Function test_randomized_search_cv

catboost/python-package/ut/medium/test.py:4054–4089

(task_type)

def test_randomized_search_cv(task_type):
    pool = Pool(TRAIN_FILE, column_description=CD_FILE)
    model = CatBoost(
        {
            "learning_rate": 0.03,
            "loss_function": "Logloss",
            "eval_metric": "AUC",
            "task_type": task_type,
            "gpu_ram_part": TEST_GPU_RAM_PART,
        }
    )

    class UniformChoice:

        def __init__(self, values):
            self.values = values

        def rvs(self):
            return np.random.choice(self.values)

    feature_border_type_list = ['Median', 'Uniform', 'UniformAndQuantiles', 'MaxLogSum']
    results = model.randomized_search(
        {
            'feature_border_type': feature_border_type_list,
            'one_hot_max_size': UniformChoice(list(range(20))),
            'iterations': UniformChoice([1, 2, 3]),
            'border_count': UniformChoice([10, 6, 20, 4])
        },
        pool,
        n_iter=2,
        search_by_train_test_split=False
    )
    assert results['params']['feature_border_type'] in feature_border_type_list
    assert results['params']['one_hot_max_size'] in range(20)
    assert results['params']['border_count'] in [10, 6, 20, 4]
    assert results['params']['iterations'] in [1, 2, 3]


def test_grid_search_with_class_weights_lists():