precision_recall_curve should raise UndefinedMetricWarning when there are no true samples #13043
Description
Description
When precision_recall_score is calculated with no true samples, a RuntimeWarning due to division by 0 is raised and nan is returned. However, when recall_score is calculated with no true labels, a UndefinedMetricWarning due to no true samples is raised.
For consistency, I think precision_recall_score should raise the same error and return 0.
Steps/Code to Reproduce
from sklearn.metrics import precision_recall_curve, recall_score
print(precision_recall_curve([0, 0], [0, 1]))
print(recall_score([0, 0], [0, 1]))Expected Results
/Users/.../scikit-learn/sklearn/metrics/classification.py:1293: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples.
(array([0., 1.]), array([0., 0.]), array([1]))
/Users/.../scikit-learn/sklearn/metrics/classification.py:1293: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples.
'recall', 'true', average, warn_for)
0.0
Actual Results
/Users/.../scikit-learn/sklearn/metrics/ranking.py:601: RuntimeWarning: invalid value encountered in true_divide
recall = tps / tps[-1]
(array([0., 1.]), array([nan, 0.]), array([1]))
/Users/.../scikit-learn/sklearn/metrics/classification.py:1293: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples.
'recall', 'true', average, warn_for)
0.0
Versions
System:
python: 3.7.2 (default, Dec 29 2018, 00:00:04) [Clang 4.0.1 (tags/RELEASE_401/final)]
executable: /Users/.../anaconda3/envs/sklearn-py37/bin/python
machine: Darwin-18.2.0-x86_64-i386-64bit
BLAS:
macros: SCIPY_MKL_H=None, HAVE_CBLAS=None
lib_dirs: /Users/.../anaconda3/envs/sklearn-py37/lib
cblas_libs: mkl_rt, pthread
Python deps:
pip: 18.1
setuptools: 40.6.3
sklearn: 0.21.dev0
numpy: 1.15.4
scipy: 1.1.0
Cython: 0.29.2
pandas: 0.23.4