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Diffstat (limited to 'lj_matrix/__main__.py')
| -rw-r--r-- | lj_matrix/__main__.py | 238 |
1 files changed, 238 insertions, 0 deletions
diff --git a/lj_matrix/__main__.py b/lj_matrix/__main__.py new file mode 100644 index 000000000..4e13f4995 --- /dev/null +++ b/lj_matrix/__main__.py @@ -0,0 +1,238 @@ +"""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 +from multiprocessing import Process, Pipe +# import matplotlib.pyplot as plt +import pandas as pd +from lj_matrix.misc import printc +from lj_matrix.read_qm7_data import read_qm7_data +from lj_matrix.c_matrix import c_matrix_multiple +from lj_matrix.lj_matrix import lj_matrix_multiple +from lj_matrix.do_ml import do_ml + + +# Test +def ml(): + """ + Main function that does the whole ML process. + """ + # Initialization time. + init_time = time.perf_counter() + + # Data reading. + zi_data, molecules, nuclear_charge, energy_pbe0, energy_delta =\ + read_qm7_data() + + # Matrices calculation. + procs = [] + pipes = [] + + # cm_recv, cm_send = Pipe(False) + # p1 = Process(target=c_matrix_multiple, + # args=(molecules, nuclear_charge, cm_send)) + # procs.append(p1) + # pipes.append(cm_recv) + # p1.start() + + ljm_recv, ljm_send = Pipe(False) + p2 = Process(target=lj_matrix_multiple, + args=(molecules, nuclear_charge, ljm_send, 1, 0.25)) + procs.append(p2) + pipes.append(ljm_recv) + p2.start() + + # cm_data = pipes[0].recv() + ljm_data = pipes[0].recv() + + for proc in procs: + proc.join() + + # ML calculation. + procs = [] + # cm_pipes = [] + ljm_pipes = [] + for i in range(1500, 6500 + 1, 500): + # cm_recv, cm_send = Pipe(False) + # p1 = Process(target=do_ml, + # args=(cm_data, energy_pbe0, i, 'CM', cm_send)) + # procs.append(p1) + # cm_pipes.append(cm_recv) + # p1.start() + + ljm_recv, ljm_send = Pipe(False) + p2 = Process(target=do_ml, + args=(ljm_data, energy_pbe0, i, 'L-JM', ljm_send)) + procs.append(p2) + ljm_pipes.append(ljm_recv) + p2.start() + + # cm_bench_results = [] + ljm_bench_results = [] + for ljd_pipe in ljm_pipes: # cd_pipe, ljd_pipe in zip(cm_pipes, ljm_pipes): + # cm_bench_results.append(cd_pipe.recv()) + ljm_bench_results.append(ljd_pipe.recv()) + + for proc in procs: + proc.join() + + with open('data\\benchmarks.csv', 'a') as save_file: + # save_file.write(''.join(['ml_type,tr_size,te_size,kernel_s,', + # 'mae,time,lj_s,lj_e,date_ran\n'])) + date = '/'.join([str(field) for field in time.localtime()[:3][::-1]]) + for ljm in ljm_bench_results: # cm, ljm, in zip(cm_bench_results, ljm_bench_results): + # cm_text = ','.join([str(field) for field in cm])\ + # + ',' + date + '\n' + ljm_text = ','.join([str(field) for field in ljm])\ + + ',1,0.25,' + date + '\n' + # save_file.write(cm_text) + save_file.write(ljm_text) + + # End of program + end_time = time.perf_counter() + printc('Program took {:.4f} seconds.'.format(end_time - init_time), + 'CYAN') + + +def pl(): + """ + Function for plotting the benchmarks. + """ + # Original columns. + or_cols = ['ml_type', + 'tr_size', + 'te_size', + 'kernel_s', + 'mae', + 'time', + 'lj_s', + 'lj_e', + 'date_ran'] + # Drop some original columns. + dor_cols = ['te_size', + 'kernel_s', + 'time', + 'date_ran'] + + # Read benchmarks data and drop some columns. + data_temp = pd.read_csv('data\\benchmarks.csv',) + data = pd.DataFrame(data_temp, columns=or_cols) + data = data.drop(columns=dor_cols) + + # Get the data of the first benchmarks and drop unnecesary columns. + first_data = pd.DataFrame(data, index=range(0, 22)) + first_data = first_data.drop(columns=['lj_s', 'lj_e']) + + # Columns to keep temporarily. + fd_columns = ['ml_type', + 'tr_size', + 'mae'] + + # Create new dataframes for each matrix descriptor and fill them. + first_data_cm = pd.DataFrame(columns=fd_columns) + first_data_ljm = pd.DataFrame(columns=fd_columns) + for i in range(first_data.shape[0]): + temp_df = first_data.iloc[[i]] + if first_data.at[i, 'ml_type'] == 'CM': + first_data_cm = first_data_cm.append(temp_df) + else: + first_data_ljm = first_data_ljm.append(temp_df) + + # Drop unnecesary column and rename 'mae' for later use. + first_data_cm = first_data_cm.drop(columns=['ml_type'])\ + .rename(columns={'mae': 'cm_mae'}) + first_data_ljm = first_data_ljm.drop(columns=['ml_type'])\ + .rename(columns={'mae': 'ljm_mae'}) + # print(first_data_cm) + # print(first_data_ljm) + + # Get the cm data axis so it can be joined with the ljm data axis. + cm_axis = first_data_cm.plot(x='tr_size', + y='cm_mae', + kind='line') + # Get the ljm data axis and join it with the cm one. + plot_axis = first_data_ljm.plot(ax=cm_axis, + x='tr_size', + y='ljm_mae', + kind='line') + plot_axis.set_xlabel('tr_size') + plot_axis.set_ylabel('mae') + plot_axis.set_title('mae for different tr_sizes') + # Get the figure and save it. + # plot_axis.get_figure().savefig('.figs\\mae_diff_tr_sizes.pdf') + + # Get the rest of the benchmark data and drop unnecesary column. + new_data = data.drop(index=range(0, 22)) + new_data = new_data.drop(columns=['ml_type']) + + # Get the first set and rename it. + nd_first = first_data_ljm.rename(columns={'ljm_mae': '1, 1'}) + ndf_axis = nd_first.plot(x='tr_size', + y='1, 1', + kind='line') + last_axis = ndf_axis + for i in range(22, 99, 11): + lj_s = new_data['lj_s'][i] + lj_e = new_data['lj_e'][i] + new_mae = '{}, {}'.format(lj_s, lj_e) + nd_temp = pd.DataFrame(new_data, index=range(i, i + 11))\ + .drop(columns=['lj_s', 'lj_e'])\ + .rename(columns={'mae': new_mae}) + last_axis = nd_temp.plot(ax=last_axis, + x='tr_size', + y=new_mae, + kind='line') + print(nd_temp) + + last_axis.set_xlabel('tr_size') + last_axis.set_ylabel('mae') + last_axis.set_title('mae for different parameters of lj(s)') + + last_axis.get_figure().savefig('.figs\\mae_diff_param_lj_s.pdf') + + ndf_axis = nd_first.plot(x='tr_size', + y='1, 1', + kind='line') + last_axis = ndf_axis + for i in range(99, data.shape[0], 11): + lj_s = new_data['lj_s'][i] + lj_e = new_data['lj_e'][i] + new_mae = '{}, {}'.format(lj_s, lj_e) + nd_temp = pd.DataFrame(new_data, index=range(i, i + 11))\ + .drop(columns=['lj_s', 'lj_e'])\ + .rename(columns={'mae': new_mae}) + last_axis = nd_temp.plot(ax=last_axis, + x='tr_size', + y=new_mae, + kind='line') + print(nd_temp) + + last_axis.set_xlabel('tr_size') + last_axis.set_ylabel('mae') + last_axis.set_title('mae for different parameters of lj(e)') + + last_axis.get_figure().savefig('.figs\\mae_diff_param_lj_e.pdf') + + +if __name__ == '__main__': + # ml() + pl() |