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import os
import sys
import torch
import selfies as sf # selfies>=2.1.1
import pickle
import pandas as pd
import numpy as np
from datasets import Dataset
from rdkit import Chem
from transformers import AutoTokenizer, AutoModel
class SELFIES(torch.nn.Module):
def __init__(self):
super().__init__()
self.model = None
self.tokenizer = None
self.invalid = []
def get_selfies(self, smiles_list):
self.invalid = []
spaced_selfies_batch = []
for i, smiles in enumerate(smiles_list):
try:
selfies = sf.encoder(smiles.rstrip())
except:
try:
smiles = Chem.MolToSmiles(Chem.MolFromSmiles(smiles.rstrip()))
selfies = sf.encoder(smiles)
except:
selfies = "[]"
self.invalid.append(i)
spaced_selfies_batch.append(selfies.replace('][', '] ['))
return spaced_selfies_batch
def get_embedding(self, selfies):
encoding = self.tokenizer(selfies["selfies"], return_tensors='pt', max_length=128, truncation=True, padding='max_length')
input_ids = encoding['input_ids']
attention_mask = encoding['attention_mask']
outputs = self.model.encoder(input_ids=input_ids, attention_mask=attention_mask)
model_output = outputs.last_hidden_state
input_mask_expanded = attention_mask.unsqueeze(-1).expand(model_output.size()).float()
sum_embeddings = torch.sum(model_output * input_mask_expanded, 1)
sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
model_output = sum_embeddings / sum_mask
encoding["embedding"] = model_output
return encoding
def load(self, checkpoint="bart-2908.pickle"):
"""
inputs :
checkpoint (pickle object)
"""
self.tokenizer = AutoTokenizer.from_pretrained("ibm/materials.selfies-ted")
self.model = AutoModel.from_pretrained("ibm/materials.selfies-ted")
"""if os.path.isfile(checkpoint):
with open(checkpoint, "rb") as input_file:
self.model, self.tokenizer = pickle.load(input_file)
for p in sys.path:
file = p + "/" + checkpoint
if os.path.isfile(file):
with open(file, "rb") as input_file:
self.model, self.tokenizer = pickle.load(input_file)"""
# TODO: remove `use_gpu` argument in validation pipeline
def encode(self, smiles_list=[], use_gpu=False, return_tensor=False):
"""
inputs :
checkpoint (pickle object)
:return: embedding
"""
selfies = self.get_selfies(smiles_list)
selfies_df = pd.DataFrame(selfies,columns=["selfies"])
data = Dataset.from_pandas(selfies_df)
embedding = data.map(self.get_embedding, batched=True, num_proc=1, batch_size=128)
emb = np.asarray(embedding["embedding"].copy())
for idx in self.invalid:
emb[idx] = np.nan
print("Cannot encode {0} to selfies and embedding replaced by NaN".format(smiles_list[idx]))
if return_tensor:
return torch.tensor(emb)
return pd.DataFrame(emb)
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