def test_LightGBM(self):
train_data = lightgbm.Dataset(self.x_train, label=np.argmax(self.y_train, axis=1))
test_data = lightgbm.Dataset(self.x_test, label=np.argmax(self.y_test, axis=1))
parameters = {
"objective": "multiclass",
"num_class": self.n_classes,
"metric": "multi_logloss",
"is_unbalance": "true",
"boosting": "gbdt",
"num_leaves": 5,
"feature_fraction": 0.5,
"bagging_fraction": 0.5,
"bagging_freq": 0,
"learning_rate": 0.05,
"verbose": 0,
}
model = lightgbm.train(
parameters, train_data, valid_sets=test_data, num_boost_round=2, early_stopping_rounds=10
)
classifier = LightGBMClassifier(model=model)
rt = RobustnessVerificationTreeModelsCliqueMethod(classifier=classifier)
average_bound, verified_error = rt.verify(
x=self.x_test, y=self.y_test, eps_init=0.3, nb_search_steps=10, max_clique=2, max_level=2
)
self.assertEqual(average_bound, 0.07634765624999999)
self.assertEqual(verified_error, 0.85)
def setUpClass(cls):
master_seed(seed=1234)
super().setUpClass()
cls.y_train_iris_index = np.argmax(cls.y_train_iris, axis=1)
cls.y_test_iris_index = np.argmax(cls.y_test_iris, axis=1)
num_round = 10
param = {
"objective": "multiclass",
"metric": "multi_logloss",
"num_class": 3
}
train_data = lgb.Dataset(cls.x_train_iris,
label=cls.y_train_iris_index)
model = lgb.train(param,
train_data,
num_round,
valid_sets=[train_data])
cls.classifier = LightGBMClassifier(model=model)