notebooks/example_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e9698d3d-f63c-4d8c-af6e-4e9996b3fe28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill in the wandb_id assigned to your demo run!\n",
    "\n",
    "wandb_id = 'YOUR_ID'\n",
    "if wandb_id == 'YOUR_ID':\n",
    "    raise ValueError('Need to provide wandb_id of demo run!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ff8faff-38fa-49cb-be73-624dc88fcd13",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import os\n",
    "import json\n",
    "import wandb\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "32bfe743-5d8c-4ae6-804d-c5800a4209f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper Functions\n",
    "\n",
    "def restore_file(wandb_id, filename='wandb-summary.json'):\n",
    "    files_dir = 'notebooks/restored_files'    \n",
    "    os.system(f'mkdir -p {files_dir}')\n",
    "\n",
    "    api = wandb.Api()\n",
    "    run = api.run(f'semantic_uncertainty/{wandb_id}')\n",
    "\n",
    "    path = f'{files_dir}/{filename}'\n",
    "    os.system(f'rm -rf {path}')\n",
    "    run.file(filename).download(root=files_dir, replace=True, exist_ok=False)\n",
    "    with open(path, 'r') as f:\n",
    "        out = json.load(f)\n",
    "    return out\n",
    "\n",
    "def get_uncertainty_df(metrics):\n",
    "    data = []\n",
    "    for method in metrics['uncertainty']:\n",
    "        for metric in metrics['uncertainty'][method]:\n",
    "            mean = metrics['uncertainty'][method][metric]['mean']\n",
    "            data.append([method, metric, mean])\n",
    "    df = pd.DataFrame(data, columns=['method', 'metric', 'means'])\n",
    "    main_methods = ['semantic_entropy', 'cluster_assignment_entropy', 'regular_entropy', 'p_false', 'p_ik']\n",
    "    df = df.set_index('method').loc[main_methods].reset_index()\n",
    "    main_names = ['Semantic entropy', 'Discrete Semantic Entropy', 'Naive Entropy', 'p(True)', 'Embedding Regression']\n",
    "    conversion = dict(zip(main_methods, main_names))\n",
    "    df['method'] = df.method.map(lambda x: conversion[x])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "56409203-8734-4b9e-8618-638a26be7ccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = restore_file(wandb_id)\n",
    "unc_df = get_uncertainty_df(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6acb4184-0c9a-4ec5-b860-e050bf7544a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6, 0.8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric = 'AUROC'\n",
    "unc_df.set_index('metric').loc[metric].plot.bar(x='method', y='means')\n",
    "plt.gca().set_ylabel(metric)\n",
    "plt.gca().grid(axis='y')\n",
    "plt.gca().set_ylim(0.6, 0.8)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}