来自边缘列表的 Scipy 稀疏矩阵

Question

如何将边缘列表（ data ）转换为 python scipy 稀疏矩阵以获得此结果：

数据集（其中“agn”是节点类别一，“fct”是节点类别二）：

data['agn'].tolist()
['p1', 'p1', 'p1', 'p1', 'p1', 'p2', 'p2', 'p2', 'p2', 'p3', 'p3', 'p3', 'p4', 'p4', 'p5']

data['fct'].tolist()
['f1', 'f2', 'f3', 'f4', 'f5', 'f3', 'f4', 'f5', 'f6', 'f5', 'f6', 'f7', 'f7', 'f8', 'f9']

（不工作）python代码：

from scipy.sparse import csr_matrix, coo_matrix

csr_matrix((data_sub['agn'].values, data['fct'].values), 
                    shape=(len(set(data['agn'].values)), len(set(data_sub['fct'].values))))

->错误：“TypeError: invalid input format” 我真的需要三个数组来构造矩阵，就像 scipy csr 文档中的例子那样建议（只能使用两个链接，抱歉！）？

（工作）用于构造只有两个向量的矩阵的 R 代码：

library(Matrix)

grph_tim <- sparseMatrix(i = as.numeric(data$agn), 
                     j = as.numeric(data$fct),  
                     dims = c(length(levels(data$agn)),
                              length(levels(data$fct))),
                     dimnames = list(levels(data$agn),
                                     levels(data$fct)))

---

编辑：我从这里修改了代码并添加了所需的数组后，它终于起作用了：

import numpy as np
import pandas as pd
import scipy.sparse as ss

def read_data_file_as_coo_matrix(filename='edges.txt'):
    "Read data file and return sparse matrix in coordinate format."

    # if the nodes are integers, use 'dtype = np.uint32'
    data = pd.read_csv(filename, sep = '\t', encoding = 'utf-8')

    # where 'rows' is node category one and 'cols' node category 2
    rows = data['agn']  # Not a copy, just a reference.
    cols = data['fct']

    # crucial third array in python, which can be left out in r
    ones = np.ones(len(rows), np.uint32)
    matrix = ss.coo_matrix((ones, (rows, cols)))
    return matrix

此外，我将节点的字符串名称转换为整数。 因此data['agn']变为[0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 4]并且data['fct']变为[0, 1, 2, 3, 4, 2, 3, 4, 5, 4, 5, 6, 6, 7, 8] 。

我得到这个稀疏矩阵：

(0, 0) 1 (0, 1) 1 (0, 2) 1 (0, 3) 1 (0, 4) 1 (1, 2) 1 (1, 3) 1 (1, 4) 1 (1) , 5) 1 (2, 4) 1 (2, 5) 1 (2, 6) 1 (3, 6) 1 (3, 7) 1 (4, 8) 1

Answer 1

我从这里修改了代码并添加了所需的数组后，它终于起作用了：

import numpy as np
import pandas as pd
import scipy.sparse as ss

def read_data_file_as_coo_matrix(filename='edges.txt'):
    "Read data file and return sparse matrix in coordinate format."

    # if the nodes are integers, use 'dtype = np.uint32'
    data = pd.read_csv(filename, sep = '\t', encoding = 'utf-8')

    # where 'rows' is node category one and 'cols' node category 2
    rows = data['agn']  # Not a copy, just a reference.
    cols = data['fct']

    # crucial third array in python, which can be left out in r
    ones = np.ones(len(rows), np.uint32)
    matrix = ss.coo_matrix((ones, (rows, cols)))
    return matrix

此外，我将节点的字符串名称转换为整数。 因此data['agn']变为[0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 4]并且data['fct']变为[0, 1, 2, 3, 4, 2, 3, 4, 5, 4, 5, 6, 6, 7, 8] 。

我得到这个稀疏矩阵：

(0, 0) 1 (0, 1) 1 (0, 2) 1 (0, 3) 1 (0, 4) 1 (1, 2) 1 (1, 3) 1 (1, 4) 1 (1) , 5) 1 (2, 4) 1 (2, 5) 1 (2, 6) 1 (3, 6) 1 (3, 7) 1 (4, 8) 1