parent
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20 changed files with 455 additions and 355 deletions
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# Default ignored files |
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/shelf/ |
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/workspace.xml |
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# Editor-based HTTP Client requests |
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/httpRequests/ |
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# Datasource local storage ignored files |
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/dataSources/ |
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/dataSources.local.xml |
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<?xml version="1.0" encoding="UTF-8"?> |
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<module type="PYTHON_MODULE" version="4"> |
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<component name="NewModuleRootManager"> |
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<content url="file://$MODULE_DIR$" /> |
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<orderEntry type="jdk" jdkName="Python 3.7 (BGANDTI)" jdkType="Python SDK" /> |
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<orderEntry type="sourceFolder" forTests="false" /> |
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</component> |
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<component name="PyDocumentationSettings"> |
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<option name="format" value="PLAIN" /> |
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<option name="myDocStringFormat" value="Plain" /> |
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</component> |
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</module> |
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<component name="ProjectCodeStyleConfiguration"> |
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<state> |
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<option name="PREFERRED_PROJECT_CODE_STYLE" value="Default" /> |
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</state> |
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</component> |
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<component name="InspectionProjectProfileManager"> |
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<settings> |
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<option name="USE_PROJECT_PROFILE" value="false" /> |
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<version value="1.0" /> |
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</settings> |
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</component> |
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<?xml version="1.0" encoding="UTF-8"?> |
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<project version="4"> |
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (BGANDTI)" project-jdk-type="Python SDK" /> |
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</project> |
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<?xml version="1.0" encoding="UTF-8"?> |
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<project version="4"> |
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<component name="ProjectModuleManager"> |
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<modules> |
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<module fileurl="file://$PROJECT_DIR$/.idea/BGANDTI-main.iml" filepath="$PROJECT_DIR$/.idea/BGANDTI-main.iml" /> |
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</modules> |
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</component> |
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</project> |
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<?xml version="1.0" encoding="UTF-8"?> |
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<project version="4"> |
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<component name="PySciProjectComponent"> |
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<option name="PY_SCI_VIEW_SUGGESTED" value="true" /> |
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</component> |
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</project> |
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<?xml version="1.0" encoding="UTF-8"?> |
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<project version="4"> |
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<component name="VcsDirectoryMappings"> |
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<mapping directory="$PROJECT_DIR$" vcs="Git" /> |
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</component> |
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</project> |
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import numpy as np |
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from sklearn import metrics |
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from sklearn.metrics import average_precision_score |
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from sklearn.metrics import roc_auc_score, auc |
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|
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|
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class Evaluator(object): |
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def __init__(self, edges_pos, edges_neg): |
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self.edges_pos = edges_pos |
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self.edges_neg = edges_neg |
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|
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def get_roc_score(self, emb, feas): |
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# if emb is None: |
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# feed_dict.update({placeholders['dropout']: 0}) |
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# emb = sess.run(model.z_mean, feed_dict=feed_dict) |
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|
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def sigmoid(x): |
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return 1 / (1 + np.exp(-x)) |
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# Predict on test set of edges |
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adj_rec = np.dot(emb, emb.T) |
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preds = [] |
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pos = [] |
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for e in self.edges_pos: |
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preds.append(sigmoid(adj_rec[e[0], e[1]])) |
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pos.append(feas['adj_orig'][e[0], e[1]]) |
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|
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preds_neg = [] |
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neg = [] |
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for e in self.edges_neg: |
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preds_neg.append(sigmoid(adj_rec[e[0], e[1]])) |
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neg.append(feas['adj_orig'][e[0], e[1]]) |
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preds_all = np.hstack([preds, preds_neg]) |
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labels_all = np.hstack([np.ones(len(preds)), np.zeros(len(preds))]) |
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roc_score = roc_auc_score(labels_all, preds_all) |
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ap_score = average_precision_score(labels_all, preds_all) |
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precision, recall, _thresholds = metrics.precision_recall_curve(labels_all, preds_all) |
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aupr_score = auc(recall, precision) |
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return roc_score, ap_score, emb, aupr_score |
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import inspect |
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import pickle |
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import numpy as np |
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import scipy.sparse as sp |
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|
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def sparse_to_tuple(sparse_mx): |
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if not sp.isspmatrix_coo(sparse_mx): |
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sparse_mx = sparse_mx.tocoo() |
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coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose() |
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values = sparse_mx.data |
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shape = sparse_mx.shape |
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return coords, values, shape |
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|
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def preprocess_graph(adj): |
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adj = sp.coo_matrix(adj) |
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adj_ = adj + sp.eye(adj.shape[0]) # A* = A+I,即对邻接矩阵加入自连接 |
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rowsum = np.array(adj_.sum(1)) # 对行求和,即得到节点的度 |
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degree_mat_inv_sqrt = sp.diags(np.power(rowsum, -0.5).flatten()) # 得到D的-1/2次方矩阵d |
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adj_normalized = adj_.dot(degree_mat_inv_sqrt).transpose().dot(degree_mat_inv_sqrt).tocoo() # 这一步的实质是做归一化,即A* × d转置 × d |
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return sparse_to_tuple(adj_normalized) |
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|
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def load_data(dataset): |
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adj = np.loadtxt('../data/partitioned_data/{0}/orig/{0}_adj_orig.txt'.format(dataset), dtype=int) |
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adj = sp.csr_matrix(adj) |
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features = pickle.load(open("../data/partitioned_data/{0}/feature/{0}_feature.pkl".format(dataset), 'rb')) |
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y_test = 0 |
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tx = 0 |
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ty = 0 |
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test_mask = 0 |
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labels = 0 |
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return adj, features, y_test, tx, ty, test_mask, labels |
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|
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def mask_test_edges(adj): |
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# Function to build test set with 10% positive links |
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# NOTE: Splits are randomized and results might slightly deviate from reported numbers in the paper. |
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# TODO: Clean up. |
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# sp.matrix(data,offsets)是将data的元素每列的元素,按offset里的顺序在列上进行重新排列,offset里的值是偏移量 |
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# 具体可以参考https://blog.csdn.net/ChenglinBen/article/details/84424379 |
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# .diagonal()就是提取对角线元素 |
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# Remove diagonal elements删除对角线元素 |
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adj = adj - sp.dia_matrix((adj.diagonal()[np.newaxis, :], [0]), shape=adj.shape) |
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# 把零元素都消除掉 |
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adj.eliminate_zeros() |
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# Check that diag is zero: |
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# np.diag(matrix)即提取matrix的对角线元素,todense() like toarray(),区别是一个是将存储方式由稀疏矩阵转成正常矩阵,另一个是转成array |
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# assert检查是否对角线元素是否都被清空了 |
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assert np.diag(adj.todense()).sum() == 0 |
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# sp.triu(matrix)获取matrix的上三角矩阵,相应的,tril()是获取下三角矩阵 |
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adj_triu = sp.triu(adj) |
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adj_tuple = sparse_to_tuple(adj_triu) |
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# edges相当于组合,因为是上三角矩阵的edge,所以减少了一半的重复量,(4.6)与(6,4)不会同时存在,而只会保留(4,6) |
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# edges_all相当于排列,就都包含了 |
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edges = adj_tuple[0] |
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edges_all = sparse_to_tuple(adj)[0] |
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# 取edge的10%作为test |
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# 取edge的20%作为val |
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num_test = int(np.floor(edges.shape[0] / 10.)) |
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num_val = int(np.floor(edges.shape[0] / 20.)) |
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# 随机选取一部分作为test与val |
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all_edge_idx = list(range(edges.shape[0])) |
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np.random.shuffle(all_edge_idx) |
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val_edge_idx = all_edge_idx[:num_val] |
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test_edge_idx = all_edge_idx[num_val:(num_val + num_test)] |
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test_edges = edges[test_edge_idx] |
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val_edges = edges[val_edge_idx] |
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train_edges = np.delete(edges, np.hstack([test_edge_idx, val_edge_idx]), axis=0) |
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# 该函数请参考github中gae的写法,应该是更新了,这种方法应该是错的,或者说与python3不兼容 |
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# 其中,return部分或许应该改成np.any(rows_close) |
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def ismember(a, b, tol=5): |
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# 该函数的作用就是判断a元素是否存在于b集合中 |
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rows_close = np.all(np.round(a - b[:, None], tol) == 0, axis=-1) |
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return np.any(rows_close) |
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# return (np.all(np.any(rows_close, axis=-1), axis=-1) and |
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# np.all(np.any(rows_close, axis=0), axis=0)) |
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# test_edges_false是去生成一些本来就不存在的edges |
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test_edges_false = [] |
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while len(test_edges_false) < len(test_edges): |
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idx_i = np.random.randint(0, adj.shape[0]) |
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idx_j = np.random.randint(0, adj.shape[0]) |
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if idx_i == idx_j: |
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continue |
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if ismember([idx_i, idx_j], edges_all): |
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continue |
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if test_edges_false: |
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if ismember([idx_j, idx_i], np.array(test_edges_false)): |
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continue |
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if ismember([idx_i, idx_j], np.array(test_edges_false)): |
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continue |
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test_edges_false.append([idx_i, idx_j]) |
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|
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# val_edges_false生成一些不存在于train与val的edges |
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val_edges_false = [] |
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while len(val_edges_false) < len(val_edges): |
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idx_i = np.random.randint(0, adj.shape[0]) |
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idx_j = np.random.randint(0, adj.shape[0]) |
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if idx_i == idx_j: |
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continue |
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if ismember([idx_i, idx_j], train_edges): |
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continue |
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if ismember([idx_j, idx_i], train_edges): |
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continue |
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if ismember([idx_i, idx_j], val_edges): |
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continue |
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if ismember([idx_j, idx_i], val_edges): |
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continue |
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if val_edges_false: |
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if ismember([idx_j, idx_i], np.array(val_edges_false)): |
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continue |
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if ismember([idx_i, idx_j], np.array(val_edges_false)): |
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continue |
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val_edges_false.append([idx_i, idx_j]) |
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assert ~ismember(test_edges_false, edges_all) |
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# assert ~ismember(val_edges_false, edges_all) |
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assert ~ismember(val_edges, train_edges) |
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assert ~ismember(test_edges, train_edges) |
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assert ~ismember(val_edges, test_edges) |
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data = np.ones(train_edges.shape[0]) |
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# Re-build adj matrix |
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# 如英文注释所说,这里将处理好的train_edges再重建出adj_train |
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adj_train = sp.csr_matrix((data, (train_edges[:, 0], train_edges[:, 1])), shape=adj.shape) |
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adj_train = adj_train + adj_train.T |
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# NOTE: these edge lists only contain single direction of edge! |
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return adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false |
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def retrieve_name(var): |
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callers_local_vars = inspect.currentframe().f_back.f_locals.items() |
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print([var_name for var_name, var_val in callers_local_vars if var_val is var]) |
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return [var_name for var_name, var_val in callers_local_vars if var_val is var][0] |
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def get_data(dataset): |
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# Load data |
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# adj, features, y_test, tx, ty, test_maks, true_labels = load_data(data_name) |
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adj, features, y_test, tx, ty, test_maks, true_labels = load_data(dataset) # e ic gpcr nr luo |
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# Store original adjacency matrix (without diagonal entries) for later |
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adj_orig = adj |
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# 删除对角线元素 |
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adj_orig = adj_orig - sp.dia_matrix((adj_orig.diagonal()[np.newaxis, :], [0]), shape=adj_orig.shape) |
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adj_orig.eliminate_zeros() |
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adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false = mask_test_edges(adj) |
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adj = adj_train |
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adj_dense = adj.toarray() |
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# Some preprocessing |
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adj_norm = preprocess_graph(adj) |
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num_nodes = adj.shape[0] |
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features_dense = features.tocoo().toarray() |
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features = sparse_to_tuple(features.tocoo()) |
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# num_features是feature的维度 |
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num_features = features[2][1] |
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# features_nonzero就是非零feature的个数 |
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features_nonzero = features[1].shape[0] |
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pos_weight = float(adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum() |
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norm = adj.shape[0] * adj.shape[0] / float((adj.shape[0] * adj.shape[0] - adj.sum()) * 2) |
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adj_label = adj_train + sp.eye(adj_train.shape[0]) |
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adj_label = sparse_to_tuple(adj_label) |
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items = [ |
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adj, num_features, num_nodes, features_nonzero, |
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pos_weight, norm, adj_norm, adj_label, |
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features, true_labels, train_edges, val_edges, |
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val_edges_false, test_edges, test_edges_false, adj_orig, features_dense, adj_dense, features_dense |
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] |
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feas = {} |
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print('num_features is:', num_features) |
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print('num_nodes is:', num_nodes) |
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print('features_nonzero is:', features_nonzero) |
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print('pos_weight is:', pos_weight) |
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print('norm is:', norm) |
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for item in items: |
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# item_name = [ k for k,v in locals().iteritems() if v == item][0] |
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feas[retrieve_name(item)] = item |
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feas['num_features'] = num_features |
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feas['num_nodes'] = num_nodes |
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return feas |
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import numpy as np |
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import tensorflow as tf |
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class OptimizerCycle(object): |
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def __init__(self, preds, labels, pos_weight, norm, d_real, d_fake, GD_real, GD_fake, preds_z2g, labels_z2g, preds_cycle, labels_cycle, gradient, gradient_z, settings): |
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preds_sub = preds |
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labels_sub = labels |
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self.real = d_real |
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self.settings = settings |
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# Discrimminator Loss |
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self.dc_loss_real = tf.reduce_mean( |
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tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(self.real), logits=self.real, name='dclreal')) |
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# self.dc_loss_real = - tf.reduce_mean(self.real) |
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self.dc_loss_fake = tf.reduce_mean( |
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tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_fake), logits=d_fake, name='dcfake')) |
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# self.dc_loss_fake = tf.reduce_mean(d_fake) |
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# GP_loss = tf.reduce_mean(tf.square(tf.sqrt(tf.reduce_mean(tf.square(gradient), axis = [0, 1])) - 1)) |
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# GP_loss_z = tf.reduce_mean(tf.square(tf.sqrt(tf.reduce_mean(tf.square(gradient_z), axis = [0, 1])) - 1)) |
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# self.dc_loss = self.dc_loss_fake + self.dc_loss_real + 10.0 * GP_loss |
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self.GD_loss_real = tf.reduce_mean( |
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tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(GD_real), logits=GD_real, name='GD_real')) |
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# self.GD_loss_real = - tf.reduce_mean(GD_real) |
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self.GD_loss_fake = tf.reduce_mean( |
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tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(GD_fake), logits=GD_fake, name='GD_fake')) |
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# self.GD_loss_fake = tf.reduce_mean(GD_fake) |
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self.dc_loss = self.dc_loss_fake + self.dc_loss_real |
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self.GD_loss = self.GD_loss_fake + self.GD_loss_real |
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# Generator loss |
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generator_loss = tf.reduce_mean( |
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tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_fake), logits=d_fake, name='gl')) |
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# generator_loss = -self.dc_loss_fake |
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generator_loss_z2g = tf.reduce_mean( |
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tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(GD_fake), logits=GD_fake, name='G_z2g')) |
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# generator_loss_z2g = -self.GD_loss_fake |
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# pos_weight,允许人们通过向上或向下加权相对于负误差的正误差的成本来权衡召回率和精确度 |
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self.cost = norm * tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(logits=preds_sub, targets=labels_sub, pos_weight=pos_weight)) |
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cost_cycle = norm * tf.reduce_mean(tf.square(preds_cycle - labels_cycle)) |
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cost_z2g = norm * tf.reduce_mean(tf.square(preds_z2g - labels_z2g)) |
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self.cost = self.cost + cost_cycle |
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self.generator_loss = generator_loss + self.cost |
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self.generator_loss_z2g = generator_loss_z2g |
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all_variables = tf.trainable_variables() |
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dc_var = [var for var in all_variables if 'dc_' in var.name] |
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en_var = [var for var in all_variables if 'e_' in var.name] |
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GG_var = [var for var in all_variables if 'GG' in var.name] |
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GD_var = [var for var in all_variables if 'GD' in var.name] |
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with tf.variable_scope(tf.get_variable_scope()): |
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self.discriminator_optimizer = tf.train.AdamOptimizer(learning_rate=self.settings.discriminator_learning_rate, |
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beta1=0.9, name='adam1').minimize(self.dc_loss, |
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var_list=dc_var) # minimize(dc_loss_real, var_list=dc_var) |
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self.generator_optimizer = tf.train.AdamOptimizer(learning_rate=self.settings.discriminator_learning_rate, |
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beta1=0.9, name='adam2').minimize(self.generator_loss, var_list=en_var) |
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self.discriminator_optimizer_z2g = tf.train.AdamOptimizer(learning_rate=self.settings.discriminator_learning_rate, |
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beta1=0.9, name='adam1').minimize(self.GD_loss, var_list=GD_var) |
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self.generator_optimizer_z2g = tf.train.AdamOptimizer(learning_rate=self.settings.discriminator_learning_rate, |
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beta1=0.9, name='adam2').minimize(self.generator_loss_z2g, var_list=GG_var) |
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# 值得注意的是,这个地方,除了对抗优化之外, |
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# 还单纯用cost损失又优化了一遍, |
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# 待会儿看训练的时候注意看是在哪部分进行的这部分优化操作 |
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self.optimizer = tf.train.AdamOptimizer(learning_rate=self.settings.learning_rate) # Adam Optimizer |
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self.opt_op = self.optimizer.minimize(self.cost) |
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# self.grads_vars = self.optimizer.compute_gradients(self.cost) |
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# self.optimizer_z2g = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) # Adam Optimizer |
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# self.opt_op_z2g = self.optimizer.minimize(cost_z2g) |
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# self.grads_vars_z2g = self.optimizer.compute_gradients(cost_z2g) |
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|
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|
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class Optimizer(object): |
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def __init__(self, model, model_z2g, D_Graph, discriminator, placeholders, pos_weight, norm, d_real, num_nodes, GD_real, settings): |
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self.opt = self.construct_optimizer(model, model_z2g, D_Graph, discriminator, placeholders, pos_weight, norm, d_real, num_nodes, GD_real, settings) |
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|
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def construct_optimizer(self, model, model_z2g, D_Graph, discriminator, placeholders, pos_weight, norm, d_real, num_nodes, GD_real, settings): |
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z2g = model_z2g.construct() |
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hidden = z2g[1] |
||||
z2g = z2g[0] |
||||
preds_z2g = model.construct(hidden=hidden, reuse=True)[0] |
||||
g2z = model.construct() |
||||
|
||||
embeddings = g2z[0] |
||||
reconstructions = g2z[1] |
||||
d_fake = discriminator.construct(embeddings, reuse=True) |
||||
GD_fake = D_Graph.construct(z2g, reuse=True) |
||||
|
||||
epsilon = tf.random_uniform(shape=[1], minval=0.0, maxval=1.0) |
||||
interpolated_input = epsilon * placeholders['real_distribution'] + (1 - epsilon) * embeddings |
||||
gradient = tf.gradients(discriminator.construct(interpolated_input, reuse=True), [interpolated_input])[0] |
||||
|
||||
epsilon = tf.random_uniform(shape=[1], minval=0.0, maxval=1.0) |
||||
interpolated_input = epsilon * placeholders['features_dense'] + (1 - epsilon) * z2g |
||||
gradient_z = tf.gradients(D_Graph.construct(interpolated_input, reuse=True), [interpolated_input])[0] |
||||
|
||||
opt = OptimizerCycle(preds=reconstructions, |
||||
labels=tf.reshape(tf.sparse_tensor_to_dense(placeholders['adj_orig'], validate_indices=False), [-1]), |
||||
pos_weight=pos_weight, |
||||
norm=norm, |
||||
d_real=d_real, |
||||
d_fake=d_fake, |
||||
GD_real=GD_real, |
||||
GD_fake=GD_fake, |
||||
preds_z2g=preds_z2g, |
||||
labels_z2g=placeholders['real_distribution'], |
||||
preds_cycle=model_z2g.construct(embeddings, reuse=True)[0], |
||||
labels_cycle=placeholders['features_dense'], |
||||
gradient=gradient, |
||||
gradient_z=gradient_z, |
||||
settings=settings) |
||||
return opt |
||||
|
||||
def construct_feed_dict(adj_normalized, adj, features, placeholders): |
||||
# construct feed dictionary |
||||
# .update()用法就是将()内的字段增加到dict当中 |
||||
feed_dict = dict() # 创建一个空字典 |
||||
feed_dict.update({placeholders['features']: features}) |
||||
feed_dict.update({placeholders['adj']: adj_normalized}) |
||||
feed_dict.update({placeholders['adj_orig']: adj}) |
||||
return feed_dict |
||||
|
||||
def update(model, opt, sess, adj_norm, adj_label, features, placeholders, adj, distribution, adj_dense, settings): |
||||
# Construct feed dictionary |
||||
feed_dict = construct_feed_dict(adj_norm, adj_label, features, placeholders) |
||||
feed_dict.update({placeholders['dropout']: settings.dropout}) |
||||
feed_dict.update({placeholders['features_dense']: adj_dense}) |
||||
feed_dict.update({placeholders['dropout']: 0}) |
||||
z_real_dist = np.random.randn(adj.shape[0], settings.hidden2) |
||||
z_real_dist = distribution.sample(adj.shape[0]) |
||||
feed_dict.update({placeholders['real_distribution']: z_real_dist}) |
||||
|
||||
for j in range(5): |
||||
_, reconstruct_loss = sess.run([opt.opt_op, opt.cost], feed_dict=feed_dict) |
||||
g_loss, _ = sess.run([opt.generator_loss, opt.generator_optimizer], feed_dict=feed_dict) |
||||
d_loss, _ = sess.run([opt.dc_loss, opt.discriminator_optimizer], feed_dict=feed_dict) |
||||
|
||||
GD_loss, _ = sess.run([opt.GD_loss, opt.discriminator_optimizer_z2g], feed_dict=feed_dict) |
||||
GG_loss, _ = sess.run([opt.generator_loss_z2g, opt.generator_optimizer_z2g], feed_dict=feed_dict) |
||||
# GD_loss = sess.run(opt.GD_loss, feed_dict=feed_dict) |
||||
# GG_loss = sess.run(opt.generator_loss_z2g, feed_dict=feed_dict) |
||||
# g_loss = sess.run(opt.generator_loss, feed_dict=feed_dict) |
||||
# d_loss = sess.run(opt.dc_loss, feed_dict=feed_dict) |
||||
emb = sess.run(model.z_mean, feed_dict=feed_dict) |
||||
avg_cost = [reconstruct_loss, d_loss, g_loss, GD_loss, GG_loss] |
||||
|
||||
return emb, avg_cost |
@ -1,94 +0,0 @@ |
||||
import os |
||||
import random |
||||
|
||||
import numpy as np |
||||
import scipy.sparse as sp |
||||
|
||||
from src import config |
||||
|
||||
|
||||
def load_luo_data(dataset): |
||||
dp = np.loadtxt('../../data/RawData/luo/mat_drug_protein.txt'.format(dataset), dtype=int) |
||||
dd = np.loadtxt('../../data/RawData/luo/mat_drug_drug.txt'.format(dataset), dtype=int) |
||||
pp = np.loadtxt('../../data/RawData/luo/mat_protein_protein.txt'.format(dataset), dtype=int) |
||||
adj = np.vstack((np.hstack((dd, dp)), np.hstack((dp.T, pp)))) |
||||
return sp.csr_matrix(adj + sp.eye(adj.shape[0])), dd.shape[0] |
||||
|
||||
|
||||
def load_yam_data(dataset): |
||||
dp = np.loadtxt('../../data/RawData/Yamanishi/{}_admat_dgc.txt'.format(dataset), dtype=str, delimiter='\t')[1:, 1:].astype(np.int).T |
||||
dd = np.loadtxt('../../data/RawData/Yamanishi/{}_simmat_dc.txt'.format(dataset), dtype=str, delimiter='\t')[1:, 1:].astype(np.float) |
||||
pp = np.loadtxt('../../data/RawData/Yamanishi/{}_simmat_dg.txt'.format(dataset), dtype=str, delimiter='\t')[1:, 1:].astype(np.float) |
||||
dd = np.where(dd < 0.5, 0, 1) |
||||
pp = np.where(pp < 0.5, 0, 1) |
||||
adj = np.vstack((np.hstack((dd, dp)), np.hstack((dp.T, pp)))) |
||||
return sp.csr_matrix(adj), dd.shape[0] |
||||
|
||||
|
||||
def is_symmetry(adj): |
||||
for i in range(adj.shape[0]): |
||||
for j in range(adj.shape[1]): |
||||
if adj[i][j] != adj[j][i]: |
||||
return False |
||||
return True |
||||
|
||||
|
||||
def is_1_diag(adj): |
||||
if sum(np.diagonal(adj)) != adj.shape[0]: |
||||
return False |
||||
return True |
||||
|
||||
|
||||
def change_unbalanced(adj, percent, dp_line, dataset): |
||||
""" |
||||
note: percent控制屏蔽掉的节点所占的百分比 |
||||
:param adj: |
||||
:param percent: |
||||
:return: 返回去除部分已知关联的邻接矩阵 |
||||
""" |
||||
# 判断是否对称 |
||||
# assert is_symmetry(adj.A) |
||||
adj = adj - sp.dia_matrix((adj.diagonal()[np.newaxis, :], [0]), shape=adj.shape) + sp.eye(adj.shape[0]) |
||||
# 判断对角线是否全为1 |
||||
assert is_1_diag(adj.A) |
||||
adj = (sp.triu(adj) + sp.triu(adj).T - sp.eye(adj.shape[0])).A |
||||
|
||||
row = list(range(0, dp_line)) |
||||
col = list(range(dp_line, adj.shape[0])) |
||||
|
||||
idx = [] |
||||
for i in row: |
||||
for j in col: |
||||
if i != j and adj[i][j] == 1: |
||||
idx.append((i, j)) |
||||
num = int(np.floor(percent * len(idx))) |
||||
count = 0 |
||||
# random.seed(config.seed) |
||||
while count < num: |
||||
row, col = random.choice(idx) |
||||
idx.remove((row, col)) |
||||
adj[row][col] = 0 |
||||
adj[col][row] = 0 |
||||
count += 1 |
||||
|
||||
# idx = [] |
||||
# for i in range(adj.shape[0]): |
||||
# for j in range(i + 1, adj.shape[0]): |
||||
# if adj[i][j] == 1: |
||||
# idx.append((i, j)) |
||||
# num = int(np.floor(percent * len(idx))) |
||||
# count = 0 |
||||
# # random.seed(config.seed) |
||||
# while count < num: |
||||
# row, col = random.choice(idx) |
||||
# idx.remove((row, col)) |
||||
# adj[row][col] = 0 |
||||
# adj[col][row] = 0 |
||||
# count += 1 |
||||
|
||||
# 保存改变不平衡性后新的dp |
||||
new_dp = adj[0:dp_line, dp_line:] |
||||
# if not os.path.exists('../../data/partitioned_data/{0}/feature'.format(dataset)): |
||||
# os.mkdir('../../data/partitioned_data/{0}/feature'.format(dataset)) |
||||
# np.savetxt('../../data/partitioned_data/{0}/feature/{0}_new_admat_dgc.txt'.format(dataset), new_dp, fmt='%d', delimiter='\t') |
||||
return sp.csr_matrix(adj.astype(np.int)) |
@ -1,88 +0,0 @@ |
||||
import os |
||||
import pickle |
||||
|
||||
import numpy as np |
||||
|
||||
from src import config |
||||
import scipy.sparse as sp |
||||
|
||||
from load_data import load_yam_data, change_unbalanced, load_luo_data |
||||
from utils import divide_vgae_datasets, sparse_to_tuple, divide_datasets |
||||
|
||||
for dataset in config.datasets: |
||||
g = os.walk(r"../../data/partitioned_data/{}".format(dataset)) |
||||
for path, dir_list, file_list in g: |
||||
for file_name in file_list: |
||||
os.remove(os.path.join(path, file_name)) |
||||
print("清除缓存完成!") |
||||
|
||||
# Load data 得到一个邻接矩阵,双向边 |
||||
if dataset == 'luo': |
||||
adj, dp_line = load_luo_data(dataset) |
||||
else: |
||||
adj, dp_line = load_yam_data(dataset) |
||||
|
||||
if not os.path.exists("../../data/partitioned_data"): |
||||
os.mkdir("../../data/partitioned_data") |
||||
if not os.path.exists("../../data/partitioned_data/{}".format(dataset)): |
||||
os.mkdir("../../data/partitioned_data/{}".format(dataset)) |
||||
if not os.path.exists("../../data/partitioned_data/{}/orig".format(dataset)): |
||||
os.mkdir("../../data/partitioned_data/{}/orig/".format(dataset)) |
||||
np.savetxt("../../data/partitioned_data/{}/orig/dp_line.txt".format(dataset), np.array([dataset, str(dp_line)]), fmt='%s') |
||||
|
||||
# 获得不同不平衡性的数据 |
||||
adj = change_unbalanced(adj, config.percent, dp_line, dataset) |
||||
|
||||
# Store original adjacency matrix (without diagonal entries) for later 保存原始邻接矩阵(不含对角线项)以备后用 |
||||
adj_orig = adj |
||||
adj_orig = adj_orig - sp.dia_matrix((adj_orig.diagonal()[np.newaxis, :], [0]), shape=adj_orig.shape) # 假设对角线有元素,去除对角线 |
||||
adj_orig.eliminate_zeros() # 假设有0,移除矩阵中的0 |
||||
path = "../../data/partitioned_data/{}/orig/".format(dataset) |
||||
if not os.path.exists(path): |
||||
os.makedirs(path) |
||||
pickle.dump(adj_orig, open(path + dataset + "_adj_orig.pkl", 'wb')) |
||||
np.savetxt(path + dataset + "_adj_orig.txt", adj_orig.A, fmt='%d') |
||||
|
||||
# 为获取嵌入划分数据, 划分数据集, 并记录边 |
||||
for i in range(10): |
||||
# Remove diagonal elements # 删除对角线元素 |
||||
adj = adj - sp.dia_matrix((adj.diagonal()[np.newaxis, :], [0]), shape=adj.shape) # 梅开二度 |
||||
adj.eliminate_zeros() |
||||
# Check that diag is zero: # 检查diag是否为零: |
||||
assert np.diag(adj.todense()).sum() == 0 |
||||
|
||||
# 为graphgan划分数据 |
||||
g_adj = adj[0:dp_line, dp_line:] |
||||
g_edges = sparse_to_tuple(g_adj)[0] |
||||
g_num_test = int(np.floor(g_edges.shape[0] / 10.)) # np.floor()是向下取整。测试集10分之一,训练集20分之一 |
||||
g_num_val = int(np.floor(g_edges.shape[0] / 20.)) |
||||
|
||||
adj_pd, train_edges, test_edges, test_edges_false = divide_datasets(g_adj, g_edges, g_num_test, i, dp_line) |
||||
adj[0:dp_line, dp_line:] = adj_pd |
||||
|
||||
# 将训练集分给vgae |
||||
edges = sparse_to_tuple(sp.triu(adj))[0] |
||||
edges_all = sparse_to_tuple(adj)[0] # 将邻接矩阵转换成三元组,然后只取坐标,即所有的边 |
||||
num_test = int(np.floor(edges.shape[0] / 10.)) # np.floor()是向下取整。测试集10分之一,训练集20分之一 |
||||
num_val = int(np.floor(edges.shape[0] / 20.)) |
||||
|
||||
adj_train, vgae_train_edges, vgae_test_edges, vgae_test_edges_false = divide_vgae_datasets(adj, edges, edges_all, num_test, num_val, |
||||
i) # val_edges, val_edges_false, |
||||
|
||||
# 保存划分好的数据 |
||||
path = "../../data/partitioned_data/{}/{}fold/".format(dataset, i) |
||||
if not os.path.exists(path): |
||||
os.makedirs(path) |
||||
|
||||
pickle.dump(adj_train, open(path + dataset + "_adj_train.pkl", 'wb')) |
||||
|
||||
np.savetxt(path + dataset + "_vgae_train.txt", vgae_train_edges, fmt='%d') |
||||
np.savetxt(path + dataset + "_vgae_test.txt", vgae_test_edges, fmt='%d') |
||||
np.savetxt(path + dataset + "_vgae_test_neg.txt", vgae_test_edges_false, fmt='%d') |
||||
|
||||
np.savetxt(path + dataset + "_train.txt", vgae_train_edges, fmt='%d') |
||||
np.savetxt(path + dataset + "_pd_train.txt", train_edges, fmt='%d') |
||||
np.savetxt(path + dataset + "_test.txt", test_edges, fmt='%d') |
||||
np.savetxt(path + dataset + "_test_neg.txt", test_edges_false, fmt='%d') |
||||
|
||||
print("OK") |
@ -1,130 +0,0 @@ |
||||
import numpy as np |
||||
import scipy.sparse as sp |
||||
|
||||
from src import config |
||||
|
||||
|
||||
def sparse_to_tuple(sparse_mx): |
||||
if not sp.isspmatrix_coo(sparse_mx): |
||||
sparse_mx = sparse_mx.tocoo() |
||||
coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose() |
||||
values = sparse_mx.data |
||||
shape = sparse_mx.shape |
||||
return coords, values, shape |
||||
|
||||
|
||||
def divide_vgae_datasets(adj, edges, edges_all, num_test, num_val, i): |
||||
# 构建具有10%正向链接的测试集的函数 |
||||
# 注:拆分是随机的,结果可能与论文中报告的数字略有偏差。 |
||||
|
||||
if i == 9: |
||||
start_test = num_test * i |
||||
end_test = edges.shape[0] |
||||
start_val = 0 |
||||
end_val = num_val |
||||
else: |
||||
start_test = num_test * i |
||||
end_test = num_test * (i + 1) |
||||
start_val = end_test |
||||
end_val = end_test + num_val |
||||
|
||||
all_edge_idx = list(range(edges.shape[0])) |
||||
np.random.seed(config.seed) |
||||
np.random.shuffle(edges) |
||||
# val_edge_idx = all_edge_idx[start_val:end_val] |
||||
test_edge_idx = all_edge_idx[start_test:end_test] |
||||
test_edges = edges[test_edge_idx] |
||||
# val_edges = edges[val_edge_idx] |
||||
train_edges = np.delete(edges, np.hstack([test_edge_idx]), axis=0) # , val_edge_idx |
||||
|
||||
def ismember(a: list, b, tol=5): |
||||
rows_close = np.all(np.round(a - b[:, None], tol) == 0, axis=-1) |
||||
return np.any(rows_close) |
||||
|
||||
test_edges_false = [] |
||||
while len(test_edges_false) < len(test_edges): |
||||
idx_i = np.random.randint(0, adj.shape[0]) # 随机生成横坐标 |
||||
idx_j = np.random.randint(0, adj.shape[0]) # 随机生成纵坐标 |
||||
if idx_i == idx_j: # 对角线的不要 |
||||
continue |
||||
if ismember([idx_i, idx_j], edges_all): # 是已知边不要 |
||||
continue |
||||
if test_edges_false: # 已选负边不要,a-b或b-a有一个是都不要 |
||||
if ismember([idx_j, idx_i], np.array(test_edges_false)): |
||||
continue |
||||
if ismember([idx_i, idx_j], np.array(test_edges_false)): |
||||
continue |
||||
test_edges_false.append([idx_i, idx_j]) |
||||
|
||||
# val_edges_false = [] |
||||
# while len(val_edges_false) < len(val_edges): |
||||
# idx_i = np.random.randint(0, adj.shape[0]) |
||||
# idx_j = np.random.randint(0, adj.shape[0]) |
||||
# if idx_i == idx_j: # 对角线不要 |
||||
# continue |
||||
# if ismember([idx_i, idx_j], edges_all): # 是已知边不要 |
||||
# continue |
||||
# if val_edges_false: |
||||
# if ismember([idx_j, idx_i], np.array(val_edges_false)): |
||||
# continue |
||||
# if ismember([idx_i, idx_j], np.array(val_edges_false)): |
||||
# continue |
||||
# val_edges_false.append([idx_i, idx_j]) |
||||
|
||||
assert ~ismember(test_edges_false, edges_all) |
||||
# assert ~ismember(val_edges_false, edges_all) |
||||
# assert ~ismember(val_edges, train_edges) |
||||
assert ~ismember(test_edges, train_edges) |
||||
# assert ~ismember(val_edges, test_edges) |
||||
|
||||
# Re-build adj matrix 重建邻接矩阵 |
||||
adj_train = sp.csr_matrix((np.ones(train_edges.shape[0]), (train_edges[:, 0], train_edges[:, 1])), shape=adj.shape) |
||||
adj_train = adj_train + adj_train.T # 因为train_edges是单向的,所以把它变成对称的 |
||||
|
||||
# NOTE: these edge lists only contain single direction of edge! 注意:这些边列表只包含边的单一方向! |
||||
return adj, train_edges, test_edges, np.array(test_edges_false) # , val_edges, np.array(val_edges_false) |
||||
|
||||
|
||||
def divide_datasets(adj, edges, num_test, i, dp_line): |
||||
if i == 9: |
||||
start_test = num_test * i |
||||
end_test = edges.shape[0] |
||||
else: |
||||
start_test = num_test * i |
||||
end_test = num_test * (i + 1) |
||||
|
||||
all_edge_idx = list(range(edges.shape[0])) |
||||
np.random.seed(config.seed) |
||||
np.random.shuffle(edges) |
||||
test_edge_idx = all_edge_idx[start_test:end_test] |
||||
test_edges = edges[test_edge_idx] |
||||
train_edges = np.delete(edges, np.hstack([test_edge_idx]), axis=0) # , val_edge_idx |
||||
|
||||
def ismember(a: list, b, tol=5): |
||||
rows_close = np.all(np.round(a - b[:, None], tol) == 0, axis=-1) |
||||
return np.any(rows_close) |
||||
|
||||
test_edges_false = [] |
||||
while len(test_edges_false) < len(test_edges): |
||||
idx_i = np.random.randint(0, adj.shape[0]) # 随机生成横坐标 |
||||
idx_j = np.random.randint(0, adj.shape[1]) # 随机生成纵坐标 |
||||
if idx_i == idx_j: # 自身不要 |
||||
continue |
||||
if ismember([idx_i, idx_j], edges): # 是已知边不要 |
||||
continue |
||||
test_edges_false.append([idx_i, idx_j]) |
||||
|
||||
adj_pd = sp.csr_matrix((np.ones(train_edges.shape[0]), (train_edges[:, 0], train_edges[:, 1])), shape=adj.shape) |
||||
|
||||
# 把列索引编号加上dp_line |
||||
def add_index(edges): |
||||
edges = np.array(edges) |
||||
colu = edges[:, 1] + dp_line |
||||
edges[:, 1] = colu |
||||
return edges |
||||
|
||||
train_edges = add_index(train_edges) |
||||
test_edges = add_index(test_edges) |
||||
test_edges_false = add_index(test_edges_false) |
||||
|
||||
return adj_pd, train_edges, test_edges, test_edges_false |
@ -1,2 +0,0 @@ |
||||
from __future__ import print_function |
||||
from __future__ import division |
@ -1,18 +0,0 @@ |
||||
import numpy as np |
||||
import scipy.sparse as sp |
||||
|
||||
|
||||
def load_yam_feature(dataset): |
||||
dp = np.loadtxt('../../data/RawData/Yamanishi/{}_admat_dgc.txt'.format(dataset), dtype=str, delimiter='\t')[1:, 1:].astype(np.float).T |
||||
dd = np.loadtxt('../../data/RawData/Yamanishi/{}_simmat_dc.txt'.format(dataset), dtype=str, delimiter='\t')[1:, 1:].astype(np.float) |
||||
pp = np.loadtxt('../../data/RawData/Yamanishi/{}_simmat_dg.txt'.format(dataset), dtype=str, delimiter='\t')[1:, 1:].astype(np.float) |
||||
feature = np.vstack((np.hstack((dd, dp)), np.hstack((dp.T, pp)))) |
||||
return sp.lil_matrix(feature) |
||||
|
||||
|
||||
def load_luo_feature(dataset): |
||||
dp = np.loadtxt('../../data/RawData/luo/mat_drug_protein.txt'.format(dataset), dtype=float) |
||||
dd = np.loadtxt('../../data/RawData/luo/Similarity_Matrix_Drugs.txt'.format(dataset), dtype=float) |
||||
pp = np.loadtxt('../../data/RawData/luo/Similarity_Matrix_Proteins.txt'.format(dataset), dtype=float) / 100 |
||||
feature = np.vstack((np.hstack((dd, dp)), np.hstack((dp.T, pp)))) |
||||
return sp.lil_matrix(feature) |
@ -1,20 +0,0 @@ |
||||
import os |
||||
import pickle |
||||
|
||||
from src import config |
||||
from src.p2_preprocessing_feature.load_feature import load_yam_feature, load_luo_feature |
||||
|
||||
for dataset in config.datasets: |
||||
# feature: lil_matrix |
||||
if dataset == 'luo': |
||||
feature = load_luo_feature(dataset) |
||||
else: |
||||
feature = load_yam_feature(dataset) |
||||
|
||||
# 保存特征 |
||||
path = "../../data/partitioned_data/{}/feature/".format(dataset) |
||||
if not os.path.exists(path): |
||||
os.makedirs(path) |
||||
pickle.dump(feature, open(path + dataset + "_feature.pkl", 'wb')) |
||||
|
||||
print("ok") |
Loading…
Reference in new issue