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Commit e049ca0f authored by Thibault Hallouin's avatar Thibault Hallouin
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add new deterministic metric CONT_TBL

parent 62b72d97
main dev v0.1.2.0 v0.1.1.0 v0.1.0.0
1 merge request!1release v0.1.0.0
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with 293 additions and 33 deletions
+293 -33
tests/expected/evald/CONT_TBL.csv 0 → 100644
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− 0
View file @ e049ca0f
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tests/test_determinist.py
+ 89
− 33
View file @ e049ca0f
...@@ -8,12 +8,19 @@ import evalhyd ...@@ -8,12 +8,19 @@ import evalhyd
_prd = numpy.genfromtxt("./data/q_prd.csv", delimiter=',')[:, :] _prd = numpy.genfromtxt("./data/q_prd.csv", delimiter=',')[:, :]
_obs = numpy.genfromtxt("./data/q_obs.csv", delimiter=',')[numpy.newaxis, :] _obs = numpy.genfromtxt("./data/q_obs.csv", delimiter=',')[numpy.newaxis, :]
_thr = numpy.repeat(
numpy.array([[690, 534, 445, numpy.nan]]), repeats=_prd.shape[0], axis=0
)
_events = "high"
# list all available deterministic metrics # list all available deterministic metrics
_all_metrics = ( _all_metrics = (
# errors-based # errors-based
'MAE', 'MARE', 'MSE', 'RMSE', 'MAE', 'MARE', 'MSE', 'RMSE',
# efficiencies-based # efficiencies-based
'NSE', 'KGE', 'KGE_D', 'KGEPRIME', 'KGEPRIME_D', 'NSE', 'KGE', 'KGE_D', 'KGEPRIME', 'KGEPRIME_D',
# contingency table-based
'CONT_TBL'
) )
# list all available deterministic diagnostics # list all available deterministic diagnostics
...@@ -30,12 +37,18 @@ class TestMetrics(unittest.TestCase): ...@@ -30,12 +37,18 @@ class TestMetrics(unittest.TestCase):
[:, numpy.newaxis, numpy.newaxis] [:, numpy.newaxis, numpy.newaxis]
) for metric in _all_metrics ) for metric in _all_metrics
} }
# /!\ stacked-up thresholds in CSV file for CONT_TBL
# because 5D metric so need to reshape array
expected['CONT_TBL'] = (
expected['CONT_TBL'].reshape(expected['NSE'].shape + (4, 4))
)
def test_metrics_2d(self): def test_metrics_2d(self):
for metric in self.expected.keys(): for metric in self.expected.keys():
with self.subTest(metric=metric): with self.subTest(metric=metric):
numpy.testing.assert_almost_equal( numpy.testing.assert_almost_equal(
evalhyd.evald(_obs, _prd, [metric])[0], evalhyd.evald(_obs, _prd, [metric],
q_thr=_thr, events=_events)[0],
self.expected[metric] self.expected[metric]
) )
...@@ -43,7 +56,8 @@ class TestMetrics(unittest.TestCase): ...@@ -43,7 +56,8 @@ class TestMetrics(unittest.TestCase):
for metric in self.expected.keys(): for metric in self.expected.keys():
with self.subTest(metric=metric): with self.subTest(metric=metric):
numpy.testing.assert_almost_equal( numpy.testing.assert_almost_equal(
evalhyd.evald(_obs[0], _prd[0], [metric])[0], evalhyd.evald(_obs[0], _prd[0], [metric],
q_thr=_thr[0], events=_events)[0],
[self.expected[metric][0]] [self.expected[metric][0]]
) )
...@@ -51,31 +65,48 @@ class TestMetrics(unittest.TestCase): ...@@ -51,31 +65,48 @@ class TestMetrics(unittest.TestCase):
class TestTransform(unittest.TestCase): class TestTransform(unittest.TestCase):
def test_transform_sqrt(self): def test_transform_sqrt(self):
numpy.testing.assert_almost_equal( for metric in _all_metrics:
evalhyd.evald(_obs, _prd, ["NSE"], transform="sqrt")[0], with self.subTest(metric=metric):
evalhyd.evald(_obs ** 0.5, _prd ** 0.5, ["NSE"])[0] numpy.testing.assert_almost_equal(
) evalhyd.evald(_obs, _prd, ["NSE"], transform="sqrt",
q_thr=_thr, events=_events)[0],
evalhyd.evald(_obs ** 0.5, _prd ** 0.5, ["NSE"],
q_thr=_thr ** 0.5, events=_events)[0]
)
def test_transform_inv(self): def test_transform_inv(self):
eps = 0.01 * numpy.mean(_obs) eps = 0.01 * numpy.mean(_obs)
numpy.testing.assert_almost_equal( for metric in _all_metrics:
evalhyd.evald(_obs, _prd, ["NSE"], transform="inv")[0], with self.subTest(metric=metric):
evalhyd.evald(1 / (_obs + eps), 1 / (_prd + eps), ["NSE"])[0] numpy.testing.assert_almost_equal(
) evalhyd.evald(_obs, _prd, ["NSE"], transform="inv",
q_thr=_thr, events=_events)[0],
evalhyd.evald(1 / (_obs + eps), 1 / (_prd + eps), ["NSE"],
q_thr=1 / (_thr + eps), events=_events)[0]
)
def test_transform_log(self): def test_transform_log(self):
eps = 0.01 * numpy.mean(_obs) eps = 0.01 * numpy.mean(_obs)
numpy.testing.assert_almost_equal( for metric in _all_metrics:
evalhyd.evald(_obs, _prd, ["NSE"], transform="log")[0], with self.subTest(metric=metric):
evalhyd.evald(numpy.log(_obs + eps), numpy.log(_prd + eps), numpy.testing.assert_almost_equal(
["NSE"])[0] evalhyd.evald(_obs, _prd, ["NSE"], transform="log",
) q_thr=_thr, events=_events)[0],
evalhyd.evald(numpy.log(_obs + eps), numpy.log(_prd + eps),
["NSE"],
q_thr=numpy.log(_thr + eps), events=_events)[0]
)
def test_transform_pow(self): def test_transform_pow(self):
numpy.testing.assert_almost_equal( for metric in _all_metrics:
evalhyd.evald(_obs, _prd, ["NSE"], transform="pow", exponent=0.3)[0], with self.subTest(metric=metric):
evalhyd.evald(_obs ** 0.3, _prd ** 0.3, ["NSE"])[0] numpy.testing.assert_almost_equal(
) evalhyd.evald(_obs, _prd, ["NSE"],
q_thr=_thr, events=_events,
transform="pow", exponent=0.3)[0],
evalhyd.evald(_obs ** 0.3, _prd ** 0.3, ["NSE"],
q_thr=_thr ** 0.3, events=_events)[0]
)
class TestMasking(unittest.TestCase): class TestMasking(unittest.TestCase):
...@@ -90,8 +121,10 @@ class TestMasking(unittest.TestCase): ...@@ -90,8 +121,10 @@ class TestMasking(unittest.TestCase):
prd = _prd[..., 99:].copy() prd = _prd[..., 99:].copy()
numpy.testing.assert_almost_equal( numpy.testing.assert_almost_equal(
evalhyd.evald(_obs, _prd, ["NSE"], t_msk=msk)[0], evalhyd.evald(_obs, _prd, ["NSE"],
evalhyd.evald(obs, prd, ["NSE"])[0] q_thr=_thr, events=_events, t_msk=msk)[0],
evalhyd.evald(obs, prd, ["NSE"],
q_thr=_thr, events=_events)[0]
) )
def test_conditions(self): def test_conditions(self):
...@@ -106,8 +139,10 @@ class TestMasking(unittest.TestCase): ...@@ -106,8 +139,10 @@ class TestMasking(unittest.TestCase):
prd = _prd[..., msk].copy() prd = _prd[..., msk].copy()
numpy.testing.assert_almost_equal( numpy.testing.assert_almost_equal(
evalhyd.evald(_obs, _prd, ["NSE"], m_cdt=cdt)[0], evalhyd.evald(_obs, _prd, ["NSE"],
evalhyd.evald(obs, prd, ["NSE"])[0] q_thr=_thr, events=_events, m_cdt=cdt)[0],
evalhyd.evald(obs, prd, ["NSE"],
q_thr=_thr, events=_events)[0]
) )
with self.subTest(conditions="observed streamflow statistics"): with self.subTest(conditions="observed streamflow statistics"):
...@@ -121,8 +156,10 @@ class TestMasking(unittest.TestCase): ...@@ -121,8 +156,10 @@ class TestMasking(unittest.TestCase):
prd = _prd[..., msk].copy() prd = _prd[..., msk].copy()
numpy.testing.assert_almost_equal( numpy.testing.assert_almost_equal(
evalhyd.evald(_obs, _prd, ["NSE"], m_cdt=cdt)[0], evalhyd.evald(_obs, _prd, ["NSE"],
evalhyd.evald(obs, prd, ["NSE"])[0] q_thr=_thr, events=_events, m_cdt=cdt)[0],
evalhyd.evald(obs, prd, ["NSE"],
q_thr=_thr, events=_events)[0]
) )
with self.subTest(conditions="time indices"): with self.subTest(conditions="time indices"):
...@@ -138,15 +175,17 @@ class TestMasking(unittest.TestCase): ...@@ -138,15 +175,17 @@ class TestMasking(unittest.TestCase):
prd = _prd[..., 20:].copy() prd = _prd[..., 20:].copy()
numpy.testing.assert_almost_equal( numpy.testing.assert_almost_equal(
evalhyd.evald(_obs, _prd, ["NSE"], m_cdt=cdt)[0], evalhyd.evald(_obs, _prd, ["NSE"],
evalhyd.evald(obs, prd, ["NSE"])[0] q_thr=_thr, events=_events, m_cdt=cdt)[0],
evalhyd.evald(obs, prd, ["NSE"],
q_thr=_thr, events=_events)[0]
) )
class TestMissingData(unittest.TestCase): class TestMissingData(unittest.TestCase):
def test_nan(self): def test_nan(self):
for metric in ('RMSE', 'NSE', 'KGE', 'KGEPRIME'): for metric in _all_metrics:
obs = numpy.array( obs = numpy.array(
[[4.7, numpy.nan, 5.5, 2.7, 4.1]] [[4.7, numpy.nan, 5.5, 2.7, 4.1]]
) )
...@@ -155,9 +194,16 @@ class TestMissingData(unittest.TestCase): ...@@ -155,9 +194,16 @@ class TestMissingData(unittest.TestCase):
[numpy.nan, 4.2, 4.7, 4.3, 3.3], [numpy.nan, 4.2, 4.7, 4.3, 3.3],
[5.3, 5.2, 5.7, numpy.nan, 3.9]] [5.3, 5.2, 5.7, numpy.nan, 3.9]]
) )
thr = numpy.array(
[[4., 5.],
[4., 5.],
[4., 5.]]
)
events = "low"
with self.subTest(metric=metric): with self.subTest(metric=metric):
res = evalhyd.evald(obs, prd, [metric])[0] res = evalhyd.evald(obs, prd, [metric],
q_thr=thr, events=events)[0]
for i in range(prd.shape[0]): for i in range(prd.shape[0]):
msk = ~numpy.isnan(obs[0]) & ~numpy.isnan(prd[i]) msk = ~numpy.isnan(obs[0]) & ~numpy.isnan(prd[i])
...@@ -169,7 +215,8 @@ class TestMissingData(unittest.TestCase): ...@@ -169,7 +215,8 @@ class TestMissingData(unittest.TestCase):
evalhyd.evald( evalhyd.evald(
obs[:, msk], obs[:, msk],
prd[i, msk][numpy.newaxis], prd[i, msk][numpy.newaxis],
[metric] [metric],
q_thr=thr[i, :][numpy.newaxis], events=events
)[0] )[0]
) )
...@@ -190,21 +237,30 @@ class TestUncertainty(unittest.TestCase): ...@@ -190,21 +237,30 @@ class TestUncertainty(unittest.TestCase):
obs_3yrs = numpy.hstack((obs_1yr,) * 3) obs_3yrs = numpy.hstack((obs_1yr,) * 3)
prd_3yrs = numpy.hstack((prd_1yr,) * 3) prd_3yrs = numpy.hstack((prd_1yr,) * 3)
for metric in ('RMSE', 'NSE', 'KGE', 'KGEPRIME'): thr = numpy.repeat(
numpy.array([[690, 534, 445, numpy.nan]]),
repeats=prd_1yr.shape[0], axis=0
)
events = "low"
for metric in _all_metrics:
with self.subTest(metric=metric): with self.subTest(metric=metric):
numpy.testing.assert_almost_equal( numpy.testing.assert_almost_equal(
# bootstrap with only one year of data # bootstrap with only one year of data
# (compare last sample only to have matching dimensions) # (compare last sample only to have matching dimensions)
evalhyd.evald( evalhyd.evald(
obs_1yr, prd_1yr, [metric], obs_1yr, prd_1yr, [metric],
q_thr=thr,
events=events,
bootstrap={ bootstrap={
"n_samples": 10, "len_sample": 3, "summary": 0 "n_samples": 10, "len_sample": 3, "summary": 0
}, },
dts=dts_1yr dts=dts_1yr
)[0][..., [0]], )[0][:, :, [0]],
# repeat year of data three times to correspond to a # repeat year of data three times to correspond to a
# bootstrap sample of length 3 # bootstrap sample of length 3
evalhyd.evald(obs_3yrs, prd_3yrs, [metric])[0] evalhyd.evald(obs_3yrs, prd_3yrs, [metric],
q_thr=thr, events=events)[0]
) )
......
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