{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Looking at cluster consistency\n", "\n", "After clustering some data, you often want to (and should!) inspect the results. Obviously, visualization is a big part of this. It is also helpful to look at some representative examples. HDBSCAN provides methods to get an approximate representative point for each cluster. It does this by calculating the centroid/medoid for each cluster, but weighting each point by its cluster membership strength (i.e. the probability of being in that cluster). One way to get representative samples for a cluster is to look for the points closest and furthest to this representative point. This allows you to look at the cluster and determine how consistent it is. For example, when clustering text this might mean \"are the documents in my cluster talking about one topic?\". \n", "\n", "Let's get some test data and cluster it:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import hdbscan\n", "from scipy.spatial.distance import cdist\n", "#Some plotting libraries\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib notebook\n", "\n", "sns.set_context('poster')\n", "sns.set_color_codes()\n", "plot_kwds = {'alpha' : 0.25, 's' : 40, 'linewidths':0}" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data = np.load('clusterable_data.npy')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "clusterer = hdbscan.HDBSCAN(min_cluster_size=15)\n", "clusterer.fit(data)\n", "labels = clusterer.labels_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's plot our sample data. We will see that the clustering is pretty good." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
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