具有自定义距离指标的"KD树"

Question

我想使用'KDtree'(这是最好的选择.其他'KNN'算法对我的项目来说不是最佳选择)和自定义距离度量.我在这里检查了一些类似问题的答案,这应该有用......但是没有.

distance_matrix是syme​​tric,应该是定义:

array([[ 1.,  0.,  5.,  5.,  0.,  3.,  2.],
   [ 0.,  1.,  0.,  0.,  0.,  0.,  0.],
   [ 5.,  0.,  1.,  5.,  0.,  2.,  3.],
   [ 5.,  0.,  5.,  1.,  0.,  4.,  4.],
   [ 0.,  0.,  0.,  0.,  1.,  0.,  0.],
   [ 3.,  0.,  2.,  4.,  0.,  1.,  0.],
   [ 2.,  0.,  3.,  4.,  0.,  0.,  1.]])

我知道我的度量标准不是"正式度量标准",但在文档中它说我的函数必须是"正式度量",只有当我使用'ball tr​​ee'(下User-defined distance:)时.这是我的代码:

from sklearn.neighbors import DistanceMetric
def dist(x, y):
    dist = 0
    for elt_x, elt_y in zip(x, y):
        dist += distance_matrix[elt_x, elt_y]
    return dist
X = np.array([[1,0], [1,2], [1,3]])
tree = KDtree(X, metric=dist)

我收到此错误:

NameError
Traceback (most recent call last)   
<ipython-input-27-b5fac7810091> in <module>()
  7     return dist
  8 X = np.array([[1,0], [1,2], [1,3]])
----> 9 tree = KDtree(X, metric=dist)
NameError: name 'KDtree' is not defined

我也尝试过:

from sklearn.neighbors import KDTree
def dist(x, y):
    dist = 0
    for elt_x, elt_y in zip(x, y):
        dist += distance_matrix[elt_x, elt_y]
    return dist
X = np.array([[1,0], [1,2], [1,3]])
tree = KDTree(X, metric=lambda a,b: dist(a,b))

我收到此错误:

ValueError
Traceback (most recent call last)   
<ipython-input-27-b5fac7810091> in <module>()
  7     return dist
  8 X = np.array([[1,0], [1,2], [1,3]])
----> 9 tree = KDtree(X, metric=dist)
ValueError: metric PyFuncDistance is not valid for KDTree

我也尝试过:

from sklearn.neighbors import NearestNeighbors
nbrs = NearestNeighbors(n_neighbors=1, algorithm='kd_tree',    metric=dist_metric)

我收到以下错误:

ValueError                                Traceback (most recent call last)
<ipython-input-32-c78d02cacb5a> in <module>()
      1 from sklearn.neighbors import NearestNeighbors
----> 2 nbrs = NearestNeighbors(n_neighbors=1, algorithm='kd_tree',     metric=dist_metric)

/usr/local/lib/python3.5/dist-packages/sklearn/neighbors/unsupervised.py    in __init__(self, n_neighbors, radius, algorithm, leaf_size, metric, p, metric_params, n_jobs, **kwargs)
    121                           algorithm=algorithm,
    122                           leaf_size=leaf_size, metric=metric, p=p,
--> 123                           metric_params=metric_params,     n_jobs=n_jobs, **kwargs)

/usr/local/lib/python3.5/dist-packages/sklearn/neighbors/base.py in     _init_params(self, n_neighbors, radius, algorithm, leaf_size, metric, p, metric_params, n_jobs)
    138                 raise ValueError(
    139                     "kd_tree algorithm does not support callable     metric '%s'"
--> 140                     % metric)
     141         elif metric not in VALID_METRICS[alg_check]:
    142             raise ValueError("Metric '%s' not valid for algorithm     '%s'"

ValueError: kd_tree algorithm does not support callable metric '<function     dist_metric at 0x7f58c2b3fd08>'

我尝试了所有其他算法(auto,brute,...),但它发出相同的错误.

我必须使用距离矩阵作为向量的元素,因为元素是特征的代码,5可以比3更接近1.我需要的是获得前3个邻居(从最近到最远排序).

Answer 1

Scikit-learn KDTree不支持自定义距离指标.在BallTree不支持自定义的距离度量,但要小心:它是由用户进行某些所提供的度量是实际上是一个有效的指标:如果不是,该算法将愉快地返回查询的结果,但结果将是不正确.

此外,您应该知道,使用自定义Python函数作为度量标准通常太慢而无法使用,因为在遍历树时Python回调的开销很大.