def checkArgs(self):
if not CmdOptions.checkArgs(self):
return False
if self.getString('select_pos') != 'random' and self.getInt('nr_pos_bags') > 1:
printError(self.__class__.__name__, "given select_pos=random, nr_pos_bags shall be 1")
return False
return True
def checkArgs(self):
if not CmdOptions.checkArgs(self):
return False
if self.getString('select_pos') != 'random' and self.getInt(
'nr_pos_bags') > 1:
printError(self.__class__.__name__,
"given select_pos=random, nr_pos_bags shall be 1")
return False
return True
def precompute_annotator(self, concept):
INFO = 'dataengine.%s.precompute_annotator'%self.__class__.__name__
topn = 100
NegativeEngine.precompute_annotator(self, concept)
for subconcept in concept.split('-'):
expandedTagSet = set([subconcept] + wn_expand(subconcept))
try:
datafile = os.path.join(ROOT_PATH, self.collection, 'SimilarityIndex', 'ngd', '%s.txt' % subconcept)
rankedtags = readRankingResults(datafile)
expandedTagSet = expandedTagSet.union(set([x[0] for x in rankedtags[:topn]]))
except:
printError(INFO, 'failed to load ranktag file for %s' % subconcept)
self.annotator = self.annotator.union(expandedTagSet)
printStatus(INFO, 'precomputing the virtual annotator for %s: %d tags' % (concept, len(self.annotator)))
def precompute_annotator(self, concept):
INFO = 'dataengine.%s.precompute_annotator' % self.__class__.__name__
topn = 100
NegativeEngine.precompute_annotator(self, concept)
for subconcept in concept.split('-'):
expandedTagSet = set([subconcept] + wn_expand(subconcept))
try:
datafile = os.path.join(ROOT_PATH, self.collection,
'SimilarityIndex', 'ngd',
'%s.txt' % subconcept)
rankedtags = readRankingResults(datafile)
expandedTagSet = expandedTagSet.union(
set([x[0] for x in rankedtags[:topn]]))
except:
printError(INFO,
'failed to load ranktag file for %s' % subconcept)
self.annotator = self.annotator.union(expandedTagSet)
printStatus(
INFO, 'precomputing the virtual annotator for %s: %d tags' %
(concept, len(self.annotator)))
def process(options, testCollection, trainCollection, annotationName,
tagrelMethod, tagfeature):
rootpath = options.rootpath
overwrite = options.overwrite
concepts = readConcepts(trainCollection, annotationName, rootpath)
nr_of_concepts = len(concepts)
mapping = dict(zip(concepts, range(nr_of_concepts)))
feat_dir = os.path.join(rootpath, testCollection, 'FeatureData',
tagfeature)
binary_file = os.path.join(feat_dir, 'feature.bin')
id_file = os.path.join(feat_dir, 'id.txt')
shape_file = os.path.join(feat_dir, 'shape.txt')
if checkToSkip(binary_file, overwrite):
sys.exit(0)
inputfile = os.path.join(rootpath, testCollection, 'autotagging',
testCollection, trainCollection, tagrelMethod,
'id.tagvotes.txt')
if not os.path.exists(inputfile):
printError(INFO, '%s does not exist' % inputfile)
sys.exit(0)
makedirsforfile(binary_file)
fw = open(binary_file, 'wb')
processed = set()
imset = []
count_line = 0
for line in open(inputfile):
count_line += 1
elems = str.split(line.strip())
name = elems[0]
if name in processed:
continue
processed.add(name)
del elems[0]
assert (len(elems) == 2 * nr_of_concepts)
vec = [0] * nr_of_concepts
for i in range(0, len(elems), 2):
tag = elems[i]
idx = mapping[tag]
score = float(elems[i + 1])
vec[idx] = score
s = float(sum(vec)) # l_1 normalized
vec = np.array([x / s for x in vec], dtype=np.float32)
vec.tofile(fw)
imset.append(name)
fw.close()
fw = open(id_file, 'w')
fw.write(' '.join(imset))
fw.close()
fw = open(shape_file, 'w')
fw.write('%d %d' % (len(imset), nr_of_concepts))
fw.close()
print('%d lines parsed, %d ids -> %d unique ids' %
(count_line, len(processed), len(imset)))
def process(options, testCollection, trainCollection, annotationName, tagrelMethod, tagfeature):
rootpath = options.rootpath
overwrite = options.overwrite
concepts = readConcepts(trainCollection, annotationName, rootpath)
nr_of_concepts = len(concepts)
mapping = dict(zip(concepts,range(nr_of_concepts)))
feat_dir = os.path.join(rootpath, testCollection, 'FeatureData', tagfeature)
binary_file = os.path.join(feat_dir, 'feature.bin')
id_file = os.path.join(feat_dir, 'id.txt')
shape_file = os.path.join(feat_dir,'shape.txt')
if checkToSkip(binary_file, overwrite):
sys.exit(0)
inputfile = os.path.join(rootpath, testCollection, 'autotagging', testCollection, trainCollection, tagrelMethod, 'id.tagvotes.txt')
if not os.path.exists(inputfile):
printError(INFO, '%s does not exist' % inputfile)
sys.exit(0)
makedirsforfile(binary_file)
fw = open(binary_file, 'wb')
processed = set()
imset = []
count_line = 0
for line in open(inputfile):
count_line += 1
elems = str.split(line.strip())
name = elems[0]
if name in processed:
continue
processed.add(name)
del elems[0]
assert(len(elems) == 2 * nr_of_concepts)
vec = [0] * nr_of_concepts
for i in range(0, len(elems), 2):
tag = elems[i]
idx = mapping[tag]
score = float(elems[i+1])
vec[idx] = score
s = float(sum(vec)) # l_1 normalized
vec = np.array([x/s for x in vec], dtype=np.float32)
vec.tofile(fw)
imset.append(name)
fw.close()
fw = open(id_file, 'w')
fw.write(' '.join(imset))
fw.close()
fw = open(shape_file, 'w')
fw.write('%d %d' % (len(imset), nr_of_concepts))
fw.close()
print ('%d lines parsed, %d ids -> %d unique ids' % (count_line, len(processed), len(imset)))