3_benchmark_results_worst_npw.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10, 8)\n",
    "plt.rcParams.update({'font.size': 20})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "quality_name_map = {\n",
    "    \"crowdlab\": \"CROWDLAB\",\n",
    "    \"label_quality_score\": \"Label Quality Score\",\n",
    "    \"agreement\": \"Agreement\",\n",
    "    \"active_label_cleaning\": \"Active Label Cleaning\",\n",
    "    \"empirical_bayes\": \"Empirical Bayes\",\n",
    "    \"no_perannotator_weights\": \"No Perannotator Weights\",\n",
    "    \"dawid_skene\": \"Dawid-Skene\",\n",
    "    \"glad\": \"GLAD\",\n",
    "    \"dawid_skene_with_model\": \"Dawid-Skene with Model\",\n",
    "    \"glad_with_model\": \"GLAD with Model\"\n",
    "}\n",
    "\n",
    "consensus_name_map = {\n",
    "    \"majority\": \"Majority Vote (Agreement)\",\n",
    "    \"dawid_skene\": \"Dawid-Skene\",\n",
    "    \"glad\": \"GLAD\",\n",
    "    \"dawid_skene_with_model\": \"Dawid-Skene with Model\",\n",
    "    \"glad_with_model\": \"GLAD with Model\",\n",
    "    \"crowdlab\": \"CROWDLAB\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.read_csv(\"benchmark_results/results_worst.csv\")\n",
    "\n",
    "results_qual_score = results[\n",
    "    (results[\"quality_method\"] == \"crowdlab\") | \n",
    "    (results[\"quality_method\"] == \"no_perannotator_weights\")\n",
    "    ][\n",
    "        [\n",
    "            \"dataset\",\n",
    "            \"model\",\n",
    "            \"quality_method\",\n",
    "            \"consensus_quality_auroc\",\n",
    "            \"consensus_quality_auprc\", \n",
    "            \"annotator_quality_spearman_corr\"\n",
    "        ]\n",
    "    ]\n",
    "\n",
    "results_accuracy = results[\n",
    "        [\n",
    "            \"dataset\",\n",
    "            \"model\",\n",
    "            \"consensus_method\",\n",
    "            \"consensus_labels_accuracy\",\n",
    "        ]\n",
    "    ].drop_duplicates(ignore_index=True)\n",
    "\n",
    "results_qual_score[\"quality_method\"] = results_qual_score[\"quality_method\"].apply(lambda s: quality_name_map[s])\n",
    "results_accuracy[\"consensus_method\"] = results_accuracy[\"consensus_method\"].apply(lambda s: consensus_name_map[s])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "qual_score_order = {\n",
    "    \"Agreement\": 0,\n",
    "    \"No Perannotator Weights\": 1,\n",
    "    \"CROWDLAB\": 2\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "resnet_qual_score = results_qual_score[results_qual_score[\"model\"] == \"resnet18\"].sort_values(\"quality_method\", key=lambda s: s.map(qual_score_order))\n",
    "swin_qual_score = results_qual_score[results_qual_score[\"model\"] == \"swin\"].sort_values(\"quality_method\", key=lambda s: s.map(qual_score_order))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(len(resnet_qual_score[\"quality_method\"]) - 1, -1, -1)\n",
    "width = 0.4 # the width of the bars\n",
    "\n",
    "plt.barh(x + width/2, resnet_qual_score[\"consensus_quality_auroc\"], width, label=\"ResNet-18\") \n",
    "plt.barh(x - width/2, swin_qual_score[\"consensus_quality_auroc\"], width, label=\"Swin\") \n",
    "\n",
    "plt.xlim(np.min(resnet_qual_score[\"consensus_quality_auroc\"]) - 0.03, min(np.max(swin_qual_score[\"consensus_quality_auroc\"]) + 0.03, 1))\n",
    "plt.yticks(x, resnet_qual_score[\"quality_method\"])\n",
    "plt.title(\"AUROC for Consensus Quality Score\")\n",
    "\n",
    "plt.savefig('benchmark_graphs/auroc_worst_npw.png', bbox_inches = \"tight\")  \n",
    "plt.savefig('benchmark_graphs/auroc_worst_npw.pdf', bbox_inches = \"tight\")  \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(len(resnet_qual_score[\"quality_method\"]) - 1, -1, -1)\n",
    "width = 0.4 # the width of the bars\n",
    "\n",
    "plt.barh(x + width/2, resnet_qual_score[\"consensus_quality_auprc\"], width, label=\"ResNet-18\") \n",
    "plt.barh(x - width/2, swin_qual_score[\"consensus_quality_auprc\"], width, label=\"Swin\") \n",
    "\n",
    "plt.xlim(np.min(resnet_qual_score[\"consensus_quality_auprc\"]) - 0.03, min(np.max(swin_qual_score[\"consensus_quality_auprc\"]) + 0.03, 1))\n",
    "plt.yticks(x, resnet_qual_score[\"quality_method\"])\n",
    "plt.title(\"AUPRC for Consensus Quality Score\")\n",
    "\n",
    "plt.savefig('benchmark_graphs/auprc_worst_npw.png', bbox_inches = \"tight\")  \n",
    "plt.savefig('benchmark_graphs/auprc_worst_npw.pdf', bbox_inches = \"tight\")  \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(len(resnet_qual_score[\"quality_method\"]) - 1, -1, -1)\n",
    "width = 0.4 # the width of the bars\n",
    "\n",
    "plt.barh(x + width/2, resnet_qual_score[\"annotator_quality_spearman_corr\"], width, label=\"ResNet-18\") \n",
    "plt.barh(x - width/2, swin_qual_score[\"annotator_quality_spearman_corr\"], width, label=\"Swin\") \n",
    "\n",
    "plt.xlim(np.min(resnet_qual_score[\"annotator_quality_spearman_corr\"]) - 0.03, min(np.max(swin_qual_score[\"annotator_quality_spearman_corr\"]) + 0.03, 1))\n",
    "plt.yticks(x, resnet_qual_score[\"quality_method\"])\n",
    "plt.title(\"Spearman Correlation for Annotator Quality\")\n",
    "\n",
    "plt.savefig('benchmark_graphs/spearman_worst_npw.png', bbox_inches = \"tight\")  \n",
    "plt.savefig('benchmark_graphs/spearman_worst_npw.pdf', bbox_inches = \"tight\")  \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture \n",
    "\n",
    "fig = plt.figure(\"Line plot\")\n",
    "legend_fig = plt.figure(\"Legend plot\")\n",
    "ax = fig.add_subplot()\n",
    "\n",
    "resnet = ax.barh(x + width/2, resnet_qual_score[\"consensus_quality_auroc\"], width) \n",
    "swin = ax.barh(x - width/2, resnet_qual_score[\"consensus_quality_auroc\"], width) \n",
    "\n",
    "legend_fig.legend([resnet, swin], [\"ResNet-18\", \"Swin Transformer\"], loc=\"center\")\n",
    "\n",
    "legend_fig.set_size_inches(11.5, 7)\n",
    "legend_fig.savefig(\"benchmark_graphs/legend_worst_npw.pdf\")\n",
    "legend_fig.savefig(\"benchmark_graphs/legend_worst_npw.png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 64-bit",
   "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": "3.10.8 (main, Oct 21 2022, 22:22:30) [Clang 14.0.0 (clang-1400.0.29.202)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
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