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main.py
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main.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Apr 10 11:16:05 2020
@author: Saint8312
"""
import time
import numpy as np
from scipy.optimize import differential_evolution
from differential_evolution import diff_evol, diff_evol_max
from Clustering import cluster_DE, cluster_DE_mm, mat_R_ij, transformation_matrix
import multiprocessing
import pickle
import HP_Protein_2D as hp2d
if __name__ == "__main__":
start = time.time() #timer
np.random.seed(13) #set seed if necessary
pool = multiprocessing.Pool()
#inputs
settings={
"E_contact": {"HH":-1., "HP":0., "PH":0., "PP":0.},
"ranges":np.array(((0,3),(3,6),(6,9)))
}
lower_bound = settings["ranges"][0][0]
upper_bound = settings["ranges"][settings["ranges"].shape[0]-1][settings["ranges"].shape[1]-1]
# hp_seq = hp2d.string_seq_to_bin("HHPPHPPHPPHPPHPPHPPHPPHH")
# len_hp_seq = hp_seq.shape[0]
# print(len_hp_seq)
## x = np.random.uniform(lower_bound,upper_bound,len_hp_seq-2) #x should be generated from optimization algorithms
## print(hp2d.func_2D_protein(x, hp_seq, settings))
# bounds = [(lower_bound, upper_bound)]*(len_hp_seq-2)
# #opt algo
# result = differential_evolution(hp2d.func_2D_protein, bounds, (hp_seq, settings), strategy='best1bin', disp=True, popsize=len_hp_seq*20, polish=True, workers=-1, maxiter=750)
# print(result)
# print("coor = ", hp2d.to_2D_cartesian_from_real(result.x, settings))
#
# ret_val = np.array(hp2d.return_val)
# np.save("data/UM24_data", hp2d.return_val, allow_pickle=True)
# ret_v = np.load("data/UM24_data.npy", allow_pickle=True)
# print(ret_v, ret_v.shape)
# end = time.time()
# print("elapsed time = ", end-start)
#
sequences = ["HPHPPHHPHPPHPHHPPHPH",
"HHPPHPPHPPHPPHPPHPPHPPHH",
"PPHPPHHPPPPHHPPPPHHPPPPHH",
"PPPHHPPHHPPPPPHHHHHHHPPHHPPPPHHPPHPP",
"PPHPHHHPHHHPPPPPHHHHHHHHHHPPPPPPHHPPHHPPHPPHHHHH",
"HHPHPHPHPHHHHPHPPPHPPPHPPPPHPPPHPPPHPHHHHPHPHPHPHH",
"PPHHHPHHHHHHHHPPPHHHHHHHHHHPHPPPHHHHHHHHHHHHPPPPHHHHHHPHHPHH",
"HHHHHHHHHHHHPHPHPPHHPPHHPPHPPHHPPHHPPHPPHHPPHHPPHPHPHHHHHHHHHHHH",
"HPPHPH"
]
files = ["UM20", "UM24", "UM25", "UM36", "UM48", "UM50", "UM60", "UM64", "test"]
for i in range(0, 1):
start = time.time() #timer
hp_seq = hp2d.string_seq_to_bin(sequences[i])
len_hp_seq = hp_seq.shape[0]
print(len_hp_seq)
bounds = [(lower_bound, upper_bound)]*(len_hp_seq-2)
# result = differential_evolution(hp2d.func_2D_protein, bounds, (hp_seq, settings), strategy='best1bin', disp=True, popsize=len_hp_seq*20, polish=False, workers=1, maxiter=20)
# result = diff_evol(hp2d.func_2D_protein, bounds, hp_seq,settings, popsize=len_hp_seq*25, maxiter=300, sobol=True)
# result = diff_evol_max(hp2d.F_2DHP, bounds, hp_seq,settings, popsize=len_hp_seq*25, maxiter=300, sobol=True)
spiral_settings = {"S":transformation_matrix, "R":mat_R_ij, "r":0.95, "m":250, "theta":45, "kmax":250}
DE_settings = {'mut':0.8, 'crossp':0.7, 'popsize':len_hp_seq*25, 'maxiter':40}
cluster_settings = {'m_cluster':250, 'gamma':0.2, 'epsilon':3, 'delta':0.1, 'k_cluster':20}
# result = cluster_DE(hp2d.F_2DHP, bounds, spiral_settings, DE_settings, hp_seq, settings,
# m_cluster=cluster_settings['m_cluster'], gamma=cluster_settings['gamma'],
# epsilon=cluster_settings['epsilon'], delta=cluster_settings['delta'], k_cluster=cluster_settings['k_cluster'])
result = cluster_DE_mm(hp2d.G_2DHP, bounds, spiral_settings, DE_settings, hp_seq, settings,
m_cluster=cluster_settings['m_cluster'], epsilon=cluster_settings['epsilon'],
delta=cluster_settings['delta'], k_cluster=cluster_settings['k_cluster'])
print(result)
coors = np.array([hp2d.to_2D_cartesian_from_real(res_, settings) for res_ in result])
Fs = np.array([hp2d.func_2D_protein(res_, hp_seq, settings) for res_ in result])
print("coor = ", coors)
end = time.time()
elapsed_time = end-start
print("elapsed time = ", end-start)
data = {'hp_seq':hp_seq, 'x': result, 'coor':coors, 'F':Fs, 'time':elapsed_time, 'params':[settings, spiral_settings, DE_settings, cluster_settings]}
with open("data/"+files[i]+"_mm_pkl", 'wb') as handle:
pickle.dump(data, handle)
with open("data/"+files[i]+"_mm_pkl", 'rb') as handle:
b = pickle.load(handle)
print(b)
# ret_val = np.array(hp2d.return_val)
# np.save("data/"+files[i]+"_data", hp2d.return_val, allow_pickle=True)
# ret_v = np.load("data/"+files[i]+"_data.npy", allow_pickle=True)
# print(ret_v, ret_v.shape)
# hp_seq = hp2d.string_seq_to_bin("HPPHPH")
# print("coor = ", hp2d.to_2D_cartesian_from_real(np.array([ 7.99533929,7.29496199,-0.58728589,6.26379575]), settings))
# print(hp2d.func_2D_protein(np.array([ 7.99533929,7.29496199,-0.58728589,6.26379575]), hp_seq, settings))