def get_confidence(alms, scorer, ref='lexstatid', gap_weight=1):
"""
Function creates confidence scores for a given set of alignments.
Parameters
----------
alms : :py:class`~lingpy.align.sca.Alignments`
An *Alignments* object containing already aligned strings.
scorer : :py:class:`~lingpy.algorithm._misc.ScoreDict`
A *ScoreDict* object which gives similarity scores for all segments in
the alignment.
ref : str (default="lexstatid")
The reference entry-type, referring to the cognate-set to be used for
the analysis.
"""
# store all values for average scores
values = []
# store all correspondences
corrs = {}
# store occurrences
occs = {}
for key, msa in alms.msa[ref].items():
# get basic stuff
idxs = msa['ID']
taxa = msa['taxa']
concept = cgi.escape(alms[idxs[0], 'concept'], True)
# get numerical representation of alignments
if scorer:
alignment = [class2tokens(
alms[idxs[i], 'numbers'],
msa['alignment'][i]) for i in range(len(idxs))]
else:
alignment = msa['alignment']
# create new array for confidence
confidence_matrix = []
character_matrix = []
# iterate over each taxon
for i, taxon in enumerate(taxa):
idx = alms.taxa.index(taxon) + 1
# get the numerical sequence
nums = alignment[i]
# store confidences per line
confidences = []
# store chars per line
chars = []
# iterate over the sequence
for j, num in enumerate(nums):
col = [alm[j] for alm in alignment]
score = 0
count = 0
# get the char
if num != '-':
charA = dotjoin(taxa[i], msa['alignment'][i][j], num.split('.')[2])
chars += [charA]
try:
occs[charA] += [concept]
except:
occs[charA] = [concept]
else:
chars += ['-']
for k, numB in enumerate(col):
if k != i:
if num == '-' and numB == '-':
pass
else:
if numB != '-' and num != '-':
# get the second char
charB = dotjoin(
taxa[k], msa['alignment'][k][j], numB.split('.')[2])
try:
corrs[charA][charB] += 1
except:
try:
corrs[charA][charB] = 1
except:
corrs[charA] = {charB: 1}
gaps = False
if num == '-' and numB != '-':
numA = charstring(idx)
gaps = True
elif numB == '-' and num != '-':
numB = charstring(alms.taxa.index(taxa[k]))
numA = num
gaps = True
else:
numA = num
scoreA = scorer[numA, numB]
scoreB = scorer[numB, numA]
this_score = max(scoreA, scoreB)
if not gaps:
score += this_score
count += 1
else:
score += this_score * gap_weight
count += gap_weight
if count:
score = score / count
else:
score = -25
confidences += [int(score + 0.5)]
values += [int(score + 0.5)]
confidence_matrix += [confidences]
character_matrix += [chars]
# append confidence matrix to alignments
alms.msa[ref][key]['confidence'] = confidence_matrix
alms.msa[ref][key]['_charmat'] = character_matrix
# sort the values
values = sorted(set(values + [1]))
# make conversion to scale of 100 values
converter = {}
valsA = values[:values.index(1)]
valsB = values[values.index(1):]
stepA = 50 / (len(valsA) + 1)
stepB = 75 / (len(valsB) + 1)
for i, score in enumerate(valsA): # values[:values.index(0)):
converter[score] = int((stepA * i) / 4 + 0.5)
for i, score in enumerate(valsB):
converter[score] = int(stepB * i + 0.5) + 50
# iterate over keys again
for key, msa in alms.msa[ref].items():
# get basic stuff
for i, line in enumerate(msa['confidence']):
for j, cell in enumerate(line):
alms.msa[ref][key]['confidence'][i][j] = converter[cell]
jsond = {}
for key, corr in corrs.items():
splits = [c.split('.') + [o] for c, o in corr.items()]
sorts = sorted(splits, key=lambda x: (x[0], -x[3]))
new_sorts = []
# check for rowspan
spans = {}
for a, b, c, d in sorts:
if a in spans:
if spans[a] < 3 and d > 1:
spans[a] += 1
new_sorts += [[a, b, c, d]]
else:
if d > 1:
spans[a] = 1
new_sorts += [[a, b, c, d]]
bestis = []
old_lang = ''
counter = 0
for a, b, c, d in new_sorts:
new_lang = a
if new_lang != old_lang:
old_lang = new_lang
tmp = '<tr class="display">'
tmp += '<td class="display" rowspan={0}>'.format(spans[a])
tmp += a + '</td>'
tmp += '<td class="display" onclick="show({0});"><span '.format(
"'" + dotjoin(a, b, c) + "'")
tmp += 'class="char {0}">' + b + '</span></td>'
tmp += '<td class="display">'
tmp += c + '</td>'
tmp += '<td class="display">' + str(d) + '</td>'
tmp += '<td class="display">' + str(len(occs[dotjoin(a, b, c)])) + '</td>'
tmp += '</tr>'
t = 'dolgo_' + token2class(b, rcParams['dolgo'])
# bad check for three classes named differently
if t == 'dolgo__':
t = 'dolgo_X'
elif t == 'dolgo_1':
t = 'dolgo_TONE'
elif t == 'dolgo_0':
t = 'dolgo_ERROR'
bestis += [tmp.format(t)]
counter += 1
elif counter > 0:
tmp = '<tr class="display">'
tmp += '<td class="display" onclick="show({0});"><span '.format(
"'" + dotjoin(a, b, c) + "'")
tmp += 'class="char {0}">' + b + '</span></td>'
tmp += '<td class="display">' + c + '</td>'
tmp += '<td class="display">' + str(d) + '</td>'
tmp += '<td class="display">' + str(len(occs[dotjoin(a, b, c)])) + '</td>'
tmp += '</tr>'
t = 'dolgo_' + token2class(b, rcParams['dolgo'])
# bad check for three classes named differently
if t == 'dolgo__':
t = 'dolgo_X'
elif t == 'dolgo_1':
t = 'dolgo_TONE'
elif t == 'dolgo_0':
t = 'dolgo_ERROR'
bestis += [tmp.format(t)]
counter += 1
old_lang = new_lang
else:
old_lang = new_lang
counter = 0
jsond[key] = [''.join(bestis), occs[key]]
return jsond
def get_partial_scorer(self, **keywords):
"""
Create a scoring function based on sound correspondences.
Parameters
----------
method : str (default='shuffle')
Select between "markov", for automatically generated random
strings, and "shuffle", for random strings taken directly from the
data.
ratio : tuple (default=3,2)
Define the ratio between derived and original score for
sound-matches.
vscale : float (default=0.5)
Define a scaling factor for vowels, in order to decrease their
score in the calculations.
runs : int (default=1000)
Choose the number of random runs that shall be made in order to
derive the random distribution.
threshold : float (default=0.7)
The threshold which used to select those words that are compared
in order to derive the attested distribution.
modes : list (default = [("global",-2,0.5),("local",-1,0.5)])
The modes which are used in order to derive the distributions from
pairwise alignments.
factor : float (default=0.3)
The scaling factor for sound segments with identical prosodic
environment.
force : bool (default=False)
Force recalculation of existing distribution.
preprocessing: bool (default=False)
Select whether SCA-analysis shall be used to derive a preliminary
set of cognates from which the attested distribution shall be
derived.
rands : int (default=1000)
If "method" is set to "markov", this parameter defines the number
of strings to produce for the calculation of the random
distribution.
limit : int (default=10000)
If "method" is set to "markov", this parameter defines the limit
above which no more search for unique strings will be carried out.
cluster_method : {"upgma" "single" "complete"} (default="upgma")
Select the method to be used for the calculation of cognates in the
preprocessing phase, if "preprocessing" is set to c{True}.
gop : int (default=-2)
If "preprocessing" is selected, define the gap opening penalty for
the preprocessing calculation of cognates.
unattested : {int, float} (default=-5)
If a pair of sounds is not attested in the data, but expected by
the alignment algorithm that computes the expected distribution,
the score would be -infinity. Yet in order to allow to smooth this
behaviour and to reduce the strictness, we set a default negative
value which does not necessarily need to be too high, since it may
well be that we miss a potentially good pairing in the first runs
of alignment analyses. Use this keyword to adjust this parameter.
unexpected : {int, float} (default=0.000001)
If a pair is encountered in a given alignment but not expected
according to the randomized alignments, the score would be not
calculable, since we had to divide by zero. For this reason, we set
a very small constant, by which the score is divided in this case.
Not that this constant is only relevant in those cases where the
shuffling procedure was not carried out long enough.
"""
kw = dict(
method=rcParams['lexstat_scoring_method'],
ratio=rcParams['lexstat_ratio'],
vscale=rcParams['lexstat_vscale'],
runs=rcParams['lexstat_runs'],
threshold=rcParams['lexstat_scoring_threshold'],
modes=rcParams['lexstat_modes'],
factor=rcParams['align_factor'],
restricted_chars=rcParams['restricted_chars'],
force=False,
preprocessing=False,
rands=rcParams['lexstat_rands'],
limit=rcParams['lexstat_limit'],
cluster_method=rcParams['lexstat_cluster_method'],
gop=rcParams['align_gop'],
preprocessing_threshold=rcParams[
'lexstat_preprocessing_threshold'],
preprocessing_method=rcParams['lexstat_preprocessing_method'],
subset=False,
defaults=False,
unattested=-5,
unexpected=0.00001,
smooth=1)
kw.update(keywords)
if kw['defaults']:
return kw
# get parameters and store them in string
params = dict(
ratio=kw['ratio'],
vscale=kw['vscale'],
runs=kw['runs'],
scoring_threshold=kw['threshold'],
preprocessing_threshold=kw['preprocessing_threshold'],
modestring=':'.join('{0}-{1}-{2:.2f}'.format(a, abs(b), c)
for a, b, c in kw['modes']),
factor=kw['factor'],
restricted_chars=kw['restricted_chars'],
method=kw['method'],
preprocessing='{0}:{1}:{2}'.format(kw['preprocessing'],
kw['cluster_method'],
kw['gop']),
unattested=kw['unattested'],
unexpected=kw['unexpected'],
smooth=kw['smooth'])
parstring = '_'.join([
'{ratio[0]}:{ratio[1]}', '{vscale:.2f}', '{runs}',
'{scoring_threshold:.2f}', '{modestring}', '{factor:.2f}',
'{restricted_chars}', '{method}', '{preprocessing}',
'{preprocessing_threshold}', '{unexpected:.2f}', '{unattested:.2f}'
]).format(**params)
# check for existing attributes
if hasattr(self, 'cscorer') and not kw['force']:
log.warning(
"An identical scoring function has already been calculated, "
"force recalculation by setting 'force' to 'True'.")
return
# check for attribute
if hasattr(self, 'params') and not kw['force']:
if 'cscorer' in self.params:
if self.params['cscorer'] == params:
log.warning(
"An identical scoring function has already been "
"calculated, force recalculation by setting 'force'"
" to 'True'.")
return
else:
log.warning(
"A different scoring function has already been calculated, "
"overwriting previous settings.")
# store parameters
self.params = {'cscorer': params}
self._meta['params'] = self.params
self._stamp += "# Parameters: " + parstring + '\n'
# get the correspondence distribution
self._corrdist = self._get_partial_corrdist(**kw)
# get the random distribution
self._randist = self._get_partial_randist(**kw)
# get the average gop
gop = sum([m[1] for m in kw['modes']]) / len(kw['modes'])
# create the new scoring matrix
matrix = [[c for c in line] for line in self.bscorer.matrix]
char_dict = self.bscorer.chars2int
for (i, tA), (j, tB) in util.multicombinations2(enumerate(self.cols)):
for charA, charB in product(
list(self.freqs[tA]) + [util.charstring(i + 1)],
list(self.freqs[tB]) + [util.charstring(j + 1)]):
exp = self._randist.get((tA, tB), {}).get((charA, charB),
False)
att = self._corrdist.get((tA, tB), {}).get((charA, charB),
False)
# in the following we follow the former lexstat protocol
if att <= kw['smooth'] and i != j:
att = False
if att and exp:
score = np.log2((att**2) / (exp**2))
elif att and not exp:
score = np.log2((att**2) / kw['unexpected'])
elif exp and not att:
score = kw['unattested'] # XXX gop ???
else: # elif not exp and not att:
score = -90 # ???
# combine the scores
if rcParams['gap_symbol'] not in charA + charB:
sim = self.bscorer[charA, charB]
else:
sim = gop
# get the real score
rscore = (kw['ratio'][0] * score + kw['ratio'][1] * sim) \
/ sum(kw['ratio'])
try:
iA = char_dict[charA]
iB = char_dict[charB]
# use the vowel scale
if charA[4] in self.vowels and charB[4] in self.vowels:
matrix[iA][iB] = matrix[iB][iA] = kw['vscale'] * rscore
else:
matrix[iA][iB] = matrix[iB][iA] = rscore
except:
pass
self.cscorer = misc.ScoreDict(self.chars, matrix)
self._meta['scorer']['cscorer'] = self.cscorer
def _get_partial_corrdist(self, **keywords):
"""
Use alignments to get a correspondences statistics.
"""
kw = dict(
cluster_method='upgma',
factor=rcParams['align_factor'],
gop=rcParams['align_gop'],
modes=rcParams['lexstat_modes'],
preprocessing=False,
preprocessing_method=rcParams['lexstat_preprocessing_method'],
preprocessing_threshold=rcParams[
'lexstat_preprocessing_threshold'],
split_on_tones=False,
ref='scaid',
restricted_chars=rcParams['restricted_chars'],
threshold=rcParams['lexstat_scoring_threshold'],
subset=False)
kw.update(keywords)
self._included = {}
corrdist = {}
if kw['preprocessing']:
if kw['ref'] not in self.header:
self.cluster(method=kw['preprocessing_method'],
threshold=kw['preprocessing_threshold'],
gop=kw['gop'],
cluster_method=kw['cluster_method'],
ref=kw['ref'])
with util.pb(desc='CORRESPONDENCE CALCULATION',
total=self.width**2 / 2) as pb:
for (i, tA), (j,
tB) in util.multicombinations2(enumerate(self.cols)):
pb.update(1)
log.info("Calculating alignments for pair {0} / {1}.".format(
tA, tB))
corrdist[tA, tB] = defaultdict(float)
for mode, gop, scale in kw['modes']:
pairs = self.pairs[tA, tB]
if kw['subset']:
pairs = [
pair for pair in pairs
if pair in self.subsets[tA, tB]
]
# threshold and preprocessing, make sure threshold is
# different from pre-processing threshold when
# preprocessing is set to false
if kw['preprocessing']:
pairs = [
pair for pair in pairs
if self[pair, kw['ref']][0] == self[pair,
kw['ref']][1]
]
threshold = 10.0
else:
threshold = kw['threshold']
# create morpheme-segmented pairs
new_nums, new_weights, new_pros = [], [], []
for idxA, idxB in pairs:
for iA, iB in self._slices[idxA]:
for jA, jB in self._slices[idxB]:
new_nums += [(self[idxA, self._numbers][iA:iB],
self[idxB,
self._numbers][jA:jB])]
new_weights += [(self[idxA,
self._weights][iA:iB],
self[idxB,
self._weights][jA:jB])]
new_pros += [(self[idxA,
self._prostrings][iA:iB],
self[idxB,
self._prostrings][jA:jB])]
corrs, self._included[tA, tB] = calign.corrdist(
threshold, new_nums, new_weights, new_pros, gop, scale,
kw['factor'], self.bscorer, mode,
kw['restricted_chars'])
# change representation of gaps
for (a, b), d in corrs.items():
# XXX check for bias XXX
if a == '-':
a = util.charstring(i + 1)
elif b == '-':
b = util.charstring(j + 1)
corrdist[tA, tB][a, b] += d / float(len(kw['modes']))
return corrdist
def _get_partial_randist(self, **keywords):
"""
Return the aligned results of randomly aligned sequences.
"""
kw = dict(modes=rcParams['lexstat_modes'],
factor=rcParams['align_factor'],
restricted_chars=rcParams['restricted_chars'],
runs=rcParams['lexstat_runs'],
rands=rcParams['lexstat_rands'],
limit=rcParams['lexstat_limit'],
method=rcParams['lexstat_scoring_method'])
kw.update(keywords)
# determine the mode
method = 'markov' if kw['method'] in ['markov', 'markov-chain', 'mc'] \
else 'shuffle'
corrdist = {}
tasks = (self.width**2) / 2
with util.pb(desc='RANDOM CORRESPONDENCE CALCULATION',
total=tasks) as progress:
for (i, tA), (j,
tB) in util.multicombinations2(enumerate(self.cols)):
progress.update(1)
log.info("Calculating random alignments"
"for pair {0}/{1}.".format(tA, tB))
corrdist[tA, tB] = defaultdict(float)
# create morpheme-segmented pairs
pairs = self.pairs[tA, tB]
new_nums, new_weights, new_pros = [], [], []
for idxA, idxB in pairs:
for iA, iB in self._slices[idxA]:
for jA, jB in self._slices[idxB]:
new_nums += [(self[idxA, self._numbers][iA:iB],
self[idxB, self._numbers][jA:jB])]
new_weights += [(self[idxA, self._weights][iA:iB],
self[idxB, self._weights][jA:jB])]
new_pros += [(self[idxA, self._prostrings][iA:iB],
self[idxB, self._prostrings][jA:jB])]
# get the number pairs etc.
sample = [(x, y) for x in range(len(new_nums))
for y in range(len(new_nums))]
if len(sample) > kw['runs']:
sample = random.sample(sample, kw['runs'])
for mode, gop, scale in kw['modes']:
corrs, included = calign.corrdist(
10.0, [(new_nums[s[0]][0], new_nums[s[1]][1])
for s in sample],
[(new_weights[s[0]][0], new_weights[s[1]][1])
for s in sample],
[(new_pros[s[0]][0], new_pros[s[1]][1])
for s in sample], gop, scale, kw['factor'],
self.bscorer, mode, kw['restricted_chars'])
# change representation of gaps
for a, b in list(corrs.keys()):
# get the correspondence count
d = corrs[a, b] * self._included[tA, tB] / included
# XXX check XXX* len(self.pairs[tA,tB]) / runs
# check for gaps
if a == '-':
a = util.charstring(i + 1)
elif b == '-':
b = util.charstring(j + 1)
corrdist[tA, tB][a, b] += d / len(kw['modes'])
return corrdist
def get_confidence(alms, scorer, ref='lexstatid', gap_weight=1):
"""
Function creates confidence scores for a given set of alignments.
Parameters
----------
alms : :py:class`~lingpy.align.sca.Alignments`
An *Alignments* object containing already aligned strings.
scorer : :py:class:`~lingpy.algorithm._misc.ScoreDict`
A *ScoreDict* object which gives similarity scores for all segments in
the alignment.
ref : str (default="lexstatid")
The reference entry-type, referring to the cognate-set to be used for
the analysis.
"""
# store all values for average scores
values = []
# store all correspondences
corrs = {}
# store occurrences
occs = {}
for key, msa in alms.msa[ref].items():
# get basic stuff
idxs = msa['ID']
taxa = msa['taxa']
concept = cgi.escape(alms[idxs[0], 'concept'], True)
# get numerical representation of alignments
if scorer:
alignment = [class2tokens(
alms[idxs[i], 'numbers'],
msa['alignment'][i]) for i in range(len(idxs))]
else:
alignment = msa['alignment']
# create new array for confidence
confidence_matrix = []
character_matrix = []
# iterate over each taxon
for i, taxon in enumerate(taxa):
idx = alms.taxa.index(taxon) + 1
# get the numerical sequence
nums = alignment[i]
# store confidences per line
confidences = []
# store chars per line
chars = []
# iterate over the sequence
for j, num in enumerate(nums):
col = [alm[j] for alm in alignment]
score = 0
count = 0
# get the char
if num != '-':
charA = dotjoin(taxa[i], msa['alignment'][i][j], num.split('.')[2])
chars += [charA]
try:
occs[charA] += [concept]
except:
occs[charA] = [concept]
else:
chars += ['-']
for k, numB in enumerate(col):
if k != i:
if num == '-' and numB == '-':
pass
else:
if numB != '-' and num != '-':
# get the second char
charB = dotjoin(
taxa[k], msa['alignment'][k][j], numB.split('.')[2])
try:
corrs[charA][charB] += 1
except:
try:
corrs[charA][charB] = 1
except:
corrs[charA] = {charB: 1}
gaps = False
if num == '-' and numB != '-':
numA = charstring(idx)
gaps = True
elif numB == '-' and num != '-':
numB = charstring(alms.taxa.index(taxa[k]))
numA = num
gaps = True
else:
numA = num
scoreA = scorer[numA, numB]
scoreB = scorer[numB, numA]
this_score = max(scoreA, scoreB)
if not gaps:
score += this_score
count += 1
else:
score += this_score * gap_weight
count += gap_weight
if count:
score = score / count
else:
score = -25
confidences += [int(score + 0.5)]
values += [int(score + 0.5)]
confidence_matrix += [confidences]
character_matrix += [chars]
# append confidence matrix to alignments
alms.msa[ref][key]['confidence'] = confidence_matrix
alms.msa[ref][key]['_charmat'] = character_matrix
# sort the values
values = sorted(set(values + [1]))
# make conversion to scale of 100 values
converter = {}
valsA = values[:values.index(1)]
valsB = values[values.index(1):]
stepA = 50 / (len(valsA) + 1)
stepB = 75 / (len(valsB) + 1)
for i, score in enumerate(valsA): # values[:values.index(0)):
converter[score] = int((stepA * i) / 4 + 0.5)
for i, score in enumerate(valsB):
converter[score] = int(stepB * i + 0.5) + 50
# iterate over keys again
for key, msa in alms.msa[ref].items():
# get basic stuff
for i, line in enumerate(msa['confidence']):
for j, cell in enumerate(line):
alms.msa[ref][key]['confidence'][i][j] = converter[cell]
jsond = {}
for key, corr in corrs.items():
splits = [c.split('.') + [o] for c, o in corr.items()]
sorts = sorted(splits, key=lambda x: (x[0], -x[3]))
new_sorts = []
# check for rowspan
spans = {}
for a, b, c, d in sorts:
if a in spans:
if spans[a] < 3 and d > 1:
spans[a] += 1
new_sorts += [[a, b, c, d]]
else:
if d > 1:
spans[a] = 1
new_sorts += [[a, b, c, d]]
bestis = []
old_lang = ''
counter = 0
for a, b, c, d in new_sorts:
new_lang = a
if new_lang != old_lang:
old_lang = new_lang
tmp = '<tr class="display">'
tmp += '<td class="display" rowspan={0}>'.format(spans[a])
tmp += a + '</td>'
tmp += '<td class="display" onclick="show({0});"><span '.format(
"'" + dotjoin(a, b, c) + "'")
tmp += 'class="char {0}">' + b + '</span></td>'
tmp += '<td class="display">'
tmp += c + '</td>'
tmp += '<td class="display">' + str(d) + '</td>'
tmp += '<td class="display">' + str(len(occs[dotjoin(a, b, c)])) + '</td>'
tmp += '</tr>'
t = 'dolgo_' + token2class(b, rcParams['dolgo'])
# bad check for three classes named differently
if t == 'dolgo__':
t = 'dolgo_X'
elif t == 'dolgo_1':
t = 'dolgo_TONE'
elif t == 'dolgo_0':
t = 'dolgo_ERROR'
bestis += [tmp.format(t)]
counter += 1
elif counter > 0:
tmp = '<tr class="display">'
tmp += '<td class="display" onclick="show({0});"><span '.format(
"'" + dotjoin(a, b, c) + "'")
tmp += 'class="char {0}">' + b + '</span></td>'
tmp += '<td class="display">' + c + '</td>'
tmp += '<td class="display">' + str(d) + '</td>'
tmp += '<td class="display">' + str(len(occs[dotjoin(a, b, c)])) + '</td>'
tmp += '</tr>'
t = 'dolgo_' + token2class(b, rcParams['dolgo'])
# bad check for three classes named differently
if t == 'dolgo__':
t = 'dolgo_X'
elif t == 'dolgo_1':
t = 'dolgo_TONE'
elif t == 'dolgo_0':
t = 'dolgo_ERROR'
bestis += [tmp.format(t)]
counter += 1
old_lang = new_lang
else:
old_lang = new_lang
counter = 0
jsond[key] = [''.join(bestis), occs[key]]
return jsond
