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Both Linux and Windows 10 builds use Intel(R) MKL(R) library. We used 2017 version; other versions might work as well. Suppose you have installed MKL at <MKL_ROOT>. In Linux, this is typically /opt/intel/mkl. In Windows 10, this is typically C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2017\windows\mkl

We built this project on Ubuntu 16.04LTS with gcc 5.4. Other linux versions with gcc 5+ could also work.

cd <ISLE_ROOT>
export LD_LIBRARY_PATH=<MKL_ROOT>/lib/intel64/:.
make -j

This should generate two executables ISLETrain and ISLEInfer in the <ISLE_ROOT> directory.

Open <ISLE_ROOT>\win\ISLE.sln in VS2015, and build the ISLETrain and ISLEInfer projects. The Debug build uses dynamic linking for C runtime, OpenMP, and MKL libraries. The Release build links all these dependencies statically into a self-contained exe.

If there is a problem with this, you could configure project file properties as follows:

• Under VC++ Directories >

  • To Include directories, add <ISLE_ROOT> and <ISLE_ROOT>\include. This will include include, Eigen and Spectra directories.
  • To Library Directories, add <MKL_ROOT>\lib\intel64_win (for MKL libs) and <MKL_ROOT>\..\compiler\lib\intel64_win\ (for OpenMP lib)

• Under C/C++ >

  • Enable optimizations, and disable runtime and SCL checks (this conflicts with turning on optimizations).
  • Code Generation > Enable Parallel Code
  • Languages > Enable OpenMP support

• Under 'Intel Performance Libraries' >

  • Enable 'Parallel' option.
  • Enable MKL_ILP64 option for 8-byte MKL_INTs.

• Under Linker > Input > Additional dependencies: Add

  • Add the prefix libiomp5md.lib;mkl_core.lib;mkl_intel_ilp64.lib;mkl_sequential.lib; to existing libraries

1. Create a <tdf_file> which has one <doc_id> <word_id> <frequency> entry per line.

   • The <doc_id> entries are 1-based and range between 1 and <num_docs>.
   • The <word_id> enties are 1-based and range between 1 and <vocab_size>.
   • Let <max_entries> be the number of entries (or lines) in this file.

2. Create a <vocab_file> file with mapping from word id to the word; the i-th line of the file is the word with word_id=i.

3. Run

ISLETrain <tdf_file> <vocab_file> <output_dir> <vocab_size> <num_docs> <max_entries> <num_topics> <sample(0/1)> <sample_rate> <edge topics(0/1) <max_edge_topics>

• <num_topics> is the number of topics you want to recover from the <tdf_file>.
• If the dataset is too large and you wish to use importance sampling, set <sample> to 1 (otherwise 0).
• When <sample> is enabled by setting it to 1, you can specify the sampling rate with <sample_rate>. For example, 0.1.
• when <edge topic> is set to one, a larger "edge" topic model with up to <max_edge_topics> will be computed.
• The output will be stored in a log directory under <output_dir>

1. Locate the trained <sparse_model_file> in the log directory under the trainer's <output_dir>.

2. Prepare <infer_file> for the documents you want to infer in the same way <tdf_file> was prepared from training. <vocab_size>, <num_docs_in_infer_file> and <nnzs_in_infer_file> are the vocabulary size, the number of documents in the <infer_file>, and the number of entries(lines) in the <infer_file> respectively. Note that the <vocab_size> of the infer file can not be larger than the <vocab_size> of the <tdf_file> on which the model was trained.

3. Run

ISLEInfer <sparse_model_file> <infer_file> <output_dir> <num_topics> <vocab_size> <num_docs_in_infer_file> <nnzs_in_infer_file> <nnzs_in_sparse_model_file> <iters> <Lifschitz_constant>

• <output_dir> is the location where the file containing the inferred weights is written.
• <num_topics> is the number of topics in the trained model.
• <nnzs_in_sparse_model_file> is the number of entries (lines) in <sparse_model_file>.
• <iters> is the number if iterations of multiplicative weight update. Set to 0 to use default number of iterations.
• <Lifschitz_constant> is an estimate of the Lifschitz constant for the gradient of the log-likelihood terms. Set to 0 to use default.

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