def cdu_feats(anno):
"""Get CDU features that are not immediate.
Parameters
----------
anno: Schema
The schema that codes this CDU in the glozz format
Returns
-------
res: dict(string, int)
Features on this CDU, currently
'nb_edus_tot' (total number of EDUs spanned by this CDU),
'nb_cdus_imm' (number of CDUs immediately embedded in this CDU),
'nb_cdus_tot' (total number of CDUs recursively embedded in this
CDU),
'max_lvl' (maximal degree of CDU nesting in this CDU).
"""
nb_members = len(anno.members)
nb_cdus_imm = len([m for m in anno.members if is_cdu(m)])
nb_edus_tot = 0
nb_cdus_tot = 0
max_lvl = 0
cdus_to_expand = [(0, anno)]
while cdus_to_expand:
lvl, cur_cdu = cdus_to_expand.pop()
mem_lvl = lvl + 1
for member in cur_cdu.members:
if is_edu(member):
nb_edus_tot += 1
elif is_cdu(member):
nb_cdus_tot += 1
if mem_lvl > max_lvl:
max_lvl = mem_lvl
cdus_to_expand.append((mem_lvl, member))
else:
raise ValueError('Unexpected type for a CDU member')
# TODO new features:
# * nb_gaps: CDUs spans can be discontinuous
# * gap_max_len: max len of a gap (in #EDUs)
# * over_nb_turns: nb of turns this CDU (partly) spans over
# * over_nb_tstars: nb of tstars this CDU (partly) spans over
res = {
'members': nb_members,
'members_cdu': nb_cdus_imm,
'spanned_cdus': nb_cdus_tot,
'spanned_edus': nb_edus_tot,
'depth': max_lvl,
}
return res
def cdu_feats(anno):
"""Get CDU features that are not immediate.
Parameters
----------
anno: Schema
The schema that codes this CDU in the glozz format
Returns
-------
res: dict(string, int)
Features on this CDU, currently
'nb_edus_tot' (total number of EDUs spanned by this CDU),
'nb_cdus_imm' (number of CDUs immediately embedded in this CDU),
'nb_cdus_tot' (total number of CDUs recursively embedded in this
CDU),
'max_lvl' (maximal degree of CDU nesting in this CDU).
"""
nb_members = len(anno.members)
nb_cdus_imm = len([m for m in anno.members if is_cdu(m)])
nb_edus_tot = 0
nb_cdus_tot = 0
max_lvl = 0
cdus_to_expand = [(0, anno)]
while cdus_to_expand:
lvl, cur_cdu = cdus_to_expand.pop()
mem_lvl = lvl + 1
for member in cur_cdu.members:
if is_edu(member):
nb_edus_tot += 1
elif is_cdu(member):
nb_cdus_tot += 1
if mem_lvl > max_lvl:
max_lvl = mem_lvl
cdus_to_expand.append((mem_lvl, member))
else:
raise ValueError('Unexpected type for a CDU member')
# TODO new features:
# * nb_gaps: CDUs spans can be discontinuous
# * gap_max_len: max len of a gap (in #EDUs)
# * over_nb_turns: nb of turns this CDU (partly) spans over
# * over_nb_tstars: nb of tstars this CDU (partly) spans over
res = {
'members': nb_members,
'members_cdu': nb_cdus_imm,
'spanned_cdus': nb_cdus_tot,
'spanned_edus': nb_edus_tot,
'depth': max_lvl,
}
return res
def rel_feats(doc, ctx, anno, debug=False):
"""Get features for relations.
Parameters
----------
doc:
ctx:
anno:
Returns
-------
res: dict
Features for this relation
"""
# get all EDUs from document, sorted by their span
doc_edus = sorted([u for u in doc.units if is_edu(u)],
key=lambda u: u.span)
# TODO doc_tstars = ...
src = anno.source
if is_cdu(src):
src_type = 'CDU'
src_edus = sorted(src.terminals(), key=lambda e: e.span)
elif is_edu(src):
src_type = 'EDU'
src_edus = [src]
else:
# covered by stac-check ("non-DU endpoints")
return {}
tgt = anno.target
if is_cdu(tgt):
tgt_type = 'CDU'
tgt_edus = sorted(tgt.terminals(), key=lambda e: e.span)
elif is_edu(tgt):
tgt_type = 'EDU'
tgt_edus = [tgt]
else:
# covered by stac-check ("non-DU endpoints")
return {}
# get the index of the EDUs in the interval between src and tgt
src_idc = [doc_edus.index(e) for e in src_edus]
tgt_idc = [doc_edus.index(e) for e in tgt_edus]
# error case covered at least partially by stac-check, either
# as "bizarre relation instance" or as "CDU punctures"
if set(src_idc).intersection(set(tgt_idc)):
if debug:
direction = 'messed up'
print('* {}: {} {}'.format(doc.origin, direction, anno.type))
print('\t' + ', '.join([
'[{}] {}'.format(str(e.span), doc.text(e.span))
for e in src_edus
]))
print('\t' + ', '.join([
'[{}] {}'.format(str(e.span), doc.text(e.span))
for e in tgt_edus
]))
return {}
# src ... tgt
if src_idc[-1] < tgt_idc[0]:
direction = 'right'
fst_idc = src_idc
snd_idc = tgt_idc
interv_edus = doc_edus[(fst_idc[-1] + 1):snd_idc[0]]
# tgt ... src
elif tgt_idc[-1] < src_idc[0]:
direction = 'left'
fst_idc = tgt_idc
snd_idc = src_idc
interv_edus = doc_edus[(fst_idc[-1] + 1):snd_idc[0]]
# tgt and src are interwoven
else:
direction = 'interwoven' # FIXME
src_tgt_idc = set(src_idc).union(tgt_idc)
interv_edus = []
gap_edus = [
e for i, e in enumerate(doc_edus)
if (i not in src_tgt_idc and i > min(src_tgt_idc)
and i < max(src_tgt_idc))
]
if debug:
print('* {}: {} {}'.format(doc.origin, direction, anno.type))
print('\t' + ', '.join([
'[{}] {}'.format(str(e.span), doc.text(e.span))
for e in src_edus
]))
print('\t' + ', '.join([
'[{}] {}'.format(str(e.span), doc.text(e.span))
for e in tgt_edus
]))
print('\t' + ', '.join([
'[{}] {}'.format(str(e.span), doc.text(e.span))
for e in gap_edus
]))
edu_dist = len(interv_edus) + 1
# turn-stars distance
src_tstars = [ctx[e].tstar for e in src_edus]
tgt_tstars = [ctx[e].tstar for e in tgt_edus]
interv_tstars = [ctx[e].tstar for e in interv_edus]
# turn-stars from the interval that don't overlap with src nor tgt
skipped_tstars = set(interv_tstars) - set(src_tstars) - set(tgt_tstars)
# we define:
# * tstar_dist = 0 if (part of) src and tgt belong to the same tstar
# * tstar_dist = len(skipped_tstars) + 1 otherwise
tstar_dist = (len(skipped_tstars) + 1
if not set(src_tstars).intersection(set(tgt_tstars)) else 0)
res = {
'src_type': src_type,
'tgt_type': tgt_type,
'direction': direction,
'edu_dist': edu_dist,
'tstar_dist': tstar_dist,
}
return res
def create_dfs(corpus):
"""Create pandas DataFrames for the corpus.
Returns
-------
res: dict(string, DataFrame)
A DataFrame for each kind of structure present in the corpus.
"""
rows = {
anno_type: list()
for anno_type in ['edu', 'turn', 'tstar', 'dialogue', 'cdu', 'rel']
}
for file_id, doc in corpus.items():
# common stuff: get general info (doc, subdoc, annotator)
doc_name = file_id.doc
subdoc_name = file_id.subdoc
stage = file_id.stage
annotator = file_id.annotator
# context: yerk
ctx = Context.for_edus(doc)
# doc.annotations() := doc.units + doc.relations + doc.schemas
for anno in doc.annotations():
common_cols = {
'anno_id': anno.identifier(),
'doc': doc_name,
'subdoc': subdoc_name,
'stage': stage,
'annotator': annotator,
'type': anno.type, # ? maybe not
}
if is_edu(anno):
row = dict(common_cols.items() +
edu_feats(doc, ctx, anno).items())
rows['edu'].append(row)
elif is_cdu(anno):
row = dict(common_cols.items() + cdu_feats(anno).items())
rows['cdu'].append(row)
elif is_relation_instance(anno):
row = dict(common_cols.items() +
rel_feats(doc, ctx, anno).items())
rows['rel'].append(row)
elif is_dialogue(anno):
row = dict(common_cols.items() + dlg_feats(anno).items())
rows['dialogue'].append(row)
elif is_turn(anno):
row = dict(common_cols.items() + turn_feats(anno).items())
rows['turn'].append(row)
elif is_turn_star(anno):
row = dict(common_cols.items() + tstar_feats(anno).items())
rows['tstar'].append(row)
elif anno.type in [
'paragraph', 'Resource', 'Anaphora', 'Several_resources',
'Preference'
]:
# each paragraph (normally) corresponds to a Turn
# so just ignore them ;
# the situation is less clear-cut for 'Resource',
# 'Anaphora', 'Several_resources'
continue
else:
err_msg = 'Unsupported annotation: {}'.format(anno)
# raise ValueError(err_msg)
print('W: {}'.format(err_msg))
continue
res = {
anno_type: pd.DataFrame(data=row_list)
for anno_type, row_list in rows.items() if row_list
}
return res
def rel_feats(doc, ctx, anno, debug=False):
"""Get features for relations.
Parameters
----------
doc : GlozzDocument
Surrounding document
ctx :
anno :
Returns
-------
res : dict(string, string?)
Features for this relation
"""
# get all EDUs from document, sorted by their span
doc_edus = sorted([u for u in doc.units if is_edu(u)],
key=lambda u: u.span)
# TODO doc_tstars = ...
src = anno.source
if is_cdu(src):
src_type = 'CDU'
src_edus = sorted(src.terminals(), key=lambda e: e.span)
elif is_edu(src):
src_type = 'EDU'
src_edus = [src]
else:
# covered by stac-check ("non-DU endpoints")
return {}
tgt = anno.target
if is_cdu(tgt):
tgt_type = 'CDU'
tgt_edus = sorted(tgt.terminals(), key=lambda e: e.span)
elif is_edu(tgt):
tgt_type = 'EDU'
tgt_edus = [tgt]
else:
# covered by stac-check ("non-DU endpoints")
return {}
# get the index of the EDUs in the interval between src and tgt
src_idc = [doc_edus.index(e) for e in src_edus]
tgt_idc = [doc_edus.index(e) for e in tgt_edus]
# error case covered at least partially by stac-check, either
# as "bizarre relation instance" or as "CDU punctures"
if set(src_idc).intersection(set(tgt_idc)):
if debug:
direction = 'messed up'
print('* {}: {} {}'.format(doc.origin, direction, anno.type))
print('\t' + ', '.join(['[{}] {}'.format(str(e.span),
doc.text(e.span))
for e in src_edus]))
print('\t' + ', '.join(['[{}] {}'.format(str(e.span),
doc.text(e.span))
for e in tgt_edus]))
return {}
# src ... tgt
if src_idc[-1] < tgt_idc[0]:
direction = 'right'
fst_idc = src_idc
snd_idc = tgt_idc
interv_edus = doc_edus[(fst_idc[-1] + 1):snd_idc[0]]
# tgt ... src
elif tgt_idc[-1] < src_idc[0]:
direction = 'left'
fst_idc = tgt_idc
snd_idc = src_idc
interv_edus = doc_edus[(fst_idc[-1] + 1):snd_idc[0]]
# tgt and src are interwoven
else:
direction = 'interwoven' # FIXME
src_tgt_idc = set(src_idc).union(tgt_idc)
interv_edus = []
gap_edus = [e for i, e in enumerate(doc_edus)
if (i not in src_tgt_idc and
i > min(src_tgt_idc) and
i < max(src_tgt_idc))]
if debug:
print('* {}: {} {}'.format(doc.origin, direction, anno.type))
print('\t' + ', '.join(['[{}] {}'.format(str(e.span),
doc.text(e.span))
for e in src_edus]))
print('\t' + ', '.join(['[{}] {}'.format(str(e.span),
doc.text(e.span))
for e in tgt_edus]))
print('\t' + ', '.join(['[{}] {}'.format(str(e.span),
doc.text(e.span))
for e in gap_edus]))
edu_dist = len(interv_edus) + 1
# turn-stars distance
src_tstars = [ctx[e].tstar for e in src_edus]
tgt_tstars = [ctx[e].tstar for e in tgt_edus]
interv_tstars = [ctx[e].tstar for e in interv_edus]
# turn-stars from the interval that don't overlap with src nor tgt
skipped_tstars = set(interv_tstars) - set(src_tstars) - set(tgt_tstars)
# we define:
# * tstar_dist = 0 if (part of) src and tgt belong to the same tstar
# * tstar_dist = len(skipped_tstars) + 1 otherwise
tstar_dist = (len(skipped_tstars) + 1
if not set(src_tstars).intersection(set(tgt_tstars))
else 0)
res = {
'src_type': src_type,
'tgt_type': tgt_type,
'direction': direction,
'edu_dist': edu_dist,
'tstar_dist': tstar_dist,
}
return res
def create_dfs(corpus):
"""Create pandas DataFrames for the corpus.
Returns
-------
res: dict(string, DataFrame)
A DataFrame for each kind of structure present in the corpus.
"""
rows = {anno_type: list()
for anno_type in ['edu', 'turn', 'tstar', 'dialogue',
'cdu', 'rel']}
for file_id, doc in corpus.items():
# common stuff: get general info (doc, subdoc, annotator)
doc_name = file_id.doc
subdoc_name = file_id.subdoc
stage = file_id.stage
annotator = file_id.annotator
# context: yerk
ctx = Context.for_edus(doc)
# doc.annotations() := doc.units + doc.relations + doc.schemas
for anno in doc.annotations():
common_cols = {
'anno_id': anno.identifier(),
'doc': doc_name,
'subdoc': subdoc_name,
'stage': stage,
'annotator': annotator,
'type': anno.type, # ? maybe not
}
if is_edu(anno):
row = dict(common_cols.items() +
edu_feats(doc, ctx, anno).items())
rows['edu'].append(row)
elif is_cdu(anno):
row = dict(common_cols.items() +
cdu_feats(anno).items())
rows['cdu'].append(row)
elif is_relation_instance(anno):
row = dict(common_cols.items() +
rel_feats(doc, ctx, anno).items())
rows['rel'].append(row)
elif is_dialogue(anno):
row = dict(common_cols.items() +
dlg_feats(anno).items())
rows['dialogue'].append(row)
elif is_turn(anno):
row = dict(common_cols.items() +
turn_feats(anno).items())
rows['turn'].append(row)
elif is_turn_star(anno):
row = dict(common_cols.items() +
tstar_feats(anno).items())
rows['tstar'].append(row)
elif anno.type in ['paragraph',
'Resource', 'Anaphora',
'Several_resources', 'Preference']:
# each paragraph (normally) corresponds to a Turn
# so just ignore them ;
# the situation is less clear-cut for 'Resource',
# 'Anaphora', 'Several_resources'
continue
else:
err_msg = 'Unsupported annotation: {}'.format(anno)
# raise ValueError(err_msg)
print('W: {}'.format(err_msg))
continue
res = {anno_type: pd.DataFrame(data=row_list)
for anno_type, row_list in rows.items()
if row_list}
return res
