titanic/titanic-speedml-0.9.2-release-updates-py3.x.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Speedml no longer requires IPython magic command for `matplotlib inline`. We automatically configure inline plots when running Speedml from a notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-12T15:37:05.977538",
     "start_time": "2017-05-12T15:37:05.973810"
    },
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from speedml import Speedml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-12T15:37:06.000433",
     "start_time": "2017-05-12T15:37:05.980455"
    },
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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 = Speedml('../input/titanic/train.csv', \n",
    "              '../input/titanic/test.csv', \n",
    "              target = 'Survived',\n",
    "              uid = 'PassengerId')\n",
    "sml.shape()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-12T15:37:06.148147",
     "start_time": "2017-05-12T15:37:06.002458"
    },
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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>[(Cabin, 147), (Ticket, 681)]</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                           [(Cabin, 147), (Ticket, 681)]   \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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sml.eda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-12T15:37:07.202175",
     "start_time": "2017-05-12T15:37:06.150541"
    },
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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hNueOAZaSBSI4cf4CtlQnTpeb3ScTuaVYxotKly9kpXyhSIa1rMILjStRwBIG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\nwlT/D/yKM7sNUeYFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11721ab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sml.plot.correlate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-12T15:57:58.614110",
     "start_time": "2017-05-12T15:57:58.163675"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LWw6fS5KkNqrc874BOD4i7qBxxuyMiFhG4xD5D4DXA98BvhERAB/LzBsqzCNJ0qxQWXk3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1176b36a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sml.plot.bar('Parch', 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-12T15:58:52.657283",
     "start_time": "2017-05-12T15:58:52.335233"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zcmfHMlxBiJjdRzv55VMH0HQTWZK4/ZpZXDS3KNZhCUKIaVvTPtzgYm99N5bc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\nEELECSnaQgghRJyQoi2EEELEif8DmIunVyv6Fu8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119f5d0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sml.plot.strip('Parch', 'Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-12T15:39:16.445634",
     "start_time": "2017-05-12T15:39:16.212473"
    },
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "'Replaced matching list of strings across train and test datasets.'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "'Replaced 2 matching values across train and test datasets.'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "'Replaced 2 matching values across train and test datasets.'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "'Replaced 1 matching values across train and test datasets.'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name Title\n",
       "0                            Braund, Mr. Owen Harris    Mr\n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...   Mrs\n",
       "2                             Heikkinen, Miss. Laina  Miss\n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)   Mrs\n",
       "4                           Allen, Mr. William Henry    Mr"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sml.feature.extract(new='Title', a='Name', regex=' ([A-Za-z]+)\\.')\n",
    "sml.plot.crosstab('Title', 'Sex')\n",
    "sml.feature.replace(a='Title', match=['Lady', 'Countess','Capt', 'Col',\\\n",
    "'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], new='Rare')\n",
    "sml.feature.replace('Title', 'Mlle', 'Miss')\n",
    "sml.feature.replace('Title', 'Ms', 'Miss')\n",
    "sml.feature.replace('Title', 'Mme', 'Mrs')\n",
    "sml.train[['Name', 'Title']].head()"
   ]
  }
 ],
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