Question

我正在使用Scipy的KDTree实现来读取300 MB的大文件。现在，有没有办法可以将数据结构保存到磁盘并再次加载它，还是我每次启动程序时都会从文件中读取原始点并构建数据结构？我正在构建KDTree，如下所示：

def buildKDTree(self):
        self.kdpoints = numpy.fromfile("All", sep=' ')
        self.kdpoints.shape = self.kdpoints.size / self.NDIM, NDIM
        self.kdtree = KDTree(self.kdpoints, leafsize = self.kdpoints.shape[0]+1)
        print "Preparing KDTree... Ready!"

有什么建议吗？

Answer 1

KDtree使用嵌套类来定义其节点类型（innernode，leafnode）。 Pickle仅适用于模块级类定义，因此嵌套类将其启动：

import cPickle

class Foo(object):
    class Bar(object):
        pass

obj = Foo.Bar()
print obj.__class__
cPickle.dumps(obj)

<class '__main__.Bar'>
cPickle.PicklingError: Can't pickle <class '__main__.Bar'>: attribute lookup __main__.Bar failed

然而，有一个（hacky）解决方法是将类定义修改到模块范围内的scipy.spatial.kdtree，以便pickler可以找到它们。如果所有读取和写入pickle KDtree对象的代码都安装了这些补丁，那么这个hack应该可以正常工作：

import cPickle
import numpy
from scipy.spatial import kdtree

# patch module-level attribute to enable pickle to work
kdtree.node = kdtree.KDTree.node
kdtree.leafnode = kdtree.KDTree.leafnode
kdtree.innernode = kdtree.KDTree.innernode

x, y = numpy.mgrid[0:5, 2:8]
t1 = kdtree.KDTree(zip(x.ravel(), y.ravel()))
r1 = t1.query([3.4, 4.1])
raw = cPickle.dumps(t1)

# read in the pickled tree
t2 = cPickle.loads(raw)
r2 = t2.query([3.4, 4.1])
print t1.tree.__class__
print repr(raw)[:70]
print t1.data[r1[1]], t2.data[r2[1]]

输出：

<class 'scipy.spatial.kdtree.innernode'>
"ccopy_reg\n_reconstructor\np1\n(cscipy.spatial.kdtree\nKDTree\np2\nc_
[3 4] [3 4]

在Python中保存KDTree对象？

1 个答案: