extendedIris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the iris dataset, split data into X and classification into y, this time there are three types. check it using head() or tail() or other function! \n",
    "We will be using sklearn, please check the documentation for more indepth knowledge of the algorithm implementation.\n",
    "http://scikit-learn.org/stable/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import numpy as np\n",
    "#Handling that X and y actually contain 3 different iris types.\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:, [2, 3]]\n",
    "y = iris.target\n",
    "T = []\n",
    "u = []\n",
    "for i in range(0,len(y)):\n",
    "    if y[i] == 0 or y[i] == 1:\n",
    "        T.append(X[i])\n",
    "        u.append(y[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a predifined function to train our data into training and test sets, lets use it! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/phallsjo/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(T, u, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good practice is to start by normalizing the data, look at X_train and X_train_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "sc.fit(X_train)\n",
    "X_train_std = sc.transform(X_train)\n",
    "X_test_std = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the sklearn perceptron, fit the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/phallsjo/anaconda/lib/python2.7/site-packages/sklearn/linear_model/stochastic_gradient.py:117: DeprecationWarning: n_iter parameter is deprecated in 0.19 and will be removed in 0.21. Use max_iter and tol instead.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Perceptron(alpha=0.0001, class_weight=None, eta0=0.1, fit_intercept=True,\n",
       "      max_iter=None, n_iter=40, n_jobs=1, penalty=None, random_state=0,\n",
       "      shuffle=True, tol=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Perceptron\n",
    "ppn = Perceptron(n_iter=40, eta0=0.1, random_state=0)\n",
    "ppn.fit(X_train_std, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using our test data, how many are missclassified?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missclassified samples: 0\n"
     ]
    }
   ],
   "source": [
    "y_pred = ppn.predict(X_test_std)\n",
    "print('Missclassified samples: %d' % (y_test != y_pred).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.00\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar code as before, plot the regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_decision_regions(X, y, classifier,\n",
    "                       test_idx=None, resolution=0.02):\n",
    "    # setup marker generator and color map\n",
    "    markers = ('s', 'x', 'o', '^', 'v')\n",
    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "    # plot the decision surface\n",
    "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
    "                            np.arange(x2_min, x2_max, resolution))\n",
    "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    Z = Z.reshape(xx1.shape)\n",
    "    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n",
    "    plt.xlim(xx1.min(), xx1.max())\n",
    "    plt.ylim(xx2.min(), xx2.max())\n",
    "    # plot all samples\n",
    "    X_test, y_test = X[test_idx, :], y[test_idx]\n",
    "    for idx, cl in enumerate(np.unique(y)):\n",
    "        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],\n",
    "                       alpha=0.8, c=cmap(idx),\n",
    "                       marker=markers[idx], label=cl)\n",
    "    # highlight test samples\n",
    "    if test_idx:\n",
    "        X_test, y_test = X[test_idx, :], y[test_idx]\n",
    "        plt.scatter(X_test[:, 0], X_test[:, 1], c='',\n",
    "                   alpha=1.0, linewidth=1, marker='o',\n",
    "                   s=55, label='test set')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine the test data and the train data to plot it easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEKCAYAAAASByJ7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERGJKCGIiAgA/x9S7Tdi5zC+GwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d1a3cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_combined_std = np.vstack((X_train_std, X_test_std))\n",
    "y_combined = np.hstack((y_train, y_test))\n",
    "#plot_decision_regions(X=X_combined_std,y=y_combined,classifier=ppn,test_idx=range(105,150))\n",
    "plot_decision_regions(X=X_combined_std,y=y_combined,classifier=ppn)\n",
    "plt.xlabel('petal length [standardized]')\n",
    "plt.ylabel('petal width [standardized]')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}