notebooks/evaluate_predictions_on_subset_substring_matched.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.chdir('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to /Users/alishri/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import itertools\n",
    "import glob\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "from statsmodels.stats.proportion import proportion_confint\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "import configs\n",
    "from utils import laion_utils as laionu\n",
    "from utils import wordnet_utils as wnu\n",
    "from core.ilsvrc_predictors import all_model_names_1k, all_model_names_pt21k_ft1k, all_model_names_21k\n",
    "from core.queries import QueryType"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LAION with predictions\n",
    "laion_path = 'laion400m'\n",
    "laion_until_part = 31\n",
    "\n",
    "laion_file_name = configs.LAIONConfig.SUBSET_SM_FILTERED_PREFIX \\\n",
    "                + configs.LAIONConfig.PREDICTED_PREFIX \\\n",
    "                + laionu.get_laion_subset_file_name(0, laion_until_part)\n",
    "\n",
    "# ILSVRC predictions\n",
    "ilsvrc_path = 'ilsvrc2012'\n",
    "\n",
    "ilsvrc_val_file_name = 'ILSVRC2012_val_predicted.parquet'\n",
    "\n",
    "# Labels (maps)\n",
    "laion_labels_path = os.path.join('laion400m', 'processed', 'ilsvrc_labels')\n",
    "wnid2laionindices_file_name = 'wnid2laionindices(substring_matched).pkl'\n",
    "\n",
    "imagename2wnid_path = os.path.join('ilsvrc2012', 'processed', 'labels', 'imagename2wnid.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Misc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_k_col = lambda k, mdl: f'top_{k}_is_correct_{mdl}'\n",
    "\n",
    "chosen_q_type = QueryType.NAME_DEF\n",
    "\n",
    "# Image-query similarity\n",
    "image_query_sim_col = f'image_to_{chosen_q_type}_wnid_similarity'\n",
    "\n",
    "# Text-query similarity\n",
    "text_query_sim_col = f'text_to_{chosen_q_type}_wnid_similarity'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resnet-18\n",
      "resnet-34\n",
      "resnet-50\n",
      "resnet-101\n",
      "resnet-152\n",
      "convnext-tiny-224\n",
      "convnext-small-224\n",
      "convnext-base-224\n",
      "convnext-large-224\n",
      "convnext-base-384\n",
      "convnext-large-384\n",
      "vit-base-patch16-224\n",
      "vit-base-patch16-384\n",
      "vit-base-patch32-384\n",
      "vit-large-patch16-224\n",
      "vit-large-patch16-384\n",
      "vit-large-patch32-384\n",
      "convnext-base-224-22k-1k\n",
      "convnext-large-224-22k-1k\n",
      "convnext-base-384-22k-1k\n",
      "convnext-large-384-22k-1k\n",
      "beit-base-patch16-224\n",
      "beit-base-patch16-384\n",
      "beit-large-patch16-224\n",
      "beit-large-patch16-384\n",
      "convnext-base-224-22k\n",
      "convnext-large-224-22k\n",
      "beit-base-patch16-224-pt22k-ft22k\n",
      "beit-large-patch16-224-pt22k-ft22k\n"
     ]
    }
   ],
   "source": [
    "model_names = all_model_names_1k + all_model_names_pt21k_ft1k + all_model_names_21k\n",
    "\n",
    "print('\\n'.join(model_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelname2color = {}\n",
    "\n",
    "for model_name in all_model_names_1k:\n",
    "    modelname2color[model_name] = 'r'\n",
    "\n",
    "for model_name in all_model_names_pt21k_ft1k:\n",
    "    modelname2color[model_name] = 'y'\n",
    "    \n",
    "for model_name in all_model_names_21k:\n",
    "    modelname2color[model_name] = 'g'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load LAION subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAMPLE_ID</th>\n",
       "      <th>URL</th>\n",
       "      <th>TEXT</th>\n",
       "      <th>HEIGHT</th>\n",
       "      <th>WIDTH</th>\n",
       "      <th>LICENSE</th>\n",
       "      <th>NSFW</th>\n",
       "      <th>similarity</th>\n",
       "      <th>a_photo_of_name_def_wnid</th>\n",
       "      <th>text_to_a_photo_of_name_def_wnid_similarity</th>\n",
       "      <th>...</th>\n",
       "      <th>top_5_is_correct_beit-large-patch16-224-pt22k-ft22k</th>\n",
       "      <th>top_1_is_correct_beit-base-patch16-224</th>\n",
       "      <th>top_5_is_correct_beit-base-patch16-224</th>\n",
       "      <th>top_1_is_correct_beit-base-patch16-384</th>\n",
       "      <th>top_5_is_correct_beit-base-patch16-384</th>\n",
       "      <th>top_1_is_correct_beit-large-patch16-224</th>\n",
       "      <th>top_5_is_correct_beit-large-patch16-224</th>\n",
       "      <th>top_1_is_correct_beit-large-patch16-384</th>\n",
       "      <th>top_5_is_correct_beit-large-patch16-384</th>\n",
       "      <th>image_to_text_similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1200056513</th>\n",
       "      <td>4.745710e+11</td>\n",
       "      <td>https://t1.ftcdn.net/jpg/00/75/08/12/240_F_750...</td>\n",
       "      <td>Colorful patchwork quilt pattern</td>\n",
       "      <td>373.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>?</td>\n",
       "      <td>UNLIKELY</td>\n",
       "      <td>0.328658</td>\n",
       "      <td>a photo of quilt which is bedding made of two ...</td>\n",
       "      <td>0.805882</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.325056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200090089</th>\n",
       "      <td>1.129810e+11</td>\n",
       "      <td>https://farm6.staticflickr.com/5132/5411062185...</td>\n",
       "      <td>basket quilt top</td>\n",
       "      <td>320.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>?</td>\n",
       "      <td>UNLIKELY</td>\n",
       "      <td>0.310124</td>\n",
       "      <td>a photo of quilt which is bedding made of two ...</td>\n",
       "      <td>0.823052</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.314476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200091068</th>\n",
       "      <td>4.145784e+12</td>\n",
       "      <td>https://theplaidporticodotcom4.files.wordpress...</td>\n",
       "      <td>QuiltCon charity quilt</td>\n",
       "      <td>584.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>?</td>\n",
       "      <td>UNLIKELY</td>\n",
       "      <td>0.307640</td>\n",
       "      <td>a photo of quilt which is bedding made of two ...</td>\n",
       "      <td>0.846488</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 88 columns</p>\n",
       "</div>"
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      "text/plain": [
       "               SAMPLE_ID                                                URL  \\\n",
       "1200056513  4.745710e+11  https://t1.ftcdn.net/jpg/00/75/08/12/240_F_750...   \n",
       "1200090089  1.129810e+11  https://farm6.staticflickr.com/5132/5411062185...   \n",
       "1200091068  4.145784e+12  https://theplaidporticodotcom4.files.wordpress...   \n",
       "\n",
       "                                        TEXT  HEIGHT  WIDTH LICENSE      NSFW  \\\n",
       "1200056513  Colorful patchwork quilt pattern   373.0  240.0       ?  UNLIKELY   \n",
       "1200090089                  basket quilt top   320.0  285.0       ?  UNLIKELY   \n",
       "1200091068            QuiltCon charity quilt   584.0  820.0       ?  UNLIKELY   \n",
       "\n",
       "            similarity                           a_photo_of_name_def_wnid  \\\n",
       "1200056513    0.328658  a photo of quilt which is bedding made of two ...   \n",
       "1200090089    0.310124  a photo of quilt which is bedding made of two ...   \n",
       "1200091068    0.307640  a photo of quilt which is bedding made of two ...   \n",
       "\n",
       "            text_to_a_photo_of_name_def_wnid_similarity  ...  \\\n",
       "1200056513                                     0.805882  ...   \n",
       "1200090089                                     0.823052  ...   \n",
       "1200091068                                     0.846488  ...   \n",
       "\n",
       "           top_5_is_correct_beit-large-patch16-224-pt22k-ft22k  \\\n",
       "1200056513                                               True    \n",
       "1200090089                                               True    \n",
       "1200091068                                               None    \n",
       "\n",
       "            top_1_is_correct_beit-base-patch16-224  \\\n",
       "1200056513                                    True   \n",
       "1200090089                                    True   \n",
       "1200091068                                    None   \n",
       "\n",
       "           top_5_is_correct_beit-base-patch16-224  \\\n",
       "1200056513                                   True   \n",
       "1200090089                                   True   \n",
       "1200091068                                   None   \n",
       "\n",
       "            top_1_is_correct_beit-base-patch16-384  \\\n",
       "1200056513                                    True   \n",
       "1200090089                                    True   \n",
       "1200091068                                    None   \n",
       "\n",
       "            top_5_is_correct_beit-base-patch16-384  \\\n",
       "1200056513                                    True   \n",
       "1200090089                                    True   \n",
       "1200091068                                    None   \n",
       "\n",
       "           top_1_is_correct_beit-large-patch16-224  \\\n",
       "1200056513                                    True   \n",
       "1200090089                                    True   \n",
       "1200091068                                    None   \n",
       "\n",
       "           top_5_is_correct_beit-large-patch16-224  \\\n",
       "1200056513                                    True   \n",
       "1200090089                                    True   \n",
       "1200091068                                    None   \n",
       "\n",
       "           top_1_is_correct_beit-large-patch16-384  \\\n",
       "1200056513                                    True   \n",
       "1200090089                                    True   \n",
       "1200091068                                    None   \n",
       "\n",
       "           top_5_is_correct_beit-large-patch16-384 image_to_text_similarity  \n",
       "1200056513                                    True                 0.325056  \n",
       "1200090089                                    True                 0.314476  \n",
       "1200091068                                    None                      NaN  \n",
       "\n",
       "[3 rows x 88 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_parquet(\n",
    "    os.path.join(laion_path, laion_file_name)\n",
    ")\n",
    "\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load ILSVRC val set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 74)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_to_name_def_wnid_similarity</th>\n",
       "      <th>name_def_wnid</th>\n",
       "      <th>top_1_is_correct_ResNet-50-1k</th>\n",
       "      <th>top_5_is_correct_ResNet-50-1k</th>\n",
       "      <th>top_1_is_correct_ConvNeXT-1k</th>\n",
       "      <th>top_5_is_correct_ConvNeXT-1k</th>\n",
       "      <th>top_1_is_correct_ConvNeXT-21k-1k</th>\n",
       "      <th>top_5_is_correct_ConvNeXT-21k-1k</th>\n",
       "      <th>top_1_is_correct_ViT-21k-1k</th>\n",
       "      <th>top_5_is_correct_ViT-21k-1k</th>\n",
       "      <th>...</th>\n",
       "      <th>top_1_is_correct_beit-large-patch16-224-pt22k-ft22k</th>\n",
       "      <th>top_5_is_correct_beit-large-patch16-224-pt22k-ft22k</th>\n",
       "      <th>top_1_is_correct_beit-base-patch16-224</th>\n",
       "      <th>top_5_is_correct_beit-base-patch16-224</th>\n",
       "      <th>top_1_is_correct_beit-base-patch16-384</th>\n",
       "      <th>top_5_is_correct_beit-base-patch16-384</th>\n",
       "      <th>top_1_is_correct_beit-large-patch16-224</th>\n",
       "      <th>top_5_is_correct_beit-large-patch16-224</th>\n",
       "      <th>top_1_is_correct_beit-large-patch16-384</th>\n",
       "      <th>top_5_is_correct_beit-large-patch16-384</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ILSVRC2012_val_00049998.JPEG</th>\n",
       "      <td>0.305767</td>\n",
       "      <td>border collie which is developed in the area b...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ILSVRC2012_val_00049999.JPEG</th>\n",
       "      <td>0.251934</td>\n",
       "      <td>groom which is a man participant in his own ma...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ILSVRC2012_val_00050000.JPEG</th>\n",
       "      <td>0.303315</td>\n",
       "      <td>llama which is wild or domesticated South Amer...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "                              image_to_name_def_wnid_similarity  \\\n",
       "ILSVRC2012_val_00049998.JPEG                           0.305767   \n",
       "ILSVRC2012_val_00049999.JPEG                           0.251934   \n",
       "ILSVRC2012_val_00050000.JPEG                           0.303315   \n",
       "\n",
       "                                                                  name_def_wnid  \\\n",
       "ILSVRC2012_val_00049998.JPEG  border collie which is developed in the area b...   \n",
       "ILSVRC2012_val_00049999.JPEG  groom which is a man participant in his own ma...   \n",
       "ILSVRC2012_val_00050000.JPEG  llama which is wild or domesticated South Amer...   \n",
       "\n",
       "                             top_1_is_correct_ResNet-50-1k  \\\n",
       "ILSVRC2012_val_00049998.JPEG                         False   \n",
       "ILSVRC2012_val_00049999.JPEG                          True   \n",
       "ILSVRC2012_val_00050000.JPEG                          True   \n",
       "\n",
       "                             top_5_is_correct_ResNet-50-1k  \\\n",
       "ILSVRC2012_val_00049998.JPEG                          True   \n",
       "ILSVRC2012_val_00049999.JPEG                          True   \n",
       "ILSVRC2012_val_00050000.JPEG                          True   \n",
       "\n",
       "                             top_1_is_correct_ConvNeXT-1k  \\\n",
       "ILSVRC2012_val_00049998.JPEG                        False   \n",
       "ILSVRC2012_val_00049999.JPEG                         True   \n",
       "ILSVRC2012_val_00050000.JPEG                         True   \n",
       "\n",
       "                             top_5_is_correct_ConvNeXT-1k  \\\n",
       "ILSVRC2012_val_00049998.JPEG                         True   \n",
       "ILSVRC2012_val_00049999.JPEG                         True   \n",
       "ILSVRC2012_val_00050000.JPEG                         True   \n",
       "\n",
       "                             top_1_is_correct_ConvNeXT-21k-1k  \\\n",
       "ILSVRC2012_val_00049998.JPEG                             True   \n",
       "ILSVRC2012_val_00049999.JPEG                             True   \n",
       "ILSVRC2012_val_00050000.JPEG                             True   \n",
       "\n",
       "                             top_5_is_correct_ConvNeXT-21k-1k  \\\n",
       "ILSVRC2012_val_00049998.JPEG                             True   \n",
       "ILSVRC2012_val_00049999.JPEG                             True   \n",
       "ILSVRC2012_val_00050000.JPEG                             True   \n",
       "\n",
       "                             top_1_is_correct_ViT-21k-1k  \\\n",
       "ILSVRC2012_val_00049998.JPEG                       False   \n",
       "ILSVRC2012_val_00049999.JPEG                        True   \n",
       "ILSVRC2012_val_00050000.JPEG                        True   \n",
       "\n",
       "                             top_5_is_correct_ViT-21k-1k  ...  \\\n",
       "ILSVRC2012_val_00049998.JPEG                        True  ...   \n",
       "ILSVRC2012_val_00049999.JPEG                        True  ...   \n",
       "ILSVRC2012_val_00050000.JPEG                        True  ...   \n",
       "\n",
       "                             top_1_is_correct_beit-large-patch16-224-pt22k-ft22k  \\\n",
       "ILSVRC2012_val_00049998.JPEG                                              False    \n",
       "ILSVRC2012_val_00049999.JPEG                                              False    \n",
       "ILSVRC2012_val_00050000.JPEG                                               True    \n",
       "\n",
       "                             top_5_is_correct_beit-large-patch16-224-pt22k-ft22k  \\\n",
       "ILSVRC2012_val_00049998.JPEG                                               True    \n",
       "ILSVRC2012_val_00049999.JPEG                                              False    \n",
       "ILSVRC2012_val_00050000.JPEG                                               True    \n",
       "\n",
       "                             top_1_is_correct_beit-base-patch16-224  \\\n",
       "ILSVRC2012_val_00049998.JPEG                                   True   \n",
       "ILSVRC2012_val_00049999.JPEG                                   True   \n",
       "ILSVRC2012_val_00050000.JPEG                                   True   \n",
       "\n",
       "                             top_5_is_correct_beit-base-patch16-224  \\\n",
       "ILSVRC2012_val_00049998.JPEG                                   True   \n",
       "ILSVRC2012_val_00049999.JPEG                                   True   \n",
       "ILSVRC2012_val_00050000.JPEG                                   True   \n",
       "\n",
       "                             top_1_is_correct_beit-base-patch16-384  \\\n",
       "ILSVRC2012_val_00049998.JPEG                                   True   \n",
       "ILSVRC2012_val_00049999.JPEG                                   True   \n",
       "ILSVRC2012_val_00050000.JPEG                                   True   \n",
       "\n",
       "                             top_5_is_correct_beit-base-patch16-384  \\\n",
       "ILSVRC2012_val_00049998.JPEG                                   True   \n",
       "ILSVRC2012_val_00049999.JPEG                                   True   \n",
       "ILSVRC2012_val_00050000.JPEG                                   True   \n",
       "\n",
       "                             top_1_is_correct_beit-large-patch16-224  \\\n",
       "ILSVRC2012_val_00049998.JPEG                                    True   \n",
       "ILSVRC2012_val_00049999.JPEG                                    True   \n",
       "ILSVRC2012_val_00050000.JPEG                                    True   \n",
       "\n",
       "                             top_5_is_correct_beit-large-patch16-224  \\\n",
       "ILSVRC2012_val_00049998.JPEG                                    True   \n",
       "ILSVRC2012_val_00049999.JPEG                                    True   \n",
       "ILSVRC2012_val_00050000.JPEG                                    True   \n",
       "\n",
       "                             top_1_is_correct_beit-large-patch16-384  \\\n",
       "ILSVRC2012_val_00049998.JPEG                                   False   \n",
       "ILSVRC2012_val_00049999.JPEG                                    True   \n",
       "ILSVRC2012_val_00050000.JPEG                                    True   \n",
       "\n",
       "                             top_5_is_correct_beit-large-patch16-384  \n",
       "ILSVRC2012_val_00049998.JPEG                                    True  \n",
       "ILSVRC2012_val_00049999.JPEG                                    True  \n",
       "ILSVRC2012_val_00050000.JPEG                                    True  \n",
       "\n",
       "[3 rows x 74 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ilsvrc_val_df = pd.read_parquet(os.path.join(ilsvrc_path, ilsvrc_val_file_name))\n",
    "\n",
    "print(ilsvrc_val_df.shape)\n",
    "\n",
    "ilsvrc_val_df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(laion_labels_path, wnid2laionindices_file_name), 'rb') as f:\n",
    "    wnid2laionindices = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(imagename2wnid_path, 'rb') as f:\n",
    "    imagename2wnid = pickle.load(f)\n",
    "    \n",
    "# Find the inverse map\n",
    "wnid2imagenames = {}\n",
    "for image_name, wnid in imagename2wnid.items():\n",
    "    if image_name not in ilsvrc_val_df.index:\n",
    "        continue\n",
    "    \n",
    "    if wnid not in wnid2imagenames:\n",
    "        wnid2imagenames[wnid] = []\n",
    "    wnid2imagenames[wnid].append(image_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calc. accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find ILSVRC val. accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "topk2model2wnid2ilsvrcrecall = {1: {}, 5: {}}\n",
    "\n",
    "for k in [1, 5]:\n",
    "    for model_name in model_names:\n",
    "        topk2model2wnid2ilsvrcrecall[k][model_name] = {}\n",
    "        for wnid, image_names in wnid2imagenames.items():\n",
    "            vals = np.array(ilsvrc_val_df.loc[image_names, top_k_col(k, model_name)].tolist())\n",
    "            mask = vals != None\n",
    "            \n",
    "            topk2model2wnid2ilsvrcrecall[k][model_name][wnid] = np.mean(vals[mask])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_recall_per_wnid(k, txt_query_sim_th, img_query_sim_th, n_q_res):\n",
    "    mdl2wnid2recall = {}\n",
    "    mdl2wnid2recallse = {}  # standard err\n",
    "\n",
    "    for model_name in model_names:\n",
    "        mdl2wnid2recall[model_name] = {}\n",
    "        mdl2wnid2recallse[model_name] = {}\n",
    "\n",
    "        for wnid, indices in wnid2laionindices.items():\n",
    "            indices = np.array(indices)\n",
    "            txt_query_sims = np.array(df.loc[indices, text_query_sim_col].tolist())\n",
    "            img_query_sims = np.array(df.loc[indices, image_query_sim_col].tolist())\n",
    "            \n",
    "            # Filter based on text-to-query similarity\n",
    "            filt_tq = txt_query_sims > txt_query_sim_th\n",
    "            \n",
    "            # Filter based on image-to-query similarity\n",
    "            filt_iq = img_query_sims > img_query_sim_th\n",
    "            \n",
    "            # Filter\n",
    "            filt = np.logical_and(filt_tq, filt_iq)\n",
    "            indices = indices[filt]\n",
    "            txt_query_sims = txt_query_sims[filt]\n",
    "\n",
    "            # Keep top n_q_res\n",
    "            top_results = np.argsort(txt_query_sims)[-n_q_res:]\n",
    "            indices = indices[top_results]\n",
    "\n",
    "            # Calc. recall\n",
    "            col_list = df.loc[indices, top_k_col(k, model_name)].to_numpy()\n",
    "            \n",
    "            mask = col_list != None\n",
    "            \n",
    "            n = np.sum(mask)\n",
    "\n",
    "            if n == 0:\n",
    "                mdl2wnid2recall[model_name][wnid] = np.nan\n",
    "                mdl2wnid2recallse[model_name][wnid] = np.nan\n",
    "                continue\n",
    "\n",
    "            count = np.sum(col_list[mask])\n",
    "\n",
    "            recall = count/n\n",
    "\n",
    "            mdl2wnid2recall[model_name][wnid] = recall\n",
    "            ci_l, ci_u = proportion_confint(count, n, method='wilson', alpha=alpha)\n",
    "            mdl2wnid2recallse[model_name][wnid] = (ci_u - ci_l)/2/z\n",
    "                \n",
    "    return mdl2wnid2recall, mdl2wnid2recallse\n",
    "\n",
    "\n",
    "def combine_recalls(means, ses, w):\n",
    "    nan_filt = ~np.isnan(means)\n",
    "    means = means[nan_filt]\n",
    "    ses = ses[nan_filt]\n",
    "    w = w[nan_filt]\n",
    "    \n",
    "    mean = np.sum(means * w)/np.sum(w)\n",
    "    se = np.sqrt(np.sum((ses * w)**2))/np.sum(w)\n",
    "    \n",
    "    return mean, se\n",
    "\n",
    "\n",
    "def calc_equi_acc(mdl2wnid2recall, mdl2wnid2recallse):\n",
    "    mdl2acc = {}\n",
    "    mdl2nwnid = {}\n",
    "    for model_name in model_names:\n",
    "        recalls = []\n",
    "        recallses = []\n",
    "        for wnid, recall in mdl2wnid2recall[model_name].items():\n",
    "            recalls.append(recall)\n",
    "            recallses.append(mdl2wnid2recallse[model_name][wnid])\n",
    "\n",
    "        recalls = np.array(recalls)\n",
    "        recallses = np.array(recallses)\n",
    "\n",
    "        acc, acc_se = combine_recalls(recalls, recallses, np.ones(recalls.shape))\n",
    "\n",
    "        mdl2acc[model_name] = {\n",
    "            'acc': acc, \n",
    "            'acc_se': acc_se,\n",
    "        }\n",
    "        \n",
    "        mdl2nwnid[model_name] = np.sum(~np.isnan(recalls))\n",
    "            \n",
    "    return mdl2acc, mdl2nwnid\n",
    "\n",
    "\n",
    "def calc_ilsvrc_val_acc(mdl2wnid2recall):\n",
    "    mdl2ilsvrcacc ={} \n",
    "\n",
    "    for model_name in mdl2wnid2recall:\n",
    "        wnids = [wnid for wnid, recall in mdl2wnid2recall[model_name].items() if not np.isnan(recall)]\n",
    "        recalls = []\n",
    "        for wnid in wnids:\n",
    "            if wnid not in topk2model2wnid2ilsvrcrecall[top_k][model_name]:\n",
    "                continue\n",
    "\n",
    "            recalls.append(topk2model2wnid2ilsvrcrecall[top_k][model_name][wnid])\n",
    "\n",
    "        mdl2ilsvrcacc[model_name] = np.mean(recalls)\n",
    "        \n",
    "    return mdl2ilsvrcacc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_query_sim_th = 0.82  # 0.82\n",
    "image_query_sim_th = 0\n",
    "n_q_result = 0  # 0 means all\n",
    "\n",
    "top_k = 5\n",
    "\n",
    "alpha = 0.05\n",
    "z = 1.96"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calc. recall per WNID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model2wnid2recall, model2wnid2recallse = \\\n",
    "    calc_recall_per_wnid(\n",
    "        top_k, \n",
    "        text_query_sim_th, \n",
    "        image_query_sim_th,\n",
    "        n_q_result\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calc. accuracy with equi-weighted classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2acc, model2nwnid = calc_equi_acc(model2wnid2recall, model2wnid2recallse)\n",
    "\n",
    "model2ilsvrcacc = calc_ilsvrc_val_acc(model2wnid2recall)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot accuracy vs. papers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 320x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3.2, 3.2))\n",
    "\n",
    "min_acc, max_acc = np.inf, -np.inf\n",
    "modelname2handle = {}\n",
    "for model_name in model_names:\n",
    "    cl = modelname2color[model_name]\n",
    "\n",
    "    acc = model2acc[model_name]['acc']\n",
    "    acc_se = model2acc[model_name]['acc_se']\n",
    "\n",
    "    h = plt.plot(acc, model2ilsvrcacc[model_name], 'o', color=cl)\n",
    "    plt.errorbar(\n",
    "        acc, \n",
    "        model2ilsvrcacc[model_name],\n",
    "        xerr=acc_se*z, \n",
    "        capsize=6,\n",
    "        color=cl\n",
    "    )\n",
    "\n",
    "    min_acc = np.minimum(min_acc, acc)\n",
    "    max_acc = np.maximum(max_acc, acc)\n",
    "\n",
    "    modelname2handle[model_name] = h[0]\n",
    "\n",
    "\n",
    "h = plt.plot([min_acc, max_acc], [min_acc, max_acc], 'k--')\n",
    "\n",
    "plt.legend(\n",
    "    handles=[\n",
    "        modelname2handle[all_model_names_1k[0]],\n",
    "        modelname2handle[all_model_names_pt21k_ft1k[0]],\n",
    "        modelname2handle[all_model_names_21k[0]],\n",
    "        h[0]\n",
    "    ],\n",
    "    labels=['1k models', 'pt22k-ft1k models', 'pt22k models', 'y=x'],\n",
    "    loc='lower right',\n",
    "    fontsize=8\n",
    ")\n",
    "\n",
    "plt.title(f'Top {top_k} accuracy')\n",
    "plt.xlabel('LAIONet \\n (equi-weighted classes)')\n",
    "plt.ylabel('ImageNet \\n (validation set)')\n",
    "\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(os.path.join('figs', 'top%d_equi-acc_vs_ilsvrc-val-acc(tqsim%.2f).pdf' % (top_k, text_query_sim_th)), bbox_inches='tight')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calc. accuracy per text-to-query similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "text_query_sim_ths = np.linspace(0.82, 0.9, 15)\n",
    "chosen_image_query_sim_th = 0\n",
    "chosen_n_q_result = 0\n",
    "\n",
    "tqsim2model2acc = {}\n",
    "tqsim2model2ilsvrcacc = {}\n",
    "tqsim2model2nwnid = {}\n",
    "\n",
    "for tqsim in tqdm(text_query_sim_ths):    \n",
    "    model2wnid2recall_i, model2wnid2recallse_i = \\\n",
    "        calc_recall_per_wnid(\n",
    "            top_k, \n",
    "            tqsim,\n",
    "            chosen_image_query_sim_th,\n",
    "            chosen_n_q_result\n",
    "        )\n",
    "    \n",
    "    tqsim2model2acc[tqsim], tqsim2model2nwnid[tqsim] = calc_equi_acc(model2wnid2recall_i, model2wnid2recallse_i)\n",
    "    tqsim2model2ilsvrcacc[tqsim] = calc_ilsvrc_val_acc(model2wnid2recall_i)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot acc. vs. text-query sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(5, 4))\n",
    "\n",
    "chosen_model_names = ['convnext-base-224', 'convnext-base-224-22k-1k', 'convnext-base-224-22k']\n",
    "\n",
    "handles = []\n",
    "for model_name in chosen_model_names:\n",
    "    cl = modelname2color[model_name]\n",
    "    \n",
    "    accs = np.array([tqsim2model2acc[tqsim][model_name]['acc'] for tqsim in text_query_sim_ths])\n",
    "    acc_ses = np.array([tqsim2model2acc[tqsim][model_name]['acc_se'] for tqsim in text_query_sim_ths])\n",
    "    \n",
    "    h = plt.plot(text_query_sim_ths, accs, color=cl)\n",
    "    plt.fill_between(text_query_sim_ths, accs + acc_ses*z, accs - acc_ses*z, color=cl, alpha=0.1, label='')\n",
    "    \n",
    "    handles.extend(h)\n",
    "    \n",
    "    # Plot the ILSVRC performance\n",
    "    ilsvrc_accs = np.array([tqsim2model2ilsvrcacc[tqsim][model_name] for tqsim in text_query_sim_ths])\n",
    "    \n",
    "    plt.plot(text_query_sim_ths, ilsvrc_accs, '--', color=cl)\n",
    "\n",
    "    \n",
    "plt.legend(handles=handles, labels=chosen_model_names)\n",
    "\n",
    "plt.xlabel('min. text-query CLIP similarity')\n",
    "plt.ylabel(f'top{top_k} accuracy (LAION, equi-weighted classes)')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(os.path.join('figs', 'top%d_equi-acc_vs_tqsim.pdf' % top_k))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot number of classes vs. text-query sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(5, 4))\n",
    "\n",
    "for model_name in chosen_model_names:\n",
    "    cl = modelname2color[model_name]\n",
    "    \n",
    "    nwnids = np.array([tqsim2model2nwnid[tqsim][model_name] for tqsim in text_query_sim_ths])\n",
    "\n",
    "    plt.plot(text_query_sim_ths, nwnids, color=cl)\n",
    "        \n",
    "plt.legend(chosen_model_names)\n",
    "\n",
    "plt.xlabel('min. image-image CLIP similarity')\n",
    "plt.ylabel('num. classes with at least one example')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(os.path.join('figs', 'nwnid_vs_tqsim.pdf'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Relative frequency of the classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = np.sum([len(laion_indices) for _, laion_indices in wnid2laionindices.items()])\n",
    "\n",
    "wnid2freq = {wnid: len(laion_indices) for wnid, laion_indices in wnid2laionindices.items() if len(laion_indices) > 0}\n",
    "\n",
    "# Sort it by freq\n",
    "wnid2freq = dict(sorted(wnid2freq.items(), key=lambda item: -item[1]))\n",
    "\n",
    "# Find prob.\n",
    "wnid2prob = {wnid: freq/n for wnid, freq in wnid2freq.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calc. stat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "wnids = list(wnid2freq.keys())\n",
    "\n",
    "synsets = []\n",
    "freqs = []\n",
    "for wnid in wnids:\n",
    "    synsets.append(wnu.get_synset(wnid).name().split('.n')[0])\n",
    "    freqs.append(wnid2freq[wnid])\n",
    "\n",
    "imagenet_tot = np.sum(freqs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of roughly 400M LAION samples, 879221 (0.22%) of them have at least one ImageNet class.\n"
     ]
    }
   ],
   "source": [
    "print('Out of roughly 400M LAION samples, %d (%.2f%%) of them have at least one ImageNet class.'\n",
    "      % (imagenet_tot, imagenet_tot/4e8*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot freq."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 3))\n",
    "\n",
    "freqs = freqs[:500]\n",
    "synsets = synsets[:500]\n",
    "\n",
    "n = len(freqs)\n",
    "\n",
    "plt.bar(synsets, freqs/imagenet_tot, color='b', alpha=0.5)\n",
    "plt.plot([0, n - 1], [1/n]*2, 'r--', alpha=0.5)\n",
    "\n",
    "plt.ylabel('relative frequency')\n",
    "# plt.xlabel('synsets (sampled)', fontsize=18)\n",
    "\n",
    "plt.xticks(rotation=-50, ha='left', rotation_mode='anchor', fontsize=8)\n",
    "\n",
    "ax = plt.gca()\n",
    "xticks = ax.xaxis.get_major_ticks()\n",
    "for i_x, xtick in enumerate(xticks):\n",
    "    if i_x % 20 != 0:\n",
    "        xtick.set_visible(False)\n",
    "        \n",
    "plt.yscale('log')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(os.path.join('figs', 'relative_freq(all).pdf'), bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accuracy vs. frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0.82 0 0\n"
     ]
    }
   ],
   "source": [
    "print(top_k, text_query_sim_th, image_query_sim_th, n_q_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2wnid2recall, model2wnid2recallse = \\\n",
    "    calc_recall_per_wnid(\n",
    "        top_k, \n",
    "        text_query_sim_th, \n",
    "        image_query_sim_th,\n",
    "        n_q_result\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "chosen_model_names = [\n",
    "    'resnet-50',\n",
    "    'convnext-base-224',\n",
    "    'convnext-base-224-22k-1k',\n",
    "    'vit-base-patch16-224', \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_m = len(chosen_model_names)\n",
    "\n",
    "plt.figure(figsize=(n_m*2.2, 2))\n",
    "\n",
    "for i_m, model_name in enumerate(chosen_model_names):\n",
    "    recalls = []\n",
    "    probs = []\n",
    "    for wnid, prob in wnid2prob.items():\n",
    "        if wnid2freq[wnid] < 30:\n",
    "            continue\n",
    "        recalls.append(model2wnid2recall[model_name][wnid])\n",
    "        probs.append(prob)\n",
    "        \n",
    "    plt.subplot(1, n_m, i_m + 1)\n",
    "    cl = modelname2color[model_name]\n",
    "    \n",
    "    plt.plot(probs, recalls, 'o', alpha=0.1, color=cl)\n",
    "    \n",
    "    plt.legend([model_name], fontsize=7)\n",
    "    plt.xlabel('relative frequency')\n",
    "    plt.ylabel('recall@%d' % top_k)\n",
    "\n",
    "    plt.xscale('log')\n",
    "    \n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(os.path.join('figs', 'top%d_acc_vs_rel_freq.pdf' % top_k))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accuracy vs. intra-class similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load sims and find the average sim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_image_sim_prefix = 'img_img_sims'\n",
    "cross_image_sim_path = os.path.join('laion400m', 'processed', 'clip_image_similarities')\n",
    "\n",
    "laion_prefix = configs.LAIONConfig.SUBSET_SM_FILTERED_PREFIX\n",
    "ilsvrc_prefix = 'imagenet_captions_'\n",
    "\n",
    "sim_key = 'similarities'\n",
    "\n",
    "def vec(x):\n",
    "    msk = np.tril(np.eye(len(x)) < 0.5)\n",
    "    return x[msk]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 978/978 [00:03<00:00, 278.17it/s]\n"
     ]
    }
   ],
   "source": [
    "wnid2avglsim = {}\n",
    "wnid2avgisim = {}\n",
    "for wnid in tqdm(wnid2laionindices):\n",
    "    l_sim_path = os.path.join(cross_image_sim_path, laion_prefix + cross_image_sim_prefix + f'({wnid}).pkl')\n",
    "    i_sim_path = os.path.join(cross_image_sim_path, ilsvrc_prefix + cross_image_sim_prefix + f'({wnid}).pkl')\n",
    "    \n",
    "    if (not os.path.exists(l_sim_path)) or (not os.path.exists(i_sim_path)):\n",
    "        continue\n",
    "\n",
    "    with open(l_sim_path, 'rb') as f:\n",
    "        l_sim_dict = pickle.load(f)\n",
    "    with open(i_sim_path, 'rb') as f:\n",
    "        i_sim_dict = pickle.load(f)\n",
    "        \n",
    "    if (not isinstance(l_sim_dict, dict)):\n",
    "        continue\n",
    "        \n",
    "    wnid2avglsim[wnid] = np.mean(vec(l_sim_dict[sim_key]))\n",
    "    wnid2avgisim[wnid] = np.mean(vec(i_sim_dict[sim_key]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_m = len(chosen_model_names)\n",
    "\n",
    "plt.figure(figsize=(n_m*2.2, 2))\n",
    "\n",
    "for i_m, model_name in enumerate(chosen_model_names):\n",
    "    recalls = []\n",
    "    sims = []\n",
    "    for wnid, sim in wnid2avglsim.items():\n",
    "        if wnid not in model2wnid2recall[model_name]:\n",
    "            continue\n",
    "            \n",
    "        recalls.append(model2wnid2recall[model_name][wnid])\n",
    "        sims.append(sim - wnid2avgisim[wnid])\n",
    "        \n",
    "    plt.subplot(1, n_m, i_m + 1)\n",
    "    cl = modelname2color[model_name]\n",
    "    \n",
    "    plt.plot(sims, recalls, 'o', alpha=0.1, color=cl)\n",
    "    \n",
    "    plt.legend([model_name], fontsize=7)\n",
    "    plt.xlabel(r'$\\Delta$ intra-class similarity')\n",
    "    plt.ylabel('recall@%d' % top_k)\n",
    "    \n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(os.path.join('figs', 'top%d_acc_vs_intra-class_sim.pdf' % top_k))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intra-class similarity vs. frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 275x275 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_m = len(chosen_model_names)\n",
    "\n",
    "plt.figure(figsize=(2.75, 2.75))\n",
    "\n",
    "probs = []\n",
    "sims = []\n",
    "for wnid, sim in wnid2avglsim.items():\n",
    "    probs.append(wnid2prob[wnid])\n",
    "    sims.append(sim - 0*wnid2avgisim[wnid])\n",
    "\n",
    "plt.plot(sims, probs, 'o', alpha=0.1, color='b')\n",
    "\n",
    "plt.xlabel('LAIONet intra-class similarity')\n",
    "plt.ylabel('relative frequency')\n",
    "\n",
    "plt.yscale('log')\n",
    "    \n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(os.path.join('figs', 'intra-class_sim_vs_rel_freq.pdf'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=-0.2645336715916952, pvalue=7.619530816017947e-16)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spearmanr(np.log(probs), sims)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LAION-weighted accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0000000000000002\n"
     ]
    }
   ],
   "source": [
    "# Sanity check\n",
    "print(np.sum([prob for _, prob in wnid2prob.items()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_laion_acc(mdl2wnid2recall, mdl2wnid2recallse, wnid2p):\n",
    "    mdl2acc = {}\n",
    "    mdl2nwnid = {}\n",
    "    for model_name in model_names:\n",
    "        recalls = []\n",
    "        recallses = []\n",
    "        weights = []\n",
    "        for wnid, recall in mdl2wnid2recall[model_name].items():\n",
    "            recalls.append(recall)\n",
    "            recallses.append(mdl2wnid2recallse[model_name][wnid])\n",
    "            weights.append(wnid2p.get(wnid, 0))\n",
    "\n",
    "        recalls = np.array(recalls)\n",
    "        recallses = np.array(recallses)\n",
    "        weights = np.array(weights)\n",
    "\n",
    "        acc, acc_se = combine_recalls(recalls, recallses, weights)\n",
    "\n",
    "        mdl2acc[model_name] = {\n",
    "            'acc': acc, \n",
    "            'acc_se': acc_se,\n",
    "        }\n",
    "        \n",
    "        mdl2nwnid[model_name] = np.sum(~np.isnan(recalls))\n",
    "            \n",
    "    return mdl2acc, mdl2nwnid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calc. acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0.82 0 0\n"
     ]
    }
   ],
   "source": [
    "print(top_k, text_query_sim_th, image_query_sim_th, n_q_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2wnid2recall, model2wnid2recallse = \\\n",
    "    calc_recall_per_wnid(\n",
    "        top_k, \n",
    "        text_query_sim_th, \n",
    "        image_query_sim_th,\n",
    "        n_q_result\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2acc, model2nwnid = calc_equi_acc(model2wnid2recall, model2wnid2recallse)\n",
    "\n",
    "model2laionacc, _ = calc_laion_acc(model2wnid2recall, model2wnid2recallse, wnid2prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot LAION-weighted accuracy vs. equi-weighted accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 320x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3.2, 3.2))\n",
    "\n",
    "min_acc, max_acc = np.inf, -np.inf\n",
    "modelname2handle = {}\n",
    "for model_name in model_names:\n",
    "    cl = modelname2color[model_name]\n",
    "\n",
    "    acc = model2acc[model_name]['acc']\n",
    "    acc_se = model2acc[model_name]['acc_se']\n",
    "    \n",
    "    acc_laion = model2laionacc[model_name]['acc']\n",
    "    acc_laion_se = model2laionacc[model_name]['acc_se']\n",
    "\n",
    "    h = plt.plot(acc, acc_laion, 'o', color=cl)\n",
    "    plt.errorbar(\n",
    "        acc, \n",
    "        acc_laion,\n",
    "        xerr=acc_se*z, \n",
    "        yerr=acc_laion_se*z,\n",
    "        capsize=6,\n",
    "        color=cl\n",
    "    )\n",
    "\n",
    "    min_acc = np.minimum(min_acc, acc)\n",
    "    max_acc = np.maximum(max_acc, acc)\n",
    "\n",
    "    modelname2handle[model_name] = h[0]\n",
    "\n",
    "\n",
    "h = plt.plot([min_acc, max_acc], [min_acc, max_acc], 'k--')\n",
    "\n",
    "plt.legend(\n",
    "    handles=[\n",
    "        modelname2handle[all_model_names_1k[0]],\n",
    "        modelname2handle[all_model_names_pt21k_ft1k[0]],\n",
    "        modelname2handle[all_model_names_21k[0]],\n",
    "        h[0]\n",
    "    ],\n",
    "    labels=['1k models', 'pt22k-ft1k models', 'pt22k models', 'y=x'],\n",
    "    loc='upper left',\n",
    "    fontsize=8\n",
    ")\n",
    "\n",
    "plt.title(f'Top {top_k} accuracy')\n",
    "plt.xlabel('LAIONet \\n (equi-weighted classes)')\n",
    "plt.ylabel('LAIONet \\n (LAION-weighted classes)')\n",
    "\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(os.path.join('figs', 'top%d_natural-acc_vs_equi-acc(tqsim%.2f).pdf' % (top_k, text_query_sim_th)), bbox_inches='tight')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "laionvenv",
   "language": "python",
   "name": "laionvenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}