def test_predict_proba_hardclassifier(self):
# task 1 (test server) is important, as it is a task with an unused class
tasks = [1, 3, 115]
for task_id in tasks:
task = openml.tasks.get_task(task_id)
clf1 = sklearn.pipeline.Pipeline(steps=[(
'imputer',
sklearn.preprocessing.Imputer()), ('estimator', GaussianNB())])
clf2 = sklearn.pipeline.Pipeline(
steps=[('imputer', sklearn.preprocessing.Imputer()
), ('estimator', HardNaiveBayes())])
arff_content1, arff_header1, _, _, _ = _run_task_get_arffcontent(
clf1, task)
arff_content2, arff_header2, _, _, _ = _run_task_get_arffcontent(
clf2, task)
# verifies last two arff indices (predict and correct)
# TODO: programmatically check wether these are indeed features (predict, correct)
predictionsA = np.array(arff_content1)[:, -2:]
predictionsB = np.array(arff_content2)[:, -2:]
np.testing.assert_array_equal(predictionsA, predictionsB)
def test_run_on_dataset_with_missing_labels(self):
# Check that _run_task_get_arffcontent works when one of the class
# labels only declared in the arff file, but is not present in the
# actual data
task = openml.tasks.get_task(2)
class_labels = task.class_labels
model = Pipeline(steps=[('Imputer', Imputer(
strategy='median')), ('Estimator', DecisionTreeClassifier())])
data_content, _, _, _, _ = _run_task_get_arffcontent(model, task)
# 2 folds, 5 repeats; keep in mind that this task comes from the test
# server, the task on the live server is different
self.assertEqual(len(data_content), 4490)
for row in data_content:
# repeat, fold, row_id, 6 confidences, prediction and correct label
self.assertEqual(len(row), 12)
def test_run_on_dataset_with_missing_labels(self):
# Check that _run_task_get_arffcontent works when one of the class
# labels only declared in the arff file, but is not present in the
# actual data
task = openml.tasks.get_task(2)
class_labels = task.class_labels
model = Pipeline(steps=[('Imputer', Imputer(strategy='median')),
('Estimator', DecisionTreeClassifier())])
data_content, _, _, _ = _run_task_get_arffcontent(model, task, class_labels)
# 2 folds, 5 repeats; keep in mind that this task comes from the test
# server, the task on the live server is different
self.assertEqual(len(data_content), 4490)
for row in data_content:
# repeat, fold, row_id, 6 confidences, prediction and correct label
self.assertEqual(len(row), 11)