homework/Day_028_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業 : (Kaggle)鐵達尼生存預測\n",
    "https://www.kaggle.com/c/titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業目標]\n",
    "- 試著調整特徵篩選的門檻值, 觀察會有什麼影響效果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 調整相關係數過濾法的篩選門檻, 看看篩選結果的影響 (In[5]~In[8], Out[5]~Out[8])\n",
    "- 調整L1 嵌入法篩選門檻, 看看篩選結果的影響 (In[9]~In[11], Out[9]~Out[11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-20T01:57:07.627905Z",
     "start_time": "2019-05-20T01:57:06.309680Z"
    }
   },
   "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>Survived</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>0</td>\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>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>1</td>\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>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>0</td>\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": [
       "   Survived  Pclass                                               Name  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2         1       3                             Heikkinen, Miss. Laina   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4         0       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      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.linear_model import LogisticRegression\n",
    "\n",
    "data_path = 'data/'\n",
    "df = pd.read_csv(data_path + 'titanic_train.csv')\n",
    "\n",
    "train_Y = df['Survived']\n",
    "df = df.drop(['PassengerId'] , axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-20T02:03:10.688785Z",
     "start_time": "2019-05-20T02:03:10.680760Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "5      0\n",
       "6      0\n",
       "7      0\n",
       "8      1\n",
       "9      1\n",
       "10     1\n",
       "11     1\n",
       "12     0\n",
       "13     0\n",
       "14     0\n",
       "15     1\n",
       "16     0\n",
       "17     1\n",
       "18     0\n",
       "19     1\n",
       "20     0\n",
       "21     1\n",
       "22     1\n",
       "23     1\n",
       "24     0\n",
       "25     1\n",
       "26     0\n",
       "27     0\n",
       "28     1\n",
       "29     0\n",
       "      ..\n",
       "861    0\n",
       "862    1\n",
       "863    0\n",
       "864    0\n",
       "865    1\n",
       "866    1\n",
       "867    0\n",
       "868    0\n",
       "869    1\n",
       "870    0\n",
       "871    1\n",
       "872    0\n",
       "873    0\n",
       "874    1\n",
       "875    1\n",
       "876    0\n",
       "877    0\n",
       "878    0\n",
       "879    1\n",
       "880    1\n",
       "881    0\n",
       "882    0\n",
       "883    0\n",
       "884    0\n",
       "885    0\n",
       "886    0\n",
       "887    1\n",
       "888    0\n",
       "889    1\n",
       "890    0\n",
       "Name: Survived, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-20T01:57:32.692085Z",
     "start_time": "2019-05-20T01:57:32.687837Z"
    }
   },
   "outputs": [],
   "source": [
    "# 計算df整體相關係數, 並繪製成熱圖\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-20T01:57:36.407926Z",
     "start_time": "2019-05-20T01:57:36.142417Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1bE1Z0nRJu6T5tSTdRNZifkrSB1sRoJlZLfpqmMqo2om+w4GH0/zRaftNgfcD/1FgXGZmdelV/qmMqpUv3qgoU3wE+FFE9AIPSvLVgGZWOmVtAedVraX8uqTdJG0K7Af8uuKxdYoLy8ysPp1evqjW2j0RuJasZPGfEbEEQNL/AH5fcGxmZjXr8Fv0Dd9Sjoi7I2KXiBgXEWdUrJ8TEUcWH56ZWW2a2VKWdICkhyU9IumUQR5fS9JV6fG7JW3baPx5x74YJ+nbku6VdI+kc9Ol1mZmpdJbwzQcST3ABcBHgYnAkZImDtjsGOD5iNgR+E/grEbjz3uZ9ZXA08ChZAMSPQ1c1ejOzcyarYmXWe8NPBIRj0bEG2R58OAB2xwMzErz1wIfkBob6ShvUt44Is6IiCVp+hqwYSM7NjMrQhPLF1vx1jF+lqZ1g24TESuBF4GGqgh5k/LNko6QNCpNU4FfNrJjM7Mi1JKUJU2TNL9imlbxUoO1eAdeyZxnm5oM2/tC0stpBwJOAi5PD/UAy4F/b2TnZmbNVktGjIiZwMwhHl4KjK9Y3hpYNsQ2S9O1GxsAz9UQwmqGTcoR4QGHzKyjNHHsi3nABEnbkY2UeQTwyQHbzCa72vlOsvNtNzU6LlC1lvIuEfGQpHcO9nhE3NvIzs3Mmq1Zg9xHxEpJJwDXk1UHLo2IxZJmAPMjYjZwCXC5pEfIWshHNLrfahePnER2B5GzK2OtmN+/2g62X9FZPbmf62l3BLXbZtQG7Q6hZi8d/dl2h1CT9Wd9v90h1Gz9SSe3O4S26Gvi4J0RMQeYM2Dd9Ir514BPNG2HVD/Rd7GkzSNiv4jYD/gBWS35frKmuplZqXT6ZdbVkvJ3gDcAJP134OtkffJeZOjiuJlZ20QNUxlVK1/0RET/mcTDgZkRcR1wnaQFxYZmZla7sraA86qalCWtkTpFf4B0h+qczzUza7mVKmsbOJ9qifVHwK2SngFeBW4HkLQjWQnDzKxUOjslV++nfKakG4EtgF9X9L8bBfxz0cGZmdWq28sXRMRdg6z7QzHhmJk1ppld4trBdWEz6yqdnZKdlM2sy3R9+cLMrJP0dnhb2UnZzLqKW8pmZiUSbimbmZWHW8pmZiXiLnFmZiXS2SnZSdnMuszKDk/LuZOypK2AbSqfExG3FRGUmVm9RsSJPklnkQ3d+QBv3m0lgEGTcroj7DSAz22wN/uvM6HxSM3MchgpJ/o+BuwcEa/n2bjyDrFXbPmpzv7aMrOOMiJaysCjwJpArqRsZtYuXd1SlnQeWZnib8CCNIznqsQcEV8oNjwzs9r0Rne3lOen/+8BZhcci5lZw7q6n3JEzAKQtC7wWkT0puUeYK3iwzMzq02n15Sr3c26343A2hXLawO/aX44ZmaN6athKqO8J/rGRMTy/oWIWC5pnYJiMjOrW6eXL/K2lF+R9M7+BUnvIruRqplZqUQN/8oob0v5ROAaScvS8hZkF5OYmZVKt/e+QNIoYDSwC7AzIOChiFhRcGxmZjXr9PJFnrtZ90k6OyL2Be5vQUxmZnUr6wm8vPLWlH8t6VBJKjQaM7MGjZSa8knAusBKSa+RlTAiItYvLDIzszp0ffkCICLGFh2ImVkzRLef6OsnaSNgAjCmf53HUzazsukdCS1lSceSdYvbGlgAvAe4E9i/uNDMzGrXqvKFpI2Bq4BtgT8DUyPi+QHbbAP8GOghG2nzvIj4znCvm/dE34nAu4G/RMR+wF7A0zXEb2bWEhGRe2rQKcCNETGBbCiKUwbZ5gngvRGxJ7APcIqkLYd70bzli9ci4jVJSForIh6StHOeJ05a68WcuyiHvR6/t90h1OyKcVPaHULN7p0/ut0h1GT9SSe3O4SavfO+b7U7hLZo4Ym+g4EpaX4WcAvwb5UbRMQbFYtrkaMhnDcpL5W0IfBT4AZJzwPLqjzHzKzlWtjVbbOIeAIgIp6Q9LbBNpI0HvglsCPwpYgYNnfm7X3x8TR7mqSbgQ2AX+WN3MysVWq5zLryfqLJzHQ7u/7HfwNsPshTv5J3HxHxGDAplS1+KunaiHhqqO2r3XlkDHAcWYZfBFwSEbfmDcbMrNVqKV9U3k90iMc/ONRjkp6StEVqJW8B/LXKvpZJWgz8HXDtUNtVq2/MAiaTJeSPAmdX2d7MrK36iNxTg2YDR6f5o4GfDdxA0taS1k7zGwHvAx4e7kWrlS8mRsTu6QUvAebWGLSZWUu18OKRbwBXSzoG+C/gEwCSJgPHRcSxwDuAsyUF2ZXQ34qIRcO9aLWkvGokuIhY6aEvzKzsWtX7IiKeBT4wyPr5wLFp/gZgUi2vWy0p7yHppTQvYO207LEvzKyUyjrQUF7Vbpza06pAzMyaoTc6e/DO3GNfmJl1ghEzIJGZWScYEUN3mpl1iq6uKZuZdZo+ly/MzMrDLWUzsxJx7wszsxJx+cLMrEQ6vXyR684jynxK0vS0/HZJexcbmplZ7foick9llPd2UBcC+wJHpuWXgQsKicjMrAFRw78yypuU94mI44HXANLNAYe8n4+kaZLmS5p/7ct/aUKYZmb59EZv7qmM8taUV0jqgeyrRdKmwJCnOCsHjl603T+U8+vIzLrSSLnM+tvAT4C3SToTOAw4tbCozMzqNCIus46IKyTdQzZ2qICPRcSDhUZmZlaHrm8pSxoF3BcRuwEPFR+SmVn9ytqrIq+qJ/oiog9YKOntLYjHzKwhnd77Im9NeQtgsaS5wCv9KyPioEKiMjOr00i5zPr0QqMwM2uSrq8pA0TErUUHYmbWDF1fUwaQ9B5J8yQtl/SGpN6KG6qamZVGROSeyihv+eJ84AjgGmAy8GlgQlFBmZnVa0T0UwaIiEck9UREL/B9Sb8rMC4zs7qUtQWcV96k/DdJo4EFkr4JPAGsW1xYZmb16fTeF3kHJPrHtO0JZF3ixgOHFhWUmVm9On3ozmFbypLeHhH/FRH9Q729hrvHmVmJdXr5olpL+af9M5KuKzgWM7OGdfsVfaqY377IQMzMmqHTW8rVknIMMW9mVkplrRXnpeG+VST1kp3YE7A28Lf+h4CIiPULj3AYkqalAfU7QqfFC50Xc6fFC47Z3mrYpFx2kuZHxOR2x5FXp8ULnRdzp8ULjtneKm+XODMzawEnZTOzEun0pNxpNa1Oixc6L+ZOixccs1Xo6JqymVm36fSWsplZV2lZUpb0FUmLJd0naYGkfZrwmgdJOqVJ8S2vYdve9B7ul3SNpHWG2fY0SSc3I8YiSPq4pJC0S7tjGcxgx42kiyVNTI8P+nNLY4DfnZ7zoKTTWhRv7mOjhtf8jKTzmxFfjn31x98/bduK/dqbcg/d2QhJ+wJ/D7wzIl6XtAkwOudz14iIlYM9FhGzgdnNizS3VyNiTwBJVwDHAee0IY5mOBL4Ldl42ae1N5S3Guq4iYhjczx9FjA1IhZK6gF2LjLWCnUfGxVD47bTqvhrUZLYu0KrWspbAM9ExOsAEfFMRCyT9Of0i4akyZJuSfOnSZop6dfAZanFs2v/i0m6RdK7+lsQkjZIrzUqPb6OpMckrSlpB0m/knSPpNv7W4SStpN0Z7qjyhkNvLfbgR3Ta346tegWSrp84IaSPp/2t1DSdf2tKEmfSC2rhZJuS+t2lTQ3tVbuk9T0mwpIWg94H3AMWVJG0ihJF6bW6S8kzZF0WHrsXZJuTZ/l9ZK2aHZMAwx13NwiaVUfWUlnS7pX0o2SNk2r30Y2xCwR0RsRD6RtT5N0uaSbJP1R0ucLjL/y2Php+twWS5pWEftySTMk3Q3sK+ndkn6XjoW5ksamTbdMx/EflQ2f2zKStk2/O/em6b1p/RRJN0v6IbAorftUxXH73fSFaLWo5dYp9U7AesAC4A/AhcD70/o/A5uk+cnALWn+NOAeYO20/EXg9DS/BfCHNP8Z4Pw0/zNgvzR/OHBxmr8RmJDm9wFuSvOzgU+n+eOB5TW8n+Xp/zXSfv8nsCvwcMX72bjivZyc5sdVvMbXgH9O84uArdL8hun/84Cj0vzo/s+iyT+XTwGXpPnfAe8EDgPmkH1hbw48n9atmbbZtOIzvrRNx80twOQ0HxWf0/SK42F6iv0nwD8BYyp+HgvJrlDdBHgM2LKJMa92bAw4HtYG7u8/FlL8Uyt+zo8C707L66fX+UxavwEwBvgLML6gz7w3feYLgJ+kdetUfH4TgPlpfgrZFb/bpeV3AD8H1kzLF5J+xzzln1pSvoiI5ZLeBfwdsB9wlarXgmdHxKtp/mrgBuDfgalkt6Ua6CqyRHEzWavvwtQSfC9wjbRqbKW10v/v480xoS8HzqrhLa0taUGavx24hOwX/9qIeAYgIp4b5Hm7SfoasCFZwrk+rb8D+IGkq4Efp3V3Al+RtDXw44j4Yw3x5XUk8H/S/JVpeU3gmojoA56UdHN6fGdgN+CG9Fn2kFqiRcl53PSR/ewB/i/p84uIGal88GHgk2TvbUra7mfp2Ho1vb+9qRgRsUGDHRsAX5D08TQ/niy5PUuWBPtHYNwZeCIi5qX38BJA+rxvjIgX0/IDwDZkXyjNNlj5Yk3gfEl7pnh3qnhsbkQsSfMfAN4FzEsxrw38tYAYu1pLkjJkf0KStXBukbQIOBpYyZsllDEDnvJKxXMfl/SspElkifefBtnFbODrkjYmOzBuIrs7yguDHGSrXrrOt7PagavsKKz2ej8APhZZnfMzpCQREccpO/F5INndXfaMiB+mP2kPBK6XdGxE3FRnvKuRNA7Yn+yLIsiSbJC1LAd9CrA4IvZtVgx5DHHcDPuUiuf+CbhI0veAp9N7fss2Qyw3YrBjYwrwQWDfiPibsjJd//H+WrxZix3uGHq9Yr6XFv7ukv2l+hSwB9nv62sVj71SMS9gVkR8uYWxdZ2W1JQl7TygJron2Z9gfyZLoFD9TiZXAv8KbBARiwY+GBHLgbnAucAvIqsjvgQskfSJFIck7ZGecgepjgocVfu7Ws2NwNT+X/z05TDQWOAJSWtW7lPSDhFxd0RMB54BxkvaHng0Ir5N9oUzqQkxVjoMuCwitomIbSNiPLAk7f/QVFvejDdblw8Dmyo7+Yayev2ug71wswxz3FQald4LZC3i36bnHqg3/zyaQJbIXkjLB0sak35WU4B5BYRfaQPg+ZSQdwHeM8R2D5HVjt8NIGmspFYm36FsQNaC7yO7C9FQdeIbgcMkvQ2y3wFJ27Qoxq7RqhN96wGzJD0g6T5gIllt73TgXEm3k/3SDOdasiR69TDbXEVWJ72qYt1RwDGSFgKLgYPT+hOB4yXNIzvoGhIRi4EzgVvTvgY74/5V4G6yUsxDFev/t6RFku4HbiOreR4O3J/+FN4FuKzRGAc4ktVbxdcBWwJLyeqe303xvhgRb5Alv7PS+1tAVhoq0lDHTaVXgF0l3UPW8p+R1v8j8HD6/C4nqzv3H2NzgV8CdwFnRMSyYt8GvwLWSO/hjLTf1aTP+HDgvPQZ38Dqf0G2w4XA0ZLuIitdvDLYRpGdTD0V+HV6rzeQnQOyGviKPluNpPVSPXccWQJ7X0Q82e64mkFZf+XlEfGtdsdiNpgy/Glk5fMLSRuS9QY4o1sSslkncEvZzKxEPPaFmVmJOCmbmZWIk7KZWYk4KZuZlYiTsplZiTgpm5mVyP8HR2Lo51OxrKQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "sns.heatmap(corr)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-20T01:58:54.377203Z",
     "start_time": "2019-05-20T01:58:54.353151Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 Numeric Features : ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']\n",
      "\n"
     ]
    },
    {
     "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>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age  SibSp  Parch     Fare\n",
       "0       3  22.0      1      0   7.2500\n",
       "1       1  38.0      1      0  71.2833\n",
       "2       3  26.0      0      0   7.9250\n",
       "3       1  35.0      1      0  53.1000\n",
       "4       3  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 記得刪除 Survived\n",
    "df = df.drop(['Survived'] , axis = 1)\n",
    "\n",
    "#只取 int64, float64 兩種數值型欄位, 存於 num_features 中\n",
    "num_features = []\n",
    "\n",
    "for dtype, feature in zip(df.dtypes, df.columns):\n",
    "    if dtype == 'float64' or dtype == 'int64':\n",
    "        num_features.append(feature)\n",
    "\n",
    "print(f'{len(num_features)} Numeric Features : {num_features}\\n')\n",
    "\n",
    "# 削減文字型欄位, 只剩數值型欄位\n",
    "df = df[num_features]\n",
    "df = df.fillna(-1)\n",
    "MMEncoder = MinMaxScaler()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業1\n",
    "* 鐵達尼生存率預測中，試著變更兩種以上的相關係數門檻值，觀察預測能力是否提升?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-20T01:59:10.812734Z",
     "start_time": "2019-05-20T01:59:10.769752Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7038635542329971"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 原始特徵 + 邏輯斯迴歸\n",
    "train_X = MMEncoder.fit_transform(df)\n",
    "estimator = LogisticRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv = 5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-20T02:04:45.749476Z",
     "start_time": "2019-05-20T02:04:45.741868Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Pclass', 'Fare']\n"
     ]
    }
   ],
   "source": [
    "# 篩選相關係數1\n",
    "# 篩選相關係數大於 0.1 或小於 -0.1 的特徵\n",
    "high_list = list(corr[(corr['Survived'] > 0.1) | (corr['Survived'] < -0.1)].index)\n",
    "high_list.pop(0)\n",
    "print(high_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-20T02:04:46.557769Z",
     "start_time": "2019-05-20T02:04:46.517176Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6791567235397566"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特徵1 + 邏輯斯迴歸\n",
    "train_X = MMEncoder.fit_transform(df[high_list])\n",
    "cross_val_score(estimator, train_X, train_Y, cv = 5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-20T02:05:06.708720Z",
     "start_time": "2019-05-20T02:05:06.701104Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']\n"
     ]
    }
   ],
   "source": [
    "# 篩選相關係數2\n",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "high_list = list(corr[(corr['Survived'] > 0.01) | (corr['Survived'] < -0.01)].index)\n",
    "high_list.pop(0)\n",
    "print(high_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-20T02:05:07.560447Z",
     "start_time": "2019-05-20T02:05:07.521021Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7038635542329971"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特徵2 + 邏輯斯迴歸\n",
    "train_X = MMEncoder.fit_transform(df[high_list])\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作業2\n",
    "* 續上題，使用 L1 Embedding 做特徵選擇(自訂門檻)，觀察預測能力是否提升?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-20T02:06:26.816312Z",
     "start_time": "2019-05-20T02:06:26.812442Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\"\"\"\n",
    "Your Code Here, select parameter alpha \n",
    "\"\"\"\n",
    "L1_Reg = Lasso(alpha = 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-20T02:06:27.668113Z",
     "start_time": "2019-05-20T02:06:27.653681Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-0.39684291, -0.25338753, -0.19144856,  0.28161304,  0.28188094])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X = MMEncoder.fit_transform(df)\n",
    "L1_Reg.fit(train_X, train_Y)\n",
    "L1_Reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-20T02:06:29.915294Z",
     "start_time": "2019-05-20T02:06:29.905570Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import compress\n",
    "L1_mask = list((L1_Reg.coef_ > 0) | (L1_Reg.coef_ < 0))\n",
    "L1_list = list(compress(list(df), list(L1_mask)))\n",
    "L1_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-20T02:06:30.713959Z",
     "start_time": "2019-05-20T02:06:30.681831Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7038635542329971"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# L1_Embedding 特徵 + 線性迴歸\n",
    "train_X = MMEncoder.fit_transform(df[L1_list])\n",
    "cross_val_score(estimator, train_X, train_Y, cv = 5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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