cluster_news.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "from sklearn.datasets import fetch_20newsgroups\n",
      "from pprint import pprint\n",
      "\n",
      "import sys\n",
      "from time import time\n",
      "\n",
      "import numpy as np\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['clf', 'e']\n",
        "`%matplotlib` prevents importing * from pylab and numpy\n"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dataset = fetch_20newsgroups(subset='train')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(\"%d documents\" % len(dataset.data))\n",
      "print(\"%d categories\" % len(dataset.target_names))\n",
      "print()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "11314 documents\n",
        "20 categories\n",
        "()\n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "labels = dataset.target\n",
      "true_k = np.unique(labels).shape[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.feature_extraction.text import TfidfVectorizer\n",
      "vectorizer = TfidfVectorizer(max_df=0.5,min_df=2, stop_words='english',use_idf=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t0 = time()\n",
      "X = vectorizer.fit_transform(dataset.data)\n",
      "\n",
      "print(\"done in %fs\" % (time() - t0))\n",
      "print(\"n_samples: %d, n_features: %d\" % X.shape)\n",
      "print()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done in 3.360153s\n",
        "n_samples: 11314, n_features: 56122\n",
        "()\n"
       ]
      }
     ],
     "prompt_number": 52
    },
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     "collapsed": false,
     "input": [
      "X.shape"
     ],
     "language": "python",
     "metadata": {},
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       "metadata": {},
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       "prompt_number": 67,
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       "text": [
        "<11314x56122 sparse matrix of type '<type 'numpy.float64'>'\n",
        "\twith 1180487 stored elements in Compressed Sparse Row format>"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy.sparse"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
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      "XT = scipy.sparse.csr_matrix.transpose(X)"
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     "language": "python",
     "metadata": {},
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     "prompt_number": 83
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     "language": "python",
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     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 87
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     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A.shape"
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     "language": "python",
     "metadata": {},
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       "metadata": {},
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       "prompt_number": 88,
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        "(56122, 56122)"
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      "e = scipy.sparse.linalg.eigsh(A,10)"
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     "language": "python",
     "metadata": {},
     "outputs": [],
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    {
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     "input": [
      "e"
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     "metadata": {},
     "outputs": []
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      "A.shape"
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     "language": "python",
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      {
       "metadata": {},
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       "prompt_number": 123,
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        "(56122, 56122)"
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     ],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.preprocessing import Normalizer\n",
      "from sklearn import metrics\n",
      "\n",
      "from sklearn.cluster import KMeans, MiniBatchKMeans"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "  km = KMeans(n_clusters=20, init='k-means++', max_iter=100, n_init=1,verbose=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "km.fit(X)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Initialization complete\n",
        "Iteration  0, inertia 21125.031"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration  1, inertia 11015.579"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration  2, inertia 10962.996"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration  3, inertia 10937.959"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration  4, inertia 10924.570"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration  5, inertia 10917.009"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration  6, inertia 10910.716"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration  7, inertia 10903.537"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration  8, inertia 10897.802"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration  9, inertia 10896.361"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration 10, inertia 10895.680"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration 11, inertia 10895.077"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration 12, inertia 10894.385"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration 13, inertia 10893.487"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration 14, inertia 10892.448"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration 15, inertia 10891.038"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration 16, inertia 10890.493"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration 17, inertia 10890.295"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration 18, inertia 10890.204"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration 19, inertia 10890.135"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration 20, inertia 10890.029"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration 21, inertia 10889.974"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration 22, inertia 10889.936"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Iteration 23, inertia 10889.916"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Converged at iteration 23\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 126,
       "text": [
        "KMeans(copy_x=True, init='k-means++', max_iter=100, n_clusters=20, n_init=1,\n",
        "    n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,\n",
        "    verbose=True)"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels, km.labels_))\n",
      "print(\"Completeness: %0.3f\" % metrics.completeness_score(labels, km.labels_))\n",
      "print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels, km.labels_))\n",
      "print(\"Adjusted Rand-Index: %.3f\"\n",
      "      % metrics.adjusted_rand_score(labels, km.labels_))\n",
      "print(\"Silhouette Coefficient: %0.3f\"\n",
      "      % metrics.silhouette_score(X, labels, sample_size=1000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Homogeneity: 0.365\n",
        "Completeness: 0.508\n",
        "V-measure: 0.425\n",
        "Adjusted Rand-Index: 0.085\n",
        "Silhouette Coefficient: 0.006"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(\"Top terms per cluster:\")\n",
      "order_centroids = km.cluster_centers_.argsort()[:, ::-1]\n",
      "terms = vectorizer.get_feature_names()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Top terms per cluster:\n"
       ]
      }
     ],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
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     "input": [
      "terms"
     ],
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        " u'1212',\n",
        " u'121236',\n",
        " u'1213',\n",
        " u'12134',\n",
        " u'12139',\n",
        " u'1214',\n",
        " u'121411',\n",
        " u'1215',\n",
        " u'12176',\n",
        " u'12180',\n",
        " u'12187',\n",
        " u'122',\n",
        " u'1220',\n",
        " ...]"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(true_k):\n",
      "    print(\"Cluster %d:\" % i)\n",
      "    for ind in order_centroids[i, :10]:\n",
      "        print terms[ind]\n",
      "    print"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cluster 0:\n",
        "turkish\n",
        "armenian\n",
        "armenians\n",
        "armenia\n",
        "turks\n",
        "argic\n",
        "turkey\n",
        "serdar\n",
        "genocide\n",
        "zuma\n",
        "\n",
        "Cluster 1:\n",
        "com\n",
        "sandvik\n",
        "stratus\n",
        "cramer\n",
        "optilink\n",
        "fbi\n",
        "kent\n",
        "people\n",
        "article\n",
        "islam\n",
        "\n",
        "Cluster 2:\n",
        "drive\n",
        "scsi\n",
        "ide\n",
        "controller\n",
        "drives\n",
        "hard\n",
        "disk\n",
        "floppy\n",
        "bus\n",
        "mac\n",
        "\n",
        "Cluster 3:\n",
        "baseball\n",
        "year\n",
        "game\n",
        "team\n",
        "games\n",
        "players\n",
        "runs\n",
        "braves\n",
        "pitching\n",
        "article\n",
        "\n",
        "Cluster 4:\n",
        "intercon\n",
        "amanda\n",
        "walker\n",
        "clipper\n",
        "com\n",
        "corporation\n",
        "systems\n",
        "herndon\n",
        "crypto\n",
        "chaos\n",
        "\n",
        "Cluster 5:\n",
        "nasa\n",
        "space\n",
        "gov\n",
        "henry\n",
        "alaska\n",
        "toronto\n",
        "moon\n",
        "zoo\n",
        "larc\n",
        "spencer\n",
        "\n",
        "Cluster 6:\n",
        "geb\n",
        "banks\n",
        "gordon\n",
        "pitt\n",
        "cs\n",
        "dsl\n",
        "n3jxp\n",
        "cadre\n",
        "chastity\n",
        "shameful\n",
        "\n",
        "Cluster 7:\n",
        "hockey\n",
        "team\n",
        "ca\n",
        "game\n",
        "nhl\n",
        "play\n",
        "players\n",
        "season\n",
        "leafs\n",
        "toronto\n",
        "\n",
        "Cluster 8:\n",
        "uk\n",
        "ac\n",
        "university\n",
        "cam\n",
        "dcs\n",
        "ed\n",
        "mathew\n",
        "posting\n",
        "host\n",
        "nntp\n",
        "\n",
        "Cluster 9:\n",
        "card\n",
        "video\n",
        "drivers\n",
        "vga\n",
        "monitor\n",
        "cards\n",
        "bus\n",
        "windows\n",
        "driver\n",
        "diamond\n",
        "\n",
        "Cluster 10:\n",
        "key\n",
        "clipper\n",
        "encryption\n",
        "chip\n",
        "keys\n",
        "escrow\n",
        "government\n",
        "com\n",
        "nsa\n",
        "algorithm\n",
        "\n",
        "Cluster 11:\n",
        "__\n",
        "___\n",
        "berkeley\n",
        "ax\n",
        "simms\n",
        "_____\n",
        "____\n",
        "baalke\n",
        "vram\n",
        "jpl\n",
        "\n",
        "Cluster 12:\n",
        "israel\n",
        "israeli\n",
        "jews\n",
        "arab\n",
        "jake\n",
        "arabs\n",
        "lebanese\n",
        "israelis\n",
        "adam\n",
        "policy\n",
        "\n",
        "Cluster 13:\n",
        "keith\n",
        "caltech\n",
        "livesey\n",
        "sgi\n",
        "solntze\n",
        "wpd\n",
        "jon\n",
        "schneider\n",
        "cco\n",
        "morality\n",
        "\n",
        "Cluster 14:\n",
        "car\n",
        "cars\n",
        "com\n",
        "engine\n",
        "oil\n",
        "dealer\n",
        "radar\n",
        "article\n",
        "just\n",
        "good\n",
        "\n",
        "Cluster 15:\n",
        "access\n",
        "digex\n",
        "pat\n",
        "express\n",
        "net\n",
        "online\n",
        "prb\n",
        "communications\n",
        "com\n",
        "greenbelt\n",
        "\n",
        "Cluster 16:\n",
        "windows\n",
        "window\n",
        "dos\n",
        "file\n",
        "files\n",
        "ms\n",
        "program\n",
        "com\n",
        "mouse\n",
        "use\n",
        "\n",
        "Cluster 17:\n",
        "god\n",
        "jesus\n",
        "bible\n",
        "christians\n",
        "people\n",
        "christ\n",
        "christian\n",
        "faith\n",
        "believe\n",
        "church\n",
        "\n",
        "Cluster 18:\n",
        "gun\n",
        "guns\n",
        "people\n",
        "firearms\n",
        "com\n",
        "weapons\n",
        "don\n",
        "crime\n",
        "control\n",
        "militia\n",
        "\n",
        "Cluster 19:\n",
        "com\n",
        "university\n",
        "posting\n",
        "host\n",
        "article\n",
        "nntp\n",
        "know\n",
        "like\n",
        "ca\n",
        "just\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 143
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}