cursor.execute("SELECT COUNT(*) FROM commits")
print
progress.start(cursor.fetchone()[0], prefix="Fetching commits ...")
cursor.execute("SELECT id, sha1 FROM commits")
for commit_id, commit_sha1 in cursor:
commits[commit_id] = commit_sha1
pending_commits.add(commit_id)
progress.update()
progress.end(" %d commits." % len(commits))
print
cursor.execute("SELECT MAX(CHARACTER_LENGTH(name)) FROM repositories")
repository_name_length = cursor.fetchone()[0]
cursor.execute("SELECT id FROM repositories ORDER BY id ASC")
repositories = [repository_id for (repository_id,) in cursor]
def processCommits(process_commits):
global commits
processed_commits = set()
try: commits = getReviewCommits(repository, getReview(branch_id))
except KeyboardInterrupt: sys.exit(1)
except:
progress.write(" review meta-data references missing commits")
incorrect_reviews.append((getReview(branch_id), "branches.head = %s" % branch_sha1))
continue
heads = commits.getHeads()
if len(heads) > 1:
progress.write(" multiple heads: r/%d" % review_id)
continue
head = heads.pop()
try:
if force: repository.run("update-ref", "refs/heads/%s" % branch_name, head.sha1, "0" * 40)
progress.write(" re-created review branch")
except KeyboardInterrupt: sys.exit(1)
except:
progress.write(" failed to re-create review branch")
incorrect_reviews.append((getReview(branch_id), "failed to re-create review branch"))
progress.end(".")
if incorrect_reviews:
print "\nReviews that need attention:"
for review_id, message in incorrect_reviews:
print " %5d: %s" % (review_id, message)
def exists_in_db(branch_name):
return branch_name in branches_in_db
def process(path, prefix=None):
for entry in os.listdir(path):
entry_path = os.path.join(path, entry)
branch_name = os.path.join(prefix, entry) if prefix else entry
if os.path.isdir(entry_path):
process(entry_path, branch_name)
elif not exists_in_db(branch_name):
progress.write("WARNING[%s]: %s exists in the repository but not in the database!" % (repository.name, branch_name))
if force: repository.run("update-ref", "-d", "refs/heads/%s" % branch_name)
progress.write(" deleted from repository")
processed.add(branch_name)
for branch_name in refs.keys():
if branch_name not in processed and not exists_in_db(branch_name):
progress.write("WARNING[%s]: %s exists in the repository but not in the database!" % (repository.name, branch_name))
if force: repository.run("update-ref", "-d", "refs/heads/%s" % branch_name)
progress.write(" deleted from repository")
process(heads_path)
progress.end(".")
if incorrect_reviews:
print "\nReviews that need attention:"
for review_id, message in incorrect_reviews:
print " %5d: %s" % (review_id, message)
cursor.execute("SELECT COUNT(*) FROM commits")
print
progress.start(cursor.fetchone()[0], prefix="Fetching commits ...")
cursor.execute("SELECT id, sha1 FROM commits")
for commit_id, commit_sha1 in cursor:
commits[commit_id] = commit_sha1
pending_commits.add(commit_id)
progress.update()
progress.end(" %d commits." % len(commits))
print
cursor.execute("SELECT MAX(CHARACTER_LENGTH(name)) FROM repositories")
repository_name_length = cursor.fetchone()[0]
cursor.execute("SELECT id FROM repositories ORDER BY id ASC")
repositories = [repository_id for (repository_id, ) in cursor]
def processCommits(process_commits):
global commits
def test_ndcg(net, data, num_users, ratio, save_file=None, gpus=[0]):
"""Evaluate net."""
progress = ProgressBar()
posi_scores = [[] for u in range(num_users)]
posi_binary = [[] for u in range(num_users)]
net.eval()
parallel = len(gpus) > 1
dtype = torch.FloatTensor if parallel else torch.cuda.FloatTensor
#data.loader.dataset.set_to_posi()
progress.reset(len(data.loader), messsage='Computing postiive outfits')
for idx, inputv in enumerate(data.loader):
items_text, nega_text, items_img, nega_img, uidx = inputv
text = tuple(Variable(v.type(dtype)) for v in items_text[0])
img = tuple(Variable(v.type(dtype)) for v in items_img[0])
uidx = uidx.view(-1, 1)
uidxv = torch.zeros(uidx.shape[0], num_users).scatter_(1, uidx, 1.0)
uidxv = Variable(uidxv.type(dtype))
inputv = (text, img, uidxv)
if parallel:
scores, binary = data_parallel(net, inputv, gpus)
else:
scores, binary = net(*inputv)
for n, u in enumerate(uidx.view(-1)):
posi_binary[u].append(binary[n].item())
posi_scores[u].append(scores[n].data[0])
progress.forward()
progress.end()
# compute scores for negative outfits
nega_scores = [[] for u in range(num_users)]
nega_binary = [[] for u in range(num_users)]
#data.loader.dataset.set_to_nega(ratio=6)
progress.reset(len(data.loader), messsage='Computing negative outfits')
for idx, inputv in enumerate(data.loader):
posi_text, items_text, posi_img, items_img, uidx = inputv
for i in range(ratio):
text = tuple(Variable(v.type(dtype)) for v in items_text[i])
img = tuple(Variable(v.type(dtype)) for v in items_img[i])
uidx = uidx.view(-1, 1)
uidxv = torch.zeros(uidx.shape[0], num_users).scatter_(1, uidx, 1.0)
uidxv = Variable(uidxv.type(dtype))
inputv = (text,img, uidxv)
if parallel:
scores, binary = data_parallel(net, inputv, gpus)
else:
scores, binary = net(*inputv)
for n, u in enumerate(uidx.view(-1)):
nega_binary[u].append(binary[n].data[0])
nega_scores[u].append(scores[n].data[0])
progress.forward()
progress.end()
mean_ndcg, avg_ndcg = metrics.NDCG(posi_scores, nega_scores)
mean_ndcg_bianry, avg_ndcg_binary = metrics.NDCG(
posi_binary, nega_binary)
aucs, mean_auc = metrics.ROC(posi_scores, nega_scores)
aucs_binary, mean_auc_binary = metrics.ROC(posi_binary, nega_binary)
results = dict(
mean_ndcg=mean_ndcg,
avg_ndcg=avg_ndcg,
mean_ndcg_bianry=mean_ndcg_bianry,
avg_ndcg_binary=avg_ndcg_binary,
aucs=aucs,
mean_auc=mean_auc,
aucs_binary=aucs_binary,
mean_auc_binary=mean_auc_binary)
print('avg_mean_ndcg:{} avg_mean_auc:{}'.format(
mean_ndcg.mean(), mean_auc))
# save results
if os.path.exists(save_file):
results.update(np.load(save_file))
np.savez(save_file, **results)
if "LT" in filename:
h2key = pattern.match(filename).group(1)
if h2key not in lt_h2:
lt_h2[h2key] = ROOT.TH2F(args.name + h2key,
args.name + ' ' + h2key, ncat, 0,
ncat, ncat, 0, ncat)
else:
h2key = None
progress.update(100. * i / chain.GetEntries())
if entry.smearerCat[0] >= 0:
x = cat1[entry.smearerCat[0]]
y = cat2[entry.smearerCat[0]]
h2.Fill(x, y)
if h2key in lt_h2:
lt_h2[h2key].Fill(x, y)
progress.end()
h2.Draw("colz")
c.SetLogz()
c.SaveAs("plots/smearerCat_" + h2.GetName() + "_" + region_name + ".png")
for key in lt_h2:
lt_h2[key].Draw("colz")
c.SaveAs("plots/smearerCat_" + lt_h2[key].GetName() + "_" + region_name +
".png")
from distutils.version import LooseVersion
header = "{} {:35} {:35} {:11}".format(args.name, "cat1", "cat2", args.name)
sorted_keys = lt_h2.keys()
sorted_keys.sort(key=LooseVersion)
for key in sorted_keys:
header += " {:11}".format(key)
