CIFAR2/methods/04_if/get_indices_gen.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c58e928-3b34-44c8-894a-458bff33d6e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "245db4ad-9e11-41df-8fca-0bab14c60e25",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0737dcf0-06e0-4f2b-afb2-77f4d2225706",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "641323a8-3ea1-4e42-9e95-4d5da7cf95fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "561527f8-aff9-4445-9c7e-47fa01b2676e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"   \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='0'\n",
    "\n",
    "os.environ[\"HF_HOME\"]=\"~/codes/.cache/huggingface\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0e2fef04-47f7-4e77-9328-e51281539152",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "af2782e7-72c5-4287-8100-fb615760485c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def show_images(images, scores, test_artist, train_artists):\n",
    "    n: int = len(images)\n",
    "    f = plt.figure(figsize=(16, 2))\n",
    "    for i in range(n):\n",
    "        # Debug, plot figure\n",
    "        ax = f.add_subplot(1, n, i + 1)\n",
    "        if i==0:\n",
    "            pass\n",
    "            ax.title.set_text(test_artist)\n",
    "        else:\n",
    "            ax.title.set_text(str(np.round(scores[i-1], 4))+'\\n'+train_artists[i-1])\n",
    "            ax.axis('off')\n",
    "        if images[i]==None:\n",
    "            pass\n",
    "        else:\n",
    "            plt.imshow(images[i])\n",
    "\n",
    "    plt.show(block=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "41675e7f-8365-4f94-a0ba-4a3c89d3f23c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff902dbc-45c6-442e-be2f-d5a8e65d1355",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0e00606e-2565-4ac8-b6bd-93d4c0bac37d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('../../data/indices/5000-0.5/idx-train.pkl', 'rb')  as handle:\n",
    "    idx_train = pickle.load(handle)\n",
    "len(idx_train)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a012e677-0046-4b39-a2a1-275465caa000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('../../data/indices/5000-0.5/idx-val.pkl', 'rb')  as handle:\n",
    "    idx_val = pickle.load(handle)\n",
    "len(idx_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b8d9cb60-42c0-4559-8496-2e788515ab5b",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'load_dataset' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# from datasets import load_dataset\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mload_dataset\u001b[49m(\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcifar10\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;66;03m# args.dataset_config_name,\u001b[39;00m\n\u001b[1;32m      6\u001b[0m     \u001b[38;5;66;03m# cache_dir=args.cache_dir,\u001b[39;00m\n\u001b[1;32m      7\u001b[0m     \u001b[38;5;66;03m# split=\"train\",\u001b[39;00m\n\u001b[1;32m      8\u001b[0m )\n",
      "\u001b[0;31mNameError\u001b[0m: name 'load_dataset' is not defined"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\n",
    "    'cifar10',\n",
    "    # args.dataset_config_name,\n",
    "    # cache_dir=args.cache_dir,\n",
    "    # split=\"train\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8ab5978-ec28-4d35-a3d2-d54d10c0cdea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import DatasetDict, Dataset, load_dataset, Image\n",
    "\n",
    "train_dataset = dataset['train'].select(idx_train)\n",
    "train_dataset[0][\"img\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd982af9-1790-4399-8f2f-de5baf09115e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_dataset =  dataset['test'].select(idx_val)\n",
    "val_dataset[0][\"img\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1080aec7-09fe-4123-bb26-2ab750860b53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame()\n",
    "df['path'] = ['{}/{}.png'.format('../../saved/5000-0.5/gen', i) for i in range(1000)]\n",
    "\n",
    "from datasets import DatasetDict, Dataset, load_dataset, Image\n",
    "dataset = DatasetDict({\n",
    "\"train\": Dataset.from_dict({\n",
    "    \"img\": df['path'].tolist(),\n",
    "}).cast_column(\"img\", Image()),})\n",
    "gen_dataset = dataset[\"train\"]\n",
    "gen_dataset[0]['img']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc29a64c-cca3-4b54-b147-cfdeeab35be9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "34f3123f-606c-40f0-b85a-df861cab66e1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[521,\n",
       " 737,\n",
       " 740,\n",
       " 660,\n",
       " 411,\n",
       " 678,\n",
       " 626,\n",
       " 513,\n",
       " 859,\n",
       " 136,\n",
       " 811,\n",
       " 76,\n",
       " 636,\n",
       " 973,\n",
       " 938,\n",
       " 899,\n",
       " 280,\n",
       " 883,\n",
       " 761,\n",
       " 319,\n",
       " 549,\n",
       " 174,\n",
       " 371,\n",
       " 527,\n",
       " 210,\n",
       " 235,\n",
       " 101,\n",
       " 986,\n",
       " 902,\n",
       " 947,\n",
       " 346,\n",
       " 139,\n",
       " 621,\n",
       " 499,\n",
       " 370,\n",
       " 198,\n",
       " 687,\n",
       " 584,\n",
       " 901,\n",
       " 59,\n",
       " 328,\n",
       " 96,\n",
       " 312,\n",
       " 974,\n",
       " 299,\n",
       " 277,\n",
       " 924,\n",
       " 601,\n",
       " 439,\n",
       " 837,\n",
       " 570,\n",
       " 879,\n",
       " 261,\n",
       " 578,\n",
       " 23,\n",
       " 30,\n",
       " 617,\n",
       " 10,\n",
       " 221,\n",
       " 820]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "_, test_index = train_test_split(range(1000), test_size=60, random_state=42)\n",
    "test_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d53af53a-8a61-4569-b7bd-4f3fe30417d7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "72ce01c9-1426-400c-86bb-4ffe5f0257cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"../../data/indices/5000-0.5/counter/idx-gen-sampled.pkl\", 'wb') as handle:\n",
    "    pickle.dump(test_index, handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "34e7940e-c1b4-4e62-82ea-1cf787a3326f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[521,\n",
       " 737,\n",
       " 740,\n",
       " 660,\n",
       " 411,\n",
       " 678,\n",
       " 626,\n",
       " 513,\n",
       " 859,\n",
       " 136,\n",
       " 811,\n",
       " 76,\n",
       " 636,\n",
       " 973,\n",
       " 938,\n",
       " 899,\n",
       " 280,\n",
       " 883,\n",
       " 761,\n",
       " 319,\n",
       " 549,\n",
       " 174,\n",
       " 371,\n",
       " 527,\n",
       " 210,\n",
       " 235,\n",
       " 101,\n",
       " 986,\n",
       " 902,\n",
       " 947,\n",
       " 346,\n",
       " 139,\n",
       " 621,\n",
       " 499,\n",
       " 370,\n",
       " 198,\n",
       " 687,\n",
       " 584,\n",
       " 901,\n",
       " 59,\n",
       " 328,\n",
       " 96,\n",
       " 312,\n",
       " 974,\n",
       " 299,\n",
       " 277,\n",
       " 924,\n",
       " 601,\n",
       " 439,\n",
       " 837,\n",
       " 570,\n",
       " 879,\n",
       " 261,\n",
       " 578,\n",
       " 23,\n",
       " 30,\n",
       " 617,\n",
       " 10,\n",
       " 221,\n",
       " 820]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff4dd931-ff12-4ef1-b872-0619333e9bfa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "824bf26e-99dd-4f42-aa04-63e184f24e6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_dataset[test_index[0]]['img']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "df83f924-97c1-4855-a53b-44187af73fe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_dataset[test_index[1]]['img']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e0babcbd-5655-4eae-8a94-14960dbb3f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_dataset[test_index[2]]['img']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0961c88d-c5bd-478d-ae5d-fc96eb5d84b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_dataset[test_index[3]]['img']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b77ea35a-d78e-4c13-8e47-249d7b3b5a31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_dataset[test_index[4]]['img']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac29c8bf-20cc-42f3-82e3-7c559d469526",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9357fae4-9636-4a91-ad77-209629fb1af4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4695603e-b3f0-4ca8-97fd-fcdb298b0473",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bb932ea8-0d72-426b-9eb4-971295bfdb56",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bca3e398-bc9a-438f-bd47-7a34f2daa01a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 5000)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"gen_5000_0.5_loss_100_5000000.0_0.16133635662548987.pkl\", 'rb') as handle:\n",
    "    scores = pickle.load(handle)\n",
    "scores_list.append(scores)\n",
    "scores.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "26da60aa-8030-4bf9-be0c-f96431cbab78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 5000)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"gen_5000_0.5_mean-squared-l2-norm_100_5000000.0_0.2601777247421162.pkl\", 'rb') as handle:\n",
    "    scores = pickle.load(handle)\n",
    "scores_list.append(scores)\n",
    "scores.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c23a1528-6b9c-4ab0-8bc8-66720d521cdc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c45180c1-e3fd-4b71-976f-52b6f3bf9fd2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c4c9e4e-4e90-47e3-ba29-f8d0ba3d8f22",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6ea0894f-d8d0-41f1-b04d-fa65a7b3774f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load ground truth\n",
    "my_list = [\n",
    "    0,1,2,3,\n",
    "    4,5,6,7,\n",
    "    8,9,10,11,\n",
    "    12,13,14,15,\n",
    "    16,17,18,19,\n",
    "    20,21,22,23,\n",
    "    24,25,26,27,\n",
    "    28,29,30,31,\n",
    "    32,33,34,35,\n",
    "    36,37,38,39,\n",
    "    40,41,42,43,\n",
    "    44,45,46,47,\n",
    "    48,49,50,51,\n",
    "    52,53,54,55,\n",
    "    56,57,58,59,\n",
    "    60,61,62,63,\n",
    "          ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "89ea4842-d7a9-4215-a464-25471d7127be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 1000, 1000)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_array_list = []\n",
    "\n",
    "for i in my_list:\n",
    "    for seed in [\n",
    "        0,\n",
    "                 1,\n",
    "                 2,\n",
    "                 # 3,\n",
    "                 # 4,\n",
    "                ]:\n",
    "        for e_seed in [\n",
    "            0, \n",
    "                       1, \n",
    "                       2\n",
    "                      ]:\n",
    "            with open('../../saved/5000-0.5/lds-val/ddpm-sub-{}-{}/e-{}-gen.pkl'.format(i, seed, e_seed), 'rb')  as handle:\n",
    "                loss_list = pickle.load(handle)\n",
    "            margins = np.concatenate(loss_list, axis=-1) # -logp\n",
    "            ####\n",
    "            if (seed==0) and (e_seed)==0:\n",
    "                loss_array = margins\n",
    "            else:\n",
    "                loss_array += margins\n",
    "            \n",
    "    loss_array = loss_array/(3*3)\n",
    "    \n",
    "    loss_array_list.append(loss_array)\n",
    "lds_loss_array = np.stack(loss_array_list)\n",
    "lds_loss_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e71e1ab2-0b85-4dcc-a6a5-480ff9c8b608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    for j in range(8):\n",
    "        plt.plot(lds_loss_array[i, :, j], color=\"C{}\".format(j))\n",
    "    # break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "af28cad4-eb5b-40ca-bcda-dbdd678b416e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 5000)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask_array_list = []\n",
    "\n",
    "for i in my_list:\n",
    "    # print(i)\n",
    "    with open('../../data/indices/5000-0.5/lds-val/sub-idx-{}.pkl'.format(i), 'rb')  as handle:\n",
    "        sub_idx_train = pickle.load(handle)\n",
    "    # print(len(sub_idx_train))\n",
    "    mask_array = np.in1d(idx_train, sub_idx_train)\n",
    "        \n",
    "    mask_array_list.append(mask_array)\n",
    "    \n",
    "lds_mask_array = np.stack(mask_array_list)\n",
    "lds_mask_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f677d157-4449-4abb-9007-ea2aee116eb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 1000)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lds_testset_correctness = lds_loss_array.mean(axis=1)\n",
    "lds_testset_correctness.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8b779648-2442-4313-a237-ef27b79b71ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for j in range(4):\n",
    "    plt.plot(lds_testset_correctness[:, j], color=\"C{}\".format(j))\n",
    "    # break\n",
    "# plt.ylim(0.15, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faf7d1b8-7115-46d0-aa8a-2f01e3026f65",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8a86cfd1-167c-48ed-8d22-f02199d7eaf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation: 0.161 (avg p value 0.299659)\n"
     ]
    }
   ],
   "source": [
    "# compute lds\n",
    "from scipy.stats import spearmanr, pearsonr\n",
    "####\n",
    "margins = lds_testset_correctness\n",
    "infl_est_ = -scores_list[0]\n",
    "# infl_est_ = -tmp\n",
    "preds = lds_mask_array @ infl_est_.T\n",
    "####\n",
    "rs = []\n",
    "ps = []\n",
    "\n",
    "for ind in range(1000):\n",
    "    r, p = spearmanr(preds[:, ind], margins[:, ind])\n",
    "    # r, p = pearsonr(preds[:, ind], margins[:, ind])\n",
    "    rs.append(r)\n",
    "    ps.append(p)\n",
    "    \n",
    "rs, ps = np.array(rs), np.array(ps)\n",
    "print(f'Correlation: {rs.mean():.3f} (avg p value {ps.mean():.6f})')\n",
    "\n",
    "# tmp_1 = rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c8cf0a77-c261-452c-af49-f86a0f0496f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation: 0.198 (avg p value 0.244832)\n"
     ]
    }
   ],
   "source": [
    "# compute lds\n",
    "from scipy.stats import spearmanr, pearsonr\n",
    "####\n",
    "margins = lds_testset_correctness\n",
    "infl_est_ = -scores_list[0]\n",
    "# infl_est_ = -tmp\n",
    "preds = lds_mask_array @ infl_est_.T\n",
    "####\n",
    "rs = []\n",
    "ps = []\n",
    "\n",
    "for ind in test_index:\n",
    "    r, p = spearmanr(preds[:, ind], margins[:, ind])\n",
    "    # r, p = pearsonr(preds[:, ind], margins[:, ind])\n",
    "    rs.append(r)\n",
    "    ps.append(p)\n",
    "    \n",
    "rs, ps = np.array(rs), np.array(ps)\n",
    "print(f'Correlation: {rs.mean():.3f} (avg p value {ps.mean():.6f})')\n",
    "\n",
    "tmp_1 = rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f0cd9ac3-05ff-4729-a705-029ff53894ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00086996,  0.12820513,  0.13379121,  0.08127289,  0.16144689,\n",
       "        0.36767399,  0.03127289,  0.38745421,  0.07783883,  0.46694139,\n",
       "        0.33695055,  0.04184982,  0.06451465,  0.19688645,  0.07637363,\n",
       "        0.19652015,  0.3154304 ,  0.08255495,  0.39674908,  0.27312271,\n",
       "        0.26048535,  0.18956044, -0.0650641 ,  0.29574176,  0.09102564,\n",
       "       -0.1117674 ,  0.2260989 ,  0.03200549,  0.34729853,  0.25444139,\n",
       "        0.0746337 ,  0.33452381,  0.15169414,  0.04015568,  0.24285714,\n",
       "        0.43328755,  0.18173077,  0.24001832,  0.32728938,  0.22371795,\n",
       "        0.09340659,  0.09388844,  0.04276557,  0.22055861,  0.29688645,\n",
       "        0.18104396,  0.17271062,  0.13836996,  0.41414835,  0.39848901,\n",
       "        0.3125    ,  0.17129121,  0.28351648, -0.01529304,  0.29184982,\n",
       "        0.29716117,  0.23598901,  0.18021978,  0.23937729,  0.23901099])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6d2b8bd8-a821-474c-8cce-4d2c21f35b39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation: 0.260 (avg p value 0.142673)\n"
     ]
    }
   ],
   "source": [
    "# compute lds\n",
    "from scipy.stats import spearmanr, pearsonr\n",
    "####\n",
    "margins = lds_testset_correctness\n",
    "infl_est_ = -scores_list[1]\n",
    "# infl_est_ = -tmp\n",
    "preds = lds_mask_array @ infl_est_.T\n",
    "####\n",
    "rs = []\n",
    "ps = []\n",
    "\n",
    "for ind in range(1000):\n",
    "    r, p = spearmanr(preds[:, ind], margins[:, ind])\n",
    "    # r, p = pearsonr(preds[:, ind], margins[:, ind])\n",
    "    rs.append(r)\n",
    "    ps.append(p)\n",
    "    \n",
    "rs, ps = np.array(rs), np.array(ps)\n",
    "print(f'Correlation: {rs.mean():.3f} (avg p value {ps.mean():.6f})')\n",
    "\n",
    "# tmp_2 = rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dfa113fd-76d0-4c52-86cc-38bc894a3e04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation: 0.287 (avg p value 0.101265)\n"
     ]
    }
   ],
   "source": [
    "# compute lds\n",
    "from scipy.stats import spearmanr, pearsonr\n",
    "####\n",
    "margins = lds_testset_correctness\n",
    "infl_est_ = -scores_list[1]\n",
    "# infl_est_ = -tmp\n",
    "preds = lds_mask_array @ infl_est_.T\n",
    "####\n",
    "rs = []\n",
    "ps = []\n",
    "\n",
    "for ind in test_index:\n",
    "    r, p = spearmanr(preds[:, ind], margins[:, ind])\n",
    "    # r, p = pearsonr(preds[:, ind], margins[:, ind])\n",
    "    rs.append(r)\n",
    "    ps.append(p)\n",
    "    \n",
    "rs, ps = np.array(rs), np.array(ps)\n",
    "print(f'Correlation: {rs.mean():.3f} (avg p value {ps.mean():.6f})')\n",
    "\n",
    "tmp_2 = rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6a840cef-578b-4343-9a47-66d9b5eb05d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.05695971,  0.21098901,  0.33672161,  0.3625    ,  0.2510989 ,\n",
       "        0.2771978 ,  0.10338828,  0.40027473,  0.37811355,  0.38328755,\n",
       "        0.47454212,  0.0375    ,  0.24766484,  0.22229853,  0.37129121,\n",
       "        0.32449634,  0.31936813,  0.35874542,  0.2474359 ,  0.36643773,\n",
       "        0.47261905,  0.47152015,  0.28800366,  0.30567766,  0.07005495,\n",
       "        0.15013736,  0.27568681,  0.29368132,  0.38411172,  0.16025641,\n",
       "        0.31936813,  0.27257326,  0.12628205,  0.39555861,  0.50315934,\n",
       "        0.46181319,  0.30906593,  0.26923077,  0.39239927,  0.05952381,\n",
       "        0.15961538,  0.30648245,  0.16739927,  0.30654762,  0.54354396,\n",
       "        0.2385989 ,  0.26588828,  0.35837912,  0.36886447,  0.44688645,\n",
       "        0.2106685 ,  0.26350733,  0.23099817,  0.33447802,  0.18086081,\n",
       "        0.18846154,  0.2632326 ,  0.14977106,  0.54029304,  0.10384615])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6e828ff0-a5f3-4f71-b47e-e2a6b274b559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 5000)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_list[0][test_index].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dc2e0f02-d5bc-41f4-8065-3b9770d18aba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3ff9c1bc70>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(scores_list[0][test_index][:, 0:128])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "631e1fcb-51ec-402d-8ce1-e58bcc48e724",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3ff9bffd90>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(scores_list[1][test_index][:, 0:128])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b829670a-1150-487d-ad48-698c52b7fd14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "scores_list.append(np.random.rand(1000, 5000))\n",
    "len(scores_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "71b4fb24-558b-4338-b58d-7d4c0f8f44d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0.19617224880382775\n",
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      "[17488 17769 25061  7882  4701]\n",
      "0.2360939431396786\n",
      "[17488 17769 25061  7882  4701]\n",
      "[17488 25061  4701 38681 11482]\n",
      "[17488 17769 25061  7882 38681]\n",
      "0.43719459614831846\n",
      "[17488 17769  7882  4701 38681]\n",
      "[ 4701 11482 37495 39588  5729]\n",
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      "1 737\n",
      "0.19331742243436753\n",
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      "[17488 17769 25061  7882  4701]\n",
      "[17488 17769 25061  7882  4701]\n",
      "0.20409391932570742\n",
      "[17488 17769  7882  4701 11482]\n",
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      "0.3947001394700139\n",
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      "0.21951219512195122\n",
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      "0.23839009287925697\n",
      "[17769 25061  7882 38681 11482]\n",
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      "0.416029453412631\n",
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      "3 660\n",
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      "0.41402714932126694\n",
      "[17488 17769 25061  7882  4701]\n",
      "[17488 17769 25061  7882  4701]\n",
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      "5 678\n",
      "0.2106537530266344\n",
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      "0.437607820586544\n",
      "[17769  7882  4701 38681 11482]\n",
      "[ 7882 11482 37495 39588 25183]\n",
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      "6 626\n",
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      "0.21802679658952498\n",
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      "[ 4701 11482 39588 25183  3832]\n",
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      "0.19047619047619047\n",
      "[17769 25061  7882  4701 38681]\n",
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      "0.20192307692307693\n",
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      "[25061  7882 38681 11482  5729]\n",
      "8 859\n",
      "0.20192307692307693\n",
      "[17488 17769 25061  7882 38681]\n",
      "[17769 25061  7882  4701 38681]\n",
      "[17488 17769 25061  7882  4701]\n",
      "0.24378109452736318\n",
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      "[17488 17769 25061  7882  4701]\n",
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      "0.4124293785310734\n",
      "[ 4701 11482 37495  5729 25183]\n",
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      "[17488 17769 25061  4701 39588]\n",
      "10 811\n",
      "0.21951219512195122\n",
      "[17488 17769 25061  7882  4701]\n",
      "[17488 25061  7882 38681 11482]\n",
      "[17488 25061  7882  4701 38681]\n",
      "0.24610591900311526\n",
      "[17488 25061  7882 38681 11482]\n",
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      "[17488 25061  4701 38681 11482]\n",
      "0.43184421534937\n",
      "[17488 25061  7882 38681 25183]\n",
      "[ 7882 38681 39588  5729 20314]\n",
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      "11 76\n",
      "0.19189511323003575\n",
      "[17488 17769 25061  7882  4701]\n",
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      "[25061  7882  4701 38681 11482]\n",
      "0.23839009287925697\n",
      "[17488 17769 25061  7882  4701]\n",
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      "[25061  7882  4701 38681 11482]\n",
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      "[17488 17769 25061  7882 39588]\n",
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      "12 636\n",
      "0.22699386503067484\n",
      "[17488 17769 25061  7882  4701]\n",
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      "0.2634238787113076\n",
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      "0.4302059496567506\n",
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      "[17488 17769  7882  4701 11482]\n",
      "[17769 11482 37495 39588  5729]\n",
      "13 973\n",
      "0.16686114352392065\n",
      "[17488 17769 25061  7882  4701]\n",
      "[17488 17769 25061  7882  4701]\n",
      "[17488 17769 25061  7882  4701]\n",
      "0.1954572624028691\n",
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      "[17488  7882 38681 11482 37495]\n",
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      "[17488 17769 25061  7882 38681]\n",
      "14 938\n",
      "0.22399020807833536\n",
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    }
   ],
   "source": [
    "for index, my_i in enumerate(test_index):\n",
    "    print(index, my_i)\n",
    "    for k in [\n",
    "        # 250, \n",
    "        500, 1000, 2500, \n",
    "              # 5000\n",
    "             ]:\n",
    "        D_trak=-scores_list[0][my_i]\n",
    "        topK_trak = np.arange(10000)[D_trak.argsort()[0:k]]\n",
    "\n",
    "        D_ours=-scores_list[1][my_i]\n",
    "        topK_ours = np.arange(10000)[D_ours.argsort()[0:k]]\n",
    "\n",
    "        ####\n",
    "        D_random=-scores_list[2][my_i]\n",
    "        topK_random = np.arange(10000)[D_random.argsort()[0:k]]\n",
    "        ####\n",
    "\n",
    "        # print(len(np.intersect1d(topK_trak, topK_ours)))\n",
    "        # print(len(np.union1d(topK_trak, topK_ours)))\n",
    "        print(len(np.intersect1d(topK_trak, topK_ours))/len(np.union1d(topK_trak, topK_ours))) # Jaccard similarity\n",
    "\n",
    "        ####\n",
    "        new_idx_trak = np.delete(idx_train, topK_trak)\n",
    "        # print(len(new_idx_trak))\n",
    "        print(new_idx_trak[0:5])\n",
    "\n",
    "        with open(\"../../data/indices/5000-0.5/counter/{}-{}-TRAK.pkl\".format(index, k), 'wb') as handle:\n",
    "            pickle.dump(new_idx_trak, handle)\n",
    "        ####\n",
    "        new_idx_ours = np.delete(idx_train, topK_ours)\n",
    "        # print(len(new_idx_ours))\n",
    "        print(new_idx_ours[0:5])\n",
    "\n",
    "        with open(\"../../data/indices/5000-0.5/counter/{}-{}-Ours.pkl\".format(index, k), 'wb') as handle:\n",
    "            pickle.dump(new_idx_ours, handle)\n",
    "        ####\n",
    "        new_idx_random = np.delete(idx_train, topK_random)\n",
    "        # print(len(new_idx_random))\n",
    "        print(new_idx_random[0:5])\n",
    "\n",
    "        with open(\"../../data/indices/5000-0.5/counter/{}-{}-Random.pkl\".format(index, k), 'wb') as handle:\n",
    "            pickle.dump(new_idx_random, handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e00e5ae2-1db4-438b-ab6d-41a62c54c7b9",
   "metadata": {},
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "758611c8-ea34-4974-a9b3-7c520389e8b6",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "54f156ae-8b5a-487f-905f-36f6efe25542",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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