├── .gitignore ├── LICENSE ├── README.md ├── data ├── M10 │ ├── adjedges.txt │ ├── docs.txt │ └── labels.txt ├── cora │ ├── docs.txt │ ├── edgelist.txt │ └── labels.txt └── dblp │ ├── adjedges.txt │ ├── docs.txt │ └── labels.txt ├── example_gcn.py └── gnn ├── __init__.py ├── data ├── __init__.py ├── dataset.py ├── example.py ├── meta_network.py └── old_meta_network.py ├── evaluation └── __init__.py ├── model ├── __init__.py └── gcn.py └── util ├── __init__.py ├── evaluation.py └── sparse.py /.gitignore: -------------------------------------------------------------------------------- 1 | /.idea/ 2 | **/__pycache__/ -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # TF-GNN 2 | TensorFlow Implementation of Graph Attention Network and Graph Convolutional Network 3 | 4 | This repository is deprecated. Use [tf_geometric](https://github.com/CrawlScript/tf_geometric) instead: [https://github.com/CrawlScript/tf_geometric](https://github.com/CrawlScript/tf_geometric) 5 | -------------------------------------------------------------------------------- /data/cora/labels.txt: -------------------------------------------------------------------------------- 1 | 31336 Neural_Networks 2 | 1061127 Rule_Learning 3 | 1106406 Reinforcement_Learning 4 | 13195 Reinforcement_Learning 5 | 37879 Probabilistic_Methods 6 | 1126012 Probabilistic_Methods 7 | 1107140 Theory 8 | 1102850 Neural_Networks 9 | 31349 Neural_Networks 10 | 1106418 Theory 11 | 1123188 Neural_Networks 12 | 1128990 Genetic_Algorithms 13 | 109323 Probabilistic_Methods 14 | 217139 Case_Based 15 | 31353 Neural_Networks 16 | 32083 Neural_Networks 17 | 1126029 Reinforcement_Learning 18 | 1118017 Neural_Networks 19 | 49482 Neural_Networks 20 | 753265 Neural_Networks 21 | 249858 Theory 22 | 1113739 Reinforcement_Learning 23 | 48766 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Probabilistic_Methods 2681 | 1131149 Neural_Networks 2682 | 1104258 Neural_Networks 2683 | 1152991 Probabilistic_Methods 2684 | 447250 Neural_Networks 2685 | 115188 Neural_Networks 2686 | 102879 Theory 2687 | 1131150 Neural_Networks 2688 | 56708 Reinforcement_Learning 2689 | 1128943 Genetic_Algorithms 2690 | 134060 Theory 2691 | 102884 Theory 2692 | 1131163 Neural_Networks 2693 | 4274 Case_Based 2694 | 1131172 Probabilistic_Methods 2695 | 767763 Theory 2696 | 152226 Theory 2697 | 152227 Theory 2698 | 626530 Probabilistic_Methods 2699 | 626531 Probabilistic_Methods 2700 | 1131180 Probabilistic_Methods 2701 | 1130454 Probabilistic_Methods 2702 | 1131184 Neural_Networks 2703 | 1128974 Genetic_Algorithms 2704 | 1128975 Genetic_Algorithms 2705 | 1128977 Genetic_Algorithms 2706 | 1128978 Genetic_Algorithms 2707 | 117328 Case_Based 2708 | 24043 Neural_Networks 2709 | -------------------------------------------------------------------------------- /example_gcn.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | from gnn.data.dataset import GraphDataset, WhiteSpaceTokenizer 3 | from gnn.data.example import load_M10, load_cora, load_dblp 4 | from gnn.model.gcn import GCN, GCNTrainer 5 | import tensorflow as tf 6 | 7 | # eager mode must be enabled 8 | from tensorflow.contrib.eager.python import tfe 9 | 10 | import os 11 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 12 | 13 | tfe.enable_eager_execution() 14 | 15 | # read graph dataset: data/M10 data/dblp 16 | # dataset = GraphDataset("data/dblp", ignore_featureless_node=True) 17 | dataset = load_M10("data/M10", ignore_featureless_node=True) 18 | 19 | adj = dataset.adj_matrix(sparse=True) 20 | feature_matrix, feature_masks = dataset.feature_matrix(bag_of_words=True, sparse=True) 21 | labels, label_masks = dataset.label_list_or_matrix(one_hot=False) 22 | 23 | train_node_indices, test_node_indices, train_masks, test_masks = dataset.split_train_and_test(training_rate=0.3) 24 | 25 | gcn_model = GCN([16, dataset.num_classes()], drop_rate=0.1) 26 | gcn_trainer = GCNTrainer(gcn_model) 27 | gcn_trainer.train(adj, feature_matrix, labels, train_masks, test_masks, learning_rate=1e-3, l2_coe=1e-3) 28 | -------------------------------------------------------------------------------- /gnn/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CrawlScript/TF-GNN/dd72d190759d00e935221c32aa139950f460dfdb/gnn/__init__.py -------------------------------------------------------------------------------- /gnn/data/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CrawlScript/TF-GNN/dd72d190759d00e935221c32aa139950f460dfdb/gnn/data/__init__.py -------------------------------------------------------------------------------- /gnn/data/dataset.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | 3 | import os 4 | import numpy as np 5 | import re 6 | 7 | from scipy.sparse import csr_matrix 8 | 9 | from gnn.data.meta_network import MetaNetwork, N_TYPE_NODE, N_TYPE_LABEL, IdIndexer 10 | 11 | 12 | class Tokenizer(object): 13 | def __init__(self): 14 | self.token_id_index_dict = {} 15 | self.token_index_id_dict = {} 16 | 17 | # get token_index if token_id exists 18 | # otherwise create token_index for token_id 19 | def get_or_create_token_index(self, token_id): 20 | if token_id in self.token_id_index_dict: 21 | return self.token_id_index_dict[token_id] 22 | else: 23 | token_index = self.num_tokens() 24 | self.token_index_id_dict[token_index] = token_id 25 | self.token_id_index_dict[token_id] = token_index 26 | return token_index 27 | 28 | def get_token_index(self, token_id): 29 | return self.token_id_index_dict[token_id] 30 | 31 | def num_tokens(self): 32 | return len(self.token_id_index_dict) 33 | 34 | # sentence:str => words:list[str] 35 | def tokenize(self, s): 36 | raise NotImplementedError() 37 | 38 | def tokenize_to_indices(self, s, create_token_index=True): 39 | token_ids = self.tokenize(s) 40 | if create_token_index: 41 | token_indices = [self.get_or_create_token_index(token_id) for token_id in token_ids] 42 | else: 43 | token_indices = [self.get_token_index(token_id) for token_id in token_ids if token_id in self.token_id_index_dict] 44 | return token_indices 45 | 46 | 47 | # default tokenizer, splitting by white spaces 48 | class WhiteSpaceTokenizer(Tokenizer): 49 | def tokenize(self, s): 50 | return s.split() 51 | 52 | 53 | class EnglishWordTokenizer(Tokenizer): 54 | def __init__(self): 55 | super().__init__() 56 | self.punc_re = re.compile("[^a-zA-Z]") 57 | 58 | def tokenize(self, s): 59 | s = s.lower() 60 | s = self.punc_re.sub(" ", s) 61 | return s.split() 62 | 63 | 64 | # construct dataset from a data directory 65 | # the data directory should contain: 66 | # - adjedges.txt or edgelist.txt 67 | # - docs.txt 68 | # - labels.txt 69 | class GraphDataset(object): 70 | FORMAT_ADJEDGES = "adjedges" 71 | FORMAT_EDGELIST = "edgelist" 72 | 73 | # read data from data_dir 74 | # if ignore_featureless_node is True, nodes without content or features will be ignored 75 | def __init__(self, data_dir, data_format=FORMAT_ADJEDGES, ignore_featureless_node=True, 76 | tokenizer=None, 77 | label_indexer=None, 78 | fit_dataset=True): 79 | super().__init__() 80 | self.network = MetaNetwork() 81 | self.label_indexer = IdIndexer() if label_indexer is None else label_indexer 82 | self.data_dir = data_dir 83 | self.data_format = data_format 84 | self.ignore_featureless_node = ignore_featureless_node 85 | 86 | self.fit_dataset = fit_dataset 87 | 88 | self.num_nodes_with_features = 0 89 | 90 | if tokenizer is None: 91 | self.tokenizer = EnglishWordTokenizer() 92 | else: 93 | self.tokenizer = tokenizer 94 | 95 | # read document first such that nodes with content will have small index 96 | self._read_docs() 97 | self._read_labels() 98 | self._read_structure() 99 | self.network.build_cache() 100 | 101 | def _read_structure(self): 102 | if self.data_format == GraphDataset.FORMAT_ADJEDGES: 103 | self._read_adjedges() 104 | else: 105 | self._read_edgelist() 106 | 107 | def _read_adjedges(self): 108 | adjedges_fpath = os.path.join(self.data_dir, "adjedges.txt") 109 | with open(adjedges_fpath, "r", encoding="utf-8") as f: 110 | for line in f: 111 | node_ids = line.split() 112 | node_id0 = node_ids[0] 113 | if self.ignore_featureless_node and not self.network.has_node_id(N_TYPE_NODE, node_id0): 114 | continue 115 | node_index0 = self.network.get_node_index(N_TYPE_NODE, node_id0, create=True) 116 | for node_id1 in node_ids[1:]: 117 | if self.ignore_featureless_node and not self.network.has_node_id(N_TYPE_NODE, node_id1): 118 | continue 119 | node_index1 = self.network.get_node_index(N_TYPE_NODE, node_id1, create=True) 120 | if node_index0 != node_index1: 121 | self.network.add_edges((N_TYPE_NODE, N_TYPE_NODE), node_index0, node_index1, 1.0) 122 | 123 | def _read_edgelist(self): 124 | edgelist_fpath = os.path.join(self.data_dir, "edgelist.txt") 125 | with open(edgelist_fpath, "r", encoding="utf-8") as f: 126 | for line in f: 127 | items = line.split() 128 | node_id0 = items[0] 129 | node_id1 = items[1] 130 | if len(items) == 3: 131 | weight = float(items[2]) 132 | else: 133 | weight = 1.0 134 | if self.ignore_featureless_node and not self.network.has_node_id(N_TYPE_NODE, node_id0): 135 | continue 136 | node_index0 = self.network.get_node_index(N_TYPE_NODE, node_id0, create=True) 137 | if self.ignore_featureless_node and not self.network.has_node_id(N_TYPE_NODE, node_id1): 138 | continue 139 | node_index1 = self.network.get_node_index(N_TYPE_NODE, node_id1, create=True) 140 | if node_index0 != node_index1: 141 | self.network.add_edges((N_TYPE_NODE, N_TYPE_NODE), node_index0, node_index1, weight) 142 | 143 | def _read_docs(self): 144 | docs_fpath = os.path.join(self.data_dir, "docs.txt") 145 | with open(docs_fpath, "r", encoding="utf-8") as f: 146 | for line in f: 147 | node_id, sentence = re.split(r"\s+", line, 1) 148 | node_index = self.network.get_node_index(N_TYPE_NODE, node_id, create=True) 149 | token_indices = self.tokenizer.tokenize_to_indices(sentence, create_token_index=self.fit_dataset) 150 | self.network.set_node_attr(N_TYPE_NODE, node_index, "features", token_indices) 151 | self.num_nodes_with_features += 1 152 | 153 | def _read_labels(self): 154 | labels_fpath = os.path.join(self.data_dir, "labels.txt") 155 | with open(labels_fpath, "r", encoding="utf-8") as f: 156 | for line in f: 157 | node_id, label_id = line.split() 158 | if self.ignore_featureless_node: 159 | node_index = self.network.get_node_index(N_TYPE_NODE, node_id) 160 | else: 161 | node_index = self.network.get_node_index(N_TYPE_NODE, node_id, create=True) 162 | label_index = self.label_indexer.get_index(label_id, create=self.fit_dataset) 163 | # label_index = self.network.get_node_index(N_TYPE_LABEL, label_id, create=True) 164 | self.network.set_node_attr(N_TYPE_NODE, node_index, "label", label_index) 165 | 166 | def feature_matrix(self, bag_of_words=False, sparse=True): 167 | # if bag of words, return sparse 168 | if bag_of_words: 169 | feature_dim = self.tokenizer.num_tokens() 170 | num_nodes = self.num_nodes() 171 | data = [] 172 | row = [] 173 | col = [] 174 | feature_masks = [] 175 | for node_index in range(num_nodes): 176 | if self.has_features(node_index): 177 | token_indices = self.network.get_node_attr(N_TYPE_NODE, node_index, "features") 178 | for token_index in token_indices: 179 | data.append(1) 180 | row.append(node_index) 181 | col.append(token_index) 182 | feature_masks.append(1) 183 | else: 184 | feature_masks.append(0) 185 | 186 | feature_matrix = csr_matrix((data, (row, col)), shape=(num_nodes, feature_dim)) 187 | if not sparse: 188 | feature_matrix = feature_matrix.todense().astype(np.float32) 189 | return feature_matrix, np.array(feature_masks) 190 | 191 | # feature_matrix = np.zeros((num_nodes, feature_dim), dtype=np.float32) 192 | # for node_index in range(num_nodes): 193 | # token_indices = self.get_node_attr(N_TYPE_NODE, node_index, "features") 194 | # feature_matrix[node_index][token_indices] = 1.0 195 | # return feature_matrix 196 | 197 | else: 198 | raise NotImplementedError() 199 | 200 | def num_classes(self): 201 | return len(self.label_indexer) 202 | 203 | def num_nodes(self): 204 | return self.network.num_nodes(N_TYPE_NODE) 205 | 206 | def label_list_or_matrix(self, one_hot=False): 207 | # label_indices = self.network.get_node_attrs(N_TYPE_NODE, range(self.network.num_nodes(N_TYPE_NODE)), "label") 208 | label_indices = [] 209 | label_masks = [] 210 | num_nodes = self.network.num_nodes(N_TYPE_NODE) 211 | for node_index in range(num_nodes): 212 | if self.has_label(node_index): 213 | label_indices.append(self.get_label_index(node_index)) 214 | label_masks.append(1) 215 | else: 216 | label_indices.append(0) 217 | label_masks.append(0) 218 | if one_hot: 219 | label_matrix = np.zeros((num_nodes, self.num_classes()), dtype=np.int32) 220 | label_matrix[np.arange(num_nodes), label_indices] = 1 221 | return label_matrix 222 | else: 223 | return np.array(label_indices), np.array(label_masks) 224 | 225 | def adj_matrix(self, sparse=False): 226 | return self.network.adj_matrix((N_TYPE_NODE, N_TYPE_NODE), sparse) 227 | 228 | def get_label_index(self, node_index): 229 | return self.network.get_node_attr(N_TYPE_NODE, node_index, "label") 230 | 231 | def has_label(self, node_index): 232 | return self.network.has_node_attr(N_TYPE_NODE, node_index, "label") 233 | 234 | def has_features(self, node_index): 235 | return self.network.has_node_attr(N_TYPE_NODE, node_index, "features") 236 | 237 | def split_train_and_test(self, training_rate=0.3): 238 | def func_should_mask(node_index): 239 | return self.has_label(node_index) 240 | return self.network.split_train_and_test(N_TYPE_NODE, training_rate, func_should_mask) 241 | 242 | -------------------------------------------------------------------------------- /gnn/data/example.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | from gnn.data.dataset import GraphDataset, WhiteSpaceTokenizer 3 | 4 | 5 | def load_cora(data_dir): 6 | return GraphDataset(data_dir, data_format=GraphDataset.FORMAT_EDGELIST, tokenizer=WhiteSpaceTokenizer()) 7 | 8 | 9 | def load_M10(data_dir, ignore_featureless_node=True): 10 | return GraphDataset(data_dir, ignore_featureless_node=ignore_featureless_node) 11 | 12 | 13 | def load_dblp(data_dir, ignore_featureless_node=True): 14 | return GraphDataset(data_dir, ignore_featureless_node=ignore_featureless_node) 15 | -------------------------------------------------------------------------------- /gnn/data/meta_network.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | from scipy.sparse import csr_matrix, _sparsetools 3 | import numpy as np 4 | import random 5 | from multiprocessing.dummy import Pool as ThreadPool 6 | 7 | # N_TYPE denotes node type 8 | N_TYPE_NODE = "N_NODE" 9 | N_TYPE_LABEL = "N_LABEL" 10 | 11 | 12 | def dict_get_or_create_value(dict_object, key, default_value): 13 | if key in dict_object: 14 | return dict_object[key] 15 | else: 16 | dict_object[key] = default_value 17 | return default_value 18 | 19 | 20 | class IdIndexer(object): 21 | def __init__(self): 22 | self.id_index_dict = {} 23 | self.index_id_dict = {} 24 | 25 | def get_index(self, id, create=False): 26 | if not create or id in self.id_index_dict: 27 | return self.id_index_dict[id] 28 | index = len(self.id_index_dict) 29 | self.id_index_dict[id] = index 30 | self.index_id_dict[index] = id 31 | return index 32 | 33 | def get_indices(self, ids): 34 | return [self.get_index(id) for id in ids] 35 | 36 | def get_id(self, index): 37 | return self.index_id_dict[index] 38 | 39 | def get_ids(self, indices): 40 | return [self.index_id_dict[index] for index in indices] 41 | 42 | def has_id(self, id): 43 | return id in self.id_index_dict 44 | 45 | def list_ids(self): 46 | return list(self.id_index_dict) 47 | 48 | def list_indices(self): 49 | return list(self.index_id_dict) 50 | 51 | def __len__(self): 52 | return len(self.id_index_dict) 53 | 54 | 55 | 56 | 57 | class Adj(object): 58 | def __init__(self): 59 | self.data = [] 60 | self.row = [] 61 | self.col = [] 62 | self.cached_csr = None 63 | self.cached_sample_list = None 64 | 65 | def build_cache(self, shape=None): 66 | self.cached_csr = self.to_csr(shape=shape) 67 | self.cached_sample_list = self.get_sample_list() 68 | 69 | def to_csr(self, shape): 70 | return csr_matrix((self.data, (self.row, self.col)), shape=shape) 71 | 72 | def add_edge(self, node_index0, node_index1, weight=1.0): 73 | self.data.append(weight) 74 | self.row.append(node_index0) 75 | self.col.append(node_index1) 76 | 77 | def add_edges(self, node_index0, node_index1, weight=1.0): 78 | self.add_edge(node_index0, node_index1, weight) 79 | self.add_edge(node_index1, node_index0, weight) 80 | 81 | def get_neighbor_dict(self, node_index): 82 | return {col_index: self.cached_csr[node_index, col_index] for col_index in self.get_neighbors(node_index)} 83 | 84 | def get_neighbors(self, node_index): 85 | return [col_index for col_index in self.cached_csr.getrow(node_index).indices] 86 | 87 | def get_sample_list(self): 88 | return list(set(self.row)) 89 | 90 | def sample_node(self, excluded_node_indices=None): 91 | while True: 92 | node_index = self.cached_sample_list[random.randrange(0, len(self.cached_sample_list))] 93 | if excluded_node_indices is None or node_index not in excluded_node_indices: 94 | return node_index 95 | 96 | def sample_neighbor(self, node_index): 97 | neighbors = self.get_neighbors() 98 | if len(neighbors) == 0: 99 | return None 100 | return neighbors[random.randrange(0, len(neighbors))] 101 | 102 | def sample_triple(self, node_a=None): 103 | if node_a is None: 104 | node_a = self.sample_node() 105 | neighbors = self.get_neighbors(node_a) 106 | node_b = self.sample_neighbor(node_a) 107 | excluded = set([node_a] + neighbors) 108 | node_neg = self.sample_node(excluded_node_indices=excluded) 109 | return node_a, node_b, node_neg 110 | 111 | 112 | 113 | # Heterogeneous Network 114 | # edges are based on meta-paths 115 | class MetaNetwork(object): 116 | 117 | # read data from data_dir 118 | # if ignore_featureless_node is True, nodes without content or features will be ignored 119 | def __init__(self): 120 | self.node_type_indexer_dict = {} 121 | self.meta_adj_dict = {} 122 | # node_type:str => node_index:int => node_attrdict: dict 123 | self.node_type_index_attrdict_dict = {} 124 | # node_type0:str => node_type1:str => node_index:int => neighbor_node_indices:list 125 | self.meta_neighbors_dict = {} 126 | # key0: node_type0 => key1: node_type1 => key2: node_index0 => key3 => node_index1 => value: weight 127 | self.meta_adj_dict = {} 128 | # key0: node_type0 => key1: node_type1 => sample_list: list[int] 129 | self.meta_sample_list_dict = {} 130 | 131 | def get_indexer(self, node_type, create=False): 132 | if not create or node_type in self.node_type_indexer_dict: 133 | return self.node_type_indexer_dict[node_type] 134 | indexer = IdIndexer() 135 | self.node_type_indexer_dict[node_type] = indexer 136 | return indexer 137 | 138 | def get_adj(self, meta, create=False): 139 | if not create or meta in self.meta_adj_dict: 140 | return self.meta_adj_dict[meta] 141 | adj = Adj() 142 | self.meta_adj_dict[meta] = adj 143 | return adj 144 | 145 | def list_node_ids(self, meta): 146 | return self.get_indexer(meta).list_ids() 147 | 148 | def list_node_indices(self, meta): 149 | return self.get_indexer(meta).list_indices() 150 | 151 | def get_node_attrdict(self, node_type, node_index, create=False): 152 | if not create: 153 | return self.node_type_index_attrdict_dict[node_type][node_index] 154 | node_index_attrdict = dict_get_or_create_value(self.node_type_index_attrdict_dict, node_type, {}) 155 | attrdict = dict_get_or_create_value(node_index_attrdict, node_index, {}) 156 | return attrdict 157 | 158 | def get_node_attr(self, node_type, node_index, attr_name, return_none_if_not_exist=False): 159 | try: 160 | return self.get_node_attrdict(node_type, node_index, create=False)[attr_name] 161 | except Exception as e: 162 | if return_none_if_not_exist: 163 | return None 164 | else: 165 | raise e 166 | 167 | def has_node_attr(self, node_type, node_index, attr_name): 168 | return self.get_node_attr(node_type, node_index, attr_name, return_none_if_not_exist=True) is not None 169 | 170 | def get_node_attrs(self, node_type, node_indices, attr_name, return_none_if_not_exist=False): 171 | return [self.get_node_attr(node_type, node_index, attr_name, return_none_if_not_exist) for node_index in node_indices] 172 | 173 | def set_node_attr(self, node_type, node_index, attr_name, attr_value): 174 | attrdict = self.get_node_attrdict(node_type, node_index, create=True) 175 | attrdict[attr_name] = attr_value 176 | 177 | # get node_index if node_id exists 178 | # otherwise create node_index for node_id 179 | def get_node_index(self, node_type, node_id, create=False): 180 | return self.get_indexer(node_type, create).get_index(node_id, create) 181 | 182 | def get_node_indices(self, node_type, node_ids): 183 | return self.get_indexer(node_type, create=False).get_ids(node_ids) 184 | 185 | def get_node_id(self, node_type, node_index, create=False): 186 | return self.get_indexer(node_type, create).get_id(node_index) 187 | 188 | def get_node_ids(self, node_type, node_indices): 189 | return self.get_indexer(node_type, create=False).get_ids(node_indices) 190 | 191 | def has_node_id(self, node_type, node_id): 192 | return self.get_indexer(node_type, create=False).has_id(node_id) 193 | 194 | def add_edge(self, meta, node_index0, node_index1, weight=1.0): 195 | self.get_adj(meta, create=True).add_edge(node_index0, node_index1, weight=weight) 196 | 197 | def add_edges(self, meta, node_index0, node_index1, weight=1.0): 198 | self.get_adj(meta, create=True).add_edges(node_index0, node_index1, weight=weight) 199 | 200 | def num_nodes(self, node_type): 201 | return len(self.get_indexer(node_type, create=False)) 202 | 203 | # if sparse, return a csr_matrix 204 | def adj_matrix(self, meta, sparse=False): 205 | csr = self.get_adj(meta, create=False).cached_csr 206 | return csr if sparse else csr.todense() 207 | 208 | def split_train_and_test(self, node_type, training_rate, func_should_mask=None): 209 | masked_node_indices = [] 210 | num_nodes = self.num_nodes(node_type) 211 | if func_should_mask is not None: 212 | for node_index in range(num_nodes): 213 | if func_should_mask(node_index): 214 | masked_node_indices.append(node_index) 215 | random_node_indices = np.random.permutation(masked_node_indices) 216 | else: 217 | random_node_indices = np.random.permutation(num_nodes) 218 | 219 | training_size = int(len(random_node_indices) * training_rate) 220 | train_node_indices = random_node_indices[:training_size] 221 | test_node_indices = random_node_indices[training_size:] 222 | 223 | train_masks = np.zeros([num_nodes], dtype=np.int32) 224 | train_masks[train_node_indices] = 1 225 | test_masks = np.zeros([num_nodes], dtype=np.int32) 226 | test_masks[test_node_indices] = 1 227 | return train_node_indices, test_node_indices, train_masks, test_masks 228 | 229 | def sample_node(self, node_type, excluded_node_indices=None): 230 | indexer = self.get_indexer(node_type, create=False) 231 | while True: 232 | node_index = random.randrange(0, len(indexer)) 233 | if excluded_node_indices is None or node_index not in excluded_node_indices: 234 | return node_index 235 | 236 | def sample_meta_node(self, meta, excluded_node_indices=None): 237 | return self.get_adj(meta, create=False).sample_node(excluded_node_indices) 238 | 239 | def sample_meta_neighbor(self, meta, node_index): 240 | return self.get_adj(meta, create=False).sample_neighbor(node_index) 241 | 242 | def random_walk(self, node_types, start_node_index=None, padding=True): 243 | if start_node_index is None: 244 | start_node_index = self.sample_meta_node(tuple(node_types[:2])) 245 | path = [start_node_index] 246 | for i, node_type in enumerate(node_types[:-1]): 247 | meta = tuple(node_types[i:i+2]) 248 | node_index0 = path[-1] 249 | node_index1 = self.sample_meta_neighbor(meta, node_index0) 250 | if node_index1 is None: 251 | break 252 | path.append(node_index1) 253 | 254 | while len(path) < len(node_types): 255 | node_type = node_types[len(path)] 256 | random_node_index = self.sample_node(node_type, excluded_node_indices=path) 257 | path.append(random_node_index) 258 | return path 259 | 260 | def multi_random_walk(self, node_types, start_node_indices=None, num_paths=None, num_threads=None): 261 | if (start_node_indices is None) == (num_paths is None): 262 | print("please specify either 'start_node_indices' or 'num_paths'") 263 | if start_node_indices is None: 264 | start_node_indices = [None] * num_paths 265 | if num_threads is None: 266 | num_paths = num_paths 267 | 268 | def random_walk_func(start_node_index): 269 | return self.random_walk(node_types, start_node_index) 270 | 271 | pool = ThreadPool(4) 272 | paths = pool.map(random_walk_func, start_node_indices) 273 | 274 | return paths 275 | 276 | def get_adj_shape(self, meta): 277 | return [self.num_nodes(meta[0]), self.num_nodes(meta[1])] 278 | 279 | 280 | def build_cache(self): 281 | for meta, adj in self.meta_adj_dict.items(): 282 | adj.build_cache(shape=self.get_adj_shape(meta)) 283 | 284 | def sample_triple(self, meta, node_a=None): 285 | return self.get_adj(meta, create=False).sample_triple(node_a) 286 | 287 | def sample_triples(self, meta, num): 288 | samples = [] 289 | for i in range(num): 290 | samples.append(self.sample_triple(meta)) 291 | return [list(t) for t in list(zip(*samples))] 292 | 293 | -------------------------------------------------------------------------------- /gnn/data/old_meta_network.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | from scipy.sparse import csr_matrix 3 | import numpy as np 4 | import random 5 | from multiprocessing.dummy import Pool as ThreadPool 6 | 7 | # N_TYPE denotes node type 8 | N_TYPE_NODE = "N_NODE" 9 | N_TYPE_LABEL = "N_LABEL" 10 | 11 | 12 | def dict_get_or_create_value(dict_object, key, default_value): 13 | if key in dict_object: 14 | return dict_object[key] 15 | else: 16 | dict_object[key] = default_value 17 | return default_value 18 | 19 | 20 | # Heterogeneous Network 21 | # edges are based on meta-paths 22 | class MetaNetwork(object): 23 | 24 | # read data from data_dir 25 | # if ignore_featureless_node is True, nodes without content or features will be ignored 26 | def __init__(self): 27 | # node_type:str => node_id:str => node_index:int 28 | self.node_type_id_index_dict = {} 29 | # node_type:str => node_index:int => node_id:str 30 | self.node_type_index_id_dict = {} 31 | # node_type:str => node_index:int => node_attrdict: dict 32 | self.node_type_index_attrdict_dict = {} 33 | # node_type0:str => node_type1:str => node_index:int => neighbor_node_indices:list 34 | self.meta_neighbors_dict = {} 35 | # key0: node_type0 => key1: node_type1 => key2: node_index0 => key3 => node_index1 => value: weight 36 | self.meta_adj_dict = {} 37 | # key0: node_type0 => key1: node_type1 => sample_list: list[int] 38 | self.meta_sample_list_dict = {} 39 | 40 | def list_node_ids(self, node_type): 41 | return list(self.node_type_id_index_dict[node_type].keys()) 42 | 43 | def list_node_indices(self, node_type): 44 | return list(self.node_type_index_id_dict[node_type].keys()) 45 | 46 | def get_or_create_adj_dict(self, node_type0, node_type1): 47 | sub_meta_adj_dict = dict_get_or_create_value(self.meta_adj_dict, node_type0, {}) 48 | adj_dict = dict_get_or_create_value(sub_meta_adj_dict, node_type1, {}) 49 | return adj_dict 50 | 51 | def get_adj_dict(self, node_type0, node_type1): 52 | return self.meta_adj_dict[node_type0][node_type1] 53 | 54 | # neighbor_weight_dict 55 | def get_weight_dict(self, node_type0, node_type1, node_index): 56 | return self.get_adj_dict(node_type0, node_type1)[node_index] 57 | 58 | def get_or_create_neighbors_dict(self, node_type0, node_type1): 59 | sub_meta_neighbors_dict = dict_get_or_create_value(self.meta_neighbors_dict, node_type0, {}) 60 | neighbors_dict = dict_get_or_create_value(sub_meta_neighbors_dict, node_type1, {}) 61 | return neighbors_dict 62 | 63 | def get_or_create_neighbors(self, node_type0, node_type1, node_index): 64 | neighbors_dict = self.get_or_create_neighbors_dict(node_type0, node_type1) 65 | neighbors = dict_get_or_create_value(neighbors_dict, node_index, []) 66 | return neighbors 67 | 68 | def get_neighbors(self, node_type0, node_type1, node_index): 69 | return self.meta_neighbors_dict[node_type0][node_type1][node_index] 70 | 71 | def get_or_create_node_id_index_dict(self, node_type): 72 | return dict_get_or_create_value(self.node_type_id_index_dict, node_type, {}) 73 | 74 | def get_or_create_node_index_id_dict(self, node_type): 75 | return dict_get_or_create_value(self.node_type_index_id_dict, node_type, {}) 76 | 77 | def get_node_id_index_dict(self, node_type): 78 | return self.node_type_id_index_dict[node_type] 79 | 80 | def get_node_index_id_dict(self, node_type): 81 | return self.node_type_index_id_dict[node_type] 82 | 83 | def get_or_create_node_attrdict(self, node_type, node_index): 84 | node_index_attrdict = dict_get_or_create_value(self.node_type_index_attrdict_dict, node_type, {}) 85 | attrdict = dict_get_or_create_value(node_index_attrdict, node_index, {}) 86 | return attrdict 87 | 88 | def get_or_create_node_attr(self, node_type, node_index, attr_name, attr_value): 89 | attr_dict = self.get_or_create_node_attrdict(node_type, node_index) 90 | if attr_name in attr_dict: 91 | return attr_dict[attr_name] 92 | else: 93 | attr_dict[attr_name] = attr_value 94 | return attr_value 95 | 96 | def get_node_attrdict(self, node_type, node_index): 97 | return self.node_type_index_attrdict_dict[node_type][node_index] 98 | 99 | def get_node_attr(self, node_type, node_index, attr_name): 100 | return self.get_node_attrdict(node_type, node_index)[attr_name] 101 | 102 | def has_node_attr(self, node_type, node_index, attr_name): 103 | node_index_attrdict_dict = self.node_type_index_attrdict_dict[node_type] 104 | if node_index not in node_index_attrdict_dict: 105 | return False 106 | attrdict = node_index_attrdict_dict[node_index] 107 | return attr_name in attrdict 108 | 109 | def get_node_attrs(self, node_type, node_indices, attr_name): 110 | return [self.get_node_attr(node_type, node_index, attr_name) for node_index in node_indices] 111 | 112 | def set_node_attr(self, node_type, node_index, attr_name, attr_value): 113 | attrdict = self.get_or_create_node_attrdict(node_type, node_index) 114 | attrdict[attr_name] = attr_value 115 | 116 | # add_edge by node indices and node types 117 | def add_edge(self, node_type0, node_type1, node_index0, node_index1, weight=1.0): 118 | adj_dict = self.get_or_create_adj_dict(node_type0, node_type1) 119 | weight_dict = dict_get_or_create_value(adj_dict, node_index0, {}) 120 | 121 | if node_index1 not in weight_dict: 122 | neighbors = self.get_or_create_neighbors(node_type0, node_type1, node_index0) 123 | neighbors.append(node_index1) 124 | 125 | weight_dict[node_index1] = weight 126 | 127 | def add_edges(self, node_type0, node_type1, node_index0, node_index1, weight=1.0): 128 | self.add_edge(node_type0, node_type1, node_index0, node_index1, weight=weight) 129 | self.add_edge(node_type1, node_type0, node_index1, node_index0, weight=weight) 130 | 131 | # get node_index if node_id exists 132 | # otherwise create node_index for node_id 133 | def get_or_create_node_index(self, node_type, node_id): 134 | node_id_index_dict = self.get_or_create_node_id_index_dict(node_type) 135 | if node_id in node_id_index_dict: 136 | return node_id_index_dict[node_id] 137 | else: 138 | node_index = self.num_nodes(node_type) 139 | node_id_index_dict[node_id] = node_index 140 | node_index_id_dict = self.get_or_create_node_index_id_dict(node_type) 141 | node_index_id_dict[node_index] = node_id 142 | return node_index 143 | 144 | # will raise exception when node_id does not exist 145 | def get_node_index(self, node_type, node_id): 146 | node_id_index_dict = self.get_node_id_index_dict(node_type) 147 | return node_id_index_dict[node_id] 148 | 149 | def get_node_indices(self, node_type, node_ids): 150 | return [self.get_node_index(node_type, node_id) for node_id in node_ids] 151 | 152 | def get_node_id(self, node_type, node_index): 153 | node_index_id_dict = self.get_node_index_id_dict(node_type) 154 | return node_index_id_dict[node_index] 155 | 156 | def get_node_ids(self, node_type, node_indices): 157 | return [self.get_node_id(node_type, node_index) for node_index in node_indices] 158 | 159 | def has_node_id(self, node_type, node_id): 160 | node_id_index_dict = self.get_node_id_index_dict(node_type) 161 | return node_id in node_id_index_dict 162 | 163 | def num_nodes(self, node_type): 164 | return len(self.node_type_id_index_dict[node_type]) 165 | 166 | # if sparse, return a csr_matrix 167 | def adj_matrix(self, node_type0, node_type1, sparse=False): 168 | data = [] 169 | row = [] 170 | col = [] 171 | adj_dict = self.get_adj_dict(node_type0, node_type1) 172 | for node_index0 in adj_dict: 173 | weight_dict = adj_dict[node_index0] 174 | for node_index1 in weight_dict: 175 | data.append(weight_dict[node_index1]) 176 | row.append(node_index0) 177 | col.append(node_index1) 178 | adj = csr_matrix((data, (row, col)), shape=(self.num_nodes(node_type0), self.num_nodes(node_type1))) 179 | if sparse: 180 | return adj 181 | else: 182 | return adj.todense() 183 | # if sparse: 184 | # adj = csr_matrix((self.num_nodes(), self.num_nodes()), dtype=np.float32) 185 | # else: 186 | # adj = np.zeros((self.num_nodes(), self.num_nodes()), dtype=np.float32) 187 | # 188 | # for node_index0 in self.adj_dict: 189 | # weight_dict = self.adj_dict[node_index0] 190 | # for node_index1 in weight_dict: 191 | # adj[node_index0, node_index1] = weight_dict[node_index1] 192 | # return adj 193 | 194 | def split_train_and_test(self, node_type, training_rate, func_should_mask=None): 195 | masked_node_indices = [] 196 | num_nodes = self.num_nodes(node_type) 197 | if func_should_mask is not None: 198 | for node_index in range(num_nodes): 199 | if func_should_mask(node_index): 200 | masked_node_indices.append(node_index) 201 | random_node_indices = np.random.permutation(masked_node_indices) 202 | else: 203 | random_node_indices = np.random.permutation(num_nodes) 204 | 205 | training_size = int(len(random_node_indices) * training_rate) 206 | train_node_indices = random_node_indices[:training_size] 207 | test_node_indices = random_node_indices[training_size:] 208 | 209 | train_masks = np.zeros([num_nodes], dtype=np.int32) 210 | train_masks[train_node_indices] = 1 211 | test_masks = np.zeros([num_nodes], dtype=np.int32) 212 | test_masks[test_node_indices] = 1 213 | return train_node_indices, test_node_indices, train_masks, test_masks 214 | 215 | def random_node_index(self, node_type, excluded_node_indices=None): 216 | while True: 217 | random_node_index = random.randint(0, self.num_nodes(node_type) - 1) 218 | if excluded_node_indices is not None and random_node_index in excluded_node_indices: 219 | continue 220 | return random_node_index 221 | 222 | def random_neighbor_node_index(self, node_type0, node_type1, node_index): 223 | neighbors = self.get_or_create_neighbors(node_type0, node_type1, node_index) 224 | if len(neighbors) == 0: 225 | return None 226 | i = random.randint(0, len(neighbors) - 1) 227 | return neighbors[i] 228 | 229 | def random_walk(self, node_types, start_node_index=None, padding=True): 230 | if start_node_index is None: 231 | start_node_index = self.random_node_index(node_types[0]) 232 | path = [start_node_index] 233 | for i, node_type in enumerate(node_types[:-1]): 234 | node_type0 = node_types[i] 235 | node_type1 = node_types[i+1] 236 | node_index0 = path[-1] 237 | node_index1 = self.random_neighbor_node_index(node_type0, node_type1, node_index0) 238 | if node_index1 is None: 239 | break 240 | path.append(node_index1) 241 | 242 | while len(path) < len(node_types): 243 | node_type = node_types[len(path)] 244 | random_node_index = self.random_node_index(node_type, excluded_node_indices=path) 245 | path.append(random_node_index) 246 | return path 247 | 248 | def multi_random_walk(self, node_types, start_node_indices=None, num_paths=None, num_threads=None): 249 | if (start_node_indices is None) == (num_paths is None): 250 | print("please specify either 'start_node_indices' or 'num_paths'") 251 | if start_node_indices is None: 252 | start_node_indices = [None] * num_paths 253 | if num_threads is None: 254 | num_paths = num_paths 255 | 256 | def random_walk_func(start_node_index): 257 | return self.random_walk(node_types, start_node_index) 258 | 259 | pool = ThreadPool(4) 260 | paths = pool.map(random_walk_func, start_node_indices) 261 | 262 | return paths 263 | 264 | def build_sample_list(self): 265 | for node_type0 in self.meta_adj_dict: 266 | sub_meta_sample_list_dict = {} 267 | self.meta_sample_list_dict[node_type0] = sub_meta_sample_list_dict 268 | for node_type1 in self.meta_adj_dict[node_type0]: 269 | sub_meta_sample_list_dict[node_type1] = list(self.meta_adj_dict[node_type0][node_type1].keys()) 270 | 271 | def sample_node(self, node_type): 272 | num_nodes = self.num_nodes(node_type) 273 | return random.randint(0, num_nodes - 1) 274 | 275 | def meta_sample_node(self, node_type0, node_type1): 276 | sample_list = self.meta_sample_list_dict[node_type0][node_type1] 277 | return sample_list[random.randint(0, len(sample_list) - 1)] 278 | 279 | def sample_triple(self, node_type0, node_type1, node_a=None): 280 | if node_a is None: 281 | node_a = self.meta_sample_node(node_type0, node_type1) 282 | neighbors = self.get_neighbors(node_type0, node_type1, node_a) 283 | node_b = neighbors[random.randint(0, len(neighbors)) - 1] 284 | while True: 285 | node_neg = self.sample_node(node_type1) 286 | if node_neg in neighbors or node_neg == node_a: 287 | continue 288 | else: 289 | break 290 | return node_a, node_b, node_neg 291 | 292 | def sample_triples(self, node_type0, node_type1, num): 293 | samples = [] 294 | for i in range(num): 295 | samples.append(self.sample_triple(node_type0, node_type1)) 296 | return [list(t) for t in list(zip(*samples))] 297 | 298 | -------------------------------------------------------------------------------- /gnn/evaluation/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CrawlScript/TF-GNN/dd72d190759d00e935221c32aa139950f460dfdb/gnn/evaluation/__init__.py -------------------------------------------------------------------------------- /gnn/model/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CrawlScript/TF-GNN/dd72d190759d00e935221c32aa139950f460dfdb/gnn/model/__init__.py -------------------------------------------------------------------------------- /gnn/model/gcn.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | 3 | import numpy as np 4 | import tensorflow as tf 5 | from tensorflow.python import keras 6 | import scipy.sparse as sp 7 | from scipy.sparse.base import spmatrix 8 | 9 | from gnn.util.evaluation import evaluate 10 | 11 | 12 | class GCNLayer(keras.Model): 13 | 14 | def __init__(self, num_units, activation=tf.nn.relu, *args, **kwargs): 15 | super().__init__(*args, **kwargs) 16 | self.num_units = num_units 17 | self.activation = activation 18 | self.W = None 19 | self.b = None 20 | 21 | def build(self, input_shape): 22 | input_dim = int(input_shape[1][1]) 23 | self.W = self.add_weight("W", shape=[input_dim, self.num_units],initializer=tf.glorot_uniform_initializer) 24 | self.b = self.add_weight("b", shape=[self.num_units], initializer=tf.zeros_initializer) 25 | super().build(input_shape) 26 | 27 | def call(self, inputs, training=None, mask=None): 28 | A, H = inputs 29 | A_is_sparse = isinstance(A, tf.SparseTensor) 30 | H_is_sparse = isinstance(H, tf.SparseTensor) 31 | 32 | if H_is_sparse: 33 | HW = tf.sparse_tensor_dense_matmul(H, self.W) + self.b 34 | else: 35 | HW = tf.matmul(H, self.W) + self.b 36 | 37 | if A_is_sparse: 38 | AHW = tf.sparse_tensor_dense_matmul(A, HW) 39 | else: 40 | AHW = tf.matmul(A, HW) 41 | 42 | if self.activation is not None: 43 | AHW = self.activation(AHW) 44 | return AHW 45 | 46 | def l2_loss(self): 47 | return tf.nn.l2_loss(self.W) 48 | 49 | 50 | class GCN(keras.Model): 51 | def __init__(self, num_units_list, drop_rate, *args, **kwargs): 52 | super().__init__(*args, **kwargs) 53 | self.num_units_list = num_units_list 54 | self.drop_rate = drop_rate 55 | self.gcn_funcs = [] 56 | 57 | for i, num_units in enumerate(num_units_list): 58 | activation = tf.nn.relu if i < len(num_units_list) - 1 else None 59 | gcn_func = GCNLayer(num_units, activation) 60 | setattr(self, "gcn_func{}".format(i), gcn_func) 61 | self.gcn_funcs.append(gcn_func) 62 | 63 | self.dropout_layer = keras.layers.Dropout(drop_rate) 64 | 65 | def l2_loss(self): 66 | return tf.add_n([gcn_func.l2_loss() for gcn_func in self.gcn_funcs]) 67 | 68 | def call(self, inputs, training=None, mask=None): 69 | A, H = inputs 70 | for i, gcn_func in enumerate(self.gcn_funcs): 71 | H = gcn_func([A, H], training=training) 72 | if i < len(self.gcn_funcs) - 1: 73 | H = self.dropout_layer(H, training=training) 74 | return H 75 | 76 | @classmethod 77 | def gcn_kernal(cls, adj): 78 | inv_D = np.array(adj.sum(axis=1)).flatten() 79 | inv_D = np.power(inv_D, -0.5) 80 | inv_D[np.isinf(inv_D)] = 0.0 81 | inv_D = sp.diags(inv_D) 82 | return inv_D.dot(adj).dot(inv_D) + sp.eye(inv_D.shape[0]) 83 | 84 | @classmethod 85 | def gcn_kernal_tensor(cls, adj, sparse=True): 86 | adj = GCN.gcn_kernal(adj) 87 | if sparse: 88 | A = adj.tocoo().astype(np.float32) 89 | A = tf.SparseTensor(indices=np.stack((A.row, A.col), axis=1), values=A.data, dense_shape=A.shape) 90 | else: 91 | A = tf.Variable(adj.todense().astype(np.float32), trainable=False) 92 | return A 93 | 94 | 95 | class GCNTrainer(object): 96 | def __init__(self, gcn_model): 97 | self.model = gcn_model 98 | 99 | def train(self, 100 | adj, 101 | feature_matrix, 102 | labels, 103 | train_masks, 104 | test_masks, 105 | steps=1000, 106 | learning_rate=1e-3, 107 | l2_coe=1e-3, 108 | drop_rate=1e-3, 109 | show_interval=20, 110 | eval_interval=20): 111 | 112 | if test_masks is None: 113 | test_masks = 1 - np.array(train_masks) 114 | 115 | A = GCN.gcn_kernal_tensor(adj, sparse=True) 116 | num_classes = self.model.num_units_list[-1] 117 | one_hot_labels = tf.one_hot(labels, num_classes) 118 | optimizer = tf.train.AdamOptimizer(learning_rate) 119 | 120 | if feature_matrix is None: 121 | feature_matrix = sp.diags(range(adj.shape[0])) 122 | 123 | if isinstance(feature_matrix, spmatrix): 124 | coo_feature_matrix = feature_matrix.tocoo().astype(np.float32) 125 | x = tf.SparseTensor(indices=np.stack((coo_feature_matrix.row, coo_feature_matrix.col), axis=1), 126 | values=coo_feature_matrix.data, dense_shape=coo_feature_matrix.shape) 127 | else: 128 | x = tf.Variable(feature_matrix, trainable=False) 129 | 130 | num_masked = tf.cast(tf.reduce_sum(train_masks), tf.float32) 131 | for step in range(steps): 132 | with tf.GradientTape() as tape: 133 | logits = self.model([A, x], training=True) 134 | losses = tf.nn.softmax_cross_entropy_with_logits( 135 | logits=logits, 136 | labels=one_hot_labels 137 | ) 138 | losses *= train_masks 139 | mean_loss = tf.reduce_sum(losses) / num_masked 140 | loss = mean_loss + self.model.l2_loss() * l2_coe 141 | 142 | watched_vars = tape.watched_variables() 143 | grads = tape.gradient(loss, watched_vars) 144 | optimizer.apply_gradients(zip(grads, watched_vars)) 145 | 146 | if step % show_interval == 0: 147 | print("step = {}\tloss = {}".format(step, loss)) 148 | 149 | if step % eval_interval == 0: 150 | preds = self.model([A, x]) 151 | preds = tf.argmax(preds, axis=-1).numpy() 152 | accuracy, macro_f1, micro_f1 = evaluate(preds, labels, test_masks) 153 | print("step = {}\taccuracy = {}\tmacro_f1 = {}\tmicro_f1 = {}".format(step, accuracy, macro_f1, micro_f1)) 154 | -------------------------------------------------------------------------------- /gnn/util/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CrawlScript/TF-GNN/dd72d190759d00e935221c32aa139950f460dfdb/gnn/util/__init__.py -------------------------------------------------------------------------------- /gnn/util/evaluation.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | import numpy as np 3 | from sklearn.metrics import accuracy_score, f1_score 4 | 5 | 6 | def evaluate(preds, labels, masks): 7 | masked_node_indices = np.nonzero(masks) 8 | masked_preds = preds[masked_node_indices] 9 | masked_labels = labels[masked_node_indices] 10 | 11 | accuracy = accuracy_score(masked_labels, masked_preds) 12 | macro_f1 = f1_score(masked_labels, masked_preds, pos_label=None, average='macro') 13 | micro_f1 = f1_score(masked_labels, masked_preds, pos_label=None, average='micro') 14 | 15 | return accuracy, macro_f1, micro_f1 16 | 17 | # corrects = (masked_preds == masked_labels).astype(np.float32) 18 | # accuracy = corrects.mean() 19 | # return accuracy 20 | 21 | 22 | -------------------------------------------------------------------------------- /gnn/util/sparse.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | import numpy as np 3 | import tensorflow as tf 4 | import scipy.sparse as sp 5 | 6 | 7 | def dense_to_sparse_tensor(dense_matrix, dtype=np.float32): 8 | coo_matrix = sp.coo_matrix(dense_matrix, dtype=dtype) 9 | return coo_to_sparse_tensor(coo_matrix) 10 | 11 | 12 | def csr_to_sparse_tensor(csr_matrix, dtype=np.float32): 13 | coo_matrix = csr_matrix.tocoo().astype(dtype) 14 | return coo_to_sparse_tensor(coo_matrix) 15 | 16 | 17 | def coo_to_sparse_tensor(coo_matrix): 18 | t = tf.SparseTensor(indices=np.stack((coo_matrix.row, coo_matrix.col), axis=1), values=coo_matrix.data, dense_shape=coo_matrix.shape) 19 | return t --------------------------------------------------------------------------------