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)