{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![title](header.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "K Nearest Neighbours is a supervised machine learning algorithm used for classification. It takes in labelled data, and then classifies a new point based on the training data that is most similar. K Nearest Neighbours (KNN) is used heavily in search engines to recommend similar results, and is also used in face recognition software to idenify a person by their facial features. By the end of this guide you should be confident in your understanding in how the algorithm works, as well as be able to apply it to some simple examples.\n", "\n", "## The K Nearest Neighbours Algorithm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The KNN Algorithm is very simple. We start with pre-classified data, and when we want to test a point, we check for the K points with the shortest distance away. The obvious question here is what value of K we should use, and there's no easy answer. A small K value gives us a higher \"definition\" of model, but with more noise, whereas a large K gives us a very general idea of how our data is distributed, but with less definition. Below we can see the \"local\" effect of small and large Ks - a larger K will sometimes \"pick up\" other points around the test point which can skew results:\n", "\n", "![Image of KNN](https://upload.wikimedia.org/wikipedia/commons/e/e7/KnnClassification.svg)\n", "\n", "We'll also look at what happens in a more general sense with different values of k in the simple example below:\n", "\n", "## A Simple Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're going to start with a very simple example to get used to how to implement this model in Python. Let's start by importing all the libraries and data we'll need: " ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " class x data y data\n", "0 0 27.095400 28.338278\n", "1 0 36.357474 26.011727\n", "2 0 21.115430 1.768997\n", "3 0 11.524589 16.216907\n", "4 0 31.990294 21.280534\n", " class x data y data\n", "195 1 42.981247 38.108244\n", "196 1 37.655655 29.933272\n", "197 1 29.341556 23.683485\n", "198 1 41.456223 39.274803\n", "199 1 30.655903 31.704547\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "df = pd.read_csv(\"test_data.csv\")\n", "\n", "print(df.head())\n", "print(df.tail())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since our data is 2 dimensional, we can graph the data to see the classes:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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WSU1FxBj43+DIieP59BU3wRphLxFhn4P3gIrjUsSQI1V/Bj8y0iW9yWmKRux6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\nKeS01quBM5ccvgX4IOP7D4C+pRpUCdNaH9dab8z4PoXAP/gaRPC4dcD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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(df['x data'], df['y data'], c=df['class'])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! From here we can build our model using the KNeighborsClassifier function - here we're using k=5 for our data." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n", " weights='uniform')" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = df.drop('class', axis=1).as_matrix()\n", "classes = df['class']\n", "\n", "\n", "knn = KNeighborsClassifier(n_neighbors=5)\n", "knn.fit(data,classes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can predict any number of points and get the class they correspond with using the predict method on our model:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 1]\n" ] } ], "source": [ "points = [[0,0], [40,40], [30,30]]\n", "\n", "print(knn.predict(points))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes it's helpful to see a breakdown of classification by area. To do this we can create a meshgrid using numpy, and use it to draw a background on our plot. This is a bit tough, but it's very powerful and worth it in the end:" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "image/png": 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Cr6dalaqMYpTPdhlksJ71fsbIho3v+Z4OdMAVacGU7n+TdZuMSKOBn/iJDWzw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\nZ3v0YNnZuXNTl87dt4+Puewy3mbP7n5t9nQIBpN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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Setting up the meshgrid require a minimum and maximum bound - it makes sense to use the min and max values of our data for this:\n", "\n", "x_min, x_max = data[:, 0].min() - 1, data[:, 0].max() + 1\n", "y_min, y_max = data[:, 1].min() - 1, data[:, 1].max() + 1\n", "\n", "#h is the step size of the mesh - lower h means higher resolution.\n", "\n", "h=0.1\n", "\n", "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", "\n", "#Ravel will take the 2 dimensional xx and yy arrays and \"unravel\" them into a 1d array.\n", "#c_ then pairs off these points to give a 1 dimensional array with x and y points for every point in the meshgrid.\n", "#We then predict every point and call this Z, then reshape it so it's the same shape as xx and yy\n", "\n", "Z = knn.predict(np.c_[xx.ravel(),yy.ravel()]) \n", "Z = Z.reshape(xx.shape)\n", "\n", "#We then define our own colormaps (you can skip this step if you want), and plot our data using a colormesh and a scatter plot.\n", "\n", "from matplotlib.colors import ListedColormap\n", "\n", "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA'])\n", "cmap_bold = ListedColormap(['#FF0000', '#00FF00'])\n", "\n", "plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n", "plt.scatter(data[:, 0], data[:, 1], c=classes, cmap = cmap_bold)\n", "plt.xlim(xx.min(), xx.max())\n", "plt.ylim(yy.min(), yy.max())\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we can see what happens when we graph for different values of K, using a for loop." ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "image/png": 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zYft2SE0l7cQNpD8YbJx8FZuxcJzjIctGMYpf+dX/eilLMWHCihWBiJgZYsYc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\nlDAffMDRagCrF6NRpvc5Xb1psWULU/iam9k/4/zzM+utYgx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0hZp2sYvRjPbV6tbQWMc6niKGXqkxcorYJm1t2JhOqOWtofEbv/Ef/sN93MdO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\nnttNMbr0UqbE5yOTJ9OKXrKEi4LGUMzTyQi2qiUP0KdeX5/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vUz6NeoxF3QDCWdN55BmWPXDgCNtxKZ74kAJlZYl2uWjKngMHYNq00D6fkyZB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\nTSlqa3ndhg/npFkI11BpEduiXIwxS0Skd9zh0QCGhu8/DuA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DQzzEDnbEZHyf8mnUY8zvBhDOms4m27DsgQOHYZldgMpUDilQVpxol4um+Dl4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\njL/v/v1sSlFWxvN2yim8aObCOVSaxLYoF2PMMhHpGbN5NID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ldKMbRzhCAgmlnlYZDRF56EKI+sANwIRcq/sAk3y/TwJuju3QNJoiICWkpMBPP8G6deAt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\nMoWWuVXRrUCAK6Tx4+tb4V4vWyjaVUzN7U5cU71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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for k in [1,3,5,10,15]:\n", "\n", " knn = KNeighborsClassifier(n_neighbors=k)\n", " knn.fit(data,classes)\n", "\n", " x_min, x_max = data[:, 0].min() - 1, data[:, 0].max() + 1\n", " y_min, y_max = data[:, 1].min() - 1, data[:, 1].max() + 1\n", " \n", " h=0.1 #h is the step size of the mesh - lower h means higher resolution.\n", " \n", " xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", " \n", " #Ravel will take the 2 dimensional xx and yy arrays and \"unravel\" them into a 1d array.\n", " #c_ then pairs off these points to give a 1 dimensional array with x and y points for every point in the meshgrid.\n", " #We then predict every point and call this Z, then reshape it so it's the same shape as xx and yy\n", " \n", " Z = knn.predict(np.c_[xx.ravel(),yy.ravel()]) \n", " Z = Z.reshape(xx.shape)\n", " \n", " #We then define our own colormaps (you can skip this step if you want), and plot our data using a colormesh and a scatter plot.\n", " \n", " from matplotlib.colors import ListedColormap\n", " \n", " cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA'])\n", " cmap_bold = ListedColormap(['#FF0000', '#00FF00'])\n", " \n", " plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n", " plt.scatter(data[:, 0], data[:, 1], c=classes, cmap = cmap_bold)\n", " plt.xlim(xx.min(), xx.max())\n", " plt.ylim(yy.min(), yy.max())\n", " plt.title(\"KNN with k = \" + str(k))\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mini Project" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we have some more training and testing data for you to try this out for yourself. Try following the above steps to create a model using the training data, and predict what class the test data is part of. We're switching it up a bit by giving you three dimensional data, with three classes to work with. If you want to see how this data is distributed in a plot, try plotting it using matplotlib's mplot3d sublibrary, but remember you don't need to do that to get the predicitions right!" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " class x data y data z data\n", "0 0 80.543502 41.318858 33.818395\n", "1 0 78.291164 127.006373 41.923430\n", "2 0 121.484717 120.776238 74.294293\n", "3 0 22.123624 70.748242 -17.645028\n", "4 0 63.964959 113.604554 98.920726\n", " class x data y data z data\n", "395 2 362.127598 413.353847 378.205861\n", "396 2 379.437545 386.067748 381.397157\n", "397 2 364.903396 374.196921 374.635584\n", "398 2 378.062759 428.275662 383.364453\n", "399 2 355.313112 332.401421 399.892779\n" ] } ], "source": [ "training_data = pd.read_csv(\"project_training.csv\")\n", "test_data = [[50,50,50], [100,100,100], [150,150,150], [200,200,200],[250,250,250],[300,300,300],[350,350,350],[400,400,400]]\n", "\n", "print(training_data.head())\n", "print(training_data.tail())" ] }, { "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }