python – Scipy稀疏 – 距离矩阵(Scikit或Scipy)

我试图在scikit-learn的DictVectorizer返回的Scipy稀疏矩阵上计算最近邻居聚类.但是,当我尝试使用scikit-learn计算距离矩阵时,我通过pairwise.euclidean_distances和pairwise.pairwise_distances使用’euclidean’距离得到错误消息.我的印象是scikit-learn可以计算这些距离矩阵.

我的矩阵非常稀疏,形状为：< 364402x223209稀疏矩阵类型< class'numpy.float64'>
使用压缩稀疏行格式的728804存储元素>.

我也在Scipy中尝试了诸如pdist和kdtree之类的方法,但是还收到了其他无法处理结果的错误.

任何人都可以请我指出一个有效地允许我计算距离矩阵和/或最近邻结果的解决方案吗？

一些示例代码：

import numpy as np
from sklearn.feature_extraction import DictVectorizer
from sklearn.neighbors import NearestNeighbors
from sklearn.metrics import pairwise
import scipy.spatial

file = 'FileLocation'
data = []
FILE = open(file,'r')
for line in FILE:
    templine = line.strip().split(',')
    data.append({'user':str(int(templine[0])),str(int(templine[1])):int(templine[2])})
FILE.close()

vec = DictVectorizer()
X = vec.fit_transform(data)

result = scipy.spatial.KDTree(X)

错误：

Traceback (most recent call last):
  File "python3.2/site-packages/scipy/spatial/kdtree.py",line 227,in __init__
    self.n,self.m = np.shape(self.data)
ValueError: need more than 0 values to unpack

同样,如果我跑：

scipy.spatial.distance.pdist(X,'euclidean')

我得到以下内容：

Traceback (most recent call last):
  File "python3.2/site-packages/scipy/spatial/distance.py",line 1169,in pdist
    [X] = _copy_arrays_if_base_present([_convert_to_double(X)])
  File "/Library/Frameworks/Python.framework/Versions/3.2/lib/python3.2/site-packages/scipy/spatial/distance.py",line 113,in _convert_to_double
    X = X.astype(np.double)
ValueError: setting an array element with a sequence.

最后,在scikit-learn中运行NearestNeighbor会导致内存错误,使用：

nbrs = NearestNeighbors(n_neighbors=10,algorithm='brute')

最佳答案

首先,你不能使用稀疏矩阵的KDTree和pdist,你必须将它转换为密集(你的选择是否是你的选择)：

>>> X
<2x3 sparse matrix of type 'pressed Sparse Row format>

>>> scipy.spatial.KDTree(X.todense())
distance.pdist(X.todense(),'euclidean')
array([ 6.55743852])

第二,从the docs：

Efficient brute-force neighbors searches can be very competitive for small data samples. However,as the number of samples N grows,the brute-force approach quickly becomes infeasible.

您可能想尝试’ball_tree’算法并查看它是否可以处理您的数据.

numpy

python – Scipy稀疏 – 距离矩阵(Scikit或Scipy)

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