NumPy之计算两个矩阵的成对平方欧氏距离

问题描述

设 ({X_{m imes k}} = left[ {vec x_1^T;vec x_2^T; cdots ;vec x_m^T} ight]) (; 表示纵向连接) 和 ({Y_{n imes k}} = left[ {vec y_1^T;vec y_2^T; cdots ;vec y_n^T} ight]), 计算矩阵 ({X_{m imes k}}) 中每一个行向量和矩阵 ({Y_{n imes k}}) 中每一个行向量的平方欧氏距离 (pairwise squared Euclidean distance), 即计算:

(left[ {egin{array}{*{20}{c}} {left| {{{vec x}_1} - {{vec y}_1}} ight|_2^2}&{left| {{{vec x}_1} - {{vec y}_2}} ight|_2^2}& cdots &{left| {{{vec x}_1} - {{vec y}_n}} ight|_2^2} \ {left| {{{vec x}_2} - {{vec y}_1}} ight|_2^2}&{left| {{{vec x}_2} - {{vec y}_2}} ight|_2^2}& cdots &{left| {{{vec x}_2} - {{vec y}_n}} ight|_2^2} \ vdots & vdots & ddots & vdots \ {left| {{{vec x}_m} - {{vec y}_1}} ight|_2^2}&{left| {{{vec x}_m} - {{vec y}_2}} ight|_2^2}& cdots &{left| {{{vec x}_m} - {{vec y}_n}} ight|_2^2} end{array}} ight]) (这是一个 (m imes n) 矩阵).

这个计算在度量学习, 图像检索, 行人重识别等算法的性能评估中有着广泛的应用.

公式转化

在 NumPy 中直接利用上述原式来计算两个矩阵的成对平方欧氏距离, 要显式地使用二重循环, 而在 Python 中循环的效率是相当低下的. 如果想提高计算效率, 最好是利用 NumPy 的特性将原式转化为数组/矩阵运算. 下面就尝试进行这种转化.

先将原式展开为:
(left[ {egin{array}{*{20}{c}} {left| {{{vec x}_1}} ight|_2^2}&{left| {{{vec x}_1}} ight|_2^2}& cdots &{left| {{{vec x}_1}} ight|_2^2} \ {left| {{{vec x}_2}} ight|_2^2}&{left| {{{vec x}_2}} ight|_2^2}& cdots &{left| {{{vec x}_2}} ight|_2^2} \ vdots & vdots & ddots & vdots \ {left| {{{vec x}_m}} ight|_2^2}&{left| {{{vec x}_m}} ight|_2^2}& cdots &{left| {{{vec x}_m}} ight|_2^2} end{array}} ight] + left[ {egin{array}{*{20}{c}} {left| {{{vec y}_1}} ight|_2^2}&{left| {{{vec y}_2}} ight|_2^2}& cdots &{left| {{{vec y}_n}} ight|_2^2} \ {left| {{{vec y}_1}} ight|_2^2}&{left| {{{vec y}_2}} ight|_2^2}& cdots &{left| {{{vec y}_n}} ight|_2^2} \ vdots & vdots & ddots & vdots \ {left| {{{vec y}_1}} ight|_2^2}&{left| {{{vec y}_2}} ight|_2^2}& cdots &{left| {{{vec y}_n}} ight|_2^2} end{array}} ight] - 2left[ {egin{array}{*{20}{c}} {leftlangle {{{vec x}_1},{{vec y}_1}} ight angle }&{leftlangle {{{vec x}_1},{{vec y}_2}} ight angle }& cdots &{leftlangle {{{vec x}_1},{{vec y}_n}} ight angle } \ {leftlangle {{{vec x}_2},{{vec y}_1}} ight angle }&{leftlangle {{{vec x}_2},{{vec y}_2}} ight angle }& cdots &{leftlangle {{{vec x}_2},{{vec y}_n}} ight angle } \ vdots & vdots & ddots & vdots \ {leftlangle {{{vec x}_m},{{vec y}_1}} ight angle }&{leftlangle {{{vec x}_m},{{vec y}_2}} ight angle }& cdots &{leftlangle {{{vec x}_m},{{vec y}_n}} ight angle } end{array}} ight])

下面逐项地化简或转化为数组/矩阵运算的形式:
(left[ {egin{array}{*{20}{c}} {left| {{{vec x}_1}} ight|_2^2}&{left| {{{vec x}_1}} ight|_2^2}& cdots &{left| {{{vec x}_1}} ight|_2^2} \ {left| {{{vec x}_2}} ight|_2^2}&{left| {{{vec x}_2}} ight|_2^2}& cdots &{left| {{{vec x}_2}} ight|_2^2} \ vdots & vdots & ddots & vdots \ {left| {{{vec x}_m}} ight|_2^2}&{left| {{{vec x}_m}} ight|_2^2}& cdots &{left| {{{vec x}_m}} ight|_2^2} end{array}} ight] = left[ {egin{array}{*{20}{c}} {left| {{{vec x}_1}} ight|_2^2} \ {left| {{{vec x}_2}} ight|_2^2} \ vdots \ {left| {{{vec x}_m}} ight|_2^2} end{array}} ight]vec 1_n^T = left( {left( {X circ X} ight){{vec 1}_k}} ight)vec 1_n^T = left( {X circ X} ight){vec 1_k}vec 1_n^T)

式中, (circ) 表示按元素积 (element-wise product), 又称为 Hadamard 积; ({vec 1_k}) 表示维的全1向量 (all-ones vector), 余者类推. 上式中 ({vec 1_k}) 的作用是计算 (X circ X) 每行元素的和, 返回一个列向量; (vec 1_n^T) 的作用类似于 NumPy 中的广播机制, 在这里是将一个列向量扩展为一个矩阵, 矩阵的每一列都是相同的.

(left[ {egin{array}{*{20}{c}} {left| {{{vec y}_1}} ight|_2^2}&{left| {{{vec y}_2}} ight|_2^2}& cdots &{left| {{{vec y}_n}} ight|_2^2} \ {left| {{{vec y}_1}} ight|_2^2}&{left| {{{vec y}_2}} ight|_2^2}& cdots &{left| {{{vec y}_n}} ight|_2^2} \ vdots & vdots & ddots & vdots \ {left| {{{vec y}_1}} ight|_2^2}&{left| {{{vec y}_2}} ight|_2^2}& cdots &{left| {{{vec y}_n}} ight|_2^2} end{array}} ight] = {vec 1_m}{left[ {egin{array}{*{20}{c}} {left| {{{vec y}_1}} ight|_2^2} \ {left| {{{vec y}_2}} ight|_2^2} \ vdots \ {left| {{{vec y}_n}} ight|_2^2} end{array}} ight]^T} = {vec 1_m}{left( {left( {Y circ Y} ight){{vec 1}_k}} ight)^T} = {vec 1_m}vec 1_k^T{left( {Y circ Y} ight)^T})

(left[ {egin{array}{*{20}{c}} {leftlangle {{{vec x}_1},{{vec y}_1}} ight angle }&{leftlangle {{{vec x}_1},{{vec y}_2}} ight angle }& cdots &{leftlangle {{{vec x}_1},{{vec y}_n}} ight angle } \ {leftlangle {{{vec x}_2},{{vec y}_1}} ight angle }&{leftlangle {{{vec x}_2},{{vec y}_2}} ight angle }& cdots &{leftlangle {{{vec x}_2},{{vec y}_n}} ight angle } \ vdots & vdots & ddots & vdots \ {leftlangle {{{vec x}_m},{{vec y}_1}} ight angle }&{leftlangle {{{vec x}_m},{{vec y}_2}} ight angle }& cdots &{leftlangle {{{vec x}_m},{{vec y}_n}} ight angle } end{array}} ight] = left[ {egin{array}{*{20}{c}} {vec x_1^T} \ {vec x_2^T} \ vdots \ {vec x_m^T} end{array}} ight]left[ {egin{array}{*{20}{c}} {{{vec y}_1}}&{{{vec y}_2}}& cdots &{{{vec y}_n}} end{array}} ight] = X{Y^T})

所以:

(left[ {egin{array}{*{20}{c}} {left| {{{vec x}_1} - {{vec y}_1}} ight|_2^2}&{left| {{{vec x}_1} - {{vec y}_2}} ight|_2^2}& cdots &{left| {{{vec x}_1} - {{vec y}_n}} ight|_2^2} \ {left| {{{vec x}_2} - {{vec y}_1}} ight|_2^2}&{left| {{{vec x}_2} - {{vec y}_2}} ight|_2^2}& cdots &{left| {{{vec x}_2} - {{vec y}_n}} ight|_2^2} \ vdots & vdots & ddots & vdots \ {left| {{{vec x}_m} - {{vec y}_1}} ight|_2^2}&{left| {{{vec x}_m} - {{vec y}_2}} ight|_2^2}& cdots &{left| {{{vec x}_m} - {{vec y}_n}} ight|_2^2} end{array}} ight] = left( {X circ X} ight){vec 1_k}vec 1_n^T + {vec 1_m}vec 1_k^T{left( {Y circ Y} ight)^T} - 2X{Y^T})

上述转化式中出现了 (X{Y^T}) (矩阵乘) , 矩阵乘在 NumPy 等在很多库中都有高效的实现, 对代码的优化是有好处的.

特别地, 当 (X=Y) 时, 原式等于 (left( {X circ X} ight){vec 1_k}vec 1_m^T + {vec 1_m}vec 1_k^T{left( {X circ X} ight)^T} - 2X{X^T}) , 注意到第一项和第二项互为转置. 当 (left( {X circ X} ight){vec 1_k} =vec 1_m) 且 (left( {Y circ Y} ight){vec 1_k} =vec 1_n) (即 (X) 和 (Y) 的每一个行向量的范数均为 1 时), 原式等于 (2vec 1_m vec 1_n^T - 2X{Y^T} = 2left( vec 1_m vec 1_n^T -X{Y^T} ight)), (vec 1_m vec 1_n^T) 是 (m imes n) 全1矩阵.

代码实现

sklearn 中已经包含了用 NumPy 实现的计算 "两个矩阵的成对平方欧氏距离" 的函数 (sklearn.metrics.euclidean_distances), 它利用的就是上面的转化公式. 这里, 我们利用上面的转化公式并借鉴 sklearn, 用 NumPy 重新实现一个轻量级且易于理解的版本:

import numpy as np

def euclidean_distances(x, y, squared=True):
    """Compute pairwise (squared) Euclidean distances.
    """
    assert isinstance(x, np.ndarray) and x.ndim == 2
    assert isinstance(y, np.ndarray) and y.ndim == 2
    assert x.shape[1] == y.shape[1]
    
    x_square = np.sum(x*x, axis=1, keepdims=True)
    if x is y:
        y_square = x_square.T
    else:
        y_square = np.sum(y*y, axis=1, keepdims=True).T
    distances = np.dot(x, y.T)
    # use inplace operation to accelerate
    distances *= -2
    distances += x_square
    distances += y_square
    # result maybe less than 0 due to floating point rounding errors.
    np.maximum(distances, 0, distances)
    if x is y:
        # Ensure that distances between vectors and themselves are set to 0.0.
        # This may not be the case due to floating point rounding errors.
        distances.flat[::distances.shape[0] + 1] = 0.0
    if not squared:
        np.sqrt(distances, distances)
    return distances

如果想进一步加速, 可以将

x_square = np.sum(x*x, axis=1, keepdims=True)

替换为

x_square = np.expand_dims(np.einsum('ij,ij->i', x, x), axis=1)

以及将

y_square = np.sum(y*y, axis=1, keepdims=True).T

替换为

y_square = np.expand_dims(np.einsum('ij,ij->i', y, y), axis=0)

使用 np.einsum 的好处是不会产生一个和 x 或 y 同样形状的临时数组 (x*x 或 y*y 会产生一个和 x 或 y 同样形状的临时数组).

PyTorch 中也包含了计算 "两个矩阵的成对平方欧氏距离" 的函数, 不过它利用了如下的转化公式, 感兴趣的朋友可以自己用 NumPy 实现一下.
(egin{aligned} left( {X circ X} ight){{vec 1}_k}vec 1_n^T + {{vec 1}_m}vec 1_k^T{left( {Y circ Y} ight)^T} - 2X{Y^T} &= left[ {egin{array}{*{20}{c}} { - 2X}&{left( {X circ X} ight){{vec 1}_k}}&{{{vec 1}_m}} end{array}} ight]left[ {egin{array}{*{20}{c}} {{Y^T}} \ {vec 1_n^T} \ {vec 1_k^T{{left( {Y circ Y} ight)}^T}} end{array}} ight] \ &= left[ {egin{array}{*{20}{c}} { - 2X}&{left( {X circ X} ight){{vec 1}_k}}&{{{vec 1}_m}} end{array}} ight]{left[ {egin{array}{*{20}{c}} Y&{{{vec 1}_n}}&{left( {Y circ Y} ight){{vec 1}_k}} end{array}} ight]^T} \ end{aligned})

另外上述的转化公式也可以用在其他 Python 框架 (如 TensorFlow) 或其他语言中, 这里就不展开叙述了.

参考

• sklearn.metrics.euclidean_distances
• _euclidean_dist in PyTorch

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