File size: 7,304 Bytes
29134bd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 |
import numpy as np
import timeit
import torch
from utils import rna2vec
#Node
class Node:
#init
def __init__(self, letter="", parent=None, root=False, last=False, depth=0, states=8):
self.exploitation_score = 0 # Exploitaion score
self.visits = 1 #How many visits
self.letter = letter #This node's letter
self.parent = parent #This node's parent node
self.states = states #How many states in node
self.children = np.array([None for _ in range(self.states)]) #This node's children
self.children_stat = np.zeros(self.states, dtype=bool) #Which stat are expanded
self.root = root # Is root? boolean
self.last = last # Is last node?
self.depth = depth # My depth
self.letters =["A_", "C_", "G_", "U_", "_A", "_C", "_G", "_U"]
#next_node
def next_node(self, child=0): #Return next node
assert self.children_stat[child] == True, "No child in here."
return self.children[child]
#back_parent
def back_parent(self): #Go back to parent
return self.parent, letters_map[self.letter]
#generate_child
def generate_child(self, child=0, last=False): #Generate child
assert self.children_stat[child] == False, "Already tree generated child at here"
self.children[child] = Node(letter=self.letters[child], parent=self, last=last, depth=self.depth+1, states=self.states) #New node
self.children_stat[child] = True #Stat = True
return self.children[child]
#backpropagation
def backpropagation(self, score=0):
self.visits += 1 # +1 to visit
if self.root == True: # if root, then stop
return self.exploitation_score
else:
self.exploitation_score += score #Add score to exploitation score
return self.parent.backpropagation(score=score) #Backpropagation to parent node
#UCT
def UCT(self):
return (self.exploitation_score / self.visits) + np.sqrt(np.log(self.parent.visits) / (2 * self.visits)) #UCT score
#MCTS
class MCTS:
def __init__(self, target_encoded, depth=20, iteration=1000, states=8, target_protein="", device='cpu'):
self.states = states #How many states
self.root = Node(letter="", parent=None, root=True, last=False, states=self.states) #root node
self.depth = depth #Maximum depth
self.iteration = iteration #iteration for expand
self.target_protein = target_protein #target protein's amino acid sequence
self.device = device
self.encoded_targetprotein = target_encoded
self.base = ""
self.candidate = ""
self.letters =["A_", "C_", "G_", "U_", "_A", "_C", "_G", "_U"]
def make_candidate(self, classifier):
now = self.root
n = 0 # rounds
start_time = timeit.default_timer() #timer start
while len(self.base) < self.depth * 2: #If now is last node, then stop
n += 1
print(n, "round start!!!")
for _ in range(self.iteration):
now = self.select(classifier, now=now) #Select & Expand
terminate_time = timeit.default_timer()
time = terminate_time-start_time
base = self.find_best_subsequence() #Find best subsequence
self.base = base
print("best subsequence:", base)
print("Depth:", int(len(base)/2))
print("%02d:%02d:%2f" % ((time//3600), (time//60)%60, time%60))
print("=" * 80)
self.root = Node(letter="", parent=None, root=True, last=False, states=self.states, depth=len(self.base)/2)
now = self.root
self.candidate = self.base
return self.candidate
#selection
def select(self, classifier, now=None):
if now.depth == self.depth: #If last node, then stop
return self.root
next_node = 0
if np.sum(now.children_stat) == self.states: #If every child is expanded, then go to best child
best = 0
for i in range(self.states):
if best < now.children[i].UCT():
next_node = i
best = now.children[i].UCT()
else: #If not, then random
next_node = np.random.randint(0, self.states)
if now.children_stat[next_node] == False: #If selected child is not expanded, then expand and simulate
next_node = self.expand(classifier, child=next_node, now=now)
return self.root #start iteration at this node
return now.next_node(child=next_node)
#expand
def expand(self, classifier, child=None, now=None):
last = False
if now.depth == (self.depth-1): #If depth of this node is maximum depth -1, then next node is last
last = True
expanded_node = now.generate_child(child=child, last=last) #Expand
score = self.simulate(classifier, target=expanded_node) #Simulate
expanded_node.backpropagation(score=score) #Backporpagation
return child
#simulate
def simulate(self, classifier, target=None):
now = target #Target node
sim_seq = ""
while now.root != True: #Parent's letters
sim_seq = now.letter + sim_seq
now = now.parent
sim_seq = self.base + sim_seq
for i in range((self.depth * 2) - len(sim_seq)): #Random child letters
r = np.random.randint(0,self.states)
sim_seq += self.letters[r]
sim_seq = self.reconstruct(sim_seq)
scores = []
classifier.eval()
with torch.no_grad():
sim_seq = self.reconstruct(sim_seq)
sim_seq = np.array([sim_seq])
apta = torch.tensor(rna2vec(sim_seq), dtype=torch.int64).to('cuda')
score = classifier(apta, self.encoded_targetprotein)
return score
#recommend
def get_candidate(self):
return self.reconstruct(self.candidate)
def find_best_subsequence(self):
now = self.root
stop = False
base = self.base
for _ in range((self.depth*2) - len(base)):
best = 0
next_node = 0
for j in range(self.states):
if now.children_stat[j] == True:
if best < now.children[j].UCT():
next_node = j
best = now.children[j].UCT()
now = now.next_node(child=next_node)
base += now.letter
if np.sum(now.children_stat) == 0:
break
return base
#reconstruct
def reconstruct(self, seq=""):
r_seq = ""
for i in range(0, len(seq), 2):
if seq[i] == '_':
r_seq = r_seq + seq[i+1]
else:
r_seq = seq[i] + r_seq
return r_seq
def reset(self):
self.base = ""
self.candidate = ""
self.root = Node(letter="", parent=None, root=True, last=False, states=self.states) |