) if labels: text_block_tag_report = classification_report(y_true=standard_tag, y_pred=test_tag, labels=labels, output_dict=True, zero_division=0) # 删除不需要的平均值报告，以简化输出 text_block_tag_report.pop("macro avg", None) text_block_tag_report.pop("weighted avg", None) else: # print("警告：无有效标签进行匹配，跳过标签匹配指标的计算。") text_block_tag_report = {} return text_block_report, text_block_tag_report def handle_multi_deletion(test_page, test_page_tag, test_page_bbox, standard_page_tag, standard_page_bbox): """ 处理多删情况，即测试页面的边界框或标签数量多于标准页面。 """ excess_count = len(test_page) + test_page_bbox.count(0) - len(standard_page_tag) if excess_count > 0: # 对于多出的项，将它们视为正确匹配的边界框，但标签视为'None' test_page_bbox.extend([1] * excess_count) standard_page_bbox.extend([0] * excess_count) test_page_tag.extend(['None'] * excess_count) standard_page_tag.extend(['None'] * excess_count) def read_json_files(standard_file, test_file): """ 读取JSON文件内容 """ with open(standard_file, 'r', encoding='utf-8') as sf: pdf_json_standard = [json.loads(line) for line in sf] with open(test_file, 'r', encoding='utf-8') as tf: pdf_json_test = [json.loads(line) for line in tf] json_standard_origin = pd.DataFrame(pdf_json_standard) json_test = pd.DataFrame(pdf_json_test) return json_standard_origin, json_test def merge_json_data(json_test_df, json_standard_df): """ 基于ID合并测试和标准数据集，并返回合并后的数据及存在性检查结果。 参数: - json_test_df: 测试数据的DataFrame。 - json_standard_df: 标准数据的DataFrame。 返回: - inner_merge: 内部合并的DataFrame，包含匹配的数据行。 - standard_exist: 标准数据存在性的Series。 - test_exist: 测试数据存在性的Series。 """ test_data = json_test_df[['id', 'mid_json']].drop_duplicates(subset='id', keep='first').reset_index(drop=True) standard_data = json_standard_df[['id', 'mid_json', 'pass_label']].drop_duplicates(subset='id', keep='first').reset_index(drop=True) outer_merge = pd.merge(test_data, standard_data, on='id', how='outer') outer_merge.columns = ['id', 'test_mid_json', 'standard_mid_json', 'pass_label'] standard_exist = outer_merge.standard_mid_json.notnull() test_exist = outer_merge.test_mid_json.notnull() inner_merge = pd.merge(test_data, standard_data, on='id', how='inner') inner_merge.columns = ['id', 'test_mid_json', 'standard_mid_json', 'pass_label'] return inner_merge, standard_exist, test_exist def process_equations_and_blocks(json_data): """ 处理JSON数据，提取公式、文本块、图片块和表格块的边界框和文本信息。 参数: - json_data: 列表，包含标准文档或测试文档的JSON数据。 返回: - 字典，包含处理后的数据。 """ equations_bboxs = {"inline": [], "interline": []} equations_texts = {"inline": [], "interline": []} dropped_bboxs = {"text": [], "image": [], "table": []} dropped_tags = {"text": []} para_texts = [] para_nums = [] preproc_nums = [] for i in json_data: mid_json = pd.DataFrame(i).iloc[:,:-1] page_data = { "equations_bboxs_list": {"inline": [], "interline": []}, "equations_texts_list": {"inline": [], "interline": []}, "dropped_bboxs_list": {"text": [], "image": [], "table": []}, "dropped_tags_list": {"text": []}, "para_texts_list": [], "para_nums_list": [], "preproc_nums_list":[] } for eq_type in ["inline", "interline"]: for equations in mid_json.loc[f"{eq_type}_equations", :]: bboxs = [eq['bbox'] for eq in equations] texts = [eq['latex_text'] for eq in equations] page_data["equations_bboxs_list"][eq_type].append(bboxs) page_data["equations_texts_list"][eq_type].append(texts) equations_bboxs["inline"].append(page_data["equations_bboxs_list"]["inline"]) equations_bboxs["interline"].append(page_data["equations_bboxs_list"]["interline"]) equations_texts["inline"].append(page_data["equations_texts_list"]["inline"]) equations_texts["interline"].append(page_data["equations_texts_list"]["interline"]) # 提取丢弃的文本块信息 for dropped_text_blocks in mid_json.loc['droped_text_block',:]: bboxs, tags = [], [] for block in dropped_text_blocks: bboxs.append(block['bbox']) tags.append(block.get('tag', 'None')) page_data["dropped_bboxs_list"]["text"].append(bboxs) page_data["dropped_tags_list"]["text"].append(tags) dropped_bboxs["text"].append(page_data["dropped_bboxs_list"]["text"]) dropped_tags["text"].append(page_data["dropped_tags_list"]["text"]) # 同时处理删除的图片块和表格块 for block_type in ['image', 'table']: # page_blocks_list = [] for blocks in mid_json.loc[f'droped_{block_type}_block', :]: # 如果是标准数据，直接添加整个块的列表 page_data["dropped_bboxs_list"][block_type].append(blocks) # 将当前页面的块边界框列表添加到结果字典中 dropped_bboxs['image'].append(page_data["dropped_bboxs_list"]['image']) dropped_bboxs['table'].append(page_data["dropped_bboxs_list"]['table']) # 处理段落 for para_blocks in mid_json.loc['para_blocks', :]: page_data["para_nums_list"].append(len(para_blocks)) # 计算段落数 for para_block in para_blocks: page_data["para_texts_list"].append(para_block['text']) for preproc_blocks in mid_json.loc['preproc_blocks', :]: numbers=[] for preproc_block in preproc_blocks: numbers.append(preproc_block['number']) page_data["preproc_nums_list"].append(numbers) para_texts.append(page_data["para_texts_list"]) para_nums.append(page_data["para_nums_list"]) preproc_nums.append(page_data["preproc_nums_list"]) return { "equations_bboxs": equations_bboxs, "equations_texts": equations_texts, "dropped_bboxs": dropped_bboxs, "dropped_tags": dropped_tags, "para_texts": para_texts, "para_nums": para_nums, "preproc_nums": preproc_nums } def consolidate_data(test_data, standard_data, key_path): """ Consolidates data from test and standard datasets based on the provided key path. :param test_data: Dictionary containing the test dataset. :param standard_data: Dictionary containing the standard dataset. :param key_path: List of keys leading to the desired data within the dictionaries. :return: List containing all items from both test and standard data at the specified key path. """ # Initialize an empty list to hold the consolidated data overall_data_standard = [] overall_data_test = [] # Helper function to recursively navigate through the dictionaries based on the key path def extract_data(source_data, keys): for key in keys[:-1]: source_data = source_data.get(key, {}) return source_data.get(keys[-1], []) for data in extract_data(standard_data, key_path): # 假设每个 single_table_tags 已经是一个列表，直接将它的元素添加到总列表中 overall_data_standard.extend(data) for data in extract_data(test_data, key_path): overall_data_test.extend(data) # Extract and extend the overall data list with items from both test and standard datasets return overall_data_standard, overall_data_test def calculate_metrics(inner_merge, json_test, json_standard, json_standard_origin): """ 计算指标 """ # 创建ID到file_id的映射 id_to_file_id_map = pd.Series(json_standard_origin.file_id.values, index=json_standard_origin.id).to_dict() # 处理标准数据和测试数据 process_data_standard = process_equations_and_blocks(json_standard) process_data_test = process_equations_and_blocks(json_test) # 从inner_merge中筛选出pass_label为'yes'的数据 test_para_text = np.asarray(process_data_test['para_texts'], dtype=object)[inner_merge['pass_label'] == 'yes'] standard_para_text = np.asarray(process_data_standard['para_texts'], dtype=object)[inner_merge['pass_label'] == 'yes'] ids_yes = inner_merge['id'][inner_merge['pass_label'] == 'yes'].tolist() pdf_dis = {} pdf_bleu = {} # 对pass_label为'yes'的数据计算编辑距离和BLEU得分 for idx, (a, b, id) in enumerate(zip(test_para_text, standard_para_text, ids_yes)): a1 = ''.join(a) b1 = ''.join(b) pdf_dis[id] = Levenshtein_Distance(a, b) pdf_bleu[id] = sentence_bleu([a1], b1) result_dict = {} acc_para=[] # 对所有数据计算其他指标 for index, id_value in enumerate(inner_merge['id'].tolist()): result = {} # 增加file_id到结果中 file_id = id_to_file_id_map.get(id_value, "Unknown") result['file_id'] = file_id # 根据id判断是否需要计算pdf_dis和pdf_bleu if id_value in ids_yes: result['pdf_dis'] = pdf_dis[id_value] result['pdf_bleu'] = pdf_bleu[id_value] # 阅读顺序编辑距离的均值 preproc_num_dis=[] for a,b in zip(process_data_test['preproc_nums'][index],process_data_standard['preproc_nums'][index]): preproc_num_dis.append(Levenshtein_Distance(a,b)) result['阅读顺序编辑距离']=np.mean(preproc_num_dis) # 计算分段准确率 single_test_para_num = np.array(process_data_test['para_nums'][index]) single_standard_para_num = np.array(process_data_standard['para_nums'][index]) acc_para.append(np.mean(single_test_para_num == single_standard_para_num)) result['分段准确率'] = acc_para[index] # 行内公式准确率和编辑距离、bleu result['行内公式准确率'] = bbox_match_indicator_general( process_data_test["equations_bboxs"]["inline"][index], process_data_standard["equations_bboxs"]["inline"][index]) result['行内公式编辑距离'], result['行内公式bleu'] = equations_indicator( process_data_test["equations_bboxs"]["inline"][index], process_data_standard["equations_bboxs"]["inline"][index], process_data_test["equations_texts"]["inline"][index], process_data_standard["equations_texts"]["inline"][index]) # 行间公式准确率和编辑距离、bleu result['行间公式准确率'] = bbox_match_indicator_general( process_data_test["equations_bboxs"]["interline"][index], process_data_standard["equations_bboxs"]["interline"][index]) result['行间公式编辑距离'], result['行间公式bleu'] = equations_indicator( process_data_test["equations_bboxs"]["interline"][index], process_data_standard["equations_bboxs"]["interline"][index], process_data_test["equations_texts"]["interline"][index], process_data_standard["equations_texts"]["interline"][index]) # 丢弃文本准确率，丢弃文本标签准确率 result['丢弃文本准确率'], result['丢弃文本标签准确率'] = bbox_match_indicator_dropped_text_block( process_data_test["dropped_bboxs"]["text"][index], process_data_standard["dropped_bboxs"]["text"][index], process_data_standard["dropped_tags"]["text"][index], process_data_test["dropped_tags"]["text"][index]) # 丢弃图片准确率 result['丢弃图片准确率'] = bbox_match_indicator_general( process_data_test["dropped_bboxs"]["image"][index], process_data_standard["dropped_bboxs"]["image"][index]) # 丢弃表格准确率 result['丢弃表格准确率'] = bbox_match_indicator_general( process_data_test["dropped_bboxs"]["table"][index], process_data_standard["dropped_bboxs"]["table"][index]) # 将结果存入result_dict result_dict[id_value] = result return result_dict def overall_calculate_metrics(inner_merge, json_test, json_standard,standard_exist, test_exist): """ 计算整体指标：包括准确性、精确度、召回率、F1分数以及不同方面的详细指标。 参数: - inner_merge: 合并后的内部数据，包含测试和标准数据的合并结果。 - json_test: 测试数据的JSON格式。 - json_standard: 标准数据的JSON格式。 - standard_exist: 标准存在的标签数据。 - test_exist: 测试存在的标签数据。 返回值: - overall_report: 包含各种指标的字典。 """ # 处理标准数据和测试数据，提取方程式和块 process_data_standard = process_equations_and_blocks(json_standard) process_data_test = process_equations_and_blocks(json_test) # 初始化整体报告，并计算基础指标 overall_report = {} overall_report['accuracy']=metrics.accuracy_score(standard_exist,test_exist) overall_report['precision']=metrics.precision_score(standard_exist,test_exist) overall_report['recall']=metrics.recall_score(standard_exist,test_exist) overall_report['f1_score']=metrics.f1_score(standard_exist,test_exist) overall_report # 提取通过标签的数据，并计算编辑距离和BLEU得分 test_para_text = np.asarray(process_data_test['para_texts'], dtype=object)[inner_merge['pass_label'] == 'yes'] standard_para_text = np.asarray(process_data_standard['para_texts'], dtype=object)[inner_merge['pass_label'] == 'yes'] ids_yes = inner_merge['id'][inner_merge['pass_label'] == 'yes'].tolist() pdf_dis = {} pdf_bleu = {} for idx,(a, b, id) in enumerate(zip(test_para_text, standard_para_text, ids_yes)): a1 = ''.join(a) b1 = ''.join(b) pdf_dis[id] = Levenshtein_Distance(a, b) pdf_bleu[id] = sentence_bleu([a1], b1) overall_report['pdf间的平均编辑距离'] = np.mean(list(pdf_dis.values())) overall_report['pdf间的平均bleu'] = np.mean(list(pdf_bleu.values())) # 合并数据中的方程式bbox和inline数据 overall_equations_bboxs_inline_standard,overall_equations_bboxs_inline_test = consolidate_data(process_data_test, process_data_standard, ["equations_bboxs", "inline"]) # 合并数据中的方程式文本和inline数据 overall_equations_texts_inline_standard,overall_equations_texts_inline_test = consolidate_data(process_data_test, process_data_standard, ["equations_texts", "inline"]) # 合并数据中的方程式bbox和interline数据 overall_equations_bboxs_interline_standard,overall_equations_bboxs_interline_test = consolidate_data(process_data_test, process_data_standard, ["equations_bboxs", "interline"]) # 合并数据中的方程式文本和interline数据 overall_equations_texts_interline_standard,overall_equations_texts_interline_test = consolidate_data(process_data_test, process_data_standard, ["equations_texts", "interline"]) # 合并丢弃的bbox和text数据 overall_dropped_bboxs_text_standard,overall_dropped_bboxs_text_test = consolidate_data(process_data_test, process_data_standard, ["dropped_bboxs","text"]) # 合并丢弃的tags和text数据 overall_dropped_tags_text_standard,overall_dropped_tags_text_test = consolidate_data(process_data_test, process_data_standard, ["dropped_tags","text"]) # 合并丢弃的bbox和image数据 overall_dropped_bboxs_image_standard,overall_dropped_bboxs_image_test = consolidate_data(process_data_test, process_data_standard, ["dropped_bboxs","image"]) # 合并丢弃的bbox和table数据 overall_dropped_bboxs_table_standard,overall_dropped_bboxs_table_test=consolidate_data(process_data_test, process_data_standard,["dropped_bboxs","table"]) # 合并阅读顺序的编辑距离 overall_preproc_standard,overall_preproc_test = consolidate_data(process_data_test, process_data_standard, ["preproc_nums"]) # 计算测试和标准数据的段落数量 para_nums_test = process_data_test['para_nums'] para_nums_standard=process_data_standard['para_nums'] overall_para_nums_standard = [item for sublist in para_nums_standard for item in (sublist if isinstance(sublist, list) else [sublist])] overall_para_nums_test = [item for sublist in para_nums_test for item in (sublist if isinstance(sublist, list) else [sublist])] preproc_num_dis=[] for a,b in zip(overall_preproc_standard,overall_preproc_test): preproc_num_dis.append(Levenshtein_Distance(a,b)) overall_report['阅读顺序编辑距离']=np.mean(preproc_num_dis) # 计算段落匹配准确性 test_para_num=np.array(overall_para_nums_test) standard_para_num=np.array(overall_para_nums_standard) acc_para=np.mean(test_para_num==standard_para_num) overall_report['分段准确率'] = acc_para # 计算并更新报告中的各种指标 overall_report['行内公式准确率'] = bbox_match_indicator_general( overall_equations_bboxs_inline_test, overall_equations_bboxs_inline_standard) overall_report['行内公式编辑距离'], overall_report['行内公式bleu'] = equations_indicator( overall_equations_bboxs_inline_test, overall_equations_bboxs_inline_standard, overall_equations_texts_inline_test, overall_equations_texts_inline_standard) overall_report['行间公式准确率'] = bbox_match_indicator_general( overall_equations_bboxs_interline_test, overall_equations_bboxs_interline_standard) overall_report['行间公式编辑距离'], overall_report['行间公式bleu'] = equations_indicator( overall_equations_bboxs_interline_test, overall_equations_bboxs_interline_standard, overall_equations_texts_interline_test, overall_equations_texts_interline_standard) overall_report['丢弃文本准确率'], overall_report['丢弃文本标签准确率'] = bbox_match_indicator_dropped_text_block( overall_dropped_bboxs_text_test, overall_dropped_bboxs_text_standard, overall_dropped_tags_text_standard, overall_dropped_tags_text_test) overall_report['丢弃图片准确率'] = bbox_match_indicator_general( overall_dropped_bboxs_image_test, overall_dropped_bboxs_image_standard) overall_report['丢弃表格准确率'] = bbox_match_indicator_general( overall_dropped_bboxs_table_test, overall_dropped_bboxs_table_standard) return overall_report def check_json_files_in_zip_exist(zip_file_path, standard_json_path_in_zip, test_json_path_in_zip): """ 检查ZIP文件中是否存在指定的JSON文件 """ with zipfile.ZipFile(zip_file_path, 'r') as z: # 获取ZIP文件中所有文件的列表 all_files_in_zip = z.namelist() # 检查标准文件和测试文件是否都在ZIP文件中 if standard_json_path_in_zip not in all_files_in_zip or test_json_path_in_zip not in all_files_in_zip: raise FileNotFoundError("One or both of the required JSON files are missing from the ZIP archive.") def read_json_files_from_streams(standard_file_stream, test_file_stream): """ 从文件流中读取JSON文件内容 """ pdf_json_standard = [json.loads(line) for line in standard_file_stream] pdf_json_test = [json.loads(line) for line in test_file_stream] json_standard_origin = pd.DataFrame(pdf_json_standard) json_test_origin = pd.DataFrame(pdf_json_test) return json_standard_origin, json_test_origin def read_json_files_from_zip(zip_file_path, standard_json_path_in_zip, test_json_path_in_zip): """ 从ZIP文件中读取两个JSON文件并返回它们的DataFrame """ with zipfile.ZipFile(zip_file_path, 'r') as z: with z.open(standard_json_path_in_zip) as standard_file_stream, \ z.open(test_json_path_in_zip) as test_file_stream: standard_file_text_stream = TextIOWrapper(standard_file_stream, encoding='utf-8') test_file_text_stream = TextIOWrapper(test_file_stream, encoding='utf-8') json_standard_origin, json_test_origin = read_json_files_from_streams( standard_file_text_stream, test_file_text_stream ) return json_standard_origin, json_test_origin def merge_json_data(json_test_df, json_standard_df): """ 基于ID合并测试和标准数据集，并返回合并后的数据及存在性检查结果。 参数: - json_test_df: 测试数据的DataFrame。 - json_standard_df: 标准数据的DataFrame。 返回: - inner_merge: 内部合并的DataFrame，包含匹配的数据行。 - standard_exist: 标准数据存在性的Series。 - test_exist: 测试数据存在性的Series。 """ test_data = json_test_df[['id', 'mid_json']].drop_duplicates(subset='id', keep='first').reset_index(drop=True) standard_data = json_standard_df[['id', 'mid_json', 'pass_label']].drop_duplicates(subset='id', keep='first').reset_index(drop=True) outer_merge = pd.merge(test_data, standard_data, on='id', how='outer') outer_merge.columns = ['id', 'test_mid_json', 'standard_mid_json', 'pass_label'] standard_exist = outer_merge.standard_mid_json.notnull() test_exist = outer_merge.test_mid_json.notnull() inner_merge = pd.merge(test_data, standard_data, on='id', how='inner') inner_merge.columns = ['id', 'test_mid_json', 'standard_mid_json', 'pass_label'] return inner_merge, standard_exist, test_exist def save_results(result_dict,overall_report_dict,badcase_path,overall_path,): """ 将结果字典保存为JSON文件至指定路径。 参数: - result_dict: 包含计算结果的字典。 - overall_path: 结果文件的保存路径，包括文件名。 """ # 打开指定的文件以写入 with open(badcase_path, 'w', encoding='utf-8') as f: # 将结果字典转换为JSON格式并写入文件 json.dump(result_dict, f, ensure_ascii=False, indent=4) print(f"评测badcase输出报告查看：{badcase_path}") with open(overall_path, 'w', encoding='utf-8') as f: # 将结果字典转换为JSON格式并写入文件 json.dump(overall_report_dict, f, ensure_ascii=False, indent=4) print(f"整体评测指标结果请查看：{overall_path}") def upload_to_s3(file_path, bucket_name, s3_directory, AWS_ACCESS_KEY, AWS_SECRET_KEY, END_POINT_URL): """ 上传文件到Amazon S3 """ # 创建S3客户端 s3 = boto3.client('s3', aws_access_key_id=AWS_ACCESS_KEY, aws_secret_access_key=AWS_SECRET_KEY, endpoint_url=END_POINT_URL) try: # 从文件路径中提取文件名 file_name = os.path.basename(file_path) # 创建S3对象键，将s3_directory和file_name连接起来 s3_object_key = f"{s3_directory}/{file_name}" # 使用斜杠直接连接 # 上传文件到S3 s3.upload_file(file_path, bucket_name, s3_object_key) print(f"文件 {file_path} 成功上传到S3存储桶 {bucket_name} 中的目录 {s3_directory}，文件名为 {file_name}") except FileNotFoundError: print(f"文件 {file_path} 未找到，请检查文件路径是否正确。") except NoCredentialsError: print("无法找到AWS凭证，请确认您的AWS访问密钥和密钥ID是否正确。") except ClientError as e: print(f"上传文件时发生错误：{e}") def generate_filename(badcase_path,overall_path): """ 生成带有当前时间戳的输出文件名。 参数: - base_path: 基础路径和文件名前缀。 返回: - 带有当前时间戳的完整输出文件名。 """ # 获取当前时间并格式化为字符串 current_time = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') # 构建并返回完整的输出文件名 return f"{badcase_path}_{current_time}.json",f"{overall_path}_{current_time}.json" def compare_edit_distance(json_file, overall_report): with open(json_file, 'r',encoding='utf-8') as f: json_data = json.load(f) json_edit_distance = json_data['pdf间的平均编辑距离'] if overall_report['pdf间的平均编辑距离'] > json_edit_distance: return 0 else: return 1 def main(standard_file, test_file, zip_file, badcase_path, overall_path,base_data_path, s3_bucket_name=None, s3_file_directory=None, aws_access_key=None, aws_secret_key=None, end_point_url=None): """ 主函数，执行整个评估流程。 参数: - standard_file: 标准文件的路径。 - test_file: 测试文件的路径。 - zip_file: 压缩包的路径的路径。 - badcase_path: badcase文件的基础路径和文件名前缀。 - overall_path: overall文件的基础路径和文件名前缀。 - s3_bucket_name: S3桶名称（可选）。 - s3_file_directory: S3上的文件保存目录（可选）。 - AWS_ACCESS_KEY, AWS_SECRET_KEY, END_POINT_URL: AWS访问凭证和端点URL（可选）。 """ # 检查文件是否存在 check_json_files_in_zip_exist(zip_file, standard_file, test_file) # 读取JSON文件内容 json_standard_origin, json_test_origin = read_json_files_from_zip(zip_file, standard_file, test_file) # 合并JSON数据 inner_merge, standard_exist, test_exist = merge_json_data(json_test_origin, json_standard_origin) #计算总体指标 overall_report_dict=overall_calculate_metrics(inner_merge, inner_merge['test_mid_json'], inner_merge['standard_mid_json'],standard_exist, test_exist) # 计算指标 result_dict = calculate_metrics(inner_merge, inner_merge['test_mid_json'], inner_merge['standard_mid_json'], json_standard_origin) # 生成带时间戳的输出文件名 badcase_file,overall_file = generate_filename(badcase_path,overall_path) # 保存结果到JSON文件 save_results(result_dict, overall_report_dict,badcase_file,overall_file) result=compare_edit_distance(base_data_path, overall_report_dict) if all([s3_bucket_name, s3_file_directory, aws_access_key, aws_secret_key, end_point_url]): try: upload_to_s3(badcase_file, s3_bucket_name, s3_file_directory, aws_access_key, aws_secret_key, end_point_url) upload_to_s3(overall_file, s3_bucket_name, s3_file_directory, aws_access_key, aws_secret_key, end_point_url) except Exception as e: print(f"上传到S3时发生错误: {e}") print(result) assert result == 1 if __name__ == "__main__": parser = argparse.ArgumentParser(description="主函数，执行整个评估流程。") parser.add_argument('standard_file', type=str, help='标准文件的路径。') parser.add_argument('test_file', type=str, help='测试文件的路径。') parser.add_argument('zip_file', type=str, help='压缩包的路径。') parser.add_argument('badcase_path', type=str, help='badcase文件的基础路径和文件名前缀。') parser.add_argument('overall_path', type=str, help='overall文件的基础路径和文件名前缀。') parser.add_argument('base_data_path', type=str, help='基准文件的基础路径和文件名前缀。') parser.add_argument('--s3_bucket_name', type=str, help='S3桶名称。', default=None) parser.add_argument('--s3_file_directory', type=str, help='S3上的文件名。', default=None) parser.add_argument('--AWS_ACCESS_KEY', type=str, help='AWS访问密钥。', default=None) parser.add_argument('--AWS_SECRET_KEY', type=str, help='AWS秘密密钥。', default=None) parser.add_argument('--END_POINT_URL', type=str, help='AWS端点URL。', default=None) args = parser.parse_args() main(args.standard_file, args.test_file, args.zip_file, args.badcase_path,args.overall_path,args.base_data_path,args.s3_bucket_name, args.s3_file_directory, args.AWS_ACCESS_KEY, args.AWS_SECRET_KEY, args.END_POINT_URL)