def cc_setup(calc_pred_int=True):
PI = PredictionIntervals()
results = gf.setup_pseudo_results()
PI._assign_features_from_results_structure(results=results)
testchain, s2chain, lims, sstype, nsample, iisample = PI._setup_generation_requirements(
nsample=400, calc_pred_int=calc_pred_int, sstype=0)
DS = gf.basic_data_structure()
datapred = PI._setup_data_structure_for_prediction(data=DS, ndatabatches=1)
return PI, results, testchain, s2chain, lims, sstype, nsample, iisample, datapred
def test_feature_assignment(self):
PI = PredictionIntervals()
results = gf.setup_pseudo_results()
PI._assign_features_from_results_structure(results=results)
PID = PI.__dict__
check_these = [
'chain', 's2chain', 'parind', 'local', 'theta', 'sstype'
]
self.check_dictionary(check_these, PID, results)
def test_second_feature_assignment(self):
PI = PredictionIntervals()
results = gf.setup_pseudo_results()
results = gf.removekey(results, 'sstype')
PI._assign_features_from_results_structure(results=results)
PID = PI.__dict__
check_these = ['chain', 's2chain', 'parind', 'local', 'theta']
self.check_dictionary(check_these, PID, results)
self.assertEqual(PID['_PredictionIntervals__sstype'],
0,
msg='Should default to 0')
def setup_generation(cls):
aa = np.random.rand([400, 1])
PI = PredictionIntervals()
results = gf.setup_pseudo_results()
PI._assign_features_from_results_structure(results=results)
return PI, aa, results
