https://github.com/tensorly/tensorly
Tip revision: de78d0dd6ed1a935cc87a6c8ff6c73740ee3fcd0 authored by Aaron Meyer on 15 August 2024, 14:54:50 UTC
Virtually all tests working across backends (#572)
Virtually all tests working across backends (#572)
Tip revision: de78d0d
plot_cp_line_search.py
"""
Using line search with PARAFAC
==============================
Example on how to use :func:`tensorly.decomposition.parafac` with line search to accelerate convergence.
"""
import matplotlib.pyplot as plt
from time import time
import numpy as np
import tensorly as tl
from tensorly.random import random_cp
from tensorly.decomposition import CP, parafac
tol = np.logspace(-1, -9)
err = np.empty_like(tol)
err_ls = np.empty_like(tol)
tt = np.empty_like(tol)
tt_ls = np.empty_like(tol)
tensor = random_cp((10, 10, 10), 3, random_state=1234, full=True)
# Get a high-accuracy decomposition for comparison
fac = parafac(tensor, rank=3, n_iter_max=2000000, tol=1.0e-15, linesearch=True)
err_min = tl.norm(tl.cp_to_tensor(fac) - tensor)
for ii, toll in enumerate(tol):
# Run PARAFAC decomposition without line search and time
start = time()
cp = CP(rank=3, n_iter_max=2000000, tol=toll, linesearch=False)
fac = cp.fit_transform(tensor)
tt[ii] = time() - start
err[ii] = tl.norm(tl.cp_to_tensor(fac) - tensor)
# Run PARAFAC decomposition with line search and time
for ii, toll in enumerate(tol):
start = time()
cp = CP(rank=3, n_iter_max=2000000, tol=toll, linesearch=True)
fac_ls = cp.fit_transform(tensor)
tt_ls[ii] = time() - start
# Calculate the error of both decompositions
err_ls[ii] = tl.norm(tl.cp_to_tensor(fac_ls) - tensor)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.loglog(tt, err - err_min, ".", label="No line search")
ax.loglog(tt_ls, err_ls - err_min, ".r", label="Line search")
ax.legend()
ax.set_ylabel("Time")
ax.set_xlabel("Error")
plt.show()