def test_default_configuration(self):
for i in range(10):
predictions, targets = \
_test_classifier(BernoulliNB)
self.assertAlmostEqual(
0.26000000000000001,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_digits(self):
for i in range(10):
predictions, targets = \
_test_classifier(classifier=QDA, dataset='digits')
self.assertAlmostEqual(0.18882817243472982,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration(self):
for i in range(10):
predictions, targets = \
_test_classifier(GradientBoostingClassifier)
self.assertAlmostEqual(
0.95999999999999996,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_sparse(self):
for i in range(10):
predictions, targets = \
_test_classifier(ExtraTreesClassifier, sparse=True)
self.assertAlmostEqual(0.71999999999999997,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_iris(self):
for i in range(10):
predictions, targets = \
_test_classifier(QDA)
self.assertAlmostEqual(1.0,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_digits(self):
for i in range(10):
predictions, targets = \
_test_classifier(classifier=PassiveAggressive, dataset='digits')
self.assertAlmostEqual(0.91924711596842745,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration(self):
for i in range(10):
predictions, targets = \
_test_classifier(KNearestNeighborsClassifier)
self.assertAlmostEqual(
0.959999999999999,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_sparse_data(self):
for i in range(10):
predictions, targets = \
_test_classifier(KNearestNeighborsClassifier, sparse=True)
self.assertAlmostEqual(0.82,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_iris_sparse(self):
for i in range(10):
predictions, targets = \
_test_classifier(AdaboostClassifier, sparse=True)
self.assertAlmostEqual(0.88,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_iris(self):
for i in range(10):
predictions, targets = \
_test_classifier(AdaboostClassifier)
self.assertAlmostEqual(
0.93999999999999995,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration(self):
for i in range(10):
predictions, targets = \
_test_classifier(MultinomialNB)
self.assertAlmostEqual(
0.97999999999999998,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_digits(self):
for i in range(10):
predictions, targets = \
_test_classifier(classifier=QDA, dataset='digits')
self.assertAlmostEqual(
0.18882817243472982,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_sparse(self):
for i in range(10):
predictions, targets = \
_test_classifier(ExtraTreesClassifier, sparse=True)
self.assertAlmostEqual(
0.71999999999999997,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_digits(self):
for i in range(10):
predictions, targets = _test_classifier(ProjLogitCLassifier,
dataset='digits')
self.assertAlmostEqual(
0.8986035215543412,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_digits(self):
for i in range(10):
predictions, targets = \
_test_classifier(classifier=AdaboostClassifier,
dataset='digits')
self.assertAlmostEqual(0.6915604128718883,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_digits(self):
for i in range(10):
predictions, targets = \
_test_classifier(SGD, dataset='digits')
self.assertAlmostEqual(
0.89313904068002425,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration(self):
for i in range(10):
predictions, targets = \
_test_classifier(BernoulliNB)
self.assertAlmostEqual(0.26000000000000001,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_digits(self):
for i in range(10):
predictions, targets = \
_test_classifier(classifier=LDA, dataset='digits')
self.assertAlmostEqual(0.88585306618093507,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration(self):
for i in range(10):
predictions, targets = \
_test_classifier(GaussianNB)
self.assertAlmostEqual(0.95999999999999996,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_digits(self):
for i in range(10):
predictions, targets = \
_test_classifier(SGD, dataset='digits')
self.assertAlmostEqual(0.89313904068002425,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_digits(self):
for i in range(10):
predictions, targets = \
_test_classifier(classifier=AdaboostClassifier,
dataset='digits')
self.assertAlmostEqual(
0.6915604128718883,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration(self):
for i in range(10):
predictions, targets = _test_classifier(DecisionTree, dataset="iris")
self.assertAlmostEqual(0.92, sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration(self):
for i in range(10):
predictions, targets = _test_classifier(SGD)
self.assertAlmostEqual(
1.0, sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration(self):
for i in range(10):
predictions, targets = _test_classifier(LibSVM_SVC, dataset='iris')
self.assertAlmostEqual(
0.96, sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration(self):
for i in range(10):
predictions, targets = _test_classifier(LibLinear_SVC,
dataset='iris')
self.assertTrue(all(targets == predictions))
def test_default_configuration_sparse_data(self):
for i in range(10):
predictions, targets = \
_test_classifier(KNearestNeighborsClassifier, sparse=True)
self.assertAlmostEqual(
0.82, sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_sparse(self):
for i in range(10):
predictions, targets = _test_classifier(DecisionTree, sparse=True)
self.assertAlmostEqual(0.69999999999999996, sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_sparse(self):
for i in range(10):
predictions, targets = _test_classifier(RandomForest, sparse=True)
self.assertAlmostEqual(
0.85999999999999999,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_digits(self):
for i in range(10):
predictions, targets = _test_classifier(ProjLogitCLassifier,
dataset='digits')
self.assertAlmostEqual(0.8986035215543412,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration(self):
for i in range(10):
predictions, targets = \
_test_classifier(KNearestNeighborsClassifier)
self.assertAlmostEqual(0.959999999999999,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration(self):
for i in range(10):
predictions, targets = _test_classifier(ProjLogitCLassifier, dataset='iris')
self.assertAlmostEqual(0.98,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration(self):
for i in range(10):
predictions, targets = _test_classifier(PassiveAggressive)
self.assertAlmostEqual(0.97999999999999998,
sklearn.metrics.accuracy_score(predictions,
targets))