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Tensor Decomposition with Missing Indices

The implementation of the proposed method in our paper "Tensor Decomposition with Missing Indices", which will be published upcoming IJCAI 2017.



You'll need to prepare the input tensor file (file format shown below), regularization parameter (lamb), and the number of feature dimensions (k).

$ python train.py -h
usage: train.py [-h] -i INFILE -o OUTFILE -l LAMB -k K [-n [NEPOCHS]] [-v]

optional arguments:
  -h, --help            show this help message and exit
  -i INFILE, --infile INFILE
                        input tensor file path
  -o OUTFILE, --outfile OUTFILE
                        output model file path
  -l LAMB, --lamb LAMB  weight of regularization
  -k K                  # feature dimensions
  -n [NEPOCHS], --nepochs [NEPOCHS]
                        # epochs (default=30)
  -v, --verbose         verbosity


You'll need to prepare the test file (file format shown below), and the trained model file. predict.py outputs predicted values to stdout.

$ python predict.py -h
usage: predict.py [-h] -m MODELFILE -t TESTFILE

optional arguments:
  -h, --help            show this help message and exit
  -m MODELFILE, --modelfile MODELFILE
                        input model file path
  -t TESTFILE, --testfile TESTFILE
                        input test data file path

Input file

Input file must contain the data shape of the input tensor in the first line, and then the data follows.

$ cat data.dat
25297 2616 49 -1
0 0 1 1
12 1 3 1
8 -1 14 1
2 9 -1 1
5 1 5 1

If the mode of the input tensor is three, then each line contains four numbers separated by space.

In the first line, the first three numbers define the number of dimensions of each mode, and the fourth number will be ignored, which is needed for implementation reason :(

After that, in each line, the first three numbers show the index for each mode, and the last (fourth) number shows the value of the tensor in the corresponding position.

If the index is -1, then that index is missing, in other words, the position of the corresponding value is not exactly determined.

Test file

Test file contains index of tensor in each line, which is separated by space. predict.py predicts the values corresponding to these indices.

$ cat test.dat
3 1 4
5 10 2


$ sh demo.sh


Yuto Yamaguchi, Kohei Hayashi, "Tensor Decomposition with Missing Indices", International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australia, Aug 19-25, 2017.