找到了这个gist https://gist.github.com/FedericoV/0e7d6d8c8794a99a7a42使用 numba 快速计算余弦相似度。
import numba
@numba.jit(target='cpu', nopython=True)
def fast_cosine(u, v):
m = u.shape[0]
udotv = 0
u_norm = 0
v_norm = 0
for i in range(m):
if (np.isnan(u[i])) or (np.isnan(v[i])):
continue
udotv += u[i] * v[i]
u_norm += u[i] * u[i]
v_norm += v[i] * v[i]
u_norm = np.sqrt(u_norm)
v_norm = np.sqrt(v_norm)
if (u_norm == 0) or (v_norm == 0):
ratio = 1.0
else:
ratio = udotv / (u_norm * v_norm)
return ratio
结果看起来很有希望（我的机器中没有 jit 装饰器时为 500ns，而只有 200us）。
我想用 numba 来并行化向量之间的计算u和一个候选矩阵M-- 即每行的余弦。
Example:
def fast_cosine_matrix(u, M):
"""
Return array of cosine similarity between u and rows in M
>>> import numpy as np
>>> u = np.random.rand(100)
>>> M = np.random.rand(10, 100)
>>> fast_cosine_matrix(u, M)
"""
一种方法是用第二个输入重写矩阵。但我得到一个NotImplementedError如果我尝试迭代矩阵的行。将尝试仅使用切片。
我想过使用vectorize但我无法让它发挥作用。
解决方案重写一下：
import numpy as np
import numba
@numba.jit(target='cpu', nopython=True, parallel=True)
def fast_cosine_matrix(u, M):
scores = np.zeros(M.shape[0])
for i in numba.prange(M.shape[0]):
v = M[i]
m = u.shape[0]
udotv = 0
u_norm = 0
v_norm = 0
for j in range(m):
if (np.isnan(u[j])) or (np.isnan(v[j])):
continue
udotv += u[j] * v[j]
u_norm += u[j] * u[j]
v_norm += v[j] * v[j]
u_norm = np.sqrt(u_norm)
v_norm = np.sqrt(v_norm)
if (u_norm == 0) or (v_norm == 0):
ratio = 1.0
else:
ratio = udotv / (u_norm * v_norm)
scores[i] = ratio
return scores
u = np.random.rand(100)
M = np.random.rand(100000, 100)
fast_cosine_matrix(u, M)
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