XAI_QUANTUS-4Evaluation.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### **QUANTUS**\n",
        "\n",
        "The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness. Until now, no tool exists that exhaustively and speedily allows researchers to quantitatively evaluate explanations of neural network predictions. To increase transparency and reproducibility in the field, we therefore built Quantus - a comprehensive, open-source toolkit in Python that includes a growing, well-organised collection of evaluation metrics and tutorials for evaluating explainable methods. The toolkit has been thoroughly tested and is available under open source license on PyPi\n",
        "\n",
        "**Github**: https://github.com/understandable-machine-intelligence-lab/Quantus\n",
        "\n",
        "**Paper**: https://arxiv.org/abs/2202.06861\n",
        "\n",
        "**More tutorials here**: https://github.com/understandable-machine-intelligence-lab/Quantus/tree/main/tutorials"
      ],
      "metadata": {
        "id": "V_qXTED8YV5V"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bukochgPFg7s"
      },
      "source": [
        "# Getting started\n",
        "\n",
        "This notebook shows how to get started with Quantus, using a very simple example. For this purpose, we use a LeNet model and MNIST dataset.\n",
        "\n",
        "- Make sure to have GPUs enabled to speed up computation.\n",
        "- Skip running the first cell if you do not use Google Colab."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A8-GsXIAcigw"
      },
      "source": [
        "# Mount Google Drive.\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)\n",
        "\n",
        "# Install packages.\n",
        "from IPython.display import clear_output\n",
        "!pip install captum opencv-python\n",
        "!pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html\n",
        "clear_output()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install quantus[torch] # or pip install quantus[tensorflow]"
      ],
      "metadata": {
        "id": "Be-oWLzDbApF",
        "outputId": "856a263c-ae2c-4724-c786-da5d6cc5fee5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting quantus[torch]\n",
            "  Downloading quantus-0.1.1-py3-none-any.whl (89 kB)\n",
            "\u001b[K     |████████████████████████████████| 89 kB 3.8 MB/s \n",
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            "\u001b[K     |██████████████████████████████▎ | 834.1 MB 32.3 MB/s eta 0:00:02tcmalloc: large alloc 1147494400 bytes == 0x561ccc864000 @  0x7f7b602d4615 0x561c6700d3bc 0x561c670ee18a 0x561c670101cd 0x561c67102b3d 0x561c67084458 0x561c6707f02f 0x561c67011aba 0x561c670842c0 0x561c6707f02f 0x561c67011aba 0x561c67080cd4 0x561c67103986 0x561c67080350 0x561c67103986 0x561c67080350 0x561c67103986 0x561c67080350 0x561c67011f19 0x561c67055a79 0x561c67010b32 0x561c670841dd 0x561c6707f02f 0x561c67011aba 0x561c67080cd4 0x561c6707f02f 0x561c67011aba 0x561c6707feae 0x561c670119da 0x561c67080108 0x561c6707f02f\n",
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            "  Downloading toml-0.10.2-py2.py3-none-any.whl (16 kB)\n",
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            "Collecting coverage[toml]>=5.2.1\n",
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            "  Downloading coverage-6.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (207 kB)\n",
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            "Installing collected packages: importlib-metadata, toml, pluggy, numpy, coverage, scipy, pytest, pyflakes, pycodestyle, mccabe, torch, scikit-learn, scikit-image, pytest-lazy-fixture, pytest-cov, opencv-python, matplotlib, flake8, torchvision, quantus\n",
            "  Attempting uninstall: importlib-metadata\n",
            "    Found existing installation: importlib-metadata 4.11.0\n",
            "    Uninstalling importlib-metadata-4.11.0:\n",
            "      Successfully uninstalled importlib-metadata-4.11.0\n",
            "  Attempting uninstall: pluggy\n",
            "    Found existing installation: pluggy 0.7.1\n",
            "    Uninstalling pluggy-0.7.1:\n",
            "      Successfully uninstalled pluggy-0.7.1\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.21.5\n",
            "    Uninstalling numpy-1.21.5:\n",
            "      Successfully uninstalled numpy-1.21.5\n",
            "  Attempting uninstall: coverage\n",
            "    Found existing installation: coverage 3.7.1\n",
            "    Uninstalling coverage-3.7.1:\n",
            "      Successfully uninstalled coverage-3.7.1\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.4.1\n",
            "    Uninstalling scipy-1.4.1:\n",
            "      Successfully uninstalled scipy-1.4.1\n",
            "  Attempting uninstall: pytest\n",
            "    Found existing installation: pytest 3.6.4\n",
            "    Uninstalling pytest-3.6.4:\n",
            "      Successfully uninstalled pytest-3.6.4\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 1.8.0+cu111\n",
            "    Uninstalling torch-1.8.0+cu111:\n",
            "      Successfully uninstalled torch-1.8.0+cu111\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.0.2\n",
            "    Uninstalling scikit-learn-1.0.2:\n",
            "      Successfully uninstalled scikit-learn-1.0.2\n",
            "  Attempting uninstall: scikit-image\n",
            "    Found existing installation: scikit-image 0.18.3\n",
            "    Uninstalling scikit-image-0.18.3:\n",
            "      Successfully uninstalled scikit-image-0.18.3\n",
            "  Attempting uninstall: opencv-python\n",
            "    Found existing installation: opencv-python 4.1.2.30\n",
            "    Uninstalling opencv-python-4.1.2.30:\n",
            "      Successfully uninstalled opencv-python-4.1.2.30\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.2.2\n",
            "    Uninstalling matplotlib-3.2.2:\n",
            "      Successfully uninstalled matplotlib-3.2.2\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.9.0+cu111\n",
            "    Uninstalling torchvision-0.9.0+cu111:\n",
            "      Successfully uninstalled torchvision-0.9.0+cu111\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.8.0 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\n",
            "torchtext 0.11.0 requires torch==1.10.0, but you have torch 1.10.1 which is incompatible.\n",
            "torchaudio 0.10.0+cu111 requires torch==1.10.0, but you have torch 1.10.1 which is incompatible.\n",
            "tensorflow 2.8.0 requires numpy>=1.20, but you have numpy 1.19.5 which is incompatible.\n",
            "markdown 3.3.6 requires importlib-metadata>=4.4; python_version < \"3.10\", but you have importlib-metadata 4.2.0 which is incompatible.\n",
            "datascience 0.10.6 requires coverage==3.7.1, but you have coverage 6.2 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "coveralls 0.5 requires coverage<3.999,>=3.6, but you have coverage 6.2 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed coverage-6.2 flake8-4.0.1 importlib-metadata-4.2.0 matplotlib-3.3.4 mccabe-0.6.1 numpy-1.19.5 opencv-python-4.5.5.62 pluggy-1.0.0 pycodestyle-2.8.0 pyflakes-2.4.0 pytest-6.2.5 pytest-cov-3.0.0 pytest-lazy-fixture-0.6.3 quantus-0.1.1 scikit-image-0.19.1 scikit-learn-0.24.2 scipy-1.5.4 toml-0.10.2 torch-1.10.1 torchvision-0.11.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "cv2",
                  "matplotlib",
                  "mpl_toolkits",
                  "numpy",
                  "torch",
                  "torchvision"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MuA8v9jLp-pf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0040d64f-8bf7-4e56-a390-794e28bc415d"
      },
      "source": [
        "# Imports general.\n",
        "import sys\n",
        "import gc\n",
        "import warnings\n",
        "import pathlib\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "import captum\n",
        "from captum.attr import *\n",
        "import random\n",
        "import os\n",
        "import cv2\n",
        "\n",
        "# Import package.\n",
        "path = \"/content/drive/MyDrive/Projects\"\n",
        "sys.path.append(f'{path}/quantus')\n",
        "import quantus\n",
        "\n",
        "# Collect garbage.\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "# Notebook settings.\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\") \n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
        "%load_ext autoreload\n",
        "%autoreload 2\n",
        "# clear_output()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The autoreload extension is already loaded. To reload it, use:\n",
            "  %reload_ext autoreload\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mGhP4bTuoWYF"
      },
      "source": [
        "## 1. Preliminaries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XqKzag4VFjHT"
      },
      "source": [
        "### 1.1 Load datasets\n",
        "\n",
        "We will then load a batch of input, output pairs that we generate explanations for, then to evaluate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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            "b00cd2e0fc56459484f57716199b0018",
            "f88a2b8992c24a1fae3fe6d546a3d4eb",
            "1bbb034eee724adca05653bafe6b92b3",
            "ebfb54e3d1bc477cb041998f8855c2ca",
            "2aecdd06f5644b6eb73d1f4fd1a4ecdf",
            "23ad942ab7a9428d9d4f2e86ffaf6d7c",
            "41a9a7d2e0364f8e9b11e333b034afd2",
            "4cda3a4d4bcb4e45b393dfc80d40cd59",
            "6b7096be2efe4c7abf8c21f7c31035a3",
            "f5dcb6fc418f456eb58b086cf4ce38cf",
            "5629a56bef0f4f48a738099bec0424e1",
            "f08867bd8bbe4925b1250da32f846af7",
            "549fce6d84874a31bebc0a9510840407",
            "1d240b83295143468a7034e0f931a3cf",
            "687eca13b5724190a9bd98205cf4a754",
            "7db6c7aa372e4c6d99be6397c0cf42aa",
            "448eff9eedf64669b13647da3adbc1d9",
            "7f671e056649478887f490a53f8f2193",
            "32e723f6fcee47d4835dd7c4313aaae0",
            "5558d3c6ca404038ad95315da8a1c79f",
            "0ae36efe7e5b4d9eb8295a5b893e78e4",
            "267c14b10b8a4f3cb116b61da7210a04",
            "6d0760f666724f8297486f504e112eca",
            "a4e457f17ab24796b81dee77aec920be",
            "d2fed8cd9f77469ebc9162fbae15c921",
            "d59e9b4b87934097b53fbf1dbbbc79f2",
            "8ad05b53acfb43a8989f12ae73f3fc87",
            "bac42f6ea9974f1689db1e38bf85b875",
            "8048595e822f4ca495dd46ab63c2c37f",
            "a138c5acbab84451be9b7ef7d6bf781d",
            "23a7e4d3484947d687754ac94a4071c1",
            "b9c52ea23fe840bcbc9a0f026e9489d5"
          ]
        },
        "outputId": "b1106ba4-7930-43fc-cde6-485e150da30c"
      },
      "source": [
        "# Load datasets and make loaders.\n",
        "test_samples = 24\n",
        "transformer = transforms.Compose([transforms.ToTensor()])\n",
        "train_set = torchvision.datasets.MNIST(root='./sample_data', train=True, transform=transformer, download=True)\n",
        "test_set = torchvision.datasets.MNIST(root='./sample_data', train=False, transform=transformer, download=True)\n",
        "train_loader = torch.utils.data.DataLoader(train_set, batch_size=128, shuffle=True, pin_memory=True) # num_workers=4,\n",
        "test_loader = torch.utils.data.DataLoader(test_set, batch_size=200, pin_memory=True)\n",
        "\n",
        "# Load a batch of inputs and outputs to use for evaluation.\n",
        "x_batch, y_batch = iter(test_loader).next()\n",
        "x_batch, y_batch = x_batch.to(device), y_batch.to(device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./sample_data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "32076d65f7af4beea5cbc8607a8db48b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./sample_data/MNIST/raw/train-images-idx3-ubyte.gz to ./sample_data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./sample_data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3fd01adc2cfa442faec998a3af55b766",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./sample_data/MNIST/raw/train-labels-idx1-ubyte.gz to ./sample_data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./sample_data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5629a56bef0f4f48a738099bec0424e1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./sample_data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./sample_data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./sample_data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "267c14b10b8a4f3cb116b61da7210a04",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./sample_data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./sample_data/MNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "aAR67-cnOS67",
        "outputId": "0d410c33-18a8-4d4a-e4d2-d266bc0127c5"
      },
      "source": [
        "# Plot some inputs!\n",
        "nr_images = 5\n",
        "fig, axes = plt.subplots(nrows=1, ncols=nr_images, figsize=(nr_images*3, int(nr_images*2/3)))\n",
        "for i in range(nr_images):\n",
        "    axes[i].imshow((np.reshape(x_batch[i].cpu().numpy(), (28, 28)) * 255).astype(np.uint8), vmin=0.0, vmax=1.0, cmap=\"gray\")\n",
        "    axes[i].title.set_text(f\"MNIST class - {y_batch[i].item()}\")\n",
        "    axes[i].axis(\"off\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x216 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmccxpA0n6MY"
      },
      "source": [
        "### 1.2 Train a LeNet model\n",
        "\n",
        "(or any other model of choice). \n",
        "Network architecture from: https://github.com/ChawDoe/LeNet5-MNIST-PyTorch."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUghaOhXddLU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "937fea9e-2c5e-48d9-cf12-2cf22d80167e"
      },
      "source": [
        "class LeNet(torch.nn.Module):\n",
        "    \"\"\"Network architecture from: https://github.com/ChawDoe/LeNet5-MNIST-PyTorch.\"\"\"\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.conv_1 = torch.nn.Conv2d(1, 6, 5)\n",
        "        self.pool_1 = torch.nn.MaxPool2d(2, 2)\n",
        "        self.relu_1 = torch.nn.ReLU()\n",
        "        self.conv_2 = torch.nn.Conv2d(6, 16, 5)\n",
        "        self.pool_2 = torch.nn.MaxPool2d(2, 2)\n",
        "        self.relu_2 = torch.nn.ReLU()\n",
        "        self.fc_1 = torch.nn.Linear(256, 120)\n",
        "        self.relu_3 = torch.nn.ReLU()\n",
        "        self.fc_2 = torch.nn.Linear(120, 84)\n",
        "        self.relu_4 = torch.nn.ReLU()\n",
        "        self.fc_3 = torch.nn.Linear(84, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool_1(self.relu_1(self.conv_1(x)))\n",
        "        x = self.pool_2(self.relu_2(self.conv_2(x)))\n",
        "        x = x.view(x.shape[0], -1)\n",
        "        x = self.relu_3(self.fc_1(x))\n",
        "        x = self.relu_4(self.fc_2(x))\n",
        "        x = self.fc_3(x)\n",
        "        return x\n",
        "\n",
        "# Load model architecture.\n",
        "model = LeNet()\n",
        "print(f\"\\n Model architecture: {model.eval()}\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Model architecture: LeNet(\n",
            "  (conv_1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
            "  (pool_1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (relu_1): ReLU()\n",
            "  (conv_2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
            "  (pool_2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (relu_2): ReLU()\n",
            "  (fc_1): Linear(in_features=256, out_features=120, bias=True)\n",
            "  (relu_3): ReLU()\n",
            "  (fc_2): Linear(in_features=120, out_features=84, bias=True)\n",
            "  (relu_4): ReLU()\n",
            "  (fc_3): Linear(in_features=84, out_features=10, bias=True)\n",
            ")\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "olAfyOHzevne"
      },
      "source": [
        "def train_model(model, \n",
        "                train_data: torchvision.datasets,\n",
        "                test_data: torchvision.datasets, \n",
        "                device: torch.device, \n",
        "                epochs: int = 20,\n",
        "                criterion: torch.nn = torch.nn.CrossEntropyLoss(), \n",
        "                optimizer: torch.optim = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9), \n",
        "                evaluate: bool = False):\n",
        "    \"\"\"Train torch model.\"\"\"\n",
        "    \n",
        "    model.train()\n",
        "    \n",
        "    for epoch in range(epochs):\n",
        "\n",
        "        for images, labels in train_data:\n",
        "            images, labels = images.to(device), labels.to(device)\n",
        "        \n",
        "            optimizer.zero_grad()\n",
        "            \n",
        "            logits = model(images)\n",
        "            loss = criterion(logits, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "        # Evaluate model!\n",
        "        if evaluate:\n",
        "            predictions, labels = evaluate_model(model, test_data, device)\n",
        "            test_acc = np.mean(np.argmax(predictions.cpu().numpy(), axis=1) == labels.cpu().numpy())\n",
        "        \n",
        "        print(f\"Epoch {epoch+1}/{epochs} - test accuracy: {(100 * test_acc):.2f}% and CE loss {loss.item():.2f}\")\n",
        "\n",
        "    return model\n",
        "\n",
        "def evaluate_model(model, data, device):\n",
        "    \"\"\"Evaluate torch model.\"\"\"\n",
        "    model.eval()\n",
        "    logits = torch.Tensor().to(device)\n",
        "    targets = torch.LongTensor().to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for images, labels in data:\n",
        "            images, labels = images.to(device), labels.to(device)\n",
        "            logits = torch.cat([logits, model(images)])\n",
        "            targets = torch.cat([targets, labels])\n",
        "    \n",
        "    return torch.nn.functional.softmax(logits, dim=1), targets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t6_qEwhee1WH",
        "outputId": "4169a05f-3b81-4bf8-afab-c875d8939e75"
      },
      "source": [
        "path_model_weights = \"drive/MyDrive/Projects/quantus/tutorials/assets/mnist\"\n",
        "\n",
        "if pathlib.Path(path_model_weights).is_file():\n",
        "    model.load_state_dict(torch.load(path_model_weights))\n",
        "    \n",
        "else:\n",
        "\n",
        "    # Train and evaluate model.\n",
        "    model = train_model(model=model.to(device),\n",
        "                        train_data=train_loader,\n",
        "                        test_data=test_loader,\n",
        "                        device=device,\n",
        "                        epochs=20,\n",
        "                        criterion=torch.nn.CrossEntropyLoss().to(device),\n",
        "                        optimizer=torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9),\n",
        "                        evaluate=True)\n",
        "\n",
        "    # Save model.\n",
        "    torch.save(model.state_dict(), path_model_weights)\n",
        "\n",
        "# Model to GPU and eval mode.\n",
        "model.to(device)\n",
        "model.eval()\n",
        "\n",
        "# Check test set performance.\n",
        "predictions, labels = evaluate_model(model, test_loader, device)\n",
        "test_acc = np.mean(np.argmax(predictions.cpu().numpy(), axis=1) == labels.cpu().numpy())        \n",
        "print(f\"Model test accuracy: {(100 * test_acc):.2f}%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20 - test accuracy: 98.96% and CE loss 0.00\n",
            "Epoch 2/20 - test accuracy: 98.94% and CE loss 0.00\n",
            "Epoch 3/20 - test accuracy: 99.02% and CE loss 0.00\n",
            "Epoch 4/20 - test accuracy: 99.03% and CE loss 0.00\n",
            "Epoch 5/20 - test accuracy: 98.95% and CE loss 0.00\n",
            "Epoch 6/20 - test accuracy: 98.98% and CE loss 0.00\n",
            "Epoch 7/20 - test accuracy: 99.00% and CE loss 0.00\n",
            "Epoch 8/20 - test accuracy: 99.03% and CE loss 0.00\n",
            "Epoch 9/20 - test accuracy: 99.04% and CE loss 0.00\n",
            "Epoch 10/20 - test accuracy: 99.03% and CE loss 0.00\n",
            "Epoch 11/20 - test accuracy: 99.02% and CE loss 0.00\n",
            "Epoch 12/20 - test accuracy: 99.01% and CE loss 0.00\n",
            "Epoch 13/20 - test accuracy: 99.02% and CE loss 0.00\n",
            "Epoch 14/20 - test accuracy: 99.02% and CE loss 0.00\n",
            "Epoch 15/20 - test accuracy: 98.99% and CE loss 0.00\n",
            "Epoch 16/20 - test accuracy: 98.99% and CE loss 0.00\n",
            "Epoch 17/20 - test accuracy: 99.04% and CE loss 0.00\n",
            "Epoch 18/20 - test accuracy: 99.02% and CE loss 0.00\n",
            "Epoch 19/20 - test accuracy: 99.00% and CE loss 0.00\n",
            "Epoch 20/20 - test accuracy: 99.02% and CE loss 0.00\n",
            "Model test accuracy: 99.02%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4vY9mZQanaxr"
      },
      "source": [
        "### 1.3 Generate explanations\n",
        "\n",
        "There exist multiple ways to generate explanations for neural network models e.g., using `captum` or `innvestigate` libraries. In this example, we rely on the `quantus.explain` functionality (a simple wrapper around `captum`) however use whatever approach or library you'd like to create your explanations.\n",
        "\n",
        "**Requirements.**\n",
        "\n",
        "* **Data type.** Similar to the x-y pairs, the attributions should also be of type `np.ndarray`\n",
        "* **Shape.** Sharing all the same dimensions as the input (expect for nr_channels which for explanations is equal to 1). For example, if x_batch is of size (128, 3, 224, 224) then the attributions should be of size (128, 1, 224, 224)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNxAtc2Co1pL"
      },
      "source": [
        "# Generate normalised Saliency and Integrated Gradients attributions of the first batch of the test set.\n",
        "a_batch_saliency = quantus.normalise_by_negative(Saliency(model).attribute(inputs=x_batch, target=y_batch, abs=True).sum(axis=1).cpu().numpy())\n",
        "a_batch_intgrad = quantus.normalise_by_negative(IntegratedGradients(model).attribute(inputs=x_batch, target=y_batch, baselines=torch.zeros_like(x_batch)).sum(axis=1).cpu().numpy())\n",
        "\n",
        "# Save x_batch and y_batch as numpy arrays that will be used to call metric instances.\n",
        "x_batch, y_batch = x_batch.cpu().numpy(), y_batch.cpu().numpy()\n",
        "\n",
        "\n",
        "# Quick assert.\n",
        "assert [isinstance(obj, np.ndarray) for obj in [x_batch, y_batch, a_batch_saliency, a_batch_intgrad]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iRDwzUUp8bR2"
      },
      "source": [
        "Visualise attributions given model and pairs of input-output."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "id": "82WWNmyoilXo",
        "outputId": "4ce24fa8-e4ae-4aba-e727-197d2534aa6d"
      },
      "source": [
        "# Plot explanations!\n",
        "nr_images = 3\n",
        "fig, axes = plt.subplots(nrows=nr_images, ncols=3, figsize=(nr_images*2.5, int(nr_images*3)))\n",
        "for i in range(nr_images):\n",
        "    axes[i, 0].imshow((np.reshape(x_batch[i], (28, 28)) * 255).astype(np.uint8), vmin=0.0, vmax=1.0, cmap=\"gray\")\n",
        "    axes[i, 0].title.set_text(f\"MNIST digit {y_batch[i].item()}\")\n",
        "    axes[i, 0].axis(\"off\")\n",
        "    axes[i, 1].imshow(a_batch_saliency[i], cmap=\"seismic\")\n",
        "    axes[i, 1].title.set_text(f\"Saliency\")\n",
        "    axes[i, 1].axis(\"off\")\n",
        "    a = axes[i, 2].imshow(a_batch_intgrad[i], cmap=\"seismic\")\n",
        "    axes[i, 2].title.set_text(f\"Integrated Gradients\")\n",
        "    axes[i, 2].axis(\"off\")\n",
        "plt.savefig(f'{path}/quantus/tutorials/assets/mnist_example.png', dpi=400)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XXlknQ7ZOxs5JPj5M3bMOipFh+rOfxdtz6aVtu2Buvz3r5jg9yWcnefZ4NzseWHFwhAIAABSjoAAAAMUoKAAAQDEKCgAAUKxXnZTp7t29Cf9mZt29CeiQuUmefY30y0kenzAobRCm73rXtvG1Ble71VbxzOuvH3/l9OP3PhfmZ1763TA/4ojx8RVo/zAdN26fZHz2deV3Jvnnkvy2JI+/Jv2WWz4S5kce2TY74oi+4djRow9OrvOcJH84ybOTL7M8+8r23unYY9tmb/pYclLuiSfG+VVXhfE9e5wc5n9Jvt0++4p4dB+OUAAAgGIUFAAAoBgFBQAAKEZBAQAAilFQAACAYr2qy6Mn6UkdJ12td3e0ZN0cmeyrtLOvXI498UScnxV8O/aECfHY6dOTyc87L4y/dO+9YX7oSxeH+ZvfHH89uDQxyc9P8uzrrtdO8luTPDZu3GVhfu+9/9cmmzo1+1rvrBPlkSTfOMkfTPIVw5vuCDp0tt46Hnxp/JXy5/3wh/H4U04J40OTbVnlyfir6TV8eBjfdEtz75932SWe/5vfjLuvZs6M53nHO+L8hhvaZjvuGI99tZc0C3GEAgAAFKOgAAAAxSgoAABAMQoKAABQjIICAAAUo8sDLS7rNMjWXtgkybNOhh3DdPbsuCvkggvGB+nEcOxaa30nzO2o+H1A375xN8d+8fIWkmYk+dlhOmzY2DA/7LB4lm9/O+6g2H77eM2G22Ymp8PvumsYHz8wus7z4jlSyToUSjoIlN2Zi5q83t7p8Et2aJPdemvbTJJGJsvfnPP0D8J8yL3Xx79w9dUd2rbFPndo/PwYO/aXyW+MS/LTwvT4Qx4P81kbbhjmTySz/9d667XJnrtoSjg26g7riThCAQAAilFQAACAYhQUAACgGAUFAAAoRkEBAACK9aouj+5YU2JFWrNjxZKt+TA3yeN1L/r2HRTmCxfenszT9gzxz3427ub45Xt+FuY/fvXIML/yyvgax46N18Lo0+fTYb7hhnE3x9+PSc6ST05Bv2Tkn8M8WWpBT98+KZ5+UpyPObDtOi3f1tfjydN1SLJun/uaHN9LFlsotO66bbNDDmlujnPPzS7ZLUwXDo3zq3aPZ8nWw5BGJfkGYbrxxnHH12/j3VGvXB3/rbhvj/jv1luCBX8evSOeu7fgCAUAAChGQQEAAIpRUAAAgGIUFAAAoBgFBQAAKNarujy6Q3d0liwLulEyryX5gCQfkuTJwgSpbA2Rh9okY8f+JBw5dmzcWZJ1qBxzzJphvs02cTfHUUfFsw956an4gqNvjPOV4s6H7Gz7s86aFuYnxsO1Z5Jrn33aZhfEc+drc7TtFKnE3Tt5F9CK3f3Rlfomy6eMHt3cPCedtEX5xkiaODHOjxsdr/ExOJln09NPb5Nd1fbloVfhCAUAAChGQQEAAIpRUAAAgGIUFAAAoBgFBQAAKEaXRy/Tld0cvaWjpTnJKeKp7Kz/P4TpwoUPJuPjdQCi9QrWWivoVpA0c+apYb7++vGaBN///pnJdd4ZpnfffVGYDx78tjD/zd57hPk9G30mzF9N12z4WpjutH68b0+ZsiieZo/bgnBicp3ZfjA7yZMFR/RCk/NnXSHorebPj/MXh8ZrgvT9S7xfHxesvdOv37JuVc/AEQoAAFCMggIAABSjoAAAAMUoKAAAQDEKCgAAUIwuD7S4bG2HEUm+apI/nORTkvzgDl/vWWfFIz+5+//EF9x4TRj/as6X4i05eNcwv/32z4Z5//5jw9zGxWerDxsWxpo+Pe7+kOI1SqZMyRZnyNZd+VCQZd0Z2YoK2VobjyZ51s1Bl8eKIuvEuPbaOB8zJs7f9a622eabL9Mm9RgcoQAAAMUoKAAAQDEKCgAAUIyCAgAAFKOgAAAAxejy6KG6cs0OqVXX7YiskuRZ90e2lsdqSb5zkp+T5P+Z5IEDDgjjB664IswP2muvMB9+62/C/JBD4m6OqVODRQYkZetYTJ9+fzI+u8/ibhQpaRdJuzxuCLJtk7Hx+ifSwiTPPNLkePRWW28d5ztfley/R58RxjNmxK9B2ZogvRlHKAAAQDEKCgAAUIyCAgAAFKOgAAAAxSgoAABAMbo80OKyNRYy2doLi5I8q8m3SPKz2yRXXhl3inxydLy2xZYjR4b5jCOOD/OPDB2fbMtKSR6v/fGFL8RdG/934jPJPCfG8fBkvZRDDw1j+/mYZP6bgmx6MjbrIMm6fbK1PF5O8mxNEPRWO48/Lsyv+/nPw3z3leM/p/OH/rjTtqmn4wgFAAAoRkEBAACKUVAAAIBiFBQAAKAYBQUAAChm7a0ZYWZdu6AEWLNjGbh7h2+U2Z7JHZytMzE4yfskeby+RT7PwCDbIRzZt2+8/sQTT8QzDx2arTPxvSTfIMmzTolvhan/bmKYP7TLLmF+VzL7/xsV3953zPtzmE+adFKQZt0WQ5J8QZLPTvL7kjzrmIm3x/3aDu3DJ58sXoO72MCBcf7eI+KH6N5kno8/GT9UF17Y/Db1dMcdp/DO4QgFAAAoRkEBAACKUVAAAIBiFBQAAKAYBQUAACjGWh4tohW7OTpHtobD8CTfKMmnJHm2FkSWbxJkt4YjFy6Mt3Ho0KyDJOvOiLtIpFXCdN11vx7mU6deE+a2S3wf+69/HeYD9t473pyjjw7j54+Mh0tf7WAmSROSPO4syfeP7HHNun2yHD1FsmSOnj4izg/rE3d8ndyC3RzN4ggFAAAoRkEBAACKUVAAAIBiFBQAAKAYBQUAAChGl8dy0tVrdiAzN8mztRqys/izLoGPJPmuSd43yE5Nxo5P8n2TPHNTkkcdJ9LUqdn47L58Mkxt7/3D/Prr4+fCsclZ8knzh26+ue3Z9jfe+N54cNLRIi1M8gzdHK1mjUt+Fuff+U6Y37VTvKZN+nRdgXCEAgAAFKOgAAAAxSgoAABAMQoKAABQjIICAAAUo8ujk3V1NwdrdjTr5SSPOxOkZ5N8+yTfJ0zf//61w7xfv7bZzTdn60ZcluSPJ3nWaRB3W0jxmgTSmUm+RZJnHTPxYgi77TYoGR93nVxySdYxs1KQ3ZmMnZHk2boo2X6TdYVE2yJJryY5lrfj9vlHfMGl8XP+uS/H3Rzjz+qsLWo9HKEAAADFKCgAAEAxCgoAAFCMggIAABSjoAAAAMXo8lhGrM3RWyxK8qzTILNamPbpE3dzHHpoPMsTT7TNZs06Jhz717/ul2zLlCS/Ick3DdORI9cP80mTdkjmuS3J4zU+PvShuAPmoYfiWWbOzNYKOSPJow6NrBvnmiTPujMeSfL48WaNj54jW/tFY84L438ccnKYn3daPE3UqYUKRygAAEAxCgoAAFCMggIAABSjoAAAAMU4KbOH4iu2O0v29dKzkjz7Kua7wnTRovirmD//+cPC/Gc/a7s9u+8eX+NWW8VfyT127JD4F/TuJP9GmA4b9sMwf9e7+ob5JZdkX0u+V5iOHh2P/uIX4xNl119/QJi/+c3xVyA//3zbbPr0B+MrVXImqCYneXayZpavkuSclNlVBidP1VXu+H2YP3d0fPLlqclJnBtttCxbtWLjCAUAAChGQQEAAIpRUAAAgGIUFAAAoBgFBQAAKEaXx1LwFdu9XXZWfvxV2tLbkvyZJH85yc8I0yOO2K1Nttde8Vdjr7dePPPVV8edK3fcEeff//6RYf6HP5wTX4HuT/Jtw3S//eKukDvuiGc57LB4O1ddNR5/991xPmlS9FXdcTeOlHV/xJ0lUty9k8v2A5SaOjXON9wwzt+004fDPPvK7PSrutE0jlAAAIBiFBQAAKAYBQUAAChGQQEAAIpRUAAAgGJ0eXQz1uzoaq8mebb2wlNJvn6SZ+tq3Jnk49sk48bFnQZ77RWv5TE8jvW9U18L86FDR4T5UUdl3RzZOiezw/TSS+PR7vGaHdKfkzxzeZJH3SXZtmddPc2utZGNz/YzlDrrrPi5tPHGcdfRIYfE8yxMGndW5q9gp+EIBQAAKEZBAQAAilFQAACAYhQUAACgGAUFAAAoxvmtNdbsaFVx50N+Vv7AJI/WjZCkTZocf2uQDQpHjhu3IMk/lMwdr6mRr0uRtIsk2yMNC1P345Lx8fbn66IMTvKsw+a+IMu6MOL1Q/L9IMuz+5K1PEpNnJhdEndkDU52l6FD4zwbn3V/oHkcoQAAAMUoKAAAQDEKCgAAUIyCAgAAFKOgAAAAxejyQIvLzr7P8ilJvlKSX9zk+KgTY3IyNus0+EOSX5/kGyV5tu7Fqkk+Lsmz+zK7D7IOmOx0+2w7o7P/s26ObO5sG7P7nm6OrrLddnF+1FGjwnz8+Hj87HjJmVS/fs2NR44jFAAAoBgFBQAAKEZBAQAAilFQAACAYhQUAACgGF0ey4mZdfcmoEOaXQ8jW5diQBN5tCZFe9uyKMl3TvK7kzzrZMhuU7NWS/LsdjW77sr8Tpgjy7G8DRkS5488EudrrRXnr/KQdhuOUAAAgGIUFAAAoBgFBQAAKEZBAQAAilFQAACAYnR51OjCQOWFJsdn61JkeTOydSZmNJln609k25h1Z2T3TTY+63Rp9j7OTtvPOm/QGz32WHdvAUpxhAIAABSjoAAAAMUoKAAAQDEKCgAAUIyCAgAAFDN37+5tAAAAvRxHKAAAQDEKCgAAUIyCAgAAFKOgAAAAxSgoAABAMQoKAABQjIICAAAUo6AAAADFKCgAAEAxCgoAAFCMggIAABSjoAAAAMUoKAAAQDEKCgAAUIyCAgAAFKOgAAAAxSgoAABAMQoKAABQjIICAAAUo6AAAADFKCgAAEAxCgoAAFCMggIAABSjoAAAAMUoKAAAQDEKCgAAUIyCAgAAFKOg6GZmtqOZTWv4+W9mtmMHf7fDY4GMmT1hZjvV/3+cmf2iu7cJ6Axmtp6ZuZmt3M3bsUI8x1qqoKgftJfNbPAS+V/rnWq9+ufz6p/f2zBmIzPzhp/Hm9mhDT8fZ2ZTzGy+mU0zs8vq/G91Nt/MXjWzhQ0/H9fsbXD3zdx9fLNjzWyMmV2YjTWzN5vZuWb2pJnNM7OJZrZrs9uHnsvMtjOzu8zseTN7zszuNLNRzczh7ie7+6FLH4neqvGPWwfGvuF1cHlb2utaJ13HaDO7x8xeMLNn6v//kplZV1xfZz3Hekqx1KilCoraFEn7Lf7BzLaQ9JZg3HOSTuzIhGZ2sKQDJe3k7v0kbS3pVunff9T71fntkr68+Gd3P7nspnSqlSVNlfRBSatL+oakyxcXWejdzGyApOsk/UTSGpLeKuk7kl7qzu0CMj3hD6GZHSXpR5K+L2mopCGSDpO0raRVkt9ZabltYC/TigXFBZIOavj5YEm/CsadL2lLM/tgB+YcJel37v6YJLn70+7+f8uycWa2an2EZLaZPVzP3Xh546GxVc3s/Hrs383sq0t8PPKEme1kZh+TdJykT9dHRu5f8nrd/QV3H+PuT7j7a+5+nari6z3LcjvQ47xdktz9End/1d0XuPtN7v6AmW1oZr83s2fNbJaZXWRmA6NJlnxHaGbb1Ec95pjZ/Y0fsdXvXk+oj4TMM7ObGo8ONhwxmWNmU83sEDMbZWYzGl+UzeyT0T6Lrlc/JneY2Wn168yUxUcuzewkSdtL+mn9uvLTOh9pZjfXR8EeMbN9G+Zb08yuNbO5ZjbBzE40szsaLnczO8LMJkuaXGc/qvePuWb2FzPbvs7D1zUzW70+2jrdzP5ZX8dK9WUr1bdllpk9Lmm3dm776pKOl/Qld7/S3ed55a/u/hl3f6ked56Z/dzMfmtmL0j6kJntZtWR77n1to9ZYu4DrToa/KyZfX2JyzrrOXZb/d859f3zfquOtP/RqqOUs6w+kr68tGJBcbekAWb2jnonGy0pOmT2oqSTJZ3UwTkPMrNjzGxrK6tQvy1pw/rfLqoKnvbGridpA0kflXRANMjdb1R1Wy6rj4y8c2kbYWZDVP0R+lszG48e6x+SXrWqAN3VzAY1XGaSTpG0jqR3SFpX0pilTWhmb5V0vaojeWtIOlrSr81srYZh+0v6rKS1Vb2jO7r+3RGSblB1xGQtSVtJmujuEyQ9K2nnhjkOVFz0Y/l4n6RHJA2W9D1J55qZufvX9cajrl82s9Uk3SzpYlWP+WhJZ5rZpvVcP5P0gqp3+wcrfn3bs77Oxb8zQdX+sUY97xVm1red17XzJL0iaSNJ71K1Ly3+COELknav860l7dPO7X6/pDdLunop949U7ecnSeov6Y76Nh4kaaCqouVwM9tTkur74ueq9ut1JK0paXg0aclzTNIO9X8H1vfPnySdIOkmSYPq6/xJB25bp2nFgkJ6/SjFRyX9XdI/k3FnS3qbLeVcAne/UNJ/qioA/ijpGTP72jJu276STnL359x9qqQfL2Xsye4+292nLWVsh5lZH0kXSTrf3Sd1xpzoXu4+V9J2klzSOZJmmtk1ZjbE3R9195vd/SV3nynpB6o++lqaAyT91t1/Wx/VulnSvZI+3jBmrLv/w90XSLpc1R8GqXoRvKU+YrLI3Z9194n1ZefXc8vM1lD1vLq44OajzJPufo67v6rqsRmm6tB/ZHdJT7j7WHd/xd3/KunXkj5Vv9HaW9K33f1Fd3+4nm9Jp9Svfwuk6vW13j9ecffTVf2R3yS68vqN0MclHVkfdX1G0g9VFTZS9Zp5hrtPdffnVBXSmcGSZrn7Kw3zLz5SsMDMdmgYe7W731k/Dxa6+3h3f7D++QFJl+j159Q+kq5z99vqoxzflPRasg0lz7HIIkkjJK1Tb+cd7YztdK1cUOwv6RC1886nfrBPqP+1y90vcvedVFWkh0k6wcx2WYZtW0fVuQyLPdnE2KnZwI4yszepun9elvTl0vnQc7j73939EHcfLmlzVfvPGWY2xMwurQ8Pz1V1xG5wu5NVRqj6QzFn8T9VRcuwhjFPN/z/i5L61f+/rqTHknkvlPSJ+t3uvpJud/fpHbyZ6Hz/fgzd/cX6f/slY0dIet8S+8RnVB2RWEuvn6u1WPSa9YbMzI626iPd5+v5Vle+f46Q1EfS9IbrP1vVu3epudfXZyUNtoZzOdz9A+4+sL6s8e/jktv8PjP7g5nNNLPnVf1NWLzNb9gGd3+hni+7Pcv6HIt8VdURyT9b1TDwuXbGdrqWLCjc/UlV5wd8XNJvljJ8rKoi4ZMdnHuRu18h6QFVL9rNmq7qxXaxty1lbOOhsnWzgarembbLzEzSuarefezt7ouW9jvoneojT+ep2kdPVrV/bOHuA1S9K+rIGexTJV3g7gMb/q3m7qd28Hc3TLbtn5L+pOo5d6CqAhc905KvK1Ml/XGJfaKfux8uaaaqjyKW9prV2E23vao/gvtKGlT/MX9er++f0fW/JGlww/UPcPfN6subeX39Uz3XHu2MabPNtYslXSNpXXdfXdJZDdv8hm0ws7eo+tgjUvIca/Oa79X5fV9w93UkfVHVx1EbdWCuTtGSBUXt85I+XFeHqfpw17clpR9hWHXi0m5m1t/M3lR/RLKZpHuWYbsul/S/ZjbIzIar+iilI2PfqvaPKMyQtF59BCLzc1WfoX9i8eFGtAarTpQ7qt6nZGbrqup2ulvV577zJT1f70fHdHDaxUcSdrHqZLe+Vn1vSvh58BIukrSTme1rZitbdbLeVg2X/0rVH5IttPSiH91nhqpzuBa7TtLb65MO+9T/RpnZO+qPTH4jaYyZvcXMRuqNJ8hH+qsqQmZKWtnMviVpwBLX/+/XtfpI1k2STjezAfXr8Yb2+sn1l0v6LzMbXp9HdGx2xe4+R1Un1Jlmtk/D6/tWklbrwHY/5+4Lrfr6gf0bLrtS0u5WnZS8iqoTP7PX5ZLn2ExVH6X8+/Exs081/O5sVUVH9nFLp2vZgsLdH3P3ezs4/BJVVWVmrqqzjZ+SNEfViUuHL+PnU99RdRhuiqonRnvvzo6XNK0ee4uqHTVrA7yi/u+zZnbfkhfWJ8l9UdXnb0/b69+V8ZlluA3oeeapOtHtHqvORL9b0kOSjlK1z71b1Tu/69XBP+D1OT57qNr3Z6p6N3WMOvC64e5PqTpCeJSqFu2JkhpPFh6n6nDvuIbD7Oh5fiRpH6s6QH7s7vNUnQQ5WtK/VB2O/66q8x6k6k3P6nV+garX1vZal38n6UZVJxU/KWmh3vjxQvS6dpCqkxMfVvVH80q9/hHBOfWc90u6T0vZ1939e5L+R1VxO6P+d7aqN5h3tfOrX5J0vJnNk/QtVYXM4jn/JukIVUcxptfbOC2apPA59qKqE0XvrD8u2UZV1+A9ZjZf1RGUr7j740ubq7OY+1KPlKOHMLPDJY12946cUAf0aGb2mKQvuvst3b0t6Bpm9l1JQ929vW42tIiWPULRCsxsmJltWx+G20TVu71x3b1dQCkz21vV4djfd/e2oPPUH71taZX3qvromdesFUS3f1MZ2rWKqsNv66v6qOVSSWd25wYBpcxsvKrvIDjQ3Zfb57tYLvqr+phjHVUfH5yujn3PA1oAH3kAAIBifOQBAACKtfuRhzWsvgn0FO7e4VUAzfbspH341SQP1w9S9b1hHR2fzd2sbJ7sm+L7Njk+025ndqDZ+7JPk/N0hma/oqW5bXG/tkP78AMPLP37ZYDlbcst4++x4QgFAAAoRkEBAACKUVAAAIBiFBQAAKAYBQUAACjGF1thBdVsR8SAJG+2g2JuE3Nk6xNlXRXZekLRdUrSJkmerficzZPd1oVJ3ux9mXV5RLL7Jps7y7Muj2x8V3acAL0DRygAAEAxCgoAAFCMggIAABSjoAAAAMUoKAAAQDG6PNDisrPvs86EbD2JQU1eb9YNEK3xkXVDbJPkI5rMN0/y8Ume3QePJ/nsJG+2A+aZJN80ySOrJvn0JM86Y7JukWyNFgAcoQAAAMUoKAAAQDEKCgAAUIyCAgAAFKOgAAAAxejyqLl7d29Ch5hZd29Ci8i6AbL1M7KOhYFJnnUsROtYZGtbZN0fWTfHrCSfkuTnJPkFSZ6t8TEuybM1OO5L8i2SfFSSR4/JhGTstCRfO8mz91rZfclaHqWeeCLOZyW79Y47xvkGT98VX7DeemH8+MJ1wvyVV+JpFmZPS3CEAgAAlKOgAAAAxSgoAABAMQoKAABQjIICAAAUW+G6PHpLN0cm2366PzLNrKnRnmxth2FJvlWS3xlkC5Kx2ZoXDyZ5vKbGyJEfCfN3vevSMN9uu3j2iRM3CvNf/OKYMHe/P55Ityd59lhl9/2MIMu6dLI1O7LHL+vayLpF6PLoqJEj43zL4c/FF9xyS5yPnx/nWVvIlLhDZ4N3vSvMXxsZryEzcWI8/cor3F/TtjhCAQAAilFQAACAYhQUAACgGAUFAAAoRkEBAACKtex5qb29mwOdJeuUyDoK4k6JvBug2euNugHODUcOGhSv8fHcN34QTz086WTY/cUwPu7Et4R5dhb7wIFxnp2Ef8AB7wzz6dP3jX9BDyX5dUke3d5snZNsHZI1k3xukr+W5Nn+tOJ2f2y5XnIf7r5PnGdtEul+vXucZ21KTz8d51deGcZv2nPPMB88eMswnzMnnn5FwhEKAABQjIICAAAUo6AAAADFKCgAAEAxCgoAAFCs13d59LRujmxNjZ62nSuObG2HQU3m2RoOWTdAdnb/JkG2MBy5+eZxl4cGD47z884L4xkfjLsqbrwxnuaJJ+J8nyZPzt9jjzg/66z3xhcoXjtBytYEuS3IsjVakvtMjyR5tn5IZpUkb/0uj379kguytocmuzD+9Y0zm5r+lfjppJUHvz3MN+13b/wL98b50APo8shwhAIAABSjoAAAAMUoKAAAQDEKCgAAUMzaO1nQzHr8mYRdfbJjdpJls3rLdvYG7t7hG2t2UHLHZ1+NPT3Js5M1s6/qzk7Si76OeqtkbPyVwNJ3wvTcc9cP8zvuiGcZO/bsZP7kq47TEx43CtP+/bcI83nzshNZz0/yWUkenay5azL28STPTsrs2pMp3a/t0D78wAPq8a/B48fH+RlnxPlVV8V5dnLn/PlNblBio3g31VvujU7uVXp28oxdDorzGcuwUb3Ullsq3H85QgEAAIpRUAAAgGIUFAAAoBgFBQAAKEZBAQAAivX6Lo9Ms10VXd0lQZdH52muy2Pv5I5PvtY6rbGzzoTXkjz6im2pf/+T22RbbRXPsPXWcf7DHx6dXGe2jYuS/OtJnnU+/CXJJyT5Dkm+dpJn3RxPJXnUjTIsGZvdN7cmedblsVKSZ/tN/D3QrdTlkZk4Mc6zbov0K7w7yUMPxXn2/Nt08DNh/q9X4v13Vrb7tiC6PAAAQJehoAAAAMUoKAAAQDEKCgAAUIyCAgAAFFu5uzegq3RX10NXd3OgWVnNnK3BsWmSx2fr5x0LcZfH6NFtsx13jGcYODDOr7vutKbGT5jwZJi/9NKIZP74NPy9945vkzQlyf+V5MnaCfqPJP9IkkedGNnc05J8zSTPZN0iXbv2R2+UdU90tQMOiPPJk+P8oovifOSeSTdH0i0CjlAAAIBOQEEBAACKUVAAAIBiFBQAAKAYBQUAACjWsl0eXa27ujlWpDU7Oke2jkV2dn9yKni6hkPW/RF3kUTrGyxMpvjVYXeF+ZoXfCDM3zcqWVfktMvifOFhYfzJkVlHxLNJ3ifJd0/ybO2P65M87kaR5gfZ4GRstO5He3NnXUAvJHm2n6GrjBkT5w8+uHfyG5eH6fz58XN75szmt2lFxxEKAABQjIICAAAUo6AAAADFKCgAAEAxCgoAAFCMLo8eim6OzpJ1ZwxI8n5JntXeqyZ5vL7FhAn3BdkW4dif/jTp5rjlN/FVDto8jF8+8qthnp0lP2ZMvJ7JfvvF4y+5ZGJ8ga5J8mw9jGwdlfj+iR+TrNtiRpJnXTpZnnV5sJbH8vbKK9kleyX59GSeuANoRrbLIMURCgAAUIyCAgAAFKOgAAAAxSgoAABAMQoKAABQjC6PpeiuNTvQWV5O8mTdC/VN8qwD4aYk3yrJTwqy+Czz1Vf/jzDfb79PhvnTP42vMVo/RJJmz74hvkC7humjjybDFW9nfFslabUkz+7jgUkedVzMScY22/2RrVuS7R+ZrCsEHXXiiXG+555xPn78AWE+enQ8frPNmt8mxDhCAQAAilFQAACAYhQUAACgGAUFAAAoRkEBAACK0eVR665uDtbs6GqDk3xWkm+Q5Gsn+TZJPiHJo3UG4k4Rs4+E+SWXnJ/MnW37w0n+YJieckq8Pknfvjsm82Trpeye5Fcn+eQkfzzJZwdZs2ttZGt2rJLk2RowdHOUWrAgzi8/419h/uLAdcJ8/vx4npEj43zVbDkeNI0jFAAAoBgFBQAAKEZBAQAAilFQAACAYhQUAACgGF0eywndHN1lzSSfnuRPJvmOSZ51VmTazvPZz8ZrD4wZE89w6KEHh/nNN382uc6Tw7Rv3y+G+cKF8TwLF2adDBsl+bZJnnXYZN0iWYdG1L2SdW2MSPLs8cuuM9s/sutFR22xRXLBD8eG8VvWXTfMN9/8oDAfnDV8odNwhAIAABSjoAAAAMUoKAAAQDEKCgAAUIyCAgAAFFvhujy6a80OdJfsbP0sXy3Js/Uwdk7yt4XpRz86vE3Wr18yw1U/DvPbb/+vMDeLz4Y/7bR4/rPOivPJkzeJL0jvs/Fh+olPxF0k1133uTB3vzWZ/84kjzo3sk6RrMtjYJJPSfJ4/QhpbpJjSdOmxfmWRyfPpcnJGi9nnx3Gq2VPYXQ5jlAAAIBiFBQAAKAYBQUAAChGQQEAAIpRUAAAgGIt2+XRXd0crNnR08xJ8lWSfECST0zytl0b7eU337yoTXbqqX3CsT++I+7mWLjwjDD3T90Vb8rK24XxUZPfH49Pb9OgJI+7P7KT893b3geVrLvk8SQfGWTvTsa+nOTPJPlTST47ybO1PLL9rPXNmRPnHx/85zAff/PNYR73S0kb7LRTmK/60FI2DF2GIxQAAKAYBQUAAChGQQEAAIpRUAAAgGIUFAAAoFjLdnkAleys/OwL/1dN8keanH96km/RJnnPe25Kxv4lTP3CDcN8zAFXxPmhhybzX5bkWbfFpCS/PR49aVgyPrvPnk3y7DHZtE1y2GHx4/rSS3H+6KNx58rtt2fdPi8k+YrbzZF585vjfNr73hfm45N5xnziE2H+wEO8H+5peEQAAEAxCgoAAFCMggIAABSjoAAAAMUoKAAAQDFrb80LM+ueBTE6QVev5cGaHd3H3Tt855vtnewIWQfCfyT5Skl+cZLvkeRR50a8toW/emKY37VSvC3ZqhRnfyy+C269NR6/aFHWdbJxkt+Z5Nn6Gdsmedb98XCSR50VfZOx70nyZMER/STJt0nyB5M8XuPD/aoO7cMPPKBe+xq85R7rh/k1TzwR5i8m8+zwz/gumDVrGTYKnWLLLRXuvxyhAAAAxSgoAABAMQoKAABQjIICAAAUo6AAAADFWMtjGXV1F0km6y7pru3pSp3TSZN1GmTdAI8medaBMDjJr0zyqEsg7ix5Junm+HMy85FDh4b5XjdeEuaPPLJfmO+4485hvtNO8fU+9FB8Nv9f/3pD/As6Psk/kuRxF4w0Ish2TMbG643k3RkHJPlTSR53c+T7Wet7OOnmmJ+M3zrJX3klzqdNi/N+/Zqbp2/yEGXzdKXO2paBA+N83rw4nzGjufkzHKEAAADFKCgAAEAxCgoAAFCMggIAABSjoAAAAMXo8uhlWrGbo2tla3BknQPZ+GyljNWSPFvzoe1p3P/7v8PDkYNPjB/rIw/YP5762GPj/J0nhPHb52wY5v+aFp9v/3+/iN9/XHBBcrq94u4S6bQwNYvXVxk/Pp5lh75Bv8vw+HT1716wY5gfe+yr8eS6Jsmz25rNk+1nrW/T/eIuok2vTDqgDog7a355Szz885//RHLN2Vox+4Tp6advFOaHHhrPMmD+v+IL5if9KzfeGOeTJrXNsnaOwUk3WdZ6NXjzMH76hWj9G2nlpBLIOmMyHKEAAADFKCgAAEAxCgoAAFCMggIAABTjpEysoLKv0r4tybdI8oPDtH//+ATDefNuapOdcsp/h2NPOSU54UrnhOmp78xOEI1P9FrjY+8N8zFj4lnuuCOZXpeFqdmvwjw70etNl1wUX7De9nH+aNuT4L57wTrh0GOPzU6O/GWSZ1+ZvXaSZ/tT9pXcre8fYy4O840ujPM3PfRAmI+Pz+GVdFSST07yPmGandc44KzvxReMGxfnC5PHetSoOI++Z3u77eKxyUmZM9767jB/NllBINvE7KTMZnGEAgAAFKOgAAAAxSgoAABAMQoKAABQjIICAAAUs/a+ytnMWu57nvnq6pyZdfcmdIi7d3hDzUYnD3jWEZF1A2ya5LslefJVubo1yP6SjN01ybOvB98gyddM8uzrpR9J8virlLMuD2lkmO61VzxP8s3L+ulP4zz6Sm73M5Jtybot7k7y9ZucZ3qSx1/J7X5th/bhBx5Qy71gRc0NkrT66nHev3+cP/10nF91VZyfcUacZ11Nn9s67jpJ25QGDgzj19aLn5dPPNE2y769u6fZckuF+y9HKAAAQDEKCgAAUIyCAgAAFKOgAAAAxSgoAABAsRWuywO9X3NdHp9I9uGsCyPr/piR5FlnxYgkfzLIpiVj5yZ5vLjBxhtvEuZHHhnP8pOfxPmkSVn3x/AkfznJs/vm6CTP1s+IOyWkF4JsSDI2e/y2SvIFSf5UkmePVXzfrMhdHuj96PIAAABdhoICAAAUo6AAAADFKCgAAEAxCgoAAFBs5e7eAKBrZeteZJ0Dw5I860B4PMlHJXnURbJDMvbKJI/X2th887jLI1rzQpL69EmmD7snJOl7Sb5Pkm+V5AcnefZYTUjyyOQkH5TkWVdPdh9k+8HsdIuAFQVHKAAAQDEKCgAAUIyCAgAAFKOgAAAAxSgoAABAMbo80OKybo7srPwHk3zVJM+6B+JOjHgNkT8nY7NtjDsZxo17OBk/Jcmz652e5NlaHjckebRuSXuyNU0WJXm0Tsazydhmu3RWaeI6pXw/A1YcHKEAAADFKCgAAEAxCgoAAFCMggIAABSjoAAAAMXo8kCLa3Ytj0w2flaT1xt1D/RLxu6c5FkXxk+SfIMk3zTJ10nyu5N8YJIvTPK5ST4yybP1ObJ5ItnaHI8m+YAm5wHAEQoAAFCMggIAABSjoAAAAMUoKAAAQDEKCgAAUIwuD7S4Zrs5srP4mz27P1uX4pkgy9aZyDoNsu6GbJ5JSZ6ttZGtIZJ1bWSdLtm6GtntytYiyW7vgiDL1tpoVjMdJAAkjlAAAIBOQEEBAACKUVAAAIBiFBQAAKAYBQUAAChm7t7d2wAAAHo5jlAAAIBiFBQAAKAYBQUAAChGQQEAAIpRUAAAgGIUFAAAoBgFBQAAKEZBAQAAilFQAACAYhQUAACgGAUFAAAoRkEBAACKUVAAAIBiFBQAAKAYBQUAAChGQQEAAIpRUAAAgGIUFAAAoBgFBQAAKEZBAQAAilFQAACAYhQUAACgGAUFAAAoRkEBAACKUVAAAIBiFBQAAKAYBQUAAChGQdHNzGxHM5vW8PPfzGzHDv5uh8cCGTN7wsx2qv//ODP7RXdvE9AZzGw9M3MzW7mbt2OFeI61VEFRP2gvm9ngJfK/1jvVevXP59U/v7dhzEZm5g0/jzezQxt+Ps7MppjZfDObZmaX1fnf6my+mb1qZgsbfj6u2dvg7pu5+/hmx5rZGDO7sL3xZvZlM7vXzF4ys/Oa3Tb0bGa2nZndZWbPm9lzZnanmY1qZg53P9ndD136SPRWjX/cOjD2Da+Dy1tHXtc64TpGm9k9ZvaCmT1T//+XzMy64vo66znWU4qlRi1VUNSmSNpv8Q9mtoWktwTjnpN0YkcmNLODJR0oaSd37ydpa0m3Sv/+o96vzm+X9OXFP7v7yWU3pdP9S9Vt/mV3bwg6l5kNkHSdpJ9IWkPSWyV9R9JL3bldQKYn/CE0s6Mk/UjS9yUNlTRE0mGStpW0SvI7Ky23DexlWrGguEDSQQ0/HyzpV8G48yVtaWYf7MCcoyT9zt0fkyR3f9rd/29ZNs7MVq2PkMw2s4fruRsvbzw0tqqZnV+P/buZfXWJj0eeMLOdzOxjko6T9On6yMj90XW7+2/c/SpJzy7LtqNHe7skufsl7v6quy9w95vc/QEz29DMfm9mz5rZLDO7yMwGRpMs+Y7QzLapj3rMMbP7Gz9iq9+9nlAfCZlnZjc1Hh1sOGIyx8ymmtkhZjbKzGY0viib2SezfRZdq35M7jCz0+rXmSlmtmt92UmStpf00/p15ad1PtLMbq6Pgj1iZvs2zLemmV1rZnPNbIKZnWhmdzRc7mZ2hJlNljS5zn5U7x9zzewvZrZ9nYeva2a2upmda2bTzeyf9XWsVF+2Un1bZpnZ45J2a+e2ry7peElfcvcr3X2eV/7q7p9x95fqceeZ2c/N7Ldm9oKkD5nZblYd+Z5bb/uYJeY+0MyerJ9zX1/iss56jt1W/3dOff+836oj7X+06ijlLKuPpC8vrVhQ3C1pgJm9o97JRkuKDpm9KOlkSSd1cM6DzOwYM9vayirUb0vasP63i6qCp72x60naQNJHJR0QDXL3G1XdlsvqIyPvLNg+9E7/kPRqXYDuamaDGi4zSadIWkfSOyStK2nM0iY0s7dKul7VUa01JB0t6ddmtlbDsP0lfVbS2qre0R1d/+4ISTeoOmKylqStJE109wmqCtqdG+Y4UHHRj+XjfZIekTRY0vcknWtm5u5f1xuPun7ZzFaTdLOki1U95qMlnWlmm9Zz/UzSC6re7R+s+PVtz/o6F//OBFX7xxr1vFeYWd92XtfOk/SKpI0kvUvVvrT4I4QvSNq9zreWtE87t/v9kt4s6eql3D9StZ+fJKm/pDvq23iQpIGqipbDzWxPSarvi5+r2q/XkbSmpOHRpCXPMUk71P8dWN8/f5J0gqSbJA2qr/MnHbhtnaYVCwrp9aMUH5X0d0n/TMadLeltiyvyjLtfKOk/VRUAf5T0jJl9bRm3bV9JJ7n7c+4+VdKPlzL2ZHef7e7TljIWKzB3nytpO0ku6RxJM83sGjMb4u6PuvvN7v6Su8+U9ANJHTkyd4Ck37r7b939NXe/WdK9kj7eMGasu//D3RdIulzVHwapehG8pT5issjdn3X3ifVl59dzy8zWUPW8urjg5qPMk+5+jru/quqxGabq0H9kd0lPuPtYd3/F3f8q6deSPlW/0dpb0rfd/UV3f7ieb0mn1K9/C6Tq9bXeP15x99NV/ZHfJLpyMxuiav870t1fcPdnJP1QVWEjVa+ZZ7j7VHd/TlUhnRksaZa7v9Iw/+IjBQvMbIeGsVe7+53182Chu4939wfrnx+QdIlef07tI+k6d7+tPsrxTUmvJdtQ8hyLLJI0QtI69Xbe0c7YTtfKBcX+kg5RO+986gf7hPpfu9z9InffSVVFepikE8xsl2XYtnUkTW34+ckmxk7NBgLu/nd3P8Tdh0vaXNX+c4aZDTGzS+vDw3NVHbEb3O5klRGq/lDMWfxPVdEyrGHM0w3//6KkfvX/ryvpsWTeCyV9on63u6+k2919egdvJjrfvx9Dd3+x/t9+ydgRkt63xD7xGVVHJNaStLKW/pr1hszMjrbqI93n6/lWV75/jpDUR9L0hus/W9W7d6m519dnJQ22hnM53P0D7j6wvqzx7+OS2/w+M/uDmc00s+dV/U1YvM1v2AZ3f0H5x8wlz7HIV1UdkfyzVQ0Dn2tnbKdryYLC3Z9UdXLmxyX9ZinDx6oqEj7ZwbkXufsVkh5Q9aLdrOmqXmwXe9tSxjYeKls3G6jqnSkgSXL3SaoODW+u6rCxS9rC3QeoelfUkTPYp0q6wN0HNvxbzd1P7eDvbphs2z8l/UnVc+5AVW8A0DMt+boyVdIfl9gn+rn74ZJmqvooYmmvWY3ddNur+iO4r6RB9R/z5/X6/hld/0uSBjdc/wB336y+vJnX1z/Vc+3Rzpg221y7WNI1ktZ199UlndWwzW/YBjN7i6qPPSIlz7E2r/n1+X1fcPd1JH1R1cdRG3Vgrk7RkgVF7fOSPlxXh6n6cNe3JaUfYVh14tJuZtbfzN5Uf0SymaR7lmG7Lpf0v2Y2yMyGq/oopSNj3yrpy+2MnSFpPTNLH1MzW9nM+kpaSdJKZtbXesCZ1ihn1YlyR9X7lMxsXVXdTner+tx3vqTn6/3omA5Ou/hIwi5WnezW16rvTQk/D17CRZJ2MrN96/1uTTPbquHyX6n6Q7KFll70o/vMUHUO12LXSXp7fdJhn/rfKDN7R/2RyW8kjTGzt5jZSL3xBPlIf1VFyExJK5vZtyQNWOL6//26Vh/JuknS6WY2oH493tBeP7n+ckn/ZWbD6/OIjs2u2N3nqOqEOtPM9ml4fd9K0mod2O7n3H2hVV8/sH/DZVdK2t2qk5JXUXXiZ/a6XPIcm6nqo5R/Pz5m9qmG352tqujIPm7pdC1bULj7Y+5+bweHX6KqqszMVXW28VOS5qg6cenwZfx86juqDsNNUfXEaO/d2fGSptVjb1G1o2ZtgFfU/33WzO5LxnxD0gJVT7ID6v//RjMbjx5rnqoT3e6x6kz0uyU9JOkoVfvcu1W987teHfwDXp/js4eqfX+mqndTx6gDrxvu/pSqI4RHqWrRniip8WThcaoO945rOMyOnudHkvaxqgPkx+4+T9VJkKNVtaE/Lem7qs57kKo3PavX+QWqXlvba13+naQbVZ1U/KSkhXrjxwvR69pBqk5OfFjVH80r9fpHBOfUc94v6T4tZV939+9J+h9Vxe2M+t/Zqt5g3tXOr35J0vFmNk/St1QVMovn/JukI1QdxZheb+O0aJLC59iLqk4UvbP+uGQbVV2D95jZfFVHUL7i7o8vba7OYu4cKe8tzOxwSaPdvSMn1AE9mpk9JumL7n5Ld28LuoaZfVfSUHdvr5sNLaJlj1C0AjMbZmbb1ofhNlH1bm9cd28XUMrM9lZ1OPb33b0t6Dz1R29bWuW9qj565jVrBcHn5z3bKqoOv62v6qOWSyWd2Z0bBJQys/GqvoPgQHdfbp/vYrnor+pjjnVUfXxwujr2PQ9oAXzkAQAAivGRBwAAKNbuRx7WsPom0FO4e4dXATT7RLIPZ9+e3rfJrcnmybqVo/GvJmPXTvJnmtyWgUk+KMkfTfI+Sb4wyTPhmkuSXk7y7HZl91szczSrmevMuV/boX3Y7DJeg9HjuH863H85QgEAAIpRUAAAgGIUFAAAoBgFBQAAKEZBAQAAivHFVmhxzZ7dn3UsdFaXQNTJkHVzLEryTZI828ZHkjzrFsk6XbLtycZn31mVjR+Q5FlnRXR7m+1Eya5zVpPzZDqnKwToDThCAQAAilFQAACAYhQUAACgGAUFAAAoRkEBAACK0eWBFVSzXRtZZ0XWJTA7yacF2QbJ2GwbN0ryuUn+YJJnt2lIks9P8qeSfGCSz0ny1ZI867iIuk42TcZm3RbZeibZfR89fu3ND6w4OEIBAACKUVAAAIBiFBQAAKAYBQUAAChGQQEAAIrR5YEWl519n60nkXU4ZOttZF0C2foZ0TyDk7HDk/zJJL8tyXdN8h2T/NEkz+6zbH2LbP6Hk/y+JN8qyScHWbaNw5I864xp9r1W1hVC9wdWHByhAAAAxSgoAABAMQoKAABQjIICAAAUo6AAAADF6PLoody9U+Yxs06Zp/fKzr7POhMWJHm2NsczST4wyd8TZC8kY7Muj78kedZxsmaSZ9uezRN1VUj5eiZZt0h2vc2uwxF1aLycjM22sbPW7KCbo/Vk+2mW75vkWVdT6+EIBQAAKEZBAQAAilFQAACAYhQUAACgGAUFAAAoRpcHWlx2Fn+25kOzZ3ZnnRirJXl0xnfWQTIpyXdP8k3CtE+feBsXLcrWG5mQ5M2u8TE9yVdJ8mx7svsykq3NkcluU9YFlO1PWZ51naDni59Pze/vKw6OUAAAgGIUFAAAoBgFBQAAKEZBAQAAilFQAACAYnR5tAjW7GhWs90fGyd5toZDtnbE40G2TTJ2SFNzf/SjcTfHSslNXWut+Cz2Cy64IbnebcP0m9/cLcxHjoxn+cxnzk/mz97fZPfPlCB7MBnbbIdK9rhmXRvZPOit/vu/dw7zG5Knx6RJl3Xh1vQOHKEAAADFKCgAAEAxCgoAAFCMggIAABTjpMxu5u7dvQkrqOykuyzPau8sj06+lOKv582+cnp8kn8uTBcm3xZ9++3xbVprrezE1Phkzf79dw3zHXeMZ/nwse8N86s+9ecwv+KK/062J/uq7j8E2QbJ2IlJnp34mt032bZkX9WNni8+2figH8Ynus/5bPyaPSn7pvwVCEcoAABAMQoKAABQjIICAAAUo6AAAADFKCgAAEAxujzQ4pr9auxBSb5Fkq+W5E8l+fZBtmo4sk+f9cN80aL4K35vvz3ukhg58odhfvjhYayvfGVCmA8dGnd5fHjW5WF+04R4nsu/clGY2xX7xBuUdsFEX4f+zmRs1oUxLMlnJ3mzX7GdfVU3eopPfCL+yvqrro3HX3VV121Lb8cRCgAAUIyCAgAAFKOgAAAAxSgoAABAMQoKAABQjC4PtLisCyM76z87u//RJN+0yTzq6Ig7AYYOjWeYOnVkMvf9YTpp0plhfuqpX0rm2T1MJ08+Oh5+2m1hfFcy+867xt0i0neS/ANJHnXwPJuMnZbk8Rn+zeO9WW91zVbfCvOzki6P2bNv6MKt6d14FgAAgGIUFAAAoBgFBQAAKEZBAQAAilFQAACAYubu+YVm+YVoSnv3czPMrFPm6c3cvcN3gtlnkju+2TUWVkryVZJ85ySPrnduMjbrFHkhyac3Of7IJP/fJN8jTP2xt8bDkzYVW+3TYb7ffvFp9ZdcsmeyPZFs7ZasmyNb0yXr9skeq+x6424i92s7tA+bXcZrcKeJ18zxvyT7xvA4tyF/6KwN6rXcPx3uvxyhAAAAxSgoAABAMQoKAABQjIICAAAUo6AAAADFWMsDLS5bsyOrpRcleXyGeC77vv94nYzYnUk+J8mzdUseSfLDkvy9YfqFL2wf5sf9Ip7llFNGh/m558bdHJ/b/Zkwv+SSrGMmul1ZZ8zdSZ7J9pusm+O1JufH8vfOMP3uzSPC/Nhjr+nKjWlJHKEAAADFKCgAAEAxCgoAAFCMggIAABSjoAAAAMXo8kCLy9bsyDoi+iV5trbD+kmeravxgSB7KhmbrT9xf5JPS/JM1rmyb5jOnx+PfuihbP6LwvRzZ8VdJPr8hDC+//54OYtDD22bTZgwPtmWWUmePa5ZN0c2Hj3fkDC9+ups/GVJ/h+dsTEtiSMUAACgGAUFAAAoRkEBAACKUVAAAIBiFBQAAKAYXR6dzD0+Ix3dZZUkH9BknnVcNNslcHyQrZOMzer9eO0B6cEkj9cwkB5N8rhbZI89BoX5+98fzzJiRNCGIemuM/4c5iutFM+zzTvnxhfo7CAblox9ock8W9Mlk2x8uh+g6wwO01NP7RvmL70Uz/KnPzWz7g4kjlAAAIBOQEEBAACKUVAAAIBiFBQAAKAYBQUAAChGl0cPZWbdvQktIj6zO7dxkicLWaRredye5NFaIaPa3aKO2zTJH0nyHcN0++23CPNP3/M/8TS/mxPGgwaNDfNtt70y2Z7xSf6jMD3mmGPaZB/7WDzDRz6yMJn7M0merfXS7P5El8fyt02Yfm37u8L8l5Oi9XUk3m83j3sMAAAUo6AAAADFKCgAAEAxCgoAAFCMggIAABSjywMtLju7P1sfIl5nIh+frf3xcpJHZ/2v3eTcdyb5JmF62GHHhvlZZ8XrVWSdEgu//sPkemNbf/SXYX7zzXuF+WOP7RPmG6z3Wpg/8FDb7J3vzNYz+UGSb5Tk2X6Trd2SreWB5e2jH006dE48MYzPm//bLtyaFQtHKAAAQDEKCgAAUIyCAgAAFKOgAAAAxSgoAABAMXP3/EKz/MIVXHv3WzNYs6N57t7hO81s7+SBanZNhnh9AGlwkm+e5DcF2bPJ2HhNDenhJM86HOLuj6y75Prrjwrzj997fDzN0KFh/Ku+/y/MBw6Mp7nuujgfMybOd9yxbTZ58jnx4LRjJu50kaYl+cQkz9b+eCZM3a/t0D5sdhmvwam4s+bss+Nuof838rYwtw9O77QtWlG4fzrcfzlCAQAAilFQAACAYhQUAACgGAUFAAAoRkEBAACKsZYHWly0doaUn5WfnfWfrfmwcZiutVY8fubMDYL09GTubM2ObN2IPkker0uRdnPMvijMD3r0W2F+wbevSa43XstDejTJ4y6Vc87J7vtojY/dkrGrJnl2H9+d5IOSfJUkR1fp0yfp5tgqXo9n39N2SGa6rJO2CByhAAAAxSgoAABAMQoKAABQjIICAAAUo6AAAADF6PJAi8s6H7Luj5eT/JEkHxGmr7wSj95447bdJZMn7x+OHTUq7m6YMCHb9midkNzHd4pv63FjPhPmF1wQd2f4XufFV7D33mE8Y6eTw/xnP4unOeGEbI2SaJ2MYcnYF5I86+YYnuSZBU2OR6mPfCTOL5vy3jC/4oqvJTO9u3M2CByhAAAA5SgoAABAMQoKAABQjIICAAAUo6AAAADF6PJAi4vXscjXdsjGT0jyuEtg9uz3JPnn2mRbbBF3c5xxRnyNY8bEa3ksXLhrmN9++1fCfNc94vE33vjNML///hPC/OV3jgvz+b/4TZiffmoY67zz4lzql+SXB1nWEZKtB7JNks9N8ulJPj/J0VWefTbOR4++P/mNrFOLLo/OwhEKAABQjIICAAAUo6AAAADFKCgAAEAxCgoAAFCMLo+lcPfu3gQU6Zvk2doOayZ52zU4KoOTfFKSn9cmefDBHcKRl146Ksw/9rHkGpOr3GyzH4X5WWedE/+C1g7TrOvkkD/Gz5FDkwaKyZMvTK53VpJnHTnTgizuXMnP5H88ybNujmwbX0tylBsfphMmZI/1N5I87o5C5+EIBQAAKEZBAQAAilFQAACAYhQUAACgmLV30qGZrfBnJHb1SZlm1qXztyJ37/CdZrZn8gAOSn4jO4kzO6lveJJnJxJGX9V9ZzL2p2E6atSIMN9uu3iWq66K8ylTspMyxyd59nXU/5Hk2YmvGyR5tj3Z+H8FWXaiXvZ4ZLdpQZJnX+2d3dY4d7+2Q/uw2WUr/Gtw/BXrUv616dnXrGdfq8/76ma5fzrcf7knAQBAMQoKAABQjIICAAAUo6AAAADFKCgAAEAxvnq7RjdHq8q6NrKv0s7GZ1+xvWmY9u8fd2LMmxd1hfRJ5v5amE6YEHcsTJiQdSwMSPLsPtg5TPv2PTjMFy68PZlnWJJnX2uddcxk988qQfZoMjbzcpLPTvI5Tc6DcvslefaczMwo3RAsBUcoAABAMQoKAABQjIICAAAUo6AAAADFKCgAAEAxujzQ4hYleXbG90pJnnUPxJ0J8+Zl6wbMCrL9k7GfTPL5SZ5tY9xxIv0yyceF6cKFzyTjM9l2Ppzk2X2fdYU0Mza636W8qyd7/DLZtr/a5Dxoa6skn5jk2XOe989djXsYAAAUo6AAAADFKCgAAEAxCgoAAFCMggIAABSjy6PGWhutKquZs7P4s/Ut/pXk1yV5vMaH9FCQLUjGbpzk2ZodWX5xkg9K8qwz4cEkz6yd5BskedZFknWpRPPflYzN1hXJNNtxQjdH1/lLk+N5n9xduOcBAEAxCgoAAFCMggIAABSjoAAAAMUoKAAAQDG6PNDiXkvy7Cz+rPsjO7s/60y4r4nrHZCMzToHpiV5JlvbILtNWadL1rWRjc/uy5eTvE+SZ90r0f2QzZ09Ttl9nO0f2TZm47PtAVoPRygAAEAxCgoAAFCMggIAABSjoAAAAMUoKAAAQDG6PNDi+jY5fpUkn5XkWYdGdtZ/tH5G1iWRdagMSfInk7zZLozsPsg6IrLtzLozXmgy74z3Pdl+kF1ns2tzsJYHwBEKAABQjIICAAAUo6AAAADFKCgAAEAxCgoAAFCMLg+0uKzToLvmj8bPT8b266TrzLo5MguS/KkkzzocelLnQ9bN0ayedJuAnoUjFAAAoBgFBQAAKEZBAQAAilFQAACAYhQUAACgmLl7d28DAADo5ThCAQAAilFQAACAYhQUAACgGAUFAAAoRkEBAACKUVAAAIBi/x+BsnUWGW+7VQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 540x648 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tuBkEBv3mihR"
      },
      "source": [
        "## 2. Quantative evaluation using Quantus\n",
        "\n",
        "We can evaluate our explanations on a variety of quantuative criteria but as a motivating example we test the Max-Sensitivity (Yeh at el., 2019) of the explanations. This metric tests how the explanations maximally change while subject to slight perturbations."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define params for evaluation.\n",
        "params_eval = {\n",
        "    \"nr_samples\": 10,\n",
        "    \"perturb_radius\": 0.1,\n",
        "    \"norm_numerator\": quantus.fro_norm,\n",
        "    \"norm_denominator\": quantus.fro_norm,\n",
        "    \"perturb_func\": quantus.uniform_sampling,\n",
        "    \"similarity_func\": quantus.difference,\n",
        "    \"disable_warnings\": True,\n",
        "}"
      ],
      "metadata": {
        "id": "aLjrKsT6mS9X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Return max sensitivity scores in an one-liner - by calling the metric instance.\n",
        "scores_saliency = quantus.MaxSensitivity(**params_eval)(model=model, \n",
        "   x_batch=x_batch,\n",
        "   y_batch=y_batch,\n",
        "   a_batch=a_batch_saliency,\n",
        "   **{\"explain_func\": quantus.explain, \"method\": \"Saliency\", \"device\": device, \"img_size\": 28, \"normalise\": False, \"abs\": False})"
      ],
      "metadata": {
        "id": "NlV_43TAmJll"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Return max sensitivity scores in an one-liner - by calling the metric instance.\n",
        "scores_intgrad = quantus.MaxSensitivity(**params_eval)(model=model, \n",
        "   x_batch=x_batch,\n",
        "   y_batch=y_batch,\n",
        "   a_batch=a_batch_intgrad,\n",
        "   **{\"explain_func\": quantus.explain, \"method\": \"IntegratedGradients\", \"device\": device, \"img_size\": 28, \"normalise\": False, \"abs\": False})"
      ],
      "metadata": {
        "id": "iq7qqDfSmIdj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kBrG51Lpuq9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9c4d9b0-6251-4b55-c460-2474046660d1"
      },
      "source": [
        "print(f\"max-Sensitivity scores by Yeh et al., 2019\\n\" \\\n",
        "      f\"\\n • Saliency = {np.mean(scores_saliency):.2f} ({np.std(scores_saliency):.2f}).\" \\\n",
        "      f\"\\n • Integrated Gradients = {np.mean(scores_intgrad):.2f} ({np.std(scores_intgrad):.2f}).\"\n",
        "      )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "max-Sensitivity scores by Yeh et al., 2019\n",
            "\n",
            " • Saliency = 1.67 (0.48).\n",
            " • Integrated Gradients = 0.76 (0.16).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uGYHGu1Esya7"
      },
      "source": [
        "# metrics = {\"max-Sensitivity\": quantus.MaxSensitivity(**params_eval)}\n",
        "\n",
        "# xai_methods = {\"Saliency\": a_batch_saliency,\n",
        "#                \"IntegratedGradients\": a_batch_intgrad}\n",
        "\n",
        "# results = quantus.evaluate(evaluation_metrics=metrics,\n",
        "#                            explanation_methods=xai_methods,\n",
        "#                            model=model,\n",
        "#                            x_batch=x_batch,\n",
        "#                            y_batch=y_batch,\n",
        "#                            agg_func=np.mean,\n",
        "#                            **{\"explain_func\": quantus.explain, \"device\": device, \"img_size\": 28, \"normalise\": False, \"abs\": False})\n",
        "\n",
        "# df = pd.DataFrame(results)\n",
        "# df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9DkYz4BuySK"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}