def run_method(method, tree, m = 300, kappa = 2, mutation_rate=0.05, threshold = None, verbose = False):
start_time = time.time()
observations, taxa_meta = generation.simulate_sequences(m, tree_model=tree, seq_model=generation.HKY(kappa = kappa), mutation_rate=mutation_rate, alphabet="DNA")
runtime = time.time() - start_time
print("Simulation took %s seconds" % runtime)
if method == "RaXML":
raxml_HKY = reconstruct_tree.RAxML()
start_time = time.time()
tree_rec = raxml_HKY(observations, taxa_meta, raxml_args="-T 2 --HKY85 -c 1")
if method == "SNJ":
snj = reconstruct_tree.SpectralNeighborJoining(reconstruct_tree.HKY_similarity_matrix)
start_time = time.time()
tree_rec = snj(observations, taxa_meta)
if method == "NJ":
nj = reconstruct_tree.NeighborJoining(reconstruct_tree.HKY_similarity_matrix)
start_time = time.time()
tree_rec = nj(observations, taxa_meta)
if method == "STR+NJ":
spectral_method = reconstruct_tree.STDR(reconstruct_tree.NeighborJoining, reconstruct_tree.HKY_similarity_matrix)
start_time = time.time()
tree_rec = spectral_method.deep_spectral_tree_reconstruction(observations, reconstruct_tree.HKY_similarity_matrix,
taxa_metadata = taxa_meta,
threshhold = threshold, min_split = 5, verbose = verbose)
if method == "STR+SNJ":
spectral_method = reconstruct_tree.STDR(reconstruct_tree.SpectralNeighborJoining, reconstruct_tree.HKY_similarity_matrix)
start_time = time.time()
tree_rec = spectral_method.deep_spectral_tree_reconstruction(observations, reconstruct_tree.HKY_similarity_matrix,
taxa_metadata = taxa_meta,
threshhold = threshold, min_split = 5, verbose = verbose)
if method == "STR+RaXML":
spectral_method = reconstruct_tree.STDR(reconstruct_tree.RAxML, reconstruct_tree.HKY_similarity_matrix)
start_time = time.time()
tree_rec = spectral_method.deep_spectral_tree_reconstruction(observations, reconstruct_tree.HKY_similarity_matrix,
taxa_metadata = taxa_meta,
threshhold = threshold,
raxml_args = "-T 2 --HKY85 -c 1", min_split = 5, verbose = verbose)
runtime = time.time() - start_time
RF,F1 = reconstruct_tree.compare_trees(tree_rec, tree)
print(method)
if threshold is not None: print(threshold)
print("--- %s seconds ---" % runtime)
print("RF = ",RF)
print("F1% = ",F1)
return([method, str(threshold), runtime, RF, F1])
sys.path.append(os.path.join(sys.path[0], 'spectraltree'))
#import spectraltree
import utils
import generation
import reconstruct_tree
import str
from dendropy.model.discrete import simulate_discrete_chars, Jc69
from dendropy.calculate.treecompare import symmetric_difference
import cProfile
N = 300
num_taxa = 512
threshold = 32
# jc = generation.Jukes_Cantor()
hky = generation.HKY(kappa=2)
mutation_rate = 0.05
# mutation_rate = [jc.p2t(0.95)]
np.random.seed(0)
# reference_tree = utils.unrooted_birth_death_tree(num_taxa, birth_rate=1)
# reference_tree = utils.lopsided_tree(num_taxa)
reference_tree = utils.balanced_binary(num_taxa)
# for x in reference_tree.preorder_edge_iter():
# x.length = 1
np.random.seed(0)
t0 = time.time()
observations, meta = generation.simulate_sequences(N,
tree_model=reference_tree,
seq_model=hky,
mutation_rate=mutation_rate,
rng=np.random,
