def test_default_configuration_digits(self):
for i in range(2):
predictions, targets = \
_test_classifier(SGD, dataset='digits')
self.assertAlmostEqual(
0.89981785063752273,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_binary_sparse(self):
for i in range(2):
predictions, targets = _test_classifier(
XGradientBoostingClassifier, make_binary=True, sparse=True)
self.assertAlmostEqual(
0.95999999999999996,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration(self):
for i in range(10):
predictions, targets = _test_classifier(LogReg, dataset='iris')
acc_score = sklearn.metrics.accuracy_score(y_true=targets,
y_pred=predictions)
print(acc_score)
self.assertAlmostEqual(0.28, acc_score)
def test_default_configuration_digits(self):
for i in range(2):
predictions, targets = \
_test_classifier(classifier=PassiveAggressive, dataset='digits')
self.assertAlmostEqual(
0.92046144505160898,
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_binary(self):
for i in range(10):
predictions, targets = _test_classifier(
DecisionTree, make_binary=True)
self.assertAlmostEqual(1.0,
sklearn.metrics.accuracy_score(
targets, predictions))
def test_default_configuration_sparse(self):
for i in range(10):
predictions, targets = \
_test_classifier(DeepFeedNet, sparse=True)
acc_score = sklearn.metrics.accuracy_score(y_pred=predictions, y_true=targets)
print(acc_score)
self.assertAlmostEqual(0.4, acc_score)
def test_default_configuration_multilabel(self):
for i in range(2):
predictions, targets = \
_test_classifier(DeepNetIterative, make_multilabel=True)
self.assertAlmostEqual(
0.71361111111111108,
sklearn.metrics.average_precision_score(targets, predictions))
def test_default_configuration(self):
for i in range(10):
predictions, targets = _test_classifier(LogReg, dataset='iris')
acc_score = sklearn.metrics.accuracy_score(y_true=targets,
y_pred=predictions)
print(acc_score)
self.assertAlmostEqual(0.28, acc_score)
def test_default_configuration_binary(self):
for i in range(10):
predictions, targets = _test_classifier(GaussianNB,
make_binary=True)
self.assertAlmostEqual(1.0,
sklearn.metrics.average_precision_score(
predictions, targets))
def test_default_configuration_multilabel(self):
for i in range(10):
predictions, targets = _test_classifier(LibLinear_SVC,
make_multilabel=True)
self.assertAlmostEquals(
0.84479797979797977,
sklearn.metrics.average_precision_score(targets, predictions))
def test_default_configuration_iris_multilabel(self):
for i in range(10):
predictions, targets = \
_test_classifier(LDA, make_multilabel=True)
self.assertEqual(predictions.shape, ((50, 3)))
self.assertAlmostEqual(
0.66, 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_multilabel(self):
for i in range(10):
predictions, targets = \
_test_classifier(QDA, make_multilabel=True)
self.assertAlmostEqual(0.99456140350877187,
sklearn.metrics.average_precision_score(
predictions, targets))
def test_default_configuration_binary(self):
for i in range(2):
predictions, targets = \
_test_classifier(BernoulliNB, make_binary=True)
self.assertAlmostEqual(
0.73999999999999999,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration_multilabel(self):
for i in range(10):
predictions, targets = \
_test_classifier(ExtraTreesClassifier, make_multilabel=True)
self.assertAlmostEqual(0.97060428849902536,
sklearn.metrics.average_precision_score(
targets, predictions))
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_multilabel(self):
for i in range(10):
predictions, targets = _test_classifier(RandomForest,
make_multilabel=True)
self.assertAlmostEqual(0.95999999999999996,
sklearn.metrics.accuracy_score(
predictions, targets))
def test_default_configuration_multilabel(self):
for i in range(10):
predictions, targets = _test_classifier(RandomForest,
make_multilabel=True)
self.assertAlmostEqual(
0.95999999999999996,
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_digits(self):
for i in range(10):
predictions, targets = \
_test_classifier(SGD, dataset='digits')
self.assertAlmostEqual(0.91438979963570133,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_binary(self):
for i in range(10):
predictions, targets = \
_test_classifier(BernoulliNB, make_binary=True)
self.assertAlmostEqual(0.73999999999999999,
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(
0.91438979963570133,
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_iris_sparse(self):
for i in range(10):
predictions, targets = \
_test_classifier(AdaboostClassifier, sparse=True)
self.assertAlmostEqual(0.85999999999999999,
sklearn.metrics.accuracy_score(targets,
predictions))
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_multilabel(self):
for i in range(10):
predictions, targets = \
_test_classifier(QDA, make_multilabel=True)
self.assertAlmostEqual(
0.99456140350877187,
sklearn.metrics.average_precision_score(predictions, targets))
def test_default_configuration_binary(self):
for i in range(2):
predictions, targets = \
_test_classifier(DeepNetIterative, make_binary=True)
self.assertAlmostEqual(
0.9599999999999,
sklearn.metrics.accuracy_score(targets, predictions))
def test_default_configuration_binary(self):
for i in range(10):
predictions, targets = _test_classifier(
DecisionTree, make_binary=True)
self.assertAlmostEqual(1.0,
sklearn.metrics.accuracy_score(
targets, predictions))
def test_default_configuration(self):
for i in range(2):
predictions, targets = \
_test_classifier(KNearestNeighborsClassifier)
self.assertAlmostEqual(
0.959999999999999,
sklearn.metrics.accuracy_score(predictions, targets))
def test_default_configuration(self):
for i in range(2):
predictions, targets = \
_test_classifier(DeepNetIterative)
self.assertAlmostEqual(
0.57999999999999996,
sklearn.metrics.accuracy_score(targets, predictions))
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=QDA, dataset='digits')
self.assertAlmostEqual(0.18882817243472982,
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(
0.85999999999999999,
sklearn.metrics.accuracy_score(targets, predictions))
def test_default_configuration_sparse(self):
for i in range(10):
predictions, targets = _test_classifier(XGradientBoostingClassifier,
sparse=True)
self.assertAlmostEqual(0.88,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration(self):
for i in range(2):
predictions, targets = \
_test_classifier(MultinomialNB)
self.assertAlmostEqual(
0.97999999999999998,
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(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_binary(self):
for i in range(10):
predictions, targets = _test_classifier(
GradientBoostingClassifier, make_binary=True)
self.assertAlmostEqual(1.0,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_binary(self):
for i in range(10):
predictions, targets = _test_classifier(GaussianNB,
make_binary=True)
self.assertAlmostEqual(
1.0,
sklearn.metrics.average_precision_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_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_multilabel(self):
for i in range(10):
predictions, targets = _test_classifier(DecisionTree,
make_multilabel=True)
self.assertAlmostEqual(
0.81108108108108112,
sklearn.metrics.average_precision_score(targets, predictions))
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(targets,
predictions))
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_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(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(BernoulliNB)
self.assertAlmostEqual(0.26000000000000001,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_binary(self):
for i in range(2):
predictions, targets = _test_classifier(LibSVM_SVC,
make_binary=True)
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(0.90710382513661203,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_multilabel(self):
for i in range(10):
predictions, targets = _test_classifier(DeepFeedNet,
make_multilabel=True)
self.assertEqual(predictions.shape, (50, 3))
ave_precision_score = sklearn.metrics.average_precision_score(targets, predictions)
print(ave_precision_score)
self.assertAlmostEqual(0.767777777778, ave_precision_score)
def test_default_configuration_multilabel(self):
for i in range(10):
predictions, targets = _test_classifier(
DecisionTree, make_multilabel=True)
print(predictions, targets)
self.assertAlmostEqual(0.81108108108108112,
sklearn.metrics.average_precision_score(
targets, predictions))
def test_default_configuration_iris_multilabel(self):
for i in range(10):
predictions, targets = \
_test_classifier(LDA, make_multilabel=True)
self.assertEqual(predictions.shape, ((50, 3)))
self.assertAlmostEqual(0.66,
sklearn.metrics.accuracy_score(predictions,
targets))
def test_default_configuration_multilabel(self):
for i in range(10):
predictions, targets = \
_test_classifier(classifier=SGD,
dataset='digits',
make_multilabel=True)
self.assertAlmostEqual(0.87079069751567639,
sklearn.metrics.average_precision_score(
targets, predictions))
def test_default_configuration_multilabel(self):
for i in range(10):
predictions, targets = \
_test_classifier(classifier=GradientBoostingClassifier,
dataset='digits',
make_multilabel=True)
self.assertAlmostEqual(0.84004577632243804,
sklearn.metrics.average_precision_score(
targets, predictions))
def test_default_configuration_multilabel(self):
for i in range(10):
predictions, targets = \
_test_classifier(classifier=BernoulliNB,
dataset='digits',
make_multilabel=True)
self.assertAlmostEqual(0.73112394623587451,
sklearn.metrics.average_precision_score(
targets, predictions))
def test_default_configuration_multilabel(self):
for i in range(10):
predictions, targets = \
_test_classifier(classifier=AdaboostClassifier,
dataset='digits',
make_multilabel=True)
self.assertAlmostEqual(0.79529966660329099,
sklearn.metrics.average_precision_score(
targets, predictions))
def test_default_configuration_binary(self):
for i in range(10):
predictions, targets = \
_test_classifier(classifier=AdaboostClassifier,
dataset='digits', sparse=True,
make_binary=True)
self.assertAlmostEqual(0.93564055859137829,
sklearn.metrics.accuracy_score(
targets, predictions))
