CIFAR2/03_counter_eval_clip.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f04439e8-3684-4e71-a58d-1b222008c74d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"   \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='0'\n",
    "os.environ[\"HF_HOME\"]=\"~/codes/.cache/huggingface\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "379a0758-ffa9-4c0f-8828-6976b5752918",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7c672bc-d87e-450e-bf89-86799b5ba6ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from accelerate.utils import ProjectConfiguration, set_seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8820a3ed-9b69-423b-868b-79c11c67762e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import random\n",
    "import numpy as np\n",
    "\n",
    "def set_seeds(seed):\n",
    "    set_seed(seed)\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    \n",
    "set_seeds(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e9bc9238-5dc8-4d43-8551-a2ce62bfd120",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import transforms\n",
    "from diffusers import DDPMPipeline, DDIMPipeline, DDPMScheduler, DDIMScheduler, UNet2DModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af017ec5-da0e-43ed-b9d1-8544336a4736",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c7aa6f4-4adf-494a-bfd2-dd414ef7d8e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'config.json'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Args():\n",
    "    \n",
    "    model_config_name_or_path=\"config.json\"\n",
    "    \n",
    "    dataset_name=\"cifar10\"\n",
    "\n",
    "    resolution=32\n",
    "    center_crop=True\n",
    "    random_flip=False\n",
    "\n",
    "    dataloader_num_workers=8\n",
    "    \n",
    "    seed=42\n",
    "    gen_seed=0    \n",
    "\n",
    "    train_batch_size=256\n",
    "\n",
    "    ddpm_num_steps=1000\n",
    "    ddpm_num_inference_steps=50\n",
    "    ddpm_beta_schedule='linear'\n",
    "\n",
    "args=Args()\n",
    "args.model_config_name_or_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "11f8d9d3-a6f0-4113-802d-964b459666de",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./data/indices/5000-0.5/counter/idx-gen-sampled.pkl', 'rb') as handle:\n",
    "    test_index = pickle.load(handle)\n",
    "# test_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ae2ce82-e7e2-41bc-a208-8c63c4262b38",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f4b8133-2eda-41cb-9ec9-85b8e964ca55",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df86fcb8-af99-499e-83f9-b60d74b7acc0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04f73514-a407-49c6-90e5-22262cec9def",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./counter/saved.pkl\", 'rb') as handle:\n",
    "    index_image_list = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b685379f-65f1-4c98-990b-5bbc39b9cd15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_image_list[0][0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "da93aa94-9e34-463f-aacb-ec3305b1ae83",
   "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": [
    "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": 11,
   "id": "41122a48-e6b2-4fe5-a0a5-349ef6d2d824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(index_image_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ccc1816e-129e-437e-b6cb-839b9374cd9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_images = [gen_dataset[i]['img'] for i in test_index]\n",
    "gen_images[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "371b1704-2c31-460f-91dd-f8d620d577e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_images = [index_image_list[i][0][0] for i in range(60)]\n",
    "len(random_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1253cd76-d79d-4f47-8ad6-697496b94182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_images[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ffd8ab02-5fc4-4db4-ad92-b1f5052d79d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trak_images = [index_image_list[i][0][1] for i in range(60)]\n",
    "len(trak_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e5bc4262-e856-4dc0-bc4b-0f3a930f0bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trak_images[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cc57274b-d591-44f0-b9f8-53fda6c34fa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ours_images = [index_image_list[i][0][2] for i in range(60)]\n",
    "len(ours_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "db088da1-30f2-429a-be78-74dd2ea06099",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ours_images[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0b38b852-3fe1-4d0d-a760-ae8de5d1f34c",
   "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": [
    "import pandas as pd\n",
    "\n",
    "from datasets import DatasetDict, Dataset, load_dataset, Image\n",
    "\n",
    "dataset = DatasetDict({\n",
    "\"train\": Dataset.from_dict({\n",
    "    \"img\": gen_images,\n",
    "})}\n",
    "                     )\n",
    "gen_dataset = dataset[\"train\"]\n",
    "gen_dataset[0][\"img\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5e01b83a-e639-4b2d-afeb-a886411a3676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = DatasetDict({\n",
    "\"train\": Dataset.from_dict({\n",
    "    \"img\": random_images,\n",
    "})}\n",
    "                     )\n",
    "random_dataset = dataset[\"train\"]\n",
    "random_dataset[0][\"img\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0ef5867c-7855-4ff1-a526-4c3ccec15cb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = DatasetDict({\n",
    "\"train\": Dataset.from_dict({\n",
    "    \"img\": trak_images,\n",
    "})}\n",
    "                     )\n",
    "trak_dataset = dataset[\"train\"]\n",
    "trak_dataset[0][\"img\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7af27bc1-97ed-4fa1-9bb6-a3b0e7b66d9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = DatasetDict({\n",
    "\"train\": Dataset.from_dict({\n",
    "    \"img\": ours_images,\n",
    "})}\n",
    "                     )\n",
    "ours_dataset = dataset[\"train\"]\n",
    "ours_dataset[0][\"img\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cbbba78-5c51-45ed-a044-4001a18949bf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3ac1bf0e-897b-4ca7-a287-6f470dc956a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Torch version: 2.0.1+cu117\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "from pkg_resources import packaging\n",
    "\n",
    "print(\"Torch version:\", torch.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fb0345d7-02df-4b53-a8bf-1f95d148ee12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['RN50',\n",
       " 'RN101',\n",
       " 'RN50x4',\n",
       " 'RN50x16',\n",
       " 'RN50x64',\n",
       " 'ViT-B/32',\n",
       " 'ViT-B/16',\n",
       " 'ViT-L/14',\n",
       " 'ViT-L/14@336px']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import clip\n",
    "\n",
    "clip.available_models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "01dac425-48c4-401e-8778-9c2e1852c7f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model parameters: 151,277,313\n",
      "Input resolution: 224\n",
      "Context length: 77\n",
      "Vocab size: 49408\n"
     ]
    }
   ],
   "source": [
    "model, preprocess = clip.load(\"ViT-B/32\")\n",
    "model.cuda().eval()\n",
    "input_resolution = model.visual.input_resolution\n",
    "context_length = model.context_length\n",
    "vocab_size = model.vocab_size\n",
    "\n",
    "print(\"Model parameters:\", f\"{np.sum([int(np.prod(p.shape)) for p in model.parameters()]):,}\")\n",
    "print(\"Input resolution:\", input_resolution)\n",
    "print(\"Context length:\", context_length)\n",
    "print(\"Vocab size:\", vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8c1e0a5e-b6bc-4838-810d-6eb5ac74c4f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 512])\n",
      "torch.Size([28, 512])\n"
     ]
    }
   ],
   "source": [
    "gen_features = []\n",
    "for i in range(0, len(gen_dataset), 32):\n",
    "    batch = gen_dataset[i:i+32]['img']\n",
    "    batch = [preprocess(b) for b in batch]\n",
    "    batch = torch.tensor(np.stack(batch)).cuda()\n",
    "    with torch.no_grad():\n",
    "        image_features = model.encode_image(batch).float()\n",
    "    print(image_features.size())\n",
    "    gen_features.append(image_features.cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a8a60136-229c-4a0f-8900-4c59beabb523",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 512])\n",
      "torch.Size([28, 512])\n"
     ]
    }
   ],
   "source": [
    "random_features = []\n",
    "for i in range(0, len(random_dataset), 32):\n",
    "    batch = random_dataset[i:i+32]['img']\n",
    "    batch = [preprocess(b) for b in batch]\n",
    "    batch = torch.tensor(np.stack(batch)).cuda()\n",
    "    with torch.no_grad():\n",
    "        image_features = model.encode_image(batch).float()\n",
    "    print(image_features.size())\n",
    "    random_features.append(image_features.cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5d08ce3d-6115-4b58-9094-c4876d59c671",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 512])\n",
      "torch.Size([28, 512])\n"
     ]
    }
   ],
   "source": [
    "trak_features = []\n",
    "for i in range(0, len(trak_dataset), 32):\n",
    "    batch = trak_dataset[i:i+32]['img']\n",
    "    batch = [preprocess(b) for b in batch]\n",
    "    batch = torch.tensor(np.stack(batch)).cuda()\n",
    "    with torch.no_grad():\n",
    "        image_features = model.encode_image(batch).float()\n",
    "    print(image_features.size())\n",
    "    trak_features.append(image_features.cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "43e39d81-57d8-4654-9cc2-117026756f33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 512])\n",
      "torch.Size([28, 512])\n"
     ]
    }
   ],
   "source": [
    "ours_features = []\n",
    "for i in range(0, len(ours_dataset), 32):\n",
    "    batch = ours_dataset[i:i+32]['img']\n",
    "    batch = [preprocess(b) for b in batch]\n",
    "    batch = torch.tensor(np.stack(batch)).cuda()\n",
    "    with torch.no_grad():\n",
    "        image_features = model.encode_image(batch).float()\n",
    "    print(image_features.size())\n",
    "    ours_features.append(image_features.cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2ae3ce3-bcb1-45e6-afc3-5f438f61cf0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dd5ab3bf-1943-40dd-b657-6183d2d1315e",
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_features_array = np.vstack(gen_features)\n",
    "random_features_array = np.vstack(random_features)\n",
    "trak_features_array = np.vstack(trak_features)\n",
    "ours_features_array = np.vstack(ours_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6d291f63-d573-4d27-86ca-a8583455b7ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "90cce4b8-4c5d-43b7-be92-cf255df63a12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # cos\n",
    "random_scores = np.sum(random_features_array*gen_features_array, axis=1)/(\n",
    "    np.linalg.norm(random_features_array, axis=1)*np.linalg.norm(gen_features_array, axis=1))\n",
    "trak_scores = np.sum(trak_features_array*gen_features_array, axis=1)/(\n",
    "    np.linalg.norm(trak_features_array, axis=1)*np.linalg.norm(gen_features_array, axis=1))\n",
    "ours_scores = np.sum(ours_features_array*gen_features_array, axis=1)/(\n",
    "    np.linalg.norm(ours_features_array, axis=1)*np.linalg.norm(gen_features_array, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1cadc249-fd10-4085-b6ef-30df1a103282",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95280945"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9d02e618-7b2b-4757-b4e2-a6a8b308a76e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9683572"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(random_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "84e96291-00f1-42e9-bb8c-89caca63b351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93293405"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trak_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "45c71487-a3a3-46b4-b97c-e83e03d4de1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94327044"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(trak_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1c6d7429-34d7-4d28-a40b-771f47d3f245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.87493867"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ours_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "dd851925-a96b-41b8-af7d-95e0934d7c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88101745"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(ours_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "80ea5261-7b8c-4477-ab11-1900691f4fd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0e81fd05-290a-43b4-9e0f-8ea15d095d50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd1c805b190>]"
      ]
     },
     "execution_count": 39,
     "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.plot(trak_scores)\n",
    "plt.plot(ours_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6c05501b-c797-4985-b9e1-4f822fc53d00",
   "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>Random</th>\n",
       "      <th>TRAK</th>\n",
       "      <th>D-TRAK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.989640</td>\n",
       "      <td>0.941574</td>\n",
       "      <td>0.823037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.967145</td>\n",
       "      <td>0.964336</td>\n",
       "      <td>0.771188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.964233</td>\n",
       "      <td>0.918932</td>\n",
       "      <td>0.881133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.929516</td>\n",
       "      <td>0.961797</td>\n",
       "      <td>0.893696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.971159</td>\n",
       "      <td>0.915916</td>\n",
       "      <td>0.904690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Random      TRAK    D-TRAK\n",
       "0  0.989640  0.941574  0.823037\n",
       "1  0.967145  0.964336  0.771188\n",
       "2  0.964233  0.918932  0.881133\n",
       "3  0.929516  0.961797  0.893696\n",
       "4  0.971159  0.915916  0.904690"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df.head()\n",
    "df['Random'] = random_scores\n",
    "df['TRAK'] = trak_scores\n",
    "df['D-TRAK'] = ours_scores\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3337e29a-f4b2-49f7-8454-1b16cd70aec4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30efb435-c9be-4cc8-8e81-21cab69c0376",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
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}