def main(path, task, representation, use_pca):
"""
:param path: str specifying path to dataset.
:param task: str specifying the task. One of ['e_iso_pi', 'z_iso_pi', 'e_iso_n', 'z_iso_n']
:param representation: str specifying the molecular representation. One of ['fingerprints, 'fragments', 'fragprints']
:param use_pca: bool. If True apply PCA to perform Principal Components Regression.
"""
data_loader = TaskDataLoader(task, path)
smiles_list, y = data_loader.load_property_data()
X = featurise_mols(smiles_list, representation)
if use_pca:
n_components = 50
else:
n_components = None
# Set random state to be different to the splits used for evaluation
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=30)
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
X_train, y_train, _, _, y_scaler = transform_data(X_train, y_train, X_test,
y_test, n_components,
use_pca)
estim = HyperoptEstimator(regressor=random_forest_regression('my_RF'))
estim.fit(X_train, y_train, valid_size=0.1, n_folds=5, cv_shuffle=True)
print(estim.best_model())
with open(f'saved_hypers/RF/tuning_for_{task}', 'w') as f:
print(estim.best_model(), file=f)
def main(path, task, representation, use_pca, n_trials, test_set_size, use_rmse_conf):
"""
:param path: str specifying path to dataset.
:param task: str specifying the task. One of ['e_iso_pi', 'z_iso_pi', 'e_iso_n', 'z_iso_n']
:param representation: str specifying the molecular representation. One of ['fingerprints, 'fragments', 'fragprints']
:param use_pca: bool. If True apply PCA to perform Principal Components Regression.
:param n_trials: int specifying number of random train/test splits to use
:param test_set_size: float in range [0, 1] specifying fraction of dataset to use as test set
:param use_rmse_conf: bool specifying whether to compute the rmse confidence-error curves or the mae confidence-
error curves. True is the option for rmse.
"""
data_loader = TaskDataLoader(task, path)
smiles_list, y = data_loader.load_property_data()
X = featurise_mols(smiles_list, representation)
# If True we perform Principal Components Regression
if use_pca:
n_components = 100
else:
n_components = None
# We define the Gaussian Process Regression Model using the Tanimoto kernel
m = None
def objective_closure():
return -m.log_marginal_likelihood()
r2_list = []
rmse_list = []
mae_list = []
# We pre-allocate arrays for plotting confidence-error curves
_, _, _, y_test = train_test_split(X, y, test_size=test_set_size) # To get test set size
n_test = len(y_test)
rmse_confidence_list = np.zeros((n_trials, n_test))
mae_confidence_list = np.zeros((n_trials, n_test))
print('\nBeginning training loop...')
for i in range(0, n_trials):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_set_size, random_state=i)
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
# We standardise the outputs but leave the inputs unchanged
_, y_train, _, y_test, y_scaler = transform_data(X_train, y_train, X_test, y_test, n_components=n_components, use_pca=use_pca)
X_train = X_train.astype(np.float64)
X_test = X_test.astype(np.float64)
k = Tanimoto()
m = gpflow.models.GPR(data=(X_train, y_train), mean_function=Constant(np.mean(y_train)), kernel=k, noise_variance=1)
# e_iso_pi best params:
# {'learner': RandomForestRegressor(max_features=0.9348473830061558, n_estimators=381,
# n_jobs=1, random_state=2, verbose=False)}
# e_iso_n best params:
# {'learner': RandomForestRegressor(bootstrap=False, max_features=0.09944870853556087,
# min_samples_leaf=3, n_estimators=1295, n_jobs=1,
# random_state=0, verbose=False)}
# z_iso_pi best params:
# {'learner': RandomForestRegressor(max_depth=4, max_features=0.33072121415416944,
# n_estimators=2755, n_jobs=1, random_state=2,
# verbose=False)}
# z_iso_n best params:
# {'learner': RandomForestRegressor(max_features=None, n_estimators=892, n_jobs=1,
# random_state=3, verbose=False)}
regr_rf = RandomForestRegressor(max_features=None, n_estimators=892, n_jobs=1,
random_state=3, verbose=False)
regr_rf.fit(X_train, y_train)
# Optimise the kernel variance and noise level by the marginal likelihood
opt = gpflow.optimizers.Scipy()
opt.minimize(objective_closure, m.trainable_variables, options=dict(maxiter=100))
print_summary(m)
# mean and variance GP prediction and RF prediction
y_pred, y_var = m.predict_f(X_test)
y_pred_rf = regr_rf.predict(X_test)
y_pred_av = (y_pred + y_pred_rf.reshape(-1, 1)) / 2.0
y_pred = y_scaler.inverse_transform(y_pred_av)
y_test = y_scaler.inverse_transform(y_test)
# Output Standardised RMSE and RMSE on Train Set
y_pred_train, _ = m.predict_f(X_train)
y_pred_train_rf = regr_rf.predict(X_train)
y_pred_train = (y_pred_train + y_pred_train_rf.reshape(-1, 1)) / 2.0
train_rmse_stan = np.sqrt(mean_squared_error(y_train, y_pred_train))
train_rmse = np.sqrt(mean_squared_error(y_scaler.inverse_transform(y_train), y_scaler.inverse_transform(y_pred_train)))
print("\nStandardised Train RMSE: {:.3f}".format(train_rmse_stan))
print("Train RMSE: {:.3f}".format(train_rmse))
score = r2_score(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
mae = mean_absolute_error(y_test, y_pred)
print("\nR^2: {:.3f}".format(score))
print("RMSE: {:.3f}".format(rmse))
print("MAE: {:.3f}".format(mae))
r2_list.append(score)
rmse_list.append(rmse)
mae_list.append(mae)
r2_list = np.array(r2_list)
rmse_list = np.array(rmse_list)
mae_list = np.array(mae_list)
print("\nmean R^2: {:.4f} +- {:.4f}".format(np.mean(r2_list), np.std(r2_list)/np.sqrt(len(r2_list))))
print("mean RMSE: {:.4f} +- {:.4f}".format(np.mean(rmse_list), np.std(rmse_list)/np.sqrt(len(rmse_list))))
print("mean MAE: {:.4f} +- {:.4f}\n".format(np.mean(mae_list), np.std(mae_list)/np.sqrt(len(mae_list))))
def main(path, task, representation, use_pca, n_trials, test_set_size,
use_rmse_conf):
"""
:param path: str specifying path to dataset.
:param task: str specifying the task. One of ['e_iso_pi', 'z_iso_pi', 'e_iso_n', 'z_iso_n']
:param representation: str specifying the molecular representation. One of ['fingerprints, 'fragments', 'fragprints']
:param use_pca: bool. If True apply PCA to perform Principal Components Regression.
:param n_trials: int specifying number of random train/test splits to use
:param test_set_size: float in range [0, 1] specifying fraction of dataset to use as test set
:param use_rmse_conf: bool specifying whether to compute the rmse confidence-error curves or the mae confidence-
error curves. True is the option for rmse.
"""
data_loader = TaskDataLoader(task, path)
smiles_list, y = data_loader.load_property_data()
X = featurise_mols(smiles_list, representation)
# If True we perform Principal Components Regression
if use_pca:
n_components = 100
else:
n_components = None
# We define the Gaussian Process Regression Model using the Tanimoto kernel
m = None
def objective_closure():
return -m.log_marginal_likelihood()
r2_list = []
rmse_list = []
mae_list = []
# We pre-allocate arrays for plotting confidence-error curves
_, _, _, y_test = train_test_split(
X, y, test_size=test_set_size) # To get test set size
n_test = len(y_test)
rmse_confidence_list = np.zeros((n_trials, n_test))
mae_confidence_list = np.zeros((n_trials, n_test))
print('\nBeginning training loop...')
for i in range(0, n_trials):
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=test_set_size, random_state=i)
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
# We standardise the outputs but leave the inputs unchanged
_, y_train, _, y_test, y_scaler = transform_data(
X_train,
y_train,
X_test,
y_test,
n_components=n_components,
use_pca=use_pca)
X_train = X_train.astype(np.float64)
X_test = X_test.astype(np.float64)
k = Tanimoto()
m = gpflow.models.GPR(data=(X_train, y_train),
mean_function=Constant(np.mean(y_train)),
kernel=k,
noise_variance=1)
# Optimise the kernel variance and noise level by the marginal likelihood
opt = gpflow.optimizers.Scipy()
opt.minimize(objective_closure,
m.trainable_variables,
options=dict(maxiter=10000))
print_summary(m)
# mean and variance GP prediction
y_pred, y_var = m.predict_f(X_test)
y_pred = y_scaler.inverse_transform(y_pred)
y_test = y_scaler.inverse_transform(y_test)
# Compute scores for confidence curve plotting.
ranked_confidence_list = np.argsort(y_var, axis=0).flatten()
for k in range(len(y_test)):
# Construct the RMSE error for each level of confidence
conf = ranked_confidence_list[0:k + 1]
rmse = np.sqrt(mean_squared_error(y_test[conf], y_pred[conf]))
rmse_confidence_list[i, k] = rmse
# Construct the MAE error for each level of confidence
mae = mean_absolute_error(y_test[conf], y_pred[conf])
mae_confidence_list[i, k] = mae
# Output Standardised RMSE and RMSE on Train Set
y_pred_train, _ = m.predict_f(X_train)
train_rmse_stan = np.sqrt(mean_squared_error(y_train, y_pred_train))
train_rmse = np.sqrt(
mean_squared_error(y_scaler.inverse_transform(y_train),
y_scaler.inverse_transform(y_pred_train)))
print("\nStandardised Train RMSE: {:.3f}".format(train_rmse_stan))
print("Train RMSE: {:.3f}".format(train_rmse))
score = r2_score(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
mae = mean_absolute_error(y_test, y_pred)
print("\nR^2: {:.3f}".format(score))
print("RMSE: {:.3f}".format(rmse))
print("MAE: {:.3f}".format(mae))
r2_list.append(score)
rmse_list.append(rmse)
mae_list.append(mae)
r2_list = np.array(r2_list)
rmse_list = np.array(rmse_list)
mae_list = np.array(mae_list)
print("\nmean R^2: {:.4f} +- {:.4f}".format(
np.mean(r2_list),
np.std(r2_list) / np.sqrt(len(r2_list))))
print("mean RMSE: {:.4f} +- {:.4f}".format(
np.mean(rmse_list),
np.std(rmse_list) / np.sqrt(len(rmse_list))))
print("mean MAE: {:.4f} +- {:.4f}\n".format(
np.mean(mae_list),
np.std(mae_list) / np.sqrt(len(mae_list))))
# Plot confidence-error curves
confidence_percentiles = np.arange(
1e-14, 100, 100 / len(y_test)
) # 1e-14 instead of 0 to stop weirdness with len(y_test) = 29
if use_rmse_conf:
rmse_mean = np.mean(rmse_confidence_list, axis=0)
rmse_std = np.std(rmse_confidence_list, axis=0)
# We flip because we want the most confident predictions on the right-hand side of the plot
rmse_mean = np.flip(rmse_mean)
rmse_std = np.flip(rmse_std)
# One-sigma error bars
lower = rmse_mean - rmse_std
upper = rmse_mean + rmse_std
plt.plot(confidence_percentiles, rmse_mean, label='mean')
plt.fill_between(confidence_percentiles, lower, upper, alpha=0.2)
plt.xlabel('Confidence Percentile')
plt.ylabel('RMSE (nm)')
plt.ylim([0, np.max(upper) + 1])
plt.xlim([0, 100 * ((len(y_test) - 1) / len(y_test))])
plt.yticks(np.arange(0, np.max(upper) + 1, 5.0))
plt.savefig(
task +
'/results/gpr/{}_confidence_curve_rmse.png'.format(representation))
plt.show()
else:
# We plot the Mean-absolute error confidence-error curves
mae_mean = np.mean(mae_confidence_list, axis=0)
mae_std = np.std(mae_confidence_list, axis=0)
mae_mean = np.flip(mae_mean)
mae_std = np.flip(mae_std)
lower = mae_mean - mae_std
upper = mae_mean + mae_std
plt.plot(confidence_percentiles, mae_mean, label='mean')
plt.fill_between(confidence_percentiles, lower, upper, alpha=0.2)
plt.xlabel('Confidence Percentile')
plt.ylabel('MAE (nm)')
plt.ylim([0, np.max(upper) + 1])
plt.xlim([0, 100 * ((len(y_test) - 1) / len(y_test))])
plt.yticks(np.arange(0, np.max(upper) + 1, 5.0))
plt.savefig(
task +
'/results/gpr/{}_confidence_curve_mae.png'.format(representation))
plt.show()
def main(path, path_to_dft_dataset, task, representation, theory_level):
"""
:param path: str specifying path to photoswitches.csv file.
:param path_to_dft_dataset: str specifying path to dft_comparison.csv file.
:param task: str specifying the task. e_iso_pi only supported task for the TD-DFT comparison.
:param representation: str specifying the molecular representation. One of ['fingerprints, 'fragments', 'fragprints']
:param theory_level: str giving the level of theory to compare against - CAM-B3LYP or PBE0 ['CAM-B3LYP', 'PBE0']
"""
data_loader = TaskDataLoader(task, path)
smiles_list, _, pbe0_vals, cam_vals, experimental_vals = data_loader.load_dft_comparison_data(path_to_dft_dataset)
X = featurise_mols(smiles_list, representation)
# Keep only non-duplicate entries because we're not considering effects of solvent
non_duplicate_indices = np.array([i for i, smiles in enumerate(smiles_list) if smiles not in smiles_list[:i]])
X = X[non_duplicate_indices, :]
experimental_vals = experimental_vals[non_duplicate_indices]
non_dup_pbe0 = np.array([i for i, smiles in enumerate(smiles_list) if smiles not in smiles_list[:i]])
non_dup_cam = np.array([i for i, smiles in enumerate(smiles_list) if smiles not in smiles_list[:i]])
pbe0_vals = pbe0_vals[non_dup_pbe0]
cam_vals = cam_vals[non_dup_cam]
# molecules with dft values to be split into train/test
if theory_level == 'CAM-B3LYP':
X_with_dft = np.delete(X, np.argwhere(np.isnan(cam_vals)), axis=0)
y_with_dft = np.delete(experimental_vals, np.argwhere(np.isnan(cam_vals)))
# DFT values for the CAM-B3LYP level of theory
dft_vals = np.delete(cam_vals, np.argwhere(np.isnan(cam_vals)))
# molecules with no dft vals must go into the training set.
X_no_dft = np.delete(X, np.argwhere(~np.isnan(cam_vals)), axis=0)
y_no_dft = np.delete(experimental_vals, np.argwhere(~np.isnan(cam_vals)))
else:
X_with_dft = np.delete(X, np.argwhere(np.isnan(pbe0_vals)), axis=0)
y_with_dft = np.delete(experimental_vals, np.argwhere(np.isnan(pbe0_vals)))
# DFT values for the PBE0 level of theory
dft_vals = np.delete(pbe0_vals, np.argwhere(np.isnan(pbe0_vals)))
# molecules with no dft vals must go into the training set.
X_no_dft = np.delete(X, np.argwhere(~np.isnan(pbe0_vals)), axis=0)
y_no_dft = np.delete(experimental_vals, np.argwhere(~np.isnan(pbe0_vals)))
mae_list = []
dft_mae_list = []
# We define the Gaussian Process optimisation objective
m = None
def objective_closure():
return -m.log_marginal_likelihood()
print('\nBeginning training loop...')
for i in range(len(y_with_dft)):
X_train = np.delete(X_with_dft, i, axis=0)
y_train = np.delete(y_with_dft, i)
X_test = X_with_dft[i].reshape(1, -1)
y_test = y_with_dft[i]
dft_test = dft_vals[i]
X_train = np.concatenate((X_train, X_no_dft))
y_train = np.concatenate((y_train, y_no_dft))
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
# We standardise the outputs but leave the inputs unchanged
_, y_train, _, y_test, y_scaler = transform_data(X_train, y_train, X_test, y_test)
X_train = X_train.astype(np.float64)
X_test = X_test.astype(np.float64)
k = Tanimoto()
m = gpflow.models.GPR(data=(X_train, y_train), mean_function=Constant(np.mean(y_train)), kernel=k, noise_variance=1)
# Optimise the kernel variance and noise level by the marginal likelihood
opt = gpflow.optimizers.Scipy()
opt.minimize(objective_closure, m.trainable_variables, options=dict(maxiter=100))
print_summary(m)
# Output Standardised RMSE and RMSE on Train Set
y_pred_train, _ = m.predict_f(X_train)
train_rmse_stan = np.sqrt(mean_squared_error(y_train, y_pred_train))
train_rmse = np.sqrt(mean_squared_error(y_scaler.inverse_transform(y_train), y_scaler.inverse_transform(y_pred_train)))
print("\nStandardised Train RMSE: {:.3f}".format(train_rmse_stan))
print("Train RMSE: {:.3f}".format(train_rmse))
# mean and variance GP prediction
y_pred, y_var = m.predict_f(X_test)
y_pred = y_scaler.inverse_transform(y_pred)
y_test = y_scaler.inverse_transform(y_test)
# Output MAE for this trial
mae = abs(y_test - y_pred)
print("MAE: {}".format(mae))
# Store values in order to compute the mean and standard error of the statistics across trials
mae_list.append(mae)
# DFT prediction scores on the same trial
dft_mae = abs(y_test - dft_test)
dft_mae_list.append(dft_mae)
mae_list = np.array(mae_list)
dft_mae_list = np.array(dft_mae_list)
print("\nmean GP-Tanimoto MAE: {:.4f} +- {:.4f}\n".format(np.mean(mae_list), np.std(mae_list)/np.sqrt(len(mae_list))))
print("mean {} MAE: {:.4f} +- {:.4f}\n".format(theory_level, np.mean(dft_mae_list), np.std(dft_mae_list)/np.sqrt(len(dft_mae_list))))
task = 'e_iso_pi'
path = '../dataset/photoswitches.csv'
# New candidates to predict wavelength values for
# candidate_list = ['O=C(OC)C(C=C1)=CC=C1C2=C[N-][N+]3=C(C=CN32)/N=N/C4=CC=CC=C4',
# 'O=C(OC)C(C=C1)=CC=C1C2=CN3[N+]([N-]2)=CC=C3/N=N/C4=CC=CC=C4']
df = pd.read_csv('../dataset/purchasable_switch.csv')
candidate_list = df['SMILES'].to_list()
if __name__ == '__main__':
data_loader = TaskDataLoader(task, path)
smiles_list, y_train = data_loader.load_property_data()
X_train = featurise_mols(smiles_list, representation)
X_test = featurise_mols(candidate_list, representation)
num_features = np.shape(X_train)[1]
# We define the Gaussian Process Regression Model using the Tanimoto kernel
m = None
def objective_closure():
return -m.log_marginal_likelihood()
# We standardise the outputs but leave the inputs unchanged. Equivalent to transform data used in other scripts.
y_train = y_train.reshape(-1, 1)
y_scaler = StandardScaler()
def main(task, path, representation, use_pca, test_set_size, r_size,
det_encoder_n_hidden, lat_encoder_n_hidden, decoder_n_hidden):
"""
:param task: str specifying the task name. One of [Photoswitch, ESOL, FreeSolv, Lipophilicity].
:param path: str specifying the path to the photoswitches.csv file
:param representation: str specifying the representation. One of [fingerprints, fragments, fragprints]
:param use_pca: bool specifying whether or not to use PCA to perform Principal Components Regression
:param test_set_size: float specifying the train/test split ratio. e.g. 0.2 is 80/20 train/test split
:param r_size: Dimensionality of context encoding r.
:param det_encoder_n_hidden: Number of deterministic encoder hidden layers.
:param lat_encoder_n_hidden: Number of latent encoder hidden layers.
:param decoder_n_hidden: Number of decoder hidden layers.
:return:
"""
data_loader = TaskDataLoader(task, path)
smiles_list, y = data_loader.load_property_data()
X = featurise_mols(smiles_list, representation)
y_size = 1
# If True we perform Principal Components Regression
if use_pca:
n_components = 50
else:
n_components = None
print('\nBeginning training loop...')
j = 0 # index for saving results
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=test_set_size, random_state=42)
if task != 'Photoswitch':
# Artificially create a 80/10/10 train/validation/test split discarding the validation set.
split_in_two = int(len(y_test) / 2)
X_test = X_test[0:split_in_two]
y_test = y_test[0:split_in_two]
else:
# We subdivide the train set in order to run cross-validation.
X_train, X_test, y_train, y_test = train_test_split(X_train,
y_train,
test_size=0.1,
random_state=42)
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
# We standardise the outputs but leave the inputs unchanged
X_train, y_train, X_test, _, y_scaler = transform_data(
X_train,
y_train,
X_test,
y_test,
n_components=n_components,
use_pca=use_pca)
X_train = torch.from_numpy(X_train).float().unsqueeze(dim=0)
X_test = torch.from_numpy(X_test).float().unsqueeze(dim=0)
y_train = torch.from_numpy(y_train).float().unsqueeze(dim=0)
det_encoder_hidden_sizes = [8, 16]
lat_encoder_hidden_sizes = [8, 16]
decoder_hidden_sizes = [8, 16]
learning_rates = [0.01, 0.001]
batch_sizes = [16, 32]
iteration_numbers = [250, 500]
best_rmse = 10000000 # a big number
best_params = {
'det_encs': 0,
'lat_encs': 0,
'dec_hid': 0,
'lr': 0,
'batch_size': 0,
'iterations': 0
}
for det_encs in det_encoder_hidden_sizes:
for lat_encs in lat_encoder_hidden_sizes:
for dec_hid in decoder_hidden_sizes:
for l_rate in learning_rates:
for batch_s in batch_sizes:
for iter_num in iteration_numbers:
m = AttentiveNP(
x_size=X_train.shape[2],
y_size=y_size,
r_size=r_size,
det_encoder_hidden_size=det_encs,
det_encoder_n_hidden=det_encoder_n_hidden,
lat_encoder_hidden_size=lat_encs,
lat_encoder_n_hidden=lat_encoder_n_hidden,
decoder_hidden_size=dec_hid,
decoder_n_hidden=decoder_n_hidden,
lr=l_rate,
attention_type="multihead")
print('...training.')
m.train(X_train,
y_train,
batch_size=batch_s,
iterations=iter_num,
print_freq=None)
# Now, the context set comprises the training x / y values, the target set comprises the test x values.
y_pred, y_var = m.predict(X_train,
y_train,
X_test,
n_samples=100)
# Output Standardised RMSE and RMSE on Train Set
score = r2_score(
y_test, y_scaler.inverse_transform(y_pred))
rmse = np.sqrt(
mean_squared_error(
y_test,
y_scaler.inverse_transform(y_pred)))
mae = mean_absolute_error(
y_test, y_scaler.inverse_transform(y_pred))
print("\nR^2: {:.3f}".format(score))
print("RMSE: {:.3f}".format(rmse))
print("MAE: {:.3f}".format(mae))
if rmse < best_rmse:
best_rmse = rmse
best_params['det_encs'] = det_encs
best_params['lat_encs'] = lat_encs
best_params['dec_hid'] = dec_hid
best_params['lr'] = l_rate
best_params['batch_size'] = batch_s
best_params['iterations'] = iter_num
print('Best parameters are \n')
print(best_params)
print('Final best parameters are \n')
print(best_params)
with open(f'cross_val_hypers/{task}/ANP/hypers_{representation}.txt',
'w') as f:
f.write(str(best_params))
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from data_utils import TaskDataLoader, featurise_mols
from kernels import Tanimoto
representation = 'fragprints'
task = 'e_iso_pi'
path = '../dataset/photoswitches.csv'
df = pd.read_csv('../dataset/purchasable_switch.csv')
candidate_list = df['SMILES'].to_list()
if __name__ == '__main__':
X_test = featurise_mols(candidate_list, representation)
data_loader_e_iso_pi = TaskDataLoader('e_iso_pi', path)
data_loader_z_iso_pi = TaskDataLoader('z_iso_pi', path)
data_loader_e_iso_n = TaskDataLoader('e_iso_n', path)
data_loader_z_iso_n = TaskDataLoader('z_iso_n', path)
smiles_list_e_iso_pi, y_e_iso_pi = data_loader_e_iso_pi.load_property_data()
smiles_list_z_iso_pi, y_z_iso_pi = data_loader_z_iso_pi.load_property_data()
smiles_list_e_iso_n, y_e_iso_n = data_loader_e_iso_n.load_property_data()
smiles_list_z_iso_n, y_z_iso_n = data_loader_z_iso_n.load_property_data()
y_e_iso_pi = y_e_iso_pi.reshape(-1, 1)
y_z_iso_pi = y_z_iso_pi.reshape(-1, 1)
y_e_iso_n = y_e_iso_n.reshape(-1, 1)
y_z_iso_n = y_z_iso_n.reshape(-1, 1)
def main(path, path_to_dft_dataset, representation, theory_level):
"""
:param path: str specifying path to photoswitches.csv file.
:param path_to_dft_dataset: str specifying path to dft_comparison.csv file.
:param representation: str specifying the molecular representation. One of ['fingerprints, 'fragments', 'fragprints']
:param theory_level: str giving the level of theory to compare against - CAM-B3LYP or PBE0 ['CAM-B3LYP', 'PBE0']
"""
task = 'e_iso_pi' # e_iso_pi only task supported for TD-DFT comparison
data_loader = TaskDataLoader(task, path)
smiles_list, _, pbe0_vals, cam_vals, experimental_vals = data_loader.load_dft_comparison_data(path_to_dft_dataset)
X = featurise_mols(smiles_list, representation)
# Keep only non-duplicate entries because we're not considering effects of solvent
non_duplicate_indices = np.array([i for i, smiles in enumerate(smiles_list) if smiles not in smiles_list[:i]])
X = X[non_duplicate_indices, :]
experimental_vals = experimental_vals[non_duplicate_indices]
non_dup_pbe0 = np.array([i for i, smiles in enumerate(smiles_list) if smiles not in smiles_list[:i]])
non_dup_cam = np.array([i for i, smiles in enumerate(smiles_list) if smiles not in smiles_list[:i]])
pbe0_vals = pbe0_vals[non_dup_pbe0]
cam_vals = cam_vals[non_dup_cam]
# molecules with dft values to be split into train/test
if theory_level == 'CAM-B3LYP':
X_with_dft = np.delete(X, np.argwhere(np.isnan(cam_vals)), axis=0)
y_with_dft = np.delete(experimental_vals, np.argwhere(np.isnan(cam_vals)))
# DFT values for the CAM-B3LYP level of theory
dft_vals = np.delete(cam_vals, np.argwhere(np.isnan(cam_vals)))
# molecules with no dft vals must go into the training set.
X_no_dft = np.delete(X, np.argwhere(~np.isnan(cam_vals)), axis=0)
y_no_dft = np.delete(experimental_vals, np.argwhere(~np.isnan(cam_vals)))
else:
X_with_dft = np.delete(X, np.argwhere(np.isnan(pbe0_vals)), axis=0)
y_with_dft = np.delete(experimental_vals, np.argwhere(np.isnan(pbe0_vals)))
# DFT values for the PBE0 level of theory
dft_vals = np.delete(pbe0_vals, np.argwhere(np.isnan(pbe0_vals)))
# molecules with no dft vals must go into the training set.
X_no_dft = np.delete(X, np.argwhere(~np.isnan(pbe0_vals)), axis=0)
y_no_dft = np.delete(experimental_vals, np.argwhere(~np.isnan(pbe0_vals)))
# Load in the other property values for multitask learning. e_iso_pi is a always the task in this instance.
data_loader_z_iso_pi = TaskDataLoader('z_iso_pi', path)
data_loader_e_iso_n = TaskDataLoader('e_iso_n', path)
data_loader_z_iso_n = TaskDataLoader('z_iso_n', path)
smiles_list_z_iso_pi, y_z_iso_pi = data_loader_z_iso_pi.load_property_data()
smiles_list_e_iso_n, y_e_iso_n = data_loader_e_iso_n.load_property_data()
smiles_list_z_iso_n, y_z_iso_n = data_loader_z_iso_n.load_property_data()
y_z_iso_pi = y_z_iso_pi.reshape(-1, 1)
y_e_iso_n = y_e_iso_n.reshape(-1, 1)
y_z_iso_n = y_z_iso_n.reshape(-1, 1)
X_z_iso_pi = featurise_mols(smiles_list_z_iso_pi, representation)
X_e_iso_n = featurise_mols(smiles_list_e_iso_n, representation)
X_z_iso_n = featurise_mols(smiles_list_z_iso_n, representation)
output_dim = 4 # Number of outputs
rank = 1 # Rank of W
feature_dim = len(X_no_dft[0, :])
tanimoto_active_dims = [i for i in range(feature_dim)] # active dims for Tanimoto base kernel.
mae_list = []
dft_mae_list = []
# We define the Gaussian Process optimisation objective
m = None
def objective_closure():
return -m.log_marginal_likelihood()
print('\nBeginning training loop...')
for i in range(len(y_with_dft)):
X_train = np.delete(X_with_dft, i, axis=0)
y_train = np.delete(y_with_dft, i)
X_test = X_with_dft[i].reshape(1, -1)
y_test = y_with_dft[i]
dft_test = dft_vals[i]
X_train = np.concatenate((X_train, X_no_dft))
y_train = np.concatenate((y_train, y_no_dft))
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
X_train = X_train.astype(np.float64)
X_test = X_test.astype(np.float64)
# Augment the input with zeroes, ones, twos, threes to indicate the required output dimension
X_augmented = np.vstack((np.append(X_train, np.zeros((len(X_train), 1)), axis=1),
np.append(X_z_iso_pi, np.ones((len(X_z_iso_pi), 1)), axis=1),
np.append(X_e_iso_n, np.ones((len(X_e_iso_n), 1)) * 2, axis=1),
np.append(X_z_iso_n, np.ones((len(X_z_iso_n), 1)) * 3, axis=1)))
X_test = np.append(X_test, np.zeros((len(X_test), 1)), axis=1)
X_train = np.append(X_train, np.zeros((len(X_train), 1)), axis=1)
# Augment the Y data with zeroes, ones, twos and threes that specify a likelihood from the list of likelihoods
Y_augmented = np.vstack((np.hstack((y_train, np.zeros_like(y_train))),
np.hstack((y_z_iso_pi, np.ones_like(y_z_iso_pi))),
np.hstack((y_e_iso_n, np.ones_like(y_e_iso_n) * 2)),
np.hstack((y_z_iso_n, np.ones_like(y_z_iso_n) * 3))))
y_test = np.hstack((y_test, np.zeros_like(y_test)))
# Base kernel
k = Tanimoto(active_dims=tanimoto_active_dims)
#set_trainable(k.variance, False)
# Coregion kernel
coreg = gpflow.kernels.Coregion(output_dim=output_dim, rank=rank, active_dims=[feature_dim])
# Create product kernel
kern = k * coreg
# This likelihood switches between Gaussian noise with different variances for each f_i:
lik = gpflow.likelihoods.SwitchedLikelihood([gpflow.likelihoods.Gaussian(), gpflow.likelihoods.Gaussian(),
gpflow.likelihoods.Gaussian(), gpflow.likelihoods.Gaussian()])
# now build the GP model as normal
m = gpflow.models.VGP((X_augmented, Y_augmented), mean_function=Constant(np.mean(y_train[:, 0])), kernel=kern, likelihood=lik)
# fit the covariance function parameters
maxiter = ci_niter(1000)
gpflow.optimizers.Scipy().minimize(m.training_loss, m.trainable_variables, options=dict(maxiter=maxiter), method="L-BFGS-B",)
print_summary(m)
# Output Standardised RMSE and RMSE on Train Set
y_pred_train, _ = m.predict_f(X_train)
train_rmse_stan = np.sqrt(mean_squared_error(y_train, y_pred_train))
train_rmse = np.sqrt(mean_squared_error(y_train, y_pred_train))
print("\nStandardised Train RMSE: {:.3f}".format(train_rmse_stan))
print("Train RMSE: {:.3f}".format(train_rmse))
# mean and variance GP prediction
y_pred, y_var = m.predict_f(X_test)
# Output MAE for this trial
mae = abs(y_test[:, 0] - y_pred)
print("MAE: {}".format(mae))
# Store values in order to compute the mean and standard error of the statistics across trials
mae_list.append(mae)
# DFT prediction scores on the same trial
dft_mae = abs(y_test[:, 0] - dft_test)
dft_mae_list.append(dft_mae)
mae_list = np.array(mae_list)
dft_mae_list = np.array(dft_mae_list)
print("\nmean GP-Tanimoto MAE: {:.4f} +- {:.4f}\n".format(np.mean(mae_list), np.std(mae_list)/np.sqrt(len(mae_list))))
print("mean {} MAE: {:.4f} +- {:.4f}\n".format(theory_level, np.mean(dft_mae_list), np.std(dft_mae_list)/np.sqrt(len(dft_mae_list))))
def main(path, task, representation, use_pca, n_trials, test_set_size):
"""
:param path: str specifying path to dataset.
:param task: str specifying the task. One of ['e_iso_pi', 'z_iso_pi', 'e_iso_n', 'z_iso_n']
:param representation: str specifying the molecular representation. One of ['fingerprints, 'fragments', 'fragprints']
:param use_pca: bool. If True apply PCA to perform Principal Components Regression.
:param n_trials: int specifying number of random train/test splits to use
:param test_set_size: float in range [0, 1] specifying fraction of dataset to use as test set.
"""
data_loader = TaskDataLoader(task, path)
smiles_list, y = data_loader.load_property_data()
X = featurise_mols(smiles_list, representation)
if use_pca:
n_components = 50
else:
n_components = None
r2_list = []
rmse_list = []
mae_list = []
print('\nBeginning training loop...')
for i in range(0, n_trials):
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=test_set_size, random_state=i)
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
X_train, y_train, X_test, y_test, y_scaler = transform_data(
X_train, y_train, X_test, y_test, n_components, use_pca)
regr_rf = RandomForestRegressor(n_estimators=1519,
random_state=4,
max_features=0.086,
bootstrap=False,
min_samples_leaf=2)
regr_rf.fit(X_train, y_train)
# Output Standardised RMSE and RMSE on Train Set
y_pred_train = regr_rf.predict(X_train)
train_rmse_stan = np.sqrt(mean_squared_error(y_train, y_pred_train))
train_rmse = np.sqrt(
mean_squared_error(y_scaler.inverse_transform(y_train),
y_scaler.inverse_transform(y_pred_train)))
print("\nStandardised Train RMSE: {:.3f}".format(train_rmse_stan))
print("Train RMSE: {:.3f}".format(train_rmse))
# Predict on new data
y_rf = regr_rf.predict(X_test)
y_rf = y_scaler.inverse_transform(y_rf)
y_test = y_scaler.inverse_transform(y_test)
score = r2_score(y_test, y_rf)
rmse = np.sqrt(mean_squared_error(y_test, y_rf))
mae = mean_absolute_error(y_test, y_rf)
print("\nR^2: {:.3f}".format(score))
print("RMSE: {:.3f}".format(rmse))
print("MAE: {:.3f}".format(mae))
r2_list.append(score)
rmse_list.append(rmse)
mae_list.append(mae)
r2_list = np.array(r2_list)
rmse_list = np.array(rmse_list)
mae_list = np.array(mae_list)
print("\nmean R^2: {:.4f} +- {:.4f}".format(
np.mean(r2_list),
np.std(r2_list) / np.sqrt(len(r2_list))))
print("mean RMSE: {:.4f} +- {:.4f}".format(
np.mean(rmse_list),
np.std(rmse_list) / np.sqrt(len(rmse_list))))
print("mean MAE: {:.4f} +- {:.4f}\n".format(
np.mean(mae_list),
np.std(mae_list) / np.sqrt(len(mae_list))))
def main(path, task, representation, use_pca, n_trials, test_set_size):
"""
:param path: str specifying path to dataset.
:param task: str specifying the task. One of ['e_iso_pi', 'z_iso_pi', 'e_iso_n', 'z_iso_n']
:param representation: str specifying the molecular representation. One of ['fingerprints, 'fragments', 'fragprints']
:param use_pca: bool. If True apply PCA to perform Principal Components Regression.
:param n_trials: int specifying number of random train/test splits to use
:param test_set_size: float in range [0, 1] specifying fraction of dataset to use as test set
"""
data_loader = TaskDataLoader(task, path)
smiles_list, y = data_loader.load_property_data()
X = featurise_mols(smiles_list, representation)
# If True we perform Principal Components Regression
if use_pca:
n_components = 100
else:
n_components = None
r2_list = []
rmse_list = []
mae_list = []
print('\nBeginning training loop...')
for i in range(0, n_trials):
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=test_set_size, random_state=i)
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
# We standardise the outputs but leave the inputs unchanged
_, y_train, _, y_test, y_scaler = transform_data(
X_train,
y_train,
X_test,
y_test,
n_components=n_components,
use_pca=use_pca)
X_train = X_train.astype(np.float64)
X_test = X_test.astype(np.float64)
gp_kernel = TanimotoKernel()
gpr = GaussianProcessRegressor(kernel=gp_kernel)
gpr.fit(X_train, y_train)
# mean GP prediction
X_test = np.tile(X_test, (10000, 1))
import time
start = time.time()
y_pred = gpr.predict(X_test, return_std=False)
end = time.time()
print(f'time elapsed is {end - start}')
y_pred = y_scaler.inverse_transform(y_pred)
y_test = y_scaler.inverse_transform(y_test)
# Output Standardised RMSE and RMSE on Train Set
y_pred_train = gpr.predict(X_train, return_std=False)
train_rmse_stan = np.sqrt(mean_squared_error(y_train, y_pred_train))
train_rmse = np.sqrt(
mean_squared_error(y_scaler.inverse_transform(y_train),
y_scaler.inverse_transform(y_pred_train)))
print("\nStandardised Train RMSE: {:.3f}".format(train_rmse_stan))
print("Train RMSE: {:.3f}".format(train_rmse))
score = r2_score(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
mae = mean_absolute_error(y_test, y_pred)
print("\nR^2: {:.3f}".format(score))
print("RMSE: {:.3f}".format(rmse))
print("MAE: {:.3f}".format(mae))
r2_list.append(score)
rmse_list.append(rmse)
mae_list.append(mae)
r2_list = np.array(r2_list)
rmse_list = np.array(rmse_list)
mae_list = np.array(mae_list)
print("\nmean R^2: {:.4f} +- {:.4f}".format(
np.mean(r2_list),
np.std(r2_list) / np.sqrt(len(r2_list))))
print("mean RMSE: {:.4f} +- {:.4f}".format(
np.mean(rmse_list),
np.std(rmse_list) / np.sqrt(len(rmse_list))))
print("mean MAE: {:.4f} +- {:.4f}\n".format(
np.mean(mae_list),
np.std(mae_list) / np.sqrt(len(mae_list))))
def main(path, task, representation, use_pca, n_trials, test_set_size):
"""
Train a multioutput GP simultaneously on all tasks of the photoswitch dataset.
:param path: str specifying path to dataset.
:param task: str specifying the task. One of ['e_iso_pi', 'z_iso_pi', 'e_iso_n', 'z_iso_n']
:param representation: str specifying the molecular representation. One of ['fingerprints, 'fragments', 'fragprints']
:param use_pca: bool. If True apply PCA to perform Principal Components Regression.
:param n_trials: int specifying number of random train/test splits to use
:param test_set_size: float in range [0, 1] specifying fraction of dataset to use as test set
"""
# If True we perform Principal Components Regression
if use_pca:
n_components = 100
else:
n_components = None
data_loader_e_iso_pi = TaskDataLoader('e_iso_pi', path)
data_loader_z_iso_pi = TaskDataLoader('z_iso_pi', path)
data_loader_e_iso_n = TaskDataLoader('e_iso_n', path)
data_loader_z_iso_n = TaskDataLoader('z_iso_n', path)
smiles_list_e_iso_pi, y_e_iso_pi = data_loader_e_iso_pi.load_property_data(
)
smiles_list_z_iso_pi, y_z_iso_pi = data_loader_z_iso_pi.load_property_data(
)
smiles_list_e_iso_n, y_e_iso_n = data_loader_e_iso_n.load_property_data()
smiles_list_z_iso_n, y_z_iso_n = data_loader_z_iso_n.load_property_data()
y_e_iso_pi = y_e_iso_pi.reshape(-1, 1)
y_z_iso_pi = y_z_iso_pi.reshape(-1, 1)
y_e_iso_n = y_e_iso_n.reshape(-1, 1)
y_z_iso_n = y_z_iso_n.reshape(-1, 1)
X_e_iso_pi = featurise_mols(smiles_list_e_iso_pi, representation)
X_z_iso_pi = featurise_mols(smiles_list_z_iso_pi, representation)
X_e_iso_n = featurise_mols(smiles_list_e_iso_n, representation)
X_z_iso_n = featurise_mols(smiles_list_z_iso_n, representation)
output_dim = 4 # Number of outputs
rank = 1 # Rank of W
feature_dim = len(X_e_iso_pi[0, :])
tanimoto_active_dims = [i for i in range(feature_dim)
] # active dims for Tanimoto base kernel.
r2_list = []
rmse_list = []
mae_list = []
print('\nBeginning training loop...')
for i in range(0, n_trials):
if task == 'e_iso_pi':
X_task = X_e_iso_pi
y_task = y_e_iso_pi
elif task == 'z_iso_pi':
X_task = X_z_iso_pi
y_task = y_z_iso_pi
elif task == 'e_iso_n':
X_task = X_e_iso_n
y_task = y_e_iso_n
else:
X_task = X_z_iso_n
y_task = y_z_iso_n
X_train, X_test, y_train, y_test = train_test_split(
X_task, y_task, test_size=test_set_size, random_state=i)
X_train = X_train.astype(np.float64)
X_test = X_test.astype(np.float64)
if task == 'e_iso_pi':
# Augment the input with zeroes, ones, twos, threes to indicate the required output dimension
X_augmented = np.vstack((np.append(X_train,
np.zeros((len(X_train), 1)),
axis=1),
np.append(X_z_iso_pi,
np.ones((len(X_z_iso_pi), 1)),
axis=1),
np.append(X_e_iso_n,
np.ones(
(len(X_e_iso_n), 1)) * 2,
axis=1),
np.append(X_z_iso_n,
np.ones(
(len(X_z_iso_n), 1)) * 3,
axis=1)))
X_test = np.append(X_test, np.zeros((len(X_test), 1)), axis=1)
X_train = np.append(X_train, np.zeros((len(X_train), 1)), axis=1)
# Augment the Y data with zeroes, ones, twos and threes that specify a likelihood from the list of likelihoods
Y_augmented = np.vstack(
(np.hstack((y_train, np.zeros_like(y_train))),
np.hstack((y_z_iso_pi, np.ones_like(y_z_iso_pi))),
np.hstack((y_e_iso_n, np.ones_like(y_e_iso_n) * 2)),
np.hstack((y_z_iso_n, np.ones_like(y_z_iso_n) * 3))))
y_test = np.hstack((y_test, np.zeros_like(y_test)))
elif task == 'z_iso_pi':
# Augment the input with zeroes, ones, twos, threes to indicate the required output dimension
X_augmented = np.vstack((np.append(X_e_iso_pi,
np.zeros((len(X_e_iso_pi), 1)),
axis=1),
np.append(X_train,
np.ones((len(X_train), 1)),
axis=1),
np.append(X_e_iso_n,
np.ones(
(len(X_e_iso_n), 1)) * 2,
axis=1),
np.append(X_z_iso_n,
np.ones(
(len(X_z_iso_n), 1)) * 3,
axis=1)))
X_test = np.append(X_test, np.ones((len(X_test), 1)), axis=1)
X_train = np.append(X_train, np.ones((len(X_train), 1)), axis=1)
# Augment the Y data with zeroes, ones, twos and threes that specify a likelihood from the list of likelihoods
Y_augmented = np.vstack(
(np.hstack((y_e_iso_pi, np.zeros_like(y_e_iso_pi))),
np.hstack((y_train, np.ones_like(y_train))),
np.hstack((y_e_iso_n, np.ones_like(y_e_iso_n) * 2)),
np.hstack((y_z_iso_n, np.ones_like(y_z_iso_n) * 3))))
y_test = np.hstack((y_test, np.ones_like(y_test)))
elif task == 'e_iso_n':
# Augment the input with zeroes, ones, twos, threes to indicate the required output dimension
X_augmented = np.vstack((np.append(X_e_iso_pi,
np.zeros((len(X_e_iso_pi), 1)),
axis=1),
np.append(X_z_iso_pi,
np.ones((len(X_z_iso_pi), 1)),
axis=1),
np.append(X_train,
np.ones((len(X_train), 1)) * 2,
axis=1),
np.append(X_z_iso_n,
np.ones(
(len(X_z_iso_n), 1)) * 3,
axis=1)))
X_test = np.append(X_test, np.ones((len(X_test), 1)) * 2, axis=1)
X_train = np.append(X_train,
np.ones((len(X_train), 1)) * 2,
axis=1)
# Augment the Y data with zeroes, ones, twos and threes that specify a likelihood from the list of likelihoods
Y_augmented = np.vstack(
(np.hstack((y_e_iso_pi, np.zeros_like(y_e_iso_pi))),
np.hstack((y_z_iso_pi, np.ones_like(y_z_iso_pi))),
np.hstack((y_train, np.ones_like(y_train) * 2)),
np.hstack((y_z_iso_n, np.ones_like(y_z_iso_n) * 3))))
y_test = np.hstack((y_test, np.ones_like(y_test) * 2))
else:
# Augment the input with zeroes, ones, twos, threes to indicate the required output dimension
X_augmented = np.vstack((np.append(X_e_iso_pi,
np.zeros((len(X_e_iso_pi), 1)),
axis=1),
np.append(X_z_iso_pi,
np.ones((len(X_z_iso_pi), 1)),
axis=1),
np.append(X_e_iso_n,
np.ones(
(len(X_e_iso_n), 1)) * 2,
axis=1),
np.append(X_train,
np.ones((len(X_train), 1)) * 3,
axis=1)))
X_test = np.append(X_test, np.ones((len(X_test), 1)) * 3, axis=1)
X_train = np.append(X_train,
np.ones((len(X_train), 1)) * 3,
axis=1)
# Augment the Y data with zeroes, ones, twos and threes that specify a likelihood from the list of likelihoods
Y_augmented = np.vstack(
(np.hstack((y_e_iso_pi, np.zeros_like(y_e_iso_pi))),
np.hstack((y_z_iso_pi, np.ones_like(y_z_iso_pi))),
np.hstack((y_e_iso_n, np.ones_like(y_e_iso_n) * 2)),
np.hstack((y_train, np.ones_like(y_train) * 3))))
y_test = np.hstack((y_test, np.ones_like(y_test) * 3))
# Base kernel
k = Tanimoto(active_dims=tanimoto_active_dims)
#set_trainable(k.variance, False)
# Coregion kernel
coreg = gpflow.kernels.Coregion(output_dim=output_dim,
rank=rank,
active_dims=[feature_dim])
# Create product kernel
kern = k * coreg
# This likelihood switches between Gaussian noise with different variances for each f_i:
lik = gpflow.likelihoods.SwitchedLikelihood([
gpflow.likelihoods.Gaussian(),
gpflow.likelihoods.Gaussian(),
gpflow.likelihoods.Gaussian(),
gpflow.likelihoods.Gaussian()
])
# now build the GP model as normal
m = gpflow.models.VGP((X_augmented, Y_augmented),
mean_function=Constant(np.mean(y_train[:, 0])),
kernel=kern,
likelihood=lik)
# fit the covariance function parameters
maxiter = ci_niter(1000)
gpflow.optimizers.Scipy().minimize(
m.training_loss,
m.trainable_variables,
options=dict(maxiter=maxiter),
method="L-BFGS-B",
)
print_summary(m)
# mean and variance GP prediction
y_pred, y_var = m.predict_f(X_test)
# Output Standardised RMSE and RMSE on Train Set
y_pred_train, _ = m.predict_f(X_train)
train_rmse_stan = np.sqrt(mean_squared_error(y_train, y_pred_train))
train_rmse = np.sqrt(mean_squared_error(y_train, y_pred_train))
print("\nStandardised Train RMSE: {:.3f}".format(train_rmse_stan))
print("Train RMSE: {:.3f}".format(train_rmse))
score = r2_score(y_test[:, 0], y_pred)
rmse = np.sqrt(mean_squared_error(y_test[:, 0], y_pred))
mae = mean_absolute_error(y_test[:, 0], y_pred)
print("\nR^2: {:.3f}".format(score))
print("RMSE: {:.3f}".format(rmse))
print("MAE: {:.3f}".format(mae))
r2_list.append(score)
rmse_list.append(rmse)
mae_list.append(mae)
B = coreg.output_covariance().numpy()
print("B =", B)
_ = plt.imshow(B)
r2_list = np.array(r2_list)
rmse_list = np.array(rmse_list)
mae_list = np.array(mae_list)
print("\nmean R^2: {:.4f} +- {:.4f}".format(
np.mean(r2_list),
np.std(r2_list) / np.sqrt(len(r2_list))))
print("mean RMSE: {:.4f} +- {:.4f}".format(
np.mean(rmse_list),
np.std(rmse_list) / np.sqrt(len(rmse_list))))
print("mean MAE: {:.4f} +- {:.4f}\n".format(
np.mean(mae_list),
np.std(mae_list) / np.sqrt(len(mae_list))))
def main(path, task, representation, use_pca, n_trials, test_set_size,
use_rmse_conf, precompute_repr):
"""
:param path: str specifying path to dataset.
:param task: str specifying the task. One of ['Photoswitch', 'ESOL', 'FreeSolv', 'Lipophilicity']
:param representation: str specifying the molecular representation. One of ['SMILES, fingerprints, 'fragments', 'fragprints']
:param use_pca: bool. If True apply PCA to perform Principal Components Regression.
:param n_trials: int specifying number of random train/test splits to use
:param test_set_size: float in range [0, 1] specifying fraction of dataset to use as test set
:param use_rmse_conf: bool specifying whether to compute the rmse confidence-error curves or the mae confidence-
error curves. True is the option for rmse.
:param precompute_repr: bool indicating whether to precompute representations or not.
"""
data_loader = TaskDataLoader(task, path)
smiles_list, y = data_loader.load_property_data()
X = featurise_mols(smiles_list, representation)
if precompute_repr:
if representation == 'SMILES':
with open(
f'precomputed_representations/{task}_{representation}.txt',
'w') as f:
for smiles in X:
f.write(smiles + '\n')
else:
np.savetxt(
f'precomputed_representations/{task}_{representation}.txt', X)
# If True we perform Principal Components Regression
if use_pca:
n_components = 100
else:
n_components = None
r2_list = []
rmse_list = []
mae_list = []
# We pre-allocate arrays for plotting confidence-error curves
_, _, _, y_test = train_test_split(X,
y,
test_size=test_set_size,
random_state=42) # To get test set size
# Photoswitch dataset requires 80/20 splitting. Other datasets are 80/10/10.
if task != 'Photoswitch':
split_in_two = int(len(y_test) / 2)
n_test = split_in_two
else:
n_test = len(y_test)
rmse_confidence_list = np.zeros((n_trials, n_test))
mae_confidence_list = np.zeros((n_trials, n_test))
# For Calibration curve
prediction_prop = [[] for _ in range(n_trials)]
print('\nBeginning training loop...')
for i in range(0, n_trials):
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=test_set_size, random_state=i)
if representation == 'SMILES':
np.savetxt(f'fixed_train_test_splits/{task}/X_train_split_{i}.txt',
X_train,
fmt="%s")
np.savetxt(f'fixed_train_test_splits/{task}/X_test_split_{i}.txt',
X_test,
fmt="%s")
np.savetxt(f'fixed_train_test_splits/{task}/y_train_split_{i}.txt',
y_train)
np.savetxt(f'fixed_train_test_splits/{task}/y_test_split_{i}.txt',
y_test)
else:
if task != 'Photoswitch':
# Artificially create a 80/10/10 train/validation/test split discarding the validation set.
split_in_two = int(len(y_test) / 2)
X_test = X_test[0:split_in_two]
y_test = y_test[0:split_in_two]
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
# We standardise the outputs but leave the inputs unchanged
_, y_train, _, y_test, y_scaler = transform_data(
X_train,
y_train,
X_test,
y_test,
n_components=n_components,
use_pca=use_pca)
X_train = X_train.astype(np.float64)
X_test = X_test.astype(np.float64)
np.random.seed(42)
datasets, n, d, mean_y_train, std_y_train = load_reg_data(
X_train, y_train, X_test, y_test)
train_set_x, train_set_y = datasets[0]
test_set_x, test_set_y = datasets[1]
N_train = train_set_x.get_value(borrow=True).shape[0]
N_test = test_set_x.get_value(borrow=True).shape[0]
layer_sizes = [d, 20, 20, len(mean_y_train)]
n_samples = 100
alpha = 0.5
learning_rate = 0.01
v_prior = 1.0
batch_size = 32
print('... building model')
sys.stdout.flush()
bb_alpha = BB_alpha(layer_sizes, n_samples, alpha, learning_rate,
v_prior, batch_size, train_set_x, train_set_y,
N_train, test_set_x, test_set_y, N_test,
mean_y_train, std_y_train)
print('... training')
sys.stdout.flush()
test_error, test_ll = bb_alpha.train_ADAM(100)
print('Test RMSE: ', test_error)
print('Test ll: ', test_ll)
samples = bb_alpha.sample_predictive_distribution(X_test)
y_pred = np.mean(samples, axis=0)
var = np.var(samples, axis=0)
# For producing the calibration curve
for k in [
0.13, 0.26, 0.39, 0.53, 0.68, 0.85, 1.04, 1.15, 1.28, 1.44,
1.645, 1.96
]:
a = (y_scaler.inverse_transform(y_test) <
y_scaler.inverse_transform(y_pred + k * np.sqrt(var)))
b = (y_scaler.inverse_transform(y_test) >
y_scaler.inverse_transform(y_pred - k * np.sqrt(var)))
prediction_prop[i].append(
np.argwhere((a == True) & (b == True)).shape[0] /
len(y_test))
# We transform the standardised predictions back to the original data space
y_pred = y_scaler.inverse_transform(y_pred)
y_test = y_scaler.inverse_transform(y_test)
# Compute scores for confidence curve plotting.
ranked_confidence_list = np.argsort(var, axis=0).flatten()
for k in range(len(y_test)):
# Construct the RMSE error for each level of confidence
conf = ranked_confidence_list[0:k + 1]
rmse = np.sqrt(mean_squared_error(y_test[conf], y_pred[conf]))
rmse_confidence_list[i, k] = rmse
# Construct the MAE error for each level of confidence
mae = mean_absolute_error(y_test[conf], y_pred[conf])
mae_confidence_list[i, k] = mae
# Output Standardised RMSE and RMSE on Train Set
train_samples = bb_alpha.sample_predictive_distribution(X_train)
y_pred_train = np.mean(train_samples, axis=0)
train_rmse_stan = np.sqrt(mean_squared_error(
y_train, y_pred_train))
train_rmse = np.sqrt(
mean_squared_error(y_scaler.inverse_transform(y_train),
y_scaler.inverse_transform(y_pred_train)))
print("\nStandardised Train RMSE: {:.3f}".format(train_rmse_stan))
print("Train RMSE: {:.3f}".format(train_rmse))
score = r2_score(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
mae = mean_absolute_error(y_test, y_pred)
print("\nR^2: {:.3f}".format(score))
print("RMSE: {:.3f}".format(rmse))
print("MAE: {:.3f}".format(mae))
r2_list.append(score)
rmse_list.append(rmse)
mae_list.append(mae)
if representation != 'SMILES':
r2_list = np.array(r2_list)
rmse_list = np.array(rmse_list)
mae_list = np.array(mae_list)
print("\nmean R^2: {:.4f} +- {:.4f}".format(np.mean(r2_list),
np.std(r2_list)))
print("mean RMSE: {:.4f} +- {:.4f}".format(np.mean(rmse_list),
np.std(rmse_list)))
print("mean MAE: {:.4f} +- {:.4f}\n".format(np.mean(mae_list),
np.std(mae_list)))
# Plot confidence-error curves
confidence_percentiles = np.arange(
1e-14, 100, 100 / len(y_test)
) # 1e-14 instead of 0 to stop weirdness with len(y_test) = 29
if use_rmse_conf:
rmse_mean = np.mean(rmse_confidence_list, axis=0)
rmse_std = np.std(rmse_confidence_list, axis=0)
# We flip because we want the most confident predictions on the right-hand side of the plot
rmse_mean = np.flip(rmse_mean)
rmse_std = np.flip(rmse_std)
# One-sigma error bars
lower = rmse_mean - rmse_std
upper = rmse_mean + rmse_std
plt.plot(confidence_percentiles, rmse_mean, label='mean')
plt.fill_between(confidence_percentiles, lower, upper, alpha=0.2)
plt.xlabel('Confidence Percentile')
plt.ylabel('RMSE')
plt.ylim([0, np.max(upper) + 1])
plt.xlim([0, 100 * ((len(y_test) - 1) / len(y_test))])
plt.yticks(np.arange(0, np.max(upper) + 1, 5.0))
plt.savefig(task +
'/results/BNN/{}_{}_confidence_curve_rmse.png'.format(
representation, task))
plt.show()
else:
# We plot the Mean-absolute error confidence-error curves
mae_mean = np.mean(mae_confidence_list, axis=0)
mae_std = np.std(mae_confidence_list, axis=0)
mae_mean = np.flip(mae_mean)
mae_std = np.flip(mae_std)
lower = mae_mean - mae_std
upper = mae_mean + mae_std
plt.plot(confidence_percentiles, mae_mean, label='mean')
plt.fill_between(confidence_percentiles, lower, upper, alpha=0.2)
plt.xlabel('Confidence Percentile')
plt.ylabel('MAE')
plt.ylim([0, np.max(upper) + 1])
plt.xlim([0, 100 * ((len(y_test) - 1) / len(y_test))])
plt.yticks(np.arange(0, np.max(upper) + 1, 5.0))
plt.savefig(task +
'/results/BNN/{}_{}_confidence_curve_mae.png'.format(
representation, task))
plt.show()
# Plot the calibration curve
mean_props = np.mean(prediction_prop, axis=0)
sd_props = np.std(prediction_prop, axis=0)
lower = mean_props - sd_props
upper = mean_props + sd_props
qs = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95]
plt.plot(qs, mean_props, label='mean')
plt.fill_between(qs, lower, upper, alpha=0.2)
plt.plot(qs, qs, color="red")
plt.xlabel('q')
plt.ylabel('C(q)')
plt.savefig(task + '/results/BNN/{}_{}_calibration_curve.png'.format(
representation, task))
plt.show()
np.savetxt(
task +
'/results/BNN/{}_{}_mean_props'.format(representation, task),
mean_props)
np.savetxt(
task + '/results/BNN/{}_{}_sd_props'.format(representation, task),
sd_props)
def main(path, path_to_large_dataset, task, representation, test_set_size,
augment_photo_dataset, n_trials):
"""
:param path: str giving path to the photoswitches.csv file.
:param path_to_large_dataset: str giving path to paper_allDB.csv file
:param task: str specifying the task. Always e_iso_pi for the generalization experiment
:param representation: str specifying the molecular representation. One of [fingerprints, fragments, fragprints].'
:param test_set_size: float in range [0, 1] specifying fraction of dataset to use as test set
:param augment_photo_dataset: If True augment the photoswitch dataset with the Beard et al. 2019 dataset
:param n_trials: int specifying the number of random train/test splits.
"""
data_loader = TaskDataLoader(task, path)
photo_smiles_list, y_vals_photo = data_loader.load_property_data()
beard_smiles_list, y_vals_beard = data_loader.load_large_comparison_data(
path_to_large_dataset)
r2_list = []
rmse_list = []
mae_list = []
if not augment_photo_dataset:
# test set is now fixed
n_trials = 1
# We train on the Beard dataset and test on the photoswitch dataset
X_train = featurise_mols(beard_smiles_list, representation)
X_test = featurise_mols(photo_smiles_list, representation)
y_train = y_vals_beard
y_test = y_vals_photo
for i in range(0, n_trials):
if augment_photo_dataset:
# We add the Beard dataset as additional training data
X_train, X_test, y_train, y_test = train_test_split(
photo_smiles_list,
y_vals_photo,
test_size=test_set_size,
random_state=i)
X_train = X_train + beard_smiles_list
y_train = np.concatenate((y_train, y_vals_beard))
X_train = featurise_mols(X_train, representation)
X_test = featurise_mols(X_test, representation)
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
X_train, y_train, X_test, y_test, y_scaler = transform_data(
X_train, y_train, X_test, y_test)
regr_rf = RandomForestRegressor(n_estimators=1000,
max_depth=300,
random_state=2)
regr_rf.fit(X_train, y_train)
# Output Standardised RMSE and RMSE on Train Set
y_pred_train = regr_rf.predict(X_train)
train_rmse_stan = np.sqrt(mean_squared_error(y_train, y_pred_train))
train_rmse = np.sqrt(
mean_squared_error(y_scaler.inverse_transform(y_train),
y_scaler.inverse_transform(y_pred_train)))
print("\nStandardised Train RMSE: {:.3f}".format(train_rmse_stan))
print("Train RMSE: {:.3f}".format(train_rmse))
# Predict on new data
y_rf = regr_rf.predict(X_test)
y_rf = y_scaler.inverse_transform(y_rf)
y_test = y_scaler.inverse_transform(y_test)
score = r2_score(y_test, y_rf)
rmse = np.sqrt(mean_squared_error(y_test, y_rf))
mae = mean_absolute_error(y_test, y_rf)
r2_list.append(score)
rmse_list.append(rmse)
mae_list.append(mae)
print("\nR^2: {:.3f}".format(score))
print("RMSE: {:.3f}".format(rmse))
print("MAE: {:.3f}".format(mae))
r2_list = np.array(r2_list)
rmse_list = np.array(rmse_list)
mae_list = np.array(mae_list)
print("\nmean R^2: {:.4f} +- {:.4f}".format(
np.mean(r2_list),
np.std(r2_list) / np.sqrt(len(r2_list))))
print("mean RMSE: {:.4f} +- {:.4f}".format(
np.mean(rmse_list),
np.std(rmse_list) / np.sqrt(len(rmse_list))))
print("mean MAE: {:.4f} +- {:.4f}\n".format(
np.mean(mae_list),
np.std(mae_list) / np.sqrt(len(mae_list))))
def main(path, representation):
"""
:param path: str specifying path to dataset.
:param representation: str specifying the molecular representation. One of ['fingerprints, 'fragments', 'fragprints']
"""
task = 'e_iso_pi' # task always e_iso_pi with human performance comparison
data_loader = TaskDataLoader(task, path)
smiles_list, y = data_loader.load_property_data()
X = featurise_mols(smiles_list, representation)
# 5 test molecules
test_smiles = [
'BrC1=CC=C(/N=N/C2=CC=CC=C2)C=C1',
'O=[N+]([O-])C1=CC=C(/N=N/C2=CC=CC=C2)C=C1',
'CC(C=C1)=CC=C1/N=N/C2=CC=C(N(C)C)C=C2',
'BrC1=CC([N+]([O-])=O)=CC([N+]([O-])=O)=C1/N=N/C2=CC([H])=C(C=C2[H])N(CC)CC',
'ClC%11=CC([N+]([O-])=O)=CC(C#N)=C%11/N=N/C%12=CC([H])=C(C=C%12OC)N(CC)CC'
]
# and their indices in the loaded data
test_smiles_indices = [116, 131, 168, 221, 229]
X_train = np.delete(X, np.array(test_smiles_indices), axis=0)
y_train = np.delete(y, np.array(test_smiles_indices))
X_test = X[[116, 131, 168, 221, 229]]
# experimental wavelength values in EtOH. Main csv file has 400nm instead of 407nm because measurement was
# under a different solvent
y_test = y[[116, 131, 168, 221, 229]]
y_test[2] = 407.
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
# # We standardise the outputs but leave the inputs unchanged
#
# _, y_train, _, y_test, y_scaler = transform_data(X_train, y_train, X_test, y_test)
X_train = X_train.astype(np.float64)
X_test = X_test.astype(np.float64)
data_loader_z_iso_pi = TaskDataLoader('z_iso_pi', path)
data_loader_e_iso_n = TaskDataLoader('e_iso_n', path)
data_loader_z_iso_n = TaskDataLoader('z_iso_n', path)
smiles_list_z_iso_pi, y_z_iso_pi = data_loader_z_iso_pi.load_property_data(
)
smiles_list_e_iso_n, y_e_iso_n = data_loader_e_iso_n.load_property_data()
smiles_list_z_iso_n, y_z_iso_n = data_loader_z_iso_n.load_property_data()
y_z_iso_pi = y_z_iso_pi.reshape(-1, 1)
y_e_iso_n = y_e_iso_n.reshape(-1, 1)
y_z_iso_n = y_z_iso_n.reshape(-1, 1)
X_z_iso_pi = featurise_mols(smiles_list_z_iso_pi, representation)
X_e_iso_n = featurise_mols(smiles_list_e_iso_n, representation)
X_z_iso_n = featurise_mols(smiles_list_z_iso_n, representation)
output_dim = 4 # Number of outputs
rank = 1 # Rank of W
feature_dim = len(X_train[0, :])
tanimoto_active_dims = [i for i in range(feature_dim)
] # active dims for Tanimoto base kernel.
# We define the Gaussian Process Regression Model using the Tanimoto kernel
m = None
def objective_closure():
return -m.log_marginal_likelihood()
# Augment the input with zeroes, ones, twos, threes to indicate the required output dimension
X_augmented = np.vstack((np.append(X_train,
np.zeros((len(X_train), 1)),
axis=1),
np.append(X_z_iso_pi,
np.ones((len(X_z_iso_pi), 1)),
axis=1),
np.append(X_e_iso_n,
np.ones((len(X_e_iso_n), 1)) * 2,
axis=1),
np.append(X_z_iso_n,
np.ones((len(X_z_iso_n), 1)) * 3,
axis=1)))
X_test = np.append(X_test, np.zeros((len(X_test), 1)), axis=1)
X_train = np.append(X_train, np.zeros((len(X_train), 1)), axis=1)
# Augment the Y data with zeroes, ones, twos and threes that specify a likelihood from the list of likelihoods
Y_augmented = np.vstack(
(np.hstack((y_train, np.zeros_like(y_train))),
np.hstack((y_z_iso_pi, np.ones_like(y_z_iso_pi))),
np.hstack((y_e_iso_n, np.ones_like(y_e_iso_n) * 2)),
np.hstack((y_z_iso_n, np.ones_like(y_z_iso_n) * 3))))
y_test = np.hstack((y_test, np.zeros_like(y_test)))
# Base kernel
k = Tanimoto(active_dims=tanimoto_active_dims)
# set_trainable(k.variance, False)
# Coregion kernel
coreg = gpflow.kernels.Coregion(output_dim=output_dim,
rank=rank,
active_dims=[feature_dim])
# Create product kernel
kern = k * coreg
# This likelihood switches between Gaussian noise with different variances for each f_i:
lik = gpflow.likelihoods.SwitchedLikelihood([
gpflow.likelihoods.Gaussian(),
gpflow.likelihoods.Gaussian(),
gpflow.likelihoods.Gaussian(),
gpflow.likelihoods.Gaussian()
])
# now build the GP model as normal
m = gpflow.models.VGP((X_augmented, Y_augmented),
mean_function=Constant(np.mean(y_train[:, 0])),
kernel=kern,
likelihood=lik)
# fit the covariance function parameters
maxiter = ci_niter(1000)
gpflow.optimizers.Scipy().minimize(
m.training_loss,
m.trainable_variables,
options=dict(maxiter=maxiter),
method="L-BFGS-B",
)
print_summary(m)
# mean and variance GP prediction
y_pred, y_var = m.predict_f(X_test)
# Output Standardised RMSE and RMSE on Train Set
y_pred_train, _ = m.predict_f(X_train)
train_rmse_stan = np.sqrt(mean_squared_error(y_train, y_pred_train))
train_rmse = np.sqrt(mean_squared_error(y_train, y_pred_train))
print("\nStandardised Train RMSE: {:.3f}".format(train_rmse_stan))
print("Train RMSE: {:.3f}".format(train_rmse))
r2 = r2_score(y_test[:, 0], y_pred)
rmse = np.sqrt(mean_squared_error(y_test[:, 0], y_pred))
mae = mean_absolute_error(y_test[:, 0], y_pred)
per_molecule = np.diag(abs(y_pred - y_test[:, 0]))
print("\n Averaged test statistics are")
print("\nR^2: {:.3f}".format(r2))
print("RMSE: {:.3f}".format(rmse))
print("MAE: {:.3f}".format(mae))
print("\nAbsolute error per molecule is {} ".format(per_molecule))
def main(path, representation):
"""
:param path: str specifying path to dataset.
:param representation: str specifying the molecular representation. One of ['fingerprints, 'fragments', 'fragprints']
"""
task = 'e_iso_pi' # Always e_iso_pi for human performance comparison
data_loader = TaskDataLoader(task, path)
smiles_list, y = data_loader.load_property_data()
X = featurise_mols(smiles_list, representation)
# 5 test molecules
test_smiles = [
'BrC1=CC=C(/N=N/C2=CC=CC=C2)C=C1',
'O=[N+]([O-])C1=CC=C(/N=N/C2=CC=CC=C2)C=C1',
'CC(C=C1)=CC=C1/N=N/C2=CC=C(N(C)C)C=C2',
'BrC1=CC([N+]([O-])=O)=CC([N+]([O-])=O)=C1/N=N/C2=CC([H])=C(C=C2[H])N(CC)CC',
'ClC%11=CC([N+]([O-])=O)=CC(C#N)=C%11/N=N/C%12=CC([H])=C(C=C%12OC)N(CC)CC'
]
# and their indices in the loaded data
test_smiles_indices = [116, 131, 168, 221, 229]
X_train = np.delete(X, np.array(test_smiles_indices), axis=0)
y_train = np.delete(y, np.array(test_smiles_indices))
X_test = X[[116, 131, 168, 221, 229]]
# experimental wavelength values in EtOH. Main csv file has 400nm instead of 407nm because measurement was
# under a different solvent
y_test = y[[116, 131, 168, 221, 229]]
y_test[2] = 407.
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
# We standardise the outputs but leave the inputs unchanged
_, y_train, _, y_test, y_scaler = transform_data(X_train, y_train, X_test,
y_test)
X_train = X_train.astype(np.float64)
X_test = X_test.astype(np.float64)
num_features = np.shape(X)[1]
# We define the Gaussian Process Regression Model using the Tanimoto kernel
m = None
def objective_closure():
return -m.log_marginal_likelihood()
# for plotting confidence-error curves
rmse_confidence_list = []
mae_confidence_list = []
k = Tanimoto()
m = gpflow.models.GPR(data=(X_train, y_train),
mean_function=Constant(np.mean(y_train)),
kernel=k,
noise_variance=1)
# Optimise the kernel variance and noise level by the marginal likelihood
opt = gpflow.optimizers.Scipy()
opt.minimize(objective_closure,
m.trainable_variables,
options=dict(maxiter=100))
print_summary(m)
# mean and variance GP prediction
y_pred, y_var = m.predict_f(X_test)
y_pred = y_scaler.inverse_transform(y_pred)
y_test = y_scaler.inverse_transform(y_test)
# Output Standardised RMSE and RMSE on Train Set
y_pred_train, _ = m.predict_f(X_train)
train_rmse_stan = np.sqrt(mean_squared_error(y_train, y_pred_train))
train_rmse = np.sqrt(
mean_squared_error(y_scaler.inverse_transform(y_train),
y_scaler.inverse_transform(y_pred_train)))
print("\nStandardised Train RMSE: {:.3f}".format(train_rmse_stan))
print("Train RMSE: {:.3f}".format(train_rmse))
r2 = r2_score(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
mae = mean_absolute_error(y_test, y_pred)
per_molecule = abs(y_pred - y_test)
print("\n Averaged test statistics are")
print("\nR^2: {:.3f}".format(r2))
print("RMSE: {:.3f}".format(rmse))
print("MAE: {:.3f}".format(mae))
print("\nAbsolute error per molecule is {} ".format(per_molecule))
def main(path, task, representation, use_pca, test_set_size):
"""
:param path: str specifying path to dataset.
:param task: str specifying the task. One of ['Photoswitch', 'ESOL', 'FreeSolv', 'Lipophilicity']
:param representation: str specifying the molecular representation. One of [fingerprints, 'fragments', 'fragprints']
:param use_pca: bool. If True apply PCA to perform Principal Components Regression.
:param test_set_size: float in range [0, 1] specifying fraction of dataset to use as test set
"""
if representation == 'SMILES':
raise Exception('SMILES is not a valid representation for the BNN')
data_loader = TaskDataLoader(task, path)
smiles_list, y = data_loader.load_property_data()
X = featurise_mols(smiles_list, representation)
# If True we perform Principal Components Regression
if use_pca:
n_components = 100
else:
n_components = None
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=test_set_size, random_state=42)
if task != 'Photoswitch':
# Artificially create a 80/10/10 train/validation/test split discarding the validation set.
split_in_two = int(len(y_test) / 2)
X_test = X_test[0:split_in_two]
y_test = y_test[0:split_in_two]
else:
# We subdivide the train set in order to run cross-validation.
X_train, X_test, y_train, y_test = train_test_split(X_train,
y_train,
test_size=0.1,
random_state=42)
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
# We standardise the outputs but leave the inputs unchanged
_, y_train, _, y_test, y_scaler = transform_data(X_train,
y_train,
X_test,
y_test,
n_components=n_components,
use_pca=use_pca)
X_train = X_train.astype(np.float64)
X_test = X_test.astype(np.float64)
datasets, n, d, mean_y_train, std_y_train = load_reg_data(
X_train, y_train, X_test, y_test)
train_set_x, train_set_y = datasets[0]
test_set_x, test_set_y = datasets[1]
layer_sizes = [10, 20]
learning_rates = [0.01, 0.001]
batch_sizes = [16, 32, 64]
iters = [20, 50, 100]
best_rmse = 10000000 # a large number
best_params = {'layer_size': 0, 'lr': 0, 'batch_size': 0, 'iterations': 0}
for layer_size in layer_sizes:
for lr in learning_rates:
for batch_size in batch_sizes:
for iteration in iters:
N_train = train_set_x.get_value(borrow=True).shape[0]
N_test = test_set_x.get_value(borrow=True).shape[0]
layer_sizes = [
d, layer_size, layer_size,
len(mean_y_train)
]
n_samples = 100
alpha = 0.5
learning_rate = lr
v_prior = 1.0
batch_size = batch_size
print('... building model')
sys.stdout.flush()
bb_alpha = BB_alpha(layer_sizes, n_samples, alpha,
learning_rate, v_prior, batch_size,
train_set_x, train_set_y, N_train,
test_set_x, test_set_y, N_test,
mean_y_train, std_y_train)
print('... training')
sys.stdout.flush()
test_error, test_ll = bb_alpha.train_ADAM(iteration)
print('Test RMSE: ', test_error)
print('Test ll: ', test_ll)
samples = bb_alpha.sample_predictive_distribution(X_test)
y_pred = np.mean(samples, axis=0)
# Output Standardised RMSE and RMSE on Train Set
train_samples = bb_alpha.sample_predictive_distribution(
X_train)
y_pred_train = np.mean(train_samples, axis=0)
train_rmse_stan = np.sqrt(
mean_squared_error(y_train, y_pred_train))
train_rmse = np.sqrt(
mean_squared_error(
y_scaler.inverse_transform(y_train),
y_scaler.inverse_transform(y_pred_train)))
print("\nStandardised Train RMSE: {:.3f}".format(
train_rmse_stan))
print("Train RMSE: {:.3f}".format(train_rmse))
score = r2_score(y_scaler.inverse_transform(y_test),
y_scaler.inverse_transform(y_pred))
rmse = np.sqrt(
mean_squared_error(y_scaler.inverse_transform(y_test),
y_scaler.inverse_transform(y_pred)))
mae = mean_absolute_error(
y_scaler.inverse_transform(y_test),
y_scaler.inverse_transform(y_pred))
print("\nR^2: {:.3f}".format(score))
print("RMSE: {:.3f}".format(rmse))
print("MAE: {:.3f}".format(mae))
if rmse < best_rmse:
best_rmse = rmse
best_params['lr'] = lr
best_params['batch_size'] = batch_size
best_params['iterations'] = iteration
best_params['layer_size'] = layer_size
print('Best parameters are \n')
print(best_params)
print('Final best parameters are \n')
print(best_params)
with open(f'cross_val_hypers/{task}/BNN/hypers_{representation}.txt',
'w') as f:
f.write(str(best_params))
def main(task, path, representation, use_pca, n_trials, test_set_size,
batch_size, lr, iterations, r_size, det_encoder_hidden_size,
det_encoder_n_hidden, lat_encoder_hidden_size, lat_encoder_n_hidden,
decoder_hidden_size, decoder_n_hidden):
"""
:param task: str specifying the task name. One of [e_iso_pi, e_iso_n, z_iso_pi, z_iso_n]
:param path: str specifying the path to the photoswitches.csv file
:param representation: str specifying the representation. One of [fingerprints, fragments, fragprints]
:param use_pca: bool specifying whether or not to use PCA to perform Principal Components Regression
:param n_trials: int specifying the number of random train/test splits.
:param test_set_size: float specifying the train/test split ratio. e.g. 0.2 is 80/20 train/test split
:param batch_size: int specifying the number of samples to take of the context set, given the number of
context points that should be selected.
:param lr: float specifying the learning rate.
:param iterations: int specifying the number of training iterations
:param r_size: Dimensionality of context encoding r.
:param det_encoder_hidden_size: Dimensionality of deterministic encoder hidden layers.
:param det_encoder_n_hidden: Number of deterministic encoder hidden layers.
:param lat_encoder_hidden_size: Dimensionality of latent encoder hidden layers.
:param lat_encoder_n_hidden: Number of latent encoder hidden layers.
:param decoder_hidden_size: Dimensionality of decoder hidden layers.
:param decoder_n_hidden: Number of decoder hidden layers.
:return:
"""
path_to_save = task + '/results/anp/'
data_loader = TaskDataLoader(task, path)
smiles_list, y = data_loader.load_property_data()
y_size = 1
if args.representation == 'fingerprints':
X = featurise_mols(smiles_list, representation)
elif args.representation == 'fragments':
X = featurise_mols(smiles_list, representation)
else:
X = featurise_mols(smiles_list, representation)
# If True we perform Principal Components Regression
if use_pca:
n_components = 50
else:
n_components = None
r2_list = []
rmse_list = []
mae_list = []
# We pre-allocate arrays for plotting confidence-error curves
_, _, _, y_test = train_test_split(
X, y, test_size=test_set_size) # To get test set size
n_test = len(y_test)
rmse_confidence_list = np.zeros((n_trials, n_test))
mae_confidence_list = np.zeros((n_trials, n_test))
print('\nBeginning training loop...')
j = 0 # index for saving results
for i in range(0, n_trials):
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=test_set_size, random_state=i)
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
# We standardise the outputs but leave the inputs unchanged
X_train, y_train, X_test, _, y_scaler = transform_data(
X_train,
y_train,
X_test,
y_test,
n_components=n_components,
use_pca=use_pca)
X_train = torch.from_numpy(X_train).float().unsqueeze(dim=0)
X_test = torch.from_numpy(X_test).float().unsqueeze(dim=0)
y_train = torch.from_numpy(y_train).float().unsqueeze(dim=0)
m = AttentiveNP(x_size=X_train.shape[2],
y_size=y_size,
r_size=r_size,
det_encoder_hidden_size=det_encoder_hidden_size,
det_encoder_n_hidden=det_encoder_n_hidden,
lat_encoder_hidden_size=lat_encoder_hidden_size,
lat_encoder_n_hidden=lat_encoder_n_hidden,
decoder_hidden_size=decoder_hidden_size,
decoder_n_hidden=decoder_n_hidden,
lr=lr,
attention_type="multihead")
print('...training.')
m.train(X_train,
y_train,
batch_size=batch_size,
iterations=iterations,
print_freq=None)
# Now, the context set comprises the training x / y values, the target set comprises the test x values.
y_pred, y_var = m.predict(X_train, y_train, X_test, n_samples=100)
y_pred = y_scaler.inverse_transform(y_pred)
# Compute scores for confidence curve plotting.
ranked_confidence_list = np.argsort(y_var.numpy(), axis=0).flatten()
for k in range(len(y_test)):
# Construct the RMSE error for each level of confidence
conf = ranked_confidence_list[0:k + 1]
rmse = np.sqrt(mean_squared_error(y_test[conf], y_pred[conf]))
rmse_confidence_list[i, k] = rmse
# Construct the MAE error for each level of confidence
mae = mean_absolute_error(y_test[conf], y_pred[conf])
mae_confidence_list[i, k] = mae
# Output Standardised RMSE and RMSE on Train Set
score = r2_score(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
mae = mean_absolute_error(y_test, y_pred)
print("\nR^2: {:.3f}".format(score))
print("RMSE: {:.3f}".format(rmse))
print("MAE: {:.3f}".format(mae))
r2_list.append(score)
rmse_list.append(rmse)
mae_list.append(mae)
np.savetxt(
path_to_save + '_seed_' + str(j) + '_ypred_' + representation +
'.txt', y_pred)
np.savetxt(path_to_save + '_seed_' + str(j) + '_ytest.txt', y_test)
np.savetxt(
path_to_save + '_seed_' + str(j) + '_ystd_' + representation +
'.txt', np.sqrt(y_var))
j += 1
r2_list = np.array(r2_list)
rmse_list = np.array(rmse_list)
mae_list = np.array(mae_list)
print("\nmean R^2: {:.4f} +- {:.4f}".format(
np.mean(r2_list),
np.std(r2_list) / np.sqrt(len(r2_list))))
print("mean RMSE: {:.4f} +- {:.4f}".format(
np.mean(rmse_list),
np.std(rmse_list) / np.sqrt(len(rmse_list))))
print("mean MAE: {:.4f} +- {:.4f}\n".format(
np.mean(mae_list),
np.std(mae_list) / np.sqrt(len(mae_list))))
with open(path_to_save + representation + '.txt', 'w+') as f:
f.write('\n Representation = ' + str(representation))
f.write('\n Task = ' + str(task))
f.write('\n Use PCA? = ' + str(use_pca))
f.write('\n Number of trials = {} \n'.format(n_trials))
f.write('\n Deterministic encoder hidden size = ' +
str(det_encoder_hidden_size))
f.write('\n Deterministic encoder number of layers = ' +
str(det_encoder_n_hidden))
f.write('\n Latent encoder hidden size = ' +
str(lat_encoder_hidden_size))
f.write('\n Latent encoder number of layers = ' +
str(lat_encoder_n_hidden))
f.write('\n Decoder hidden size = ' + str(decoder_hidden_size))
f.write('\n Decoder number of layers = ' + str(decoder_n_hidden))
f.write('\n Latent variable size = ' + str(r_size))
f.write('\n Batch size = {}'.format(batch_size))
f.write('\n Learning rate = {}'.format(lr))
f.write('\n Number of iterations = {} \n'.format(iterations))
f.write("\nmean R^2: {:.4f} +- {:.4f}".format(
np.mean(r2_list),
np.std(r2_list) / np.sqrt(len(r2_list))))
f.write("\nmean RMSE: {:.4f} +- {:.4f}".format(
np.mean(rmse_list),
np.std(rmse_list) / np.sqrt(len(rmse_list))))
f.write("\nmean MAE: {:.4f} +- {:.4f}\n".format(
np.mean(mae_list),
np.std(mae_list) / np.sqrt(len(mae_list))))
f.flush()
# Plot confidence-error curves
# 1e-14 instead of 0 to stop weirdness with len(y_test) = 29
confidence_percentiles = np.arange(1e-14, 100, 100 / len(y_test))
rmse_mean = np.mean(rmse_confidence_list, axis=0)
rmse_std = np.std(rmse_confidence_list, axis=0)
# We flip because we want the most confident predictions on the right-hand side of the plot
rmse_mean = np.flip(rmse_mean)
rmse_std = np.flip(rmse_std)
# One-sigma error bars
lower = rmse_mean - rmse_std
upper = rmse_mean + rmse_std
plt.plot(confidence_percentiles, rmse_mean, label='mean')
plt.fill_between(confidence_percentiles, lower, upper, alpha=0.2)
plt.xlabel('Confidence Percentile')
plt.ylabel('RMSE (nm)')
plt.ylim([0, np.max(upper) + 1])
plt.xlim([0, 100 * ((len(y_test) - 1) / len(y_test))])
plt.yticks(np.arange(0, np.max(upper) + 1, 5.0))
plt.savefig(path_to_save + 'confidence_curve_rmse.png')
# We plot the Mean-absolute error confidence-error curves
mae_mean = np.mean(mae_confidence_list, axis=0)
mae_std = np.std(mae_confidence_list, axis=0)
mae_mean = np.flip(mae_mean)
mae_std = np.flip(mae_std)
lower = mae_mean - mae_std
upper = mae_mean + mae_std
plt.plot(confidence_percentiles, mae_mean, label='mean')
plt.fill_between(confidence_percentiles, lower, upper, alpha=0.2)
plt.xlabel('Confidence Percentile')
plt.ylabel('MAE (nm)')
plt.ylim([0, np.max(upper) + 1])
plt.xlim([0, 100 * ((len(y_test) - 1) / len(y_test))])
plt.yticks(np.arange(0, np.max(upper) + 1, 5.0))
plt.savefig(path_to_save + 'confidence_curve_mae.png')
