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ch01 - added clustering method comparison graph
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WandrilleD committed Jun 15, 2022
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"# %load -r 89- solutions/solution_01_kmean.py"
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"[Back to ToC](#toc)\n",
"\n",
"## What is the best method for clustering ? <a id='best'></a>\n",
"\n",
"As you surely suspect by now, there is no perfect method. \n",
"Each algorithm makes different assumptions about the structure of your data and will thus behave well or bad depending on howyour data is actually structured.\n",
"\n",
"Let's demonstrate by displaying how different algorithm perform on different dataset (example taken from the [sklearn documentation](https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html)):"
]
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"outputs": [],
"source": [
"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, 13))\n",
"plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.95, 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",
" ('MiniBatch\\nKMeans', two_means),\n",
" ('Affinity\\nPropagation', affinity_propagation),\n",
" ('MeanShift', ms),\n",
" ('Spectral\\nClustering', spectral),\n",
" ('Ward', ward),\n",
" ('Agglomerative\\nClustering', average_linkage),\n",
" ('DBSCAN', dbscan),\n",
" ('OPTICS', optics),\n",
" ('BIRCH', birch),\n",
" ('Gaussian\\nMixture', 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(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()"
]
},
{
"cell_type": "markdown",
"metadata": {},
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