scikit-learn from watching Jake's (http://jakevdp.github.com/) PyData 2012 video: http://youtu.be/cHZONQ2-x7I

sklearn.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "X = np.random.random((100, 4))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "(100, 4)"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import datasets"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = datasets.load_iris()\n",
      "iris = datasets.load_iris()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "sklearn.datasets.base.Bunch"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "x = data.data\n",
      "y = data.target"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "(150, 4)"
       ]
      }
     ],
     "prompt_number": 5
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     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y.shape"
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     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "(150,)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "array([ 5.1,  3.5,  1.4,  0.2])"
       ]
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     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x[:,0]"
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     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "array([ 5.1,  4.9,  4.7,  4.6,  5. ,  5.4,  4.6,  5. ,  4.4,  4.9,  5.4,\n",
        "        4.8,  4.8,  4.3,  5.8,  5.7,  5.4,  5.1,  5.7,  5.1,  5.4,  5.1,\n",
        "        4.6,  5.1,  4.8,  5. ,  5. ,  5.2,  5.2,  4.7,  4.8,  5.4,  5.2,\n",
        "        5.5,  4.9,  5. ,  5.5,  4.9,  4.4,  5.1,  5. ,  4.5,  4.4,  5. ,\n",
        "        5.1,  4.8,  5.1,  4.6,  5.3,  5. ,  7. ,  6.4,  6.9,  5.5,  6.5,\n",
        "        5.7,  6.3,  4.9,  6.6,  5.2,  5. ,  5.9,  6. ,  6.1,  5.6,  6.7,\n",
        "        5.6,  5.8,  6.2,  5.6,  5.9,  6.1,  6.3,  6.1,  6.4,  6.6,  6.8,\n",
        "        6.7,  6. ,  5.7,  5.5,  5.5,  5.8,  6. ,  5.4,  6. ,  6.7,  6.3,\n",
        "        5.6,  5.5,  5.5,  6.1,  5.8,  5. ,  5.6,  5.7,  5.7,  6.2,  5.1,\n",
        "        5.7,  6.3,  5.8,  7.1,  6.3,  6.5,  7.6,  4.9,  7.3,  6.7,  7.2,\n",
        "        6.5,  6.4,  6.8,  5.7,  5.8,  6.4,  6.5,  7.7,  7.7,  6. ,  6.9,\n",
        "        5.6,  7.7,  6.3,  6.7,  7.2,  6.2,  6.1,  6.4,  7.2,  7.4,  7.9,\n",
        "        6.4,  6.3,  6.1,  7.7,  6.3,  6.4,  6. ,  6.9,  6.7,  6.9,  5.8,\n",
        "        6.8,  6.7,  6.7,  6.3,  6.5,  6.2,  5.9])"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(x[:,0], x[:,1], c=y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.collections.PathCollection at 0x2be5110>"
       ]
      },
      {
       "output_type": "display_data",
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MIBQ3xzeEoLq6xuJ4CwoKIBT6Qj/xGAB4gs93RFlZmcX77mwKCgrgl+gLsbN+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\n"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "set(y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "set([0, 1, 2])"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.svm import LinearSVC"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "clf = LinearSVC() # clf == classifier"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
        "     intercept_scaling=1, loss='l2', multi_class='ovr', penalty='l2',\n",
        "     random_state=None, tol=0.0001, verbose=0)"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LinearSVC?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.fit(x,y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
        "     intercept_scaling=1, loss='l2', multi_class='ovr', penalty='l2',\n",
        "     random_state=None, tol=0.0001, verbose=0)"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.coef_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "array([[ 0.18423255,  0.45122479, -0.80794238, -0.45071612],\n",
        "       [ 0.05222543, -0.89358974,  0.40548688, -0.93937329],\n",
        "       [-0.85064228, -0.98663656,  1.38093043,  1.86529426]])"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_new = np.array([[5.0, 3.6, 1.3, 0.25]])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_new.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "(1, 4)"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict(x_new)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "array([0])"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "iris.target_names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "array(['setosa', 'versicolor', 'virginica'], \n",
        "      dtype='|S10')"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "PCA"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.decomposition import PCA"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "(150, 4)"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pca = PCA(n_components=2, whiten=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pca.fit(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "PCA(copy=True, n_components=2, whiten=True)"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = pca.transform(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "(150, 2)"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(y[:,0], y[:,1], c=data.target)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<matplotlib.collections.PathCollection at 0x37874d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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W0pKUlBQ2b95MmZIleadFC8qXKcOsn37i+JEjuGdkoIbsFrkKsFAqqVKlCtNn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\nRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cluster import KMeans\n",
      "from numpy.random import RandomState\n",
      "rng = RandomState(42)\n",
      "kmeans = KMeans(3, random_state=rng).fit(y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "KMeans(copy_x=True, init='k-means++', k=None, max_iter=300, n_clusters=3,\n",
        "    n_init=10, n_jobs=1, precompute_distances=True,\n",
        "    random_state=<mtrand.RandomState object at 0x7f957b8f0840>, tol=0.0001,\n",
        "    verbose=0)"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmeans.labels_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
        "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2,\n",
        "       2, 2, 2, 2, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1,\n",
        "       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0,\n",
        "       1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1], dtype=int32)"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(y[:,0], y[:,1], c=kmeans.labels_)\n",
      "# Take a look at the unsupervised classification"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "<matplotlib.collections.PathCollection at 0x3d1d290>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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wh5plazFi1ogXurMyJCSEkJAQ3m7zNtp3LVGqsoaNyr5blpOTTrx0XAaDATMz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\nIQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!wget https://raw.github.com/scikit-learn/scikit-learn/master/examples/applications/svm_gui.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "--2013-02-09 14:45:50--  https://raw.github.com/scikit-learn/scikit-learn/master/examples/applications/svm_gui.py\r\n",
        "Resolving raw.github.com (raw.github.com)... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "199.27.75.130\r\n",
        "Connecting to raw.github.com (raw.github.com)|199.27.75.130|:443... connected.\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "HTTP request sent, awaiting response... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "200 OK\r\n",
        "Length: 11112 (11K) [text/plain]\r\n",
        "Saving to: `svm_gui.py'\r\n",
        "\r\n",
        "\r",
        " 0% [                                       ] 0           --.-K/s              \r",
        "100%[======================================>] 11,112      --.-K/s   in 0.004s  \r\n",
        "\r\n",
        "2013-02-09 14:45:50 (2.50 MB/s) - `svm_gui.py' saved [11112/11112]\r\n",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}