def prepare(self, testSet, rand, numSamples=20, samplePercent=50):
"""Prepares for bootstrap estimation of lift of one ANN.
@param testSet: test set on which to test the ANN
@type testSet: input.DataSet
@param rand: source of randomness for bootstrap samples
@type rand: random.Random
@param numSamples: number of bootstrap samples
@type numSamples: int
@param samplePercent: size of each sample, in percent of the testSet
@type samplePercent: int
"""
self.sampleSets = []
self.anns = []
for sampleIndex in range(numSamples):
sampleSet = testSet.sample(samplePercent, rand)
self.sampleSets.append(sampleSet)
a = ANN()
a.prepare(sampleSet, popSize=1)
self.anns.append(a)
self.sampleSize = self.sampleSets[0].size
def main():
import input
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
np.set_printoptions(precision=3, edgeitems=3, threshold=20)
random.seed(80085) # used by the GA
randSample = random.Random(input.SAMPLE_SEED) # used for data set sampling
inp = input.Input("train3-std.tsv", randSample)
print "Train set:",
inp.trainSet.show()
print "Test set:",
inp.testSet.show()
n = inp.trainSet.size * 20/100
a = ANN()
a.prepare(inp.trainSet, POPSIZE)
tester = SampleTester()
tester.prepare(inp.testSet, randSample)
tester.showSampleSets()
params = []
generatePop(params)
for genIndex in range(TOTAL_GENS):
print "Generation", genIndex, "starting."
logFP("Population", params)
outputValues = a.evaluate(params, returnOutputs=True)
logFP("Outputs", outputValues)
thresholds = a.nlargest(n)
logFP("Thresholds", thresholds)
lifts = a.lift(n)
logFP("Lifts", lifts)
taggedParams = sorted(zip(lifts, params, range(len(params))),
key=lambda (l, p, i): l,
reverse=True)
sortedParams = [p for l, p, i in taggedParams]
logFP("Sorted pop", sortedParams)
testLift, _ = tester.test(sortedParams[0])
genplot.addGeneration(lifts, testLift, genIndex)
mutateValue = 1.0
params = generateGeneration(sortedParams, mutateValue)
args = sys.argv[1:]
if len(args) == 1:
open(args[0], "w").write(repr(sortedParams[0]))
genplot.plot()
