diff --git a/doc/source/tutorials/notebooks/7_outlier_detection.ipynb b/doc/source/tutorials/notebooks/7_outlier_detection.ipynb new file mode 100755 index 0000000000..e8a3440d0b --- /dev/null +++ b/doc/source/tutorials/notebooks/7_outlier_detection.ipynb @@ -0,0 +1,261 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Anomaly Detection with the Local Outlier Factor\n", + "\n", + "The **Local Outlier Factor (LOF)** is a density-based anomaly detection algorithm that quantifies how isolated each data point is relative to its local neighborhood.\n", + "Instead of applying a single global distance threshold, LOF estimates the **local density** around every sample and compares it with the densities of its nearest neighbors.\n", + "Points located in regions that are substantially less dense than those of their neighbors are considered potential outliers.\n", + "\n", + "\n", + "This localized approach makes LOF particularly effective for datasets with **non-uniform density**, where clusters vary in compactness or spread.\n", + "The algorithm produces a **LOF score** for each sample, which can be interpreted as follows:\n", + "\n", + "\n", + "- **LOF < 1:** The sample lies in a denser region than its neighbors (potentially part of a very compact cluster).\n", + "- **LOF ≈ 1:** The sample has a density comparable to its neighbors and is considered *normal*.\n", + "- **LOF >> 1:** The sample resides in a region of much lower density and is likely an *outlier*.\n", + "\n", + "\n", + "In this way, LOF captures *relative density deviations* rather than assuming a single global notion of normality, enabling robust detection of local anomalies in complex datasets.\n", + "\n", + "\n", + "This notebook shows how to:\n", + "1. Generate a small synthetic dataset with normal points and outliers.\n", + "2. Apply Heat’s distributed LOF implementation.\n", + "3. Visualize and interpret the resulting anomaly scores." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on device: gpu:0\n", + "MPI world size: 1\n" + ] + } + ], + "source": [ + "import heat as ht\n", + "import numpy as np\n", + "import torch\n", + "from heat.classification.localoutlierfactor import LocalOutlierFactor\n", + "from mpi4py import MPI\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Uncomment the following line to enable GPU usage if necessary\n", + "# ht.use_device(\"gpu\")\n", + "\n", + "print(f\"Running on device: {ht.get_device()}\")\n", + "print(f\"MPI world size: {ht.MPI_WORLD.size}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### About the setup\n", + "\n", + "The code below initializes all required modules:\n", + "- **Heat (ht)** for distributed array operations\n", + "- **NumPy** for basic data generation\n", + "- **Matplotlib** for visualization\n", + "\n", + "> 💡 You can uncomment `ht.use_device(\"gpu\")` to run this example on a GPU if available." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Generate synthetic data\n", + "\n", + "To illustrate the LOF algorithm, we will create a small two-dimensional dataset.\n", + "It consists of two dense clusters of points and a few uniformly distributed **outliers**." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated dataset with shape: (220, 2)\n" + ] + } + ], + "source": [ + "def generate_test_data(random_seed:int = 42, points: int = 100, outliers: int = 20):\n", + " np.random.seed(random_seed)\n", + "\n", + " # Generate two Gaussian clusters\n", + " X_inliers = 0.3 * np.random.randn(points, 2)\n", + " X_inliers = np.r_[X_inliers + 2, X_inliers - 2]\n", + "\n", + " # Add uniformly distributed outliers\n", + " X_outliers = np.random.uniform(low=-4, high=4, size=(outliers, 2))\n", + " X = np.r_[X_outliers, X_inliers]\n", + "\n", + " # Convert to Heat tensor (distributed array)\n", + " ht_X = ht.array(X, split=0)\n", + " return ht_X\n", + "\n", + "ht_data = generate_test_data()\n", + "data = ht_data.numpy()\n", + "print(f\"Generated dataset with shape: {ht_data.shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> 🔍 The dataset contains 220 samples in total: 200 inliers forming two clusters and 20 outliers scattered randomly." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Compute Local Outlier Factor\n", + "\n", + "We now fit the LOF model to the generated data.\n", + "\n", + "The current implementation allows for adjusting two parameters:\n", + "- **`n_neighbors`**: number of neighboring points used to estimate local density.\n", + "- **`threshold`**: points with a higher LOF score thean thus cut-off value are labeled as anomalies.\n", + "\n", + "The Heat implementation distributes both the neighbor search and LOF computation across all available MPI processes." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computed LOF scores. Number of detected outliers: 18\n" + ] + } + ], + "source": [ + "lof = LocalOutlierFactor(n_neighbors=10, threshold=2)\n", + "lof.fit(ht_data)\n", + "\n", + "lof_scores = lof.lof_scores.numpy()\n", + "anomaly = lof.anomaly.numpy()\n", + "print(f\"Computed LOF scores. Number of detected outliers: {np.sum(np.where(anomaly==-1, 0, 1))}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After fitting, each point receives a **LOF score**:\n", + "- Values around **1** correspond to normal points (similar local density).\n", + "- Values significantly larger than **1** indicate potential outliers.\n", + "\n", + "The `threshold` parameter determines which points are finally classified as anomalies." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Visualize LOF scores\n", + "\n", + "The plot below shows the data points colored by their LOF scores.\n", + "- The color intensity represents how “different” a point’s local density is compared to its neighbors.\n", + "- Points outlined with a black circle are flagged as **outliers**." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_lof(data: np.array, lof_scores: np.array, anomaly: np.array): \n", + " plt.rc(\"font\", family=\"serif\")\n", + " plt.figure(figsize=(10, 6))\n", + " scatter = plt.scatter(\n", + " data[:, 0], data[:, 1], c=lof_scores, cmap=\"coolwarm\", edgecolors=\"k\", s=60, alpha=0.8\n", + " )\n", + "\n", + " outlier_indices = np.where(anomaly == 1)[0]\n", + "\n", + " plt.scatter(\n", + " data[outlier_indices, 0],\n", + " data[outlier_indices, 1],\n", + " facecolors=\"none\",\n", + " edgecolors=\"black\",\n", + " s=120,\n", + " linewidths=2,\n", + " label=\"Outliers\",\n", + " )\n", + "\n", + " plt.colorbar(scatter, label=\"LOF Score\")\n", + " plt.xlabel(\"Feature 1\")\n", + " plt.ylabel(\"Feature 2\")\n", + " plt.title(\"Local Outlier Factor (LOF) - Anomaly Detection\")\n", + " plt.legend()\n", + " if ht.MPI_WORLD.rank == 0:\n", + " plt.show()\n", + "\n", + "plot_lof(data, lof_scores, anomaly)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> 💡 Interpretation: Points with darker (red) colors have higher LOF scores and are more likely to be outliers. The black-circled points are those identified as anomalies according to the chosen threshold." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "heatenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}