Skip to content
GitLab
Commit fee6e1c2 authored by Thibault Hallouin's avatar Thibault Hallouin
Browse files

add contingency table as probabilistic evaluation metric

parent 4b977b7a
No related merge requests found
Pipeline #52655 failed with stage
in 4 minutes and 30 seconds
Hide whitespace changes
Inline Side-by-side
Showing
with 217 additions and 4 deletions
+217 -4
xtensor @ 44b56bba
Compare e534928c...44b56bba
Subproject commit e534928cc30eb3a4a05539747d98e1d6868c2d62 Subproject commit 44b56bbae2185ebf19e6f617ac5690344b9e35a4
environment.yml
+ 1
− 1
View file @ fee6e1c2
...@@ -12,7 +12,7 @@ dependencies: ...@@ -12,7 +12,7 @@ dependencies:
- numpy - numpy
- pybind11 - pybind11
- xtl==0.7.5 - xtl==0.7.5
- xtensor==0.24.6 - xtensor==0.24.7
- xtensor-python==0.26.1 - xtensor-python==0.26.1
# - evalhyd-cpp==0.1.0 # - evalhyd-cpp==0.1.0
# Test dependencies # Test dependencies
......
tests/expected/evalp/CONT_TBL.csv 0 → 100644
+ 208
− 0
View file @ fee6e1c2
0.44694534,0.55305466,0.,0.
0.33118971,0.66881029,0.,0.
0.2733119,0.7266881,0.,0.
nan,nan,nan,nan
0.38585209,0.04823151,0.06109325,0.50482315
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04823151,0.06109325,0.50482315
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04823151,0.06109325,0.50482315
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04823151,0.06109325,0.50482315
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.38585209,0.04501608,0.06109325,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.37942122,0.04501608,0.06752412,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.37942122,0.04501608,0.06752412,0.50803859
0.28938907,0.02572347,0.04180064,0.64308682
0.20578778,0.0192926,0.06752412,0.7073955
nan,nan,nan,nan
0.37942122,0.04501608,0.06752412,0.50803859
0.28617363,0.02572347,0.04501608,0.64308682
0.20578778,0.01607717,0.06752412,0.71061093
nan,nan,nan,nan
tests/test_probabilist.py
+ 7
− 2
View file @ fee6e1c2
...@@ -20,7 +20,7 @@ _all_metrics = ( ...@@ -20,7 +20,7 @@ _all_metrics = (
# quantile-based # quantile-based
'QS', 'CRPS_FROM_QS', 'QS', 'CRPS_FROM_QS',
# contingency table-based # contingency table-based
'POD', 'POFD', 'FAR', 'CSI', 'ROCSS', 'CONT_TBL', 'POD', 'POFD', 'FAR', 'CSI', 'ROCSS',
# ranks-based # ranks-based
'RANK_HIST', 'DS', 'AS', 'RANK_HIST', 'DS', 'AS',
# intervals # intervals
...@@ -68,8 +68,13 @@ class TestMetrics(unittest.TestCase): ...@@ -68,8 +68,13 @@ class TestMetrics(unittest.TestCase):
metric: ( metric: (
numpy.genfromtxt(f"./expected/evalp/{metric}.csv", delimiter=',') numpy.genfromtxt(f"./expected/evalp/{metric}.csv", delimiter=',')
[numpy.newaxis, numpy.newaxis, numpy.newaxis, numpy.newaxis, ...] [numpy.newaxis, numpy.newaxis, numpy.newaxis, numpy.newaxis, ...]
) for metric in ('POD', 'POFD', 'FAR', 'CSI', 'ROCSS') ) for metric in ('CONT_TBL', 'POD', 'POFD', 'FAR', 'CSI', 'ROCSS')
} }
# /!\ stacked-up thresholds in CSV file for CONT_TBL
# because 7D metric so need to reshape array
expected_ct['CONT_TBL'] = (
expected_ct['CONT_TBL'].reshape(expected_ct['POD'].shape + (4,))
)
expected_rk = { expected_rk = {
metric: ( metric: (
......
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment