def __init__(self, experiment_name):
# Read parameter used during training
self._config = configparser.ConfigParser()
self._config.read('../experiments/' + experiment_name + '.ini')
self._model_type = self._config['MODEL']['model']
self._experiment_name = experiment_name
self._file_name = self._config['DATA']['data']
self._encoding_size = int(self._config['DATA']['encoding_size'])
self._molecular_size = int(self._config['DATA']['molecular_size'])
self._epochs = int(self._config['TRAINING']['epochs'])
self._n_folds = int(self._config['TRAINING']['n_folds'])
self._learning_rate = float(self._config['TRAINING']['learning_rate'])
self._batch_size = int(self._config['TRAINING']['batch_size'])
self._samples = int(self._config['EVALUATION']['samples'])
self._T = float(self._config['EVALUATION']['temp'])
self._starting_token = self._config['EVALUATION']['starting_token']
if os.path.isfile('../data/' + self._file_name + '.csv'):
self._data = pd.read_csv('../data/' + self._file_name + '.csv',
header=None).values[:, 0]
elif os.path.isfile('../data/' + self._file_name + '.tar.xz'):
# Skip first line since empty and last line since nan
self._data = pd.read_csv('../data/' + self._file_name + '.tar.xz',
compression='xz',
header=None).values[1:-1, 0]
# Clean data from start, end and padding token
for i, mol_dat in enumerate(self._data):
self._data[i] = clean_molecule(mol_dat, self._model_type)
def sample(self,
N=100,
stor_dir='../evaluation',
T=0.7,
fold=[1],
epoch=[9],
valid=True,
novel=True,
unique=True,
write_csv=True):
'''Sample from a model where the number of novel valid unique molecules is fixed
:param stor_dir: directory where the generated SMILES are saved
:param N: number of samples
:param T: Temperature
:param fold: Folds to use for sampling
:param epoch: Epochs to use for sampling
:param valid: If True, only accept valid SMILES
:param novel: If True, only accept novel SMILES
:param unique: If True, only accept unique SMILES
:param write_csv If True, the generated SMILES are written in stor_dir
:return: res_molecules: list with all the generated SMILES
'''
res_molecules = []
print('Sampling: started')
for f in fold:
for e in epoch:
self._model.build(stor_dir + '/' + self._experiment_name +
'/models/model_fold_' + str(f) + '_epochs_' +
str(e))
new_molecules = []
while len(new_molecules) < N:
new_mol = self._encoder.decode(
self._model.sample(self._starting_token, T))
# Remove remains from generation
new_mol = clean_molecule(new_mol[0], self._model_type)
# If not valid, get new molecule
if valid and not check_valid(new_mol):
continue
# If not unique, get new molecule
if unique and (new_mol in new_molecules):
continue
# If not novel, get molecule
if novel and (new_mol in self._data):
continue
# If all conditions checked, add new molecule
new_molecules.append(new_mol)
# Prepare name for file
name = 'molecules_fold_' + str(f) + '_epochs_' + str(
e) + '_T_' + str(T) + '_N_' + str(N) + '.csv'
if unique:
name = 'unique_' + name
if valid:
name = 'valid_' + name
if novel:
name = 'novel_' + name
# Store final molecules
if write_csv:
if not os.path.exists(stor_dir + '/' +
self._experiment_name +
'/molecules/'):
os.makedirs(stor_dir + '/' + self._experiment_name +
'/molecules/')
mol = np.array(new_molecules).reshape(-1)
pd.DataFrame(mol).to_csv(stor_dir + '/' +
self._experiment_name +
'/molecules/' + name,
header=None)
res_molecules.append(new_molecules)
print('Sampling: done')
return res_molecules
def fine_tuning(self, stor_dir='../evaluation/', restart=False):
'''Perform fine-tuning and store statistic,
NOTE: Directory should be prepared with the correct name and model
NOTE: Molecules are not generated or validation is not performed. To sample molecules sampler should be used'
:param stor_dir: directory to store data
:return:
'''
# Create directories
if not os.path.exists(stor_dir + '/' + self._experiment_name + '/models'):
os.makedirs(stor_dir + '/' + self._experiment_name + '/models')
if not os.path.exists(stor_dir + '/' + self._experiment_name + '/statistic'):
os.makedirs(stor_dir + '/' + self._experiment_name + '/statistic')
if not os.path.exists(stor_dir + '/' + self._experiment_name + '/molecules'):
os.makedirs(stor_dir + '/' + self._experiment_name + '/molecules')
# Compute labels
label = np.argmax(self._data, axis=-1).astype(int)
# Special preprocessing in the case of NADE random
if self._model_type == 'NADE' and self._generation == 'random':
# First column stores correct SMILES and second column stores SMILES with missing values
label = np.argmax(self._data[:, 0], axis=-1).astype(int)
aug = self._data.shape[1] - 1
label = np.repeat(label[:, np.newaxis, :], aug, axis=1)
self._data = self._data[:, 1:]
# Build model
self._model.build(stor_dir + '/' + self._experiment_name + '/' + self._start_model)
# Store total Statistics
tot_stat = []
# only single fold
fold = 1
for i in range(self._epochs):
print('Fold:', fold)
print('Epoch:', i)
if restart:
# Read existing files
tmp_stat_file = pd.read_csv(
stor_dir + '/' + self._experiment_name + '/statistic/stat_fold_' + str(fold) + '.csv',
header=None).to_numpy()
# Check if current epoch is successfully completed else continue with normal training
if check_model(self._model_type, self._experiment_name, stor_dir, fold, i) and check_molecules(
self._experiment_name, stor_dir, fold, i) and tmp_stat_file.shape[0] > i:
# Load model
self._model.build(
stor_dir + '/' + self._experiment_name + '/models/model_fold_' + str(fold) + '_epochs_' + str(i))
# Fill statistic and loss list
tot_stat.append(tmp_stat_file[i, 1:].reshape(1, -1).tolist())
# Skip this epoch
continue
else:
restart = False
# Train model (Data reshaped from (N_samples, N_augmentation, molecular_size, encoding_size)
# to (all_SMILES, molecular_size, encoding_size))
statistic = self._model.train(self._data.reshape(-1, self._molecular_size, self._encoding_size),
label.reshape(-1, self._molecular_size), epochs=1,
batch_size=self._batch_size)
tot_stat.append(statistic.tolist())
# Store model
self._model.save(
stor_dir + '/' + self._experiment_name + '/models/model_fold_' + str(fold) + '_epochs_' + str(i) )
# Sample new molecules
new_molecules = []
for s in range(self._samples):
mol = self._encoder.decode(self._model.sample(self._starting_token, self._T))
new_molecules.append(clean_molecule(mol[0], self._model_type))
# Store new molecules
new_molecules = np.array(new_molecules)
pd.DataFrame(new_molecules).to_csv(
stor_dir + '/' + self._experiment_name + '/molecules/molecule_fold_' + str(fold) + '_epochs_' + str(
i) + '.csv', header=None)
# Store statistic
store_stat = np.array(tot_stat).reshape(i + 1, -1)
pd.DataFrame(np.array(store_stat)).to_csv(
stor_dir + '/' + self._experiment_name + '/statistic/stat_fold_' + str(fold) + '.csv',
header=None)
def cross_validation(self, stor_dir='../evaluation/', restart=False):
'''Perform cross-validation and store data'''
# Create directories
if not os.path.exists(stor_dir + '/' + self._experiment_name +
'/models'):
os.makedirs(stor_dir + '/' + self._experiment_name + '/models')
if not os.path.exists(stor_dir + '/' + self._experiment_name +
'/molecules'):
os.makedirs(stor_dir + '/' + self._experiment_name + '/molecules')
if not os.path.exists(stor_dir + '/' + self._experiment_name +
'/statistic'):
os.makedirs(stor_dir + '/' + self._experiment_name + '/statistic')
if not os.path.exists(stor_dir + '/' + self._experiment_name +
'/validation'):
os.makedirs(stor_dir + '/' + self._experiment_name + '/validation')
self._kf = KFold(n_splits=self._n_folds, shuffle=True, random_state=2)
# Count iterations
fold = 0
# Compute labels
label = np.argmax(self._data, axis=-1).astype(int)
# Special preprocessing in the case of NADE
if (self._model_type == 'NADE') and self._generation == 'random':
# First column stores correct SMILES and second column stores SMILES with missing values
label = np.argmax(self._data[:, 0], axis=-1).astype(int)
aug = self._data.shape[1] - 1
label = np.repeat(label[:, np.newaxis, :], aug, axis=1)
self._data = self._data[:, 1:]
# Split data into train and test data
for train, test in self._kf.split(self._data):
# Shuffle index within test and train set
np.random.shuffle(train)
np.random.shuffle(test)
fold += 1
self._model.build()
# Store total statistics
tot_stat = []
# Store validation loss
tot_loss = []
for i in range(self._epochs):
print('Fold:', fold)
print('Epoch:', i)
if restart:
tmp_val_file = pd.read_csv(
stor_dir + '/' + self._experiment_name +
'/validation/val_fold_' + str(fold) + '.csv',
header=None).to_numpy()
tmp_stat_file = pd.read_csv(
stor_dir + '/' + self._experiment_name +
'/statistic/stat_fold_' + str(fold) + '.csv',
header=None).to_numpy()
# Check if current epoch is successfully complete[0]d else continue with normal training
if check_model(
self._model_type, self._experiment_name, stor_dir,
fold, i) and check_molecules(
self._experiment_name, stor_dir, fold,
i) and tmp_val_file.shape[
0] > i and tmp_stat_file.shape[0] > i:
# Load model
self._model.build(stor_dir + '/' +
self._experiment_name +
'/models/model_fold_' + str(fold) +
'_epochs_' + str(i))
# Fill statistic and loss list
tot_stat.append(tmp_stat_file[i,
1:].reshape(1,
-1).tolist())
tot_loss.append(tmp_val_file[i, 1])
# Skip this epoch
continue
else:
restart = False
# Train model (Data reshaped from (N_samples, N_augmentation, molecular_size, encoding_size)
# to (all_SMILES, molecular_size, encoding_size))
statistic = self._model.train(
self._data[train].reshape(-1, self._molecular_size,
self._encoding_size),
label[train].reshape(-1, self._molecular_size),
epochs=1,
batch_size=self._batch_size)
tot_stat.append(statistic.tolist())
# Store model
self._model.save(stor_dir + '/' + self._experiment_name +
'/models/model_fold_' + str(fold) +
'_epochs_' + str(i))
# Test model on validation set
tot_loss.append(
self._model.validate(
self._data[test].reshape(-1, self._molecular_size,
self._encoding_size),
label[test].reshape(-1, self._molecular_size)))
# Sample new molecules
new_molecules = []
for s in range(self._samples):
mol = self._encoder.decode(
self._model.sample(self._starting_token, self._T))
new_molecules.append(
clean_molecule(mol[0], self._model_type))
# Store new molecules
new_molecules = np.array(new_molecules)
pd.DataFrame(new_molecules).to_csv(
stor_dir + '/' + self._experiment_name +
'/molecules/molecule_fold_' + str(fold) + '_epochs_' +
str(i) + '.csv',
header=None)
# Store statistic
store_stat = np.array(tot_stat).reshape(i + 1, -1)
pd.DataFrame(np.array(store_stat)).to_csv(
stor_dir + '/' + self._experiment_name +
'/statistic/stat_fold_' + str(fold) + '.csv',
header=None)
# Store validation data
pd.DataFrame(np.array(tot_loss).reshape(-1, 1)).to_csv(
stor_dir + '/' + self._experiment_name +
'/validation/val_fold_' + str(fold) + '.csv',
header=None)
def eval_molecule(self, stor_dir='.'):
'''Plot percentage of novel, valid and unique SMILES
:return:
'''
valid = np.zeros((self._n_folds, self._epochs))
unique = np.zeros((self._n_folds, self._epochs))
novel = np.zeros((self._n_folds, self._epochs))
for i in range(self._n_folds):
for j in range(self._epochs):
mol = pd.read_csv(stor_dir + '/' + self._experiment_name +
'/molecules/molecule_fold_' + str(i + 1) +
'_epochs_' + str(j) + '.csv',
header=None).values[:, 1].astype(str)
# Remove padding
for k, m in enumerate(mol):
mol[k] = clean_molecule(m, self._model_type)
# Compute unique molecules
unique[i, j] = len(set(mol)) / self._samples
# Remove duplicates
mol = np.array(list(set(mol)))
# Check validity and remove non-valid molecules
to_delete = []
for k, m in enumerate(mol):
if not check_valid(m):
to_delete.append(k)
valid_mol = np.delete(mol, to_delete)
valid[i, j] = len(valid_mol) / self._samples
# Compute molecules unequal to training data
if valid_mol.size != 0:
new_m = self.check_with_training_data(list(valid_mol))
novel[i, j] = len(new_m) / self._samples
# Get percentage
unique *= 100
novel *= 100
valid *= 100
# Get mean values
mean_unique = np.mean(unique, axis=0)
mean_valid = np.mean(valid, axis=0)
mean_novel = np.mean(novel, axis=0)
# Get standard deviation
std_unique = np.std(unique, axis=0)
std_valid = np.std(valid, axis=0)
std_novel = np.std(novel, axis=0)
print(mean_unique)
print(mean_valid)
print(mean_novel)
# PLot
plt.figure(1)
plt.errorbar(np.arange(1, self._epochs + 1),
mean_unique,
yerr=std_unique,
capsize=3,
label='unique')
plt.errorbar(np.arange(1, self._epochs + 1),
mean_valid,
yerr=std_valid,
capsize=3,
label='valid & unique')
plt.errorbar(np.arange(1, self._epochs + 1),
mean_novel,
yerr=std_novel,
capsize=3,
label='novel, valid & unique',
linestyle=':')
plt.yticks(np.arange(0, 110, step=10))
plt.legend()
plt.ylim(0, 105)
plt.title('SMILES T=' + str(self._T))
plt.ylabel('% SMILES')
plt.xlabel('Epoch')
plt.savefig(stor_dir + '/' + self._experiment_name +
'/molecules/novel_valid_unique_molecules.png')
# Store data
data = np.vstack((mean_unique, std_unique, mean_valid, std_valid,
mean_novel, std_novel))
pd.DataFrame(data).to_csv(self._experiment_name + '/molecules/' +
self._experiment_name + '_data.csv')
plt.show()
