diff options
| -rw-r--r-- | do_ml.py | 52 |
1 files changed, 50 insertions, 2 deletions
@@ -20,7 +20,55 @@ 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 +from colorama import Fore, Style +import numpy as np +from gauss_kernel import gauss_kernel +from cholesky_solve import cholesky_solve -def do_ml(): - pass +def printc(text, color): + """ + Prints texts normaly, but in color. Using colorama. + text: string with the text to print. + """ + print(color + text + Style.RESET_ALL) + + +def do_ml(desc_data, + desc_type, + energy_data, + training_size, + test_size, + sigma=1000.0): + """ + Does the ML methodology. + desc_data: descriptor (or representation) data. + desc_type: string with the name of the descriptor used. + energy_data: energy data associated with desc_data. + training_size: size of the training set to use. + test_size: size of the test set to use. + sigma: depth of the kernel. + NOTE: desc_type 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() + printc('{} ML started.'.format(desc_type), Fore.CYAN) + + Xcm_training = desc_data[:training_size] + Ycm_training = energy_data[:training_size] + Kcm_training = gauss_kernel(Xcm_training, Xcm_training, sigma) + alpha_cm = cholesky_solve(Kcm_training, Ycm_training) + + Xcm_test = desc_data[-test_size:] + Ycm_test = energy_data[-test_size:] + Kcm_test = gauss_kernel(Xcm_test, Xcm_training, sigma) + Ycm_predicted = np.dot(Kcm_test, alpha_cm) + + print('\tMAE for {}: {}'.format(desc_type, + np.mean(np.abs(Ycm_predicted - Ycm_test)))) + + toc = time.perf_counter() + printc('\t{} ML took {:.4f} seconds.'.format(desc_type, toc-tic), + Fore.GREEN) |