titanic/titanic-speedml-0.9.2-release-updates-py2.x.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-12T15:05:46.488099",
     "start_time": "2017-05-12T15:05:38.283849"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from speedml import Speedml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-12T15:05:46.720528",
     "start_time": "2017-05-12T15:05:46.490527"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sml = Speedml('../input/titanic/train.csv', '../input/titanic/test.csv', \n",
    "              target='Survived', uid='PassengerId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-12T15:05:46.774682",
     "start_time": "2017-05-12T15:05:46.722532"
    },
    "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>Results</th>\n",
       "      <th>Observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Speedml Release</th>\n",
       "      <td>v0.9.2</td>\n",
       "      <td>Visit https://speedml.com for release notes.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Null Values</th>\n",
       "      <td>1014</td>\n",
       "      <td>Aim for zero nulls.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#Samples Train</th>\n",
       "      <td>891</td>\n",
       "      <td>Much larger than #Features to avoid over-fitting.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#Samples Test</th>\n",
       "      <td>418</td>\n",
       "      <td>Cannot drop Test samples.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#Features</th>\n",
       "      <td>11</td>\n",
       "      <td>Compare with n=count during feature selection.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Numerical over Text</th>\n",
       "      <td>54%</td>\n",
       "      <td>Aim for 100% numerical.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Numerical High-cardinality</th>\n",
       "      <td>[(Age, 88), (Fare, 248)]</td>\n",
       "      <td>(&gt;10) categories. Engineer with density method.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Numerical Categorical</th>\n",
       "      <td>[(Survived, 2), (Pclass, 3), (SibSp, 7), (Parc...</td>\n",
       "      <td>Violin plots for outliers.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Numerical Continuous</th>\n",
       "      <td>[]</td>\n",
       "      <td>~90% unique. Scatter plots for outliers.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Text High-cardinality</th>\n",
       "      <td>[(Ticket, 681), (Cabin, 147)]</td>\n",
       "      <td>(&gt;10) categories. Labels to numeric.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Text Categorical</th>\n",
       "      <td>[(Sex, 2), (Embarked, 3)]</td>\n",
       "      <td>Mapping or Labels to numeric.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Text Unique</th>\n",
       "      <td>[(Name, 891)]</td>\n",
       "      <td>~90% unique. Extract or drop.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target Analysis (Survived)</th>\n",
       "      <td>Model ready.</td>\n",
       "      <td>Use classification models.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                      Results  \\\n",
       "Speedml Release                                                        v0.9.2   \n",
       "Null Values                                                              1014   \n",
       "#Samples Train                                                            891   \n",
       "#Samples Test                                                             418   \n",
       "#Features                                                                  11   \n",
       "Numerical over Text                                                       54%   \n",
       "Numerical High-cardinality                           [(Age, 88), (Fare, 248)]   \n",
       "Numerical Categorical       [(Survived, 2), (Pclass, 3), (SibSp, 7), (Parc...   \n",
       "Numerical Continuous                                                       []   \n",
       "Text High-cardinality                           [(Ticket, 681), (Cabin, 147)]   \n",
       "Text Categorical                                    [(Sex, 2), (Embarked, 3)]   \n",
       "Text Unique                                                     [(Name, 891)]   \n",
       "Target Analysis (Survived)                                       Model ready.   \n",
       "\n",
       "                                                                 Observations  \n",
       "Speedml Release                  Visit https://speedml.com for release notes.  \n",
       "Null Values                                               Aim for zero nulls.  \n",
       "#Samples Train              Much larger than #Features to avoid over-fitting.  \n",
       "#Samples Test                                       Cannot drop Test samples.  \n",
       "#Features                      Compare with n=count during feature selection.  \n",
       "Numerical over Text                                   Aim for 100% numerical.  \n",
       "Numerical High-cardinality    (>10) categories. Engineer with density method.  \n",
       "Numerical Categorical                              Violin plots for outliers.  \n",
       "Numerical Continuous                 ~90% unique. Scatter plots for outliers.  \n",
       "Text High-cardinality                    (>10) categories. Labels to numeric.  \n",
       "Text Categorical                                Mapping or Labels to numeric.  \n",
       "Text Unique                                     ~90% unique. Extract or drop.  \n",
       "Target Analysis (Survived)                         Use classification models.  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sml.eda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-12T15:05:47.102808",
     "start_time": "2017-05-12T15:05:46.778601"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Survived', 2), ('Pclass', 3), ('SibSp', 7), ('Parch', 7)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sml.eda().get_value('Numerical Categorical', 'Results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-12T15:05:47.398535",
     "start_time": "2017-05-12T15:05:47.105095"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: train (891, 11) test (418, 10)\n",
      "Numerical: train_n (6) test_n (5)\n"
     ]
    }
   ],
   "source": [
    "sml.shape()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-12T15:05:47.427103",
     "start_time": "2017-05-12T15:05:47.402475"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      "Survived    891 non-null int64\n",
      "Pclass      891 non-null int64\n",
      "Name        891 non-null object\n",
      "Sex         891 non-null object\n",
      "Age         714 non-null float64\n",
      "SibSp       891 non-null int64\n",
      "Parch       891 non-null int64\n",
      "Ticket      891 non-null object\n",
      "Fare        891 non-null float64\n",
      "Cabin       204 non-null object\n",
      "Embarked    889 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 76.6+ KB\n",
      "----------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 10 columns):\n",
      "Pclass      418 non-null int64\n",
      "Name        418 non-null object\n",
      "Sex         418 non-null object\n",
      "Age         332 non-null float64\n",
      "SibSp       418 non-null int64\n",
      "Parch       418 non-null int64\n",
      "Ticket      418 non-null object\n",
      "Fare        417 non-null float64\n",
      "Cabin       91 non-null object\n",
      "Embarked    418 non-null object\n",
      "dtypes: float64(2), int64(3), object(5)\n",
      "memory usage: 32.7+ KB\n"
     ]
    }
   ],
   "source": [
    "sml.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-12T15:05:47.923945",
     "start_time": "2017-05-12T15:05:47.430512"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Crj+7yRiRrkcxcPjLv/wkLS26jiVyzOrVHdx00z/wvvd9EK/hZeLpfsYe7SE5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\nx2Arkj3+3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116a47150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "u'Fixed 6 or 0.67% upper outliers. '"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A9PcTnbqAOdWfWAQEoL6+gZtvvoX77ntL3n3oZNKFC318+9uPcPLk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116b983d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sml.plot.ordinal('Parch')\n",
    "sml.feature.outliers('Parch', upper=99)\n",
    "sml.plot.ordinal('Parch')"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "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"
  }
 },
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