08_logistic/08_01_gaussiannb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes With Gaussian Predictors\n",
    "\n",
    "We've played around with NB using Multinomial (and possibly Bernoulli) likelihood models through the `naive_bayes` classses:\n",
    "\n",
    "* MultinomialNB()\n",
    "* BernoulliNB()\n",
    "\n",
    "We're going to try using the `GaussianNB` class now. Now, our features should be continuous (and normally distributed) in predicting our response variable `y` (which is binomial).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import naive_bayes\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sb\n",
    "\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston House Prices dataset\n",
      "\n",
      "Notes\n",
      "------\n",
      "Data Set Characteristics:  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive\n",
      "    \n",
      "    :Median Value (attribute 14) is usually the target\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "http://archive.ics.uci.edu/ml/datasets/Housing\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      "**References**\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data = datasets.load_boston()\n",
    "print data.DESCR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM  ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  \n",
       "0  396.90   4.98  \n",
       "1  396.90   9.14  \n",
       "2  392.83   4.03  \n",
       "3  394.63   2.94  \n",
       "4  396.90   5.33  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert data into a Pandas dataframe for ease\n",
    "data_df = pd.DataFrame( data.data, columns=data.feature_names)\n",
    "data_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Let's say we want to predict whether a home is next to the Charles river\n",
    "# Based on the median value and how much crime is in the area\n",
    "X = pd.DataFrame( {'MEDV': data.target,'CRIM':(data_df.CRIM)} )\n",
    "y = data_df['CHAS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.cross_validation import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91305882352941181"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validate to get accuracy\n",
    "gnb = naive_bayes.GaussianNB()\n",
    "cvscores = cross_val_score(gnb,X,y,cv=KFold(len(X),n_folds=10,shuffle=True))\n",
    "np.mean(cvscores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12edfaa10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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RcgDQt3QVhcPhYMOGDYiIiIC7uzvN4qMG0I1I6g3VWc1FLtetu3fvIjc3F6GhoeDz+Xj0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\npOQ/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e58ae50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take a look at the histograms of the features\n",
    "f = plt.figure(figsize=(5,5))\n",
    "f = data_df.hist(grid=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wQF/84hc1b948jRkzRpJUUlKir3/966qrq9PevXuVkJCgBQsWqLCwMALtAwAwuHQZzG+/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\nZ86c0bPPPnvL5Uy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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13029d250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ah, the CRIM feature isn't really normally distributed, which is one of the\n",
    "# Assumptions in using GaussianNB\n",
    "plt.subplot(1,2,1)\n",
    "f = plt.hist( data_df.CRIM)\n",
    "plt.subplot(1,2,2)\n",
    "f = plt.hist( np.log(data_df.CRIM) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Let's transform the CRIM to be more normal through a log distribution\n",
    "X = pd.DataFrame( {'MEDV': data.target,'CRIM':np.log(data_df.CRIM)} )\n",
    "y = data_df['CHAS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9210196078431373"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slightly better cross validated results\n",
    "gnb = naive_bayes.GaussianNB()\n",
    "cvscores = cross_val_score(gnb,X,y,cv=KFold(len(X),n_folds=10,shuffle=True))\n",
    "np.mean(cvscores)"
   ]
  }
 ],
 "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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}