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import numpy as np
import timeit
import torch
from utils import rna2vec
from transformers import AutoTokenizer
#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_", "T_", "_A", "_C", "_G", "_T"]
#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', esm_alphabet=None):
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_", "T_", "_A", "_C", "_G", "_T"]
self.esm_alphabet = esm_alphabet
self.nt_tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/nucleotide-transformer-v2-50m-multi-species", trust_remote_code=True)
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().to('cuda')
with torch.no_grad():
sim_seq = np.array([sim_seq])
apta_toks = self.nt_tokenizer.batch_encode_plus(sim_seq, return_tensors='pt', padding='max_length', max_length=275)['input_ids']
apta_attention_mask = apta_toks != self.nt_tokenizer.pad_token_id
prot_attention_mask = self.encoded_targetprotein != self.esm_alphabet.padding_idx
score, _, _, _ = classifier(apta_toks.to('cuda'), self.encoded_targetprotein.to('cuda'), apta_attention_mask.to('cuda'), prot_attention_mask.to('cuda'))
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 current node has no expanded children, stop reconstructing.
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) |