def main():
import input
import random
logging.basicConfig(level=logging.DEBUG)
np.set_printoptions(precision=3, edgeitems=3, threshold=20)
randSample = random.Random(input.SAMPLE_SEED)
a = ANN()
inp = input.Input("train3.tsv", randSample)
popSize = 10
timefun(a.prepare, inp.trainSet, popSize)
params = []
for paramsIndex in range(popSize):
p = Parameters()
for i in range(19):
p.ih[0][i] = 0.0
p.ih[1][i] = p.ih[2][i] = p.ih[3][i] = 0.0
p.c[0][i] = 0.0
p.c[1][i] = p.c[2][i] = p.c[3][i] = 0.0
p.ih[0][0] = 1.0
p.ih[1][11] = 1.0
p.w[0] = -1e-0
p.w[1] = -1.0 / (10.0 ** linterp(0, 4, float(paramsIndex) / popSize))
p.w[2] = p.w[3] = 0.0
p.ho[0] = 1.0
p.ho[1] = 0.0 #-1.0
p.ho[2] = p.ho[3] = 0.0
params.append(p)
outputValues = timefun(a.evaluate, params, True)
n = inp.trainSet.size * 20/100
thresholds = timefun(a.nlargest, n)
lifts = timefun(a.lift, n)
print "Lifts:", lifts
def benchImpl(impl):
a = impl.ANN()
a.prepare(trainSet, len(population))
def benchmarkee():
a.evaluate(population, returnOutputs=False)
a.nlargest(n)
a.lift(n)
avg, std = bench.timefun(benchmarkee, repeat=10, stats=True)
sys.stdout.write("%12.01f" % (avg * 1000.0))
def main():
import input
logging.basicConfig(level=logging.DEBUG)
np.set_printoptions(precision=3, edgeitems=3, threshold=10)
# don't clutter the screen with details
logging.getLogger("ann").setLevel(logging.ERROR)
fullTrainSet = list(input.Input("train.tsv"))
popSizes = [10, 100, 1000]
trainSizes = [90, 900, 9000, 90000]
benchParams = list(product(enumerate(popSizes), enumerate(trainSizes)))
benchGpuAvg = np.zeros((len(popSizes), len(trainSizes)), np.float32)
benchGpuStd = np.zeros_like(benchGpuAvg)
benchCpuAvg = np.zeros_like(benchGpuAvg)
benchCpuStd = np.zeros_like(benchGpuAvg)
for index, ((popIndex, popSize), (trainIndex, trainSize)) in enumerate(benchParams):
log.info("benchmarking %.01f%% popSize %d trainSize %d",
100.0 * index / len(benchParams), popSize, trainSize)
a = ann.ANN()
trainSet = fullTrainSet[:trainSize]
assert len(trainSet) == trainSize
a.prepare(trainSet, popSize)
params = [generateParams(linterp(1e-4, 1e-5, float(index) / popSize))
for index in range(popSize)]
a.evaluate(params)
n = len(trainSet) * 20/100
avg, std = stats = timefun(a.nlargest, n, repeat=10, stats=True)
benchGpuAvg[popIndex, trainIndex] = avg
benchGpuStd[popIndex, trainIndex] = std
outputs = a.evaluate(params, returnOutputs=True)
avg, std = stats = timefun(nlargest_cpu, a, n, repeat=10, stats=True)
benchCpuAvg[popIndex, trainIndex] = avg
benchCpuStd[popIndex, trainIndex] = std
