tb5_kaggle_book_ch5-3-2_5-3-3.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# warningsを無視する\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.3.2 連続変数の離散化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(\"./input/train.csv\")\n",
    "df_test = pd.read_csv(\"./input/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "% matplotlib inline\n",
    "\n",
    "# 日本語表示用 ##カーネルでは日本語表示できない\n",
    "plt.rcParams[\"font.size\"] = 18\n",
    "plt.rcParams['font.family'] = 'IPAPGothic' \n",
    "\n",
    "# サイズの設定\n",
    "plt.rcParams['figure.figsize'] = (8.0, 6.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Embarkedの補完\n",
    "df_train.loc[df_train['PassengerId'].isin([62, 830]), 'Embarked'] = 'C'\n",
    "\n",
    "# Fareの補完\n",
    "df_test.loc[df_test['PassengerId'] == 1044, 'Fare'] = 13.675550\n",
    "\n",
    "#Age変換のための関数\n",
    "def impute_age(cols):\n",
    "    Age = cols[0]\n",
    "    Pclass = cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):        \n",
    "        if Pclass == 1:\n",
    "            return 39\n",
    "        elif Pclass == 2:\n",
    "            return 30\n",
    "        else:\n",
    "            return 25    \n",
    "    else:\n",
    "        return Age\n",
    "\n",
    "\n",
    "data = [df_train, df_test]\n",
    "for df in data:\n",
    "    # Ageの補完\n",
    "    df['Age'] = df[['Age','Pclass']].apply(impute_age, axis = 1) \n",
    "\n",
    "    # 性別の変換\n",
    "    df['Sex'] = df['Sex'].map({\"male\": 0, \"female\": 1})\n",
    "        \n",
    "    # Embarked\n",
    "    df['Embarked'] = df['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "\n",
    "df_train = pd.get_dummies(df_train, columns = ['Embarked'])\n",
    "df_test = pd.get_dummies(df_test, columns = ['Embarked'])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [df_train, df_test]\n",
    "for df in data:    \n",
    "    # Fareのカテゴリ変数化\n",
    "    df.loc[ df['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    df.loc[(df['Fare'] > 7.91) & (df['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    df.loc[(df['Fare'] > 14.454) & (df['Fare'] <= 31), 'Fare']   = 2\n",
    "    df.loc[ df['Fare'] > 31, 'Fare'] = 3\n",
    "    df['Fare'] = df['Fare'].astype(int)\n",
    "\n",
    "    # Ageのカテゴリ変数化\n",
    "    df.loc[ df['Age'] <= 16, 'Age'] = 0\n",
    "    df.loc[(df['Age'] > 16) & (df['Age'] <= 32), 'Age'] = 1\n",
    "    df.loc[(df['Age'] > 32) & (df['Age'] <= 48), 'Age'] = 2\n",
    "    df.loc[ df['Age'] > 48, 'Age']  = 3\n",
    "    df['Age'] = df['Age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.drop(['Name', 'Cabin', 'Ticket'], axis=1, inplace=True)\n",
    "df_test.drop(['Name', 'Cabin', 'Ticket'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df_train.drop([\"PassengerId\", \"Survived\"], axis=1) # 不要な列を削除\n",
    "Y_train = df_train['Survived'] # Y_trainは、df_trainのSurvived列\n",
    "X_test  = df_test.drop('PassengerId', axis=1).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# 学習と予測を行う\n",
    "forest = RandomForestClassifier(random_state=1)\n",
    "forest.fit(X_train, Y_train)\n",
    "Y_prediction = forest.predict(X_test)\n",
    "submission = pd.DataFrame({\n",
    "        'PassengerId': df_test['PassengerId'],\n",
    "        'Survived': Y_prediction\n",
    "    })\n",
    "submission.to_csv('submission.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.3.3 特徴量の重要度を確認する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.13924783, 0.41004894, 0.11409453, 0.09676177, 0.07999719,\n",
       "       0.10232023, 0.02503838, 0.02067357, 0.01181757])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass 0.1392\n",
      "Sex 0.41\n",
      "Age 0.1141\n",
      "SibSp 0.0968\n",
      "Parch 0.08\n",
      "Fare 0.1023\n",
      "Embarked_0 0.025\n",
      "Embarked_1 0.0207\n",
      "Embarked_2 0.0118\n"
     ]
    }
   ],
   "source": [
    "for i,k in zip(X_train.columns,forest.feature_importances_):\n",
    "  print(i,round(k,4))"
   ]
  }
 ],
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