简化这些代码：import numpy as np import matplotlib.pyplot as plt import networkx as nx G1 = nx.DiGraph() G1.add_edge('V1', 'V2', capacity=70) G1.add_edge('V1', 'V3', capacity=100) G1.add_edge('V1', 'V4', capacity=90) G1.add_edge('V2', 'V6', capacity=80) G1.add_edge('V3', 'V4', capacity=40) G1.add_edge('V3', 'V5', capacity=70) G1.add_edge('V4', 'V5', capacity=40) G1.add_edge('V4', 'V6', capacity=100) G1.add_edge('V5', 'V6', capacity=90) from networkx.algorithms.flow import edmonds_karp maxFlowValue, maxFlowDict = nx.maximum_flow(G1, 'V1', 'V6', flow_func=edmonds_karp) edgeCapacity = nx.get_edge_attributes(G1, 'capacity') edgeLabel = {} for i in edgeCapacity.keys(): edgeLabel[i] = f'c={edgeCapacity[i]:}' edgeLists = [] for i in maxFlowDict.keys(): for j in maxFlowDict[i].keys(): edgeLabel[(i, j)] += ',f=' + str(maxFlowDict[i][j]) if maxFlowDict[i][j] > 0: edgeLists.append((i,j)) print("最大流值: ", maxFlowValue) print("最大流的路径：", edgeLists) print("最大流的途径及流量: ", maxFlowDict) fig, ax = plt.subplots(figsize=(8, 6)) pos = {'V1': (3, 3.5), 'V2': (4, 4), 'V3': (4, 3), 'V4': (5, 3.5), 'V5': (5.5, 3), 'V6': (6.5, 3.5) } edge_labels = nx.get_edge_attributes(G1, 'capacity') ax.set_title("abcd") nx.draw(G1, pos, with_labels=True, node_color='c', node_size=300, font_size=10) nx.draw_networkx_edge_labels(G1, pos, edgeLabel, font_color='navy') nx.draw_networkx_edges(G1, pos, edgelist=edgeLists, edge_color='m') plt.axis('on') plt.show()

import numpy as np import matplotlib.pyplot as plt import math

import numpy as np import matplotlib.pyplot as plt import math # 解决图标题中文乱码问题 import matplotlib as mpl mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体 mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题 #原数据 data=np.array([[72.03,241.2,1592.74],[73.84,241.2,1855.36],[74.49,244.8,2129.60],[76.68,250.9,2486.86],[78.00,250.9,2728.94],[79.68,252.2,3038.90]]) #要预测数据的真实值 data_T=np.array([[81.21,256.5,3458.05],[82.84,259.4,3900.27],[84.5,262.4,4399.06],[86.19,265.3,4961.62],[87

python 3.74 运行import numpy as np 报错lib\site-packages\numpy\init.py

import numpy as np import os,sys #获取当前文件夹，并根据文件名 def path(fileName): p=sys.path[0]+'\\'+fileName return p #读文件 def readFile(fileName): f=open(path(fileName)) str=f.read() ...

import pandas as pd import numpy as np import networkx as nx import matplotlib.pyplot as plt df = pd.read_excel(r"C:\Users\li'yi'jie\Desktop\运筹学网络规划数据.xlsx") edges = [] for i in range(len(df)): edge = { "id": df.loc[i, "边的编号"], "tail": df.loc[i, "边的尾节点"], "head": df.loc[i, "边的头节点"], "length": df.loc[i, "长度"], "capacity": df.loc[i, "容量"] } edges.append(edge) plt.figure(figsize=(15,15)) G = nx.DiGraph() for edge in edges: G.add_edge(edge["tail"], edge["head"], weight=edge["length"]) pos = nx.spring_layout(G) nx.draw(G, pos, with_labels=True) labels = nx.get_edge_attributes(G, "weight") nx.draw_networkx_edge_labels(G, pos, edge_labels=labels) #nx.draw_networkx_edge_labels(G, pos, edge_labels=labels, label_pos=0.3) plt.show() all_pairs = dict(nx.all_pairs_dijkstra_path_length(G)) rows = [] for start_node, dist_dict in all_pairs.items(): for end_node, dist in dist_dict.items(): rows.append({'起始节点': start_node, '终止节点': end_node, '最短路径长度': dist}) df_result = pd.DataFrame(rows) df_result.to_excel('all_pairs.xlsx', index=False)请问在写入excel的时候，能否把经过的边的编号一起写入呢？麻烦给下修改后的代码，谢谢你！

import matplotlib.pyplot as plt df = pd.read_excel(r"C:\Users\li'yi'jie\Desktop\运筹学网络规划数据.xlsx") edges = [] for i in range(len(df)): edge = { "id": df.loc[i, "边的编号"], "tail": df.loc...

import pandas as pd import numpy as np import networkx as nx import matplotlib.pyplot as plt # 读取Excel文件中的邻接矩阵 adjacency_matrix = pd.read_excel('output.xlsx', index_col=0) # 将邻接矩阵转换为numpy数组 adjacency_matrix = adjacency_matrix.to_numpy() # 创建有向图对象 G = nx.DiGraph(adjacency_matrix) def preprocess(G): p = 0 directedGraph = nx.DiGraph() for u in G.nodes(): for v in G.neighbors(u): if (v != u): propProb = G.number_of_edges(u, v) / G.degree(v) directedGraph.add_edge(u, v, pp=propProb) return directedGraph def simulate(G, seedNode, propProbability): newActive = True currentActiveNodes = seedNode.copy() newActiveNodes = set() activatedNodes = seedNode.copy() influenceSpread = len(seedNode) while newActive: for node in currentActiveNodes: for neighbor in G.neighbors(node): if neighbor not in activatedNodes: if G[node][neighbor]['pp'] > propProbability: newActiveNodes.add(neighbor) activatedNodes.append(neighbor) influenceSpread += len(newActiveNodes) if newActiveNodes: currentActiveNodes = list(newActiveNodes) newActiveNodes = set() else: newActive = False return influenceSpread def flipCoin(probability): return np.random.random() < probability # 可视化传播过程 def visualizePropagation(G, seedNode, propProbability): pos = nx.spring_layout(G) # 选择布局算法 labels = {node: node for node in G.nodes()} # 节点标签为节点名 colors = ['r' if node in seedNode else 'b' for node in G.nodes()] # 种子节点为红色，其他节点为蓝色 plt.figure(figsize=(10,6)) nx.draw_networkx_nodes(G, pos, node_color=colors) nx.draw_networkx_edges(G, pos) nx.draw_networkx_labels(G, pos, labels) plt.title('Propagation Visualization') plt.show() # 示例用法 seedNode = [7,36,17] propProbability = 0.7 directedGraph = preprocess(G) influenceSpread = simulate(directedGraph, seedNode, propProbability) print("Influence Spread:", influenceSpread) visualizePropagation(directedGraph, seedNode, propProbability)修改这个代码使得输出图形节点之间间隔合理能够看清

你可以尝试修改nx.spring_layout()函数的参数，例如k，来调整节点之间的距离。这个参数控制着节点之间的斥力大小，较大的k值会使节点之间的距离增大，从而使得图形更容易观察。下面是修改后的代码示例： ...

import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.stats import pointbiserialr import networkx as nx import pingouin as pg from statsmodels.stats.outliers_influence import variance_inflation_factor import statsmodels.api as sm # 设置中文支持和图像清晰度 plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False plt.rcParams['figure.dpi'] = 300 # 固定随机种子 np.random.seed(123) # %% 1. 模拟符合因素效应特征的数据 np_samples = 1500 # 样本量 # 初始化数据字典 data = {} # T0阶段：转运前基础状态 data['GCS'] = np.round(np.random.uniform(3, 15, np_samples)).astype(int) # GCS评分 data['APACHE'] = np.round(np.random.uniform(0, 30, np_samples)).astype(int) # APACHE II评分 data['age'] = np.round(np.random.normal(60, 15, np_samples)).astype(int) # 年龄 data['hypertension'] = np.random.randint(0, 2, np_samples) # 高血压史 # T1阶段：转运中过程变量 data['transport_time'] = np.round(np.random.normal(18, 8, np_samples)).astype(int) # 转运时长 data['spo2_mean'] = np.round(np.random.normal(94, 6, np_samples)).astype(int) # 平均血氧 data['sedative'] = np.random.randint(0, 2, np_samples) # 镇静剂使用 data['respiratory_rate'] = np.round(np.random.normal(18, 5, np_samples)).astype(int) # 呼吸频率 data['transport_distance'] = np.round(np.random.normal(10, 5, np_samples)).astype(int) # 距离 data['transport_personnel'] = np.random.randint(1, 4, np_samples) # 人员 data['temperature'] = np.random.normal(36.5, 0.8, np_samples) # 体温 # T2阶段：转运后状态 data['spo2_final'] = (data['spo2_mean'] + np.random.normal(0, 2, np_samples)).round().astype(int) data['heart_rate'] = np.round(np.random.normal(85, 20, np_samples)).astype(int) data['resp_support'] = np.random.randint(0, 3, np_samples) # 呼吸支持 # %% 构建结局变量：转运后不良事件 gcs_effect = np.zeros(np_samples) for i in range(np_samples): if data['GCS'][i] < 8: gcs_effect[i] = -0.223 * (8 - data['GCS'][i]) else: gcs_effect[i] = -0.087 * (15 - data['GCS'][i]) ht_effect = 0.185 * data['hypertension'] time_effect = np.zeros(np_samples) for i in range(np_samples): if data['transport_time'][i] > 20: time_effect[i] = 0.032 * (data['transport_time'][i] - 20) else: time_effect[i] = 0.015 * data['transport_time'][i] age_effect = np.zeros(np_samples) for i in range(np_samples): if data['age'][i] > 65: age_effect[i] = 0.037 * (data['age'][i] - 65) / 5 else: age_effect[i] = 0.016 * data['age'][i] / 5 resp_support_effect = 0.096 * data['resp_support'] spo2_effect = np.zeros(np_samples) for i in range(np_samples): if data['spo2_final'][i] < 90: spo2_effect[i] = -0.123 * (90 - data['spo2_final'][i]) else: spo2_effect[i] = -0.021 * (data['spo2_final'][i] - 90) hr_effect = np.zeros(np_samples) for i in range(np_samples): if data['heart_rate'][i] < 50: hr_effect[i] = 0.42 * (50 - data['heart_rate'][i]) / 10 elif data['heart_rate'][i] > 120: hr_effect[i] = 0.38 * (data['heart_rate'][i] - 120) / 10 sedative_effect = -0.1839 * data['sedative'] rr_effect = np.zeros(np_samples) for i in range(np_samples): if data['respiratory_rate'][i] < 10: rr_effect[i] = 0.35 elif data['respiratory_rate'][i] > 24: rr_effect[i] = 0.31 temp_effect = np.zeros(np_samples) for i in range(np_samples): if data['temperature'][i] < 36: temp_effect[i] = 0.28 elif data['temperature'][i] > 38.5: temp_effect[i] = 0.25 distance_effect = -0.028 * data['transport_distance'] / 5 personnel_effect = -0.025 * (data['transport_personnel'] - 1) apache_effect = 0.011 * data['APACHE'] / 10 # 总logit计算 logit = (0.2 + 0.257 * gcs_effect + 0.185 * ht_effect + 0.172 * time_effect + 0.153 * age_effect + 0.096 * resp_support_effect + 0.078 * sedative_effect + 0.062 * spo2_effect + 0.058 * hr_effect + 0.049 * rr_effect + 0.045 * temp_effect + 0.028 * distance_effect + 0.025 * personnel_effect + 0.011 * apache_effect) # 计算概率和结局 prob = 1 / (1 + np.exp(-logit)) data['adverse_event'] = np.random.binomial(1, prob) # %% 2. 核心因素筛选（不剔除任何变量，不显示 0.00） factors = { '心率': data['heart_rate'], '血氧饱和度': data['spo2_final'], 'GCS评分': data['GCS'], '转运时长': data['transport_time'], '镇静剂使用': data['sedative'], '呼吸支持': data['resp_support'], '年龄': data['age'], 'APACHE II评分': data['APACHE'] } rpb = [] p_values = [] for name, values in factors.items(): r, p = pointbiserialr(values, data['adverse_event']) rpb.append(r) p_values.append(p) # 不进行变量剔除，直接保留所有变量 filtered_factors = factors.copy() print("=== 因素筛选结果（不显示 0.00） ===") for i, (name, values) in enumerate(factors.items()): if abs(rpb[i]) > 0.001 or p_values[i] > 0.001: print(f"{name}: rpb={rpb[i]:.2f}, P={p_values[i]:.2f}") # %% 3. 共线性处理（VIF） X = pd.DataFrame(filtered_factors) vif_data = pd.DataFrame() vif_data["特征"] = X.columns vif_data["VIF值"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])] print("\n=== VIF共线性分析 ===") print(vif_data) # %% 4. 偏相关分析 df = pd.DataFrame(filtered_factors) df['adverse_event'] = data['adverse_event'] # 自定义偏相关矩阵函数 def partial_corr_matrix(df): cols = df.columns pcorr = pd.DataFrame(np.zeros((len(cols), len(cols))), columns=cols, index=cols) for i, col1 in enumerate(cols): for j, col2 in enumerate(cols): if i == j: pcorr.loc[col1, col2] = 1.0 elif j > i: controls = list(set(cols) - {col1, col2}) r = pg.partial_corr(data=df, x=col1, y=col2, covar=controls).r[0] pcorr.loc[col1, col2] = r pcorr.loc[col2, col1] = r return pcorr # 计算偏相关矩阵 partial_corr = partial_corr_matrix(df) print("\n=== 显著直接关联（偏相关|r|>0.001） ===") for col in df.columns: if col != 'adverse_event': corr = partial_corr.loc[col, 'adverse_event'] if abs(corr) > 0.001: print(f"{col} 与转运后不良事件: r={corr:.3f}") # %% 5. 动态网络模型构建 T0_nodes = ['GCS评分', 'APACHE II评分', '年龄'] T1_nodes = ['转运时长', '血氧饱和度', '镇静剂使用'] T2_nodes = ['心率', '呼吸支持', 'adverse_event'] G = nx.DiGraph() G.add_nodes_from(df.columns) # 添加边（显著偏相关） for i in df.columns: for j in df.columns: if i != j: pc = partial_corr.loc[i, j] if abs(pc) > 0.2: G.add_edge(i, j, weight=pc) # %% 6. 动态特征量化分析 # 节点核心度 node_core = {} for node in G.nodes: core = sum(abs(G[u][v]['weight']) for u, v in G.edges if u == node or v == node) node_core[node] = core print("\n=== 节点核心度排序 ===") for node, score in sorted(node_core.items(), key=lambda x: x[1], reverse=True): print(f"{node}: {score:.3f}") # %% 7. 网络可视化 pos = nx.spring_layout(G, seed=42) plt.figure(figsize=(12, 8)) # 节点颜色 color_map = [] for node in G.nodes: if node in T0_nodes: color_map.append((0.8, 0.9, 1)) elif node in T1_nodes: color_map.append((0.9, 0.8, 1)) elif node in T2_nodes: color_map.append((1, 0.8, 0.8)) # 边颜色 edge_colors = ['red' if G[u][v]['weight'] > 0 else 'blue' for u, v in G.edges] # 绘图 nx.draw(G, pos, with_labels=True, node_color=color_map, edge_color=edge_colors, width=[abs(G[u][v]['weight']) * 5 for u, v in G.edges], node_size=[core * 100 for core in node_core.values()], font_size=10, font_weight='bold', alpha=0.8) plt.title('转运后不良事件动态关联网络', fontsize=16) plt.legend(handles=[ plt.Line2D([0], [0], color='red', lw=2, label='正相关'), plt.Line2D([0], [0], color='blue', lw=2, label='负相关') ]) plt.show() 此代码的结果 C:\python\py\.venv\Scripts\python.exe C:\python\py\7.22.py === 因素筛选结果（不显示 0.00） === 心率: rpb=-0.01, P=0.58 血氧饱和度: rpb=0.01, P=0.69 GCS评分: rpb=0.02, P=0.40 转运时长: rpb=-0.02, P=0.36 镇静剂使用: rpb=0.00, P=0.92 呼吸支持: rpb=0.00, P=0.88 年龄: rpb=0.02, P=0.34 APACHE II评分: rpb=0.00, P=0.94 === VIF共线性分析 === 特征 VIF值 0 心率 17.976832 1 血氧饱和度 42.486776 2 GCS评分 7.331784 3 转运时长 6.108660 4 镇静剂使用 2.009300 5 呼吸支持 2.615991 6 年龄 16.590947 7 APACHE II评分 3.918573 C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] C:\python\py\7.22.py:161: FutureWarning: Series.getitem treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use ser.iloc[pos] === 显著直接关联（偏相关|r|>0.001） === 心率与转运后不良事件: r=-0.015 血氧饱和度与转运后不良事件: r=0.010 GCS评分与转运后不良事件: r=0.022 转运时长与转运后不良事件: r=-0.023 镇静剂使用与转运后不良事件: r=0.002 呼吸支持与转运后不良事件: r=0.004 年龄与转运后不良事件: r=0.024 APACHE II评分与转运后不良事件: r=0.004 === 节点核心度排序 === 心率: 0.000 血氧饱和度: 0.000 GCS评分: 0.000 转运时长: 0.000 镇静剂使用: 0.000 呼吸支持: 0.000 年龄: 0.000 APACHE II评分: 0.000 adverse_event: 0.000 进程已结束，退出代码为 0 显著直接关联生成的值要使偏相关|r|>0.3 节点核心度排序的值不要0.000 你可以适当调整（数据也行）

import matplotlib.pyplot as plt from scipy.stats import pointbiserialr import networkx as nx import pingouin as pg from statsmodels.stats.outliers_influence import variance_inflation_factor import ...

import numpy as np import pandas as pd import matplotlib.pyplot as plt import networkx as nx from pgmpy.models import BayesianNetwork from pgmpy.factors.discrete import TabularCPD from pgmpy.estimators import MaximumLikelihoodEstimator from pgmpy.inference import VariableElimination # 网络结构定义 model_structure = [ ('Pollution', 'Cancer'), ('Smoker', 'Cancer'), ('Cancer', 'Xray'), ('Cancer', 'Dyspnoea') ] # 节点状态定义 states = { 'Pollution': ['low', 'high'], 'Smoker': ['yes', 'no'], 'Cancer': ['yes', 'no'], 'Xray': ['yes', 'no'], 'Dyspnoea': ['yes', 'no'] } # 专家条件概率表（修复行顺序） expert_cpds = { 'Pollution': TabularCPD( variable='Pollution', variable_card=2, values=[[0.9], [0.1]] ), 'Smoker': TabularCPD( variable='Smoker', variable_card=2, values=[[0.3], [0.7]] ), 'Cancer': TabularCPD( variable='Cancer', variable_card=2, # 行顺序修正：第一行对应 Cancer=yes values=[ [0.97, 0.95, 0.999, 0.98], # Cancer=yes [0.03, 0.05, 0.001, 0.02] # Cancer=no ], evidence=['Smoker', 'Pollution'], evidence_card=[2, 2] ), 'Xray': TabularCPD( variable='Xray', variable_card=2, # 行顺序修正：第一行对应 Xray=yes values=[ [0.1, 0.8], # Xray=yes | Cancer=no, Xray=yes | Cancer=yes [0.9, 0.2] # Xray=no ], evidence=['Cancer'], evidence_card=[2] ), 'Dyspnoea': TabularCPD( variable='Dyspnoea', variable_card=2, # 行顺序修正：第一行对应 Dyspnoea=yes values=[ [0.35, 0.7], # Dyspnoea=yes | Cancer=no, Dyspnoea=yes | Cancer=yes [0.65, 0.3] # Dyspnoea=no ], evidence=['Cancer'], evidence_card=[2] ) } # 数据生成函数（修复索引类型） def generate_random_data(model, n_samples=1000): np.random.seed(42) data = pd.DataFrame() G = nx.DiGraph(model.edges()) sorted_nodes = list(nx.topological_sort(G)) # 预生成状态到索引的映射 state_to_index = {node: {state: idx for idx, state in enumerate(states[node])} for node in states} for node in sorted_nodes: cpd = model.get_cpds(node) if len(cpd.variables) == 1: # 根节点 values = cpd.values.flatten() data[node] = np.random.choice(states[node], p=values, size=n_samples) else: # 非根节点 evidence = cpd.variables[1:] # 父节点列表 evidence_card = [len(states[ev]) for ev in evidence] parent_data = data[evidence] # 检查父节点数据完整性 if parent_data.isnull().any().any(): missing = parent_data.columns[parent_data.isnull().any()].tolist() raise ValueError(f"父节点 {missing} 数据未生成") # 批量计算父节点索引 evidence_indices = np.array([ [state_to_index[ev][val] for ev, val in zip(evidence, parent_data.iloc[i])] for i in range(n_samples) ], dtype=int) # 强制类型转换为整数 # 计算 CPD 列索引 try: cpd_indices = np.ravel_multi_index(evidence_indices.T, evidence_card) cpd_indices = cpd_indices.astype(int) # 强制转换为整数 except ValueError as e: raise ValueError(f"节点 {node} 的索引计算错误: {e}") # 检查索引范围 if cpd_indices.max() >= cpd.values.shape[1]: raise IndexError( f"CPD索引越界: 最大索引 {cpd_indices.max()}, " f"允许最大索引 {cpd.values.shape[1] - 1}" ) # 生成当前节点数据（确保索引为整数） data[node] = [ np.random.choice(states[node], p=cpd.values[:, int(idx)].flatten()) for idx in cpd_indices ] return data这是相关代码片段请帮我解决上述问题

嗯，用户提供的这段代码看起来是构建贝叶斯网络并进行数据生成的。首先，我需要仔细阅读代码，看看哪里可能出问题。用户提到要修复索引类型，可能在数据生成函数中遇到问题。首先看数据生成函数generate_random_...

import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.stats import pointbiserialr from sklearn.linear_model import LinearRegression import networkx as nx import pingouin as pg from statsmodels.stats.outliers_influence import variance_inflation_factor # 设置中文支持和图像清晰度 plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False plt.rcParams['figure.dpi'] = 300 # 固定随机种子 np.random.seed(123) # %% 1. 模拟符合因素效应特征的数据 np_samples = 1500 # 样本量 # 初始化数据字典 data = {} # T0阶段：转运前基础状态 data['GCS'] = np.round(np.random.uniform(3, 15, np_samples)).astype(int) # GCS评分 data['APACHE'] = np.round(np.random.uniform(0, 30, np_samples)).astype(int) # APACHE II评分 data['age'] = np.round(np.random.normal(60, 15, np_samples)).astype(int) # 年龄 data['hypertension'] = np.random.randint(0, 2, np_samples) # 高血压史 # T1阶段：转运中过程变量 data['transport_time'] = np.round(np.random.normal(18, 8, np_samples)).astype(int) # 转运时长 data['spo2_mean'] = np.round(np.random.normal(94, 6, np_samples)).astype(int) # 平均血氧 data['sedative'] = np.random.randint(0, 2, np_samples) # 镇静剂使用 data['respiratory_rate'] = np.round(np.random.normal(18, 5, np_samples)).astype(int) # 呼吸频率 data['transport_distance'] = np.round(np.random.normal(10, 5, np_samples)).astype(int) # 距离 data['transport_personnel'] = np.random.randint(1, 4, np_samples) # 人员 data['temperature'] = np.random.normal(36.5, 0.8, np_samples) # 体温 # T2阶段：转运后状态 data['spo2_final'] = (data['spo2_mean'] + np.random.normal(0, 2, np_samples)).round().astype(int) data['heart_rate'] = np.round(np.random.normal(85, 20, np_samples)).astype(int) data['resp_support'] = np.random.randint(0, 3, np_samples) # 呼吸支持 # %% 构建结局变量：转运后不良事件（增强效应） gcs_effect = np.zeros(np_samples) for i in range(np_samples): if data['GCS'][i] < 8: gcs_effect[i] = -0.4 * (8 - data['GCS'][i]) else: gcs_effect[i] = -0.2 * (15 - data['GCS'][i]) ht_effect = 0.3 * data['hypertension'] time_effect = np.zeros(np_samples) for i in range(np_samples): if data['transport_time'][i] > 20: time_effect[i] = 0.05 * (data['transport_time'][i] - 20) else: time_effect[i] = 0.03 * data['transport_time'][i] age_effect = np.zeros(np_samples) for i in range(np_samples): if data['age'][i] > 65: age_effect[i] = 0.06 * (data['age'][i] - 65) / 5 else: age_effect[i] = 0.03 * data['age'][i] / 5 resp_support_effect = 0.15 * data['resp_support'] spo2_effect = np.zeros(np_samples) for i in range(np_samples): if data['spo2_final'][i] < 90: spo2_effect[i] = -0.2 * (90 - data['spo2_final'][i]) else: spo2_effect[i] = -0.05 * (data['spo2_final'][i] - 90) hr_effect = np.zeros(np_samples) for i in range(np_samples): if data['heart_rate'][i] < 50: hr_effect[i] = 0.6 * (50 - data['heart_rate'][i]) / 10 elif data['heart_rate'][i] > 120: hr_effect[i] = 0.5 * (data['heart_rate'][i] - 120) / 10 sedative_effect = -0.3 * data['sedative'] rr_effect = np.zeros(np_samples) for i in range(np_samples): if data['respiratory_rate'][i] < 10: rr_effect[i] = 0.5 elif data['respiratory_rate'][i] > 24: rr_effect[i] = 0.4 temp_effect = np.zeros(np_samples) for i in range(np_samples): if data['temperature'][i] < 36: temp_effect[i] = 0.4 elif data['temperature'][i] > 38.5: temp_effect[i] = 0.35 distance_effect = -0.05 * data['transport_distance'] / 5 personnel_effect = -0.05 * (data['transport_personnel'] - 1) apache_effect = 0.03 * data['APACHE'] / 10 # 总logit计算（增强影响） logit = (0.5 + 0.3 * gcs_effect + 0.25 * ht_effect + 0.2 * time_effect + 0.18 * age_effect + 0.15 * resp_support_effect + 0.12 * sedative_effect + 0.1 * spo2_effect + 0.09 * hr_effect + 0.08 * rr_effect + 0.07 * temp_effect + 0.05 * distance_effect + 0.04 * personnel_effect + 0.03 * apache_effect) # 计算概率和结局 prob = 1 / (1 + np.exp(-logit)) data['adverse_event'] = np.random.binomial(1, prob) # %% 2. 核心因素筛选（不剔除任何变量，不显示 0.00） factors = { '心率': data['heart_rate'], '血氧饱和度': data['spo2_final'], 'GCS评分': data['GCS'], '转运时长': data['transport_time'], '镇静剂使用': data['sedative'], '呼吸支持': data['resp_support'], '年龄': data['age'], 'APACHE II评分': data['APACHE'] } rpb = [] p_values = [] for name, values in factors.items(): r, p = pointbiserialr(values, data['adverse_event']) rpb.append(r) p_values.append(p) # 不进行变量剔除，直接保留所有变量 filtered_factors = factors.copy() print("=== 因素筛选结果（不显示 0.00） ===") for i, (name, values) in enumerate(factors.items()): if abs(rpb[i]) > 0.001 or p_values[i] > 0.001: print(f"{name}: rpb={rpb[i]:.2f}, P={p_values[i]:.2f}") # %% 3. 共线性处理（VIF） X = pd.DataFrame(filtered_factors) vif_data = pd.DataFrame() vif_data["特征"] = X.columns vif_data["VIF值"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])] print("\n=== VIF共线性分析 ===") print(vif_data) # %% 4. 偏相关分析 df = pd.DataFrame(filtered_factors) df['adverse_event'] = data['adverse_event'] def partial_corr_matrix(df): cols = df.columns pcorr = pd.DataFrame(np.zeros((len(cols), len(cols))), columns=cols, index=cols) for i, col1 in enumerate(cols): for j, col2 in enumerate(cols): if i == j: pcorr.loc[col1, col2] = 1.0 elif j > i: controls = list(set(cols) - {col1, col2}) r = pg.partial_corr(data=df, x=col1, y=col2, covar=controls).r[0] pcorr.loc[col1, col2] = r pcorr.loc[col2, col1] = r return pcorr partial_corr = partial_corr_matrix(df) print("\n=== 显著直接关联（偏相关|r|>0.001） ===") for col in df.columns: if col != 'adverse_event': corr = partial_corr.loc[col, 'adverse_event'] if abs(corr) > 0.001: print(f"{col} 与转运后不良事件: r={corr:.3f}") # %% 5. 动态网络模型构建 T0_nodes = ['GCS评分', 'APACHE II评分', '年龄'] T1_nodes = ['转运时长', '血氧饱和度', '镇静剂使用'] T2_nodes = ['心率', '呼吸支持', 'adverse_event'] G = nx.DiGraph() G.add_nodes_from(df.columns) # 添加边（显著偏相关） for i in df.columns: for j in df.columns: if i != j: pc = partial_corr.loc[i, j] if abs(pc) > 0.02: G.add_edge(i, j, weight=pc) # %% 6. 动态特征量化分析 # 节点核心度 node_core = {} for node in G.nodes: core = sum(abs(G[u][v]['weight']) for u, v in G.edges if u == node or v == node) node_core[node] = core print("\n=== 节点核心度排序 ===") for node, score in sorted(node_core.items(), key=lambda x: x[1], reverse=True): if score > 0.001: print(f"{node}: {score:.3f}") # %% 7. 网络可视化 pos = nx.spring_layout(G, seed=42) plt.figure(figsize=(12, 8)) # 节点颜色 color_map = [] for node in G.nodes: if node in T0_nodes: color_map.append((0.8, 0.9, 1)) elif node in T1_nodes: color_map.append((0.9, 0.8, 1)) elif node in T2_nodes: color_map.append((1, 0.8, 0.8)) # 边颜色 edge_colors = ['red' if G[u][v]['weight'] > 0 else 'blue' for u, v in G.edges] # 绘图 nx.draw(G, pos, with_labels=True, node_color=color_map, edge_color=edge_colors, width=[abs(G[u][v]['weight']) * 5 for u, v in G.edges], node_size=[core * 100 for core in node_core.values()], font_size=10, font_weight='bold', alpha=0.8) plt.title('转运后不良事件动态关联网络', fontsize=16) plt.legend(handles=[ plt.Line2D([0], [0], color='red', lw=2, label='正相关'), plt.Line2D([0], [0], color='blue', lw=2, label='负相关') ]) plt.show() 这个代码生成的图不太清晰，线太细了，字太小了，字上还有线修改一下

好的，用户提供的代码生成的图表不够清晰，线太细，字体太小，而且文字被线条遮挡。我需要分析问题并提出修改方案。首先，用户提到线太细了。在代码中，边的宽度是通过width=[abs(G[u][v]['weight']) * 5 for u, ...

NetworkXimport matplotlib.pyplot as plt import networkx as nx import numpy as np # 初始化画布 fig, ax = plt.subplots(figsize=(15, 12), facecolor='#1a1a1a') ax.axis('off') # 创建有向图 G = nx.DiGraph() # 添加节点（主要人物） characters = { 'Hamlet': {'color': '#4CAF50', 'size': 3000}, 'Claudius': {'color': '#F44336', 'size': 2500}, 'Gertrude': {'color': '#9C27B0', 'size': 2200}, 'Ophelia': {'color': '#2196F3', 'size': 2000}, 'Ghost': {'color': '#607D8B', 'size': 1800}, 'Horatio': {'color': '#FF9800', 'size': 1500} } for char, props in characters.items(): G.add_node(char, color=props['color'], size=props['size']) # 添加人物关系边 edges = [ ('Hamlet', 'Claudius', {'weight': 5, 'label': '复仇'}), ('Claudius', 'Hamlet', {'weight': 4, 'label': '猜忌'}), ('Gertrude', 'Hamlet', {'weight': 3, 'label': '母子矛盾'}), ('Hamlet', 'Ophelia', {'weight': 3, 'label': '爱情悲剧'}), ('Ghost', 'Hamlet', {'weight': 4, 'label': '真相启示'}), ('Horatio', 'Hamlet', {'weight': 4, 'label': '忠诚友谊'}) ] for edge in edges: G.add_edge(edge[0], edge[1], weight=edge[2]['weight'], label=edge[2]['label'])

import matplotlib.pyplot as plt import numpy as np # 创建一个示例图 G = nx.Graph() edges_with_weights = [('A', 'B', 0.5), ('B', 'C', 0.7), ('C', 'D', 0.9), ('D', 'E', 0.3), ('E', 'F', 0.6)] G.add_...

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import StandardScaler import statsmodels.api as sm # 用于计算P值 # ---------------------- # 1. 生成模拟数据（贴合转运场景） # ---------------------- np.random.seed(42) # 固定随机数，保证结果可复现 n = 500 # 样本量 data = pd.DataFrame({ # 自变量：候选影响因素 'GCS评分': np.random.randint(3, 16, size=n), # GCS评分3-15分，越低病情越重 '管道数量': np.random.randint(0, 6, size=n), # 0-5根管道 '转运时长': np.random.randint(5, 60, size=n), # 5-60分钟 '镇静程度': np.random.randint(0, 3, size=n), # 0=未镇静，1=浅镇静，2=深镇静 '生命体征波动': np.random.randint(0, 3, size=n), # 0=稳定，1=轻度，2=显著 # 因变量：病情变化（问题1核心） # 模拟逻辑：GCS低、管道多、转运久、镇静不足、生命体征波动大→更易病情变化 '病情变化': np.where( (np.random.rand(n) < 0.1) + # 基础概率 (data['GCS评分'] < 9) * 0.3 + # GCS<9（昏迷）增加30%概率 (data['管道数量'] > 2) * 0.2 + # 管道>2根增加20%概率 (data['转运时长'] > 30) * 0.15 + # 转运>30分钟增加15%概率 (data['镇静程度'] == 0) * 0.25 + # 未镇静增加25%概率 (data['生命体征波动'] == 2) * 0.3 # 显著波动增加30%概率 > 0.5, 1, 0 ) }) # ---------------------- # 2. 逻辑回归筛选变量（计算P值，保留P<0.05） # ---------------------- # 提取自变量和因变量 X = data[['GCS评分', '管道数量', '转运时长', '镇静程度', '生命体征波动']] y = data['病情变化'] # 用statsmodels计算P值（更适合筛选变量） X_sm = sm.add_constant(X) # 添加常数项（截距） logit_model = sm.Logit(y, X_sm) result = logit_model.fit() # 输出变量系数和P值 var_pvalues = result.pvalues.drop('const') # 排除常数项 significant_vars = var_pvalues[var_pvalues < 0.05].index.tolist() # 显著变量列表 print("步骤1：逻辑回归筛选的显著影响因素（P<0.05）：") print(significant_vars) print("\n各变量P值：") print(var_pvalues.round(4)) # ---------------------- # 3. 可视化：变量重要性（系数绝对值） # ---------------------- plt.figure(figsize=(8, 5)) coef = result.params.drop('const').abs() # 系数绝对值（代表影响强度） sns.barplot(x=coef.index, y=coef.values) plt.title('逻辑回归筛选的变量重要性（系数绝对值）', fontsize=12) plt.ylabel('系数绝对值（越大影响越显著）') plt.xticks(rotation=30) plt.tight_layout() plt.show() from pgmpy.models import BayesianNetwork from pgmpy.estimators import HillClimbSearch, BicScore from pgmpy.inference import VariableElimination import networkx as nx # ---------------------- # 1. 准备数据（仅保留显著变量+新增不良事件因变量） # ---------------------- # 新增“不良事件”因变量（模拟：由病情变化、管道数量、镇静程度共同影响） data['不良事件'] = np.where( (data['病情变化'] == 1) * 0.4 + # 病情变化增加40%概率 (data['管道数量'] > 2) * 0.3 + # 管道多增加30%概率 (data['镇静程度'] == 0) * 0.25 # 未镇静增加25%概率 > 0.5, 1, 0 ) # 仅保留步骤1筛选的显著变量+结局变量 bn_data = data[significant_vars + ['病情变化', '不良事件']] # ---------------------- # 2. 构建贝叶斯网络（学习结构和参数） # ---------------------- # 用Hill-Climb算法学习网络结构（基于数据关联） hc = HillClimbSearch(bn_data) model = hc.estimate(scoring_method=BicScore(bn_data)) # 用BIC评分优化结构 # 学习条件概率表（CPT） from pgmpy.estimators import MaximumLikelihoodEstimator model.fit(bn_data, estimator=MaximumLikelihoodEstimator) # ---------------------- # 3. 可视化：网络关联路径 # ---------------------- plt.figure(figsize=(10, 6)) pos = nx.spring_layout(model) # 布局 nx.draw(model, pos, with_labels=True, node_size=3000, node_color='lightblue', font_size=10, arrowsize=20) # 标注关键路径（示例：手动标注临床关注的路径） critical_edges = [('GCS评分', '生命体征波动'), ('生命体征波动', '病情变化'), ('管道数量', '不良事件'), ('病情变化', '不良事件')] nx.draw_networkx_edges(model, pos, edgelist=critical_edges, edge_color='red', width=2) plt.title('贝叶斯网络：转运相关因素关联路径（红色为关键路径）', fontsize=12) plt.tight_layout() plt.show() # ---------------------- # 4. 推理：关键路径概率验证 # ---------------------- infer = VariableElimination(model) # 例：计算“GCS评分低（<9）”时，“不良事件”的发生概率 prob_bad = infer.query(variables=['不良事件'], evidence={'GCS评分': 6}) # GCS=6（低） print("\n步骤2：贝叶斯网络推理结果：") print(f"GCS评分低（6分）时，不良事件发生概率：{prob_bad.values[1]:.2f}") from scipy.stats import spearmanr # ---------------------- # 1. 提取贝叶斯网络中的核心变量对 # ---------------------- # 基于贝叶斯网络的关键路径，选择待分析的变量对 core_pairs = [ ('GCS评分', '生命体征波动'), # GCS低是否与生命体征波动相关 ('生命体征波动', '病情变化'), # 波动是否与病情变化相关 ('管道数量', '不良事件') # 管道数量是否与不良事件相关 ] # ---------------------- # 2. 斯皮尔曼相关分析 # ---------------------- corr_results = [] for var1, var2 in core_pairs: rho, p = spearmanr(data[var1], data[var2]) corr_results.append({ '变量对': f"{var1}-{var2}", '相关系数ρ': round(rho, 2), 'P值': round(p, 4), '关联强度': '强' if abs(rho) > 0.5 else '中' if abs(rho) > 0.3 else '弱' }) # 输出结果 corr_df = pd.DataFrame(corr_results) print("\n步骤3：斯皮尔曼相关分析结果（核心变量对）：") print(corr_df) # ---------------------- # 3. 可视化：相关系数热力图 # ---------------------- plt.figure(figsize=(6, 4)) corr_matrix = data[significant_vars + ['病情变化', '不良事件']].corr(method='spearman') sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1) plt.title('斯皮尔曼相关系数热力图（核心变量）', fontsize=12) plt.tight_layout() plt.show()怎么改，不能运行

import matplotlib.pyplot as plt import seaborn as sns from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import StandardScaler import statsmodels.api as sm from pgmpy....

不要0.000，=== 显著直接关联（偏相关|r|>0.3） ===要有数据 === 节点核心度排序 === 年龄: 0.127 APACHE II评分: 0.127 心率: 0.000 血氧饱和度: 0.000 GCS评分: 0.000 转运时长: 0.000 镇静剂使用: 0.000 呼吸支持: 0.000 adverse_event: 0.000 进程已结束，退出代码为 0 你适当调整，调整数据也行

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import matplotlib.pyplot as plt class LinkedGraph: def __init__(self, vers, edges): self.vers = int(vers) self.edges = [] for edge in edges: u, v = map(int, edge.split()) self.edges.append((u, ...

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