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问题:
I was trying to use DBSCAN algorithm from scikit-learn library with cosine metric but was stuck with the error. The line of code is
db = DBSCAN(eps=1, min_samples=2, metric='cosine').fit(X)
where X is a csr_matrix. The error is the following:
Metric 'cosine' not valid for algorithm 'auto',
though the documentation says that it is possible to use this metric. I tried to use option algorithm='kd_tree' and 'ball_tree' but got the same. However, there is no error if I use euclidean or, say, l1 metric.
The matrix X is large, so I can't use a precomputed matrix of pairwise distances.
I use python 2.7.6 and scikit-learn 0.16.1. My dataset doesn't have a full row of zeros, so cosine metric is well-defined.
回答1:
The indexes in sklearn (probably - this may change with new versions) cannot accelerate cosine.
Try algorithm='brute'.
For a list of metrics that your version of sklearn can accelerate, see the supported metrics of the ball tree:
from sklearn.neighbors.ball_tree import BallTree print(BallTree.valid_metrics)
回答2:
If you want a normalized distance like the cosine distance, you can also normalize your vectors first and then use the euclidean metric. Notice that for two normalized vectors u and v the euclidean distance is equal to sqrt(2-2*cos(u, v)) (see this discussion)
You can hence do something like:
Xnorm = np.linalg.norm(X,axis = 1) Xnormed = np.divide(X,Xnorm.reshape(Xnorm.shape[0],1)) db = DBSCAN(eps=0.5, min_samples=2, metric='euclidean').fit(Xnormed)
The distances will lie in [0,2] so make sure you adjust your parameters accordingly.
