test sklearn.ipynb

{
 "metadata": {
  "name": "test sklearn"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Simple tests for sklearn"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "sys.path.append('/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/')\n",
      "sys.path.append('/Users/doug/SW_Dev/NLTK_Experiments/')\n",
      "from sklearn.feature_extraction import DictVectorizer\n",
      "from sklearn.feature_extraction import DictVectorizer\n",
      "from sklearn.feature_extraction.text import TfidfTransformer"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test1 = ({'a':'foo', 'b':1, 'c':3}, {'a':1, 'b':1, 'c':3}, {'a':1, 'b':1, 'c':3})\n",
      "test2 = (('a_foo', 'b', 'c'), ('a', 'b', 'c'), ('a', 'b', 'c'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#from sklearn.feature_extraction import DictVectorizer\n",
      "vectorizer = DictVectorizer()\n",
      "vectorized_sparse = vectorizer.fit_transform(test1)\n",
      "vectorized_sparse\n",
      "vectorized_array = vectorized_sparse.toarray()\n",
      "print vectorizer.get_feature_names()\n",
      "vectorized_array"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['a', 'a=foo', 'b', 'c']\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "array([[ 0.,  1.,  1.,  3.],\n",
        "       [ 1.,  0.,  1.,  3.],\n",
        "       [ 1.,  0.,  1.,  3.]])"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pos_window = [\n",
      "    {\n",
      "        'word-2': 'the',\n",
      "        'pos-2': 'DT',\n",
      "        'word-1': 'cat',\n",
      "        'pos-1': 'NN',\n",
      "        'word+1': 'on',\n",
      "        'pos+1': 'PP',\n",
      "    },\n",
      "    # in a real application one would extract many such dictionaries\n",
      "]\n",
      "vec = DictVectorizer()\n",
      "pos_vectorized = vec.fit_transform(pos_window)\n",
      "print pos_vectorized                \n",
      "print pos_vectorized.toarray()\n",
      "print vec.get_feature_names()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  (0, 1)\t1.0\n",
        "  (0, 2)\t1.0\n",
        "  (0, 4)\t1.0\n",
        "  (0, 5)\t1.0\n",
        "  (0, 3)\t1.0\n",
        "  (0, 0)\t1.0\n",
        "[[ 1.  1.  1.  1.  1.  1.]]\n",
        "['pos+1=PP', 'pos-1=NN', 'pos-2=DT', 'word+1=on', 'word-1=cat', 'word-2=the']\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def tokens2dict (tokens):\n",
      "    result = {}\n",
      "    for t in tokens:\n",
      "            result[t]=True\n",
      "    return result\n",
      "\n",
      "a =(('a', 'b', 'c'),)\n",
      "print tokens2dict(a[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'a': True, 'c': True, 'b': True}\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vectorizer = DictVectorizer()\n",
      "vectorized_sparse = vectorizer.fit_transform([tokens2dict(tokens) for tokens in test2])\n",
      "vectorized_sparse\n",
      "vectorized_array = vectorized_sparse.toarray()\n",
      "labels =  vectorizer.get_feature_names()\n",
      "vectorized_array"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array([[ 0.,  1.,  1.,  1.],\n",
        "       [ 1.,  0.,  1.,  1.],\n",
        "       [ 1.,  0.,  1.,  1.]])"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print labels"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['a', 'a_foo', 'b', 'c']\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TfidfTransformer().fit_transform(vectorized_sparse).toarray()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "array([[ 0.        ,  0.76749457,  0.45329466,  0.45329466],\n",
        "       [ 0.67325467,  0.        ,  0.52284231,  0.52284231],\n",
        "       [ 0.67325467,  0.        ,  0.52284231,  0.52284231]])"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cluster import KMeans, MiniBatchKMeans\n",
      "km = KMeans(n_clusters=2,init='k-means++', max_iter=100, n_init=1, verbose=True)\n",
      "#km = KMeans(n_clusters=2,init='k-means++', max_iter=100, n_init=1, verbose=True, n_jobs=-2)\n",
      "#print km.fit(vectorized_sparse)\n",
      "km.fit(vectorized_sparse)\n",
      "print km.labels_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Initialization complete\n",
        "Iteration  0, inertia 0.000\n",
        "Converged at iteration 0\n",
        "[1 0 0]\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print labels"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['a', 'a_foo', 'b', 'c']\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import metrics\n",
      "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": [
      {
       "ename": "ValueError",
       "evalue": "labels_true and labels_pred must have same size, got 4 and 3",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-11-2aef135192bf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Homogeneity: %0.3f\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhomogeneity_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Completeness: %0.3f\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompleteness_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"V-measure: %0.3f\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mv_measure_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m print(\"Adjusted Rand-Index: %.3f\"\n",
        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/metrics/cluster/supervised.pyc\u001b[0m in \u001b[0;36mhomogeneity_score\u001b[0;34m(labels_true, labels_pred)\u001b[0m\n\u001b[1;32m    331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    332\u001b[0m     \"\"\"\n\u001b[0;32m--> 333\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mhomogeneity_completeness_v_measure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/metrics/cluster/supervised.pyc\u001b[0m in \u001b[0;36mhomogeneity_completeness_v_measure\u001b[0;34m(labels_true, labels_pred)\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0mv_measure_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m     \"\"\"\n\u001b[0;32m--> 242\u001b[0;31m     \u001b[0mlabels_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_clusterings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels_true\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/metrics/cluster/supervised.pyc\u001b[0m in \u001b[0;36mcheck_clusterings\u001b[0;34m(labels_true, labels_pred)\u001b[0m\n\u001b[1;32m     41\u001b[0m         raise ValueError(\n\u001b[1;32m     42\u001b[0m             \u001b[0;34m\"labels_true and labels_pred must have same size, got %d and %d\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m             % (labels_true.shape[0], labels_pred.shape[0]))\n\u001b[0m\u001b[1;32m     44\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mlabels_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mValueError\u001b[0m: labels_true and labels_pred must have same size, got 4 and 3"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}