k-means-sklearn-digits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import scale\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (10,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_digits, y_digits = load_digits(return_X_y=True)\n",
    "data = scale(X_digits)\n",
    "\n",
    "n_samples, n_features = data.shape\n",
    "n_digits = len(np.unique(y_digits))\n",
    "labels = y_digits\n",
    "\n",
    "sample_size = 300\n",
    "\n",
    "print(\"n_digits: %d, \\t n_samples %d, \\t n_features %d\"\n",
    "      % (n_digits, n_samples, n_features))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(82 * '_')\n",
    "print('init\\t\\ttime\\tinertia\\thomo\\tcompl\\tv-meas\\tARI\\tAMI\\tsilhouette')\n",
    "\n",
    "\n",
    "def bench_k_means(estimator, name, data):\n",
    "    t0 = time()\n",
    "    estimator.fit(data)\n",
    "    print('%-9s\\t%.2fs\\t%i\\t%.3f\\t%.3f\\t%.3f\\t%.3f\\t%.3f\\t%.3f'\n",
    "          % (name, (time() - t0), estimator.inertia_,\n",
    "             metrics.homogeneity_score(labels, estimator.labels_),\n",
    "             metrics.completeness_score(labels, estimator.labels_),\n",
    "             metrics.v_measure_score(labels, estimator.labels_),\n",
    "             metrics.adjusted_rand_score(labels, estimator.labels_),\n",
    "             metrics.adjusted_mutual_info_score(labels,  estimator.labels_),\n",
    "             metrics.silhouette_score(data, estimator.labels_,\n",
    "                                      metric='euclidean',\n",
    "                                      sample_size=sample_size)))\n",
    "\n",
    "bench_k_means(KMeans(init='k-means++', n_clusters=n_digits, n_init=10),\n",
    "              name=\"k-means++\", data=data)\n",
    "\n",
    "bench_k_means(KMeans(init='random', n_clusters=n_digits, n_init=10),\n",
    "              name=\"random\", data=data)\n",
    "\n",
    "# in this case the seeding of the centers is deterministic, hence we run the\n",
    "# kmeans algorithm only once with n_init=1\n",
    "pca = PCA(n_components=n_digits).fit(data)\n",
    "bench_k_means(KMeans(init=pca.components_, n_clusters=n_digits, n_init=1),\n",
    "              name=\"PCA-based\",\n",
    "              data=data)\n",
    "print(82 * '_')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #############################################################################\n",
    "# Visualize the results on PCA-reduced data\n",
    "\n",
    "reduced_data = PCA(n_components=2).fit_transform(data)\n",
    "kmeans = KMeans(init='k-means++', n_clusters=n_digits, n_init=10)\n",
    "kmeans.fit(reduced_data)\n",
    "\n",
    "# Step size of the mesh. Decrease to increase the quality of the VQ.\n",
    "h = .02     # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\n",
    "y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "# Obtain labels for each point in mesh. Use last trained model.\n",
    "Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(1)\n",
    "plt.clf()\n",
    "plt.imshow(Z, interpolation='nearest',\n",
    "           extent=(xx.min(), xx.max(), yy.min(), yy.max()),\n",
    "           cmap=plt.cm.Paired,\n",
    "           aspect='auto', origin='lower')\n",
    "\n",
    "plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)\n",
    "# Plot the centroids as a white X\n",
    "centroids = kmeans.cluster_centers_\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1],\n",
    "            marker='x', s=169, linewidths=3,\n",
    "            color='w', zorder=10)\n",
    "plt.title('K-means clustering on the digits dataset (PCA-reduced data)\\n'\n",
    "          'Centroids are marked with white cross')\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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