def lcsr(srcw, tgtw, slang, tlang):
if slang == tlang or not isc.is_supported_language(
slang) or not isc.is_supported_language(tlang):
return lcsr_any(srcw, tgtw, slang, tlang)
else:
return lcsr_indic(srcw, tgtw, slang, tlang)
def is_consonant(self,c,lang):
if isc.is_supported_language(lang):
return isc.is_consonant(isc.get_phonetic_feature_vector(c,lang))
elif lang in self.consonant_set:
return c in self.consonant_set[lang]
else:
raise Exception('Language no supported. Add list of consonants for this language')
def is_vowel(self, c, lang):
if isc.is_supported_language(lang):
return isc.is_vowel(isc.get_phonetic_feature_vector(c, lang))
elif lang in self.vowel_set:
return c in self.vowel_set[lang]
else:
raise Exception(
'Language no supported. Add list of vowels for this language')
def get_label(x, lang):
if isc.is_supported_language(lang):
if isc.in_coordinated_range(x, lang):
return indtrans.ItransTransliterator.to_itrans(
x, lang) + '({:2x})'.format(isc.get_offset(x, lang))
else:
return str(hex(ord(x)))
else:
return x
def get_column_name(x, tlang):
"""
Get column name (char) in ascii (romanized)
"""
if isc.is_supported_language(tlang):
#return x if tlang=='hi' else indtrans.UnicodeIndicTransliterator.transliterate(x,tlang,'hi')
if isc.in_coordinated_range(x, tlang):
return indtrans.ItransTransliterator.to_itrans(
x, tlang) + '({:2x})'.format(isc.get_offset(x, tlang))
else:
return str(hex(ord(x)))
elif tlang == 'ar':
return a2r_xlit.transliterate(x)
else:
return x
def input_chars_to_analyze():
"""
Input in code: what characters to input
"""
chars_to_analyze = []
if FLAGS.lang == 'en':
chars_to_analyze = ['A', 'E', 'I', 'O', 'U']
elif isc.is_supported_language(FLAGS.lang):
offsets_to_analyze = range(0x3e,
0x4d) ## only vowel diacritics included
chars_to_analyze = [
isc.offset_to_char(x, FLAGS.lang) for x in offsets_to_analyze
]
elif slavic_characters.is_supported_language_latin(FLAGS.lang):
chars_to_analyze = slavic_characters.latin_vowels
#chars_to_analyze=['A','E','I','O','U']
#chars_to_analyze=['K','C','F','V','P','B']
return chars_to_analyze
def is_supported_language(self, lang):
return isc.is_supported_language(lang) or lang in self.vowel_set
def run_sort_errors(basedir, exp_conf_fname):
## read the list of experiments to be analyzed
print 'Read list of experiments'
conf_df = pd.read_csv(exp_conf_fname, header=0, sep=',')
augmented_data = []
for rec in [x[1] for x in conf_df.iterrows()]:
slang = rec['src']
tlang = rec['tgt']
epoch = rec['epoch']
edir = get_edir(rec)
exp_dirname = '{basedir}/results/sup/{dataset}/{exp}/{rep}/{edir}'.format(
basedir=basedir,
dataset=rec['dataset'],
rep=rec['representation'],
exp=rec['exp'],
edir=edir)
out_dirname = '{exp_dirname}/outputs/{epoch:03d}_analysis_{slang}-{tlang}'.format(
exp_dirname=exp_dirname, epoch=epoch, slang=slang, tlang=tlang)
print 'Starting Experiment: ' + exp_dirname
if os.path.isdir(out_dirname):
a_df = align.read_align_count_file(
'{}/alignment_count.csv'.format(out_dirname))
err_df = a_df[a_df.ref_char != a_df.out_char].copy(deep=True)
if isc.is_supported_language(tlang):
err_df['roman_ref'] = err_df.apply(
lambda x: (indtrans.ItransTransliterator.to_itrans(
x['ref_char'], tlang)),
axis=1)
err_df['roman_out'] = err_df.apply(
lambda x: (indtrans.ItransTransliterator.to_itrans(
x['out_char'], tlang)),
axis=1)
err_df['unicode_ref'] = err_df.apply(
lambda x:
('{:2x}'.format(isc.get_offset(x['ref_char'], tlang))),
axis=1)
err_df['unicode_out'] = err_df.apply(
lambda x:
('{:2x}'.format(isc.get_offset(x['out_char'], tlang))),
axis=1)
if tlang == 'ar':
err_df['roman_ref'] = err_df.apply(
lambda x: (a2r_xlit.transliterate(x['ref_char'])), axis=1)
err_df['roman_out'] = err_df.apply(
lambda x: (a2r_xlit.transliterate(x['out_char'])), axis=1)
err_df['unicode_ref'] = err_df.apply(
lambda x: ('{:4x}'.format(ord(x['ref_char']))), axis=1)
err_df['unicode_out'] = err_df.apply(
lambda x: ('{:4x}'.format(ord(x['out_char']))), axis=1)
if align.cci.is_supported_language(tlang):
err_df['charcat_ref'] = err_df.apply(
lambda x: align.cci.get_char_type(x['ref_char'], tlang),
axis=1)
err_df['charcat_out'] = err_df.apply(
lambda x: align.cci.get_char_type(x['out_char'], tlang),
axis=1)
err_df.sort_values(by='count',
axis=0,
ascending=False,
inplace=True)
err_df.to_csv('{}/err_count.csv'.format(out_dirname),
encoding='utf-8')
else:
print 'WARNING (run_sort_errors): Could not analyze following experiment: {} {} {} {} {} epoch: {}'.format(
rec['dataset'], rec['exp'], rec['representation'], slang,
tlang, epoch)
print 'End Experiment: ' + exp_dirname
def is_supported_language(self,lang):
return isc.is_supported_language(lang) or lang in self.vowel_set
