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_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(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_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))