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ModelPredictorTraining.py
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#!/usr/bin/env python3
## Fewer nodes in inter layers. Less layers in u models.
import subprocess
import os
import itertools
import numpy as np
import tensorflow as tf
import random
import math
import sys
import argparse
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV, train_test_split
from tensorboard.plugins.hparams import api as hp
# Argparse arguments
parser = argparse.ArgumentParser()
parser.add_argument('-sequence_type', type=str, default='DNA',
help='Compulsory argument. Nucleotide or Amino acids data. Enter DNA for nucleotide sequences. Enter AA for amino acid sequences.', required=True)
parser.add_argument('-neural_network', type=str, default='b1', choices=['b1','b2','b3','b10','u','cu'],
help='Optional argument. Which neural network should be trained. Default: b1', required=False)
parser.add_argument('-seqlen', type=int, default=None,
help='Optional argument.The length of the training sequences. Default for DNA: 100000\tDefault for amino acids: 1000000',required=False)
parser.add_argument('-seqnum', type=int, default=None,
help='Optional argument. The number of sequences used for training. Actual number is multiplied by 30 for DNA and by 24 for amino acids. Default for DNA: 30000(x30)\tDefault for amino acids: 500(x24)', required=False)
parser.add_argument('-epochs', type=int, default=500,
help='Optional argument. The number of epochs is a hyperparameter that defines the number times that the learning algorithm will work through the entire training dataset. Default: 500', required=False)
parser.add_argument('-predict_mode', type=int, default=0,
help='Optional argument. Type of predicted sequences either normal internal branches(0) or short internal branches(1). Default: 0',required=False)
parser.add_argument('-predict_seqlen', type=int, default=10000,
help='Optional argument. Optional argument. The length of the prediction sequences. Default:10000', required=False)
parser.add_argument('-predict_seqnum', type=int, default=1000,
help='Optional argument. The number of the prediction sequences. Default: 1000', required=False)
args = parser.parse_args()
python_random_seed = 13
os.environ['MKL_NUM_THREADS'] = '10'
os.environ['GOTO_NUM_THREADS'] = '10'
os.environ['OMP_NUM_THREADS'] = '10'
os.environ['openmp'] = 'True'
tf.config.threading.set_inter_op_parallelism_threads(10)
tf.config.threading.set_intra_op_parallelism_threads(10)
tmpdir = os.getcwd() + "/"
runID = str(os.getpid()*random.randint(1,100000))
## Switches:
predict_mode = args.predict_mode ## 0: normal, 1: short internal branches
random.seed(python_random_seed)
#global variables:
if (args.sequence_type == 'DNA' and args.seqlen == None and args.seqnum == None):
global_seqlen = 100000
global_replicates = 30000
global_replicates_short_internalbranch = 30000
elif (args.sequence_type == 'AA' and args.seqlen == None and args.seqnum == None):
global_seqlen = 1000000
global_replicates = 500
global_replicates_short_internalbranch = 500
elif (args.sequence_type == 'DNA' and args.seqlen == None):
global_seqlen = 100000
global_replicates = args.seqnum
global_replicates_short_internalbranch = args.seqnum
elif (args.sequence_type == 'AA' and args.seqlen == None):
global_seqlen = 1000000
global_replicates = args.seqnum
global_replicates_short_internalbranch = args.seqnum
elif (args.sequence_type == 'DNA' and args.seqnum == None):
global_seqlen = args.seqlen
global_replicates = 30000
global_replicates_short_internalbranch = 30000
elif (args.sequence_type == 'AA' and args.seqnum == None):
global_seqlen = args.seqlen
global_replicates = 500
global_replicates_short_internalbranch = 500
else:
global_seqlen = args.seqlen
global_replicates = args.seqnum
global_replicates_short_internalbranch = args.seqnum
prediction_replicates = args.predict_seqnum
global_predict_seqlen = args.predict_seqlen
total_replicates = 3*global_replicates + 3*global_replicates_short_internalbranch
if args.sequence_type == 'DNA' :
frequency_array = np.empty((5 * total_replicates, 256))
evo_mod_array = np.empty((5 * total_replicates, 1))
if args.sequence_type == 'AA' :
frequency_array = np.empty((4 * total_replicates, 160000))
evo_mod_array = np.empty((4 * total_replicates, 1))
global_min_branchlength = 0.1
global_max_branchlength = 0.5
global_min_short_internal_branchlength = 0.001
global_max_short_internal_branchlength = 0.02
base_models_choice_list = ['JC','K2P','F81','HKY','GTR']
base_models_choice_aa = ['JTT', 'LG', 'WAG', 'DAY']
# Parameters for nucleotide substitution models
# All models
global_min_pinv = 0
global_max_pinv = 0.50
global_min_shape = 0.01
global_max_shape = 4
# K2P, F84, HKY, GTR
global_min_tstv = 1.0
global_max_tstv = 3.0
# F81, F84, HKY, GTR
global_min_basefreq = 0.2
global_max_basefreq = 0.3
# GTR
global_min_rrates_GTR = 0.1
global_max_rrates_GTR = 1.0
print("Parameters used:")
print("prediction_replicates_DNA: ",prediction_replicates)
print("prediction_replicates_AA: ",prediction_replicates)
print("global_predict_seqlen_DNA: ",global_predict_seqlen)
print("global_predict_seqlen_AA: ",global_predict_seqlen)
print("global_min_branchlength: ",global_min_branchlength)
print("global_max_branchlength: ",global_max_branchlength)
print("global_min_short_internal_branchlength: ",global_min_short_internal_branchlength)
print("global_max_short_internal_branchlength: ",global_max_short_internal_branchlength )
print("global_min_pinv: ", global_min_pinv)
print("global_max_pinv: ", global_max_pinv)
print("global_min_shape: ", global_min_shape)
print("global_max_shape: ", global_max_shape)
print("base_models_choice_list: ", base_models_choice_list)
print("base_models_choice_aa: ", base_models_choice_aa)
##### Grid search parameters:
ad = tf.keras.optimizers.Adam()
def get_optimizer(name):
if name == "ad":
return ad
### HPARAM
HP_ACTIVATION = hp.HParam('activation', hp.Discrete(['tanh']))
HP_DROPOUT = hp.HParam('dropout', hp.Discrete([0.3]))
HP_OPTIMIZER = hp.HParam('optimizer', hp.Discrete(['ad']))
METRIC_ACCURACY = 'accuracy'
HPARAMS = [
HP_ACTIVATION,
HP_DROPOUT,
HP_OPTIMIZER,
]
### Evaluation routines:
def highest_likelihood (predictions):
topo = []
if args.sequence_type == 'DNA':
for prediction in predictions:
if prediction[0] > prediction[1] and prediction[0] > prediction[2] and prediction[0] > prediction[3] and prediction[0] > prediction[4]:
topo.append('JC')
if prediction[1] > prediction[0] and prediction[1] > prediction[2] and prediction[1] > prediction[3] and prediction[1] > prediction[4]:
topo.append('K2P')
if prediction[2] > prediction[0] and prediction[2] > prediction[1] and prediction[2] > prediction[3] and prediction[2] > prediction[4]:
topo.append('F81')
if prediction[3] > prediction[0] and prediction[3] > prediction[1] and prediction[3] > prediction[2] and prediction[3] > prediction[4]:
topo.append('HKY')
if prediction[4] > prediction[0] and prediction[4] > prediction[1] and prediction[4] > prediction[2] and prediction[4] > prediction[3]:
topo.append('GTR')
if args.sequence_type == 'AA':
for prediction in predictions:
if prediction[0] > prediction[1] and prediction[0] > prediction[2] and prediction[0] > prediction[3] :
topo.append('JTT')
if prediction[1] > prediction[0] and prediction[1] > prediction[2] and prediction[1] > prediction[3] :
topo.append('LG')
if prediction[2] > prediction[0] and prediction[2] > prediction[1] and prediction[2] > prediction[3] :
topo.append('WAG')
if prediction[3] > prediction[0] and prediction[3] > prediction[1] and prediction[3] > prediction[2] :
topo.append('DAY')
return topo
def count_evo_mod_by_likelihood(predictions):
topo = highest_likelihood (predictions)
if args.sequence_type == 'DNA' :
return [topo.count('JC')/len(topo), topo.count('K2P')/len(topo), topo.count('F81')/len(topo), topo.count('HKY')/len(topo), topo.count('GTR')/len(topo)]
if args.sequence_type == 'AA' :
return [topo.count('JTT')/len(topo), topo.count('LG')/len(topo), topo.count('WAG')/len(topo), topo.count('DAY')/len(topo)]
def print_highest_likelihood_evaluation(model, d0, d1, d2,d3,d4=None):
pred0 = model.predict(d0)
pred1 = model.predict(d1)
pred2 = model.predict(d2)
pred3 = model.predict(d3)
print(count_evo_mod_by_likelihood(pred0))
print(count_evo_mod_by_likelihood(pred1))
print(count_evo_mod_by_likelihood(pred2))
print(count_evo_mod_by_likelihood(pred3))
if args.sequence_type == 'DNA':
pred4 = model.predict(d4)
print(count_evo_mod_by_likelihood(pred4))
def find_frequency(freq_file):
with open(freq_file,"r") as frequency_file:
frequency_list = []
for line in frequency_file:
column = line.split("\t")
if len(column[0]) == 4:
frequency_list.append(np.float32(column[1]))
else :
f_e = open('file_error.txt', 'a')
f_e.write(line)
f_e.write('\n')
f_e.close()
return frequency_list
def make_tree_file_model(file_name,topology,B1,B2,B3,B4,B5,B6, seqlen, modelname):
with open(file_name,"w") as tree_file:
if topology == 0:
string = "1.0 {0} {1} ((A:{2},B:{3}):{4},(C:{5},D:{6}):{7})\n".format(seqlen, modelname, B1,B2,B3,B4,B5,B6)
if topology ==1 :
string = "1.0 {0} {1} ((A:{2},C:{3}):{4},(B:{5},D:{6}):{7})\n".format(seqlen, modelname, B1,B2,B3,B4,B5,B6)
if topology ==2:
string = "1.0 {0} {1} ((A:{2},D:{3}):{4},(C:{5},B:{6}):{7})\n".format(seqlen, modelname, B1,B2,B3,B4,B5,B6)
print(string)
tree_file.write(string)
def make_model_file(file_name, model, alpha, pinv, tstv,basefreq_1,basefreq_2,GTR_A_C,GTR_A_G,GTR_A_T,GTR_C_G,GTR_C_T,GTR_G_T, model_name):
with open(file_name, "w") as model_file:
if model == 'JC':
model_file.write("begin model\n")
model_file.write("name: {0}\n".format(model_name))
model_file.write('modeltype: {0}\n'.format(model))
string = "shape: {0}\n".format(alpha)
model_file.write(string)
string = "pinv: {0}\n".format(pinv)
model_file.write(string)
model_file.write("end model\n")
elif model == 'K2P':
model_file.write("begin model\n")
model_file.write("name: {0}\n".format(model_name))
model_file.write('modeltype: {0}\n'.format(model))
string = "shape: {0}\n".format(alpha)
model_file.write(string)
string = "pinv: {0}\n".format(pinv)
model_file.write(string)
string = 'tstv: {0}\n'.format(tstv)
model_file.write(string)
model_file.write("end model\n")
elif model == 'F81':
model_file.write("begin model\n")
model_file.write("name: {0}\n".format(model_name))
model_file.write('modeltype: {0}\n'.format(model))
string = "shape: {0}\n".format(alpha)
model_file.write(string)
string = "pinv: {0}\n".format(pinv)
model_file.write(string)
string = 'basefreq: {0} {1} {2} {3}\n'.format(basefreq_1,basefreq_2,basefreq_1,basefreq_2)
model_file.write(string)
model_file.write("end model\n")
elif model == 'HKY':
model_file.write("begin model\n")
model_file.write("name: {0}\n".format(model_name))
model_file.write('modeltype: {0}\n'.format(model))
string = "shape: {0}\n".format(alpha)
model_file.write(string)
string = "pinv: {0}\n".format(pinv)
model_file.write(string)
string = 'tstv: {0}\n'.format(tstv)
model_file.write(string)
string = 'basefreq: {0} {1} {2} {3}\n'.format(basefreq_1, basefreq_2, basefreq_1, basefreq_2)
model_file.write(string)
model_file.write("end model\n")
elif model == 'GTR':
model_file.write("begin model\n")
model_file.write("name: {0}\n".format(model_name))
model_file.write('modeltype: {0}\n'.format(model))
string = "shape: {0}\n".format(alpha)
model_file.write(string)
string = "pinv: {0}\n".format(pinv)
model_file.write(string)
string = 'basefreq: {0} {1} {2} {3}\n'.format(basefreq_1, basefreq_2, basefreq_1, basefreq_2)
model_file.write(string)
string = 'rrates: {} {} {} {} {} {}\n'.format(GTR_A_C,GTR_A_G,GTR_A_T,GTR_C_G,GTR_C_T,GTR_G_T)
model_file.write(string)
model_file.write("end model\n")
elif model in base_models_choice_aa :
model_file.write("begin aa-model\n")
model_file.write("name: {0}\n".format(model_name))
model_file.write('modeltype: {0}\n '.format(model))
string = "shape: {0}\n".format(alpha)
model_file.write(string)
string = "pinv: {0}\n".format(pinv)
model_file.write(string)
model_file.write("end model\n")
print("Model file written:")
def simulate_random_lengths_random_free_model(topology, seed, base_model_list, min_brlen, max_brlen, min_internal_brlen,
max_internal_brlen, min_pinv, max_pinv,
min_shape, max_shape, seqlen, base_model_list_aa,global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR, md):
model_filename = tmpdir + "Modelfile_free" + runID + "_" + str(seed) + ".txt"
seq_filename = tmpdir + "sim-sequences" + runID + "_" + str(seed) + ".txt"
sim_filename = tmpdir + "sim_tree" + runID + "_" + str(seed) + ".txt"
if args.sequence_type == 'DNA':
model = md
modelname = model + '_model'
elif args.sequence_type == 'AA':
model = md
modelname = model + '_model'
shape = random.uniform(min_shape, max_shape)
pinv = random.uniform(min_pinv, max_pinv)
r1 = random.uniform(min_brlen, max_brlen)
r2 = random.uniform(min_brlen, max_brlen)
r3 = random.uniform(min_internal_brlen, max_internal_brlen)
r4 = random.uniform(min_brlen, max_brlen)
r5 = random.uniform(min_brlen, max_brlen)
r3 = r3 / 2
r6 = r3
tstv = random.uniform(global_min_tstv, global_max_tstv)
basefreq_1 = np.random.normal(0.5,0.03,1)/2
while basefreq_1 > 0.3 or basefreq_1 < 0.2:
basefreq_1 = np.random.normal(0.5,0.03,1)/2
basefreq_2 = (1 - 2*basefreq_1)/2
basefreq_1 = float(basefreq_1)
basefreq_2 = float(basefreq_2)
GTR_A_C = random.uniform(global_min_rrates_GTR,global_max_rrates_GTR)
GTR_A_G = random.uniform(global_min_rrates_GTR, global_max_rrates_GTR)
GTR_A_T = random.uniform(global_min_rrates_GTR, global_max_rrates_GTR)
GTR_C_G = random.uniform(global_min_rrates_GTR, global_max_rrates_GTR)
GTR_C_T = random.uniform(global_min_rrates_GTR, global_max_rrates_GTR)
GTR_G_T = random.uniform(global_min_rrates_GTR, global_max_rrates_GTR)
print("Calling make_model_file: ", model_filename, " ", model, " ", shape, " ", pinv, " ", modelname)
make_model_file(model_filename, model, shape, pinv, tstv,basefreq_1,basefreq_2,GTR_A_C,GTR_A_G,GTR_A_T,GTR_C_G,GTR_C_T,GTR_G_T, modelname)
make_tree_file_model(sim_filename, topology, r1, r2, r3, r4, r5, r6, seqlen, modelname)
all_sim_filename = "all_sim_tree" + runID + ".txt"
os.system("echo " + str(seed) + " >> " + all_sim_filename)
os.system("cat " + sim_filename + " >> " + all_sim_filename)
command = "PolyMoSim-v1.1.4 -s " + str(
seed) + " -m " + model_filename + " -t " + sim_filename + " -f site_pattern_freq_relative_fill -n 1 1> " + seq_filename
subprocess.run([command], shell=True)
individual_frequencies = find_frequency(seq_filename)
os.remove(seq_filename)
os.remove(sim_filename)
os.remove(model_filename)
return individual_frequencies
####################################
# my main program starts here:
####################################
topology_list = []
evo_mod_list = []
frequency_simulations_filename = 'trainFrequency' + '_all_evo_mod' + '_R_' + str(global_replicates) + "_B_" + str(
global_min_branchlength) + "_" + str(global_max_branchlength) + "_r_" + str(
global_replicates_short_internalbranch) + "_b_" + str(global_min_short_internal_branchlength) + "_" + str(
global_max_short_internal_branchlength) + "_i_" + str(global_min_pinv) + "_" + str(
global_max_pinv) + "_a_" + str(
global_min_shape) + "_" + str(global_max_shape) + '_tstv_' + str(global_min_tstv) + '_' + str(
global_max_tstv) + "_pseed_" + str(python_random_seed) + ".npy"
evo_mod_simulations_filename = "trainEvoModClass" + '_all_evo_mod'+ '_R_' + str(global_replicates) + "_B_" + str(
global_min_branchlength) + "_" + str(global_max_branchlength) + "_r_" + str(
global_replicates_short_internalbranch) + "_b_" + str(global_min_short_internal_branchlength) + "_" + str(
global_max_short_internal_branchlength) + "_i_" + str(global_min_pinv) + "_" + str(
global_max_pinv) + "_a_" + str(
global_min_shape) + "_" + str(global_max_shape) + '_tstv_' + str(
global_min_tstv) + '_' + str(
global_max_tstv) + "_pseed_" + str(python_random_seed) + ".npy"
### Frequency files:
print("Filenames training:")
print(frequency_simulations_filename)
print(evo_mod_simulations_filename)
sys.stdout.flush()
if os.path.isfile(frequency_simulations_filename):
frequency_array = np.load(frequency_simulations_filename)
evo_mod_array= np.load(evo_mod_simulations_filename)
else:
if args.sequence_type == 'DNA':
for j in base_models_choice_list:
if j == 'JC':
counter = 0
x = 0
elif j == 'K2P':
counter = total_replicates
x = 1
elif j == 'F81':
counter = 2*total_replicates
x = 2
elif j == 'HKY':
counter = 3*total_replicates
x = 3
elif j == 'GTR':
counter = 4*total_replicates
x = 4
for i in range(0, global_replicates):
temporary_array = simulate_random_lengths_random_free_model(0, i, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_branchlength, global_max_branchlength,
global_min_pinv, global_max_pinv, global_min_shape,
global_max_shape, global_seqlen,
base_models_choice_aa,global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR, j)
frequency_array[i+counter] = temporary_array
evo_mod_list.append(x)
for i in range(global_replicates, 2 * global_replicates):
temporary_array = simulate_random_lengths_random_free_model(0, i, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_branchlength, global_max_branchlength,
global_min_pinv, global_max_pinv, global_min_shape,
global_max_shape, global_seqlen,
base_models_choice_aa,
global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR, j)
frequency_array[i+counter] = temporary_array
evo_mod_list.append(x)
for i in range(2 * global_replicates, 3 * global_replicates):
temporary_array = simulate_random_lengths_random_free_model(0, i, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_branchlength, global_max_branchlength,
global_min_pinv, global_max_pinv, global_min_shape,
global_max_shape, global_seqlen,
base_models_choice_aa,\
global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR, j)
frequency_array[i+counter] = temporary_array
evo_mod_list.append(x)
for i in range(3 * global_replicates, 3 * global_replicates + global_replicates_short_internalbranch):
temporary_array = simulate_random_lengths_random_free_model(0, i, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_short_internal_branchlength,
global_max_short_internal_branchlength,
global_min_pinv, global_max_pinv, global_min_shape,
global_max_shape, global_seqlen,
base_models_choice_aa,
global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR, j)
frequency_array[i+counter] = temporary_array
evo_mod_list.append(x)
for i in range(3 * global_replicates + global_replicates_short_internalbranch,
3 * global_replicates + 2 * global_replicates_short_internalbranch):
temporary_array = simulate_random_lengths_random_free_model(0, i, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_short_internal_branchlength,
global_max_short_internal_branchlength,
global_min_pinv, global_max_pinv, global_min_shape,
global_max_shape, global_seqlen,
base_models_choice_aa,global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR, j)
frequency_array[i+counter] = temporary_array
evo_mod_list.append(x)
for i in range(3 * global_replicates + 2 * global_replicates_short_internalbranch,
3 * global_replicates + 3 * global_replicates_short_internalbranch):
temporary_array = simulate_random_lengths_random_free_model(0, i, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_short_internal_branchlength,
global_max_short_internal_branchlength,
global_min_pinv, global_max_pinv, global_min_shape,
global_max_shape, global_seqlen,
base_models_choice_aa,global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR, j)
frequency_array[i+counter] = temporary_array
evo_mod_list.append(x)
print('Freq_shape: ', frequency_array.shape)
evo_mod_array = np.array(evo_mod_list)
evo_mod_array = np.reshape(evo_mod_array,(5*total_replicates,1))
print('Evo_mod_shape', evo_mod_array.shape)
np.save(frequency_simulations_filename, frequency_array)
np.save(evo_mod_simulations_filename, evo_mod_array)
elif args.sequence_type == 'AA':
for j in base_models_choice_aa:
if j == 'JTT':
counter = 0
x = 0
elif j == 'LG':
counter = total_replicates
x = 1
elif j == 'WAG':
counter = 2 * total_replicates
x = 2
elif j == 'DAY':
counter = 3 * total_replicates
x = 3
for i in range(0, global_replicates):
temporary_array = simulate_random_lengths_random_free_model(0, i, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_branchlength, global_max_branchlength,
global_min_pinv, global_max_pinv, global_min_shape,
global_max_shape, global_seqlen,
base_models_choice_aa,global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR,j)
frequency_array[i+counter] = temporary_array
evo_mod_list.append(x)
for i in range(global_replicates, 2 * global_replicates):
temporary_array = simulate_random_lengths_random_free_model(1, i, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_branchlength, global_max_branchlength,
global_min_pinv, global_max_pinv, global_min_shape,
global_max_shape, global_seqlen,
base_models_choice_aa,global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR,j)
frequency_array[i+counter] = temporary_array
evo_mod_list.append(x)
for i in range(2 * global_replicates, 3 * global_replicates):
temporary_array = simulate_random_lengths_random_free_model(2, i, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_branchlength, global_max_branchlength,
global_min_pinv, global_max_pinv, global_min_shape,
global_max_shape, global_seqlen,
base_models_choice_aa,global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR,j)
frequency_array[i+counter] = temporary_array
evo_mod_list.append(x)
for i in range(3 * global_replicates, 3 * global_replicates + global_replicates_short_internalbranch):
temporary_array = simulate_random_lengths_random_free_model(0, i, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_short_internal_branchlength,
global_max_short_internal_branchlength,
global_min_pinv, global_max_pinv, global_min_shape,
global_max_shape, global_seqlen,
base_models_choice_aa,global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR,j)
frequency_array[i+counter] = temporary_array
evo_mod_list.append(x)
for i in range(3 * global_replicates + global_replicates_short_internalbranch,
3 * global_replicates + 2 * global_replicates_short_internalbranch):
temporary_array = simulate_random_lengths_random_free_model(1, i, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_short_internal_branchlength,
global_max_short_internal_branchlength,
global_min_pinv, global_max_pinv, global_min_shape,
global_max_shape, global_seqlen,
base_models_choice_aa,global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR,j)
frequency_array[i+counter] = temporary_array
evo_mod_list.append(x)
for i in range(3 * global_replicates + 2 * global_replicates_short_internalbranch,
3 * global_replicates + 3 * global_replicates_short_internalbranch):
temporary_array = simulate_random_lengths_random_free_model(2, i, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_short_internal_branchlength,
global_max_short_internal_branchlength,
global_min_pinv, global_max_pinv, global_min_shape,
global_max_shape, global_seqlen,
base_models_choice_aa,global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR,j)
frequency_array[i+counter] = temporary_array
evo_mod_list.append(x)
print('Freq_shape: ', frequency_array.shape)
evo_mod_array = np.array(evo_mod_list)
evo_mod_array = np.reshape(evo_mod_array,(4*total_replicates,1))
print('Evo_mod_shape', evo_mod_array.shape)
np.save(frequency_simulations_filename, frequency_array)
np.save(evo_mod_simulations_filename, evo_mod_array)
print('Shape of the frequency array: ', frequency_array.shape)
print(frequency_array)
temporary_JC = np.empty((prediction_replicates, 256))
temporary_K2P = np.empty((prediction_replicates, 256))
temporary_F81 = np.empty((prediction_replicates, 256))
temporary_HKY = np.empty((prediction_replicates, 256))
temporary_GTR = np.empty((prediction_replicates, 256))
temporary_JTT = np.empty((prediction_replicates, 160000))
temporary_LG = np.empty((prediction_replicates, 160000))
temporary_WAG = np.empty((prediction_replicates, 160000))
temporary_DAY = np.empty((prediction_replicates, 160000))
python_random_seed += 1
random.seed(python_random_seed)
temporary_JC_filename = " "
temporary_K2P_filename = " "
temporary_F81_filename = " "
temporary_HKY_filename = " "
temporary_GTR_filename = " "
temporary_JTT_filename = " "
temporary_LG_filename = " "
temporary_WAG_filename = " "
temporary_DAY_filename = " "
if predict_mode == 0:
if args.sequence_type == 'DNA' :
temporary_JC_filename = "predict_JC" +"_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + "_pseed_" + str(python_random_seed) + ".npy"
temporary_K2P_filename = "predict_K2P" + "_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + "_pseed_" + str(python_random_seed) + ".npy"
temporary_F81_filename = "predict_F81" + "_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + "_pseed_" + str(python_random_seed) + ".npy"
temporary_HKY_filename = "predict_HKY" + "_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + "_pseed_" + str(python_random_seed) + ".npy"
temporary_GTR_filename = "predict_GTR" + "_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + "_pseed_" + str(python_random_seed) + ".npy"
elif args.sequence_type == 'AA' :
temporary_JTT_filename = "predict_JTT" +"_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + "_pseed_" + str(python_random_seed) + ".npy"
temporary_LG_filename = "predict_LG" +"_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + "_pseed_" + str(python_random_seed) + ".npy"
temporary_WAG_filename = "predict_WAG" +"_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + "_pseed_" + str(python_random_seed) + ".npy"
temporary_DAY_filename = "predict_DAY" +"_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + "_pseed_" + str(python_random_seed) + ".npy"
if predict_mode == 1:
if args.sequence_type == 'DNA' :
temporary_JC_filename = "predict_JC" + "_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_short_internal_branchlength) + "_" + str(
global_max_short_internal_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + "_pseed_" + str(python_random_seed) + ".npy"
temporary_K2P_filename = "predict_K2P" + "_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_short_internal_branchlength) + "_" + str(
global_max_short_internal_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + '_tstv_' + str(global_min_tstv) + '_' + str(
global_max_tstv) +"_pseed_" + str(python_random_seed) + ".npy"
temporary_F81_filename = "predict_F81" + "_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_short_internal_branchlength) + "_" + str(
global_max_short_internal_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + '_bf_' + str(global_min_basefreq) + '_' + str(
global_max_basefreq) + "_pseed_" + str(python_random_seed) + ".npy"
temporary_HKY_filename = "predict_HKY" + "_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_short_internal_branchlength) + "_" + str(
global_max_short_internal_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + '_tstv_' + str(global_min_tstv) + '_' + str(
global_max_tstv) + '_bf_' + str(global_min_basefreq) + '_' + str(
global_max_basefreq) + "_pseed_" + str(python_random_seed) + ".npy"
temporary_GTR_filename = "predict_GTR" + "_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_short_internal_branchlength) + "_" + str(
global_max_short_internal_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + '_bf_' + str(global_min_basefreq) + '_' + str(
global_max_basefreq) + '_rt_' + str(global_min_rrates_GTR) + '_' + str(global_max_rrates_GTR) + "_pseed_" + str(python_random_seed) + ".npy"
elif args.sequence_type == 'AA':
temporary_JTT_filename = "predict_JTT" +"_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_short_internal_branchlength) + "_" + str(
global_max_short_internal_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + "_pseed_" + str(python_random_seed) + ".npy"
temporary_LG_filename = "predict_LG" +"_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_short_internal_branchlength) + "_" + str(
global_max_short_internal_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + "_pseed_" + str(python_random_seed) + ".npy"
temporary_WAG_filename = "predict_WAG" +"_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_short_internal_branchlength) + "_" + str(
global_max_short_internal_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + "_pseed_" + str(python_random_seed) + ".npy"
temporary_DAY_filename = "predict_DAY" +"_L_" + str(global_predict_seqlen) + "_r_" + str(
prediction_replicates) + "_B_" + str(global_min_branchlength) + "_" + str(
global_max_branchlength) + "_b_" + str(global_min_short_internal_branchlength) + "_" + str(
global_max_short_internal_branchlength) + "_a_" + str(global_min_shape) + "_" + str(global_max_shape) + "_i_" + str(
global_min_pinv) + "_" + str(global_max_pinv) + "_pseed_" + str(python_random_seed) + ".npy"
print("Filenames for prediction files:")
if args.sequence_type == 'DNA' :
print("predict_JC: ", temporary_JC_filename)
print("predict_K2P: ", temporary_K2P_filename)
print("predict_F81: ", temporary_F81_filename)
print("predict_HKY: ", temporary_HKY_filename)
print("predict_GTR: ", temporary_GTR_filename)
elif args.sequence_type == 'AA' :
print("predict_JTT: ", temporary_JTT_filename)
print("predict_LG: ", temporary_LG_filename)
print("predict_WAG: ", temporary_WAG_filename)
print("predict_DAY: ", temporary_DAY_filename)
sys.stdout.flush()
if os.path.isfile(temporary_JC_filename or temporary_JTT_filename):
if args.sequence_type == 'DNA' :
temporary_JC = np.load(temporary_JC_filename,allow_pickle=True)
temporary_K2P = np.load(temporary_K2P_filename,allow_pickle=True)
temporary_F81 = np.load(temporary_F81_filename,allow_pickle=True)
temporary_HKY = np.load(temporary_HKY_filename, allow_pickle=True)
temporary_GTR = np.load(temporary_GTR_filename, allow_pickle=True)
elif args.sequence_type == 'AA' :
temporary_JTT = np.load(temporary_JTT_filename,allow_pickle=True)
temporary_LG = np.load(temporary_LG_filename,allow_pickle=True)
temporary_WAG = np.load(temporary_WAG_filename,allow_pickle=True)
temporary_DAY = np.load(temporary_DAY_filename, allow_pickle=True)
else:
if args.sequence_type == 'DNA':
if predict_mode == 0:
base_models_choice_list = ['JC','K2P','F81','HKY','GTR']
for i in base_models_choice_list:
for j in range(0, prediction_replicates):
if i == 'JC':
temporary_JC[j] = simulate_random_lengths_random_free_model(0, 10000000 + j, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_branchlength, global_max_branchlength,
global_min_pinv, global_max_pinv,
global_min_shape, global_max_shape,
global_predict_seqlen,base_models_choice_aa,global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR,i)
if i == 'K2P':
temporary_K2P[j] = simulate_random_lengths_random_free_model(0, 20000000 + j, base_models_choice_list,
global_min_branchlength,
global_max_branchlength,
global_min_branchlength,
global_max_branchlength,
global_min_pinv, global_max_pinv,
global_min_shape, global_max_shape,
global_predict_seqlen,
base_models_choice_aa, global_min_tstv,
global_max_tstv,
global_min_rrates_GTR,
global_max_rrates_GTR, i)
if i == 'F81':
temporary_F81[j] = simulate_random_lengths_random_free_model(0, 30000000 + j, base_models_choice_list,
global_min_branchlength,
global_max_branchlength,
global_min_branchlength,
global_max_branchlength,
global_min_pinv, global_max_pinv,
global_min_shape, global_max_shape,
global_predict_seqlen,
base_models_choice_aa, global_min_tstv,
global_max_tstv,
global_min_rrates_GTR,
global_max_rrates_GTR, i)
if i == 'HKY':
temporary_HKY[j] = simulate_random_lengths_random_free_model(0, 50000000 + j, base_models_choice_list,
global_min_branchlength,
global_max_branchlength,
global_min_branchlength,
global_max_branchlength,
global_min_pinv, global_max_pinv,
global_min_shape, global_max_shape,
global_predict_seqlen,
base_models_choice_aa, global_min_tstv,
global_max_tstv,
global_min_rrates_GTR,
global_max_rrates_GTR, i)
if i == 'GTR':
temporary_GTR[j] = simulate_random_lengths_random_free_model(0, 60000000 + j, base_models_choice_list,
global_min_branchlength,
global_max_branchlength,
global_min_branchlength,
global_max_branchlength,
global_min_pinv, global_max_pinv,
global_min_shape, global_max_shape,
global_predict_seqlen,
base_models_choice_aa, global_min_tstv,
global_max_tstv,
global_min_rrates_GTR,
global_max_rrates_GTR, i)
if predict_mode == 1:
for i in base_models_choice_list:
for j in range(0, prediction_replicates):
if i == 'JC':
temporary_JC[j] = simulate_random_lengths_random_free_model(0, 10000000 + j, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_short_internal_branchlength, global_max_short_internal_branchlength,
global_min_pinv, global_max_pinv,
global_min_shape, global_max_shape,
global_predict_seqlen,base_models_choice_aa,global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR,i)
if i == 'K2P':
temporary_K2P[j] = simulate_random_lengths_random_free_model(0, 20000000 + j, base_models_choice_list,
global_min_branchlength,
global_max_branchlength,
global_min_short_internal_branchlength,
global_max_short_internal_branchlength,
global_min_pinv, global_max_pinv,
global_min_shape, global_max_shape,
global_predict_seqlen,
base_models_choice_aa, global_min_tstv,
global_max_tstv,
global_min_rrates_GTR,
global_max_rrates_GTR, i)
if i == 'F81':
temporary_F81[j] = simulate_random_lengths_random_free_model(0, 30000000 + j, base_models_choice_list,
global_min_branchlength,
global_max_branchlength,
global_min_short_internal_branchlength,
global_max_short_internal_branchlength,
global_min_pinv, global_max_pinv,
global_min_shape, global_max_shape,
global_predict_seqlen,
base_models_choice_aa, global_min_tstv,
global_max_tstv,
global_min_rrates_GTR,
global_max_rrates_GTR, i)
if i == 'HKY':
temporary_HKY[j] = simulate_random_lengths_random_free_model(0, 50000000 + j, base_models_choice_list,
global_min_branchlength,
global_max_branchlength,
global_min_short_internal_branchlength,
global_max_short_internal_branchlength,
global_min_pinv, global_max_pinv,
global_min_shape, global_max_shape,
global_predict_seqlen,
base_models_choice_aa, global_min_tstv,
global_max_tstv,
global_min_rrates_GTR,
global_max_rrates_GTR, i)
if i == 'GTR':
temporary_GTR[j] = simulate_random_lengths_random_free_model(0, 60000000 + j, base_models_choice_list,
global_min_branchlength,
global_max_branchlength,
global_min_short_internal_branchlength,
global_max_short_internal_branchlength,
global_min_pinv, global_max_pinv,
global_min_shape, global_max_shape,
global_predict_seqlen,
base_models_choice_aa, global_min_tstv,
global_max_tstv,
global_min_rrates_GTR,
global_max_rrates_GTR, i)
np.save(temporary_JC_filename, temporary_JC)
np.save(temporary_K2P_filename, temporary_K2P)
np.save(temporary_F81_filename, temporary_F81)
np.save(temporary_HKY_filename, temporary_HKY)
np.save(temporary_GTR_filename, temporary_GTR)
if args.sequence_type == 'AA':
if predict_mode == 0:
for i in base_models_choice_aa:
for j in range(0, prediction_replicates):
if i == 'JTT':
temporary_JTT[j] = simulate_random_lengths_random_free_model(0, 10000000 + j, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_branchlength, global_max_branchlength,
global_min_pinv, global_max_pinv,
global_min_shape, global_max_shape,
global_predict_seqlen,base_models_choice_aa,global_min_tstv,global_max_tstv,
global_min_rrates_GTR,global_max_rrates_GTR,i)
if i == 'LG':
temporary_LG[j] = simulate_random_lengths_random_free_model(0, 20000000 + j, base_models_choice_list,
global_min_branchlength,
global_max_branchlength,
global_min_branchlength,
global_max_branchlength,
global_min_pinv, global_max_pinv,
global_min_shape, global_max_shape,
global_predict_seqlen,
base_models_choice_aa, global_min_tstv,
global_max_tstv,
global_min_rrates_GTR,
global_max_rrates_GTR, i)
if i == 'WAG':
temporary_WAG[j] = simulate_random_lengths_random_free_model(0, 30000000 + j, base_models_choice_list,
global_min_branchlength,
global_max_branchlength,
global_min_branchlength,
global_max_branchlength,
global_min_pinv, global_max_pinv,
global_min_shape, global_max_shape,
global_predict_seqlen,
base_models_choice_aa, global_min_tstv,
global_max_tstv,
global_min_rrates_GTR,
global_max_rrates_GTR, i)
if i == 'DAY':
temporary_DAY[j] = simulate_random_lengths_random_free_model(0, 60000000 + j, base_models_choice_list,
global_min_branchlength,
global_max_branchlength,
global_min_branchlength,
global_max_branchlength,
global_min_pinv, global_max_pinv,
global_min_shape, global_max_shape,
global_predict_seqlen,
base_models_choice_aa, global_min_tstv,
global_max_tstv,
global_min_rrates_GTR,
global_max_rrates_GTR, i)
if predict_mode == 1:
for i in base_models_choice_aa:
for j in range(0, prediction_replicates):
if i == 'JTT':
temporary_JTT[j] = simulate_random_lengths_random_free_model(0, 10000000 + j, base_models_choice_list,
global_min_branchlength, global_max_branchlength,
global_min_short_internal_branchlength, global_max_short_internal_branchlength,
global_min_pinv, global_max_pinv,
global_min_shape, global_max_shape,