Refactor code

1 files changed, 2 insertions, 123 deletions

diff --git a/lj_matrix/__main__.py b/lj_matrix/__main__.py
index f7e4065da..98f341e1e 100644
--- a/lj_matrix/__main__.py
+++ b/lj_matrix/__main__.py
@@ -20,131 +20,10 @@ 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 pandas as pd
 from lj_matrix.do_ml import do_ml
-
-
-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')
-
+# from lj_matrix.misc import plot_benchmarks
 
 if __name__ == '__main__':
     do_ml(min_training_size=1500, max_training_size=3000)
-    # pl()
+    # plot_benchmarks()
     print('OK!')