def __getitem__(self, idx):
tokenized_smiles_list_tmp = self.smiles_set[idx * self.batch_size:(idx + 1) * self.batch_size]
# self.max_length + 1 padding
if self.soft_padding:
max_length_tmp = np.max([len(ismiles) for ismiles in tokenized_smiles_list_tmp])
else:
max_length_tmp = self.max_length
batch_x = token.int_vec_encode(tokenized_smiles_list = tokenized_smiles_list_tmp,
max_length = max_length_tmp+1,
vocab = self.vocab)
#batch_x = self.batch[idx * self.batch_size:(idx + 1) * self.batch_size]
batch_y = self.props_set[idx * self.batch_size:(idx + 1) * self.batch_size]
return np.array(batch_x), np.array(batch_y)
def Inference(data_name,
smiles_list = ['CC','CCC','C=O'],
data_units = '',
k_fold_number = 8,
augmentation = False,
outdir = "../data/"):
if augmentation:
p_dir_temp = 'Augm'
else:
p_dir_temp = 'Can'
input_dir = outdir+'Main/'+'{}/{}/'.format(data_name,p_dir_temp)
save_dir = outdir+'Inference/'+'{}/{}/'.format(data_name,p_dir_temp)
os.makedirs(save_dir, exist_ok=True)
print("***SMILES_X for inference starts...***\n\n")
print("***Checking the SMILES list for inference***\n")
smiles_checked = list()
smiles_rejected = list()
for ismiles in smiles_list:
mol_tmp = Chem.MolFromSmiles(ismiles)
if mol_tmp != None:
smiles_can = Chem.MolToSmiles(mol_tmp)
smiles_checked.append(smiles_can)
else:
smiles_rejected.append(ismiles)
if len(smiles_rejected) > 0:
with open(save_dir+'rejected_smiles.txt','w') as f:
for ismiles in smiles_rejected:
f.write("%s\n" % ismiles)
if len(smiles_checked) == 0:
print("***Process of inference automatically aborted!***")
print("The provided SMILES are all incorrect and could not be verified via RDKit.")
return
smiles_x = np.array(smiles_checked)
smiles_y = np.array([[np.nan]*len(smiles_checked)]).flatten()
# data augmentation or not
if augmentation == True:
print("***Data augmentation.***\n")
canonical = False
rotation = True
else:
print("***No data augmentation has been required.***\n")
canonical = True
rotation = False
smiles_x_enum, smiles_x_enum_card, smiles_y_enum = \
augm.Augmentation(smiles_x, smiles_y, canon=canonical, rotate=rotation)
print("Enumerated SMILES: {}\n".format(smiles_x_enum.shape[0]))
print("***Tokenization of SMILES.***\n")
# Tokenize SMILES
smiles_x_enum_tokens = token.get_tokens(smiles_x_enum)
# models ensembling
smiles_y_pred_mean_array = np.empty(shape=(0,len(smiles_checked)), dtype='float')
for ifold in range(k_fold_number):
# Tokens as a list
tokens = token.get_vocab(input_dir+data_name+'_tokens_set_fold_'+str(ifold)+'.txt')
# Add 'pad', 'unk' tokens to the existing list
vocab_size = len(tokens)
tokens, vocab_size = token.add_extra_tokens(tokens, vocab_size)
# Transformation of tokenized SMILES to vector of intergers and vice-versa
token_to_int = token.get_tokentoint(tokens)
int_to_token = token.get_inttotoken(tokens)
# Best architecture to visualize from
model_train = load_model(input_dir+'LSTMAtt_'+data_name+'_model.best_fold_'+str(ifold)+'.hdf5',
custom_objects={'AttentionM': model.AttentionM()})
if ifold == 0:
# Maximum of length of SMILES to process
max_length = model_train.layers[0].output_shape[-1]
print("Full vocabulary: {}\nOf size: {}\n".format(tokens, vocab_size))
print("Maximum length of tokenized SMILES: {} tokens\n".format(max_length))
model_train.compile(loss="mse", optimizer='adam', metrics=[metrics.mae,metrics.mse])
# predict and compare for the training, validation and test sets
smiles_x_enum_tokens_tointvec = token.int_vec_encode(tokenized_smiles_list = smiles_x_enum_tokens,
max_length = max_length,
vocab = tokens)
smiles_y_pred = model_train.predict(smiles_x_enum_tokens_tointvec)
# compute a mean per set of augmented SMILES
smiles_y_pred_mean, _ = utils.mean_median_result(smiles_x_enum_card, smiles_y_pred)
smiles_y_pred_mean_array = np.append(smiles_y_pred_mean_array, smiles_y_pred_mean.reshape(1,-1), axis = 0)
if ifold == (k_fold_number-1):
smiles_y_pred_mean_ensemble = np.mean(smiles_y_pred_mean_array, axis = 0)
smiles_y_pred_sd_ensemble = np.std(smiles_y_pred_mean_array, axis = 0)
pred_from_ens = pd.DataFrame(data=[smiles_x,
smiles_y_pred_mean_ensemble,
smiles_y_pred_sd_ensemble]).T
pred_from_ens.columns = ['SMILES', 'ens_pred_mean', 'ens_pred_sd']
print("***Inference of SMILES property done.***")
return pred_from_ens
def Main(data,
data_name,
bayopt_bounds,
data_units = '',
k_fold_number = 8,
augmentation = False,
outdir = "../data/",
bayopt_n_epochs = 10,
bayopt_n_rounds = 25,
bayopt_it_factor = 1,
bayopt_on = True,
lstmunits_ref = 512,
denseunits_ref = 512,
embedding_ref = 512,
batch_size_ref = 64,
alpha_ref = 3,
n_gpus = 1,
bridge_type = 'None',
patience = 25,
n_epochs = 1000):
if augmentation:
p_dir_temp = 'Augm'
else:
p_dir_temp = 'Can'
save_dir = outdir+'Main/'+'{}/{}/'.format(data_name,p_dir_temp)
os.makedirs(save_dir, exist_ok=True)
print("***SMILES_X starts...***\n\n")
np.random.seed(seed=123)
seed_list = np.random.randint(int(1e6), size = k_fold_number).tolist()
# Train/validation/test data splitting - 80/10/10 % at random with diff. seeds for k_fold_number times
for ifold in range(k_fold_number):
print("******")
print("***Fold #{} initiated...***".format(ifold))
print("******")
print("***Sampling and splitting of the dataset.***\n")
x_train, x_valid, x_test, y_train, y_valid, y_test, scaler = \
utils.random_split(smiles_input=data.smiles,
prop_input=np.array(data.iloc[:,1]),
random_state=seed_list[ifold],
scaling = True)
# data augmentation or not
if augmentation == True:
print("***Data augmentation to {}***\n".format(augmentation))
canonical = False
rotation = True
else:
print("***No data augmentation has been required.***\n")
canonical = True
rotation = False
x_train_enum, x_train_enum_card, y_train_enum = \
augm.Augmentation(x_train, y_train, canon=canonical, rotate=rotation)
x_valid_enum, x_valid_enum_card, y_valid_enum = \
augm.Augmentation(x_valid, y_valid, canon=canonical, rotate=rotation)
x_test_enum, x_test_enum_card, y_test_enum = \
augm.Augmentation(x_test, y_test, canon=canonical, rotate=rotation)
print("Enumerated SMILES:\n\tTraining set: {}\n\tValidation set: {}\n\tTest set: {}\n".\
format(x_train_enum.shape[0], x_valid_enum.shape[0], x_test_enum.shape[0]))
print("***Tokenization of SMILES.***\n")
# Tokenize SMILES per dataset
x_train_enum_tokens = token.get_tokens(x_train_enum)
x_valid_enum_tokens = token.get_tokens(x_valid_enum)
x_test_enum_tokens = token.get_tokens(x_test_enum)
print("Examples of tokenized SMILES from a training set:\n{}\n".\
format(x_train_enum_tokens[:5]))
# Vocabulary size computation
all_smiles_tokens = x_train_enum_tokens+x_valid_enum_tokens+x_test_enum_tokens
# Check if the vocabulary for current dataset exists already
if os.path.exists(save_dir+data_name+'_Vocabulary.txt'):
tokens = token.get_vocab(save_dir+data_name+'_Vocabulary.txt')
else:
tokens = token.extract_vocab(all_smiles_tokens)
token.save_vocab(tokens, save_dir+data_name+'_Vocabulary.txt')
tokens = token.get_vocab(save_dir+data_name+'_Vocabulary.txt')
vocab_size = len(tokens)
train_unique_tokens = token.extract_vocab(x_train_enum_tokens)
print("Number of tokens only present in a training set: {}\n".format(len(train_unique_tokens)))
valid_unique_tokens = token.extract_vocab(x_valid_enum_tokens)
print("Number of tokens only present in a validation set: {}".format(len(valid_unique_tokens)))
print("Is the validation set a subset of the training set: {}".\
format(valid_unique_tokens.issubset(train_unique_tokens)))
print("What are the tokens by which they differ: {}\n".\
format(valid_unique_tokens.difference(train_unique_tokens)))
test_unique_tokens = token.extract_vocab(x_test_enum_tokens)
print("Number of tokens only present in a test set: {}".format(len(test_unique_tokens)))
print("Is the test set a subset of the training set: {}".\
format(test_unique_tokens.issubset(train_unique_tokens)))
print("What are the tokens by which they differ: {}".\
format(test_unique_tokens.difference(train_unique_tokens)))
print("Is the test set a subset of the validation set: {}".\
format(test_unique_tokens.issubset(valid_unique_tokens)))
print("What are the tokens by which they differ: {}\n".\
format(test_unique_tokens.difference(valid_unique_tokens)))
print("Full vocabulary: {}\nOf size: {}\n".format(tokens, vocab_size))
# Add 'pad', 'unk' tokens to the existing list
tokens, vocab_size = token.add_extra_tokens(tokens, vocab_size)
# Maximum of length of SMILES to process
max_length = np.max([len(ismiles) for ismiles in all_smiles_tokens])
print("Maximum length of tokenized SMILES: {} tokens (termination spaces included)\n".format(max_length))
print("***Bayesian Optimization of the SMILESX's architecture.***\n")
if bayopt_on:
# Operate the bayesian optimization of the neural architecture
def create_mod(params):
print('Model: {}'.format(params))
model_tag = data_name
K.clear_session()
if n_gpus > 1:
if bridge_type == 'NVLink':
model_opt = model.LSTMAttModel.create(inputtokens = max_length+1,
vocabsize = vocab_size,
lstmunits=int(params[:,0][0]),
denseunits = int(params[:,1]),
embedding = int(params[:,2][0]))
else:
with tf.device('/cpu'): # necessary to multi-GPU scaling
model_opt = model.LSTMAttModel.create(inputtokens = max_length+1,
vocabsize = vocab_size,
lstmunits=int(params[:,0][0]),
denseunits = int(params[:,1]),
embedding = int(params[:,2][0]))
multi_model = model.ModelMGPU(model_opt, gpus=n_gpus, bridge_type=bridge_type)
else: # single GPU
model_opt = model.LSTMAttModel.create(inputtokens = max_length+1,
vocabsize = vocab_size,
lstmunits=int(params[:,0][0]),
denseunits = int(params[:,1]),
embedding = int(params[:,2][0]))
multi_model = model_opt
batch_size = int(params[:,3][0])
custom_adam = Adam(lr=math.pow(10,-float(params[:,4][0])))
multi_model.compile(loss='mse', optimizer=custom_adam, metrics=[metrics.mae,metrics.mse])
history = multi_model.fit_generator(generator = DataSequence(x_train_enum_tokens,
vocab = tokens,
max_length = max_length,
props_set = y_train_enum,
batch_size = batch_size),
steps_per_epoch = math.ceil(len(x_train_enum_tokens)/batch_size)//bayopt_it_factor,
validation_data = DataSequence(x_valid_enum_tokens,
vocab = tokens,
max_length = max_length,
props_set = y_valid_enum,
batch_size = min(len(x_valid_enum_tokens), batch_size)),
validation_steps = math.ceil(len(x_valid_enum_tokens)/min(len(x_valid_enum_tokens), batch_size))//bayopt_it_factor,
epochs = bayopt_n_epochs,
shuffle = True,
initial_epoch = 0,
verbose = 0)
best_epoch = np.argmin(history.history['val_loss'])
mae_valid = history.history['val_mean_absolute_error'][best_epoch]
mse_valid = history.history['val_mean_squared_error'][best_epoch]
if math.isnan(mse_valid): # discard diverging architectures (rare event)
mae_valid = math.inf
mse_valid = math.inf
print('Valid MAE: {0:0.4f}, RMSE: {1:0.4f}'.format(mae_valid, mse_valid))
return mse_valid
print("Random initialization:\n")
Bayes_opt = GPyOpt.methods.BayesianOptimization(f=create_mod,
domain=bayopt_bounds,
acquisition_type = 'EI',
initial_design_numdata = bayopt_n_rounds,
exact_feval = False,
normalize_Y = True,
num_cores = multiprocessing.cpu_count()-1)
print("Optimization:\n")
Bayes_opt.run_optimization(max_iter=bayopt_n_rounds)
best_arch = Bayes_opt.x_opt
else:
best_arch = [lstmunits_ref, denseunits_ref, embedding_ref, batch_size_ref, alpha_ref]
print("\nThe architecture for this datatset is:\n\tLSTM units: {}\n\tDense units: {}\n\tEmbedding dimensions {}".\
format(int(best_arch[0]), int(best_arch[1]), int(best_arch[2])))
print("\tBatch size: {0:}\n\tLearning rate: 10^-({1:.1f})\n".format(int(best_arch[3]), float(best_arch[4])))
print("***Training of the best model.***\n")
# Train the model and predict
K.clear_session()
# Define the multi-gpus model if necessary
if n_gpus > 1:
if bridge_type == 'NVLink':
model_train = model.LSTMAttModel.create(inputtokens = max_length+1,
vocabsize = vocab_size,
lstmunits= int(best_arch[0]),
denseunits = int(best_arch[1]),
embedding = int(best_arch[2]))
else:
with tf.device('/cpu'):
model_train = model.LSTMAttModel.create(inputtokens = max_length+1,
vocabsize = vocab_size,
lstmunits= int(best_arch[0]),
denseunits = int(best_arch[1]),
embedding = int(best_arch[2]))
print("Best model summary:\n")
print(model_train.summary())
print("\n")
multi_model = model.ModelMGPU(model_train, gpus=n_gpus, bridge_type=bridge_type)
else:
model_train = model.LSTMAttModel.create(inputtokens = max_length+1,
vocabsize = vocab_size,
lstmunits= int(best_arch[0]),
denseunits = int(best_arch[1]),
embedding = int(best_arch[2]))
print("Best model summary:\n")
print(model_train.summary())
print("\n")
multi_model = model_train
batch_size = int(best_arch[3])
custom_adam = Adam(lr=math.pow(10,-float(best_arch[4])))
# Compile the model
multi_model.compile(loss="mse", optimizer=custom_adam, metrics=[metrics.mae,metrics.mse])
# Checkpoint, Early stopping and callbacks definition
filepath=save_dir+'LSTMAtt_'+data_name+'_model.best_fold_'+str(ifold)+'.hdf5'
checkpoint = ModelCheckpoint(filepath,
monitor='val_loss',
verbose=0,
save_best_only=True,
mode='min')
earlystopping = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=patience,
verbose=0,
mode='min')
callbacks_list = [checkpoint, earlystopping]
# Fit the model
history = multi_model.fit_generator(generator = DataSequence(x_train_enum_tokens,
vocab = tokens,
max_length = max_length,
props_set = y_train_enum,
batch_size = batch_size),
validation_data = DataSequence(x_valid_enum_tokens,
vocab = tokens,
max_length = max_length,
props_set = y_valid_enum,
batch_size = min(len(x_valid_enum_tokens), batch_size)),
epochs = n_epochs,
shuffle = True,
initial_epoch = 0,
callbacks = callbacks_list)
# Summarize history for losses per epoch
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper right')
plt.savefig(save_dir+'History_fit_LSTMAtt_'+data_name+'_model_weights.best_fold_'+str(ifold)+'.png', bbox_inches='tight')
plt.close()
print("Best val_loss @ Epoch #{}\n".format(np.argmin(history.history['val_loss'])+1))
print("***Predictions from the best model.***\n")
model_train.load_weights(save_dir+'LSTMAtt_'+data_name+'_model.best_fold_'+str(ifold)+'.hdf5')
model_train.compile(loss="mse", optimizer='adam', metrics=[metrics.mae,metrics.mse])
# predict and compare for the training, validation and test sets
x_train_enum_tokens_tointvec = token.int_vec_encode(tokenized_smiles_list = x_train_enum_tokens,
max_length = max_length+1,
vocab = tokens)
x_valid_enum_tokens_tointvec = token.int_vec_encode(tokenized_smiles_list = x_valid_enum_tokens,
max_length = max_length+1,
vocab = tokens)
x_test_enum_tokens_tointvec = token.int_vec_encode(tokenized_smiles_list = x_test_enum_tokens,
max_length = max_length+1,
vocab = tokens)
y_pred_train = model_train.predict(x_train_enum_tokens_tointvec)
y_pred_valid = model_train.predict(x_valid_enum_tokens_tointvec)
y_pred_test = model_train.predict(x_test_enum_tokens_tointvec)
# compute a mean per set of augmented SMILES
y_pred_train_mean, _ = utils.mean_median_result(x_train_enum_card, y_pred_train)
y_pred_valid_mean, _ = utils.mean_median_result(x_valid_enum_card, y_pred_valid)
y_pred_test_mean, _ = utils.mean_median_result(x_test_enum_card, y_pred_test)
# inverse transform the scaling of the property and plot 'predictions VS observations'
y_pred_VS_true_train = scaler.inverse_transform(y_train) - \
scaler.inverse_transform(y_pred_train_mean.reshape(-1,1))
mae_train = np.mean(np.absolute(y_pred_VS_true_train))
mse_train = np.mean(np.square(y_pred_VS_true_train))
corrcoef_train = r2_score(scaler.inverse_transform(y_train), \
scaler.inverse_transform(y_pred_train_mean.reshape(-1,1)))
print("For the training set:\nMAE: {0:0.4f} RMSE: {1:0.4f} R^2: {2:0.4f}\n".\
format(mae_train, np.sqrt(mse_train), corrcoef_train))
y_pred_VS_true_valid = scaler.inverse_transform(y_valid) - \
scaler.inverse_transform(y_pred_valid_mean.reshape(-1,1))
mae_valid = np.mean(np.absolute(y_pred_VS_true_valid))
mse_valid = np.mean(np.square(y_pred_VS_true_valid))
corrcoef_valid = r2_score(scaler.inverse_transform(y_valid), \
scaler.inverse_transform(y_pred_valid_mean.reshape(-1,1)))
print("For the validation set:\nMAE: {0:0.4f} RMSE: {1:0.4f} R^2: {2:0.4f}\n".\
format(mae_valid, np.sqrt(mse_valid), corrcoef_valid))
y_pred_VS_true_test = scaler.inverse_transform(y_test) - \
scaler.inverse_transform(y_pred_test_mean.reshape(-1,1))
mae_test = np.mean(np.absolute(y_pred_VS_true_test))
mse_test = np.mean(np.square(y_pred_VS_true_test))
corrcoef_test = r2_score(scaler.inverse_transform(y_test), \
scaler.inverse_transform(y_pred_test_mean.reshape(-1,1)))
print("For the test set:\nMAE: {0:0.4f} RMSE: {1:0.4f} R^2: {2:0.4f}\n".\
format(mae_test, np.sqrt(mse_test), corrcoef_test))
# Plot the final result
# Unscaling the data
y_train = scaler.inverse_transform(y_train)
y_pred_train_mean = scaler.inverse_transform(y_pred_train_mean.reshape(-1,1))
y_valid = scaler.inverse_transform(y_valid)
y_pred_valid_mean = scaler.inverse_transform(y_pred_valid_mean.reshape(-1,1))
y_test = scaler.inverse_transform(y_test)
y_pred_test_mean = scaler.inverse_transform(y_pred_test_mean.reshape(-1,1))
# Changed colors, scaling and sizes
plt.figure(figsize=(12, 8))
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Setting plot limits
y_true_min = min(np.min(y_train), np.min(y_valid), np.min(y_test))
y_true_max = max(np.max(y_train), np.max(y_valid), np.max(y_test))
y_pred_min = min(np.min(y_pred_train_mean), np.min(y_pred_valid_mean), np.min(y_pred_test_mean))
y_pred_max = max(np.max(y_pred_train_mean), np.max(y_pred_valid_mean), np.max(y_pred_test_mean))
# Expanding slightly the canvas around the data points (by 10%)
axmin = y_true_min-0.1*(y_true_max-y_true_min)
axmax = y_true_max+0.1*(y_true_max-y_true_min)
aymin = y_pred_min-0.1*(y_pred_max-y_pred_min)
aymax = y_pred_max+0.1*(y_pred_max-y_pred_min)
plt.xlim(min(axmin, aymin), max(axmax, aymax))
plt.ylim(min(axmin, aymin), max(axmax, aymax))
plt.errorbar(y_train,
y_pred_train_mean,
fmt='o',
label="Train",
elinewidth = 0,
ms=5,
mfc='#519fc4',
markeredgewidth = 0,
alpha=0.7)
plt.errorbar(y_valid,
y_pred_valid_mean,
elinewidth = 0,
fmt='o',
label="Validation",
ms=5,
mfc='#db702e',
markeredgewidth = 0,
alpha=0.7)
plt.errorbar(y_test,
y_pred_test_mean,
elinewidth = 0,
fmt='o',
label="Test",
ms=5,
mfc='#cc1b00',
markeredgewidth = 0,
alpha=0.7)
# Plot X=Y line
plt.plot([max(plt.xlim()[0], plt.ylim()[0]),
min(plt.xlim()[1], plt.ylim()[1])],
[max(plt.xlim()[0], plt.ylim()[0]),
min(plt.xlim()[1], plt.ylim()[1])],
':', color = '#595f69')
plt.xlabel('Observations ' + data_units, fontsize = 12)
plt.ylabel('Predictions ' + data_units, fontsize = 12)
plt.legend()
# Added fold number
plt.savefig(save_dir+'TrainValid_Plot_LSTMAtt_'+data_name+'_model_weights.best_fold_'+str(ifold)+'.png', bbox_inches='tight', dpi=80)
plt.close()
def Interpretation(data,
data_name,
data_units = '',
k_fold_number = 8,
k_fold_index=0,
augmentation = False,
outdir = "../data/",
smiles_toviz = 'CCC',
font_size = 15,
font_rotation = 'horizontal'):
if augmentation:
p_dir_temp = 'Augm'
else:
p_dir_temp = 'Can'
input_dir = outdir+'Main/'+'{}/{}/'.format(data_name,p_dir_temp)
save_dir = outdir+'Interpretation/'+'{}/{}/'.format(data_name,p_dir_temp)
os.makedirs(save_dir, exist_ok=True)
print("***SMILES_X Interpreter starts...***\n\n")
np.random.seed(seed=123)
seed_list = np.random.randint(int(1e6), size = k_fold_number).tolist()
# Train/validation/test data splitting - 80/10/10 % at random with diff. seeds for k_fold_number times
selection_seed = seed_list[k_fold_index]
print("******")
print("***Fold #{} initiated...***".format(selection_seed))
print("******")
print("***Sampling and splitting of the dataset.***\n")
x_train, x_valid, x_test, y_train, y_valid, y_test, scaler = \
utils.random_split(smiles_input=data.smiles,
prop_input=np.array(data.iloc[:,1]),
random_state=selection_seed,
scaling = True)
np.savetxt(save_dir+'smiles_train.txt', np.asarray(x_train), newline="\n", fmt='%s')
np.savetxt(save_dir+'smiles_valid.txt', np.asarray(x_valid), newline="\n", fmt='%s')
np.savetxt(save_dir+'smiles_test.txt', np.asarray(x_test), newline="\n", fmt='%s')
mol_toviz = Chem.MolFromSmiles(smiles_toviz)
if mol_toviz != None:
smiles_toviz_can = Chem.MolToSmiles(mol_toviz)
else:
print("***Process of visualization automatically aborted!***")
print("The smiles_toviz is incorrect and cannot be canonicalized by RDKit.")
return
smiles_toviz_x = np.array([smiles_toviz_can])
if smiles_toviz_can in np.array(data.smiles):
smiles_toviz_y = np.array([[data.iloc[np.where(data.smiles == smiles_toviz_x[0])[0][0],1]]])
else:
smiles_toviz_y = np.array([[np.nan]])
# data augmentation or not
if augmentation == True:
print("***Data augmentation.***\n")
canonical = False
rotation = True
else:
print("***No data augmentation has been required.***\n")
canonical = True
rotation = False
x_train_enum, x_train_enum_card, y_train_enum = \
augm.Augmentation(x_train, y_train, canon=canonical, rotate=rotation)
x_valid_enum, x_valid_enum_card, y_valid_enum = \
augm.Augmentation(x_valid, y_valid, canon=canonical, rotate=rotation)
x_test_enum, x_test_enum_card, y_test_enum = \
augm.Augmentation(x_test, y_test, canon=canonical, rotate=rotation)
smiles_toviz_x_enum, smiles_toviz_x_enum_card, smiles_toviz_y_enum = \
augm.Augmentation(smiles_toviz_x, smiles_toviz_y, canon=canonical, rotate=rotation)
print("Enumerated SMILES:\n\tTraining set: {}\n\tValidation set: {}\n\tTest set: {}\n".\
format(x_train_enum.shape[0], x_valid_enum.shape[0], x_test_enum.shape[0]))
print("***Tokenization of SMILES.***\n")
# Tokenize SMILES per dataset
x_train_enum_tokens = token.get_tokens(x_train_enum)
x_valid_enum_tokens = token.get_tokens(x_valid_enum)
x_test_enum_tokens = token.get_tokens(x_test_enum)
smiles_toviz_x_enum_tokens = token.get_tokens(smiles_toviz_x_enum)
print("Examples of tokenized SMILES from a training set:\n{}\n".\
format(x_train_enum_tokens[:5]))
# Vocabulary size computation
all_smiles_tokens = x_train_enum_tokens+x_valid_enum_tokens+x_test_enum_tokens
tokens = token.extract_vocab(all_smiles_tokens)
vocab_size = len(tokens)
train_unique_tokens = list(token.extract_vocab(x_train_enum_tokens))
print(train_unique_tokens)
print("Number of tokens only present in a training set: {}\n".format(len(train_unique_tokens)))
train_unique_tokens.insert(0,'pad')
# Tokens as a list
tokens = token.get_vocab(input_dir+data_name+'_Vocabulary.txt')
# Add 'pad', 'unk' tokens to the existing list
tokens, vocab_size = token.add_extra_tokens(tokens, vocab_size)
print("Full vocabulary: {}\nOf size: {}\n".format(tokens, vocab_size))
# Maximum of length of SMILES to process
max_length = np.max([len(ismiles) for ismiles in all_smiles_tokens])
print("Maximum length of tokenized SMILES: {} tokens\n".format(max_length))
# Transformation of tokenized SMILES to vector of intergers and vice-versa
token_to_int = token.get_tokentoint(tokens)
int_to_token = token.get_inttotoken(tokens)
# Best architecture to visualize from
model_topredict = load_model(input_dir+'LSTMAtt_'+data_name+'_model.best_seed_'+str(selection_seed)+'.hdf5',
custom_objects={'AttentionM': model.AttentionM()})
best_arch = [model_topredict.layers[2].output_shape[-1]/2,
model_topredict.layers[3].output_shape[-1],
model_topredict.layers[1].output_shape[-1]]
# Architecture to return attention weights
model_att = model.LSTMAttModel.create(inputtokens = max_length+1,
vocabsize = vocab_size,
lstmunits= int(best_arch[0]),
denseunits = int(best_arch[1]),
embedding = int(best_arch[2]),
return_proba = True)
print("Best model summary:\n")
print(model_att.summary())
print("\n")
print("***Interpretation from the best model.***\n")
model_att.load_weights(input_dir+'LSTMAtt_'+data_name+'_model.best_seed_'+str(selection_seed)+'.hdf5')
model_att.compile(loss="mse", optimizer='adam', metrics=[metrics.mae,metrics.mse])
smiles_toviz_x_enum_tokens_tointvec = token.int_vec_encode(tokenized_smiles_list= smiles_toviz_x_enum_tokens,
max_length = max_length+1,
vocab = tokens)
intermediate_layer_model = Model(inputs=model_att.input,
outputs=model_att.layers[-2].output)
intermediate_output = intermediate_layer_model.predict(smiles_toviz_x_enum_tokens_tointvec)
smiles_toviz_x_card_cumsum_viz = np.cumsum(smiles_toviz_x_enum_card)
smiles_toviz_x_card_cumsum_shift_viz = shift(smiles_toviz_x_card_cumsum_viz, 1, cval=0)
mols_id = 0
ienumcard = smiles_toviz_x_card_cumsum_shift_viz[mols_id]
smiles_len_tmp = len(smiles_toviz_x_enum_tokens[ienumcard])
intermediate_output_tmp = intermediate_output[ienumcard,-smiles_len_tmp+1:-1].flatten().reshape(1,-1)
max_intermediate_output_tmp = np.max(intermediate_output_tmp)
plt.matshow(intermediate_output_tmp,
cmap='Reds')
plt.tick_params(axis='x', bottom = False)
plt.xticks([ix for ix in range(smiles_len_tmp-2)])
plt.xticks(range(smiles_len_tmp-2),
[int_to_token[iint].replace('pad','') \
for iint in smiles_toviz_x_enum_tokens_tointvec[ienumcard,-smiles_len_tmp+1:-1]],
fontsize = font_size,
rotation = font_rotation)
plt.yticks([])
plt.savefig(save_dir+'Interpretation_1D_'+data_name+'_seed_'+str(selection_seed)+'.png', bbox_inches='tight')
#plt.show()
smiles_tmp = smiles_toviz_x_enum[ienumcard]
mol_tmp = Chem.MolFromSmiles(smiles_tmp)
smiles_len_tmp = len(smiles_toviz_x_enum_tokens[ienumcard])
mol_df_tmp = pd.DataFrame([smiles_toviz_x_enum_tokens[ienumcard][1:-1],intermediate_output[ienumcard].\
flatten().\
tolist()[-smiles_len_tmp+1:-1]]).transpose()
bond = ['-','=','#','$','/','\\','.','(',')']
mol_df_tmp = mol_df_tmp[~mol_df_tmp.iloc[:,0].isin(bond)]
mol_df_tmp = mol_df_tmp[[not itoken.isdigit() for itoken in mol_df_tmp.iloc[:,0].values.tolist()]]
minmaxscaler = MinMaxScaler(feature_range=(0,1))
norm_weights = minmaxscaler.fit_transform(mol_df_tmp.iloc[:,1].values.reshape(-1,1)).flatten().tolist()
fig = GetSimilarityMapFromWeights(mol=mol_tmp,
size = (250,250),
scale=-1,
sigma=0.05,
weights=norm_weights,
colorMap='Reds',
contourLines = 10,
alpha = 0.25)
fig.savefig(save_dir+'Interpretation_2D_'+data_name+'_seed_'+str(selection_seed)+'.png', bbox_inches='tight')
#fig.show()
model_topredict.compile(loss="mse", optimizer='adam', metrics=[metrics.mae,metrics.mse])
y_pred_test_tmp = model_topredict.predict(smiles_toviz_x_enum_tokens_tointvec[ienumcard].reshape(1,-1))[0,0]
y_test_tmp = smiles_toviz_y_enum[ienumcard,0]
if not np.isnan(y_test_tmp):
print("True value: {0:.2f} Predicted: {1:.2f}".format(y_test_tmp,
scaler.inverse_transform(y_pred_test_tmp.reshape(1, -1))[0][0]))
else:
print("Predicted: {0:.2f}".format(scaler.inverse_transform(y_pred_test_tmp.reshape(1, -1))[0][0]))
smiles_len_tmp = len(smiles_toviz_x_enum_tokens[ienumcard])
diff_topred_list = list()
diff_totrue_list = list()
for csubsmiles in range(1,smiles_len_tmp):
isubsmiles = smiles_toviz_x_enum_tokens[ienumcard][:csubsmiles]+[' ']
isubsmiles_tointvec= token.int_vec_encode(tokenized_smiles_list = [isubsmiles],
max_length = max_length+1,
vocab = tokens)
predict_prop_tmp = model_topredict.predict(isubsmiles_tointvec)[0,0]
diff_topred_tmp = (predict_prop_tmp-y_pred_test_tmp)/np.abs(y_pred_test_tmp)
diff_topred_list.append(diff_topred_tmp)
diff_totrue_tmp = (predict_prop_tmp-y_test_tmp)/np.abs(y_test_tmp)
diff_totrue_list.append(diff_totrue_tmp)
max_diff_topred_tmp = np.max(diff_topred_list)
max_diff_totrue_tmp = np.max(diff_totrue_list)
plt.figure(figsize=(15,7))
markers, stemlines, baseline = plt.stem([ix for ix in range(smiles_len_tmp-1)],
diff_topred_list,
'k.-',
use_line_collection=True)
plt.setp(baseline, color='k', linewidth=2, linestyle='--')
plt.setp(markers, linewidth=1, marker='o', markersize=10, markeredgecolor = 'black')
plt.setp(stemlines, color = 'k', linewidth=0.5, linestyle='-')
plt.xticks(range(smiles_len_tmp-1),
smiles_toviz_x_enum_tokens[ienumcard][:-1],
fontsize = font_size,
rotation = font_rotation)
plt.yticks(fontsize = 20)
plt.ylabel('Temporal relative distance', fontsize = 25, labelpad = 15)
plt.savefig(save_dir+'Interpretation_temporal_'+data_name+'_seed_'+str(selection_seed)+'.png', bbox_inches='tight')
