Python Sklearn kNN 使用与用户定义的度量
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Sklearn kNN usage with a user defined metric
提问by user2926523
Currently I'm doing a project which may require using a kNN algorithm to find the top k nearest neighbors for a given point, say P. im using python, sklearn package to do the job, but our predefined metric is not one of those default metrics. so I have to use the user defined metric, from the documents of sklearn, which can be find hereand here.
目前我正在做一个项目，它可能需要使用 kNN 算法来找到给定点的前 k 个最近邻，比如 P。im 使用 python、sklearn 包来完成这项工作，但我们的预定义指标不是这些默认指标之一指标。所以我必须使用用户定义的指标，来自 sklearn 的文档，可以在这里和这里找到。
It seems that the latest version of sklearn kNN support the user defined metric, but i cant find how to use it:
似乎最新版本的 sklearn kNN 支持用户定义的指标，但我找不到如何使用它：
import sklearn
from sklearn.neighbors import NearestNeighbors
import numpy as np
from sklearn.neighbors import DistanceMetric
from sklearn.neighbors.ball_tree import BallTree
BallTree.valid_metrics
say i have defined a metric called mydist=max(x-y), then use DistanceMetric.get_metric to make it a DistanceMetric object:
假设我定义了一个名为 mydist=max(xy) 的度量，然后使用 DistanceMetric.get_metric 使其成为一个 DistanceMetric 对象：
dt=DistanceMetric.get_metric('pyfunc',func=mydist)
from the document, the line should looks like this
从文档中，该行应如下所示
nbrs = NearestNeighbors(n_neighbors=4, algorithm='auto',metric='pyfunc').fit(A)
distances, indices = nbrs.kneighbors(A)
but where can i put the dtin? Thanks
但是我可以把它dt放在哪里？谢谢
采纳答案by alko
You pass a metric as metricparam, and additional metric arguments as keyword paramethers to NN constructor:
您将度量作为metric参数传递，并将其他度量参数作为关键字参数传递给 NN 构造函数：
>>> def mydist(x, y):
... return np.sum((x-y)**2)
...
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> nbrs = NearestNeighbors(n_neighbors=4, algorithm='ball_tree',
... metric='pyfunc', func=mydist)
>>> nbrs.fit(X)
NearestNeighbors(algorithm='ball_tree', leaf_size=30, metric='pyfunc',
n_neighbors=4, radius=1.0)
>>> nbrs.kneighbors(X)
(array([[ 0., 1., 5., 8.],
[ 0., 1., 2., 13.],
[ 0., 2., 5., 25.],
[ 0., 1., 5., 8.],
[ 0., 1., 2., 13.],
[ 0., 2., 5., 25.]]), array([[0, 1, 2, 3],
[1, 0, 2, 3],
[2, 1, 0, 3],
[3, 4, 5, 0],
[4, 3, 5, 0],
[5, 4, 3, 0]]))
回答by Mahmoud
A small addition to the previous answer. How to use a user defined metric that takes additional arguments.
对上一个答案的一个小补充。如何使用用户定义的带有附加参数的指标。
>>> def mydist(x, y, **kwargs):
... return np.sum((x-y)**kwargs["metric_params"]["power"])
...
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> Y = np.array([-1, -1, -2, 1, 1, 2])
>>> nbrs = KNeighborsClassifier(n_neighbors=4, algorithm='ball_tree',
... metric=mydist, metric_params={"power": 2})
>>> nbrs.fit(X, Y)
KNeighborsClassifier(algorithm='ball_tree', leaf_size=30,
metric=<function mydist at 0x7fd259c9cf50>, n_neighbors=4, p=2,
weights='uniform')
>>> nbrs.kneighbors(X)
(array([[ 0., 1., 5., 8.],
[ 0., 1., 2., 13.],
[ 0., 2., 5., 25.],
[ 0., 1., 5., 8.],
[ 0., 1., 2., 13.],
[ 0., 2., 5., 25.]]),
array([[0, 1, 2, 3],
[1, 0, 2, 3],
[2, 1, 0, 3],
[3, 4, 5, 0],
[4, 3, 5, 0],
[5, 4, 3, 0]]))
