kimiyoung/ planetoid
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Description
Semi-supervised learning with graph embeddings
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Planetoid
Introduction
This is an implementation of Planetoid, a graph-based semi-supervised learning method proposed in the following paper:
Revisiting Semi-Supervised Learning with Graph Embeddings. Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov. ICML 2016.
Please cite the above paper if you use the datasets or code in this repo.
Run the demo
We include the Citeseer dataset in the directory
data, where the data structures needed are pickled.
To run the transductive version,
python test_trans.py
To run the inductive version,
python test_ind.py
You can refer to
test_trans.pyand
test_ind.pyfor example usages of our model.
Models
The models are implemented mainly in
trans_model.py(transductive) and
ind_model.py(inductive), with inheritance from
base_model.py. You might refer to the source files for detailed API documentation.
Prepare the data
Transductive learning
The input to the transductive model contains: -
x, the feature vectors of the training instances, -
y, the one-hot labels of the training instances, -
graph, a
dictin the format
{index: [index_of_neighbor_nodes]}, where the neighbor nodes are organized as a list. The current version only supports binary graphs.
Let L be the number of training instances. The indices in
graphfrom 0 to L - 1 must correspond to the training instances, with the same order as in
x.
Inductive learning
The input to the inductive model contains: -
x, the feature vectors of the labeled training instances, -
y, the one-hot labels of the labeled training instances, -
allx, the feature vectors of both labeled and unlabeled training instances (a superset of
x), -
graph, a
dictin the format
{index: [index_of_neighbor_nodes]}.
Let n be the number of both labeled and unlabeled training instances. These n instances should be indexed from 0 to n - 1 in
graphwith the same order as in
allx.
Preprocessed datasets
Datasets for Citeseet, Cora, and Pubmed are available in the directory
data, in a preprocessed format stored as numpy/scipy files.
The dataset for DIEL is available at http://www.cs.cmu.edu/~lbing/data/emnlp-15-diel/emnlp-15-diel.tar.gz. We also provide a much more succinct version of the dataset that only contains necessary files and some (not very well-organized) pre-processing code here at http://cs.cmu.edu/~zhiliny/data/diel_data.tar.gz.
The NELL dataset can be found here at http://www.cs.cmu.edu/~zhiliny/data/nell_data.tar.gz.
In addition to
x,
y,
allx, and
graphas described above, the preprocessed datasets also include: -
tx, the feature vectors of the test instances, -
ty, the one-hot labels of the test instances, -
test.index, the indices of test instances in
graph, for the inductive setting, -
ally, the labels for instances in
allx.
The indices of test instances in
graphfor the transductive setting are from
#xto
#x + #tx - 1, with the same order as in
tx.
You can use
cPickle.load(open(filename))to load the numpy/scipy objects
x,
y,
tx,
ty,
allx,
ally, and
graph.
test.indexis stored as a text file.
Hyper-parameter tuning
Refer to
test_ind.pyand
test_trans.pyfor the definition of different hyper-parameters (passed as arguments). Hyper-parameters are tuned by randomly shuffle the training/test split (i.e., randomly shuffling the indices in
x,
y,
tx,
ty, and
graph). For the DIEL dataset, we tune the hyper-parameters on one of the ten runs, and then keep the same hyper-parameters for all the ten runs.