From b9dab7a7b9618cf6e5cf537d610120baf9f3ad47 Mon Sep 17 00:00:00 2001 From: ines-chami Date: Fri, 1 May 2020 05:38:33 -0700 Subject: [PATCH] added airport data --- README.md | 5 + data/airport/airport.p | Bin 0 -> 554100 bytes data/airport/airport_alldata.p | Bin 0 -> 1163337 bytes data/airport/routes.dat | 67663 +++++++++++++++++++++++++++++++ utils/data_utils.py | 41 +- 5 files changed, 67704 insertions(+), 5 deletions(-) create mode 100644 data/airport/airport.p create mode 100644 data/airport/airport_alldata.p create mode 100644 data/airport/routes.dat diff --git a/README.md b/README.md index 46080f5..105c211 100644 --- a/README.md +++ b/README.md @@ -57,6 +57,7 @@ The ```data/``` folder contains source files for: * Cora * Pubmed * Disease + * Airport To run this code on new datasets, please add corresponding data processing and loading in ```load_data_nc``` and ```load_data_lp``` functions in ```utils/data_utils.py```. @@ -168,6 +169,10 @@ We provide examples of training commands used to train HGCN and other graph embe ```python train.py --task lp --dataset disease_lp --model HGCN --lr 0.01 --dim 16 --num-layers 2 --num-layers 2 --act relu --bias 1 --dropout 0 --weight-decay 0 --manifold PoincareBall --normalize-feats 0 --log-freq 5``` + * Airport (Test ROC-AUC=97.43): + +```python train.py --task lp --dataset airport --model HGCN --lr 0.01 --dim 16 --num-layers 2 --act relu --bias 1 --dropout 0.0 --weight-decay 0 --manifold PoincareBall --log-freq 5 --cuda 0 --c None``` + #### Node classification * Cora and Pubmed: diff --git a/data/airport/airport.p b/data/airport/airport.p new file mode 100644 index 0000000000000000000000000000000000000000..882353482df7073d5ed837de4e2bcb370aa2f1b7 GIT binary patch literal 554100 zcmaH!bwE|m_VzKrz%C3_Of1Agu{fyc5fQQY+KK@d28d!EMeM@B!otE11VsV$fT$?l z-Hj-sVqx$;d(S#Ye)r5Pf85Kx@_hE1*fVR^teNdpW8q3`2ggWaeH)s@+4ivM$-V69y}nO*&E<|i~| zb`7T2#2?vM&v&qEILEG0H>o7cAyVCQ!ZoQRy7A>=qth3q@)P=R7W_KVM=E)1S1q`% 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za+A4|xsth(36lwv36lwv36lwv36lwv36lwv30EBx=1E*qcT#s!cT)GNqwc(9dC79| z%Ec=euVlhx!eqi^!eqi^!eqi^!d1zHc@mcccU2DDTyt{G$*G%DH+SF3gvo@-gvo@- zgvo@-gvo@-gvo@-gsYAT^CT{*JE=RVJE?osQFmUlykxm}<>Hl#S2AHTVKQMdVKQMd zVKQMd;i_c9Jc-MJyDA56t~t5puNUb0-ga`DQ=E158vFqtr!Fqtr!Fqtr!a8)v4p2X$AU6lhj z*PL8)a_Z*P&E0o0VKQMdVKQMdVKQMdVKQMd;SXIze)+Vm`s>Vm`s>Vm`s>Vm`s>Vm`r$lOqeHe$JcK;<8sF3$jQA&?mdzTlL?avlL?av zlL?avlL?avlL?avlL?avm&b&85|<-pc_SuSHd!|3OwO6yZzdBa6DAWT6DAWT6DAWT z6DAWT6DAWT6CNKE=1JV~^;^!koN+mFa_^CQk7UAR!k^8ABh%&Tm@9inc9(17@A;!^ zMo&Ffu0>oodfMpv;i22ha3n{caYwl>{%$W@q#S?s7 5] = 5 + deg_onehot = torch.tensor(np.eye(6)[deg], dtype=torch.float).squeeze() + const_f = torch.ones(features.size(0), 1) + features = torch.cat((features, deg_onehot, const_f), dim=1) + return features + + # ############### DATA SPLITS ##################################################### @@ -111,6 +122,11 @@ def split_data(labels, val_prop, test_prop, seed): return idx_val_pos + idx_val_neg, idx_test_pos + idx_test_neg, idx_train_pos + idx_train_neg +def bin_feat(feat, bins): + digitized = np.digitize(feat, bins) + return digitized - digitized.min() + + # ############### LINK PREDICTION DATA LOADERS #################################### @@ -119,6 +135,8 @@ def load_data_lp(dataset, use_feats, data_path): adj, features = load_citation_data(dataset, use_feats, data_path)[:2] elif dataset == 'disease_lp': adj, features = load_synthetic_data(dataset, use_feats, data_path)[:2] + elif dataset == 'airport': + adj, features = load_data_airport(dataset, data_path, return_label=False) else: raise FileNotFoundError('Dataset {} is not supported.'.format(dataset)) data = {'adj_train': adj, 'features': features} @@ -135,19 +153,17 @@ def load_data_nc(dataset, use_feats, data_path, split_seed): ) else: if dataset == 'disease_nc': - val_prop, test_prop = 0.10, 0.60 adj, features, labels = load_synthetic_data(dataset, use_feats, data_path) + val_prop, test_prop = 0.10, 0.60 elif dataset == 'airport': - # TODO + adj, features, labels = load_data_airport(dataset, data_path, return_label=True) val_prop, test_prop = 0.15, 0.15 - pass else: raise FileNotFoundError('Dataset {} is not supported.'.format(dataset)) idx_val, idx_test, idx_train = split_data(labels, val_prop, test_prop, seed=split_seed) labels = torch.LongTensor(labels) - data = {'adj_train': adj, 'features': features, 'labels': labels, 'idx_train': idx_train, 'idx_val': idx_val, - 'idx_test': idx_test} + data = {'adj_train': adj, 'features': features, 'labels': labels, 'idx_train': idx_train, 'idx_val': idx_val, 'idx_test': idx_test} return data @@ -223,3 +239,18 @@ def load_synthetic_data(dataset_str, use_feats, data_path): features = sp.eye(adj.shape[0]) labels = np.load(os.path.join(data_path, "{}.labels.npy".format(dataset_str))) return sp.csr_matrix(adj), features, labels + + +def load_data_airport(dataset_str, data_path, return_label=False): + graph = pkl.load(open(os.path.join(data_path, dataset_str + '.p'), 'rb')) + adj = nx.adjacency_matrix(graph) + features = np.array([graph.node[u]['feat'] for u in graph.nodes()]) + if return_label: + label_idx = 4 + labels = features[:, label_idx] + features = features[:, :label_idx] + labels = bin_feat(labels, bins=[7.0/7, 8.0/7, 9.0/7]) + return sp.csr_matrix(adj), features, labels + else: + return sp.csr_matrix(adj), features +

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