"/home/mattjones/projects/scLineages/SingleCellLineageTracing/Alex_Solver")
from simulation_tools.simulation_utils import get_leaves_of_tree
from simulation_tools.validation import check_triplets_correct, tree_collapse
from lineage_solver.solution_evaluation_metrics import cci_score
from data_pipeline import newick_to_network
parser = argparse.ArgumentParser()
parser.add_argument("true_net", type=str)
parser.add_argument("typ", type=str)
args = parser.parse_args()
true_netfp = args.true_net
t = args.typ
name = true_netfp.split("/")[-1]
spl = name.split("_")
param = spl[-3]
run = spl[-1].split(".")[0]
true_network = pic.load(open(true_netfp, "rb"))
target_nodes = get_leaves_of_tree(true_network, clip_identifier=True)
cci_upper = cci_score(target_nodes, bound="upper")
cci_lower = cci_score(target_nodes, bound="lower")
print(
str(param) + "\t" + str(run) + "\t" + str(cci_lower) + "\t" +
str(cci_upper) + '\t' + str(cci_upper - cci_lower) + '\t' + str(t))
46: 0.8870967741935477
}
dropout_prob_map = {
i: random.choice(data_dropout_rates.values())
for i in range(0, 40)
}
# Generate simulated network
true_network = generate_simulated_full_tree(prior_probabilities,
dropout_prob_map,
characters=number_of_characters,
subsample_percentage=.4,
depth=10)
target_nodes = get_leaves_of_tree(true_network, clip_identifier=True)
target_nodes_original_network = get_leaves_of_tree(true_network,
clip_identifier=False)
pic.dump(target_nodes, open("test_target_nodes.pkl", "wb"))
print("CCI complexity of reconstruction: " + str(cci_score(target_nodes)))
# Hybrid solution
reconstructed_network_hybrid = solve_lineage_instance(
target_nodes,
method="greedy",
prior_probabilities=prior_probabilities,
hybrid_subset_cutoff=200,
time_limit=60,
threads=10)