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POHow to produce different random results with DEAP?
 

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  1. POHow to produce different random results with DEAP?
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    <p>I am using the DEAP library to maximize a metric, and I noticed that whenever I restart the algorithm (which is supposed to create a random list of binary values - 1s and 0s) it is producing the same initial values.</p> <p>I became suspicious and copied their <a href="https://code.google.com/p/deap/" rel="nofollow noreferrer">basic DEAP example here</a> and re-ran the algorithms again:</p> <pre><code>import array, random from deap import creator, base, tools, algorithms creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 10) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalOneMax(individual): return sum(individual), toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoints) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) population = toolbox.population(n=10) NGEN=40 for gen in range(NGEN): offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1) fits = toolbox.map(toolbox.evaluate, offspring) for fit, ind in zip(fits, offspring): ind.fitness.values = fit population = offspring </code></pre> <p>The code above is exactly their example, but with the population and individual size reduced to 10. I ran the algorithm 5 times and it produced exact copies of each other. I also added a print statement to get the below output:</p> <pre><code>&gt;python testGA.py [1, 0, 1, 0, 1, 0, 1, 1, 1, 1] Starting the Evolution Algorithm... Evaluating Individual: [0, 1, 0, 1, 0, 1, 1, 1, 1, 0] Evaluating Individual: [1, 1, 0, 1, 0, 1, 0, 1, 0, 0] Evaluating Individual: [0, 0, 1, 0, 0, 1, 1, 0, 0, 1] Evaluating Individual: [1, 0, 0, 0, 0, 0, 0, 0, 0, 0] Evaluating Individual: [0, 1, 1, 0, 1, 0, 1, 1, 0, 1] Evaluating Individual: [1, 0, 1, 1, 1, 0, 0, 1, 0, 0] Evaluating Individual: [0, 1, 0, 0, 0, 1, 0, 0, 0, 1] Evaluating Individual: [1, 1, 0, 1, 0, 1, 0, 1, 1, 1] Evaluating Individual: [1, 1, 1, 1, 0, 0, 1, 0, 0, 0] Evaluating Individual: [0, 0, 1, 1, 1, 1, 0, 1, 1, 1] </code></pre> <p>This output is generated every time I call the function - In that order. They are exactly identical.</p> <p>I have read that I shouldn't have to seed the random.randint function, and I tested it by writing a basic script that just prints out a list of 10 random ints ranged 0 to 1. This workd fine, it just seems to produce the same values when I feed it through DEAP.</p> <p>Is this normal? How can I ensure that, when I run the algorithm, I get different 'individuals' every time?</p> <p><strong>EDIT:</strong></p> <p>Sorry for the late reply, here is the full source I am using:</p> <pre><code>import random, sys from deap import creator, base, tools class Max(): def __init__(self): creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) INDIVIDUAL_SIZE = 10 self.toolbox = base.Toolbox() self.toolbox.register("attr_bool", random.randint, 0, 1) self.toolbox.register("individual", tools.initRepeat, creator.Individual, self.toolbox.attr_bool, n=INDIVIDUAL_SIZE) self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual) self.toolbox.register("mate", tools.cxTwoPoints) self.toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) self.toolbox.register("select", tools.selTournament, tournsize=3) self.toolbox.register("evaluate", self.evaluate) print self.main() def evaluate(self, individual): # Some debug code print 'Evaluating Individual: ' + str(individual) return sum(individual), def main(self): CXPB, MUTPB, NGEN = 0.5, 0.2, 40 random.seed(64) pop = self.toolbox.population(n=10) print "Starting the Evolution Algorithm..." fitnesses = list(map(self.toolbox.evaluate, pop)) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit # ---------------------------------------------------------- # Killing the program here - just want to see the population created sys.exit() print "Evaluated %i individuals" % (len(pop)) for g in range(NGEN): print "-- Generation %i --" % (g) # Select the next genereation individuals offspring = self.toolbox.select(pop, len(pop)) # Clone the selected individuals offspring = list(map(self.toolbox.clone, offspring)) # Apply crossover and mutation on the offspring for child1, child2 in zip(offspring[::2], offspring[1::2]): if random.random() &lt; CXPB: self.toolbox.mate(child1, child2) del child1.fitness.values del child2.fitness.values for mutant in offspring: if random.random() &lt; MUTPB: self.toolbox.mutate(mutant) del mutant.fitness.values # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = map(self.toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit print "\tEvaluated %i individuals" % (len(pop)) pop[:] = offspring fits = [ind.fitness.values[0] for ind in pop] length = len(pop) mean = sum(fits) / length sum2 = sum(x*x for x in fits) std = abs(sum2 / length - mean**2)**0.5 print "\tMin %s" % (min(fits)) print "\tMax %s" % (max(fits)) print "\tAvg %s" % (mean) print "\tStd %s" % (std) class R_Test: def __init__(self): print str([random.randint(0, 1) for i in range(10)]) if __name__ == '__main__': #rt = R_Test() mx = Max() </code></pre> <p>The R_Test class is there to test the random generation in Python. I read <a href="https://stackoverflow.com/questions/8423288/seed-and-random-numbers-in-python">here</a> that the seed is dynamically called if not given in Python, and I wanted to test this.</p> <p>How I have been executing the above code has been as such:</p> <pre><code>&gt; python testGA.py ... the 10 outputs &gt; python testGA.py ... the exact same outputs &gt; python testGA.py ... the exact same outputs &gt; python testGA.py ... the exact same outputs &gt; python testGA.py ... the exact same outputs </code></pre> <p>Obviously 5 times isn't exactly a strenuous test, but the fact that all 10 values are the same 5 times in a row raised a red flag.</p>
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    1. COThere don't seem to be any references to `random.seed` in the source.
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    2. COWhat happens if you explicitly call `random.seed()`? Do the values change at all?
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      1. USMartijn Pieters
    3. COI am one of DEAP lead developer. What you are observing is not normal, but I could not reproduce it using the code you provided. Could you put up the exact code that you used?
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