def main():
args = parse_args()
data_dir = args.data_dir
plot_dir = data_dir+"/plots/benchmarks/"
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
# create benchmark object
b = Benchmark(data_dir+"/pdb/")
#specify methods to benchmark
b.add_method("pseudo-likelihood APC", data_dir +"/predictions_pll/", "apc.mat")
b.add_method("pseudo-likelihood raw", data_dir +"/predictions_pll/", "raw.mat")
b.add_method("persistent contrastive divergence APC", data_dir +"/predictions_pcd/", "apc.mat")
b.add_method("persistent contrastive divergence raw", data_dir +"/predictions_pcd/", "raw.mat")
#add constraint that all MRF optimizations have exist status 0
b.add_constraint("opt_code", 0, "greater_equal")
#compute the precision of predictions
b.compute_evaluation_statistics(seqsep=6, contact_thr=8, noncontact_thr=8)
#generate a benchmark plot
plot = b.plot_precision_vs_rank()
#format that benchmark plot to resemble the one in Fig 1C
plot_pll_vs_pcd_benchmark_figure(plot, plot_dir, height=500, width=1000)
def main():
args = parse_args()
data_dir = args.data_dir
plot_dir = data_dir + "/plots/benchmarks/"
pdb_dir = data_dir + "/pdb/"
sequence_separation = 6
contact_thr = 8
non_contact_thr = 8
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
### create benchmark plot for star-tree topologies
# create benchmark object
b = Benchmark(pdb_dir)
#specify methods to benchmark
b.add_method("APC", data_dir + "/recover_pcd_constrained/", "apc.star.mat")
b.add_method("EC", data_dir + "/recover_pcd_constrained/", "ec.star.mat")
b.add_method("no APC", data_dir + "/recover_pcd_constrained/",
"raw.star.mat")
#add constraint that all MRF optimizations have exist status 0
b.add_constraint("opt_code", 0, "greater_equal")
#compute the precision of predictions
b.compute_evaluation_statistics(seqsep=sequence_separation,
contact_thr=contact_thr,
noncontact_thr=non_contact_thr)
#generate a benchmark plot
benchmark_plot_star = b.plot_precision_vs_rank()
plot_file = plot_dir + "/" + "fig_6b.html"
plot_ccmgen_benchmark_figure(benchmark_plot_star,
'star topology',
plot_file,
height=350,
width=500)
### create benchmark plot for binary-tree topologies
# create benchmark object
b = Benchmark(pdb_dir)
# specify methods to benchmark
b.add_method("APC", data_dir + "/recover_pcd_constrained/",
"apc.binary.mat")
b.add_method("EC", data_dir + "/recover_pcd_constrained/", "ec.binary.mat")
b.add_method("no APC", data_dir + "/recover_pcd_constrained/",
"raw.binary.mat")
# add constraint that all MRF optimizations have exist status 0
b.add_constraint("opt_code", 0, "greater_equal")
# compute the precision of predictions
b.compute_evaluation_statistics(seqsep=sequence_separation,
contact_thr=contact_thr,
noncontact_thr=non_contact_thr)
# generate a benchmark plot
benchmark_plot_binary = b.plot_precision_vs_rank()
plot_file = plot_dir + "/" + "fig_6a.html"
plot_ccmgen_benchmark_figure(benchmark_plot_binary,
'binary topology',
plot_file,
height=350,
width=500)
### create qunatification of noise plot
plot_file = plot_dir + "/" + "fig_6c.html"
plot_ccmgen_noise_quant_figure(benchmark_plot_star,
benchmark_plot_binary,
plot_file,
height=350,
width=500)