{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c542d51c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import json\n",
    "import glob\n",
    "from functools import reduce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1592981c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_results(dataset, model, pretrained, ckpt, metric, n_sample, seed):\n",
    "    if pretrained:\n",
    "        result_dir = f'../results/{dataset}/pretrained/{model}_{seed}-{ckpt}/{metric}_{n_sample}'\n",
    "    else:\n",
    "        result_dir = f'../results/{dataset}/scratch/{model}_{seed}-{ckpt}/{metric}_{n_sample}'\n",
    "      \n",
    "    result_fs = glob.glob(f'{result_dir}/*.json')\n",
    "    results = []\n",
    "    for file in result_fs:\n",
    "        with open(file, 'r') as f:\n",
    "            data = json.load(f)\n",
    "        results.extend(data)\n",
    "    \n",
    "    print(f'{metric}: {len(results)}')\n",
    "        \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "54321e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_sample_dict = {\n",
    "    'CIFAR-10': -1, \n",
    "    'CIFAR-100': -1, \n",
    "    'Living-17': -1,\n",
    "    'Nonliving-26': -1,\n",
    "    'Entity-13': -1,\n",
    "    'Entity-30': -1,\n",
    "    'ImageNet': -1,\n",
    "    'RxRx1': -1,\n",
    "    'FMoW': -1,\n",
    "    'Amazon': -1,\n",
    "    'CivilComments': -1,\n",
    "    'Camelyon17': -1\n",
    "}\n",
    "\n",
    "n_epoch_dict = {\n",
    "    'CIFAR-10': 300, \n",
    "    'CIFAR-100': 300, \n",
    "    'Living-17': 450,\n",
    "    'Nonliving-26': 450,\n",
    "    'Entity-13': 300,\n",
    "    'Entity-30': 300,\n",
    "    'ImageNet': 10,\n",
    "    'FMoW': 50,\n",
    "    'RxRx1': 90,\n",
    "    'Amazon': 3,\n",
    "    'CivilComments': 5,\n",
    "    'Camelyon17': 5,\n",
    "}\n",
    "\n",
    "pretrained_dict = {\n",
    "    'CIFAR-10': False, \n",
    "    'CIFAR-100': False, \n",
    "    'Living-17': False,\n",
    "    'Nonliving-26': False,\n",
    "    'Entity-13': False,\n",
    "    'Entity-30': False,\n",
    "    'ImageNet': True,\n",
    "    'FMoW': True,\n",
    "    'RxRx1': True,\n",
    "    'Amazon': True,\n",
    "    'Camelyon17': True,\n",
    "    'CivilComments': True\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "13c79cfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed: 0\n",
      "AC: 192\n",
      "DoC: 192\n",
      "IM: 192\n",
      "GDE: 0\n",
      "ATC-MC: 192\n",
      "ATC-NE: 192\n",
      "COT: 192\n",
      "COTT-MC: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "metrics = ['AC', 'DoC', 'IM', 'GDE', 'ATC-MC', 'ATC-NE', 'COT', 'COTT-MC']\n",
    "dataset = 'Nonliving-26'\n",
    "arch = 'resnet50' # \"distilbert-base-uncased\" #  \n",
    "n_sample = n_sample_dict[dataset]\n",
    "seeds = [0]\n",
    "model_ckpt = n_epoch_dict[dataset]\n",
    "pretrained = pretrained_dict[dataset]\n",
    "results = []\n",
    "for seed in seeds: \n",
    "    print(f'seed: {seed}')\n",
    "    results.append(\n",
    "        sum([load_results(dataset, arch, pretrained, model_ckpt, metric, n_sample, seed) for metric in metrics], [])\n",
    "    )\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "edb585d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ef90531d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_corr_chart(data, subpop, scale_min=0, scale_max=1):\n",
    "    corr = alt.Chart(alt.Data(values=data), title=subpop).mark_point(size=40, filled=True).encode(\n",
    "        x=alt.X('metric:Q', scale=alt.Scale(domain=[scale_min, scale_max])),\n",
    "        y=alt.X('error:Q', scale=alt.Scale(domain=[scale_min, scale_max]), title='Test Error'),\n",
    "        color=alt.Color('ref:N'),\n",
    "        shape=alt.Color('ref:N'),\n",
    "    ).properties(\n",
    "        width=200,\n",
    "        height=200\n",
    "    )\n",
    "    return corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "86df21ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed_ind = 0\n",
    "same_pop_results =  [i for i in results[seed_ind] if i['subpopulation'] == 'same']\n",
    "natural_pop_results =  [i for i in results[seed_ind] if i['subpopulation'] == 'natural']\n",
    "novel_pop_results = [i for i in results[seed_ind] if i['subpopulation'] == 'novel']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "dc6e0c13",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_min = max( min( min([i['metric'] for i in results[seed_ind]]), min([i['error'] for i in results[seed_ind]]))  - 0.1,  0)\n",
    "scale_max = min( max( max([i['metric'] for i in results[seed_ind]]), max([i['error'] for i in results[seed_ind]]))  + 0.1,  1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "fd94e84f",
   "metadata": {},
   "outputs": [],
   "source": [
    "line = pd.DataFrame({'metric': [scale_min, scale_max], 'error': [scale_min, scale_max]})\n",
    "line_plot = alt.Chart(line).mark_line(color='black', strokeDash=[5, 8]).encode(\n",
    "    x='metric',\n",
    "    y='error',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a2681d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "same_corr = get_corr_chart(same_pop_results, 'same', scale_min=scale_min, scale_max=scale_max)\n",
    "same_plt = same_corr + line_plot\n",
    "\n",
    "natural_corr = get_corr_chart(natural_pop_results, 'natural', scale_min=scale_min, scale_max=scale_max)\n",
    "natural_plt = natural_corr + line_plot\n",
    "\n",
    "novel_corr = get_corr_chart(novel_pop_results, 'novel', scale_min=scale_min, scale_max=scale_max)\n",
    "novel_plt = novel_corr + line_plot\n",
    "\n",
    "plt = same_plt | natural_plt | novel_plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "3c3d3a92",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/workspace/lu35/anaconda3/envs/ood/lib/python3.9/site-packages/altair/utils/core.py:317: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for col_name, dtype in df.dtypes.iteritems():\n"
     ]
    },
    {
     "data": {
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       "\n",
       "<div id=\"altair-viz-95cfed49eb1244088a6067f5e2e9ab98\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-95cfed49eb1244088a6067f5e2e9ab98\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-95cfed49eb1244088a6067f5e2e9ab98\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
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       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
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       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.configure_axis(\n",
    "    labelFontSize=14,\n",
    "    titleFontSize=16\n",
    ").configure_axis(\n",
    "    labelFontSize=14,\n",
    "    titleFontSize=16,\n",
    ").configure_legend(\n",
    "    titleFontSize=14,\n",
    "    labelFontSize=16\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "3ccfbce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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0.97, \"ref\": \"COTT-MC\", \"acc\": 0.07235293835401535, \"error\": 0.9276470616459846, \"subpopulation\": \"same\", \"pretrained\": false}, {\"corruption\": \"contrast\", \"corruption level\": 5, \"metric\": 0.9588235294117647, \"ref\": \"COTT-MC\", \"acc\": 0.05529411509633064, \"error\": 0.9447058849036694, \"subpopulation\": \"novel\", \"pretrained\": false}]}, \"mark\": {\"type\": \"point\", \"filled\": true, \"size\": 40}, \"encoding\": {\"color\": {\"field\": \"ref\", \"type\": \"nominal\"}, \"shape\": {\"field\": \"ref\", \"type\": \"nominal\"}, \"x\": {\"field\": \"metric\", \"scale\": {\"domain\": [0.15470588235294117, 1]}, \"type\": \"quantitative\"}, \"y\": {\"field\": \"error\", \"scale\": {\"domain\": [0.15470588235294117, 1]}, \"title\": \"Test Error\", \"type\": \"quantitative\"}}, \"height\": 200, \"title\": \"Living-17\", \"width\": 200}, {\"data\": {\"name\": \"data-55cc1c487e8b42cf39f3db1ac778f185\"}, \"mark\": {\"type\": \"line\", \"color\": \"black\", \"strokeDash\": [5, 8]}, \"encoding\": {\"x\": {\"field\": \"metric\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"error\", \"type\": \"quantitative\"}}}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-55cc1c487e8b42cf39f3db1ac778f185\": [{\"metric\": 0.15470588235294117, \"error\": 0.15470588235294117}, {\"metric\": 1.0, \"error\": 1.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_corr_chart(results[seed_ind], dataset, scale_min=scale_min, scale_max=scale_max) + line_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f6c96c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def polyfit(x, y, degree=1):\n",
    "    results = {}\n",
    "\n",
    "    coeffs = np.polyfit(x, y, degree)\n",
    "\n",
    "    results['polynomial'] = coeffs.tolist()\n",
    "\n",
    "    p = np.poly1d(coeffs)\n",
    "\n",
    "    yhat = p(x)                \n",
    "    ybar = np.sum(y)/len(y)          \n",
    "    ssreg = np.sum((yhat - ybar)**2)   \n",
    "    sstot = np.sum((y - ybar)**2)    \n",
    "    results['determination'] = ssreg / sstot\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "73c59c8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import spearmanr\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "08834324",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_corr_stats(all_results, metric):\n",
    "    if len(all_results) == 0:\n",
    "        return\n",
    "    \n",
    "    results = [i for i in all_results if i['ref'] == metric]\n",
    "    d = [i['metric'] for i in results]\n",
    "    e = [i['error'] for i in results]\n",
    "    \n",
    "    if len(results) > 1:\n",
    "        r2 = polyfit(d, e)['determination']\n",
    "\n",
    "        coef, p = spearmanr(d, e)\n",
    "        slope = polyfit(d, e)['polynomial'][0]\n",
    "        bias = polyfit(d, e)['polynomial'][1]\n",
    "\n",
    "    yhat = np.array(d)\n",
    "    y = np.array(e)\n",
    "    mae = round(np.abs(yhat - y).mean() * 100, 2)\n",
    "    \n",
    "    return mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9eb490d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_summary(results, seeds, subpop):\n",
    "    pop_sum = []\n",
    "    for i, seed in enumerate(seeds):\n",
    "        maes = []\n",
    "        for metric in metrics:\n",
    "            pop_result = [r for r in results[i] if r['subpopulation'] == subpop]\n",
    "            if len(pop_result) == 0:\n",
    "                return\n",
    "            maes.append(\n",
    "                compute_corr_stats(pop_result, metric)\n",
    "            )\n",
    "        pop_sum.append(maes)\n",
    "\n",
    "    pop_sum = np.array(pop_sum).T\n",
    "    for j, metric in enumerate(metrics):\n",
    "        print(f'{metric}:', round(pop_sum[j].mean(), 2), u\"\\u00B1\", round(pop_sum[j].std(), 2))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "23828d93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- same pop results -----\n",
      "AC: 11.36 ± 0.0\n",
      "DoC: 11.57 ± 0.0\n",
      "IM: 13.38 ± 0.0\n",
      "GDE: nan ± nan\n",
      "ATC-MC: 5.4 ± 0.0\n",
      "ATC-NE: 4.64 ± 0.0\n",
      "COT: 4.16 ± 0.0\n",
      "COTT-MC: 2.24 ± 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/tmp/lu35/ipykernel_3738105/2605032691.py:18: RuntimeWarning: Mean of empty slice.\n",
      "  mae = round(np.abs(yhat - y).mean() * 100, 2)\n",
      "/usr/workspace/lu35/anaconda3/envs/ood/lib/python3.9/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    }
   ],
   "source": [
    "print(\"----- same pop results -----\")\n",
    "generate_summary(results, seeds, 'same')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "bf70a29c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- natural pop results -----\n"
     ]
    }
   ],
   "source": [
    "print(\"----- natural pop results -----\")\n",
    "generate_summary(results, seeds, 'natural')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "05c3ac30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- novel pop results -----\n",
      "AC: 25.9 ± 0.0\n",
      "DoC: 26.11 ± 0.0\n",
      "IM: 28.54 ± 0.0\n",
      "GDE: nan ± nan\n",
      "ATC-MC: 16.43 ± 0.0\n",
      "ATC-NE: 14.3 ± 0.0\n",
      "COT: 17.1 ± 0.0\n",
      "COTT-MC: 10.59 ± 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/tmp/lu35/ipykernel_3738105/2605032691.py:18: RuntimeWarning: Mean of empty slice.\n",
      "  mae = round(np.abs(yhat - y).mean() * 100, 2)\n"
     ]
    }
   ],
   "source": [
    "print(\"----- novel pop results -----\")\n",
    "generate_summary(results, seeds, 'novel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4599063",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ood",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
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