diff options
| -rw-r--r-- | ml_exp/krr.py | 195 |
1 files changed, 134 insertions, 61 deletions
diff --git a/ml_exp/krr.py b/ml_exp/krr.py index 4a87884d4..358816a39 100644 --- a/ml_exp/krr.py +++ b/ml_exp/krr.py @@ -42,7 +42,7 @@ def krr(descriptors, sigma=1000.0, opt=True, identifier=None, - laplauss='gauss', + kernel='gaussian', use_tf=True, show_msgs=True): """ @@ -55,6 +55,7 @@ def krr(descriptors, sigma: depth of the kernel. opt: if the optimized algorithm should be used. For benchmarking purposes. identifier: string with the name of the descriptor used. + kernel: which kernel to use. use_tf: if tensorflow should be used. show_msgs: if debug messages should be shown. NOTE: identifier is just a string and is only for identification purposes. @@ -98,11 +99,26 @@ def krr(descriptors, with tf.device('GPU:0'): X_tr = descriptors[:training_size] Y_tr = labels[:training_size] - K_tr = laplauss_kernel(X_tr, - X_tr, - sigma, - laplauss=laplauss, - use_tf=use_tf) + if kernel == 'gaussian': + K_tr = gaussian_kernel(X_tr, + X_tr, + sigma, + use_tf=use_tf) + + elif kernel == 'laplacian': + K_tr = laplacian_kernel(X_tr, + X_tr, + sigma, + use_tf=use_tf) + + elif kernel == 'wasserstein': + K_tr = wasserstein_kernel(X_tr, + X_tr, + sigma, + use_tf=use_tf) + + else: + raise TypeError(f'{kernel} kernel not found.') # Adding a small value on the diagonal for cho_solve. dv = tf.linalg.tensor_diag(tf.constant(1e-8, @@ -115,11 +131,26 @@ def krr(descriptors, X_te = descriptors[-test_size:] Y_te = labels[-test_size:] - K_te = laplauss_kernel(X_te, - X_tr, - sigma, - laplauss=laplauss, - use_tf=use_tf) + if kernel == 'gaussian': + K_te = gaussian_kernel(X_te, + X_tr, + sigma, + use_tf=use_tf) + + elif kernel == 'laplacian': + K_te = laplacian_kernel(X_te, + X_tr, + sigma, + use_tf=use_tf) + + elif kernel == 'wasserstein': + K_te = wasserstein_kernel(X_te, + X_tr, + sigma, + use_tf=use_tf) + + else: + raise TypeError(f'{kernel} kernel not found.') Y_te = tf.expand_dims(Y_te, 1) Y_pr = tf.tensordot(K_te, alpha, 1) @@ -130,11 +161,26 @@ def krr(descriptors, else: X_tr = descriptors[:training_size] Y_tr = labels[:training_size] - K_tr = laplauss_kernel(X_tr, - X_tr, - sigma, - laplauss=laplauss, - use_tf=use_tf) + if kernel == 'gaussian': + K_tr = gaussian_kernel(X_tr, + X_tr, + sigma, + use_tf=use_tf) + + elif kernel == 'laplacian': + K_tr = laplacian_kernel(X_tr, + X_tr, + sigma, + use_tf=use_tf) + + elif kernel == 'wasserstein': + K_tr = wasserstein_kernel(X_tr, + X_tr, + sigma, + use_tf=use_tf) + + else: + raise TypeError(f'{kernel} kernel not found.') # Adding a small value on the diagonal for cho_solve. K_tr[np.diag_indices_from(K_tr)] += 1e-8 @@ -143,11 +189,26 @@ def krr(descriptors, X_te = descriptors[-test_size:] Y_te = labels[-test_size:] - K_te = laplauss_kernel(X_te, - X_tr, - sigma, - laplauss=laplauss, - use_tf=use_tf) + if kernel == 'gaussian': + K_te = gaussian_kernel(X_te, + X_tr, + sigma, + use_tf=use_tf) + + elif kernel == 'laplacian': + K_te = laplacian_kernel(X_te, + X_tr, + sigma, + use_tf=use_tf) + + elif kernel == 'wasserstein': + K_te = wasserstein_kernel(X_te, + X_tr, + sigma, + use_tf=use_tf) + + else: + raise TypeError(f'{kernel} kernel not found.') Y_pr = np.dot(K_te, alpha) mae = np.mean(np.abs(Y_pr - Y_te)) @@ -168,8 +229,10 @@ def multi_krr(db_path='data', lj_sigma=1.0, lj_epsilon=1.0, use_forces=False, - stuff='bonds', + acount={'C':7, 'H':16, 'N':3, 'O':3, 'S':1}, size=23, + sort=False, + flatten=True, as_eig=True, bohr_ru=False, training_size=1500, @@ -187,8 +250,10 @@ def multi_krr(db_path='data', lj_sigma: sigma value. lj_epsilon: epsilon value. use_forces: if the use of forces instead of k_cx should be used. - stuff: elements of the bag, by default the known bag of bonds. + acount: atom count for the compound, defaults to qm7 sizes. size: compound size. + sort: if the representation should be sorted row-norm or bag-wise. + flatten: if the representation should be 1D. as_eig: if the representation should be as the eigenvalues. bohr_ru: if radius units should be in bohr's radius units. training_size: size of the training set to use. @@ -224,6 +289,8 @@ def multi_krr(db_path='data', for compound in compounds: if 'CM' in identifiers: compound.gen_cm(size=size, + sort=sort, + flatten=flatten, as_eig=as_eig, bohr_ru=bohr_ru) if 'LJM' in identifiers: @@ -231,15 +298,21 @@ def multi_krr(db_path='data', sigma=lj_sigma, epsilon=lj_epsilon, size=size, + sort=sort, + flatten=flatten, as_eig=as_eig, bohr_ru=bohr_ru) if 'AM' in identifiers: compound.gen_hd(size=size, bohr_ru=bohr_ru) compound.gen_am(use_forces=use_forces, - size=size) + size=size, + sort=sort, + flatten=flatten) if 'BOB' in identifiers: - compound.gen_bob(size=size) + compound.gen_bob(size=size, + sort=sort, + acount=acount) # Create a numpy array (or tensorflow tensor) for the descriptors. if 'CM' in identifiers: @@ -272,45 +345,45 @@ def multi_krr(db_path='data', # ML calculation. if 'CM' in identifiers: - cm_mae, cm_tictoc = simple_ml(cm_data, - energy_pbe0, - training_size=training_size, - test_size=test_size, - sigma=sigma, - identifier='CM', - laplauss='gauss', - use_tf=use_tf, - show_msgs=show_msgs) + cm_mae, cm_tictoc = krr(cm_data, + energy_pbe0, + training_size=training_size, + test_size=test_size, + sigma=sigma, + identifier='CM', + kernel='gaussian', + use_tf=use_tf, + show_msgs=show_msgs) if 'LJM' in identifiers: - ljm_mae, ljm_tictoc = simple_ml(ljm_data, - energy_pbe0, - training_size=training_size, - test_size=test_size, - sigma=sigma, - identifier='LJM', - laplauss='gauss', - use_tf=use_tf, - show_msgs=show_msgs) + ljm_mae, ljm_tictoc = krr(ljm_data, + energy_pbe0, + training_size=training_size, + test_size=test_size, + sigma=sigma, + identifier='LJM', + laplauss='gaussian', + use_tf=use_tf, + show_msgs=show_msgs) if 'AM' in identifiers: - am_mae, am_tictoc = simple_ml(am_data, - energy_pbe0, - training_size=training_size, - test_size=test_size, - sigma=sigma, - identifier='AM', - laplauss='gauss', - use_tf=use_tf, - show_msgs=show_msgs) + am_mae, am_tictoc = krr(am_data, + energy_pbe0, + training_size=training_size, + test_size=test_size, + sigma=sigma, + identifier='AM', + laplauss='gaussian', + use_tf=use_tf, + show_msgs=show_msgs) if 'BOB' in identifiers: - bob_mae, bob_tictoc = simple_ml(bob_data, - energy_pbe0, - training_size=training_size, - test_size=test_size, - sigma=sigma, - identifier='BOB', - laplauss='laplace', - use_tf=use_tf, - show_msgs=show_msgs) + bob_mae, bob_tictoc = krr(bob_data, + energy_pbe0, + training_size=training_size, + test_size=test_size, + sigma=sigma, + identifier='BOB', + laplauss='laplacian', + use_tf=use_tf, + show_msgs=show_msgs) # End of program end_time = time.perf_counter() |