def regressor(self):
fit = self.fit
if fit is None:
fit = 'linear'
self.fit = fit
lfit = fit.lower()
is_mean = 'average' in lfit
is_expo = lfit == 'exponential'
is_poly = not (is_mean or is_expo)
reg = self._regressor
if reg is None:
if is_mean:
reg = MeanRegressor()
elif is_expo:
reg = ExponentialRegressor()
else:
reg = PolynomialRegressor()
elif is_poly and not isinstance(reg, PolynomialRegressor):
reg = PolynomialRegressor()
elif is_mean and not isinstance(reg, MeanRegressor):
reg = MeanRegressor()
elif is_expo and not isinstance(reg, ExponentialRegressor):
reg = ExponentialRegressor()
if is_poly:
reg.set_degree(fit_to_degree(fit), refresh=False)
reg.trait_set(xs=self.offset_xs,
ys=self.ys,
error_calc_type=self.error_type or 'SEM',
filter_outliers_dict=self.filter_outliers_dict,
tag=self.name)
if self.truncate:
reg.set_truncate(self.truncate)
reg.calculate()
self._regressor = reg
return reg
class TruncateRegressionTest(TestCase):
def setUp(self):
self.reg = MeanRegressor()
def test_pre_truncate(self):
xs, ys, sol = pre_truncated_data()
self.reg.trait_set(xs=xs, ys=ys)
self.solution = sol
self.reg.trait_set(xs=xs, ys=ys)
self.reg.set_truncate('x<5')
self.assertEqual(self.reg.mean, self.solution['pre_mean'])
def test_post_truncate(self):
xs, ys, sol = pre_truncated_data()
self.reg.trait_set(xs=xs, ys=ys)
self.solution = sol
self.reg.trait_set(xs=xs, ys=ys)
self.reg.set_truncate('x>=5')
self.assertEqual(self.reg.mean, self.solution['post_mean'])
class TruncateRegressionTest(TestCase):
def setUp(self):
self.reg = MeanRegressor()
def test_pre_truncate(self):
xs, ys, sol = pre_truncated_data()
self.reg.trait_set(xs=xs, ys=ys)
self.solution = sol
self.reg.trait_set(xs=xs, ys=ys)
self.reg.set_truncate('x<5')
self.assertEqual(self.reg.mean, self.solution['pre_mean'])
def test_post_truncate(self):
xs, ys, sol = pre_truncated_data()
self.reg.trait_set(xs=xs, ys=ys)
self.solution = sol
self.reg.trait_set(xs=xs, ys=ys)
self.reg.set_truncate('x>=5')
self.assertEqual(self.reg.mean, self.solution['post_mean'])
def regressor(self):
fit = self.fit
if fit is None:
fit = 'linear'
self.fit = fit
lfit = fit.lower()
is_mean = 'average' in lfit
is_expo = lfit == 'exponential'
is_poly = not (is_mean or is_expo)
reg = self._regressor
if reg is None:
if is_mean:
reg = MeanRegressor()
elif is_expo:
reg = ExponentialRegressor()
else:
reg = PolynomialRegressor()
elif is_poly and not isinstance(reg, PolynomialRegressor):
reg = PolynomialRegressor()
elif is_mean and not isinstance(reg, MeanRegressor):
reg = MeanRegressor()
elif is_expo and not isinstance(reg, ExponentialRegressor):
reg = ExponentialRegressor()
if is_poly:
reg.set_degree(fit_to_degree(fit), refresh=False)
reg.trait_set(xs=self.offset_xs, ys=self.ys,
error_calc_type=self.error_type or 'SEM',
filter_outliers_dict=self.filter_outliers_dict,
tag=self.name)
if self.truncate:
reg.set_truncate(self.truncate)
reg.calculate()
self._regressor = reg
return reg
