def test_hercprand_35():
"""
test hercprand 3 5 for complicated case
"""
I=50
J=50
K=50
r=10 # rank
n_samples=int(10*r*np.log(r)+1) # nb of randomized samples
fac_true,noise=init_factors(I,J,K,r,True)
t=tl.cp_to_tensor((None,fac_true))+noise
factors=random_init_fac(t,r)
weights1,factors1,it1,error1,cpt1,time1=her_Als(t,r,factors=copy.deepcopy(factors),it_max=200,time_rec=True)
print("her als Complicated case pct restart",cpt1)
weights3,factors3,it3,error3,error_es3,cpt3,time3=her_CPRAND(t,r,n_samples,factors=copy.deepcopy(factors),exact_err=True,it_max=200,err_it_max=100,time_rec=True)
print("3 Complicated case pct restart",cpt3)
print("3 Complicated case time err_rand",np.cumsum(time3)[len(time3)-1])
print("3 min error", np.min(error3))
print("3 min error es", np.min(error_es3))
weights5,factors5,it5,error5,error_es5,cpt5,time5=her_CPRAND5(t,r,n_samples,factors=copy.deepcopy(factors),exact_err=True,it_max=200,err_it_max=100,time_rec=True)
print("5 Complicated case pct restart",cpt5)
print("5 Complicated case time err_rand",np.cumsum(time5)[len(time5)-1])
print("5 min error", np.min(error5))
print("5 min error es", np.min(error_es5))
plt.figure(0)
plt.plot(range(len(error3)),error3,'b-',label="exact 3")
plt.plot(range(len(error_es3)),error_es3,'r--',label="err rand 3")
plt.plot(range(len(error5)),error5,'g-',label="exact 5")
plt.plot(range(len(error_es5)),error_es5,'k--',label="err rand 5")
plt.xlabel('it')
plt.yscale('log')
plt.title('hercprand3 5 for complicated case')
plt.ylabel('terminaison criterion')
plt.legend(loc='best')
def test_nbrestart(i):
"""
Compute the nb restart for 4 cases :
1 - simple case with exact error
2 - simple case with estimated error
3 - complicated case with exact error
4 - complicated case with estimated error
In each case, we simulate nb_rand noised I*J*K rank r random tensors.
For each tensor, we do 5 factor matrices initializations.
Parameters
----------
i : int
choice of case.
Returns
-------
float
mean value of nbrestart
"""
I = 50
J = 50
K = 50
r = 10 # rank
n_samples = int(10 * r * np.log(r) + 1) # nb of randomized samples
nb_rand = 10 # nb of random initialization
list_pct = []
for i in range(nb_rand):
# Random initialization of a noised cp_tensor
np.random.seed(i)
if i == 1 or i == 2:
A, B, C, noise = init_factors(I, J, K, r, scale=False)
else:
A, B, C, noise = init_factors(I, J, K, r, scale=True)
t = tl.cp_to_tensor((None, [A, B, C])) + noise
for j in range(5):
factors = random_init_fac(t, r)
if i == 1:
# simple case with exact error
weights, factors, it, error, _, pct = her_CPRAND(
t,
r,
n_samples,
factors=copy.deepcopy(factors),
exact_err=True,
it_max=500,
err_it_max=100)
list_pct.append(pct)
if i == 2:
# simple case with estimated error
weights, factors, it, _, error, pct = her_CPRAND(
t,
r,
n_samples,
factors=copy.deepcopy(factors),
exact_err=False,
it_max=500,
err_it_max=100)
list_pct.append(pct)
if i == 3:
# complicated case with exact error
weights, factors, it, error, _, pct = her_CPRAND(
t,
r,
n_samples,
factors=copy.deepcopy(factors),
exact_err=True,
it_max=500,
err_it_max=100)
list_pct.append(pct)
if i == 4:
# complicated case with estimated error
weights, factors, it, _, error, pct = her_CPRAND(
t,
r,
n_samples,
factors=copy.deepcopy(factors),
exact_err=False,
it_max=500,
err_it_max=400)
list_pct.append(pct)
return (np.mean(list_pct))
def test_hercprand():
"""
Run herCPRAND1 2 3 4 5 for the simple and complicated case, plot exact/estimated error.
Print restart percentage.
Compare running time with herCPRAND for complicated case.
"""
I=50
J=50
K=50
r=10 # rank
n_samples=int(10*r*np.log(r)+1) # nb of randomized samples
fac_true,noise=init_factors(I,J,K,r,True)
t=tl.cp_to_tensor((None,fac_true))+noise
factors=random_init_fac(t,r)
weights1,factors1,it1,error1,error_es1,cpt1,time1=her_CPRAND1(t,r,n_samples,factors=copy.deepcopy(factors),exact_err=True,it_max=200,err_it_max=100,time_rec=True)
print("1 Complicated case pct restart",cpt1)
print("1 Complicated case time err_rand",np.cumsum(time1)[len(time1)-1])
weights2,factors2,it2,error2,error_es2,cpt2,time2=her_CPRAND2(t,r,n_samples,factors=copy.deepcopy(factors),exact_err=True,it_max=200,err_it_max=100,time_rec=True)
print("2 Complicated case pct restart",cpt2)
print("2 Complicated case time err_rand",np.cumsum(time2)[len(time2)-1])
weights3,factors3,it3,error3,error_es3,cpt3,time3=her_CPRAND3(t,r,n_samples,factors=copy.deepcopy(factors),exact_err=True,it_max=200,err_it_max=100,time_rec=True)
print("3 Complicated case pct restart",cpt3)
print("3 Complicated case time err_rand",np.cumsum(time3)[len(time3)-1])
weights4,factors4,it4,error4,error_es4,cpt4,time4=her_CPRAND4(t,r,n_samples,factors=copy.deepcopy(factors),exact_err=True,it_max=200,err_it_max=100,time_rec=True)
print("4 Complicated case pct restart",cpt4)
print("4 Complicated case time err_rand",np.cumsum(time4)[len(time4)-1])
weights5,factors5,it5,error5,error_es5,cpt5,time5=her_CPRAND5(t,r,n_samples,factors=copy.deepcopy(factors),exact_err=True,it_max=200,err_it_max=100,time_rec=True)
print("5 Complicated case pct restart",cpt5)
print("5 Complicated case time err_rand",np.cumsum(time5)[len(time5)-1])
plt.figure(0)
plt.plot(range(len(error1)),error1,'b-',label="exact")
plt.plot(range(len(error_es1)),error_es1,'r--',label="err rand")
plt.xlabel('it')
plt.yscale('log')
plt.title('hercprand1 for complicated case')
plt.ylabel('terminaison criterion')
plt.legend(loc='best')
plt.figure(1)
plt.plot(range(len(error2)),error2,'b-',label="exact")
plt.plot(range(len(error_es2)),error_es2,'r--',label="err rand")
plt.xlabel('it')
plt.yscale('log')
plt.title('hercprand2 for complicated case')
plt.ylabel('terminaison criterion')
plt.legend(loc='best')
plt.figure(2)
plt.plot(range(len(error3)),error3,'b-',label="exact")
plt.plot(range(len(error_es3)),error_es3,'r--',label="err rand")
plt.xlabel('it')
plt.yscale('log')
plt.title('hercprand3 for complicated case')
plt.ylabel('terminaison criterion')
plt.legend(loc='best')
plt.figure(3)
plt.plot(range(len(error4)),error4,'b-',label="exact")
plt.plot(range(len(error_es4)),error_es4,'r--',label="err rand")
plt.xlabel('it')
plt.yscale('log')
plt.title('hercprand4 for complicated case')
plt.ylabel('terminaison criterion')
plt.legend(loc='best')
plt.figure(4)
plt.plot(range(len(error5)),error5,'b-',label="exact")
plt.plot(range(len(error_es5)),error_es5,'r--',label="err rand")
plt.xlabel('it')
plt.yscale('log')
plt.title('hercprand5 for complicated case')
plt.ylabel('terminaison criterion')
plt.legend(loc='best')
fac_true,noise=init_factors(I,J,K,r,False)
t=tl.cp_to_tensor((None,fac_true))+noise
factors=random_init_fac(t,r)
weights1,factors1,it1,error1,error_es1,cpt1,time1=her_CPRAND1(t,r,n_samples,factors=copy.deepcopy(factors),exact_err=True,it_max=200,err_it_max=100,time_rec=True)
print("Simple case pct restart",cpt1)
weights2,factors2,it2,error2,error_es2,cpt2,time2=her_CPRAND2(t,r,n_samples,factors=copy.deepcopy(factors),exact_err=True,it_max=200,err_it_max=100,time_rec=True)
print("Simple case pct restart",cpt2)
weights3,factors3,it3,error3,error_es3,cpt3,time3=her_CPRAND(t,r,n_samples,factors=copy.deepcopy(factors),exact_err=True,it_max=200,err_it_max=100,time_rec=True)
print("Simple case pct restart",cpt3)
weights4,factors4,it4,error4,error_es4,cpt4,time4=her_CPRAND4(t,r,n_samples,factors=copy.deepcopy(factors),exact_err=True,it_max=200,err_it_max=100,time_rec=True)
print("Simple case pct restart",cpt4)
weights5,factors5,it5,error5,error_es5,cpt5,time5=her_CPRAND5(t,r,n_samples,factors=copy.deepcopy(factors),exact_err=True,it_max=200,err_it_max=100,time_rec=True)
print("Simple case pct restart",cpt5)
plt.figure(5)
plt.plot(range(len(error1)),error1,'b-',label="exact")
plt.plot(range(len(error_es1)),error_es1,'r--',label="err rand")
plt.xlabel('it')
plt.yscale('log')
plt.title('hercprand1 for simple case')
plt.ylabel('terminaison criterion')
plt.legend(loc='best')
plt.figure(6)
plt.plot(range(len(error2)),error2,'b-',label="exact")
plt.plot(range(len(error_es2)),error_es2,'r--',label="err rand")
plt.xlabel('it')
plt.yscale('log')
plt.title('hercprand2 for simple case')
plt.ylabel('terminaison criterion')
plt.legend(loc='best')
plt.figure(7)
plt.plot(range(len(error3)),error3,'b-',label="exact")
plt.plot(range(len(error_es3)),error_es3,'r--',label="err rand")
plt.xlabel('it')
plt.yscale('log')
plt.title('hercprand3 for simple case')
plt.ylabel('terminaison criterion')
plt.legend(loc='best')
plt.figure(8)
plt.plot(range(len(error4)),error4,'b-',label="exact")
plt.plot(range(len(error_es4)),error_es4,'r--',label="err rand")
plt.xlabel('it')
plt.yscale('log')
plt.title('hercprand4 for simple case')
plt.ylabel('terminaison criterion')
plt.legend(loc='best')
plt.figure(9)
plt.plot(range(len(error5)),error5,'b-',label="exact")
plt.plot(range(len(error_es5)),error_es5,'r--',label="err rand")
plt.xlabel('it')
plt.yscale('log')
plt.title('hercprand5 for simple case')
plt.ylabel('terminaison criterion')
plt.legend(loc='best')
def compar_time(I,
J,
K,
r,
nb_rand,
n_samples,
exact_err=False,
list_factors=False,
scale=False):
"""
plot data fitting/factors error over nb_rand noised I*J*K rank r random tensors,
with the median value in bold.
x aixs is time.
For each tensor, we have 5 factors initializations.
Need to change plot x,y label and title to run the test.
Parameters
----------
I : int
dimension of mode 1.
J : int
dimension of mode 2.
K : int
dimension of mode 3.
r : int
rank.
nb_rand : int
nb of tensors.
n_samples : int
sample size used for herCPRAND.
exact_err : boolean, optional
whether use exact error computation or not for herCPRAND. The default is False.
list_factors : TYPE, optional
DESCRIPTION. The default is False.
scale : TYPE, optional
DESCRIPTION. The default is False.
Returns
-------
None.
"""
list_err1 = []
list_time1 = []
list_err2 = []
list_time2 = []
list_err3 = []
list_time3 = []
list_err4 = []
list_time4 = []
min_e = None
for i in range(nb_rand):
# Random initialization of a noised cp_tensor
np.random.seed(i)
A, B, C, noise = init_factors(I, J, K, r, scale)
fac_true = [A, B, C]
t = tl.cp_to_tensor((None, fac_true)) + noise
norm_tensor = tl.norm(t, 2)
if (min_e == None): min_e = norm_tensor
for k in range(5):
factors = random_init_fac(t, r)
if list_factors == False:
weights4, factors4, it4, error4, _, cpt, time4 = her_CPRAND(
t,
r,
n_samples,
factors=copy.deepcopy(factors),
exact_err=True,
it_max=500,
err_it_max=250,
time_rec=True)
weights1, factors1, it1, error1, cpt, time1 = her_Als(
t,
r,
factors=copy.deepcopy(factors),
it_max=500,
time_rec=True)
weights3, factors3, it3, error3, _, time3 = CPRAND(
t,
r,
n_samples,
factors=copy.deepcopy(factors),
exact_err=True,
it_max=500,
err_it_max=250,
time_rec=True)
weights2, factors2, it2, error2, time2 = als(
t,
r,
factors=copy.deepcopy(factors),
it_max=500,
time_rec=True)
error1 = [i * norm_tensor for i in error1]
del error1[0]
error2 = [i * norm_tensor for i in error2]
del error2[0]
error3 = [i * norm_tensor for i in error3]
del error3[0]
error4 = [i * norm_tensor for i in error4]
del error4[0]
else:
weights4, factors4, it4, error, _, cpt, l4, time4 = her_CPRAND(
t,
r,
n_samples,
factors=copy.deepcopy(factors),
exact_err=True,
it_max=500,
err_it_max=250,
list_factors=list_factors,
time_rec=True)
weights1, factors1, it1, error, cpt, l1, time1 = her_Als(
t,
r,
factors=copy.deepcopy(factors),
it_max=500,
list_factors=list_factors,
time_rec=True)
weights3, factors3, it3, error, _, l3, time3 = CPRAND(
t,
r,
n_samples,
factors=copy.deepcopy(factors),
exact_err=True,
it_max=500,
err_it_max=250,
list_factors=list_factors,
time_rec=True)
weights2, factors2, it2, error, l2, time2 = als(
t,
r,
factors=copy.deepcopy(factors),
it_max=500,
list_factors=list_factors,
time_rec=True)
error1 = [err_fac(fac_true, i) for i in l1]
del error1[0]
error2 = [err_fac(fac_true, i) for i in l2]
del error2[0]
error3 = [err_fac(fac_true, i) for i in l3]
del error3[0]
error4 = [err_fac(fac_true, i) for i in l4]
del error4[0]
if (min_e > min(min(error1), min(error2), min(error3),
min(error4))):
min_e = min(min(error1), min(error2), min(error3), min(error4))
list_err1.append(error1)
list_err2.append(error2)
list_err3.append(error3)
list_err4.append(error4)
list_time1.append(time1)
list_time2.append(time2)
list_time3.append(time3)
list_time4.append(time4)
list_err1 = np.array([np.array(i) for i in list_err1])
list_err2 = np.array([np.array(i) for i in list_err2])
list_err3 = np.array([np.array(i) for i in list_err3])
list_err4 = np.array([np.array(i) for i in list_err4])
list_err1 = list_err1 - min_e
list_err2 = list_err2 - min_e
list_err3 = list_err3 - min_e
list_err4 = list_err4 - min_e
for i in range(len(list_err1)):
plt.plot(np.cumsum(list_time1[i]), list_err1[i], 'b-', linewidth=.3)
for i in range(len(list_err2)):
plt.plot(np.cumsum(list_time2[i]), list_err2[i], 'r-', linewidth=.3)
for i in range(len(list_err3)):
plt.plot(np.cumsum(list_time3[i]), list_err3[i], 'y-', linewidth=.3)
for i in range(len(list_err4)):
plt.plot(np.cumsum(list_time4[i]), list_err4[i], 'g-', linewidth=.3)
n_max1 = len(max(list_err1, key=len)) # length of the longest error
n_max2 = len(max(list_err2, key=len))
n_max3 = len(max(list_err3, key=len))
n_max4 = len(max(list_err4, key=len))
mat1 = np.array(
[i.tolist() + [i[len(i) - 1]] * (n_max1 - len(i)) for i in list_err1])
mat2 = np.array(
[i.tolist() + [i[len(i) - 1]] * (n_max2 - len(i)) for i in list_err2])
mat3 = np.array(
[i.tolist() + [i[len(i) - 1]] * (n_max3 - len(i)) for i in list_err3])
mat4 = np.array(
[i.tolist() + [i[len(i) - 1]] * (n_max4 - len(i)) for i in list_err4])
t_max1 = len(max(list_time1, key=len))
t_max2 = len(max(list_time2, key=len))
t_max3 = len(max(list_time3, key=len))
t_max4 = len(max(list_time4, key=len))
mat_time1 = np.array([i + [0] * (t_max1 - len(i)) for i in list_time1])
mat_time2 = np.array([i + [0] * (t_max2 - len(i)) for i in list_time2])
mat_time3 = np.array([i + [0] * (t_max3 - len(i)) for i in list_time3])
mat_time4 = np.array([i + [0] * (t_max4 - len(i)) for i in list_time4])
# plot
plt.plot(np.cumsum(np.median(mat_time1, axis=0)),
np.median(mat1, axis=0),
'b-',
linewidth=3,
label="her als")
plt.plot(np.cumsum(np.median(mat_time2, axis=0)),
np.median(mat2, axis=0),
'r-',
linewidth=3,
label="als")
plt.plot(np.cumsum(np.median(mat_time3, axis=0)),
np.median(mat3, axis=0),
'y-',
linewidth=3,
label="CPRAND")
plt.plot(np.cumsum(np.median(mat_time4, axis=0)),
np.median(mat4, axis=0),
'g-',
linewidth=3,
label="herCPRAND")
plt.yscale("log")
plt.xlabel('time')
plt.ylabel('data fitting error')
plt.legend(loc='best')
plt.title('Simple case exact')
def comparaison(I,
J,
K,
r,
nb_rand,
n_samples,
exact_err=False,
list_factors=False,
scale=False):
"""
plot data fitting/factors error over nb_rand noised I*J*K rank r random tensors,
with the median value in bold.
x aixs is it.
For each tensor, we have 5 random factors initializations.
Need to change plot x,y label and title to run the test.
Parameters
----------
I : int
dimension of mode 1.
J : int
dimension of mode 2.
K : int
dimension of mode 3.
r : int
rank.
nb_rand : int
nb of tensors.
n_samples : int
sample size used for (her)CPRAND.
exact_err : boolean, optional
whether use exact error computation or not for (her)CPRAND. The default is False.
list_factors : boolean, optional
whether compute factor error or data fitting error. The default is False.
scale : boolean, optional
whether to scale the singular values of matrices or not. The default is False.
Returns
-------
None.
"""
list_err1 = []
list_err2 = []
list_err3 = []
list_err4 = []
list_pct = []
min_e = None
for i in range(nb_rand):
# Random initialization of a noised cp_tensor
# np.random.seed(i)
A, B, C, noise = init_factors(I, J, K, r, scale)
fac_true = [A, B, C]
t = tl.cp_to_tensor((None, fac_true)) + noise
norm_tensor = tl.norm(t, 2)
if (min_e == None): min_e = norm_tensor
for k in range(5): # 5 initializations
factors = random_init_fac(t, r)
if list_factors == False:
weights4, factors4, it4, error4, _, cpt1 = her_CPRAND(
t,
r,
n_samples,
factors=copy.deepcopy(factors),
exact_err=True,
it_max=500,
err_it_max=100)
weights1, factors1, it1, error1, cpt = her_Als(
t, r, factors=copy.deepcopy(factors), it_max=500)
weights3, factors3, it3, error3, _ = CPRAND(
t,
r,
n_samples,
factors=copy.deepcopy(factors),
exact_err=True,
it_max=500,
err_it_max=100)
weights2, factors2, it2, error2 = als(
t, r, factors=copy.deepcopy(factors), it_max=500)
error1 = [i * norm_tensor for i in error1]
error2 = [i * norm_tensor for i in error2]
error3 = [i * norm_tensor for i in error3]
error4 = [i * norm_tensor for i in error4]
else:
weights4, factors4, it4, error, _, cpt1, l4 = her_CPRAND(
t,
r,
n_samples,
factors=copy.deepcopy(factors),
exact_err=True,
it_max=500,
err_it_max=100,
list_factors=list_factors)
weights1, factors1, it1, error, cpt, l1 = her_Als(
t,
r,
factors=copy.deepcopy(factors),
it_max=500,
list_factors=list_factors)
weights3, factors3, it3, error, _, l3 = CPRAND(
t,
r,
n_samples,
factors=copy.deepcopy(factors),
exact_err=True,
it_max=500,
err_it_max=100,
list_factors=list_factors)
weights2, factors2, it2, error, l2 = als(
t,
r,
factors=copy.deepcopy(factors),
it_max=500,
list_factors=list_factors)
error1 = [err_fac(fac_true, i) for i in l1]
error2 = [err_fac(fac_true, i) for i in l2]
error3 = [err_fac(fac_true, i) for i in l3]
error4 = [err_fac(fac_true, i) for i in l4]
list_pct.append(cpt1)
if (min_e > min(min(error1), min(error2), min(error3),
min(error4))):
min_e = min(min(error1), min(error2), min(error3), min(error4))
list_err1.append(error1)
list_err2.append(error2)
list_err3.append(error3)
list_err4.append(error4)
list_err1 = np.array([np.array(i) for i in list_err1])
list_err2 = np.array([np.array(i) for i in list_err2])
list_err3 = np.array([np.array(i) for i in list_err3])
list_err4 = np.array([np.array(i) for i in list_err4])
list_err1 = list_err1 - min_e
list_err2 = list_err2 - min_e
list_err3 = list_err3 - min_e
list_err4 = list_err4 - min_e
for i in list_err1:
plt.plot(range(len(i)), i, 'b-', linewidth=.3)
for i in list_err2:
plt.plot(range(len(i)), i, 'r-', linewidth=.3)
for i in list_err3:
plt.plot(range(len(i)), i, 'y-', linewidth=.3)
for i in list_err4:
plt.plot(range(len(i)), i, 'g-', linewidth=.3)
n_max1 = len(max(list_err1, key=len)) # length of the longest error
n_max2 = len(max(list_err2, key=len))
n_max3 = len(max(list_err3, key=len))
n_max4 = len(max(list_err4, key=len))
mat1 = np.array(
[i.tolist() + [i[len(i) - 1]] * (n_max1 - len(i)) for i in list_err1])
mat2 = np.array(
[i.tolist() + [i[len(i) - 1]] * (n_max2 - len(i)) for i in list_err2])
mat3 = np.array(
[i.tolist() + [i[len(i) - 1]] * (n_max3 - len(i)) for i in list_err3])
mat4 = np.array(
[i.tolist() + [i[len(i) - 1]] * (n_max4 - len(i)) for i in list_err4])
# plot
plt.plot(range(n_max1),
np.median(mat1, axis=0),
'b-',
linewidth=3,
label="her_als")
plt.plot(range(n_max2),
np.median(mat2, axis=0),
'r-',
linewidth=3,
label="als")
plt.plot(range(n_max3),
np.median(mat3, axis=0),
'y-',
linewidth=3,
label="CPRAND")
plt.plot(range(n_max4),
np.median(mat4, axis=0),
'g-',
linewidth=3,
label="herCPRAND")
plt.yscale("log")
plt.xlabel('it')
plt.ylabel('data fitting error')
plt.legend(loc='best')
plt.title('Complicated case')
def compar_filter(I,
J,
K,
r,
nb_rand,
n_samples,
n_samples_err,
exact_err=False,
scale=False,
noise_level=0.1,
tol=0.11):
"""
boxplot for fits, scores, it, time, restarts in order to compare the filter used in HER-CPRAND.
We generate nb_rand noised I*J*K rank r random tensors, for each tensor, we have 5 factors initializations.
Then we run the 4 algorithms.
Parameters
----------
I : int
dimension of mode 1.
J : int
dimension of mode 2.
K : int
dimension of mode 3.
r : int
rank.
nb_rand : int
nb of tensors.
n_samples : int
sample size used for (her)CPRAND.
n_samples_err : int
sample size used for error in (her)cprand
exact_err : boolean, optional
whether use exact error computation or not for (her)CPRAND. The default is False.
scale : boolean, optional
whether to scale the singular values of matrices or not. The default is False.
Returns
-------
None.
"""
fit = {1: [], 2: [], 3: [], 4: []}
score_tot = {1: [], 2: [], 3: [], 4: []}
it_tot = {1: [], 2: [], 3: [], 4: []}
time_tot = {1: [], 2: [], 3: [], 4: []}
restart = {1: [], 2: [], 3: [], 4: []}
# local variables
error = {1: [], 2: [], 3: [], 4: []}
l_fac = {1: [], 2: [], 3: [], 4: []}
it = {1: 0, 2: 0, 3: 0, 4: 0}
time = {1: [], 2: [], 3: [], 4: []}
for i in range(nb_rand):
# Random initialization of a noised cp_tensor
fac_true, noise = init_factors(I, J, K, r, noise_level, scale)
t = tl.cp_to_tensor((None, fac_true)) + noise
for k in range(5):
# random initialization of factors
factors = random_init_fac(t, r)
# run 4 methods
weights1, l_fac[1], it[1], error[1], cpt1, l_fac1, time[
1] = her_Als(t,
r,
factors=copy.deepcopy(factors),
it_max=200,
tol=tol,
list_factors=True,
time_rec=True)
weights4, l_fac[2], it[2], error[2], err_es4, cpt4, l_fac4, time[
2] = her_CPRAND(t,
r,
n_samples,
n_samples_err,
factors=copy.deepcopy(factors),
exact_err=exact_err,
it_max=200,
err_it_max=200,
tol=tol,
list_factors=True,
time_rec=True)
weights6, l_fac[3], it[3], error[3], err_es6, cpt6, l_fac6, time[
3] = her_CPRAND(t,
r,
n_samples,
n_samples_err,
factors=copy.deepcopy(factors),
exact_err=exact_err,
it_max=200,
err_it_max=200,
tol=tol,
list_factors=True,
time_rec=True,
filter=5)
# information storage
restart[1].append(cpt1)
restart[2].append(cpt4)
restart[3].append(cpt6)
for j in range(1, 4):
fit[j].append(1 - (error[j][len(error[j]) - 1]))
score_tot[j].append(score(fac_true, l_fac[j]))
it_tot[j].append(it[j])
time_tot[j].append(np.cumsum(time[j])[len(time[j]) - 1])
# figure
labels = ["herals", "her cprand 10", "hercprand 5"]
_, dataf = [*zip(*fit.items())]
_, datas = [*zip(*score_tot.items())]
_, datai = [*zip(*it_tot.items())]
_, datat = [*zip(*time_tot.items())]
_, datar = [*zip(*restart.items())]
plt.figure(0)
plt.boxplot(dataf, vert=False)
plt.yticks(range(1, len(labels) + 1), labels)
plt.title('fits')
plt.figure(1)
plt.boxplot(datas, vert=False)
plt.yticks(range(1, len(labels) + 1), labels)
plt.title('scores')
plt.figure(2)
plt.boxplot(datai, vert=False)
plt.yticks(range(1, len(labels) + 1), labels)
plt.title('it')
plt.figure(3)
plt.boxplot(datat, vert=False)
plt.yticks(range(1, len(labels) + 1), labels)
plt.title('time')
plt.figure(4)
plt.boxplot(datar, vert=False)
plt.yticks(range(1, 4), labels)
plt.title('restarts')
def param_research(I,
J,
K,
r,
nb_rand,
n_samples,
exact_err=True,
beta=True,
eta=False,
gamma=False):
"""
plot data fitting error for different values of beta, eta and gamma.
For each parameter (beta for example), we initialize nb_rand noised I*J*K rank r
random tensors.
For each tensor, we have 5 random factors initializations.
Need to change plot label to run the test.
Parameters
----------
I : int
dimension of mode 1.
J : int
dimension of mode 2.
K : int
dimension of mode 3.
r : int
rank.
nb_rand : int
nb of tensors.
n_samples : int
sample size used for herCPRAND.
exact_err : boolean, optional
whether use exact error computation or not for herCPRAND. The default is True.
beta : boolean, optional
plot figure for different values of beta. The default is True.
eta : boolean, optional
plot figure for different values of eta. The default is False.
gamma : boolean, optional
plot figure for different values of gamma. The default is False.
Returns
-------
None.
"""
list_err1 = []
list_err2 = []
list_err3 = []
min_e = None
for i in range(nb_rand):
# Random initialization of a noised cp_tensor
factors, noise = init_factors(I, J, K, r, scale=True, nn=False)
tensor = tl.cp_to_tensor((None, factors)) + noise
norm_tensor = tl.norm(tensor, 2)
if (min_e == None): min_e = norm_tensor
for j in range(5):
factors = random_init_fac(tensor, r)
# parameter choice
if (beta == True):
weights1, factors1, it1, error1, cpt1 = her_CPRAND(
tensor,
r,
n_samples,
factors=copy.deepcopy(factors),
it_max=400,
exact_err=exact_err,
err_it_max=100,
beta=0.01) # beta0=0.1 bien
weights2, factors2, it2, error2, cpt2 = her_CPRAND(
tensor,
r,
n_samples,
factors=copy.deepcopy(factors),
it_max=400,
exact_err=exact_err,
err_it_max=100,
beta=0.5) # beta0=0.3
weights3, factors3, it3, error3, cpt3 = her_CPRAND(
tensor,
r,
n_samples,
factors=copy.deepcopy(factors),
it_max=400,
exact_err=exact_err,
err_it_max=100,
beta=0.99) # beta0=0.5
if (eta == True):
weights1, factors1, it1, error1, cpt1 = her_CPRAND(
tensor,
r,
n_samples,
factors=copy.deepcopy(factors),
it_max=400,
exact_err=exact_err,
err_it_max=100,
eta=1.1) # eta=1.1
weights2, factors2, it2, error2, cpt2 = her_CPRAND(
tensor,
r,
n_samples,
factors=copy.deepcopy(factors),
it_max=400,
exact_err=exact_err,
err_it_max=100,
eta=3) # eta=2
weights3, factors3, it3, error3, cpt3 = her_CPRAND(
tensor,
r,
n_samples,
factors=copy.deepcopy(factors),
it_max=400,
exact_err=exact_err,
err_it_max=100,
eta=5) # eta=3
if (gamma == True):
weights1, factors1, it1, error1, cpt1 = her_CPRAND(
tensor,
r,
n_samples,
factors=copy.deepcopy(factors),
it_max=100,
exact_err=exact_err,
err_it_max=100,
gamma=1.01,
gamma_bar=1.005)
weights2, factors2, it2, error2, cpt2 = her_CPRAND(
tensor,
r,
n_samples,
factors=copy.deepcopy(factors),
it_max=100,
exact_err=exact_err,
err_it_max=100,
gamma=1.05,
gamma_bar=1.01)
weights3, factors3, it3, error3, cpt3 = her_CPRAND(
tensor,
r,
n_samples,
factors=copy.deepcopy(factors),
it_max=100,
exact_err=exact_err,
err_it_max=100,
gamma=1.9,
gamma_bar=1.5)
error1 = [i * norm_tensor for i in error1]
list_err1.append(error1)
error2 = [i * norm_tensor for i in error2]
list_err2.append(error2)
error3 = [i * norm_tensor for i in error3]
list_err3.append(error3)
if (min_e > min(min(error1), min(error2), min(error3))):
min_e = min(min(error1), min(error2), min(error3))
list_err1 = [x - min_e for x in list_err1]
list_err2 = [x - min_e for x in list_err2]
list_err3 = [x - min_e for x in list_err3]
# plot
for i in range(len(list_err1)):
if i == 0:
plt.plot(range(len(list_err1[i])),
list_err1[i],
'b-',
label='beta=0.01')
else:
plt.plot(range(len(list_err1[i])), list_err1[i], 'b-')
for i in range(len(list_err2)):
if i == 0:
plt.plot(range(len(list_err2[i])),
list_err2[i],
'r-',
label='beta=0.5')
else:
plt.plot(range(len(list_err2[i])), list_err2[i], 'r-')
for i in range(len(list_err3)):
if i == 0:
plt.plot(range(len(list_err3[i])),
list_err3[i],
'g-',
label='beta=0.99')
else:
plt.plot(range(len(list_err3[i])), list_err3[i], 'g-')
plt.yscale("log")
plt.legend(loc='best')
plt.xlabel('it')
plt.ylabel('f')
plt.title('f(iteration)')