问题
I faced the problem of quickly finding the nearest neighbors in a given range.
Example of dataset:
id | string | float
0 | AA | 0.1
12 | BB | 0.5
2 | CC | 0.3
102| AA | 1.1
33 | AA | 2.8
17 | AA | 0.5
For each line, print the number of lines satisfying the following conditions:
- string field is equal to current
- float field <= current float - del
For this example with del = 1.5:
id | count
0 | 0
12 | 0
2 | 0
102| 2 (string is equal row with id=0,33,17 but only in row id=0,17 float value: 1.1-1.5<=0.1, 1.1-1.5<=0.5)
33 | 0 (string is equal row with id=0,102,17 but 2.8-1.5>=0.1/1.1/1.5)
17 | 1
To solve this problem, I used a class BallTree
with custom metric, but it works for a very long time due to a reverse tree walk (on a large dataset).
Can someone suggest other solutions or how you can increase the speed of custom metrics to the speed of the metrics from the sklearn.neighbors.DistanceMetric
?
My code:
from sklearn.neighbors import BallTree
def distance(x, y):
if(x[0]==y[0] and x[1]>y[1]):
return (x[1] - y[1])
else:
return (x[1] + y[1])
tree2 = BallTree(X, leaf_size=X.shape[0], metric=distance)
mas=tree2.query_radius(X, r=del, count_only = True)
来源:https://stackoverflow.com/questions/54569023/nearest-neighbors-in-a-given-range