machine_learning_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Following post at http://rforwork.info/2015/05/28/an-r-enthusiast-goes-pythonic/\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import os\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from scipy import stats\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mat_perf = pd.read_csv(os.path.expanduser('~/Downloads/student/student-mat.csv'), delimiter=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        6   5   6   6  \n",
       "1      5        3      3     1     1      3        4   5   5   6  \n",
       "2      4        3      2     2     3      3       10   7   8  10  \n",
       "3      3        2      2     1     1      5        2  15  14  15  \n",
       "4      4        3      2     1     2      5        4   6  10  10  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat_perf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "TkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b6599e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_palette(\"deep\", desat=.6)\n",
    "sns.set_context(context='poster', font_scale=1)\n",
    "sns.set_context(rc={\"figure.figsize\": (8, 4)})\n",
    "sns.set_style(\"darkgrid\")\n",
    "mat_perf.G3.hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Python Code\n",
    "test_stats = {'variable': [], 'test_type' : [], 'test_value' : []}\n",
    " \n",
    "for col in mat_perf.columns[:-3]:\n",
    "    test_stats['variable'].append(col)\n",
    "    \n",
    "    if mat_perf[col].dtype == 'O':\n",
    "        # Do ANOVA\n",
    "        aov = smf.ols(formula='G3 ~ C(' + col + ')', data=mat_perf, missing='drop').fit()\n",
    "        test_stats['test_type'].append('F Test')\n",
    "        test_stats['test_value'].append(round(aov.fvalue,2))\n",
    "    else:\n",
    "        # Do correlation\n",
    "        # print(col + '\\n')\n",
    "        model = smf.ols(formula='G3 ~ ' + col, data=mat_perf, missing='drop').fit()\n",
    "        value = round(model.tvalues[1],2)\n",
    "        test_stats['test_type'].append('t Test')\n",
    "        test_stats['test_value'].append(value)\n",
    "        \n",
    "test_stats = pd.DataFrame(test_stats)\n",
    "test_stats.sort(columns='test_value', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b696cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b73a6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group by test type\n",
    "groups = test_stats.groupby(by=\"test_type\")\n",
    "\n",
    "lab_to_axes = groups.plot(\"variable\", \"test_value\", kind=\"bar\", legend=False, rot=55)\n",
    "for lab, ax in lab_to_axes.items():\n",
    "    ax.set_ylabel(lab.replace(\"Test\", \"Values\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'F Test': [20, 22, 3, 1, 17, 21, 8, 15, 4, 10, 11, 5, 9, 19, 0, 16, 18],\n",
       " 't Test': [6, 7, 13, 23, 29, 24, 27, 26, 28, 12, 25, 2, 14]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "groups.get_group(\"F Test\")\n",
    "groups.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Python code\n",
    "usevars =  [x for x in test_stats.variable[test_stats.test_value.map(lambda z: abs(z)) <= 50]]\n",
    "\n",
    "mat_perf['randu'] = np.array([np.random.uniform(0,1) for x in range(0,mat_perf.shape[0])])\n",
    " \n",
    "mp_X = mat_perf[usevars]\n",
    "mp_X_train = mp_X[mat_perf.randu <= .67]\n",
    "mp_X_test = mp_X[mat_perf.randu > .67]\n",
    " \n",
    "mp_Y_train = mat_perf.G3[mat_perf.randu <= .67]\n",
    "mp_Y_test = mat_perf.G3[mat_perf.randu > .67]\n",
    "\n",
    "def get_dummies(ser):\n",
    "    vals = ser.unique().tolist()\n",
    "    print(vals)\n",
    "    return ser.map(lambda s: vals.index(s))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['yes', 'no']\n",
      "['no', 'yes']\n",
      "['U', 'R']\n",
      "['F', 'M']\n",
      "['no', 'yes']\n",
      "['no', 'yes']\n",
      "['at_home', 'services', 'other', 'teacher', 'health']\n",
      "['yes', 'no']\n",
      "['GT3', 'LE3']\n",
      "['course', 'reputation', 'home', 'other']\n",
      "['mother', 'father', 'other']\n",
      "['A', 'T']\n",
      "['teacher', 'other', 'health', 'services', 'at_home']\n",
      "['yes', 'no']\n",
      "['GP', 'MS']\n",
      "['no', 'yes']\n",
      "['no', 'yes']\n",
      "['yes', 'no']"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/huziy/virtualenvs/py3.4-default/lib/python3.4/site-packages/IPython/kernel/__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/huziy/virtualenvs/py3.4-default/lib/python3.4/site-packages/IPython/kernel/__main__.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "['no', 'yes']\n",
      "['U', 'R']\n",
      "['F', 'M']\n",
      "['yes', 'no']\n",
      "['yes', 'no']\n",
      "['at_home', 'health', 'other', 'services', 'teacher']\n",
      "['yes', 'no']\n",
      "['LE3', 'GT3']\n",
      "['other', 'home', 'reputation', 'course']\n",
      "['mother', 'father', 'other']\n",
      "['T', 'A']\n",
      "['other', 'services', 'at_home', 'teacher', 'health']\n",
      "['yes', 'no']\n",
      "['GP', 'MS']\n",
      "['no', 'yes']\n",
      "['no', 'yes']"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/huziy/virtualenvs/py3.4-default/lib/python3.4/site-packages/IPython/kernel/__main__.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/huziy/virtualenvs/py3.4-default/lib/python3.4/site-packages/IPython/kernel/__main__.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "r squared value of 0.347015760782\n",
      "RMSE of 3.72195986261\n"
     ]
    },
    {
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       "gg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b8377f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for the training set\n",
    "cat_cols = [x for x in mp_X_train.columns if mp_X_train[x].dtype == \"O\"]\n",
    "for col in cat_cols:\n",
    "    mp_X_train[col + '_num'] = get_dummies(mp_X_train[col])\n",
    "\n",
    "\n",
    "# for the testing set\n",
    "cat_cols = [x for x in mp_X_test.columns if mp_X_test[x].dtype == \"O\"]\n",
    "for col in cat_cols:\n",
    "    mp_X_test[col + '_num'] = get_dummies(mp_X_test[col])\n",
    "    \n",
    "mp_X_train.drop(cat_cols, inplace=True, axis=1)\n",
    "mp_X_test.drop(cat_cols, inplace=True, axis=1)\n",
    " \n",
    "rf = RandomForestRegressor(bootstrap=True,\n",
    "           criterion='mse', max_depth=2, max_features='auto',\n",
    "           min_samples_leaf=1, min_samples_split=2,\n",
    "           n_estimators=100, n_jobs=1, oob_score=True, random_state=None,\n",
    "           verbose=0)\n",
    "rf.fit(mp_X_train, mp_Y_train)\n",
    "\n",
    "#Python Code\n",
    "y_pred = rf.predict(mp_X_test)\n",
    "first_test = pd.DataFrame({\"pred.G3.keepvars\" : y_pred, \"G3\" : mp_Y_test})\n",
    "sns.lmplot(\"G3\", \"pred.G3.keepvars\", first_test, size=7, aspect=1.5)\n",
    "print('r squared value of', stats.pearsonr(mp_Y_test, y_pred)[0]**2)\n",
    "print('RMSE of', np.sqrt(mean_squared_error(mp_Y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['higher',\n",
       " 'romantic',\n",
       " 'address',\n",
       " 'sex',\n",
       " 'paid',\n",
       " 'internet',\n",
       " 'Mjob',\n",
       " 'schoolsup',\n",
       " 'famsize',\n",
       " 'reason',\n",
       " 'guardian',\n",
       " 'Pstatus',\n",
       " 'Fjob',\n",
       " 'nursery',\n",
       " 'school',\n",
       " 'famsup',\n",
       " 'activities']"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat_perf.Fjob.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat_perf[\"Fedu\"].dtype == \"O\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['schoolsup', 'famsize', 'reason', 'studytime', 'guardian', 'Pstatus', 'Fjob', 'nursery', 'famrel', 'school', 'absences', 'famsup', 'freetime', 'activities', 'Walc', 'Dalc', 'health', 'traveltime', 'goout'], dtype='object')"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp_X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       teacher\n",
       "1         other\n",
       "2         other\n",
       "3      services\n",
       "4         other\n",
       "5         other\n",
       "6         other\n",
       "7       teacher\n",
       "8         other\n",
       "9         other\n",
       "10       health\n",
       "11        other\n",
       "12     services\n",
       "13        other\n",
       "14        other\n",
       "15        other\n",
       "16     services\n",
       "17        other\n",
       "18     services\n",
       "19        other\n",
       "20        other\n",
       "21       health\n",
       "22        other\n",
       "23        other\n",
       "24       health\n",
       "25     services\n",
       "26        other\n",
       "27     services\n",
       "28        other\n",
       "29      teacher\n",
       "         ...   \n",
       "365       other\n",
       "366    services\n",
       "367    services\n",
       "368    services\n",
       "369     teacher\n",
       "370    services\n",
       "371    services\n",
       "372     at_home\n",
       "373       other\n",
       "374       other\n",
       "375       other\n",
       "376       other\n",
       "377    services\n",
       "378       other\n",
       "379       other\n",
       "380     teacher\n",
       "381       other\n",
       "382    services\n",
       "383    services\n",
       "384       other\n",
       "385       other\n",
       "386     at_home\n",
       "387       other\n",
       "388    services\n",
       "389       other\n",
       "390    services\n",
       "391    services\n",
       "392       other\n",
       "393       other\n",
       "394     at_home\n",
       "Name: Fjob, dtype: object"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat_perf.Fjob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
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    "name": "ipython",
    "version": 3
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