def source_competence(self):
"""The source of competence C_src at the validation point :math:`\mathbf{x}_{k}` is calculated by
logarithm function in the support obtained by the base classifier.
Returns
----------
C_src : array of shape = [n_samples, n_classifiers]
The competence source for each base classifier at each data point.
"""
C_src = np.zeros((self.n_samples, self.n_classifiers))
for clf_index in range(self.n_classifiers):
supports = self.dsel_scores[:, clf_index, :]
support_correct = supports[np.arange(self.n_samples),
self.DSEL_target]
C_src[:, clf_index] = log_func(self.n_classes, support_correct)
return C_src
def source_competence(self):
"""The source of competence C_src at the validation point xk is calculated by logarithm in the support
obtained by the base classifier.
Returns
----------
C_src : array of shape = [n_samples, n_classifiers]
The competence source for each base classifier at each data point.
"""
C_src = np.zeros((self.n_samples, self.n_classifiers))
for clf_index in range(self.n_classifiers):
supports = self._get_scores_dsel(clf_index)
support_correct = [
supports[sample_idx, i]
for sample_idx, i in enumerate(self.DSEL_target)
]
support_correct = np.array(support_correct)
C_src[:, clf_index] = log_func(self.n_classes, support_correct)
return C_src
def test_log_func_multi_class(supports_correct, expected):
n_classes = 3
result = log_func(n_classes, supports_correct)
assert np.isclose(result, expected, atol=0.01)
def test_log_func_two_classes(supports_correct, expected):
n_classes = 2
result = log_func(n_classes, supports_correct)
assert np.isclose(result, expected)