oe-c10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import sys\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"MIG-GPU-ff380879-d5b2-8469-da3d-71267d28a645/0/0\"\n",
    "\n",
    "n_epochs = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytorch_ood.model import WideResNet\n",
    "from pytorch_ood.detector import EnergyBased, MaxSoftmax\n",
    "from pytorch_ood.utils import OODMetrics, ToUnknown, ToRGB\n",
    "from pytorch_ood.dataset.img import Textures, LSUNCrop, LSUNResize, TinyImageNetCrop, TinyImageNetResize\n",
    "from torchvision.datasets import CIFAR10\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "from torchvision.transforms import Compose, ToPILImage, Normalize, ToTensor, Resize, ToPILImage\n",
    "import torch \n",
    "\n",
    "def test(model):\n",
    "    \n",
    "    prep = Compose([\n",
    "        ToRGB(),\n",
    "        Resize([32,32]),\n",
    "        ToTensor(),\n",
    "        Normalize(\n",
    "            std=[0.24705882352941176470, 0.24352941176470588235, 0.26156862745098039215], \n",
    "            mean=[0.49137254901960784313, 0.48235294117647058823, 0.44666666666666666666]), \n",
    "        ])\n",
    "\n",
    "    metrics = [] \n",
    "    \n",
    "    with torch.no_grad():\n",
    "        model.eval()\n",
    "\n",
    "        data_in = CIFAR10(root=\"/data_fast/kirchheim/datasets/\", download=True, transform=prep, train=False)\n",
    "\n",
    "        for Data_clz in [Textures, LSUNCrop, LSUNResize, TinyImageNetCrop, TinyImageNetResize]:\n",
    "            ood_name = Data_clz.__name__\n",
    "\n",
    "            data_out = Data_clz(root=\"/data_fast/kirchheim/datasets/\", transform=prep, download=True, \n",
    "                                target_transform=ToUnknown())\n",
    "\n",
    "            data_loader = DataLoader(data_in + data_out, batch_size=128, num_workers=10)\n",
    "\n",
    "            # create detector\n",
    "            softmax = MaxSoftmax(model)\n",
    "            energy = EnergyBased(model)\n",
    "\n",
    "            # evaluate\n",
    "            metric_softmax = OODMetrics()\n",
    "            metric_energy = OODMetrics()\n",
    "\n",
    "            for x, y in data_loader:\n",
    "                logits = model(x.cuda())\n",
    "                metric_softmax.update(softmax.score(logits), y)\n",
    "                metric_energy.update(energy.score(logits), y)\n",
    "\n",
    "            # print(ood_name, metric_softmax.compute())\n",
    "\n",
    "            m = metric_softmax.compute()\n",
    "            m.update({\n",
    "                \"Dataset\": ood_name,\n",
    "                \"Method\": \"Softmax\"\n",
    "            })\n",
    "            metrics.append(m)\n",
    "            \n",
    "            m = metric_energy.compute()\n",
    "            m.update({\n",
    "                \"Dataset\": ood_name,\n",
    "                \"Method\": \"Energy\"\n",
    "            })\n",
    "            metrics.append(m)\n",
    "    \n",
    "    model.train()\n",
    "    \n",
    "    return metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `AUROC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
      "  warnings.warn(*args, **kwargs)\n",
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `PrecisionRecallCurve` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
      "  warnings.warn(*args, **kwargs)\n",
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: `pos_label` automatically set 1.\n",
      "  warnings.warn(*args, **kwargs)\n",
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/functional/classification/precision_recall_curve.py:156: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  thresholds = tensor(reversed(thresholds[sl]))\n"
     ]
    }
   ],
   "source": [
    "# create Neural Network\n",
    "model = WideResNet(pretrained=\"cifar10-pt\", num_classes=10).eval().cuda()\n",
    "metrics = test(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "{} &  AUROC &  AUPR-IN &  AUPR-OUT &  FPR95TPR \\\\\n",
      "Method  &        &          &           &           \\\\\n",
      "\\midrule\n",
      "Energy  &  96.50 &    95.34 &     97.10 &     16.03 \\\\\n",
      "Softmax &  95.20 &    93.13 &     96.45 &     17.46 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "data = pd.DataFrame(metrics)\n",
    "print((tmp.groupby([\"Method\"]).mean() * 100)[[\"AUROC\", \"AUPR-IN\", \"AUPR-OUT\", \"FPR95TPR\"]]\n",
    "      .to_latex(float_format=\"%.2f\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Epoch 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:46<00:00,  8.50it/s, loss=0.978]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:45<00:00,  8.51it/s, loss=0.754]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:45<00:00,  8.52it/s, loss=0.672]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:46<00:00,  8.49it/s, loss=0.688]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:45<00:00,  8.57it/s, loss=0.648]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:45<00:00,  8.58it/s, loss=0.637]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:45<00:00,  8.51it/s, loss=0.637]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:45<00:00,  8.52it/s, loss=0.646]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:45<00:00,  8.54it/s, loss=0.631]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:46<00:00,  8.49it/s, loss=0.644]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:46<00:00,  8.50it/s, loss=0.622]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:45<00:00,  8.56it/s, loss=0.622]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:45<00:00,  8.54it/s, loss=0.628]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:45<00:00,  8.55it/s, loss=0.591]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 17\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 18\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 19\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:45<00:00,  8.55it/s, loss=0.623]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 21\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:46<00:00,  8.49it/s, loss=0.609]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 22\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:45<00:00,  8.55it/s, loss=0.646]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 23\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 24\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "from pytorch_ood.model import WideResNet\n",
    "from pytorch_ood.detector import EnergyBased, MaxSoftmax\n",
    "from pytorch_ood.utils import OODMetrics\n",
    "from pytorch_ood.dataset.img import Textures\n",
    "from torchvision.datasets import CIFAR10 \n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "from torchvision.transforms import Compose, ToPILImage, Normalize, ToTensor, Resize, ToPILImage\n",
    "import torch \n",
    "\n",
    "from pytorch_ood.loss import OutlierExposureLoss\n",
    "from torch.optim import SGD\n",
    "from tqdm import tqdm\n",
    "\n",
    "torch.manual_seed(10)\n",
    "\n",
    "prep = Compose([\n",
    "    ToPILImage(),\n",
    "    Resize([32,32]),\n",
    "    ToTensor(),\n",
    "    Normalize(\n",
    "        std=[0.24705882352941176470, 0.24352941176470588235, 0.26156862745098039215], \n",
    "        mean=[0.49137254901960784313, 0.48235294117647058823, 0.44666666666666666666]), \n",
    "    ])\n",
    "\n",
    "mt = Compose([\n",
    "    #ToPILImage(),\n",
    "    Resize([32,32]),\n",
    "    ToTensor(),\n",
    "    Normalize(\n",
    "        std=[0.24705882352941176470, 0.24352941176470588235, 0.26156862745098039215], \n",
    "        mean=[0.49137254901960784313, 0.48235294117647058823, 0.44666666666666666666]), \n",
    "    ])\n",
    "\n",
    "# TODO: use 80,000?\n",
    "x = torch.load(\"samples-c10.pt\")[:80000]\n",
    "gan_oe_data = torch.stack([prep(t) for t in x])\n",
    "gan_oe_label = torch.ones(size=(len(x),)).long() * -1\n",
    "\n",
    "data_in_train = CIFAR10(root=\"data/\", download=True, transform=mt, target_transform=torch.tensor)\n",
    "data_out_train = TensorDataset(gan_oe_data,gan_oe_label)\n",
    "train_loader = DataLoader(data_in_train + data_out_train, batch_size=256, shuffle=True, num_workers=20)\n",
    "      \n",
    "# create Neural Network\n",
    "model = WideResNet(pretrained=\"cifar10-pt\", num_classes=10).cuda()\n",
    "opti = SGD(model.parameters(), lr=0.001)\n",
    "\n",
    "criterion = OutlierExposureLoss(alpha=0.5)\n",
    "\n",
    "mav = 0.0\n",
    "mavs = []\n",
    "\n",
    "metrics_goe = []\n",
    "\n",
    "for i in range(n_epochs):\n",
    "    print(f\"Epoch {i}\")\n",
    "    \n",
    "    # train \n",
    "    model.train()\n",
    "    bar = tqdm(train_loader)\n",
    "    for x, y in bar:\n",
    "        opti.zero_grad()\n",
    "        y_hat = model(x.cuda())\n",
    "        loss = criterion(y_hat, y.cuda())\n",
    "        mav = 0.2 * loss.item() + 0.8 * mav\n",
    "        mavs.append(mav)\n",
    "\n",
    "        loss.backward()\n",
    "        opti.step()\n",
    "        bar.set_postfix({\"loss\": mav})\n",
    "        \n",
    "    print(80*\"#\")\n",
    "    \n",
    "    r = test(model)\n",
    "    for entry in r:\n",
    "        entry.update({\"Epoch\": i})\n",
    "    \n",
    "    metrics_goe += r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "{} &  AUROC &  AUPR-IN &  AUPR-OUT &  FPR95TPR \\\\\n",
      "Method  &        &          &           &           \\\\\n",
      "\\midrule\n",
      "Energy  &  95.51 &    94.04 &     96.52 &     18.50 \\\\\n",
      "Softmax &  95.54 &    94.20 &     96.54 &     18.31 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data = pd.DataFrame(metrics_goe)\n",
    "tmp = data[data[\"Epoch\"] == n_epochs -1]\n",
    "print((tmp.groupby([\"Method\"]).mean() * 100)[[\"AUROC\", \"AUPR-IN\", \"AUPR-OUT\", \"FPR95TPR\"]]\n",
    "      .to_latex(float_format=\"%.2f\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Epoch', ylabel='AUROC'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sb.lineplot(data=data.groupby([\"Epoch\", \"Method\"]).mean(), y=\"AUROC\", x=\"Epoch\", hue=\"Method\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Noise Outlier Exposure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Epoch 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 508/508 [00:59<00:00,  8.59it/s, loss=0.806]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `AUROC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
      "  warnings.warn(*args, **kwargs)\n",
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `PrecisionRecallCurve` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
      "  warnings.warn(*args, **kwargs)\n",
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: `pos_label` automatically set 1.\n",
      "  warnings.warn(*args, **kwargs)\n",
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/functional/classification/precision_recall_curve.py:156: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  thresholds = tensor(reversed(thresholds[sl]))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 508/508 [00:59<00:00,  8.58it/s, loss=0.767]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 508/508 [00:59<00:00,  8.54it/s, loss=0.765]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 508/508 [00:58<00:00,  8.61it/s, loss=0.745]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 508/508 [00:59<00:00,  8.60it/s, loss=0.751]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 508/508 [00:59<00:00,  8.56it/s, loss=0.729]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████| 508/508 [00:59<00:00,  8.51it/s, loss=0.77]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 7\n"
     ]
    },
    {
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      "Epoch 24\n"
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    },
    {
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      "################################################################################\n",
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    }
   ],
   "source": [
    "\"\"\"\n",
    "Do it with uniformely distributed outlier samples \n",
    "\"\"\"\n",
    "from pytorch_ood.model import WideResNet\n",
    "from pytorch_ood.detector import EnergyBased, MaxSoftmax\n",
    "from pytorch_ood.utils import OODMetrics\n",
    "from pytorch_ood.dataset.img import Textures\n",
    "from torchvision.datasets import CIFAR10 \n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "from torchvision.transforms import Compose, ToPILImage, Normalize, ToTensor, Resize, ToPILImage\n",
    "import torch \n",
    "\n",
    "from pytorch_ood.loss import OutlierExposureLoss\n",
    "from torch.optim import SGD\n",
    "from tqdm import tqdm\n",
    "\n",
    "torch.manual_seed(10)\n",
    "\n",
    "\n",
    "# TODO: use 80,000?\n",
    "x = torch.randint(0, 255, size=(80000, 3, 32, 32), dtype=torch.uint8) #  torch.load(\"samples.pt\")[:80000]\n",
    "gan_oe_data = torch.stack([prep(t) for t in x])\n",
    "gan_oe_label = torch.ones(size=(len(x),)).long() * -1\n",
    "\n",
    "data_in_train = CIFAR10(root=\"data\", download=True, transform=mt, target_transform=torch.tensor)\n",
    "data_out_train = TensorDataset(gan_oe_data,gan_oe_label)\n",
    "train_loader = DataLoader(data_in_train + data_out_train, batch_size=256, shuffle=True, num_workers=20)\n",
    "      \n",
    "# create Neural Network\n",
    "model = WideResNet(pretrained=\"cifar10-pt\", num_classes=10).cuda()\n",
    "opti = SGD(model.parameters(), lr=0.001)\n",
    "\n",
    "criterion = OutlierExposureLoss(alpha=0.5)\n",
    "\n",
    "mav = 0.0\n",
    "mavs = []\n",
    "\n",
    "metrics_noise = []\n",
    "\n",
    "for i in range(n_epochs):\n",
    "    print(f\"Epoch {i}\")\n",
    "    \n",
    "    # train \n",
    "    model.train()\n",
    "    bar = tqdm(train_loader)\n",
    "    for x, y in bar:\n",
    "        opti.zero_grad()\n",
    "        y_hat = model(x.cuda())\n",
    "        loss = criterion(y_hat, y.cuda())\n",
    "        mav = 0.2 * loss.item() + 0.8 * mav\n",
    "        mavs.append(mav)\n",
    "\n",
    "        loss.backward()\n",
    "        opti.step()\n",
    "        bar.set_postfix({\"loss\": mav})\n",
    "        \n",
    "    print(80*\"#\")\n",
    "    \n",
    "    r = test(model)\n",
    "    for entry in r:\n",
    "        entry.update({\"Epoch\": i})\n",
    "    \n",
    "    metrics_noise += r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "{} &  AUROC &  AUPR-IN &  AUPR-OUT &  FPR95TPR \\\\\n",
      "Method  &        &          &           &           \\\\\n",
      "\\midrule\n",
      "Energy  &  97.36 &    96.32 &     97.95 &     11.54 \\\\\n",
      "Softmax &  94.49 &    91.75 &     96.11 &     17.83 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data = pd.DataFrame(metrics_noise)\n",
    "tmp = data[data[\"Epoch\"] == n_epochs - 1]\n",
    "print((tmp.groupby([\"Method\"]).mean() * 100)[[\"AUROC\", \"AUPR-IN\", \"AUPR-OUT\", \"FPR95TPR\"]]\n",
    "      .to_latex(float_format=\"%.2f\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Epoch', ylabel='AUROC'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sb.lineplot(data=data.groupby([\"Epoch\", \"Method\"]).mean(), y=\"AUROC\", x=\"Epoch\", hue=\"Method\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Real Outlier Exposure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Epoch 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████| 1368/1368 [02:35<00:00,  8.79it/s, loss=1.26]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `AUROC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
      "  warnings.warn(*args, **kwargs)\n",
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `PrecisionRecallCurve` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
      "  warnings.warn(*args, **kwargs)\n",
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: `pos_label` automatically set 1.\n",
      "  warnings.warn(*args, **kwargs)\n",
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/functional/classification/precision_recall_curve.py:156: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  thresholds = tensor(reversed(thresholds[sl]))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████| 1368/1368 [02:36<00:00,  8.75it/s, loss=1.16]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████| 1368/1368 [02:35<00:00,  8.79it/s, loss=1.14]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 1368/1368 [02:35<00:00,  8.79it/s, loss=1.1]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████| 1368/1368 [02:36<00:00,  8.76it/s, loss=1.11]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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   ],
   "source": [
    "\"\"\"\n",
    "Do it with the usual (non-synthetic outliers)\n",
    "\"\"\"\n",
    "from pytorch_ood.model import WideResNet\n",
    "from pytorch_ood.detector import EnergyBased, MaxSoftmax\n",
    "from pytorch_ood.utils import OODMetrics\n",
    "from pytorch_ood.dataset.img import Textures, TinyImages300k\n",
    "from torchvision.datasets import CIFAR10 \n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "from torchvision.transforms import Compose, ToPILImage, Normalize, ToTensor, Resize, ToPILImage\n",
    "import torch \n",
    "\n",
    "from pytorch_ood.loss import OutlierExposureLoss\n",
    "from torch.optim import SGD\n",
    "from tqdm import tqdm\n",
    "\n",
    "torch.manual_seed(10)\n",
    "\n",
    "# prepare data \n",
    "# data_in_test = CIFAR100(root=\".\", download=True, transform=mt2, train=False)\n",
    "# data_out_test = Textures(root=\".\", transform=mt2, download=True)\n",
    "# test_loader = DataLoader(data_in_test + data_out_test, batch_size=512, num_workers=20)\n",
    "\n",
    "# TODO: use 80,000?\n",
    "# x = torch.randint(0, 255, size=(80000, 3, 32, 32), dtype=torch.uint8) #  torch.load(\"samples.pt\")[:80000]\n",
    "# gan_oe_data = torch.stack([mt(t) for t in x])\n",
    "# gan_oe_label = torch.ones(size=(len(x),)).long() * -1\n",
    "\n",
    "data_out_train = TinyImages300k(root=\"/data_fast/kirchheim/datasets/\", transform=mt, download=True, \n",
    "                                target_transform=ToUnknown())\n",
    "\n",
    "data_in_train = CIFAR10(root=\"data/\", download=True, transform=mt)\n",
    "train_loader = DataLoader(data_in_train + data_out_train, batch_size=256, shuffle=True, num_workers=20)\n",
    "      \n",
    "# create Neural Network\n",
    "model = WideResNet(pretrained=\"cifar10-pt\", num_classes=10).cuda()\n",
    "opti = SGD(model.parameters(), lr=0.001)\n",
    "\n",
    "criterion = OutlierExposureLoss(alpha=0.5)\n",
    "\n",
    "mav = 0.0\n",
    "mavs = []\n",
    "\n",
    "metrics_oe = []\n",
    "\n",
    "for i in range(n_epochs):\n",
    "    print(f\"Epoch {i}\")\n",
    "    \n",
    "    # train \n",
    "    model.train()\n",
    "    bar = tqdm(train_loader)\n",
    "    for x, y in bar:\n",
    "        opti.zero_grad()\n",
    "        y_hat = model(x.cuda())\n",
    "        loss = criterion(y_hat, y.cuda())\n",
    "        mav = 0.2 * loss.item() + 0.8 * mav\n",
    "        mavs.append(mav)\n",
    "\n",
    "        loss.backward()\n",
    "        opti.step()\n",
    "        bar.set_postfix({\"loss\": mav})\n",
    "        \n",
    "    print(80*\"#\")\n",
    "    \n",
    "    r = test(model)\n",
    "    for entry in r:\n",
    "        entry.update({\"Epoch\": i})\n",
    "    \n",
    "    metrics_oe += r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "{} &  AUROC &  AUPR-IN &  AUPR-OUT &  FPR95TPR \\\\\n",
      "Method  &        &          &           &           \\\\\n",
      "\\midrule\n",
      "Energy  &  98.63 &    97.91 &     98.99 &      5.88 \\\\\n",
      "Softmax &  98.56 &    97.86 &     98.92 &      6.12 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data = pd.DataFrame(metrics_oe)\n",
    "tmp = data[data[\"Epoch\"] == n_epochs -1]\n",
    "\n",
    "print((tmp.groupby([\"Method\"]).mean() * 100)[[\"AUROC\", \"AUPR-IN\", \"AUPR-OUT\", \"FPR95TPR\"]]\n",
    "      .to_latex(float_format=\"%.2f\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Epoch', ylabel='AUROC'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sb.lineplot(data=data.groupby([\"Epoch\", \"Method\"]).mean(), y=\"AUROC\", x=\"Epoch\", hue=\"Method\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Epoch 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:46<00:00,  8.48it/s, loss=0.711]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `AUROC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
      "  warnings.warn(*args, **kwargs)\n",
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `PrecisionRecallCurve` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
      "  warnings.warn(*args, **kwargs)\n",
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: `pos_label` automatically set 1.\n",
      "  warnings.warn(*args, **kwargs)\n",
      "/home/kirchheim/anaconda3/envs/myenv/lib/python3.8/site-packages/torchmetrics/functional/classification/precision_recall_curve.py:156: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  thresholds = tensor(reversed(thresholds[sl]))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "Epoch 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:46<00:00,  8.39it/s, loss=0.664]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "################################################################################\n",
      "Epoch 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:46<00:00,  8.48it/s, loss=0.648]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "################################################################################\n",
      "Epoch 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:46<00:00,  8.48it/s, loss=0.652]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "################################################################################\n",
      "Epoch 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:46<00:00,  8.48it/s, loss=0.611]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "################################################################################\n",
      "Epoch 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:46<00:00,  8.47it/s, loss=0.609]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "################################################################################\n",
      "Epoch 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 391/391 [00:46<00:00,  8.48it/s, loss=0.619]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "100%|███████████████████████████████████████████████████| 508/508 [01:00<00:00,  8.46it/s, loss=0.744]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "################################################################################\n",
      "Epoch 18\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 508/508 [00:59<00:00,  8.49it/s, loss=0.757]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "################################################################################\n",
      "Epoch 19\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 508/508 [00:59<00:00,  8.51it/s, loss=0.771]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "################################################################################\n",
      "Epoch 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 508/508 [00:59<00:00,  8.47it/s, loss=0.754]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "################################################################################\n",
      "Epoch 21\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 508/508 [00:59<00:00,  8.54it/s, loss=0.756]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "################################################################################\n",
      "Epoch 22\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 508/508 [00:59<00:00,  8.56it/s, loss=0.746]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "################################################################################\n",
      "Epoch 23\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 508/508 [00:59<00:00,  8.51it/s, loss=0.756]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "################################################################################\n",
      "Epoch 24\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 508/508 [01:00<00:00,  8.47it/s, loss=0.751]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "################################################################################\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "\n",
    "# 1.0, 2.0, 5.0, 10.0, 20.0, 30.0, 40.0\n",
    "\n",
    "for var in [0.0, 0.5, 1.0, 2.0, 5.0, 10.0, 20.0, 30.0, 40.0, 50.0]:\n",
    "    data = np.load(f\"cifar10/samples-{var}.npz\")\n",
    "    x = torch.tensor(data[\"x\"])[:80000]\n",
    "\n",
    "    # x = torch.load(\"samples/samples-{var}.pt\")[:80000]\n",
    "    gan_oe_data = torch.stack([prep(t) for t in x])\n",
    "    gan_oe_label = torch.ones(size=(len(x),)).long() * -1\n",
    "\n",
    "    data_in_train = CIFAR10(root=\"data/\", download=True, transform=mt, target_transform=torch.tensor)\n",
    "    data_out_train = TensorDataset(gan_oe_data,gan_oe_label)\n",
    "    train_loader = DataLoader(data_in_train + data_out_train, batch_size=256, shuffle=True, num_workers=20)\n",
    "\n",
    "    # create Neural Network\n",
    "    model = WideResNet(pretrained=\"cifar10-pt\", num_classes=10).cuda()\n",
    "    opti = SGD(model.parameters(), lr=0.001)\n",
    "\n",
    "    criterion = OutlierExposureLoss(alpha=0.5)\n",
    "\n",
    "    mav = 0.0\n",
    "    mavs = []\n",
    "\n",
    "    for i in range(n_epochs):\n",
    "        print(f\"Epoch {i}\")\n",
    "\n",
    "        # train \n",
    "        model.train()\n",
    "        bar = tqdm(train_loader)\n",
    "        for x, y in bar:\n",
    "            opti.zero_grad()\n",
    "            y_hat = model(x.cuda())\n",
    "            loss = criterion(y_hat, y.cuda())\n",
    "            mav = 0.2 * loss.item() + 0.8 * mav\n",
    "            mavs.append(mav)\n",
    "\n",
    "            loss.backward()\n",
    "            opti.step()\n",
    "            bar.set_postfix({\"loss\": mav})\n",
    "            \n",
    "        \n",
    "        r = test(model)\n",
    "        for entry in r:\n",
    "            entry.update({\n",
    "                \"Epoch\": i,\n",
    "                \"Variance\": var,\n",
    "            })\n",
    "        results += r \n",
    "        \n",
    "        print(80*\"#\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmpdf = pd.DataFrame(results)\n",
    "tmpdf.to_csv(\"results/c10-auroc-vs-epoch-vs-variance.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 762.93x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set_style(\"whitegrid\")\n",
    "sb.set_context(\"paper\")\n",
    "\n",
    "from matplotlib import rcParams\n",
    "rcParams['pgf.texsystem'] = \"pdflatex\"\n",
    "\n",
    "tmpdf[\"$\\sigma^2$\"] = tmpdf[\"Variance\"]\n",
    "\n",
    "sb.relplot(data=tmpdf.groupby([\"Epoch\", \"Method\", \"Variance\"]).mean(), \n",
    "            x=\"Epoch\", y=\"AUROC\", hue=\"$\\sigma^2$\", palette=\"rocket\", kind=\"line\", col=\"Method\")\n",
    "\n",
    "plt.savefig(\"img/c10-auroc-vs-epoch-vs-variance.png\", dpi=300)\n",
    "plt.savefig(\"img/c10-auroc-vs-epoch-vs-variance.pgf\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 840.68x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.relplot(data=tmpdf[tmpdf[\"Epoch\"] == 24], \n",
    "            x=\"$\\sigma^2$\", y=\"AUROC\", kind=\"line\", col=\"Method\", hue=\"Dataset\", marker=\"o\")\n",
    "\n",
    "plt.savefig(\"img/c10-auroc-vs-variance.png\", dpi=300)\n",
    "plt.savefig(\"img/c10-auroc-vs-variance.pgf\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th></th>\n",
       "      <th>AUROC</th>\n",
       "      <th>AUPR-IN</th>\n",
       "      <th>AUPR-OUT</th>\n",
       "      <th>ACC95TPR</th>\n",
       "      <th>FPR95TPR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epoch</th>\n",
       "      <th>Method</th>\n",
       "      <th>Variance</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">Energy</th>\n",
       "      <th>0.0</th>\n",
       "      <td>0.867851</td>\n",
       "      <td>0.831112</td>\n",
       "      <td>0.889360</td>\n",
       "      <td>0.731984</td>\n",
       "      <td>0.43980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.750507</td>\n",
       "      <td>0.729155</td>\n",
       "      <td>0.774432</td>\n",
       "      <td>0.601787</td>\n",
       "      <td>0.69448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.762882</td>\n",
       "      <td>0.737570</td>\n",
       "      <td>0.782122</td>\n",
       "      <td>0.611229</td>\n",
       "      <td>0.67578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.780458</td>\n",
       "      <td>0.755620</td>\n",
       "      <td>0.795922</td>\n",
       "      <td>0.629167</td>\n",
       "      <td>0.64088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.802884</td>\n",
       "      <td>0.780730</td>\n",
       "      <td>0.813577</td>\n",
       "      <td>0.650680</td>\n",
       "      <td>0.59966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">24</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">Softmax</th>\n",
       "      <th>10.0</th>\n",
       "      <td>0.911178</td>\n",
       "      <td>0.888301</td>\n",
       "      <td>0.917226</td>\n",
       "      <td>0.790205</td>\n",
       "      <td>0.34750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>0.944412</td>\n",
       "      <td>0.926199</td>\n",
       "      <td>0.955047</td>\n",
       "      <td>0.858592</td>\n",
       "      <td>0.21648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>0.949081</td>\n",
       "      <td>0.932628</td>\n",
       "      <td>0.959929</td>\n",
       "      <td>0.866235</td>\n",
       "      <td>0.20208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.0</th>\n",
       "      <td>0.952508</td>\n",
       "      <td>0.937975</td>\n",
       "      <td>0.962723</td>\n",
       "      <td>0.872884</td>\n",
       "      <td>0.19080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>0.956178</td>\n",
       "      <td>0.942074</td>\n",
       "      <td>0.966194</td>\n",
       "      <td>0.880872</td>\n",
       "      <td>0.17624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           AUROC   AUPR-IN  AUPR-OUT  ACC95TPR  FPR95TPR\n",
       "Epoch Method  Variance                                                  \n",
       "0     Energy  0.0       0.867851  0.831112  0.889360  0.731984   0.43980\n",
       "              0.5       0.750507  0.729155  0.774432  0.601787   0.69448\n",
       "              1.0       0.762882  0.737570  0.782122  0.611229   0.67578\n",
       "              2.0       0.780458  0.755620  0.795922  0.629167   0.64088\n",
       "              5.0       0.802884  0.780730  0.813577  0.650680   0.59966\n",
       "...                          ...       ...       ...       ...       ...\n",
       "24    Softmax 10.0      0.911178  0.888301  0.917226  0.790205   0.34750\n",
       "              20.0      0.944412  0.926199  0.955047  0.858592   0.21648\n",
       "              30.0      0.949081  0.932628  0.959929  0.866235   0.20208\n",
       "              40.0      0.952508  0.937975  0.962723  0.872884   0.19080\n",
       "              50.0      0.956178  0.942074  0.966194  0.880872   0.17624\n",
       "\n",
       "[500 rows x 5 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "tmpdf.groupby([\"Epoch\", \"Method\", \"Variance\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10000000, 3, 32, 32])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.load(f\"samples-c10.npz\")\n",
    "x = torch.tensor(data[\"x\"])\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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     "output_type": "stream",
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      "Files already downloaded and verified\n",
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      "################################################################################\n",
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    },
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    },
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    },
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    },
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    },
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     ]
    },
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     ]
    },
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     ]
    },
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      "Epoch 15\n"
     ]
    },
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      "################################################################################\n",
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      "Epoch 16\n"
     ]
    },
    {
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     "output_type": "stream",
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      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 17\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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      "################################################################################\n",
      "Files already downloaded and verified\n",
      "Epoch 18\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 37%|██████████████████▉                                | 73/196 [00:10<00:13,  9.03it/s, loss=0.0205]"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(10)\n",
    "\n",
    "data = np.load(f\"samples-c10.npz\")\n",
    "\n",
    "metrics_l = []\n",
    "    \n",
    "for l in [100, 1000, 10000, 20000, 50000, 100000]:\n",
    "    xn = x[:l]\n",
    "    gan_oe_data = torch.stack([prep(t) for t in xn])\n",
    "    gan_oe_label = torch.ones(size=(len(xn),)).long() * -1\n",
    "\n",
    "    data_in_train = CIFAR10(root=\"data/\", download=True, transform=mt, target_transform=torch.tensor)\n",
    "    data_out_train = TensorDataset(gan_oe_data,gan_oe_label)\n",
    "    train_loader = DataLoader(data_in_train + data_out_train, batch_size=256, shuffle=True, num_workers=20)\n",
    "\n",
    "    # create Neural Network\n",
    "    model = WideResNet(pretrained=\"cifar10-pt\", num_classes=10).cuda()\n",
    "    opti = SGD(model.parameters(), lr=0.001)\n",
    "\n",
    "    criterion = OutlierExposureLoss(alpha=0.5)\n",
    "\n",
    "    mav = 0.0\n",
    "    mavs = []\n",
    "    \n",
    "    r = test(model)\n",
    "    for entry in r:\n",
    "        entry.update({\n",
    "            \"Epoch\": -1,\n",
    "            \"Length\": l\n",
    "        })\n",
    "\n",
    "    metrics_l += r\n",
    "\n",
    "    for i in range(n_epochs):\n",
    "        print(f\"Epoch {i}\")\n",
    "\n",
    "        # train \n",
    "        model.train()\n",
    "        bar = tqdm(train_loader)\n",
    "        for x, y in bar:\n",
    "            opti.zero_grad()\n",
    "            y_hat = model(x.cuda())\n",
    "            loss = criterion(y_hat, y.cuda())\n",
    "            mav = 0.2 * loss.item() + 0.8 * mav\n",
    "            mavs.append(mav)\n",
    "\n",
    "            loss.backward()\n",
    "            opti.step()\n",
    "            bar.set_postfix({\"loss\": mav})\n",
    "\n",
    "        print(80*\"#\")\n",
    "\n",
    "        r = test(model)\n",
    "        for entry in r:\n",
    "            entry.update({\n",
    "                \"Epoch\": i,\n",
    "                \"Length\": l\n",
    "            })\n",
    "\n",
    "        metrics_l += r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "ename": "BrokenPipeError",
     "evalue": "[Errno 32] Broken pipe",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mBrokenPipeError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1488639/152827861.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"img/size-vs-auroc-c10.png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"img/size-vs-auroc-c10.pgf\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    964\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    965\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    967\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m# need this if 'transparent=True' to reset colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   3013\u001b[0m                 \u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_edgecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'none'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3014\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3015\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3016\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3017\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtransparent\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2253\u001b[0m                 \u001b[0;31m# force the figure dpi to 72), so we need to set it again here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2254\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2255\u001b[0;31m                     result = print_method(\n\u001b[0m\u001b[1;32m   2256\u001b[0m                         \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2257\u001b[0m                         \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfacecolor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/backends/backend_pgf.py\u001b[0m in \u001b[0;36mprint_pgf\u001b[0;34m(self, fname_or_fh, *args, **kwargs)\u001b[0m\n\u001b[1;32m    852\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_requires_unicode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    853\u001b[0m                 \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcodecs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetwriter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 854\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_print_pgf_to_fh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    855\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    856\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_pdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname_or_fh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1667\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1669\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1671\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/backends/backend_pgf.py\u001b[0m in \u001b[0;36m_print_pgf_to_fh\u001b[0;34m(self, fh, bbox_inches_restore)\u001b[0m\n\u001b[1;32m    837\u001b[0m                                      \u001b[0mRendererPgf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    838\u001b[0m                                      bbox_inches_restore=bbox_inches_restore)\n\u001b[0;32m--> 839\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    840\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    841\u001b[0m         \u001b[0;31m# end the pgfpicture environment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   2788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2789\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2790\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   2791\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   2792\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    429\u001b[0m                          \u001b[0;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m                 **kwargs)\n\u001b[0;32m--> 431\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2919\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2920\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2921\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2922\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2923\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1141\u001b[0m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1142\u001b[0;31m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[0m\u001b[1;32m   1143\u001b[0m                                                                 renderer)\n\u001b[1;32m   1144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_tick_bboxes\u001b[0;34m(self, ticks, renderer)\u001b[0m\n\u001b[1;32m   1066\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_tick_bboxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1067\u001b[0m         \u001b[0;34m\"\"\"Return lists of bboxes for ticks' label1's and label2's.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1068\u001b[0;31m         return ([tick.label1.get_window_extent(renderer)\n\u001b[0m\u001b[1;32m   1069\u001b[0m                  for tick in ticks if tick.label1.get_visible()],\n\u001b[1;32m   1070\u001b[0m                 [tick.label2.get_window_extent(renderer)\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1066\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_tick_bboxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1067\u001b[0m         \u001b[0;34m\"\"\"Return lists of bboxes for ticks' label1's and label2's.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1068\u001b[0;31m         return ([tick.label1.get_window_extent(renderer)\n\u001b[0m\u001b[1;32m   1069\u001b[0m                  for tick in ticks if tick.label1.get_visible()],\n\u001b[1;32m   1070\u001b[0m                 [tick.label2.get_window_extent(renderer)\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36mget_window_extent\u001b[0;34m(self, renderer, dpi)\u001b[0m\n\u001b[1;32m    901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 903\u001b[0;31m             \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    904\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    905\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    312\u001b[0m             \u001b[0mclean_line\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_preprocess_math\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mclean_line\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 314\u001b[0;31m                 w, h, d = renderer.get_text_width_height_descent(\n\u001b[0m\u001b[1;32m    315\u001b[0m                     clean_line, self._fontproperties, ismath=ismath)\n\u001b[1;32m    316\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/backends/backend_pgf.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    730\u001b[0m         \u001b[0;31m# get text metrics in units of latex pt, convert to display units\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 731\u001b[0;31m         w, h, d = (LatexManager._get_cached_or_new()\n\u001b[0m\u001b[1;32m    732\u001b[0m                    .get_width_height_descent(s, prop))\n\u001b[1;32m    733\u001b[0m         \u001b[0;31m# TODO: this should be latex_pt_to_in instead of mpl_pt_to_in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/backends/backend_pgf.py\u001b[0m in \u001b[0;36mget_width_height_descent\u001b[0;34m(self, text, prop)\u001b[0m\n\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m         \u001b[0;31m# send textbox to LaTeX and wait for prompt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 342\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stdin_writeln\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtextbox\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    343\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expect_prompt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/backends/backend_pgf.py\u001b[0m in \u001b[0;36m_stdin_writeln\u001b[0;34m(self, s)\u001b[0m\n\u001b[1;32m    248\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlatex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlatex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlatex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_expect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mBrokenPipeError\u001b[0m: [Errno 32] Broken pipe"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import rcParams\n",
    "rcParams['pgf.texsystem'] = \"pdflatex\"\n",
    "\n",
    "tmp = pd.DataFrame(metrics_l)\n",
    "tmp.to_csv(f\"results/c10-size-vs-auroc.csv\")\n",
    "tmp[\"Samples\"] = tmp[\"Length\"]\n",
    "tmp[\"Epoch\"] = tmp[\"Epoch\"] + 1 \n",
    "tmp = tmp.groupby([\"Epoch\", \"Samples\", \"Method\"]).mean()\n",
    "\n",
    "sb.lineplot(data=tmp,  x=\"Epoch\", y=\"AUROC\", hue=\"Samples\", style=\"Method\") \n",
    "plt.tight_layout()\n",
    "plt.savefig(\"img/size-vs-auroc-c10.png\", dpi=300)\n",
    "plt.savefig(\"img/size-vs-auroc-c10.pgf\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv",
   "language": "python",
   "name": "myenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}