如何将数据帧转换与串列到csr_matrix

Question

我工作的一个PMI的问题，到目前为止，我有这样一个数据帧：

w = ['by', 'step', 'by', 'the', 'is', 'step', 'is', 'by', 'is'] 
c = ['step', 'what', 'is', 'what', 'the', 'the', 'step', 'the', 'what'] 
ppmi = [1, 3, 12, 3, 123, 1, 321, 1, 23] 
df = pd.DataFrame({'w':w, 'c':c, 'ppmi': ppmi}) 

我想这个数据帧转换成稀疏矩阵。由于w和c是字符串列表，如果我做csr_matrix((ppmi, (w, c)))，它会给我一个错误TypeError: cannot perform reduce with flexible type。什么是转换此数据框的另一种方法？

Answer 1

也许你可以尝试用coo_matrix：

import pandas as pd 
import scipy.sparse as sps 
w = ['by', 'step', 'by', 'the', 'is', 'step', 'is', 'by', 'is'] 
c = ['step', 'what', 'is', 'what', 'the', 'the', 'step', 'the', 'what'] 
ppmi = [1, 3, 12, 3, 123, 1, 321, 1, 23] 
df = pd.DataFrame({'w':w, 'c':c, 'ppmi': ppmi}) 
df.set_index(['w', 'c'], inplace=True) 
mat = sps.coo_matrix((df['ppmi'],(df.index.labels[0], df.index.labels[1]))) 
print(mat.todense()) 

输出：

[[ 12 1 1 0] 
[ 0 321 123 23] 
[ 0 0 1 3] 
[ 0 0 0 3]]