notebook/parameter_size.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "NT_50m=[0.7918,0.9644,0.9417,0.7334,0.9083,0.9377,0.8791,0.7564]\n",
    "NT_100m=[0.7796,0.9697,0.948,0.7463,0.9157,0.9391,0.9167,0.762]\n",
    "NT_500m=[0.8512,0.9751,0.9576,0.7612,0.9244,0.9403,0.9252,0.8042]\n",
    "\n",
    "#plot\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15,6))\n",
    "#plot barchat for NT_50, NT_100, NT_500, make them around center of label\n",
    "plt.bar([0,1,2,3,4,5,6,7],NT_50m,width=0.2,label='NT_50m')\n",
    "plt.bar([0.2,1.2,2.2,3.2,4.2,5.2,6.2,7.2],NT_100m,width=0.2,label='NT_100m')\n",
    "plt.bar([0.4,1.4,2.4,3.4,4.4,5.4,6.4,7.4],NT_500m,width=0.2,label='NT_500m')\n",
    "#enlarge the image\n",
    "\n",
    "#assign labels for each x-axis value\n",
    "#fit the length of x ticks,make label position left\n",
    "plt.tick_params(axis='x', labelsize=18,  labelleft=True)\n",
    "#rotate x ticks\n",
    "# plt.xticks(rotation=45)\n",
    "# plt.xticks([0.2,1.2,2.2,3.2,4.2,5.2,6.2,7.2],['dummy_mouse_enhancer_ensemble','demo_human_or_worm','demo_coding_vs_iner_genomic_seq','human_enhancer_cohn','human_enhancer_ensamble','human_enhancer_regulatory','human_notata_promoters','human_ocr_ensemble'])\n",
    "plt.xticks([0.2,1.2,2.2,3.2,4.2,5.2,6.2,7.2],['DMEE','DHOW','DCIGS','HEC','HEE','HER','HNP','HOE'])\n",
    "#move x label left\n",
    "#add title and legend\n",
    "plt.legend()\n",
    "#y axis label\n",
    "plt.ylabel('Accuracy', fontsize=18)\n",
    "# make y axis from 0.6 to 1.0\n",
    "plt.ylim(0.7,1.0)\n",
    "plt.title('Impact of the Nucleotide Transformer Parameter Size on Accuracy of Short Range Tasks', fontsize=18)\n",
    "#add grid  backgound\n",
    "plt.grid(axis='y', alpha=0.5)\n",
    "plt.grid(axis='x', alpha=0.5)\n",
    "#show plot\n",
    "# plt.show()\n",
    "plt.savefig('NT_parameter_size_impact.pdf')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}