tutorials/Tutorial_Getting_Started.ipynb

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   "cell_type": "markdown",
   "metadata": {
    "id": "bukochgPFg7s"
   },
   "source": [
    "# Getting Started!\n",
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/understandable-machine-intelligence-lab/Quantus/main?labpath=tutorials%2FTutorial_Getting_Started.ipynb)\n",
    "\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "4Y7_mNf9Bic0"
   },
   "outputs": [],
   "source": [
    "from IPython.display import clear_output\n",
    "!pip install torch torchvision captum quantus seaborn\n",
    "clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "RV7X-Ss9-16F"
   },
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import quantus\n",
    "import torch\n",
    "import torchvision\n",
    "from captum.attr import *\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "# Enable GPU.\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\") \n",
    "clear_output()"
   ]
  },
  {
   "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",
   "execution_count": 17,
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    "executionInfo": {
     "elapsed": 8767,
     "status": "ok",
     "timestamp": 1665156018107,
     "user": {
      "displayName": "Anna Hedström",
      "userId": "05540180366077551505"
     },
     "user_tz": -120
    },
    "id": "TmsZxFhuc0mm",
    "outputId": "386071bc-8741-4371-d680-a4a56ba92f4a"
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   "outputs": [],
   "source": [
    "# Load datasets and make loaders.\n",
    "test_samples = 24\n",
    "transformer = torchvision.transforms.Compose([torchvision.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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
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    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1665156018109,
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      "displayName": "Anna Hedström",
      "userId": "05540180366077551505"
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   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1080x216 with 5 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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()"
   ]
  },
  {
   "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",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1665156018110,
     "user": {
      "displayName": "Anna Hedström",
      "userId": "05540180366077551505"
     },
     "user_tz": -120
    },
    "id": "CUghaOhXddLU",
    "outputId": "7ba0cf49-d0da-44d4-c5ef-6df5fc036d57"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "olAfyOHzevne"
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 83532,
     "status": "ok",
     "timestamp": 1665156101636,
     "user": {
      "displayName": "Anna Hedström",
      "userId": "05540180366077551505"
     },
     "user_tz": -120
    },
    "id": "t6_qEwhee1WH",
    "outputId": "bb872f39-3308-42f5-d7c2-c9988ba7f3df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1 - test accuracy: 95.71% and CE loss 0.24\n",
      "Model test accuracy: 95.71%\n"
     ]
    }
   ],
   "source": [
    "# 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=1,\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",
    "# 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}%\")"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 843,
     "status": "ok",
     "timestamp": 1665156102476,
     "user": {
      "displayName": "Anna Hedström",
      "userId": "05540180366077551505"
     },
     "user_tz": -120
    },
    "id": "gNxAtc2Co1pL",
    "outputId": "25573a18-e66a-45d7-b686-56c181ecdbce"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/quantus/lib/python3.8/site-packages/captum/_utils/gradient.py:56: UserWarning: Input Tensor 0 did not already require gradients, required_grads has been set automatically.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Generate normalised Saliency and Integrated Gradients attributions of the first batch of the test set.\n",
    "a_batch_saliency = quantus.normalise_func.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_func.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",
    "# Quick assert.\n",
    "assert [isinstance(obj, np.ndarray) for obj in [x_batch, y_batch, a_batch_saliency, a_batch_intgrad]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iRDwzUUp8bR2"
   },
   "source": [
    "Visualise attributions given model and pairs of input-output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 630
    },
    "executionInfo": {
     "elapsed": 329,
     "status": "ok",
     "timestamp": 1665156102804,
     "user": {
      "displayName": "Anna Hedström",
      "userId": "05540180366077551505"
     },
     "user_tz": -120
    },
    "id": "82WWNmyoilXo",
    "outputId": "6f9ba1de-16e5-4678-9932-b59b57b464de"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 540x648 with 9 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "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",
   "execution_count": 24,
   "metadata": {
    "id": "aLjrKsT6mS9X"
   },
   "outputs": [],
   "source": [
    "# Define metric for evaluation.\n",
    "metric_init = quantus.MaxSensitivity(nr_samples=10,\n",
    "    lower_bound=0.1,\n",
    "    norm_numerator=quantus.norm_func.fro_norm,\n",
    "    norm_denominator=quantus.norm_func.fro_norm,\n",
    "    perturb_func=quantus.perturb_func.uniform_noise,\n",
    "    similarity_func=quantus.similarity_func.difference,\n",
    "    disable_warnings=True,\n",
    "    normalise=True,\n",
    "    abs=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "NlV_43TAmJll"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dilyarabareeva/Documents/UMI_Lab/Quantus/quantus/helpers/warn.py:263: UserWarning: The settings for perturbing input e.g., 'perturb_func' didn't cause change in input. Reconsider the parameter settings.\n",
      "  warnings.warn(\n",
      "/Users/dilyarabareeva/Documents/UMI_Lab/Quantus/quantus/functions/explanation_func.py:392: UserWarning: Softmax argument has been passed to the explanation function. Different XAI methods may or may not require the output to go through softmax activation. Make sure that your softmax argument choice aligns with the method intended usage.\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/quantus/lib/python3.8/site-packages/captum/_utils/gradient.py:56: UserWarning: Input Tensor 0 did not already require gradients, required_grads has been set automatically.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Return Max-Sensitivity scores for Saliency.\n",
    "scores_saliency = metric_init(model=model, \n",
    "                                  x_batch=x_batch,\n",
    "                                  y_batch=y_batch,\n",
    "                                  a_batch=a_batch_intgrad,\n",
    "                                  device=device,\n",
    "                                  explain_func=quantus.explain,\n",
    "                                  explain_func_kwargs={\"method\": \"Saliency\", \"softmax\": False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "iq7qqDfSmIdj"
   },
   "outputs": [],
   "source": [
    "# Return Max-Sensitivity scores for Integrated Gradients.\n",
    "scores_intgrad = metric_init(model=model, \n",
    "                            x_batch=x_batch,\n",
    "                            y_batch=y_batch,\n",
    "                            a_batch=a_batch_intgrad,\n",
    "                            device=device,\n",
    "                            explain_func=quantus.explain,\n",
    "                            explain_func_kwargs={\"method\": \"IntegratedGradients\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 202,
     "status": "ok",
     "timestamp": 1665156140018,
     "user": {
      "displayName": "Anna Hedström",
      "userId": "05540180366077551505"
     },
     "user_tz": -120
    },
    "id": "3kBrG51Lpuq9",
    "outputId": "97c19adf-53bb-45c7-afb5-014239e72797"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max-Sensitivity scores by Yeh et al., 2019\n",
      "\n",
      " • Saliency = 0.39 (0.11).\n",
      " • Integrated Gradients = 0.25 (0.08).\n"
     ]
    }
   ],
   "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",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "executionInfo": {
     "elapsed": 204,
     "status": "ok",
     "timestamp": 1665156209547,
     "user": {
      "displayName": "Anna Hedström",
      "userId": "05540180366077551505"
     },
     "user_tz": -120
    },
    "id": "uGYHGu1Esya7",
    "outputId": "ad92c308-a3ae-4a33-88ec-e97e8ea2e709"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                                  Saliency        IntegratedGradients\nmax-Sensitivity  {'0': 0.3584188719838858}  {'0': 0.2516450374849362}",
      "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>Saliency</th>\n      <th>IntegratedGradients</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>max-Sensitivity</th>\n      <td>{'0': 0.3584188719838858}</td>\n      <td>{'0': 0.2516450374849362}</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the quantus.evaluate functionality of Quantus to do a more comprehensive quantification.\n",
    "metrics = {\"max-Sensitivity\": metric_init}\n",
    "\n",
    "xai_methods = {\"Saliency\": a_batch_saliency,\n",
    "               \"IntegratedGradients\": a_batch_intgrad}\n",
    "\n",
    "results = quantus.evaluate(metrics=metrics,\n",
    "                           xai_methods=xai_methods,\n",
    "                           model=model.cpu(),\n",
    "                           x_batch=x_batch,\n",
    "                           y_batch=y_batch,\n",
    "                           agg_func=np.mean,\n",
    "                           explain_func_kwargs={\"method\": \"IntegratedGradients\", \"device\": device},\n",
    "                           call_kwargs={\"0\": {}})\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "df"
   ]
  }
 ],
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