peacock-data-public-datasets-idc-llm_eval
/
env-llmeval
/lib
/python3.10
/site-packages
/scipy
/sparse
/_spfuncs.py
""" Functions that operate on sparse matrices | |
""" | |
__all__ = ['count_blocks','estimate_blocksize'] | |
from ._base import issparse | |
from ._csr import csr_array | |
from ._sparsetools import csr_count_blocks | |
def estimate_blocksize(A,efficiency=0.7): | |
"""Attempt to determine the blocksize of a sparse matrix | |
Returns a blocksize=(r,c) such that | |
- A.nnz / A.tobsr( (r,c) ).nnz > efficiency | |
""" | |
if not (issparse(A) and A.format in ("csc", "csr")): | |
A = csr_array(A) | |
if A.nnz == 0: | |
return (1,1) | |
if not 0 < efficiency < 1.0: | |
raise ValueError('efficiency must satisfy 0.0 < efficiency < 1.0') | |
high_efficiency = (1.0 + efficiency) / 2.0 | |
nnz = float(A.nnz) | |
M,N = A.shape | |
if M % 2 == 0 and N % 2 == 0: | |
e22 = nnz / (4 * count_blocks(A,(2,2))) | |
else: | |
e22 = 0.0 | |
if M % 3 == 0 and N % 3 == 0: | |
e33 = nnz / (9 * count_blocks(A,(3,3))) | |
else: | |
e33 = 0.0 | |
if e22 > high_efficiency and e33 > high_efficiency: | |
e66 = nnz / (36 * count_blocks(A,(6,6))) | |
if e66 > efficiency: | |
return (6,6) | |
else: | |
return (3,3) | |
else: | |
if M % 4 == 0 and N % 4 == 0: | |
e44 = nnz / (16 * count_blocks(A,(4,4))) | |
else: | |
e44 = 0.0 | |
if e44 > efficiency: | |
return (4,4) | |
elif e33 > efficiency: | |
return (3,3) | |
elif e22 > efficiency: | |
return (2,2) | |
else: | |
return (1,1) | |
def count_blocks(A,blocksize): | |
"""For a given blocksize=(r,c) count the number of occupied | |
blocks in a sparse matrix A | |
""" | |
r,c = blocksize | |
if r < 1 or c < 1: | |
raise ValueError('r and c must be positive') | |
if issparse(A): | |
if A.format == "csr": | |
M,N = A.shape | |
return csr_count_blocks(M,N,r,c,A.indptr,A.indices) | |
elif A.format == "csc": | |
return count_blocks(A.T,(c,r)) | |
return count_blocks(csr_array(A),blocksize) | |