def test_generation(test_file, lexicon_file):
lexicon = Lexicon(lexicon_file)
counter = Counter()
with open(test_file) as f:
for test in yaml.load(f):
lemma = test.pop("lemma")
location = test.pop("location", "")
for parse, form in test.items():
predicted = lexicon.generate(lemma, parse, context=location)
if predicted is None:
counter.fail("didn't know how to work out {} {} {}".format(lemma, parse, form))
elif strip_length(form) == strip_length(predicted):
counter.success()
continue
elif strip_length(form) not in [strip_length(p) for p in predicted.split("/")]:
counter.fail("{} {} got {} instead of {} in {}".format(lemma, parse, predicted, form, location))
else:
counter.skip("{} {} {} {} {}".format(lemma, parse, form, predicted, location))
counter.results()
ccat_parse = row["ccat-parse"]
norm = row["norm"]
lemma = row["lemma"]
if ccat_pos != "V-":
continue
if ccat_parse[3] == "N":
parse = ccat_parse[1:4]
elif ccat_parse[3] == "P":
parse = ccat_parse[1:4] + "." + ccat_parse[4:7]
elif ccat_parse[3] == "I":
parse = ccat_parse[1:4] + "." + ccat_parse[0] + ccat_parse[5]
else:
continue
predicted = lexicon.generate(lemma, parse)
if predicted is None:
counter.fail("didn't know how to work out {} {} {}".format(
lemma, parse, norm))
elif strip_length(norm) == strip_length(predicted):
counter.success()
continue
elif strip_length(norm) not in [
strip_length(p) for p in predicted.split("/")
]:
counter.fail("{} {} got {} instead of {} in {}".format(
lemma, parse, predicted, norm, row["bcv"]))
else:
counter.skip("{} {} {} {} {}".format(lemma, parse, norm, predicted,
row["bcv"]))
words[lemma]["present.mp1.actual"].add(norm)
if ccat_parse[1:4] == "AAN":
words[lemma]["aorist.act.actual"].add(norm)
if ccat_parse[1:4] == "AMN":
words[lemma]["aorist.mp1.actual"].add(norm)
if ccat_parse[1:4] == "APN":
words[lemma]["aorist.mp2.actual"].add(norm)
lexicon = Lexicon("lexicons/morphgnt.yaml")
for k in sorted(words.keys(), key=collator.sort_key):
PAN_generated = lexicon.generate(k, "PAN")
PMN_generated = lexicon.generate(k, "PMN")
PPN_generated = lexicon.generate(k, "PPN")
AAN_generated = lexicon.generate(k, "AAN")
AMN_generated = lexicon.generate(k, "AMN")
APN_generated = lexicon.generate(k, "APN")
if PAN_generated:
if "present.act.actual" not in words[k]:
words[k]["present.act.generated"].add(strip_length(PAN_generated))
if PMN_generated:
if "present.mp1.actual" not in words[k]:
words[k]["present.mp1.generated"].add(strip_length(PMN_generated))
if PPN_generated:
if "present.mp1.actual" not in words[k]:
words[k]["present.mp1.generated"].add(strip_length(PPN_generated))
if ccat_parse[1:4] == "PPN":
words[lemma]["present.mp1.actual"].add(norm)
if ccat_parse[1:4] == "AAN":
words[lemma]["aorist.act.actual"].add(norm)
if ccat_parse[1:4] == "AMN":
words[lemma]["aorist.mp1.actual"].add(norm)
if ccat_parse[1:4] == "APN":
words[lemma]["aorist.mp2.actual"].add(norm)
lexicon = Lexicon("lexicons/morphgnt.yaml")
for k in sorted(words.keys(), key=collator.sort_key):
PAN_generated = lexicon.generate(k, "PAN")
PMN_generated = lexicon.generate(k, "PMN")
PPN_generated = lexicon.generate(k, "PPN")
AAN_generated = lexicon.generate(k, "AAN")
AMN_generated = lexicon.generate(k, "AMN")
APN_generated = lexicon.generate(k, "APN")
if PAN_generated:
if "present.act.actual" not in words[k]:
words[k]["present.act.generated"].add(strip_length(PAN_generated))
if PMN_generated:
if "present.mp1.actual" not in words[k]:
words[k]["present.mp1.generated"].add(strip_length(PMN_generated))
if PPN_generated:
if "present.mp1.actual" not in words[k]:
words[k]["present.mp1.generated"].add(strip_length(PPN_generated))
for book_num in range(1, 28):
for row in morphgnt_rows(book_num):
ccat_pos = row["ccat-pos"]
ccat_parse = row["ccat-parse"]
norm = row["norm"]
lemma = row["lemma"]
if ccat_pos != "V-":
continue
if ccat_parse[3] == "N":
parse = ccat_parse[1:4]
elif ccat_parse[3] == "P":
parse = ccat_parse[1:4] + "." + ccat_parse[4:7]
elif ccat_parse[3] == "I":
parse = ccat_parse[1:4] + "." + ccat_parse[0] + ccat_parse[5]
else:
continue
predicted = lexicon.generate(lemma, parse)
if predicted is None:
counter.fail("didn't know how to work out {} {} {}".format(lemma, parse, norm))
elif strip_length(norm) == strip_length(predicted):
counter.success()
continue
elif strip_length(norm) not in [strip_length(p) for p in predicted.split("/")]:
counter.fail("{} {} got {} instead of {} in {}".format(lemma, parse, predicted, norm, row["bcv"]))
else:
counter.skip("{} {} {} {} {}".format(lemma, parse, norm, predicted, row["bcv"]))
counter.results()
