maxlikely

def node_mean_avg_prec(G, node, predictions):
    '''
    Calculates mean average precision for a user.
    '''

    gold = set(G[node])
    indicator = [1 if p in gold else 0 for p in predictions]
    prec = np.cumsum(indicator)
    prec = [indicator[i] * p / float(i+1) for i, p in enumerate(prec)]

maxlikely

def is_permutation(first_string, second_string):
    '''
    checks if two strings are permutations of eachother
    '''
    from collections import Counter
    return Counter(first_string) == Counter(second_string)

maxlikely

def dicts_equal(first_dict, second_dict):
    '''
    check if two dicts are equal
    '''
    
    # same length, same keys, same values
    return  len(first) == len(second) and \
            all([k in first for k in second]) and \
            all([v == second[k] for k,v in first.items()])