Issue 1:
Since I am tired of getting tons of emails because of Jenkins failing, I tried to look it up.

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ERROR: sklearn.metrics.tests.test_metrics.test_classification_report_multiclass_with_unicode_label
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Traceback (most recent call last):
File "/home/slave/virtualenvs/cpython-2.6/lib/python2.6/site-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "<https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.6-numpy-1.3.0-scipy-0.7.2/ws/sklearn/metrics/tests/test_metrics.py",> line 855, in test_classification_report_multiclass_with_unicode_label
report = classification_report(y_true, y_pred)
File "<https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.6-numpy-1.3.0-scipy-0.7.2/ws/sklearn/metrics/metrics.py",> line 1768, in classification_report
average=None)
File "<https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.6-numpy-1.3.0-scipy-0.7.2/ws/sklearn/metrics/metrics.py",> line 1515, in precision_recall_fscore_support
precision = precision[indices]
IndexError: index 3 out of bounds 0<=index<3

If I am not wrong, this bug originates from indices = np.searchsorted(labels, label_order) in metrics.py when labels contains unicode strings. It seems related to the following known (and fixed) issue in Numpy: http://mail.scipy.org/pipermail/numpy-discussion/2012-March/061495.html

What shall we do about this?

Issue 2:
A Test failure on Travis presumably due to changes in lbfgs:

=================================== FAILURES ===================================
________________________________ test_max_iter _________________________________
@ignore_warnings
def test_max_iter():
# Test that the maximum number of iteration is reached
X, y_bin = iris.data, iris.target.copy()
y_bin[y_bin == 2] = 0

solvers = ['newton-cg', 'liblinear', 'sag', 'saga', 'lbfgs']

for max_iter in range(1, 5):
for solver in solvers:
for multi_class in ['ovr', 'multinomial']:
if solver == 'liblinear' and multi_class == 'multinomial':
continue
lr = LogisticRegression(max_iter=max_iter, tol=1e-15,
multi_class=multi_class,
random_state=0, solver=solver)
lr.fit(X, y_bin)
> assert_equal(lr.n_iter_[0], max_iter)
X = array([[ 5.1, 3.5, 1.4, 0.2],
[ 4.9, 3. , 1.4, 0.2],
[ 4.7, 3.2, 1.3, 0.2],
[ 4.6, 3.1,... 2.5, 5. , 1.9],
[ 6.5, 3. , 5.2, 2. ],
[ 6.2, 3.4, 5.4, 2.3],
[ 5.9, 3. , 5.1, 1.8]])
lr = LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=1...r', n_jobs=1,
penalty='l2', random_state=0, solver='lbfgs', tol=1e-15,
verbose=0, warm_start=False)
max_iter = 1
multi_class = 'ovr'
solver = 'lbfgs'
solvers = ['newton-cg', 'liblinear', 'sag', 'saga', 'lbfgs']
y_bin = array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,... 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
/home/travis/build/scikit-learn/scikit-learn/sklearn/linear_model/tests/test_logistic.py:1053:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/python/3.6.3/lib/python3.6/unittest/case.py:829: in assertEqual
assertion_func(first, second, msg=msg)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <sklearn.utils._unittest_backport.TestCase testMethod=__init__>
first = 0, second = 1, msg = '0 != 1'
def _baseAssertEqual(self, first, second, msg=None):
"""The default assertEqual implementation, not type specific."""
if not first == second:
standardMsg = '%s != %s' % _common_shorten_repr(first, second)
msg = self._formatMessage(msg, standardMsg)
> raise self.failureException(msg)
E AssertionError: 0 != 1
first = 0
msg = '0 != 1'
second = 1
self = <sklearn.utils._unittest_backport.TestCase testMethod=__init__>
standardMsg = '0 != 1'
/opt/python/3.6.3/lib/python3.6/unittest/case.py:822: AssertionError