def main():
parser = argparse.ArgumentParser(
description='Computes an LSA model correlating associations and lexsem model.')
parser.add_argument('--lexspace', '-l', metavar='FILE',
help='The input lexical space.')
parser.add_argument('--assoc', '-a', metavar='FILE',
help='The input association space.')
args = parser.parse_args()
lex = norm2_matrix(read_vector_file(openfile(args.lexspace)))
assoc = norm2_matrix(read_vector_file(openfile(args.assoc)))
together = pd.concat(lex, assoc, keys=("lex", "assoc"))
org_matrix = together.as_matrix()
U, S, V = svd(org_matrix)
np.savez("svd.npz", U, S, V)
def main():
parser = argparse.ArgumentParser(
description=
'Computes an LSA model correlating associations and lexsem model.')
parser.add_argument('--lexspace',
'-l',
metavar='FILE',
help='The input lexical space.')
parser.add_argument('--assoc',
'-a',
metavar='FILE',
help='The input association space.')
args = parser.parse_args()
lex = norm2_matrix(read_vector_file(openfile(args.lexspace)))
assoc = norm2_matrix(read_vector_file(openfile(args.assoc)))
together = pd.concat(lex, assoc, keys=("lex", "assoc"))
org_matrix = together.as_matrix()
U, S, V = svd(org_matrix)
np.savez("svd.npz", U, S, V)
def main():
parser = argparse.ArgumentParser(
description='Computes correlations with compositionality ratings.')
parser.add_argument('--input', '-i', action="append", type=openfile,
metavar="FILE", help='Input vector space.')
parser.add_argument('--ratings', '-r', metavar='COMPFILE', type=openfile,
help='The compositionality ratings file.')
parser.add_argument('--self', '-s', action="store_true",
help='Whether we should include self-comp ratings.')
parser.add_argument('--no-tsv', '-T', action="store_true",
help="*Don't* output the TSV containing comp and model ratings.")
parser.add_argument('--corrs', '-c', action="store_true",
help='Specifies whether correlations should be computed and outputed.')
parser.add_argument('--pdf', '-p', metavar="FILE", default=None,
help='Output plots as a PDF to the given filename.')
args = parser.parse_args()
compratings = pd.read_table(args.ratings)
if not args.self:
compratings = compratings[compratings["compound"] != compratings["const"]]
word_pairs = set(zip(compratings['compound'], compratings['const']))
named_vector_spaces = [
(basename(f.name), norm2_matrix(df_remove_pos(read_vector_file(f))))
for f in args.input
]
if len(named_vector_spaces) > 1:
# need to do concatenation
names, vses = zip(*named_vector_spaces)
concat_space = pd.concat(vses, keys=names)
named_vector_spaces.append(("<concat>", concat_space))
# compute all the distances AND keep the different measures independently named
distances = [
cdm(vs, word_pairs, [DISTANCE_METRIC])
.rename(columns={DISTANCE_METRIC.name: fn + ":" + DISTANCE_METRIC.name})
for fn, vs in named_vector_spaces
]
# now we need to join all the distance calculations:
joined_measures = reduce(pd.merge, distances).rename(
columns={"left": "compound", "right": "const"})
# finally join the similarity measures with the human ratings
dm_and_comp = pd.merge(compratings, joined_measures)
# output dm_and_comp unless the user specified not to
if not args.no_tsv:
dm_and_comp.to_csv(sys.stdout, index=False, sep="\t")
# nicer output
if not args.no_tsv and args.corrs:
# let's compute our correlations
print "\n" + "-" * 80 + "\n"
# compute and output correlations if the user asked
if args.corrs:
corrs = correlations(dm_and_comp).to_csv(sys.stdout, index=False, sep="\t")
# plot the measures if the user asked.
if args.pdf:
scatters(dm_and_comp, args.pdf)
def main():
parser = argparse.ArgumentParser(
description='Computes correlations with compositionality ratings.')
parser.add_argument('--input',
'-i',
action="append",
type=openfile,
metavar="FILE",
help='Input vector space.')
parser.add_argument('--ratings',
'-r',
metavar='COMPFILE',
type=openfile,
help='The compositionality ratings file.')
parser.add_argument('--self',
'-s',
action="store_true",
help='Whether we should include self-comp ratings.')
parser.add_argument(
'--no-tsv',
'-T',
action="store_true",
help="*Don't* output the TSV containing comp and model ratings.")
parser.add_argument(
'--corrs',
'-c',
action="store_true",
help='Specifies whether correlations should be computed and outputed.')
parser.add_argument('--pdf',
'-p',
metavar="FILE",
default=None,
help='Output plots as a PDF to the given filename.')
args = parser.parse_args()
compratings = pd.read_table(args.ratings)
if not args.self:
compratings = compratings[
compratings["compound"] != compratings["const"]]
word_pairs = set(zip(compratings['compound'], compratings['const']))
named_vector_spaces = [(basename(f.name),
norm2_matrix(df_remove_pos(read_vector_file(f))))
for f in args.input]
if len(named_vector_spaces) > 1:
# need to do concatenation
names, vses = zip(*named_vector_spaces)
concat_space = pd.concat(vses, keys=names)
named_vector_spaces.append(("<concat>", concat_space))
# compute all the distances AND keep the different measures independently named
distances = [
cdm(vs, word_pairs, [DISTANCE_METRIC]).rename(
columns={DISTANCE_METRIC.name: fn + ":" + DISTANCE_METRIC.name})
for fn, vs in named_vector_spaces
]
# now we need to join all the distance calculations:
joined_measures = reduce(pd.merge, distances).rename(columns={
"left": "compound",
"right": "const"
})
# finally join the similarity measures with the human ratings
dm_and_comp = pd.merge(compratings, joined_measures)
# output dm_and_comp unless the user specified not to
if not args.no_tsv:
dm_and_comp.to_csv(sys.stdout, index=False, sep="\t")
# nicer output
if not args.no_tsv and args.corrs:
# let's compute our correlations
print "\n" + "-" * 80 + "\n"
# compute and output correlations if the user asked
if args.corrs:
corrs = correlations(dm_and_comp).to_csv(sys.stdout,
index=False,
sep="\t")
# plot the measures if the user asked.
if args.pdf:
scatters(dm_and_comp, args.pdf)
