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Description
Description
LabelPropagation with the rbf kernel should ignore underflow errors.
Steps/Code to Reproduce
Example taken from #9292.
from sklearn.datasets import fetch_mldata
from sklearn.semi_supervised import label_propagation
import numpy
numpy.seterr(all='raise')
mnist = fetch_mldata('MNIST original', data_home="./tmp")
X = mnist.data[1:10000]
y = mnist.target[1:10000]
# Use only 300 labeled examples
y[300:] = -1
lp_model = label_propagation.LabelSpreading(kernel='rbf', n_neighbors=7, n_jobs=-1)
lp_model.fit(X,y)Expected Results
No error should be thrown.
Actual Results
Traceback (most recent call last):
File "reproduce.py", line 21, in <module>
lp_model.fit(X,y)
File "/share/mug/gentoo/anaconda3/envs/ssl-py3/lib/python3.6/site-packages/sklearn/semi_supervised/label_propagation.py", line 234, in fit
graph_matrix = self._build_graph()
File "/share/mug/gentoo/anaconda3/envs/ssl-py3/lib/python3.6/site-packages/sklearn/semi_supervised/label_propagation.py", line 511, in _build_graph
affinity_matrix = self._get_kernel(self.X_)
File "/share/mug/gentoo/anaconda3/envs/ssl-py3/lib/python3.6/site-packages/sklearn/semi_supervised/label_propagation.py", line 131, in _get_kernel
return rbf_kernel(X, X, gamma=self.gamma)
File "/share/mug/gentoo/anaconda3/envs/ssl-py3/lib/python3.6/site-packages/sklearn/metrics/pairwise.py", line 837, in rbf_kernel
np.exp(K, K) # exponentiate K in-place
FloatingPointError: underflow encountered in exp
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