plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "!pip install pandas matplotlib seaborn"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 3600x1800 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "results = pd.read_csv('results.csv')\n",
    "plt.figure(figsize=(12, 6), dpi=300)\n",
    "# increase font size\n",
    "sns.set(font_scale=1.5)\n",
    "\n",
    "sns.barplot(results, x=\"tool calling\", y=\"ast_percentage\", hue=\"model\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.ylabel('AST Percentage')\n",
    "plt.xlabel('Tool Calling')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'results.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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