chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,305 @@
|
||||
"""Tree-structured data.
|
||||
Including:
|
||||
- Stanford Sentiment Treebank
|
||||
"""
|
||||
from __future__ import absolute_import
|
||||
|
||||
import os
|
||||
|
||||
from collections import OrderedDict
|
||||
|
||||
import networkx as nx
|
||||
|
||||
import numpy as np
|
||||
|
||||
from .. import backend as F
|
||||
from ..convert import from_networkx
|
||||
|
||||
from .dgl_dataset import DGLBuiltinDataset
|
||||
from .utils import (
|
||||
_get_dgl_url,
|
||||
deprecate_property,
|
||||
load_graphs,
|
||||
load_info,
|
||||
save_graphs,
|
||||
save_info,
|
||||
)
|
||||
|
||||
__all__ = ["SST", "SSTDataset"]
|
||||
|
||||
|
||||
class SSTDataset(DGLBuiltinDataset):
|
||||
r"""Stanford Sentiment Treebank dataset.
|
||||
|
||||
Each sample is the constituency tree of a sentence. The leaf nodes
|
||||
represent words. The word is a int value stored in the ``x`` feature field.
|
||||
The non-leaf node has a special value ``PAD_WORD`` in the ``x`` field.
|
||||
Each node also has a sentiment annotation: 5 classes (very negative,
|
||||
negative, neutral, positive and very positive). The sentiment label is a
|
||||
int value stored in the ``y`` feature field.
|
||||
Official site: `<http://nlp.stanford.edu/sentiment/index.html>`_
|
||||
|
||||
Statistics:
|
||||
|
||||
- Train examples: 8,544
|
||||
- Dev examples: 1,101
|
||||
- Test examples: 2,210
|
||||
- Number of classes for each node: 5
|
||||
|
||||
Parameters
|
||||
----------
|
||||
mode : str, optional
|
||||
Should be one of ['train', 'dev', 'test', 'tiny']
|
||||
Default: train
|
||||
glove_embed_file : str, optional
|
||||
The path to pretrained glove embedding file.
|
||||
Default: None
|
||||
vocab_file : str, optional
|
||||
Optional vocabulary file. If not given, the default vacabulary file is used.
|
||||
Default: None
|
||||
raw_dir : str
|
||||
Raw file directory to download/contains the input data directory.
|
||||
Default: ~/.dgl/
|
||||
force_reload : bool
|
||||
Whether to reload the dataset. Default: False
|
||||
verbose : bool
|
||||
Whether to print out progress information. Default: True.
|
||||
transform : callable, optional
|
||||
A transform that takes in a :class:`~dgl.DGLGraph` object and returns
|
||||
a transformed version. The :class:`~dgl.DGLGraph` object will be
|
||||
transformed before every access.
|
||||
|
||||
Attributes
|
||||
----------
|
||||
vocab : OrderedDict
|
||||
Vocabulary of the dataset
|
||||
num_classes : int
|
||||
Number of classes for each node
|
||||
pretrained_emb: Tensor
|
||||
Pretrained glove embedding with respect the vocabulary.
|
||||
vocab_size : int
|
||||
The size of the vocabulary
|
||||
|
||||
Notes
|
||||
-----
|
||||
All the samples will be loaded and preprocessed in the memory first.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> # get dataset
|
||||
>>> train_data = SSTDataset()
|
||||
>>> dev_data = SSTDataset(mode='dev')
|
||||
>>> test_data = SSTDataset(mode='test')
|
||||
>>> tiny_data = SSTDataset(mode='tiny')
|
||||
>>>
|
||||
>>> len(train_data)
|
||||
8544
|
||||
>>> train_data.num_classes
|
||||
5
|
||||
>>> glove_embed = train_data.pretrained_emb
|
||||
>>> train_data.vocab_size
|
||||
19536
|
||||
>>> train_data[0]
|
||||
Graph(num_nodes=71, num_edges=70,
|
||||
ndata_schemes={'x': Scheme(shape=(), dtype=torch.int64), 'y': Scheme(shape=(), dtype=torch.int64), 'mask': Scheme(shape=(), dtype=torch.int64)}
|
||||
edata_schemes={})
|
||||
>>> for tree in train_data:
|
||||
... input_ids = tree.ndata['x']
|
||||
... labels = tree.ndata['y']
|
||||
... mask = tree.ndata['mask']
|
||||
... # your code here
|
||||
"""
|
||||
|
||||
PAD_WORD = -1 # special pad word id
|
||||
UNK_WORD = -1 # out-of-vocabulary word id
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
mode="train",
|
||||
glove_embed_file=None,
|
||||
vocab_file=None,
|
||||
raw_dir=None,
|
||||
force_reload=False,
|
||||
verbose=False,
|
||||
transform=None,
|
||||
):
|
||||
assert mode in ["train", "dev", "test", "tiny"]
|
||||
_url = _get_dgl_url("dataset/sst.zip")
|
||||
self._glove_embed_file = glove_embed_file if mode == "train" else None
|
||||
self.mode = mode
|
||||
self._vocab_file = vocab_file
|
||||
super(SSTDataset, self).__init__(
|
||||
name="sst",
|
||||
url=_url,
|
||||
raw_dir=raw_dir,
|
||||
force_reload=force_reload,
|
||||
verbose=verbose,
|
||||
transform=transform,
|
||||
)
|
||||
|
||||
def process(self):
|
||||
from nltk.corpus.reader import BracketParseCorpusReader
|
||||
|
||||
# load vocab file
|
||||
self._vocab = OrderedDict()
|
||||
vocab_file = (
|
||||
self._vocab_file
|
||||
if self._vocab_file is not None
|
||||
else os.path.join(self.raw_path, "vocab.txt")
|
||||
)
|
||||
with open(vocab_file, encoding="utf-8") as vf:
|
||||
for line in vf.readlines():
|
||||
line = line.strip()
|
||||
self._vocab[line] = len(self._vocab)
|
||||
|
||||
# filter glove
|
||||
if self._glove_embed_file is not None and os.path.exists(
|
||||
self._glove_embed_file
|
||||
):
|
||||
glove_emb = {}
|
||||
with open(self._glove_embed_file, "r", encoding="utf-8") as pf:
|
||||
for line in pf.readlines():
|
||||
sp = line.split(" ")
|
||||
if sp[0].lower() in self._vocab:
|
||||
glove_emb[sp[0].lower()] = np.asarray(
|
||||
[float(x) for x in sp[1:]]
|
||||
)
|
||||
files = ["{}.txt".format(self.mode)]
|
||||
corpus = BracketParseCorpusReader(self.raw_path, files)
|
||||
sents = corpus.parsed_sents(files[0])
|
||||
|
||||
# initialize with glove
|
||||
pretrained_emb = []
|
||||
fail_cnt = 0
|
||||
for line in self._vocab.keys():
|
||||
if self._glove_embed_file is not None and os.path.exists(
|
||||
self._glove_embed_file
|
||||
):
|
||||
if not line.lower() in glove_emb:
|
||||
fail_cnt += 1
|
||||
pretrained_emb.append(
|
||||
glove_emb.get(
|
||||
line.lower(), np.random.uniform(-0.05, 0.05, 300)
|
||||
)
|
||||
)
|
||||
|
||||
self._pretrained_emb = None
|
||||
if self._glove_embed_file is not None and os.path.exists(
|
||||
self._glove_embed_file
|
||||
):
|
||||
self._pretrained_emb = F.tensor(np.stack(pretrained_emb, 0))
|
||||
print(
|
||||
"Miss word in GloVe {0:.4f}".format(
|
||||
1.0 * fail_cnt / len(self._pretrained_emb)
|
||||
)
|
||||
)
|
||||
# build trees
|
||||
self._trees = []
|
||||
for sent in sents:
|
||||
self._trees.append(self._build_tree(sent))
|
||||
|
||||
def _build_tree(self, root):
|
||||
g = nx.DiGraph()
|
||||
|
||||
def _rec_build(nid, node):
|
||||
for child in node:
|
||||
cid = g.number_of_nodes()
|
||||
if isinstance(child[0], str) or isinstance(child[0], bytes):
|
||||
# leaf node
|
||||
word = self.vocab.get(child[0].lower(), self.UNK_WORD)
|
||||
g.add_node(cid, x=word, y=int(child.label()), mask=1)
|
||||
else:
|
||||
g.add_node(
|
||||
cid, x=SSTDataset.PAD_WORD, y=int(child.label()), mask=0
|
||||
)
|
||||
_rec_build(cid, child)
|
||||
g.add_edge(cid, nid)
|
||||
|
||||
# add root
|
||||
g.add_node(0, x=SSTDataset.PAD_WORD, y=int(root.label()), mask=0)
|
||||
_rec_build(0, root)
|
||||
ret = from_networkx(g, node_attrs=["x", "y", "mask"])
|
||||
return ret
|
||||
|
||||
@property
|
||||
def graph_path(self):
|
||||
return os.path.join(self.save_path, self.mode + "_dgl_graph.bin")
|
||||
|
||||
@property
|
||||
def vocab_path(self):
|
||||
return os.path.join(self.save_path, "vocab.pkl")
|
||||
|
||||
def has_cache(self):
|
||||
return os.path.exists(self.graph_path) and os.path.exists(
|
||||
self.vocab_path
|
||||
)
|
||||
|
||||
def save(self):
|
||||
save_graphs(self.graph_path, self._trees)
|
||||
save_info(self.vocab_path, {"vocab": self.vocab})
|
||||
if self.pretrained_emb:
|
||||
emb_path = os.path.join(self.save_path, "emb.pkl")
|
||||
save_info(emb_path, {"embed": self.pretrained_emb})
|
||||
|
||||
def load(self):
|
||||
emb_path = os.path.join(self.save_path, "emb.pkl")
|
||||
|
||||
self._trees = load_graphs(self.graph_path)[0]
|
||||
self._vocab = load_info(self.vocab_path)["vocab"]
|
||||
self._pretrained_emb = None
|
||||
if os.path.exists(emb_path):
|
||||
self._pretrained_emb = load_info(emb_path)["embed"]
|
||||
|
||||
@property
|
||||
def vocab(self):
|
||||
r"""Vocabulary
|
||||
|
||||
Returns
|
||||
-------
|
||||
OrderedDict
|
||||
"""
|
||||
return self._vocab
|
||||
|
||||
@property
|
||||
def pretrained_emb(self):
|
||||
r"""Pre-trained word embedding, if given."""
|
||||
return self._pretrained_emb
|
||||
|
||||
def __getitem__(self, idx):
|
||||
r"""Get graph by index
|
||||
|
||||
Parameters
|
||||
----------
|
||||
idx : int
|
||||
|
||||
Returns
|
||||
-------
|
||||
:class:`dgl.DGLGraph`
|
||||
|
||||
graph structure, word id for each node, node labels and masks.
|
||||
|
||||
- ``ndata['x']``: word id of the node
|
||||
- ``ndata['y']:`` label of the node
|
||||
- ``ndata['mask']``: 1 if the node is a leaf, otherwise 0
|
||||
"""
|
||||
if self._transform is None:
|
||||
return self._trees[idx]
|
||||
else:
|
||||
return self._transform(self._trees[idx])
|
||||
|
||||
def __len__(self):
|
||||
r"""Number of graphs in the dataset."""
|
||||
return len(self._trees)
|
||||
|
||||
@property
|
||||
def vocab_size(self):
|
||||
r"""Vocabulary size."""
|
||||
return len(self._vocab)
|
||||
|
||||
@property
|
||||
def num_classes(self):
|
||||
r"""Number of classes for each node."""
|
||||
return 5
|
||||
|
||||
|
||||
SST = SSTDataset
|
||||
Reference in New Issue
Block a user