deep_learning/course1/lesson4/dl-on-tabular-data-using-fastai.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from fastai.tabular import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = untar_data(URLs.ADULT_SAMPLE)\n",
    "df = pd.read_csv(path/'adult.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "9b84e09b8019768d0a2bba7b1e8746faaf26e66d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>101320</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>1902</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;=50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>236746</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>10520</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;=50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>96185</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>112847</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;=50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>82297</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;50k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt  ...   hours-per-week  native-country salary\n",
       "0   49            Private  101320  ...               40   United-States  >=50k\n",
       "1   44            Private  236746  ...               45   United-States  >=50k\n",
       "2   38            Private   96185  ...               32   United-States   <50k\n",
       "3   38       Self-emp-inc  112847  ...               40   United-States  >=50k\n",
       "4   42   Self-emp-not-inc   82297  ...               50   United-States   <50k\n",
       "\n",
       "[5 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "a4acbaecdb78e8c1ec5e8d20f96675a07e1058a3",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dep_var = 'salary'\n",
    "cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race']\n",
    "cont_names = ['age', 'fnlwgt', 'education-num']\n",
    "procs = [FillMissing, Categorify]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "333cc9c32a6a8b1a9fe3ccd33dfebff537f926a6",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = TabularList.from_df(df.iloc[800:1000].copy(), path=path, cat_names=cat_names, cont_names=cont_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "8c26dad0c865be1d86baceb160add3dc314231c0",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = (TabularList.from_df(df, path=path, cat_names=cat_names, cont_names=cont_names, procs=procs)\n",
    "                           .split_by_idx(list(range(800,1000)))\n",
    "                           .label_from_df(cols=dep_var)\n",
    "                           .add_test(test)\n",
    "                           .databunch())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "482fc5adb0734e434c6dbf4e1980233487c59954"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>education-num_na</th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Local-gov</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Amer-Indian-Eskimo</td>\n",
       "      <td>False</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>67187.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>&lt;50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>False</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>325355.0000</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>&gt;=50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Private</td>\n",
       "      <td>9th</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>False</td>\n",
       "      <td>49.0000</td>\n",
       "      <td>39986.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>&lt;50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>10th</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>False</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>233933.0000</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>&lt;50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>False</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>314310.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>&lt;50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>State-gov</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>False</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>231043.0000</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>&lt;50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>False</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>133937.0000</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>&gt;=50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Private</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>False</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>192793.0000</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>&gt;=50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>False</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>186126.0000</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>&lt;50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>False</td>\n",
       "      <td>59.0000</td>\n",
       "      <td>157303.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>&lt;50k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_batch(rows=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "cebca75f5c5dbd5b5eab65b2a3c9eeb7249286aa",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learn = tabular_learner(data, layers=[200,100], metrics=accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "6d36e4c94b5e2e6ebddcc0a93e4e28aeab161dbe"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 00:15 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.367805</td>\n",
       "      <td>0.379517</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.365418</td>\n",
       "      <td>0.362761</td>\n",
       "      <td>0.845000</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.364398</td>\n",
       "      <td>0.380435</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.351031</td>\n",
       "      <td>0.361564</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit(4, 1e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "882911a5d3eb035361f43d034e0225f50d535f49",
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.6.1"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}