def test_CC ():
nac = np.allclose
n = 1000
r = 100
nt = 200
rcross = 0
X,Y = createFakeData3(n, r, nt, rcross)
num_eval = 50
pi = sum(Y)/len(Y)
init_pt = 5
# import IPython
# IPython.embed()
A = X.T.dot(X)
t1 = time.time()
verbose = True
prms = ASI.Parameters(pi=pi,sparse=False, verbose=verbose)
kAS = ASI.kernelAS(prms)
kAS.initialize(X)
sAS = ASI.shariAS(prms)
sAS.initialize(A)
#sAS2 = ASI.naiveShariAS(prms)
kAS.firstMessage(init_pt)
sAS.firstMessage(init_pt)
# fs2 = [kAS.f]
for i in range(num_eval):
idx1 = kAS.getNextMessage()
kAS.setLabelCurrent(Y[idx1])
# init_lbls[idx1] = Y[idx1]
idx2 = sAS.getNextMessage()
sAS.setLabelCurrent(Y[idx2])
print('NEXT')
print idx1==idx2
print nac(kAS.f, sAS.f)
# fs2.append(kAS.f)
# fs3.append(sAS.f)
import IPython
IPython.embed()
activeSearch = None
# when firstMessage is called we reinitialize the kernel algorithm. However calling
# initialize again requires us to invert C so we could be smarter and save that
# For now the invert time is a couple of seconds so we can do that as future work
restart_save = None
first_run = True
if (args.method == "kernel"):
print "Using kernelAS"
activeSearch = asI.kernelAS()
wMat = dataConn.getFinalFeatureMatrix(args.wordlimit,args.skip_stemmer, args.num_cpus, message_count, args.out_to_database, args.in_from_database, 0,0)
restart_save = wMat.copy()
activeSearch.initialize(wMat)
elif (args.method == "shari"):
print "Using shariAS"
activeSearch = asI.shariAS()
A = dataConn.getAffinityMatrix(args.wordlimit,args.skip_stemmer,args.num_cpus, message_count, args.out_to_database, args.in_from_database, 0,0)
# Feeding in the dense version to shari's code because the sparse version is not implemented
activeSearch.initialize(np.array(A.todense()))
elif (args.method == "naiveshari"):
print "Using naieveShariAS"
activeSearch = asI.naiveShariAS()
A = dataConn.getAffinityMatrix(args.wordlimit,args.skip_stemmer,args.num_cpus, message_count, args.out_to_database, args.in_from_database, 0,0)
# Feeding in the dense version to shari's code because the sparse version is not implemented
activeSearch.initialize(np.array(A.todense()))
else:
print "Invalid method argument. See help (run with -h)"
sys.exit()
# track the message ID that we're currently presenting the user for evaluation
currentMessage = -1
def test_warm_start ():
verbose = True
nac = np.allclose
#ts_data = ef.load_timestamps (tsfile)
Xfull = load_sparse_csr('Xfull1.npz')
# print Xfull.shape
# Xfull = Xfull[np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=1))[0])),:]
# Xfull = Xfull[:,np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=0))[1]))]
# # r,n = Xfull.shape
# print Xfull.shape
# Xfull = Xfull[np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=1))[0])),:]
# Xfull = Xfull[:,np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=0))[1]))]
# getting rid of features which are zero for all these elements
n = 300
r = 600
X = Xfull[:,:n]
X = X[np.squeeze(np.array(np.nonzero(X.sum(axis=1))[0])),:]
X = X[:,np.squeeze(np.array(np.nonzero(X.sum(axis=0))[1]))]
X = X[:r,:]
X = X[np.squeeze(np.array(np.nonzero(X.sum(axis=1))[0])),:]
X = X[:,np.squeeze(np.array(np.nonzero(X.sum(axis=0))[1]))]
print X.shape
#X = np.load('X11.npy')
r,n = X.shape
nt = int(0.05*n)
num_eval = 50
Y = np.array([1]*nt + [0]*(n-nt), dtype=int)
nr.shuffle(Y)
pi = sum(Y)/len(Y)
init_pt = 5
# import IPython
# IPython.embed()
A = np.array((X.T.dot(X)).todense())
t1 = time.time()
prms = ASI.Parameters(pi=pi,sparse=True, verbose=verbose)
kAS = ASI.kernelAS(prms)
kAS.initialize(X)
kAS2 = ASI.kernelAS(prms)
sAS = ASI.shariAS(prms)
sAS2 = ASI.naiveShariAS(prms)
# import IPython
# IPython.embed()
init_lbls = {init_pt:1}
kAS.firstMessage(init_pt)
fs2 = [kAS.f]
for i in range(num_eval):
idx1 = kAS.getNextMessage()
kAS.setLabelCurrent(Y[idx1])
init_lbls[idx1] = Y[idx1]
# sAS.setLabelCurrent(Y[idx2])
# fs2.append(kAS.f)
# fs3.append(sAS.f)
print("Batch initializing:")
print("Kernel AS:")
kAS2.initialize(X, init_lbls)
print("Shari AS:")
sAS.initialize(A, init_lbls)
print("Naive Shari AS:")
sAS2.initialize(A, init_lbls)
import IPython
IPython.embed()
def test_interface3 ():
verbose = True
nac = np.allclose
#ts_data = ef.load_timestamps (tsfile)
Xfull = load_sparse_csr('Xfull1.npz')
print Xfull.shape
Xfull = Xfull[np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=1))[0])),:]
Xfull = Xfull[:,np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=0))[1]))]
# r,n = Xfull.shape
print Xfull.shape
Xfull = Xfull[np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=1))[0])),:]
Xfull = Xfull[:,np.squeeze(np.asarray(np.nonzero(Xfull.sum(axis=0))[1]))]
# getting rid of features which are zero for all these elements
# n = 300
# r = 600
X = Xfull#[:,:n]
# X = X[np.squeeze(np.array(np.nonzero(X.sum(axis=1))[0])),:]
# X = X[:,np.squeeze(np.array(np.nonzero(X.sum(axis=0))[1]))]
# X = X[:r,:]
# X = X[np.squeeze(np.array(np.nonzero(X.sum(axis=1))[0])),:]
# X = X[:,np.squeeze(np.array(np.nonzero(X.sum(axis=0))[1]))]
# print X.shape
# #X = np.load('X11.npy')
r,n = X.shape
nt = int(0.05*n)
num_eval = 50
Y = np.array([1]*nt + [0]*(n-nt), dtype=int)
pi = sum(Y)/len(Y)
init_pt = 5
# import IPython
# IPython.embed()
A = np.array((X.T.dot(X)).todense())
t1 = time.time()
prms = ASI.Parameters(pi=pi,sparse=True, verbose=verbose)
kAS = ASI.kernelAS(prms)
kAS.initialize(X)
sAS = ASI.shariAS(prms)
sAS.initialize(A)
# ofk = kAS.f
# ofs = sAS.f
# import IPython
# IPython.embed()
kAS.firstMessage(init_pt)
fs2 = [kAS.f]
sAS.firstMessage(init_pt)
fs3 = [sAS.f]
# #
# lbl = 1
# idx = 5
# B = np.ones(n)/(1+prms.w0)
# D = A.sum(axis=1)
# BDinv = np.diag(np.squeeze(B*1./D))
# IA = np.eye(n) - BDinv.dot(A)
# IAi = np.matrix(nlg.inv(IA))
# IAk = nlg.inv(np.eye(n) + kAS.BDinv.dot(X.T.dot(nlg.inv(np.eye(r) - X.dot(kAS.BDinv.dot(X.T))))).dot(X.todense()))
# IAki = nlg.inv(IAk)
# t = (1+prms.w0)*(1-prms.eta)
# e = np.zeros((n,1))
# e[idx] = 1
# IA2 = IA + (1-t)*e.dot(e.T).dot(BDinv.dot(A))
# ai = (1./D)[idx]/(1+ prms.w0)*A[idx,:]
# Ad = (1-t)*IAi[:,idx].dot(ai.dot(IAi))/(1 + (1-t)*ai.dot(IAi[:,idx]))
# IA2i = IAi - Ad
#
# import IPython
# IPython.embed()
for i in range(num_eval):
idx1 = kAS.getNextMessage()
idx2 = sAS.getNextMessage()
print('NEXT')
print idx1==idx2
print nac(kAS.f, sAS.f)
# import IPython
# IPython.embed()
kAS.setLabelCurrent(Y[idx1])
sAS.setLabelCurrent(Y[idx2])
fs2.append(kAS.f)
fs3.append(sAS.f)
t2 = time.time()
# f1,h1,s1,fs1,dtinv1 = AS.kernel_AS (Xfull, Y, pi=pi, num_eval=num_eval, init_pt=init_pt, verbose=verbose,all_fs=True,tinv=True,sparse=True)
t3 = time.time()
# checks = [np.allclose(fs1[i],fs2[i]) for i in range(len(fs1))]
import IPython
IPython.embed()
# initialize again requires us to invert C so we could be smarter and save that
# For now the invert time is a couple of seconds so we can do that as future work
restart_save = None
first_run = True
if (args.method == "kernel"):
print "Using kernelAS"
activeSearch = asI.kernelAS()
wMat = dataConn.getFinalFeatureMatrix(args.wordlimit, args.skip_stemmer,
args.num_cpus, message_count,
args.out_to_database,
args.in_from_database, 0, 0)
restart_save = wMat.copy()
activeSearch.initialize(wMat)
elif (args.method == "shari"):
print "Using shariAS"
activeSearch = asI.shariAS()
A = dataConn.getAffinityMatrix(args.wordlimit, args.skip_stemmer,
args.num_cpus, message_count,
args.out_to_database, args.in_from_database,
0, 0)
# Feeding in the dense version to shari's code because the sparse version is not implemented
activeSearch.initialize(np.array(A.todense()))
elif (args.method == "naiveshari"):
print "Using naieveShariAS"
activeSearch = asI.naiveShariAS()
A = dataConn.getAffinityMatrix(args.wordlimit, args.skip_stemmer,
args.num_cpus, message_count,
args.out_to_database, args.in_from_database,
0, 0)
# Feeding in the dense version to shari's code because the sparse version is not implemented
activeSearch.initialize(np.array(A.todense()))
