def test_rules(tokenizer_with_profile, tokenizer, testdata):
assert tokenizer.rules('abc') == 'abc'
assert tokenizer_with_profile.rules("aabchonn-ih") == " ii-ii"
assert Tokenizer(profile=testdata / 'profile_without_rules.prf').rules('aa') != \
tokenizer_with_profile.rules('aa')
rules = Rules((r'(a|á|e|é|i|í|o|ó|u|ú)(n)(\s)(a|á|e|é|i|í|o|ó|u|ú)', r'\1 \2 \4'))
assert rules.apply('tan ab') == 'ta n ab'
def test_normalization():
specs = [
{'Grapheme': 'ä'},
{'Grapheme': 'aa'},
{'Grapheme': 'a'},
]
prf = Profile(*specs, **{'form': 'NFD'})
t = Tokenizer(profile=prf)
# "aa" matches, because the "ä" is decomposed:
assert t(unicodedata.normalize('NFD', 'aä')) == 'aa ' + REPLACEMENT_MARKER
# A composed "ä" doesn't match anymore:
assert t(unicodedata.normalize('NFC', 'aä')) == 'a ' + REPLACEMENT_MARKER
prf = Profile(*specs, **{'form': 'NFC'})
t = Tokenizer(profile=prf)
# "aa" doesn't match here, this is typically the behaviour one wants:
assert t(unicodedata.normalize('NFC', 'aä')) == 'a ä'
assert t(unicodedata.normalize('NFD', 'aä')) == 'aa ' + REPLACEMENT_MARKER
def tokenize(args):
"""
Tokenize a string (passed as argument or read from stdin)
segments [--profile=PATH/TO/PROFILE] tokenize [STRING]
"""
if args.profile and not Path(args.profile).exists(): # pragma: no cover
raise ParserError('--profile must be a path for an existing file')
_write(args,
Tokenizer(profile=args.profile)(_read(args), column=args.mapping))
def test_errors(profile_path):
t = Tokenizer(profile_path, errors_replace=lambda c: '<{0}>'.format(c))
assert t('habe') == '<i> a b <e>'
with pytest.raises(ValueError):
t('habe', form='xyz')
with pytest.raises(ValueError):
t('habe', errors='strict')
assert t('habe', errors='ignore') == 'a b'
def test_profile():
prf = Profile(
{'Grapheme': 'bischen', 'Out': 'b i s ch e n'},
{'Grapheme': 'sch', 'Out': 'sch'},
{'Grapheme': 'n', 'Out': 'n'},
{'Grapheme': 'a', 'Out': 'a'},
{'Grapheme': 'e', 'Out': 'e'},
{'Grapheme': 'n', 'Out': 'n'},
)
t = Tokenizer(profile=prf)
assert t('bischen', column='Out') == 'b i s ch e n'
assert t('naschen', column='Out') == 'n a sch e n'
assert t('x', column='Out') == REPLACEMENT_MARKER
prf = Profile(
{'Grapheme': 'uu'},
{'Grapheme': 'b'},
{'Grapheme': 'o'},
)
t = Tokenizer(profile=prf)
assert t('uubo uubo') == 'uu b o # uu b o'
def tokenizer(self):
"""
Datasets can provide support for segmentation (aka tokenization) in two ways:
- by providing an orthography profile at etc/orthography.tsv or
- by overwriting this method to return a custom tokenizer callable.
:return: A callable to do segmentation.
The expected signature of the callable is
def t(item, string, **kw)
where
- `item` is a `dict` representing the complete CLDF FormTable row
- `string` is the string to be segmented
- `kw` may be used to pass any context info to the tokenizer, when called
explicitly.
"""
tokenizers = {
k: Tokenizer(profile=p, errors_replace=lambda c: '<{0}>'.format(c))
for k, p in self.orthography_profile_dict.items()
}
if tokenizers:
def _tokenizer(item, string, **kw):
"""
Adds `Profile` and `Graphemes` keys to `item`, returns `list` of segments.
"""
kw.setdefault("column", "IPA")
kw.setdefault("separator", " + ")
profile = kw.pop('profile', None)
if profile:
tokenizer = tokenizers[profile]
item['Profile'] = profile
elif isinstance(item, dict) \
and 'Language_ID' in item \
and item['Language_ID'] in tokenizers:
tokenizer = tokenizers[item['Language_ID']]
item['Profile'] = item['Language_ID']
else:
tokenizer = tokenizers[None]
item['Profile'] = 'default'
form = self.form_for_segmentation(string)
res = tokenizer(form, **kw).split()
kw['column'] = Profile.GRAPHEME_COL
item['Graphemes'] = tokenizer(form, **kw)
return res
return _tokenizer
def tokenizer(self):
"""
Datasets can provide support for segmentation (aka tokenization) in two ways:
- by providing an orthography profile at etc/orthography.tsv or
- by overwriting this method to return a custom tokenizer callable.
:return: A callable to do segmentation.
The expected signature of the callable is
def t(item, string, **kw)
where
- `item` is a `dict` representing the complete CLDF FormTable row
- `string` is the string to be segmented
- `kw` may be used to pass any context info to the tokenizer, when called
explicitly.
"""
profile = self.dir / 'etc' / 'orthography.tsv'
if profile.exists():
profile = Profile.from_file(str(profile), form='NFC')
default_spec = list(next(iter(profile.graphemes.values())).keys())
for grapheme in ['^', '$']:
if grapheme not in profile.graphemes:
profile.graphemes[grapheme] = {
k: None
for k in default_spec
}
profile.tree = Tree(list(profile.graphemes.keys()))
tokenizer = Tokenizer(profile=profile,
errors_replace=lambda c: '<{0}>'.format(c))
def _tokenizer(item, string, **kw):
kw.setdefault("column", "IPA")
kw.setdefault("separator", " + ")
return tokenizer(
unicodedata.normalize('NFC', '^' + string + '$'),
**kw).split()
return _tokenizer
def main(args):
# Initiate tokenizer and profile
profile = Profile.from_file(args.profile)
tokenizer = Tokenizer(profile=profile)
# Open file and check items
errors = []
with open(args.wordlist) as handler:
reader = csv.DictReader(handler, delimiter="\t")
for count, row in enumerate(reader):
segments = my_tokenizer(row[args.form], tokenizer)
reference = row[args.segments]
if segments != reference:
errors.append([row["ID"], row[args.form], segments, reference])
if args.l:
if count > args.l:
break
# Output
print(tabulate(errors, headers=["ID", "Form", "Result", "Reference"]))
print("Errors: %i/%i (%.2f%%)" % (len(errors), count + 1,
(len(errors) / (count + 1)) * 100))
def tokenizer():
return Tokenizer()
def test_tokenize_with_profile_from_object():
prf = Profile(dict(Grapheme='aa', mapping=['x', 'y']),
dict(Grapheme='b', mapping='z'))
assert Tokenizer(profile=prf)('aab', column='mapping') == 'x y z'
def tokenizer_with_profile(profile_path):
return Tokenizer(profile_path)
def cmd_makecldf(self, args):
data = self.raw_dir.read_csv("tukano.csv", dicts=True)
args.writer.add_sources()
# Get our own tokenizer from the orthography profile
# because of multi-profile support, the orthography profile dict
# has a single item, keyed by `None`.
tokenizer = Tokenizer(profile=self.orthography_profile_dict[None])
def _re_tokenize(segmented):
""" Generator of re-tokenized sequences.
Used to re-tokenize alignments, which is needed due to changes
in the orthography profile
Args:
segmented: list of strings
Generates: tokenized segments
"""
preserve_chars = {"(", ")", "-"}
for seg in segmented:
if seg in preserve_chars:
yield seg
else:
normalized = self.form_for_segmentation(seg)
tokenized = tokenizer(normalized, column="IPA")
for seg in tokenized.split(" "):
yield seg
concept_lookup = {}
for concept in self.conceptlists[0].concepts.values():
c_id = "{0}-{1}".format(
concept.id.split("-")[-1], slug(concept.english))
concept_lookup[concept.english] = c_id
args.writer.add_concept(
ID=c_id,
Concepticon_ID=concept.concepticon_id,
Concepticon_Gloss=concept.concepticon_gloss,
Name=concept.english,
)
language_lookup = {}
for language in self.languages:
args.writer.add_language(ID=language["ID"],
Glottocode=language["Glottocode"],
Name=language["Name"])
language_lookup[language["ID_in_raw"]] = language["ID"]
# add data
for row in pylexibank.progressbar(data):
language_id = language_lookup[row["DOCULECT"]]
c_id = concept_lookup[row["CONCEPT"]]
# The alignments were corrected by hand,
# when they differ from the segments,
# the correct notation is in the alignments
tokens = row["TOKENS"].split()
alignment = row["ALIGNMENT"].split(" ")
stripped_alignments = [
s for s in alignment if s not in {"(", "-", ")"}
]
if tokens != stripped_alignments:
tokens = stripped_alignments
lex = args.writer.add_form(
Language_ID=language_id,
Parameter_ID=c_id,
Value=row["IPA"],
# This is a workaround to re-tokenize tokens
Form=".".join(tokens),
Source=["Chacon2014"],
)
# add cognates -- make sure Cognateset_ID is global!
args.writer.add_cognate(
lexeme=lex,
Cognateset_ID="{0}-{1}".format(c_id, row["COGID"]),
Source=["Chacon2014"],
Alignment=list(_re_tokenize(alignment)),
Alignment_Method="expert",
Alignment_Source="Chacon2014",
)
tone += tones[c]
else:
new_segment += c
if len(new_segment) > 0: result.append(new_segment)
if len(tone) > 0: result.append(tone)
return " ".join(result)
# Load tones.csv as lookup table
with open('op/tones.csv', mode='r') as infile:
reader = csv.reader(infile)
tones = {rows[0]: rows[1] for rows in reader}
# Heath2016 orthography profile
t = Tokenizer("op/Heath2016-profile.tsv")
# Dogon data to tokenize
df = pd.read_csv("data/dogon-wordlist-long.csv", index_col="ID")
# Tokenize
tokenizer = lambda x: t.transform(x, column="IPA")
tone_changer = lambda x: convert_tone(x)
df['TOKENS'] = pd.Series(df['COUNTERPART'].apply(tokenizer))
df['TOKENS_CHAO'] = pd.Series(df['TOKENS'].apply(tone_changer))
df['TOKENS'] = df['TOKENS'].str.strip()
df['TOKENS_CHAO'] = df['TOKENS_CHAO'].str.strip()
df.to_csv('data/dogon-wordlist-lingpy-format.csv')
# df.to_csv('final-wordlist-new-tone.tsv', sep="\t")
#!/usr/bin/env python
"""Similarity code tentative cognates in a word list and align them"""
import sys
from pycldf.util import Path
import hashlib
import argparse
import lingpy
import lingpy.compare.partial
from pylexirumah import get_dataset
from segments import Tokenizer
from pyclts import TranscriptionSystem
tokenizer = Tokenizer()
bipa = TranscriptionSystem("bipa")
def sha1(path):
return hashlib.sha1(str(path).encode('utf-8')).hexdigest()[:12]
def clean_segments(row):
"""Reduce the row's segments to not contain empty morphemes.
This function removes all unknown sound segments (/0/) from the "Segments"
list of the `row` dict it is passed, and removes empty morphemes by
collapsing subsequent morpheme boundaries (_#◦+→←) into one. The `row` is
modified in-place, the resulting cleaned segment list is returned.
"e͡i",
"a͡i",
"o͡i",
"u͡i",
"a͡e",
"o͡e",
"e͡o",
"a͡o",
"i͡u",
"e͡u",
"a͡u",
"o͡u",
])
tokenizer = Tokenizer(Profile(*({
"Grapheme": x,
"mapping": x
} for x in sounds)),
errors_ignore=lambda c: c)
from pylexirumah import get_dataset, repository
def needleman_wunsch(x,
y,
lodict={},
gop=-2.5,
gep=-1.75,
local=False,
indel=''):
"""Needleman-Wunsch algorithm with affine gaps penalties.
def test_characters():
t = Tokenizer()
assert t.characters(
"ĉháɾã̌ctʼɛ↗ʐː| k͡p") == "c ̂ h a ́ ɾ a ̃ ̌ c t ʼ ɛ ↗ ʐ ː | # k ͡ p"
assert t.characters('abc def', segment_separator='_',
separator='|') == 'a_b_c|d_e_f'
def test_jipa(lang, testdata):
tokenize = Tokenizer()
assert tokenize(_read_data(testdata / (lang + '_input.txt')), ipa=True) ==\
_read_data(testdata / (lang + '_output.txt'))
def clean_for_lemmatization(word):
word = remove_other_chars(word)
word = replace_greek_word(word)
word = replace_salus(word)
word = replace_lacuna(word)
word = replace_full_stop(word)
word = replace_j(word)
word = word.lower()
return word
# Tokenize
tokenize_graphemes = Tokenizer(GRAPHEME_PROFILE)
def clean_and_tokenize(word, for_lemmatization=False):
if for_lemmatization:
word = clean_for_lemmatization(word)
else:
word = clean(word)
graphemes = tokenize_graphemes(
word, segment_separator=GRAPHEME_SEPARATOR, column="mapping"
)
if graphemes:
return GRAPHEME_SEPARATOR.join([START_WORD, graphemes, END_WORD])
return ""
def recode(s):
t = Tokenizer(profile=_orthography_profile(s))
return t(s.replace('\n', NEWLINE),
column='IPA',
segment_separator='',
separator=' ').replace(SPACE, ' ')
def test_single_combining_character():
assert Tokenizer()("ˈ", ipa=True) == "ˈ"
assert Tokenizer()("ʲ", ipa=True) == "ʲ"
def cmd_install(self, **kw):
# Read individual orthographic profiles, extract the corresponding
# doculect ids (here, glottocodes), and build the appropriate
# tokenizers
profile_files = sorted(glob.glob(str(self.dir / "etc" / "*.prof")))
doculect_codes = [
os.path.splitext(os.path.basename(pf))[0] for pf in profile_files
]
self.doc_tokenizers = {
doculect: Tokenizer(
profile=Profile.from_file(pf, form="NFC"),
errors_replace=lambda c: "<{0}>".format(c),
)
for pf, doculect in zip(profile_files, doculect_codes)
}
# Cache the Concepticon IDs
concepticon = {
x.attributes["wold_id"]: x.concepticon_id
for x in self.conceptlist.concepts.values()
}
# cache the field names for CLDF output
fields = self.lexeme_class.fieldnames()
# Write data to CLDF
with self.cldf as ds:
vocab_ids = [
v["ID"] for v in self.original_cldf["contributions.csv"]
]
# add sources
self.add_sources(ds)
# add languages and build map for choosing the right profile
lang_map = {}
for row in self.original_cldf["LanguageTable"]:
gc, iso = row["Glottocode"], row["ISO639P3code"]
if gc == "tzot1264":
gc, iso = "tzot1259", "tzo"
if row["ID"] in vocab_ids:
ds.add_language(ID=row["ID"],
Name=row["Name"],
Glottocode=gc,
ISO639P3code=iso)
# Add to map only those which are receivers
if int(row["ID"]) <= 41:
lang_map[row["ID"]] = gc
# add parameters
for row in self.original_cldf["ParameterTable"]:
ds.add_concept(
ID=row["ID"],
Name=row.pop("Name"),
Concepticon_ID=concepticon.get(row["ID"]),
)
# Being explicit on what we are adding
for row in self.original_cldf["FormTable"]:
if row["Language_ID"] in vocab_ids:
# Copy the raw Form to Value, clean form, and tokenize
row["Value"] = row["Form"]
row["Form"] = self.clean_form(row["Form"])
row["Segments"] = self.tokenizer(
row["Form"], lang_map[row["Language_ID"]])
# Note: We count words marked as "probably borrowed" as loans.
row["Loan"] = float(row["BorrowedScore"]) > 0.6
ds.add_form_with_segments(
**{k: v
for k, v in row.items() if k in fields})
if c in tones:
tone += tones[c]
else:
new_segment += c
if len(new_segment) > 0: result.append(new_segment)
if len(tone) > 0: result.append(tone)
return " ".join(result)
# Load tones.csv as lookup table
with open('op/tones.csv', mode='r') as infile:
reader = csv.reader(infile)
tones = {rows[0]: rows[1] for rows in reader}
# Heath2016 orthography profile
t = Tokenizer("op/Heath2016-profile.tsv")
# Dogon data to tokenize
df = pd.read_csv("data/dogon-wordlist-long.csv", index_col="ID")
# Tokenize
tokenizer = lambda x: t.transform(x, column="IPA")
tone_changer = lambda x: convert_tone(x)
df['TOKENS'] = pd.Series(df['COUNTERPART'].apply(tokenizer))
df['TOKENS_CHAO'] = pd.Series(df['TOKENS'].apply(tone_changer))
df['TOKENS'] = df['TOKENS'].str.strip()
df['TOKENS_CHAO'] = df['TOKENS_CHAO'].str.strip()
df.to_csv('data/dogon-wordlist-lingpy-format.csv')
# df.to_csv('final-wordlist-new-tone.tsv', sep="\t")
def get_orthography(name):
return Tokenizer(Profile.from_file(profile_path(name + '.tsv'),
form='NFD'))