def create_representation(args):
rep_type = args['<representation>']
path = args['<representation_path>']
neg = int(args['--neg'])
w_c = args['--w+c']
eig = float(args['--eig'])
if rep_type == 'PPMI':
if w_c:
raise Exception('w+c is not implemented for PPMI.')
else:
return PositiveExplicit(path, True, neg)
elif rep_type == 'SVD':
if w_c:
return EnsembleEmbedding(SVDEmbedding(path, False, eig, False), SVDEmbedding(path, False, eig, True), True)
else:
return SVDEmbedding(path, True, eig)
elif rep_type == 'GLOVE':
return GLOVEEmbedding(path, True)
else:
if w_c:
return EnsembleEmbedding(Embedding(path + '.words', False), Embedding(path + '.contexts', False), True)
else:
return Embedding(path + '.words', True)
def main():
args = docopt("""
Usage:
svd2text.py [options] <svd_path> <output_path>
Options:
--w+c Use ensemble of word and context vectors
--eig NUM Weighted exponent of the eigenvalue matrix [default: 0.5]
""")
svd_path = args['<svd_path>']
output_path = args['<output_path>']
w_c = args['--w+c']
eig = float(args['--eig'])
if w_c:
svd = EnsembleEmbedding(SVDEmbedding(svd_path, False, eig, False),
SVDEmbedding(svd_path, False, eig, True), True)
else:
svd = SVDEmbedding(svd_path, True, eig)
with open(output_path, 'w') as f:
for i, w in enumerate(svd.iw):
print >> f, w, ' '.join([str(x) for x in svd.m[i]])
def __init__(self, path, years, **kwargs):
self.embeds = collections.OrderedDict()
for year in years:
self.embeds[year] = SVDEmbedding(path + "/" + str(year), **kwargs)
parser.add_argument("test_path", help="Path to test data")
parser.add_argument("--word-path",
help="Path to sorted list of context words",
default="")
parser.add_argument("--num-context",
type=int,
help="Number context words to use",
default=-1)
parser.add_argument("--type", default="PPMI")
args = parser.parse_args()
if args.type == "PPMI":
year = int(args.vec_path.split("/")[-1].split(".")[0])
if args.num_context != -1 and args.word_path == "":
raise Exception(
"Must specify path to context word file if the context words are to be restricted!"
)
elif args.word_path != "":
_, context_words = ioutils.load_target_context_words(
[year], args.word_path, -1, args.num_context)
context_words = context_words[year]
else:
context_words = None
rep = Explicit.load(args.vec_path, restricted_context=context_words)
elif args.type == "SVD":
rep = SVDEmbedding(args.vec_path, eig=0.0)
else:
rep = Embedding.load(args.vec_path, add_context=False)
data = read_test_set(args.test_path)
correlation = evaluate(rep, data)
print "Correlation: " + str(correlation)
def folder2svd(folder, raw=False):
if raw:
return SVDEmbedding(join(folder, "svd_pmi"))
return SVDEmbedding(join(folder, "svd_pmi")).similarity