def runActualTest(points):
actual = True
print 'RUNNING PREDICTION: ', ['Actual' if actual else 'Test']
points = '33.46503917 26.76995952 85.97458381 82.61217489 59.33400217 49.29506539'.split(
)
#points = '5 51 4 73 55 36'.split()
points = map(float, points)
#points = np.array(zip(points[::2],points[1::2]))
print points
testTimes = pd.read_csv('test_times.csv')
tags = generatetest.listTags()
if actual:
trainTags = tags[0:100]
else:
tags = tags[0:100]
trainTags = tags[0:10]
print 'Training Tags: ', trainTags
print 'Test Tags: ', tags
optFunc = makeOptFunc(testTimes, trainTags, tags, visualise=True)
result = optFunc(np.array([points]))
print result
quit()
def runActualTest(points):
actual = True
print 'RUNNING PREDICTION: ', ['Actual' if actual else 'Test']
points = '33.46503917 26.76995952 85.97458381 82.61217489 59.33400217 49.29506539'.split()
#points = '5 51 4 73 55 36'.split()
points = map(float, points)
#points = np.array(zip(points[::2],points[1::2]))
print points
testTimes = pd.read_csv('test_times.csv')
tags = generatetest.listTags()
if actual:
trainTags = tags[0:100]
else:
tags = tags[0:100]
trainTags = tags[0:10]
print 'Training Tags: ', trainTags
print 'Test Tags: ', tags
optFunc = makeOptFunc(testTimes, trainTags, tags, visualise=True)
result = optFunc(np.array([points]))
print result
quit()
print 'Training Tags: ', trainTags
print 'Test Tags: ', tags
optFunc = makeOptFunc(testTimes, trainTags, tags, visualise=True)
result = optFunc(np.array([points]))
print result
quit()
if __name__ == '__main__':
if len(sys.argv) > 1:
runActualTest(sys.argv[1:])
testTimes = pd.read_csv('test_times.csv')
tags = generatetest.listTags()[0:100]
trainTags, testTags = generatetest.splitTags(tags, proportion=0.1)
print 'Training Tags: ', trainTags
print 'Test Tags: ', tags
acquisition_par = 0.01
max_iter = 10
bounds = [(0, 100)] * 6
optFunc = makeOptFunc(testTimes, trainTags, tags, visualise=True)
bOpt = GPyOpt.methods.BayesianOptimization(optFunc,
bounds=bounds,
acquisition='LCB',
acquisition_par=acquisition_par)
bOpt.run_optimization(max_iter,
print 'Training Tags: ', trainTags
print 'Test Tags: ', tags
optFunc = makeOptFunc(testTimes, trainTags, tags, visualise=True)
result = optFunc(np.array([points]))
print result
quit()
if __name__ == '__main__':
if len(sys.argv) > 1:
runActualTest(sys.argv[1:])
testTimes = pd.read_csv('test_times.csv')
tags = generatetest.listTags()[0:100]
trainTags, testTags = generatetest.splitTags(tags, proportion=0.1)
print 'Training Tags: ', trainTags
print 'Test Tags: ', tags
acquisition_par = 0.01
max_iter = 10
bounds = [(0, 100)] * 6
optFunc = makeOptFunc(testTimes, trainTags, tags, visualise=True)
bOpt = GPyOpt.methods.BayesianOptimization(optFunc,
bounds=bounds,
acquisition='LCB',
acquisition_par=acquisition_par)
