{
 "metadata": {
  "name": "Machine Learning II"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rcParams['figure.figsize'] = (16, 4)\n",
      "\n",
      "# A lot of code below borrowed from the scikits learn docs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html\n",
      "\n",
      "![http://1.bp.blogspot.com/-ME24ePzpzIM/UQLWTwurfXI/AAAAAAAAANw/W3EETIroA80/s1600/drop_shadows_background.png](http://1.bp.blogspot.com/-ME24ePzpzIM/UQLWTwurfXI/AAAAAAAAANw/W3EETIroA80/s1600/drop_shadows_background.png)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import load_iris\n",
      "data = load_iris()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Desicion Trees"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://scikit-learn.org/stable/modules/tree.html"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import tree"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = tree.DecisionTreeClassifier()\n",
      "clf.fit(data.data, data.target)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "DecisionTreeClassifier(compute_importances=None, criterion='gini',\n",
        "            max_depth=None, max_features=None, min_density=None,\n",
        "            min_samples_leaf=1, min_samples_split=2, random_state=None,\n",
        "            splitter='best')"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict(data.data[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array([0])"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict(data.data[10])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "array([0])"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict(data.data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 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, 2, 2, 2, 2, 2,\n",
        "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image #needed to render in notebook\n",
      "import StringIO, pydot  #needed to convert dot format to png\n",
      "\n",
      "dot_data = StringIO.StringIO() \n",
      "tree.export_graphviz(clf, out_file=dot_data) \n",
      "graph = pydot.graph_from_dot_data(dot_data.getvalue())\n",
      "Image(graph.create_png())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "png": 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GAADqQiaTxcTETJgwoUaj1q1bN2fOHB8fnwMHDtRTMKDuxo8fL4SIi4uTOggAwOyxMygA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\nCSE8PT01Nz08PIQQ8fHxmpvbtm2ruCl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NatWytN+ctf/mK5QUBA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\nowEAAAAAANTn/wB7iADvI7/NXgAAAABJRU5ErkJggg==\n",
       "prompt_number": 9,
       "text": [
        "<IPython.core.display.Image at 0x3a10a50>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Cross-validation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://scikit-learn.org/stable/modules/classes.html#module-sklearn.cross_validation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.naive_bayes import GaussianNB\n",
      "\n",
      "clf2 = GaussianNB()\n",
      "\n",
      "clf2.fit(data.data, data.target)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "GaussianNB()"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import cross_validation"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kf = cross_validation.KFold(len(data.data), n_folds=10)\n",
      "len(kf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "10"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pc_folds = []\n",
      "for train_index, test_index in kf:\n",
      "    clf2 = GaussianNB()\n",
      "    clf2.fit(data.data[train_index], data.target[train_index])\n",
      "    predictions = clf2.predict(data.data[test_index])\n",
      "    pc_right = sum(predictions == data.target[test_index])/float(len(test_index))\n",
      "    pc_folds.append(pc_right)\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pc_folds"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "[1.0,\n",
        " 1.0,\n",
        " 1.0,\n",
        " 0.93333333333333335,\n",
        " 0.93333333333333335,\n",
        " 0.8666666666666667,\n",
        " 1.0,\n",
        " 0.8666666666666667,\n",
        " 0.8666666666666667,\n",
        " 1.0]"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean(pc_folds)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "0.94666666666666688"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "min(pc_folds)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "0.8666666666666667"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Metrics"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import metrics"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predictions = clf2.predict(data.data)\n",
      "predictions"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,\n",
        "       2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2,\n",
        "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "metrics.accuracy_score(data.target, predictions)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "0.96666666666666667"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print metrics.classification_report(data.target, predictions)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "             precision    recall  f1-score   support\n",
        "\n",
        "          0       1.00      1.00      1.00        50\n",
        "          1       0.94      0.96      0.95        50\n",
        "          2       0.96      0.94      0.95        50\n",
        "\n",
        "avg / total       0.97      0.97      0.97       150\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "metrics.confusion_matrix(data.target, predictions)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "array([[50,  0,  0],\n",
        "       [ 0, 48,  2],\n",
        "       [ 0,  3, 47]])"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "confmat = metrics.confusion_matrix(data.target, predictions)\n",
      "imshow(confmat,interpolation='nearest', cmap=cm.gray_r)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<matplotlib.image.AxesImage at 0x3d88690>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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m55xzGzdudC+99NKk6yoqKty5c+cKNsfY2JhbuHCh++mnn9zIyIiLx+Pu+PHj49Z88skn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\nCfEAYEI8AJgQDwAm/w+HMkJGLyd0QwAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "confmat = metrics.confusion_matrix(data.target, predictions)\n",
      "\n",
      "imshow(confmat.max() - confmat,interpolation='nearest', cmap=cm.gray)\n",
      "\n",
      "for rownum, row in enumerate(confmat):\n",
      "    for colnum, val in enumerate(row):\n",
      "        text(colnum, rownum, str(val/sum(data.target == rownum).astype(float)), fontsize=24, color='black' if val < confmat.max()/2 else 'white', ha='center', va='center' )\n",
      "        \n",
      "xticks(arange(len(data.target_names)), data.target_names)\n",
      "yticks(arange(len(data.target_names)), data.target_names);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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QoIqKipTZbFazZ8+ustzLy0ulpaUps9msvvnmm5vWmz9/vjKbzWrChAlq27Zt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\nIYTh/hcrGxTIWIvgLwAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://media.aau.dk/null_space_pursuits/2012/01/frustrations-with-music-genre.html"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By Andr\u00e9s Cabrera mantaraya36@gmail.com\n",
      "\n",
      "For course MAT 240E at UCSB\n",
      "\n",
      "This ipython notebook is licensed under the CC-BY-NC-SA license: http://creativecommons.org/licenses/by-nc-sa/4.0/\n",
      "\n",
      "![http://i.creativecommons.org/l/by-nc-sa/3.0/88x31.png](http://i.creativecommons.org/l/by-nc-sa/3.0/88x31.png)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}