parser.set_defaults(maxn=sys.maxint)
parser.add_argument("--maxN", dest="maxn", type=int, help="")
parser.set_defaults(knn=1)
parser.add_argument("-k",
"--knn",
dest="knn",
type=int,
help="use k of the nearest neighbors (default 1)")
args = parser.parse_args()
test_file = args.test
topK = args.knn
maxN = args.maxn
if args.modelfile:
logging.info("load trained model file")
modelfile = args.modelfile
model = Category2Vec.load(modelfile)
else:
input_file = args.train[0]
p_dir = re.compile("^.*/")
basename = p_dir.sub("", input_file)
if args.outdir:
outdir = args.outdir
else:
m = p_dir.search(input_file)
outdir = m.group(0) if m else ""
if args.split:
input_file = args.train
logging.info("train from input file")
model = Category2Vec(CatSentence(input_file,
cont_col=3,
split=args.split),
parser.add_argument("-k",
"--knn",
dest="knn",
type=int,
help="use k of the nearest neighbors (default 1)")
args = parser.parse_args()
test_file = args.test
topK = args.knn
maxN = args.maxn
if not args.modelfile1 or not args.modelfile2:
print "Specify modelfile1 and modelfile2"
quit(-1)
logging.info("load trained model file")
modelfile1 = args.modelfile1
model1 = Category2Vec.load(modelfile1)
modelfile2 = args.modelfile2
model2 = Category2Vec.load(modelfile2)
logging.info("initializing pairnorm")
model1.init_pairnorm()
model2.init_pairnorm()
#pairtable = np.empty((model1.pair_len, model1.layer1_size * 2), dtype=REAL)
#init_joint_pairtable(model1, model2, pairtable)
test_sentences = CatSentence(test_file)
confusion_mtx = {}
def prepare_sentences():
count = 0
for sent_tuple in test_sentences:
yield sent_tuple
if not os.path.isfile(wikip_data):
logger.info("downloading Wikipedia data")
urllib.urlretrieve(wiki_url, wikip_data)
logger.info("downloaded in %s" % wikip_data)
sentences = WikiSentence(wikip_data)
if not os.path.isfile(c2v_model_name):
model = Category2Vec(sentences,
iteration=20,
model="cb",
hs=1,
negative=0,
size=300)
model.save(c2v_model_name)
else:
model = Category2Vec.load(c2v_model_name)
print "Input a category name or an article title (type EXIT to exit)"
sys.stdout.write("Name: ")
line = sys.stdin.readline()
while line:
line = utils.to_unicode(line.rstrip())
if line == "EXIT":
break
try:
if model.cat_no_hash.has_key(line):
cat_no = model.cat_no_hash[line]
cat_vec = model.cats[cat_no]
ncats = model.most_similar_category(cat_vec, 11)
print "Similar categories similarity"
print "-" * 45
