# Experimental Class for Smiles Enumeration, Iterator and SmilesIterator
# adapted from Keras 1.2.2
from rdkit import Chem
import numpy as np
import threading
[docs]class Iterator(object):
"""Abstract base class for data iterators.
# Arguments
n: Integer, total number of samples in the dataset to loop over.
batch_size: Integer, size of a batch.
shuffle: Boolean, whether to shuffle the data between epochs.
seed: Random seeding for data shuffling.
"""
def __init__(self, n, batch_size, shuffle, seed):
self.n = n
self.batch_size = batch_size
self.shuffle = shuffle
self.batch_index = 0
self.total_batches_seen = 0
self.lock = threading.Lock()
self.index_generator = self._flow_index(n, batch_size, shuffle, seed)
if n < batch_size:
raise ValueError('Input data length is shorter than batch_size' 'Adjust batch_size')
[docs] def reset(self):
self.batch_index = 0
def _flow_index(self, n, batch_size=32, shuffle=False, seed=None):
# Ensure self.batch_index is 0.
self.reset()
while 1:
if seed is not None:
np.random.seed(seed + self.total_batches_seen)
if self.batch_index == 0:
index_array = np.arange(n)
if shuffle:
index_array = np.random.permutation(n)
current_index = (self.batch_index * batch_size) % n
if n > current_index + batch_size:
current_batch_size = batch_size
self.batch_index += 1
else:
current_batch_size = n - current_index
self.batch_index = 0
self.total_batches_seen += 1
yield (index_array[current_index:current_index + current_batch_size], current_index, current_batch_size)
def __iter__(self):
# Needed if we want to do something like:
# for x, y in data_gen.flow(...):
return self
def __next__(self, *args, **kwargs):
return self.next(*args, **kwargs)
[docs]class SmilesIterator(Iterator):
"""Iterator yielding data from a SMILES array.
# Arguments
x: Numpy array of SMILES input data.
y: Numpy array of targets data.
smiles_data_generator: Instance of `SmilesEnumerator`
to use for random SMILES generation.
batch_size: Integer, size of a batch.
shuffle: Boolean, whether to shuffle the data between epochs.
seed: Random seed for data shuffling.
dtype: dtype to use for returned batch.
Set to keras.backend.floatx if using Keras
"""
def __init__(self, x, y, smiles_data_generator, batch_size=32, shuffle=False, seed=None, dtype=np.float32):
if y is not None and len(x) != len(y):
raise ValueError('X (images tensor) and y (labels) '
'should have the same length. '
'Found: X.shape = %s, y.shape = %s' % (np.asarray(x).shape, np.asarray(y).shape))
self.x = np.asarray(x)
if y is not None:
self.y = np.asarray(y)
else:
self.y = None
self.smiles_data_generator = smiles_data_generator
self.dtype = dtype
super(SmilesIterator, self).__init__(x.shape[0], batch_size, shuffle, seed)
[docs] def next(self):
"""For python 2.x.
# Returns
The next batch.
"""
# Keeps under lock only the mechanism which advances
# the indexing of each batch.
with self.lock:
index_array, current_index, current_batch_size =\
next(self.index_generator)
# The transformation of images is not under thread lock
# so it can be done in parallel
batch_x = np.zeros(tuple([current_batch_size] +
[self.smiles_data_generator.pad, self.smiles_data_generator._charlen]),
dtype=self.dtype)
for i, j in enumerate(index_array):
smiles = self.x[j:j + 1]
x = self.smiles_data_generator.transform(smiles)
batch_x[i] = x
if self.y is None:
return batch_x
batch_y = self.y[index_array]
return batch_x, batch_y
[docs]class SmilesEnumerator(object):
"""SMILES Enumerator, vectorizer and devectorizer
#Arguments
charset: string containing the characters for the vectorization
can also be generated via the .fit() method
pad: Length of the vectorization
leftpad: Add spaces to the left of the SMILES
isomericSmiles: Generate SMILES containing information about stereogenic centers
enum: Enumerate the SMILES during transform
canonical: use canonical SMILES during transform (overrides enum)
"""
def __init__(self,
charset='@C)(=cOn1S2/H[N]\\',
pad=120,
leftpad=True,
isomericSmiles=True,
enum=True,
canonical=False):
self._charset = None
self.charset = charset
self.pad = pad
self.leftpad = leftpad
self.isomericSmiles = isomericSmiles
self.enumerate = enum
self.canonical = canonical
@property
def charset(self):
return self._charset
@charset.setter
def charset(self, charset):
self._charset = charset
self._charlen = len(charset)
self._char_to_int = dict((c, i) for i, c in enumerate(charset))
self._int_to_char = dict((i, c) for i, c in enumerate(charset))
[docs] def fit(self, smiles, extra_chars=[], extra_pad=5):
"""Performs extraction of the charset and length of a SMILES datasets
and sets self.pad and self.charset
#Arguments
smiles: Numpy array or Pandas series containing smiles as strings
extra_chars: List of extra chars to add to the charset
(e.g. "\\\\" when "/" is present)
extra_pad: Extra padding to add before or after the
SMILES vectorization
"""
charset = set("".join(list(smiles)))
self.charset = "".join(charset.union(set(extra_chars)))
self.pad = max([len(smile) for smile in smiles]) + extra_pad
[docs] def randomize_smiles(self, smiles):
"""Perform a randomization of a SMILES string
must be RDKit sanitizable"""
m = Chem.MolFromSmiles(smiles)
ans = list(range(m.GetNumAtoms()))
np.random.shuffle(ans)
nm = Chem.RenumberAtoms(m, ans)
return Chem.MolToSmiles(nm, canonical=self.canonical, isomericSmiles=self.isomericSmiles)
if __name__ == "__main__":
smiles = np.array(
["CCC(=O)O[C@@]1(CC[NH+](C[C@H]1CC=C)C)c2ccccc2", "CCC[S@@](=O)c1ccc2c(c1)[nH]/c(=N/C(=O)OC)/[nH]2"] * 10)
# Test canonical SMILES vectorization
sm_en = SmilesEnumerator(canonical=True, enum=False)
sm_en.fit(smiles, extra_chars=["\\"])
v = sm_en.transform(smiles)
transformed = sm_en.reverse_transform(v)
if len(set(transformed)) > 2:
print("Too many different canonical SMILES generated")
# Test enumeration
sm_en.canonical = False
sm_en.enumerate = True
v2 = sm_en.transform(smiles)
transformed = sm_en.reverse_transform(v2)
if len(set(transformed)) < 3: print("Too few enumerated SMILES generated")
# Reconstruction
reconstructed = sm_en.reverse_transform(v[0:5])
for i, smile in enumerate(reconstructed):
if smile != smiles[i]:
print("Error in reconstruction %s %s" % (smile, smiles[i]))
break
# test Pandas
import pandas as pd
df = pd.DataFrame(smiles)
v = sm_en.transform(df[0])
if v.shape != (20, 52, 18): print("Possible error in pandas use")
# BUG, when batchsize > x.shape[0], then it only returns x.shape[0]!
# Test batch generation
sm_it = SmilesIterator(smiles, np.array([1, 2] * 10), sm_en, batch_size=10, shuffle=True)
X, y = sm_it.next()
if sum(y == 1) - sum(y == 2) > 1:
print("Unbalanced generation of batches")
if len(X) != 10: print("Error in batchsize generation")