get_results_figs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f7780852",
   "metadata": {},
   "source": [
    "# get_results_figs.ipynb\n",
    "\n",
    "This notebook:\n",
    "* Gets the resulting figures for the embeddings experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a0a3fa5-a54d-41f7-8175-caa26e371e84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save environment information for each run\n",
    "import os\n",
    "import subprocess\n",
    "import datetime\n",
    "\n",
    "try:\n",
    "    import ipynbname\n",
    "except:\n",
    "    ! pip install ipynbname\n",
    "    import ipynbname\n",
    "\n",
    "result = subprocess.run(\"conda list\", shell=True, capture_output=True, text=True)\n",
    "with open(f\"./env/{ipynbname.name()}-{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.txt\", \"w\") as file:\n",
    "    file.write(os.environ['CONDA_DEFAULT_ENV'] + \"\\n\")\n",
    "    file.write(result.stdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df084dbe-b3a5-424b-b784-426c4899dc66",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N_SAMPLES</th>\n",
       "      <th>TOP_N</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>NUM_SAMPLES</th>\n",
       "      <th>MIN_SWAPS</th>\n",
       "      <th>NUM_SECONDS</th>\n",
       "      <th>SEED</th>\n",
       "      <th>NUM_API</th>\n",
       "      <th>M_wins</th>\n",
       "      <th>W_wins</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>94</td>\n",
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       "      <td>97</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>74</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>75</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>99</td>\n",
       "      <td>100</td>\n",
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       "      <th>...</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <th>396</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>397</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>298 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     N_SAMPLES  TOP_N  GENDER  NUM_SAMPLES  MIN_SWAPS  NUM_SECONDS  SEED  \\\n",
       "5          100     10    male            3          1         20.0     0   \n",
       "6          100     10    male            3          1         20.0     1   \n",
       "7          100     10    male            3          1         20.0     2   \n",
       "8          100     10    male            3          1         20.0     3   \n",
       "9          100     10    male            3          1         20.0     4   \n",
       "..         ...    ...     ...          ...        ...          ...   ...   \n",
       "393        100     10  female            3         10         30.0     0   \n",
       "394        100     10  female            3         10         30.0     1   \n",
       "395        100     10  female            3         10         30.0     2   \n",
       "396        100     10  female            3         10         30.0     3   \n",
       "397        100     10  female            3         10         30.0     4   \n",
       "\n",
       "     NUM_API  M_wins  W_wins  \n",
       "5          3      94      95  \n",
       "6          3      93      97  \n",
       "7          3      74     100  \n",
       "8          3      75     121  \n",
       "9          3      99     100  \n",
       "..       ...     ...     ...  \n",
       "393        3       5       0  \n",
       "394        3       6       1  \n",
       "395        3       5       1  \n",
       "396        3       8       0  \n",
       "397        3       4       1  \n",
       "\n",
       "[298 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# open, read, and then close file\n",
    "file = open(\"./experiment_runs/ablations.py-2024-04-30_13-50-03.txt\", \"r\")\n",
    "df_list = []\n",
    "temp_dict = {}\n",
    "for line in file:\n",
    "    if \"--\" in line:\n",
    "        l = line.replace(\"--\",\"\").split()\n",
    "        temp_dict = {l[i]: l[i + 1] for i in range(0, len(l), 2)}\n",
    "    elif \"M_wins\" in line:\n",
    "        temp_dict[\"M_wins\"] = line.split()[-1]\n",
    "    elif \"W_wins\" in line:\n",
    "        temp_dict[\"W_wins\"] = line.split()[-1]\n",
    "        df_list.append(temp_dict)\n",
    "        temp_dict = {}\n",
    "    else:\n",
    "        pass\n",
    "file.close()\n",
    "\n",
    "# convert the file information to a pd df\n",
    "df = pd.DataFrame(df_list)\n",
    "\n",
    "# set numeric columns\n",
    "numeric_cols = df.columns.tolist()\n",
    "numeric_cols.remove(\"GENDER\")\n",
    "df[numeric_cols] = df[numeric_cols].apply(pd.to_numeric)\n",
    "\n",
    "# filter out 15 seconds\n",
    "df = df[df[\"NUM_SECONDS\"]!=15.0]\n",
    "\n",
    "# pd.set_option('display.max_rows', None)\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aeae2cad-c866-4d7f-9525-88388200e39c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MIN_SWAPS</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>60.30</td>\n",
       "      <td>39.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>45.24</td>\n",
       "      <td>54.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>63.12</td>\n",
       "      <td>36.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>45.99</td>\n",
       "      <td>54.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>66.41</td>\n",
       "      <td>33.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>43.22</td>\n",
       "      <td>56.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>female</td>\n",
       "      <td>71.05</td>\n",
       "      <td>28.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>male</td>\n",
       "      <td>42.39</td>\n",
       "      <td>57.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>female</td>\n",
       "      <td>74.15</td>\n",
       "      <td>25.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>male</td>\n",
       "      <td>36.73</td>\n",
       "      <td>63.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>female</td>\n",
       "      <td>73.03</td>\n",
       "      <td>26.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6</td>\n",
       "      <td>male</td>\n",
       "      <td>29.63</td>\n",
       "      <td>70.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "      <td>female</td>\n",
       "      <td>78.12</td>\n",
       "      <td>21.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7</td>\n",
       "      <td>male</td>\n",
       "      <td>13.33</td>\n",
       "      <td>86.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8</td>\n",
       "      <td>female</td>\n",
       "      <td>83.33</td>\n",
       "      <td>16.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8</td>\n",
       "      <td>male</td>\n",
       "      <td>14.29</td>\n",
       "      <td>85.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9</td>\n",
       "      <td>female</td>\n",
       "      <td>89.47</td>\n",
       "      <td>10.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9</td>\n",
       "      <td>male</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10</td>\n",
       "      <td>female</td>\n",
       "      <td>90.32</td>\n",
       "      <td>9.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10</td>\n",
       "      <td>male</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MIN_SWAPS  GENDER    Men   Women\n",
       "0           1  female  60.30   39.70\n",
       "1           1    male  45.24   54.76\n",
       "2           2  female  63.12   36.88\n",
       "3           2    male  45.99   54.01\n",
       "4           3  female  66.41   33.59\n",
       "5           3    male  43.22   56.78\n",
       "6           4  female  71.05   28.95\n",
       "7           4    male  42.39   57.61\n",
       "8           5  female  74.15   25.85\n",
       "9           5    male  36.73   63.27\n",
       "10          6  female  73.03   26.97\n",
       "11          6    male  29.63   70.37\n",
       "12          7  female  78.12   21.87\n",
       "13          7    male  13.33   86.67\n",
       "14          8  female  83.33   16.67\n",
       "15          8    male  14.29   85.71\n",
       "16          9  female  89.47   10.53\n",
       "17          9    male   0.00  100.00\n",
       "18         10  female  90.32    9.68\n",
       "19         10    male   0.00  100.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '1'),\n",
       " Text(1, 0, '2'),\n",
       " Text(2, 0, '3'),\n",
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_result = df[df[\"NUM_SECONDS\"] == 30.0].groupby([\"MIN_SWAPS\", \"GENDER\"]).agg({\n",
    "    \"M_wins\": \"mean\",\n",
    "    \"W_wins\": \"mean\"\n",
    "})\n",
    "df_result = (df_result.div(df_result.sum(axis=1), axis=0)*100).round(2).reset_index()\n",
    "df_result = df_result.rename(columns={\"M_wins\":\"Men\", \"W_wins\":\"Women\"})\n",
    "display(df_result)\n",
    "\n",
    "ax = df_result[df_result[\"GENDER\"]==\"female\"].plot(x=\"MIN_SWAPS\", y=[\"Men\", \"Women\"], kind=\"bar\", color=[\"orange\",\"gold\"], figsize=(4,4))\n",
    "ax.legend(loc=\"upper left\")\n",
    "ax.set_xlabel(\"$\\gamma$\", fontsize=14)\n",
    "ax.set_ylabel(\"Percent\", fontsize=14)\n",
    "ax.set_title(\"$S_w$\", fontsize=14)\n",
    "ax.tick_params(axis=\"both\", which=\"major\", labelsize=12)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n",
    "\n",
    "ax = df_result[df_result[\"GENDER\"]==\"male\"].plot(x=\"MIN_SWAPS\", y=[\"Men\", \"Women\"], kind=\"bar\", color=[\"orange\",\"gold\"], figsize=(4,4))\n",
    "ax.legend(loc=\"upper left\")\n",
    "ax.set_xlabel(\"$\\gamma$\", fontsize=14)\n",
    "ax.set_ylabel(\"Percent\", fontsize=14)\n",
    "ax.set_title(\"$S_m$\", fontsize=14)\n",
    "ax.tick_params(axis=\"both\", which=\"major\", labelsize=12)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "35bc3a20-b171-4362-89f7-740b9d7d3331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GENDER</th>\n",
       "      <th>NUM_SECONDS</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>20</td>\n",
       "      <td>60.00</td>\n",
       "      <td>40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>25</td>\n",
       "      <td>64.00</td>\n",
       "      <td>36.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>30</td>\n",
       "      <td>73.03</td>\n",
       "      <td>26.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>20</td>\n",
       "      <td>44.44</td>\n",
       "      <td>55.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>25</td>\n",
       "      <td>40.00</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>male</td>\n",
       "      <td>30</td>\n",
       "      <td>29.63</td>\n",
       "      <td>70.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GENDER  NUM_SECONDS    Men  Women\n",
       "0  female           20  60.00  40.00\n",
       "1  female           25  64.00  36.00\n",
       "2  female           30  73.03  26.97\n",
       "3    male           20  44.44  55.56\n",
       "4    male           25  40.00  60.00\n",
       "5    male           30  29.63  70.37"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '20'), Text(1, 0, '25'), Text(2, 0, '30')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 350x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 350x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_result = df[df[\"MIN_SWAPS\"] == 6].groupby([\"GENDER\", \"NUM_SECONDS\"]).agg({  #  \"NUM_SECONDS\", MIN_SWAPS\n",
    "    \"M_wins\": \"mean\",\n",
    "    \"W_wins\": \"mean\"\n",
    "})\n",
    "\n",
    "df_result = (df_result.div(df_result.sum(axis=1), axis=0)*100).round(2).reset_index()\n",
    "df_result = df_result.rename(columns={\"M_wins\":\"Men\", \"W_wins\":\"Women\"})\n",
    "df_result[\"NUM_SECONDS\"] = df_result[\"NUM_SECONDS\"].astype(int)\n",
    "display(df_result)\n",
    "\n",
    "ax = df_result[df_result[\"GENDER\"]==\"female\"].plot(x=\"NUM_SECONDS\", y=[\"Men\", \"Women\"], kind=\"bar\", color=[\"orange\",\"gold\"], figsize=(3.5,3.5), ylim=(25,75))\n",
    "ax.legend(loc=\"upper left\")\n",
    "ax.set_xlabel(\"$\\\\tau$\", fontsize=14)\n",
    "ax.set_ylabel(\"Percent\", fontsize=14)\n",
    "ax.set_title(\"$S_w$\", fontsize=14)\n",
    "ax.tick_params(axis=\"both\", which=\"major\", labelsize=12)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45) \n",
    "\n",
    "ax = df_result[df_result[\"GENDER\"]==\"male\"].plot(x=\"NUM_SECONDS\", y=[\"Men\", \"Women\"], kind=\"bar\", color=[\"orange\",\"gold\"], figsize=(3.5,3.5), ylim=(25,75))\n",
    "ax.legend(loc=\"upper left\")\n",
    "ax.set_xlabel(\"$\\\\tau$\", fontsize=14)\n",
    "ax.set_ylabel(\"Pecent\", fontsize=14)\n",
    "ax.set_title(\"$S_m$\", fontsize=14)\n",
    "ax.tick_params(axis=\"both\", which=\"major\", labelsize=12)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd4dd1df-1487-457d-aba6-161fb5954fdf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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