{ "cells": [ { "cell_type": "markdown", "id": "11ff8da5", "metadata": {}, "source": [ "# Performance Benchmarks\n", "\n", "The ``fast-plscan`` library is a re-implementation of the ``fast-hdbscan``\n", "library, while retaining most of the features from the classic ``hdbscan``\n", "library. In this notebook, we compare the computational performance of these\n", "libraries. Since all three implementations use *space trees*, we are\n", "particularly interested in understanding how their performance changes as the\n", "number of dimensions increases." ] }, { "cell_type": "code", "execution_count": 3, "id": "32686d54", "metadata": {}, "outputs": [], "source": [ "import time\n", "import warnings\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from tqdm import tqdm\n", "from sklearn.datasets import make_blobs\n", "\n", "from hdbscan import HDBSCAN\n", "from fast_hdbscan import HDBSCAN as FastHDBSCAN\n", "from sklearn.cluster import KMeans\n", "from fast_plscan import PLSCAN\n", "\n", "plt.rcParams[\"figure.dpi\"] = 150\n", "plt.rcParams[\"figure.figsize\"] = (2.75, 0.618 * 2.75)\n", "warnings.simplefilter(action=\"ignore\", category=FutureWarning)" ] }, { "cell_type": "code", "execution_count": 4, "id": "00b3f86f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fast-plscan 0.1.0.post3.dev13+g3c7f25aaa.d20260328\n", "hdbscan 0.8.42\n", "fast-hdbscan 0.3.1\n", "scikit-learn 1.8.0\n" ] } ], "source": [ "from importlib.metadata import version\n", "\n", "for name in [\"fast-plscan\", \"hdbscan\", \"fast-hdbscan\", \"scikit-learn\"]:\n", " print(name, version(name))" ] }, { "cell_type": "markdown", "id": "66bca131", "metadata": {}, "source": [ "To make things easily repeatable we'll write a simple benchmarking function that\n", "can time how long it takes the different implementations to run on the same\n", "randomly generated dataset. To generate the datasets we'll just use\n", "``make_blobs`` from sklearn. This will not produce the most interesting datasets\n", "for clustering, but it will be good enough to benchmark runtimes. Since we will\n", "be interested in varying the dimension of the dataset we'll leave that as a\n", "parameter, and we'll scale the size of the blobs accordingly (there is more\n", "space in high dimensions so we can have a larger standard deviation on the\n", "blobs) to make it more realistic." ] }, { "cell_type": "code", "execution_count": 5, "id": "5e362ebf", "metadata": {}, "outputs": [], "source": [ "def benchmark(\n", " data_sizes,\n", " dimensions,\n", " alg_classes,\n", " alg_params,\n", " alg_names,\n", " *,\n", " n_clusters=100,\n", " n_runs=5,\n", " min_samples=10,\n", " min_cluster_size=100,\n", " timeout=20,\n", "):\n", " rows = []\n", " pbar = tqdm(total=n_runs * len(data_sizes) * len(dimensions) * len(alg_classes))\n", " for d in dimensions:\n", " max_size = {}\n", " for n_samples in data_sizes:\n", " min_cluster_size = min(n_samples // n_clusters, min_cluster_size)\n", " for n in range(n_runs):\n", " data_sample, _ = make_blobs(\n", " n_samples=n_samples,\n", " n_features=d,\n", " centers=n_clusters,\n", " cluster_std=min((10**d / (n_clusters * 9)), 2.0),\n", " )\n", " for Alg, param, name in zip(alg_classes, alg_params, alg_names):\n", " # Skip algorithms that took too long on previous runs\n", " if n_samples >= max_size.get(name, np.inf):\n", " pbar.update()\n", " continue\n", "\n", " if name == \"kmeans\":\n", " start_time = time.time()\n", " with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " Alg(n_clusters=n_clusters, **param).fit_predict(data_sample)\n", " elapsed = time.time() - start_time\n", " else:\n", " start_time = time.time()\n", " with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " Alg(\n", " min_samples=min_samples,\n", " min_cluster_size=min_cluster_size,\n", " **param,\n", " ).fit_predict(data_sample)\n", " elapsed = time.time() - start_time\n", " if elapsed > timeout:\n", " max_size[name] = n_samples\n", " rows.append((name, n_samples, d, n, elapsed))\n", " pbar.update()\n", "\n", " return pd.DataFrame(\n", " rows, columns=(\"algorithm\", \"n_samples\", \"n_features\", \"run\", \"elapsed_time\")\n", " )" ] }, { "cell_type": "markdown", "id": "2ef96f38", "metadata": {}, "source": [ "With that in hand we can run the benchmark. For reference this notebook was run\n", "on an 8-core machine. If you have fewer cores you'll see less improvement, and\n", "if you have more cores, well, things will only look better." ] }, { "cell_type": "code", "execution_count": 6, "id": "53277ed6", "metadata": {}, "outputs": [], "source": [ "timeout = 20\n", "dimensions = [2, 5, 10, 15, 20]\n", "data_sizes = [5000, 10000, 20000, 40000, 80000, 160000, 320000, 640000, 1280000]\n", "alg_classes = [PLSCAN, PLSCAN, FastHDBSCAN, HDBSCAN, KMeans]\n", "alg_params = [{}, {\"space_tree\": \"ball_tree\"}, {}, {}, {}]\n", "alg_names = [\n", " \"plscan (kd-tree)\",\n", " \"plscan (ball-tree)\",\n", " \"fast_hdbscan\",\n", " \"hdbscan\",\n", " \"kmeans\",\n", "]" ] }, { "cell_type": "code", "execution_count": 7, "id": "7cb29d8d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1125/1125 [40:00<00:00, 2.13s/it] \n" ] } ], "source": [ "benchmark_results = benchmark(\n", " data_sizes, dimensions, alg_classes, alg_params, alg_names, timeout=timeout\n", ")\n", "benchmark_results.to_parquet(\"data/generated/benchmark_time.parquet\", index=False)" ] }, { "cell_type": "markdown", "id": "97262a64", "metadata": {}, "source": [ "Plotting the results, we observe considerable differences between ``hdbscan``\n", "and the ``fast-hdbscan`` and ``fast-plscan`` libraries on low-dimensional data.\n", "The parallel implementations offer substantial improvements in scalability." ] }, { "cell_type": "code", "execution_count": 8, "id": "270359da", "metadata": {}, "outputs": [], "source": [ "benchmark_results = pd.read_parquet(\"data/generated/benchmark_time.parquet\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "9781896a", "metadata": {}, "outputs": [], "source": [ "def to_display_name(input):\n", " if input == \"kmeans\":\n", " return \"k-Means\"\n", " if input == \"hdbscan\":\n", " return \"HDBSCAN*\"\n", " if input == \"fast_hdbscan\":\n", " return \"HDBSCAN* (fast)\"\n", " if input == \"plscan (kd-tree)\":\n", " return \"PLSCAN (kD-tree)\"\n", " if input == \"plscan (ball-tree)\":\n", " return \"PLSCAN (ball-tree)\"\n", " return input" ] }, { "cell_type": "markdown", "id": "aa8cbc20", "metadata": {}, "source": [ "At 10 dimensions, the advantage of parallelization remains, but the scaling\n", "curves become steeper than that of ``kmeans``, which is largely unaffected by\n", "the number of dimensions. Interestingly, the ball-tree implementation in\n", "``fast-plscan`` scales considerably worse than the kd-tree implementation.\n", "\n", "At 20 dimensions, the parallel implementations provide little benefit when\n", "compared to ``kmeans``, which continues to perform efficiently regardless of\n", "dimensionality.\n", "\n", "Plotting over the dimensions we see only ``kmeans`` does not need more time with\n", "higher dimensional data. The scaling trends are a bit more complicated.\n", "``hdbscan`` starts of steepest but appears to level off a bit. Ball-tree\n", "``fast-plscan`` quickly approaches the ``hdbscan`` curve with higher dimensions.\n", "The curves for ``fast_hdbscan`` and kd-tree ``fast-plscan`` remain fairly\n", "shallow, with ``fast-plscan`` consistently being slightly quicker. ``kmeans`` is\n", "unaffected by the number of dimensions, achieving quick times in all cases." ] }, { "cell_type": "code", "execution_count": 10, "id": "89d11344", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "legend_ids = [[], [], [0, 1, 2], [3, 4]]\n", "palette = [\"C0\", \"C1\", \"C2\", \"C3\", \"k\"]\n", "ticks = [0, 10, 20]\n", "\n", "plt.figure(figsize=(2.75 * 3, 3.25))\n", "for i, d in enumerate([2, 10, 20]):\n", " plt.subplot(2, 4, i + 1)\n", " plt.title(f\"{d}D\")\n", " for j, name in enumerate(alg_names):\n", " sns.regplot(\n", " benchmark_results.query(f\"n_features == {d} & algorithm == '{name}'\"),\n", " x=\"n_samples\",\n", " y=\"elapsed_time\",\n", " color=palette[j],\n", " order=2,\n", " line_kws={\"linewidth\": 1},\n", " scatter_kws={\"s\": 5, \"edgecolor\": \"none\"},\n", " )\n", " plt.xlabel(\"Num. samples\")\n", " plt.ylim(0, timeout)\n", " plt.yticks(ticks)\n", " if i == 0:\n", " plt.ylabel(\"Time (s)\")\n", " else:\n", " plt.ylabel(\"\")\n", " plt.gca().set_yticklabels([\"\" for t in ticks])\n", "plt.subplot(2, 4, 4)\n", "for i, name in enumerate(alg_names):\n", " with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " sns.regplot(\n", " benchmark_results.query(f\"algorithm == '{name}' & n_samples == 80000\"),\n", " x=\"n_features\",\n", " y=\"elapsed_time\",\n", " color=palette[i],\n", " order=2,\n", " line_kws={\"linewidth\": 1},\n", " scatter_kws={\"s\": 5, \"edgecolor\": \"none\"},\n", " )\n", "\n", "plt.xticks([2, 5, 10, 15, 20])\n", "plt.ylim(0, timeout)\n", "plt.yticks(ticks)\n", "plt.gca().set_yticklabels([\"\" for t in ticks])\n", "plt.ylabel(\"\")\n", "plt.xlabel(\"Num. dimensions\")\n", "plt.title(\"80k samples\")\n", "plt.suptitle(\"Computational cost scaling\", y=1)\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.legend(\n", " handles=[\n", " plt.Line2D([0], [0], color=p, lw=1, label=to_display_name(n))\n", " for p, n in zip(palette, alg_names)\n", " ],\n", " loc=\"lower center\",\n", " bbox_to_anchor=(0.5, 0.3),\n", " handlelength=1,\n", " ncol=5,\n", ")\n", "plt.axis(\"off\")\n", "\n", "plt.subplots_adjust(0.06, 0.05, 0.99, 0.85, wspace=0.1)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.14.3" } }, "nbformat": 4, "nbformat_minor": 5 }