def fit(
real,
known,
latent_d=1,
ret_pmf=False,
subtract_mean=False,
sig_u=1e10,
sig_v=1e10,
sig=1,
do_bayes=False,
burnin=10,
samps=200,
stop_thresh=1e-10,
min_learning_rate=1e-20,
):
ratings = np.zeros((known.sum(), 3))
for idx, (i, j) in enumerate(np.transpose(known.nonzero())):
ratings[idx] = [i, j, real[i, j]]
pmf = ProbabilisticMatrixFactorization(ratings, latent_d, subtract_mean)
pmf.sigma_sq = sig
pmf.sigma_u_sq = sig_u
pmf.sigma_v_sq = sig_v
pmf.stop_thresh = stop_thresh
pmf.min_learning_rate = min_learning_rate
pmf.fit()
if not do_bayes:
pred = pmf.predicted_matrix()
return (pmf, pred) if ret_pmf else pred
else:
bpmf = BayesianPMF(ratings, 1)
bpmf.__setstate__(pmf.__getstate__())
sampler = bpmf.samples()
# do burn-in
next(islice(sampler, burnin, burnin), None)
pred = bpmf.predict(islice(sampler, samps))
return (bpmf, pred) if ret_pmf else pred
def fit(real,
known,
latent_d=1,
ret_pmf=False,
subtract_mean=False,
sig_u=1e10,
sig_v=1e10,
sig=1,
do_bayes=False,
burnin=10,
samps=200,
stop_thresh=1e-10,
min_learning_rate=1e-20):
ratings = np.zeros((known.sum(), 3))
for idx, (i, j) in enumerate(np.transpose(known.nonzero())):
ratings[idx] = [i, j, real[i, j]]
pmf = ProbabilisticMatrixFactorization(ratings, latent_d, subtract_mean)
pmf.sigma_sq = sig
pmf.sigma_u_sq = sig_u
pmf.sigma_v_sq = sig_v
pmf.stop_thresh = stop_thresh
pmf.min_learning_rate = min_learning_rate
pmf.fit()
if not do_bayes:
pred = pmf.predicted_matrix()
return (pmf, pred) if ret_pmf else pred
else:
bpmf = BayesianPMF(ratings, 1)
bpmf.__setstate__(pmf.__getstate__())
sampler = bpmf.samples()
# do burn-in
next(islice(sampler, burnin, burnin), None)
pred = bpmf.predict(islice(sampler, samps))
return (bpmf, pred) if ret_pmf else pred
