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_basic_datapred(self):
PI = PredictionIntervals()
DS = gf.basic_data_structure()
datapred = PI._setup_data_structure_for_prediction(data=DS,
ndatabatches=1)
self.assertTrue(np.array_equal(datapred[0].xdata[0], DS.xdata[0]),
msg='Arrays should match')
self.assertTrue(np.array_equal(datapred[0].ydata[0], DS.ydata[0]),
msg='Arrays should match')
def test_non_basic_modelfunction(self):
PI = PredictionIntervals()
DS = gf.non_basic_data_structure()
datapred = PI._setup_data_structure_for_prediction(data=DS,
ndatabatches=2)
nrow, ncol = PI._determine_shape_of_response(
modelfunction=gf.predmodelfun,
ndatabatches=2,
datapred=datapred,
theta=[3.0, 5.0])
self.assertEqual(nrow, [100, 100], msg='Expect [100, 100]')
self.assertEqual(ncol, [1, 1], msg='Expect [1, 1]')
def setup_interval(cls):
PI = PredictionIntervals()
DS = gf.basic_data_structure()
datapred = PI._setup_data_structure_for_prediction(data=DS,
ndatabatches=1)
return PI, datapred