Day_031_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業 : (Kaggle)鐵達尼生存預測"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業目標]\n",
    "- 試著模仿範例寫法, 在鐵達尼生存預測中, 練習特徵重要性的寫作與觀察"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 仿造範例, 完成特徵重要性的計算, 並觀察對預測結果的影響 (In[3]~[5], Out[3]~[5]) \n",
    "- 仿造範例, 將兩個特徵重要性最高的特徵重組出新特徵, 並觀察對預測結果的影響 (In[8], Out[8]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass                                               Name     Sex   Age  \\\n",
       "0       3                            Braund, Mr. Owen Harris    male  22.0   \n",
       "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "2       3                             Heikkinen, Miss. Laina  female  26.0   \n",
       "3       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "4       3                           Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "   SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1      1      0          PC 17599  71.2833   C85        C  \n",
       "2      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      1      0            113803  53.1000  C123        S  \n",
       "4      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 做完特徵工程前的所有準備 (與前範例相同)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import copy\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "data_path = 'C:/Users/user/OneDrive/cupoy/Machine Learning/教材/data/'\n",
    "df = pd.read_csv(data_path + 'titanic_train.csv')\n",
    "\n",
    "train_Y = df['Survived']\n",
    "df = df.drop(['PassengerId', 'Survived'] , axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.121348</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.283951</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.769118</td>\n",
       "      <td>0.014151</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.213483</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.481481</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.876471</td>\n",
       "      <td>0.139136</td>\n",
       "      <td>0.557823</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.396629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.983824</td>\n",
       "      <td>0.015469</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.305618</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.072059</td>\n",
       "      <td>0.103644</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.016854</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.694118</td>\n",
       "      <td>0.015713</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass      Name  Sex       Age  SibSp  Parch    Ticket      Fare  \\\n",
       "0     1.0  0.121348  1.0  0.283951  0.125    0.0  0.769118  0.014151   \n",
       "1     0.0  0.213483  0.0  0.481481  0.125    0.0  0.876471  0.139136   \n",
       "2     1.0  0.396629  0.0  0.333333  0.000    0.0  0.983824  0.015469   \n",
       "3     0.0  0.305618  0.0  0.444444  0.125    0.0  0.072059  0.103644   \n",
       "4     1.0  0.016854  1.0  0.444444  0.000    0.0  0.694118  0.015713   \n",
       "\n",
       "      Cabin  Embarked  \n",
       "0  0.000000  1.000000  \n",
       "1  0.557823  0.333333  \n",
       "2  0.000000  1.000000  \n",
       "3  0.380952  1.000000  \n",
       "4  0.000000  1.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 因為需要把類別型與數值型特徵都加入, 故使用最簡版的特徵工程\n",
    "LEncoder = LabelEncoder()\n",
    "MMEncoder = MinMaxScaler()\n",
    "for c in df.columns:\n",
    "    df[c] = df[c].fillna(-1)\n",
    "    if df[c].dtype == 'object':\n",
    "        df[c] = LEncoder.fit_transform(list(df[c].values))\n",
    "    df[c] = MMEncoder.fit_transform(df[c].values.reshape(-1, 1))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex         0.254448\n",
       "Ticket      0.155346\n",
       "Name        0.138297\n",
       "Fare        0.132174\n",
       "Age         0.106977\n",
       "Pclass      0.072771\n",
       "Cabin       0.071077\n",
       "SibSp       0.028818\n",
       "Embarked    0.020480\n",
       "Parch       0.019612\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 隨機森林擬合後, 將結果依照重要性由高到低排序\n",
    "estimator = RandomForestClassifier()\n",
    "estimator.fit(df.values, train_Y)\n",
    "feats = pd.Series(data=estimator.feature_importances_, index=df.columns)\n",
    "feats = feats.sort_values(ascending=False)\n",
    "feats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 先用隨機森林對鐵達尼生存預測做訓練，再用其特徵重要性回答下列問題\n",
    "\n",
    "# 作業1\n",
    "* 將特徵重要性較低的一半特徵刪除後，再做生存率預估，正確率是否有變化?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8136880196979417"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 高重要性特徵 + 隨機森林\n",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "high_feature=list(feats[:5].index)\n",
    "train_X = MMEncoder.fit_transform(df[high_feature])\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8058605135777986"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 原始特徵 + 隨機森林\n",
    "train_X = MMEncoder.fit_transform(df)\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作業2\n",
    "* 將特徵重要性最高的兩個特徵做特徵組合，是否能再進一步提升預測力?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 觀察重要特徵與目標的分布\n",
    "# 第一名              \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.regplot(x=train_Y, y=df['Sex'], fit_reg=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第二名       \n",
    "sns.regplot(x=train_Y, y=df['Ticket'], fit_reg=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8227523922906232"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 製作新特徵看效果\n",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "df['Add_char'] = (df['Sex'] + df['Ticket']) / 2\n",
    "df['Multi_char'] = df['Sex'] * df['Ticket']\n",
    "df['GO_div1p'] = df['Sex'] / (df['Ticket']+1) * 2\n",
    "df['OG_div1p'] = df['Ticket'] / (df['Sex']+1) * 2\n",
    "train_X = MMEncoder.fit_transform(df)\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}