def test_fit_profile_simple_b(self):
_fit_with_constraint = HistFit(
self._data_container,
model_density_function=self._model_function,
bin_evaluation=self._model_function_antiderivative)
_fit_with_constraint.add_parameter_constraint('b', self._means[1],
np.sqrt(self._vars[1]))
self._test_consistency(_fit_with_constraint,
self._cov_mat_simple_b_inv)
def test_fit_profile_cov_mat_correlated(self):
_fit_with_constraint = HistFit(
self._data_container,
model_density_function=self._model_function,
bin_evaluation=self._model_function_antiderivative)
_fit_with_constraint.add_matrix_parameter_constraint(['a', 'b'],
self._means,
self._cov_mat_cor)
self._test_consistency(_fit_with_constraint, self._cov_mat_cor_inv)
def setUp(self):
_bin_edges = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
_data = [
0.5, 1.5, 1.5, 2.5, 2.5, 2.5, 3.5, 3.5, 3.5, 3.5, 4.5, 4.5, 4.5,
4.5, 4.5
]
self._means = np.array([3.654, 7.789])
self._vars = np.array([2.467, 1.543])
self._cov_mat_uncor = np.array([[self._vars[0], 0.0],
[0.0, self._vars[1]]])
self._cov_mat_uncor_inv = np.linalg.inv(self._cov_mat_uncor)
self._cov_mat_cor = np.array([[self._vars[0], 0.1],
[0.1, self._vars[1]]])
self._cov_mat_cor_inv = np.linalg.inv(self._cov_mat_cor)
self._cov_mat_simple_a_inv = np.array([[1.0 / self._vars[0], 0.0],
[0.0, 0.0]])
self._cov_mat_simple_b_inv = np.array([[0.0, 0.0],
[0.0, 1.0 / self._vars[1]]])
self._data_container = HistContainer(n_bins=5,
bin_range=(0.0, 5.0),
fill_data=_data,
dtype=float)
self._data_container.add_error(err_val=1.0)
_a_test = np.linspace(start=1, stop=2, num=9, endpoint=True)
_b_test = np.linspace(start=2, stop=3, num=9, endpoint=True)
self._test_par_values = np.zeros((4, 2, 9))
self._test_par_values[0, 0] = _a_test
self._test_par_values[1, 1] = _b_test
self._test_par_values[2, 0] = _a_test
self._test_par_values[2, 1] = _b_test
self._test_par_values[3, 0] = _a_test
self._test_par_values[3, 1] = _b_test[::-1] # reverse order
self._test_par_res = self._test_par_values - self._means.reshape(
(1, 2, 1))
self._test_par_res = np.transpose(self._test_par_res, axes=(0, 2, 1))
self._fit_no_constraints = HistFit(
self._data_container,
model_density_function=self._model_function,
bin_evaluation=self._model_function_antiderivative)
self._fit_no_constraints.do_fit()
_cost_function = self._fit_no_constraints._fitter._fcn_wrapper
self._profile_no_constraints = np.zeros((4, 9))
for _i in range(4):
for _j in range(9):
self._profile_no_constraints[_i, _j] = _cost_function(
self._test_par_values[_i, 0, _j],
self._test_par_values[_i, 1, _j])
def test_bad_input_exception(self):
_fit_with_constraint = HistFit(self._data_container, model_density_function=self._model_function,
model_density_antiderivative=self._model_function_antiderivative)
with self.assertRaises(HistFitException):
_fit_with_constraint.add_parameter_constraint('c', 1.0, 1.0)
with self.assertRaises(HistFitException):
_fit_with_constraint.add_matrix_parameter_constraint(['a', 'c'], [1.0, 2.0], [[0.2, 0.0], [0.0, 0.1]])
with self.assertRaises(HistFitException):
_fit_with_constraint.add_matrix_parameter_constraint(['a'], [1.0, 2.0], [[0.2, 0.0], [0.0, 0.1]])
def test_fit_profile_cov_mat_uncorrelated(self):
_fit_with_constraint = HistFit(self._data_container, model_density_function=self._model_function,
model_density_antiderivative=self._model_function_antiderivative)
_fit_with_constraint.add_matrix_parameter_constraint(['a', 'b'], self._means, self._cov_mat_uncor)
self._test_consistency(_fit_with_constraint, self._cov_mat_uncor_inv)
_fit_with_constraint_alt = HistFit(self._data_container, model_density_function=self._model_function,
model_density_antiderivative=self._model_function_antiderivative)
_fit_with_constraint_alt.add_parameter_constraint('a', self._means[0], np.sqrt(self._vars[0]))
_fit_with_constraint_alt.add_parameter_constraint('b', self._means[1], np.sqrt(self._vars[1]))
self._test_consistency(_fit_with_constraint_alt, self._cov_mat_uncor_inv)
def _get_fit(self, model_density_function=None, model_density_antiderivative=None, cost_function=None):
'''convenience'''
model_density_function = model_density_function or hist_model_density
# TODO: fix default
cost_function = cost_function or HistCostFunction_NegLogLikelihood(
data_point_distribution='poisson')
_fit = HistFit(
data=self._ref_hist_cont,
model_density_function=model_density_function,
model_density_antiderivative=model_density_antiderivative,
cost_function=cost_function,
minimizer=self.MINIMIZER
)
_fit.add_error(1.0) # only considered for chi2
return _fit
def test_model_no_pars_raise(self):
def dummy_model():
pass
with self.assertRaises(HistModelFunctionException) as _exc:
HistFit(data=self._ref_hist_cont,
model_density_function=dummy_model,
bin_evaluation=dummy_model,
minimizer=self.MINIMIZER)
self.assertIn("needs at least one parameter", _exc.exception.args[0])
def test_reserved_parameter_names_raise(self):
def dummy_model(x, data):
pass
with self.assertRaises(HistFitException) as _exc:
HistFit(data=self._ref_hist_cont,
model_density_function=dummy_model,
minimizer=self.MINIMIZER)
self.assertIn('reserved', _exc.exception.args[0])
self.assertIn('data', _exc.exception.args[0])
def test_model_and_antiderivative_no_defaults(self):
def legendre_grade_2(x, a=1, b=2, c=3):
return a + b * x + c * 0.5 * (3 * x**2 - 1)
def legendre_grade_2_integrated(x, a, b, c):
return 0.5 * x * (2 * a + b * x + c * (x**2 - 1))
# should not raise an error
HistFit(data=self._ref_hist_cont,
model_density_function=legendre_grade_2,
bin_evaluation=legendre_grade_2_integrated,
minimizer=self.MINIMIZER)
def test_model_and_antiderivative_different_signatures_raise(self):
def dummy_model(x, mu, sigma):
pass
def dummy_model_antiderivative(x, mu, bogus):
pass
with self.assertRaises(ValueError) as _exc:
HistFit(data=self._ref_hist_cont,
model_density_function=dummy_model,
bin_evaluation=dummy_model_antiderivative,
minimizer=self.MINIMIZER)
def test_model_varargs_varkwargs_raise(self):
# TODO: raise even without 'par'
def dummy_model(x, par, *varargs, **varkwargs):
pass
with self.assertRaises(HistModelFunctionException) as _exc:
HistFit(data=self._ref_hist_cont,
model_density_function=dummy_model,
bin_evaluation=dummy_model,
minimizer=self.MINIMIZER)
self.assertIn('variable', _exc.exception.args[0])
self.assertIn('varargs', _exc.exception.args[0])
def test_model_and_antiderivative_different_signatures_raise(self):
def dummy_model(x, data):
pass
def dummy_model_antiderivative(x, bogus):
pass
with self.assertRaises(HistModelFunctionException) as _exc:
HistFit(data=self._ref_hist_cont,
model_density_function=dummy_model,
model_density_antiderivative=dummy_model_antiderivative,
minimizer=self.MINIMIZER)
self.assertIn('require the same argument structures', _exc.exception.args[0])
self.assertIn('data', _exc.exception.args[0])
class TestParameterConstraintInHistFit(unittest.TestCase):
@staticmethod
def _model_function(x, a, b):
return a * x + b
@staticmethod
def _model_function_antiderivative(x, a, b):
return 0.5 * a * x ** 2 + b * x
def _expected_profile_diff(self, res, cov_mat_inv):
return res.dot(cov_mat_inv).dot(res)
def _test_consistency(self, constrained_fit, par_cov_mat_inv):
constrained_fit.do_fit()
_cost_function = constrained_fit._fitter._fcn_wrapper
for _i in range(4):
for _j in range(9):
_a = self._test_par_values[_i, 0, _j]
_b = self._test_par_values[_i, 1, _j]
_profile_constrained = _cost_function(self._test_par_values[_i, 0, _j], self._test_par_values[_i, 1, _j])
_diff = _profile_constrained - self._profile_no_constraints[_i, _j]
_expected_profile_diff = self._expected_profile_diff(self._test_par_res[_i, _j], par_cov_mat_inv)
self.assertTrue(np.abs(_diff - _expected_profile_diff) < 1e-12)
def setUp(self):
_bin_edges = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
_data = [
0.5,
1.5, 1.5,
2.5, 2.5, 2.5,
3.5, 3.5, 3.5, 3.5,
4.5, 4.5, 4.5, 4.5, 4.5
]
self._means = np.array([3.654, 7.789])
self._vars = np.array([2.467, 1.543])
self._cov_mat_uncor = np.array([[self._vars[0], 0.0], [0.0, self._vars[1]]])
self._cov_mat_uncor_inv = np.linalg.inv(self._cov_mat_uncor)
self._cov_mat_cor = np.array([[self._vars[0], 0.1], [0.1, self._vars[1]]])
self._cov_mat_cor_inv = np.linalg.inv(self._cov_mat_cor)
self._cov_mat_simple_a_inv = np.array([[1.0 / self._vars[0], 0.0], [0.0, 0.0]])
self._cov_mat_simple_b_inv = np.array([[0.0, 0.0], [0.0, 1.0 / self._vars[1]]])
self._data_container = HistContainer(n_bins=5, bin_range=(0.0, 5.0), fill_data=_data, dtype=float)
self._data_container.add_error(err_val=1.0)
_a_test = np.linspace(start=1, stop=2, num=9, endpoint=True)
_b_test = np.linspace(start=2, stop=3, num=9, endpoint=True)
self._test_par_values = np.zeros((4, 2, 9))
self._test_par_values[0, 0] = _a_test
self._test_par_values[1, 1] = _b_test
self._test_par_values[2, 0] = _a_test
self._test_par_values[2, 1] = _b_test
self._test_par_values[3, 0] = _a_test
self._test_par_values[3, 1] = _b_test[::-1] # reverse order
self._test_par_res = self._test_par_values - self._means.reshape((1, 2, 1))
self._test_par_res = np.transpose(self._test_par_res, axes=(0, 2, 1))
self._fit_no_constraints = HistFit(self._data_container, model_density_function=self._model_function,
model_density_antiderivative=self._model_function_antiderivative)
self._fit_no_constraints.do_fit()
_cost_function = self._fit_no_constraints._fitter._fcn_wrapper
self._profile_no_constraints = np.zeros((4, 9))
for _i in range(4):
for _j in range(9):
self._profile_no_constraints[_i, _j] = _cost_function(
self._test_par_values[_i, 0, _j],
self._test_par_values[_i, 1, _j])
def test_bad_input_exception(self):
_fit_with_constraint = HistFit(self._data_container, model_density_function=self._model_function,
model_density_antiderivative=self._model_function_antiderivative)
with self.assertRaises(HistFitException):
_fit_with_constraint.add_parameter_constraint('c', 1.0, 1.0)
with self.assertRaises(HistFitException):
_fit_with_constraint.add_matrix_parameter_constraint(['a', 'c'], [1.0, 2.0], [[0.2, 0.0], [0.0, 0.1]])
with self.assertRaises(HistFitException):
_fit_with_constraint.add_matrix_parameter_constraint(['a'], [1.0, 2.0], [[0.2, 0.0], [0.0, 0.1]])
def test_fit_profile_cov_mat_uncorrelated(self):
_fit_with_constraint = HistFit(self._data_container, model_density_function=self._model_function,
model_density_antiderivative=self._model_function_antiderivative)
_fit_with_constraint.add_matrix_parameter_constraint(['a', 'b'], self._means, self._cov_mat_uncor)
self._test_consistency(_fit_with_constraint, self._cov_mat_uncor_inv)
_fit_with_constraint_alt = HistFit(self._data_container, model_density_function=self._model_function,
model_density_antiderivative=self._model_function_antiderivative)
_fit_with_constraint_alt.add_parameter_constraint('a', self._means[0], np.sqrt(self._vars[0]))
_fit_with_constraint_alt.add_parameter_constraint('b', self._means[1], np.sqrt(self._vars[1]))
self._test_consistency(_fit_with_constraint_alt, self._cov_mat_uncor_inv)
def test_fit_profile_cov_mat_correlated(self):
_fit_with_constraint = HistFit(self._data_container, model_density_function=self._model_function,
model_density_antiderivative=self._model_function_antiderivative)
_fit_with_constraint.add_matrix_parameter_constraint(['a', 'b'], self._means, self._cov_mat_cor)
self._test_consistency(_fit_with_constraint, self._cov_mat_cor_inv)
def test_fit_profile_simple_a(self):
_fit_with_constraint = HistFit(self._data_container, model_density_function=self._model_function,
model_density_antiderivative=self._model_function_antiderivative)
_fit_with_constraint.add_parameter_constraint('a', self._means[0], np.sqrt(self._vars[0]))
self._test_consistency(_fit_with_constraint, self._cov_mat_simple_a_inv)
def test_fit_profile_simple_b(self):
_fit_with_constraint = HistFit(self._data_container, model_density_function=self._model_function,
model_density_antiderivative=self._model_function_antiderivative)
_fit_with_constraint.add_parameter_constraint('b', self._means[1], np.sqrt(self._vars[1]))
self._test_consistency(_fit_with_constraint, self._cov_mat_simple_b_inv)