def test_valid_tests2():
"""test valid test input; a bit ugly unit test. Should die and therefore pass the test"""
#error here: numpy.linspace(0)
t1 = [{
'metrics': [
'numpy.std', 'numpy.median', 'bh_photo_z_validation.sigma_68',
'bh_photo_z_validation.outlier_fraction'
],
'weights':
'WEIGHTS',
'error_function': ['bh_photo_z_validation.bootstrap_mean_error'],
'tolerance': [0.4, 0.001, 0.02, 5],
'truths':
'Z_SPEC',
'predictions': ['MODE_Z', 'MEAN_Z', 'Z_MC'],
'bins': [{
'MAG_DETMODEL_I': '[ 17.5, 19, 22, 25]'
}, {
'MODE_Z': 'numpy.linspace(0)'
}]
}]
try:
pval.valid_tests(t1)
np.testing.assert_equal(False, True)
except:
np.testing.assert_equal(True, True)
def test_valid_tests1():
"""test valid test input"""
t1 = [{
'individual': {
'metrics': ['bh_photo_z_validation.eval_pdf_point'],
'truths': 'Z_SPEC',
'metric_bins': [{
'MAG_DETMODEL_I': '[ 17.5, 19, 22, 25]'
}],
'tolerance': [0.7, 20],
'weights': 'WEIGHTS'
},
'stacks': {
'metrics': [
'bh_photo_z_validation.kstest',
'bh_photo_z_validation.npoisson',
'bh_photo_z_validation.log_loss'
],
'tolerance': [0.7, 20],
'metric_bins': [{
'MAG_DETMODEL_I': '[ 17.5, 19, 22, 25]'
}],
'weights':
'WEIGHTS',
'truth_bins': [{
'Z_SPEC': 'numpy.linspace(0, 2, 4)'
}]
}
}]
try:
r = pval.valid_tests(t1)
np.testing.assert_equal(True, r)
except:
np.testing.assert_equal(True, False)
def test_valid_tests3():
"""test valid test input; a bit ugly unit test. Should die and therefore pass the test"""
#error here: '[ 17.5, ,19, 22, 25]'
t1 = [{
'individual': {
'metrics': ['bh_photo_z_validation.eval_pdf_point'],
'truths': 'Z_SPEC',
'metric_bins': [{
'MAG_DETMODEL_I': '[ 17.5, ,19, 22, 25]'
}],
'tolerance': [0.7, 20],
'weights': 'WEIGHTS'
},
'stacks': {
'metrics': [
'bh_photo_z_validation.kstest',
'bh_photo_z_validation.npoisson',
'bh_photo_z_validation.log_loss'
],
'tolerance': [0.7, 20],
'metric_bins': [{
'MAG_DETMODEL_I': '[ 17.5, 19, 22, 25]'
}],
'weights':
'WEIGHTS',
'truth_bins': [{
'Z_SPEC': 'numpy.linspace(0, 2, 4)'
}]
}
}]
try:
pval.valid_tests(t1)
np.testing.assert_equal(False, True)
except:
np.testing.assert_equal(True, True)
if pval.key_not_none(config, 'test_name'):
test_name = config['test_name']
#First point predictions
ptype = 'point'
#do we have any files of this type to work with?
if len(files[ptype]) > 0:
#results dictionary
res[ptype] = {}
#obtain the tests and required cols
tests = testProperties[ptype]
#check these test are "valid"
cont = pval.valid_tests(tests)
reqcols = pval.required_cols(tests, ptype)
#loop over all files
for f in files[ptype]:
#load a file, and complain if it's not formatted correctly.
d = load_file(f, reqcols)
res[ptype][f] = {}
#calculate all unweighted metrics for deltaz and deltaz/(1+z)
for tst in tests:
if test_name is None: