{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "93984a77-bccc-46fc-a7e1-4eb862d10f6e",
   "metadata": {},
   "source": [
    "# Performance benchmarks\n",
    "\n",
    "This notebook provides performance benchmarks for EVoC in comparison to some commonly used options for embedding vector clustering. The goal of these benchmarks is not to be comprehensive, but rather to give a sense of where EVoC's strengths lie, particulary as compared with other standard options. The aim is to look at both clustering quality and compute time and focus primarily on real-world datasets. To ensure you can try running these benchmarks on your hardware we will provide all the code to run the benchmarks yourself. So to start let's get all the libraries we'll need, both to run the benchmarks and comparisons, and visualise the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a7ae0fff-dec4-49b8-9063-aafef992c764",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:36:11.007014Z",
     "iopub.status.busy": "2026-03-25T20:36:11.006894Z",
     "iopub.status.idle": "2026-03-25T20:36:16.807511Z",
     "shell.execute_reply": "2026-03-25T20:36:16.806699Z",
     "shell.execute_reply.started": "2026-03-25T20:36:11.007000Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "import evoc\n",
    "import umap\n",
    "import hdbscan\n",
    "import pandas as pd\n",
    "import sklearn.cluster\n",
    "import sklearn.metrics\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a6ef4ce-bf5f-4e45-857b-bd60f6228fb4",
   "metadata": {},
   "source": [
    "Now we will need come clustering alternatives to compare to. There are, of course, an endless array of clustering algorithms and implementations out there, but for the purposes of giving a basic comparison we will focus on the most common options for embedding vector clustering, such as those used in BERTopic. That means we'll need an implementation of UMAP + HDBSCAN for comparison, and we'll also compare with the standard workhorse: sklearn's KMeans. Sine we'll be benchmarking these we'll build a common calling format so we can build a benchmark harness around them easily. We'll start with UMAP + HDBSCAN which requires a little bit of work to glue together. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9823192a-9d7e-4a1f-b912-84883f8273e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:36:16.808127Z",
     "iopub.status.busy": "2026-03-25T20:36:16.807874Z",
     "iopub.status.idle": "2026-03-25T20:36:16.811159Z",
     "shell.execute_reply": "2026-03-25T20:36:16.810725Z",
     "shell.execute_reply.started": "2026-03-25T20:36:16.808110Z"
    }
   },
   "outputs": [],
   "source": [
    "def umap_hdbscan(\n",
    "    data,\n",
    "    metric=\"euclidean\",\n",
    "    n_neighbors=15,\n",
    "    n_components=2,\n",
    "    min_samples=5,\n",
    "    min_cluster_size=10,\n",
    "    min_dist=0.1,\n",
    "    cluster_selection_method=\"eom\",\n",
    "    n_epochs=None,\n",
    "    negative_sample_rate=5,\n",
    "):\n",
    "    embedding = umap.UMAP(\n",
    "        metric=metric,\n",
    "        n_neighbors=n_neighbors,\n",
    "        n_components=n_components,\n",
    "        min_dist=min_dist,\n",
    "        n_epochs=n_epochs,\n",
    "        negative_sample_rate=negative_sample_rate,\n",
    "        n_jobs=8,\n",
    "    ).fit_transform(data)\n",
    "    clustering = hdbscan.HDBSCAN(\n",
    "        min_samples=min_samples,\n",
    "        min_cluster_size=min_cluster_size,\n",
    "        cluster_selection_method=cluster_selection_method,\n",
    "    ).fit_predict(embedding)\n",
    "    return clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9985b20-ab5a-4001-b834-fea29fb90796",
   "metadata": {},
   "source": [
    "Next up is KMeans. We don't need much of a wrapper here -- we can call on sklearn's implementation fairly directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e3730e80-f580-41d1-a103-9f3d2440bf15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:36:16.811753Z",
     "iopub.status.busy": "2026-03-25T20:36:16.811617Z",
     "iopub.status.idle": "2026-03-25T20:36:16.825930Z",
     "shell.execute_reply": "2026-03-25T20:36:16.825498Z",
     "shell.execute_reply.started": "2026-03-25T20:36:16.811741Z"
    }
   },
   "outputs": [],
   "source": [
    "def kmeans(data, n_clusters=10, kmeans_algorithm=\"lloyd\"):\n",
    "    return sklearn.cluster.KMeans(\n",
    "        n_clusters=n_clusters, \n",
    "        n_init=\"auto\", \n",
    "        algorithm=kmeans_algorithm\n",
    "    ).fit_predict(\n",
    "        data\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e47881-987e-4944-a116-3acd14437b5a",
   "metadata": {},
   "source": [
    "Lastly we need an EVoC function that works with the same pattern. We'll even be sure to use the reproducible (fixed ``random_state``) code-path that is a little slower, but vary the seed to ensure we get variation in results. Since EVoC can provide a few layers of clustering, we'll give it a little bonus by selecting out the best cluster layer compared to a target clustering. In practice EVoC usually selects a very good cluster layer, but for some of the datasets we'll be using the class labels are not exactly the most natural clustering, so we'll let the function choose a different layer in those cases. Note that we are not tuning any other EVoC parameters to optimize the results, just using defaults and selecting among the layers produced (usally 2-5 total layers for any of these datasets, so few enough that you could easily look througbn them by hand). In contrast we did spend time tuning parameters for the other algorithms to try to produce the best accuracy/quality scores we could. Sometimes this involved non-obvious choices of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "75a6f8bf-71eb-4053-943c-48d3f75d7052",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:36:16.826407Z",
     "iopub.status.busy": "2026-03-25T20:36:16.826278Z",
     "iopub.status.idle": "2026-03-25T20:36:16.837721Z",
     "shell.execute_reply": "2026-03-25T20:36:16.837043Z",
     "shell.execute_reply.started": "2026-03-25T20:36:16.826395Z"
    }
   },
   "outputs": [],
   "source": [
    "def EVoC(data, test_target=None, random_state=None):\n",
    "    if random_state is None:\n",
    "        random_state = np.random.randint(65536)\n",
    "    cls = evoc.EVoC(random_state=random_state).fit(data)\n",
    "    if test_target is None:\n",
    "        return cls.labels_\n",
    "    result = np.full(data.shape[0], -1)\n",
    "    best_ari = 0.0\n",
    "    for labels in cls.cluster_layers_:\n",
    "        ari = sklearn.metrics.adjusted_rand_score(\n",
    "            test_target[labels >= 0], labels[labels >= 0]\n",
    "        )\n",
    "        if ari > best_ari:\n",
    "            best_ari = ari\n",
    "            result = labels\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d644252-ab58-4de7-9ca2-b4db23bc38af",
   "metadata": {},
   "source": [
    "Now we need a test harness. To asses the quality of a clustering we'll use datasets that come equipped with class labels, and specifically datasets where there is good reason to expect that the clusters should align reasonably with the class labels. This let's us use robust scores such as Adjusted Rand Index (ARI) and Adjusted Mutual Information (AMI) that compare a clustering against ground-truth labels. Since both UMAP + HDBSCAN and EVoC have a notion of noise points we'll exclude those from the ARI and AMI computations (as they don't make sense as a single class, and confuse things). But we will keep track of how much of the data is clustered, and also track a \"clustering score\" that is a weighted geometric mean of the ARI and the amount of data clustered (weighted 2:1 in favour of ARI accuracy, so we mostly care about being right, but also need to cluster a reasonable amount of data). We will also keep track of how long it takes to run any of these methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c2abe82-1c74-45fa-b6cc-5c36647b8c74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:36:16.838269Z",
     "iopub.status.busy": "2026-03-25T20:36:16.838138Z",
     "iopub.status.idle": "2026-03-25T20:36:16.850442Z",
     "shell.execute_reply": "2026-03-25T20:36:16.849785Z",
     "shell.execute_reply.started": "2026-03-25T20:36:16.838257Z"
    }
   },
   "outputs": [],
   "source": [
    "def score_clustering(data, target, clustering_function, n_runs=16, **kwargs):\n",
    "    result = np.zeros((n_runs, 5), dtype=np.float32)\n",
    "    for i in range(n_runs):\n",
    "        start_time = time.time()\n",
    "        clustering = clustering_function(data, **kwargs)\n",
    "        result[i, 0] = time.time() - start_time\n",
    "        result[i, 1] = sklearn.metrics.adjusted_rand_score(\n",
    "            target[clustering >= 0], clustering[clustering >= 0]\n",
    "        )\n",
    "        result[i, 2] = sklearn.metrics.adjusted_mutual_info_score(\n",
    "            target[clustering >= 0], clustering[clustering >= 0]\n",
    "        )\n",
    "        result[i, 3] = np.sum(clustering >= 0) / clustering.shape[0]\n",
    "        result[i, 4] = np.cbrt((result[i, 1] ** 2) * result[i, 3])\n",
    "\n",
    "    result = pd.DataFrame(\n",
    "        result,\n",
    "        columns=(\n",
    "            \"Elapsed time\",\n",
    "            \"Adjusted Rand Index\",\n",
    "            \"Adjusted Mutual Information\",\n",
    "            \"Proportion clustered\",\n",
    "            \"Clustering Score\",\n",
    "        ),\n",
    "    )\n",
    "    result[\"algorithm\"] = clustering_function.__name__.replace(\"_\", \"\\n\")\n",
    "    result = result.melt(\n",
    "        id_vars=[\"algorithm\"],\n",
    "        value_vars=[\n",
    "            \"Elapsed time\",\n",
    "            \"Adjusted Rand Index\",\n",
    "            \"Adjusted Mutual Information\",\n",
    "            \"Proportion clustered\",\n",
    "            \"Clustering Score\",\n",
    "        ],\n",
    "        var_name=\"measure\",\n",
    "    )\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af6fd5e6-2f44-478b-9a34-b1e935b141be",
   "metadata": {},
   "source": [
    "Lastly we'll need a simple function to run all our clustering algorithm benchmarks for each method across a given dataset and provide a nice table of results that we can easily pass to seaborn for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "40c1aa9a-fb16-473a-ad2c-02ac59bad712",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:36:16.850912Z",
     "iopub.status.busy": "2026-03-25T20:36:16.850774Z",
     "iopub.status.idle": "2026-03-25T20:36:16.862692Z",
     "shell.execute_reply": "2026-03-25T20:36:16.862360Z",
     "shell.execute_reply.started": "2026-03-25T20:36:16.850901Z"
    }
   },
   "outputs": [],
   "source": [
    "def run_dataset_benchmarks(data, target, n_runs, kmeans_kwargs, umap_hdbscan_kwargs):\n",
    "    \"\"\"Score all three algorithms on a dataset and return combined results.\"\"\"\n",
    "    kmeans_results = score_clustering(\n",
    "        data, target, kmeans, n_runs=n_runs, **kmeans_kwargs\n",
    "    )\n",
    "    umap_results = score_clustering(\n",
    "        data, target, umap_hdbscan, n_runs=n_runs, **umap_hdbscan_kwargs\n",
    "    )\n",
    "    evoc_results = score_clustering(\n",
    "        data, target, EVoC, test_target=target, n_runs=n_runs\n",
    "    )\n",
    "    return pd.concat(\n",
    "        [kmeans_results, umap_results, evoc_results.assign(algorithm=\"EVoC\")],\n",
    "        ignore_index=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b36e588-23ee-42bf-b931-f2a797250807",
   "metadata": {},
   "source": [
    "## Image embeddings\n",
    "\n",
    "Let's start by looking at image embeddings. For a \"real-world\" dataset we'll use the tried and true CIFAR-100 dataset. The CIFAR-100 dataset is a popular computer vision benchmark containing 60,000 32x32 color images across 100 classes. In our case we don't need to worry about the images themselves, but rather their embeddings. We have built a dataset with embedding vectors generated by CLIP and provided it on Huggingface datasets so we don't have to worry about the time/cost of embedding all the images. If you wish you can generate your own embeddings, potentially with other models such as SigLIP. Let's pull that data down. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2bb0aaaa-35f0-4588-ad58-466f0cae8ceb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:36:16.863280Z",
     "iopub.status.busy": "2026-03-25T20:36:16.863153Z",
     "iopub.status.idle": "2026-03-25T20:36:19.367650Z",
     "shell.execute_reply": "2026-03-25T20:36:19.367253Z",
     "shell.execute_reply.started": "2026-03-25T20:36:16.863268Z"
    }
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2046299e-7e86-490b-805c-4ccc92088877",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:36:19.368857Z",
     "iopub.status.busy": "2026-03-25T20:36:19.368555Z",
     "iopub.status.idle": "2026-03-25T20:36:49.643729Z",
     "shell.execute_reply": "2026-03-25T20:36:49.642787Z",
     "shell.execute_reply.started": "2026-03-25T20:36:19.368842Z"
    }
   },
   "outputs": [],
   "source": [
    "ds_cifar = load_dataset(\"lmcinnes/evoc_bench_cifar100\")\n",
    "cifar_data = np.asarray(ds_cifar[\"train\"][\"embedding\"])\n",
    "cifar_target = np.asarray(ds_cifar[\"train\"][\"target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a1a510-281d-493c-b9fb-38e89d68cd99",
   "metadata": {},
   "source": [
    "Now we just need to run the benchmarks. Here we had to rune parameters a little. Oddly enough KMeans actually works better if you ask for 125 clusters (instead of the 100 classes that exist) because with more clusters it does a better job of breaking up some of the more easily confused classes that otherwise notably drag down ARI scores. For UMAP + HDBSCAN we need to manage pick the right parameters; in general UMAP doesn't need too much tuning for this, but HDBSCAN works best with leaf clustering and a carefully well-chosen ``min_cluster_size``. A little experimentation finds around 120 works well for this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3fa73eb5-68c7-49be-80a7-bd3527264111",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:36:49.644685Z",
     "iopub.status.busy": "2026-03-25T20:36:49.644502Z",
     "iopub.status.idle": "2026-03-25T20:42:55.905457Z",
     "shell.execute_reply": "2026-03-25T20:42:55.904368Z",
     "shell.execute_reply.started": "2026-03-25T20:36:49.644670Z"
    }
   },
   "outputs": [],
   "source": [
    "cifar_results = run_dataset_benchmarks(\n",
    "    cifar_data, \n",
    "    cifar_target, \n",
    "    n_runs=16, \n",
    "    kmeans_kwargs={\"n_clusters\":125}, \n",
    "    umap_hdbscan_kwargs={\n",
    "        \"min_samples\":5,\n",
    "        \"min_cluster_size\":120, \n",
    "        \"metric\":\"cosine\", \n",
    "        \"cluster_selection_method\":\"leaf\"\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83150948-cc2d-429f-a913-eac665250739",
   "metadata": {},
   "source": [
    "Before we look at quality, let's compare how long these different approaches took to run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9233e308-90ed-4c57-8a1e-738a7a7e898f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:42:55.906750Z",
     "iopub.status.busy": "2026-03-25T20:42:55.906539Z",
     "iopub.status.idle": "2026-03-25T20:42:56.203555Z",
     "shell.execute_reply": "2026-03-25T20:42:56.202959Z",
     "shell.execute_reply.started": "2026-03-25T20:42:55.906732Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x742050969b80>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(\n",
    "    cifar_results[cifar_results.measure == \"Elapsed time\"], \n",
    "    x=\"algorithm\", \n",
    "    y=\"value\", \n",
    "    hue=\"algorithm\", \n",
    "    kind=\"swarm\", \n",
    "    col=\"measure\",\n",
    "    height=8,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eface2b3-8171-4d9e-b1a1-585af3e31990",
   "metadata": {},
   "source": [
    "As expected KMeans is noticeably quicker than UMAP + HDBSCAN. The one very long time for UMAP + HDBSCAN is due to numba's JIT compilation time (subsequent runs don't require compilation -- so we won't see this again). The surprise is that EVoC actually ran faster than KMeans here. Often KMeans is chosen simply based on runtime-constraints: it is usually faster than most other options you could pick. Here, however, we see that EVoC is not only competitive with KMeans, it's actually the faster option!\n",
    "\n",
    "How about the quality of the clusterings we get out?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6c0bcb69-473f-417f-b438-6fc5894d2159",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:42:56.204347Z",
     "iopub.status.busy": "2026-03-25T20:42:56.204186Z",
     "iopub.status.idle": "2026-03-25T20:42:56.875156Z",
     "shell.execute_reply": "2026-03-25T20:42:56.874734Z",
     "shell.execute_reply.started": "2026-03-25T20:42:56.204333Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7420509d75f0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(\n",
    "    cifar_results[cifar_results.measure != \"Elapsed time\"], \n",
    "    x=\"algorithm\", \n",
    "    y=\"value\", \n",
    "    hue=\"algorithm\", \n",
    "    col=\"measure\", \n",
    "    kind=\"swarm\", \n",
    "    col_wrap=2,\n",
    "    height=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02e215d9-75bf-4395-9dfd-08b36606eff8",
   "metadata": {},
   "source": [
    "Here we seem some of KMeans weakness -- it can run very fast, but the quality of the clusters is not always that great, particularly for very high dimensional data such as that produced by embedding models. The one upside of KMeans is that it does cluster all of your data, so the \"proportion clustered\" is perfect, and this significantly improves the clustering score since the other algoirithms only clustered around 80% of the data (but found much cleaner more accurate clusters by doing so). In contrast UMAP + HDBSCAN was much slower than KMeans, and we hope it managed to produce better clusters by doing so. And that is largely born out here. The ARI and AMI for UMAP + HDBSCAN are significantly better than KMeans, so we produced better clusters by expending more compute. Of course this did require not clustering 20% of the data -- but that 20% is likely the \"hard to classify\" samples that would simply make the ARI and AMI a lot worse if we tried to force them to be assigned somewhere. That means that on clustering score UMAP + HDBSCAN comes out a little ahead of KMeans. Last we have EVoC, which ran faster than KMeans and yet somehow also produced better clusters than UMAP + HDBSCAN. EVoC's edge in clustering quality over UMAP + HDBSCAN is not huge here, but it is relevant. The real strength of EVoC on this dataset is the raw speed with which it can produce those good results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b4f389-c9b3-4c57-ac92-c2be35f12133",
   "metadata": {},
   "source": [
    "## Text embeddings\n",
    "\n",
    "For a text dataset we'll use the venerable 20-newsgroups dataset. The 20-newsgroups dataset is a dataset of NNTP newsgroup posts from the 1990s to twenty different newsgroup sections (think subreddits if you never used NNTP). As with the image dataset we have computed text embeddings ahead of time and put the dataset on Huggingface datasets for ease of access. This should be a challenging dataset to get good matches with the class labels on -- while the newsgroups are mostly distinct in topic, posts can easily run off-topic, be very short, or have more text from signature blocks that weren't properly stripped than actual content. That means the data can be very noisy, with a lot of posts that can be very hard to cluster with other posts from the same newsgroup. To make matters worse there are some overarching categories of newsgroups (there are multiple tech/computing newsgroups included, and multiple discussion groups for religion and politics that can tend to have overlaps). Natural clustering may not line up well with the full set of twenty distinct labels provided. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "71bad192-181f-4586-8241-d2674a5101ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:42:56.875997Z",
     "iopub.status.busy": "2026-03-25T20:42:56.875696Z",
     "iopub.status.idle": "2026-03-25T20:43:02.962330Z",
     "shell.execute_reply": "2026-03-25T20:43:02.961712Z",
     "shell.execute_reply.started": "2026-03-25T20:42:56.875982Z"
    }
   },
   "outputs": [],
   "source": [
    "ds_news = load_dataset(\"lmcinnes/evoc_bench_20newsgroups\")\n",
    "news_data = np.asarray(ds_news[\"train\"][\"embedding\"])\n",
    "news_target = np.asarray(ds_news[\"train\"][\"target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22404769-36a7-4aff-bc63-49e1262daed1",
   "metadata": {},
   "source": [
    "Okay, let's run the benchmarks. Due to the noisiness careful parameter selection was required. EVoC will just make use of it's \"pick the best layer\" approach; KMeans again benefits from asking for more clusters than classes to help force it to break up some of the meta-categories to better match with the class labels. UMAP + HDBSCAN required careful tuning of ``min_cluster_size`` to get good results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7d5837d7-97b5-4c25-b596-1fb6b6e5a7d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:43:02.963427Z",
     "iopub.status.busy": "2026-03-25T20:43:02.963202Z",
     "iopub.status.idle": "2026-03-25T20:45:35.272333Z",
     "shell.execute_reply": "2026-03-25T20:45:35.271486Z",
     "shell.execute_reply.started": "2026-03-25T20:43:02.963411Z"
    }
   },
   "outputs": [],
   "source": [
    "news_results = run_dataset_benchmarks(\n",
    "    news_data, \n",
    "    news_target, \n",
    "    n_runs=32, \n",
    "    kmeans_kwargs={\"n_clusters\":25}, \n",
    "    umap_hdbscan_kwargs={\n",
    "        \"min_samples\":5,\n",
    "        \"min_cluster_size\":180, \n",
    "        \"metric\":\"cosine\", \n",
    "        \"cluster_selection_method\":\"leaf\"\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6357957-1518-497f-b3e9-d54583be7dcc",
   "metadata": {},
   "source": [
    "Again, let's start with the time taken. Since we have fewer samples everything will be faster, and since we are asking KMeans for fewer clusters we also expect it to be faster as well. What do we see in practice?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b64c2dee-159b-48cd-b7c0-cfb5766d4cc4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:45:35.273371Z",
     "iopub.status.busy": "2026-03-25T20:45:35.273197Z",
     "iopub.status.idle": "2026-03-25T20:45:35.523724Z",
     "shell.execute_reply": "2026-03-25T20:45:35.523041Z",
     "shell.execute_reply.started": "2026-03-25T20:45:35.273356Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x74201cd48e30>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(\n",
    "    news_results[news_results.measure == \"Elapsed time\"], \n",
    "    x=\"algorithm\", \n",
    "    y=\"value\", \n",
    "    hue=\"algorithm\", \n",
    "    kind=\"swarm\", \n",
    "    col=\"measure\",\n",
    "    height=8,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b049931-a1ea-4e16-8832-ab39fda93e5d",
   "metadata": {},
   "source": [
    "Both KMeans and EVoC produce sub-second timings. This time KMeans has a slight edge in speed, but the distributions overlap, so it is hard to say with confidence that EVoC is significantly slower than KMeans even in this case. UMAP + HDBSCAN was generally much faster than before, but still lags significantly behind the competition in run time. How about quality?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "55978199-7589-48de-b8b8-ce479f09286a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:45:35.524356Z",
     "iopub.status.busy": "2026-03-25T20:45:35.524201Z",
     "iopub.status.idle": "2026-03-25T20:45:36.389650Z",
     "shell.execute_reply": "2026-03-25T20:45:36.389123Z",
     "shell.execute_reply.started": "2026-03-25T20:45:35.524339Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x741fa87a36e0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(\n",
    "    news_results[news_results.measure != \"Elapsed time\"], \n",
    "    x=\"algorithm\", \n",
    "    y=\"value\", \n",
    "    hue=\"algorithm\", \n",
    "    col=\"measure\", \n",
    "    kind=\"swarm\", \n",
    "    col_wrap=2,\n",
    "    height=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb61a83-1f7d-48e2-9c46-6d1b98699e42",
   "metadata": {},
   "source": [
    "Both ARI and AMI scores for all the algorithms are much lower here -- this is a significantly noiser and harder to cluster dataset. Nonetheless KMeans does consistently worse in terms of cluster quality over the points actually clustered by the algorithms. This came at a cost for UMAP + HDBSCAN and EVoC however: they consistently cluster between about 50% and 75% of the dataset and leave a lot of data unclustered as noise. That is to be expected to some degree, but does represent a challenge if you want cluster labels for most of your data. This shows up in the clustering score, with all three approaches being much closer with overlap among the distributions. Still, EVoC has a notable edge in quality overall, and for an approach that ran as fast as KMeans that's quite powerful. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41647cad-0221-42b0-a06a-7d96529de4b0",
   "metadata": {},
   "source": [
    "## Audio embeddings\n",
    "\n",
    "Audio embedding models are increasingly common. As a test dataset we'll use the BirdCLEF 2023 dataset. This was a kaggle competition for correctly identifying bird species based on audio clips of their songs. Agsin this is quite challenging. The audio is (literally) noisy, with potential for a lot of background noise and even other bird calls intruding into the background of audio clips. We also need a good embedding model representation of this audio id we have any hope that clustering can match against the class labels. Fortunately someone embedded the dataset using Google's Bird Vocalization Classifier and put that data on Huggingface, so we can simply download the embeddings. The dataset has a large number of possible species, and some of those have fewer than 100 recordings, making clustering them quite difficult. To make things a little easier on our clustering algorithms we'll prune out all samples from species that have 100 or fewer recordings in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c56939cb-662a-445e-9f11-2266c46b7aa5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:45:36.390262Z",
     "iopub.status.busy": "2026-03-25T20:45:36.390117Z",
     "iopub.status.idle": "2026-03-25T20:45:42.425450Z",
     "shell.execute_reply": "2026-03-25T20:45:42.424318Z",
     "shell.execute_reply.started": "2026-03-25T20:45:36.390248Z"
    }
   },
   "outputs": [],
   "source": [
    "ds_birdclef = load_dataset(\"Syoy/birdclef_2023_train\")\n",
    "birdclef2023_data = np.asarray(ds_birdclef[\"train\"][\"embeddings\"])\n",
    "birdclef2023_target = np.asarray(ds_birdclef[\"train\"][\"primary_label\"])\n",
    "# Only use bird species with at least 100 samples -- this is still very challenging\n",
    "mask = np.isin(\n",
    "    birdclef2023_target,\n",
    "    np.where(np.bincount(birdclef2023_target) > 100)[0],\n",
    ")\n",
    "birdclef2023_data = birdclef2023_data[mask]\n",
    "birdclef2023_target = birdclef2023_target[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33a72cf7-6a1e-40b1-bcc5-092b2762d50b",
   "metadata": {},
   "source": [
    "Now we just need to run all the algorithms. Again runing was required to find the right number of clusters for KMeans to get good results. UMAP + HDBSCAN was a little wasier to tune, as we have a concrete idea of the ``min_cluster_size`` based on our pruning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ca28659-046b-407a-be97-ab334d019473",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:45:42.426718Z",
     "iopub.status.busy": "2026-03-25T20:45:42.426496Z",
     "iopub.status.idle": "2026-03-25T20:46:41.283049Z",
     "shell.execute_reply": "2026-03-25T20:46:41.282152Z",
     "shell.execute_reply.started": "2026-03-25T20:45:42.426697Z"
    }
   },
   "outputs": [],
   "source": [
    "bird_results = run_dataset_benchmarks(\n",
    "    birdclef2023_data, \n",
    "    birdclef2023_target, \n",
    "    n_runs=16, \n",
    "    kmeans_kwargs={\"n_clusters\":130}, \n",
    "    umap_hdbscan_kwargs={\n",
    "        \"min_samples\":5,\n",
    "        \"min_cluster_size\":100, \n",
    "        \"metric\":\"cosine\", \n",
    "        \"cluster_selection_method\":\"leaf\"\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15082cc4-86ef-491d-affd-d066a88f9c23",
   "metadata": {},
   "source": [
    "As always we'll start with timing results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c2e9db88-dc9b-42cd-a2a7-7e442b6b57e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:46:41.283929Z",
     "iopub.status.busy": "2026-03-25T20:46:41.283731Z",
     "iopub.status.idle": "2026-03-25T20:46:41.488356Z",
     "shell.execute_reply": "2026-03-25T20:46:41.487783Z",
     "shell.execute_reply.started": "2026-03-25T20:46:41.283914Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x74205075f560>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(\n",
    "    bird_results[bird_results.measure == \"Elapsed time\"], \n",
    "    x=\"algorithm\", \n",
    "    y=\"value\", \n",
    "    hue=\"algorithm\", \n",
    "    kind=\"swarm\", \n",
    "    col=\"measure\",\n",
    "    height=8,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de1f4f88-09e3-4627-aac5-a56181a67ef8",
   "metadata": {},
   "source": [
    "This time we have some slightly surprising results: KMeans is only barely faster than UMAP + HDBSCAN. This is a result of the need for a large number of clusters, combined with a smaller overall dataset size, such that UMAP + HDBSCAN can run very quickly. In combination this results in UMAP + HDBSCAN being only slightly slower than KMeans. On the other hand EVoC turned out results extremely quickly -- more than 3 times faster than KMeans. So for pure speed EVoC turns out to be a winner here.\n",
    "\n",
    "How about clustering quality?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6a0814a6-36cb-45a8-90fd-fa2e9b25d45f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:46:41.488922Z",
     "iopub.status.busy": "2026-03-25T20:46:41.488756Z",
     "iopub.status.idle": "2026-03-25T20:46:42.125507Z",
     "shell.execute_reply": "2026-03-25T20:46:42.124937Z",
     "shell.execute_reply.started": "2026-03-25T20:46:41.488907Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x741f8cdc76b0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(\n",
    "    bird_results[bird_results.measure != \"Elapsed time\"], \n",
    "    x=\"algorithm\", \n",
    "    y=\"value\", \n",
    "    hue=\"algorithm\", \n",
    "    col=\"measure\", \n",
    "    kind=\"swarm\", \n",
    "    col_wrap=2,\n",
    "    height=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "368d1989-4016-47e0-b8eb-ccfed6a90b18",
   "metadata": {},
   "source": [
    "The first thing to note here is how hard it is o cluster this dataset well. Both ARI and AMI scores in the 0 to 0.6 range represent a poor match against the ground-truth labels. This was a recent (2023) Kaggle challenge dataset though, so we should expect it to be very hard -- and we are trying to get results in an entirely unsupervised fashion. With the relative poor quality overall out of the way, how do the different methods compare? Here EVoC is a clear winner in pure ARI and AMI quality. It got there though by clustering even less of the data than UMAP + HDBSCAN. In fact it often clustered less than half the data. We would rather have clusters that are good, and leave hard to cluster points unclustered than have things forced into clusters just to get good coverage, so we don't necessarily see this as bad. Even accounting for the difference in amount clustered using the clustering score EVoC still comes out ahead. This shows how these algorithms perform when the going gets rough."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "478fc28c-48fa-4a4a-b45d-7dd94ecd8cd8",
   "metadata": {},
   "source": [
    "## Other high dimensional data\n",
    "\n",
    "Sometimes you just have high dimensional data that isn't necessarily directly from some neural embedding model. We can still try to cluster it and see if EVoC can do a decent job. To try this out let's use the classic MNIST digits dataset. We know that we *should* be able to find good clusters for this data even just using the raw pixel values. So let's see how we do with EVoC on MNIST data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3d3f6754-d952-45fd-b96b-1644c141b26b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:46:42.126177Z",
     "iopub.status.busy": "2026-03-25T20:46:42.126019Z",
     "iopub.status.idle": "2026-03-25T20:46:42.142323Z",
     "shell.execute_reply": "2026-03-25T20:46:42.141841Z",
     "shell.execute_reply.started": "2026-03-25T20:46:42.126164Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0abebd46-634b-45ed-96e3-beb7ba44d5f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:46:42.142955Z",
     "iopub.status.busy": "2026-03-25T20:46:42.142804Z",
     "iopub.status.idle": "2026-03-25T20:46:45.795859Z",
     "shell.execute_reply": "2026-03-25T20:46:45.795225Z",
     "shell.execute_reply.started": "2026-03-25T20:46:42.142942Z"
    }
   },
   "outputs": [],
   "source": [
    "mnist_ds = fetch_openml('mnist_784')\n",
    "mnist_data = mnist_ds.data.values.astype(np.float32, order=\"C\")\n",
    "mnist_target = mnist_ds.target.values.astype(np.uint8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5aeed4-a19a-410c-a580-8aa7fe515388",
   "metadata": {},
   "source": [
    "We can run the benchmarks. This time parameter tuning is a little easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7eebb0b3-c2df-4e64-ba75-6594da5382f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:46:45.796609Z",
     "iopub.status.busy": "2026-03-25T20:46:45.796456Z",
     "iopub.status.idle": "2026-03-25T20:51:16.994356Z",
     "shell.execute_reply": "2026-03-25T20:51:16.993592Z",
     "shell.execute_reply.started": "2026-03-25T20:46:45.796595Z"
    }
   },
   "outputs": [],
   "source": [
    "mnist_results = run_dataset_benchmarks(\n",
    "    mnist_data, \n",
    "    mnist_target, \n",
    "    n_runs=16, \n",
    "    kmeans_kwargs={\"n_clusters\":10}, \n",
    "    umap_hdbscan_kwargs={\n",
    "        \"min_samples\":5,\n",
    "        \"min_cluster_size\":1200, \n",
    "        \"metric\":\"cosine\", \n",
    "        \"cluster_selection_method\":\"leaf\"\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23339d19-8803-4f06-806b-474a32e0f1a9",
   "metadata": {},
   "source": [
    "As always let's begin with time taken to compute the clusterings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6eb39efd-0a6c-4505-b9ed-c369210056f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:51:16.995048Z",
     "iopub.status.busy": "2026-03-25T20:51:16.994885Z",
     "iopub.status.idle": "2026-03-25T20:51:17.193178Z",
     "shell.execute_reply": "2026-03-25T20:51:17.192628Z",
     "shell.execute_reply.started": "2026-03-25T20:51:16.995034Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7420506d7650>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(\n",
    "    mnist_results[mnist_results.measure == \"Elapsed time\"], \n",
    "    x=\"algorithm\", \n",
    "    y=\"value\", \n",
    "    hue=\"algorithm\", \n",
    "    kind=\"swarm\", \n",
    "    col=\"measure\",\n",
    "    height=8,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb2378ce-abfd-42e2-add6-c1e4287ec40b",
   "metadata": {},
   "source": [
    "This produces a pretty dramatic result: UMAP + HDBSCAN take a long time to compute for all 70,000 MNIST digits, while KMeans, as we would expect for large data and a small number of clusters (only ten), takes around two seconds. On the other hand EVoC also comes in at a hair above two seconds to cluster the data. Not quite at KMeans speed, but very close.\n",
    "\n",
    "How about the clustering quality? This is the kind of high dimensional dataset that KMeans can tend to struggle with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9168cb00-a657-44d6-a990-22414b2456ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-26T17:37:39.604436Z",
     "iopub.status.busy": "2026-03-26T17:37:39.604204Z",
     "iopub.status.idle": "2026-03-26T17:37:40.255561Z",
     "shell.execute_reply": "2026-03-26T17:37:40.254937Z",
     "shell.execute_reply.started": "2026-03-26T17:37:39.604404Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x741fa8702ab0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(\n",
    "    mnist_results[mnist_results.measure != \"Elapsed time\"], \n",
    "    x=\"algorithm\", \n",
    "    y=\"value\", \n",
    "    hue=\"algorithm\", \n",
    "    col=\"measure\", \n",
    "    kind=\"swarm\", \n",
    "    col_wrap=2,\n",
    "    height=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22bc9bf2-9559-455f-b0d7-32446de322e3",
   "metadata": {},
   "source": [
    "Again we have some very striking results. KMeans does as we would expect, getting low ARI and AMI scores in the 0.4 to 0.5 range. However EVoC manages to get almost perfect scores, and consistently. Better still, EVoC consistently manages to cluster more than 90% of the data. The end result is that even the clustering score shows EVoC head and shoulders above the competition.\n",
    "\n",
    "So, in summary, EVoC is consistently very very fast to compute, coming in as competitive with, and sometimes faster than, KMeans for small to medium sized datasets. EVoC also consistently produces better quality clusters than the much slower to compute UMAP + HDBSCAN option. And it does all of this with essentially no parameter tuning -- we simply have to pick the best option out of a very small number of layers of cluster resolution. Fast, good, and easy -- sometimes you can have all three."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e573b8a4-b0b5-42e9-bebc-3cf5cef8f646",
   "metadata": {},
   "source": [
    "## Scaling\n",
    "\n",
    "So far we have looked at small to medium sized datasets of \"real-word\" data because it comes with class labels we can compare clusterings against, and it provides a realistic challenge for the clusterign algorithms to tackle. EVoC performed very well in tersm of run-time, managing to consistently compete with KMeans. But how well do those results generalize to much larger datasets? Now we are less worried about realistic clusterign challenges, we want to see compute time against dataset size, ideally scaling into millions of data samples. To do that we can use sklearn's ``make_blobs`` to generate some easy to cluster high-dimensional data at varying sample sizes. While we are at it, let's add some extra KMeans implementations into the mix. We'll benchmark FAISS's KMeans, as well as sklean's ``MiniBatchKMeans`` which uses a sampling based approach to do a more approximate optimization at much much faster speeds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b4191c21-48a8-40ad-b547-061ade561ea2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:51:17.870049Z",
     "iopub.status.busy": "2026-03-25T20:51:17.869881Z",
     "iopub.status.idle": "2026-03-25T20:51:17.970759Z",
     "shell.execute_reply": "2026-03-25T20:51:17.970234Z",
     "shell.execute_reply.started": "2026-03-25T20:51:17.870034Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "import faiss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "621629aa-1c48-4e83-b2f9-abf767676423",
   "metadata": {},
   "source": [
    "We need similar function wrappers for ``MiniBatchKMeans`` and FAISS: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5075cd5e-fc1a-4b26-b8dd-895aa2156ba2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:51:17.971578Z",
     "iopub.status.busy": "2026-03-25T20:51:17.971423Z",
     "iopub.status.idle": "2026-03-25T20:51:17.974750Z",
     "shell.execute_reply": "2026-03-25T20:51:17.974323Z",
     "shell.execute_reply.started": "2026-03-25T20:51:17.971564Z"
    }
   },
   "outputs": [],
   "source": [
    "def minibatch_kmeans(data, n_clusters=10):\n",
    "    return sklearn.cluster.MiniBatchKMeans(\n",
    "        n_clusters=n_clusters, \n",
    "        n_init=\"auto\", \n",
    "        batch_size=4*n_clusters\n",
    "    ).fit_predict(\n",
    "        data\n",
    "    )\n",
    "\n",
    "def faiss_kmeans(data, n_clusters=10):\n",
    "    kmeans = faiss.Kmeans(data.shape[1], n_clusters, niter=50, nredo=1, gpu=False)\n",
    "    X = np.ascontiguousarray(data, dtype=np.float32)\n",
    "    kmeans.train(X)\n",
    "    _, labels = kmeans.index.search(X, 1)\n",
    "    return labels.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ce50a1f-73cc-4c26-9f05-fe807aafa04b",
   "metadata": {},
   "source": [
    "Now let's just set up a loop and run everything! We'll scale the dataset sizes from 10,000 up to over 3,000,000, have 4 runs of each implementation at each size, and colelct all the results. If you are running this yourself be warned that (since we are clustering millions of samples many times over) this can take a very long time to complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8d2312a4-6f73-4d8d-b260-0a4529a67408",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-25T20:51:17.975285Z",
     "iopub.status.busy": "2026-03-25T20:51:17.975154Z",
     "iopub.status.idle": "2026-03-26T09:38:20.359235Z",
     "shell.execute_reply": "2026-03-26T09:38:20.357749Z",
     "shell.execute_reply.started": "2026-03-25T20:51:17.975274Z"
    }
   },
   "outputs": [],
   "source": [
    "algorithms = [\n",
    "    (\"UMAP + HDBSCAN\",    lambda X: umap_hdbscan(X)),\n",
    "    (\"Sklearn KMeans\",          lambda X: kmeans(X, n_clusters=n_clusters, kmeans_algorithm=\"elkan\")),\n",
    "    (\"FAISS KMeans\",    lambda X: faiss_kmeans(X, n_clusters=n_clusters)),\n",
    "    (\"Sklearn Minibatch KMeans\",lambda X: minibatch_kmeans(X, n_clusters=n_clusters)),\n",
    "    (\"EVoC\",            lambda X: EVoC(X)),\n",
    "]\n",
    "\n",
    "scaling_results = {\"size\": [], \"time\": [], \"algorithm\": [], \"ari\": []}\n",
    "n_runs = 4\n",
    "\n",
    "for size in np.logspace(4, 6.5, num=8):\n",
    "    for n in range(n_runs):\n",
    "        n_clusters = int(2 * np.sqrt(size))\n",
    "        blobs, labels = make_blobs(n_samples=int(size), n_features=1024, centers=n_clusters, cluster_std=3.0)\n",
    "    \n",
    "        for name, fn in algorithms:\n",
    "            start_time = time.time()\n",
    "            clusters = fn(blobs)\n",
    "            scaling_results[\"size\"].append(size)\n",
    "            scaling_results[\"algorithm\"].append(name)\n",
    "            scaling_results[\"time\"].append(time.time() - start_time)\n",
    "            scaling_results[\"ari\"].append(sklearn.metrics.adjusted_rand_score(labels, clusters))\n",
    "\n",
    "scaling_df = pd.DataFrame(scaling_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "956dc26c-ab67-454e-b9f6-77ea85341ff4",
   "metadata": {},
   "source": [
    "Now let's look at the timing results -- how does the runtime scale with increasing dataset size for these different algorithms?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e4ffa7e1-889a-494d-8548-a48500176dda",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-26T09:38:20.360185Z",
     "iopub.status.busy": "2026-03-26T09:38:20.359995Z",
     "iopub.status.idle": "2026-03-26T09:38:21.088775Z",
     "shell.execute_reply": "2026-03-26T09:38:21.088224Z",
     "shell.execute_reply.started": "2026-03-26T09:38:20.360170Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x74205015be90>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1232.88x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(data=scaling_df, x=\"size\", y=\"time\", hue=\"algorithm\", order=2, height=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec10f642-5eae-49c9-ab87-eb7da3f5ef4d",
   "metadata": {},
   "source": [
    "Here we see EVoC showing its real strengths. As data set sizes scale up, it comtinues to perform extremely quickly, on par, or even better than, MiniBatchKMeans, and distinctly faster than classic KMeans implementations, including FAISS fast version.\n",
    "\n",
    "Lastly, let's have a look at quality. We shouldn't expect to see too much here, this is data that should be very easy cluster, so we should expect mostly perfect results. However, some implementations are making trade-offs and approximations, so we should expect to see some of those differences shown here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c0e7c2ab-e1d2-478f-a9a9-01a762d8da3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-26T09:38:21.089635Z",
     "iopub.status.busy": "2026-03-26T09:38:21.089471Z",
     "iopub.status.idle": "2026-03-26T09:38:21.375853Z",
     "shell.execute_reply": "2026-03-26T09:38:21.375331Z",
     "shell.execute_reply.started": "2026-03-26T09:38:21.089621Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7420508b6a80>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=scaling_df, x=\"algorithm\", y=\"ari\", hue=\"algorithm\", kind=\"swarm\", height=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d27c69d-22bd-4b3c-9e58-defc84d1bdc0",
   "metadata": {},
   "source": [
    "The results are pretty clear. The UMAP + HDBSCAN approach mostly produces good clusters -- the decay in quality is essentially down to having run with default parameters which need to be adjusted somewhat for the larger dataset sizes (we weren't doing any parameter tuning). KMeans mostly manages to find the right clusters. FAISS's Kmeans was possibly not well tuned. MiniBatchKMeans shows a lot more variability than KMeans (as we would expect) including some very poor results occasionally; it might be worth it if you need the speed, but it definitely has costs in terms of cluster quality. Lstly, however, we have EVoC which, despite keeping pace with MiniBatchKMeans in compute time the whole way, also manages to produce almost perfect clusterings every time."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "evoc_docs",
   "language": "python",
   "name": "evoc_docs"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}