From d48adb6ca31cab1d86d81aa5c88bfeb198f19af8 Mon Sep 17 00:00:00 2001 From: David Luevano Alvarado <55825613+luevano@users.noreply.github.com> Date: Wed, 11 Mar 2020 10:12:27 -0700 Subject: Change naming to krr --- ml_exp/__init__.py | 6 +- ml_exp/do_ml.py | 319 ----------------------------------------------------- ml_exp/krr.py | 319 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 322 insertions(+), 322 deletions(-) delete mode 100644 ml_exp/do_ml.py create mode 100644 ml_exp/krr.py diff --git a/ml_exp/__init__.py b/ml_exp/__init__.py index d304f581c..02819d6a1 100644 --- a/ml_exp/__init__.py +++ b/ml_exp/__init__.py @@ -26,7 +26,7 @@ from ml_exp.representations import coulomb_matrix, lennard_jones_matrix,\ from ml_exp.qm7db import qm7db from ml_exp.data import NUCLEAR_CHARGE, POSSIBLE_BONDS from ml_exp.kernels import laplauss_kernel -from ml_exp.do_ml import simple_ml, do_ml +from ml_exp.krr import krr, multi_krr __all__ = ['Compound', 'coulomb_matrix', @@ -38,7 +38,7 @@ __all__ = ['Compound', 'bag_of_bonds', 'qm7db', 'laplauss_kernel', - 'simple_ml', - 'do_ml', + 'krr', + 'multi_krr', 'NUCLEAR_CHARGE', 'POSSIBLE_BONDS'] diff --git a/ml_exp/do_ml.py b/ml_exp/do_ml.py deleted file mode 100644 index b37553c24..000000000 --- a/ml_exp/do_ml.py +++ /dev/null @@ -1,319 +0,0 @@ -"""MIT License - -Copyright (c) 2019 David Luevano Alvarado - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. -""" -import time -import numpy as np -from scipy import linalg as LA -try: - import tensorflow as tf - TF_AV = True -except ImportError: - print('Tensorflow couldn\'t be imported. Maybe it is not installed.') - TF_AV = False -from ml_exp.misc import printc -from ml_exp.kernels import laplauss_kernel -from ml_exp.qm7db import qm7db - - -def simple_ml(descriptors, - energies, - training_size=1500, - test_size=None, - sigma=1000.0, - opt=True, - identifier=None, - laplauss='gauss', - use_tf=True, - show_msgs=True): - """ - Basic ML methodology for a single descriptor type. - descriptors: array of descriptors. - energies: array of energies. - training_size: size of the training set to use. - test_size: size of the test set to use. If no size is given, - the last remaining molecules are used. - 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. - 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. - Also, training is done with the first part of the data and - testing with the ending part of the data. - """ - tic = time.perf_counter() - # Initial calculations for later use. - data_size = descriptors.shape[0] - - if not identifier: - identifier = 'NOT SPECIFIED' - - if not data_size == energies.shape[0]: - raise ValueError('Energies size is different than descriptors size.') - - if training_size >= data_size: - raise ValueError('Training size is greater or equal to the data size.') - - # If tf is to be used but couldn't be imported, don't try to use it. - if use_tf and not TF_AV: - use_tf = False - - # If test_size is not set, it is set to a maximum size of 1500. - if not test_size: - test_size = data_size - training_size - if test_size > 1500: - test_size = 1500 - - if show_msgs: - printc(f'{identifier} ML started.', 'GREEN') - printc(f'\tTraining size: {training_size}', 'CYAN') - printc(f'\tTest size: {test_size}', 'CYAN') - printc(f'\tSigma: {test_size}', 'CYAN') - - if use_tf: - 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 = laplauss_kernel(X_tr, - X_tr, - sigma, - laplauss=laplauss, - use_tf=use_tf) - - # Adding a small value on the diagonal for cho_solve. - 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 = laplauss_kernel(X_te, - X_tr, - sigma, - laplauss=laplauss, - 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: - raise TypeError('No GPU found, could not create Tensor objects.') - else: - X_tr = descriptors[:training_size] - Y_tr = energies[:training_size] - K_tr = laplauss_kernel(X_tr, - X_tr, - sigma, - laplauss=laplauss, - use_tf=use_tf) - - # Adding a small value on the diagonal for cho_solve. - 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 = laplauss_kernel(X_te, - X_tr, - sigma, - laplauss=laplauss, - 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') - - toc = time.perf_counter() - tictoc = toc - tic - if show_msgs: - printc(f'\t{identifier} ML took {tictoc:.4f} seconds.', 'GREEN') - printc(f'\t\tTraining size: {training_size}', 'CYAN') - printc(f'\t\tTest size: {test_size}', 'CYAN') - printc(f'\t\tSigma: {sigma}', 'CYAN') - - return mae, tictoc - - -def do_ml(db_path='data', - is_shuffled=True, - r_seed=111, - diag_value=None, - lj_sigma=1.0, - lj_epsilon=1.0, - use_forces=False, - stuff='bonds', - size=23, - as_eig=True, - bohr_ru=False, - training_size=1500, - test_size=None, - sigma=1000.0, - identifiers=['CM'], - use_tf=True, - show_msgs=True): - """ - Main function that does the whole ML process. - db_path: path to the database directory. - is_shuffled: if the resulting list of compounds should be shuffled. - r_seed: random seed to use for the shuffling. - diag_value: if special diagonal value is to be used. - 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. - size: compound size. - 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. - test_size: size of the test set to use. If no size is given, - the last remaining molecules are used. - sigma: depth of the kernel. - identifiers: list of names (strings) of descriptors to use. - use_tf: if tensorflow should be used. - show_msgs: if debug messages should be shown. - """ - if type(identifiers) != list: - raise TypeError('\'identifiers\' is not a list.') - - # If tf is to be used but couldn't be imported, don't try to use it. - if use_tf and not TF_AV: - use_tf = False - - init_time = time.perf_counter() - - # Data reading. - tic = time.perf_counter() - compounds, energy_pbe0, energy_delta = qm7db(db_path=db_path, - is_shuffled=is_shuffled, - r_seed=r_seed, - use_tf=use_tf) - toc = time.perf_counter() - tictoc = toc - tic - if show_msgs: - printc(f'Data reading took {tictoc:.4f} seconds.', 'CYAN') - - # Matrices calculation. - tic = time.perf_counter() - for compound in compounds: - if 'CM' in identifiers: - compound.gen_cm(size=size, - as_eig=as_eig, - bohr_ru=bohr_ru) - if 'LJM' in identifiers: - compound.gen_ljm(diag_value=diag_value, - sigma=lj_sigma, - epsilon=lj_epsilon, - size=size, - 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) - if 'BOB' in identifiers: - compound.gen_bob(size=size) - - # Create a numpy array (or tensorflow tensor) for the descriptors. - if 'CM' in identifiers: - cm_data = np.array([comp.cm for comp in compounds], dtype=np.float64) - if 'LJM' in identifiers: - ljm_data = np.array([comp.ljm for comp in compounds], dtype=np.float64) - if 'AM' in identifiers: - am_data = np.array([comp.am for comp in compounds], dtype=np.float64) - if 'BOB' in identifiers: - bob_data = np.array([comp.bob for comp in compounds], dtype=np.float64) - - if use_tf: - if tf.config.experimental.list_physical_devices('GPU'): - with tf.device('GPU:0'): - if 'CM' in identifiers: - cm_data = tf.convert_to_tensor(cm_data) - if 'LJM' in identifiers: - ljm_data = tf.convert_to_tensor(ljm_data) - if 'AM' in identifiers: - am_data = tf.convert_to_tensor(am_data) - if 'BOB' in identifiers: - bob_data = tf.convert_to_tensor(bob_data) - else: - raise TypeError('No GPU found, could not create Tensor objects.') - - toc = time.perf_counter() - tictoc = toc - tic - if show_msgs: - printc(f'Matrices calculation took {tictoc:.4f} seconds.', 'CYAN') - - # 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) - 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) - 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) - 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) - - # End of program - end_time = time.perf_counter() - totaltime = end_time - init_time - printc(f'Program took {totaltime:.4f} seconds.', 'CYAN') diff --git a/ml_exp/krr.py b/ml_exp/krr.py new file mode 100644 index 000000000..dca1e14e6 --- /dev/null +++ b/ml_exp/krr.py @@ -0,0 +1,319 @@ +"""MIT License + +Copyright (c) 2019 David Luevano Alvarado + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +""" +import time +import numpy as np +from scipy import linalg as LA +try: + import tensorflow as tf + TF_AV = True +except ImportError: + print('Tensorflow couldn\'t be imported. Maybe it is not installed.') + TF_AV = False +from ml_exp.misc import printc +from ml_exp.kernels import laplauss_kernel +from ml_exp.qm7db import qm7db + + +def krr(descriptors, + energies, + training_size=1500, + test_size=None, + sigma=1000.0, + opt=True, + identifier=None, + laplauss='gauss', + use_tf=True, + show_msgs=True): + """ + Basic krr methodology for a single descriptor type. + descriptors: array of descriptors. + energies: array of energies. + training_size: size of the training set to use. + test_size: size of the test set to use. If no size is given, + the last remaining molecules are used. + 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. + 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. + Also, training is done with the first part of the data and + testing with the ending part of the data. + """ + tic = time.perf_counter() + # Initial calculations for later use. + data_size = descriptors.shape[0] + + if not identifier: + identifier = 'NOT SPECIFIED' + + if not data_size == energies.shape[0]: + raise ValueError('Energies size is different than descriptors size.') + + if training_size >= data_size: + raise ValueError('Training size is greater or equal to the data size.') + + # If tf is to be used but couldn't be imported, don't try to use it. + if use_tf and not TF_AV: + use_tf = False + + # If test_size is not set, it is set to a maximum size of 1500. + if not test_size: + test_size = data_size - training_size + if test_size > 1500: + test_size = 1500 + + if show_msgs: + printc(f'{identifier} ML started.', 'GREEN') + printc(f'\tTraining size: {training_size}', 'CYAN') + printc(f'\tTest size: {test_size}', 'CYAN') + printc(f'\tSigma: {test_size}', 'CYAN') + + if use_tf: + 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 = laplauss_kernel(X_tr, + X_tr, + sigma, + laplauss=laplauss, + use_tf=use_tf) + + # Adding a small value on the diagonal for cho_solve. + 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 = laplauss_kernel(X_te, + X_tr, + sigma, + laplauss=laplauss, + 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: + raise TypeError('No GPU found, could not create Tensor objects.') + else: + X_tr = descriptors[:training_size] + Y_tr = energies[:training_size] + K_tr = laplauss_kernel(X_tr, + X_tr, + sigma, + laplauss=laplauss, + use_tf=use_tf) + + # Adding a small value on the diagonal for cho_solve. + 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 = laplauss_kernel(X_te, + X_tr, + sigma, + laplauss=laplauss, + 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') + + toc = time.perf_counter() + tictoc = toc - tic + if show_msgs: + printc(f'\t{identifier} ML took {tictoc:.4f} seconds.', 'GREEN') + printc(f'\t\tTraining size: {training_size}', 'CYAN') + printc(f'\t\tTest size: {test_size}', 'CYAN') + printc(f'\t\tSigma: {sigma}', 'CYAN') + + return mae, tictoc + + +def multi_krr(db_path='data', + is_shuffled=True, + r_seed=111, + diag_value=None, + lj_sigma=1.0, + lj_epsilon=1.0, + use_forces=False, + stuff='bonds', + size=23, + as_eig=True, + bohr_ru=False, + training_size=1500, + test_size=None, + sigma=1000.0, + identifiers=['CM'], + use_tf=True, + show_msgs=True): + """ + Main function that does the whole ML process. + db_path: path to the database directory. + is_shuffled: if the resulting list of compounds should be shuffled. + r_seed: random seed to use for the shuffling. + diag_value: if special diagonal value is to be used. + 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. + size: compound size. + 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. + test_size: size of the test set to use. If no size is given, + the last remaining molecules are used. + sigma: depth of the kernel. + identifiers: list of names (strings) of descriptors to use. + use_tf: if tensorflow should be used. + show_msgs: if debug messages should be shown. + """ + if type(identifiers) != list: + raise TypeError('\'identifiers\' is not a list.') + + # If tf is to be used but couldn't be imported, don't try to use it. + if use_tf and not TF_AV: + use_tf = False + + init_time = time.perf_counter() + + # Data reading. + tic = time.perf_counter() + compounds, energy_pbe0, energy_delta = qm7db(db_path=db_path, + is_shuffled=is_shuffled, + r_seed=r_seed, + use_tf=use_tf) + toc = time.perf_counter() + tictoc = toc - tic + if show_msgs: + printc(f'Data reading took {tictoc:.4f} seconds.', 'CYAN') + + # Matrices calculation. + tic = time.perf_counter() + for compound in compounds: + if 'CM' in identifiers: + compound.gen_cm(size=size, + as_eig=as_eig, + bohr_ru=bohr_ru) + if 'LJM' in identifiers: + compound.gen_ljm(diag_value=diag_value, + sigma=lj_sigma, + epsilon=lj_epsilon, + size=size, + 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) + if 'BOB' in identifiers: + compound.gen_bob(size=size) + + # Create a numpy array (or tensorflow tensor) for the descriptors. + if 'CM' in identifiers: + cm_data = np.array([comp.cm for comp in compounds], dtype=np.float64) + if 'LJM' in identifiers: + ljm_data = np.array([comp.ljm for comp in compounds], dtype=np.float64) + if 'AM' in identifiers: + am_data = np.array([comp.am for comp in compounds], dtype=np.float64) + if 'BOB' in identifiers: + bob_data = np.array([comp.bob for comp in compounds], dtype=np.float64) + + if use_tf: + if tf.config.experimental.list_physical_devices('GPU'): + with tf.device('GPU:0'): + if 'CM' in identifiers: + cm_data = tf.convert_to_tensor(cm_data) + if 'LJM' in identifiers: + ljm_data = tf.convert_to_tensor(ljm_data) + if 'AM' in identifiers: + am_data = tf.convert_to_tensor(am_data) + if 'BOB' in identifiers: + bob_data = tf.convert_to_tensor(bob_data) + else: + raise TypeError('No GPU found, could not create Tensor objects.') + + toc = time.perf_counter() + tictoc = toc - tic + if show_msgs: + printc(f'Matrices calculation took {tictoc:.4f} seconds.', 'CYAN') + + # 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) + 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) + 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) + 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) + + # End of program + end_time = time.perf_counter() + totaltime = end_time - init_time + printc(f'Program took {totaltime:.4f} seconds.', 'CYAN') -- cgit v1.2.3-54-g00ecf