def test_rst_to_dt(self): lw_trees = [ "(R:rel (S x) (N y))", """ (R:rel (S x) (N:rel (N h) (S t))) """, """ (R:r (S x) (N:r (N:r (S t1) (N h)) (S t2))) """ ] for lstr in lw_trees: rst1 = parse_lightweight_tree(lstr) dep = RstDepTree.from_simple_rst_tree(rst1) rst2 = deptree_to_simple_rst_tree(dep) self.assertEqual(rst1, rst2, "round-trip on " + lstr) for name, tree in self._test_trees().items(): rst1 = SimpleRSTTree.from_rst_tree(tree) dep = RstDepTree.from_simple_rst_tree(rst1) rst2 = deptree_to_simple_rst_tree(dep) self.assertEqual( treenode(rst1).span, treenode(rst2).span, "span equality on " + name) self.assertEqual( treenode(rst1).edu_span, treenode(rst2).edu_span, "edu span equality on " + name)
def test_rst_to_dt(self): lw_trees = ["(R:rel (S x) (N y))", """ (R:rel (S x) (N:rel (N h) (S t))) """, """ (R:r (S x) (N:r (N:r (S t1) (N h)) (S t2))) """ ] for lstr in lw_trees: rst1 = parse_lightweight_tree(lstr) dep = RstDepTree.from_simple_rst_tree(rst1) rst2 = deptree_to_simple_rst_tree(dep) self.assertEqual(rst1, rst2, "round-trip on " + lstr) for name, tree in self._test_trees().items(): rst1 = SimpleRSTTree.from_rst_tree(tree) dep = RstDepTree.from_simple_rst_tree(rst1) rst2 = deptree_to_simple_rst_tree(dep) self.assertEqual(treenode(rst1).span, treenode(rst2).span, "span equality on " + name) self.assertEqual(treenode(rst1).edu_span, treenode(rst2).edu_span, "edu span equality on " + name)
def convert(corpus, multinuclear, odir): """ Convert every RST tree in the corpus to a dependency tree (and back, but simplified using a set of relation types that will be systematically treated as multinuclear) """ bin_dir = os.path.join(odir, "rst-binarised") dt_dir = os.path.join(odir, "rst-to-dt") rst2_dir = os.path.join(odir, "dt-to-rst") for subdir in [bin_dir, dt_dir, rst2_dir]: if not os.path.exists(subdir): os.makedirs(subdir) for k in corpus: suffix = os.path.splitext(k.doc)[0] stree = SimpleRSTTree.from_rst_tree(corpus[k]) with open(os.path.join(bin_dir, suffix), 'w') as fout: fout.write(str(stree)) dtree = RstDepTree.from_simple_rst_tree(stree) with open(os.path.join(dt_dir, suffix), 'w') as fout: fout.write(str(dtree)) stree2 = deptree_to_simple_rst_tree(dtree) with open(os.path.join(rst2_dir, suffix), 'w') as fout: fout.write(str(stree2))
def convert(corpus, multinuclear, odir): """ Convert every RST tree in the corpus to a dependency tree (and back, but simplified using a set of relation types that will be systematically treated as multinuclear) """ bin_dir = os.path.join(odir, "rst-binarised") dt_dir = os.path.join(odir, "rst-to-dt") rst2_dir = os.path.join(odir, "dt-to-rst") for subdir in [bin_dir, dt_dir, rst2_dir]: if not os.path.exists(subdir): os.makedirs(subdir) for k in corpus: suffix = os.path.splitext(k.doc)[0] stree = SimpleRSTTree.from_rst_tree(corpus[k]) with open(os.path.join(bin_dir, suffix), 'w') as fout: fout.write(str(stree)) dtree = RstDepTree.from_simple_rst_tree(stree) with open(os.path.join(dt_dir, suffix), 'w') as fout: fout.write(str(dtree)) stree2 = deptree_to_simple_rst_tree(dtree) with open(os.path.join(rst2_dir, suffix), 'w') as fout: fout.write(str(stree2))
def test_dt_to_rst_order(self): lw_trees = [ "(R:r (N:r (N h) (S r1)) (S r2))", "(R:r (S:r (S l2) (N l1)) (N h))", "(R:r (N:r (S l1) (N h)) (S r1))", """ (R:r (N:r (N:r (S l2) (N:r (S l1) (N h))) (S r1)) (S r2)) """, # ((l2 <- l1 <- h) -> r1 -> r2) """ (R:r (N:r (S l2) (N:r (N:r (S l1) (N h)) (S r1))) (S r2)) """, # (l2 <- ((l1 <- h) -> r1)) -> r2 ] for lstr in lw_trees: rst1 = parse_lightweight_tree(lstr) dep = RstDepTree.from_simple_rst_tree(rst1) dep_a = dep rst2a = deptree_to_simple_rst_tree(dep_a) self.assertEqual(rst1, rst2a, "round-trip on " + lstr) dep_b = copy.deepcopy(dep) dep_b.deps(0).reverse() rst2b = deptree_to_simple_rst_tree(dep_b) # TODO assertion on rst2b? dep_c = copy.deepcopy(dep) random.shuffle(dep_c.deps(0)) rst2c = deptree_to_simple_rst_tree(dep_c)
def test_rst_to_dt_nuclearity_loss(self): """ Test that we still get sane tree structure with nuclearity loss """ tricky = """ (R:r (S t) (N h)) """ nuked = """ (R:r (N t) (N h)) """ # tricky = """ # (R:r # (S x) # (N:r (N:r (S t1) (N h)) # (S t2))) # """ # # nuked = """ # (R:r # (N x) # (N:r (N:r (N t1) (N h)) # (N t2))) # """ rst0 = parse_lightweight_tree(nuked) rst1 = parse_lightweight_tree(tricky) # a little sanity check first dep0 = RstDepTree.from_simple_rst_tree(rst0) rev0 = deptree_to_simple_rst_tree(dep0) # was:, ['r']) self.assertEqual(rst0, rev0, "same structure " + nuked) # sanity # now the real test dep1 = RstDepTree.from_simple_rst_tree(rst1) rev1 = deptree_to_simple_rst_tree(dep1) # was:, ['r'])
def test_rst_to_dt_nuclearity_loss(self): """ Test that we still get sane tree structure with nuclearity loss """ tricky = """ (R:r (S t) (N h)) """ nuked = """ (R:r (N t) (N h)) """ # tricky = """ # (R:r # (S x) # (N:r (N:r (S t1) (N h)) # (S t2))) # """ # # nuked = """ # (R:r # (N x) # (N:r (N:r (N t1) (N h)) # (N t2))) # """ rst0 = parse_lightweight_tree(nuked) rst1 = parse_lightweight_tree(tricky) # a little sanity check first dep0 = RstDepTree.from_simple_rst_tree(rst0) rev0 = deptree_to_simple_rst_tree(dep0) # was:, ['r']) self.assertEqual(rst0, rev0, "same structure " + nuked) # sanity # now the real test dep1 = RstDepTree.from_simple_rst_tree(rst1) rev1 = deptree_to_simple_rst_tree(dep1) # was:, ['r'])
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