{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8abbf1c8",
   "metadata": {},
   "source": [
    "## Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "85579924",
   "metadata": {},
   "outputs": [],
   "source": [
    "from chemml.visualization import scatter2D, SavePlot, decorator\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "257b6a43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Plot has been saved at:  ./images/Parity.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df[\"Actual\"] = np.random.random(50)\n",
    "df[\"Predicted\"] = np.random.random(50)\n",
    "sc = scatter2D('b', marker='.')\n",
    "fig = sc.plot(dfx=df, dfy=df, x=\"Actual\", y=\"Predicted\")\n",
    "\n",
    "dec = decorator(title='Actual vs. Predicted',xlabel='Actual', ylabel='Predicted',\n",
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    "                grid_color='g', grid_linestyle=':', grid_linewidth=0.5)\n",
    "fig = dec.fit(fig)\n",
    "# print(type(fig))\n",
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    "sa.save(obj=fig)\n",
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