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Poisson_ccd.py
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Poisson_ccd.py
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import numpy as np
import numpy.linalg as la
import time
import csv
import ctf
import random
def subtract_sparse(T,M):
[inds,data] = T.read_local_nnz()
[inds,data2] = M.read_local_nnz()
new_data = data-data2
new_tensor = ctf.tensor(T.shape, sp=T.sp)
new_tensor.write(inds,new_data)
return new_tensor
def getOmega(T):
[inds, data] = T.read_local_nnz()
data[:] = 1.
Omega = ctf.tensor(T.shape, sp=T.sp)
Omega.write(inds, data)
return Omega
def elementwise_prod(T,M):
[inds,data] = T.read_local_nnz()
[inds,data2] = M.read_local_nnz()
new_data= data2*data
new_tensor = ctf.tensor(T.shape, sp=T.sp)
new_tensor.write(inds,new_data)
return new_tensor
def elementwise_exp(T):
[inds,data] = T.read_local_nnz()
new_data = np.exp(data)
new_tensor = ctf.tensor(T.shape, sp=T.sp)
new_tensor.write(inds,new_data)
return new_tensor
def elementwise_log(T):
[inds,data] = T.read_local_nnz()
new_data = np.log(data)
new_tensor = ctf.tensor(T.shape, sp=T.sp)
new_tensor.write(inds,new_data)
return new_tensor
class Poisson_ccd_Completer():
#Current implementation is using \lambda = e^m and replacing it in the function to get: e^m - xm
def __init__(self,tenpy, T, Omega, A):
self.tenpy = tenpy
self.T = T
self.Omega = Omega
self.A = A
self.rank = self.A[0].shape[1]
def Get_Denom(self,num,r,M,regu):
lst_vec = []
for j in range(len(self.A)):
if j != num :
lst_vec.append((self.A[j][:,r])**2)
else:
lst_vec.append(self.tenpy.zeros(self.A[num].shape[0]))
self.tenpy.MTTKRP(M,lst_vec,num)
#self.tenpy.printf('The norm is ',self.tenpy.vecnorm(lst_mat[num]))
#self.tenpy.printf("Performing sum with reg")
lst_vec[num] += regu
return lst_vec[num]
def Get_Num(self,num,r,M,regu):
#The gradient of the loss function is Mttkrp(e^m - x) ............... Need negative of this
lst_vec = []
for j in range(len(self.A)):
if num==j:
lst_vec.append(self.tenpy.zeros(self.A[num].shape[0]))
else:
lst_vec.append(self.A[j][:,r])
#inter = subtract_sparse(self.T,M)
ctf.Sparse_add(M,self.T,alpha=-1)
#inter = self.T - M
self.tenpy.MTTKRP(M,lst_vec,num)
#inter.set_zero()
#self.tenpy.printf("Performing sum with reg*factor")
lst_vec[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 lst_vec[num]
def step(self,regu):
M = self.tenpy.TTTP(self.Omega,self.A)
ctf.Sparse_exp(M)
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])
for t in range(5):
numerator = self.Get_Num(i,r,M,regu)
denominator = self.Get_Denom(i,r,M,regu)
delta = numerator/denominator
lst_vec[i] = delta
step_nrm = self.tenpy.norm(delta)/self.tenpy.norm(self.A[i][:,r])
#self.tenpy.printf("ratio of norm of delta is ",step_nrm)
#self.tenpy.printf("Performing sum with delta")
self.A[i][:,r]+= delta
M_ = self.tenpy.TTTP(self.Omega,lst_vec)
ctf.Sparse_exp(M_)
ctf.Sparse_mul(M,M_)
if step_nrm<= 1e-03:
break
#self.tenpy.printf("Completed for ",i)
return self.A
def ccd_poisson(tenpy, T_in, T, O, U, V, W, reg_als,I,J,K,R, num_iter_als,tol,csv_file):
opt = Poisson_ccd_Completer(tenpy, T_in, O, [U,V,W])
#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_ccd")
regu = reg_als
tenpy.printf("--------------------------------Poisson_ccd-----------------------")
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))
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
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)
#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
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,'PCCD'])
csv_file.flush()
if abs(rmse) < tol:
tenpy.printf("Ending algo due to tolerance")
break
end= time.time()
tenpy.printf('Poisson ccd time taken is ',end - start)
return [U,V,W]