Repository: CCChenhao997/DualGCN-ABSA Branch: main Commit: e8975264b080 Files: 133 Total size: 62.8 MB Directory structure: gitextract_bclkhkuw/ ├── DualGCN/ │ ├── build_vocab.sh │ ├── data_utils.py │ ├── dataset/ │ │ ├── Laptops_allennlp/ │ │ │ ├── test.json │ │ │ └── train.json │ │ ├── Laptops_corenlp/ │ │ │ ├── test.json │ │ │ └── train.json │ │ ├── Laptops_stanza/ │ │ │ ├── test.json │ │ │ └── train.json │ │ ├── Restaurants_allennlp/ │ │ │ ├── test.json │ │ │ └── train.json │ │ ├── Restaurants_corenlp/ │ │ │ ├── test.json │ │ │ └── train.json │ │ ├── Restaurants_stanza/ │ │ │ ├── test.json │ │ │ └── train.json │ │ ├── Tweets_allennlp/ │ │ │ ├── test.json │ │ │ └── train.json │ │ ├── Tweets_corenlp/ │ │ │ ├── test.json │ │ │ └── train.json │ │ └── Tweets_stanza/ │ │ ├── test.json │ │ └── train.json │ ├── glove/ │ │ └── readme.md │ ├── layers.py │ ├── models/ │ │ ├── atae_lstm.py │ │ ├── dualgcn.py │ │ ├── dualgcn_bert.py │ │ ├── ian.py │ │ ├── semgcn.py │ │ └── syngcn.py │ ├── prepare_vocab.py │ ├── run.sh │ ├── train.py │ └── tree.py ├── LAL-Parser/ │ ├── EVALB/ │ │ ├── COLLINS.prm │ │ ├── LICENSE │ │ ├── Makefile │ │ ├── README │ │ ├── bug/ │ │ │ ├── bug.gld │ │ │ ├── bug.rsl-new │ │ │ ├── bug.rsl-old │ │ │ └── bug.tst │ │ ├── evalb │ │ ├── evalb.c │ │ ├── evalb.dSYM/ │ │ │ └── Contents/ │ │ │ ├── Info.plist │ │ │ └── Resources/ │ │ │ └── DWARF/ │ │ │ └── evalb │ │ ├── new.prm │ │ ├── nk.prm │ │ ├── sample/ │ │ │ ├── sample.gld │ │ │ ├── sample.prm │ │ │ ├── sample.rsl │ │ │ └── sample.tst │ │ └── tgrep_proc.prl │ ├── best_model/ │ │ └── readme.md │ └── src_joint/ │ ├── KM_parser.py │ ├── absa_parser.py │ ├── const_decoder.pyx │ ├── dep_eval.py │ ├── dep_reader.py │ ├── evaluate.py │ ├── hpsg_decoder.pyx │ ├── main.py │ ├── makehp.py │ ├── pretrained_bert/ │ │ ├── __init__.py │ │ ├── __main__.py │ │ ├── convert_tf_checkpoint_to_pytorch.py │ │ ├── file_utils.py │ │ ├── modeling.py │ │ ├── optimization.py │ │ └── tokenization.py │ ├── pytorch_transformers/ │ │ ├── __init__.py │ │ ├── __main__.py │ │ ├── convert_gpt2_checkpoint_to_pytorch.py │ │ ├── convert_openai_checkpoint_to_pytorch.py │ │ ├── convert_pytorch_checkpoint_to_tf.py │ │ ├── convert_roberta_checkpoint_to_pytorch.py │ │ ├── convert_tf_checkpoint_to_pytorch.py │ │ ├── convert_transfo_xl_checkpoint_to_pytorch.py │ │ ├── convert_xlm_checkpoint_to_pytorch.py │ │ ├── convert_xlnet_checkpoint_to_pytorch.py │ │ ├── file_utils.py │ │ ├── modeling_auto.py │ │ ├── modeling_bert.py │ │ ├── modeling_gpt2.py │ │ ├── modeling_openai.py │ │ ├── modeling_roberta.py │ │ ├── modeling_transfo_xl.py │ │ ├── modeling_transfo_xl_utilities.py │ │ ├── modeling_utils.py │ │ ├── modeling_xlm.py │ │ ├── modeling_xlnet.py │ │ ├── optimization.py │ │ ├── tests/ │ │ │ ├── __init__.py │ │ │ ├── conftest.py │ │ │ ├── fixtures/ │ │ │ │ ├── input.txt │ │ │ │ ├── sample_text.txt │ │ │ │ └── test_sentencepiece.model │ │ │ ├── modeling_auto_test.py │ │ │ ├── modeling_bert_test.py │ │ │ ├── modeling_common_test.py │ │ │ ├── modeling_gpt2_test.py │ │ │ ├── modeling_openai_test.py │ │ │ ├── modeling_roberta_test.py │ │ │ ├── modeling_transfo_xl_test.py │ │ │ ├── modeling_xlm_test.py │ │ │ ├── modeling_xlnet_test.py │ │ │ ├── optimization_test.py │ │ │ ├── tokenization_auto_test.py │ │ │ ├── tokenization_bert_test.py │ │ │ ├── tokenization_gpt2_test.py │ │ │ ├── tokenization_openai_test.py │ │ │ ├── tokenization_roberta_test.py │ │ │ ├── tokenization_tests_commons.py │ │ │ ├── tokenization_transfo_xl_test.py │ │ │ ├── tokenization_utils_test.py │ │ │ ├── tokenization_xlm_test.py │ │ │ └── tokenization_xlnet_test.py │ │ ├── tokenization_auto.py │ │ ├── tokenization_bert.py │ │ ├── tokenization_gpt2.py │ │ ├── tokenization_openai.py │ │ ├── tokenization_roberta.py │ │ ├── tokenization_transfo_xl.py │ │ ├── tokenization_utils.py │ │ ├── tokenization_xlm.py │ │ └── tokenization_xlnet.py │ ├── test.py │ ├── transliterate.py │ ├── trees.py │ ├── utils.py │ ├── utils_io.py │ └── vocabulary.py ├── LICENSE ├── README.md └── requirements.txt ================================================ FILE CONTENTS ================================================ ================================================ FILE: DualGCN/build_vocab.sh ================================================ #!/bin/bash # build vocab for different datasets python ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Restaurants_corenlp --vocab_dir DualGCN/dataset/Restaurants_corenlp python ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Laptops_corenlp --vocab_dir DualGCN/dataset/Laptops_corenlp python ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Tweets_corenlp --vocab_dir DualGCN/dataset/Tweets_corenlp python ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Restaurants_allennlp --vocab_dir DualGCN/dataset/Restaurants_allennlp python ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Laptops_allennlp --vocab_dir DualGCN/dataset/Laptops_allennlp python ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Tweets_allennlp --vocab_dir DualGCN/dataset/Tweets_allennlp python ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Restaurants_stanza --vocab_dir DualGCN/dataset/Restaurants_stanza python ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Laptops_stanza --vocab_dir DualGCN/dataset/Laptops_stanza python ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Tweets_stanza --vocab_dir DualGCN/dataset/Tweets_stanza ================================================ FILE: DualGCN/data_utils.py ================================================ ''' Description: version: Author: chenhao Date: 2021-06-09 14:17:37 ''' import os import sys sys.path.append(r'./LAL-Parser/src_joint') import re import json import pickle import numpy as np from tqdm import tqdm from transformers import BertTokenizer from torch.utils.data import Dataset def ParseData(data_path): with open(data_path) as infile: all_data = [] data = json.load(infile) for d in data: for aspect in d['aspects']: text_list = list(d['token']) tok = list(d['token']) # word token length = len(tok) # real length # if args.lower == True: tok = [t.lower() for t in tok] tok = ' '.join(tok) asp = list(aspect['term']) # aspect asp = [a.lower() for a in asp] asp = ' '.join(asp) label = aspect['polarity'] # label pos = list(d['pos']) # pos_tag head = list(d['head']) # head deprel = list(d['deprel']) # deprel # position aspect_post = [aspect['from'], aspect['to']] post = [i-aspect['from'] for i in range(aspect['from'])] \ +[0 for _ in range(aspect['from'], aspect['to'])] \ +[i-aspect['to']+1 for i in range(aspect['to'], length)] # aspect mask if len(asp) == 0: mask = [1 for _ in range(length)] # for rest16 else: mask = [0 for _ in range(aspect['from'])] \ +[1 for _ in range(aspect['from'], aspect['to'])] \ +[0 for _ in range(aspect['to'], length)] sample = {'text': tok, 'aspect': asp, 'pos': pos, 'post': post, 'head': head,\ 'deprel': deprel, 'length': length, 'label': label, 'mask': mask, \ 'aspect_post': aspect_post, 'text_list': text_list} all_data.append(sample) return all_data def build_tokenizer(fnames, max_length, data_file): parse = ParseData if os.path.exists(data_file): print('loading tokenizer:', data_file) tokenizer = pickle.load(open(data_file, 'rb')) else: tokenizer = Tokenizer.from_files(fnames=fnames, max_length=max_length, parse=parse) pickle.dump(tokenizer, open(data_file, 'wb')) return tokenizer class Vocab(object): ''' vocabulary of dataset ''' def __init__(self, vocab_list, add_pad, add_unk): self._vocab_dict = dict() self._reverse_vocab_dict = dict() self._length = 0 if add_pad: self.pad_word = '' self.pad_id = self._length self._length += 1 self._vocab_dict[self.pad_word] = self.pad_id if add_unk: self.unk_word = '' self.unk_id = self._length self._length += 1 self._vocab_dict[self.unk_word] = self.unk_id for w in vocab_list: self._vocab_dict[w] = self._length self._length += 1 for w, i in self._vocab_dict.items(): self._reverse_vocab_dict[i] = w def word_to_id(self, word): if hasattr(self, 'unk_id'): return self._vocab_dict.get(word, self.unk_id) return self._vocab_dict[word] def id_to_word(self, id_): if hasattr(self, 'unk_word'): return self._reverse_vocab_dict.get(id_, self.unk_word) return self._reverse_vocab_dict[id_] def has_word(self, word): return word in self._vocab_dict def __len__(self): return self._length @staticmethod def load_vocab(vocab_path: str): with open(vocab_path, "rb") as f: return pickle.load(f) def save_vocab(self, vocab_path): with open(vocab_path, "wb") as f: pickle.dump(self, f) class Tokenizer(object): ''' transform text to indices ''' def __init__(self, vocab, max_length, lower, pos_char_to_int, pos_int_to_char): self.vocab = vocab self.max_length = max_length self.lower = lower self.pos_char_to_int = pos_char_to_int self.pos_int_to_char = pos_int_to_char @classmethod def from_files(cls, fnames, max_length, parse, lower=True): corpus = set() pos_char_to_int, pos_int_to_char = {}, {} for fname in fnames: for obj in parse(fname): text_raw = obj['text'] if lower: text_raw = text_raw.lower() corpus.update(Tokenizer.split_text(text_raw)) return cls(vocab=Vocab(corpus, add_pad=True, add_unk=True), max_length=max_length, lower=lower, pos_char_to_int=pos_char_to_int, pos_int_to_char=pos_int_to_char) @staticmethod def pad_sequence(sequence, pad_id, maxlen, dtype='int64', padding='post', truncating='post'): x = (np.zeros(maxlen) + pad_id).astype(dtype) if truncating == 'pre': trunc = sequence[-maxlen:] else: trunc = sequence[:maxlen] trunc = np.asarray(trunc, dtype=dtype) if padding == 'post': x[:len(trunc)] = trunc else: x[-len(trunc):] = trunc return x def text_to_sequence(self, text, reverse=False, padding='post', truncating='post'): if self.lower: text = text.lower() words = Tokenizer.split_text(text) sequence = [self.vocab.word_to_id(w) for w in words] if len(sequence) == 0: sequence = [0] if reverse: sequence.reverse() return Tokenizer.pad_sequence(sequence, pad_id=self.vocab.pad_id, maxlen=self.max_length, padding=padding, truncating=truncating) @staticmethod def split_text(text): # for ch in ["\'s", "\'ve", "n\'t", "\'re", "\'m", "\'d", "\'ll", ",", ".", "!", "*", "/", "?", "(", ")", "\"", "-", ":"]: # text = text.replace(ch, " "+ch+" ") return text.strip().split() class SentenceDataset(Dataset): ''' PyTorch standard dataset class ''' def __init__(self, fname, tokenizer, opt, vocab_help): parse = ParseData post_vocab, pos_vocab, dep_vocab, pol_vocab = vocab_help data = list() polarity_dict = {'positive':0, 'negative':1, 'neutral':2} for obj in tqdm(parse(fname), total=len(parse(fname)), desc="Training examples"): text = tokenizer.text_to_sequence(obj['text']) aspect = tokenizer.text_to_sequence(obj['aspect']) # max_length=10 post = [post_vocab.stoi.get(t, post_vocab.unk_index) for t in obj['post']] post = tokenizer.pad_sequence(post, pad_id=opt.pad_id, maxlen=opt.max_length, dtype='int64', padding='post', truncating='post') pos = [pos_vocab.stoi.get(t, pos_vocab.unk_index) for t in obj['pos']] pos = tokenizer.pad_sequence(pos, pad_id=opt.pad_id, maxlen=opt.max_length, dtype='int64', padding='post', truncating='post') deprel = [dep_vocab.stoi.get(t, dep_vocab.unk_index) for t in obj['deprel']] deprel = tokenizer.pad_sequence(deprel, pad_id=opt.pad_id, maxlen=opt.max_length, dtype='int64', padding='post', truncating='post') mask = tokenizer.pad_sequence(obj['mask'], pad_id=opt.pad_id, maxlen=opt.max_length, dtype='int64', padding='post', truncating='post') adj = np.ones(opt.max_length) * opt.pad_id if opt.parseadj: from absa_parser import headparser # * adj headp, syntree = headparser.parse_heads(obj['text']) adj = softmax(headp[0]) adj = np.delete(adj, 0, axis=0) adj = np.delete(adj, 0, axis=1) adj -= np.diag(np.diag(adj)) if not opt.direct: adj = adj + adj.T adj = adj + np.eye(adj.shape[0]) adj = np.pad(adj, (0, opt.max_length - adj.shape[0]), 'constant') if opt.parsehead: from absa_parser import headparser headp, syntree = headparser.parse_heads(obj['text']) syntree2head = [[leaf.father for leaf in tree.leaves()] for tree in syntree] head = tokenizer.pad_sequence(syntree2head[0], pad_id=opt.pad_id, maxlen=opt.max_length, dtype='int64', padding='post', truncating='post') else: head = tokenizer.pad_sequence(obj['head'], pad_id=opt.pad_id, maxlen=opt.max_length, dtype='int64', padding='post', truncating='post') length = obj['length'] polarity = polarity_dict[obj['label']] data.append({ 'text': text, 'aspect': aspect, 'post': post, 'pos': pos, 'deprel': deprel, 'head': head, 'adj': adj, 'mask': mask, 'length': length, 'polarity': polarity }) self._data = data def __getitem__(self, index): return self._data[index] def __len__(self): return len(self._data) def _load_wordvec(data_path, embed_dim, vocab=None): with open(data_path, 'r', encoding='utf-8', newline='\n', errors='ignore') as f: word_vec = dict() if embed_dim == 200: for line in f: tokens = line.rstrip().split() if tokens[0] == '' or tokens[0] == '': # avoid them continue if vocab is None or vocab.has_word(tokens[0]): word_vec[tokens[0]] = np.asarray(tokens[1:], dtype='float32') elif embed_dim == 300: for line in f: tokens = line.rstrip().split() if tokens[0] == '': # avoid them continue elif tokens[0] == '': word_vec[''] = np.random.uniform(-0.25, 0.25, 300) word = ''.join((tokens[:-300])) if vocab is None or vocab.has_word(tokens[0]): word_vec[word] = np.asarray(tokens[-300:], dtype='float32') else: print("embed_dim error!!!") exit() return word_vec def build_embedding_matrix(vocab, embed_dim, data_file): if os.path.exists(data_file): print('loading embedding matrix:', data_file) embedding_matrix = pickle.load(open(data_file, 'rb')) else: print('loading word vectors...') embedding_matrix = np.zeros((len(vocab), embed_dim)) fname = './DualGCN/glove/glove.840B.300d.txt' word_vec = _load_wordvec(fname, embed_dim, vocab) for i in range(len(vocab)): vec = word_vec.get(vocab.id_to_word(i)) if vec is not None: embedding_matrix[i] = vec pickle.dump(embedding_matrix, open(data_file, 'wb')) return embedding_matrix def softmax(x): if len(x.shape) > 1: # matrix tmp = np.max(x, axis=1) x -= tmp.reshape((x.shape[0], 1)) x = np.exp(x) tmp = np.sum(x, axis=1) x /= tmp.reshape((x.shape[0], 1)) else: # vector tmp = np.max(x) x -= tmp x = np.exp(x) tmp = np.sum(x) x /= tmp return x class Tokenizer4BertGCN: def __init__(self, max_seq_len, pretrained_bert_name): self.max_seq_len = max_seq_len self.tokenizer = BertTokenizer.from_pretrained(pretrained_bert_name) self.cls_token_id = self.tokenizer.cls_token_id self.sep_token_id = self.tokenizer.sep_token_id def tokenize(self, s): return self.tokenizer.tokenize(s) def convert_tokens_to_ids(self, tokens): return self.tokenizer.convert_tokens_to_ids(tokens) class ABSAGCNData(Dataset): def __init__(self, fname, tokenizer, opt): self.data = [] parse = ParseData polarity_dict = {'positive':0, 'negative':1, 'neutral':2} for obj in tqdm(parse(fname), total=len(parse(fname)), desc="Training examples"): polarity = polarity_dict[obj['label']] text = obj['text'] term = obj['aspect'] term_start = obj['aspect_post'][0] term_end = obj['aspect_post'][1] text_list = obj['text_list'] left, term, right = text_list[: term_start], text_list[term_start: term_end], text_list[term_end: ] from absa_parser import headparser headp, syntree = headparser.parse_heads(text) ori_adj = softmax(headp[0]) ori_adj = np.delete(ori_adj, 0, axis=0) ori_adj = np.delete(ori_adj, 0, axis=1) ori_adj -= np.diag(np.diag(ori_adj)) if not opt.direct: ori_adj = ori_adj + ori_adj.T ori_adj = ori_adj + np.eye(ori_adj.shape[0]) assert len(text_list) == ori_adj.shape[0] == ori_adj.shape[1], '{}-{}-{}'.format(len(text_list), text_list, ori_adj.shape) left_tokens, term_tokens, right_tokens = [], [], [] left_tok2ori_map, term_tok2ori_map, right_tok2ori_map = [], [], [] for ori_i, w in enumerate(left): for t in tokenizer.tokenize(w): left_tokens.append(t) # * ['expand', '##able', 'highly', 'like', '##ing'] left_tok2ori_map.append(ori_i) # * [0, 0, 1, 2, 2] asp_start = len(left_tokens) offset = len(left) for ori_i, w in enumerate(term): for t in tokenizer.tokenize(w): term_tokens.append(t) # term_tok2ori_map.append(ori_i) term_tok2ori_map.append(ori_i + offset) asp_end = asp_start + len(term_tokens) offset += len(term) for ori_i, w in enumerate(right): for t in tokenizer.tokenize(w): right_tokens.append(t) right_tok2ori_map.append(ori_i+offset) while len(left_tokens) + len(right_tokens) > tokenizer.max_seq_len-2*len(term_tokens) - 3: if len(left_tokens) > len(right_tokens): left_tokens.pop(0) left_tok2ori_map.pop(0) else: right_tokens.pop() right_tok2ori_map.pop() bert_tokens = left_tokens + term_tokens + right_tokens tok2ori_map = left_tok2ori_map + term_tok2ori_map + right_tok2ori_map truncate_tok_len = len(bert_tokens) tok_adj = np.zeros( (truncate_tok_len, truncate_tok_len), dtype='float32') for i in range(truncate_tok_len): for j in range(truncate_tok_len): tok_adj[i][j] = ori_adj[tok2ori_map[i]][tok2ori_map[j]] context_asp_ids = [tokenizer.cls_token_id]+tokenizer.convert_tokens_to_ids( bert_tokens)+[tokenizer.sep_token_id]+tokenizer.convert_tokens_to_ids(term_tokens)+[tokenizer.sep_token_id] context_asp_len = len(context_asp_ids) paddings = [0] * (tokenizer.max_seq_len - context_asp_len) context_len = len(bert_tokens) context_asp_seg_ids = [0] * (1 + context_len + 1) + [1] * (len(term_tokens) + 1) + paddings src_mask = [0] + [1] * context_len + [0] * (opt.max_length - context_len - 1) aspect_mask = [0] + [0] * asp_start + [1] * (asp_end - asp_start) aspect_mask = aspect_mask + (opt.max_length - len(aspect_mask)) * [0] context_asp_attention_mask = [1] * context_asp_len + paddings context_asp_ids += paddings context_asp_ids = np.asarray(context_asp_ids, dtype='int64') context_asp_seg_ids = np.asarray(context_asp_seg_ids, dtype='int64') context_asp_attention_mask = np.asarray(context_asp_attention_mask, dtype='int64') src_mask = np.asarray(src_mask, dtype='int64') aspect_mask = np.asarray(aspect_mask, dtype='int64') # pad adj context_asp_adj_matrix = np.zeros( (tokenizer.max_seq_len, tokenizer.max_seq_len)).astype('float32') context_asp_adj_matrix[1:context_len + 1, 1:context_len + 1] = tok_adj data = { 'text_bert_indices': context_asp_ids, 'bert_segments_ids': context_asp_seg_ids, 'attention_mask': context_asp_attention_mask, 'asp_start': asp_start, 'asp_end': asp_end, 'adj_matrix': context_asp_adj_matrix, 'src_mask': src_mask, 'aspect_mask': aspect_mask, 'polarity': polarity, } self.data.append(data) def __len__(self): return len(self.data) def __getitem__(self, idx): return self.data[idx] ================================================ FILE: DualGCN/dataset/Laptops_allennlp/test.json ================================================ [ { "token": [ "Boot", "time", "is", "super", "fast", ",", "around", "anywhere", "from", "35", "seconds", "to", "1", "minute", "." ], "pos": [ "NOUN", "NOUN", "AUX", "ADV", "ADJ", "PUNCT", "ADV", "ADV", "ADP", "NUM", "NOUN", "ADP", "NUM", "NOUN", "PUNCT" ], "head": [ 3, 3, 0, 3, 4, 5, 8, 6, 8, 9, 9, 9, 14, 12, 3 ], "deprel": [ "nsubj", "nsubj", "root", "dep", "dep", "mark", "nsubj", "dep", "dep", "pobj", "dep", "dep", "num", "dep", "dep" ], "aspects": [ { "term": [ "Boot", "time" ], "from": 0, "to": 2, "polarity": "positive" } ] }, { "token": [ "tech", "support", "would", "not", "fix", "the", "problem", "unless", "I", "bought", "your", "plan", "for", "$", "150", "plus", "." ], "pos": [ "NOUN", "NOUN", "VERB", "PART", "VERB", "DET", "NOUN", "SCONJ", "PRON", "VERB", "DET", "NOUN", "ADP", "SYM", "NUM", "CCONJ", "PUNCT" ], "head": [ 5, 1, 5, 5, 0, 7, 5, 10, 10, 5, 10, 10, 10, 13, 14, 14, 5 ], "deprel": [ "advmod", "dep", "aux", "neg", "root", "dep", "dobj", "advmod", "nsubj", "dep", "dep", "dobj", "prep", "pobj", "num", "dep", "punct" ], "aspects": [ { "term": [ "tech", "support" ], "from": 0, "to": 2, "polarity": "negative" } ] }, { "token": [ "Set", "up", "was", "easy", "." ], "pos": [ "VERB", "ADP", "AUX", "ADJ", "PUNCT" ], "head": [ 4, 1, 4, 0, 4 ], "deprel": [ "nsubj", "advmod", "cop", "root", "dep" ], "aspects": [ { "term": [ "Set", "up" ], "from": 0, "to": 2, "polarity": "positive" } ] }, { "token": [ "Did", "not", "enjoy", "the", "new", "Windows", "8", "and", "touchscreen", "functions", "." ], "pos": [ "AUX", "PART", "VERB", "DET", "ADJ", "PROPN", "NUM", "CCONJ", "NOUN", "NOUN", "PUNCT" ], "head": [ 3, 3, 0, 10, 10, 10, 10, 10, 10, 3, 3 ], "deprel": [ "nsubj", "neg", "root", "dep", "amod", "amod", "dep", "cc", "dep", "dep", "dep" ], "aspects": [ { "term": [ "Windows", "8" ], "from": 5, "to": 7, "polarity": "negative" }, { "term": [ "touchscreen", "functions" ], "from": 8, "to": 10, "polarity": "negative" } ] }, { "token": [ "Other", "than", "not", "being", "a", "fan", "of", "click", "pads", "(", "industry", "standard", "these", "days", ")", "and", "the", "lousy", "internal", "speakers", ",", "it", "'s", 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"neutral" } ] }, { "token": [ "''", "Technically", "if", "a", "girl", "wants", "to", "sparkle", "she", "can", "put", "on", "glitter", ",", "but", "I", "think", "the", "correct", "answer", "would", "be", "a", "smile", ",", "''", "-", "demi", "lovato", "." ], "pos": [ "``", "RB", "IN", "DT", "NN", "VBZ", "TO", "VB", "PRP", "MD", "VB", "IN", "NN", ",", "CC", "PRP", "VBP", "DT", "JJ", "NN", "MD", "VB", "DT", "NN", ",", "''", ",", "NNP", "NNP", "." ], "head": [ 11, 11, 6, 5, 6, 11, 8, 6, 11, 11, 0, 13, 11, 17, 17, 17, 11, 20, 20, 24, 24, 24, 24, 17, 11, 28, 28, 11, 28, 11 ], "deprel": [ "punct", "advmod", "mark", "det", "nsubj", "advcl", "mark", "xcomp", "nsubj", "aux", "root", "case", "obl", "punct", "cc", "nsubj", "conj", "det", "amod", "nsubj", "aux", "cop", "det", "ccomp", "punct", "punct", "punct", "dep", "flat", "punct" ], "aspects": [ { "term": [ ",", "''", "-", "demi", "lovato" ], "from": 24, "to": 29, "polarity": "neutral" } ] } ] ================================================ FILE: DualGCN/dataset/Tweets_stanza/train.json ================================================ [File too large to display: 11.3 MB] ================================================ FILE: DualGCN/glove/readme.md ================================================ ### Please put the pre-trained word vectors in this folder. [https://nlp.stanford.edu/projects/glove/](https://nlp.stanford.edu/projects/glove/) or [https://apache-mxnet.s3.cn-north-1.amazonaws.com.cn/gluon/embeddings/glove/glove.840B.300d.zip](https://apache-mxnet.s3.cn-north-1.amazonaws.com.cn/gluon/embeddings/glove/glove.840B.300d.zip) ================================================ FILE: DualGCN/layers.py ================================================ import math import torch import torch.nn as nn import torch.nn.functional as F class DynamicLSTM(nn.Module): ''' LSTM which can hold variable length sequence, use like TensorFlow's RNN(input, lenght...). ''' def __init__(self, input_size, hidden_size, num_layers=1, bias=True, batch_first=True, dropout=0, bidirectional=False, only_use_last_hidden_state=False, rnn_type='LSTM'): super(DynamicLSTM, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.bias = bias self.batch_first = batch_first self.dropout = dropout self.bidirectional = bidirectional self.only_use_last_hidden_state = only_use_last_hidden_state self.rnn_type = rnn_type if self.rnn_type == 'LSTM': self.RNN = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bias=bias, batch_first=batch_first, dropout=dropout, bidirectional=bidirectional) elif self.rnn_type == 'GRU': self.RNN = nn.GRU(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bias=bias, batch_first=batch_first, dropout=dropout, bidirectional=bidirectional) elif self.rnn_type == 'RNN': self.RNN = nn.RNN(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bias=bias, batch_first=batch_first, dropout=dropout, bidirectional=bidirectional) def forward(self, x, x_len): ''' sequence -> sort -> pad and pack -> process using RNN -> unpack -> unsort ''' '''sort''' x_sort_idx = torch.sort(x_len, descending=True)[1].long() x_unsort_idx = torch.sort(x_sort_idx)[1].long() x_len = x_len[x_sort_idx] x = x[x_sort_idx] '''pack''' x_emb_p = torch.nn.utils.rnn.pack_padded_sequence(x, x_len, batch_first=self.batch_first) ''' process ''' if self.rnn_type == 'LSTM': out_pack, (ht, ct) = self.RNN(x_emb_p, None) else: out_pack, ht = self.RNN(x_emb_p, None) ct = None '''unsort''' ht = ht[:, x_unsort_idx] if self.only_use_last_hidden_state: return ht else: out, _ = torch.nn.utils.rnn.pad_packed_sequence(out_pack, batch_first=self.batch_first) if self.batch_first: out = out[x_unsort_idx] else: out = out[:, x_unsort_idx] if self.rnn_type == 'LSTM': ct = ct[:, x_unsort_idx] return out, (ht, ct) class SqueezeEmbedding(nn.Module): ''' Squeeze sequence embedding length to the longest one in the batch ''' def __init__(self, batch_first=True): super(SqueezeEmbedding, self).__init__() self.batch_first = batch_first def forward(self, x, x_len): ''' sequence -> sort -> pad and pack -> unpack -> unsort ''' '''sort''' x_sort_idx = torch.sort(x_len, descending=True)[1].long() x_unsort_idx = torch.sort(x_sort_idx)[1].long() x_len = x_len[x_sort_idx] x = x[x_sort_idx] '''pack''' x_emb_p = torch.nn.utils.rnn.pack_padded_sequence(x, x_len, batch_first=self.batch_first) '''unpack''' out, _ = torch.nn.utils.rnn.pad_packed_sequence(x_emb_p, batch_first=self.batch_first) if self.batch_first: out = out[x_unsort_idx] else: out = out[:, x_unsort_idx] return out class SoftAttention(nn.Module): ''' Attention Mechanism for ATAE-LSTM ''' def __init__(self, hidden_dim, embed_dim): super(SoftAttention, self).__init__() self.embed_dim = embed_dim self.hidden_dim = hidden_dim self.w_h = nn.Linear(hidden_dim, hidden_dim, bias=False) self.w_v = nn.Linear(embed_dim, embed_dim, bias=False) self.w_p = nn.Linear(hidden_dim, hidden_dim, bias=False) self.w_x = nn.Linear(hidden_dim, hidden_dim, bias=False) self.weight = nn.Parameter(torch.Tensor(hidden_dim+embed_dim)) def forward(self, h, aspect): hx = self.w_h(h) vx = self.w_v(aspect) hv = torch.tanh(torch.cat((hx, vx), dim=-1)) ax = torch.unsqueeze(F.softmax(torch.matmul(hv, self.weight), dim=-1), dim=1) rx = torch.squeeze(torch.bmm(ax, h), dim=1) hn = h[:, -1, :] hs = torch.tanh(self.w_p(rx)+self.w_x(hn)) return hs class Attention(nn.Module): def __init__(self, embed_dim, hidden_dim=None, out_dim=None, n_head=1, score_function='dot_product', dropout=0): ''' Attention Mechanism :param embed_dim: :param hidden_dim: :param out_dim: :param n_head: num of head (Multi-Head Attention) :param score_function: scaled_dot_product / mlp (concat) / bi_linear (general dot) :return (?, q_len, out_dim,) ''' super(Attention, self).__init__() if hidden_dim is None: hidden_dim = embed_dim // n_head if out_dim is None: out_dim = embed_dim self.embed_dim = embed_dim self.hidden_dim = hidden_dim self.n_head = n_head self.score_function = score_function self.w_k = nn.Linear(embed_dim, n_head * hidden_dim) self.w_q = nn.Linear(embed_dim, n_head * hidden_dim) self.proj = nn.Linear(n_head * hidden_dim, out_dim) self.dropout = nn.Dropout(dropout) if score_function == 'mlp': self.weight = nn.Parameter(torch.Tensor(hidden_dim*2)) elif self.score_function == 'bi_linear': self.weight = nn.Parameter(torch.Tensor(hidden_dim, hidden_dim)) else: # dot_product / scaled_dot_product self.register_parameter('weight', None) self.reset_parameters() def reset_parameters(self): stdv = 1. / math.sqrt(self.hidden_dim) if self.weight is not None: self.weight.data.uniform_(-stdv, stdv) def forward(self, k, q): if len(q.shape) == 2: q = torch.unsqueeze(q, dim=1) if len(k.shape) == 2: k = torch.unsqueeze(k, dim=1) mb_size = k.shape[0] # ? k_len = k.shape[1] q_len = q.shape[1] kx = self.w_k(k).view(mb_size, k_len, self.n_head, self.hidden_dim) kx = kx.permute(2, 0, 1, 3).contiguous().view(-1, k_len, self.hidden_dim) qx = self.w_q(q).view(mb_size, q_len, self.n_head, self.hidden_dim) qx = qx.permute(2, 0, 1, 3).contiguous().view(-1, q_len, self.hidden_dim) if self.score_function == 'dot_product': kt = kx.permute(0, 2, 1) score = torch.bmm(qx, kt) elif self.score_function == 'scaled_dot_product': kt = kx.permute(0, 2, 1) qkt = torch.bmm(qx, kt) score = torch.div(qkt, math.sqrt(self.hidden_dim)) elif self.score_function == 'mlp': kxx = torch.unsqueeze(kx, dim=1).expand(-1, q_len, -1, -1) qxx = torch.unsqueeze(qx, dim=2).expand(-1, -1, k_len, -1) kq = torch.cat((kxx, qxx), dim=-1) score = F.tanh(torch.matmul(kq, self.weight)) elif self.score_function == 'bi_linear': qw = torch.matmul(qx, self.weight) kt = kx.permute(0, 2, 1) score = torch.bmm(qw, kt) else: raise RuntimeError('invalid score_function') score = F.softmax(score, dim=-1) output = torch.bmm(score, kx) output = torch.cat(torch.split(output, mb_size, dim=0), dim=-1) output = self.proj(output) output = self.dropout(output) return output, score ================================================ FILE: DualGCN/models/atae_lstm.py ================================================ ''' Description: version: Author: chenhao Date: 2021-06-09 14:17:37 ''' import torch import torch.nn as nn import torch.nn.functional as F from layers import DynamicLSTM, SqueezeEmbedding, SoftAttention class ATAE_LSTM(nn.Module): ''' Attention-based LSTM with Aspect Embedding ''' def __init__(self, embedding_matrix, opt): super(ATAE_LSTM, self).__init__() self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float), freeze=True) self.squeeze_embedding = SqueezeEmbedding() self.lstm = DynamicLSTM(opt.embed_dim*2, opt.hidden_dim, num_layers=1, batch_first=True) self.attention = SoftAttention(opt.hidden_dim, opt.embed_dim) self.dense = nn.Linear(opt.hidden_dim, opt.polarities_dim) def forward(self, inputs): text, aspect_text = inputs[0], inputs[1] x_len = torch.sum(text != 0, dim=-1) x_len_max = torch.max(x_len) aspect_len = torch.sum(aspect_text != 0, dim=-1).float() x = self.embed(text) x = self.squeeze_embedding(x, x_len) aspect = self.embed(aspect_text) aspect_pool = torch.div(torch.sum(aspect, dim=1), aspect_len.view(aspect_len.size(0), 1)) aspect = torch.unsqueeze(aspect_pool, dim=1).expand(-1, x_len_max, -1) x = torch.cat((aspect, x), dim=-1) h, _ = self.lstm(x, x_len) hs = self.attention(h, aspect) out = self.dense(hs) return out, None ================================================ FILE: DualGCN/models/dualgcn.py ================================================ ''' Description: version: Author: chenhao Date: 2021-06-09 14:17:37 ''' import copy import math import torch import numpy as np import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable from tree import head_to_tree, tree_to_adj class DualGCNClassifier(nn.Module): def __init__(self, embedding_matrix, opt): super().__init__() in_dim = opt.hidden_dim self.opt = opt self.gcn_model = GCNAbsaModel(embedding_matrix=embedding_matrix, opt=opt) self.classifier = nn.Linear(in_dim*2, opt.polarities_dim) def forward(self, inputs): outputs1, outputs2, adj_ag, adj_dep = self.gcn_model(inputs) final_outputs = torch.cat((outputs1, outputs2), dim=-1) logits = self.classifier(final_outputs) adj_ag_T = adj_ag.transpose(1, 2) identity = torch.eye(adj_ag.size(1)).cuda() identity = identity.unsqueeze(0).expand(adj_ag.size(0), adj_ag.size(1), adj_ag.size(1)) ortho = adj_ag@adj_ag_T for i in range(ortho.size(0)): ortho[i] -= torch.diag(torch.diag(ortho[i])) ortho[i] += torch.eye(ortho[i].size(0)).cuda() penal = None if self.opt.losstype == 'doubleloss': penal1 = (torch.norm(ortho - identity) / adj_ag.size(0)).cuda() penal2 = (adj_ag.size(0) / torch.norm(adj_ag - adj_dep)).cuda() penal = self.opt.alpha * penal1 + self.opt.beta * penal2 elif self.opt.losstype == 'orthogonalloss': penal = (torch.norm(ortho - identity) / adj_ag.size(0)).cuda() penal = self.opt.alpha * penal elif self.opt.losstype == 'differentiatedloss': penal = (adj_ag.size(0) / torch.norm(adj_ag - adj_dep)).cuda() penal = self.opt.beta * penal return logits, penal class GCNAbsaModel(nn.Module): def __init__(self, embedding_matrix, opt): super().__init__() self.opt = opt self.embedding_matrix = embedding_matrix self.emb = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float), freeze=True) self.pos_emb = nn.Embedding(opt.pos_size, opt.pos_dim, padding_idx=0) if opt.pos_dim > 0 else None # POS emb self.post_emb = nn.Embedding(opt.post_size, opt.post_dim, padding_idx=0) if opt.post_dim > 0 else None # position emb embeddings = (self.emb, self.pos_emb, self.post_emb) # gcn layer self.gcn = GCN(opt, embeddings, opt.hidden_dim, opt.num_layers) def forward(self, inputs): tok, asp, pos, head, deprel, post, mask, l, adj = inputs # unpack inputs maxlen = max(l.data) mask = mask[:, :maxlen] if self.opt.parseadj: adj_dep = adj[:, :maxlen, :maxlen].float() else: def inputs_to_tree_reps(head, words, l): trees = [head_to_tree(head[i], words[i], l[i]) for i in range(len(l))] adj = [tree_to_adj(maxlen, tree, directed=self.opt.direct, self_loop=self.opt.loop).reshape(1, maxlen, maxlen) for tree in trees] adj = np.concatenate(adj, axis=0) adj = torch.from_numpy(adj) return adj.cuda() adj_dep = inputs_to_tree_reps(head.data, tok.data, l.data) h1, h2, adj_ag = self.gcn(adj_dep, inputs) # avg pooling asp feature asp_wn = mask.sum(dim=1).unsqueeze(-1) # aspect words num mask = mask.unsqueeze(-1).repeat(1,1,self.opt.hidden_dim) # mask for h outputs1 = (h1*mask).sum(dim=1) / asp_wn outputs2 = (h2*mask).sum(dim=1) / asp_wn return outputs1, outputs2, adj_ag, adj_dep class GCN(nn.Module): def __init__(self, opt, embeddings, mem_dim, num_layers): super(GCN, self).__init__() self.opt = opt self.layers = num_layers self.mem_dim = mem_dim self.in_dim = opt.embed_dim+opt.post_dim+opt.pos_dim self.emb, self.pos_emb, self.post_emb = embeddings # rnn layer input_size = self.in_dim self.rnn = nn.LSTM(input_size, opt.rnn_hidden, opt.rnn_layers, batch_first=True, \ dropout=opt.rnn_dropout, bidirectional=opt.bidirect) if opt.bidirect: self.in_dim = opt.rnn_hidden * 2 else: self.in_dim = opt.rnn_hidden # drop out self.rnn_drop = nn.Dropout(opt.rnn_dropout) self.in_drop = nn.Dropout(opt.input_dropout) self.gcn_drop = nn.Dropout(opt.gcn_dropout) # gcn layer self.W = nn.ModuleList() for layer in range(self.layers): input_dim = self.in_dim if layer == 0 else self.mem_dim self.W.append(nn.Linear(input_dim, self.mem_dim)) self.attention_heads = opt.attention_heads self.attn = MultiHeadAttention(self.attention_heads, self.mem_dim*2) self.weight_list = nn.ModuleList() for j in range(self.layers): input_dim = self.in_dim if j == 0 else self.mem_dim self.weight_list.append(nn.Linear(input_dim, self.mem_dim)) self.affine1 = nn.Parameter(torch.Tensor(self.mem_dim, self.mem_dim)) self.affine2 = nn.Parameter(torch.Tensor(self.mem_dim, self.mem_dim)) def encode_with_rnn(self, rnn_inputs, seq_lens, batch_size): h0, c0 = rnn_zero_state(batch_size, self.opt.rnn_hidden, self.opt.rnn_layers, self.opt.bidirect) rnn_inputs = nn.utils.rnn.pack_padded_sequence(rnn_inputs, seq_lens, batch_first=True, enforce_sorted=False) rnn_outputs, (ht, ct) = self.rnn(rnn_inputs, (h0, c0)) rnn_outputs, _ = nn.utils.rnn.pad_packed_sequence(rnn_outputs, batch_first=True) return rnn_outputs def forward(self, adj, inputs): tok, asp, pos, head, deprel, post, mask, l, _ = inputs # unpack inputs src_mask = (tok != 0).unsqueeze(-2) maxlen = max(l.data) mask_ = (torch.zeros_like(tok) != tok).float().unsqueeze(-1)[:, :maxlen] # embedding word_embs = self.emb(tok) embs = [word_embs] if self.opt.pos_dim > 0: embs += [self.pos_emb(pos)] if self.opt.post_dim > 0: embs += [self.post_emb(post)] embs = torch.cat(embs, dim=2) embs = self.in_drop(embs) # rnn layer self.rnn.flatten_parameters() gcn_inputs = self.rnn_drop(self.encode_with_rnn(embs, l, tok.size()[0])) denom_dep = adj.sum(2).unsqueeze(2) + 1 attn_tensor = self.attn(gcn_inputs, gcn_inputs, src_mask) attn_adj_list = [attn_adj.squeeze(1) for attn_adj in torch.split(attn_tensor, 1, dim=1)] outputs_dep = None adj_ag = None # * Average Multi-head Attention matrixes for i in range(self.attention_heads): if adj_ag is None: adj_ag = attn_adj_list[i] else: adj_ag += attn_adj_list[i] adj_ag /= self.attention_heads for j in range(adj_ag.size(0)): adj_ag[j] -= torch.diag(torch.diag(adj_ag[j])) adj_ag[j] += torch.eye(adj_ag[j].size(0)).cuda() adj_ag = mask_ * adj_ag denom_ag = adj_ag.sum(2).unsqueeze(2) + 1 outputs_ag = gcn_inputs outputs_dep = gcn_inputs for l in range(self.layers): # ************SynGCN************* Ax_dep = adj.bmm(outputs_dep) AxW_dep = self.W[l](Ax_dep) AxW_dep = AxW_dep / denom_dep gAxW_dep = F.relu(AxW_dep) # ************SemGCN************* Ax_ag = adj_ag.bmm(outputs_ag) AxW_ag = self.weight_list[l](Ax_ag) AxW_ag = AxW_ag / denom_ag gAxW_ag = F.relu(AxW_ag) # * mutual Biaffine module A1 = F.softmax(torch.bmm(torch.matmul(gAxW_dep, self.affine1), torch.transpose(gAxW_ag, 1, 2)), dim=-1) A2 = F.softmax(torch.bmm(torch.matmul(gAxW_ag, self.affine2), torch.transpose(gAxW_dep, 1, 2)), dim=-1) gAxW_dep, gAxW_ag = torch.bmm(A1, gAxW_ag), torch.bmm(A2, gAxW_dep) outputs_dep = self.gcn_drop(gAxW_dep) if l < self.layers - 1 else gAxW_dep outputs_ag = self.gcn_drop(gAxW_ag) if l < self.layers - 1 else gAxW_ag return outputs_ag, outputs_dep, adj_ag def rnn_zero_state(batch_size, hidden_dim, num_layers, bidirectional=True): total_layers = num_layers * 2 if bidirectional else num_layers state_shape = (total_layers, batch_size, hidden_dim) h0 = c0 = Variable(torch.zeros(*state_shape), requires_grad=False) return h0.cuda(), c0.cuda() def attention(query, key, mask=None, dropout=None): d_k = query.size(-1) scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k) if mask is not None: scores = scores.masked_fill(mask == 0, -1e9) p_attn = F.softmax(scores, dim=-1) if dropout is not None: p_attn = dropout(p_attn) return p_attn def clones(module, N): return nn.ModuleList([copy.deepcopy(module) for _ in range(N)]) class MultiHeadAttention(nn.Module): def __init__(self, h, d_model, dropout=0.1): super(MultiHeadAttention, self).__init__() assert d_model % h == 0 self.d_k = d_model // h self.h = h self.linears = clones(nn.Linear(d_model, d_model), 2) self.dropout = nn.Dropout(p=dropout) def forward(self, query, key, mask=None): mask = mask[:, :, :query.size(1)] if mask is not None: mask = mask.unsqueeze(1) nbatches = query.size(0) query, key = [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2) for l, x in zip(self.linears, (query, key))] attn = attention(query, key, mask=mask, dropout=self.dropout) return attn ================================================ FILE: DualGCN/models/dualgcn_bert.py ================================================ ''' Description: version: Author: chenhao Date: 2021-06-09 14:17:37 ''' import copy import math import torch import numpy as np import torch.nn as nn import torch.nn.functional as F class LayerNorm(nn.Module): "Construct a layernorm module (See citation for details)." def __init__(self, features, eps=1e-6): super(LayerNorm, self).__init__() self.a_2 = nn.Parameter(torch.ones(features)) self.b_2 = nn.Parameter(torch.zeros(features)) self.eps = eps def forward(self, x): mean = x.mean(-1, keepdim=True) std = x.std(-1, keepdim=True) return self.a_2 * (x - mean) / (std + self.eps) + self.b_2 class DualGCNBertClassifier(nn.Module): def __init__(self, bert, opt): super().__init__() self.opt = opt self.gcn_model = GCNAbsaModel(bert, opt=opt) self.classifier = nn.Linear(opt.bert_dim*2, opt.polarities_dim) def forward(self, inputs): outputs1, outputs2, adj_ag, adj_dep, pooled_output = self.gcn_model(inputs) final_outputs = torch.cat((outputs1, outputs2, pooled_output), dim=-1) logits = self.classifier(final_outputs) adj_ag_T = adj_ag.transpose(1, 2) identity = torch.eye(adj_ag.size(1)).cuda() identity = identity.unsqueeze(0).expand(adj_ag.size(0), adj_ag.size(1), adj_ag.size(1)) ortho = adj_ag@adj_ag_T for i in range(ortho.size(0)): ortho[i] -= torch.diag(torch.diag(ortho[i])) ortho[i] += torch.eye(ortho[i].size(0)).cuda() penal = None if self.opt.losstype == 'doubleloss': penal1 = (torch.norm(ortho - identity) / adj_ag.size(0)).cuda() penal2 = (adj_ag.size(0) / torch.norm(adj_ag - adj_dep)).cuda() penal = self.opt.alpha * penal1 + self.opt.beta * penal2 elif self.opt.losstype == 'orthogonalloss': penal = (torch.norm(ortho - identity) / adj_ag.size(0)).cuda() penal = self.opt.alpha * penal elif self.opt.losstype == 'differentiatedloss': penal = (adj_ag.size(0) / torch.norm(adj_ag - adj_dep)).cuda() penal = self.opt.beta * penal return logits, penal class GCNAbsaModel(nn.Module): def __init__(self, bert, opt): super().__init__() self.opt = opt self.gcn = GCNBert(bert, opt, opt.num_layers) def forward(self, inputs): text_bert_indices, bert_segments_ids, attention_mask, asp_start, asp_end, adj_dep, src_mask, aspect_mask = inputs h1, h2, adj_ag, pooled_output = self.gcn(adj_dep, inputs) # avg pooling asp feature asp_wn = aspect_mask.sum(dim=1).unsqueeze(-1) aspect_mask = aspect_mask.unsqueeze(-1).repeat(1, 1, self.opt.bert_dim // 2) outputs1 = (h1*aspect_mask).sum(dim=1) / asp_wn outputs2 = (h2*aspect_mask).sum(dim=1) / asp_wn return outputs1, outputs2, adj_ag, adj_dep, pooled_output class GCNBert(nn.Module): def __init__(self, bert, opt, num_layers): super(GCNBert, self).__init__() self.bert = bert self.opt = opt self.layers = num_layers self.mem_dim = opt.bert_dim // 2 self.attention_heads = opt.attention_heads self.bert_dim = opt.bert_dim self.bert_drop = nn.Dropout(opt.bert_dropout) self.pooled_drop = nn.Dropout(opt.bert_dropout) self.gcn_drop = nn.Dropout(opt.gcn_dropout) self.layernorm = LayerNorm(opt.bert_dim) # gcn layer self.W = nn.ModuleList() for layer in range(self.layers): input_dim = self.bert_dim if layer == 0 else self.mem_dim self.W.append(nn.Linear(input_dim, self.mem_dim)) self.attn = MultiHeadAttention(opt.attention_heads, self.bert_dim) self.weight_list = nn.ModuleList() for j in range(self.layers): input_dim = self.bert_dim if j == 0 else self.mem_dim self.weight_list.append(nn.Linear(input_dim, self.mem_dim)) self.affine1 = nn.Parameter(torch.Tensor(self.mem_dim, self.mem_dim)) self.affine2 = nn.Parameter(torch.Tensor(self.mem_dim, self.mem_dim)) def forward(self, adj, inputs): text_bert_indices, bert_segments_ids, attention_mask, asp_start, asp_end, adj_dep, src_mask, aspect_mask = inputs src_mask = src_mask.unsqueeze(-2) sequence_output, pooled_output = self.bert(text_bert_indices, attention_mask=attention_mask, token_type_ids=bert_segments_ids) sequence_output = self.layernorm(sequence_output) gcn_inputs = self.bert_drop(sequence_output) pooled_output = self.pooled_drop(pooled_output) denom_dep = adj.sum(2).unsqueeze(2) + 1 attn_tensor = self.attn(gcn_inputs, gcn_inputs, src_mask) attn_adj_list = [attn_adj.squeeze(1) for attn_adj in torch.split(attn_tensor, 1, dim=1)] multi_head_list = [] outputs_dep = None adj_ag = None # * Average Multi-head Attention matrixes for i in range(self.attention_heads): if adj_ag is None: adj_ag = attn_adj_list[i] else: adj_ag += attn_adj_list[i] adj_ag /= self.attention_heads for j in range(adj_ag.size(0)): adj_ag[j] -= torch.diag(torch.diag(adj_ag[j])) adj_ag[j] += torch.eye(adj_ag[j].size(0)).cuda() adj_ag = src_mask.transpose(1, 2) * adj_ag denom_ag = adj_ag.sum(2).unsqueeze(2) + 1 outputs_ag = gcn_inputs outputs_dep = gcn_inputs for l in range(self.layers): # ************SynGCN************* Ax_dep = adj.bmm(outputs_dep) AxW_dep = self.W[l](Ax_dep) AxW_dep = AxW_dep / denom_dep gAxW_dep = F.relu(AxW_dep) # ************SemGCN************* Ax_ag = adj_ag.bmm(outputs_ag) AxW_ag = self.weight_list[l](Ax_ag) AxW_ag = AxW_ag / denom_ag gAxW_ag = F.relu(AxW_ag) # * mutual Biaffine module A1 = F.softmax(torch.bmm(torch.matmul(gAxW_dep, self.affine1), torch.transpose(gAxW_ag, 1, 2)), dim=-1) A2 = F.softmax(torch.bmm(torch.matmul(gAxW_ag, self.affine2), torch.transpose(gAxW_dep, 1, 2)), dim=-1) gAxW_dep, gAxW_ag = torch.bmm(A1, gAxW_ag), torch.bmm(A2, gAxW_dep) outputs_dep = self.gcn_drop(gAxW_dep) if l < self.layers - 1 else gAxW_dep outputs_ag = self.gcn_drop(gAxW_ag) if l < self.layers - 1 else gAxW_ag return outputs_ag, outputs_dep, adj_ag, pooled_output def attention(query, key, mask=None, dropout=None): d_k = query.size(-1) scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k) if mask is not None: scores = scores.masked_fill(mask == 0, -1e9) p_attn = F.softmax(scores, dim=-1) if dropout is not None: p_attn = dropout(p_attn) return p_attn def clones(module, N): return nn.ModuleList([copy.deepcopy(module) for _ in range(N)]) class MultiHeadAttention(nn.Module): def __init__(self, h, d_model, dropout=0.1): super(MultiHeadAttention, self).__init__() assert d_model % h == 0 self.d_k = d_model // h self.h = h self.linears = clones(nn.Linear(d_model, d_model), 2) self.dropout = nn.Dropout(p=dropout) def forward(self, query, key, mask=None): mask = mask[:, :, :query.size(1)] if mask is not None: mask = mask.unsqueeze(1) nbatches = query.size(0) query, key = [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2) for l, x in zip(self.linears, (query, key))] attn = attention(query, key, mask=mask, dropout=self.dropout) return attn ================================================ FILE: DualGCN/models/ian.py ================================================ ''' Description: version: Author: chenhao Date: 2021-06-09 14:17:37 ''' import torch import torch.nn as nn import torch.nn.functional as F from layers import DynamicLSTM, Attention class IAN(nn.Module): def __init__(self, embedding_matrix, opt): super(IAN, self).__init__() self.opt = opt self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float), freeze=True) self.lstm_context = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True) self.lstm_aspect = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True) self.attention_aspect = Attention(opt.hidden_dim, score_function='bi_linear') self.attention_context = Attention(opt.hidden_dim, score_function='bi_linear') self.dense = nn.Linear(opt.hidden_dim*2, opt.polarities_dim) def forward(self, inputs): text, aspect_text = inputs[0], inputs[1] text_raw_len = torch.sum(text != 0, dim=-1) aspect_len = torch.sum(aspect_text != 0, dim=-1) context = self.embed(text) aspect = self.embed(aspect_text) context, (_, _) = self.lstm_context(context, text_raw_len) aspect, (_, _) = self.lstm_aspect(aspect, aspect_len) aspect_len = torch.tensor(aspect_len, dtype=torch.float).to(self.opt.device) aspect_pool = torch.sum(aspect, dim=1) aspect_pool = torch.div(aspect_pool, aspect_len.view(aspect_len.size(0), 1)) text_raw_len = torch.tensor(text_raw_len, dtype=torch.float).to(self.opt.device) context_pool = torch.sum(context, dim=1) context_pool = torch.div(context_pool, text_raw_len.view(text_raw_len.size(0), 1)) aspect_final, _ = self.attention_aspect(aspect, context_pool) aspect_final = aspect_final.squeeze(dim=1) context_final, _ = self.attention_context(context, aspect_pool) context_final = context_final.squeeze(dim=1) x = torch.cat((aspect_final, context_final), dim=-1) out = self.dense(x) return out, None ================================================ FILE: DualGCN/models/semgcn.py ================================================ ''' Description: version: Author: chenhao Date: 2021-06-09 14:17:37 ''' import copy import math import torch import numpy as np import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable class SemGCNClassifier(nn.Module): def __init__(self, embedding_matrix, opt): super().__init__() in_dim = opt.hidden_dim self.opt = opt self.gcn_model = GCNAbsaModel(embedding_matrix=embedding_matrix, opt=opt) self.classifier = nn.Linear(in_dim, opt.polarities_dim) def forward(self, inputs): outputs = self.gcn_model(inputs) logits = self.classifier(outputs) return logits, None class GCNAbsaModel(nn.Module): def __init__(self, embedding_matrix, opt): super().__init__() self.opt = opt self.embedding_matrix = embedding_matrix self.emb = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float), freeze=True) self.pos_emb = nn.Embedding(opt.pos_size, opt.pos_dim, padding_idx=0) if opt.pos_dim > 0 else None # POS emb self.post_emb = nn.Embedding(opt.post_size, opt.post_dim, padding_idx=0) if opt.post_dim > 0 else None # position emb embeddings = (self.emb, self.pos_emb, self.post_emb) # gcn layer self.gcn = GCN(opt, embeddings, opt.hidden_dim, opt.num_layers) def forward(self, inputs): tok, asp, pos, head, deprel, post, mask, l = inputs # unpack inputs maxlen = max(l.data) mask = mask[:, :maxlen] h = self.gcn(inputs) # avg pooling asp feature asp_wn = mask.sum(dim=1).unsqueeze(-1) # aspect words num mask = mask.unsqueeze(-1).repeat(1,1,self.opt.hidden_dim) # mask for h outputs = (h*mask).sum(dim=1) / asp_wn # mask h return outputs class GCN(nn.Module): def __init__(self, opt, embeddings, mem_dim, num_layers): super(GCN, self).__init__() self.opt = opt self.layers = num_layers self.mem_dim = mem_dim self.in_dim = opt.embed_dim+opt.post_dim+opt.pos_dim self.emb, self.pos_emb, self.post_emb = embeddings # rnn layer input_size = self.in_dim self.rnn = nn.LSTM(input_size, opt.rnn_hidden, opt.rnn_layers, batch_first=True, \ dropout=opt.rnn_dropout, bidirectional=opt.bidirect) if opt.bidirect: self.in_dim = opt.rnn_hidden * 2 else: self.in_dim = opt.rnn_hidden # drop out self.rnn_drop = nn.Dropout(opt.rnn_dropout) self.in_drop = nn.Dropout(opt.input_dropout) self.gcn_drop = nn.Dropout(opt.gcn_dropout) # gcn layer self.W = nn.ModuleList() for layer in range(self.layers): input_dim = self.in_dim if layer == 0 else self.mem_dim self.W.append(nn.Linear(input_dim, self.mem_dim)) self.attention_heads = opt.attention_heads self.head_dim = self.mem_dim // self.layers self.attn = MultiHeadAttention(self.attention_heads, self.mem_dim*2) self.weight_list = nn.ModuleList() for layer in range(self.layers): input_dim = self.in_dim if layer == 0 else self.mem_dim self.weight_list.append(nn.Linear(input_dim, self.mem_dim)) def encode_with_rnn(self, rnn_inputs, seq_lens, batch_size): h0, c0 = rnn_zero_state(batch_size, self.opt.rnn_hidden, self.opt.rnn_layers, self.opt.bidirect) rnn_inputs = nn.utils.rnn.pack_padded_sequence(rnn_inputs, seq_lens, batch_first=True, enforce_sorted=False) rnn_outputs, (ht, ct) = self.rnn(rnn_inputs, (h0, c0)) rnn_outputs, _ = nn.utils.rnn.pad_packed_sequence(rnn_outputs, batch_first=True) return rnn_outputs def forward(self, inputs): tok, asp, pos, head, deprel, post, mask, l = inputs # unpack inputs src_mask = (tok != 0).unsqueeze(-2) maxlen = max(l.data) mask_ = (torch.zeros_like(tok) != tok).float().unsqueeze(-1)[:, :maxlen] # embedding word_embs = self.emb(tok) embs = [word_embs] if self.opt.pos_dim > 0: embs += [self.pos_emb(pos)] if self.opt.post_dim > 0: embs += [self.post_emb(post)] embs = torch.cat(embs, dim=2) embs = self.in_drop(embs) # rnn layer self.rnn.flatten_parameters() gcn_inputs = self.rnn_drop(self.encode_with_rnn(embs, l, tok.size()[0])) attn_tensor = self.attn(gcn_inputs, gcn_inputs, src_mask) attn_adj_list = [attn_adj.squeeze(1) for attn_adj in torch.split(attn_tensor, 1, dim=1)] adj_ag = None # * Average Multi-head Attention matrixes for i in range(self.attention_heads): if adj_ag is None: adj_ag = attn_adj_list[i] else: adj_ag += attn_adj_list[i] adj_ag /= self.attention_heads for j in range(adj_ag.size(0)): adj_ag[j] -= torch.diag(torch.diag(adj_ag[j])) adj_ag[j] += torch.eye(adj_ag[j].size(0)).cuda() adj_ag = mask_ * adj_ag denom_ag = adj_ag.sum(2).unsqueeze(2) + 1 outputs = gcn_inputs for l in range(self.layers): Ax = adj_ag.bmm(outputs) AxW = self.weight_list[l](Ax) AxW = AxW / denom_ag gAxW = F.relu(AxW) outputs = self.gcn_drop(gAxW) if l < self.layers - 1 else gAxW return outputs def rnn_zero_state(batch_size, hidden_dim, num_layers, bidirectional=True): total_layers = num_layers * 2 if bidirectional else num_layers state_shape = (total_layers, batch_size, hidden_dim) h0 = c0 = Variable(torch.zeros(*state_shape), requires_grad=False) return h0.cuda(), c0.cuda() def attention(query, key, mask=None, dropout=None): d_k = query.size(-1) scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k) if mask is not None: scores = scores.masked_fill(mask == 0, -1e9) p_attn = F.softmax(scores, dim=-1) if dropout is not None: p_attn = dropout(p_attn) return p_attn def clones(module, N): return nn.ModuleList([copy.deepcopy(module) for _ in range(N)]) class MultiHeadAttention(nn.Module): def __init__(self, h, d_model, dropout=0.1): super(MultiHeadAttention, self).__init__() assert d_model % h == 0 self.d_k = d_model // h self.h = h self.linears = clones(nn.Linear(d_model, d_model), 2) self.dropout = nn.Dropout(p=dropout) def forward(self, query, key, mask=None): mask = mask[:, :, :query.size(1)] if mask is not None: mask = mask.unsqueeze(1) nbatches = query.size(0) query, key = [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2) for l, x in zip(self.linears, (query, key))] attn = attention(query, key, mask=mask, dropout=self.dropout) return attn ================================================ FILE: DualGCN/models/syngcn.py ================================================ ''' Description: version: Author: chenhao Date: 2021-06-09 14:17:37 ''' import torch import numpy as np import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable from tree import head_to_tree, tree_to_adj class SynGCNClassifier(nn.Module): def __init__(self, embedding_matrix, opt): super().__init__() in_dim = opt.hidden_dim self.opt = opt self.gcn_model = GCNAbsaModel(embedding_matrix=embedding_matrix, opt=opt) self.classifier = nn.Linear(in_dim, opt.polarities_dim) def forward(self, inputs): outputs = self.gcn_model(inputs) logits = self.classifier(outputs) return logits, None class GCNAbsaModel(nn.Module): def __init__(self, embedding_matrix, opt): super().__init__() self.opt = opt self.embedding_matrix = embedding_matrix self.emb = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float), freeze=True) self.pos_emb = nn.Embedding(opt.pos_size, opt.pos_dim, padding_idx=0) if opt.pos_dim > 0 else None # POS emb self.post_emb = nn.Embedding(opt.post_size, opt.post_dim, padding_idx=0) if opt.post_dim > 0 else None # position emb embeddings = (self.emb, self.pos_emb, self.post_emb) # gcn layer self.gcn = GCN(opt, embeddings, opt.hidden_dim, opt.num_layers) def forward(self, inputs): tok, asp, pos, head, deprel, post, mask, l, adj = inputs # unpack inputs maxlen = max(l.data) mask = mask[:, :maxlen] if self.opt.parseadj: adj_dep = adj[:, :maxlen, :maxlen].float() else: def inputs_to_tree_reps(head, words, l): trees = [head_to_tree(head[i], words[i], l[i]) for i in range(len(l))] adj = [tree_to_adj(maxlen, tree, directed=self.opt.direct, self_loop=self.opt.loop).reshape(1, maxlen, maxlen) for tree in trees] adj = np.concatenate(adj, axis=0) adj = torch.from_numpy(adj) return adj.cuda() adj_dep = inputs_to_tree_reps(head.data, tok.data, l.data) h = self.gcn(adj_dep, inputs) # avg pooling asp feature asp_wn = mask.sum(dim=1).unsqueeze(-1) # aspect words num mask = mask.unsqueeze(-1).repeat(1,1,self.opt.hidden_dim) # mask for h outputs = (h*mask).sum(dim=1) / asp_wn # mask h return outputs class GCN(nn.Module): def __init__(self, opt, embeddings, mem_dim, num_layers): super(GCN, self).__init__() self.opt = opt self.layers = num_layers self.mem_dim = mem_dim self.in_dim = opt.embed_dim+opt.post_dim+opt.pos_dim self.emb, self.pos_emb, self.post_emb = embeddings # rnn layer input_size = self.in_dim self.rnn = nn.LSTM(input_size, opt.rnn_hidden, opt.rnn_layers, batch_first=True, \ dropout=opt.rnn_dropout, bidirectional=opt.bidirect) if opt.bidirect: self.in_dim = opt.rnn_hidden * 2 else: self.in_dim = opt.rnn_hidden # drop out self.rnn_drop = nn.Dropout(opt.rnn_dropout) self.in_drop = nn.Dropout(opt.input_dropout) self.gcn_drop = nn.Dropout(opt.gcn_dropout) # gcn layer self.W = nn.ModuleList() for layer in range(self.layers): input_dim = self.in_dim if layer == 0 else self.mem_dim self.W.append(nn.Linear(input_dim, self.mem_dim)) def encode_with_rnn(self, rnn_inputs, seq_lens, batch_size): h0, c0 = rnn_zero_state(batch_size, self.opt.rnn_hidden, self.opt.rnn_layers, self.opt.bidirect) rnn_inputs = nn.utils.rnn.pack_padded_sequence(rnn_inputs, seq_lens, batch_first=True, enforce_sorted=False) rnn_outputs, (ht, ct) = self.rnn(rnn_inputs, (h0, c0)) rnn_outputs, _ = nn.utils.rnn.pad_packed_sequence(rnn_outputs, batch_first=True) return rnn_outputs def forward(self, adj, inputs): tok, asp, pos, head, deprel, post, mask, l, _ = inputs # unpack inputs # embedding word_embs = self.emb(tok) embs = [word_embs] if self.opt.pos_dim > 0: embs += [self.pos_emb(pos)] if self.opt.post_dim > 0: embs += [self.post_emb(post)] embs = torch.cat(embs, dim=2) embs = self.in_drop(embs) # rnn layer self.rnn.flatten_parameters() gcn_inputs = self.rnn_drop(self.encode_with_rnn(embs, l, tok.size()[0])) # gcn layer denom = adj.sum(2).unsqueeze(2) + 1 for l in range(self.layers): Ax = adj.bmm(gcn_inputs) AxW = self.W[l](Ax) AxW = AxW / denom gAxW = F.relu(AxW) gcn_inputs = self.gcn_drop(gAxW) if l < self.layers - 1 else gAxW return gcn_inputs def rnn_zero_state(batch_size, hidden_dim, num_layers, bidirectional=True): total_layers = num_layers * 2 if bidirectional else num_layers state_shape = (total_layers, batch_size, hidden_dim) h0 = c0 = Variable(torch.zeros(*state_shape), requires_grad=False) return h0.cuda(), c0.cuda() ================================================ FILE: DualGCN/prepare_vocab.py ================================================ """ Prepare vocabulary and initial word vectors. """ import json import tqdm import pickle import argparse import numpy as np from collections import Counter class VocabHelp(object): def __init__(self, counter, specials=['', '']): self.pad_index = 0 self.unk_index = 1 counter = counter.copy() self.itos = list(specials) for tok in specials: del counter[tok] # sort by frequency, then alphabetically words_and_frequencies = sorted(counter.items(), key=lambda tup: tup[0]) words_and_frequencies.sort(key=lambda tup: tup[1], reverse=True) # words_and_frequencies is a tuple for word, freq in words_and_frequencies: self.itos.append(word) # stoi is simply a reverse dict for itos self.stoi = {tok: i for i, tok in enumerate(self.itos)} def __eq__(self, other): if self.stoi != other.stoi: return False if self.itos != other.itos: return False return True def __len__(self): return len(self.itos) def extend(self, v): words = v.itos for w in words: if w not in self.stoi: self.itos.append(w) self.stoi[w] = len(self.itos) - 1 return self @staticmethod def load_vocab(vocab_path: str): with open(vocab_path, "rb") as f: return pickle.load(f) def save_vocab(self, vocab_path): with open(vocab_path, "wb") as f: pickle.dump(self, f) def parse_args(): parser = argparse.ArgumentParser(description='Prepare vocab for relation extraction.') parser.add_argument('--data_dir', help='TACRED directory.') parser.add_argument('--vocab_dir', help='Output vocab directory.') parser.add_argument('--lower', default=True, help='If specified, lowercase all words.') args = parser.parse_args() return args def main(): args = parse_args() # input files train_file = args.data_dir + '/train.json' test_file = args.data_dir + '/test.json' # output files # token vocab_tok_file = args.vocab_dir + '/vocab_tok.vocab' # position vocab_post_file = args.vocab_dir + '/vocab_post.vocab' # pos_tag vocab_pos_file = args.vocab_dir + '/vocab_pos.vocab' # dep_rel vocab_dep_file = args.vocab_dir + '/vocab_dep.vocab' # polarity vocab_pol_file = args.vocab_dir + '/vocab_pol.vocab' # load files print("loading files...") train_tokens, train_pos, train_dep, train_max_len = load_tokens(train_file) test_tokens, test_pos, test_dep, test_max_len = load_tokens(test_file) # lower tokens if args.lower: train_tokens, test_tokens = [[t.lower() for t in tokens] for tokens in\ (train_tokens, test_tokens)] # counters token_counter = Counter(train_tokens+test_tokens) pos_counter = Counter(train_pos+test_pos) dep_counter = Counter(train_dep+test_dep) max_len = max(train_max_len, test_max_len) post_counter = Counter(list(range(-max_len, max_len))) pol_counter = Counter(['positive', 'negative', 'neutral']) # build vocab print("building vocab...") token_vocab = VocabHelp(token_counter, specials=['', '']) pos_vocab = VocabHelp(pos_counter, specials=['', '']) dep_vocab = VocabHelp(dep_counter, specials=['', '']) post_vocab = VocabHelp(post_counter, specials=['', '']) pol_vocab = VocabHelp(pol_counter, specials=[]) print("token_vocab: {}, pos_vocab: {}, dep_vocab: {}, post_vocab: {}, pol_vocab: {}".format(len(token_vocab), len(pos_vocab), len(dep_vocab), len(post_vocab), len(pol_vocab))) print("dumping to files...") token_vocab.save_vocab(vocab_tok_file) pos_vocab.save_vocab(vocab_pos_file) dep_vocab.save_vocab(vocab_dep_file) post_vocab.save_vocab(vocab_post_file) pol_vocab.save_vocab(vocab_pol_file) print("all done.") def load_tokens(filename): with open(filename) as infile: data = json.load(infile) tokens = [] pos = [] dep = [] max_len = 0 for d in data: tokens.extend(d['token']) pos.extend(d['pos']) dep.extend(d['deprel']) max_len = max(len(d['token']), max_len) print("{} tokens from {} examples loaded from {}.".format(len(tokens), len(data), filename)) return tokens, pos, dep, max_len if __name__ == '__main__': main() ================================================ FILE: DualGCN/run.sh ================================================ #!/bin/bash # * laptop # * DualGCN CUDA_VISIBLE_DEVICES=0 python ./DualGCN/train.py --model_name dualgcn --dataset laptop --seed 1000 --num_epoch 50 --vocab_dir ./DualGCN/dataset/Laptops_corenlp --cuda 0 --losstype doubleloss --alpha 0.2 --beta 0.2 --parseadj # * DualGCN with Bert # CUDA_VISIBLE_DEVICES=0 python ./DualGCN/train.py --model_name dualgcnbert --dataset laptop --seed 1000 --bert_lr 2e-5 --num_epoch 15 --hidden_dim 768 --max_length 100 --cuda 0 --losstype doubleloss --alpha 0.4 --beta 0.3 --parseadj # * restaurant # * DualGCN # CUDA_VISIBLE_DEVICES=0 python ./DualGCN/train.py --model_name dualgcn --dataset restaurant --seed 1000 --num_epoch 50 --vocab_dir ./DualGCN/dataset/Restaurants_corenlp --cuda 0 --losstype doubleloss --alpha 0.2 --beta 0.3 --parseadj # * DualGCN with Bert # CUDA_VISIBLE_DEVICES=0 python ./DualGCN/train.py --model_name dualgcnbert --dataset restaurant --seed 1000 --bert_lr 2e-5 --num_epoch 15 --hidden_dim 768 --max_length 100 --cuda 0 --losstype doubleloss --alpha 0.6 --beta 0.9 --parseadj # * twitter # * DualGCN # CUDA_VISIBLE_DEVICES=0 python ./DualGCN/train.py --model_name dualgcn --dataset twitter --seed 1000 --num_epoch 50 --vocab_dir ./DualGCN/dataset/Tweets_corenlp --cuda 0 --losstype doubleloss --alpha 0.3 --beta 0.2 --parseadj # * DualGCN with Bert # CUDA_VISIBLE_DEVICES=0 python ./DualGCN/train.py --model_name dualgcnbert --dataset twitter --seed 1000 --bert_lr 2e-5 --num_epoch 15 --hidden_dim 768 --max_length 100 --cuda 0 --losstype doubleloss --alpha 0.5 --beta 0.9 --parseadj ================================================ FILE: DualGCN/train.py ================================================ ''' Description: version: Author: chenhao Date: 2021-06-09 14:17:37 ''' import os import sys import copy import random import logging import argparse import torch import torch.nn as nn import numpy as np from sklearn import metrics from time import strftime, localtime from torch.utils.data import DataLoader from transformers import BertModel, AdamW from models.ian import IAN from models.atae_lstm import ATAE_LSTM from models.syngcn import SynGCNClassifier from models.semgcn import SemGCNClassifier from models.dualgcn import DualGCNClassifier from models.dualgcn_bert import DualGCNBertClassifier from data_utils import SentenceDataset, build_tokenizer, build_embedding_matrix, Tokenizer4BertGCN, ABSAGCNData from prepare_vocab import VocabHelp logger = logging.getLogger() logger.setLevel(logging.INFO) logger.addHandler(logging.StreamHandler(sys.stdout)) def setup_seed(seed): torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) random.seed(seed) torch.backends.cudnn.deterministic = True class Instructor: ''' Model training and evaluation ''' def __init__(self, opt): self.opt = opt if 'bert' in opt.model_name: tokenizer = Tokenizer4BertGCN(opt.max_length, opt.pretrained_bert_name) bert = BertModel.from_pretrained(opt.pretrained_bert_name) self.model = opt.model_class(bert, opt).to(opt.device) trainset = ABSAGCNData(opt.dataset_file['train'], tokenizer, opt=opt) testset = ABSAGCNData(opt.dataset_file['test'], tokenizer, opt=opt) else: tokenizer = build_tokenizer( fnames=[opt.dataset_file['train'], opt.dataset_file['test']], max_length=opt.max_length, data_file='{}/{}_tokenizer.dat'.format(opt.vocab_dir, opt.dataset)) embedding_matrix = build_embedding_matrix( vocab=tokenizer.vocab, embed_dim=opt.embed_dim, data_file='{}/{}d_{}_embedding_matrix.dat'.format(opt.vocab_dir, str(opt.embed_dim), opt.dataset)) logger.info("Loading vocab...") token_vocab = VocabHelp.load_vocab(opt.vocab_dir + '/vocab_tok.vocab') # token post_vocab = VocabHelp.load_vocab(opt.vocab_dir + '/vocab_post.vocab') # position pos_vocab = VocabHelp.load_vocab(opt.vocab_dir + '/vocab_pos.vocab') # POS dep_vocab = VocabHelp.load_vocab(opt.vocab_dir + '/vocab_dep.vocab') # deprel pol_vocab = VocabHelp.load_vocab(opt.vocab_dir + '/vocab_pol.vocab') # polarity logger.info("token_vocab: {}, post_vocab: {}, pos_vocab: {}, dep_vocab: {}, pol_vocab: {}".format(len(token_vocab), len(post_vocab), len(pos_vocab), len(dep_vocab), len(pol_vocab))) # opt.tok_size = len(token_vocab) opt.post_size = len(post_vocab) opt.pos_size = len(pos_vocab) vocab_help = (post_vocab, pos_vocab, dep_vocab, pol_vocab) self.model = opt.model_class(embedding_matrix, opt).to(opt.device) trainset = SentenceDataset(opt.dataset_file['train'], tokenizer, opt=opt, vocab_help=vocab_help) testset = SentenceDataset(opt.dataset_file['test'], tokenizer, opt=opt, vocab_help=vocab_help) self.train_dataloader = DataLoader(dataset=trainset, batch_size=opt.batch_size, shuffle=True) self.test_dataloader = DataLoader(dataset=testset, batch_size=opt.batch_size) if opt.device.type == 'cuda': logger.info('cuda memory allocated: {}'.format(torch.cuda.memory_allocated(self.opt.device.index))) self._print_args() def _print_args(self): n_trainable_params, n_nontrainable_params = 0, 0 for p in self.model.parameters(): n_params = torch.prod(torch.tensor(p.shape)) if p.requires_grad: n_trainable_params += n_params else: n_nontrainable_params += n_params logger.info('n_trainable_params: {0}, n_nontrainable_params: {1}'.format(n_trainable_params, n_nontrainable_params)) logger.info('training arguments:') for arg in vars(self.opt): logger.info('>>> {0}: {1}'.format(arg, getattr(self.opt, arg))) def _reset_params(self): for p in self.model.parameters(): if p.requires_grad: if len(p.shape) > 1: self.opt.initializer(p) # xavier_uniform_ else: stdv = 1. / (p.shape[0]**0.5) torch.nn.init.uniform_(p, a=-stdv, b=stdv) def get_bert_optimizer(self, model): # Prepare optimizer and schedule (linear warmup and decay) no_decay = ['bias', 'LayerNorm.weight'] diff_part = ["bert.embeddings", "bert.encoder"] if self.opt.diff_lr: logger.info("layered learning rate on") optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay) and any(nd in n for nd in diff_part)], "weight_decay": self.opt.weight_decay, "lr": self.opt.bert_lr }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay) and any(nd in n for nd in diff_part)], "weight_decay": 0.0, "lr": self.opt.bert_lr }, { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay) and not any(nd in n for nd in diff_part)], "weight_decay": self.opt.weight_decay, "lr": self.opt.learning_rate }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay) and not any(nd in n for nd in diff_part)], "weight_decay": 0.0, "lr": self.opt.learning_rate }, ] optimizer = AdamW(optimizer_grouped_parameters, eps=self.opt.adam_epsilon) else: logger.info("bert learning rate on") optimizer_grouped_parameters = [ {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': self.opt.weight_decay}, {'params': [p for n, p in model.named_parameters() if any( nd in n for nd in no_decay)], 'weight_decay': 0.0} ] optimizer = AdamW(optimizer_grouped_parameters, lr=self.opt.bert_lr, eps=self.opt.adam_epsilon) return optimizer def _train(self, criterion, optimizer, max_test_acc_overall=0): max_test_acc = 0 max_f1 = 0 global_step = 0 model_path = '' for epoch in range(self.opt.num_epoch): logger.info('>' * 60) logger.info('epoch: {}'.format(epoch)) n_correct, n_total = 0, 0 for i_batch, sample_batched in enumerate(self.train_dataloader): global_step += 1 # switch model to training mode, clear gradient accumulators self.model.train() optimizer.zero_grad() inputs = [sample_batched[col].to(self.opt.device) for col in self.opt.inputs_cols] outputs, penal = self.model(inputs) targets = sample_batched['polarity'].to(self.opt.device) if self.opt.losstype is not None: loss = criterion(outputs, targets) + penal else: loss = criterion(outputs, targets) loss.backward() optimizer.step() if global_step % self.opt.log_step == 0: n_correct += (torch.argmax(outputs, -1) == targets).sum().item() n_total += len(outputs) train_acc = n_correct / n_total test_acc, f1 = self._evaluate() if test_acc > max_test_acc: max_test_acc = test_acc if test_acc > max_test_acc_overall: if not os.path.exists('./DualGCN/state_dict'): os.mkdir('./DualGCN/state_dict') model_path = './DualGCN/state_dict/{}_{}_acc_{:.4f}_f1_{:.4f}'.format(self.opt.model_name, self.opt.dataset, test_acc, f1) self.best_model = copy.deepcopy(self.model) logger.info('>> saved: {}'.format(model_path)) if f1 > max_f1: max_f1 = f1 logger.info('loss: {:.4f}, acc: {:.4f}, test_acc: {:.4f}, f1: {:.4f}'.format(loss.item(), train_acc, test_acc, f1)) return max_test_acc, max_f1, model_path def _evaluate(self, show_results=False): # switch model to evaluation mode self.model.eval() n_test_correct, n_test_total = 0, 0 targets_all, outputs_all = None, None with torch.no_grad(): for batch, sample_batched in enumerate(self.test_dataloader): inputs = [sample_batched[col].to(self.opt.device) for col in self.opt.inputs_cols] targets = sample_batched['polarity'].to(self.opt.device) outputs, penal = self.model(inputs) n_test_correct += (torch.argmax(outputs, -1) == targets).sum().item() n_test_total += len(outputs) targets_all = torch.cat((targets_all, targets), dim=0) if targets_all is not None else targets outputs_all = torch.cat((outputs_all, outputs), dim=0) if outputs_all is not None else outputs test_acc = n_test_correct / n_test_total f1 = metrics.f1_score(targets_all.cpu(), torch.argmax(outputs_all, -1).cpu(), labels=[0, 1, 2], average='macro') labels = targets_all.data.cpu() predic = torch.argmax(outputs_all, -1).cpu() if show_results: report = metrics.classification_report(labels, predic, digits=4) confusion = metrics.confusion_matrix(labels, predic) return report, confusion, test_acc, f1 return test_acc, f1 def _test(self): self.model = self.best_model self.model.eval() test_report, test_confusion, acc, f1 = self._evaluate(show_results=True) logger.info("Precision, Recall and F1-Score...") logger.info(test_report) logger.info("Confusion Matrix...") logger.info(test_confusion) def run(self): criterion = nn.CrossEntropyLoss() if 'bert' not in self.opt.model_name: _params = filter(lambda p: p.requires_grad, self.model.parameters()) optimizer = self.opt.optimizer(_params, lr=self.opt.learning_rate, weight_decay=self.opt.l2reg) else: optimizer = self.get_bert_optimizer(self.model) max_test_acc_overall = 0 max_f1_overall = 0 if 'bert' not in self.opt.model_name: self._reset_params() max_test_acc, max_f1, model_path = self._train(criterion, optimizer, max_test_acc_overall) logger.info('max_test_acc: {0}, max_f1: {1}'.format(max_test_acc, max_f1)) max_test_acc_overall = max(max_test_acc, max_test_acc_overall) max_f1_overall = max(max_f1, max_f1_overall) torch.save(self.best_model.state_dict(), model_path) logger.info('>> saved: {}'.format(model_path)) logger.info('#' * 60) logger.info('max_test_acc_overall:{}'.format(max_test_acc_overall)) logger.info('max_f1_overall:{}'.format(max_f1_overall)) self._test() def main(): model_classes = { 'atae_lstm': ATAE_LSTM, 'ian': IAN, 'syngcn': SynGCNClassifier, 'semgcn': SemGCNClassifier, 'dualgcn': DualGCNClassifier, 'dualgcnbert': DualGCNBertClassifier, } dataset_files = { 'restaurant': { 'train': './DualGCN/dataset/Restaurants_corenlp/train.json', 'test': './DualGCN/dataset/Restaurants_corenlp/test.json', }, 'laptop': { 'train': './DualGCN/dataset/Laptops_corenlp/train.json', 'test': './DualGCN/dataset/Laptops_corenlp/test.json' }, 'twitter': { 'train': './DualGCN/dataset/Tweets_corenlp/train.json', 'test': './DualGCN/dataset/Tweets_corenlp/test.json', } } input_colses = { 'atae_lstm': ['text', 'aspect'], 'ian': ['text', 'aspect'], 'syngcn': ['text', 'aspect', 'pos', 'head', 'deprel', 'post', 'mask', 'length', 'adj'], 'semgcn': ['text', 'aspect', 'pos', 'head', 'deprel', 'post', 'mask', 'length'], 'dualgcn': ['text', 'aspect', 'pos', 'head', 'deprel', 'post', 'mask', 'length', 'adj'], 'dualgcnbert': ['text_bert_indices', 'bert_segments_ids', 'attention_mask', 'asp_start', 'asp_end', 'adj_matrix', 'src_mask', 'aspect_mask'] } initializers = { 'xavier_uniform_': torch.nn.init.xavier_uniform_, 'xavier_normal_': torch.nn.init.xavier_normal_, 'orthogonal_': torch.nn.init.orthogonal_, } optimizers = { 'adadelta': torch.optim.Adadelta, 'adagrad': torch.optim.Adagrad, 'adam': torch.optim.Adam, 'adamax': torch.optim.Adamax, 'asgd': torch.optim.ASGD, 'rmsprop': torch.optim.RMSprop, 'sgd': torch.optim.SGD, } # Hyperparameters parser = argparse.ArgumentParser() parser.add_argument('--model_name', default='dualgcn', type=str, help=', '.join(model_classes.keys())) parser.add_argument('--dataset', default='laptop', type=str, help=', '.join(dataset_files.keys())) parser.add_argument('--optimizer', default='adam', type=str, help=', '.join(optimizers.keys())) parser.add_argument('--initializer', default='xavier_uniform_', type=str, help=', '.join(initializers.keys())) parser.add_argument('--learning_rate', default=0.002, type=float) parser.add_argument('--l2reg', default=1e-4, type=float) parser.add_argument('--num_epoch', default=20, type=int) parser.add_argument('--batch_size', default=16, type=int) parser.add_argument('--log_step', default=5, type=int) parser.add_argument('--embed_dim', default=300, type=int) parser.add_argument('--post_dim', type=int, default=30, help='Position embedding dimension.') parser.add_argument('--pos_dim', type=int, default=30, help='Pos embedding dimension.') parser.add_argument('--hidden_dim', type=int, default=50, help='GCN mem dim.') parser.add_argument('--num_layers', type=int, default=2, help='Num of GCN layers.') parser.add_argument('--polarities_dim', default=3, type=int, help='3') parser.add_argument('--input_dropout', type=float, default=0.7, help='Input dropout rate.') parser.add_argument('--gcn_dropout', type=float, default=0.1, help='GCN layer dropout rate.') parser.add_argument('--lower', default=True, help='Lowercase all words.') parser.add_argument('--direct', default=False, help='directed graph or undirected graph') parser.add_argument('--loop', default=True) parser.add_argument('--bidirect', default=True, help='Do use bi-RNN layer.') parser.add_argument('--rnn_hidden', type=int, default=50, help='RNN hidden state size.') parser.add_argument('--rnn_layers', type=int, default=1, help='Number of RNN layers.') parser.add_argument('--rnn_dropout', type=float, default=0.1, help='RNN dropout rate.') parser.add_argument('--attention_heads', default=1, type=int, help='number of multi-attention heads') parser.add_argument('--max_length', default=85, type=int) parser.add_argument('--device', default=None, type=str, help='cpu, cuda') parser.add_argument('--seed', default=0, type=int) parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight deay if we apply some.") parser.add_argument('--vocab_dir', type=str, default='./DualGCN/dataset/Laptops_corenlp') parser.add_argument('--pad_id', default=0, type=int) parser.add_argument('--parseadj', default=False, action='store_true', help='dependency probability') parser.add_argument('--parsehead', default=False, action='store_true', help='dependency tree') parser.add_argument('--cuda', default='0', type=str) parser.add_argument('--losstype', default=None, type=str, help="['doubleloss', 'orthogonalloss', 'differentiatedloss']") parser.add_argument('--alpha', default=0.25, type=float) parser.add_argument('--beta', default=0.25, type=float) # * bert parser.add_argument('--pretrained_bert_name', default='bert-base-uncased', type=str) parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument('--bert_dim', type=int, default=768) parser.add_argument('--bert_dropout', type=float, default=0.3, help='BERT dropout rate.') parser.add_argument('--diff_lr', default=False, action='store_true') parser.add_argument('--bert_lr', default=2e-5, type=float) opt = parser.parse_args() opt.model_class = model_classes[opt.model_name] opt.dataset_file = dataset_files[opt.dataset] opt.inputs_cols = input_colses[opt.model_name] opt.initializer = initializers[opt.initializer] opt.optimizer = optimizers[opt.optimizer] # print("choice cuda:{}".format(opt.cuda)) # os.environ["CUDA_VISIBLE_DEVICES"] = opt.cuda opt.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if opt.device is None else torch.device(opt.device) # set random seed setup_seed(opt.seed) if not os.path.exists('./DualGCN/log'): os.makedirs('./DualGCN/log', mode=0o777) log_file = '{}-{}-{}.log'.format(opt.model_name, opt.dataset, strftime("%Y-%m-%d_%H:%M:%S", localtime())) logger.addHandler(logging.FileHandler("%s/%s" % ('./DualGCN/log', log_file))) ins = Instructor(opt) ins.run() if __name__ == '__main__': main() ================================================ FILE: DualGCN/tree.py ================================================ """ Basic operations on trees. """ import numpy as np from collections import defaultdict import copy class Tree(object): """ Reused tree object from stanfordnlp/treelstm. """ def __init__(self): self.parent = None self.num_children = 0 self.children = list() def add_child(self, child): child.parent = self self.num_children += 1 self.children.append(child) def size(self): if getattr(self,'_size'): return self._size count = 1 for i in xrange(self.num_children): count += self.children[i].size() self._size = count return self._size def depth(self): if getattr(self,'_depth'): return self._depth count = 0 if self.num_children>0: for i in xrange(self.num_children): child_depth = self.children[i].depth() if child_depth>count: count = child_depth count += 1 self._depth = count return self._depth def __iter__(self): yield self for c in self.children: for x in c: yield x def head_to_tree(head, tokens, len_): """ Convert a sequence of head indexes into a tree object. """ if isinstance(head, list) == False: tokens = tokens[:len_].tolist() head = head[:len_].tolist() root = None nodes = [Tree() for _ in head] for i in range(len(nodes)): h = head[i] nodes[i].idx = i nodes[i].dist = -1 # just a filler if h == 0: root = nodes[i] else: try: nodes[h-1].add_child(nodes[i]) except: print(len_) exit() assert root is not None return root def tree_to_adj(sent_len, tree, directed=False, self_loop=True): """ Convert a tree object to an (numpy) adjacency matrix. """ ret = np.zeros((sent_len, sent_len), dtype=np.float32) queue = [tree] idx = [] while len(queue) > 0: t, queue = queue[0], queue[1:] idx += [t.idx] for c in t.children: ret[t.idx, c.idx] = 1 queue += t.children if not directed: ret = ret + ret.T if self_loop: for i in idx: ret[i, i] = 1 return ret ================================================ FILE: LAL-Parser/EVALB/COLLINS.prm ================================================ ##------------------------------------------## ## Debug mode ## ## 0: No debugging ## ## 1: print data for individual sentence ## ##------------------------------------------## DEBUG 0 ##------------------------------------------## ## MAX error ## ## Number of error to stop the process. ## ## This is useful if there could be ## ## tokanization error. ## ## The process will stop when this number## ## of errors are accumulated. ## ##------------------------------------------## MAX_ERROR 10 ##------------------------------------------## ## Cut-off length for statistics ## ## At the end of evaluation, the ## ## statistics for the senetnces of length## ## less than or equal to this number will## ## be shown, on top of the statistics ## ## for all the sentences ## ##------------------------------------------## CUTOFF_LEN 40 ##------------------------------------------## ## unlabeled or labeled bracketing ## ## 0: unlabeled bracketing ## ## 1: labeled bracketing ## ##------------------------------------------## LABELED 1 ##------------------------------------------## ## Delete labels ## ## list of labels to be ignored. ## ## If it is a pre-terminal label, delete ## ## the word along with the brackets. ## ## If it is a non-terminal label, just ## ## delete the brackets (don't delete ## ## deildrens). ## ##------------------------------------------## DELETE_LABEL TOP DELETE_LABEL -NONE- DELETE_LABEL , DELETE_LABEL : DELETE_LABEL `` DELETE_LABEL '' DELETE_LABEL . ##------------------------------------------## ## Delete labels for length calculation ## ## list of labels to be ignored for ## ## length calculation purpose ## ##------------------------------------------## DELETE_LABEL_FOR_LENGTH -NONE- ##------------------------------------------## ## Equivalent labels, words ## ## the pairs are considered equivalent ## ## This is non-directional. ## ##------------------------------------------## EQ_LABEL ADVP PRT # EQ_WORD Example example ================================================ FILE: LAL-Parser/EVALB/LICENSE ================================================ This is free and unencumbered software released into the public domain. Anyone is free to copy, modify, publish, use, compile, sell, or distribute this software, either in source code form or as a compiled binary, for any purpose, commercial or non-commercial, and by any means. In jurisdictions that recognize copyright laws, the author or authors of this software dedicate any and all copyright interest in the software to the public domain. We make this dedication for the benefit of the public at large and to the detriment of our heirs and successors. We intend this dedication to be an overt act of relinquishment in perpetuity of all present and future rights to this software under copyright law. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. For more information, please refer to ================================================ FILE: LAL-Parser/EVALB/Makefile ================================================ all: evalb evalb: evalb.c gcc -Wall -g -o evalb evalb.c ================================================ FILE: LAL-Parser/EVALB/README ================================================ ################################################################# # # # Bug fix and additional functionality for evalb # # # # This updated version of evalb fixes a bug in which sentences # # were incorrectly categorized as "length mismatch" when the # # the parse output had certain mislabeled parts-of-speech. # # # # The bug was the result of evalb treating one of the tags (in # # gold or test) as a label to be deleted (see sections [6],[7] # # for details), but not the corresponding tag in the other. # # This most often occurs with punctuation. See the subdir # # "bug" for an example gld and tst file demonstating the bug, # # as well as output of evalb with and without the bug fix. # # # # For the present version in case of length mismatch, the nodes # # causing the imbalance are reinserted to resolve the miscount. # # If the lengths of gold and test truly differ, the error is # # still reported. The parameter file "new.prm" (derived from # # COLLINS.prm) shows how to add new potential mislabelings for # # quotes (",``,',`). # # # # I have preserved DJB's revision for modern compilers except # # for the delcaration of "exit" which is provided by stdlib. # # # # Other changes: # # # # * output of F-Measure in addition to precision and recall # # (I did not update the documention in section [4] for this) # # # # * more comprehensive DEBUG output that includes bracketing # # information as evalb is processing each sentence # # (useful in working through this, and peraps other bugs). # # Use either the "-D" run-time switch or set DEBUG to 2 in # # the parameter file. # # # # * added DELETE_LABEL lines in new.prm for S1 nodes produced # # by the Charniak parser and "?", "!" punctuation produced by # # the Bikel parser. # # # # # # David Ellis (Brown) # # # # January.2006 # ################################################################# ################################################################# # # # Update of evalb for modern compilers # # # # This is an updated version of evalb, for use with modern C # # compilers. There are a few updates, each marked in the code: # # # # /* DJB: explanation of comment */ # # # # The updates are purely to help compilation with recent # # versions of GCC (and other C compilers). There are *NO* other # # changes to the algorithm itself. # # # # I have made these changes following recommendations from # # users of the Corpora Mailing List, especially Peet Morris and # # Ramon Ziai. # # # # David Brooks (Birmingham) # # # # September.2005 # ################################################################# ################################################################# # # # README file for evalb # # # # Satoshi Sekine (NYU) # # Mike Collins (UPenn) # # # # October.1997 # ################################################################# Contents of this README: [0] COPYRIGHT [1] INTRODUCTION [2] INSTALLATION AND RUN [3] OPTIONS [4] OUTPUT FORMAT FROM THE SCORER [5] HOW TO CREATE A GOLDFILE FROM THE TREEBANK [6] THE PARAMETER FILE [7] MORE DETAILS ABOUT THE SCORING ALGORITHM [0] COPYRIGHT The authors abandon the copyright of this program. Everyone is permitted to copy and distribute the program or a portion of the program with no charge and no restrictions unless it is harmful to someone. However, the authors are delightful for the user's kindness of proper usage and letting the authors know bugs or problems. This software is provided "AS IS", and the authors make no warranties, express or implied. To legally enforce the abandonment of copyright, this package is released under the Unlicense (see LICENSE). [1] INTRODUCTION Evaluation of bracketing looks simple, but in fact, there are minor differences from system to system. This is a program to parametarize such minor differences and to give an informative result. "evalb" evaluates bracketing accuracy in a test-file against a gold-file. It returns recall, precision, tagging accuracy. It uses an identical algorithm to that used in (Collins ACL97). [2] Installation and Run To compile the scorer, type > make To run the scorer: > evalb -p Parameter_file Gold_file Test_file For example to use the sample files: > evalb -p sample.prm sample.gld sample.tst [3] OPTIONS You can specify system parameters in the command line options. Other options concerning to evaluation metrix should be specified in parameter file, described later. -p param_file parameter file -d debug mode -e n number of error to kill (default=10) -h help [4] OUTPUT FORMAT FROM THE SCORER The scorer gives individual scores for each sentence, for example: Sent. Matched Bracket Cross Correct Tag ID Len. Stat. Recal Prec. Bracket gold test Bracket Words Tags Accracy ============================================================================ 1 8 0 100.00 100.00 5 5 5 0 6 5 83.33 At the end of the output the === Summary === section gives statistics for all sentences, and for sentences <=40 words in length. The summary contains the following information: i) Number of sentences -- total number of sentences. ii) Number of Error/Skip sentences -- should both be 0 if there is no problem with the parsed/gold files. iii) Number of valid sentences = Number of sentences - Number of Error/Skip sentences iv) Bracketing recall = (number of correct constituents) ---------------------------------------- (number of constituents in the goldfile) v) Bracketing precision = (number of correct constituents) ---------------------------------------- (number of constituents in the parsed file) vi) Complete match = percentaage of sentences where recall and precision are both 100%. vii) Average crossing = (number of constituents crossing a goldfile constituen ---------------------------------------------------- (number of sentences) viii) No crossing = percentage of sentences which have 0 crossing brackets. ix) 2 or less crossing = percentage of sentences which have <=2 crossing brackets. x) Tagging accuracy = percentage of correct POS tags (but see [5].3 for exact details of what is counted). [5] HOW TO CREATE A GOLDFILE FROM THE PENN TREEBANK The gold and parsed files are in a format similar to this: (TOP (S (INTJ (RB No)) (, ,) (NP (PRP it)) (VP (VBD was) (RB n't) (NP (NNP Black) (NNP Monday))) (. .))) To create a gold file from the treebank: tgrep -wn '/.*/' | tgrep_proc.prl will produce a goldfile in the required format. ("tgrep -wn '/.*/'" prints parse trees, "tgrep_process.prl" just skips blank lines). For example, to produce a goldfile for section 23 of the treebank: tgrep -wn '/.*/' | tail +90895 | tgrep_process.prl | sed 2416q > sec23.gold [6] THE PARAMETER (.prm) FILE The .prm file sets options regarding the scoring method. COLLINS.prm gives the same scoring behaviour as the scorer used in (Collins 97). The options chosen were: 1) LABELED 1 to give labelled precision/recall figures, i.e. a constituent must have the same span *and* label as a constituent in the goldfile. 2) DELETE_LABEL TOP Don't count the "TOP" label (which is always given in the output of tgrep) when scoring. 3) DELETE_LABEL -NONE- Remove traces (and all constituents which dominate nothing but traces) when scoring. For example .... (VP (VBD reported) (SBAR (-NONE- 0) (S (-NONE- *T*-1)))) (. .))) would be processed to give .... (VP (VBD reported)) (. .))) 4) DELETE_LABEL , -- for the purposes of scoring remove punctuation DELETE_LABEL : DELETE_LABEL `` DELETE_LABEL '' DELETE_LABEL . 5) DELETE_LABEL_FOR_LENGTH -NONE- -- don't include traces when calculating the length of a sentence (important when classifying a sentence as <=40 words or >40 words) 6) EQ_LABEL ADVP PRT Count ADVP and PRT as being the same label when scoring. [7] MORE DETAILS ABOUT THE SCORING ALGORITHM 1) The scorer initially processes the files to remove all nodes specified by DELETE_LABEL in the .prm file. It also recursively removes nodes which dominate nothing due to all their children being removed. For example, if -NONE- is specified as a label to be deleted, .... (VP (VBD reported) (SBAR (-NONE- 0) (S (-NONE- *T*-1)))) (. .))) would be processed to give .... (VP (VBD reported)) (. .))) 2) The scorer also removes all functional tags attached to non-terminals (functional tags are prefixed with "-" or "=" in the treebank). For example "NP-SBJ" is processed to give "NP", "NP=2" is changed to "NP". 3) Tagging accuracy counts tags for all words *except* any tags which are deleted by a DELETE_LABEL specification in the .prm file. (For example, for COLLINS.prm, punctuation tagged as "," ":" etc. would not be included). 4) When calculating the length of a sentence, all words with POS tags not included in the "DELETE_LABEL_FOR_LENGTH" list in the .prm file are counted. (For COLLINS.prm, only "-NONE-" is specified in this list, so traces are removed before calculating the length of the sentence). 5) There are some subtleties in scoring when either the goldfile or parsed file contains multiple constituents for the same span which have the same non-terminal label. e.g. (NP (NP the man)) If the goldfile contains n constituents for the same span, and the parsed file contains m constituents with that nonterminal, the scorer works as follows: i) If m>n, then the precision is n/m, recall is 100% ii) If n>m, then the precision is 100%, recall is m/n. iii) If n==m, recall and precision are both 100%. ================================================ FILE: LAL-Parser/EVALB/bug/bug.gld ================================================ (TOP (S (NP-SBJ (DT The) (NN Thy-1) (NN gene) (NN promoter) ) (VP (VBZ resembles) (NP (DT a) (`` ") (JJ housekeeping) ('' ") (NN promoter) ) (PP (IN in) (SBAR (IN that) (S (NP-SBJ-68 (PRP it) ) (VP-COOD (VP (VBZ is) (ADJP-PRD (JJ located) (PP (IN within) (NP (DT a) (JJ methylation-free) (NN island) )))) (, ,) (VP (VBZ lacks) (NP (DT a) (JJ canonical) (NN TATA) (NN box) )) (, ,) (CC and) (VP (VBZ displays) (NP (NN heterogeneity) ) (PP (IN in) (NP (NP (DT the) (JJ 5'-end) (NNS termini) ) (PP (IN of) (NP (DT the) (NN mRNA) )))))))))) (. .) ) ) (TOP (S (NP-SBJ (DT The) (JJ latter) (`` ") (NP (NP (JJ nuclear) (NN factor) ) (PP (IN for) (NP (VBN activated) (NN T) (NNS cells) ))) ('' ") ) (ADVP (RB likely) ) (VP (VBZ contributes) (PP (TO to) (NP (NP (DT the) (NN tissue) (NN specificity) ) (PP (IN of) (NP (NN IL-2) (NN gene) (NN expression) ))))) (. .) ) ) (TOP (S (ADVP (RB Thus) ) (, ,) (NP-SBJ (PRP we) ) (VP (VBD postulated) (SBAR-COOD (SBAR (IN that) (S (NP-SBJ (NP (DT the) (JJ circadian) (NN modification) ) (PP (IN of) (NP (NN GR) ))) (VP (VBD was) (ADJP-PRD (JJ independent) (PP (IN of) (NP-COOD (NP (NP (DT the) (JJ diurnal) (NNS fluctuations) ) (PP (IN in) (NP (NN plasma) (NN cortisol) (NN level) ))) (CC or) (NP (NP (DT the) (JJ circadian) (NNS variations) ) (PP (IN in) (NP (JJ environmental) (NN lighting) ))))))))) (CC and) (SBAR (IN that) (S (NP-SBJ-79 (DT the) (NN rhythmicity) ) (VP (MD might) (VP (VB be) (VP (VBN regulated) (NP (-NONE- *-79) ) (PP (IN by) (NP-LGS (NP (DT the) (`` ') (JJ circadian) (NN pacemaker) ('' ') ) (ADJP (JJ located) (PP (IN in) (NP (DT the) (JJ human) (JJ basal) (NN brain) )))))))))))) (. .) ) ) (TOP (S (NP-SBJ-70 (JJ Such) (NN transcription) (NNS factors) ) (VP (VBP play) (NP (DT a) (JJ key) (NN role) ) (PP (IN in) (NP (NP (DT the) (NN development) ) (PP (IN of) (NP (DT the) (JJ mature) (NN T-cell) (NN phenotype) )))) (PP (IN by) (S (NP-SBJ (-NONE- *-70) ) (VP (VBG functioning) (PP (IN as) (`` ') (NP (NP (JJ master) (NNS regulators) ) (PP (IN of) (NP (NN T-cell) (NN differentiation) ))) ('' ') ))))) (. .) ) ) (TOP (S (NP-SBJ (NP (DT The) (NN conversion) ) (PP (IN of) (NP (DT the) (NN TCEd) )) (PP (TO to) (NP (DT a) (`` ') (JJ perfect) ('' ') (NN NF-kB) (NN binding) (NN site) ))) (VP-COOD (VP (VBZ leads) (PP (TO to) (NP-19 (NP (DT a) (JJR tighter) (NN binding) ) (PP (IN of) (NP (NN NF-kB) )) (PP (TO to) (NP (NN TCEd) (NN DNA) ))))) (CC and) (, ,) (VP (PP (IN as) (NP (DT a) (JJ functional) (NN consequence) )) (, ,) (PP (TO to) (NP=19 (NP (DT the) (NN activity) ) (PP (IN of) (NP (DT the) (`` ') (VBN converted) ('' ') (NN TCEd) (NNS motifs) )) (PP (IN in) (NP (NN HeLa) (NNS cells) )))))) (. .) ) ) ================================================ FILE: LAL-Parser/EVALB/bug/bug.rsl-new ================================================ Sent. Matched Bracket Cross Correct Tag ID Len. Stat. Recal Prec. Bracket gold test Bracket Words Tags Accracy ============================================================================ 1 37 0 77.27 65.38 17 22 26 5 34 27 79.41 2 21 0 69.23 64.29 9 13 14 2 20 16 80.00 3 47 0 80.00 82.35 28 35 34 4 44 40 90.91 4 26 0 35.29 37.50 6 17 16 8 25 18 72.00 5 44 0 42.31 33.33 11 26 33 17 38 28 73.68 ============================================================================ 62.83 57.72 71 113 123 0 161 129 80.12 === Summary === -- All -- Number of sentence = 5 Number of Error sentence = 0 Number of Skip sentence = 0 Number of Valid sentence = 5 Bracketing Recall = 62.83 Bracketing Precision = 57.72 Bracketing FMeasure = 60.17 Complete match = 0.00 Average crossing = 7.20 No crossing = 0.00 2 or less crossing = 20.00 Tagging accuracy = 80.12 -- len<=40 -- Number of sentence = 3 Number of Error sentence = 0 Number of Skip sentence = 0 Number of Valid sentence = 3 Bracketing Recall = 61.54 Bracketing Precision = 57.14 Bracketing FMeasure = 59.26 Complete match = 0.00 Average crossing = 5.00 No crossing = 0.00 2 or less crossing = 33.33 Tagging accuracy = 77.22 ================================================ FILE: LAL-Parser/EVALB/bug/bug.rsl-old ================================================ Sent. Matched Bracket Cross Correct Tag ID Len. Stat. Recal Prec. Bracket gold test Bracket Words Tags Accracy ============================================================================ 1 : Length unmatch (33|35) 1 37 1 0.00 0.00 0 0 0 0 0 0 0.00 2 : Length unmatch (19|21) 2 21 1 0.00 0.00 0 0 0 0 0 0 0.00 3 : Length unmatch (44|45) 3 47 1 0.00 0.00 0 0 0 0 0 0 0.00 4 : Length unmatch (24|26) 4 26 1 0.00 0.00 0 0 0 0 0 0 0.00 5 : Length unmatch (38|39) 5 44 1 0.00 0.00 0 0 0 0 0 0 0.00 ============================================================================ 0 0 0.00 === Summary === -- All -- Number of sentence = 5 Number of Error sentence = 5 Number of Skip sentence = 0 Number of Valid sentence = 0 Bracketing Recall = 0.00 Bracketing Precision = 0.00 Bracketing FMeasure = nan Complete match = 0.00 Average crossing = 0.00 No crossing = 0.00 2 or less crossing = 0.00 Tagging accuracy = 0.00 -- len<=40 -- Number of sentence = 3 Number of Error sentence = 3 Number of Skip sentence = 0 Number of Valid sentence = 0 Bracketing Recall = 0.00 Bracketing Precision = 0.00 Bracketing FMeasure = nan Complete match = 0.00 Average crossing = 0.00 No crossing = 0.00 2 or less crossing = 0.00 Tagging accuracy = 0.00 ================================================ FILE: LAL-Parser/EVALB/bug/bug.tst ================================================ (S1 (S (NP (DT The) (JJ Thy-1) (NN gene) (NN promoter)) (VP (VP (VBZ resembles) (NP (NP (DT a) (ADJP (CD ") (NN housekeeping)) (NN ") (NN promoter)) (SBAR (WHPP (IN in) (WHNP (WDT that))) (S (NP (PRP it)) (VP (VBZ is) (VP (VBN located) (PP (IN within) (NP (DT a) (JJ methylation-free) (NN island))))))))) (, ,) (VP (VBZ lacks) (NP (DT a) (JJ canonical) (NNP TATA) (NN box))) (, ,) (CC and) (VP (VBZ displays) (NP (NP (NN heterogeneity)) (PP (IN in) (NP (NP (DT the) (JJ 5'-end) (NNS termini)) (PP (IN of) (NP (DT the) (NN mRNA)))))))) (. .))) (S1 (S (NP (NP (DT The) (JJ latter) (CD ") (JJ nuclear) (NN factor)) (PP (IN for) (NP (VBN activated) (NN T) (NNS cells)))) (VP (VBZ ") (ADJP (JJ likely) (S (VP (VBZ contributes) (PP (TO to) (NP (NP (DT the) (NN tissue) (NN specificity)) (PP (IN of) (NP (JJ IL-2) (NN gene) (NN expression))))))))) (. .))) (S1 (S (ADVP (RB Thus)) (, ,) (NP (PRP we)) (VP (VBD postulated) (SBAR (SBAR (IN that) (S (NP (NP (DT the) (JJ circadian) (NN modification)) (PP (IN of) (NP (NNP GR)))) (VP (VBD was) (ADJP (JJ independent) (PP (IN of) (NP (DT the) (JJ diurnal) (NNS fluctuations)))) (PP (IN in) (NP (NP (NN plasma) (JJ cortisol) (NN level)) (CC or) (NP (NP (DT the) (JJ circadian) (NNS variations)) (PP (IN in) (NP (JJ environmental) (NN lighting))))))))) (CC and) (SBAR (IN that) (S (NP (DT the) (NN rhythmicity)) (VP (MD might) (VP (VB be) (VP (VBN regulated) (PP (IN by) (NP (DT the) ('' ') (NP (JJ circadian) (NN pacemaker) (POS ')) (VP (VBN located) (PP (IN in) (NP (DT the) (JJ human) (JJ basal) (NN brain))))))))))))) (. .))) (S1 (S (NP (JJ Such) (NN transcription) (NNS factors)) (VP (VBP play) (NP (NP (DT a) (JJ key) (NN role)) (PP (IN in) (NP (NP (DT the) (NN development)) (PP (IN of) (NP (NP (DT the) (JJ mature) (JJ T-cell) (NN phenotype)) (PP (IN by) (NP (NP (NN functioning) (RB as) (POS ')) (NN master) (NNS regulators))))) (PP (IN of) (NP (JJ T-cell) (NN differentiation) (POS '))))))) (. .))) (S1 (S (NP (NP (DT The) (NN conversion)) (PP (IN of) (NP (DT the)))) (VP (VBD TCEd) (PP (TO to) (NP (NP (DT a) ('' ') (JJ perfect) ('' ') (NN NF-kB)) (SBAR (S (NP (JJ binding) (NN site)) (VP (VBZ leads) (PP (TO to) (NP (NP (NP (DT a) (ADJP (RBR tighter) (JJ binding)) (PP (IN of) (NP (NP (NNS NF-kB)) (PP (PP (TO to) (NP (JJ TCEd) (NN DNA))) (CC and) (PP (, ,) (PP (IN as) (NP (DT a) (JJ functional) (NN consequence))) (, ,) (TO to) (NP (NP (DT the) (NN activity)) (PP (IN of) (NP (DT the)))))))) (POS ')) (JJ converted) ('' ') (JJ TCEd) (NNS motifs)) (PP (IN in) (NP (NNP HeLa) (NNS cells))))))))))) (. .))) ================================================ FILE: LAL-Parser/EVALB/evalb.c ================================================ /*****************************************************************/ /* evalb [-p param_file] [-dh] [-e n] gold-file test-file */ /* */ /* Evaluate bracketing in test-file against gold-file. */ /* Return recall, precision, tagging accuracy. */ /* */ /*