使用稀疏矩阵与numpy数组(Using a sparse matrix versus numpy array)

 我在Python中创建了一些带有字数的numpy数组：行是文档，列是对字X的计数。如果我有很多零计数，人们建议在处理这些时进一步使用稀疏矩阵，例如在分类器中。 当将一个numpy阵列与一个稀疏矩阵进行匹配时，它似乎没有什么区别。 所以我想知道三件事情： 
 
 
 
 维基百科说 
 
  
 
   
 稀疏矩阵是大多数元素为零的矩阵 
 
  
 这是确定何时使用稀疏矩阵格式的适当方法 - 只要> 50％的值为零？ 或者是否有意义，以防万一？ 
 
 
 稀疏矩阵在像我这样的任务中有多少帮助性能，特别是与一个numpy数组或标准列表相比较？ 
 
 
 到目前为止，我将数据收集到一个numpy数组中，然后转换为Scipy中的csr_matrix。 这是做到这一点的正确方法吗？ 我无法弄清楚如何从头开始构建一个稀疏矩阵，这可能是不可能的。 
 
 
 任何帮助深表感谢！ 

I am creating some numpy arrays with word counts in Python: rows are documents, columns are counts for word X. If I have a lot of zero counts, people suggest using sparse matrices when processing these further, e.g. in a classifier. When feeding a numpy array versus a sparse matrix into the Scikit logistic regression classifier, it did not seem to make much of a difference, however. So I was wondering about three things:
 
 
 
Wikipedia says 
 
  
 
   
a sparse matrix is a matrix in which most of the elements are zero
 
  
 
Is that an appropriate way to determine when to use a sparse matrix format - as soon as > 50 % of the values are zero? Or does it make sense to use just in case?
 
 
How much does a sparse matrix help performance in a task like mine, especially compared to a numpy array or a standard list?
 
 
So far, I collect my data into a numpy array, then convert into the csr_matrix in Scipy. Is that the right way to do it? I could not figure out how to build a sparse matrix from the ground up, and that might be impossible.
 
 
Any help is much appreciated!

原文：https://stackoverflow.com/questions/36969886