def test_condMVLocal(self):
nsamples = 5
m=2
initbids = [3,3]
samples = numpy.zeros((nsamples,m))
samples[:,0] = numpy.arange(nsamples)
samples[:,1] = 2
bundles = listBundles(m)
l = 1
v = [45,20]
revenue = msListRevenue(bundles, v, l)
print bundles
print revenue
brd = {}
for b,r in zip(bundles,revenue):
brd[tuple(b)] = r
newbid = condMVLocalUpdate(brd, initbids, 0, samples, True)
print newbid
bids = condMVLocal(bundles,revenue, initbids, samples)
print bids
def test_expectedSurplus(self):
"""
[20,15] -> (45 - 35)*.1
[20,20] -> (45 -40)*.4
[30,15] -> (20 - 5)*.1
[30,20] -> (20 - 20)*.4
Expected Surplus = 3.5
"""
samples = numpy.zeros((1000,2))
samples[:100,:] = numpy.asarray([20,15])
samples[100:500,:] = numpy.asarray([20,20])
samples[500:600,:] = numpy.asarray([30,15])
samples[600:,:] = numpy.asarray([30,20])
m=2
l = 1
v = [45,20]
bundles = listBundles(m)
revenue = msListRevenue(bundles, v, l)
bids = numpy.asarray([25.,25.])
bundleRevenueDict = {}
for b,r in zip(bundles,revenue):
bundleRevenueDict[tuple(b)] = r
numpy.testing.assert_equal(expectedSurplus_(bundleRevenueDict, bids, samples),
3.5,'test_expetedSurplus failed.',True)
def test1(self):
pp = [5,5]
l = 1
v = [20,10]
bundles = listBundles(2)
rev = msListRevenue(bundles, v, l)
bid = targetPrice(bundles, rev, pp, True)
numpy.testing.assert_array_equal(bid, [5,0], "Error: targetPrice test1 failed.")
def setUp(self):
self.samples2d = numpy.atleast_2d([[1,1], [20,3], [10,7], [15,5],
[10,30], [15,26],[17,33], [2,40],
[30,30], [27,42],[30,38], [29,40],
[42,20], [38,7], [40,15], [33,10]])
self.bundles2d = listBundles(2)
self.l2d = 1
self.v2d = [20,10]
self.revenue2d = msListRevenue(self.bundles2d, self.v2d, self.l2d)
def main():
desc="Compute jointLocal bids given v's, l's initial bids and parameters"
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('-s','--samplesFile',dest='samplesFile',
required=True,type=str,
help='Samples (text file)')
parser.add_argument('-v','--vfile',dest='vfile',
required=True,type=str,
help='Valuation v vector txt file.')
parser.add_argument('-l','--lfile',dest='lfile',
required=True,type=str,
help='Valuation lambda txt file.')
parser.add_argument('-ib','--initBidFile',dest='initBidFile',
required=True,type=str,
help='Initial Bids txt file.')
parser.add_argument('-es','--evalSamples',dest='evalFile',
required=True,type=str,
help='Evaluation samples file.')
parser.add_argument('-o','--odir',dest='odir',
required=True,type=str,
help='Output directory.')
parser.add_argument('-eps','--eps',dest='eps',
required=False,default=1,type=float,
help='Eps condLocal parameter')
parser.add_argument('-mi','--maxitr', dest='maxitr',
required=False, default=100, type=int,
help='Maximum Update Iterations')
parser.add_argument('-t','--tol',dest='tol',
required=False, default=1e-5,
type=float,help='L2 Update Stopping Tolerance')
parser.add_argument('--verbose',dest='verbose',
required=False,default=False,
type=bool,help='Output debugg information')
args = parser.parse_args()
args.odir = os.path.realpath(args.odir)
jointSamples = numpy.loadtxt(os.path.realpath(args.samplesFile))
initBids = numpy.loadtxt(os.path.realpath(args.initBidFile))
vmat = numpy.loadtxt(os.path.realpath(args.vfile))
lmat = numpy.loadtxt(os.path.realpath(args.lfile))
evalSamples = numpy.loadtxt(os.path.realpath(args.evalFile))
m = initBids.shape[1]
bundles = listBundles(m)
bids = numpy.zeros(initBids.shape)
es = numpy.zeros(initBids.shape[0])
for itr, initBid, v, l in zip(xrange(vmat.shape[0]),initBids,vmat,lmat):
print 'iteration {0}'.format(itr)
revenue = msListRevenue(bundles,v,l)
bids[itr,:] = \
condLocal(bundles,revenue,
initBid,jointSamples,
maxItr = args.maxitr,
tol = args.tol,
verbose = args.verbose,
ret = 'bids',
eps = args.eps)
brd = {}
for b,r in zip(bundles,revenue):
brd[tuple(b)] = r
es[itr] = expectedSurplus_(brd, bids[itr,:], evalSamples)
numpy.savetxt(os.path.join(args.odir,'condLocalBids.txt'), bids)
numpy.savetxt(os.path.join(args.odir,'condLocalExpectedSurplus.txt'),es)
with open(os.path.join(args.odir,'condLocalStats.txt'),'w') as f:
print >> f, numpy.mean(es)
print >> f, numpy.var(es)
def test_jointLocalMc1(self):
"""
Updates computed by hand given:
v = [45,20], l = 1
p(q_1 = 20) = 0.5
p(q_1 = 30) = 0.5
p(q_2 = 15) = 0.2
p(q_2 = 20) = 0.8
Under independent prices p(q_1,q_2) = p(q_1)p(q_2)
Hence:
p(q_1 = 20, q_2 = 15) = 0.1
p(q_1 = 20, q_2 = 20) = 0.4
p(q_1 = 30, q_2 = 15) = 0.1
p(q_1 = 30, q_2 = 20) = 0.4
The joint pdf is represented with 1000 samples.
100 samples of [20,15]
400 samples of [20,20]
100 samples of [30,15]
400 samples of [30,20]
Ground Truth Anwers:
Starting at [25,25]
1.1) b1 <- 25
1.2) b2 <- 10
2.1) b1 <- 45
2.2) b2 <- 0
3.1) b1 <- 25
3.2) b2 <- 0
Therefore, starting at [25,25] converges to [45,0] after 3 iterations
"""
samples = numpy.zeros((1000,2))
samples[:100,:] = numpy.asarray([20,15])
samples[100:500,:] = numpy.asarray([20,20])
samples[500:600,:] = numpy.asarray([30,15])
samples[600:,:] = numpy.asarray([30,20])
m=2
l = 1
v = [45,20]
bundles = listBundles(m)
revenue = msListRevenue(bundles, v, l)
bids = numpy.asarray([25.,25.])
bids[0] = jointLocalUpdateMc(bundles,revenue,bids,0,samples,True)
print bids
numpy.testing.assert_equal(bids[0], 25, "Update 1.1 Failed", True)
bids[1] = jointLocalUpdateMc(bundles,revenue,bids,1,samples,True)
print bids
numpy.testing.assert_equal(bids[1], 10, "Update 1.2 Failed", True )
bids[0] = jointLocalUpdateMc(bundles,revenue,bids,0,samples,True)
print bids
numpy.testing.assert_equal(bids[0], 45, "Update 2.1 Failed", True)
bids[1] = jointLocalUpdateMc(bundles,revenue,bids,1,samples,True)
print bids
numpy.testing.assert_equal(bids[1], 0, "Update 2.2 Failed", True)
bids[0] = jointLocalUpdateMc(bundles,revenue,bids,0,samples,True)
print bids
numpy.testing.assert_equal(bids[0], 45, "Update 3.1 Failed", True)
bids[1] = jointLocalUpdateMc(bundles,revenue,bids,1,samples,True)
print bids
numpy.testing.assert_equal(bids[1], 0, "Update 3.2 Failed", True)
bids = numpy.asarray([25.,25.])
bids, converged, itr, tol = jointLocalMc(bundles, revenue, bids, samples, verbose=True,ret='all')
print bids
print converged
print itr
print tol
def test_margLocalUpdate1(self):
"""
Updates computed by hand given:
v = [45,20], l = 1
p(q_1 = 20) = 0.5
p(q_1 = 30) = 0.5
p(q_2 = 15) = 0.2
p(q_2 = 20) = 0.8
Under independent prices p(q_1,q_2) = p(q_1)p(q_2)
Hence:
p(q_1 = 20, q_2 = 15) = 0.1
p(q_1 = 20, q_2 = 20) = 0.4
p(q_1 = 30, q_2 = 15) = 0.1
p(q_1 = 30, q_2 = 20) = 0.4
The joint pdf is represented with 1000 samples.
100 samples of [20,15]
400 samples of [20,20]
100 samples of [30,15]
400 samples of [30,20]
Ground Truth Anwers:
Starting at [25,25]
1.1) b1 <- 25
1.2) b2 <- 10
2.1) b1 <- 45
2.2) b2 <- 0
3.1) b1 <- 25
3.2) b2 <- 0
Therefore, starting at [25,25] converges to [45,0] after 3 iterations
"""
samples = numpy.zeros((1000,2))
samples[:100,:] = numpy.asarray([20,15])
samples[100:500,:] = numpy.asarray([20,20])
samples[500:600,:] = numpy.asarray([30,15])
samples[600:,:] = numpy.asarray([30,20])
m=2
l = 1
v = [45,20]
bundles = listBundles(m)
revenue = msListRevenue(bundles, v, l)
bids = numpy.asarray([25.,25.])
bids[0] = margLocalUpdate(bundles,revenue,bids,0,samples,True)
numpy.testing.assert_equal(bids[0], 25, "Update 1.1 Failed", True)
bids[1] = margLocalUpdate(bundles,revenue,bids,1,samples,True)
numpy.testing.assert_equal(bids[1], 10, "Update 1.2 Failed", True )
bids[0] = margLocalUpdate(bundles,revenue,bids,0,samples,True)
numpy.testing.assert_equal(bids[0], 45, "Update 2.1 Failed", True)
bids[1] = margLocalUpdate(bundles,revenue,bids,1,samples,True)
numpy.testing.assert_equal(bids[1], 0, "Update 2.2 Failed", True)
bids[0] = margLocalUpdate(bundles,revenue,bids,0,samples,True)
numpy.testing.assert_equal(bids[0], 45, "Update 3.1 Failed", True)
bids[1] = margLocalUpdate(bundles,revenue,bids,1,samples,True)
numpy.testing.assert_equal(bids[1], 0, "Update 3.2 Failed", True)
#should converge after 3 iterations
bids = numpy.asarray([25.,25.])
bids, converged, itr, tol = margLocal(bundles, revenue, bids, samples, ret = 'all')
numpy.testing.assert_array_equal(bids, numpy.asarray([45,0]), "margLocal bids test failed", True)
numpy.testing.assert_equal(converged, True, "margLocal converged failed", True)
numpy.testing.assert_equal(itr,3,"margLocal number of iterations failed.", True)
numpy.testing.assert_almost_equal(tol, 0., 8, "margLocal tol failed", True)