145 lines
4.6 KiB
Python
145 lines
4.6 KiB
Python
import numpy as np
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import pandas as pd
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import networkx as nx
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import matplotlib.pyplot as plt
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import seaborn as sns
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from matplotlib import rcParams
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# 全局设置中文字体
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rcParams['font.sans-serif'] = ['SimHei'] # 设置中文字体为黑体
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rcParams['axes.unicode_minus'] = False # 解决坐标轴负号显示问题
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# 计算相关系数矩阵
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def row_correlations(df):
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num_rows = df.shape[0]
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corr_matrix = np.zeros((num_rows, num_rows))
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# 标准化数据，并处理标准差为零的情况
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def standardize_column(x):
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std = x.std()
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return (x - x.mean()) / std if std != 0 else np.zeros_like(x)
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standardized_df = df.apply(standardize_column, axis=0)
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for i in range(num_rows):
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for j in range(i, num_rows):
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r = np.corrcoef(standardized_df.iloc[i], standardized_df.iloc[j])[0, 1]
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corr_matrix[i, j] = r
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corr_matrix[j, i] = r
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corr_df = pd.DataFrame(corr_matrix, index=df.index, columns=df.index)
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return corr_df
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def row_euclidean_distances(df):
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# 获取DataFrame的行数
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num_rows = df.shape[0]
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# 初始化一个空的对称方阵用于存储欧氏距离
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distance_matrix = np.zeros((num_rows, num_rows))
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# 计算每两行之间的欧氏距离
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for i in range(num_rows):
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for j in range(i, num_rows):
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# 计算第i行和第j行的欧氏距离
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distance = np.linalg.norm(df.iloc[i] - df.iloc[j])
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# 存储在对称方阵中
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distance_matrix[i, j] = distance
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distance_matrix[j, i] = distance
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# 将欧氏距离矩阵转换为DataFrame
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distance_df = pd.DataFrame(distance_matrix, index=df.index, columns=df.index)
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return distance_df
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def calculate_similarity(correlation_df, distance_df):
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# 初始化一个空的对称方阵用于存储相似度
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num_rows = correlation_df.shape[0]
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similarity_matrix = np.zeros((num_rows, num_rows))
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# 计算相似度S
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for i in range(num_rows):
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for j in range(i, num_rows):
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r = correlation_df.iloc[i, j]
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d = distance_df.iloc[i, j]
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R = (1 + r) / 2
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S = 100 * R / d if d != 0 else 100 * R / 0.25
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# 存储在对称方阵中
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similarity_matrix[i, j] = S
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similarity_matrix[j, i] = S
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# 将相似度矩阵转换为DataFrame
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similarity_df = pd.DataFrame(similarity_matrix, index=correlation_df.index, columns=correlation_df.index)
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return similarity_df
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# 绘制相似度网络图
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def plot_similarity_network(similarity_df, threshold=0.5):
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G = nx.Graph()
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for i in range(len(similarity_df)):
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for j in range(i + 1, len(similarity_df)):
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if similarity_df.iloc[i, j] > threshold:
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G.add_edge(similarity_df.index[i], similarity_df.columns[j], weight=similarity_df.iloc[i, j])
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pos = nx.spring_layout(G)
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plt.figure(figsize=(10, 8))
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edges = G.edges(data=True)
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# 根据相似度调整边的宽度
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weights = [e['weight'] for u, v, e in edges]
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# 设置颜色为浅色调
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nx.draw(G, pos, with_labels=True, node_size=700, node_color='lightblue', font_size=10,
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width=2, edge_color=weights, edge_cmap=plt.cm.Blues, edge_vmin=min(weights),
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edge_vmax=max(weights)*0.5)
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# 显示边的相似度值
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edge_labels = {(u, v): f'{e["weight"]:.2f}' for u, v, e in edges}
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nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
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plt.suptitle("相似度网络图", fontsize=16)
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# plt.title('Similarity Network Graph')
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plt.show()
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# 绘制热力图
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def plot_similarity_heatmap(similarity_df):
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plt.figure(figsize=(10, 8))
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sns.heatmap(similarity_df, annot=True, fmt=".2f", cmap="coolwarm", linewidths=.5)
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plt.title("相关系数热力图", fontsize=16)
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plt.xlabel('样品编号')
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plt.ylabel('样品编号')
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plt.show()
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# 示例用法
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if __name__ == '__main__':
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# 示例矩阵,和之前的扇形图要求一样
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matrix = pd.read_csv('./radartest.csv', index_col=0)
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# 调用函数
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correlation_result = row_correlations(matrix)
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distance_result = row_euclidean_distances(matrix)
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similarity_result = calculate_similarity(correlation_result, distance_result)
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# 输出相关系数矩阵
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print("每两行之间的相关系数矩阵：")
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print(correlation_result)
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# 输出欧氏距离矩阵
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print("每两行之间的欧氏距离矩阵：")
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print(distance_result)
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# 输出相似度矩阵
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print("每两行之间的相似度矩阵：")
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print(similarity_result)
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# 绘制相关系数热力图
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plot_similarity_heatmap(correlation_result)
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# 绘制相似度网络图
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plot_similarity_network(similarity_result, threshold=0.5)
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