# tpp (tag preprocessing) has to be chosen from {'raw', 'stem', 'lemm'}

def __init__(self, collection, feature, distance, tpp='lemm', rootpath=ROOT_PATH):

feat_dir = os.path.join(rootpath, collection, "FeatureData", feature)

id_file = os.path.join(feat_dir, "id.txt")

feat_file = os.path.join(feat_dir, "feature.bin")

nr_of_images, ndims = map(int, open(os.path.join(feat_dir,'shape.txt')).readline().split())

self.searcher = simpleknn.load_model(feat_file, ndims, nr_of_images, id_file)

self.searcher.set_distance(distance)

tagfile = os.path.join(rootpath, collection, "TextData", "id.userid.%stags.txt" % tpp)

self.textstore = RecordStore(tagfile)

self.nr_neighbors = 1000

self.nr_newtags = 100

printStatus(INFO, "nr_neighbors=%d, nr_newtags=%d" % (self.nr_neighbors, self.nr_newtags))

def set_nr_neighbors(self, k):

self.nr_neighbors = k

printStatus(INFO, "setting nr_neighbors to %d" % k)

def set_nr_autotags(self, k):

self.nr_newtags = k

printStatus(INFO, "setting nr_autotags to %d" % k)

def neighbor_voting(self, neighbors, qry_tags, qry_userid):

users_voted = set([qry_userid])

tag2vote = {}

voted = 0

unlabeled = 0

thesameuser = 0

for (name, dist) in neighbors:

(userid,tags) = self.textstore.lookup(name)

if not tags:

unlabeled += 1

continue

if userid in users_voted:

thesameuser += 1

continue

tagset = set(tags.split())

for tag in tagset:

tag2vote[tag] = tag2vote.get(tag,0) + 1

users_voted.add(userid)

voted += 1

if voted >= self.nr_neighbors:

break

assert (voted >= self.nr_neighbors), 'unlabeled %d, thesameuser %d, voted %d, neighbors %d' % (unlabeled, thesameuser, voted, len(neighbors))

if not qry_tags: # no tags given, do image auto-tagging

autotags = []

for tag,vote in tag2vote.iteritems():

score = vote - self.textstore.tagprior(tag, self.nr_neighbors)

if score > 1e-6:

autotags.append((tag, score))

autotags.sort(key=lambda v:(v[1]), reverse=True)

return autotags[:self.nr_newtags]

else: # tag relevance learning

qry_tagset = set(str.split(qry_tags.lower()))

tagvotes = [(tag, tag2vote.get(tag,0) - self.textstore.tagprior(tag,self.nr_neighbors)) for tag in qry_tagset]

tagvotes.sort(key=lambda v:(v[1]), reverse=True)

return tagvotes

def estimate(self, qry_vec, qry_tags, qry_id="", qry_userid=""):

# Step 1. visual neighbor search

s_time = time.time()

neighbors = self.searcher.search_knn(qry_vec, max_hits=self.nr_neighbors*3)

search_time = time.time() - s_time

# step 2. neighbor voting

s_time = time.time()

results = self.neighbor_voting(neighbors, qry_tags, qry_userid=qry_userid)

voting_time = time.time() - s_time

#self.echo(self.estimate.__name__, "search %g, voting %g seconds" % (search_time, voting_time))