confusion_matrix_visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm, datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([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,\n",
       "       0, 0, 0, 0, 0, 0, 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,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Split the data into a training set and a test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "# Run classifier, using a model that is too regularized (C too low) to see\n",
    "# the impact on the results\n",
    "classifier = svm.SVC(kernel='linear', C=0.01, probability=True)\n",
    "y_pred = classifier.fit(X_train, y_train).predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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s/+WRzH/h6VL8jIKQMg/o5rI0llwS2yfZQfALJV0iaRTQqchxFc3SpZX06vXpHJoVFb2o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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dc5f860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "plt.title(\"title\")\n",
    "plt.colorbar()\n",
    "\n",
    "classes = [\"0\", \"1\", \"2\"]\n",
    "tick_marks = np.arange(len(classes))\n",
    "plt.xticks(tick_marks, classes, rotation=45)\n",
    "plt.yticks(tick_marks, classes)\n",
    "\n",
    "fmt = '.2f'\n",
    "thresh = cm.max() / 2.\n",
    "for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "    plt.text(j, i, format(cm[i, j], fmt),\n",
    "             horizontalalignment=\"center\",\n",
    "             color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.tight_layout()\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "classifier = classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_scores = classifier.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 0, 2, 0, 2, 0, 2, 2, 1, 2, 2, 1, 2, 2, 0, 1, 1, 0, 0, 2, 1,\n",
       "       0, 0, 2, 0, 0, 1, 1, 0, 2, 1, 0, 2, 2, 1, 0, 2])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#roc_auc_score(y_true=y_test, y_score=y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# we can also write the above plot in a function\n",
    "plot_confusion_matrix(cnf_matrix, classes=class_names,\n",
    "                      title='Confusion matrix, without normalization')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}