用Python表示和访问稀疏数据的可扩展方式

Question

I have a sparse binary matrix represented with a file like this:

p_1|m_11
p_1|m_12
p_1|m_13
...
p_1|m_1,N1
p_2|m_21
p_2|m_22
...
p_2|m_2,N2
...
p_K|m_K1
...
p_K|m_K,NK

p 's and m 's comes from two respective sets. If there are K unique p 's and L unique m 's, the above represents a sparse KXL matrix with each row corresponding to a single 1 element of the matrix.

p 's are integers; m 's are alphanum strings

I need to have fast access to both individual elements of the matrix and its rows and columns. The current implementation shown below worked fine for small values of K ( L is always about 50,000 ) but does not scale.

from scipy import sparse
from numpy import array
import numpy as np

# 1st pass: collect unique ps and ms
Ps = set()
Ms = set()
nnz = 0
with open('data.txt','r') as fin:
    for line in fin:
        parts = line.strip().split('|')
        Ps.add(parts[0])
        Ms.add(parts[1])
        nnz += 1

Ps = list(Ps).sort()    # optional but prefer sorted
Ms = list(Ms).sort()    # optional but prefer sorted
K = len(Ps)
L = len(Ms)

# 2nd pass: create sparse mx
I = np.zeros(nnz)
J = np.zeros(nnz)
V = np.ones(nnz)

ix = 0
with open('data.txt','r') as fin:
    for line in fin:
        parts = line.strip().split('|')
        I[ix] = Ps.index(parts[0])  # TAKES TOO LONG FOR LARGE K
        J[ix] = Ms.index(parts[1])
        ix += 1

data = sparse.coo_matrix((V,(I,J)),shape=(K,L)).tocsr()     

There is gotta be a different way of doing this that scales better, but what is it?

I am not married to the sparse matrix format ( dict ? ), I am willing to use any data structure that allows me fast access to individual elements, "rows" and "columns"

---

CLARIFICATION ^{( I hope )} :
I am trying to move away from retrieving elements, rows and columns of my data using integer row/column values that get extracted by searching through two long arrays of strings.

Instead I just want to use actual p s and m s as keys, so instead of data[i,j] I want to use something like data[p_10,m_15] ; and instead of data[i,:] use something like data[p_10,:] .

I also need to be able to create data fast from my data file.

Again, data does not need to be a scipy or numpy sparse matrix.

Answer 1

I was able to speed up the 2nd pass below by simply creating two inverse indices:

from scipy import sparse
from numpy import array
import numpy as np

# 1st pass: collect unique ps and ms
Ps = set()
Ms = set()
nnz = 0
with open('data.txt','r') as fin:
    for line in fin:
        parts = line.strip().split('|')
        Ps.add(parts[0])
        Ms.add(parts[1])
        nnz += 1

Ps = list(Ps).sort()    # optional but prefer sorted
Ms = list(Ms).sort()    # optional but prefer sorted
K = len(Ps)
L = len(Ms)

# create inverse indices for quick lookup
#
mapPs = dict()
for i in range(len(Ps)):
    mapPs[Ps[i]] = i

mapMs = dict()
for i in range(len(Ms)):
    mapMs[Ms[i]] = i

# 2nd pass: create sparse mx
I = np.zeros(nnz)
J = np.zeros(nnz)
V = np.ones(nnz)

ix = 0
with open('data.txt','r') as fin:
    for line in fin:
        parts = line.strip().split('|')
        #I[ix] = Ps.index(parts[0]) # TAKES TOO LONG FOR LARGE K
        #J[ix] = Ms.index(parts[1]) # TAKES TOO LONG FOR LARGE K
        I[ix] = mapPs[parts[0]]
        J[ix] = mapMs[parts[1]]
        ix += 1

data = sparse.coo_matrix((V,(I,J)),shape=(K,L)).tocsr()     

I did not have a chance to test it on a much larger dataset but on a smaller one I had problems with, execution time went from about 1 hour to about 10 seconds! So I am satisfied with this solution for now.