def test_default_configuration_iterative_fit(self):
for i in range(10):
predictions, targets = \
_test_classifier_iterative_fit(ExtraTreesClassifier)
self.assertAlmostEqual(
0.93999999999999995,
sklearn.metrics.accuracy_score(targets, predictions))
def test_default_configuration_iterative_fit(self):
for i in range(2):
predictions, targets = \
_test_classifier_iterative_fit(RandomForest)
self.assertAlmostEqual(0.95999999999999996,
sklearn.metrics.accuracy_score(
predictions, targets))
def test_default_configuration_digits_iterative_fit(self):
for i in range(10):
predictions, targets = _test_classifier_iterative_fit(classifier=PassiveAggressive,
dataset='digits')
self.assertAlmostEqual(0.91317547055251969,
sklearn.metrics.accuracy_score(
predictions, targets))
def test_default_configuration_iterative_fit(self):
for i in range(10):
predictions, targets = \
_test_classifier_iterative_fit(GradientBoostingClassifier)
self.assertAlmostEqual(0.95999999999999996,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_iterative_fit(self):
for i in range(10):
predictions, targets = \
_test_classifier_iterative_fit(BernoulliNB)
self.assertAlmostEqual(
0.26000000000000001,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_digits_iterative_fit(self):
for i in range(10):
predictions, targets = _test_classifier_iterative_fit(
SGD, dataset='digits')
self.assertAlmostEqual(
0.91438979963570133,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_iterative_fit(self):
for i in range(2):
predictions, targets = \
_test_classifier_iterative_fit(MultinomialNB)
self.assertAlmostEqual(
0.97999999999999998,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_iterative_fit(self):
for i in range(10):
predictions, targets = \
_test_classifier_iterative_fit(GradientBoostingClassifier)
self.assertAlmostEqual(0.95999999999999996,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_iterative_fit(self):
for i in range(10):
predictions, targets = _test_classifier_iterative_fit(
PassiveAggressive)
self.assertAlmostEqual(
0.97999999999999998,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_iterative_fit(self):
for i in range(10):
predictions, targets = \
_test_classifier_iterative_fit(MultinomialNB)
self.assertAlmostEqual(0.97999999999999998,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_digits_iterative_fit(self):
for i in range(10):
predictions, targets = _test_classifier_iterative_fit(
classifier=PassiveAggressive, dataset='digits')
self.assertAlmostEqual(
0.91924711596842745,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_iterative_fit(self):
for i in range(10):
predictions, targets = _test_classifier_iterative_fit(
PassiveAggressive)
self.assertAlmostEqual(0.68000000000000005,
sklearn.metrics.accuracy_score(
predictions, targets))
def test_default_configuration_iterative_fit(self):
for i in range(10):
predictions, targets = \
_test_classifier_iterative_fit(ExtraTreesClassifier)
self.assertAlmostEqual(0.93999999999999995,
sklearn.metrics.accuracy_score(targets,
predictions))
def test_default_configuration_digits_iterative_fit(self):
for i in range(2):
predictions, targets = _test_classifier_iterative_fit(
SGD, dataset='digits')
self.assertAlmostEqual(
0.89981785063752273,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_iterative_fit(self):
for i in range(10):
predictions, targets = \
_test_classifier_iterative_fit(BernoulliNB)
self.assertAlmostEqual(0.26000000000000001,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_sparse(self):
for i in range(10):
predictions, targets = \
_test_classifier_iterative_fit(DeepNetIterative, sparse=True)
acc_score = sklearn.metrics.accuracy_score(y_pred=predictions, y_true=targets)
print(acc_score)
self.assertAlmostEqual(0.54, acc_score)
def test_default_configuration_digits_iterative_fit(self):
for i in range(10):
predictions, targets = _test_classifier_iterative_fit(
SGD,
dataset='digits')
self.assertAlmostEqual(0.91438979963570133,
sklearn.metrics.accuracy_score(
predictions, targets))
def test_default_digits_iterative_fit(self):
if not hasattr(self.module, 'iterative_fit'):
return
for i in range(2):
predictions, targets = \
_test_classifier_iterative_fit(dataset="digits",
classifier=self.module)
self.assertAlmostEqual(
self.res["default_digits_iterative"],
sklearn.metrics.accuracy_score(targets, predictions),
places=self.res.get("default_digits_iterative_places", 7))
def test_default_iris_iterative_fit(self):
if not hasattr(self.module, 'iterative_fit'):
return
for i in range(2):
predictions, targets, classifier = \
_test_classifier_iterative_fit(dataset="iris",
classifier=self.module)
self.assertAlmostEqual(
self.res["default_iris_iterative"],
sklearn.metrics.accuracy_score(targets, predictions),
places=self.res.get("default_iris_iterative_places", 7))
if self.step_hyperparameter is not None:
self.assertEqual(
getattr(classifier.estimator,
self.step_hyperparameter['name']),
self.step_hyperparameter['value'])
def test_default_iris_iterative_fit(self):
if not hasattr(self.module, 'iterative_fit'):
return
for i in range(2):
predictions, targets, classifier = \
_test_classifier_iterative_fit(dataset="iris",
classifier=self.module)
self.assertAlmostEqual(self.res["default_iris_iterative"],
sklearn.metrics.accuracy_score(targets,
predictions),
places=self.res.get(
"default_iris_iterative_places", 7))
if self.step_hyperparameter is not None:
self.assertEqual(
getattr(classifier.estimator, self.step_hyperparameter['name']),
self.step_hyperparameter['value']
)
def test_default_configuration_digits_iterative_fit(self):
for i in range(10):
predictions, targets = _test_classifier_iterative_fit(classifier=PassiveAggressive, dataset="digits")
self.assertAlmostEqual(0.92349726775956287, sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_iterative_fit(self):
for i in range(2):
predictions, targets = \
_test_classifier_iterative_fit(DeepNetIterative)
self.assertAlmostEqual(
0.62, sklearn.metrics.accuracy_score(targets, predictions))
def test_default_configuration(self):
for i in range(10):
predictions, targets = _test_classifier_iterative_fit(DeepNetIterative, dataset='iris')
acc_score = sklearn.metrics.accuracy_score(y_pred=predictions, y_true=targets)
print(acc_score)
self.assertAlmostEqual(0.62, acc_score)
