def setUpClass(cls):
if MPControl.is_initialized:
MPControl.shutdown()
MPControl.set_multiprocess_engine("dask-local")
MPControl.set_processes(1)
MPControl.connect()
BSUBTILIS_2_EXPRESSION = 'expression.tsv.gz'
BSUBTILIS_2_METADATA = 'meta_data.tsv'
CV_SEEDS = list(range(42, 52))
# Multiprocessing uses the pathos implementation of multiprocessing (with dill instead of cPickle)
# This is suited for a single computer but will not work on a distributed cluster
n_cores_local = 10
local_engine = True
# Multiprocessing needs to be protected with the if __name__ == 'main' pragma
if __name__ == '__main__' and local_engine:
MPControl.set_multiprocess_engine("multiprocessing")
MPControl.client.processes = n_cores_local
MPControl.connect()
# Inference on B. subtilis data set 1 (GSE67023) with BBSR
# Using the crossvalidation wrapper
# Run the regression 10 times and hold 20% of the gold standard out of the priors for testing each time
# Each run is seeded differently (and therefore has different holdouts)
# Create a crossvalidation wrapper
cv_wrap = CrossValidationManager()
# Assign variables for grid search
cv_wrap.add_gridsearch_parameter('random_seed', CV_SEEDS)
# Create a worker
worker = inferelator_workflow(regression="bbsr", workflow="tfa")
def tearDownClass(cls):
if MPControl.is_initialized:
MPControl.shutdown()
MPControl.set_multiprocess_engine("local")
MPControl.connect()