diff options
| -rw-r--r-- | ml_exp/do_ml.py | 88 |
1 files changed, 47 insertions, 41 deletions
diff --git a/ml_exp/do_ml.py b/ml_exp/do_ml.py index 57661578a..ffe9a2d85 100644 --- a/ml_exp/do_ml.py +++ b/ml_exp/do_ml.py @@ -80,50 +80,56 @@ def simple_ml(descriptors, printc(f'\tSigma: {test_size}', 'CYAN') if use_tf: - X_training = descriptors[:training_size] - Y_training = energies[:training_size] - K_training = gaussian_kernel(X_training, - X_training, - sigma, - use_tf=use_tf) - - # Y_training = tf.expand_dims(Y_training, 1) - alpha = tf.linalg.cholesky_solve(tf.linalg.cholesky(K_training), - Y_training) - - X_test = descriptors[-test_size:] - Y_test = energies[-test_size:] - K_test = gaussian_kernel(X_test, - X_training, - sigma, - use_tf=use_tf) - - # Y_test = tf.expand_dims(Y_test, 1) - Y_predicted = tf.tensordot(K_test, alpha, 1) - - mae = tf.reduce_mean(tf.abs(Y_predicted - Y_test)) + if tf.config.experimental.list_physical_devices('GPU'): + with tf.device('GPU:0'): + X_tr = descriptors[:training_size] + Y_tr = energies[:training_size] + K_tr = gaussian_kernel(X_tr, + X_tr, + sigma, + use_tf=use_tf) + + dv = tf.linalg.tensor_diag(tf.constant(1e-8, + shape=(training_size), + dtype=tf.float64)) + K_tr += dv + # Y_tr = tf.expand_dims(Y_tr, 1) + alpha = tf.linalg.cholesky_solve(tf.linalg.cholesky(K_tr), + Y_tr) + + X_te = descriptors[-test_size:] + Y_te = energies[-test_size:] + K_te = gaussian_kernel(X_te, + X_tr, + sigma, + use_tf=use_tf) + + # Y_te = tf.expand_dims(Y_te, 1) + Y_pr = tf.tensordot(K_te, alpha, 1) + + mae = tf.reduce_mean(tf.abs(Y_pr - Y_te)) else: - X_training = descriptors[:training_size] - Y_training = energies[:training_size] - K_training = gaussian_kernel(X_training, - X_training, - sigma, - use_tf=use_tf) + X_tr = descriptors[:training_size] + Y_tr = energies[:training_size] + K_tr = gaussian_kernel(X_tr, + X_tr, + sigma, + use_tf=use_tf) # Adding a small value on the diagonal for cho_solve. - K_training[np.diag_indices_from(K_training)] += 1e-8 - alpha = LA.cho_solve(LA.cho_factor(K_training), - Y_training) - - X_test = descriptors[-test_size:] - Y_test = energies[-test_size:] - K_test = gaussian_kernel(X_test, - X_training, - sigma, - use_tf=use_tf) - Y_predicted = np.dot(K_test, alpha) - - mae = np.mean(np.abs(Y_predicted - Y_test)) + K_tr[np.diag_indices_from(K_tr)] += 1e-8 + alpha = LA.cho_solve(LA.cho_factor(K_tr), + Y_tr) + + X_te = descriptors[-test_size:] + Y_te = energies[-test_size:] + K_te = gaussian_kernel(X_te, + X_tr, + sigma, + use_tf=use_tf) + Y_pr = np.dot(K_te, alpha) + + mae = np.mean(np.abs(Y_pr - Y_te)) if show_msgs: printc(f'\tMAE for {identifier}: {mae:.4f}', 'GREEN') |