def objective(trial):
    (data, target) = sklearn.datasets.load_breast_cancer(return_X_y=True)
    (train_x, test_x, train_y, test_y) = train_test_split(data, target, test_size=0.25)
    dtrain = xgb.DMatrix(train_x, label=train_y)
    dtest = xgb.DMatrix(test_x, label=test_y)
    param = {'silent': 1, 'objective': 'binary:logistic', 'booster': trial.suggest_categorical('booster', ['gbtree', 'gblinear', 'dart']), 'lambda': trial.suggest_loguniform('lambda', 1e-08, 1.0), 'alpha': trial.suggest_loguniform('alpha', 1e-08, 1.0)}
    if ((param['booster'] == 'gbtree') or (param['booster'] == 'dart')):
        param['max_depth'] = trial.suggest_int('max_depth', 1, 9)
        param['eta'] = trial.suggest_loguniform('eta', 1e-08, 1.0)
        param['gamma'] = trial.suggest_loguniform('gamma', 1e-08, 1.0)
        param['grow_policy'] = trial.suggest_categorical('grow_policy', ['depthwise', 'lossguide'])
    if (param['booster'] == 'dart'):
        param['sample_type'] = trial.suggest_categorical('sample_type', ['uniform', 'weighted'])
        param['normalize_type'] = trial.suggest_categorical('normalize_type', ['tree', 'forest'])
        param['rate_drop'] = trial.suggest_loguniform('rate_drop', 1e-08, 1.0)
        param['skip_drop'] = trial.suggest_loguniform('skip_drop', 1e-08, 1.0)
    bst = xgb.train(param, dtrain)
    preds = bst.predict(dtest)
    pred_labels = np.rint(preds)
    accuracy = sklearn.metrics.accuracy_score(test_y, pred_labels)
    return (1.0 - accuracy)
def objective(trial):
    (data, target) = sklearn.datasets.load_breast_cancer(return_X_y=True)
    (train_x, test_x, train_y, test_y) = train_test_split(data, target, test_size=0.25)
    dtrain = xgb.DMatrix(train_x, label=train_y)
    dtest = xgb.DMatrix(test_x, label=test_y)
    param = {'silent': 1, 'objective': 'binary:logistic', 'booster': trial.suggest_categorical('booster', ['gbtree', 'gblinear', 'dart']), 'lambda': trial.suggest_loguniform('lambda', 1e-08, 1.0), 'alpha': trial.suggest_loguniform('alpha', 1e-08, 1.0)}
    if ((param['booster'] == 'gbtree') or (param['booster'] == 'dart')):
        param['max_depth'] = trial.suggest_int('max_depth', 1, 9)
        param['eta'] = trial.suggest_loguniform('eta', 1e-08, 1.0)
        param['gamma'] = trial.suggest_loguniform('gamma', 1e-08, 1.0)
        param['grow_policy'] = trial.suggest_categorical('grow_policy', ['depthwise', 'lossguide'])
    if (param['booster'] == 'dart'):
        param['sample_type'] = trial.suggest_categorical('sample_type', ['uniform', 'weighted'])
        param['normalize_type'] = trial.suggest_categorical('normalize_type', ['tree', 'forest'])
        param['rate_drop'] = trial.suggest_loguniform('rate_drop', 1e-08, 1.0)
        param['skip_drop'] = trial.suggest_loguniform('skip_drop', 1e-08, 1.0)
    bst = xgb.train(param, dtrain)
    preds = bst.predict(dtest)
    pred_labels = np.rint(preds)
    accuracy = sklearn.metrics.accuracy_score(test_y, pred_labels)
    return (1.0 - accuracy)