From 97b21438e18fb5c0ea1a8b942db90779c794c440 Mon Sep 17 00:00:00 2001 From: lichusong <2661058231@qq.com> Date: Mon, 17 Jul 2023 13:01:15 +0800 Subject: [PATCH] =?UTF-8?q?0716-END0516=E6=96=87=E4=BB=B6=E6=94=BESED?= =?UTF-8?q?=E6=A8=A1=E5=9E=8B=EF=BC=88sentence=20bert=20elmo=20...?= =?UTF-8?q?=EF=BC=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- END0516/Code/DocSimElmoPlus0516.py | 89 ++++++++++++++++ END0516/Code/DotProSim.py | 43 ++++++++ END0516/Code/PatSimBert0516.py | 59 +++++++++++ END0516/Code/handler.py | 58 ++++++++++ END0516/Code/results_s.txt | 100 ++++++++++++++++++ ...6\215\256 - \345\211\257\346\234\254.xlsx" | Bin 0 -> 23492 bytes ...\350\257\225\346\225\260\346\215\256.xlsx" | Bin 0 -> 28628 bytes 7 files changed, 349 insertions(+) create mode 100644 END0516/Code/DocSimElmoPlus0516.py create mode 100644 END0516/Code/DotProSim.py create mode 100644 END0516/Code/PatSimBert0516.py create mode 100644 END0516/Code/handler.py create mode 100644 END0516/Code/results_s.txt create mode 100644 "END0516/data/\345\257\274\345\214\273\346\265\213\350\257\225\346\225\260\346\215\256 - \345\211\257\346\234\254.xlsx" create mode 100644 "END0516/data/\345\257\274\345\214\273\346\265\213\350\257\225\346\225\260\346\215\256.xlsx" diff --git a/END0516/Code/DocSimElmoPlus0516.py b/END0516/Code/DocSimElmoPlus0516.py new file mode 100644 index 0000000..0f0aa59 --- /dev/null +++ b/END0516/Code/DocSimElmoPlus0516.py @@ -0,0 +1,89 @@ +import math +import numpy as np +import pandas as pd +from elmoformanylangs import Embedder +import handler + + +def calculate_cosine_similarity(target_indices, output_path): + e = Embedder('../../model/zhs.model') + + # 相似度目标文本 + df = pd.read_excel(r'../../data-dev/医生分词结果词性表0511.xlsx', sheet_name='Sheet1') + + # sents = df['word_list'].fillna('').tolist() + # elmo_vecs = e.sents2elmo(sents, output_layer=-1) + + # 加载Elmo处理结果的文件 + elmo_vecs = np.load('elmo_vectors.npy', allow_pickle=True) + + # 词性，词索引 + df1 = pd.read_excel(r'../../data-dev/医生分词结果词性表0511.xlsx', sheet_name='Sheet1') + + # 定义权重字典 动词 名词 形容词 副词 连词 未知词 + weight_dict = {'v': 1.2, 'n': 1.2, 'a': 0.8, 'd': 0.8, 'c': 0.6, 'u': 0.6} + + # 字符向量转词向量,并处理权重 + word_vecs_plus = [] + for i, char_vecs in enumerate(elmo_vecs): + # 定位 + pos_list = eval(df1.loc[i, 'pos_list']) + pos_index = eval(df1.loc[i, 'pos_index']) + words = [] + for j, (start, end) in enumerate(pos_index): + word_vec = sum(char_vecs[start:end]) / (end - start) # average character vectors + pos = pos_list[j] + if pos in weight_dict: + weight = weight_dict[pos] + word_vec *= weight + word = {'vector': word_vec} + words.append(word) + word_vecs_plus.append(words) + + # 格式转换 + word_vecs_plus_fix = [] + for n in word_vecs_plus: + my_array = np.zeros((0, 1024)) + for m in n: + vector = m['vector'] + my_array = np.vstack([my_array, vector]) + word_vecs_plus_fix.append(my_array) + + print("================222222==============") + results = [] + # 计算每个句子与第x个句子的余弦相似度 + for target_index in target_indices: + target_vec = word_vecs_plus_fix[target_index] + sims = [] + for vec in word_vecs_plus_fix: + vec.mean(axis=0) + if isinstance(vec, float) and math.isnan(vec): + sims.append('') + else: + sims.append(np.dot(vec.mean(axis=0), target_vec.mean(axis=0)) / ( + np.linalg.norm(vec.mean(axis=0)) * np.linalg.norm(target_vec.mean(axis=0)))) + + # 将余弦相似度添加到原DataFrame中，并将结果保存为Excel文件 + df['similarity'] = sims + # 根据相似度降序排列，并获取前五个 + top_five = df.nlargest(6, 'similarity') + + # 提取前五个的 idx 和对应的 similarity + top_five_idx = top_five['idx'].tolist() + top_five_similarity = top_five['similarity'].tolist() + + # 输出前五个的 idx 和对应的 similarity + for i in range(1, len(top_five_idx)): + idx = top_five_idx[i] + similarity = top_five_similarity[i] + find_minimum_idx = handler.find_doctor_id_by_idx(idx) + name_by_idx = handler.find_doctor_name_by_idx(find_minimum_idx) + name_doctor = name_by_idx[0] + # print(f"编号:{idx}, 医生集B: name: {name_by_idx}, Score: {similarity:.4f}") + result = { + '医生集': 'B', + '医生名称': name_doctor, + '相似度得分': similarity + } + results.append(result) + return results \ No newline at end of file diff --git a/END0516/Code/DotProSim.py b/END0516/Code/DotProSim.py new file mode 100644 index 0000000..4c0b911 --- /dev/null +++ b/END0516/Code/DotProSim.py @@ -0,0 +1,43 @@ +import pandas as pd +import numpy as np + + +def find_top_similar_doctors(target_indices): + # 计算向量的余弦相似度 + def cosine_similarity(u, v): + dot_product = np.dot(u, v) + norm_u = np.linalg.norm(u) + norm_v = np.linalg.norm(v) + similarity = dot_product / (norm_u * norm_v) + return similarity + + # 读取 Excel 文件 + df = pd.read_excel('../../data-dev/消化内科对话_已解决问题0510.xlsx', sheet_name='医生属性+') + + # 提取所需的列数据 + columns = ['医生职位', '所属医院等级', '科室', '健康顾问（元）', '图文咨询', '指定咨询（元）', '采纳率', '回答总数', + '好评数', '满意度'] + data = df[columns].values + + # 计算相似度并保存到结果列表中 + result = [] + + # 计算相似度并保存到 Excel 文件中 + similarity_df = pd.DataFrame(columns=['idx', '相似idx', '相似度']) + for i in target_indices: + row_i = data[i - 1] + similarities = [] + for j in range(len(data)): + if j != i - 1: + similarity = cosine_similarity(row_i, data[j]) + similarities.append((j + 1, similarity)) + + similarities.sort(key=lambda x: x[1], reverse=True) + top_five = similarities[:5] + result.append({'idx': i, 'similarities': top_five}) + + # 将结果保存到 Excel 文件中 + similarity_df.to_excel('../../data-dev/医生属性相似度2.xlsx', index=False) + + return result + diff --git a/END0516/Code/PatSimBert0516.py b/END0516/Code/PatSimBert0516.py new file mode 100644 index 0000000..189c478 --- /dev/null +++ b/END0516/Code/PatSimBert0516.py @@ -0,0 +1,59 @@ +from transformers import AutoTokenizer, AutoModel +import torch +import numpy as np +import pandas as pd +from sklearn.preprocessing import MinMaxScaler + +class OutputQStruct: + def __init__(self, idx, name, sentence, score): + self.idx = idx + self.name = name + self.sentence = sentence + self.score = score + + +def process_data(query): + # 创建一个空的输出值列表 + output_values = [] + + # 加载BERT模型和tokenizer + tokenizer = AutoTokenizer.from_pretrained('bert-base-chinese') + model = AutoModel.from_pretrained('bert-base-chinese') + + # 加载数据 + df = pd.read_excel('../../data-dev/消化内科对话_已解决问题0510.xlsx', sheet_name='对话数据+') + corpus = df['标题'].tolist() + + # 将所有文本向量化 + encoded_dict = tokenizer(corpus, padding=True, truncation=True, max_length=32, return_tensors='pt') + input_ids = encoded_dict['input_ids'] + attention_mask = encoded_dict['attention_mask'] + + with torch.no_grad(): + outputs = model(input_ids, attention_mask=attention_mask) + embeddings = outputs.last_hidden_state[:, 0, :].numpy() + + query_encoded = tokenizer.encode_plus( + query, add_special_tokens=True, max_length=32, padding='longest', truncation=True, return_tensors='pt') + query_input_ids = query_encoded['input_ids'] + query_attention_mask = query_encoded['attention_mask'] + + with torch.no_grad(): + query_output = model(query_input_ids, attention_mask=query_attention_mask) + query_embedding = query_output.last_hidden_state[:, 0, :].numpy() + + similarities = np.inner(query_embedding, embeddings) + + similarities = similarities.squeeze() # 移除不必要的维度 + scaler = MinMaxScaler() + similarities = scaler.fit_transform(similarities.reshape(-1, 1)).squeeze() # 归一化到[0,1] + + top200_idx = similarities.argsort()[::-1][:50] + + for idx in top200_idx: + output_struct = OutputQStruct(df.iloc[idx]['idx'], df.iloc[idx]['医生姓名'], + corpus[idx].strip(), similarities[idx]) + output_values.append(output_struct) + + # 返回输出值列表 + return output_values diff --git a/END0516/Code/handler.py b/END0516/Code/handler.py new file mode 100644 index 0000000..371cd77 --- /dev/null +++ b/END0516/Code/handler.py @@ -0,0 +1,58 @@ +import pandas as pd + + +def find_minimum_idx(matching_data): + # 存储相同sentence的数据 + same_sentence_data = {} + # 遍历匹配的数据列表 + for data in matching_data: + sentence = data.sentence + idx = data.idx + # 检查sentence是否已经存在于same_sentence_data中 + if sentence in same_sentence_data: + # 如果已存在，则更新为更小的idx + if idx < same_sentence_data[sentence]: + same_sentence_data[sentence] = idx + else: + # 如果不存在，则添加到same_sentence_data中 + same_sentence_data[sentence] = idx + + # 将最小idx组成的数组返回 + return sorted(list(same_sentence_data.values())) + + +def find_doctor_id_by_idx(idx): + excel_file_path = '../../data-dev/消化内科对话_已解决问题0510.xlsx' + # 读取包含 idx 和医生名称的 Excel 表 + df = pd.read_excel(excel_file_path, sheet_name='对话数据+') + + # 获取 idx 列和医生名称列的数据 + idx_column = df['idx'] + doctor_name_column = df['医生姓名'] + + # 查找医生名称对应的行索引 + row_index = idx_column[idx_column == idx].index[0] + + # 获取医生名称 + doctor_name = doctor_name_column[row_index] + + # 查找医生名称对应的医生 ID（假设医生 ID 在另一张工作表的 '医生名称' 和 '医生ID' 列中） + doctor_info_df = pd.read_excel(excel_file_path, sheet_name='医生属性+') + doctor_id = doctor_info_df.loc[doctor_info_df['医生姓名'] == doctor_name, '医生标识'].values[0] + + return doctor_id + + +def find_doctor_name_by_idx(idx): + # 读取 Excel 文件 + df = pd.read_excel('../../data-dev/消化内科对话_已解决问题0510.xlsx', sheet_name='医生属性+') + + # 根据 idx 列筛选匹配的行 + matching_rows = df.loc[df['医生标识'] == idx] + + # 提取医生姓名列 + doctor_names = matching_rows['医生姓名'].tolist() + + # 返回医生姓名列表 + return doctor_names + diff --git a/END0516/Code/results_s.txt b/END0516/Code/results_s.txt new file mode 100644 index 0000000..a4791c9 --- /dev/null +++ b/END0516/Code/results_s.txt @@ -0,0 +1,100 @@ +('成波', 2.9454188275686617) ('宫智超', 2.8402966704554142) ('程琤', 2.4580450750584566) ('侯云飞', 2.4549028114005296) ('高笑娜', 1.9988415715402146) ('孙瑶', 1.9965451569759185) ('宋安全', 1.869607855517698) ('刘恒新', 1.4558374544365433) ('张媛媛', 1.2874338873642452) ('颜承尧', 1.2771727353777995) +('侯云飞', 1.8356272615410427) ('宫智超', 1.6753593742668451) ('孔亮', 1.2790733386747315) ('马强', 1.2739929988201035) ('张强', 1.1144159641734666) ('孟宪华', 0.8350603303655539) +('宫智超', 2.2737457065486764) ('侯云飞', 2.2117425895474487) ('孙瑶', 1.9944149288023283) ('马静', 1.3148150736718538) ('宋安全', 1.2761902527689815) ('钱栋', 1.2386004442394702) ('曾光', 0.9643361091613769) ('刘恒新', 0.93934907913208) ('曹宏春', 0.7821239823105655) ('程琤', 0.7387110294156258) +('王坤', 3.0256132823095787) ('刘燕燕', 2.993532226250397) ('宋安全', 2.281871589449806) ('钱栋', 1.9995735173219509) ('李华', 1.999120438526238) ('李国宏', 1.5115276295493754) ('孟宪华', 1.4637304358330068) ('李崇亮', 1.1277964797212712) ('王俊玲', 0.9530253410339355) ('高宾', 0.9309257984161377) +('宫智超', 2.123563317568448) ('孔亮', 1.9986690111258365) ('马静', 1.5577969769603568) ('李博杰', 1.3567827022135246) ('黄淑玲', 1.282117398676805) ('胡晓华', 0.7953120985190215) ('王晓晨', 0.7847371759350168) ('吴芳', 0.7813442205024328) ('徐彦玲', 0.5650794577227367) +('胡良皞', 4.000000000000001) ('李文燕', 4.000000000000001) ('聂里红', 4.000000000000001) ('王艳', 4.000000000000001) ('吴寒', 4.000000000000001) ('黄淑玲', 1.9965561389923094) ('吴芳', 1.7090036611077972) ('王坤', 1.4233490711527728) ('曾光', 0.7828508681601312) ('孟宪华', 0.5742877002160591) +('赵海明', 2.2830862016303444) ('胡良皞', 2.2807389898981696) ('黄淑玲', 2.269978434550917) ('张继舜', 1.9999999738831313) ('李文燕', 1.9999998820616318) ('徐彦玲', 1.282977594208242) ('樊彩妮', 1.2710411099520305) ('张媛媛', 1.0286868095397947) ('吴芳', 0.8245323063629026) ('路华', 0.7965407330401216) +('孙瑶', 2.9994624815412525) ('宫智超', 2.7094187959037934) ('姜欣莹', 1.9997480823299223) ('薛艳', 1.9995711552926545) ('王晶', 1.4560369578962342) ('侯云飞', 1.4337721265770536) ('成波', 1.4304066516719445) ('张媛媛', 1.28527140639626) ('胡晓华', 1.273903252374241) ('许恒参', 1.0054126159776362) +('苑长青', 1.9865301221252492) ('张强', 1.7805374231807294) ('邢龙', 1.7630762467382197) ('韩金利', 1.489885344146016) ('侯云飞', 1.4895401157356838) ('周宗文', 1.2893925692637274) ('曹宏春', 1.2687105396216198) ('颜承尧', 1.2614803295409769) ('宫智超', 1.0185523509979246) ('王俊玲', 0.9882380247116088) +('许乐', 2.2773229287603107) ('朱月永', 1.9968809978353264) ('胡良皞', 1.7858052607972852) ('陈益忠', 1.4951729194509271) ('黄玉红', 1.492472829042602) ('吴芳', 1.3266995685298655) ('颜承尧', 1.275267623866931) ('潘孝聪', 1.2708027224452616) ('黄津', 0.9986678123474121) ('王俊玲', 0.9948954105377197) +('胡良皞', 1.9999999834717588) ('黄淑玲', 1.9999999834717588) ('李文燕', 1.9999999834717588) ('侯云飞', 1.728372295780077) ('李博杰', 1.4635675691022882) ('宫智超', 1.4294901359714756) ('成波', 1.3006469522184836) ('孙瑶', 1.2789077314941828) ('赵海明', 1.2738186759783319) ('王坤', 1.2621834568912702) +('胡良皞', 3.996053289348878) ('黄淑玲', 3.4051539178833545) ('聂里红', 3.000000000000001) ('王艳', 3.000000000000001) ('吴寒', 2.696239417716143) ('李文燕', 2.422249221801758) ('陈益忠', 1.858032215299379) ('颜承尧', 1.633585034588462) ('钱栋', 1.4614982658316975) +('孙瑶', 2.0728484361028148) ('吴芳', 1.341451454588975) ('宫智超', 1.278872390584215) ('李崇亮', 1.080530930415001) ('马静', 1.059712360695831) ('蒋卫兵', 0.9978378295898438) ('刘恒新', 0.9965024471282959) ('成波', 0.5695439629664139) ('颜承尧', 0.5677522951865217) ('李博杰', 0.5661868355605659) +('陈大雷', 1.798673949522608) ('成波', 1.7939733156255684) ('侯云飞', 1.5914091694564902) ('宫智超', 1.5676271551485659) ('张强', 1.5384780484657445) ('聂里红', 1.2866062520856734) ('侯加洲', 1.2790772645204926) ('关晖勇', 1.01233971118927) ('马志刚', 0.5674683917455632) +('胡良皞', 3.9999999410308167) ('李文燕', 3.9999999410308167) ('黄淑玲', 3.8289853428515537) ('聂里红', 3.000000000000001) ('王艳', 3.000000000000001) ('吴寒', 1.8168021206726293) ('马静', 1.1039206525272165) ('李崇亮', 0.850007770601215) ('宫智超', 0.5729993634330844) ('黄玉红', 0.5653701875547006) +('宫智超', 2.558723817576591) ('孙瑶', 2.282571173371422) ('王晶', 1.9993834509272057) ('陈仙萍', 1.9681855698301285) ('毛晓英', 1.9407468189541677) ('罗积忠', 1.9228224823910987) ('魏传军', 1.9033837302728662) ('徐先明', 1.897475147181563) ('侯云飞', 1.3978582561964548) ('刘恒新', 1.271037937974184) +('孟宪华', 2.53665714652163) ('李博杰', 1.8937904212606005) ('王坤', 1.5609503618526455) ('曾光', 1.5025207543125312) ('钱栋', 1.4911412347020323) ('高宾', 1.0152596050459912) ('关晖勇', 0.9427408218383788) ('宫智超', 0.776524681597643) ('宋安全', 0.7745775007698272) ('王晓晨', 0.7577484915935383) +('宫智超', 2.5551301524919423) ('孙瑶', 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