Ensemble models (and maybe others?) don't check for negative sample_weight #3774
Description
When sample weights are negative, the probabilities can come out negative as well:
>>> rng = np.random.RandomState(10)
>>> X = rng.randn(10, 4)
>>> y = rng.randint(0, 2, 10)
>>> sample_weight = rng.randn(10)
>>> clf = RandomForestClassifier().fit(X, y, sample_weight)
>>> clf.predict_proba(X)
array([[ 0.56133774, 0.43866226],
[ 1.03235924, -0.03235924],
[ 1.03235924, -0.03235924],
[ 1.03235924, -0.03235924],
[ 1.03235924, -0.03235924],
[ 1.03235924, -0.03235924],
[ 0.98071868, 0.01928132],
[ 0.56133774, 0.43866226],
[ 1.03235924, -0.03235924],
[ 1.03235924, -0.03235924]])