def process(options, collection):
rootpath = options.rootpath
overwrite = options.overwrite
feature = options.feature
method = options.method
sigma = options.sigma
# result path
ranking_result_path = os.path.join(rootpath, collection, 'SimilarityIndex',
collection, 'MetaData', method, feature)
DCG_result_path = os.path.join(rootpath, collection, 'DCG', method,
feature)
if checkToSkip(ranking_result_path, overwrite):
sys.exit(0)
if checkToSkip(DCG_result_path, overwrite):
sys.exit(0)
# inpute of query
qid_query_file = os.path.join(rootpath, collection, 'Annotations',
'qid.text.txt')
qid_list, query_list = readQidQuery(qid_query_file)
qid2query = dict(zip(qid_list, query_list))
# inpute of image
img_feat_path = os.path.join(rootpath, collection, 'FeatureData', feature)
img_feats = BigFile(img_feat_path)
# the model to calculate DCG@25
scorer = getScorer("DCG@25")
done = 0
qid2dcg = collections.OrderedDict()
qid2iid_label_score = {}
for qid in qid_list:
iid_list, label_list = readAnnotationsFrom(
collection, 'concepts%s.txt' % collection, qid, False, rootpath)
renamed, test_X = img_feats.read(iid_list)
parzen_list = []
for imidx in iid_list:
parzen_list.append(
calParzen(img_feats.read_one(imidx), test_X, sigma))
# parzen_list_suffle = calParzen_fast(test_X, len(renamed), sigma)
# parzen_list = []
# for imidx in iid_list:
# parzen_list.append(parzen_list_suffle[renamed.index(imidx)])
sorted_tuple = sorted(zip(iid_list, label_list, parzen_list),
key=lambda v: v[2],
reverse=True)
qid2iid_label_score[qid] = sorted_tuple
# calculate DCG@25
sorted_label = [x[1] for x in sorted_tuple]
qid2dcg[qid] = scorer.score(sorted_label)
printMessage("Done", qid, qid2query[qid])
done += 1
if done % 20 == 0:
writeRankingResult(ranking_result_path, qid2iid_label_score)
qid2iid_label_score = {}
writeDCGResult(DCG_result_path, qid2dcg)
writeRankingResult(ranking_result_path, qid2iid_label_score)
print "average DCG@25: %f" % (1.0 * sum(qid2dcg.values()) /
len(qid2dcg.values()))
result_path_file = "result/individual_result_pathes.txt"
if os.path.exists(result_path_file):
fout = open(result_path_file, 'a')
else:
makedirsforfile(result_path_file)
fout = open(result_path_file, 'w')
fout.write(ranking_result_path + '\n')
fout.close()
def process(options, collection):
rootpath = options.rootpath
overwrite = options.overwrite
feature = options.feature
method = options.method
sigma =options.sigma
# result path
ranking_result_path = os.path.join(rootpath, collection, 'SimilarityIndex', collection, 'MetaData', method, feature)
DCG_result_path = os.path.join(rootpath, collection, 'DCG', method, feature)
if checkToSkip(ranking_result_path, overwrite):
sys.exit(0)
if checkToSkip(DCG_result_path, overwrite):
sys.exit(0)
# inpute of query
qid_query_file = os.path.join(rootpath, collection, 'Annotations', 'qid.text.txt')
qid_list, query_list = readQidQuery(qid_query_file)
qid2query = dict(zip(qid_list, query_list))
# inpute of image
img_feat_path = os.path.join(rootpath, collection, 'FeatureData', feature)
img_feats = BigFile(img_feat_path)
# the model to calculate DCG@25
scorer = getScorer("DCG@25")
done = 0
qid2dcg = collections.OrderedDict()
qid2iid_label_score = {}
for qid in qid_list:
iid_list, label_list = readAnnotationsFrom(collection, 'concepts%s.txt' % collection, qid, False, rootpath)
renamed, test_X = img_feats.read(iid_list)
parzen_list = []
for imidx in iid_list:
parzen_list.append(calParzen(img_feats.read_one(imidx), test_X , sigma))
sorted_tuple = sorted(zip(iid_list, label_list, parzen_list), key=lambda v:v[2], reverse=True)
qid2iid_label_score[qid] = sorted_tuple
# calculate DCG@25
sorted_label = [x[1] for x in sorted_tuple]
qid2dcg[qid] = scorer.score(sorted_label)
printMessage("Done", qid, qid2query[qid])
done += 1
if done % 20 == 0:
writeRankingResult(ranking_result_path, qid2iid_label_score)
qid2iid_label_score = {}
writeDCGResult(DCG_result_path, qid2dcg)
writeRankingResult(ranking_result_path, qid2iid_label_score)
print "average DCG@25: %f" % (1.0*sum(qid2dcg.values())/ len(qid2dcg.values()))
result_path_file = "result/individual_result_pathes.txt"
if os.path.exists(result_path_file):
fout = open(result_path_file,'a')
else:
makedirsforfile(result_path_file)
fout = open(result_path_file, 'w')
fout.write(ranking_result_path + '\n')
fout.close()
def process(options, trainCollection, devCollection):
rootpath = options.rootpath
overwrite = options.overwrite
method = options.method
metric = options.metric
qrysim = options.qrysim
qrythres = options.qrythres
ntopimg = options.ntopimg
ntopqry = options.ntopqry
mincc = options.mincc
feature = options.feature
# semantic embedding
k = options.k
corpus = options.corpus
word2vec_model = options.word2vec
label_source = options.label_source
# result path
ranking_result_path = os.path.join(rootpath, devCollection, 'SimilarityIndex', devCollection, 'MetaData', method, feature)
DCG_result_path = os.path.join(rootpath, devCollection, metric, method, feature)
if checkToSkip(ranking_result_path, overwrite):
sys.exit(0)
if checkToSkip(DCG_result_path, overwrite):
sys.exit(0)
# inpute of query
qp = SimpleQueryParser()
qid_query_file = os.path.join(rootpath, devCollection, 'Annotations', 'qid.text.txt')
qid_list, query_list = readQidQuery(qid_query_file) #(qid query)
qid2query = dict(zip(qid_list, [qp.process(query) for query in query_list]))
# path of image feature
train_feat_path = os.path.join(rootpath, trainCollection, 'FeatureData', feature)
dev_feat_path = os.path.join(rootpath, devCollection, 'FeatureData', feature)
# method selection
if method =='conse':
se_searcher = ConSE(label_source, corpus, word2vec_model, dev_feat_path, rootpath)
elif method == 't2i' or method == 'ta':
nnquery_file = os.path.join(rootpath, devCollection, 'TextData','querynn', options.nnqueryfile)
qryClick_file = os.path.join(rootpath, trainCollection, 'TextData', options.queryclickfile)
t2i_searcher = Text2Image(nnquery_file, qryClick_file, dev_feat_path, train_feat_path, ntopqry)
elif method == 'i2t' or method == 'ia':
nnimage_file = os.path.join(rootpath, devCollection, 'TextData','imagenn', feature, options.nnimagefile)
imgClick_file = os.path.join(rootpath, trainCollection, 'TextData', options.imageclickfile)
i2t_searcher = Image2Text(nnimage_file, imgClick_file, qrysim, ntopimg, ntopqry)
else:
print "this model is not supported with %s" % method
sys.exit(0)
# calculate DCG@25
scorer = getScorer(metric)
done = 0
failed_count = 0
qid2dcg = collections.OrderedDict()
qid2iid_label_score = {}
for query_id in qid_list:
iid_list, label_list = readAnnotationsFrom(devCollection, 'concepts%s.txt' % devCollection, query_id, False, rootpath)
if method == 'conse':
scorelist = se_searcher.do_search(qid2query[query_id], iid_list, k)
elif method == 't2i':
scorelist = t2i_searcher.text2image(query_id, iid_list, qrythres, mincc )
elif method == 'ta':
scorelist = t2i_searcher.textAnnotation( query_id, iid_list, ntopimg, qrythres, mincc)
elif method == 'i2t':
scorelist = i2t_searcher.image2text(qid2query[query_id], iid_list, mincc )
elif method == 'ia':
scorelist = i2t_searcher.imageAnnotation( qid2query[query_id], iid_list, mincc )
if len(scorelist) == 0:
failed_count += 1
scorelist = [0]*len(iid_list)
qid2iid_label_score[query_id] = zip(iid_list, label_list, scorelist)
random.shuffle(qid2iid_label_score[query_id])
else:
qid2iid_label_score[query_id] = zip(iid_list, label_list, scorelist)
qid2iid_label_score[query_id] = sorted(qid2iid_label_score[query_id], key=lambda v:v[2], reverse=True)
# calculate the result ranking of DCG@25 from our model
qid2dcg[query_id] = scorer.score([x[1] for x in qid2iid_label_score[query_id]])
printMessage("Done", query_id, qid2query[query_id])
done += 1
if(done % 20 == 0):
writeRankingResult(ranking_result_path, qid2iid_label_score)
qid2iid_label_score = {}
writeRankingResult(ranking_result_path, qid2iid_label_score)
writeDCGResult(DCG_result_path, qid2dcg)
print "number of failed query: %d" % failed_count
print "average DCG@25: %f" % (1.0*sum(qid2dcg.values())/ len(qid2dcg.values()))
result_path_file = "result/individual_result_pathes.txt"
if os.path.exists(result_path_file):
fout = open(result_path_file,'a')
else:
makedirsforfile(result_path_file)
fout = open(result_path_file, 'w')
fout.write(ranking_result_path + '\n')
fout.close()
def process(options, trainCollection, devCollection):
rootpath = options.rootpath
overwrite = options.overwrite
method = options.method
metric = options.metric
qrysim = options.qrysim
qrythres = options.qrythres
ntopimg = options.ntopimg
ntopqry = options.ntopqry
mincc = options.mincc
feature = options.feature
# semantic embedding
k = options.k
corpus = options.corpus
word2vec_model = options.word2vec
label_source = options.label_source
# result path
ranking_result_path = os.path.join(rootpath, devCollection,
'SimilarityIndex', devCollection,
'MetaData', method, feature)
DCG_result_path = os.path.join(rootpath, devCollection, 'DCG', method,
feature)
if checkToSkip(ranking_result_path, overwrite):
sys.exit(0)
if checkToSkip(DCG_result_path, overwrite):
sys.exit(0)
# inpute of query
qp = SimpleQueryParser()
qid_query_file = os.path.join(rootpath, devCollection, 'Annotations',
'qid.text.txt')
qid_list, query_list = readQidQuery(qid_query_file) #(qid query)
qid2query = dict(zip(qid_list,
[qp.process(query) for query in query_list]))
# path of image feature
train_feat_path = os.path.join(rootpath, trainCollection, 'FeatureData',
feature)
dev_feat_path = os.path.join(rootpath, devCollection, 'FeatureData',
feature)
# method selection
if method == 'se':
se_searcher = SemanticEmbedding(label_source, corpus, word2vec_model,
dev_feat_path, rootpath)
elif method == 't2i':
nnquery_file = os.path.join(rootpath, devCollection, 'TextData',
'querynn', options.nnqueryfile)
qryClick_file = os.path.join(rootpath, trainCollection, 'TextData',
options.queryclickfile)
t2i_searcher = Text2Image(nnquery_file, qryClick_file, dev_feat_path,
train_feat_path, ntopqry)
elif method == 'i2t':
nnimage_file = os.path.join(rootpath, devCollection, 'TextData',
'imagenn', feature, options.nnimagefile)
imgClick_file = os.path.join(rootpath, trainCollection, 'TextData',
options.imageclickfile)
i2t_searcher = Image2Text(nnimage_file, imgClick_file, qrysim, ntopimg,
ntopqry)
else:
print "this model is not supported with %s" % method
sys.exit(0)
# calculate DCG@25
scorer = getScorer(metric)
done = 0
failed_count = 0
qid2dcg = collections.OrderedDict()
qid2iid_label_score = {}
for query_id in qid_list:
iid_list, label_list = readAnnotationsFrom(
devCollection, 'concepts%s.txt' % devCollection, query_id, False,
rootpath)
if method == 'se':
scorelist = se_searcher.do_search(qid2query[query_id], iid_list, k)
elif method == 't2i':
scorelist = t2i_searcher.text2image(query_id, iid_list, qrythres,
mincc)
elif method == 'i2t':
scorelist = i2t_searcher.image2text(qid2query[query_id], iid_list,
mincc)
if len(scorelist) == 0:
failed_count += 1
scorelist = [0] * len(iid_list)
qid2iid_label_score[query_id] = zip(iid_list, label_list,
scorelist)
random.shuffle(qid2iid_label_score[query_id])
else:
qid2iid_label_score[query_id] = zip(iid_list, label_list,
scorelist)
qid2iid_label_score[query_id] = sorted(
qid2iid_label_score[query_id],
key=lambda v: v[2],
reverse=True)
# calculate the result ranking of DCG@25 from our model
qid2dcg[query_id] = scorer.score(
[x[1] for x in qid2iid_label_score[query_id]])
printMessage("Done", query_id, qid2query[query_id])
done += 1
if (done % 20 == 0):
writeRankingResult(ranking_result_path, qid2iid_label_score)
qid2iid_label_score = {}
writeRankingResult(ranking_result_path, qid2iid_label_score)
writeDCGResult(DCG_result_path, qid2dcg)
print "number of failed query: %d" % failed_count
print "average DCG@25: %f" % (1.0 * sum(qid2dcg.values()) /
len(qid2dcg.values()))
result_path_file = "result/individual_result_pathes.txt"
if os.path.exists(result_path_file):
fout = open(result_path_file, 'a')
else:
makedirsforfile(result_path_file)
fout = open(result_path_file, 'w')
fout.write(ranking_result_path + '\n')
fout.close()
def process(options, trainCollection, devCollection):
rootpath = options.rootpath
overwrite = options.overwrite
metric = options.metric
qrythres = options.qrythres
ntopimg = options.ntopimg
ntopqry = options.ntopqry
mincc = options.mincc
feature = options.feature
# result path
ranking_result_path = os.path.join(rootpath, devCollection, 'SimilarityIndex', devCollection, 'MetaData', 'text2image', feature)
DCG_result_path = os.path.join(rootpath, devCollection, metric, 'text2image', feature)
if checkToSkip(ranking_result_path, overwrite):
sys.exit(0)
if checkToSkip(DCG_result_path, overwrite):
sys.exit(0)
# inpute of query
qp = SimpleQueryParser()
qid_query_file = os.path.join(rootpath, devCollection, 'Annotations', 'qid.text.txt')
qid_list, query_list = readQidQuery(qid_query_file) #(qid query)
qid2query = dict(zip(qid_list, [qp.process(query) for query in query_list]))
# random performance for specific queries
qid_randomperf_file = os.path.join(rootpath, devCollection, 'Annotations', '*****@*****.**')
qid2randomperf = {}
for line in open(qid_randomperf_file):
qid, random_perf = line.strip().split()
qid2randomperf[qid] = float(random_perf)
# path of image feature
train_feat_path = os.path.join(rootpath, trainCollection, 'FeatureData', feature)
dev_feat_path = os.path.join(rootpath, devCollection, 'FeatureData', feature)
nnquery_file = os.path.join(rootpath, devCollection, 'TextData','querynn', options.nnqueryfile)
qryClick_file = os.path.join(rootpath, trainCollection, 'TextData', options.queryclickfile)
t2i_searcher = Text2Image(nnquery_file, qryClick_file, dev_feat_path, train_feat_path, ntopqry)
# calculate DCG@25
scorer = getScorer(metric)
done = 0
failed_count = 0
qid2dcg = collections.OrderedDict()
qid2iid_label_score = {}
for query_id in qid_list:
iid_list, label_list = readAnnotationsFrom(devCollection, 'concepts%s.txt' % devCollection, query_id, False, rootpath)
scorelist = t2i_searcher.doSearch( query_id, iid_list, ntopimg, qrythres, mincc)
if len(scorelist) == 0:
failed_count += 1
qid2dcg[query_id] = qid2randomperf[query_id]
else:
qid2iid_label_score[query_id] = zip(iid_list, label_list, scorelist)
qid2iid_label_score[query_id] = sorted(qid2iid_label_score[query_id], key=lambda v:v[2], reverse=True)
# calculate the result ranking of DCG@25 from our model
qid2dcg[query_id] = scorer.score([x[1] for x in qid2iid_label_score[query_id]])
printMessage("Done", query_id, qid2query[query_id])
done += 1
if(done % 20 == 0):
writeRankingResult(ranking_result_path, qid2iid_label_score)
qid2iid_label_score = {}
writeRankingResult(ranking_result_path, qid2iid_label_score)
writeDCGResult(DCG_result_path, qid2dcg)
print "number of failed query: %d" % failed_count
print "average DCG@25: %f" % (1.0*sum(qid2dcg.values())/ len(qid2dcg.values()))
