CIFAR2/methods/04_if/01_IF_gen_5000-0.5_msl2norm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31e0858d-12e5-4eaf-8cc3-f461d2aff756",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "974ba093-b42b-4ee9-a8c0-cd79d161435c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c2f5efb-631c-4083-84ae-dc219c6a75a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fde1d3d3-3ae3-4194-b2e8-596809be9636",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e3a5e70-ec86-4af6-ad75-bdc4351b4d40",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "723df846-36f4-433c-99cf-044b96c870ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf1854ba-184b-4c9f-9a5c-9158e786e404",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c4cadde9-8859-435a-b6ba-09aa42bb468d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"   \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='2'\n",
    "\n",
    "os.environ[\"HF_HOME\"]=\"~/codes/.cache/huggingface\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f7cd96b-ca27-4ce1-ac2d-c3dd67e069b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import random\n",
    "import numpy as np\n",
    "\n",
    "def set_seeds(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": 3,
   "id": "4ff495b8-8df0-49e8-a409-4ed9601c8143",
   "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": "c4e89a09-d6ff-46a1-8a9e-9c6f9c167114",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2682e2c4-9275-4bae-a732-74b981a358d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb2dc562-293c-4e74-8e97-141fc7465938",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bda41875-8976-4852-823a-91dc1f42aebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5000"
      ]
     },
     "execution_count": 7,
     "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": null,
   "id": "773b1a12-d60d-4ed8-8f61-b03a3af61e71",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "40c86797-31d5-45d0-b46f-52f8c84dd130",
   "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": null,
   "id": "c6632d56-9134-4720-99a9-1853fe349039",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2e50aae-335f-457d-bfb7-e3991f19fb50",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5df420e1-7166-4e5b-baa3-a012fe7e32e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f2ce6b6b-ff45-4156-9606-7c87046e751a",
   "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": [
    "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": null,
   "id": "26b8b800-7147-4c3b-9f62-b99110392974",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8c2e0980-d551-48f4-8b1f-3f9d4ac788cb",
   "metadata": {},
   "outputs": [],
   "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",
    "val_dataset = dataset[\"train\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5b522cfd-98f6-488c-be5b-4314ff16a91b",
   "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": [
    "val_dataset[0]['img']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bdada2c1-66b5-4d5e-b959-e80b30420b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_dataset[1]['img']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "087ea1db-01c9-4e6a-a402-8b8a9aa93e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# val_dataset =  dataset['test'].select(idx_val)\n",
    "# val_dataset[0][\"img\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e16837b7-8ddd-46ab-9d0a-c988f2ca9514",
   "metadata": {},
   "outputs": [],
   "source": [
    "# idx_val[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dd5e770-1fed-4f65-b4e7-5373e2adcb10",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "317f972c-b763-40d3-8186-759118c12237",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Args():\n",
    "            \n",
    "    dataset_name=\"cifar10\"\n",
    "\n",
    "    output_dir=\"../../saved/5000-0.5/ddpm/checkpoint-8000\"\n",
    "    \n",
    "    K=10\n",
    "    Z=65536\n",
    "\n",
    "    used_dim=32768\n",
    "    \n",
    "    t_strategy='uniform'\n",
    "   \n",
    "args = Args()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bb9d8f5f-5370-4157-be84-f77949771866",
   "metadata": {},
   "outputs": [],
   "source": [
    "import diffusers\n",
    "from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel\n",
    "from diffusers.optimization import get_scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ffe8c0c-a6df-41e9-8cd9-4dfa6eaa4d89",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04be809b-6fd9-4a8a-9d64-94c6ef3b4f85",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad307d24-057d-43bc-9772-5ae5d62c9fa1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c37c6ab7-8c07-416a-b7b1-4b2c03a82cfa",
   "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": 23,
   "id": "7e5b270b-c5df-4b5d-a8fd-2a924a6edad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 1000, 1000)"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "id": "d823ef7b-9d53-4f31-ab11-864274554493",
   "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": 25,
   "id": "874a332c-29b1-46fe-bf83-12e7c8f64e28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 5000)"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "id": "41ebfcfb-5789-44f0-99d0-0dddc7c6f32f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 1000)"
      ]
     },
     "execution_count": 26,
     "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": 27,
   "id": "46afee77-65eb-4023-8077-d11ed785d406",
   "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": "8d92c4b4-5b21-434f-b6cc-96e968cbe495",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "649a172c-968c-4aa3-b086-b32d64d22424",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import spearmanr, pearsonr\n",
    "\n",
    "lamb_list = [\n",
    "\n",
    "        1e-2, 2e-2, 5e-2,\n",
    "        1e-1, 2e-1, 5e-1,\n",
    "        1e0, 2e0, 5e0,\n",
    "        1e1, 2e1, 5e1,\n",
    "        1e2, 2e2, 5e2,\n",
    "        1e3, 2e3, 5e3, \n",
    "        1e4, 2e4, 5e4, \n",
    "        1e5, 2e5, 5e5, \n",
    "        1e6, 2e6, 5e6, \n",
    "    ]\n",
    "\n",
    "len(lamb_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "effb0646-37be-44c2-8b2e-7bf3c67ea049",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 65536)\n",
      "(5000, 32768)\n",
      "(1000, 65536)\n",
      "(1000, 32768)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5176/1623843635.py:32: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n",
      "  gen_dstore_keys = torch.from_numpy(gen_dstore_keys).cuda()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32768, 32768])\n",
      "tensor(0.0072, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.004 (avg p value 0.502900)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0010, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.008 (avg p value 0.492959)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0233, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.001 (avg p value 0.495202)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0431, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.003 (avg p value 0.494457)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0529, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: -0.002 (avg p value 0.482744)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0769, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: -0.002 (avg p value 0.524561)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0765, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: -0.005 (avg p value 0.503290)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0633, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: -0.001 (avg p value 0.503855)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0507, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.005 (avg p value 0.495291)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0359, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: -0.003 (avg p value 0.501003)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0276, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: -0.003 (avg p value 0.494005)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0162, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.014 (avg p value 0.503644)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0098, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.022 (avg p value 0.490179)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0045, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.167 (avg p value 0.285088)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0018, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.179 (avg p value 0.266415)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0009, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.187 (avg p value 0.253685)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0005, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.191 (avg p value 0.246112)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0002, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.190 (avg p value 0.248725)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(9.7486e-05, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.185 (avg p value 0.256484)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(4.9169e-05, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.177 (avg p value 0.270055)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(1.9813e-05, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.167 (avg p value 0.288369)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(9.9401e-06, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.159 (avg p value 0.304213)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(4.9811e-06, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.151 (avg p value 0.316747)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(1.9959e-06, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.140 (avg p value 0.338286)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(9.9871e-07, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.130 (avg p value 0.354007)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(4.9959e-07, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.119 (avg p value 0.367421)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(1.9991e-07, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.106 (avg p value 0.385322)\n",
      "(5000, 65536)\n",
      "(5000, 32768)\n",
      "(1000, 65536)\n",
      "(1000, 32768)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0063, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.007 (avg p value 0.479913)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0166, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.007 (avg p value 0.503316)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0277, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.004 (avg p value 0.493283)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(-0.0149, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.004 (avg p value 0.519347)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0251, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.002 (avg p value 0.487545)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0682, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.014 (avg p value 0.489688)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0715, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: -0.002 (avg p value 0.512171)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0628, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: -0.001 (avg p value 0.497806)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0442, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: -0.002 (avg p value 0.495550)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0354, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: -0.002 (avg p value 0.492728)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0255, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: -0.002 (avg p value 0.502251)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0157, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.007 (avg p value 0.493695)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0097, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.012 (avg p value 0.509131)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0047, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.244 (avg p value 0.161457)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0019, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.256 (avg p value 0.146343)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0009, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.260 (avg p value 0.142673)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0005, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.258 (avg p value 0.147999)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(0.0002, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.247 (avg p value 0.166491)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(9.8556e-05, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.237 (avg p value 0.183783)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(4.9531e-05, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.224 (avg p value 0.200503)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(1.9895e-05, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.205 (avg p value 0.221802)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(9.9665e-06, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.188 (avg p value 0.248926)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(4.9893e-06, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.172 (avg p value 0.278162)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(1.9976e-06, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.150 (avg p value 0.323039)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(9.9921e-07, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.134 (avg p value 0.349061)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(4.9974e-07, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.120 (avg p value 0.371907)\n",
      "torch.Size([32768, 32768])\n",
      "tensor(1.9994e-07, device='cuda:0')\n",
      "torch.Size([1000, 5000])\n",
      "Correlation: 0.103 (avg p value 0.395171)\n"
     ]
    }
   ],
   "source": [
    "# for loop over functions\n",
    "lds_list = []\n",
    "for K in [10, 100, \n",
    "          # 1000\n",
    "         ]:\n",
    "    ####\n",
    "    ####\n",
    "    dstore_keys_list = []\n",
    "    for split in range(5):\n",
    "        dstore_keys = np.memmap('{}/features-0/ddpm-train-keys-{}-{}-{}-{}-{}.npy'.format(\n",
    "            args.output_dir, split, K, args.Z, 'mean-squared-l2-norm', args.t_strategy), \n",
    "                                dtype=np.float32, \n",
    "                                mode='r',\n",
    "                                shape=(1000, args.Z))\n",
    "        dstore_keys_list.append(dstore_keys) \n",
    "        \n",
    "    dstore_keys = np.vstack(dstore_keys_list)\n",
    "    print(dstore_keys.shape)  \n",
    "    dstore_keys = dstore_keys[:, :args.used_dim]\n",
    "    print(dstore_keys.shape)\n",
    "    ####        \n",
    "    gen_dstore_keys = np.memmap('{}/features-0/ddpm-gen-keys-{}-{}-{}-{}-{}.npy'.format(\n",
    "                args.output_dir, 0, K, args.Z, 'mean-squared-l2-norm', args.t_strategy), \n",
    "                                dtype=np.float32, \n",
    "                                mode='r',\n",
    "                                shape=(1000, args.Z))\n",
    "    print(gen_dstore_keys.shape)    \n",
    "    gen_dstore_keys = gen_dstore_keys[:, :args.used_dim]\n",
    "    print(gen_dstore_keys.shape)\n",
    "    ####\n",
    "    dstore_keys = torch.from_numpy(dstore_keys).cuda()\n",
    "    gen_dstore_keys = torch.from_numpy(gen_dstore_keys).cuda()\n",
    "    # dstore_keys = torch.from_numpy(dstore_keys).cuda() * 1000\n",
    "    # gen_dstore_keys = torch.from_numpy(gen_dstore_keys).cuda() * 1000\n",
    "    ####\n",
    "    kernel = dstore_keys.T@dstore_keys\n",
    "    ####\n",
    "    rs_list = []\n",
    "    ps_list = []\n",
    "\n",
    "    best_scores = None\n",
    "    best_lds = -np.inf\n",
    "    for lamb in lamb_list:\n",
    "        # kernel_ = kernel + lamb*np.eye(kernel.shape[0])\n",
    "        kernel_ = kernel + lamb*torch.eye(kernel.shape[0]).cuda()\n",
    "        # kernel_ = torch.from_numpy(kernel_).cuda()\n",
    "        ####\n",
    "        kernel_ = torch.linalg.inv(kernel_)  \n",
    "        # kernel_ = kernel_.cpu().numpy()\n",
    "        ####\n",
    "        print(kernel_.shape)\n",
    "        print(torch.mean(kernel_.diagonal()))\n",
    "        ####\n",
    "        # scores = gen_dstore_keys.dot((dstore_keys@kernel_).T)\n",
    "        scores = gen_dstore_keys@((dstore_keys@kernel_).T)\n",
    "        print(scores.size())\n",
    "        scores = scores.cpu().numpy()\n",
    "        \n",
    "        # compute lds\n",
    "\n",
    "        margins = lds_testset_correctness\n",
    "        infl_est_ = -scores\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",
    "        rs_list.append(rs.mean())   \n",
    "        ps_list.append(ps.mean())\n",
    "\n",
    "        if rs.mean()>best_lds:\n",
    "            best_scores = scores\n",
    "            best_lds = rs.mean()\n",
    "\n",
    "    with open('./gen_5000_0.5_{}_{}_{}_{}.pkl'.format('mean-squared-l2-norm', K, lamb, best_lds), 'wb') as handle:\n",
    "        pickle.dump(best_scores, handle)\n",
    "    \n",
    "    lds_list.append((rs_list, ps_list))\n",
    "\n",
    "    # break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3380ec5-8479-45b3-81c8-78d83cb98f3d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47e17534-05f6-4f8d-a9b8-7ea2c69258fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c08ec20a-9e82-4f58-b342-95145208f5b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00398951,  0.00828928,  0.00069967,  0.00298869, -0.002245  ,\n",
       "        -0.00212123, -0.00491668, -0.00125946,  0.00463198, -0.00308233,\n",
       "        -0.00250666,  0.01419614,  0.02184524,  0.16657473,  0.17947366,\n",
       "         0.18670356,  0.19124168,  0.19003493,  0.18496602,  0.177354  ,\n",
       "         0.16696272,  0.15943266,  0.15136054,  0.1397969 ,  0.13006159,\n",
       "         0.11928458,  0.10632282],\n",
       "       [ 0.50289977,  0.49295943,  0.49520223,  0.4944572 ,  0.48274443,\n",
       "         0.52456145,  0.50328968,  0.50385456,  0.49529051,  0.50100341,\n",
       "         0.49400452,  0.50364399,  0.4901789 ,  0.28508824,  0.26641509,\n",
       "         0.25368458,  0.2461122 ,  0.24872505,  0.25648414,  0.27005519,\n",
       "         0.28836887,  0.30421293,  0.31674652,  0.33828615,  0.35400701,\n",
       "         0.36742065,  0.38532193]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lds_array = np.array(lds_list)\n",
    "lds_array[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c7d99b73-0b7e-4685-af5e-20038c48751d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2, 27)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lds_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b03547d-c1b8-4470-ba4d-7548284937cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "15cc06cf-1ae9-4f04-a0a6-b3720c9ab012",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./gen_5000_0.5_{}.pkl'.format('mean-squared-l2-norm'), 'wb') as handle:\n",
    "    pickle.dump(lds_array, handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3736dd0f-e9f2-47c0-a81a-57d40c3b51a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8122043b-7cc0-4d50-a1a5-98e6eed9a4ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.19124168, 0.26017772])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lds_array[:, 0].max(axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "80995dab-f837-4566-9ea9-ccb5b1c5b0fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k in range(lds_array.shape[0]):\n",
    "    plt.plot(lds_array[k, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58582fc9-190f-42c7-a62c-1f7b2111d74a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bb0b736-25aa-4368-a617-ba6efa313cd1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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