{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Mining\n", "\n", "## Lab 4: k-Nearest Neighbours & k-Means" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from scipy.spatial import Voronoi, voronoi_plot_2d\n", "from sklearn.neighbors import KNeighborsClassifier\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "%matplotlib inline\n", "\n", "np.random.seed(1337)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## k-Nearest Neighbours" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Voronoi Tessellation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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MHx8fhIWF4cmTJzAyMsKxY8fKDV5GRUXBysoKU6ZMwdatW6UiVaCgoIA9e/ag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\nTGEwAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x1 = 3.5 + np.random.randn(10)\n", "x2 = 6.5 + np.random.randn(10)\n", "y1 = 5 + np.random.randn(10)\n", "y2 = 5 + np.random.randn(10)\n", "x = np.append(x1, x2)\n", "y = np.append(y1, y2)\n", "\n", "vor = Voronoi(np.array([x, y]).T)\n", "voronoi_plot_2d(vor, show_points=False, show_vertices=False)\n", "plt.scatter(x1, y1)\n", "plt.scatter(x2, y2)\n", "plt.grid(False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Kita mencoba mengenalkan data baru di titik (6, 5), tetangga terdekatnya adalah titik dalam region yang sama. Dengan kata lain, Voronoi tessellation ini membentuk batas keputusan (_decision boundary_) dari algoritma Nearest Neighbour." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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enggJCcGzZ89gYGCAkydPlhu8jIiIgIWFBaZNm4YdO3ZIRapATk4O+/fvR5s2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\nKZfR4lZKKZfR4lZKKZfR4lZKKZf5X423ozIxuaMuAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "voronoi_plot_2d(vor, show_points=False, show_vertices=False)\n", "plt.scatter(x1, y1)\n", "plt.scatter(x2, y2)\n", "plt.scatter([6], [5], marker='X')\n", "plt.grid(False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7-NN untuk MNIST" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import load_digits\n", "from sklearn.model_selection import train_test_split\n", "\n", "mnist = load_digits()\n", "\n", "img_rows, img_cols = 8, 8\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(mnist.data, mnist.target, test_size=0.33, random_state=1945)\n", "X_train = X_train.reshape(X_train.shape[0], img_rows * img_cols)\n", "X_test = X_test.reshape(X_test.shape[0], img_rows * img_cols)\n", "\n", "k = 7\n", "n_samples = 5\n", "clf = KNeighborsClassifier(n_neighbors=k)\n", "clf.fit(X_train, y_train)\n", "X_test = X_test[np.random.choice(len(X_test), n_samples, replace=False)]\n", "knn = clf.kneighbors(X_test)\n", "\n", "fig, ax = plt.subplots(n_samples, k+1)\n", "x = X_train[knn[1]].reshape(n_samples, k, img_rows, img_cols)\n", "for i in range(n_samples):\n", " for j in range(k):\n", " ax[i][j].imshow(x[i,j,:,:], cmap='Greys')\n", " ax[i][j].axis('off')\n", " ax[i][k].imshow(X_test[i].reshape(img_rows, img_cols), cmap='gray')\n", " ax[i][k].tick_params(axis='both', left='off', top='off', right='off', bottom='off', labelleft='off', labeltop='off', labelright='off', labelbottom='off')\n", " ax[i][k].grid('off')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Digit di paling kanan adalah kelasnya, sedangkan tujuh sampel di kirinya adalah 7-NN." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Regresi dengan k-NN\n", "\n", "k-NN juga dapat digunakan untuk regresi. Namun, regresi ini biasanya hanya bekerja baik untuk kasus interpolasi, bukan ekstrapolasi. Perhatikan grafik di bawah ini. Nilai $y$ untuk data $x_i$ dapat diestimasi dengan\n", "$$\n", "\\hat{y} = \\frac{1}{k} \\sum_{j=1}^{k} y_{ij}\n", "$$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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AWCYiS0TECeDTAN6ceICI5E54+nEAF4KPDwJ4TEQyRCQDwGPBbZZht9uRl5en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\nvYjYDCxtPiciFkXE7Qd3iog3AOcC/3vE138zIjY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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = 10 * np.random.random(20)\n", "xx = np.linspace(x.min(), x.max())\n", "y = np.sin(x)\n", "\n", "fig, ax = plt.subplots(figsize=(10, 7))\n", "ax.scatter(x, y)\n", "ax.plot(xx, np.sin(xx), c='gray', linestyle=':', linewidth=0.5)\n", "\n", "style = dict(size=10, color='gray')\n", "\n", "xn = 6.5\n", "distance = np.abs(x - xn) # menghitung jarak dengan semua instances (absolut)\n", "neighbours = np.array([distance, y]).T\n", "# print(neighbours.shape)\n", "for k in range(1, 4):\n", "# print(neighbours[neighbours[:, 0].argsort()][:k]) # k-NN\n", " yn = neighbours[neighbours[:, 0].argsort()][:k][:,1].mean() # mean value\n", " ax.scatter([xn], [yn])\n", " ax.text(xn - .75, yn, '%d-NN' % k, **style)\n", "ax.grid(True)\n", "ax.set_xlabel('x')\n", "ax.set_ylabel('y')\n", "plt.title('Interpolasi')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perhatikan bahwa pada saat kita melakukan ekstrapolasi seperti kode di bawah ini, estimasi $y$ hingga 3-NN bernilai $> 0.1$. Padahal, kita tahu bahwa fungsi yang menghasilkan data tersebut adalah fungsi $\\sin()$ yang artinya nilainya akan lebih rendah daripada itu." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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S8P7nAWye2AonIr8P4PsAOgFcAfB1pVSLiPwpgHil1HeCx30TgFsp9d+qq6sL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\nLQsdI7bU/oU1NGYunckC8M26Y/zHiZkMYtRXJ1m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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(10, 7))\n", "ax.scatter(x, y)\n", "ax.plot(xx, np.sin(xx), c='gray', linestyle=':', linewidth=0.5)\n", "\n", "style = dict(size=10, color='gray')\n", "\n", "xn = -0.5\n", "distance = np.abs(x - xn) # menghitung jarak dengan semua instances (absolut)\n", "neighbours = np.array([distance, y]).T\n", "# print(neighbours.shape)\n", "for k in range(1, 4):\n", "# print(neighbours[neighbours[:, 0].argsort()][:k]) # k-NN\n", " yn = neighbours[neighbours[:, 0].argsort()][:k][:,1].mean() # mean value\n", " ax.scatter([xn], [yn])\n", " ax.text(xn - .75, yn, '%d-NN' % k, **style)\n", "ax.grid(True)\n", "ax.set_xlabel('x')\n", "ax.set_ylabel('y')\n", "plt.title('Ekstrapolasi')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parzen Windows\n", "\n", "Ada kemiripan antara k-NN dan Parzen Windows, yaitu sama-sama mencari tetangga terdekat. Namun, saat k-NN mencari $k$ tetangga terdekat sehingga batas keputusannya berbeda-beda besarnya, Parzen Windows membuat batas keputusannya dalam radius yang nilainya tetap. Jika ditilik lebih dalam, kita bisa melihat Parzen Windows punya prinsip kerja yang mirip dengan algoritma Support Vector Machines (SVM)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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PlBLm/u9cA/WLzRRpho2NDby9vXH69GnVUfLWZRpg/8iEi/bOWcPnZs6SfzYF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\nBNQUWhFvVraEvWCQ2C6ULQq0gm330p9QgvSGK7Q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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_train = 10 * np.random.random(size=40).reshape(20,2)\n", "y_train = np.random.randint(2, size=20)\n", "X_test = np.array([2.,4.5,7.,3.]).reshape(2,2)\n", "\n", "fig, ax = plt.subplots(1, 2, figsize=(10,5))\n", "\n", "for i in range(2):\n", " ax[i].scatter(X_train[np.where(y_train == 0),0], X_train[np.where(y_train == 0),1], marker='^')\n", " ax[i].scatter(X_train[np.where(y_train == 1),0], X_train[np.where(y_train == 1),1])\n", " ax[i].scatter(X_test[:,0], X_test[:,1], marker='x')\n", "\n", "k = 3\n", "clf = KNeighborsClassifier(n_neighbors=k)\n", "clf.fit(X_train, y_train)\n", "\n", "knn = clf.kneighbors(X_test)\n", "for i, test_point in enumerate(X_test):\n", " c = plt.Circle(test_point, knn[0][i,k-1], fill=False, linestyle='--')\n", " ax[0].add_artist(c)\n", " c = plt.Circle(test_point, 2, fill=False, linestyle='--')\n", " ax[1].add_artist(c)\n", "ax[0].set_title('3-NN')\n", "ax[1].set_title('Parzen Window')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### K-D Trees\n", "\n", "Algoritma k-NN bisa berjalan sangat lambat karena perhitungan jarak harus dilakukan untuk setiap pasang data. Kompleksitasnya mencapai $O(nd)$ dengan $d$ adalah dimensi data (jumlah atribut) dan $n$ adalah jumlah data. Oleh karena itu, digunakanlah beberapa cara untuk mempercepat algoritma k-NN. Salah satu metode tersebut adalah dengan K-D Trees.\n", "\n", "K-D Trees dibentuk dengan memilih dimensi secara acak, kemudian mencari median pada dimensi tersebut, lalu membagi datanya menjadi dua region. Proses ini diulangi terus untuk setiap region yang dihasilkan pada berbagai dimensi. Pencarian k-Nearest Neighbours nantinya hanya dilakukan dengan data yang berada pada region yang sama saja." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_train = 10 * np.random.random(size=40).reshape(20,2)\n", "y_train = np.random.randint(2, size=20)\n", "\n", "med_x1 = np.median(X_train[:,0])\n", "med_x20 = np.median(X_train[X_train[:,0] < med_x1,1])\n", "med_x21 = np.median(X_train[X_train[:,0] >= med_x1,1])\n", "\n", "fig, ax = plt.subplots(figsize=(6,6))\n", "\n", "plt.scatter(X_train[np.where(y_train == 0),0], X_train[np.where(y_train == 0),1], marker='^')\n", "plt.scatter(X_train[np.where(y_train == 1),0], X_train[np.where(y_train == 1),1])\n", "plt.plot([med_x1, med_x1], [0, 10])\n", "plt.plot([0, med_x1], [med_x20, med_x20])\n", "plt.plot([med_x1, 10], [med_x21, med_x21])\n", "\n", "k = 3\n", "clf = KNeighborsClassifier(n_neighbors=k)\n", "clf.fit(X_train, y_train)\n", "\n", "X_test = np.array([[2.,7.]])\n", "y_pred = clf.predict(X_test)\n", "plt.scatter(X_test[:,0], X_test[:,1])\n", "\n", "knn = clf.kneighbors(X_test)\n", "c = plt.Circle(X_test[0], knn[0][0,k-1], fill=False, linestyle='--')\n", "ax.add_artist(c)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perhatikan bahwa tiga tetangga terdekat dari data baru (titik hijau) _sebenarnya_ berada dalam region yang berbeda. Masalah seperti ini yang membuat K-D Trees mungkin tidak memberikan solusi yang optimal." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## K-Means\n", "\n", "Berbeda dengan k-Nearest Neighbours, k-Means merupakan algoritma untuk _unsupervised learning_, dalam hal ini adalah untuk tugas _clustering_. Dalam _clustering_, kita berusaha melihat subpopulasi dalam data dengan melihat kesamaan dari elemen di tiap subpopulasi tersebut.\n", "\n", "Idenya adalah dengan menggunakan **centroids** atau titik-titik pusat yang mendefinisikan klaster-klaster yang ada. Tiap _centroid_ merupakan representasi dari nilai rata-rata dari objek yang masuk ke dalam klaster tersebut." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Kode dari http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.11-K-Means.ipynb\n", "from sklearn.datasets.samples_generator import make_blobs\n", "from sklearn.cluster import KMeans\n", "\n", "X, y_true = make_blobs(n_samples=300, centers=4,\n", " cluster_std=0.60, random_state=0)\n", "\n", "fig, ax = plt.subplots(figsize=(7,7))\n", "ax.scatter(X[:,0], X[:,1])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dalam gambar di bawah, setiap warna mewakili satu klaster. Tanda silang di gambar tersebut menunjukkan letak _centroids_-nya." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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1sgqTYIJbdMNtc2N6YRrAo2aQ33nmOxj1jGarhRU2T8xtgOmyuRBLxRBLxuCy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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(2, 2, figsize=(14,14))\n", "\n", "for n in range(4):\n", " kmeans = KMeans(n_clusters=n+1)\n", " kmeans.fit(X)\n", "\n", " for label in np.unique(kmeans.labels_):\n", " ax[n / 2][n % 2].scatter(X[np.where(kmeans.labels_ == label),0], X[np.where(kmeans.labels_ == label),1], alpha=.5)\n", " centers = kmeans.cluster_centers_\n", " ax[n / 2][n % 2].scatter(centers[:,0], centers[:,1], marker='X')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pertanyaannya adalah bagaimana cara menentukan nilai $k$?\n", "\n", "Ada beberapa cara:\n", "\n", "1. Gunakan label kelas, e.g. 10 untuk MNIST\n", "2. Gunakan V atau _inertia_ atau jarak agregat intraklaster (total jarak antara tiap data dalam suatu klaster dengan _centroid_-nya) $$V = \\sum_{j} \\sum_{x_i \\rightarrow c_j} D(c_j, x_i)^2$$ lalu gunakan _elbow method_ (saat turunan curam berubah menjadi landai paling ekstrem)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X, y_true = make_blobs(n_samples=300, centers=4,\n", " cluster_std=0.60, random_state=0)\n", "\n", "v = []\n", "for i in range(1,11):\n", " kmeans = KMeans(n_clusters=i)\n", " kmeans.fit(X)\n", " v.append(kmeans.inertia_)\n", "\n", "plt.plot(range(1,11), v, 'o-')\n", "plt.xticks(range(1,11), range(1,11))\n", "plt.xlabel('# clusters')\n", "plt.ylabel('aggregate distances')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }