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( 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)