Optimization: Picking random keys from a dictionary and mutatingvalues

Discussion in 'Python' started by blaine, May 29, 2008.

  1. blaine

    blaine Guest

    Hey everyone,
    Just a friendly question about an efficient way to do this. I have
    a graph with nodes and edges (networkx is am amazing library, check it
    out!). I also have a lookup table with weights of each edge. So:

    weights[(edge1, edge2)] = .12
    weights[(edge2, edge5)] = .53
    weights[(edge5, edge1)] = 1.23
    weights[(edge3, edge2)] = -2.34

    I would like to take this weight table and subject it to evolutionary
    mutations. So given a probability p (mutation rate), mutate edge
    weights by some random value. So in effect, if p is .25, choose 25%
    random edges, and mutate them some amount. So:
    1. Whats a good way to get the keys? I'm using a loop with
    random.choice() at the moment.
    2. Any comments on how to get a 'fair' mutation on an existing edge

    I currently am doing something like this, which seems like it leaves
    something to be desired.

    import random
    weights = generateweights() # generates table like the one above
    p = 0.25
    v = random.betavariate(2, 10)
    num = int(v*len(weights)) # How many weights should we mutate?
    keys = w.keys()
    for i in xrange(num):
    val = random.choice(keys) # Choose a single random key
    w[val] = w[val]*(random.random()*5-1) # Is this a 'good' way to
    'mutate' the value?

    This is an evolutionary search, so mutate in this sense relates to a
    genetic algorithm, perhaps a gradient decent?
    blaine, May 29, 2008
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  2. Why not keep a separate list of keys and use random.sample()?

    Raymond Hettinger, May 29, 2008
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  3. blaine

    Carl Banks Guest

    Friendly advance reminder: in many optimization problems, the
    objective function is far more expensive to calculate than the
    optimizing procedure. Your time is usually better spent optimizing
    the objective function, or tuning the optimizer.

    But what they hey.
    That's probably the best way.

    You might be able to squeeze a little more speed out of it by using a
    partial random shuffle as opposed to removing the item from the list.
    This will minimize the amount of copying. Here's an example (without
    error checking):

    def partial_random_shuffle(lst,nshuffled):
    for i in xrange(nshuffled):
    j = random.randrange(i,len(lst))
    t = lst
    lst = lst[j]
    lst[j] = t

    The first nshuffled items of lst upon return will be the selected
    items. Note: This might also be slower than removing items. It
    should scale better, though.

    random.normalvariate is usually a good choice for selecting random
    real-valued increments.

    val = random.normalvariate(val,sigma)

    where sigma, the standard deviation, is the amount

    If you don't remove keys[val], there's a chance you'll mutate the same
    key twice, which I doubt is what you want.

    A gradient descent method is not an evolutionary search and involves
    no randomness (unless noise is added to the objective, which is a
    possible way to attack a function with unimportant small scale
    features, and in that case normalvariate would be the thing used).

    Carl Banks
    Carl Banks, May 29, 2008
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