def test_read_ref_trans(self):
refset = read_ref_trans(
config["test_data_dir"] + "/lemma_sample_out_de-en.ref")
# check doc ids
assert refset.keys() == ["test"]
# check seg ids
assert refset["test"].keys() == ['1', '2', '3', '4', '5']
# check translations
assert refset["test"]["5"] == [
'all other aspect be secondary .',
'the rest be secondary .',
'All the rest be secondary .',
'All the rest be secondary .',
'the rest be secondary .']
def trans_diff(inf, score_attrs, ref_fname=None, colwidth=32,
outf=codecs.getwriter('utf8')(sys.stdout)):
"""
Report translation differences
Outputs all cases where translations differ when selected on score_attr.
If reference translations are provided, it also shows the reference
translation sentences as well as a guess of the reference lemma(s) per
source lemma.
Parameters
----------
inf: list or str
list of TransGraph instances or filename of pickled graphs
score_attrs: list of strings
list of scoring attributes
ref_fname: str
filename of reference translations in mteval xml format
col_width: int
column width
outf: file or str
file or filename for output
Notes
-----
Does not support multi-word expressions
"""
assert len(score_attrs) > 1
if isinstance(inf, basestring):
inf = cPickle.load(open(inf))
if isinstance(outf, basestring):
outf = codecs.open(outf, "w", encoding="utf-8")
no_cols = 1 + len(score_attrs)
if ref_fname:
ref_trans = read_ref_trans(ref_fname, flatten=True)
ref_counts = read_ref_trans_counts(ref_fname, flatten=True)
no_cols += 1
else:
ref_lemmas = set()
bar = no_cols * colwidth * u"=" + u"\n"
subbar = no_cols * colwidth * u"-" + u"\n"
for i, graph in enumerate(inf):
diffs = graph_trans_diff(graph, score_attrs)
if not diffs:
continue
outf.write(bar)
outf.write( u"SEGMENT {} (id={})\n".format(graph.graph.get("n"),
graph.graph.get("id")))
outf.write(bar + u"\n")
outf.write(u"SRC: {}\n".format(
graph.source_string()))
if ref_fname:
for ref_lemmas in ref_trans[i]:
outf.write(u"REF: {}\n".format(ref_lemmas))
outf.write(u"\n")
outf.write(u"SRC LEMPOS:".ljust(colwidth))
for attr in score_attrs:
outf.write((attr.upper() + ":").ljust(colwidth))
if ref_fname:
outf.write(u"REF TRANS:".ljust(colwidth))
outf.write(u"\n" + subbar)
for source_node, max_scores in diffs.iteritems():
if ref_fname:
ref_lemmas = get_ref_lemmas(graph, source_node, ref_counts[i])
outf.write(graph.lempos(source_node).ljust(colwidth))
for score, target_node in max_scores:
if score is not None:
target_lemma = graph.lemma(target_node)
else:
target_lemma = u"__NONE__"
pair = u"{}: {:.4f}: {}".format(
"+" if target_lemma in ref_lemmas else "-",
score,
target_lemma)
outf.write(pair.ljust(colwidth))
if ref_fname:
outf.write(", ".join(ref_lemmas or ["---"]))
outf.write(u"\n")
outf.write(u"\n")
