def test_basic_ds_shape_0(self):
PI = PredictionIntervals()
DS = gf.basic_data_structure()
DS.shape = [(100, )] # remove column - code should add it back
ndatabatches, ncols = PI._analyze_data_structure(data=DS)
self.assertEqual(ndatabatches, 1, msg='Expect 1 batch')
self.assertEqual(ncols, [1], msg='Expect [1]')
def pi_setup():
PI = PredictionIntervals()
results = gf.setup_pseudo_results()
DS = gf.basic_data_structure()
PI.setup_prediction_interval_calculation(results=results,
data=DS,
modelfunction=gf.predmodelfun)
return PI, results
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_basic_modelfunction(self):
PI = PredictionIntervals()
DS = gf.basic_data_structure()
datapred = PI._setup_data_structure_for_prediction(data=DS,
ndatabatches=1)
nrow, ncol = PI._determine_shape_of_response(
modelfunction=gf.predmodelfun,
ndatabatches=1,
datapred=datapred,
theta=[3.0, 5.0])
self.assertEqual(nrow, [100], msg='Expect [100]')
self.assertEqual(ncol, [1], msg='Expect [1]')
def setup_complex_pi(cls):
PI = PredictionIntervals()
results = gf.setup_pseudo_results()
results['s2chain'] = None
DS = gf.basic_data_structure()
PI.setup_prediction_interval_calculation(results=results,
data=DS,
modelfunction=gf.predmodelfun)
PI.generate_prediction_intervals(sstype=0,
nsample=500,
calc_pred_int=False,
waitbar=False)
return PI
def test_generate_credible_intervals(self):
PI = PredictionIntervals()
results = gf.setup_pseudo_results()
results['s2chain'] = None
DS = gf.basic_data_structure()
PI.setup_prediction_interval_calculation(results=results,
data=DS,
modelfunction=gf.predmodelfun)
PI.generate_prediction_intervals(sstype=None,
nsample=500,
calc_pred_int=False,
waitbar=False)
cint = PI.intervals['credible_intervals']
pint = PI.intervals['prediction_intervals']
self.common_set_1(cint, pint)
def test_generate_credible_intervals_with_waitbar(self):
PI = PredictionIntervals()
results = gf.setup_pseudo_results()
results['s2chain'] = None
DS = gf.basic_data_structure()
PI.setup_prediction_interval_calculation(results=results,
data=DS,
modelfunction=gf.predmodelfun)
PI.generate_prediction_intervals(sstype=None,
nsample=500,
calc_pred_int=False,
waitbar=True)
cint = PI.intervals['credible_intervals']
pint = PI.intervals['prediction_intervals']
self.common_set_1(cint, pint)
self.assertEqual(PI._PredictionIntervals__wbarstatus.percentage(3),
3.0,
msg='Expect 3.0')
def test_setup_pi_calc_with_s2chain_none(self):
PI = PredictionIntervals()
DS = gf.basic_data_structure()
results = gf.setup_pseudo_results()
results['s2chain'] = None
PI.setup_prediction_interval_calculation(results=results,
data=DS,
modelfunction=gf.predmodelfun)
self.assertEqual(PI._PredictionIntervals__ndatabatches,
1,
msg='Expect ndatabatches = 1')
self.assertEqual(PI.modelfunction,
gf.predmodelfun,
msg='Functions should match')
self.assertFalse(hasattr(PI, '_PredictionIntervals__s2chain_index'),
msg='Expect False')
self.assertTrue(np.array_equal(PI._PredictionIntervals__chain,
results['chain']),
msg='Arrays should match')
def test_basic_ds(self):
PI = PredictionIntervals()
DS = gf.basic_data_structure()
ndatabatches, ncols = PI._analyze_data_structure(data=DS)
self.assertEqual(ndatabatches, 1, msg='Expect 1 batch')
self.assertEqual(ncols, [1], msg='Expect [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
