Compare sampling algorithms

collect_collisions.py

def collect_collisions(arr, sample_size, n_samples, 
                       fast=False):
    """Collect total number of collisions made for each sample of arr.
    
    Parameters
    ----------
    arr: np.array
        array to sample from.
    sample_size: int
        sample size.
    n_samples: int
        number of samples to take.
    fast: bool
        use sampling optimized for fast consecutive samples 
        from the same array.
    
    Returns
    -------
    collisions: np.array
        collision number for each sample taken
    """
    samples = collect_samples(arr, 
                              sample_size,
                              n_samples,
                              fast=fast).flatten()
    
    # count collisions for *all* numbers we have sampled
    counts = Counter(samples)
    collision_sum = sum([count for k, count in counts.items() if count > 1])
    
    return collision_sum


n = 10000

arr = np.array([i for i in range(n)])
# copy arrays so all experiments will be isolated in terms of array shuffling 
arr_fast = arr.copy()

choice_num = []
fast_num = []
for i in tqdm(range(0, 500), desc="Collecting sample statistics"):
    collisions = collect_collisions(arr, 1000, 10, fast=False)
    collisions_fast = collect_collisions(arr_fast, 1000, 10, fast=True)
    choice_num.append(collisions)
    fast_num.append(collisions_fast)