plot_cluster_comparison.ipynb

{
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  {
   "cell_type": "code",
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    "%matplotlib inline"
   ]
  },
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   "source": [
    "\n",
    "# Comparing different clustering algorithms on toy datasets\n",
    "\n",
    "\n",
    "This example shows characteristics of different\n",
    "clustering algorithms on datasets that are \"interesting\"\n",
    "but still in 2D. With the exception of the last dataset,\n",
    "the parameters of each of these dataset-algorithm pairs\n",
    "has been tuned to produce good clustering results. Some\n",
    "algorithms are more sensitive to parameter values than\n",
    "others.\n",
    "\n",
    "The last dataset is an example of a 'null' situation for\n",
    "clustering: the data is homogeneous, and there is no good\n",
    "clustering. For this example, the null dataset uses the\n",
    "same parameters as the dataset in the row above it, which\n",
    "represents a mismatch in the parameter values and the\n",
    "data structure.\n",
    "\n",
    "While these examples give some intuition about the\n",
    "algorithms, this intuition might not apply to very high\n",
    "dimensional data.\n"
   ]
  },
  {
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   "source": [
    "print(__doc__)\n",
    "\n",
    "import time\n",
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import cluster, datasets, mixture\n",
    "from sklearn.neighbors import kneighbors_graph\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from itertools import cycle, islice\n",
    "\n",
    "np.random.seed(0)\n",
    "\n",
    "# ============\n",
    "# Generate datasets. We choose the size big enough to see the scalability\n",
    "# of the algorithms, but not too big to avoid too long running times\n",
    "# ============\n",
    "n_samples = 1500\n",
    "noisy_circles = datasets.make_circles(n_samples=n_samples, factor=.5,\n",
    "                                      noise=.05)\n",
    "noisy_moons = datasets.make_moons(n_samples=n_samples, noise=.05)\n",
    "blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)\n",
    "no_structure = np.random.rand(n_samples, 2), None\n",
    "\n",
    "# Anisotropicly distributed data\n",
    "random_state = 170\n",
    "X, y = datasets.make_blobs(n_samples=n_samples, random_state=random_state)\n",
    "transformation = [[0.6, -0.6], [-0.4, 0.8]]\n",
    "X_aniso = np.dot(X, transformation)\n",
    "aniso = (X_aniso, y)\n",
    "\n",
    "# blobs with varied variances\n",
    "varied = datasets.make_blobs(n_samples=n_samples,\n",
    "                             cluster_std=[1.0, 2.5, 0.5],\n",
    "                             random_state=random_state)\n",
    "\n",
    "# ============\n",
    "# Set up cluster parameters\n",
    "# ============\n",
    "plt.figure(figsize=(9 * 2 + 3, 12.5))\n",
    "plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05,\n",
    "                    hspace=.01)\n",
    "\n",
    "plot_num = 1\n",
    "\n",
    "default_base = {'quantile': .3,\n",
    "                'eps': .3,\n",
    "                'damping': .9,\n",
    "                'preference': -200,\n",
    "                'n_neighbors': 10,\n",
    "                'n_clusters': 3,\n",
    "                'min_samples': 20,\n",
    "                'xi': 0.05,\n",
    "                'min_cluster_size': 0.1}\n",
    "\n",
    "datasets = [\n",
    "    (noisy_circles, {'damping': .77, 'preference': -240,\n",
    "                     'quantile': .2, 'n_clusters': 2,\n",
    "                     'min_samples': 20, 'xi': 0.25}),\n",
    "    (noisy_moons, {'damping': .75, 'preference': -220, 'n_clusters': 2}),\n",
    "    (varied, {'eps': .18, 'n_neighbors': 2,\n",
    "              'min_samples': 5, 'xi': 0.035, 'min_cluster_size': .2}),\n",
    "    (aniso, {'eps': .15, 'n_neighbors': 2,\n",
    "             'min_samples': 20, 'xi': 0.1, 'min_cluster_size': .2}),\n",
    "    (blobs, {}),\n",
    "    (no_structure, {})]\n",
    "\n",
    "for i_dataset, (dataset, algo_params) in enumerate(datasets):\n",
    "    # update parameters with dataset-specific values\n",
    "    params = default_base.copy()\n",
    "    params.update(algo_params)\n",
    "\n",
    "    X, y = dataset\n",
    "\n",
    "    # normalize dataset for easier parameter selection\n",
    "    X = StandardScaler().fit_transform(X)\n",
    "\n",
    "    # estimate bandwidth for mean shift\n",
    "    bandwidth = cluster.estimate_bandwidth(X, quantile=params['quantile'])\n",
    "\n",
    "    # connectivity matrix for structured Ward\n",
    "    connectivity = kneighbors_graph(\n",
    "        X, n_neighbors=params['n_neighbors'], include_self=False)\n",
    "    # make connectivity symmetric\n",
    "    connectivity = 0.5 * (connectivity + connectivity.T)\n",
    "\n",
    "    # ============\n",
    "    # Create cluster objects\n",
    "    # ============\n",
    "    ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)\n",
    "    two_means = cluster.MiniBatchKMeans(n_clusters=params['n_clusters'])\n",
    "    ward = cluster.AgglomerativeClustering(\n",
    "        n_clusters=params['n_clusters'], linkage='ward',\n",
    "        connectivity=connectivity)\n",
    "    spectral = cluster.SpectralClustering(\n",
    "        n_clusters=params['n_clusters'], eigen_solver='arpack',\n",
    "        affinity=\"nearest_neighbors\")\n",
    "    dbscan = cluster.DBSCAN(eps=params['eps'])\n",
    "    optics = cluster.OPTICS(min_samples=params['min_samples'],\n",
    "                            xi=params['xi'],\n",
    "                            min_cluster_size=params['min_cluster_size'])\n",
    "    affinity_propagation = cluster.AffinityPropagation(\n",
    "        damping=params['damping'], preference=params['preference'])\n",
    "    average_linkage = cluster.AgglomerativeClustering(\n",
    "        linkage=\"average\", affinity=\"cityblock\",\n",
    "        n_clusters=params['n_clusters'], connectivity=connectivity)\n",
    "    birch = cluster.Birch(n_clusters=params['n_clusters'])\n",
    "    gmm = mixture.GaussianMixture(\n",
    "        n_components=params['n_clusters'], covariance_type='full')\n",
    "\n",
    "    clustering_algorithms = (\n",
    "        ('MiniBatchKMeans', two_means),\n",
    "        ('AffinityPropagation', affinity_propagation),\n",
    "        ('MeanShift', ms),\n",
    "        ('SpectralClustering', spectral),\n",
    "        ('Ward', ward),\n",
    "        ('AgglomerativeClustering', average_linkage),\n",
    "        ('DBSCAN', dbscan),\n",
    "        ('OPTICS', optics),\n",
    "        ('Birch', birch),\n",
    "        ('GaussianMixture', gmm)\n",
    "    )\n",
    "\n",
    "    for name, algorithm in clustering_algorithms:\n",
    "        t0 = time.time()\n",
    "\n",
    "        # catch warnings related to kneighbors_graph\n",
    "        with warnings.catch_warnings():\n",
    "            warnings.filterwarnings(\n",
    "                \"ignore\",\n",
    "                message=\"the number of connected components of the \" +\n",
    "                \"connectivity matrix is [0-9]{1,2}\" +\n",
    "                \" > 1. Completing it to avoid stopping the tree early.\",\n",
    "                category=UserWarning)\n",
    "            warnings.filterwarnings(\n",
    "                \"ignore\",\n",
    "                message=\"Graph is not fully connected, spectral embedding\" +\n",
    "                \" may not work as expected.\",\n",
    "                category=UserWarning)\n",
    "            algorithm.fit(X)\n",
    "\n",
    "        t1 = time.time()\n",
    "        if hasattr(algorithm, 'labels_'):\n",
    "            y_pred = algorithm.labels_.astype(np.int)\n",
    "        else:\n",
    "            y_pred = algorithm.predict(X)\n",
    "\n",
    "        plt.subplot(len(datasets), len(clustering_algorithms), plot_num)\n",
    "        if i_dataset == 0:\n",
    "            plt.title(name, size=18)\n",
    "\n",
    "        colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',\n",
    "                                             '#f781bf', '#a65628', '#984ea3',\n",
    "                                             '#999999', '#e41a1c', '#dede00']),\n",
    "                                      int(max(y_pred) + 1))))\n",
    "        # add black color for outliers (if any)\n",
    "        colors = np.append(colors, [\"#000000\"])\n",
    "        plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[y_pred])\n",
    "\n",
    "        plt.xlim(-2.5, 2.5)\n",
    "        plt.ylim(-2.5, 2.5)\n",
    "        plt.xticks(())\n",
    "        plt.yticks(())\n",
    "        plt.text(.99, .01, ('%.2fs' % (t1 - t0)).lstrip('0'),\n",
    "                 transform=plt.gca().transAxes, size=15,\n",
    "                 horizontalalignment='right')\n",
    "        plot_num += 1\n",
    "\n",
    "plt.show()"
   ]
  }
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