def __completef(fitinfo, method=None):
"""Completes missing values in f using matrix-completion library (Duan, 2020)."""
mof = fitinfo['mof']
f = fitinfo['f']
if method is None or method == 'svt':
# Singular value thresholding
fhat = svt_solve(f, ~mof)
elif method == 'pmf':
# Probablistic matrix factorization
fhat = pmf_solve(f, ~mof, k=10, mu=1e-2)
elif method == 'bmf':
# Biased alternating least squares
fhat = biased_mf_solve(f, ~mof, k=10, mu=1e-2)
else:
raise ValueError(
'Unsupported completion method. {:s}'.format(methodoptionstr))
if not np.isfinite(fhat).all():
raise ValueError('Completion method {:s} failed. {:s} {:s}'.format(
method, methodoptionstr, suggeststr))
fitinfo['f'] = fhat
fitinfo['completionmethod'] = method
return
V = np.random.randn(items, rank)
# R = np.random.randn(users, items) + np.dot(U, V.T) # with noise
R = np.dot(U, V.T) # without noise
mask = np.random.binomial(1, percent_shown[i], size=users * items).reshape(
(users, items))
rHat_svt = svt_solve(R, mask)
res_observed[i, 0] = calc_observed_rmse(R, rHat_svt, mask)
res_unobserved[i, 0] = calc_unobserved_rmse(R, rHat_svt, mask)
rHat_pmf = pmf_solve(R, mask, 10, 1e-2)
res_observed[i, 1] = calc_observed_rmse(R, rHat_pmf, mask)
res_unobserved[i, 1] = calc_unobserved_rmse(R, rHat_pmf, mask)
rHat_bias = biased_mf_solve(R, mask, 10, 1e-2)
res_observed[i, 2] = calc_observed_rmse(R, rHat_bias, mask)
res_unobserved[i, 2] = calc_unobserved_rmse(R, rHat_bias, mask)
plt.plot(percent_shown, res_observed[:, 0], 'r--', label='SVT')
plt.plot(percent_shown, res_observed[:, 1], 'g--', label='PMF')
plt.plot(percent_shown, res_observed[:, 2], 'b--', label='BIAS')
plt.title('Observed RMSE')
plt.legend(loc='upper left')
plt.show()
plt.plot(percent_shown, res_unobserved[:, 0], 'r--', label='SVT')
plt.plot(percent_shown, res_unobserved[:, 1], 'g--', label='PMF')
plt.plot(percent_shown, res_unobserved[:, 2], 'b--', label='BIAS')
plt.title('UnObserved RMSE')
plt.legend(loc='upper left')
import numpy as np
from matrix_completion import svt_solve, pmf_solve, biased_mf_solve
import matplotlib.pyplot as plt
ratings_100k = np.load('rating-100k.npy')
plt.imshow(ratings_100k, cmap='jet', interpolation='nearest')
plt.show()
mask = (ratings_100k > 0).astype(np.int)
rHat_svt = svt_solve(ratings_100k, mask)
plt.imshow(rHat_svt, cmap='jet', interpolation='nearest')
plt.show()
rHat_pmf = pmf_solve(ratings_100k, mask, 10, 1e-2)
plt.imshow(rHat_pmf, cmap='jet', interpolation='nearest')
plt.show()
rHat_bias = biased_mf_solve(ratings_100k, mask, 10, 1e-2)
plt.imshow(rHat_bias, cmap='jet', interpolation='nearest')
plt.show()