"""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 misc import printc from read_qm7_data import read_qm7_data from c_matrix import c_matrix_multiple from lj_matrix import lj_matrix_multiple from 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('data\\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('data\\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('data\\figs\\mae_diff_param_lj_e.pdf') if __name__ == '__main__': # ml() pl()