def Get_RHS(self,Regu):
grad = []
inter = self.tenpy.TTTP(self.Omega, self.A)
ctf.Sparse_exp(inter)
ctf.Sparse_add(inter,self.T,alpha=-1)
#inter = self.T - inter
for i in range(len(self.A)):
lst_mat = self.A[:]
lst_mat[i] = self.tenpy.zeros(self.A[i].shape)
self.tenpy.MTTKRP(inter,lst_mat,i)
grad.append(lst_mat[i]-Regu*self.A[i])
ctf.Sparse_add(inter,self.T,alpha=-1)
return grad,inter
def matvec(self, regu, delta):
N = len(self.A)
ret = []
lst_mat = self.A[:]
lst_mat[0] = delta[0].copy()
inter = self.tenpy.TTTP(self.Omega, lst_mat)
for n in range(1, N):
lst_mat = self.A[:]
lst_mat[n] = delta[n].copy()
M = self.tenpy.TTTP(self.Omega, lst_mat)
ctf.Sparse_add(inter, M)
lst_mat = self.A[:]
lst_mat[0] = self.tenpy.zeros(self.A[0].shape)
self.tenpy.MTTKRP(inter, lst_mat, 0)
ret.append(self.tenpy.zeros(self.A[0].shape))
ret[0] += lst_mat[0]
ret[0] += regu * delta[0]
for n in range(1, N):
ret.append(self.tenpy.zeros(self.A[n].shape))
lst_mat = self.A[:]
lst_mat[n] = self.tenpy.zeros(self.A[n].shape)
self.tenpy.MTTKRP(inter, lst_mat, n)
ret[n] += lst_mat[n]
ret[n] += regu * delta[n]
return ret
def Get_Num(self, num, r, regu, M):
#The gradient of the loss function is Mttkrp(e^m - x) ............... Need negative of this
lst_mat = []
for j in range(len(self.A)):
lst_mat.append(self.A[j][:, r])
#inter = subtract_sparse(self.T,M)
ctf.Sparse_add(M, self.T, alpha=-1)
lst_mat[num] = self.tenpy.zeros(self.A[num].shape[0])
self.tenpy.MTTKRP(M, lst_mat, num)
grad = lst_mat[num] - regu * self.A[num][:, r]
ctf.Sparse_add(M, self.T, alpha=-1)
#self.tenpy.printf("The norm of gradient is ",self.tenpy.norm(grad))
return grad
def Get_RHS(self, num, regu):
#The gradient of the loss function is Mttkrp(e^m - x) ............... Need negative of this
M = self.tenpy.TTTP(self.Omega, self.A)
ctf.Sparse_exp(M)
#inter = subtract_sparse(self.T,M)
ctf.Sparse_add(M, self.T, alpha=-1)
#inter = self.T - M
lst_mat = []
for j in range(len(self.A)):
if j != num:
lst_mat.append(self.A[j])
else:
lst_mat.append(self.tenpy.zeros(self.A[num].shape))
self.tenpy.MTTKRP(M, lst_mat, num)
grad = lst_mat[num] - regu * self.A[num]
ctf.Sparse_add(M, self.T, alpha=-1)
#self.tenpy.printf("The norm of gradient is ",self.tenpy.vecnorm(grad))
return [grad, M]
def ccd(tenpy, T_in, T, O, X, reg_als, num_iter_als, tol, csv_file):
opt = ccd_Completer(tenpy, T_in, O, X)
#if T_in.sp == True:
# nnz_tot = T_in.nnz_tot
#else:
# nnz_tot = ctf.sum(omega)
if tenpy.name() == 'ctf':
nnz_tot = T_in.nnz_tot
else:
nnz_tot = np.sum(O)
t_ccd = ctf.timer_epoch("ccd")
regu = reg_als
tenpy.printf("--------------------------------ccd-----------------------")
start = time.time()
# T_in = backend.einsum('ijk,ijk->ijk',T,O)
it = 0
time_all = 0
if csv_file is not None:
csv_writer = csv.writer(csv_file,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
for i in range(num_iter_als):
it += 1
s = time.time()
t_ccd.begin()
X = opt.step(regu)
t_ccd.end()
e = time.time()
time_all += e - s
M = tenpy.TTTP(O, X)
ctf.Sparse_add(M, T_in, beta=-1)
rmse = tenpy.vecnorm(M) / (nnz_tot)**0.5
M.set_zero()
if tenpy.is_master_proc():
tenpy.printf("After " + str(it) + " iterations,")
tenpy.printf("RMSE is", rmse)
#print("Full Tensor Objective",(tenpy.norm(tenpy.einsum('ir,jr,kr->ijk',X[0],X[1],X[2])-T)))
if csv_file is not None:
csv_writer.writerow([i, time_all, rmse, i, 'CCD'])
csv_file.flush()
if rmse < tol:
tenpy.printf("Ending algo due to tolerance")
break
end = time.time()
tenpy.printf('ccd time taken is ', end - start)
return X
def matvec(self,regu,delta,d_derivative):
N = len(self.A)
ret = []
lst_mat = self.A[:]
lst_mat[0] = delta[0].copy()
inter = self.tenpy.TTTP(d_derivative, lst_mat)
s_derivative = d_derivative.copy()
ctf.Sparse_add(s_derivative,self.T,beta=-1)
for n in range(1,N):
lst_mat= self.A[:]
lst_mat[n] = delta[n].copy()
M = self.tenpy.TTTP(d_derivative, lst_mat)
ctf.Sparse_add(inter,M)
lst_mat = self.A[:]
lst_mat[0] = self.tenpy.zeros(self.A[0].shape)
self.tenpy.MTTKRP(inter,lst_mat,0)
ret.append(self.tenpy.zeros(self.A[0].shape))
ret[0]+=lst_mat[0]
ret[0]+= regu*delta[0]
for n in range(1,N):
ret.append(self.tenpy.zeros(self.A[n].shape))
lst_mat = self.A[:]
lst_mat[n] = self.tenpy.zeros(self.A[n].shape)
self.tenpy.MTTKRP(inter,lst_mat,n)
ret[n]+=lst_mat[n]
ret[n]+= regu*delta[n]
'''
for n in range(N):
lst_mat = self.A[:]
lst_mat[n]=self.tenpy.zeros(self.A[n].shape)
for i in range(N):
if i != n:
lst_mat[i] = delta[i].copy()
self.tenpy.MTTKRP(s_derivative,lst_mat,n)
ret[n]+=lst_mat[n]
lst_mat[i]=self.A[i].copy()
'''
return ret
def step(self, regu):
M = self.tenpy.TTTP(self.Omega, self.A)
for r in range(self.rank):
for i in range(len(self.A)):
lst_vec = []
for j in range(len(self.A)):
lst_vec.append(self.A[j][:, r])
numerator = self.Get_Num(i, r, regu, M)
denominator = self.Get_Denom(i, r, regu)
delta = numerator / denominator
lst_vec[i] = delta
self.A[i][:, r] += delta
P = self.tenpy.TTTP(self.Omega, lst_vec)
ctf.Sparse_add(M, P)
return self.A
def getCPGN(tenpy, T_in, T, O, X, reg_GN, num_iter_GN, tol, csv_file):
opt = CP_GN_Completer(tenpy, T_in, O, X)
if tenpy.name() == 'ctf':
nnz_tot = T_in.nnz_tot
else:
nnz_tot = np.sum(O)
regu = reg_GN
tenpy.printf(
"--------------------------------GN WIth CG-----------------------------"
)
start = time.time()
# T_in = backend.einsum('ijk,ijk->ijk',T,O)
it = 0
time_all = 0
if csv_file is not None:
csv_writer = csv.writer(csv_file,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
for i in range(num_iter_GN):
it += 1
s = time.time()
[X, cg_iters] = opt.step(regu)
e = time.time()
if regu > 1e-05:
regu = regu / 2
time_all += e - s
M = tenpy.TTTP(O, X)
ctf.Sparse_add(M, T_in, beta=-1)
rmse = tenpy.vecnorm(M) / (nnz_tot)**0.5
if tenpy.is_master_proc():
tenpy.printf("After " + str(it) + " iterations, and time ",
time_all)
tenpy.printf("RMSE is", rmse)
#print("Full Tensor Objective",(tenpy.norm(tenpy.einsum('ir,jr,kr->ijk',X[0],X[1],X[2])-T)))
if csv_file is not None:
csv_writer.writerow([i, time_all, rmse, cg_iters, 'GN'])
csv_file.flush()
if rmse < tol:
tenpy.printf("Ending algo due to tolerance")
break
end = time.time()
tenpy.printf('GN time taken is ', end - start)
return X
def Get_RHS(self, num, regu):
#The gradient of the loss function is Mttkrp(e^m - x) ............... Need negative of this
Omega_ = self.sampled_T.copy()
ctf.get_index_tensor(Omega_)
M = self.tenpy.TTTP(Omega_, self.A)
#inter = elementwise_exp(self.tenpy.TTTP(getOmega(self.sampled_T),self.A))
ctf.Sparse_exp(M)
ctf.Sparse_add(M, self.sampled_T, alpha=-1)
#inter = subtract_sparse(self.sampled_T,inter)
lst_mat = []
for j in range(len(self.A)):
if j != num:
lst_mat.append(self.A[j])
else:
lst_mat.append(self.tenpy.zeros(self.A[num].shape))
self.tenpy.MTTKRP(M, lst_mat, num)
#inter.set_zero()
grad = lst_mat[num] - regu * self.A[num]
#self.tenpy.printf("The norm of gradient is ",self.tenpy.vecnorm(grad))
return grad
def getPCPGN(tenpy, T_in, T, O, X, reg_GN, num_iter_GN,tol,csv_file):
opt = Poisson_CP_GN_Completer(tenpy, T_in, O, X)
if tenpy.name() == 'ctf':
nnz_tot = T_in.nnz_tot
else:
nnz_tot = np.sum(O)
regu = reg_GN
tenpy.printf("--------------------------------Poisson GN WIth CG-----------------------------")
t_ALS = ctf.timer_epoch("Poisson_GN")
start= time.time()
# T_in = backend.einsum('ijk,ijk->ijk',T,O)
it = 0
time_all = 0
P = T_in.copy()
ctf.Sparse_log(P)
ctf.Sparse_mul(P,T_in)
ctf.Sparse_add(P,T_in,beta=-1)
val2 = ctf.sum(P)
#val2 = ctf.sum(subtract_sparse(elementwise_prod(T_in,elementwise_log(T_in)),T_in))
M = tenpy.TTTP(O,X)
#val = ctf.sum(subtract_sparse(ctf.exp(M),elementwise_prod(T_in,M) ))
P = M.copy()
ctf.Sparse_mul(P,T_in)
ctf.Sparse_exp(M)
#rmse_lsq = tenpy.vecnorm(T_in-M)/(nnz_tot)**0.5
#tenpy.printf("least square RMSE is",rmse_lsq)
ctf.Sparse_add(M,P,beta=-1)
val = ctf.sum(M)
P.set_zero()
M.set_zero()
rmse = (val+val2)/nnz_tot
P.set_zero()
if tenpy.is_master_proc():
tenpy.printf("After " + str(it) + " iterations,")
tenpy.printf("RMSE is",rmse)
if csv_file is not None:
csv_writer = csv.writer(
csv_file, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
for i in range(num_iter_GN):
it+=1
s = time.time()
t_ALS.begin()
X = opt.step(regu)
t_ALS.end()
e = time.time()
time_all+= e- s
#rmse = tenpy.vecnorm(tenpy.TTTP(O,[U,V,W])-T_in)/(nnz_tot)**0.5
M = tenpy.TTTP(O,X)
#val = ctf.sum(subtract_sparse(ctf.exp(M),elementwise_prod(T_in,M) ))
P = M.copy()
ctf.Sparse_mul(P,T_in)
ctf.Sparse_exp(M)
#rmse_lsq = tenpy.vecnorm(T_in-M)/(nnz_tot)**0.5
#tenpy.printf("least square RMSE is",rmse_lsq)
ctf.Sparse_add(M,P,beta=-1)
val = ctf.sum(M)
P.set_zero()
M.set_zero()
rmse = (val+val2)/nnz_tot
regu = regu/2
if tenpy.is_master_proc():
tenpy.printf("After " + str(it) + " iterations,")
tenpy.printf("RMSE is",rmse)
#print("Full Tensor Objective",(tenpy.norm(tenpy.einsum('ir,jr,kr->ijk',U,V,W)-T)))
if csv_file is not None:
csv_writer.writerow([i,time_all , rmse, i,'PGN'])
csv_file.flush()
if abs(rmse) < tol:
tenpy.printf("Ending algo due to tolerance")
break
end= time.time()
end= time.time()
tenpy.printf('Poisson_GN time taken is ',end - start)
return X
def sgd_poisson(tenpy, T_in, T, O, U, V, W, reg_als, I, J, K, R, num_iter_als,
tol, csv_file):
step_size = 0.03
opt = Poisson_sgd_Completer(tenpy, T_in, O, [U, V, W], step_size)
#if T_in.sp == True:
# nnz_tot = T_in.nnz_tot
#else:
# nnz_tot = ctf.sum(omega)
if tenpy.name() == 'ctf':
nnz_tot = T_in.nnz_tot
else:
nnz_tot = np.sum(O)
t_ALS = ctf.timer_epoch("poisson_sgd")
regu = reg_als
tenpy.printf(
"--------------------------------Poisson_sgd-----------------------")
start = time.time()
# T_in = backend.einsum('ijk,ijk->ijk',T,O)
it = 0
time_all = 0
#val2 = ctf.sum(subtract_sparse(elementwise_prod(T_in,elementwise_log(T_in)),T_in))
P = T_in.copy()
ctf.Sparse_log(P)
ctf.Sparse_mul(P, T_in)
ctf.Sparse_add(P, T_in, beta=-1)
val2 = ctf.sum(P)
P.set_zero()
if csv_file is not None:
csv_writer = csv.writer(csv_file,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
for i in range(num_iter_als):
it += 1
s = time.time()
#t_ALS.begin()
[U, V, W] = opt.step(regu)
#t_ALS.end()
e = time.time()
time_all += e - s
#rmse = tenpy.vecnorm(tenpy.TTTP(O,[U,V,W])-T_in)/(nnz_tot)**0.5
if it % 20 == 0:
M = tenpy.TTTP(O, [U, V, W])
#val = ctf.sum(subtract_sparse(ctf.exp(M),elementwise_prod(T_in,M) ))
P = M.copy()
ctf.Sparse_mul(P, T_in)
ctf.Sparse_exp(M)
ctf.Sparse_add(M, P, beta=-1)
val = ctf.sum(M)
P.set_zero()
M.set_zero()
rmse = (val + val2) / nnz_tot
if tenpy.is_master_proc():
tenpy.printf("After " + str(it) + " iterations, and time is",
time_all)
tenpy.printf("RMSE is", rmse)
#print("Full Tensor Objective",(tenpy.norm(tenpy.einsum('ir,jr,kr->ijk',U,V,W)-T)))
if csv_file is not None:
csv_writer.writerow([i, time_all, rmse, i, 'PALS'])
csv_file.flush()
if abs(rmse) < tol:
tenpy.printf("Ending algo due to tolerance")
break
end = time.time()
tenpy.printf('Poisson sgd time taken is ', end - start)
return [U, V, W]
