def test_balancing_get_weights_treed_single_label(self):
Y = np.array([0] * 80 + [1] * 20)
balancing = Balancing(strategy='weighting')
init_params, fit_params = balancing.get_weights(
Y, 'adaboost', None, None, None)
self.assertTrue(np.allclose(fit_params['classifier:sample_weight'],
np.array([0.4] * 80 + [1.6] * 20)))
def test_balancing_get_weights_treed_single_label(self):
Y = np.array([0] * 80 + [1] * 20)
balancing = Balancing(strategy='weighting')
init_params, fit_params = balancing.get_weights(
Y, 'adaboost', None, None, None)
self.assertTrue(
np.allclose(fit_params['classifier:sample_weight'],
np.array([0.4] * 80 + [1.6] * 20)))
def test_balancing_get_weights_treed_multilabel(self):
Y = np.array([[0, 0, 0]] * 100 + [[1, 0, 0]] * 100 + [[0, 1, 0]] * 100 +
[[1, 1, 0]] * 100 + [[0, 0, 1]] * 100 + [[1, 0, 1]] * 10)
balancing = Balancing(strategy='weighting')
init_params, fit_params = balancing.get_weights(
Y, 'adaboost', None, None, None)
self.assertTrue(np.allclose(fit_params['classifier:sample_weight'],
np.array([0.4] * 500 + [4.0] * 10)))
def test_balancing_get_weights_treed_multilabel(self):
Y = np.array([[0, 0, 0]] * 100 + [[1, 0, 0]] * 100 +
[[0, 1, 0]] * 100 + [[1, 1, 0]] * 100 +
[[0, 0, 1]] * 100 + [[1, 0, 1]] * 10)
balancing = Balancing(strategy='weighting')
init_params, fit_params = balancing.get_weights(
Y, 'adaboost', None, None, None)
self.assertTrue(
np.allclose(fit_params['classifier:sample_weight'],
np.array([0.4] * 500 + [4.0] * 10)))
def test_balancing_get_weights_svm_sgd(self):
Y = np.array([0] * 80 + [1] * 20)
balancing = Balancing(strategy='weighting')
init_params, fit_params = balancing.get_weights(
Y, 'libsvm_svc', None, None, None)
self.assertEqual(("classifier:class_weight", "auto"),
list(init_params.items())[0])
init_params, fit_params = balancing.get_weights(
Y, None, 'liblinear_svc_preprocessor', None, None)
self.assertEqual(("preprocessor:class_weight", "auto"),
list(init_params.items())[0])
def test_balancing_get_weights_svm_sgd(self):
Y = np.array([0] * 80 + [1] * 20)
balancing = Balancing(strategy='weighting')
init_params, fit_params = balancing.get_weights(
Y, 'libsvm_svc', None, None, None)
self.assertEqual(("classifier:class_weight", "auto"),
list(init_params.items())[0])
init_params, fit_params = balancing.get_weights(
Y, None, 'liblinear_svc_preprocessor', None, None)
self.assertEqual(("preprocessor:class_weight", "auto"),
list(init_params.items())[0])
def pre_transform(self, X, y, fit_params=None, init_params=None):
self.num_targets = 1 if len(y.shape) == 1 else y.shape[1]
# Weighting samples has to be done here, not in the components
if self.configuration['balancing:strategy'] == 'weighting':
balancing = Balancing(strategy='weighting')
init_params, fit_params = balancing.get_weights(
y, self.configuration['classifier:__choice__'],
self.configuration['preprocessor:__choice__'],
init_params, fit_params)
X, fit_params = super(ParamSklearnClassifier, self).pre_transform(
X, y, fit_params=fit_params, init_params=init_params)
return X, fit_params
def pre_transform(self, X, y, fit_params=None, init_params=None):
self.num_targets = 1 if len(y.shape) == 1 else y.shape[1]
# Weighting samples has to be done here, not in the components
if self.configuration['balancing:strategy'] == 'weighting':
balancing = Balancing(strategy='weighting')
init_params, fit_params = balancing.get_weights(
y, self.configuration['classifier:__choice__'],
self.configuration['preprocessor:__choice__'], init_params,
fit_params)
X, fit_params = super(ParamSklearnClassifier,
self).pre_transform(X,
y,
fit_params=fit_params,
init_params=init_params)
return X, fit_params
