{ "cells": [ { "cell_type": "markdown", "id": "296eec8a", "metadata": {}, "source": [ "# Saliency Generation with MNIST Dataset" ] }, { "cell_type": "markdown", "id": "a72e8026", "metadata": { "tags": [] }, "source": [ "\n", "## Introduction\n", "\n", "The first example showcases the use of the xaitk-saliency API to generate visual saliency maps using models from the [scikit-learn](https://scikit-learn.org/stable/) library trained on the MNIST dataset.\n", "The MNIST dataset contains grayscale images of handwritten digits (0-9), which are normalized and centered in the frame.\n", "It was developed to evaluate the performance of models classifying individual handwritten digits.\n", "\n", "[This example](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html) from scikit-learn's website uses their `LogisticRegression` class to achieve fairly high accuracy on the MNIST dataset with very short training time.\n", "As is shown by this example, it is easy to visualize the decision boundaries of each class by simply plotting their respective model's coefficients in the same dimensions as the input image.\n", "\n", "We use the xaitk-saliency high-level API to mimic this visualization by creating saliency maps for several images from the dataset and averaging them to create a global decision-boundary representation for each class.\n", "This approach achieves comparable results to those shown in scikit-learn's example while requiring zero knowledge of the intrinsic properties of the model used.\n", "\n", "We do the same while using the `MLPClassifier` class also from the scikit-learn library.\n", "Our model is taken from [another example](https://scikit-learn.org/stable/auto_examples/neural_networks/plot_mnist_filters.html#sphx-glr-auto-examples-neural-networks-plot-mnist-filters-py) on their website.\n", "The example shows a visualization of the MLP's weights as images to gain insight on the learning behavior of the model.\n", "These images, however, only show generic patterns that the model has learned and therefore the behavior of each class is still obscure.\n", "\n", "Using our approach provides per-class saliency representations and also gives some insight on the different learning behaviors portrayed by both these models.\n", "\n", "### Table of Contents\n", "* [MNIST Dataset Example](#MNIST-Dataset-Example-mnist)\n", " * [Set Up Environment](#Set-Up-Environment-mnist)\n", " * [Downloading the Dataset](#Download-the-Dataset-mnist)\n", " * [The \"Application\"](#The-Application-mnist)\n", "* [Logistic Regression Example](#Logistic-Regression-Example-mnist)\n", " * [Fitting the Model](#Fitting-the-Model-logistic-mnist)\n", " * [Black-Box Classifier](#Black-Box-Classifier-logistic-mnist)\n", " * [Heatmap Generation](#Heatmap-Generation-mnist)\n", " * [Calling the Application](#Calling-the-Application-logistic-mnist)\n", "* [MLP Example](#MLP-Example-mnist)\n", " * [Fitting the Model](#Fitting-the-Model-mlp-mnist)\n", " * [Black-Box Classifier](#Black-Box-Classifier-mlp-mnist)\n", " * [Calling the Application](#Calling-the-Application-mlp-mnist)\n", "\n", "
\n", "\n", "To run this notebook in Colab, use the link below:\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/XAITK/xaitk-saliency/blob/master/docs/examples/MNIST_scikit_saliency.ipynb)" ] }, { "cell_type": "markdown", "id": "265104e6", "metadata": { "tags": [] }, "source": [ "# MNIST Dataset Example \n", "## Set Up Environment \n", "**Note for Colab users**: after setting up the environment, you may need to \"Restart Runtime\" in order to resolve package version conflicts (see the [README](https://github.com/XAITK/xaitk-saliency/blob/master/docs/examples/README.md#run-the-notebooks-from-colab) for more info)." ] }, { "cell_type": "code", "execution_count": 1, "id": "322e6692", "metadata": {}, "outputs": [], "source": [ "import sys # noqa\n", "\n", "!{sys.executable} -m pip install -qU pip\n", "!{sys.executable} -m pip install -q xaitk-saliency\n", "# for some reason scikit-learn is needing pandas in this notebook\n", "!{sys.executable} -m pip install -q pandas" ] }, { "cell_type": "markdown", "id": "c44e6e20", "metadata": { "tags": [] }, "source": [ "## Downloading the Dataset \n", "\n", "The MNIST dataset consists of 70,000 28x28 grayscale images of handwritten numbers.\n", "Each image is stored as a column vector, resulting in a (70000,784) shape for the entire dataset." ] }, { "cell_type": "code", "execution_count": 2, "id": "e389b304", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import os\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn.datasets import fetch_openml\n", "\n", "cwd = os.getcwd()\n", "data_dir = cwd + \"/data/scikit-learn-example\"\n", "\n", "# Load data from https://www.openml.org/d/554\n", "X, y = fetch_openml(\"mnist_784\", version=1, return_X_y=True, as_frame=False, data_home=data_dir)\n", "X = X / X.max()\n", "\n", "# Find examples of each class\n", "ref_inds = [np.nonzero(np.int64(y) == i)[0][0] for i in range(10)]\n", "\n", "ref_imgs = X[ref_inds]\n", "\n", "# Plot examples\n", "plt.figure(figsize=(15, 5))\n", "for i in range(10):\n", " plt.subplot(1, 10, i + 1)\n", " plt.imshow(ref_imgs[i].reshape(28, 28), \"gray\")\n", " plt.axis(\"off\")" ] }, { "cell_type": "markdown", "id": "52a034e3", "metadata": { "tags": [] }, "source": [ "## The \"Application\" \n", "\n", "Our \"application\" will accept a set of images, a black-box image classifier, and a saliency generator and will generate saliency maps for each image provided.\n", "The saliency maps from the first image in the set will then be plotted to give an idea of the model's behavior on a single sample.\n", "\n", "Additionally, because all digits in the MNIST dataset are centered in the frame, we can average all the heatmaps generated for each respective class to produce a decision boundary visualization.\n", "The application will do this and plot the resulting averaged heatmaps for each digit class.\n", "This should compare to what is shown in the [first example](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html) discussed in the [introduction](#introduction)." ] }, { "cell_type": "code", "execution_count": 3, "id": "631c7578", "metadata": {}, "outputs": [], "source": [ "from collections.abc import Iterable, Sequence\n", "from typing import Any\n", "\n", "from smqtk_classifier import ClassifyImage\n", "from typing_extensions import override\n", "\n", "from xaitk_saliency import GenerateImageClassifierBlackboxSaliency\n", "\n", "\n", "def app(\n", " images: np.ndarray,\n", " image_classifier: ClassifyImage,\n", " saliency_generator: GenerateImageClassifierBlackboxSaliency,\n", ") -> None:\n", " \"\"\"Helper to generate and visualize saliency maps\"\"\"\n", " # Generate saliency maps\n", " sal_maps_set = []\n", " for img in images:\n", " ref_image = img.reshape(28, 28)\n", " sal_maps = saliency_generator(ref_image, image_classifier)\n", " sal_maps_set.append(sal_maps)\n", "\n", " num_classes = sal_maps_set[0].shape[0]\n", "\n", " # Plot first image in set with saliency maps\n", " plt.figure(figsize=(10, 5))\n", " plt.suptitle(\"Heatmaps for First Image\", fontsize=16)\n", " num_cols = np.ceil(num_classes / 2).astype(int) + 1\n", " plt.subplot(2, num_cols, 1)\n", " plt.imshow(images[0].reshape(28, 28), cmap=\"gray\")\n", " plt.xticks(())\n", " plt.yticks(())\n", "\n", " for c in range(num_cols - 1):\n", " plt.subplot(2, num_cols, c + 2)\n", " plt.imshow(sal_maps_set[0][c], cmap=plt.cm.RdBu, vmin=-1, vmax=1)\n", " plt.xticks(())\n", " plt.yticks(())\n", " plt.xlabel(f\"Class {c}\")\n", " for c in range(num_classes - num_cols + 1, num_classes):\n", " plt.subplot(2, num_cols, c + 3)\n", " plt.imshow(sal_maps_set[0][c], cmap=plt.cm.RdBu, vmin=-1, vmax=1)\n", " plt.xticks(())\n", " plt.yticks(())\n", " plt.xlabel(f\"Class {c}\")\n", "\n", " # Average heatmaps for each respective class\n", " global_maps = np.sum(sal_maps_set, axis=0) / len(images)\n", "\n", " # Plot average maps\n", " plt.figure(figsize=(10, 5))\n", " plt.suptitle(\"Average Heatmaps from All Images\", fontsize=16)\n", " for c in range(num_classes):\n", " vcap = np.absolute(global_maps[i]).max()\n", " plt.subplot(2, num_cols - 1, c + 1)\n", " plt.imshow(global_maps[c], cmap=plt.cm.RdBu, vmin=-vcap, vmax=vcap)\n", " plt.xticks(())\n", " plt.yticks(())\n", " plt.xlabel(f\"Class {c}\")" ] }, { "cell_type": "markdown", "id": "0e500cdd-093b-4f02-a427-485f2091c51d", "metadata": {}, "source": [ "# Logistic Regression Example \n", "## Fitting the Model \n", "\n", "We take the same `LogisticRegression` object used in the scikit-learn example and fit it to a subset of the dataset.\n", "Here, an L2 penalty and a larger training set is used to yield slightly better results than those shown in the example.\n", "The same visualization of the coefficients is shown with these new parameters." ] }, { "cell_type": "code", "execution_count": 4, "id": "80ef7b64", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test score with L2 penalty: 0.8865\n", "Example run in 2.445 s\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import time\n", "\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.model_selection import train_test_split\n", "\n", "t0 = time.time()\n", "\n", "# Split data into test and train sets\n", "train_samples = 20000\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=train_samples, test_size=10000, random_state=0)\n", "\n", "# Define model, lower C value gives higher regulation\n", "clf = LogisticRegression(C=50.0 / train_samples, penalty=\"l2\", solver=\"saga\", tol=0.1, random_state=0)\n", "\n", "# Fit model\n", "clf.fit(X_train, y_train)\n", "\n", "# Score model\n", "score = clf.score(X_test, y_test)\n", "print(f\"Test score with L2 penalty: {score:.4f}\")\n", "\n", "# Visualize coefficients\n", "coef = clf.coef_.copy()\n", "max_val = np.abs(coef).max()\n", "\n", "plt.figure(figsize=(10, 5))\n", "for i in range(10):\n", " p = plt.subplot(2, 5, i + 1)\n", " p.imshow(coef[i].reshape(28, 28), cmap=plt.cm.RdBu, vmin=-max_val, vmax=max_val)\n", " p.set_xticks(())\n", " p.set_yticks(())\n", " p.set_xlabel(f\"Class {i}\")\n", "plt.suptitle(\"Classification vector for...\")\n", "\n", "run_time = time.time() - t0\n", "print(f\"Example run in {run_time:.3f} s\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "e6d18472", "metadata": { "tags": [] }, "source": [ "## Black-Box Classifier \n", "\n", "Here we wrap our `LogisticRegression` object in [SMQTK-Classifier's](https://smqtk-classifier.readthedocs.io/en/stable/classifier_interfaces.html#classifyimage) `ClassifyImage` class to comply with the API's interface." ] }, { "cell_type": "code", "execution_count": 5, "id": "59ccb555", "metadata": {}, "outputs": [], "source": [ "class MNISTClassifierLog(ClassifyImage):\n", " \"\"\"ClassifyImage wrapper for LogisticRegression\"\"\"\n", "\n", " @override\n", " def get_labels(self) -> Sequence[int]:\n", " \"\"\"Return class labels\"\"\"\n", " return list(range(10))\n", "\n", " @override\n", " def classify_images(self, image_iter: Iterable[np.ndarray]) -> Sequence[dict[str, Any]]:\n", " \"\"\"Generate predictions\"\"\"\n", " # Yes, \"images\" in this example case are really 1-dim (28*28=784).\n", " # MLP input needs a (n_samples, n_features) matrix input.\n", " images = np.asarray(list(image_iter)) # may fail because input is not consistent in shape\n", " images = images.reshape(-1, 28 * 28) # may fail because input was not the correct shape\n", " return (dict(zip(range(10), p, strict=False)) for p in clf.decision_function(images))\n", "\n", " @override\n", " def get_config(self) -> dict[str, Any]:\n", " \"\"\"Required for implementation\"\"\"\n", " return {}\n", "\n", "\n", "image_classifier_log = MNISTClassifierLog()" ] }, { "cell_type": "markdown", "id": "dcdab7a0", "metadata": {}, "source": [ "## Heatmap Generation \n", "\n", "We create an instance of `SlidingWindowStack`, an implementation of the `GenerateImageClassifierBalckboxSaliency` interface, to carry out our image perturbation and heatmap generation." ] }, { "cell_type": "code", "execution_count": 6, "id": "99c51510", "metadata": {}, "outputs": [], "source": [ "from xaitk_saliency.impls.gen_image_classifier_blackbox_sal.slidingwindow import SlidingWindowStack\n", "\n", "gen_sliding_window = SlidingWindowStack(window_size=(2, 2), stride=(1, 1), threads=4)" ] }, { "cell_type": "markdown", "id": "48ce7713", "metadata": {}, "source": [ "## Calling the Application \n", "\n", "Finally, we call the application using the first 20 images in the MNIST dataset.\n", "Here the blue is showing positive saliency while the red is showing negative saliency.\n", "\n", "Even with a small set of images, the general shape of the digits is visible.\n", "Using a larger set of images should improve the visualized decision boundaries, but scales the computation time linearly.\n", "\n", "These results are largely expected when linear models like logistic regression are used.\n", "Each pixel from the image corresponds to a single coefficient in each of the respective classes' regressions.\n", "Occluding a pixel in the image should affect the output of the model, and therefore the resulting saliency maps, proportional to the value of the corresponding coefficients.\n", "We therefore expect the saliency maps to largely match the pattern of the learned coefficients." ] }, { "cell_type": "code", "execution_count": 7, "id": "b3e0be0f", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "app(X[0:20], image_classifier_log, gen_sliding_window)" ] }, { "cell_type": "markdown", "id": "eeceae38", "metadata": {}, "source": [ "# MLP Example \n", "## Fitting the Model \n", "\n", "Following the second [example](https://scikit-learn.org/stable/auto_examples/neural_networks/plot_mnist_filters.html#sphx-glr-auto-examples-neural-networks-plot-mnist-filters-py) from scikit-learn, we training an `MLPClassifier` on the MNIST dataset using the same hyperparameters.\n", "\n", "To shorten training time, the MLP has only one hidden layer with 50 nodes, and is only trained for 10 iterations, meaning the model does not converge." ] }, { "cell_type": "code", "execution_count": 8, "id": "e2f41616", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 1, loss = 0.32009978\n", "Iteration 2, loss = 0.15347534\n", "Iteration 3, loss = 0.11544755\n", "Iteration 4, loss = 0.09279764\n", "Iteration 5, loss = 0.07889367\n", "Iteration 6, loss = 0.07170497\n", "Iteration 7, loss = 0.06282111\n", "Iteration 8, loss = 0.05530788\n", "Iteration 9, loss = 0.04960484\n", "Iteration 10, loss = 0.04645355\n", "Training set score: 0.986800\n", "Test set score: 0.970000\n" ] } ], "source": [ "import warnings\n", "\n", "from sklearn.exceptions import ConvergenceWarning\n", "from sklearn.neural_network import MLPClassifier\n", "\n", "# use the traditional train/test split\n", "X_train, X_test = X[:60000], X[60000:]\n", "y_train, y_test = y[:60000], y[60000:]\n", "\n", "mlp = MLPClassifier(\n", " hidden_layer_sizes=(50,),\n", " max_iter=10,\n", " alpha=1e-4,\n", " solver=\"sgd\",\n", " verbose=10,\n", " random_state=1,\n", " learning_rate_init=0.1,\n", ")\n", "\n", "# this example won't converge because of CI's time constraints, so we catch the\n", "# warning and ignore it here\n", "with warnings.catch_warnings():\n", " warnings.filterwarnings(\"ignore\", category=ConvergenceWarning, module=\"sklearn\")\n", " mlp.fit(X_train, y_train)\n", "\n", "print(f\"Training set score: {mlp.score(X_train, y_train):f}\")\n", "print(f\"Test set score: {mlp.score(X_test, y_test):f}\")" ] }, { "cell_type": "markdown", "id": "ee7aa189", "metadata": {}, "source": [ "## Black-Box Classifier \n", "\n", "We wrap our `MLPClassifier` object in [SMQTK-Classifier's](https://smqtk-classifier.readthedocs.io/en/stable/classifier_interfaces.html#classifyimage) `ClassifyImage` class to comply with the API's interface." ] }, { "cell_type": "code", "execution_count": 9, "id": "fad4f2d6", "metadata": {}, "outputs": [], "source": [ "class MNISTClassifierMLP(ClassifyImage):\n", " \"\"\"ClassifyImage wrapper for MLPClassifier\"\"\"\n", "\n", " @override\n", " def get_labels(self) -> Sequence[int]:\n", " \"\"\"Return class labels\"\"\"\n", " return list(range(10))\n", "\n", " @override\n", " def classify_images(self, image_iter: Iterable[np.ndarray]) -> Sequence[dict[str, Any]]:\n", " \"\"\"Generate predictions\"\"\"\n", " # Yes, \"images\" in this example case are really 1-dim (28*28=784).\n", " # MLP input needs a (n_samples, n_features) matrix input.\n", " images = np.asarray(list(image_iter)) # may fail because input is not consistent in shape\n", " images = images.reshape(-1, 28 * 28) # may fail because input was not the correct shape\n", " return (dict(zip(range(10), p, strict=False)) for p in mlp.predict_proba(images))\n", "\n", " @override\n", " def get_config(self) -> dict[str, Any]:\n", " \"\"\"Required for implementation\"\"\"\n", " return {}\n", "\n", "\n", "image_classifier_mlp = MNISTClassifierMLP()" ] }, { "cell_type": "markdown", "id": "a7907238", "metadata": {}, "source": [ "## Calling the Application \n", "\n", "We call our application again using the same image set and saliency generator, but this time using our MLP classifier.\n", "\n", "The results show mostly negative saliency, suggesting that the MLP model has learned where the pixels are *absent* for each class more than where they are *present*." ] }, { "cell_type": "code", "execution_count": 10, "id": "ddd7d92e", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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