{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Last updated 27 August 2017" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import scipy.io as sio\n", "\n", "import matplotlib.pyplot as plt\n", "import plotly\n", "import plotly.graph_objs as go\n", "plotly.offline.init_notebook_mode(connected=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# $K$-means Clustering\n", "\n", "We first start on an example 2D dataset that will help us gain an intuition of how the $K$-means algorithm works. After that, we will use the $K$-means algorithm for image compression by reducing the number of colors that occur in an image to only those that are most common in that image." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data2 = sio.loadmat(\"./ex7data2.mat\")\n", "Xdata2 = data2[\"X\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Implementation notes for `find_closest_centroids()` function\n", "\n", "`_X` is passed as a 2D $m$ by $n$ array, where $m$ is the number of data examples, and $n$ is the number of features. `_centr` is passed as a 2D $k$ by $n$ array, where $k$ is the number of centroids, and $n$ is the number of features.\n", "\n", "Broadcasting is used extensively for high performance. The function performs the following steps:\n", "\n", "1. It reshapes `_X` from 2D to 3D, inserting a placeholder dimension to form new array `_Xresh`. Axis 0 in `_Xresh` indexes into a specific data example, while axis 2 indexes into a specific feature of the selected data example.\n", "2. `np.repeat()` is applied to axis 1 of `_Xresh` to duplicate the coordinates (or features) of each data example. The resulting `_stretch` array has axes 1 and 2 dimensions that match the axes 0 and 1 of `_centr` (trailing dimensions match), allowing for a broadcasted difference computation in the next step.\n", "3. Broadcasting is now applied to find, for each data example, the difference between that respective data example's coordinates and all the coordinates of each and every centroid. The resultant `_diff` array is 2D.\n", "4. `np.sum()` and `np.square()` are applied to `_diff` along the correct axes to give the `_eudists` array. In `_eudists`, each data example is indexed by axis 0, the euclidean distance between that data example and each centroid. For a given data example, its euclidean distance to each centroid is therefore indexed by axis 1 in `_eudists`.\n", "\n", "The function returns the closest cluster value that each data example belongs to. In other words, it assigns each centroid to the data examples which it is nearest to." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def find_closest_centroids(_X, _centr):\n", " _Xresh = _X.reshape(_X.shape[0],1, _X.shape[1])\n", " _stretch = np.repeat(_Xresh, _centr.shape[0], axis=1)\n", " _diffs = _stretch - _centr # Compute the difference \n", " _eudists = np.sum(np.square(_diffs), axis = 2) # Compute the euclidean squared distance\n", " return np.argmin(_eudists,axis = 1) # return cluster value of each centroid based on min distance" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, 3, 3, 0, 0, 1, 2, 3])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Passes the test case\n", "Xtestcase = np.reshape(np.sin(np.arange(1,51)), (10,5), 'F'); centrtestcase = Xtestcase[6:10]\n", "find_closest_centroids(Xtestcase, centrtestcase)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Implementation notes for `compute_centroid_means()` function\n", "\n", "`_X` is passed as a 2D $m$ by $n$ array, where $m$ is the number of data examples, and $n$ is the number of features. `_idx` is passed as a 1D $m$ array, where $m$ is the number of data examples. `_K` is an integer specifying the number of centroids, $k$.\n", "\n", "An array holding the new mean coordinates of each centroid is first initialized. The algorithm then iterates over each centroid, in each iteration, fancy boolean indexing is employed to obtain the data examples belonging under the given centroid's cluster. A transpose is necessary in order for `np.mean()` with `axis=1` to correctly broadcast the computation over the coordinates/features for each data example in the cluster." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def compute_centroid_means(_X, _idx, _K):\n", " _centrmeans = np.zeros((_K, _X.shape[1]))\n", " for k in range(0,_K):\n", " _centrmeans[k] = np.mean(_X[_idx == k].T, axis = 1)\n", " return _centrmeans" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1.5, 9.5, 17.5],\n", " [ 7.5, 15.5, 23.5],\n", " [ 3.5, 11.5, 19.5],\n", " [ 5.5, 13.5, 21.5]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Passes the test case\n", "Xtestcase = np.reshape(np.arange(1,25),(8,3), 'F'); idxtestcase = np.array([0,0,2,2,3,3,1,1]); Ktestcase = 4\n", "compute_centroid_means(Xtestcase, idxtestcase, Ktestcase)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def init_centroids(_X, _K):\n", " _idxs = np.arange(0, _X.shape[0])\n", " np.random.shuffle(_idxs)\n", " return _X[_idxs][0:_K]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def k_means(_X, _K, _numiter):\n", " _centr = init_centroids(_X,_K)\n", " for i in range(0, _numiter):\n", " _idx = find_closest_centroids(_X, _centr) \n", " _centr = compute_centroid_means(_X, _idx, _K) # Update the centroid positions with the new means\n", " return _centr, _idx" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "K = 3; maxiter = 10\n", "centr , idx = k_means(Xdata2,K,maxiter)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Xdata2clust = np.hstack([Xdata2, idx.reshape(idx.shape[0],1)]) # Append cluster information to the data matrix\n", "\n", "clusters = []\n", "for k in range(0,K):\n", " clustk = Xdata2[Xdata2clust[:,2] == k]\n", " clustkplot = go.Scatter(x = clustk[:,0], y= clustk[:,1], mode = \"markers\", name= \"Cluster {}\".format(k))\n", " clusters.append(clustkplot)\n", "\n", "centrplot = go.Scatter(x = centr[:,0], y= centr[:,1], mode = \"markers\",\n", " marker=dict(symbol=\"circle-cross\", size=15, color = \"black\"), name= \"Centroids\")\n", "\n", "plotly.offline.iplot({\"data\": [centrplot] + clusters,\n", " \"layout\": go.Layout(title = \"Results of clustering\",\n", " xaxis = dict(title=\"X1\"),\n", " yaxis = dict(title=\"X2\"))})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO: For animating the K-means plot, refer to following articles https://plot.ly/python/animations/ https://plot.ly/python/gapminder-example/ https://plot.ly/python/sliders/. Will also need to rewrite k_means() function to return a vector after each iteration, storing into a matrix.\n", "\n", "### Concluding remarks on clustering\n", "\n", "In general, there is no one single best way for deciding what value of $k$ to use, the problem of [determining cluster count](https://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set). However, a good approach is to always ask what is your purpose of running $k$-means, and to choose the value of $k$ that best reflects the goal you are trying to achieve with clustering your data.\n", "\n", "## Image compression with $K$-means\n", "\n", "In this exercise, we will apply K-means to image compression. In a straightforward 24-bit color representation of an image, each pixel is represented as three 8-bit unsigned integers (ranging from 0 to 255) that specify the red, green and blue intensity values. This encoding is often refered to as the RGB encoding. The image contains thousands of colors, and in this part of the exercise, we will reduce the number of colors to 16 colors by using just 16 clusters in $K$-means.\n", "\n", "Compressing an image can also be interpreted [as a form of](https://www.quora.com/What-are-the-applications-of-vector-quantization) of [vector quantization](https://en.wikipedia.org/wiki/Vector_quantization). As this exercise illustrates, vector quantization is in fact a very important [application](https://en.wikipedia.org/wiki/K-means_clustering#Vector_quantization) of $k$-means. \n", "\n", "For further discussions on the connections between vector quantization and $k$-means, see the following articles [1](https://dsp.stackexchange.com/questions/38602/lloyd-max-quantization-and-clustering-part-1), [2](https://dsp.stackexchange.com/questions/38606/difference-between-clustering-and-quantization-part-2) and [3](https://stackoverflow.com/questions/9012991/difference-between-k-means-clustering-and-vector-quantization)." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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MRYZS7TJnlJDCF0Xvy+MUJCgC+ywzyTCzUF+EQAg3dEYf0oJDotXmJYJDQ4gg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yxeS54VXd8wvX/Eg6Nk+HN4+5BtU/N8teHR5+mBslgecHB7j8cfJp2hbtfzwfufMP3m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\nwluIAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy.misc import imread\n", "\n", "bird = imread(\"bird_small.png\")\n", "\n", "fig, axes = plt.subplots(nrows=1, ncols=1, figsize = (3,3))\n", "axes.imshow(bird)\n", "axes.set(xticks=[], yticks=[])\n", "fig.suptitle(\"The original 128x128 image\", fontsize = 15)\n", "fig" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "birdresh = bird.reshape(bird.shape[0]*bird.shape[1], bird.shape[2]) # reshape image to support K-means" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "K = 16; maxiter = 500\n", "colors , idx = k_means(birdresh,K,maxiter) # Run k-means to cluster the pixels" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Reconstructing the compressed image\n", "idxim = idx.reshape(bird.shape[0],bird.shape[1])\n", "birdnew = np.zeros_like(bird)\n", "# Assign the chosen color using the clustered centroid\n", "for i in range(0,bird.shape[0]):\n", " for j in range(0,bird.shape[1]):\n", " birdnew[i][j] = colors[idxim[i][j]]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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OPVgtxDnXdM5VN5HLGPyCdfeEup8c1vGdAN6O8GHxuoz1r1X+Zuq2aZY+zIv9mu+fHf7/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\n2YJtbMfUu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uEGrP6xxsiK4W4ztQ6rrQ7DbseyE1ipPPnkV7fc5vSrjxTtdvRC0d5LBXQn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\nO/T5TkvW1XnaJ6ueYXb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"text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig, axes = plt.subplots(nrows=1, ncols=2, figsize = (16,8))\n", "axes[0].imshow(bird)\n", "axes[0].set(xticks=[], yticks=[])\n", "axes[1].imshow(birdnew)\n", "axes[1].set(xticks=[], yticks=[])\n", "fig.suptitle(\"Comparison between original and reconstructed images, after $K$-means compression\", fontsize = 20)\n", "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By decreasing $K$, we can get a higher compression rate at the expense of image quality, and increasing $K$ we can get a better image quality at the expense of a lower compression rate.\n", "\n", "# Principal Component Analysis\n", "\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data1 = sio.loadmat(\"ex7data1.mat\")\n", "Xdata1 = data1[\"X\"]\n", "data1plot = go.Scatter(x = Xdata1[:,0], y= Xdata1[:,1],mode = \"markers\", name = \"Dataset 1\")\n", "plotly.offline.iplot({\"data\": [data1plot],\n", " \"layout\": go.Layout(title = \"Example Dataset 1\")})" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Implementation of normalization follows exercise 5\n", "def feature_normalize(_X):\n", " _mu = np.mean(_X, axis=0)\n", " _Xnorm = _X-_mu\n", " _sigma = np.std(_Xnorm, axis=0, ddof=1)\n", " _Xnorm = _Xnorm / _sigma\n", " return _Xnorm, _mu, _sigma" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO: Figure out why the assignment gives covariance formula as $\\Sigma = \\frac{1}{m} X^T X$ and not $\\Sigma = \\frac{1}{m} X X^T$. Shouldn't the latter be the correct one since covariance matrix, by definition, has to be m by m (m = number of data examples)? Using the former only gives us n by n (n=number of features)." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def compute_covariance_matrix(_X):\n", " return 1/_X.shape[0] * (_X @ _X.T)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from numpy.linalg import svd\n", "\n", "def compute_pca(_Xnorm):\n", " _Xcovar = compute_covariance_matrix(_Xnorm) \n", " _U, _S, _V = svd(_Xcovar)\n", " return _U, _S" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "Xdata1norm, mu, sigma = feature_normalize(Xdata1)\n", "U,S = compute_pca(Xdata1norm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO: Figure out why Eigenvector 1 is not showing up well\n", "TODO: Understand how the eig0 and eig1 syntax works (it was copied over from the course provided code)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "eig0 = mu + 1.5 * S[0] * U[:,0].reshape(50,1)\n", "eig1 = mu + 1.5 * S[1] * U[:,1].reshape(50,1)\n", "\n", "eig0plot = go.Scatter(x = eig0[:,0], y= eig0[:,1],mode = \"line\", name=\"Eigenvector 0\")\n", "eig1plot = go.Scatter(x = eig1[:,0], y= eig1[:,1],mode = \"line\", name= \"Eigenvector 1\")\n", "plotly.offline.iplot({\"data\": [data1plot,eig0plot, eig1plot],\n", "#plotly.offline.iplot({\"data\": [data1plot,eig0plot],\n", " \"layout\": go.Layout(title = \"Example Dataset 1\")})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Further Readings\n", "\n", "PCA rings bells of linear regression in my head, the two felt very similar. There's also a method combining the two, which the following links clarify https://shankarmsy.github.io/posts/pca-vs-lr.html https://www.quora.com/Is-principal-components-regression-the-same-as-doing-PCA-and-then-linear-regression https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues http://www.cerebralmastication.com/2010/09/principal-component-analysis-pca-vs-ordinary-least-squares-ols-a-visual-explination/ https://stackoverflow.com/questions/8457279/visual-comparison-of-regression-pca\n", "\n", "Stanford CS168 course notes lecture 7 foudn on http://web.stanford.edu/class/cs168/ also explain the connections between linear regression and PCA.\n", "\n", "Help on interpreting PCA: http://setosa.io/ev/principal-component-analysis/ being derived from SVD http://www.ams.org/samplings/feature-column/fcarc-svd \n", "\n", "Useful article for PCA https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues and https://deeplearning4j.org/eigenvector#principal\n", "\n", "\n", "\n", "This chapter talks about cluster analysis in food science and technology applications, makes for interesting read showing how market segmentation in food science can be achieved http://onlinelibrary.wiley.com/doi/10.1002/9781118434635.ch7/summary\n", "\n", "This is a really end of exercise note, but just to mention that within these class of algos, also good to learn about t-SNE https://www.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm\n" ] }, { "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": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }