def run(self):
kwargs = self.kwargs
self.results = []
context = kwargs.pop('context')
if context == ContextWidget.canonical_value:
cm = CanonicalVariantContext
elif context == ContextWidget.frequent_value:
cm = MostFrequentVariantContext
elif context == ContextWidget.separate_value:
cm = SeparatedTokensVariantContext
elif context == ContextWidget.relative_value:
cm = WeightedVariantContext
with cm(kwargs['corpus'], kwargs['sequence_type'], kwargs['type_token'], frequency_threshold = kwargs['frequency_cutoff']) as c:
try:
for pair in kwargs['segment_pairs']:
res = pointwise_mi(c, pair,
halve_edges = kwargs['halve_edges'],
in_word = kwargs['in_word'],
stop_check = kwargs['stop_check'],
call_back = kwargs['call_back'])
if self.stopped:
break
self.results.append(res)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)
def doVowelHarmony(self):
text = QInputDialog.getText(self, 'Vowel harmony', 'Which feature is unique to vowels? In SPE this is [+voc]')
text = text[0].lstrip('[').rstrip(']')
if not self.corpusHasFeature(text):
return
self.harmony_feature = text
self.corpusModel.corpus.add_tier('AutoGeneratedVowels',text)
inventory = [seg for seg in self.corpusModel.corpus.inventory if seg.features[text[1:]]==text[0]]
probs = defaultdict(list)
for pair in itertools.product(inventory,repeat=2):
pair = (pair[0].symbol, pair[1].symbol)
try:
mi = mutual_information.pointwise_mi(self.corpusModel.corpus, pair, 'AutoGeneratedVowels')
probs[pair[0]].append( (pair[1], mi) )
except mutual_information.MutualInformationError:
probs[pair[0]].append( (pair[1], '*') )
harmonic_features = ['high', 'back', 'round']
commentary = list()
for feature in harmonic_features:
plus = [seg for seg in inventory if seg.features[feature]=='+']
minus = [seg for seg in inventory if not seg in plus]
avg_pp_mi = list()
avg_pm_mi = list()
avg_mm_mi = list()
avg_mp_mi = list()
for seg in inventory:
for seg2,mi in probs[seg.symbol]:
if mi == '*':
continue
seg2 = self.corpusModel.corpus.symbol_to_segment(seg2)
seg2_sign = seg.features[feature]
if seg in plus:
if seg2_sign == '+':
avg_pp_mi.append(mi)
else:
avg_pm_mi.append(mi)
elif seg in minus:
if seg2_sign == '-':
avg_mm_mi.append(mi)
else:
avg_mp_mi.append(mi)
self.resultsLayout.addWidget(QLabel('Average [+{0}][+{0}] MI = {1}'.format(feature, avg_pp_mi)))
self.resultsLayout.addWidget(QLabel('Average [+{0}][-{0}] MI = {1}'.format(feature, avg_pm_mi)))
self.resultsLayout.addWidget(QLabel('Average [-{0}][-{0}] MI = {1}'.format(feature, avg_mm_mi)))
self.resultsLayout.addWidget(QLabel('Average [-{0}][+{0}] MI = {1}'.format(feature, avg_mp_mi)))
if avg_pp_mi > avg_pm_mi and avg_mm_mi > avg_mp_mi:
commentary.append('There might be harmony based on {}'.format(feature))
commentary = '\n'.join([c for c in commentary])
self.outputHarmonyResults(avg_pp_mi,avg_pm_mi,avg_mm_mi,avg_mp_mi,commentary)
self.corpusModel.corpus.remove_attribute('AutoGeneratedVowels')
return
def doVowelHarmony(self):
text = QInputDialog.getText(self, 'Vowel harmony', 'Which feature is unique to vowels? In SPE this is [+voc]')
text = text[0].lstrip('[').rstrip(']')
if not self.corpusHasFeature(text):
return
self.harmony_feature = text
self.corpusModel.corpus.add_tier('AutoGeneratedVowels',text)
inventory = [seg for seg in self.corpusModel.corpus.inventory if seg.features[text[1:]]==text[0]]
probs = defaultdict(list)
for pair in itertools.product(inventory,repeat=2):
pair = (pair[0].symbol, pair[1].symbol)
try:
mi = mutual_information.pointwise_mi(self.corpusModel.corpus, pair, 'AutoGeneratedVowels')
probs[pair[0]].append( (pair[1], mi) )
except mutual_information.MutualInformationError:
probs[pair[0]].append( (pair[1], '*') )
harmonic_features = ['high', 'back', 'round']
commentary = list()
for feature in harmonic_features:
plus = [seg for seg in inventory if seg.features[feature]=='+']
minus = [seg for seg in inventory if not seg in plus]
avg_pp_mi = list()
avg_pm_mi = list()
avg_mm_mi = list()
avg_mp_mi = list()
for seg in inventory:
for seg2,mi in probs[seg.symbol]:
if mi == '*':
continue
seg2 = self.corpusModel.corpus.symbol_to_segment(seg2)
seg2_sign = seg.features[feature]
if seg in plus:
if seg2_sign == '+':
avg_pp_mi.append(mi)
else:
avg_pm_mi.append(mi)
elif seg in minus:
if seg2_sign == '-':
avg_mm_mi.append(mi)
else:
avg_mp_mi.append(mi)
self.resultsLayout.addWidget(QLabel('Average [+{0}][+{0}] MI = {1}'.format(feature, avg_pp_mi)))
self.resultsLayout.addWidget(QLabel('Average [+{0}][-{0}] MI = {1}'.format(feature, avg_pm_mi)))
self.resultsLayout.addWidget(QLabel('Average [-{0}][-{0}] MI = {1}'.format(feature, avg_mm_mi)))
self.resultsLayout.addWidget(QLabel('Average [-{0}][+{0}] MI = {1}'.format(feature, avg_mp_mi)))
if avg_pp_mi > avg_pm_mi and avg_mm_mi > avg_mp_mi:
commentary.append('There might be harmony based on {}'.format(feature))
commentary = '\n'.join([c for c in commentary])
self.outputHarmonyResults(avg_pp_mi,avg_pm_mi,avg_mm_mi,avg_mp_mi,commentary)
self.corpusModel.corpus.remove_attribute('AutoGeneratedVowels')
return
def run(self):
kwargs = self.kwargs
self.results = []
context = kwargs.pop('context')
if context == ContextWidget.canonical_value:
cm = CanonicalVariantContext
elif context == ContextWidget.frequent_value:
cm = MostFrequentVariantContext
elif context == ContextWidget.separate_value:
cm = SeparatedTokensVariantContext
elif context == ContextWidget.relative_value:
cm = WeightedVariantContext
with cm(kwargs['corpus'],
kwargs['sequence_type'],
kwargs['type_token'],
frequency_threshold=kwargs['frequency_cutoff']) as c:
try:
envs = kwargs.pop('envs', None)
if envs is not None: # if env is set, c(orpus context) is 'extracted'
context_output_path = kwargs.pop(
'context_output_path'
) # context_output_path for env context export
c = mi_env_filter(c, envs, context_output_path)
kwargs['in_word'] = False
for pair in kwargs['segment_pairs']:
res = pointwise_mi(c,
pair,
halve_edges=kwargs['halve_edges'],
in_word=kwargs['in_word'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
if self.stopped:
break
self.results.append(res)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)
def test_pointwise_mi(unspecified_test_corpus):
with CanonicalVariantContext(unspecified_test_corpus, 'transcription', 'type') as c:
calls = [
({'corpus_context': c,
'query':('e', 'm')}, 2.7319821866519507),
({'corpus_context': c,
'query':('t', 'n'),
'in_word':True}, 0.5849625007211564),
({'corpus_context': c,
'query':('e', 'm'),
'halve_edges':True}, 2.7319821866519507)
]
for c,v in calls:
result = pointwise_mi(**c)
assert(abs(result-v) < 0.0001)
def test_pointwise_mi(unspecified_test_corpus):
with CanonicalVariantContext(unspecified_test_corpus, 'transcription',
'type') as c:
calls = [({
'corpus_context': c,
'query': ('e', 'm')
}, 2.7319821866519507),
({
'corpus_context': c,
'query': ('t', 'n'),
'in_word': True
}, 0.5849625007211564),
({
'corpus_context': c,
'query': ('e', 'm'),
'halve_edges': True
}, 2.7319821866519507)]
for c, v in calls:
result = pointwise_mi(**c)
assert (abs(result - v) < 0.0001)
def run(self):
time.sleep(0.1)
kwargs = self.kwargs
self.results = []
context = kwargs.pop('context')
if context == ContextWidget.canonical_value:
cm = CanonicalVariantContext
elif context == ContextWidget.frequent_value:
cm = MostFrequentVariantContext
elif context == ContextWidget.separate_value:
cm = SeparatedTokensVariantContext
elif context == ContextWidget.relative_value:
cm = WeightedVariantContext
with cm(kwargs['corpus'], kwargs['sequence_type'],
kwargs['type_token']) as c:
try:
for pair in kwargs['segment_pairs']:
res = pointwise_mi(c,
pair,
halve_edges=kwargs['halve_edges'],
in_word=kwargs['in_word'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
if self.stopped:
break
self.results.append(res)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)