Refactor files

1 files changed, 0 insertions, 238 deletions

diff --git a/main.py b/main.py
deleted file mode 100644
index 3bf86572e..000000000
--- a/main.py
+++ /dev/null
@@ -1,238 +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
-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('.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()