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