def idlangs(self, tokens):
"""
Return whether a language is present and the counts from each wordlist.
@param tokens sequence of tokens to process
@returns array of booleans for language presence and tuples of wordlist scores
The ordering of return vectors will match the order of languages given
at initialization.
"""
# Create scores
short_scores = numpy.zeros(self.nlangs, dtype=numpy.int)
long_scores = numpy.zeros(self.nlangs, dtype=numpy.int)
# Count each language
for token in tokens:
for idx in range(self.nlangs):
if token in self.shorts[idx]:
short_scores[idx] += 1
if token in self.longs[idx]:
long_scores[idx] += 1
# Decide whether each language is there
langspresent = [(short_scores[idx] > 1 or
(short_scores[idx] == 1 and long_scores[idx] > 0))
for idx in range(self.nlangs)]
# Codeswitching verdict
cs = cs_langspresent(langspresent)
# Give the number of hits in the wordlists
hits = zip(short_scores, long_scores)
lid = self._pick_lang(hits)
return (lid, langspresent, hits, cs)
def idlangs(self, tokens):
"""
Return whether a language is present and the counts from each wordlist.
@param tokens: tokens to identify
"""
# Per-token ratios and langs
ratios = [self._ratios.get(token, RatioListLID.UNK_WORD_RATIO) for token in tokens]
langs = [self._ratio_lang(ratio) if not non_lid(token) else None
for ratio, token in zip(ratios, tokens)]
# Count hits, making a copy with no UNKNOWN_LANG as well
hits = [langs.count(lang) for lang in self.langs]
known_lang_hits = hits[:-1]
unknown_hits = hits[-1]
hitcount = sum(hits)
unk_rate = unknown_hits / hitcount if hitcount else 1.0
langspresent = [(langhits >= present_min)
for langhits, present_min in zip(known_lang_hits, self.present_mins)]
# Zero out langspresent based on unknown rate
langspresent[0] = langspresent[0] and (unk_rate <= self.lang1_max_unk_rate)
langspresent[1] = langspresent[1] and (unk_rate <= self.lang2_max_unk_rate)
# If we're under the acceptable unknown rate, we can have codeswitching
cs = cs_langspresent(langspresent) if (unk_rate <= self.cs_max_unk_rate) else False
# Compute LID based on the greatest number of hits that passed thresholds
lid = self._pick_lang([hit if present else 0
for hit, present in zip(known_lang_hits, langspresent)])
return (lid, langspresent, hits, ratios, langs, unk_rate, cs)
def idlangs(self, tokens, lowmethod, unkmethod, tags=None):
"""
Return whether a language is present and the counts from each wordlist.
@param tokens: tokens to identify
@param tags: optional Jerboa tags for the tokens
"""
# Per-token ratios and langs
ratios = [self._ratios.get(token, RatioListLID.UNK_WORD_RATIO) for token in tokens]
langs = [self._ratio_lang(ratio) if not non_lid(token) else None
for ratio, token in zip(ratios, tokens)]
# Put in dummy tags if needed
if not tags:
tags = [JERBOA_NOTAG] * len(tokens)
# Choose langs for
langs = [choose_lang(token, lang, self.langs, tag, ratio, lowmethod, unkmethod, False)
for token, tag, lang, ratio in zip(tokens, tags, langs, ratios)]
# Clean out any remaining unknowns
if None in langs:
langs = choose_unk_lang(langs, unkmethod)
# Count hits, making a copy with no UNKNOWN_LANG as well
hits = [langs.count(lang) for lang in self.langs]
known_lang_hits = hits[:-1]
unknown_hits = hits[-1]
hitcount = sum(hits)
unk_rate = unknown_hits / hitcount if hitcount else 1.0
langspresent = [(langhits >= present_min)
for langhits, present_min in zip(known_lang_hits, self.present_mins)]
# Zero out langspresent based on unknown rate
langspresent[0] = langspresent[0] and (unk_rate <= self.lang1_max_unk_rate)
langspresent[1] = langspresent[1] and (unk_rate <= self.lang2_max_unk_rate)
# If we're under the acceptable unknown rate, we can have codeswitching
cs = cs_langspresent(langspresent) if (unk_rate <= self.cs_max_unk_rate) else False
# Compute LID based on the greatest number of hits that passed thresholds
lid = self._pick_lang([hit if present else 0
for hit, present in zip(known_lang_hits, langspresent)])
return (lid, langspresent, hits, ratios, langs, unk_rate, cs)
