def read_rst_trees(data_dir):
# Read RST tree file
files = [os.path.join(data_dir, fname) for fname in os.listdir(data_dir) if fname.endswith('.dis')]
for i, fdis in enumerate(files):
fmerge = fdis.replace('.dis', '.merge')
if not os.path.isfile(fmerge):
print("Corresponding .fmerge file does not exist. Skipping the file.")
continue
rst_tree = RstTree(fdis, fmerge)
rst_tree.build()
yield rst_tree
def __getitem__(self, idx):
"""
Returns:
If train: features for that tree (corefs, organizational features, spans, etc.)
If test: gold tree
"""
if self.is_train:
return self.X[idx], torch.LongTensor(self.y[idx])
doc = self.data_helper.docs[self.X[idx][0]]
gold_rst = RstTree(doc.filename, doc.filename.replace('.dis', '.merge'))
gold_rst.build()
return gold_rst
def eval_parser(self,
dev_data=None,
path='./examples',
use_parseval=False):
""" Test the parsing performance"""
# Evaluation
met = Metrics(use_parseval, levels=['span', 'nuclearity'])
# ----------------------------------------
# Read all files from the given path
if dev_data is None:
eval_list = [
os.path.join(path, fname) for fname in os.listdir(path)
if fname.endswith('.merge')
]
else:
eval_list = dev_data
pred_forms = []
gold_forms = []
total_cost = 0
for eval_instance in eval_list:
# ----------------------------------------
# Read *.merge file
doc = Doc()
if dev_data is not None:
gold_rst = eval_instance
doc = eval_instance.doc
else:
doc.read_from_fmerge(eval_instance)
eval_instance = eval_instance.replace('.dis', '.merge')
fdis = eval_instance.replace('.merge', '.dis')
gold_rst = RstTree(fdis, eval_instance)
gold_rst.build()
_, gold_action_seq = gold_rst.generate_action_samples()
gold_action_seq = list(
map(lambda x: self.data_helper.action_map[x], gold_action_seq))
pred_rst, cost = self.parser.sr_parse(
[doc], [torch.cuda.LongTensor(gold_action_seq)],
None,
is_train=False)
total_cost += cost
pred_rst = pred_rst[0]
# ----------------------------------------
# Evaluate with gold RST tree
met.eval(gold_rst, pred_rst)
print("Total cost: ", total_cost)
met.report()
def read_rst_trees(data_dir, output_dir, parse_type, isFlat):
# Read RST tree file
files = [
os.path.join(data_dir, fname) for fname in os.listdir(data_dir)
if fname.endswith('.dis')
]
with open(os.path.join(output_dir, parse_type, "processed_data.p"),
'rb') as file:
fmerges = pickle.load(file)
rst_trees = []
for fdis, fmerge in zip(files, fmerges):
print("building tree " + fdis)
rst_tree = RstTree(fdis, fmerge, isFlat)
rst_tree.build()
rst_trees.append(rst_tree)
return rst_trees
def test_eval():
fdis = "../data/data_dir/train_dir/wsj_0603.out.dis"
fmerge = "../data/data_dir/train_dir/wsj_0603.out.merge"
gold_tree = RstTree(fdis, fmerge)
gold_tree.build()
eval_trees(gold_tree, gold_tree, 1, 1, 1)
sr_parser = ParsingState([], [])
sr_parser.init(gold_tree.doc)
sh, rd = 'Shift', 'Reduce'
nn, ns, sn = 'NN', 'NS', 'SN'
# Last two nuclearity actions are incorrect, last rel is incorrect
silver_nuc = [(sh, None, None), (sh, None, None), (rd, ns, 'Elaboration'),
(sh, None, None), (sh, None, None), (rd, ns, 'Elaboration'),
(rd, nn, 'Same-Unit'), (sh, None, None),
(rd, ns, 'Elaboration'), (sh, None, None), (sh, None, None),
(rd, sn, 'Attribution'), (rd, ns, 'Attribution')]
for idx, action in enumerate(silver_nuc):
sr_parser.operate(action)
tree = sr_parser.get_parse_tree()
silver_tree = RstTree()
silver_tree.assign_tree(tree)
silver_tree.assign_doc(gold_tree.doc)
silver_tree.back_prop(tree, gold_tree.doc)
eval_trees(silver_tree, gold_tree, 1, 9 / 12, 8 / 12)
eval_trees(silver_tree, gold_tree, 1, 4 / 6, 5 / 6, use_parseval=True)
sr_parser = ParsingState([], [])
sr_parser.init(gold_tree.doc)
silver_nuc = [(sh, None, None), (sh, None, None), (sh, None, None),
(rd, ns, 'Elaboration'), (sh, None, None),
(rd, ns, 'Elaboration'), (rd, nn, 'Same-Unit'),
(sh, None, None), (rd, ns, 'Elaboration'), (sh, None, None),
(sh, None, None), (rd, ns, 'Attribution'),
(rd, ns, 'Attribution')]
for idx, action in enumerate(silver_nuc):
sr_parser.operate(action)
tree = sr_parser.get_parse_tree()
silver_tree = RstTree()
silver_tree.assign_tree(tree)
silver_tree.assign_doc(gold_tree.doc)
silver_tree.back_prop(tree, gold_tree.doc)
eval_trees(silver_tree, gold_tree, 10 / 12, 7 / 12, 5 / 12)
eval_trees(silver_tree, gold_tree, 4 / 6, 3 / 6, 3 / 6, use_parseval=True)
def read_rst_trees(data_dir):
# Read RST tree file
files = [
os.path.join(data_dir, fname) for fname in os.listdir(data_dir)
if fname.endswith('.dis')
]
rst_trees = []
for fdis in files:
fmerge = fdis.replace('.dis', '.merge')
if not os.path.isfile(fmerge):
raise FileNotFoundError(
'Corresponding .fmerge file does not exist. You should do preprocessing first.'
)
rst_tree = RstTree(fdis, fmerge)
rst_tree.build()
rst_trees.append(rst_tree)
return rst_trees
def test_tree():
fdis = "../data/data_dir/train_dir/wsj_0603.out.dis"
fmerge = "../data/data_dir/train_dir/wsj_0603.out.merge"
tree = RstTree(fdis, fmerge)
tree.build()
bftbin = RstTree.BFTbin(tree.tree)
assert len(bftbin) == 13
edu_spans = [(1, 7), (1, 5), (6, 7), (1, 4), (5, 5), (6, 6), (7, 7), (1, 2), (3, 4), (1, 1), (2, 2), (3, 3), (4, 4)]
r, n, s = 'Root', 'Nucleus', 'Satellite'
props = [r, n, n, n, s, n, s, n, n, n, s, n, s]
rels = [None, 'TextualOrganization', 'TextualOrganization',
'span', 'elaboration-additional', 'span', 'attribution', 'Same-Unit',
'Same-Unit', 'span', 'elaboration-object-attribute-e', 'span',
'elaboration-object-attribute-e',]
for i, span in enumerate(bftbin):
assert span.edu_span == edu_spans[i]
assert span.prop == props[i]
assert span.relation == rels[i]
# TODO: Check where relation conversion happens? In RST-Coref paper, in generate_action_samples
fdis, action_seq = tree.generate_action_samples()
assert fdis == fdis
sh, rd = 'Shift', 'Reduce'
nn, ns, sn = 'NN', 'NS', 'SN'
gold_action_seq = [(sh, None), (sh, None), (rd, ns), (sh, None), (sh, None),
(rd, ns), (rd, nn), (sh, None), (rd, ns), (sh, None),
(sh, None), (rd, ns), (rd, nn)]
assert len(action_seq) == len(gold_action_seq)
for i in range(len(action_seq)):
assert action_seq[i] == gold_action_seq[i]
# Check that tree binarization is node correctly
fdis = "../data/data_dir/train_dir/file2.dis"
fmerge = "../data/data_dir/train_dir/file2.merge"
tree = RstTree(fdis, fmerge)
tree.build()
spans = [span.edu_span for span in RstTree.BFTbin(tree.tree)]
nonbinary_spans = [(22, 28), (22, 22), (23, 28), (23, 23), (24, 28),
(24, 24), (25, 28), (25, 25), (26, 28), (26, 26),
(27, 28), (27, 27), (28, 28), (11, 15), (11, 11),
(12, 15), (12, 12), (13, 15), (13, 14), (13, 13),
(14, 14), (15, 15), (45, 57), (45, 47), (48, 57),
(48, 53), (54, 57), (54, 56), (57, 57)]
for span in nonbinary_spans:
assert span in spans
def read_rst_trees(data_dir):
# Read RST tree file
files = [
os.path.join(data_dir, fname) for fname in os.listdir(data_dir)
if fname.endswith('.dis')
]
for i, fdis in enumerate(files):
fmerge = fdis.replace('.dis', '.merge')
if not os.path.isfile(fmerge):
print(
fmerge +
" - Corresponding .fmerge file does not exist. Skipping the file."
)
continue
rst_tree = RstTree(fdis, fmerge)
rst_tree.build()
if (rst_tree.get_parse().startswith(" ( EDU")):
print("Skipping document - tree is corrupt.")
continue
yield rst_tree
def __getitem__(self, idx):
"""
Returns:
document_edus - packed list of tensors, where each element is tensor of
word embeddings for an EDU of that document
sorted_idx - output of torch.argsort, used to return EDUs to original
linear order
y - sequence of gold actions for a particular tree
doc - Document instance of an RstTree instance
"""
doc = Doc()
eval_instance = self.X[idx].replace('.dis', '.merge')
doc.read_from_fmerge(eval_instance)
if self.is_train:
return doc, torch.cuda.LongTensor(self.y[idx])
fdis = eval_instance.replace('.merge', '.dis')
gold_rst = RstTree(fdis, eval_instance)
gold_rst.build()
return gold_rst
def eval_parser(self,
dev_data=None,
path='./examples',
save_preds=True,
use_parseval=False):
""" Test the parsing performance"""
# Evaluation
met = Metrics(levels=['span', 'nuclearity', 'relation'],
use_parseval=use_parseval)
# ----------------------------------------
# Read all files from the given path
if dev_data is None:
dev_data = [
os.path.join(path, fname) for fname in os.listdir(path)
if fname.endswith('.dis')
]
total_cost = 0
for eval_instance in dev_data:
# ----------------------------------------
fmerge = eval_instance.replace('.dis', '.merge')
doc = Doc()
doc.read_from_fmerge(fmerge)
gold_rst = RstTree(eval_instance, fmerge)
gold_rst.build()
# tok_edus = [nltk.word_tokenize(edu) for edu in doc.doc_edus]
tok_edus = [edu.split(" ") for edu in doc.doc_edus]
tokens = flatten(tok_edus)
coref_document = Document(raw_text=None,
tokens=tokens,
sents=tok_edus,
corefs=[],
speakers=["0"] * len(tokens),
genre="nw",
filename=None)
coref_document.token_dict = doc.token_dict
coref_document.edu_dict = doc.edu_dict
doc = coref_document
gold_action_seq, gold_rel_seq = gold_rst.decode_rst_tree()
gold_action_seq = [action_map[x] for x in gold_action_seq]
gold_relation_seq = [
relation_map[x.lower()] for x in gold_rel_seq if x is not None
]
pred_rst, cost = self.parser.sr_parse(
doc, torch.cuda.LongTensor(gold_action_seq),
torch.cuda.LongTensor(gold_relation_seq))
total_cost += cost
if save_preds:
if not os.path.isdir('../data/predicted_trees'):
os.mkdir('../data/predicted_trees')
filename = eval_instance.split(os.sep)[-1]
filepath = f'../data/predicted_trees/{self.config[MODEL_NAME]}_{filename}'
pred_brackets = pred_rst.bracketing()
# Write brackets into file
Evaluator.writebrackets(filepath, pred_brackets)
# ----------------------------------------
# Evaluate with gold RST tree
met.eval(gold_rst, pred_rst)
print("Total cost: ", total_cost)
if use_parseval:
print("Reporting original Parseval metric.")
else:
print("Reporting RST Parseval metric.")
met.report()
def eval_parser(self,
path='./examples',
report=False,
bcvocab=None,
draw=True):
""" Test the parsing performance"""
# Evaluation
met = Metrics(levels=['span', 'nuclearity', 'relation'])
# ----------------------------------------
# Read all files from the given path
doclist = [
os.path.join(path, fname) for fname in os.listdir(path)
if fname.endswith('.merge')
]
pred_forms = []
gold_forms = []
depth_per_relation = {}
for fmerge in doclist:
# ----------------------------------------
# Read *.merge file
doc = Doc()
doc.read_from_fmerge(fmerge)
# ----------------------------------------
# Parsing
pred_rst = self.parser.sr_parse(doc, bcvocab)
if draw:
pred_rst.draw_rst(fmerge.replace(".merge", ".ps"))
# Get brackets from parsing results
pred_brackets = pred_rst.bracketing()
fbrackets = fmerge.replace('.merge', '.brackets')
# Write brackets into file
Evaluator.writebrackets(fbrackets, pred_brackets)
# ----------------------------------------
# Evaluate with gold RST tree
if report:
fdis = fmerge.replace('.merge', '.dis')
gold_rst = RstTree(fdis, fmerge)
gold_rst.build()
met.eval(gold_rst, pred_rst)
for node in pred_rst.postorder_DFT(pred_rst.tree, []):
pred_forms.append(node.form)
for node in gold_rst.postorder_DFT(gold_rst.tree, []):
gold_forms.append(node.form)
nodes = gold_rst.postorder_DFT(gold_rst.tree, [])
inner_nodes = [
node for node in nodes
if node.lnode is not None and node.rnode is not None
]
for idx, node in enumerate(inner_nodes):
relation = node.rnode.relation if node.form == 'NS' else node.lnode.relation
rela_class = RstTree.extract_relation(relation)
if rela_class in depth_per_relation:
depth_per_relation[rela_class].append(node.depth)
else:
depth_per_relation[rela_class] = [node.depth]
lnode_text = ' '.join([
gold_rst.doc.token_dict[tid].word
for tid in node.lnode.text
])
lnode_lemmas = ' '.join([
gold_rst.doc.token_dict[tid].lemma
for tid in node.lnode.text
])
rnode_text = ' '.join([
gold_rst.doc.token_dict[tid].word
for tid in node.rnode.text
])
rnode_lemmas = ' '.join([
gold_rst.doc.token_dict[tid].lemma
for tid in node.rnode.text
])
# if rela_class == 'Topic-Change':
# print(fmerge)
# print(relation)
# print(lnode_text)
# print(rnode_text)
# print()
if report:
met.report()