在Python中保存KDTree對象？

Question

我正在使用Scipy的KDTree實現來讀取300 MB的大文件。現在，有沒有辦法將數據結構保存到磁盤並重新加載它，或者我堅持每次從文件中讀取原始數據並在每次啓動程序時構造數據結構？我正在構建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!" 

有什麼建議嗎？

Answer 1

KDtree使用嵌套類來定義其節點類型（innernode，leafnode）。泡菜只能在模塊級的類定義，所以嵌套類車次起來：

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 

但是，通過猴子打補丁的類定義爲scipy.spatial.kdtree在模塊範圍，所以，皮克勒一（哈克）解決方法可以找到他們。如果您的所有代碼的讀取和寫入醃製KDtree對象安裝這些補丁，這個技巧應該很好地工作：

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]] 

輸出：

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