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ml-workshop / 03-Classification.ipynb

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{
 "metadata": {
  "name": "03-Classification"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Load and View Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import load_iris\n",
      "iris = load_iris()\n",
      "print(iris['DESCR'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Iris Plants Database\n",
        "\n",
        "Notes\n",
        "-----\n",
        "Data Set Characteristics:\n",
        "    :Number of Instances: 150 (50 in each of three classes)\n",
        "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
        "    :Attribute Information:\n",
        "        - sepal length in cm\n",
        "        - sepal width in cm\n",
        "        - petal length in cm\n",
        "        - petal width in cm\n",
        "        - class:\n",
        "                - Iris-Setosa\n",
        "                - Iris-Versicolour\n",
        "                - Iris-Virginica\n",
        "    :Summary Statistics:\n",
        "    ============== ==== ==== ======= ===== ====================\n",
        "                    Min  Max   Mean    SD   Class Correlation\n",
        "    ============== ==== ==== ======= ===== ====================\n",
        "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
        "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
        "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
        "    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n",
        "    ============== ==== ==== ======= ===== ====================\n",
        "    :Missing Attribute Values: None\n",
        "    :Class Distribution: 33.3% for each of 3 classes.\n",
        "    :Creator: R.A. Fisher\n",
        "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
        "    :Date: July, 1988\n",
        "\n",
        "This is a copy of UCI ML iris datasets.\n",
        "http://archive.ics.uci.edu/ml/datasets/Iris\n",
        "\n",
        "The famous Iris database, first used by Sir R.A Fisher\n",
        "\n",
        "This is perhaps the best known database to be found in the\n",
        "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
        "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
        "data set contains 3 classes of 50 instances each, where each class refers to a\n",
        "type of iris plant.  One class is linearly separable from the other 2; the\n",
        "latter are NOT linearly separable from each other.\n",
        "\n",
        "References\n",
        "----------\n",
        "   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
        "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
        "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
        "   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
        "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
        "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
        "     Structure and Classification Rule for Recognition in Partially Exposed\n",
        "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
        "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
        "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
        "     on Information Theory, May 1972, 431-433.\n",
        "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
        "     conceptual clustering system finds 3 classes in the data.\n",
        "   - Many, many more ...\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pylab as pl\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "from sklearn.decomposition import PCA"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = iris.data\n",
      "y = iris.target"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
      "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
      "\n",
      "pl.figure(2, figsize=(8, 6))\n",
      "pl.clf()\n",
      "\n",
      "# Plot the training points\n",
      "pl.scatter(X[:, 0], X[:, 1], c=y, cmap=pl.cm.Paired)\n",
      "pl.xlabel('Sepal length')\n",
      "pl.ylabel('Sepal width')\n",
      "\n",
      "pl.xlim(x_min, x_max)\n",
      "pl.ylim(y_min, y_max)\n",
      "pl.xticks(())\n",
      "pl.yticks(())\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "([], <a list of 0 Text yticklabel objects>)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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C12bM4Cz/CxcuYPTYcZjw1jJY2Tlg24r3oNYswvJlyzibkxBC/otWI5MaZWVlwc7OHhvP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\n"
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot first three PCA dimensions\n",
      "fig = pl.figure(1, figsize=(8, 6))\n",
      "ax = Axes3D(fig, elev=-150, azim=110)\n",
      "X_reduced = PCA(n_components=3).fit_transform(iris.data)\n",
      "ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=y,\n",
      "           cmap=pl.cm.Paired)\n",
      "ax.set_title(\"First three PCA directions\")\n",
      "ax.set_xlabel(\"1st eigenvector\")\n",
      "ax.set_xticks(())\n",
      "ax.set_ylabel(\"2nd eigenvector\")\n",
      "ax.set_yticks(())\n",
      "ax.set_zlabel(\"3rd eigenvector\")\n",
      "ax.set_zticks(())\n",
      "\n",
      "pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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vv53Q0FDNnV2npVpF7TKZTJhMJqDyZIfx//0/Nq5ejtFk4ro7RjN69Oh6q+OZ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\nPpDDAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Bayes Classifier"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.naive_bayes import GaussianNB\n",
      "gnb = GaussianNB()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Learn a naive bayes model\n",
      "gnb_model = gnb.fit(X, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Prediction\n",
      "pred_y = gnb_model.predict(iris.data)\n",
      "num_errors = (iris.target != pred_y).sum()\n",
      "print(\"Number of mislabeled points : {}\".format(num_errors))\n",
      "print(gnb_model.score(X,y))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Number of mislabeled points : 6\n",
        "0.96\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Cross Validation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cross_validation import train_test_split\n",
      "train_data, test_data, train_target, test_target = train_test_split(X, y, test_size=0.15)\n",
      "\n",
      "print(\"Original data size\",X.shape)\n",
      "print(\"Original num target labels\",y.size)\n",
      "\n",
      "print(\"Train data:\", train_data.shape)\n",
      "print(\"Train target:\", train_target.size)\n",
      "print(\"Test data:\", test_data.shape)\n",
      "print(\"Test target:\", test_target.size)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('Original data size', (150, 4))\n",
        "('Original num target labels', 150)\n",
        "('Train data:', (127, 4))\n",
        "('Train target:', 127)\n",
        "('Test data:', (23, 4))\n",
        "('Test target:', 23)\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Learn a naive bayes model\n",
      "gnb_model = gnb.fit(train_data, train_target)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_pred_y = gnb_model.predict(test_data)\n",
      "num_errors = (test_target != test_pred_y).sum()\n",
      "print(\"Number of mislabeled points : {}\".format(num_errors))\n",
      "print(gnb_model.score(X,y))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Number of mislabeled points : 1\n",
        "0.96\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### SVM"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import svm\n",
      "clf = svm.LinearSVC()\n",
      "clf.fit(train_data, train_target)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 31,
       "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",
        "     tol=0.0001, verbose=0)"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "svm_test_pred_y = clf.predict(test_data)\n",
      "num_errors = (test_target != svm_test_pred_y).sum()\n",
      "print(\"Number of mislabeled points : {}\".format(num_errors))\n",
      "print(clf.score(X,y))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Number of mislabeled points : 1\n",
        "0.966666666667\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Decision Trees"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import tree\n",
      "clf = tree.DecisionTreeClassifier()\n",
      "clf.fit(train_data, train_target)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "DecisionTreeClassifier(compute_importances=False, criterion='gini',\n",
        "            max_depth=None, max_features=None, min_density=0.1,\n",
        "            min_samples_leaf=1, min_samples_split=2,\n",
        "            random_state=<mtrand.RandomState object at 0x1002ab2e8>)"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dt_test_pred_y = clf.predict(test_data)\n",
      "num_errors = (test_target != dt_test_pred_y).sum()\n",
      "print(\"Number of mislabeled points : {}\".format(num_errors))\n",
      "print(clf.score(X,y))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Number of mislabeled points : 1\n",
        "0.993333333333\n"
       ]
      }
     ],
     "prompt_number": 34
    }
   ],
   "metadata": {}
  }
 ]
}