roc_auc_score computation is wrong for large samples #6842
Description
Hi, we've recently found some strange inconsistency in the behavior of roc_auc_score. After long digging @tata-antares found out that it is wrongly computed for large data samples if weights passed are float32.
Minimal example
from sklearn.metrics import roc_auc_score
import numpy
numpy.random.seed(42)
n_samples = 4 * 10 ** 7
y = numpy.random.randint(2, size=n_samples)
prediction = numpy.random.normal(size=n_samples) + y * 0.01
trivial_weight = numpy.ones(n_samples)
roc_auc_score(y, prediction)
#prints 0.50273924526046887
roc_auc_score(y, prediction, sample_weight=trivial_weight.astype('float32'))
#**prints nan** and wrong warning
/moosefs/miniconda/envs/ipython_py2/lib/python2.7/site-packages/sklearn/metrics/ranking.py:530: UndefinedMetricWarning: No negative samples in y_true, false positive value should be meaningless
UndefinedMetricWarning)
roc_auc_score(y, prediction, sample_weight=trivial_weight.astype('float64'))
#prints 0.50273924526046887The value when dtype=float64 is correct, but in the case we investigated after passing float32 metric returned a regular number, which is very different from correct one (e.g 0.58 instead of 0.52). It is some occasion we detected this problem.
sklearn == '0.17.1'