def merge_vps(i,
v,
s,
l,
thresh,
lweight,
lsim,
wbias,
pdfpar,
lp,
llen,
distance_measure,
max_stdd=0.01,
outlier_stdd=1e-6):
M = v.shape[1]
num_cores = multiprocessing.cpu_count()
tryAgain = True
tries = 0
while tryAgain and M > 1:
tries += 1
angles = Parallel(n_jobs=num_cores)(
delayed(calc_angle_to_other_vp)(v, i, j) for j in range(M))
angles = np.stack(angles)
argmin_angle = numpy.unravel_index(angles.argmin(), angles.shape)
j = argmin_angle[0]
k = argmin_angle[1]
min_angle = angles[j, k]
if min_angle < thresh:
try:
p = prob.calc_probabilities(i, pdfpar, v, l, lp, s, llen,
distance_measure)
w = weight_matrix(p.vl, lweight, lsim, bias=wbias)
newVP = calc_new_vanishing_point(l, w[j, :] + w[k, :])
p_vl_sum = np.sum(p.vl[k, :] + p.vl[j, :])
s_log = np.log(np.sum(0.5 * (p.lvsq[:, j] + p.lvsq[:, k]) * (p.vl[k, :] + p.vl[j, :]))) - \
np.log(p_vl_sum)
s[k] = np.exp(s_log)
if newVP is None or s[k] > max_stdd:
tryAgain = False
continue
else:
v[i, k, :] = newVP
v = np.delete(v, j, axis=1)
s = np.delete(s, j, axis=0)
except np.linalg.linalg.LinAlgError as err:
continue
else:
tryAgain = False
M = v.shape[1]
return {'v': v, 's': s}
def main(version='100k', n_jobs=1, random_state=0, cross_val=False):
dl_params = {}
dl_params['100k'] = dict(learning_rate=1, batch_size=10, offset=0, alpha=1)
dl_params['1m'] = dict(learning_rate=.75,
batch_size=60,
offset=0,
alpha=.8)
dl_params['10m'] = dict(learning_rate=.75,
batch_size=600,
offset=0,
alpha=3)
dl_params['netflix'] = dict(learning_rate=.8,
batch_size=4000,
offset=0,
alpha=0.16)
cd_params = {
'100k': dict(alpha=.1),
'1m': dict(alpha=.03),
'10m': dict(alpha=.04),
'netflix': dict(alpha=.1)
}
if version in ['100k', '1m', '10m']:
X = load_movielens(version)
X_tr, X_te = train_test_split(X,
train_size=0.75,
random_state=random_state)
X_tr = X_tr.tocsr()
X_te = X_te.tocsr()
elif version is 'netflix':
X_tr = load(expanduser('~/spira_data/nf_prize/X_tr.pkl'))
X_te = load(expanduser('~/spira_data/nf_prize/X_te.pkl'))
cd_mf = ExplicitMF(
n_components=60,
max_iter=50,
alpha=.1,
normalize=True,
verbose=1,
)
dl_mf = DictMF(n_components=30,
n_epochs=20,
alpha=1.17,
verbose=5,
batch_size=10000,
normalize=True,
fit_intercept=True,
random_state=0,
learning_rate=.75,
impute=False,
partial=False,
backend='python')
dl_mf_partial = DictMF(n_components=60,
n_epochs=20,
alpha=1.17,
verbose=5,
batch_size=10000,
normalize=True,
fit_intercept=True,
random_state=0,
learning_rate=.75,
impute=False,
partial=True,
backend='python')
timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H' '-%M-%S')
if cross_val:
subdir = 'benches_ncv'
else:
subdir = 'benches'
output_dir = expanduser(join('~/output/recommender/', timestamp, subdir))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
alphas = np.logspace(-2, 1, 10)
mf_list = [dl_mf_partial]
dict_id = {cd_mf: 'cd', dl_mf: 'dl', dl_mf_partial: 'dl_partial'}
names = {
'cd': 'Coordinate descent',
'dl': 'Proposed online masked MF',
'dl_partial': 'Proposed algorithm (with partial projection)'
}
if os.path.exists(
join(output_dir, 'results_%s_%s.json' % (version, random_state))):
with open(
join(output_dir,
'results_%s_%s.json' % (version, random_state)),
'r') as f:
results = json.load(f)
else:
results = {}
for mf in mf_list:
results[dict_id[mf]] = {}
if not cross_val:
if isinstance(mf, DictMF):
mf.set_params(
learning_rate=dl_params[version]['learning_rate'],
batch_size=dl_params[version]['batch_size'],
alpha=dl_params[version]['alpha'])
else:
mf.set_params(alpha=cd_params[version]['alpha'])
else:
if isinstance(mf, DictMF):
mf.set_params(
learning_rate=dl_params[version]['learning_rate'],
batch_size=dl_params[version]['batch_size'])
if version != 'netflix':
cv = ShuffleSplit(n_iter=3, train_size=0.66, random_state=0)
mf_scores = Parallel(n_jobs=n_jobs, verbose=10)(
delayed(single_fit)(mf, alpha, X_tr, cv)
for alpha in alphas)
else:
mf_scores = Parallel(n_jobs=n_jobs, verbose=10)(
delayed(single_fit)(mf, alpha, X_tr, X_te, nested=False)
for alpha in alphas)
mf_scores = np.array(mf_scores).mean(axis=1)
best_alpha_arg = mf_scores.argmin()
best_alpha = alphas[best_alpha_arg]
mf.set_params(alpha=best_alpha)
cb = Callback(X_tr, X_te, refit=False)
mf.set_params(callback=cb)
mf.fit(X_tr)
results[dict_id[mf]] = dict(name=names[dict_id[mf]],
time=cb.times,
rmse=cb.rmse)
if cross_val:
results[dict_id[mf]]['alphas'] = alphas.tolist()
results[dict_id[mf]]['cv_alpha'] = mf_scores.tolist()
results[dict_id[mf]]['best_alpha'] = mf.alpha
with open(
join(output_dir,
'results_%s_%s.json' % (version, random_state)),
'w+') as f:
json.dump(results, f)
print('Done')
def main(version='100k', n_jobs=1, random_state=0, cross_val=False):
dl_params = {}
dl_params['100k'] = dict(learning_rate=1, batch_size=10, offset=0, alpha=1)
dl_params['1m'] = dict(learning_rate=.75, batch_size=60, offset=0,
alpha=.8)
dl_params['10m'] = dict(learning_rate=.75, batch_size=600, offset=0,
alpha=3)
dl_params['netflix'] = dict(learning_rate=.8, batch_size=4000, offset=0,
alpha=0.16)
cd_params = {'100k': dict(alpha=.1), '1m': dict(alpha=.03),
'10m': dict(alpha=.04),
'netflix': dict(alpha=.1)}
if version in ['100k', '1m', '10m']:
X = load_movielens(version)
X_tr, X_te = train_test_split(X, train_size=0.75,
random_state=random_state)
X_tr = X_tr.tocsr()
X_te = X_te.tocsr()
elif version is 'netflix':
X_tr = load(expanduser('~/spira_data/nf_prize/X_tr.pkl'))
X_te = load(expanduser('~/spira_data/nf_prize/X_te.pkl'))
cd_mf = ExplicitMF(n_components=60, max_iter=50, alpha=.1, normalize=True,
verbose=1, )
dl_mf = DictMF(n_components=30, n_epochs=20, alpha=1.17, verbose=5,
batch_size=10000, normalize=True,
fit_intercept=True,
random_state=0,
learning_rate=.75,
impute=False,
partial=False,
backend='python')
dl_mf_partial = DictMF(n_components=60, n_epochs=20, alpha=1.17, verbose=5,
batch_size=10000, normalize=True,
fit_intercept=True,
random_state=0,
learning_rate=.75,
impute=False,
partial=True,
backend='python')
timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H'
'-%M-%S')
if cross_val:
subdir = 'benches_ncv'
else:
subdir = 'benches'
output_dir = expanduser(join('~/output/recommender/', timestamp, subdir))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
alphas = np.logspace(-2, 1, 10)
mf_list = [dl_mf_partial]
dict_id = {cd_mf: 'cd', dl_mf: 'dl', dl_mf_partial: 'dl_partial'}
names = {'cd': 'Coordinate descent', 'dl': 'Proposed online masked MF',
'dl_partial': 'Proposed algorithm (with partial projection)'}
if os.path.exists(join(output_dir, 'results_%s_%s.json' % (version,
random_state))):
with open(join(output_dir, 'results_%s_%s.json' % (version,
random_state)),
'r') as f:
results = json.load(f)
else:
results = {}
for mf in mf_list:
results[dict_id[mf]] = {}
if not cross_val:
if isinstance(mf, DictMF):
mf.set_params(
learning_rate=dl_params[version]['learning_rate'],
batch_size=dl_params[version]['batch_size'],
alpha=dl_params[version]['alpha'])
else:
mf.set_params(alpha=cd_params[version]['alpha'])
else:
if isinstance(mf, DictMF):
mf.set_params(
learning_rate=dl_params[version]['learning_rate'],
batch_size=dl_params[version]['batch_size'])
if version != 'netflix':
cv = ShuffleSplit(n_iter=3, train_size=0.66, random_state=0)
mf_scores = Parallel(n_jobs=n_jobs, verbose=10)(
delayed(single_fit)(mf, alpha, X_tr, cv) for alpha in
alphas)
else:
mf_scores = Parallel(n_jobs=n_jobs, verbose=10)(
delayed(single_fit)(mf, alpha, X_tr, X_te,
nested=False) for alpha in alphas)
mf_scores = np.array(mf_scores).mean(axis=1)
best_alpha_arg = mf_scores.argmin()
best_alpha = alphas[best_alpha_arg]
mf.set_params(alpha=best_alpha)
cb = Callback(X_tr, X_te, refit=False)
mf.set_params(callback=cb)
mf.fit(X_tr)
results[dict_id[mf]] = dict(name=names[dict_id[mf]],
time=cb.times,
rmse=cb.rmse)
if cross_val:
results[dict_id[mf]]['alphas'] = alphas.tolist()
results[dict_id[mf]]['cv_alpha'] = mf_scores.tolist()
results[dict_id[mf]]['best_alpha'] = mf.alpha
with open(join(output_dir, 'results_%s_%s.json' % (version,
random_state)),
'w+') as f:
json.dump(results, f)
print('Done')
