def fuse_rows(self, grouper, sort=None):
""" Merge groups of rows
Return the fused table !
TODO : in-place is better
grouper : row grouping attribute
sorter : positioning attribute
"""
sorter = (lambda x: x[sort].value) if sort else (lambda x: 0)
head, fnames = self.tab_header()
mergers = valmerger.gen_vm(fnames)
groups = defaultdict(list)
for r in self.t:
groups[r[grouper].value].append(r)
with open(TMP, 'w') as f:
f.write(head)
for rl in groups.values():
# Sort turn rows by position
rlis = sorted(rl, key=sorter)
# Collect and apply mergers
vals = [rlis[0][n].value for n in fnames]
for r in rlis[1:]:
vals = list(
m(v, r[n].value)
for m, v, n in zip(mergers, vals, fnames))
f.write('\t'.join(map(str, vals)) + '\n')
self.t = Orange.data.Table(TMP)
discard(TMP)
def fuse_rows(self, grouper, sort=None):
""" Merge groups of rows
Return the fused table !
TODO : in-place is better
grouper : row grouping attribute
sorter : positioning attribute
"""
sorter = (lambda x:x[sort].value) if sort else (lambda x:0)
head, fnames = self.tab_header()
mergers = valmerger.gen_vm(fnames)
groups = defaultdict(list)
for r in self.t:
groups[r[grouper].value].append(r)
with open(TMP, 'w') as f:
f.write(head)
for rl in groups.values():
# Sort turn rows by position
rlis = sorted(rl, key=sorter)
# Collect and apply mergers
vals = [rlis[0][n].value for n in fnames]
for r in rlis[1:]:
vals = list(m(v,r[n].value) for m,v,n in zip(mergers, vals, fnames))
f.write('\t'.join(map(str,vals))+'\n')
self.t = Orange.data.Table(TMP)
discard(TMP)
mrg_table = Orange.data.Table([cf_table, df_table])
mgd = mrg_table.domain
md = Orange.data.Domain(mgd, 'is_commitment')
md = Orange.data.Domain(feat_sel, md)
# Add necessary meta information
for nm in ('id', 'turn_id', 'dialogue', 'start'):
md.add_meta(mgd.meta_id(nm), mgd.get_meta(nm))
m_table = Orange.data.Table(md, mrg_table)
m_table.save(fmerge)
print('Data built with {0} EDUs'.format(len(m_table)))
##### Part 2 : Turn merging #####
head, fnames = gen_tab_header(m_table)
mergers = gen_vm(fnames)
# Indexes for features AND metas
# (can't iterate easily on both at the same time)
inds = list(md.index(n) for n in fnames)
# Build turn-rows dict
tid_i = md.index('turn_id')
t_rows = defaultdict(list)
for r in m_table:
t_rows[r[tid_i].value].append(r)
# Merge and store data
s_id = md.index('start')
with open(fturns, 'w') as f:
f.write(head)
mrg_table = Orange.data.Table([cf_table, df_table])
mgd = mrg_table.domain
md = Orange.data.Domain(mgd, 'is_commitment')
md = Orange.data.Domain(feat_sel, md)
# Add necessary meta information
for nm in ('id', 'turn_id', 'dialogue', 'start'):
md.add_meta(mgd.meta_id(nm), mgd.get_meta(nm))
m_table = Orange.data.Table(md, mrg_table)
m_table.save(fmerge)
print('Data built with {0} EDUs'.format(len(m_table)))
##### Part 2 : Turn merging #####
head, fnames = gen_tab_header(m_table)
mergers = gen_vm(fnames)
# Indexes for features AND metas
# (can't iterate easily on both at the same time)
inds = list(md.index(n) for n in fnames)
# Build turn-rows dict
tid_i = md.index('turn_id')
t_rows = defaultdict(list)
for r in m_table:
t_rows[r[tid_i].value].append(r)
# Merge and store data
s_id = md.index('start')
with open(fturns, 'w') as f:
f.write(head)
