figures.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme()\n",
    "no_normalization = pd.read_csv('benchmark_no_normalization.csv', header=0).drop('dataset', axis=1)\n",
    "minmax = pd.read_csv('benchmark_minmax_normalization.csv', header=0).drop('dataset', axis=1)\n",
    "standard = pd.read_csv('benchmark_standard_normalization.csv', header=0).drop('dataset', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_normalization_melted = no_normalization.melt(var_name='algorithm', value_name='auc')\n",
    "minmax_melted = minmax.melt(var_name='algorithm', value_name='auc')\n",
    "standard_melted = standard.melt(var_name='algorithm', value_name='auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8), dpi=200)\n",
    "sns.boxplot(data=no_normalization_melted, x='algorithm', y='auc', palette='pastel')\n",
    "\n",
    "ax.set_title('No Normalization')\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('AUC')\n",
    "    \n",
    "plt.tight_layout()\n",
    "sns.despine(left=True, bottom=True)\n",
    "fig.savefig('boxplot_no_normalization.png', format='png', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8), dpi=200)\n",
    "sns.boxplot(data=minmax_melted, x='algorithm', y='auc', palette='pastel')\n",
    "\n",
    "ax.set_title('MinMax Normalization')\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('AUC')\n",
    "    \n",
    "plt.tight_layout()\n",
    "sns.despine(left=True, bottom=True)\n",
    "fig.savefig('boxplot_minmax.png', format='png', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8), dpi=200)\n",
    "sns.boxplot(data=standard_melted, x='algorithm', y='auc', palette='pastel')\n",
    "\n",
    "ax.set_title('Standard Normalization')\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('AUC')\n",
    "    \n",
    "plt.tight_layout()\n",
    "sns.despine(left=True, bottom=True)\n",
    "fig.savefig('boxplot_standard.png', format='png', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
  },
  "kernelspec": {
   "display_name": "Python 3.8.8 64-bit ('base': conda)",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}