def __potential_boundaries__(segmentation_a, segmentation_b, **kwargs):
boundary_format = kwargs['boundary_format']
boundary_string_a = segmentation_a
boundary_string_b = segmentation_b
# Convert from NLTK types
if boundary_format == BoundaryFormat.nltk:
boundary_string_a = convert_nltk_to_masses(segmentation_a)
boundary_string_b = convert_nltk_to_masses(segmentation_b)
boundary_format = BoundaryFormat.mass
# Check format
if boundary_format == BoundaryFormat.sets:
pass
elif boundary_format == BoundaryFormat.mass:
boundary_string_a = boundary_string_from_masses(boundary_string_a)
boundary_string_b = boundary_string_from_masses(boundary_string_b)
elif boundary_format == BoundaryFormat.position:
boundary_string_a = convert_positions_to_masses(boundary_string_a)
boundary_string_b = convert_positions_to_masses(boundary_string_b)
boundary_string_a = boundary_string_from_masses(boundary_string_a)
boundary_string_b = boundary_string_from_masses(boundary_string_b)
else:
raise SegmentationMetricError('Unsupported boundary format')
# Compute boundary types if required
boundary_types = identify_types(boundary_string_a, boundary_string_b)
return len(boundary_string_a) * len(boundary_types)
def __boundary_statistics__(
segs_a, segs_b, boundary_types, boundary_format, n_t, weight):
'''
Compute boundary similarity applying the weighting functions specified.
'''
# Convert from NLTK types
if boundary_format == BoundaryFormat.nltk:
segs_a = convert_nltk_to_masses(segs_a)
segs_b = convert_nltk_to_masses(segs_b)
boundary_format = BoundaryFormat.mass
# Check format
if boundary_format == BoundaryFormat.sets:
pass # Correct boundary format
elif boundary_format == BoundaryFormat.mass:
segs_a = boundary_string_from_masses(segs_a)
segs_b = boundary_string_from_masses(segs_b)
elif boundary_format == BoundaryFormat.position:
segs_a = convert_positions_to_masses(segs_a)
segs_b = convert_positions_to_masses(segs_b)
segs_a = boundary_string_from_masses(segs_a)
segs_b = boundary_string_from_masses(segs_b)
else:
raise SegmentationMetricError('Unsupported boundary format')
# Check length
if len(segs_a) != len(segs_b):
raise SegmentationMetricError(
'Segmentations differ in length ({0} != {1})'.format(
len(segs_a), len(segs_b)))
# Determine the boundary types
boundary_types = identify_types(segs_a, segs_b)
# Calculate the total pbs
pbs = len(segs_b) * len(boundary_types)
# Compute edits
additions, substitutions, transpositions = \
boundary_edit_distance(segs_a, segs_b, n_t=n_t)
# Apply weighting functions
fnc_weight_a, fnc_weight_s, fnc_weight_t = weight
count_additions = fnc_weight_a(additions)
count_substitutions = fnc_weight_s(substitutions,
max(boundary_types),
min(boundary_types))
count_transpositions = fnc_weight_t(transpositions, n_t)
count_edits = count_additions + count_substitutions + count_transpositions
# Compute
matches = list()
full_misses = list()
boundaries_all = 0
for set_a, set_b in zip(segs_a, segs_b):
matches.extend(set_a.intersection(set_b))
full_misses.extend(set_a.symmetric_difference(set_b))
boundaries_all += len(set_a) + len(set_b)
return {'count_edits': count_edits, 'additions': additions,
'substitutions': substitutions, 'transpositions': transpositions,
'full_misses': full_misses, 'boundaries_all': boundaries_all,
'matches': matches, 'pbs': pbs, 'boundary_types': boundary_types}
def __boundary_statistics__(segs_a, segs_b, boundary_types, boundary_format,
n_t, weight):
'''
Compute boundary similarity applying the weighting functions specified.
'''
# Convert from NLTK types
if boundary_format == BoundaryFormat.nltk:
segs_a = convert_nltk_to_masses(segs_a)
segs_b = convert_nltk_to_masses(segs_b)
boundary_format = BoundaryFormat.mass
# Check format
if boundary_format == BoundaryFormat.sets:
pass # Correct boundary format
elif boundary_format == BoundaryFormat.mass:
segs_a = boundary_string_from_masses(segs_a)
segs_b = boundary_string_from_masses(segs_b)
elif boundary_format == BoundaryFormat.position:
segs_a = convert_positions_to_masses(segs_a)
segs_b = convert_positions_to_masses(segs_b)
segs_a = boundary_string_from_masses(segs_a)
segs_b = boundary_string_from_masses(segs_b)
else:
raise SegmentationMetricError('Unsupported boundary format')
# Check length
if len(segs_a) != len(segs_b):
raise SegmentationMetricError(
'Segmentations differ in length ({0} != {1})'.format(
len(segs_a), len(segs_b)))
# Determine the boundary types
boundary_types = identify_types(segs_a, segs_b)
# Calculate the total pbs
pbs = len(segs_b) * len(boundary_types)
# Compute edits
additions, substitutions, transpositions = \
boundary_edit_distance(segs_a, segs_b, n_t=n_t)
# Apply weighting functions
fnc_weight_a, fnc_weight_s, fnc_weight_t = weight
count_additions = fnc_weight_a(additions)
count_substitutions = fnc_weight_s(substitutions, max(boundary_types),
min(boundary_types))
count_transpositions = fnc_weight_t(transpositions, n_t)
count_edits = count_additions + count_substitutions + count_transpositions
# Compute
matches = list()
full_misses = list()
boundaries_all = 0
for set_a, set_b in zip(segs_a, segs_b):
matches.extend(set_a.intersection(set_b))
full_misses.extend(set_a.symmetric_difference(set_b))
boundaries_all += len(set_a) + len(set_b)
return {
'count_edits': count_edits,
'additions': additions,
'substitutions': substitutions,
'transpositions': transpositions,
'full_misses': full_misses,
'boundaries_all': boundaries_all,
'matches': matches,
'pbs': pbs,
'boundary_types': boundary_types
}
