0716-END0516文件放SED模型（sentence bert elmo ...）

END0516/Code/DocSimElmoPlus0516.py

+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

END0516/Code/DotProSim.py

+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
+

END0516/Code/PatSimBert0516.py

+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

END0516/Code/handler.py

+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
+

END0516/Code/results_s.txt

+('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', 1.9947148900907936) ('苑长青', 1.9865301221252492) ('孟宪华', 1.7049357941890206) ('关晖勇', 1.5371908384552073) ('侯加洲', 1.287524101967818) ('王俊玲', 1.0039800453215237) ('吴芳', 0.8680275678634644) ('黄玉红', 0.5641451405567901)
+('黄淑玲', 2.999999983471759) ('胡良皞', 2.692108031703208) ('李文燕', 2.48697849051369) ('聂里红', 2.0000000000000004) ('王艳', 2.0000000000000004) ('吴寒', 2.0000000000000004) ('张作臻', 1.3542336515836435) ('李博杰', 1.3479194811595079) ('程琤', 0.8139369494058206) ('吴芳', 0.7905133469511982)
+('胡良皞', 2.5750636769252093) ('宫智超', 2.40984811052963) ('关晖勇', 1.5790383752041641) ('许瑞卿', 1.5419518315674021) ('成波', 1.4234218932948692) ('张作臻', 1.4106508708736958) ('陈莉', 0.8599011008010139) ('宋安全', 0.5706169261565581) ('高宾', 0.5597592584623368)
+('颜承尧', 1.8552309709042092) ('李国宏', 1.7099945300020232) ('李博杰', 1.6750698506163242) ('宋安全', 1.4071488472368499) ('胡良皞', 1.2880419911398722) ('黄淑玲', 1.283447963118194) ('高宾', 0.8448801040649414) ('李崇亮', 0.8158397197723388) ('成波', 0.7011309687049518)
+('胡晓华', 1.9999243616642333) ('孙瑶', 1.999881624100492) ('王晶', 1.9995715548808053) ('宫智超', 1.890355550393569) ('王坤', 1.8276355724617392) ('孔凡荣', 1.457230107488313) ('姜欣莹', 1.4487086645216818) ('刘万里', 1.4260489853514056) ('侯云飞', 1.285667384393903) ('张强', 1.2782970442296748)
+('邢龙', 2.5832393666816573) ('刘恒新', 2.1241064171362067) ('黄玉红', 1.999999842923348) ('朱月永', 1.999999842923348) ('韩金利', 1.9997350017991122) ('周宗文', 1.9994231252849652) ('于中锴', 1.9992564963683672) ('李萍', 1.9991562073007407) ('陈益忠', 1.9937767041396801) ('段婷', 1.9921102366070147)
+('吴芳', 2.743813681934311) ('成波', 1.3408727692197036) ('刘恒新', 1.0124738046836022) ('陈莉', 0.740867345923425) ('侯云飞', 0.7301467743858121) ('孔凡荣', 0.547266224014082)
+('李凤仙', 1.4856093487121296) ('韩金利', 1.2742043232651719) ('邢龙', 0.8168545878254873) ('高笑娜', 0.8154305124107504) ('胡晓华', 0.7786450875322353) ('颜承尧', 0.5619245735374021) ('成波', 0.5614500499029877) ('曹宏春', 0.5605454203396196)
+('许瑞卿', 1.5613394092699977) ('侯永强', 1.4226021761303376) ('李安', 1.2607768979386247) ('吴芳', 1.163305439877638) ('高宾', 0.9776919672949648) ('王晓晨', 0.9702457471202377) ('侯云飞', 0.8368565158782604) ('李博杰', 0.725250493718868) ('陈莉', 0.7152039398872156) ('孟宪华', 0.7150812005143434)
+('李国宏', 1.985874164715406) ('宋安全', 1.662249974954796) ('李文燕', 1.2937501431869647) ('黄淑玲', 1.273243418408509) ('颜承尧', 0.8537888525137206) ('侯云飞', 0.5471923945433859)
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