def create_model(realdata=True, force_implementation=None, filter_p=None):
if realdata:
ts = read_test_data(325278)
start, end = datetime(1998, 1, 1), datetime(2007, 1, 1)
breaktime = datetime(2002, 6, 19)
ts = ts[start:end]
ts.rename(columns={
'CCI_41_COMBINED': 'candidate',
'merra2': 'reference'
},
inplace=True)
else:
ts, breaktime, [start, end] = create_artificial_test_data('asc2')
if realdata: # bias correction
slope, inter = linreg_params(ts.dropna().candidate,
ts.dropna().reference)
ts.candidate = linreg_stored_params(ts.candidate, slope,
inter) # scale
regress = LinearRegression(ts['candidate'].loc[start:end],
ts['reference'].loc[start:end],
filter_p=filter_p,
fit_intercept=True,
force_implementation=force_implementation)
return regress
def setUpClass(cls):
ts, breaktime, timeframe = create_artificial_test_data('mean')
ts.rename(columns={
'candidate': 'can',
'reference': 'ref'
},
inplace=True)
cls.ts_full = ts # this can be used to test if values to adjust are not same as for model
start = timeframe[0]
ts_frame = ts.loc[start:, :]
lmp_kwargs = dict(regress_resample=None)
lmp = RegressPairFit(
ts_frame['can'],
ts_frame['ref'],
breaktime,
candidate_freq='D',
bias_corr_method='linreg',
filter=None, # cannot filter the test data, otherwise empty
adjust_group=0,
model_intercept=True,
**lmp_kwargs)
cls.lmp = lmp
def test_var_break():
'''Test var break detection'''
df, breaktime, timeframe = create_artificial_test_data('var')
test = TsRelBreakTest(candidate=df.candidate,
reference=df.reference,
breaktime=breaktime,
test_resample=None,
test_check_min_data=3,
test_check_spearR_sig=(-1, 1),
bias_corr_method=None,
alpha=0.01)
test.run_tests()
testresults, error_dict, checkstats = test.get_results()
assert (testresults['var']['h'] == 1)
assert (error_dict['error_code_test'] == 0)
assert (checkstats['n0'] == 182)
assert (checkstats['n1'] == 184)
nptest.assert_almost_equal(checkstats['frame_spearmanR'], 0.8944298, 4)
nptest.assert_almost_equal(checkstats['frame_corrPval'], 0, 4)
assert (test.check_test_results()[1] == 'var')
assert (test.check_test_results()[0] == True)
def setUp(self):
breaktype = self.shortDescription()
self.df, breaktime, timeframe = create_artificial_test_data(
type=breaktype)
self.base = TsRelBreakBase(candidate=self.df.candidate,
reference=self.df.reference,
breaktime=breaktime,
bias_corr_method=None)
def setUpClass(cls):
ts, breaktime, timeframe = create_artificial_test_data('norm_mean')
ts.rename(columns={
'candidate': 'can',
'reference': 'ref'
},
inplace=True)
cls.ts_full = ts # this can be used to test if values to adjust are not same as for model
qcm_kwargs = dict(categories=1, first_last='equal', fit='mean')
qcm = QuantileCatMatch(ts['can'],
ts['ref'],
breaktime,
bias_corr_method='linreg',
adjust_group=0,
**qcm_kwargs)
cls.qcm = qcm
def create_model(realdata=True, poly_order=2, filter_p=None):
if realdata:
ts = read_test_data(325278)
start, end = datetime(1998, 1, 1), datetime(2007, 1, 1)
ts.rename(columns={
'CCI_41_COMBINED': 'can',
'merra2': 'ref'
},
inplace=True)
ts_drop = ts.dropna()
slope, inter = linreg_params(ts_drop['can'], ts_drop['ref'])
ts['can'] = linreg_stored_params(ts['can'], slope, inter) # scale
else:
ts, breaktime, [start, end] = create_artificial_test_data('asc2')
regress = HigherOrderRegression(ts['can'].loc[start:end],
ts['ref'].loc[start:end],
poly_order=poly_order,
filter_p=filter_p)
return regress
def setUpClass(cls):
ts, breaktime, timeframe = create_artificial_test_data('norm_mean')
ts.rename(columns={'candidate': 'can',
'reference': 'ref'}, inplace=True)
cls.ts_full = ts # this can be used to test if values to adjust are not same as for model
start = timeframe[0]
ts_frame = ts.loc[start:, :]
hom_kwargs = dict(regress_resample=None)
hom = HigherOrderMoments(ts_frame['can'],
ts_frame['ref'],
breaktime,
bias_corr_method=None,
filter=None,
adjust_group=0,
poly_orders=2,
select_by=None,
cdf_types=None,
**hom_kwargs)
cls.hom = hom
def test_mean_break():
''' Test mean break detection'''
df_mean, breaktime, timeframe = create_artificial_test_data('mean')
test = TsRelBreakTest(candidate=df_mean.candidate,
reference=df_mean.reference,
breaktime=breaktime,
test_resample=('M', 0.3),
test_check_min_data=3,
test_check_spearR_sig=(-1, 1),
bias_corr_method=None,
alpha=0.01)
test.run_tests()
testresults, error_dict, checkstats = test.get_results()
assert (testresults['mean']['h'] == 1)
assert (error_dict['error_code_test'] == 0)
assert (checkstats['n0'] == 6)
assert (checkstats['n1'] == 6)
assert (test.check_test_results()[1] == 'mean')
assert (test.check_test_results()[0] == True)
def setUp(self):
self.df, breaktime, timeframe = create_artificial_test_data(
type='const')
self.test = HorizontalVal(candidate=self.df.candidate,
reference=self.df.reference,
breaktime=breaktime)