def token_to_segment(token, segment_list, diacritic_list):
'''Converts a string token in IPA to Segment object, given
a list of dictionaries representing segments and the same representing
diacritics.'''
diacritic_strings = [segment['IPA'] for segment in diacritic_list]
# Isolate the base IPA segment string
base_string = ''.join(filter(lambda x: x not in diacritic_strings,
token))
# Isolate an iterable of diacritics present
diacritics = [diacritic for diacritic in diacritic_list
if diacritic['IPA'] in token]
# Initialise the base Segment
segment = Segment.from_dictionary(find_segment(base_string,
segment_list))
# Add each diacritic feature to the segment
for diacritic in diacritics:
diacritic_segment = Segment(diacritic['applies'].get('positive', []),
diacritic['applies'].get('negative', []))
segment = segment + diacritic_segment
return segment
def token_to_segment(token, segment_list, diacritic_list):
'''Converts a string token in IPA to Segment object, given
a list of dictionaries representing segments and the same representing
diacritics.'''
diacritic_strings = [segment['IPA'] for segment in diacritic_list]
# Isolate the base IPA segment string
base_string = ''.join(filter(lambda x: x not in diacritic_strings, token))
# Isolate an iterable of diacritics present
diacritics = [
diacritic for diacritic in diacritic_list if diacritic['IPA'] in token
]
# Initialise the base Segment
segment = Segment.from_dictionary(find_segment(base_string, segment_list))
# Add each diacritic feature to the segment
for diacritic in diacritics:
diacritic_segment = Segment(diacritic['applies'].get('positive', []),
diacritic['applies'].get('negative', []),
diacritic['applies'].get('zero', []))
segment = segment + diacritic_segment
return segment
def test_initialisation():
feature_dictionary = {'stress': '+', 'long': '-', 'continuant': '0',
'IPA': 'b'}
segment = Segment.from_dictionary(feature_dictionary)
assert segment.positive == ['stress']
assert segment.negative == ['long']
def test_addition():
feature_dictionary = {'stress': '+', 'syllabic': '-', 'continuant': '0',
'IPA': 'b'}
segment = Segment.from_dictionary(feature_dictionary)
syllabic_diacritic = Segment(['syllabic'], ['voice'])
addition = segment + syllabic_diacritic
assert addition.positive == ['stress', 'syllabic']
assert addition.negative == ['voice']
def test_initialisation():
feature_dictionary = {
'stress': '+',
'long': '-',
'continuant': '0',
'IPA': 'b'
}
segment = Segment.from_dictionary(feature_dictionary)
assert segment.positive == ['stress']
assert segment.negative == ['long']
def benchmark_match_accuracy(segments, diacritics, filename):
'''Convert all given segments to feature strings, then convert back to
segments. Use the given feature string file. Return the percentage accuracy
of the conversion.
'''
feature_strings = load_feature_strings(
path.join(base_directory, 'engine', 'data', filename))
print('Loaded {0} feature strings'.format(len(feature_strings)))
base_matches = []
matches = []
deparse.initialise_cache()
for segment in segments:
base_segment = Segment.from_dictionary(segment)
base_matches.append((segment['IPA'],
deparse.segment_match(feature_strings,
base_segment)))
matches.append((segment['IPA'],
deparse.segment_match(feature_strings, base_segment)))
for diacritic in diacritics:
IPA_representation = segment['IPA'] + diacritic['IPA']
if base_segment.meets_conditions(diacritic['conditions']):
diacritic_segment = base_segment + Segment(
diacritic['applies'].get('positive', []),
diacritic['applies'].get('negative', []))
matches.append((IPA_representation,
deparse.segment_match(feature_strings,
diacritic_segment)))
print('Calculating base accuracy...')
base_successes = 0
for match in base_matches:
if match[0] == match[1]:
base_successes += 1
else:
print('\tExpected {0}, deparsed {1}'.format(match[0], match[1]))
print('Calculating diacritic accuracy...')
successes = len([match for match in matches if match[0] == match[1]])
return (base_successes / len(base_matches)), (successes / len(matches))
def test_addition():
feature_dictionary = {
'stress': '+',
'syllabic': '-',
'continuant': '0',
'IPA': 'b'
}
segment = Segment.from_dictionary(feature_dictionary)
syllabic_diacritic = Segment(['syllabic'], ['voice'])
addition = segment + syllabic_diacritic
assert addition.positive == ['stress', 'syllabic']
assert addition.negative == ['voice']
def benchmark_match_accuracy(segments, diacritics, filename):
'''Convert all given segments to feature strings, then convert back to
segments. Use the given feature string file. Return the percentage accuracy
of the conversion.
'''
feature_strings = load_feature_strings(path.join(base_directory, 'engine',
'data', filename))
print('Loaded {0} feature strings'.format(len(feature_strings)))
base_matches = []
matches = []
deparse.initialise_cache()
for segment in segments:
base_segment = Segment.from_dictionary(segment)
base_matches.append((segment['IPA'],
deparse.segment_match(feature_strings,
base_segment)))
matches.append((segment['IPA'], deparse.segment_match(feature_strings,
base_segment)))
for diacritic in diacritics:
IPA_representation = segment['IPA'] + diacritic['IPA']
if base_segment.meets_conditions(diacritic['conditions']):
diacritic_segment = base_segment + Segment(diacritic['applies'].get('positive', []),
diacritic['applies'].get('negative', []))
matches.append((IPA_representation,
deparse.segment_match(feature_strings,
diacritic_segment)))
print('Calculating base accuracy...')
base_successes = 0
for match in base_matches:
if match[0] == match[1]:
base_successes += 1
else:
print('\tExpected {0}, deparsed {1}'.format(match[0], match[1]))
print('Calculating diacritic accuracy...')
successes = len([match for match in matches if match[0] == match[1]])
return (base_successes / len(base_matches)), (successes / len(matches))
def test_setters():
feature_dictionary = {'stress': '+', 'long': '-', 'continuant': '0',
'IPA': 'b'}
segment = Segment.from_dictionary(feature_dictionary)
segment.add_positive('long')
assert segment.positive == ['stress', 'long']
assert segment.negative == []
segment.add_negative('stress')
assert segment.positive == ['long']
assert segment.negative == ['stress']
segment.add_negative('stress')
assert segment.positive == ['long']
assert segment.negative == ['stress']
def test_setters():
feature_dictionary = {
'stress': '+',
'long': '-',
'continuant': '0',
'IPA': 'b'
}
segment = Segment.from_dictionary(feature_dictionary)
segment.add_positive('long')
assert segment.positive == ['stress', 'long']
assert segment.negative == []
segment.add_negative('stress')
assert segment.positive == ['long']
assert segment.negative == ['stress']
segment.add_negative('stress')
assert segment.positive == ['long']
assert segment.negative == ['stress']
def main():
# Add IPA strings here to print segment values when encountered
diagnostic_targets = []
segments = load_segments(
path.join(base_directory, 'engine', 'data', 'features.csv'))
with open(path.join(base_directory, 'engine', 'data',
'diacritics.yaml')) as f:
diacritics = yaml.load(f)
print('Generating basic feature strings')
print('================================')
feature_strings = []
for segment in segments:
base_segment = Segment.from_dictionary(segment)
feature_strings.append(
(segment['IPA'], deparse.feature_string(base_segment)))
print('\nGenerated {0} feature strings'.format(len(feature_strings)))
print_duplicates(feature_strings)
print('\nGenerating diacritic feature strings')
print('================================')
feature_strings = []
for segment in segments:
base_segment = Segment.from_dictionary(segment)
feature_strings.append(
(segment['IPA'], deparse.feature_string(base_segment)))
if segment['IPA'] in diagnostic_targets:
print('Target found: {0}'.format(segment['IPA']))
print('\tPositive: {0}'.format(base_segment.positive))
print('\tNegative: {0}'.format(base_segment.negative))
for diacritic in diacritics:
IPA_representation = segment['IPA'] + diacritic['IPA']
if base_segment.meets_conditions(diacritic['conditions']):
diacritic_segment = base_segment + Segment(
diacritic['applies'].get('positive', []),
diacritic['applies'].get('negative', []))
feature_strings.append(
(IPA_representation,
deparse.feature_string(diacritic_segment)))
if base_segment.meets_conditions({
'positive': ['syllabic']
}) and not diacritic_segment.meets_conditions(
{'positive': ['long']}):
long_segment = base_segment + Segment(
diacritic['applies'].get(
'positive', []), diacritic['applies'].get(
'negative', [])) + Segment(['long'], [])
feature_strings.append(
(IPA_representation + 'ː',
deparse.feature_string(long_segment)))
if IPA_representation in diagnostic_targets:
print('Target found: {0}'.format(IPA_representation))
print('\tPositive: {0}'.format(diacritic_segment.positive))
print('\tNegative: {0}'.format(diacritic_segment.negative))
print('\nGenerated {0} feature strings'.format(len(feature_strings)))
print_duplicates(feature_strings)
with open('feature-strings-with-diacritics.csv', 'w') as f:
csv.writer(f).writerows(feature_strings)