This repository provides different implementations for extracting the same features from tensors. The input tensor format is COO (.tns file format).
For more information on this tool, please refer to the manuscript on this link.
To compile and run our main code, use following commands, replace placeholders with corresponding values:
TO COMPILE:
make
USAGE:
featen [options]
-i input : input tensor path
-o out : feature info output file
-m method : feature extraction method. Options:{map, sort, group, hybrid} [DEFAULT : map]
-c csv_out : feture out file to be in csv {1} or json {0} format [DEFAULT : 1]
-d only3d : features to be in only 3 modes {1} or M modes {0} (only valid for MAP) [DEFAULT : 0]
SIMPLEST USAGE:
./featen -i [input-tensor-path] -o [output-file-path]
EXAMPLE:
./featen -i ../data_tensors/sample_small_3D.tns -o ../features/sample_small_3D_feat.txt
All feature extraction methods find the features of the tensor exactly. Users can use whichever method they like. These methods provide different performances depending on the given tensor.
To use one, write the choice number of the method as a parameter (the placeholder : [algorithm choice]).
| Method | Description |
|---|---|
| MAP | Uses hash table implemented with std::unordered_map to calculate # of nnz per fiber & slice to do the extraction. |
| SORT | Sorts all the indices along all modes to make the calculation. |
| GROUP | Groups the slices and fibers according to their indices, similar to CSF construction. |
| HYBRID | Uses sorting-based and grouping-based algorithms for different modes, depending on mode properties. |
For 3-mode tensors, all the methods extract all features along all modes.
For M-mode tensors with
The program writes the tensor features into a file.
It first prints the general features, whose meanings are:
name order sizes[] nnz sparsity sliceCnt fiberCnt nnzSliceCnt nnzFiberCnt slice_sparsity fiber_sparsity
The description of general features is shown in the table below.
| Feature Name | Description |
|---|---|
| name | Name of the tensor |
| order | Order ( mode count) of the tensor |
| sizes[] | Sizes of the tensor, which is an array of size order |
| nnz | Number of nonzeros |
| sparsity | Nonzero density of the tensor |
| sliceCnt | # of all possible slices (even if it has no nnz) |
| fiberCnt | # of all possible fibers (even if it has no nnz) |
| nnzSliceCnt | # of nonzero slices (which has at least 1 nnz) |
| nnzFiberCnt | # of nonzero fibers (which has at least 1 nnz) |
| slice_sparsity | Density of nonzero slices |
| fiber_sparsity | Density of nonzero fibers |
The program then prints the kind- and mode-dependent features for each mode and kind (nonzeros_per_slice, nonzeros_per_fibers, fibers_per_slice).
For features of nonzeros_per_slice and fibers_per_slice kinds, the output is in the form:
id1 id2 all_cnt nz_cnt nz_density max min dev sum avg imbal stDev cv avg_onlynz imbal_onlynz stDev_onlynz cv_onlynz
Here id1 and id2 represent the ignored dimensions of mode-(id1, id2) slice
For features of nonzeros_per_fiber kind, the output is in the form:
id all_cnt nz_cnt nz_density max min dev sum avg imbal stDev cv avg_onlynz imbal_onlynz stDev_onlynz cv_onlynz
Here id represents the ignored dimension of mode-(id) fiber
The description of kind- and mode-dependent features is shown in the table below.
| Feature Name | Description |
|---|---|
| all_cnt | all count including empty |
| nz_cnt | nonzero count |
| nz_density | nonzero sparsity (nz_cnt / all_cnt) |
| max | maximum value |
| min | minimum value |
| dev | deviation (max – min) |
| sum | summation of values |
| avg | average value (sum / all_cnt) |
| imbal | imbalance : (max-avg)/max |
| stDev | standard deviation |
| cv | coefficient of variation (stDev/avg) |
| avg_onlynz | average by excluding empty (sum / nz_cnt) |
| imbal_onlynz | imbal by excluding empty (max- avg_onlynz)/max |
| stDev_onlynz | stDev by excluding empty |
| cv_onlynz | cv by excluding empty (stDev_onlynz / avg_onlynz ) |