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molecule_vae.py
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molecule_vae.py
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import nltk
import numpy as np
import zinc_grammar
import models.model_zinc
import models.model_zinc_str
def get_zinc_tokenizer(cfg):
long_tokens = filter(lambda a: len(a) > 1, cfg._lexical_index.keys())
replacements = ['$','%','^'] # ,'&']
assert len(long_tokens) == len(replacements)
for token in replacements:
assert not cfg._lexical_index.has_key(token)
def tokenize(smiles):
for i, token in enumerate(long_tokens):
smiles = smiles.replace(token, replacements[i])
tokens = []
for token in smiles:
try:
ix = replacements.index(token)
tokens.append(long_tokens[ix])
except:
tokens.append(token)
return tokens
return tokenize
def pop_or_nothing(S):
try: return S.pop()
except: return 'Nothing'
def prods_to_eq(prods):
seq = [prods[0].lhs()]
for prod in prods:
if str(prod.lhs()) == 'Nothing':
break
for ix, s in enumerate(seq):
if s == prod.lhs():
seq = seq[:ix] + list(prod.rhs()) + seq[ix+1:]
break
try:
return ''.join(seq)
except:
return ''
class ZincGrammarModel(object):
def __init__(self, weights_file, latent_rep_size=56):
""" Load the (trained) zinc encoder/decoder, grammar model. """
self._grammar = zinc_grammar
self._model = models.model_zinc
self.MAX_LEN = self._model.MAX_LEN
self._productions = self._grammar.GCFG.productions()
self._prod_map = {}
for ix, prod in enumerate(self._productions):
self._prod_map[prod] = ix
self._parser = nltk.ChartParser(self._grammar.GCFG)
self._tokenize = get_zinc_tokenizer(self._grammar.GCFG)
self._n_chars = len(self._productions)
self._lhs_map = {}
for ix, lhs in enumerate(self._grammar.lhs_list):
self._lhs_map[lhs] = ix
self.vae = self._model.MoleculeVAE()
self.vae.load(self._productions, weights_file, max_length=self.MAX_LEN, latent_rep_size=latent_rep_size)
def encode(self, smiles):
""" Encode a list of smiles strings into the latent space """
assert type(smiles) == list
tokens = map(self._tokenize, smiles)
parse_trees = [self._parser.parse(t).next() for t in tokens]
productions_seq = [tree.productions() for tree in parse_trees]
indices = [np.array([self._prod_map[prod] for prod in entry], dtype=int) for entry in productions_seq]
one_hot = np.zeros((len(indices), self.MAX_LEN, self._n_chars), dtype=np.float32)
for i in xrange(len(indices)):
num_productions = len(indices[i])
one_hot[i][np.arange(num_productions),indices[i]] = 1.
one_hot[i][np.arange(num_productions, self.MAX_LEN),-1] = 1.
self.one_hot = one_hot
return self.vae.encoderMV.predict(one_hot)[0]
def _sample_using_masks(self, unmasked):
""" Samples a one-hot vector, masking at each timestep.
This is an implementation of Algorithm ? in the paper. """
eps = 1e-100
X_hat = np.zeros_like(unmasked)
# Create a stack for each input in the batch
S = np.empty((unmasked.shape[0],), dtype=object)
for ix in xrange(S.shape[0]):
S[ix] = [str(self._grammar.start_index)]
# Loop over time axis, sampling values and updating masks
for t in xrange(unmasked.shape[1]):
next_nonterminal = [self._lhs_map[pop_or_nothing(a)] for a in S]
mask = self._grammar.masks[next_nonterminal]
masked_output = np.exp(unmasked[:,t,:])*mask + eps
sampled_output = np.argmax(np.random.gumbel(size=masked_output.shape) + np.log(masked_output), axis=-1)
X_hat[np.arange(unmasked.shape[0]),t,sampled_output] = 1.0
# Identify non-terminals in RHS of selected production, and
# push them onto the stack in reverse order
rhs = [filter(lambda a: (type(a) == nltk.grammar.Nonterminal) and (str(a) != 'None'),
self._productions[i].rhs())
for i in sampled_output]
for ix in xrange(S.shape[0]):
S[ix].extend(map(str, rhs[ix])[::-1])
return X_hat # , ln_p
def decode(self, z):
""" Sample from the grammar decoder """
assert z.ndim == 2
unmasked = self.vae.decoder.predict(z)
X_hat = self._sample_using_masks(unmasked)
# Convert from one-hot to sequence of production rules
prod_seq = [[self._productions[X_hat[index,t].argmax()]
for t in xrange(X_hat.shape[1])]
for index in xrange(X_hat.shape[0])]
return [prods_to_eq(prods) for prods in prod_seq]
class ZincCharacterModel(object):
def __init__(self, weights_file, latent_rep_size=56):
self._model = models.model_zinc_str
self.MAX_LEN = 120
self.vae = self._model.MoleculeVAE()
self.charlist = ['C', '(', ')', 'c', '1', '2', 'o', '=', 'O', 'N', '3', 'F', '[',
'@', 'H', ']', 'n', '-', '#', 'S', 'l', '+', 's', 'B', 'r', '/',
'4', '\\', '5', '6', '7', 'I', 'P', '8', ' ']
self._char_index = {}
for ix, char in enumerate(self.charlist):
self._char_index[char] = ix
self.vae.load(self.charlist, weights_file, max_length=self.MAX_LEN, latent_rep_size=latent_rep_size)
def encode(self, smiles):
""" Encode a list of smiles strings into the latent space """
indices = [np.array([self._char_index[c] for c in entry], dtype=int) for entry in smiles]
one_hot = np.zeros((len(indices), self.MAX_LEN, len(self.charlist)), dtype=np.float32)
for i in xrange(len(indices)):
num_productions = len(indices[i])
one_hot[i][np.arange(num_productions),indices[i]] = 1.
one_hot[i][np.arange(num_productions, self.MAX_LEN),-1] = 1.
return self.vae.encoderMV.predict(one_hot)[0]
def decode(self, z):
""" Sample from the character decoder """
assert z.ndim == 2
out = self.vae.decoder.predict(z)
noise = np.random.gumbel(size=out.shape)
sampled_chars = np.argmax(np.log(out) + noise, axis=-1)
char_matrix = np.array(self.charlist)[np.array(sampled_chars, dtype=int)]
return [''.join(ch).strip() for ch in char_matrix]