def main():
print("Hello World!")
print(
"This file demonstrates the usage of the DendroPy module and its functions."
)
print("\n1. Tree Generations\n")
print("\n1.1. Create two random birth-death trees and print them:\n")
# If you want to compare two trees, make sure you specify the same Taxon Namespace!
tns = dendropy.TaxonNamespace()
num_leaves = 5
t1 = dendropy.simulate.treesim.birth_death_tree(birth_rate=1.0,
death_rate=0.5,
num_extant_tips=num_leaves,
taxon_namespace=tns)
t2 = dendropy.simulate.treesim.birth_death_tree(birth_rate=1.0,
death_rate=0.2,
num_extant_tips=num_leaves,
taxon_namespace=tns)
print("\tTree 1: ", t1.as_string("newick"))
t1.print_plot()
print("\tTree 2: ", t2.as_string("newick"))
t2.print_plot()
print("\n2. Compare Trees\n")
print(
"\n2.1. Compare tree with itself and print Robinson-Foulds (RF) distance:\n"
)
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t1, t1))
print(
"\n2.2. Compare different trees and print Robinson-Foulds (RF) distance:\n"
)
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t1, t2))
print("\n3. Load Trees from Newick Files and Compare:\n")
print("\n3.1 Load trees from Newick files:\n")
# If you want to compare two trees, make sure you specify the same Taxon Namespace!
tns = dendropy.TaxonNamespace()
directory = '/Users/filipbergentoft/Desktop/Github/DD2434/Assignment 2/2_4/'
filename = directory + "data/q2_4/q2_4_tree_mixture.pkl_tree_0_newick.txt"
with open(filename, 'r') as input_file:
newick_str = input_file.read()
t0 = dendropy.Tree.get(data=newick_str,
schema="newick",
taxon_namespace=tns)
print("\tTree 0: ", t0.as_string("newick"))
t0.print_plot()
filename = directory + "data/q2_4/q2_4_tree_mixture.pkl_tree_1_newick.txt"
with open(filename, 'r') as input_file:
newick_str = input_file.read()
t1 = dendropy.Tree.get(data=newick_str,
schema="newick",
taxon_namespace=tns)
print("\tTree 1: ", t1.as_string("newick"))
t1.print_plot()
filename = directory + "data/q2_4/q2_4_tree_mixture.pkl_tree_2_newick.txt"
with open(filename, 'r') as input_file:
newick_str = input_file.read()
t2 = dendropy.Tree.get(data=newick_str,
schema="newick",
taxon_namespace=tns)
print("\tTree 2: ", t2.as_string("newick"))
t2.print_plot()
print("\n3.2 Compare trees and print Robinson-Foulds (RF) distance:\n")
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t0, t1))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t0, t2))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t1, t2))
print("\n4. Load Inferred Trees")
filename = directory + "data/q2_4/q2_4_own_results_em_topology.npy" # This is the result you have.
topology_list = np.load(filename)
print(topology_list.shape)
print(topology_list)
rt0 = Tree()
rt0.load_tree_from_direct_arrays(topology_list[0])
rt0 = dendropy.Tree.get(data=rt0.newick,
schema="newick",
taxon_namespace=tns)
print("\tInferred Tree 0: ", rt0.as_string("newick"))
rt0.print_plot()
rt1 = Tree()
rt1.load_tree_from_direct_arrays(topology_list[1])
rt1 = dendropy.Tree.get(data=rt1.newick,
schema="newick",
taxon_namespace=tns)
print("\tInferred Tree 1: ", rt1.as_string("newick"))
rt1.print_plot()
rt2 = Tree()
rt2.load_tree_from_direct_arrays(topology_list[2])
rt2 = dendropy.Tree.get(data=rt2.newick,
schema="newick",
taxon_namespace=tns)
print("\tInferred Tree 2: ", rt2.as_string("newick"))
rt2.print_plot()
print("\n4.2 Compare trees and print Robinson-Foulds (RF) distance:\n")
print("\tt0 vs inferred trees")
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t0, rt0))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t0, rt1))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t0, rt2))
print("\tt1 vs inferred trees")
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t1, rt0))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t1, rt1))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t1, rt2))
print("\tt2 vs inferred trees")
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t2, rt0))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t2, rt1))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t2, rt2))
print("\nInvestigate")
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t0, rt0))
print("\tRF distance: \t",
dendropy.calculate.treecompare.find_missing_bipartitions(t0, rt0))
print(
"\tRF distance: \t",
dendropy.calculate.treecompare.false_positives_and_negatives(t0, rt0))
print("\tRF distance: \t",
dendropy.calculate.treecompare.find_missing_bipartitions(t0, rt1))
print(
"\tRF distance: \t",
dendropy.calculate.treecompare.false_positives_and_negatives(t0, rt1))
def main():
print("\n1. Load samples from txt file.\n")
default_sample_filename = 'q_2_5_tm_10node_20sample_4clusters.pkl_samples.txt'
default_output_filename = 'q_2_5_tm_10node_20sample_4clusters_results'
default_num_clusters = 4
default_seed_val = 42
default_real_values_filename = "q_2_5_tm_10node_20sample_4clusters.pkl_tree_0_topology.npy"
samples = np.loadtxt(default_sample_filename,
delimiter="\t",
dtype=np.int32)
num_samples, num_nodes = samples.shape
print("\tnum_samples: ", num_samples, "\tnum_nodes: ", num_nodes)
print("\tSamples: \n", samples)
print("\n2. Run EM Algorithm.\n")
loglikelihood, topology_array, theta_array = em_algorithm(
default_seed_val, samples, num_clusters=default_num_clusters)
print("\n3. Save, print and plot the results.\n")
print("top array 0 = ", topology_array[0, :])
save_results(loglikelihood, topology_array, theta_array,
default_output_filename)
save_results(loglikelihood, topology_array[0, :], theta_array[0, :],
"q_2_5_tm_10node_20sample_4clusters0_results")
save_results(loglikelihood, topology_array[1, :], theta_array[1, :],
"q_2_5_tm_10node_20sample_4clusters1_results")
save_results(loglikelihood, topology_array[2, :], theta_array[2, :],
"q_2_5_tm_10node_20sample_4clusters2_results")
save_results(loglikelihood, topology_array[3, :], theta_array[3, :],
"q_2_5_tm_10node_20sample_4clusters3_results")
for i in range(default_num_clusters):
print("\n\tCluster: ", i)
print("\tTopology: \t", topology_array[i])
print("\tTheta: \t", theta_array[i])
Theta_Expected = np.array(theta_array.shape)
Topology_Expected = np.zeros((default_num_clusters, samples.shape[1]))
filename = "q_2_5_tm_10node_20sample_4clusters.pkl_tree_0_theta.npy"
theta_expected0 = np.load(filename)
filename = "q_2_5_tm_10node_20sample_4clusters.pkl_tree_0_topology.npy"
topology_expected0 = np.load(filename)
filename = "q_2_5_tm_10node_20sample_4clusters.pkl_pi.npy"
pi_expected = np.load(filename)
filename = "q_2_5_tm_10node_20sample_4clusters.pkl_tree_1_theta.npy"
theta_expected1 = np.load(filename)
filename = "q_2_5_tm_10node_20sample_4clusters.pkl_tree_1_topology.npy"
topology_expected1 = np.load(filename)
filename = "q_2_5_tm_10node_20sample_4clusters.pkl_tree_2_theta.npy"
theta_expected2 = np.load(filename)
filename = "q_2_5_tm_10node_20sample_4clusters.pkl_tree_2_topology.npy"
topology_expected2 = np.load(filename)
filename = "q_2_5_tm_10node_20sample_4clusters.pkl_tree_3_theta.npy"
theta_expected3 = np.load(filename)
filename = "q_2_5_tm_10node_20sample_4clusters.pkl_tree_3_topology.npy"
topology_expected3 = np.load(filename)
Theta_Expected = theta_array
thetafiles = [
theta_expected0, theta_expected1, theta_expected2, theta_expected3
]
topologyfiles = [
topology_expected0, topology_expected1, topology_expected2,
topology_expected3
]
for k in range(default_num_clusters):
Theta_Expected[k][:][:][:] = thetafiles[k]
Topology_Expected[k, :] = topologyfiles[k]
loglikelihood_expected = np.zeros(len(loglikelihood))
for iterations in range(len(loglikelihood)):
loglikelihood_expected[iterations] = log_likelihood(
default_num_clusters, samples, Theta_Expected, Topology_Expected,
pi_expected)
print("loglikelihood_expected = ", loglikelihood_expected[0])
plt.figure(figsize=(8, 3))
plt.subplot(121)
plt.plot(np.exp(loglikelihood_expected), label='Expected')
plt.plot(np.exp(loglikelihood), label='Estimated')
plt.ylabel("Likelihood of Mixture")
plt.xlabel("Iterations")
plt.subplot(122)
plt.plot(loglikelihood_expected, label='Expected')
plt.plot(loglikelihood, label='Estimated')
plt.ylabel("Log-Likelihood of Mixture")
plt.xlabel("Iterations")
plt.legend(loc=(1.04, 0))
plt.show()
print("\n4. Retrieve real results and compare.\n")
if default_real_values_filename != "":
print("\tComparing the results with real values...")
print("\t4.1. Make the Robinson-Foulds distance analysis.\n")
print("Hello World!")
print(
"This file demonstrates the usage of the DendroPy module and its functions."
)
print("\n1. Tree Generations\n")
print("\n1.1. Create two random birth-death trees and print them:\n")
tns = dendropy.TaxonNamespace()
num_leaves = 5
t1 = dendropy.simulate.treesim.birth_death_tree(
birth_rate=1.0,
death_rate=0.5,
num_extant_tips=num_leaves,
taxon_namespace=tns)
t2 = dendropy.simulate.treesim.birth_death_tree(
birth_rate=1.0,
death_rate=0.2,
num_extant_tips=num_leaves,
taxon_namespace=tns)
print("\tTree 1: ", t1.as_string("newick"))
t1.print_plot()
print("\tTree 2: ", t2.as_string("newick"))
t2.print_plot()
print("\n2. Compare Trees\n")
print(
"\n2.1. Compare tree with itself and print Robinson-Foulds (RF) distance:\n"
)
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t1, t1))
print(
"\n2.2. Compare different trees and print Robinson-Foulds (RF) distance:\n"
)
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t1, t2))
print("\n3. Load Trees from Newick Files and Compare:\n")
print("\n3.1 Load trees from Newick files:\n")
# If you want to compare two trees, make sure you specify the same Taxon Namespace!
tns = dendropy.TaxonNamespace()
filename = "q_2_5_tm_10node_20sample_4clusters.pkl_tree_0_newick.txt"
with open(filename, 'r') as input_file:
newick_str = input_file.read()
t0 = dendropy.Tree.get(data=newick_str,
schema="newick",
taxon_namespace=tns)
print("\tTree 0: ", t0.as_string("newick"))
t0.print_plot()
filename = "q_2_5_tm_10node_20sample_4clusters.pkl_tree_1_newick.txt"
with open(filename, 'r') as input_file:
newick_str = input_file.read()
t1 = dendropy.Tree.get(data=newick_str,
schema="newick",
taxon_namespace=tns)
print("\tTree 1: ", t1.as_string("newick"))
t1.print_plot()
filename = "q_2_5_tm_10node_20sample_4clusters.pkl_tree_2_newick.txt"
with open(filename, 'r') as input_file:
newick_str = input_file.read()
t2 = dendropy.Tree.get(data=newick_str,
schema="newick",
taxon_namespace=tns)
print("\tTree 2: ", t2.as_string("newick"))
t2.print_plot()
filename = "q_2_5_tm_10node_20sample_4clusters.pkl_tree_3_newick.txt"
with open(filename, 'r') as input_file:
newick_str = input_file.read()
t3 = dendropy.Tree.get(data=newick_str,
schema="newick",
taxon_namespace=tns)
print("\tTree 3: ", t3.as_string("newick"))
t3.print_plot()
print("\n3.2 Compare trees and print Robinson-Foulds (RF) distance:\n")
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t0, t1))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t0, t2))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t1, t2))
filename = "q_2_5_tm_10node_20sample_4clusters0_results_em_topology.npy"
topology_list = np.load(filename)
rt0 = Tree()
rt0.load_tree_from_direct_arrays(np.array(topology_list))
rt0 = dendropy.Tree.get(data=rt0.newick,
schema="newick",
taxon_namespace=tns)
print("\tInferred Tree 0: ", rt0.as_string("newick"))
rt0.print_plot()
filename = "q_2_5_tm_10node_20sample_4clusters1_results_em_topology.npy"
topology_list = np.load(filename)
rt1 = Tree()
rt1.load_tree_from_direct_arrays(np.array(topology_list))
rt1 = dendropy.Tree.get(data=rt1.newick,
schema="newick",
taxon_namespace=tns)
print("\tInferred Tree 1: ", rt1.as_string("newick"))
rt1.print_plot()
filename = "q_2_5_tm_10node_20sample_4clusters2_results_em_topology.npy"
topology_list = np.load(filename)
rt2 = Tree()
rt2.load_tree_from_direct_arrays(np.array(topology_list))
rt2 = dendropy.Tree.get(data=rt2.newick,
schema="newick",
taxon_namespace=tns)
print("\tInferred Tree 2: ", rt2.as_string("newick"))
rt2.print_plot()
filename = "q_2_5_tm_10node_20sample_4clusters3_results_em_topology.npy"
topology_list = np.load(filename)
rt3 = Tree()
rt3.load_tree_from_direct_arrays(np.array(topology_list))
rt3 = dendropy.Tree.get(data=rt3.newick,
schema="newick",
taxon_namespace=tns)
print("\tInferred Tree 3: ", rt3.as_string("newick"))
rt3.print_plot()
print("\n4.2 Compare trees and print Robinson-Foulds (RF) distance:\n")
print("\tt0 vs inferred trees")
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t0, rt0))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t0, rt1))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t0, rt2))
print("\tt1 vs inferred trees")
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t1, rt0))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t1, rt1))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t1, rt2))
print("\tt2 vs inferred trees")
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t2, rt0))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t2, rt1))
print("\tRF distance: \t",
dendropy.calculate.treecompare.symmetric_difference(t2, rt2))
print("\t4.2. Make the likelihood comparison.\n")
