def sample_data_list()

in src/nli/training.py [0:0]

• 16 lines of code
• 6 McCabe index (conditional complexity)

def sample_data_list(d_list, ratio):
    if ratio <= 0:
        raise ValueError("Invalid training weight ratio. Please change --train_weights.")
    upper_int = int(math.ceil(ratio))
    if upper_int == 1:
        return d_list # if ratio is 1 then we just return the data list
    else:
        sampled_d_list = []
        for _ in range(upper_int):
            sampled_d_list.extend(copy.deepcopy(d_list))
        if np.isclose(ratio, upper_int):
            return sampled_d_list
        else:
            sampled_length = int(ratio * len(d_list))
            random.shuffle(sampled_d_list)
            return sampled_d_list[:sampled_length]