def test_predict_proba_binary_classification(self):
X_train, Y_train, X_test, Y_test = get_dataset('iris')
eliminate_class_two = Y_train != 2
X_train = X_train[eliminate_class_two]
Y_train = Y_train[eliminate_class_two]
eliminate_class_two = Y_test != 2
X_test = X_test[eliminate_class_two]
Y_test = Y_test[eliminate_class_two]
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
class Dummy2(object):
def predict_proba(self, y, batch_size=200):
return np.array([[0.1, 0.9], [0.7, 0.3]])
model = Dummy2()
task_type = BINARY_CLASSIFICATION
D = Dummy()
D.info = {
'metric': BAC_METRIC,
'task': task_type,
'is_sparse': False,
'label_num': 3
}
D.data = {
'X_train': X_train,
'Y_train': Y_train,
'X_valid': X_valid,
'X_test': X_test
}
D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical']
configuration_space = get_configuration_space(
D.info,
include_estimators=['lda'],
include_preprocessors=['select_rates'])
configuration = configuration_space.sample_configuration()
evaluator = HoldoutEvaluator(D, configuration)
pred = evaluator.predict_proba(None, model, task_type)
expected = [[0.9], [0.3]]
for i in range(len(expected)):
self.assertEqual(expected[i], pred[i])
def test_predict_proba_binary_classification(self):
X_train, Y_train, X_test, Y_test = get_dataset('iris')
eliminate_class_two = Y_train != 2
X_train = X_train[eliminate_class_two]
Y_train = Y_train[eliminate_class_two]
eliminate_class_two = Y_test != 2
X_test = X_test[eliminate_class_two]
Y_test = Y_test[eliminate_class_two]
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
class Dummy2(object):
def predict_proba(self, y, batch_size=200):
return np.array([[0.1, 0.9], [0.7, 0.3]])
model = Dummy2()
task_type = BINARY_CLASSIFICATION
D = Dummy()
D.info = {
'metric': BAC_METRIC,
'task': task_type,
'is_sparse': False,
'label_num': 3
}
D.data = {
'X_train': X_train,
'Y_train': Y_train,
'X_valid': X_valid,
'X_test': X_test
}
D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical']
configuration_space = get_configuration_space(
D.info,
include_estimators=['lda'],
include_preprocessors=['select_rates'])
configuration = configuration_space.sample_configuration()
evaluator = HoldoutEvaluator(D, configuration)
pred = evaluator.predict_proba(None, model, task_type)
expected = [[0.9], [0.3]]
for i in range(len(expected)):
self.assertEqual(expected[i], pred[i])
def test_predict_proba_binary_classification(self):
X_train, Y_train, X_test, Y_test = get_dataset("iris")
eliminate_class_two = Y_train != 2
X_train = X_train[eliminate_class_two]
Y_train = Y_train[eliminate_class_two]
eliminate_class_two = Y_test != 2
X_test = X_test[eliminate_class_two]
Y_test = Y_test[eliminate_class_two]
X_valid = X_test[:25,]
Y_valid = Y_test[:25,]
X_test = X_test[25:,]
Y_test = Y_test[25:,]
class Dummy2(object):
def predict_proba(self, y, batch_size=200):
return np.array([[0.1, 0.9], [0.7, 0.3]])
model = Dummy2()
task_type = BINARY_CLASSIFICATION
D = Dummy()
D.info = {"metric": BAC_METRIC, "task": task_type, "is_sparse": False, "label_num": 3}
D.data = {"X_train": X_train, "Y_train": Y_train, "X_valid": X_valid, "X_test": X_test}
D.feat_type = ["numerical", "Numerical", "numerical", "numerical"]
configuration_space = get_configuration_space(
D.info, include_estimators=["lda"], include_preprocessors=["select_rates"]
)
configuration = configuration_space.sample_configuration()
evaluator = HoldoutEvaluator(D, configuration)
pred = evaluator.predict_proba(None, model, task_type)
expected = [[0.9], [0.3]]
for i in range(len(expected)):
self.assertEqual(expected[i], pred[i])
