def main():
snp_fn = "data/mouse/alldata"
out_prefix = "results/mouse_"
description = "test_run"
runner = Local()
num_causals = 500
num_repeats = 10
num_pcs = 5
# make this a tuple of function and kwargs
from GWAS_benchmark.methods import execute_lmm, execute_linear_regression, execute_dual_fs, execute_fs
for name, method in {"lmm": execute_lmm, "lr": execute_linear_regression, "dual_fs": execute_dual_fs, "fs": execute_fs}.items():
methods = [method]
combine_output = run_simulation(snp_fn, out_prefix, methods, num_causals, num_repeats, num_pcs, description, runner, plot_fn=None, seed=42)
def run_sim_and_compare(self, name, method):
logging.info('in test_all')
import fastlmm.util.runner as runner
currentFolder = os.path.dirname(os.path.realpath(__file__))
snp_fn = os.path.realpath(currentFolder + "/../../data/mouse/alldata")
out_prefix = currentFolder + "/tempdir/mouse_"
description = "test_run_{0}".format(name)
runner = Local()
num_causals = 500
num_repeats = 1
num_pcs = 5
expected_prefix = currentFolder + "/expected/"
methods = [method]
combine_output = run_simulation(snp_fn, out_prefix, methods, num_causals, num_repeats, num_pcs, description, runner, plot_fn="out.png", seed=42)
from fastlmm.util.pickle_io import load
filename = "%s%s.bzip" % (expected_prefix, name)
co = load(filename)
compare_nested(combine_output, co)
