def test_evaluate_multilabel_classification(self):
X_train, Y_train, X_test, Y_test = get_dataset('iris')
Y_train = np.array(convert_to_bin(Y_train, 3))
Y_train[:, -1] = 1
Y_test = np.array(convert_to_bin(Y_test, 3))
Y_test[:, -1] = 1
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
D = Dummy()
D.info = {
'metric': F1_METRIC,
'task': MULTILABEL_CLASSIFICATION,
'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=['extra_trees'],
include_preprocessors=['no_preprocessing'])
err = np.zeros([N_TEST_RUNS])
for i in range(N_TEST_RUNS):
print('Evaluate configuration: %d; result:' % i)
configuration = configuration_space.sample_configuration()
D_ = copy.deepcopy(D)
evaluator = HoldoutEvaluator(D_, configuration)
if not self._fit(evaluator):
continue
err[i] = evaluator.predict()
print(err[i])
self.assertTrue(np.isfinite(err[i]))
self.assertGreaterEqual(err[i], 0.0)
def test_evaluate_multilabel_classification(self):
X_train, Y_train, X_test, Y_test = get_dataset('iris')
Y_train = np.array(convert_to_bin(Y_train, 3))
Y_train[:, -1] = 1
Y_test = np.array(convert_to_bin(Y_test, 3))
Y_test[:, -1] = 1
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
D = Dummy()
D.info = {
'metric': F1_METRIC,
'task': MULTILABEL_CLASSIFICATION,
'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=['extra_trees'],
include_preprocessors=['no_preprocessing'])
err = np.zeros([N_TEST_RUNS])
for i in range(N_TEST_RUNS):
print('Evaluate configuration: %d; result:' % i)
configuration = configuration_space.sample_configuration()
D_ = copy.deepcopy(D)
evaluator = HoldoutEvaluator(D_, configuration)
if not self._fit(evaluator):
continue
err[i] = evaluator.predict()
print(err[i])
self.assertTrue(np.isfinite(err[i]))
self.assertGreaterEqual(err[i], 0.0)
def get_multilabel_classification_datamanager():
X_train, Y_train, X_test, Y_test = get_dataset('iris')
indices = list(range(X_train.shape[0]))
np.random.seed(1)
np.random.shuffle(indices)
X_train = X_train[indices]
Y_train = Y_train[indices]
Y_train = np.array(convert_to_bin(Y_train, 3))
# for i in range(Y_train_.shape[0]):
# Y_train_[:, Y_train[i]] = 1
# Y_train = Y_train_
Y_test = np.array(convert_to_bin(Y_test, 3))
# for i in range(Y_test_.shape[0]):
# Y_test_[:, Y_test[i]] = 1
# Y_test = Y_test_
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
D = Dummy()
D.info = {
'task': MULTILABEL_CLASSIFICATION,
'is_sparse': False,
'label_num': 3
}
D.data = {
'X_train': X_train,
'Y_train': Y_train,
'X_valid': X_valid,
'Y_valid': Y_valid,
'X_test': X_test,
'Y_test': Y_test
}
D.feat_type = {
0: 'numerical',
1: 'Numerical',
2: 'numerical',
3: 'numerical'
}
return D
def get_multilabel_classification_datamanager():
X_train, Y_train, X_test, Y_test = get_dataset('iris')
indices = list(range(X_train.shape[0]))
np.random.seed(1)
np.random.shuffle(indices)
X_train = X_train[indices]
Y_train = Y_train[indices]
Y_train = np.array(convert_to_bin(Y_train, 3))
#for i in range(Y_train_.shape[0]):
# Y_train_[:, Y_train[i]] = 1
#Y_train = Y_train_
Y_test = np.array(convert_to_bin(Y_test, 3))
#for i in range(Y_test_.shape[0]):
# Y_test_[:, Y_test[i]] = 1
#Y_test = Y_test_
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
D = Dummy()
D.info = {
'metric': ACC_METRIC,
'task': MULTILABEL_CLASSIFICATION,
'is_sparse': False,
'label_num': 3
}
D.data = {
'X_train': X_train,
'Y_train': Y_train,
'X_valid': X_valid,
'Y_valid': Y_valid,
'X_test': X_test,
'Y_test': Y_test
}
D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical']
return D
def test_evaluate_multilabel_classification(self):
X_train, Y_train, X_test, Y_test = get_dataset("iris")
Y_train = np.array(convert_to_bin(Y_train, 3))
Y_train[:, -1] = 1
Y_test = np.array(convert_to_bin(Y_test, 3))
Y_test[:, -1] = 1
X_valid = X_test[:25,]
Y_valid = Y_test[:25,]
X_test = X_test[25:,]
Y_test = Y_test[25:,]
D = Dummy()
D.info = {"metric": F1_METRIC, "task": MULTILABEL_CLASSIFICATION, "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=["extra_trees"], include_preprocessors=["no_preprocessing"]
)
err = np.zeros([N_TEST_RUNS])
for i in range(N_TEST_RUNS):
print("Evaluate configuration: %d; result:" % i)
configuration = configuration_space.sample_configuration()
D_ = copy.deepcopy(D)
evaluator = HoldoutEvaluator(D_, configuration)
if not self._fit(evaluator):
continue
err[i] = evaluator.predict()
print(err[i])
self.assertTrue(np.isfinite(err[i]))
self.assertGreaterEqual(err[i], 0.0)
print("Number of times it was worse than random guessing:" + str(np.sum(err > 1)))
