├── README.md ├── Text_GCN_Tutorial.pdf ├── __init__.py ├── build_graph.py ├── data ├── corpus │ └── ohsumed_3.txt └── ohsumed_3.txt ├── figure ├── TextGCN.png ├── TextGCN.svg └── ohsumed3_tsne.png ├── inits.py ├── layers.py ├── metrics.py ├── models.py ├── remove_words.py ├── train.py ├── tsne.py └── utils.py /README.md: -------------------------------------------------------------------------------- 1 | # Text GCN Tutorial 2 | 3 | This tutorial (currently under improvement) is based on the implementation of Text GCN in our paper: 4 | 5 | Liang Yao, Chengsheng Mao, Yuan Luo. "Graph Convolutional Networks for Text Classification." In 33rd AAAI Conference on Artificial Intelligence (AAAI-19) 6 | 7 | 8 | 9 | # Require 10 | 11 | Python 2.7 or 3.6 12 | 13 | Tensorflow >= 1.4.0 14 | 15 | # Example input data 16 | The Ohsumed corpus is from the MEDLINE database, which is a bibliographic database of important medical literature maintained by the National Library of Medicine 17 | 18 | In this tutorial, we created a subsample of the 2,762 unique diseases abstracts from 3 categories 19 | * C04: Neoplasms 20 | * C10: Nervous System Diseases 21 | * C14: Cardiovascular Diseases 22 | 23 | As we focus on single-label text classification, the documents belonging to multiple categories are excluded 24 | 25 | 1230 train (use 10% as validation), 1532 test 26 | 27 | 1. `/data/ohsumed_3.txt` indicates document names, training/test split, document labels. Each line is for a document. 28 | 29 | 2. `/data/corpus/ohsumed_3.txt` contains raw text of each document, each line is for the corresponding line in `/data/ohsumed_3.txt` 30 | 31 | # Reproduing Results 32 | 33 | 1. Run `python remove_words.py ohsumed_3` 34 | 35 | 2. Run `python build_graph.py ohsumed_3` 36 | 37 | 3. Run `python train.py ohsumed_3` 38 | 39 | # Example output 40 | ``` 41 | 2019-04-04 22:58:26.244395: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA 42 | Epoch: 0001 train_loss= 1.09856 train_acc= 0.41463 val_loss= 1.08209 val_acc= 0.48780 time= 29.13731 43 | Epoch: 0002 train_loss= 1.08044 train_acc= 0.49865 val_loss= 1.05469 val_acc= 0.47967 time= 23.00088 44 | Epoch: 0003 train_loss= 1.05075 train_acc= 0.49865 val_loss= 1.02113 val_acc= 0.47967 time= 21.82401 45 | Epoch: 0004 train_loss= 1.01430 train_acc= 0.49955 val_loss= 0.98582 val_acc= 0.48780 time= 21.42816 46 | Epoch: 0005 train_loss= 0.97174 train_acc= 0.50678 val_loss= 0.95375 val_acc= 0.51220 time= 21.44958 47 | Epoch: 0006 train_loss= 0.93406 train_acc= 0.51220 val_loss= 0.92789 val_acc= 0.55285 time= 24.01502 48 | ...... 49 | Epoch: 0074 train_loss= 0.01921 train_acc= 0.99819 val_loss= 0.09674 val_acc= 0.96748 time= 24.01229 50 | Epoch: 0075 train_loss= 0.02093 train_acc= 0.99909 val_loss= 0.09715 val_acc= 0.96748 time= 24.08436 51 | Early stopping... 52 | Optimization Finished! 53 | Test set results: cost= 0.24295 accuracy= 0.92167 time= 7.60145 54 | 10456 55 | Test Precision, Recall and F1-Score... 56 | precision recall f1-score support 57 | 58 | 0 0.8882 0.8363 0.8614 342 59 | 1 0.9438 0.9517 0.9477 600 60 | 2 0.9174 0.9407 0.9289 590 61 | 62 | avg / total 0.9212 0.9217 0.9212 1532 63 | 64 | ``` 65 | # Visualizing Documents 66 | Run `python tsne.py` 67 | 68 | # Example Visualization 69 | 70 | 71 | 72 | -------------------------------------------------------------------------------- /Text_GCN_Tutorial.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/luoyuanlab/text_gcn_tutorial/710847e7458872cba13003ffeb9a63ac2a6c9ccb/Text_GCN_Tutorial.pdf -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | from __future__ import division 3 | -------------------------------------------------------------------------------- /build_graph.py: -------------------------------------------------------------------------------- 1 | import os 2 | import random 3 | import numpy as np 4 | import pickle as pkl 5 | import networkx as nx 6 | import scipy.sparse as sp 7 | from utils import * 8 | from math import log 9 | from sklearn import svm 10 | from nltk.corpus import wordnet as wn 11 | from nltk.wsd import lesk 12 | 13 | from sklearn.feature_extraction.text import TfidfVectorizer 14 | import sys 15 | from scipy.spatial.distance import cosine 16 | 17 | if len(sys.argv) != 2: 18 | sys.exit("Use: python build_graph.py ") 19 | 20 | # build corpus 21 | dataset = sys.argv[1] 22 | 23 | word_embeddings_dim = 300 24 | word_vector_map = {} 25 | 26 | # shulffing 27 | doc_name_list = [] 28 | doc_train_list = [] 29 | doc_test_list = [] 30 | 31 | # train test split 32 | f = open('data/' + dataset + '.txt', 'r') # 3 columns, path, train/test, label 33 | lines = f.readlines() 34 | for line in lines: 35 | doc_name_list.append(line.strip()) 36 | temp = line.split("\t") 37 | if temp[1].find('test') != -1: 38 | doc_test_list.append(line.strip()) 39 | elif temp[1].find('train') != -1: 40 | doc_train_list.append(line.strip()) 41 | f.close() 42 | 43 | doc_content_list = [] 44 | f = open('data/corpus/' + dataset + '.clean.txt', 'r') # clean: after stop/rare words filtering 45 | lines = f.readlines() 46 | for line in lines: 47 | doc_content_list.append(line.strip()) 48 | f.close() 49 | 50 | train_ids = [] 51 | for train_name in doc_train_list: 52 | train_id = doc_name_list.index(train_name) 53 | train_ids.append(train_id) 54 | ## print(train_ids) 55 | random.shuffle(train_ids) 56 | 57 | # partial labeled data, if you only want 20% training set 58 | #train_ids = train_ids[:int(0.2 * len(train_ids))] 59 | 60 | # persisting the training set 61 | train_ids_str = '\n'.join(str(index) for index in train_ids) 62 | f = open('data/' + dataset + '.train.index', 'w') 63 | f.write(train_ids_str) 64 | f.close() 65 | 66 | # may not be necessary 67 | test_ids = [] 68 | for test_name in doc_test_list: 69 | test_id = doc_name_list.index(test_name) 70 | test_ids.append(test_id) 71 | ## print(test_ids) 72 | random.shuffle(test_ids) 73 | 74 | test_ids_str = '\n'.join(str(index) for index in test_ids) 75 | f = open('data/' + dataset + '.test.index', 'w') 76 | f.write(test_ids_str) 77 | f.close() 78 | 79 | ids = train_ids + test_ids 80 | ## print(ids) 81 | print(len(ids)) 82 | 83 | shuffle_doc_name_list = [] 84 | shuffle_doc_words_list = [] 85 | for id in ids: 86 | shuffle_doc_name_list.append(doc_name_list[int(id)]) 87 | shuffle_doc_words_list.append(doc_content_list[int(id)]) 88 | shuffle_doc_name_str = '\n'.join(shuffle_doc_name_list) 89 | shuffle_doc_words_str = '\n'.join(shuffle_doc_words_list) # content 90 | 91 | f = open('data/' + dataset + '_shuffle.txt', 'w') 92 | f.write(shuffle_doc_name_str) 93 | f.close() 94 | 95 | f = open('data/corpus/' + dataset + '_shuffle.txt', 'w') 96 | f.write(shuffle_doc_words_str) 97 | f.close() 98 | 99 | # build vocab using cleaned words and record freq. 100 | word_freq = {} 101 | word_set = set() 102 | for doc_words in shuffle_doc_words_list: 103 | words = doc_words.split() 104 | for word in words: 105 | word_set.add(word) 106 | if word in word_freq: 107 | word_freq[word] += 1 108 | else: 109 | word_freq[word] = 1 110 | 111 | vocab = list(word_set) 112 | vocab_size = len(vocab) 113 | 114 | word_doc_list = {} 115 | 116 | # keep doc occurrence list, for idf 117 | for i in range(len(shuffle_doc_words_list)): 118 | doc_words = shuffle_doc_words_list[i] 119 | words = doc_words.split() 120 | appeared = set() 121 | for word in words: 122 | if word in appeared: 123 | continue 124 | if word in word_doc_list: 125 | doc_list = word_doc_list[word] 126 | doc_list.append(i) 127 | word_doc_list[word] = doc_list 128 | else: 129 | word_doc_list[word] = [i] 130 | appeared.add(word) 131 | 132 | ## df 133 | word_doc_freq = {} 134 | for word, doc_list in word_doc_list.items(): 135 | word_doc_freq[word] = len(doc_list) 136 | 137 | ## from word to id 138 | word_id_map = {} 139 | for i in range(vocab_size): 140 | word_id_map[vocab[i]] = i 141 | 142 | vocab_str = '\n'.join(vocab) 143 | 144 | f = open('data/corpus/' + dataset + '_vocab.txt', 'w') 145 | f.write(vocab_str) 146 | f.close() 147 | 148 | # get unique label list 149 | label_set = set() 150 | for doc_meta in shuffle_doc_name_list: 151 | temp = doc_meta.split('\t') 152 | label_set.add(temp[2]) 153 | label_list = list(label_set) 154 | 155 | label_list_str = '\n'.join(label_list) 156 | f = open('data/corpus/' + dataset + '_labels.txt', 'w') 157 | f.write(label_list_str) 158 | f.close() 159 | 160 | # x: feature vectors of training docs, no initial features, one hot input 161 | # slect 90% training set 162 | train_size = len(train_ids) 163 | val_size = int(0.1 * train_size) 164 | real_train_size = train_size - val_size 165 | # different training rates 166 | 167 | real_train_doc_names = shuffle_doc_name_list[:real_train_size] 168 | real_train_doc_names_str = '\n'.join(real_train_doc_names) 169 | 170 | f = open('data/' + dataset + '.real_train.name', 'w') 171 | f.write(real_train_doc_names_str) 172 | f.close() 173 | 174 | ## not necessary if don't use preloaded embedding 175 | row_x = [] 176 | col_x = [] 177 | data_x = [] 178 | for i in range(real_train_size): 179 | doc_vec = np.array([0.0 for k in range(word_embeddings_dim)]) 180 | doc_words = shuffle_doc_words_list[i] 181 | words = doc_words.split() 182 | doc_len = len(words) 183 | for word in words: 184 | if word in word_vector_map: 185 | word_vector = word_vector_map[word] 186 | doc_vec = doc_vec #+ np.array(word_vector) 187 | 188 | for j in range(word_embeddings_dim): 189 | row_x.append(i) 190 | col_x.append(j) 191 | # np.random.uniform(-0.25, 0.25) 192 | data_x.append(doc_vec[j] / doc_len) 193 | 194 | # x = sp.csr_matrix((real_train_size, word_embeddings_dim), dtype=np.float32) 195 | x = sp.csr_matrix((data_x, (row_x, col_x)), shape=( 196 | real_train_size, word_embeddings_dim)) 197 | 198 | y = [] 199 | for i in range(real_train_size): 200 | doc_meta = shuffle_doc_name_list[i] 201 | temp = doc_meta.split('\t') 202 | label = temp[2] 203 | one_hot = [0 for l in range(len(label_list))] 204 | label_index = label_list.index(label) 205 | one_hot[label_index] = 1 206 | y.append(one_hot) 207 | y = np.array(y) 208 | ## print(y) 209 | 210 | # tx: feature vectors of test docs, no initial features 211 | test_size = len(test_ids) 212 | 213 | row_tx = [] 214 | col_tx = [] 215 | data_tx = [] 216 | for i in range(test_size): 217 | doc_vec = np.array([0.0 for k in range(word_embeddings_dim)]) 218 | doc_words = shuffle_doc_words_list[i + train_size] 219 | words = doc_words.split() 220 | doc_len = len(words) 221 | for word in words: 222 | if word in word_vector_map: 223 | word_vector = word_vector_map[word] 224 | doc_vec = doc_vec #+ np.array(word_vector) 225 | 226 | for j in range(word_embeddings_dim): 227 | row_tx.append(i) 228 | col_tx.append(j) 229 | data_tx.append(doc_vec[j] / doc_len) # doc_vec[j] / doc_len 230 | 231 | tx = sp.csr_matrix((data_tx, (row_tx, col_tx)), 232 | shape=(test_size, word_embeddings_dim)) 233 | 234 | ty = [] 235 | for i in range(test_size): 236 | doc_meta = shuffle_doc_name_list[i + train_size] 237 | temp = doc_meta.split('\t') 238 | label = temp[2] 239 | one_hot = [0 for l in range(len(label_list))] 240 | label_index = label_list.index(label) 241 | one_hot[label_index] = 1 242 | ty.append(one_hot) 243 | ty = np.array(ty) 244 | ## print(ty) 245 | 246 | # allx: the the feature vectors of both labeled and unlabeled training instances 247 | # (a superset of x) train+val+word list 248 | # unlabeled training instances -> words 249 | 250 | word_vectors = np.random.uniform(-0.01, 0.01, 251 | (vocab_size, word_embeddings_dim)) 252 | 253 | row_allx = [] 254 | col_allx = [] 255 | data_allx = [] 256 | 257 | for i in range(train_size): 258 | doc_vec = np.array([0.0 for k in range(word_embeddings_dim)]) 259 | doc_words = shuffle_doc_words_list[i] 260 | words = doc_words.split() 261 | doc_len = len(words) 262 | for word in words: 263 | if word in word_vector_map: 264 | word_vector = word_vector_map[word] 265 | doc_vec = doc_vec #+ np.array(word_vector) 266 | 267 | for j in range(word_embeddings_dim): 268 | row_allx.append(int(i)) 269 | col_allx.append(j) 270 | # np.random.uniform(-0.25, 0.25) 271 | data_allx.append(doc_vec[j] / doc_len) # doc_vec[j]/doc_len 272 | 273 | for i in range(vocab_size): 274 | for j in range(word_embeddings_dim): 275 | row_allx.append(int(i + train_size)) 276 | col_allx.append(j) 277 | data_allx.append(word_vectors.item((i, j))) 278 | 279 | 280 | row_allx = np.array(row_allx) 281 | col_allx = np.array(col_allx) 282 | data_allx = np.array(data_allx) 283 | 284 | 285 | allx = sp.csr_matrix((data_allx, (row_allx, col_allx)), 286 | shape=(train_size + vocab_size, word_embeddings_dim)) 287 | 288 | ally = [] 289 | for i in range(train_size): 290 | doc_meta = shuffle_doc_name_list[i] 291 | temp = doc_meta.split('\t') 292 | label = temp[2] 293 | one_hot = [0 for l in range(len(label_list))] 294 | label_index = label_list.index(label) 295 | one_hot[label_index] = 1 296 | ally.append(one_hot) 297 | 298 | ## dummy label for vocab, not counted in loss and learning 299 | for i in range(vocab_size): 300 | one_hot = [0 for l in range(len(label_list))] 301 | ally.append(one_hot) 302 | 303 | ally = np.array(ally) 304 | 305 | print(x.shape, y.shape, tx.shape, ty.shape, allx.shape, ally.shape) 306 | 307 | ''' 308 | Doc word heterogeneous graph 309 | ''' 310 | 311 | # word co-occurence with context windows 312 | window_size = 20 313 | windows = [] 314 | 315 | for doc_words in shuffle_doc_words_list: 316 | words = doc_words.split() 317 | length = len(words) 318 | if length <= window_size: 319 | windows.append(words) 320 | else: 321 | for j in range(length - window_size + 1): 322 | window = words[j: j + window_size] 323 | windows.append(window) 324 | # print(window) 325 | 326 | 327 | word_window_freq = {} # number of windows a word occurs in 328 | for window in windows: 329 | appeared = set() 330 | for i in range(len(window)): 331 | if window[i] in appeared: 332 | continue 333 | if window[i] in word_window_freq: 334 | word_window_freq[window[i]] += 1 335 | else: 336 | word_window_freq[window[i]] = 1 337 | appeared.add(window[i]) 338 | 339 | ## number of windows a pair of words occur in 340 | word_pair_count = {} 341 | for window in windows: 342 | for i in range(1, len(window)): 343 | for j in range(0, i): 344 | word_i = window[i] 345 | word_i_id = word_id_map[word_i] 346 | word_j = window[j] 347 | word_j_id = word_id_map[word_j] 348 | if word_i_id == word_j_id: 349 | continue 350 | word_pair_str = str(word_i_id) + ',' + str(word_j_id) 351 | if word_pair_str in word_pair_count: 352 | word_pair_count[word_pair_str] += 1 353 | else: 354 | word_pair_count[word_pair_str] = 1 355 | # two orders 356 | word_pair_str = str(word_j_id) + ',' + str(word_i_id) 357 | if word_pair_str in word_pair_count: 358 | word_pair_count[word_pair_str] += 1 359 | else: 360 | word_pair_count[word_pair_str] = 1 361 | 362 | 363 | row = [] 364 | col = [] 365 | weight = [] 366 | 367 | # pmi as weights 368 | 369 | num_window = len(windows) 370 | 371 | for key in word_pair_count: 372 | temp = key.split(',') 373 | i = int(temp[0]) 374 | j = int(temp[1]) 375 | count = word_pair_count[key] 376 | word_freq_i = word_window_freq[vocab[i]] 377 | word_freq_j = word_window_freq[vocab[j]] 378 | pmi = log((1.0 * count / num_window) / 379 | (1.0 * word_freq_i * word_freq_j/(num_window * num_window))) 380 | if pmi <= 0: 381 | continue 382 | 383 | row.append(train_size + i) 384 | col.append(train_size + j) 385 | weight.append(pmi) 386 | 387 | 388 | # doc word frequency, tf 389 | doc_word_freq = {} 390 | 391 | for doc_id in range(len(shuffle_doc_words_list)): 392 | doc_words = shuffle_doc_words_list[doc_id] 393 | words = doc_words.split() 394 | for word in words: 395 | word_id = word_id_map[word] 396 | doc_word_str = str(doc_id) + ',' + str(word_id) 397 | if doc_word_str in doc_word_freq: 398 | doc_word_freq[doc_word_str] += 1 399 | else: 400 | doc_word_freq[doc_word_str] = 1 401 | # tfidf 402 | for i in range(len(shuffle_doc_words_list)): 403 | doc_words = shuffle_doc_words_list[i] 404 | words = doc_words.split() 405 | doc_word_set = set() 406 | for word in words: 407 | if word in doc_word_set: 408 | continue 409 | j = word_id_map[word] 410 | key = str(i) + ',' + str(j) 411 | freq = doc_word_freq[key] 412 | if i < train_size: 413 | row.append(i) 414 | else: 415 | row.append(i + vocab_size) 416 | col.append(train_size + j) 417 | idf = log(1.0 * len(shuffle_doc_words_list) / 418 | word_doc_freq[vocab[j]]) 419 | weight.append(freq * idf) 420 | doc_word_set.add(word) 421 | 422 | ## adjacent matrix 423 | node_size = train_size + vocab_size + test_size 424 | adj = sp.csr_matrix( 425 | (weight, (row, col)), shape=(node_size, node_size)) 426 | 427 | # save objects as input for train.py 428 | f = open("data/ind.{}.x".format(dataset), 'wb') 429 | pkl.dump(x, f) 430 | f.close() 431 | 432 | f = open("data/ind.{}.y".format(dataset), 'wb') 433 | pkl.dump(y, f) 434 | f.close() 435 | 436 | f = open("data/ind.{}.tx".format(dataset), 'wb') 437 | pkl.dump(tx, f) 438 | f.close() 439 | 440 | f = open("data/ind.{}.ty".format(dataset), 'wb') 441 | pkl.dump(ty, f) 442 | f.close() 443 | 444 | f = open("data/ind.{}.allx".format(dataset), 'wb') 445 | pkl.dump(allx, f) 446 | f.close() 447 | 448 | f = open("data/ind.{}.ally".format(dataset), 'wb') 449 | pkl.dump(ally, f) 450 | f.close() 451 | 452 | f = open("data/ind.{}.adj".format(dataset), 'wb') 453 | pkl.dump(adj, f) 454 | f.close() 455 | -------------------------------------------------------------------------------- /data/ohsumed_3.txt: -------------------------------------------------------------------------------- 1 | data/ohsumed_single_23/test/C04/0000014 test C04 2 | data/ohsumed_single_23/test/C04/0000108 test C04 3 | data/ohsumed_single_23/test/C04/0000140 test C04 4 | data/ohsumed_single_23/test/C04/0000143 test C04 5 | data/ohsumed_single_23/test/C04/0000145 test C04 6 | data/ohsumed_single_23/test/C04/0000146 test C04 7 | data/ohsumed_single_23/test/C04/0000159 test C04 8 | data/ohsumed_single_23/test/C04/0000175 test C04 9 | data/ohsumed_single_23/test/C04/0000179 test C04 10 | data/ohsumed_single_23/test/C04/0000181 test C04 11 | data/ohsumed_single_23/test/C04/0000188 test C04 12 | data/ohsumed_single_23/test/C04/0000189 test C04 13 | data/ohsumed_single_23/test/C04/0000191 test C04 14 | data/ohsumed_single_23/test/C04/0000199 test C04 15 | data/ohsumed_single_23/test/C04/0001051 test C04 16 | data/ohsumed_single_23/test/C04/0001083 test C04 17 | data/ohsumed_single_23/test/C04/0001084 test C04 18 | data/ohsumed_single_23/test/C04/0001110 test C04 19 | data/ohsumed_single_23/test/C04/0001147 test C04 20 | data/ohsumed_single_23/test/C04/0001160 test C04 21 | data/ohsumed_single_23/test/C04/0001163 test C04 22 | data/ohsumed_single_23/test/C04/0001175 test C04 23 | data/ohsumed_single_23/test/C04/0001190 test C04 24 | data/ohsumed_single_23/test/C04/0001191 test C04 25 | data/ohsumed_single_23/test/C04/0001194 test C04 26 | data/ohsumed_single_23/test/C04/0001254 test C04 27 | data/ohsumed_single_23/test/C04/0001321 test C04 28 | data/ohsumed_single_23/test/C04/0001393 test C04 29 | data/ohsumed_single_23/test/C04/0001457 test C04 30 | data/ohsumed_single_23/test/C04/0001458 test C04 31 | data/ohsumed_single_23/test/C04/0001461 test C04 32 | 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/luoyuanlab/text_gcn_tutorial/710847e7458872cba13003ffeb9a63ac2a6c9ccb/figure/TextGCN.png -------------------------------------------------------------------------------- /figure/ohsumed3_tsne.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/luoyuanlab/text_gcn_tutorial/710847e7458872cba13003ffeb9a63ac2a6c9ccb/figure/ohsumed3_tsne.png -------------------------------------------------------------------------------- /inits.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import numpy as np 3 | 4 | 5 | def uniform(shape, scale=0.05, name=None): 6 | """Uniform init.""" 7 | initial = tf.random_uniform(shape, minval=-scale, maxval=scale, dtype=tf.float32) 8 | return tf.Variable(initial, name=name) 9 | 10 | 11 | def glorot(shape, name=None): 12 | """Glorot & Bengio (AISTATS 2010) init.""" 13 | init_range = np.sqrt(6.0/(shape[0]+shape[1])) 14 | initial = tf.random_uniform(shape, minval=-init_range, maxval=init_range, dtype=tf.float32) 15 | return tf.Variable(initial, name=name) 16 | 17 | def weight_variable_glorot(input_dim, output_dim, name=""): 18 | """Create a weight variable with Glorot & Bengio (AISTATS 2010) 19 | initialization. 20 | """ 21 | init_range = np.sqrt(6.0 / (input_dim + output_dim)) 22 | initial = tf.random_uniform([input_dim, output_dim], minval=-init_range, 23 | maxval=init_range, dtype=tf.float32) 24 | return tf.Variable(initial, name=name) 25 | 26 | 27 | def zeros(shape, name=None): 28 | """All zeros.""" 29 | initial = tf.zeros(shape, dtype=tf.float32) 30 | return tf.Variable(initial, name=name) 31 | 32 | 33 | def ones(shape, name=None): 34 | """All ones.""" 35 | initial = tf.ones(shape, dtype=tf.float32) 36 | return tf.Variable(initial, name=name) -------------------------------------------------------------------------------- /layers.py: -------------------------------------------------------------------------------- 1 | from inits import * 2 | import tensorflow as tf 3 | 4 | flags = tf.app.flags 5 | FLAGS = flags.FLAGS 6 | 7 | # global unique layer ID dictionary for layer name assignment 8 | _LAYER_UIDS = {} 9 | 10 | 11 | def get_layer_uid(layer_name=''): 12 | """Helper function, assigns unique layer IDs.""" 13 | if layer_name not in _LAYER_UIDS: 14 | _LAYER_UIDS[layer_name] = 1 15 | return 1 16 | else: 17 | _LAYER_UIDS[layer_name] += 1 18 | return _LAYER_UIDS[layer_name] 19 | 20 | 21 | def sparse_dropout(x, keep_prob, noise_shape): 22 | """Dropout for sparse tensors.""" 23 | random_tensor = keep_prob 24 | random_tensor += tf.random_uniform(noise_shape) 25 | dropout_mask = tf.cast(tf.floor(random_tensor), dtype=tf.bool) 26 | pre_out = tf.sparse_retain(x, dropout_mask) 27 | return pre_out * (1./keep_prob) 28 | 29 | def dropout_sparse(x, keep_prob, num_nonzero_elems): 30 | """Dropout for sparse tensors. Currently fails for very large sparse tensors (>1M elements) 31 | """ 32 | noise_shape = [num_nonzero_elems] 33 | random_tensor = keep_prob 34 | random_tensor += tf.random_uniform(noise_shape) 35 | dropout_mask = tf.cast(tf.floor(random_tensor), dtype=tf.bool) 36 | pre_out = tf.sparse_retain(x, dropout_mask) 37 | return pre_out * (1./keep_prob) 38 | 39 | 40 | def dot(x, y, sparse=False): 41 | """Wrapper for tf.matmul (sparse vs dense).""" 42 | if sparse: 43 | res = tf.sparse_tensor_dense_matmul(x, y) 44 | else: 45 | res = tf.matmul(x, y) 46 | return res 47 | 48 | 49 | class Layer(object): 50 | """Base layer class. Defines basic API for all layer objects. 51 | Implementation inspired by keras (http://keras.io). 52 | 53 | # Properties 54 | name: String, defines the variable scope of the layer. 55 | logging: Boolean, switches Tensorflow histogram logging on/off 56 | 57 | # Methods 58 | _call(inputs): Defines computation graph of layer 59 | (i.e. takes input, returns output) 60 | __call__(inputs): Wrapper for _call() 61 | _log_vars(): Log all variables 62 | """ 63 | 64 | def __init__(self, edge_type=(), num_types=-1, **kwargs): 65 | self.edge_type = edge_type 66 | self.num_types = num_types 67 | 68 | allowed_kwargs = {'name', 'logging'} 69 | for kwarg in kwargs.keys(): 70 | assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg 71 | name = kwargs.get('name') 72 | if not name: 73 | layer = self.__class__.__name__.lower() 74 | name = layer + '_' + str(get_layer_uid(layer)) 75 | self.name = name 76 | self.vars = {} 77 | logging = kwargs.get('logging', False) 78 | self.logging = logging 79 | self.sparse_inputs = False 80 | 81 | def _call(self, inputs): 82 | return inputs 83 | 84 | def __call__(self, inputs): 85 | with tf.name_scope(self.name): 86 | if self.logging and not self.sparse_inputs: 87 | tf.summary.histogram(self.name + '/inputs', inputs) 88 | outputs = self._call(inputs) 89 | if self.logging: 90 | tf.summary.histogram(self.name + '/outputs', outputs) 91 | return outputs 92 | 93 | def _log_vars(self): 94 | for var in self.vars: 95 | tf.summary.histogram(self.name + '/vars/' + var, self.vars[var]) 96 | 97 | 98 | class Dense(Layer): 99 | """Dense layer.""" 100 | def __init__(self, input_dim, output_dim, placeholders, dropout=0., sparse_inputs=False, 101 | act=tf.nn.relu, bias=False, featureless=False, **kwargs): 102 | super(Dense, self).__init__(**kwargs) 103 | 104 | if dropout: 105 | self.dropout = placeholders['dropout'] 106 | else: 107 | self.dropout = 0. 108 | 109 | self.act = act 110 | self.sparse_inputs = sparse_inputs 111 | self.featureless = featureless 112 | self.bias = bias 113 | 114 | # helper variable for sparse dropout 115 | self.num_features_nonzero = placeholders['num_features_nonzero'] 116 | 117 | with tf.variable_scope(self.name + '_vars'): 118 | self.vars['weights'] = glorot([input_dim, output_dim], 119 | name='weights') 120 | if self.bias: 121 | self.vars['bias'] = zeros([output_dim], name='bias') 122 | 123 | if self.logging: 124 | self._log_vars() 125 | 126 | def _call(self, inputs): 127 | x = inputs 128 | 129 | # dropout 130 | if self.sparse_inputs: 131 | x = sparse_dropout(x, 1-self.dropout, self.num_features_nonzero) 132 | else: 133 | x = tf.nn.dropout(x, 1-self.dropout) 134 | 135 | # transform 136 | output = dot(x, self.vars['weights'], sparse=self.sparse_inputs) 137 | 138 | # bias 139 | if self.bias: 140 | output += self.vars['bias'] 141 | 142 | return self.act(output) 143 | 144 | 145 | class GraphConvolution(Layer): 146 | """Graph convolution layer.""" 147 | def __init__(self, input_dim, output_dim, placeholders, dropout=0., 148 | sparse_inputs=False, act=tf.nn.relu, bias=False, 149 | featureless=False, **kwargs): 150 | super(GraphConvolution, self).__init__(**kwargs) 151 | 152 | if dropout: 153 | self.dropout = placeholders['dropout'] 154 | else: 155 | self.dropout = 0. 156 | 157 | self.act = act 158 | self.support = placeholders['support'] 159 | self.sparse_inputs = sparse_inputs 160 | self.featureless = featureless 161 | self.bias = bias 162 | 163 | # helper variable for sparse dropout 164 | self.num_features_nonzero = placeholders['num_features_nonzero'] 165 | 166 | with tf.variable_scope(self.name + '_vars'): 167 | for i in range(len(self.support)): 168 | self.vars['weights_' + str(i)] = glorot([input_dim, output_dim], 169 | name='weights_' + str(i)) 170 | if self.bias: 171 | self.vars['bias'] = zeros([output_dim], name='bias') 172 | 173 | if self.logging: 174 | self._log_vars() 175 | 176 | def _call(self, inputs): 177 | x = inputs 178 | 179 | # dropout 180 | if self.sparse_inputs: 181 | x = sparse_dropout(x, 1-self.dropout, self.num_features_nonzero) 182 | else: 183 | x = tf.nn.dropout(x, 1-self.dropout) 184 | 185 | # convolve 186 | supports = list() 187 | for i in range(len(self.support)): 188 | if not self.featureless: 189 | pre_sup = dot(x, self.vars['weights_' + str(i)], 190 | sparse=self.sparse_inputs) 191 | else: 192 | pre_sup = self.vars['weights_' + str(i)] 193 | support = dot(self.support[i], pre_sup, sparse=True) 194 | supports.append(support) 195 | output = tf.add_n(supports) # get AXW 196 | 197 | # bias 198 | if self.bias: 199 | output += self.vars['bias'] 200 | self.embedding = output #output 201 | return self.act(output) 202 | 203 | -------------------------------------------------------------------------------- /metrics.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | 3 | def masked_softmax_cross_entropy(preds, labels, mask): 4 | """Softmax cross-entropy loss with masking.""" 5 | print(preds) 6 | loss = tf.nn.softmax_cross_entropy_with_logits(logits=preds, labels=labels) 7 | #loss = tf.losses.hinge_loss(logits=preds, labels=labels) 8 | mask = tf.cast(mask, dtype=tf.float32) 9 | mask /= tf.reduce_mean(mask) 10 | loss *= mask 11 | return tf.reduce_mean(loss) 12 | 13 | 14 | def masked_accuracy(preds, labels, mask): 15 | """Accuracy with masking.""" 16 | correct_prediction = tf.equal(tf.argmax(preds, 1), tf.argmax(labels, 1)) 17 | 18 | accuracy_all = tf.cast(correct_prediction, tf.float32) 19 | mask = tf.cast(mask, dtype=tf.float32) 20 | mask /= tf.reduce_mean(mask) 21 | accuracy_all *= mask 22 | return tf.reduce_mean(accuracy_all) 23 | -------------------------------------------------------------------------------- /models.py: -------------------------------------------------------------------------------- 1 | from layers import * 2 | from metrics import * 3 | import tensorflow as tf 4 | from collections import defaultdict 5 | 6 | flags = tf.app.flags 7 | FLAGS = flags.FLAGS 8 | 9 | 10 | class Model(object): 11 | def __init__(self, **kwargs): 12 | allowed_kwargs = {'name', 'logging'} 13 | for kwarg in kwargs.keys(): 14 | assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg 15 | name = kwargs.get('name') 16 | if not name: 17 | name = self.__class__.__name__.lower() 18 | self.name = name 19 | 20 | logging = kwargs.get('logging', False) 21 | self.logging = logging 22 | 23 | self.vars = {} 24 | self.placeholders = {} 25 | 26 | self.layers = [] 27 | self.activations = [] 28 | 29 | self.inputs = None 30 | self.outputs = None 31 | 32 | self.loss = 0 33 | self.accuracy = 0 34 | self.optimizer = None 35 | self.opt_op = None 36 | 37 | def _build(self): 38 | raise NotImplementedError 39 | 40 | def build(self): 41 | """ Wrapper for _build() """ 42 | with tf.variable_scope(self.name): 43 | self._build() 44 | 45 | # Build sequential layer model 46 | self.activations.append(self.inputs) 47 | for layer in self.layers: 48 | hidden = layer(self.activations[-1]) 49 | self.activations.append(hidden) 50 | self.outputs = self.activations[-1] 51 | 52 | # Store model variables for easy access 53 | variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name) 54 | self.vars = {var.name: var for var in variables} 55 | 56 | # Build metrics 57 | self._loss() 58 | self._accuracy() 59 | 60 | self.opt_op = self.optimizer.minimize(self.loss) 61 | 62 | def predict(self): 63 | pass 64 | 65 | def _loss(self): 66 | raise NotImplementedError 67 | 68 | def _accuracy(self): 69 | raise NotImplementedError 70 | 71 | def save(self, sess=None): 72 | if not sess: 73 | raise AttributeError("TensorFlow session not provided.") 74 | saver = tf.train.Saver(self.vars) 75 | save_path = saver.save(sess, "tmp/%s.ckpt" % self.name) 76 | print("Model saved in file: %s" % save_path) 77 | 78 | def load(self, sess=None): 79 | if not sess: 80 | raise AttributeError("TensorFlow session not provided.") 81 | saver = tf.train.Saver(self.vars) 82 | save_path = "tmp/%s.ckpt" % self.name 83 | saver.restore(sess, save_path) 84 | print("Model restored from file: %s" % save_path) 85 | 86 | 87 | 88 | class GCN(Model): 89 | def __init__(self, placeholders, input_dim, **kwargs): 90 | super(GCN, self).__init__(**kwargs) 91 | 92 | self.inputs = placeholders['features'] 93 | self.input_dim = input_dim 94 | # self.input_dim = self.inputs.get_shape().as_list()[1] # To be supported in future Tensorflow versions 95 | self.output_dim = placeholders['labels'].get_shape().as_list()[1] 96 | self.placeholders = placeholders 97 | 98 | self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) 99 | 100 | self.build() 101 | 102 | def _loss(self): 103 | # Weight decay loss 104 | for var in self.layers[0].vars.values(): 105 | self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var) 106 | 107 | # Cross entropy error 108 | self.loss += masked_softmax_cross_entropy(self.outputs, self.placeholders['labels'], self.placeholders['labels_mask']) 109 | 110 | def _accuracy(self): 111 | self.accuracy = masked_accuracy(self.outputs, self.placeholders['labels'], 112 | self.placeholders['labels_mask']) 113 | self.pred = tf.argmax(self.outputs, 1) 114 | self.labels = tf.argmax(self.placeholders['labels'], 1) 115 | 116 | 117 | def _build(self): 118 | 119 | self.layers.append(GraphConvolution(input_dim=self.input_dim, 120 | output_dim=FLAGS.hidden1, 121 | placeholders=self.placeholders, 122 | act=tf.nn.relu, 123 | dropout=True, 124 | sparse_inputs=True, 125 | featureless=False, 126 | logging=self.logging)) 127 | 128 | self.layers.append(GraphConvolution(input_dim=FLAGS.hidden1, 129 | output_dim=self.output_dim, 130 | placeholders=self.placeholders, 131 | act=lambda x: x, # 132 | dropout=True, 133 | logging=self.logging)) 134 | 135 | 136 | 137 | def predict(self): 138 | 139 | return tf.nn.softmax(self.outputs) 140 | 141 | 142 | class RGCN(Model): 143 | def __init__(self, placeholders, num_feat, nonzero_feat, edge_types, **kwargs): 144 | super(RGCN, self).__init__(**kwargs) 145 | self.edge_types = edge_types 146 | self.num_edge_types = sum(self.edge_types.values()) 147 | self.num_obj_types = max([i for i, _ in self.edge_types]) + 1 148 | self.inputs = {i: placeholders['feat_%d' % i] for i, _ in self.edge_types} 149 | self.input_dim = num_feat 150 | self.output_dim = placeholders['labels'].get_shape().as_list()[1] 151 | self.nonzero_feat = nonzero_feat 152 | self.placeholders = placeholders 153 | self.dropout = placeholders['dropout'] 154 | self.adj_mats = {et: [ 155 | placeholders['adj_mats_%d,%d,%d' % (et[0], et[1], k)] for k in range(n)] 156 | for et, n in self.edge_types.items()} 157 | print(self.adj_mats) 158 | self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) 159 | self.build() 160 | 161 | def _build(self): 162 | self.hidden1 = defaultdict(list) 163 | for i, j in self.edge_types: 164 | self.hidden1[i].append(GraphConvolutionSparseMulti( 165 | input_dim=self.input_dim, output_dim=FLAGS.hidden1, 166 | edge_type=(i,j), num_types=self.edge_types[i,j], 167 | adj_mats=self.adj_mats, nonzero_feat=self.nonzero_feat, 168 | act=lambda x: x, dropout=self.dropout, 169 | logging=self.logging)(self.inputs[j])) 170 | 171 | for i, hid1 in self.hidden1.items(): 172 | self.hidden1[i] = tf.nn.relu(tf.add_n(hid1)) 173 | 174 | self.embeddings_reltyp = defaultdict(list) 175 | for i, j in self.edge_types: 176 | # i, j identity, featureless = True 177 | self.embeddings_reltyp[i].append(GraphConvolutionMulti( 178 | input_dim=FLAGS.hidden1, output_dim=self.output_dim, 179 | edge_type=(i,j), num_types=self.edge_types[i,j], 180 | adj_mats=self.adj_mats, act=lambda x: x, 181 | dropout=self.dropout, logging=self.logging)(self.hidden1[j])) 182 | 183 | self.embeddings = [None] * self.num_obj_types 184 | for i, embeds in self.embeddings_reltyp.items(): 185 | # self.embeddings[i] = tf.nn.relu(tf.add_n(embeds)) 186 | self.embeddings[i] = tf.add_n(embeds) 187 | 188 | 189 | def _loss(self): 190 | 191 | # Cross entropy error 192 | self.loss += masked_softmax_cross_entropy(self.embeddings[0], self.placeholders['labels'], self.placeholders['labels_mask']) 193 | 194 | def _accuracy(self): 195 | self.accuracy = masked_accuracy(self.embeddings[0], self.placeholders['labels'], 196 | self.placeholders['labels_mask']) 197 | self.pred = tf.argmax(self.embeddings[0], 1) 198 | self.labels = tf.argmax(self.placeholders['labels'], 1) 199 | 200 | -------------------------------------------------------------------------------- /remove_words.py: -------------------------------------------------------------------------------- 1 | from nltk.corpus import stopwords 2 | import nltk 3 | from nltk.wsd import lesk 4 | from nltk.corpus import wordnet as wn 5 | from utils import clean_str, loadWord2Vec 6 | import sys 7 | 8 | if len(sys.argv) != 2: 9 | sys.exit("Use: python remove_words.py ") 10 | 11 | dataset = sys.argv[1] 12 | 13 | # nltk.download('stopwords') 14 | stop_words = set(stopwords.words('english')) 15 | print(stop_words) 16 | 17 | doc_content_list = [] 18 | f = open('data/corpus/' + dataset + '.txt', 'rb') 19 | for line in f.readlines(): 20 | doc_content_list.append(line.strip().decode('latin1')) 21 | f.close() 22 | 23 | 24 | word_freq = {} # to remove rare words 25 | 26 | for doc_content in doc_content_list: 27 | temp = clean_str(doc_content) 28 | words = temp.split() 29 | for word in words: 30 | if word in word_freq: 31 | word_freq[word] += 1 32 | else: 33 | word_freq[word] = 1 34 | 35 | clean_docs = [] 36 | for doc_content in doc_content_list: 37 | temp = clean_str(doc_content) 38 | words = temp.split() 39 | doc_words = [] 40 | for word in words: 41 | if dataset == 'mr': 42 | doc_words.append(word) 43 | elif word not in stop_words and word_freq[word] >= 5: 44 | doc_words.append(word) 45 | 46 | doc_str = ' '.join(doc_words).strip() 47 | clean_docs.append(doc_str) 48 | 49 | clean_corpus_str = '\n'.join(clean_docs) 50 | 51 | f = open('data/corpus/' + dataset + '.clean.txt', 'w') 52 | f.write(clean_corpus_str) 53 | f.close() 54 | 55 | min_len = 10000 56 | aver_len = 0 57 | max_len = 0 58 | 59 | f = open('data/corpus/' + dataset + '.clean.txt', 'r') 60 | lines = f.readlines() 61 | for line in lines: 62 | line = line.strip() 63 | temp = line.split() 64 | aver_len = aver_len + len(temp) 65 | if len(temp) < min_len: 66 | min_len = len(temp) 67 | if len(temp) > max_len: 68 | max_len = len(temp) 69 | f.close() 70 | aver_len = 1.0 * aver_len / len(lines) 71 | print('min_len : ' + str(min_len)) 72 | print('max_len : ' + str(max_len)) 73 | print('average_len : ' + str(aver_len)) 74 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | from __future__ import print_function 3 | 4 | import time 5 | import tensorflow as tf 6 | 7 | from sklearn import metrics 8 | from utils import * 9 | from models import GCN 10 | import random 11 | import os 12 | import sys 13 | 14 | if len(sys.argv) != 2: 15 | sys.exit("Use: python train.py ") 16 | 17 | dataset = sys.argv[1] 18 | 19 | # Set random seed 20 | seed = random.randint(1, 200) 21 | tf.set_random_seed(seed) 22 | 23 | # Settings, default not using GPU 24 | os.environ["CUDA_VISIBLE_DEVICES"] = "" 25 | 26 | flags = tf.app.flags 27 | FLAGS = flags.FLAGS 28 | # 'cora', 'citeseer', 'pubmed' 29 | flags.DEFINE_string('dataset', dataset, 'Dataset string.') 30 | # 'gcn', 'gcn_cheby', 'dense' 31 | flags.DEFINE_string('model', 'gcn', 'Model string.') 32 | flags.DEFINE_float('learning_rate', 0.02, 'Initial learning rate.') 33 | flags.DEFINE_integer('epochs', 200, 'Number of epochs to train.') 34 | flags.DEFINE_integer('hidden1', 200, 'Number of units in hidden layer 1.') 35 | flags.DEFINE_float('dropout', 0.5, 'Dropout rate (1 - keep probability).') 36 | flags.DEFINE_float('weight_decay', 0, 37 | 'Weight for L2 loss on embedding matrix.') # 5e-4 38 | flags.DEFINE_integer('early_stopping', 10, 39 | 'Tolerance for early stopping (# of epochs).') 40 | flags.DEFINE_integer('max_degree', 3, 'Maximum Chebyshev polynomial degree.') 41 | 42 | # Load data 43 | adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, train_size, test_size = load_corpus( 44 | FLAGS.dataset) 45 | # print(adj) 46 | features = sp.identity(features.shape[0]) # featureless 47 | 48 | print(adj.shape) 49 | print(features.shape) 50 | 51 | # Some preprocessing 52 | features = preprocess_features(features) 53 | if FLAGS.model == 'gcn': 54 | support = [preprocess_adj(adj)] 55 | num_supports = 1 # list size 1, different from Kipf 56 | else: 57 | raise ValueError('Invalid argument for model: ' + str(FLAGS.model)) 58 | 59 | # Define placeholders 60 | placeholders = { 61 | 'support': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)], 62 | 'features': tf.sparse_placeholder(tf.float32, shape=tf.constant(features[2], dtype=tf.int64)), 63 | 'labels': tf.placeholder(tf.float32, shape=(None, y_train.shape[1])), 64 | 'labels_mask': tf.placeholder(tf.int32), 65 | 'dropout': tf.placeholder_with_default(0., shape=()), 66 | # helper variable for sparse dropout 67 | 'num_features_nonzero': tf.placeholder(tf.int32), 68 | } 69 | 70 | # Create model 71 | print(features[2][1]) # dim, note there is sparse_to_tuple in preprocess_features 72 | model = GCN(placeholders, input_dim=features[2][1], logging=True) 73 | 74 | # Initialize session 75 | session_conf = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True)) 76 | sess = tf.Session(config=session_conf) 77 | 78 | 79 | # Define model evaluation function 80 | def evaluate(features, support, labels, mask, placeholders): 81 | t_test = time.time() 82 | feed_dict_val = construct_feed_dict( 83 | features, support, labels, mask, placeholders) 84 | outs_val = sess.run([model.loss, model.accuracy, model.pred, model.labels], feed_dict=feed_dict_val) 85 | return outs_val[0], outs_val[1], outs_val[2], outs_val[3], (time.time() - t_test) 86 | 87 | 88 | # Init variables, models.py call layers.py then call init.py 89 | sess.run(tf.global_variables_initializer()) 90 | 91 | cost_val = [] 92 | 93 | # Train model 94 | for epoch in range(FLAGS.epochs): 95 | 96 | t = time.time() 97 | # Construct feed dictionary, same input for every epoch as we input full batch 98 | feed_dict = construct_feed_dict( 99 | features, support, y_train, train_mask, placeholders) 100 | feed_dict.update({placeholders['dropout']: FLAGS.dropout}) # update dropout for train, different from test 0 101 | # to inductive 102 | # Training step 103 | outs = sess.run([model.opt_op, model.loss, model.accuracy, 104 | model.layers[0].embedding], feed_dict=feed_dict) 105 | 106 | # Validation 107 | cost, acc, pred, labels, duration = evaluate( 108 | features, support, y_val, val_mask, placeholders) 109 | cost_val.append(cost) 110 | 111 | print("Epoch:", '%04d' % (epoch + 1), "train_loss=", "{:.5f}".format(outs[1]), 112 | "train_acc=", "{:.5f}".format( 113 | outs[2]), "val_loss=", "{:.5f}".format(cost), 114 | "val_acc=", "{:.5f}".format(acc), "time=", "{:.5f}".format(time.time() - t)) 115 | 116 | if epoch > FLAGS.early_stopping and cost_val[-1] > np.mean(cost_val[-(FLAGS.early_stopping+1):-1]): 117 | print("Early stopping...") 118 | break 119 | 120 | print("Optimization Finished!") 121 | 122 | # Testing 123 | test_cost, test_acc, pred, labels, test_duration = evaluate( 124 | features, support, y_test, test_mask, placeholders) 125 | print("Test set results:", "cost=", "{:.5f}".format(test_cost), 126 | "accuracy=", "{:.5f}".format(test_acc), "time=", "{:.5f}".format(test_duration)) 127 | 128 | test_pred = [] 129 | test_labels = [] 130 | print(len(test_mask)) 131 | for i in range(len(test_mask)): 132 | if test_mask[i]: 133 | test_pred.append(pred[i]) 134 | test_labels.append(labels[i]) 135 | 136 | print("Test Precision, Recall and F1-Score...") 137 | print(metrics.classification_report(test_labels, test_pred, digits=4)) 138 | print("Macro average Test Precision, Recall and F1-Score...") 139 | print(metrics.precision_recall_fscore_support(test_labels, test_pred, average='macro')) 140 | print("Micro average Test Precision, Recall and F1-Score...") 141 | print(metrics.precision_recall_fscore_support(test_labels, test_pred, average='micro')) 142 | 143 | # doc and word embeddings 144 | print('embeddings:') 145 | word_embeddings = outs[3][train_size: adj.shape[0] - test_size] 146 | train_doc_embeddings = outs[3][:train_size] # include val docs 147 | test_doc_embeddings = outs[3][adj.shape[0] - test_size:] 148 | 149 | print(len(word_embeddings), len(train_doc_embeddings), 150 | len(test_doc_embeddings)) 151 | ## print(word_embeddings) 152 | 153 | f = open('data/corpus/' + dataset + '_vocab.txt', 'r') 154 | words = f.readlines() 155 | f.close() 156 | 157 | vocab_size = len(words) 158 | word_vectors = [] 159 | for i in range(vocab_size): 160 | word = words[i].strip() 161 | word_vector = word_embeddings[i] 162 | word_vector_str = ' '.join([str(x) for x in word_vector]) 163 | word_vectors.append(word + ' ' + word_vector_str) 164 | 165 | word_embeddings_str = '\n'.join(word_vectors) 166 | f = open('data/' + dataset + '_word_vectors.txt', 'w') 167 | f.write(word_embeddings_str) 168 | f.close() 169 | 170 | doc_vectors = [] 171 | doc_id = 0 172 | for i in range(train_size): 173 | doc_vector = train_doc_embeddings[i] 174 | doc_vector_str = ' '.join([str(x) for x in doc_vector]) 175 | doc_vectors.append('doc_' + str(doc_id) + ' ' + doc_vector_str) 176 | doc_id += 1 177 | 178 | for i in range(test_size): 179 | doc_vector = test_doc_embeddings[i] 180 | doc_vector_str = ' '.join([str(x) for x in doc_vector]) 181 | doc_vectors.append('doc_' + str(doc_id) + ' ' + doc_vector_str) 182 | doc_id += 1 183 | 184 | doc_embeddings_str = '\n'.join(doc_vectors) 185 | f = open('data/' + dataset + '_doc_vectors.txt', 'w') 186 | f.write(doc_embeddings_str) 187 | f.close() 188 | -------------------------------------------------------------------------------- /tsne.py: -------------------------------------------------------------------------------- 1 | from matplotlib.backends.backend_pdf import PdfPages 2 | from sklearn.manifold import TSNE 3 | from matplotlib import pyplot as plt 4 | import numpy as np 5 | 6 | f = open('data/ohsumed_3.train.index', 'r') 7 | lines = f.readlines() 8 | f.close() 9 | train_size = len(lines) 10 | 11 | 12 | f = open('data/ohsumed_3_shuffle.txt', 'r') 13 | lines = f.readlines() 14 | f.close() 15 | 16 | target_names = set() 17 | labels = [] 18 | for line in lines: 19 | line = line.strip() 20 | temp = line.split('\t') 21 | labels.append(temp[2]) 22 | target_names.add(temp[2]) 23 | 24 | target_names = list(target_names) 25 | 26 | f = open('data/ohsumed_3_doc_vectors.txt', 'r') 27 | lines = f.readlines() 28 | f.close() 29 | 30 | docs = [] 31 | for line in lines: 32 | temp = line.strip().split() 33 | values_str_list = temp[1:] 34 | values = [float(x) for x in values_str_list] 35 | docs.append(values) 36 | 37 | fea = docs[train_size:] # int(train_size * 0.9) 38 | label = labels[train_size:] # int(train_size * 0.9) 39 | label = np.array(label) 40 | 41 | fea = TSNE(n_components=2).fit_transform(fea) 42 | pdf = PdfPages('ohsumed_3_gcn_doc_train.pdf') 43 | cls = np.unique(label) 44 | 45 | # cls=range(10) 46 | fea_num = [fea[label == i] for i in cls] 47 | for i, f in enumerate(fea_num): 48 | if cls[i] in range(10): 49 | plt.scatter(f[:, 0], f[:, 1], label=cls[i], marker='+') 50 | else: 51 | plt.scatter(f[:, 0], f[:, 1], label=cls[i]) 52 | plt.legend() 53 | # plt.legend(ncol=2, ) 54 | # plt.legend(ncol=5,loc='upper center',bbox_to_anchor=(0.48, -0.08),fontsize=11) 55 | # plt.ylim([-20,35]) 56 | # plt.title(md_file) 57 | plt.tight_layout() 58 | pdf.savefig() 59 | plt.show() 60 | pdf.close() 61 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pickle as pkl 3 | import networkx as nx 4 | import scipy.sparse as sp 5 | from scipy.sparse.linalg.eigen.arpack import eigsh 6 | import sys 7 | import re 8 | from nltk.corpus import wordnet as wn 9 | from sklearn.feature_extraction.text import TfidfVectorizer 10 | 11 | def parse_index_file(filename): 12 | """Parse index file.""" 13 | index = [] 14 | for line in open(filename): 15 | index.append(int(line.strip())) 16 | return index 17 | 18 | 19 | def sample_mask(idx, l): 20 | """Create mask.""" 21 | mask = np.zeros(l) 22 | mask[idx] = 1 23 | return np.array(mask, dtype=np.bool) 24 | 25 | 26 | def load_data(dataset_str): 27 | """ 28 | Loads input data from gcn/data directory 29 | 30 | ind.dataset_str.x => the feature vectors of the training instances as scipy.sparse.csr.csr_matrix object; 31 | ind.dataset_str.tx => the feature vectors of the test instances as scipy.sparse.csr.csr_matrix object; 32 | ind.dataset_str.allx => the feature vectors of both labeled and unlabeled training instances 33 | (a superset of ind.dataset_str.x) as scipy.sparse.csr.csr_matrix object; 34 | ind.dataset_str.y => the one-hot labels of the labeled training instances as numpy.ndarray object; 35 | ind.dataset_str.ty => the one-hot labels of the test instances as numpy.ndarray object; 36 | ind.dataset_str.ally => the labels for instances in ind.dataset_str.allx as numpy.ndarray object; 37 | ind.dataset_str.graph => a dict in the format {index: [index_of_neighbor_nodes]} as collections.defaultdict 38 | object; 39 | ind.dataset_str.test.index => the indices of test instances in graph, for the inductive setting as list object. 40 | 41 | All objects above must be saved using python pickle module. 42 | 43 | :param dataset_str: Dataset name 44 | :return: All data input files loaded (as well the training/test data). 45 | """ 46 | names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph'] 47 | objects = [] 48 | for i in range(len(names)): 49 | with open("data/ind.{}.{}".format(dataset_str, names[i]), 'rb') as f: 50 | if sys.version_info > (3, 0): 51 | objects.append(pkl.load(f, encoding='latin1')) 52 | else: 53 | objects.append(pkl.load(f)) 54 | 55 | x, y, tx, ty, allx, ally, graph = tuple(objects) 56 | test_idx_reorder = parse_index_file( 57 | "data/ind.{}.test.index".format(dataset_str)) 58 | test_idx_range = np.sort(test_idx_reorder) 59 | print(x.shape, y.shape, tx.shape, ty.shape, allx.shape, ally.shape) 60 | 61 | if dataset_str == 'citeseer': 62 | # Fix citeseer dataset (there are some isolated nodes in the graph) 63 | # Find isolated nodes, add them as zero-vecs into the right position 64 | test_idx_range_full = range( 65 | min(test_idx_reorder), max(test_idx_reorder)+1) 66 | tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1])) 67 | tx_extended[test_idx_range-min(test_idx_range), :] = tx 68 | tx = tx_extended 69 | ty_extended = np.zeros((len(test_idx_range_full), y.shape[1])) 70 | ty_extended[test_idx_range-min(test_idx_range), :] = ty 71 | ty = ty_extended 72 | 73 | features = sp.vstack((allx, tx)).tolil() 74 | features[test_idx_reorder, :] = features[test_idx_range, :] 75 | adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph)) 76 | 77 | labels = np.vstack((ally, ty)) 78 | labels[test_idx_reorder, :] = labels[test_idx_range, :] 79 | # print(len(labels)) 80 | 81 | idx_test = test_idx_range.tolist() 82 | # print(idx_test) 83 | idx_train = range(len(y)) 84 | idx_val = range(len(y), len(y)+500) 85 | 86 | train_mask = sample_mask(idx_train, labels.shape[0]) 87 | val_mask = sample_mask(idx_val, labels.shape[0]) 88 | test_mask = sample_mask(idx_test, labels.shape[0]) 89 | 90 | y_train = np.zeros(labels.shape) 91 | y_val = np.zeros(labels.shape) 92 | y_test = np.zeros(labels.shape) 93 | y_train[train_mask, :] = labels[train_mask, :] 94 | y_val[val_mask, :] = labels[val_mask, :] 95 | y_test[test_mask, :] = labels[test_mask, :] 96 | 97 | return adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask 98 | 99 | 100 | def load_corpus(dataset_str): 101 | """ 102 | Loads input corpus from gcn/data directory 103 | 104 | ind.dataset_str.x => the feature vectors of the training docs as scipy.sparse.csr.csr_matrix object; 105 | ind.dataset_str.tx => the feature vectors of the test docs as scipy.sparse.csr.csr_matrix object; 106 | ind.dataset_str.allx => the feature vectors of both labeled and unlabeled training docs/words 107 | (a superset of ind.dataset_str.x) as scipy.sparse.csr.csr_matrix object; 108 | ind.dataset_str.y => the one-hot labels of the labeled training docs as numpy.ndarray object; 109 | ind.dataset_str.ty => the one-hot labels of the test docs as numpy.ndarray object; 110 | ind.dataset_str.ally => the labels for instances in ind.dataset_str.allx as numpy.ndarray object; 111 | ind.dataset_str.adj => adjacency matrix of word/doc nodes as scipy.sparse.csr.csr_matrix object; 112 | ind.dataset_str.train.index => the indices of training docs in original doc list. 113 | 114 | All objects above must be saved using python pickle module. 115 | 116 | :param dataset_str: Dataset name 117 | :return: All data input files loaded (as well the training/test data). 118 | """ 119 | 120 | names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'adj'] 121 | objects = [] 122 | for i in range(len(names)): 123 | with open("data/ind.{}.{}".format(dataset_str, names[i]), 'rb') as f: 124 | if sys.version_info > (3, 0): 125 | objects.append(pkl.load(f, encoding='latin1')) 126 | else: 127 | objects.append(pkl.load(f)) 128 | 129 | x, y, tx, ty, allx, ally, adj = tuple(objects) 130 | print(x.shape, y.shape, tx.shape, ty.shape, allx.shape, ally.shape) 131 | 132 | features = sp.vstack((allx, tx)).tolil() 133 | labels = np.vstack((ally, ty)) 134 | print(len(labels)) 135 | 136 | train_idx_orig = parse_index_file( 137 | "data/{}.train.index".format(dataset_str)) 138 | train_size = len(train_idx_orig) 139 | 140 | val_size = train_size - x.shape[0] 141 | test_size = tx.shape[0] 142 | 143 | idx_train = range(len(y)) 144 | idx_val = range(len(y), len(y) + val_size) 145 | idx_test = range(allx.shape[0], allx.shape[0] + test_size) 146 | 147 | train_mask = sample_mask(idx_train, labels.shape[0]) 148 | val_mask = sample_mask(idx_val, labels.shape[0]) 149 | test_mask = sample_mask(idx_test, labels.shape[0]) 150 | 151 | y_train = np.zeros(labels.shape) 152 | y_val = np.zeros(labels.shape) 153 | y_test = np.zeros(labels.shape) 154 | y_train[train_mask, :] = labels[train_mask, :] 155 | y_val[val_mask, :] = labels[val_mask, :] 156 | y_test[test_mask, :] = labels[test_mask, :] 157 | 158 | adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj) 159 | 160 | return adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, train_size, test_size 161 | 162 | 163 | def load_corpus_multimodal(dataset_str): 164 | """ 165 | Loads input corpus from gcn/data directory 166 | 167 | ind.dataset_str.x => the feature vectors of the training docs as scipy.sparse.csr.csr_matrix object; 168 | ind.dataset_str.tx => the feature vectors of the test docs as scipy.sparse.csr.csr_matrix object; 169 | ind.dataset_str.allx => the feature vectors of both training and val docs 170 | (a superset of ind.dataset_str.x) as scipy.sparse.csr.csr_matrix object; 171 | ind.dataset_str.y => the one-hot labels of the labeled training docs as numpy.ndarray object; 172 | ind.dataset_str.ty => the one-hot labels of the test docs as numpy.ndarray object; 173 | ind.dataset_str.ally => the labels for instances in ind.dataset_str.allx as numpy.ndarray object; 174 | ind.dataset_str.word_adj => adjacency matrix of word nodes as scipy.sparse.csr.csr_matrix object; 175 | ind.dataset_str.doc_adj => adjacency matrix of doc nodes as scipy.sparse.csr.csr_matrix object; 176 | ind.dataset_str.doc_word_adj => adjacency matrix for doc and word nodes as scipy.sparse.csr.csr_matrix object; 177 | 178 | All objects above must be saved using python pickle module. 179 | 180 | :param dataset_str: Dataset name 181 | :return: All data input files loaded (as well the training/test data). 182 | """ 183 | 184 | names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'word_adj', 'doc_adj', 'doc_word_adj', 'word_feat'] 185 | objects = [] 186 | for i in range(len(names)): 187 | with open("data/ind.{}.{}".format(dataset_str, names[i]), 'rb') as f: 188 | if sys.version_info > (3, 0): 189 | objects.append(pkl.load(f, encoding='latin1')) 190 | else: 191 | objects.append(pkl.load(f)) 192 | 193 | x, y, tx, ty, allx, ally, word_adj, doc_adj, doc_word_adj, word_feat = tuple(objects) 194 | print(x.shape, y.shape, tx.shape, ty.shape, allx.shape, ally.shape) 195 | 196 | labels = np.vstack((ally, ty)) 197 | print(len(labels)) 198 | 199 | train_idx_orig = parse_index_file( 200 | "data/{}.train.index".format(dataset_str)) 201 | train_size = len(train_idx_orig) 202 | 203 | val_size = train_size - x.shape[0] 204 | test_size = tx.shape[0] 205 | 206 | idx_train = range(len(y)) 207 | idx_val = range(len(y), len(y) + val_size) 208 | idx_test = range(allx.shape[0], allx.shape[0] + test_size) 209 | 210 | train_mask = sample_mask(idx_train, labels.shape[0]) 211 | val_mask = sample_mask(idx_val, labels.shape[0]) 212 | test_mask = sample_mask(idx_test, labels.shape[0]) 213 | 214 | y_train = np.zeros(labels.shape) 215 | y_val = np.zeros(labels.shape) 216 | y_test = np.zeros(labels.shape) 217 | y_train[train_mask, :] = labels[train_mask, :] 218 | y_val[val_mask, :] = labels[val_mask, :] 219 | y_test[test_mask, :] = labels[test_mask, :] 220 | 221 | #word_adj = preprocess_graph(word_adj) 222 | #doc_adj = preprocess_graph(doc_adj) 223 | #doc_word_adj = preprocess_graph(doc_word_adj) 224 | #adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj) 225 | 226 | return word_adj, doc_adj, doc_word_adj, word_feat, y_train, y_val, y_test, train_mask, val_mask, test_mask, train_size, test_size 227 | 228 | def load_corpus_kg(dataset_str): 229 | """ 230 | Loads input corpus from gcn/data directory 231 | 232 | ind.dataset_str.x => the feature vectors of the training docs as scipy.sparse.csr.csr_matrix object; 233 | ind.dataset_str.tx => the feature vectors of the test docs as scipy.sparse.csr.csr_matrix object; 234 | ind.dataset_str.allx => the feature vectors of both labeled and unlabeled training docs/words 235 | (a superset of ind.dataset_str.x) as scipy.sparse.csr.csr_matrix object; 236 | ind.dataset_str.y => the one-hot labels of the labeled training docs as numpy.ndarray object; 237 | ind.dataset_str.ty => the one-hot labels of the test docs as numpy.ndarray object; 238 | ind.dataset_str.ally => the labels for instances in ind.dataset_str.allx as numpy.ndarray object; 239 | ind.dataset_str.adj => adjacency matrix of word/doc nodes as scipy.sparse.csr.csr_matrix object; 240 | ind.dataset_str.train.index => the indices of training docs in original doc list. 241 | ind.dataset_str.word_entity_adj => adjacency matrix for word and entity nodes as scipy.sparse.csr.csr_matrix object; 242 | ind.dataset_str.entity_adj_list => adjacency matrix list for knowledge graph triples (one for each relation) 243 | as a list of scipy.sparse.csr.csr_matrix objects; 244 | 245 | All objects above must be saved using python pickle module. 246 | 247 | :param dataset_str: Dataset name 248 | :return: All data input files loaded (as well the training/test data). 249 | """ 250 | 251 | names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'adj', 'word_entity_adj', 'entity_adj_list'] 252 | objects = [] 253 | for i in range(len(names)): 254 | with open("data/ind.{}.{}".format(dataset_str, names[i]), 'rb') as f: 255 | if sys.version_info > (3, 0): 256 | objects.append(pkl.load(f, encoding='latin1')) 257 | else: 258 | objects.append(pkl.load(f)) 259 | 260 | x, y, tx, ty, allx, ally, adj, word_entity_adj, entity_adj_list = tuple(objects) 261 | print(x.shape, y.shape, tx.shape, ty.shape, allx.shape, ally.shape) 262 | 263 | labels = np.vstack((ally, ty)) 264 | print(len(labels)) 265 | 266 | train_idx_orig = parse_index_file( 267 | "data/{}.train.index".format(dataset_str)) 268 | train_size = len(train_idx_orig) 269 | 270 | val_size = train_size - x.shape[0] 271 | test_size = tx.shape[0] 272 | 273 | idx_train = range(len(y)) 274 | idx_val = range(len(y), len(y) + val_size) 275 | idx_test = range(allx.shape[0], allx.shape[0] + test_size) 276 | 277 | train_mask = sample_mask(idx_train, labels.shape[0]) 278 | val_mask = sample_mask(idx_val, labels.shape[0]) 279 | test_mask = sample_mask(idx_test, labels.shape[0]) 280 | 281 | y_train = np.zeros(labels.shape) 282 | y_val = np.zeros(labels.shape) 283 | y_test = np.zeros(labels.shape) 284 | y_train[train_mask, :] = labels[train_mask, :] 285 | y_val[val_mask, :] = labels[val_mask, :] 286 | y_test[test_mask, :] = labels[test_mask, :] 287 | print(y_train.shape) 288 | 289 | return adj, word_entity_adj, entity_adj_list, y_train, y_val, y_test, train_mask, val_mask, test_mask, train_size, test_size 290 | 291 | def sparse_to_tuple(sparse_mx): 292 | """Convert sparse matrix to tuple representation.""" 293 | def to_tuple(mx): 294 | if not sp.isspmatrix_coo(mx): 295 | mx = mx.tocoo() 296 | coords = np.vstack((mx.row, mx.col)).transpose() 297 | values = mx.data 298 | shape = mx.shape 299 | return coords, values, shape 300 | 301 | if isinstance(sparse_mx, list): 302 | 303 | for i in range(len(sparse_mx)): 304 | sparse_mx[i] = to_tuple(sparse_mx[i]) 305 | else: 306 | sparse_mx = to_tuple(sparse_mx) 307 | 308 | return sparse_mx 309 | 310 | def preprocess_features(features): 311 | """Row-normalize feature matrix and convert to tuple representation""" 312 | rowsum = np.array(features.sum(1)) 313 | r_inv = np.power(rowsum, -1).flatten() 314 | r_inv[np.isinf(r_inv)] = 0. 315 | r_mat_inv = sp.diags(r_inv) 316 | features = r_mat_inv.dot(features) 317 | return sparse_to_tuple(features) 318 | 319 | 320 | def normalize_adj(adj, symmetric=True): 321 | if symmetric: 322 | d = sp.diags(np.power(np.array(adj.sum(1)), -0.5).flatten()) 323 | a_norm = adj.dot(d).transpose().dot(d).tocsr() 324 | else: 325 | d = sp.diags(np.power(np.array(adj.sum(1)), -1).flatten()) 326 | a_norm = d.dot(adj).tocsr() 327 | return a_norm 328 | 329 | def preprocess_adj(adj): 330 | """Preprocessing of adjacency matrix for simple GCN model and conversion to tuple representation.""" 331 | adj_normalized = normalize_adj(adj + sp.eye(adj.shape[0])) 332 | return sparse_to_tuple(adj_normalized) 333 | 334 | def preprocess_graph(adj, symmetric=True): 335 | # this function has bugs, return none, decagon defines and do this immediatly. here we load from pkl 336 | adj = sp.coo_matrix(adj) 337 | if adj.shape[0] == adj.shape[1]: 338 | if symmetric == True: 339 | adj_ = adj + sp.eye(adj.shape[0]) 340 | rowsum = np.array(adj_.sum(1)) 341 | degree_inv_sqrt = np.power(rowsum, -0.5).flatten() 342 | degree_inv_sqrt[np.isinf(degree_inv_sqrt)] = 0. 343 | degree_mat_inv_sqrt = sp.diags(degree_inv_sqrt) 344 | adj_normalized = adj_.dot(degree_mat_inv_sqrt).transpose().dot(degree_mat_inv_sqrt).tocoo() 345 | else: 346 | degree_inv_sqrt = np.power(np.array(adj.sum(1)), -1).flatten() 347 | degree_inv_sqrt[np.isinf(degree_inv_sqrt)] = 0. 348 | degree_mat_inv_sqrt = sp.diags(degree_inv_sqrt) 349 | adj_normalized = degree_mat_inv_sqrt.dot(adj).tocsr() 350 | else: 351 | rowsum = np.array(adj.sum(1)) 352 | rowdegree_inv = np.power(rowsum, -0.5).flatten() 353 | rowdegree_inv[np.isinf(rowdegree_inv)] = 0. 354 | rowdegree_mat_inv = sp.diags(rowdegree_inv) 355 | 356 | colsum = np.array(adj.sum(0)) 357 | coldegree_inv = np.power(colsum, -0.5).flatten() 358 | coldegree_inv[np.isinf(coldegree_inv)] = 0. 359 | coldegree_mat_inv = sp.diags(coldegree_inv) 360 | 361 | adj_normalized = rowdegree_mat_inv.dot(adj).dot(coldegree_mat_inv).tocoo() 362 | return sparse_to_tuple(adj_normalized) 363 | 364 | def construct_feed_dict(features, support, labels, labels_mask, placeholders): 365 | """Construct feed dictionary.""" 366 | feed_dict = dict() 367 | feed_dict.update({placeholders['labels']: labels}) 368 | feed_dict.update({placeholders['labels_mask']: labels_mask}) 369 | feed_dict.update({placeholders['features']: features}) 370 | feed_dict.update({placeholders['support'][i]: support[i] 371 | for i in range(len(support))}) 372 | feed_dict.update({placeholders['num_features_nonzero']: features[1].shape}) 373 | return feed_dict 374 | 375 | def build_feed_dict(labels, labels_mask, adj, edge_types, feat, placeholders): 376 | """Construct feed dictionary.""" 377 | feed_dict = dict() 378 | feed_dict.update({ 379 | placeholders['adj_mats_%d,%d,%d' % (i,j,k)]: adj[i,j][k] 380 | for i, j in edge_types for k in range(edge_types[i,j])}) 381 | #print(adj[1,1][0]) 382 | feed_dict.update({placeholders['feat_%d' % i]: feat[i] for i, _ in edge_types}) 383 | feed_dict.update({placeholders['labels']: labels}) 384 | feed_dict.update({placeholders['labels_mask']: labels_mask}) 385 | return feed_dict 386 | 387 | def chebyshev_polynomials(adj, k): 388 | """Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices (tuple representation).""" 389 | print("Calculating Chebyshev polynomials up to order {}...".format(k)) 390 | 391 | adj_normalized = normalize_adj(adj) 392 | laplacian = sp.eye(adj.shape[0]) - adj_normalized 393 | largest_eigval, _ = eigsh(laplacian, 1, which='LM') 394 | scaled_laplacian = ( 395 | 2. / largest_eigval[0]) * laplacian - sp.eye(adj.shape[0]) 396 | 397 | t_k = list() 398 | t_k.append(sp.eye(adj.shape[0])) 399 | t_k.append(scaled_laplacian) 400 | 401 | def chebyshev_recurrence(t_k_minus_one, t_k_minus_two, scaled_lap): 402 | s_lap = sp.csr_matrix(scaled_lap, copy=True) 403 | return 2 * s_lap.dot(t_k_minus_one) - t_k_minus_two 404 | 405 | for i in range(2, k+1): 406 | t_k.append(chebyshev_recurrence(t_k[-1], t_k[-2], scaled_laplacian)) 407 | 408 | return sparse_to_tuple(t_k) 409 | 410 | 411 | def loadWord2Vec(filename): 412 | """Read Word Vectors""" 413 | vocab = [] 414 | embd = [] 415 | word_vector_map = {} 416 | file = open(filename, 'r') 417 | for line in file.readlines(): 418 | row = line.strip().split(' ') 419 | if(len(row) > 2): 420 | vocab.append(row[0]) 421 | vector = row[1:] 422 | length = len(vector) 423 | for i in range(length): 424 | vector[i] = float(vector[i]) 425 | embd.append(vector) 426 | word_vector_map[row[0]] = vector 427 | print('Loaded Word Vectors!') 428 | file.close() 429 | return vocab, embd, word_vector_map 430 | 431 | def clean_str(string): 432 | """ 433 | Tokenization/string cleaning for all datasets except for SST. 434 | Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py 435 | """ 436 | string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string) 437 | string = re.sub(r"\'s", " \'s", string) 438 | string = re.sub(r"\'ve", " \'ve", string) 439 | string = re.sub(r"n\'t", " n\'t", string) 440 | string = re.sub(r"\'re", " \'re", string) 441 | string = re.sub(r"\'d", " \'d", string) 442 | string = re.sub(r"\'ll", " \'ll", string) 443 | string = re.sub(r",", " , ", string) 444 | string = re.sub(r"!", " ! ", string) 445 | string = re.sub(r"\(", " \( ", string) 446 | string = re.sub(r"\)", " \) ", string) 447 | string = re.sub(r"\?", " \? ", string) 448 | string = re.sub(r"\s{2,}", " ", string) 449 | return string.strip().lower() 450 | 451 | def word_synonyms(word): 452 | ''' 453 | look up synonyms given a word 454 | ''' 455 | synonyms = [] 456 | for syn in wn.synsets(word): 457 | for l in syn.lemmas(): 458 | synonyms.append(l.name()) 459 | return synonyms 460 | 461 | def synonimize(word, pos=None): 462 | """ Get synonyms of the word / lemma """ 463 | try: 464 | # map part of speech tags to wordnet 465 | pos = {'NN': wn.NOUN,'JJ':wn.ADJ,'VB':wn.VERB,'RB':wn.ADV}[pos[:2]] 466 | except: 467 | # or just return the original word 468 | print("OUCH {} {}".format(word, pos)) 469 | return [word] 470 | 471 | synsets = wn.synsets(word, pos) 472 | synonyms = [] 473 | for synset in synsets: 474 | for sim in synset.similar_tos(): 475 | synonyms += sim.lemma_names() 476 | 477 | # return list of synonyms or just the original word 478 | return synonyms or [word] 479 | 480 | 481 | def wordnet_id_synset_dict(): 482 | ''' 483 | synset to number mapping 484 | ''' 485 | 486 | f = open('data/WN18/wordnet-mlj12-definitions.txt', 'r') 487 | lines = f.readlines() 488 | f.close() 489 | 490 | synset_id_dict = {} 491 | 492 | count = 0 493 | for line in lines: 494 | temp = line.strip().split('\t') 495 | #print(temp[0], temp[1]) 496 | # n, v, a, r 497 | if temp[1].find('_NN_') != -1 or temp[1].find('_JJ_') != -1 or temp[1].find('_VB_') != -1 or temp[1].find('_RB_') != -1: 498 | count += 1 499 | wordnet_str = temp[1][2:] 500 | num_start = wordnet_str.rfind('_') 501 | num = wordnet_str[num_start + 1:] 502 | if len(num) == 1: 503 | num = '0' + num 504 | # print(num) 505 | pos_start = wordnet_str[:num_start].rfind('_') 506 | pos = wordnet_str[:num_start][pos_start + 1:] 507 | if pos == 'NN': 508 | pos = 'n' 509 | elif pos == 'JJ': 510 | pos = 'a' 511 | elif pos == 'VB': 512 | pos = 'v' 513 | elif pos == 'RB': 514 | pos = 'r' 515 | # print(pos) 516 | name = wordnet_str[:pos_start] 517 | # print(name) 518 | new_str = name + '.' + pos + '.' + num 519 | # print(new_str, temp[0]) 520 | synset_id_dict[new_str] = temp[0] 521 | 522 | # print(wordnet_str) 523 | # if wordnet_str.find('10') != -1: 524 | # print(wordnet_str, num, pos, name) 525 | else: 526 | print(temp[1]) 527 | print(count) 528 | 529 | return synset_id_dict 530 | 531 | 532 | def wordnet_id_num_dict(): 533 | ''' number to id mapping''' 534 | 535 | f = open('data/WN18/entity2id.txt', 'r') 536 | lines = f.readlines() 537 | f.close() 538 | 539 | id_num_dict = {} 540 | for line in lines: 541 | temp = line.strip().split('\t') 542 | if len(temp) == 2: 543 | #print(temp[0], temp[1]) 544 | id_num_dict[temp[0]] = temp[1] 545 | return id_num_dict 546 | 547 | def wordnet_defs(): 548 | ''' id to definitions ''' 549 | 550 | f = open('data/WN18/wordnet-mlj12-definitions.txt', 'r') 551 | lines = f.readlines() 552 | f.close() 553 | 554 | number_def_dict = {} 555 | for line in lines: 556 | temp = line.strip().split('\t') 557 | 558 | number_def_dict[temp[0]] = temp[2] 559 | id_num_dict = wordnet_id_num_dict() 560 | 561 | id_def_dict = {} 562 | for num in id_num_dict: 563 | entity_id = id_num_dict[num] 564 | definition = number_def_dict[num] 565 | id_def_dict[entity_id] = definition 566 | 567 | def_docs = [] 568 | for i in range(len(id_def_dict)): 569 | def_docs.append(id_def_dict[str(i)]) 570 | 571 | tfidf_vec = TfidfVectorizer() 572 | tfidf_matrix = tfidf_vec.fit_transform(def_docs) 573 | return tfidf_matrix 574 | 575 | 576 | def read_triples(file_path): 577 | '''read train, val or test triples''' 578 | f = open(file_path, 'r') 579 | lines = f.readlines() 580 | f.close() 581 | 582 | triple_list = [] 583 | for line in lines: 584 | line = line.strip() 585 | temp = line.split() 586 | if len(temp) == 3: 587 | #print(temp[0], temp[1], temp[2]) 588 | triple_list.append(line) 589 | 590 | return triple_list --------------------------------------------------------------------------------