Marginalized Beam Search (MBS)

This git repository contains the code and simulation data generator for the paper 'Marginalized Beam Search Algorithms for Hierarchical HMMs'.

For DNA data see the paper 'Lokatt: A hybrid DNA nanopore basecaller with an explicit duration hidden Markov model and a residual LSTM network'.

GMBS and LFBS

The two algorithms are implemented with C++ CUDA kernel in file beamsearch/dnaseq_beam.cu, and were wrapped as Tensorflow custom operation in file beamsearch/dnaseq_beam.cc. To compile them, run

cd beamsearch/
TF_CFLAGS=( $(python3 -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_compile_flags()))') )
TF_LFLAGS=( $(python3 -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_link_flags()))') )
nvcc -std=c++14 -c -o dnaseq_beam.cu.o dnaseq_beam.cu ${TF_CFLAGS[@]} -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC -O3 --use_fast_math -maxrregcount 32
g++ -std=c++14 -shared dnaseq_beam.cc dnaseq_beam.cu.o -o dnaseq_beam.so -fPIC -I /usr/local/cuda/include/ -L /usr/local/cuda/lib64/ -lcudart ${TF_CFLAGS[@]} ${TF_LFLAGS[@]} -O2

Simulation data

To generate n=200 simulation data pairs with observation standard deviation 2, run:

python3 simulation_writeinput.py -output ./ -n 200 -var 2

Run the MBS and Viterbi (GPU only)

To run GMBS, LFBS or Viterbi for n=10 batches with batch size 150 with data DATA.h5:

python3 simulation_c_diskinput.py -input DATA.h5 -batch 150 -n 10 -decoder gmbs/lfbs/viterbi

Please adjust the parameters (beam width 'bstest' and focus length 'lfbtest') within the file simulation_c_diskinput.py.