DeepCAE_baseline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d29e1a7c",
   "metadata": {},
   "source": [
    "Achieve AD using a deep CAE generative model: train the generative model on supposedly normal data and use the reconstruction error as the AD score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6b15df52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8013238f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "from seaborn import boxplot, heatmap\n",
    "import random\n",
    "from torch.optim.lr_scheduler import MultiStepLR\n",
    "\n",
    "from utils import *\n",
    "from networks import *\n",
    "from centroids import *\n",
    "from batchscores import *\n",
    "from epochscores import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "41ff8c51",
   "metadata": {},
   "outputs": [],
   "source": [
    "nbr_epochs = 350 # paper says 250 + 100\n",
    "batch_size = 512 # paper says 200\n",
    "# dataset = 'MNIST'\n",
    "dataset = 'FashionMNIST'\n",
    "# dataset = 'CIFAR10'\n",
    "normal_cls = [0,1,2]\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "learning_rate = 1e-4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2cef5f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "nbr_seeds = 3 # how many different random init to try\n",
    "valid_AUCs = torch.zeros((nbr_seeds, nbr_epochs+1))\n",
    "test_AUCs = torch.zeros((nbr_seeds, nbr_epochs+1))\n",
    "losses = torch.zeros((nbr_seeds, nbr_epochs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc9ba803",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/blupon/anaconda3/envs/recent/lib/python3.9/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /opt/conda/conda-bld/pytorch_1631630797748/work/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
      "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n",
      "100%|██████████| 350/350 [06:21<00:00,  1.09s/it]\n",
      "100%|██████████| 350/350 [05:13<00:00,  1.12it/s]\n",
      "100%|██████████| 350/350 [03:41<00:00,  1.58it/s]\n"
     ]
    }
   ],
   "source": [
    "for seed_idx in range(nbr_seeds):\n",
    "    \n",
    "    torch.manual_seed(seed_idx)\n",
    "    random.seed(seed_idx)\n",
    "    np.random.seed(seed_idx)\n",
    "\n",
    "    train_loader, valid_loader, test_loader, _, _, _, _, _, _ = get_dataloaders_MNIST_FashionMNIST(batch_size, normal_cls, dataset, seed=seed_idx)\n",
    "    if dataset == 'MNIST':\n",
    "        net = MNIST_LeNet_Autoencoder().to(device)\n",
    "    elif dataset == 'FashionMNIST':\n",
    "        net = FashionMNIST_LeNet_Autoencoder().to(device)\n",
    "    else:\n",
    "        net = CIFAR10_LeNet_Autoencoder().to(device)\n",
    "    optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=1e-6)\n",
    "    scheduler = MultiStepLR(optimizer, milestones=[250], gamma=0.1)\n",
    "    pytorch_total_params = sum(p.numel() for p in net.parameters() if p.requires_grad)\n",
    "    \n",
    "    valid_AUCs[seed_idx,0], test_AUCs[seed_idx,0], _, _ = get_epoch_performances_baseline_CAE(valid_loader, test_loader, device, net, normal_cls)\n",
    "    \n",
    "    for epoch in tqdm(range(nbr_epochs), position=0, leave=True):\n",
    "        net.train()\n",
    "        running_loss = 0.0\n",
    "        for i, (data,targets) in enumerate(train_loader, 0):\n",
    "            inputs, labels = data.to(device), targets.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            outputs = net(inputs)\n",
    "            reconstruction_error = torch.sum((outputs - inputs) ** 2, dim=tuple((range(1, outputs.dim()))))\n",
    "            loss = torch.mean(reconstruction_error)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            running_loss += loss.item()\n",
    "        losses[seed_idx,epoch] = running_loss\n",
    "\n",
    "        valid_AUCs[seed_idx,epoch+1],test_AUCs[seed_idx,epoch+1], scores_test, scores_labels_test = get_epoch_performances_baseline_CAE(valid_loader, test_loader, device, net, normal_cls)\n",
    "        \n",
    "        scheduler.step()\n",
    "        \n",
    "    save_pretrained_weights(net, dataset, normal_cls, seed_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d7a2ccb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "boxplot(y=scores_test, x=scores_labels_test)\n",
    "plt.xlabel('{} class'.format(dataset))\n",
    "plt.ylabel('Reconstruction error after training') # minimum over all centroids for test samples\n",
    "plt.title('Normal class: {} - LAST SEED EXPERIMENT ONLY'.format(normal_cls))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2247b12b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "for seed_idx in range(nbr_seeds):\n",
    "    plt.plot(range(nbr_epochs), losses[seed_idx,:], linestyle=':')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Mean reconstruction error')\n",
    "    plt.title('Training loss (normal class: {})'.format(normal_cls))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d321e361",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 15)) \n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "for seed_idx in range(nbr_seeds):\n",
    "    plt.scatter(range(0, nbr_epochs+1), test_AUCs[seed_idx,:], alpha=0.6, s=30)\n",
    "    plt.ylim(0.5,0.99)\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Test AUC')\n",
    "    plt.title('Test AUC - normal class: {} - {} trainable params'.format(normal_cls, pytorch_total_params))\n",
    "    \n",
    "plt.subplot(1,2,2)\n",
    "for seed_idx in range(nbr_seeds):\n",
    "    plt.scatter(range(0, nbr_epochs+1), valid_AUCs[seed_idx,:], alpha=0.6, s=30)\n",
    "    plt.ylim(0.5,0.99)\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Valid AUC')\n",
    "    plt.title('Valid AUC - normal class: {} - {} trainable params'.format(normal_cls, pytorch_total_params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba580da2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MEAN TEST AUC +/- STD: 0.8745653033256531 +/- 0.0033920679707080126\n"
     ]
    }
   ],
   "source": [
    "test_AUC_at_best_valid = torch.gather(test_AUCs,1,torch.max(valid_AUCs, dim=1)[1].long().unsqueeze(1))\n",
    "test_AUC_at_best_test = torch.gather(test_AUCs,1,torch.max(test_AUCs, dim=1)[1].long().unsqueeze(1))\n",
    "print(\"MEAN TEST AUC +/- STD: {} +/- {}\".format(torch.mean(test_AUC_at_best_valid),torch.std(test_AUC_at_best_valid)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a0dc2bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('last_results.txt', \"a\") as file:\n",
    "    file.write(\"DeepCAE-{}-Norm{}-BSize{}-LR{}-{}epochs-{}seeds - MEAN TEST AUC (best valid epoch) +/- STD: {} +/- {} | MEAN TEST AUC (best test epoch) +/- STD: {} +/- {}\\n\".format(dataset, normal_cls, batch_size, learning_rate, nbr_epochs, nbr_seeds, torch.mean(test_AUC_at_best_valid),torch.std(test_AUC_at_best_valid),torch.mean(test_AUC_at_best_test),torch.std(test_AUC_at_best_test)))"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}