{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Benchmarking Performance and Scaling of Python Clustering Algorithms\n", "\n", "There are a host of different clustering algorithms and implementations thereof for Python. The performance and scaling can depend as much on the implementation as the underlying algorithm. Obviously a well written implementation in C or C++ will beat a naive implementation on pure Python, but there is more to it than just that. The internals and data structures used can have a large impact on performance, and can even significanty change asymptotic performance. All of this means that, given some amount of data that you want to cluster your options as to algorithm and implementation maybe significantly constrained. I'm both lazy, and prefer empirical results for this sort of thing, so rather than analyzing the implementations and deriving asymptotic performance numbers for various implementations I'm just going to run everything and see what happens.\n", "\n", "To begin with we need to get together all the clustering implementations, along with some plotting libraries so we can see what is going on once we've got data. Obviously this is not an exhaustive collection of clustering implementations, so if I've left off your favourite I apologise, but one has to draw a line somewhere.\n", "\n", "The implementations being test are:\n", "\n", "* [Sklearn](http://scikit-learn.org/stable/modules/clustering.html) (which implements several algorithms):\n", " * K-Means clustering\n", " * DBSCAN clustering\n", " * Agglomerative clustering\n", " * Spectral clustering\n", " * Affinity Propagation\n", "* [Scipy](http://docs.scipy.org/doc/scipy/reference/cluster.html) (which provides basic algorithms):\n", " * K-Means clustering\n", " * Agglomerative clustering\n", "* [Fastcluster](http://danifold.net/fastcluster.html) (which provides very fast agglomerative clustering in C++)\n", "* [DeBaCl](https://github.com/CoAxLab/DeBaCl) (Density Based Clustering; similar to a mix of DBSCAN and Agglomerative)\n", "* [HDBSCAN](https://github.com/lmcinnes/hdbscan) (A robust hierarchical version of DBSCAN)\n", "\n", "Obviously a major factor in performance will be the algorithm itself. Some algorithms are simply slower -- often, but not always, because they are doing more work to provide a better clustering." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import hdbscan\n", "import debacl\n", "import fastcluster\n", "import sklearn.cluster\n", "import scipy.cluster\n", "import sklearn.datasets\n", "import numpy as np\n", "import pandas as pd\n", "import time\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline\n", "sns.set_context('poster')\n", "sns.set_palette('Paired', 10)\n", "sns.set_color_codes()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need some benchmarking code at various dataset sizes. Because some clustering algorithms have performance that can vary quite a lot depending on the exact nature of the dataset we'll also need to run several times on randomly generated datasets of each size so as to get a better idea of the average case performance.\n", "\n", "We also need to generalise over algorithms which don't necessarily all have the same API. We can resolve that by taking a clustering function, argument tuple and keywords dictionary to let us do semi-arbitrary calls (fortunately all the algorithms do at least take the dataset to cluster as the first parameter).\n", "\n", "Finally some algorithms scale poorly, and I don't want to spend forever doing clustering of random datasets so we'll cap the maximum time an algorithm can use; once it has taken longer than max time we'll just abort there and leave the remaining entries in our datasize by samples matrix unfilled.\n", "\n", "In the end this all amounts to a fairly straightforward set of nested loops (over datasizes and number of samples) with calls to sklearn to generate mock data and the clustering function inside a timer. Add in some early abort and we're done." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def benchmark_algorithm(dataset_sizes, cluster_function, function_args, function_kwds,\n", " dataset_dimension=10, dataset_n_clusters=10, max_time=45, sample_size=2):\n", " \n", " # Initialize the result with NaNs so that any unfilled entries \n", " # will be considered NULL when we convert to a pandas dataframe at the end\n", " result = np.nan * np.ones((len(dataset_sizes), sample_size))\n", " for index, size in enumerate(dataset_sizes):\n", " for s in range(sample_size):\n", " # Use sklearns make_blobs to generate a random dataset with specified size\n", " # dimension and number of clusters\n", " data, labels = sklearn.datasets.make_blobs(n_samples=size, \n", " n_features=dataset_dimension, \n", " centers=dataset_n_clusters)\n", " \n", " # Start the clustering with a timer\n", " start_time = time.time()\n", " cluster_function(data, *function_args, **function_kwds)\n", " time_taken = time.time() - start_time\n", " \n", " # If we are taking more than max_time then abort -- we don't\n", " # want to spend excessive time on slow algorithms\n", " if time_taken > max_time:\n", " result[index, s] = time_taken\n", " return pd.DataFrame(np.vstack([dataset_sizes.repeat(sample_size), \n", " result.flatten()]).T, columns=['x','y'])\n", " else:\n", " result[index, s] = time_taken\n", " \n", " # Return the result as a dataframe for easier handling with seaborn afterwards\n", " return pd.DataFrame(np.vstack([dataset_sizes.repeat(sample_size), \n", " result.flatten()]).T, columns=['x','y'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison of all ten implementations\n", "\n", "Now we need a range of dataset sizes to test out our algorithm. Since the scaling performance is wildly different over the ten implementations we're going to look at it will be beneficial to have a number of very small dataset sizes, and increasing spacing as we get larger, spanning out to 32000 datapoints to cluster (to begin with). Numpy provides convenient ways to get this done via `arange` and vector multiplication. We'll start with step sizes of 500, then shift to steps of 1000 past 3000 datapoints, and finally steps of 2000 past 6000 datapoints." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataset_sizes = np.hstack([np.arange(1, 6) * 500, np.arange(3,7) * 1000, np.arange(4,17) * 2000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now it is just a matter of running all the clustering algorithms via our benchmark function to collect up all the requsite data. This could be prettier, rolled up into functions appropriately, but sometimes brute force is good enough. More importantly (for me) since this can take a significant amount of compute time, I wanted to be able to comment out algorithms that were slow or I was uninterested in easily. Which brings me to a warning for you the reader and potential user of the notebook: this next step is very expensive. We are running ten different clustering algorithms multiple times each on twenty two different dataset sizes -- and some of the clustering algorithms are slow (we are capping out at forty five seconds per run). That means that the next cell can take an hour or more to run. That doesn't mean \"Don't try this at home\" (I actually encourage you to try this out yourself and play with dataset parameters and clustering parameters) but it does mean you should be patient if you're going to!" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "k_means = sklearn.cluster.KMeans(10)\n", "k_means_data = benchmark_algorithm(dataset_sizes, k_means.fit, (), {})\n", "\n", "dbscan = sklearn.cluster.DBSCAN()\n", "dbscan_data = benchmark_algorithm(dataset_sizes, dbscan.fit, (), {})\n", "\n", "scipy_k_means_data = benchmark_algorithm(dataset_sizes, scipy.cluster.vq.kmeans, (10,), {})\n", "\n", "scipy_single_data = benchmark_algorithm(dataset_sizes, scipy.cluster.hierarchy.single, (), {})\n", "\n", "fastclust_data = benchmark_algorithm(dataset_sizes, fastcluster.single, (), {})\n", "\n", "hdbscan_ = hdbscan.HDBSCAN()\n", "hdbscan_data = benchmark_algorithm(dataset_sizes, hdbscan_.fit, (), {})\n", "\n", "debacl_data = benchmark_algorithm(dataset_sizes, debacl.geom_tree.geomTree, (5, 5), {'verbose':False})\n", "\n", "agglomerative = sklearn.cluster.AgglomerativeClustering(10)\n", "agg_data = benchmark_algorithm(dataset_sizes, agglomerative.fit, (), {}, sample_size=4)\n", "\n", "spectral = sklearn.cluster.SpectralClustering(10)\n", "spectral_data = benchmark_algorithm(dataset_sizes, spectral.fit, (), {}, sample_size=6)\n", "\n", "affinity_prop = sklearn.cluster.AffinityPropagation()\n", "ap_data = benchmark_algorithm(dataset_sizes, affinity_prop.fit, (), {}, sample_size=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need to plot the results so we can see what is going on. The catch is that we have several datapoints for each dataset size and ultimately we would like to try and fit a curve through all of it to get the general scaling trend. Fortunately [seaborn](http://stanford.edu/~mwaskom/software/seaborn/) comes to the rescue here by providing `regplot` which plots a regression through a dataset, supports higher order regression (we should probably use order two as most algorithms are effectively quadratic) and handles multiple datapoints for each x-value cleanly (using the `x_estimator` keyword to put a point at the mean and draw an error bar to cover the range of data)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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oBiHEqazu/krBql1omykoW+hlqOhMcGpj+zrKpvhOChEkhIrO9BeU8HjM1jgAtIxSNwm1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\n6xqWZePzz03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Xe0AItHwcuxBMvBIc2+Grf/Ys+Wxp8J9Xz5G3/cVjj2ZijJ+Z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\ncA3uQ7AaMFlE4srt7bgPzbnAMb7MJ/0PRZ6/AbvgPjh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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.regplot(x='x', y='y', data=k_means_data, order=2, label='Sklearn K-Means', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=dbscan_data, order=2, label='Sklearn DBSCAN', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=scipy_k_means_data, order=2, label='Scipy K-Means', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=scipy_single_data, order=2, label='Scipy Single Linkage', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=fastclust_data, order=2, label='Fastcluster Single Linkage', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=hdbscan_data, order=2, label='HDBSCAN', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=debacl_data, order=2, label='DeBaCl Geom Tree', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=spectral_data, order=2, label='Sklearn Spectral', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=agg_data, order=2, label='Sklearn Agglomerative', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=ap_data, order=2, label='Sklearn Affinity Propagation', x_estimator=np.mean)\n", "plt.gca().axis([0, 34000, 0, 120])\n", "plt.gca().set_xlabel('Number of data points')\n", "plt.gca().set_ylabel('Time taken to cluster (s)')\n", "plt.title('Performance Comparison of Clustering Implementations')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A few features stand out. First of all there appear to be essentially two classes of implementation, with DeBaCl being an odd case that falls in the middle. The fast implementations tend to be implementations of single linkage agglomerative clustering, K-means, and DBSCAN. The slow cases are largely from sklearn and include agglomerative clustering (in this case using Ward instead of single linkage).\n", "\n", "For practical purposes this means that if you have much more than 10000 datapoints your clustering options are significantly constrained: sklearn spectral, agglomerative and affinity propagation are going to take far too long. DeBaCl may still be an option, but given that the hdbscan library provides \"robust single linkage clustering\" equivalent to what DeBaCl is doing (and with effectively the same runtime as hdbscan as it is a subset of that algorithm) it is probably not the best choice for large dataset sizes.\n", "\n", "So let's drop out those slow algorithms so we can scale out a little further and get a closer look at the various algorithms that managed 32000 points in under thirty seconds. There is almost undoubtedly more to learn as we get ever larger dataset sizes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison of fast implementations\n", "\n", "Let's compare the six fastest implementations now. We can scale out a little further as well; based on the curves above it looks like we should be able to comfortably get to 60000 data points without taking much more than a minute per run. We can also note that most of these implementations weren't that noisy so we can get away with a single run per dataset size." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "large_dataset_sizes = np.arange(1,16) * 4000\n", "\n", "hdbscan_ = hdbscan.HDBSCAN()\n", "large_hdbscan_data = benchmark_algorithm(large_dataset_sizes, \n", " hdbscan_.fit, (), {}, max_time=90, sample_size=1)\n", "\n", "k_means = sklearn.cluster.KMeans(10)\n", "large_k_means_data = benchmark_algorithm(large_dataset_sizes, \n", " k_means.fit, (), {}, max_time=90, sample_size=1)\n", "\n", "dbscan = sklearn.cluster.DBSCAN()\n", "large_dbscan_data = benchmark_algorithm(large_dataset_sizes, \n", " dbscan.fit, (), {}, max_time=90, sample_size=1)\n", "\n", "large_fastclust_data = benchmark_algorithm(large_dataset_sizes, \n", " fastcluster.single, (), {}, max_time=90, sample_size=1)\n", "\n", "large_scipy_k_means_data = benchmark_algorithm(large_dataset_sizes, \n", " scipy.cluster.vq.kmeans, (10,), {}, max_time=90, sample_size=1)\n", "\n", "large_scipy_single_data = benchmark_algorithm(large_dataset_sizes, \n", " scipy.cluster.hierarchy.single, (), {}, max_time=90, sample_size=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again we can use seaborn to do curve fitting and plotting, exactly as before." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZT+8+g7h32MNhLbcmMjk9eofTFE9MDLEZJwa18ERucpHhySDG5ZwVwhLHyvlH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\nIKyvP7ZjXy2WDqI1xnUICp0b7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auQkFLu833/ydR6FQC+QK3x+bdNLlfUPB5+2sbYbp36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\nc5jP5CnsV6JyE/vJE7+X0A9a6w/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FAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.regplot(x='x', y='y', data=large_k_means_data, order=2, label='Sklearn K-Means', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=large_dbscan_data, order=2, label='Sklearn DBSCAN', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=large_scipy_k_means_data, order=2, label='Scipy K-Means', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=large_scipy_single_data, order=2, label='Scipy Single Linkage', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=large_fastclust_data, order=2, label='Fastcluster Single Linkage', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=large_hdbscan_data, order=2, label='HDBSCAN', x_estimator=np.mean)\n", "plt.gca().axis([0, 64000, 0, 150])\n", "plt.gca().set_xlabel('Number of data points')\n", "plt.gca().set_ylabel('Time taken to cluster (s)')\n", "plt.title('Performance Comparison of Fastest Clustering Implementations')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clearly something has gone woefully wrong with the curve fitting for the two single linkage algorithms, but what exactly? If we look at the raw data we can see." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " x y\n", "5 24000 9.266714\n", "6 28000 13.266489\n", "7 32000 18.329864\n", "8 36000 24.690914\n", "9 40000 42.135954\n", "10 44000 305.774102\n", "11 48000 NaN\n", "12 52000 NaN\n", "13 56000 NaN\n", "14 60000 NaN" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "large_scipy_single_data.tail(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems that at around 44000 points we hit a wall and the runtimes spiked. A hint is that I'm running this on a laptop with 8GB of RAM. Both single linkage algorithms use `scipy.spatial.pdist` to compute pairwise distances between points, which returns an array of shape `(n(n-1)/2, 1)` of doubles. A quick computation shows that that array of distances is quite large once we nave 44000 points:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7.211998105049133" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "size_of_array = 44000 * (44000 - 1) / 2 # from pdist documentation\n", "bytes_in_array = size_of_array * 8 # Since doubles use 8 bytes\n", "gigabytes_used = bytes_in_array / (1024.0 ** 3) # divide out to get the number of GB\n", "gigabytes_used" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we assume that my laptop is keeping much other than that distance array in RAM then clearly we are going to spend time paging out the distance array to disk and back and hence we will see the runtimes increase dramatically as we become disk IO bound. If we just leave off the last element we can get a better idea of the curves, but keep in mind that both single linkage algorithms do not scale past a limit set by your available RAM." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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iXLlyfs87evQITzzRn1q1FBMmvOk3z5Ytm5g7dzYHDuyjRImSdOnSjUcffSxt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48e8/TqzmwHBnPgB1bBC+mt3LzhVgLCR7hOm/0uTphyQpVWNT4VNZBOKfUY8CrXdVuAhYADbFFK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\no9fF4sUf+to9ZnD82c+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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.regplot(x='x', y='y', data=large_k_means_data, order=2, label='Sklearn K-Means', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=large_dbscan_data, order=2, label='Sklearn DBSCAN', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=large_scipy_k_means_data, order=2, label='Scipy K-Means', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=large_scipy_single_data[:10], order=2, label='Scipy Single Linkage', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=large_fastclust_data[:10], order=2, label='Fastcluster Single Linkage', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=large_hdbscan_data, order=2, label='HDBSCAN', x_estimator=np.mean)\n", "plt.gca().axis([0, 64000, 0, 150])\n", "plt.gca().set_xlabel('Number of data points')\n", "plt.gca().set_ylabel('Time taken to cluster (s)')\n", "plt.title('Performance Comparison of Fastest Clustering Implementations')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now it becomes clear that there were really three classes, not two: the K-Means and DBSCAN implementations are all packed along the very bottom while HDBSCAN and the single linkage implementations begin to consume more and more time for larger datasets.\n", "\n", "In practice this is going to mean that for larger datasets you are going to be very constrained in what algorithms you can apply: if you get enough datapoints only K-Means and DBSCAN will be left. This is somewhat disappointing, paritcularly as [K-Means is not a particularly good clustering algorithm](http://nbviewer.jupyter.org/github/lmcinnes/hdbscan/blob/master/notebooks/Comparing%20Clustering%20Algorithms.ipynb), paricularly for exploratory data analysis. If you're willing to go for coffee while waiting for your run, however, than HDBSCAN will be able to handle 100,000 datapoints or so; if you're willing to wait over lunch you can go higher again.\n", "\n", "With this in mind it is worth looking at how the K-Means and DBSCAN implementations perform. If we restrict to just those algorithms we can scale out to even larger dataset sizes again, and start to see how those different algorithms and implementations separate." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison of K-Means and DBSCAN implementations\n", "\n", "At this point we can scale out to 100000 datapoints easily enough: K-Means and DBSCAN can use various data structures to avoid having to compute the full pairwise distance matrix and are thus not as memory constrained as some of the other algorithms we looked at." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "huge_dataset_sizes = np.arange(1,19) * 10000\n", "\n", "k_means = sklearn.cluster.KMeans(10)\n", "huge_k_means_data = benchmark_algorithm(huge_dataset_sizes, \n", " k_means.fit, (), {}, max_time=120, sample_size=5)\n", "\n", "dbscan = sklearn.cluster.DBSCAN()\n", "huge_dbscan_data = benchmark_algorithm(huge_dataset_sizes, \n", " dbscan.fit, (), {}, max_time=120, sample_size=5)\n", "\n", "huge_scipy_k_means_data = benchmark_algorithm(huge_dataset_sizes, \n", " scipy.cluster.vq.kmeans, (10,), {}, max_time=120, sample_size=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This time around we'll use a linear rather than quadratic fit (feel free to try the quadratic fit for yourself of course). Why is that? Because of the 'secret sauce' that keeps these implementations runtimes so low: using appropriate data structures such as kd-trees these algorithms have $O(n\\log n)$ asymptotics, so while they don't actually scale linearly, a linear fit is better than a quadratic one." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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BCCGEECKd2R0OTkfGEu9IOu9ydDw3YuMJ9k/d3e5/o1ev3ly7dpW1a79m+/at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\n9voIqHxB1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OX3+pcEhTlTBZ+X3+vnq08c3bTvLYQQ+42EBSH2uFJIyBJobz4k5IIJBrwe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\nXAdetHkFP33Zenas65wxMhNGBmMM7S0+K9szpKqMqIiF5ceBW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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.regplot(x='x', y='y', data=huge_k_means_data, order=1, label='Sklearn K-Means', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=huge_dbscan_data, order=1, label='Sklearn DBSCAN', x_estimator=np.mean)\n", "sns.regplot(x='x', y='y', data=huge_scipy_k_means_data, order=1, label='Scipy K-Means', x_estimator=np.mean)\n", "plt.gca().axis([0, 190000, 0, 20])\n", "plt.gca().set_xlabel('Number of data points')\n", "plt.gca().set_ylabel('Time taken to cluster (s)')\n", "plt.title('Performance Comparison of K-Means and DBSCAN')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the differences become clear, and it demonstrates how much of a difference implementation can make: the sklearn implementation of K-Means is far better than the scipy implementation. The DBSCAN implementation falls somewhere in between. Since DBSCAN clustering is a lot better than K-Means (unless you have good reasons to assume that the clusters partition your data and are all drawn from Gaussian distributions) and the scaling is still very good I would suggest that unless you have a truly stupendous amount of data you wish to cluster then the sklearn DBSCAN implementation is a good choice." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## But should I get a coffee?\n", "\n", "So we know which implementations scale and which don't; a more useful thing to know in practice is, given a dataset, what can I run interactively? What can I run while I go and grab some coffee? How about a run over lunch? What if I'm willing to wait until I get in tomorrow morning? Each of these represent significant breaks in productivity -- once you aren't working interactively anymore your productivity drops measurably, and so on.\n", "\n", "We can build a table for this. To start we'll need to be able to approximate how long a given clustering implementation will take to run. Fortunately we already gathered a lot of that data; if we load up the `statsmodels` package we can fit the data (with a quadratic or $n\\log n$ fit depending on the implementation) and use the resulting model to make our predictions. Obviously this has some caveats: if you fill your RAM with a distance matrix your runtime isn't going to fit the curve.\n", "\n", "I've hand built a `time_samples` list to give a reasonable set of potential data sizes that are nice and human readable. After that we just need a function to fit and build the curves." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import statsmodels.formula.api as sm\n", "\n", "time_samples = [1000, 2000, 5000, 10000, 25000, 50000, 75000, 100000, 250000, 500000, 750000,\n", " 1000000, 5000000, 10000000, 50000000, 100000000, 500000000, 1000000000]\n", "\n", "def get_timing_series(data, quadratic=True):\n", " if quadratic:\n", " data['x_squared'] = data.x**2\n", " model = sm.ols('y ~ x + x_squared', data=data).fit()\n", " predictions = [model.params.dot([1.0, i, i**2]) for i in time_samples]\n", " return pd.Series(predictions, index=pd.Index(time_samples))\n", " else: # assume n log(n)\n", " data['xlogx'] = data.x * np.log(data.x)\n", " model = sm.ols('y ~ x + xlogx', data=data).fit()\n", " predictions = [model.params.dot([1.0, i, i*np.log(i)]) for i in time_samples]\n", " return pd.Series(predictions, index=pd.Index(time_samples))\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we run that for each of our pre-existing datasets to extrapolate out predicted performance on the relevant dataset sizes. A little pandas wrangling later and we've produced a table of roughly how large a dataset you can tackle in each time frame with each implementation." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
InteractiveGet CoffeeOver LunchOvernight
AffinityPropagation200050002500075000
Spectral200050002500050000
Agglomerative20001000025000100000
DeBaCl500025000100000250000
Fastcluster250002500050000100000
ScipySingleLinkage2500025000100000250000
HDBSCAN250001000002500001000000
DBSCAN250000100000010000000100000000
KMeans1000000100000001000000001000000000
\n", "
" ], "text/plain": [ " Interactive Get Coffee Over Lunch Overnight\n", "AffinityPropagation 2000 5000 25000 75000\n", "Spectral 2000 5000 25000 50000\n", "Agglomerative 2000 10000 25000 100000\n", "DeBaCl 5000 25000 100000 250000\n", "Fastcluster 25000 25000 50000 100000\n", "ScipySingleLinkage 25000 25000 100000 250000\n", "HDBSCAN 25000 100000 250000 1000000\n", "DBSCAN 250000 1000000 10000000 100000000\n", "KMeans 1000000 10000000 100000000 1000000000" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ap_timings = get_timing_series(ap_data)\n", "spectral_timings = get_timing_series(spectral_data)\n", "agg_timings = get_timing_series(agg_data)\n", "debacl_timings = get_timing_series(debacl_data)\n", "fastclust_timings = get_timing_series(large_fastclust_data.ix[:10,:].copy())\n", "scipy_single_timings = get_timing_series(large_scipy_single_data.ix[:10,:].copy())\n", "hdbscan_timings = get_timing_series(large_hdbscan_data)\n", "#scipy_k_means_timings = get_timing_series(huge_scipy_k_means_data, quadratic=False)\n", "dbscan_timings = get_timing_series(huge_dbscan_data, quadratic=False)\n", "k_means_timings = get_timing_series(huge_k_means_data, quadratic=False)\n", "\n", "timing_data = pd.concat([ap_timings, spectral_timings, agg_timings, debacl_timings, \n", " fastclust_timings, scipy_single_timings, hdbscan_timings,\n", " dbscan_timings, k_means_timings\n", " ], axis=1)\n", "timing_data.columns=['AffinityPropagation', 'Spectral', 'Agglomerative',\n", " 'DeBaCl', 'Fastcluster', 'ScipySingleLinkage',\n", " 'HDBSCAN', 'DBSCAN', 'KMeans'\n", " ]\n", "def get_size(series, max_time):\n", " return series.index[series < max_time].max()\n", "\n", "datasize_table = pd.concat([\n", " timing_data.apply(get_size, max_time=30),\n", " timing_data.apply(get_size, max_time=300),\n", " timing_data.apply(get_size, max_time=3600),\n", " timing_data.apply(get_size, max_time=8*3600)\n", " ], axis=1)\n", "datasize_table.columns=('Interactive', 'Get Coffee', 'Over Lunch', 'Overnight')\n", "datasize_table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I had to leave out the scipy KMeans timings because the noise in timing results caused the model to be unrealistic at larger data sizes. It is also worth keeping in mind that some of the results of the models for larger numbers are simply false -- you'll recall that Fastcluster and Scipy's single linkage both didn't scale at all well past 40000 points on my laptop, so I'm certainly not ging to manage 50000 or 100000 over lunch. The same applies to DeBaCl and the slower Sklearn implementations as they also produce the full pairwise distance matrix during computations.\n", "\n", "The main thing to note is how the $O(n\\log n)$ algorithms utterly dominate here. In the meantime, for medium sizes data sets you can still get quite a lot done with HDBSCAN." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusions\n", "\n", "Performance obviously depends on the algorithm chosen, but can also vary significantly upon the specific implementation (HDBSCAN is far better hierarchical density based clustering than DeBaCl, and sklearn has by far the best K-Means implementation). For anything beyond toy datasets, however, your algorithm options are greatly constrained. In my (obviously biased) opinion [HDBSCAN is the best algorithm for clustering](http://nbviewer.jupyter.org/github/lmcinnes/hdbscan/blob/master/notebooks/Comparing%20Clustering%20Algorithms.ipynb) that survived the first cut in our analysis, so if your data is small enough to work with then it should probably be your first option. Beyond that the only algorithm options on the table are DBSCAN and K-Means; and obviously sklearn is the best choice there. That really means DBSCAN should be your first option for really large datasets.\n", "\n", "Ultimately, however, the reason K-Means and DBSCAN come out so strongly is their impressive asymptotic complexity, which win out easily even over impressively efficient implementations of other algorithms (such as fastcluster). Fortunately the [hdbscan](https://github.com/lmcinnes/hdbscan) development team has plans to bring down the asymptotics of the implementation to $O(n\\log n)$, although probably with larger constants than DBSCAN and K-Means. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }