From f9cd430d8e66cdac5d78a643f87445e3dd6bdf8e Mon Sep 17 00:00:00 2001
From: David Luevano <55825613+luevano@users.noreply.github.com>
Date: Sat, 28 Dec 2019 10:54:36 -0700
Subject: Refactor code

---
 lj_matrix/misc.py | 121 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 121 insertions(+)

(limited to 'lj_matrix/misc.py')

diff --git a/lj_matrix/misc.py b/lj_matrix/misc.py
index c50653a5c..e9142b05f 100644
--- a/lj_matrix/misc.py
+++ b/lj_matrix/misc.py
@@ -21,6 +21,7 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 """
 from colorama import init, Fore, Style
+import pandas as pd
 
 init()
 
@@ -51,3 +52,123 @@ def printc(text, color):
         actual_color = color_dic[color]
 
     print(actual_color + text + Style.RESET_ALL)
+
+
+def plot_benchmarks():
+    """
+    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')
-- 
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