def part21_king(num_taxa_king, Ns_king):
##### Part 2: NJ vs SNJ vs RG vs CLRG
print("Starting Part 21 for kingman tree")
snj = spectraltree.SpectralNeighborJoining(spectraltree.JC_similarity_matrix)
nj = spectraltree.NeighborJoining(spectraltree.JC_similarity_matrix)
rg = spectraltree.RG(spectraltree.JC_distance_matrix)
#clrg = spectraltree.CLRG()
methods = [snj, nj,rg]
alphabet = "DNA"
## Part 2.1: different N
king_tree = spectraltree.unrooted_pure_kingman_tree(num_taxa_king)
res_king21 = compare_methods.experiment(tree_list = [king_tree], sequence_model = sequence_model, Ns = Ns_king, methods = methods, mutation_rates=mutation_rates, reps_per_tree=5, alphabet=alphabet, verbose=True)
return res_king21
def part21_bin(num_taxa_bin, Ns_bin):
##### Part 2: NJ vs SNJ vs RG vs CLRG
print("Starting Part 21 for binary tree")
snj = spectraltree.SpectralNeighborJoining(spectraltree.JC_similarity_matrix)
nj = spectraltree.NeighborJoining(spectraltree.JC_similarity_matrix)
rg = spectraltree.RG(spectraltree.JC_distance_matrix)
#clrg = spectraltree.CLRG()
methods = [snj, nj,rg]
alphabet = "DNA"
## Part 2.1: different N
bin_tree = spectraltree.balanced_binary(num_taxa_bin)
res_bin21 = compare_methods.experiment(tree_list = [bin_tree], sequence_model = sequence_model, Ns = Ns_bin, methods = methods, mutation_rates=mutation_rates, reps_per_tree=5, alphabet=alphabet, verbose=True)
return res_bin21
def part22_cat(num_taxa_s_cat, Ns_cat):
## Part 2.2: different Taxa
print("Starting Part 22 for caterpillar trees")
snj = spectraltree.SpectralNeighborJoining(spectraltree.JC_similarity_matrix)
nj = spectraltree.NeighborJoining(spectraltree.JC_similarity_matrix)
rg = spectraltree.RG(spectraltree.JC_distance_matrix)
clrg = spectraltree.CLRG()
methods = [snj, nj,rg]
alphabet = "Binary"
cat_trees = [spectraltree.lopsided_tree(num_taxa) for num_taxa in num_taxa_s_cat]
res_cat22 = compare_methods.experiment(tree_list = cat_trees, sequence_model = sequence_model, Ns = Ns_cat, methods = methods, mutation_rates=mutation_rates, reps_per_tree=5, alphabet=alphabet , verbose=True)
return res_cat22
def part21_cat(num_taxa_cat, Ns_cat):
##### Part 2: NJ vs SNJ vs RG vs CLRG
print("Starting Part 21 for caterpillar tree")
snj = spectraltree.SpectralNeighborJoining(spectraltree.JC_similarity_matrix)
nj = spectraltree.NeighborJoining(spectraltree.JC_similarity_matrix)
rg = spectraltree.RG(spectraltree.JC_distance_matrix)
#clrg = spectraltree.CLRG()
methods = [snj, nj,rg]
alphabet = "DNA"
## Part 2.1: different N
cat_tree = spectraltree.lopsided_tree(num_taxa_cat)
res_cat21 = compare_methods.experiment(tree_list = [cat_tree], sequence_model = sequence_model, Ns = Ns_cat, methods = methods, mutation_rates=mutation_rates, reps_per_tree=5, alphabet=alphabet, verbose=True)
return res_cat21
def part22_king(num_taxa_s_king, Ns_king):
## Part 2.2: different Taxa
print("Starting Part 22 for kingman trees")
snj = spectraltree.SpectralNeighborJoining(spectraltree.JC_similarity_matrix)
nj = spectraltree.NeighborJoining(spectraltree.JC_similarity_matrix)
rg = spectraltree.RG(spectraltree.JC_distance_matrix)
clrg = spectraltree.CLRG()
methods = [snj, nj,rg]
alphabet = "DNA"
king_trees = [spectraltree.unrooted_pure_kingman_tree(num_taxa) for num_taxa in num_taxa_s_king]
res_king22 = compare_methods.experiment(tree_list = king_trees, sequence_model = sequence_model, Ns = Ns_king, methods = methods, mutation_rates=mutation_rates, reps_per_tree=5, alphabet=alphabet , verbose=True)
return res_king22
def part22_bin(num_taxa_s_bin, Ns_bin):
## Part 2.2: different Taxa
print("Starting Part 22 for binary trees")
snj = spectraltree.SpectralNeighborJoining(spectraltree.JC_similarity_matrix)
nj = spectraltree.NeighborJoining(spectraltree.JC_similarity_matrix)
rg = spectraltree.RG(spectraltree.JC_distance_matrix)
clrg = spectraltree.CLRG()
methods = [snj, nj,rg]
alphabet = "DNA"
bin_trees = [spectraltree.balanced_binary(num_taxa) for num_taxa in num_taxa_s_bin]
res_bin22 = compare_methods.experiment(tree_list = bin_trees, sequence_model = sequence_model, Ns = Ns_bin, methods = methods, mutation_rates=mutation_rates, reps_per_tree=5, alphabet=alphabet , verbose=True)
return res_bin22
def test_jukes_cantor(self):
# reference_tree = spectraltree.unrooted_birth_death_tree(num_taxa, birth_rate=1, rng=)
# reference_tree = spectraltree.lopsided_tree(num_taxa)
reference_tree = spectraltree.balanced_binary(8)
jc = spectraltree.Jukes_Cantor(num_classes=2)
observations, meta = spectraltree.simulate_sequences(
seq_len=10_000,
tree_model=reference_tree,
seq_model=jc,
mutation_rate=jc.p2t(0.98),
rng=default_rng(678),
alphabet="Binary")
rg = spectraltree.RG(spectraltree.JC_distance_matrix)
recoverd_tree = rg(observations, meta)
print("(RF distance,F1 score):",
spectraltree.compare_trees(reference_tree, recoverd_tree))
self.assertTrue(spectraltree.topos_equal(
reference_tree, recoverd_tree)) # this doesn't work very well
import spectraltree import spectraltree.compare_methods as compare_methods tree_list = [spectraltree.balanced_binary(32)] jc = spectraltree.Jukes_Cantor(num_classes=2) Ns = [100,200,300] mutation_rates = [jc.p2t(0.9)] snj = spectraltree.SpectralNeighborJoining(spectraltree.JC_similarity_matrix) nj = spectraltree.NeighborJoining(spectraltree.JC_similarity_matrix) RG = spectraltree.RG(spectraltree.JC_distance_matrix) CLRG = spectraltree.CLRG() methods = [nj,snj,RG,CLRG] num_reps = 1 results = compare_methods.experiment(tree_list = tree_list, sequence_model = jc, Ns = Ns, methods=methods, mutation_rates = mutation_rates, reps_per_tree=num_reps,savepath='balanced_binary_m_512.pkl',folder = './data/',overwrite=True) df = compare_methods.results2frame(results) print(df) a =1