<jupyter_start><jupyter_text>Similarity Pairs with websitesYou are given a list of (website, user) pairs that represent users visiting websites. Come up with a program that identifies the top k pairs of websites with the greatest similarity.For example, suppose k = 1, and the list of tuples is:```[('a', 1), ('a', 3), ('a', 5), ('b', 2), ('b', 6), ('c', 1), ('c', 2), ('c', 3), ('c', 4), ('c', 5) ('d', 4), ('d', 5), ('d', 6), ('d', 7), ('e', 1), ('e', 3), ('e': 5), ('e', 6)]``` Then a reasonable similarity metric would most likely conclude that a and e are the most similar, so your program should return [('a', 'e')].<jupyter_code># what does similarity mean? 

def compute_similarity(a,b,visitors): 
    return len(visitors[a] & visitors[b]) / len(visitors[a] | visitors[b])

def top_pairs(log, k): 
    visitors = defaultdict(set) 
    for site, user in log: 
        visitors[site].add(user)
        
    pairs = [] 
    sites = list(visitors.keys())
    
    for _ in range(k): 
        heapq.heappush(pairs, (0, ('', '')))
    
    for i in range(len(sites) -1): 
        for j in range(i + 1, len(sites)): 
            score = compute_similarity(sites[i], sites[j], visitors)
            heapq.heappushpop(pairs, (score, (sites[i], sites[j])))
        
    return [pair[1] for pair in pairs]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18468468468468469, 'fraction_numerical': 0.017267267267267267, 'mean_word_length': 3.4581939799331103, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '242256', 'license': 'MIT', 'n_tokens_mistral': 488, 'n_tokens_neox': 447, 'n_words': 158, 'path': 'Daily/Similarity Pairs with Websites.ipynb', 'repo_name': 'jasonyum/python_go', 'size': '1332'}