def get_oracle_ctrees(dep_edges,
att_edus,
nuc_strategy="unamb_else_most_frequent",
rank_strategy="closest-intra-rl-inter-rl",
prioritize_same_unit=True,
strict=False):
"""Build the oracle constituency tree(s) for a dependency tree.
Parameters
----------
dep_edges: dict(string, [(string, string, string)])
Edges for each document, indexed by doc name
Cf. type of return value from
irit-rst-dt/ctree.py:load_attelo_output_file()
att_edus: cf return type of attelo.io.load_edus
EDUs as they are known to attelo
strict: boolean, True by default
If True, any link from ROOT to an EDU that is neither 'ROOT' nor
UNRELATED raises an exception, otherwise a warning is issued.
Returns
-------
ctrees: list of RstTree
There can be several e.g. for leaky sentences.
"""
# rebuild educe EDUs from their attelo description
# and group them by doc_name
educe_edus = defaultdict(list)
edu2sent_idx = defaultdict(dict)
gid2num = dict()
for att_edu in att_edus:
# doc name
doc_name = att_edu.grouping
# EDU info
# skip ROOT (automatically added by RstDepTree.__init__)
if att_edu.id == 'ROOT':
continue
edu_num = int(att_edu.id.rsplit('_', 1)[1])
edu_span = EduceSpan(att_edu.start, att_edu.end)
edu_text = att_edu.text
educe_edus[doc_name].append(EduceEDU(edu_num, edu_span, edu_text))
# map global id of EDU to num of EDU inside doc
gid2num[att_edu.id] = edu_num
# map EDU to sentence
try:
sent_idx = int(att_edu.subgrouping.split('_sent')[1])
except IndexError:
# this EDU could not be attached to any sentence (ex: missing
# text in the PTB), so a default subgrouping identifier was used ;
# we aim for consistency with educe and map these to "None"
sent_idx = None
edu2sent_idx[doc_name][edu_num] = sent_idx
# check that our info covers only one document
assert len(educe_edus) == 1
# then restrict to this document
doc_name = educe_edus.keys()[0]
educe_edus = educe_edus[doc_name]
edu2sent_idx = edu2sent_idx[doc_name]
# sort EDUs by num
educe_edus = list(sorted(educe_edus, key=lambda e: e.num))
# rebuild educe-style edu2sent ; prepend 0 for the fake root
edu2sent = [0] + [edu2sent_idx[e.num] for e in educe_edus]
# classifiers for nuclearity and ranking
# FIXME declare, fit and predict upstream...
# nuclearity
nuc_classifier = DummyNuclearityClassifier(strategy=nuc_strategy)
nuc_classifier.fit([], []) # empty X and y for dummy fit
# ranking classifier
rank_classifier = InsideOutAttachmentRanker(
strategy=rank_strategy, prioritize_same_unit=prioritize_same_unit)
# rebuild RstDepTrees
dtree = RstDepTree(educe_edus)
for src_id, tgt_id, lbl in dep_edges:
if src_id == 'ROOT':
if lbl not in ['ROOT', UNKNOWN]:
err_msg = 'weird root label: {} {} {}'.format(
src_id, tgt_id, lbl)
if strict:
raise ValueError(err_msg)
else:
print('W: {}, using ROOT instead'.format(err_msg))
dtree.set_root(gid2num[tgt_id])
else:
dtree.add_dependency(gid2num[src_id], gid2num[tgt_id], lbl)
# add nuclearity: heuristic baseline
dtree.nucs = nuc_classifier.predict([dtree])[0]
# add rank: some strategies require a mapping from EDU to sentence
# EXPERIMENTAL attach array of sentence index for each EDU in tree
dtree.sent_idx = edu2sent
# end EXPERIMENTAL
dtree.ranks = rank_classifier.predict([dtree])[0]
# end NEW
# create pred ctree
try:
bin_srtrees = deptree_to_simple_rst_tree(dtree, allow_forest=True)
if False: # EXPERIMENTAL
# currently False to run on output that already has
# labels embedding nuclearity
bin_srtrees = [
SimpleRSTTree.incorporate_nuclearity_into_label(bin_srtree)
for bin_srtree in bin_srtrees
]
bin_rtrees = [
SimpleRSTTree.to_binary_rst_tree(bin_srtree)
for bin_srtree in bin_srtrees
]
except RstDtException as rst_e:
print(rst_e)
if False:
print('\n'.join('{}: {}'.format(edu.text_span(), edu)
for edu in educe_edus[doc_name]))
raise
ctrees = bin_rtrees
return ctrees
def get_oracle_ctrees(dep_edges, att_edus,
nuc_strategy="unamb_else_most_frequent",
rank_strategy="closest-intra-rl-inter-rl",
prioritize_same_unit=True,
strict=False):
"""Build the oracle constituency tree(s) for a dependency tree.
Parameters
----------
dep_edges: dict(string, [(string, string, string)])
Edges for each document, indexed by doc name
Cf. type of return value from
irit-rst-dt/ctree.py:load_attelo_output_file()
att_edus: cf return type of attelo.io.load_edus
EDUs as they are known to attelo
strict: boolean, True by default
If True, any link from ROOT to an EDU that is neither 'ROOT' nor
UNRELATED raises an exception, otherwise a warning is issued.
Returns
-------
ctrees: list of RstTree
There can be several e.g. for leaky sentences.
"""
# rebuild educe EDUs from their attelo description
# and group them by doc_name
educe_edus = defaultdict(list)
edu2sent_idx = defaultdict(dict)
gid2num = dict()
for att_edu in att_edus:
# doc name
doc_name = att_edu.grouping
# EDU info
# skip ROOT (automatically added by RstDepTree.__init__)
if att_edu.id == 'ROOT':
continue
edu_num = int(att_edu.id.rsplit('_', 1)[1])
edu_span = EduceSpan(att_edu.start, att_edu.end)
edu_text = att_edu.text
educe_edus[doc_name].append(EduceEDU(edu_num, edu_span, edu_text))
# map global id of EDU to num of EDU inside doc
gid2num[att_edu.id] = edu_num
# map EDU to sentence
try:
sent_idx = int(att_edu.subgrouping.split('_sent')[1])
except IndexError:
# this EDU could not be attached to any sentence (ex: missing
# text in the PTB), so a default subgrouping identifier was used ;
# we aim for consistency with educe and map these to "None"
sent_idx = None
edu2sent_idx[doc_name][edu_num] = sent_idx
# check that our info covers only one document
assert len(educe_edus) == 1
# then restrict to this document
doc_name = educe_edus.keys()[0]
educe_edus = educe_edus[doc_name]
edu2sent_idx = edu2sent_idx[doc_name]
# sort EDUs by num
educe_edus = list(sorted(educe_edus, key=lambda e: e.num))
# rebuild educe-style edu2sent ; prepend 0 for the fake root
edu2sent = [0] + [edu2sent_idx[e.num] for e in educe_edus]
# classifiers for nuclearity and ranking
# FIXME declare, fit and predict upstream...
# nuclearity
nuc_classifier = DummyNuclearityClassifier(strategy=nuc_strategy)
nuc_classifier.fit([], []) # empty X and y for dummy fit
# ranking classifier
rank_classifier = InsideOutAttachmentRanker(
strategy=rank_strategy,
prioritize_same_unit=prioritize_same_unit)
# rebuild RstDepTrees
dtree = RstDepTree(educe_edus)
for src_id, tgt_id, lbl in dep_edges:
if src_id == 'ROOT':
if lbl not in ['ROOT', UNKNOWN]:
err_msg = 'weird root label: {} {} {}'.format(
src_id, tgt_id, lbl)
if strict:
raise ValueError(err_msg)
else:
print('W: {}, using ROOT instead'.format(err_msg))
dtree.set_root(gid2num[tgt_id])
else:
dtree.add_dependency(gid2num[src_id], gid2num[tgt_id], lbl)
# add nuclearity: heuristic baseline
dtree.nucs = nuc_classifier.predict([dtree])[0]
# add rank: some strategies require a mapping from EDU to sentence
# EXPERIMENTAL attach array of sentence index for each EDU in tree
dtree.sent_idx = edu2sent
# end EXPERIMENTAL
dtree.ranks = rank_classifier.predict([dtree])[0]
# end NEW
# create pred ctree
try:
bin_srtrees = deptree_to_simple_rst_tree(dtree, allow_forest=True)
if False: # EXPERIMENTAL
# currently False to run on output that already has
# labels embedding nuclearity
bin_srtrees = [SimpleRSTTree.incorporate_nuclearity_into_label(
bin_srtree) for bin_srtree in bin_srtrees]
bin_rtrees = [SimpleRSTTree.to_binary_rst_tree(bin_srtree)
for bin_srtree in bin_srtrees]
except RstDtException as rst_e:
print(rst_e)
if False:
print('\n'.join('{}: {}'.format(edu.text_span(), edu)
for edu in educe_edus[doc_name]))
raise
ctrees = bin_rtrees
return ctrees
