Titanic/notebooks/052-Titanic_new.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Another Submission - adding more features to Random Forest. \n",
    "\n",
    "All this is taken from https://www.kaggle.com/creepykoala/titanic/study-of-tree-and-forest-algorithms/notebook \n",
    "I just write it in line by line by myself as it helps me learn and I can query things as I go. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries Ready!\n"
     ]
    }
   ],
   "source": [
    "# Import libraries\n",
    "\n",
    "import numpy as np\n",
    "from numpy.random import random_integers\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from scipy.stats import pointbiserialr, spearmanr\n",
    "%matplotlib inline\n",
    "\n",
    "print('Libraries Ready!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load training data\n",
    "\n",
    "df = pd.read_csv('/home/sophie/projects/Titanic/data/train.csv', header=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People with stronger titles tend to have more help on board. Hence, we will categorize passengers based on titles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      Mr\n",
      "1     Mrs\n",
      "2    Miss\n",
      "3     Mrs\n",
      "4      Mr\n",
      "Name: Title, dtype: object\n",
      "0                              Braund, Mr. Owen Harris\n",
      "1    Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
      "2                               Heikkinen, Miss. Laina\n",
      "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
      "4                             Allen, Mr. William Henry\n",
      "Name: Name, dtype: object\n"
     ]
    }
   ],
   "source": [
    "Title_Dictionary = {\n",
    "                    \"Capt\":       \"Officer\",\n",
    "                    \"Col\":        \"Officer\",\n",
    "                    \"Major\":      \"Officer\",\n",
    "                    \"Jonkheer\":   \"Royalty\",\n",
    "                    \"Don\":        \"Royalty\",\n",
    "                    \"Sir\" :       \"Royalty\",\n",
    "                    \"Dr\":         \"Officer\",\n",
    "                    \"Rev\":        \"Officer\",\n",
    "                    \"the Countess\":\"Royalty\",\n",
    "                    \"Dona\":       \"Royalty\",\n",
    "                    \"Mme\":        \"Mrs\",\n",
    "                    \"Mlle\":       \"Miss\",\n",
    "                    \"Ms\":         \"Mrs\",\n",
    "                    \"Mr\" :        \"Mr\",\n",
    "                    \"Mrs\" :       \"Mrs\",\n",
    "                    \"Miss\" :      \"Miss\",\n",
    "                    \"Master\" :    \"Master\",\n",
    "                    \"Lady\" :      \"Royalty\"\n",
    "                    }\n",
    "\n",
    "# for each name, i.e. Braund, Mr. Owen Harris, we extract the title using x.split and strip and use it as a key to the \n",
    "# Title Dictionary. \n",
    "# x.split(',')[1] means split the string where the , is. Take the 2nd item. i.e. Mr. Owen Harris\n",
    "# split('.')[0] means split by the . and take the first item.\n",
    "# strip() to remove all the other characters. \n",
    "df['Title'] = df['Name'].apply(lambda x: Title_Dictionary[x.split(',')[1].split('.')[0].strip()])\n",
    "\n",
    "print(df['Title'][0:5])\n",
    "print(df['Name'][0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Futrelle,', 'Mrs.', 'Jacques', 'Heath', '(Lily', 'May', 'Peel)']]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = ['Braund, Mr. Owen Harris', 'Futrelle, Mrs. Jacques Heath (Lily May Peel)']\n",
    "[name[1].split(',')[1].split('.')[0].strip()]\n",
    "[name[1].split()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Include the ticket prefix as a new column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    A/5 21171\n",
      "1     PC 17599\n",
      "Name: Ticket, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print (df['Ticket'][0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         A/5\n",
      "1          PC\n",
      "2    STON/O2.\n",
      "3      NoClue\n",
      "4      NoClue\n",
      "Name: TicketPrefix, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Extract the letters from the beginning of each element of 'Ticket'\n",
    "def Ticket_Prefix(s):\n",
    "    s=s.split()[0] # if you don't include anything in split()\n",
    "    if s.isdigit():\n",
    "        return 'NoClue'\n",
    "    else:\n",
    "        return s\n",
    "    \n",
    "df['TicketPrefix'] = df['Ticket'].apply(lambda x: Ticket_Prefix(x))\n",
    "\n",
    "print(df['TicketPrefix'][0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill in missing age with median age, based on Title, Pclass and Sex.\n",
    "\n",
    "- Make a mask with non-null values for Age. Make a new dataframe out of this new mask.\n",
    "- Make a groupby object from these values and calculate the median for each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make an array where null values are False.\n",
    "mask_Age = df.Age.notnull()\n",
    "\n",
    "# New dataframe where all rows have a value for age. \n",
    "Age_Sex_Title_Pclass = df.loc[mask_Age, [\"Age\", \"Title\", \"Sex\", \"Pclass\"]]\n",
    "\n",
    "# Groupby object to group by Title, Pclass and Sex\n",
    "Filler_Ages_1 = Age_Sex_Title_Pclass.groupby(by = [\"Title\", \"Pclass\", \"Sex\"]).median()\n",
    "\n",
    "# This moves both Sex and Pclass into column headers and does so in that order. \n",
    "Filler_Ages = Filler_Ages_1.Age.unstack(level = -1).unstack(level = -1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Age Title     Sex  Pclass\n",
      "0  22.0    Mr    male       3\n",
      "1  38.0   Mrs  female       1\n",
      "2  26.0  Miss  female       3\n",
      "3  35.0   Mrs  female       1\n",
      "4  35.0    Mr    male       3\n",
      "714\n"
     ]
    }
   ],
   "source": [
    "print(Age_Sex_Title_Pclass[0:5])\n",
    "print(len(df[mask_Age]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Title     Sex  Pclass\n",
      "5     Mr    male       3\n",
      "17    Mr    male       2\n",
      "19   Mrs  female       3\n",
      "26    Mr    male       3\n",
      "28  Miss  female       3\n"
     ]
    }
   ],
   "source": [
    "mask_Age = df.Age.isnull()  # A mask where null values are True\n",
    "\n",
    "# New DataFrame with missing values for age\n",
    "Age_Sex_Title_Pclass_missing = df.loc[mask_Age, [\"Title\", \"Sex\", \"Pclass\"]]\n",
    "\n",
    "print(Age_Sex_Title_Pclass_missing[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Age Title     Sex  Pclass\n",
      "0  22.0    Mr    male       3\n",
      "1  38.0   Mrs  female       1\n",
      "2  26.0  Miss  female       3\n",
      "3  35.0   Mrs  female       1\n",
      "4  35.0    Mr    male       3\n",
      "Before unstacking---------\n",
      "                        Age\n",
      "Title   Pclass Sex         \n",
      "Master  1      male     4.0\n",
      "        2      male     1.0\n",
      "        3      male     4.0\n",
      "Miss    1      female  30.0\n",
      "        2      female  24.0\n",
      "        3      female  18.0\n",
      "Mr      1      male    40.0\n",
      "        2      male    31.0\n",
      "        3      male    26.0\n",
      "Mrs     1      female  40.0\n",
      "        2      female  31.5\n",
      "        3      female  31.0\n",
      "Officer 1      female  49.0\n",
      "               male    51.0\n",
      "        2      male    46.5\n",
      "Royalty 1      female  40.5\n",
      "               male    40.0\n",
      "After unstacking---------\n",
      "Sex     female              male            \n",
      "Pclass       1     2     3     1     2     3\n",
      "Title                                       \n",
      "Master     NaN   NaN   NaN   4.0   1.0   4.0\n",
      "Miss      30.0  24.0  18.0   NaN   NaN   NaN\n",
      "Mr         NaN   NaN   NaN  40.0  31.0  26.0\n",
      "Mrs       40.0  31.5  31.0   NaN   NaN   NaN\n",
      "Officer   49.0   NaN   NaN  51.0  46.5   NaN\n",
      "Royalty   40.5   NaN   NaN  40.0   NaN   NaN\n"
     ]
    }
   ],
   "source": [
    "print(Age_Sex_Title_Pclass[0:5])\n",
    "print(\"Before unstacking---------\")\n",
    "print (Filler_Ages_1)\n",
    "print(\"After unstacking---------\")\n",
    "print (Filler_Ages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Make a function, Age_filler, which is applied to each of the missing dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Look-up function for the calculated median ages. \n",
    "def Age_filler(row):\n",
    "    if row.Sex == \"female\":\n",
    "        age = Filler_Ages.female.loc[row[\"Title\"], row[\"Pclass\"]]\n",
    "        return age\n",
    "    elif row.Sex == \"male\":\n",
    "        age = Filler_Ages.male.loc[row[\"Title\"], row[\"Pclass\"]]\n",
    "        return age\n",
    "    \n",
    "# Make a new column on \"missing\" dataframe and add the median value to each row. \n",
    "Age_Sex_Title_Pclass_missing[\"Age\"] = Age_Sex_Title_Pclass_missing.apply(Age_filler, axis= 1)\n",
    "\n",
    "# reform the 'Age' column.\n",
    "df[\"Age\"] = pd.concat([Age_Sex_Title_Pclass[\"Age\"], Age_Sex_Title_Pclass_missing[\"Age\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is one missing Fare value to fill in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Filling in with the mean of all fares.\n",
    "df['Fare'] = df['Fare'].fillna(value=df.Fare.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop Cabin and Ticket.   \n",
    "Combine SibSp and Parch to create FamilySize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['FamilySize'] = df['SibSp'] + df['Parch']\n",
    "df = df.drop(['Ticket', 'Cabin'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will use dummy variables to transform the categorical data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      male\n",
      "1    female\n",
      "2    female\n",
      "Name: Sex, dtype: object\n",
      "   Sex_female  Sex_male\n",
      "0         0.0       1.0\n",
      "1         1.0       0.0\n",
      "2         1.0       0.0\n"
     ]
    }
   ],
   "source": [
    "# get_dummies splits up a column into two seperate columns of 1 and 0, where they are true or false. \n",
    "dummies_Sex = pd.get_dummies(df['Sex'], prefix='Sex')\n",
    "\n",
    "print(df['Sex'][0:3])\n",
    "print(dummies_Sex[0:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Making dummies for the other categorical features\n",
    "dummies_Embarked = pd.get_dummies(df['Embarked'], prefix= 'Embarked') \n",
    "dummies_Pclass = pd.get_dummies(df['Pclass'], prefix= 'Pclass')\n",
    "dummies_Title = pd.get_dummies(df['Title'], prefix= 'Title')\n",
    "dummies_TicketPrefix = pd.get_dummies(df['TicketPrefix'], prefix='TicketPrefix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Make new dataframes which have the dummies added on to the end.\n",
    "df = pd.concat([df, dummies_Sex, dummies_Embarked, dummies_Pclass, dummies_Title, dummies_TicketPrefix], axis=1)\n",
    "\n",
    "# Drop the categorical data\n",
    "df = df.drop(['Sex','Embarked','Pclass','Title','Name','TicketPrefix'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, set PassengerId as the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>...</th>\n",
       "      <th>TicketPrefix_SOTON/O.Q.</th>\n",
       "      <th>TicketPrefix_SOTON/O2</th>\n",
       "      <th>TicketPrefix_SOTON/OQ</th>\n",
       "      <th>TicketPrefix_STON/O</th>\n",
       "      <th>TicketPrefix_STON/O2.</th>\n",
       "      <th>TicketPrefix_SW/PP</th>\n",
       "      <th>TicketPrefix_W./C.</th>\n",
       "      <th>TicketPrefix_W.E.P.</th>\n",
       "      <th>TicketPrefix_W/C</th>\n",
       "      <th>TicketPrefix_WE/P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived   Age  SibSp  Parch     Fare  FamilySize  Sex_female  \\\n",
       "PassengerId                                                                  \n",
       "1                   0  22.0      1      0   7.2500           1         0.0   \n",
       "2                   1  38.0      1      0  71.2833           1         1.0   \n",
       "3                   1  26.0      0      0   7.9250           0         1.0   \n",
       "4                   1  35.0      1      0  53.1000           1         1.0   \n",
       "5                   0  35.0      0      0   8.0500           0         0.0   \n",
       "\n",
       "             Sex_male  Embarked_C  Embarked_Q        ...          \\\n",
       "PassengerId                                          ...           \n",
       "1                 1.0         0.0         0.0        ...           \n",
       "2                 0.0         1.0         0.0        ...           \n",
       "3                 0.0         0.0         0.0        ...           \n",
       "4                 0.0         0.0         0.0        ...           \n",
       "5                 1.0         0.0         0.0        ...           \n",
       "\n",
       "             TicketPrefix_SOTON/O.Q.  TicketPrefix_SOTON/O2  \\\n",
       "PassengerId                                                   \n",
       "1                                0.0                    0.0   \n",
       "2                                0.0                    0.0   \n",
       "3                                0.0                    0.0   \n",
       "4                                0.0                    0.0   \n",
       "5                                0.0                    0.0   \n",
       "\n",
       "             TicketPrefix_SOTON/OQ  TicketPrefix_STON/O  \\\n",
       "PassengerId                                               \n",
       "1                              0.0                  0.0   \n",
       "2                              0.0                  0.0   \n",
       "3                              0.0                  0.0   \n",
       "4                              0.0                  0.0   \n",
       "5                              0.0                  0.0   \n",
       "\n",
       "             TicketPrefix_STON/O2.  TicketPrefix_SW/PP  TicketPrefix_W./C.  \\\n",
       "PassengerId                                                                  \n",
       "1                              0.0                 0.0                 0.0   \n",
       "2                              0.0                 0.0                 0.0   \n",
       "3                              1.0                 0.0                 0.0   \n",
       "4                              0.0                 0.0                 0.0   \n",
       "5                              0.0                 0.0                 0.0   \n",
       "\n",
       "             TicketPrefix_W.E.P.  TicketPrefix_W/C  TicketPrefix_WE/P  \n",
       "PassengerId                                                            \n",
       "1                            0.0               0.0                0.0  \n",
       "2                            0.0               0.0                0.0  \n",
       "3                            0.0               0.0                0.0  \n",
       "4                            0.0               0.0                0.0  \n",
       "5                            0.0               0.0                0.0  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index(['PassengerId'])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Selection\n",
    "\n",
    "To select features we will correlate each feature against Survived. We need to use different algorithms for the different data types:\n",
    "\n",
    "- Spearman-Rank correlation for nominal vs nominal data\n",
    "- Point-Biserial correlation for nominal vs continuous data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Put the column names into a list called columns\n",
    "columns = df.columns.values\n",
    "\n",
    "# initiate lists to store results\n",
    "param = []\n",
    "correlation = []\n",
    "abs_corr=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64\n",
      "Survived\n",
      "Age\n",
      "SibSp\n",
      "Parch\n",
      "Fare\n",
      "FamilySize\n",
      "Sex_female\n",
      "Sex_male\n",
      "Embarked_C\n",
      "Embarked_Q\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(columns))\n",
    "#[print(type(df[c])) for c in columns][0:10]\n",
    "[print(\"%s\"%c) for c in columns[0:10]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for c in columns:\n",
    "    # Check if binary or continuous\n",
    "    if len(df[c].unique())<=2:\n",
    "        corr = spearmanr(df['Survived'], df[c])[0]   # apply spearmanr if\n",
    "    else:\n",
    "        corr = pointbiserialr(df['Survived'], df[c])[0]\n",
    "    \n",
    "    param.append(c) # name of feature\n",
    "    correlation.append(corr) # associated correlation\n",
    "    abs_corr.append(abs(corr))# get rid of -ve values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0, -0.062899950141403885, -0.035322498885735583, 0.081629407083483485, 0.25730652238496243]\n",
      "[1.0, 0.062899950141403885, 0.035322498885735583, 0.081629407083483485, 0.25730652238496243]\n"
     ]
    }
   ],
   "source": [
    "print(correlation[0:5])\n",
    "print(abs_corr[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>abs_corr</th>\n",
       "      <th>correlation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parameter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title_Mr</th>\n",
       "      <td>0.549199</td>\n",
       "      <td>-0.549199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_male</th>\n",
       "      <td>0.543351</td>\n",
       "      <td>-0.543351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_female</th>\n",
       "      <td>0.543351</td>\n",
       "      <td>0.543351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title_Mrs</th>\n",
       "      <td>0.344935</td>\n",
       "      <td>0.344935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title_Miss</th>\n",
       "      <td>0.332795</td>\n",
       "      <td>0.332795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_3</th>\n",
       "      <td>0.322308</td>\n",
       "      <td>-0.322308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_1</th>\n",
       "      <td>0.285904</td>\n",
       "      <td>0.285904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.257307</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_C</th>\n",
       "      <td>0.168240</td>\n",
       "      <td>0.168240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            abs_corr  correlation\n",
       "parameter                        \n",
       "Survived    1.000000     1.000000\n",
       "Title_Mr    0.549199    -0.549199\n",
       "Sex_male    0.543351    -0.543351\n",
       "Sex_female  0.543351     0.543351\n",
       "Title_Mrs   0.344935     0.344935\n",
       "Title_Miss  0.332795     0.332795\n",
       "Pclass_3    0.322308    -0.322308\n",
       "Pclass_1    0.285904     0.285904\n",
       "Fare        0.257307     0.257307\n",
       "Embarked_C  0.168240     0.168240"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dataFrame for visualisation\n",
    "param_df = pd.DataFrame({'correlation':correlation, 'parameter':param, 'abs_corr':abs_corr})\n",
    "\n",
    "# Sort by absolute correlation\n",
    "param_df = param_df.sort_values(by=['abs_corr'], ascending=False)\n",
    "\n",
    "# Set parameter name as index\n",
    "param_df = param_df.set_index('parameter')\n",
    "\n",
    "print(len(param_df))\n",
    "param_df[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived                   0\n",
      "Age                        0\n",
      "SibSp                      0\n",
      "Parch                      0\n",
      "Fare                       0\n",
      "FamilySize                 0\n",
      "Sex_female                 0\n",
      "Sex_male                   0\n",
      "Embarked_C                 0\n",
      "Embarked_Q                 0\n",
      "Embarked_S                 0\n",
      "Pclass_1                   0\n",
      "Pclass_2                   0\n",
      "Pclass_3                   0\n",
      "Title_Master               0\n",
      "Title_Miss                 0\n",
      "Title_Mr                   0\n",
      "Title_Mrs                  0\n",
      "Title_Officer              0\n",
      "Title_Royalty              0\n",
      "TicketPrefix_A./5.         0\n",
      "TicketPrefix_A.5.          0\n",
      "TicketPrefix_A/4           0\n",
      "TicketPrefix_A/4.          0\n",
      "TicketPrefix_A/5           0\n",
      "TicketPrefix_A/5.          0\n",
      "TicketPrefix_A/S           0\n",
      "TicketPrefix_A4.           0\n",
      "TicketPrefix_C             0\n",
      "TicketPrefix_C.A.          0\n",
      "                          ..\n",
      "TicketPrefix_F.C.C.        0\n",
      "TicketPrefix_Fa            0\n",
      "TicketPrefix_LINE          0\n",
      "TicketPrefix_NoClue        0\n",
      "TicketPrefix_P/PP          0\n",
      "TicketPrefix_PC            0\n",
      "TicketPrefix_PP            0\n",
      "TicketPrefix_S.C./A.4.     0\n",
      "TicketPrefix_S.C./PARIS    0\n",
      "TicketPrefix_S.O./P.P.     0\n",
      "TicketPrefix_S.O.C.        0\n",
      "TicketPrefix_S.O.P.        0\n",
      "TicketPrefix_S.P.          0\n",
      "TicketPrefix_S.W./PP       0\n",
      "TicketPrefix_SC            0\n",
      "TicketPrefix_SC/AH         0\n",
      "TicketPrefix_SC/PARIS      0\n",
      "TicketPrefix_SC/Paris      0\n",
      "TicketPrefix_SCO/W         0\n",
      "TicketPrefix_SO/C          0\n",
      "TicketPrefix_SOTON/O.Q.    0\n",
      "TicketPrefix_SOTON/O2      0\n",
      "TicketPrefix_SOTON/OQ      0\n",
      "TicketPrefix_STON/O        0\n",
      "TicketPrefix_STON/O2.      0\n",
      "TicketPrefix_SW/PP         0\n",
      "TicketPrefix_W./C.         0\n",
      "TicketPrefix_W.E.P.        0\n",
      "TicketPrefix_W/C           0\n",
      "TicketPrefix_WE/P          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our correlation, we can use the Decision Tree classifier to see the score against feature space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scoresCV = []\n",
    "scores = []\n",
    "\n",
    "# Loop over each of the 64 different parameters\n",
    "for i in range(1, len(param_df)):\n",
    "    \n",
    "    # Make a new dataframe for each feature(selected by df[])\n",
    "    new_df = df[param_df.index[0:i+1].values]\n",
    "    \n",
    "    # .ix supports mixed labels and integer. It can do what .loc and .iloc. both do.\n",
    "    # X is abs_corr\n",
    "    X = new_df.ix[:,1::]\n",
    "    #print(X)\n",
    "    \n",
    "    # y is corr\n",
    "    y = new_df.ix[:,0]\n",
    "    \n",
    "    # initialise a DecisionTreeClassifier object\n",
    "    clf = DecisionTreeClassifier()\n",
    "    \n",
    "    # cross_val_score, returns: Array of scores of the estimator for each run of the cross validation.\n",
    "    # (estimators, data-to-fit, target-variable-to-predict, cv(determines the cross-validation splitting strategy))\n",
    "    scoreCV = sklearn.cross_validation.cross_val_score(clf, X, y, cv = 10)\n",
    "    \n",
    "    # save the mean of all the runs\n",
    "    scores.append(np.mean(scoreCV))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Survived']\n",
      "['Survived', 'Title_Mr', 'Sex_male']\n",
      "63\n"
     ]
    }
   ],
   "source": [
    "new_df = df[param_df.index[0:1].values]\n",
    "print(list(new_df))\n",
    "new_df = df[param_df.index[0:3].values]\n",
    "print(list(new_df))\n",
    "print(len(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the scores for different numbers of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wtIhUjfBYShKvgz9KrCpVgNq1gdxcr1tCRERE5L4ZM4C//c1+Ftk1+pcOkjny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\nHXHfffG1MSg++QQ47TQGfuURsQmSjjgCOP10e/1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd65c091ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(range(1,len(scores)+1), scores, '.-')\n",
    "plt.axis(\"tight\")\n",
    "plt.title('Feature Selection', fontsize=14)\n",
    "plt.xlabel('# Features', fontsize=12)\n",
    "plt.ylabel('Score', fontsize=12)\n",
    "plt.grid();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does the cross validation score not depend on WHICH features are selected? We put the parameters in order of most highly correlated to lowest. \n",
    "\n",
    "Based on the plot, a feature space of 10 dimensions (the top 10 most highly correlating features) provides the most reliable result while avoiding overfit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features:\t ['Title_Mr' 'Sex_male' 'Sex_female' 'Title_Mrs' 'Title_Miss' 'Pclass_3'\n",
      " 'Pclass_1' 'Fare' 'Embarked_C' 'Embarked_S']\n"
     ]
    }
   ],
   "source": [
    "best_features=param_df.index[1:10+1].values\n",
    "print('Best features:\\t',best_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at them in histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fd65c09f588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd655752ba8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd65571bef0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fd6556d5a90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd6556a0cc0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd65c09bc88>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fd65dbb3f28>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd65dbaf5f8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd655651ba8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fd655682dd8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd65562e908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd6555f9320>]], dtype=object)"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OPCRDPcaMe1iGGre0p/p27PQpnj7FAsbTd2PfDU2SpD30euDc9i42XwVeDTwMuCDJqbRT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\nPCXSMP8AbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd65db747f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[best_features].hist(figsize=(20,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}