def test_ultrametrize_with_empty_levels(self):
# Ultrametrize and set to 2 levels already ultrametric from tree below
# 0 1 2 3
# \/ d=1 \/ d=3
# 01 23
# \ / d=8
# 0123
src_matr = [
[0, 1, 8, 8],
[1, 0, 8, 8],
[8, 8, 0, 3],
[8, 8, 3, 0],
]
expected_matr = src_matr
res = get_ultra_metric(src_matr, UltraMetricParams(max_level=8))
self.assertEqual(expected_matr, res)
def test_ultrametrize_non_ultrametric(self):
# get matrix from sample 1 (test_ultrametrize_already_ultrametric)
# but some distances aren't ultrametric
src_matr = [
[0, 1, 7, 8], # here the difference: 7
[1, 0, 8, 8],
[7, 8, 0, 3], # here the difference: 7
[8, 8, 3, 0],
]
expected_matr = [
[0, 1, 2, 2],
[1, 0, 2, 2],
[2, 2, 0, 1],
[2, 2, 1, 0],
]
res = get_ultra_metric(src_matr, UltraMetricParams(max_level=2))
self.assertEqual(expected_matr, res)
def test_ultrametrize_to_top_level(self):
# Ultrametrize and set to 2 levels already ultrametric from tree below
# 0 1 2 3
# \/ d=1 \/ d=4.6 (here the difference, d=4.6, it will be 2nd level)
# 01 23
# \ / d=8
# 0123
src_matr = [
[0, 1, 8, 8],
[1, 0, 8, 8],
[8, 8, 0, 4.6], # here the difference - 4.6
[8, 8, 4.6, 0], # here the difference - 4.6
]
expected_matr = [
[0, 1, 2, 2],
[1, 0, 2, 2],
[2, 2, 0, 2],
[2, 2, 2, 0],
]
res = get_ultra_metric(src_matr, UltraMetricParams(max_level=2))
self.assertEqual(expected_matr, res)
chain_length_weight=0.0,
level_weight_multiplier=level_weight_multiplier,
use_flipping=use_flipping)
experiment_matrix = matrDiff.make_experiment_matrix(
global_params)
# corr = matrDiff.corrcoef(experiment_matrix=experiment_matrix)
# name = f"param_a={param_a}_corr_{corr:0.2f}_{systematic_tree}_{cluster_algorithm}_subtree_(thr,mult)=({global_params.subtree_threshold},{global_params.subtree_multiplier})_lev_mult={global_params.level_weight_multiplier}"
#print_matrix(experiment_matrix, name, matrDiff.names, corr, with_headers=True)
#experiment_array = make_experiment_array(experiment_matrix)
effective_cluster_algorithm = cluster_algorithm
if cluster_algorithm == 'ultrametric':
ultra_matrix = get_ultra_metric(
to_full_matrix(experiment_matrix),
UltraMetricParams(max_level=len(experiment_matrix)))
experiment_matrix = ultra_matrix
effective_cluster_algorithm = 'average'
plot_matrix = to_full_matrix(experiment_matrix)
# convert the redundant n*n square matrix form into a condensed nC2 array
# dist_array[{n choose 2}-{n-i choose 2} + (j-i-1)] is the distance between points i and j
dist_array = ssd.squareform(plot_matrix)
#clustered_trees = hierarchy.linkage(np.asarray(experiment_array), cluster_algorithm)
clustered_trees = hierarchy.linkage(
dist_array, effective_cluster_algorithm)
corr = corr_clustered_trees(clustered_trees, matrDiff.names,
matrDiff.make_systematic_matrix())
"../../input/systematic_tree_morph.xtg", ["Angiosperms"],
max_level=10)
init_values = [0.5, 0.1, 0.0]
x = init_values
global_params = GlobalParams(max_level=10,
param_a=x[0],
g_weight=x[1],
chain_length_weight=x[2])
experiment_matrix = matrDiff.make_full_experiment_matrix(global_params)
print(experiment_matrix)
for level_count in range(30):
name = f"ultrametric_{x[0]}_{x[1]}_{x[2]}_{level_count + 1}"
ultra_matrix = get_ultra_metric(
experiment_matrix, UltraMetricParams(max_level=level_count + 1))
ultra_array = make_experiment_array(ultra_matrix)
clustered_trees = hierarchy.linkage(np.asarray(ultra_array), 'average')
corrcoef = matrDiff.corrcoef(ultra_matrix)
corrcoef2 = common_corrcoef(experiment_matrix, ultra_matrix)
print_matrix(ultra_matrix,
name,
matrDiff.names,
corrcoef,
with_headers=True)
draw_plot(clustered_trees, matrDiff.names, name,
f"../../output/ultra_metric/{name}.png")