do not run examples for boosting algorithms in 2019,4 yet
@@ -73,11 +73,11 @@ class Base():
gen_examples = [
- ('adaboost_batch', None, None, (2019, 4)),
+ ('adaboost_batch', None, None, (2019, 5)),
('adagrad_mse_batch', 'adagrad_mse_batch.csv', 'minimum'),
('association_rules_batch', 'association_rules_batch.csv', 'confidence'),
('bacon_outlier_batch', 'multivariate_outlier_batch.csv', lambda r: r[1].weights),
- ('brownboost_batch', None, None, (2019, 4)),
+ ('brownboost_batch', None, None, (2019, 5)),
('correlation_distance_batch', 'correlation_distance_batch.csv', lambda r: [[np.amin(r.correlationDistance)],
[np.amax(r.correlationDistance)],
[np.mean(r.correlationDistance)],
@@ -109,7 +109,7 @@ gen_examples = [
('linear_regression_streaming', 'linear_regression_batch.csv', lambda r: r[1].prediction),
('log_reg_binary_dense_batch', 'log_reg_binary_dense_batch.csv', lambda r: r[1].prediction),
('log_reg_dense_batch',),
- ('logitboost_batch', None, None, (2019, 4)),
+ ('logitboost_batch', None, None, (2019, 5)),
('low_order_moms_dense_batch', 'low_order_moms_dense_batch.csv', lambda r: np.vstack((r.minimum,
r.maximum,
r.sum,
@@ -153,8 +153,8 @@ gen_examples = [
('sgd_logistic_loss_batch', 'sgd_logistic_loss_batch.csv', 'minimum'),
('sgd_mse_batch', 'sgd_mse_batch.csv', 'minimum'),
('sorting_batch',),
- ('stump_classification_batch', None, None, (2019, 4)),
- ('stump_regression_batch', None, None, (2019, 4)),
+ ('stump_classification_batch', None, None, (2019, 5)),
+ ('stump_regression_batch', None, None, (2019, 5)),
('svm_multiclass_batch', 'svm_multiclass_batch.csv', lambda r: r[0].prediction),
('univariate_outlier_batch', 'univariate_outlier_batch.csv', lambda r: r[1].weights),
]
