create_paper_supplement.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae60cb00-a38b-43f1-93c2-66362a3f089d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format ='retina'\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import h5py\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import gzip\n",
    "import shutil\n",
    "from collections import defaultdict\n",
    "from pathlib import Path\n",
    "import joblib\n",
    "from tqdm.autonotebook import tqdm, trange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a141e3f1-0491-48c3-8e1c-4b7fc0922764",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = Path('data/w4c22/')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8730a76-5594-448f-981f-17df6817aab1",
   "metadata": {},
   "source": [
    "#### Paragraph \"Dataset\" from subsection \"Model-independent configurations\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "260f4dca-fa8e-47bc-bb8f-fe278f1d3bec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1e8fa4e13a445888fd1f52e520dcb1a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/28 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "boxi_0015-train (2019): from 0.0553 to 0.1017 with mean 0.0732\n",
      "boxi_0015-val   (2019): from 0.0417 to 0.0986 with mean 0.0596\n",
      "boxi_0015-train (2020): from 0.0676 to 0.1204 with mean 0.0807\n",
      "boxi_0015-val   (2020): from 0.0454 to 0.1676 with mean 0.0888\n",
      "\n",
      "\n",
      "boxi_0034-train (2019): from 0.0341 to 0.1240 with mean 0.0602\n",
      "boxi_0034-val   (2019): from 0.0398 to 0.1282 with mean 0.0688\n",
      "boxi_0034-train (2020): from 0.0600 to 0.1486 with mean 0.0873\n",
      "boxi_0034-val   (2020): from 0.0625 to 0.1477 with mean 0.0892\n",
      "\n",
      "\n",
      "boxi_0076-train (2019): from 0.0229 to 0.1556 with mean 0.0544\n",
      "boxi_0076-val   (2019): from 0.0222 to 0.1838 with mean 0.0611\n",
      "boxi_0076-train (2020): from 0.0210 to 0.0838 with mean 0.0414\n",
      "boxi_0076-val   (2020): from 0.0264 to 0.1213 with mean 0.0671\n",
      "\n",
      "\n",
      "roxi_0004-train (2019): from 0.0447 to 0.1742 with mean 0.1050\n",
      "roxi_0004-val   (2019): from 0.0546 to 0.2093 with mean 0.1354\n",
      "roxi_0004-train (2020): from 0.0751 to 0.2264 with mean 0.1410\n",
      "roxi_0004-val   (2020): from 0.0778 to 0.2356 with mean 0.1531\n",
      "\n",
      "\n",
      "roxi_0005-train (2019): from 0.0258 to 0.2612 with mean 0.0943\n",
      "roxi_0005-val   (2019): from 0.0079 to 0.2060 with mean 0.0853\n",
      "roxi_0005-train (2020): from 0.0220 to 0.2853 with mean 0.1114\n",
      "roxi_0005-val   (2020): from 0.0120 to 0.2625 with mean 0.1132\n",
      "\n",
      "\n",
      "roxi_0006-train (2019): from 0.0762 to 0.1839 with mean 0.1074\n",
      "roxi_0006-val   (2019): from 0.0755 to 0.2005 with mean 0.1244\n",
      "roxi_0006-train (2020): from 0.0658 to 0.1732 with mean 0.1069\n",
      "roxi_0006-val   (2020): from 0.0352 to 0.1394 with mean 0.0704\n",
      "\n",
      "\n",
      "roxi_0007-train (2019): from 0.0090 to 0.0549 with mean 0.0227\n",
      "roxi_0007-val   (2019): from 0.0037 to 0.0481 with mean 0.0160\n",
      "roxi_0007-train (2020): from 0.0186 to 0.0678 with mean 0.0253\n",
      "roxi_0007-val   (2020): from 0.0190 to 0.1134 with mean 0.0479\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['masks_nan_9.joblib']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_rain = np.infty\n",
    "max_rain = -np.infty\n",
    "\n",
    "masks = defaultdict(lambda: defaultdict(dict))\n",
    "\n",
    "for filename in tqdm(sorted(data_path.glob('**/*.rates.crop.h5'), key=lambda x: (x.stem.split('.')[0], x.parts[2], x.stem.split('.')[1]))):\n",
    "    f = h5py.File(filename, 'r')\n",
    "    x = f['rates.crop'][:]\n",
    "    # x[x == -8888000.0] = np.nan\n",
    "    x[x == -9999000.0] = np.nan\n",
    "    nan_mask = np.isnan(x)\n",
    "    mask = (x >= 0.2)\n",
    "    mask[nan_mask] = np.nan\n",
    "    mask = np.nanmean(mask, axis=(0, 1))\n",
    "    rain_ratio_min, rain_ratio_mean, rain_ratio_max = mask.min(), mask.mean(), mask.max()\n",
    "    region, split, *_ = filename.stem.split('.')\n",
    "    year = filename.parts[2]\n",
    "    masks[region][int(year)][split] = mask\n",
    "    print(f\"{region}-{split: <5} ({year}): from {rain_ratio_min:0.4f} to {rain_ratio_max:0.4f} with mean {rain_ratio_mean:0.4f}\")\n",
    "    if split == 'val' and year == '2020':\n",
    "        print('\\n')\n",
    "    min_rain = min(rain_ratio_min, min_rain)\n",
    "    max_rain = max(rain_ratio_max, max_rain)\n",
    "\n",
    "joblib.dump(dict(masks), 'masks_nan_9.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d12724f8-e8a4-4ef4-b61a-53647ece49cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(252, 252)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = joblib.load('masks.joblib')\n",
    "x['boxi_0015'][2019]['train'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "77e539d5-31ae-4fb0-8b2b-cf345b160950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.02568446,  0.02568446,  0.02568446, ...,  0.01009281,\n",
       "         0.01009281,  0.0049901 ],\n",
       "       [ 0.02255187,  0.02255187,  0.02255187, ...,  0.01009281,\n",
       "         0.01009281,  0.0049901 ],\n",
       "       [ 0.02631494,  0.02255187,  0.02255187, ...,  0.00629108,\n",
       "         0.00629108,  0.00308946],\n",
       "       ...,\n",
       "       [ 0.01679661,  0.01665855,  0.01665855, ..., -0.01087119,\n",
       "        -0.01087119, -0.01087119],\n",
       "       [ 0.01575196,  0.01665855,  0.01665855, ..., -0.01087119,\n",
       "        -0.01445708, -0.01445708],\n",
       "       [ 0.01575196,  0.01668755,  0.01668755, ..., -0.01445708,\n",
       "        -0.01445708, -0.01445708]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = x['boxi_0015'][2019]['train'] - x['boxi_0015'][2019]['val']\n",
    "diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aa0d0528-4c6f-4837-98c2-e729d82d9fdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.97495871, 0.97495871, 0.97495871, ..., 0.99000804, 0.99000804,\n",
       "        0.99503468],\n",
       "       [0.9779455 , 0.9779455 , 0.9779455 , ..., 0.99000804, 0.99000804,\n",
       "        0.99503468],\n",
       "       [0.97435979, 0.9779455 , 0.9779455 , ..., 0.99374825, 0.99374825,\n",
       "        0.99692006],\n",
       "       ...,\n",
       "       [0.98348086, 0.98361441, 0.98361441, ..., 1.01099068, 1.01099068,\n",
       "        1.01099068],\n",
       "       [0.98449232, 0.98361441, 0.98361441, ..., 1.01099068, 1.01466916,\n",
       "        1.01466916],\n",
       "       [0.98449232, 0.98358635, 0.98358635, ..., 1.01466916, 1.01466916,\n",
       "        1.01466916]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha = 1 / (1 + diff)\n",
    "alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76abf757-e78c-4210-a042-55ea8daf9850",
   "metadata": {},
   "source": [
    "### Code to produce Figure 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af57de0e-e448-43bf-9f4b-49909f474138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 24, 27, 30, 32, 35, 37, 38, 42, 45, 46, 49, 50, 51, 55, 56, 20,\n",
       "       19, 29, 17,  6, 18, 10, 12, 13, 59, 15, 57, 22, 40,  8, 25, 11, 41,\n",
       "        3, 52,  9, 54, 34,  5, 26, 21, 48, 16, 44, 53, 36, 23,  7, 39, 28,\n",
       "       14, 58,  4,  1, 31,  2, 33, 43, 47])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_file = h5py.File('submission/archive/heldout/majority_vote/unzipped/majority_vote/2019/roxi_0004.pred.h5', 'r')\n",
    "sub = sub_file['submission'][:]\n",
    "sub.mean(axis=(1, 2, 3)).argsort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "067d580f-8805-4205-916a-04161cd44af0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 9 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 755,
       "width": 1570
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(20, 10), nrows=2, ncols=4)\n",
    "selected_time = 23\n",
    "# get discrete colormap\n",
    "cmap = plt.get_cmap('Paired_r', 2)\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    time = i * 4\n",
    "    sub_at_time = sub[selected_time][time]\n",
    "    im = ax.imshow(sub_at_time, cmap=cmap, vmin=0, vmax=1)\n",
    "    ax.axis('off')\n",
    "    ax.set_title(f\"Hour: {time // 4}. Mean rain ratio: {sub_at_time.mean():.4f}\", fontsize=13)\n",
    "fig.subplots_adjust(right=0.8)\n",
    "cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n",
    "fig.colorbar(im, cax=cbar_ax, ticks=[0, 1])\n",
    "cbar_ax.get_yaxis().set_ticks([])\n",
    "for j, lab in enumerate(['no rain','rain']):\n",
    "    cbar_ax.text(.5, (2 * j + 1) / 4.0, lab, ha='center', va='center', color='white' if j < 1 else 'black', fontsize=15)\n",
    "plt.savefig(f\"submission__roxi_0004_2020_at_{selected_time}.pdf\", format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "795b22dc-abb9-4249-8581-828bc79ada1c",
   "metadata": {},
   "source": [
    "### Code to generate Table 1 and 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3cae62be-e545-47df-be13-3490c51a04c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_name_to_type(x):\n",
    "    for model_type in ['baseline', 'swin', 'vivit']:\n",
    "        if model_type in x.lower():\n",
    "            return model_type\n",
    "    return 'other'\n",
    "\n",
    "def model_name_to_loss(x):\n",
    "    if 'official' in x.lower():\n",
    "        return 'bce'\n",
    "    for loss in ['bce', 'dice_focal', 'IoU']:\n",
    "        if loss.lower() in x.lower():\n",
    "            return loss\n",
    "    return 'other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a8da5a6b-f91c-485b-a36f-b949613c1e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model type</th>\n",
       "      <th>Loss</th>\n",
       "      <th>Submission name</th>\n",
       "      <th>Total mean</th>\n",
       "      <th>2019 mean</th>\n",
       "      <th>2020 mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline</td>\n",
       "      <td>bce</td>\n",
       "      <td>official baseline</td>\n",
       "      <td>0.255085</td>\n",
       "      <td>0.259016</td>\n",
       "      <td>0.251153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>baseline</td>\n",
       "      <td>other</td>\n",
       "      <td>improved_epoch_15</td>\n",
       "      <td>0.269615</td>\n",
       "      <td>0.261093</td>\n",
       "      <td>0.278137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>swin</td>\n",
       "      <td>bce</td>\n",
       "      <td>bce_epoch_3</td>\n",
       "      <td>0.281390</td>\n",
       "      <td>0.282880</td>\n",
       "      <td>0.279900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>majority_vote</td>\n",
       "      <td>0.299784</td>\n",
       "      <td>0.296496</td>\n",
       "      <td>0.303072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>take best prediction per region</td>\n",
       "      <td>0.302430</td>\n",
       "      <td>0.301438</td>\n",
       "      <td>0.303423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model type   Loss                  Submission name  Total mean  2019 mean  \\\n",
       "0   baseline    bce                official baseline    0.255085   0.259016   \n",
       "1   baseline  other                improved_epoch_15    0.269615   0.261093   \n",
       "2       swin    bce                      bce_epoch_3    0.281390   0.282880   \n",
       "3      other  other                    majority_vote    0.299784   0.296496   \n",
       "4      other  other  take best prediction per region    0.302430   0.301438   \n",
       "\n",
       "   2020 mean  \n",
       "0   0.251153  \n",
       "1   0.278137  \n",
       "2   0.279900  \n",
       "3   0.303072  \n",
       "4   0.303423  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_per_region = joblib.load('results_per_submission_heldout.joblib') # output from `parse_all_submissions.py`\n",
    "df = pd.DataFrame.from_dict(results_per_region)\n",
    "df['take best prediction per region'] = df.drop(columns='official baseline').max(axis=1)\n",
    "df = df.T\n",
    "df['Total mean'] = df.mean(axis=1)\n",
    "df['2019 mean'] = df.filter(regex='2019').mean(axis=1)\n",
    "df['2020 mean'] = df.filter(regex='2020').mean(axis=1)\n",
    "df = df[['Total mean', '2019 mean', '2020 mean']]\n",
    "df.index.name = 'Submission name'\n",
    "df = df.reset_index()\n",
    "df['Model type'] = df['Submission name'].apply(model_name_to_type)\n",
    "df['Loss'] = df['Submission name'].apply(model_name_to_loss)\n",
    "df = df[['Model type', 'Loss'] + list(df.columns[0:-2])]\n",
    "df['Submission name'] = df.apply(lambda x: '_'.join(x['Submission name'].split('_')[2:]) if x['Model type'] in {'baseline', 'swin'} and 'official' not in x['Submission name'] else x['Submission name'], axis=1)\n",
    "df\n",
    "# df.to_excel('results_per_submission.xlsx', index=False)\n",
    "# df.to_csv('results_per_submission.csv', index=False)"
   ]
  }
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