2-3-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例2-7. ニュース記事に含まれる単語数の分布の可視化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>url</th>\n",
       "      <th>timedelta</th>\n",
       "      <th>n_tokens_title</th>\n",
       "      <th>n_tokens_content</th>\n",
       "      <th>n_unique_tokens</th>\n",
       "      <th>n_non_stop_words</th>\n",
       "      <th>n_non_stop_unique_tokens</th>\n",
       "      <th>num_hrefs</th>\n",
       "      <th>num_self_hrefs</th>\n",
       "      <th>num_imgs</th>\n",
       "      <th>...</th>\n",
       "      <th>max_positive_polarity</th>\n",
       "      <th>avg_negative_polarity</th>\n",
       "      <th>min_negative_polarity</th>\n",
       "      <th>max_negative_polarity</th>\n",
       "      <th>title_subjectivity</th>\n",
       "      <th>title_sentiment_polarity</th>\n",
       "      <th>abs_title_subjectivity</th>\n",
       "      <th>abs_title_sentiment_polarity</th>\n",
       "      <th>shares</th>\n",
       "      <th>log_n_tokens_content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://mashable.com/2013/01/07/amazon-instant-...</td>\n",
       "      <td>731.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>0.663594</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.815385</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-0.350000</td>\n",
       "      <td>-0.600</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.187500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>593</td>\n",
       "      <td>2.342423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://mashable.com/2013/01/07/ap-samsung-spon...</td>\n",
       "      <td>731.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>0.604743</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.791946</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-0.118750</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>711</td>\n",
       "      <td>2.408240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://mashable.com/2013/01/07/apple-40-billio...</td>\n",
       "      <td>731.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>0.575130</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.663866</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.466667</td>\n",
       "      <td>-0.800</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1500</td>\n",
       "      <td>2.326336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://mashable.com/2013/01/07/astronaut-notre...</td>\n",
       "      <td>731.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>0.503788</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.665635</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-0.369697</td>\n",
       "      <td>-0.600</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.725912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://mashable.com/2013/01/07/att-u-verse-apps/</td>\n",
       "      <td>731.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1072.0</td>\n",
       "      <td>0.415646</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.540890</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.220192</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>505</td>\n",
       "      <td>3.030600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  timedelta  \\\n",
       "0  http://mashable.com/2013/01/07/amazon-instant-...      731.0   \n",
       "1  http://mashable.com/2013/01/07/ap-samsung-spon...      731.0   \n",
       "2  http://mashable.com/2013/01/07/apple-40-billio...      731.0   \n",
       "3  http://mashable.com/2013/01/07/astronaut-notre...      731.0   \n",
       "4   http://mashable.com/2013/01/07/att-u-verse-apps/      731.0   \n",
       "\n",
       "   n_tokens_title  n_tokens_content  n_unique_tokens  n_non_stop_words  \\\n",
       "0            12.0             219.0         0.663594               1.0   \n",
       "1             9.0             255.0         0.604743               1.0   \n",
       "2             9.0             211.0         0.575130               1.0   \n",
       "3             9.0             531.0         0.503788               1.0   \n",
       "4            13.0            1072.0         0.415646               1.0   \n",
       "\n",
       "   n_non_stop_unique_tokens  num_hrefs  num_self_hrefs  num_imgs  \\\n",
       "0                  0.815385        4.0             2.0       1.0   \n",
       "1                  0.791946        3.0             1.0       1.0   \n",
       "2                  0.663866        3.0             1.0       1.0   \n",
       "3                  0.665635        9.0             0.0       1.0   \n",
       "4                  0.540890       19.0            19.0      20.0   \n",
       "\n",
       "           ...           max_positive_polarity  avg_negative_polarity  \\\n",
       "0          ...                             0.7              -0.350000   \n",
       "1          ...                             0.7              -0.118750   \n",
       "2          ...                             1.0              -0.466667   \n",
       "3          ...                             0.8              -0.369697   \n",
       "4          ...                             1.0              -0.220192   \n",
       "\n",
       "   min_negative_polarity  max_negative_polarity  title_subjectivity  \\\n",
       "0                 -0.600              -0.200000            0.500000   \n",
       "1                 -0.125              -0.100000            0.000000   \n",
       "2                 -0.800              -0.133333            0.000000   \n",
       "3                 -0.600              -0.166667            0.000000   \n",
       "4                 -0.500              -0.050000            0.454545   \n",
       "\n",
       "   title_sentiment_polarity  abs_title_subjectivity  \\\n",
       "0                 -0.187500                0.000000   \n",
       "1                  0.000000                0.500000   \n",
       "2                  0.000000                0.500000   \n",
       "3                  0.000000                0.500000   \n",
       "4                  0.136364                0.045455   \n",
       "\n",
       "   abs_title_sentiment_polarity  shares  log_n_tokens_content  \n",
       "0                      0.187500     593              2.342423  \n",
       "1                      0.000000     711              2.408240  \n",
       "2                      0.000000    1500              2.326336  \n",
       "3                      0.000000    1200              2.725912  \n",
       "4                      0.136364     505              3.030600  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Online News Popularity データセットを UCI リポジトリからダウンロードし、\n",
    "# Pandas を使ってデータフレームとして読み込む。\n",
    "df = pd.read_csv('data/news/OnlineNewsPopularity.csv', delimiter=', ', engine='python')\n",
    "\n",
    "# ニュース記事に含まれる単語数 'n_tokens_content' に対数変換を施す\n",
    "df['log_n_tokens_content'] = np.log10(df['n_tokens_content'] + 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Articles')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2,1)\n",
    "df['n_tokens_content'].hist(ax=ax1, bins=100)\n",
    "ax1.tick_params(labelsize=14)\n",
    "ax1.set_xlabel('Number of Words in Article', fontsize=14)\n",
    "ax1.set_ylabel('Number of Articles', fontsize=14)\n",
    "\n",
    "df['log_n_tokens_content'].hist(ax=ax2, bins=100)\n",
    "ax2.tick_params(labelsize=14)\n",
    "ax2.set_xlabel('Log of Number of Words', fontsize=14)\n",
    "ax2.set_ylabel('Number of Articles', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例2-8. 平均評価を予測するためにYelpレビュー件数の対数変換を使う"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>business_id</th>\n",
       "      <th>categories</th>\n",
       "      <th>city</th>\n",
       "      <th>full_address</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>name</th>\n",
       "      <th>neighborhoods</th>\n",
       "      <th>open</th>\n",
       "      <th>review_count</th>\n",
       "      <th>stars</th>\n",
       "      <th>state</th>\n",
       "      <th>type</th>\n",
       "      <th>log_review_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rncjoVoEFUJGCUoC1JgnUA</td>\n",
       "      <td>[Accountants, Professional Services, Tax Servi...</td>\n",
       "      <td>Peoria</td>\n",
       "      <td>8466 W Peoria Ave\\nSte 6\\nPeoria, AZ 85345</td>\n",
       "      <td>33.581867</td>\n",
       "      <td>-112.241596</td>\n",
       "      <td>Peoria Income Tax Service</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>AZ</td>\n",
       "      <td>business</td>\n",
       "      <td>0.602060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0FNFSzCFP_rGUoJx8W7tJg</td>\n",
       "      <td>[Sporting Goods, Bikes, Shopping]</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>2149 W Wood Dr\\nPhoenix, AZ 85029</td>\n",
       "      <td>33.604054</td>\n",
       "      <td>-112.105933</td>\n",
       "      <td>Bike Doctor</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>AZ</td>\n",
       "      <td>business</td>\n",
       "      <td>0.778151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3f_lyB6vFK48ukH6ScvLHg</td>\n",
       "      <td>[]</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>1134 N Central Ave\\nPhoenix, AZ 85004</td>\n",
       "      <td>33.460526</td>\n",
       "      <td>-112.073933</td>\n",
       "      <td>Valley Permaculture Alliance</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>AZ</td>\n",
       "      <td>business</td>\n",
       "      <td>0.698970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>usAsSV36QmUej8--yvN-dg</td>\n",
       "      <td>[Food, Grocery]</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>845 W Southern Ave\\nPhoenix, AZ 85041</td>\n",
       "      <td>33.392210</td>\n",
       "      <td>-112.085377</td>\n",
       "      <td>Food City</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>AZ</td>\n",
       "      <td>business</td>\n",
       "      <td>0.778151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PzOqRohWw7F7YEPBz6AubA</td>\n",
       "      <td>[Food, Bagels, Delis, Restaurants]</td>\n",
       "      <td>Glendale Az</td>\n",
       "      <td>6520 W Happy Valley Rd\\nSte 101\\nGlendale Az, ...</td>\n",
       "      <td>33.712797</td>\n",
       "      <td>-112.200264</td>\n",
       "      <td>Hot Bagels &amp; Deli</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "      <td>3.5</td>\n",
       "      <td>AZ</td>\n",
       "      <td>business</td>\n",
       "      <td>1.176091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id                                         categories  \\\n",
       "0  rncjoVoEFUJGCUoC1JgnUA  [Accountants, Professional Services, Tax Servi...   \n",
       "1  0FNFSzCFP_rGUoJx8W7tJg                  [Sporting Goods, Bikes, Shopping]   \n",
       "2  3f_lyB6vFK48ukH6ScvLHg                                                 []   \n",
       "3  usAsSV36QmUej8--yvN-dg                                    [Food, Grocery]   \n",
       "4  PzOqRohWw7F7YEPBz6AubA                 [Food, Bagels, Delis, Restaurants]   \n",
       "\n",
       "          city                                       full_address   latitude  \\\n",
       "0       Peoria         8466 W Peoria Ave\\nSte 6\\nPeoria, AZ 85345  33.581867   \n",
       "1      Phoenix                  2149 W Wood Dr\\nPhoenix, AZ 85029  33.604054   \n",
       "2      Phoenix              1134 N Central Ave\\nPhoenix, AZ 85004  33.460526   \n",
       "3      Phoenix              845 W Southern Ave\\nPhoenix, AZ 85041  33.392210   \n",
       "4  Glendale Az  6520 W Happy Valley Rd\\nSte 101\\nGlendale Az, ...  33.712797   \n",
       "\n",
       "    longitude                          name neighborhoods  open  review_count  \\\n",
       "0 -112.241596     Peoria Income Tax Service            []  True             3   \n",
       "1 -112.105933                   Bike Doctor            []  True             5   \n",
       "2 -112.073933  Valley Permaculture Alliance            []  True             4   \n",
       "3 -112.085377                     Food City            []  True             5   \n",
       "4 -112.200264             Hot Bagels & Deli            []  True            14   \n",
       "\n",
       "   stars state      type  log_review_count  \n",
       "0    5.0    AZ  business          0.602060  \n",
       "1    5.0    AZ  business          0.778151  \n",
       "2    5.0    AZ  business          0.698970  \n",
       "3    3.5    AZ  business          0.778151  \n",
       "4    3.5    AZ  business          1.176091  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "with open('data/yelp/yelp_academic_dataset_business.json') as biz_file:\n",
    "    biz_df = pd.DataFrame([json.loads(x) for x in biz_file.readlines()])\n",
    "    \n",
    "# 例2-2で読み込んだ Yelp データセットの\n",
    "# データフレーム biz_df を使用して、レビュー件数を対数変換する。\n",
    "# レビュー件数 0 を対数変換してマイナス無限大になるのを防ぐために\n",
    "# 対数変換の前に生データに 1 を加算していることに注意。\n",
    "biz_df['log_review_count'] = np.log10(biz_df['review_count'] + 1)\n",
    "biz_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared score without log transform: 0.00215 (+/- 0.00329)\n",
      "R-squared score with log transform: 0.00136 (+/- 0.00328)\n"
     ]
    }
   ],
   "source": [
    "# 各店舗の平均評価（stars）を予測するために線形回帰モデルを学習する。\n",
    "# 特徴量 review_count を対数変換した場合としない場合とで\n",
    "# 10分割クロスバリデーションにより R2 スコアを比較する。\n",
    "m_orig = linear_model.LinearRegression()\n",
    "scores_orig = cross_val_score(m_orig, biz_df[['review_count']],\n",
    "                              biz_df['stars'], cv=10)\n",
    "m_log = linear_model.LinearRegression()\n",
    "scores_log = cross_val_score(m_log, biz_df[['log_review_count']],\n",
    "                             biz_df['stars'], cv=10)\n",
    "print('R-squared score without log transform: %0.5f (+/- %0.5f)' % (scores_orig.mean(), scores_orig.std() * 2))\n",
    "print('R-squared score with log transform: %0.5f (+/- %0.5f)' % (scores_log.mean(), scores_log.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例2-9. Online News Popularity データセットの記事の人気を予測するために単語数を対数変換する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared score without log transform: -0.00242 (+/- 0.00509)\n",
      "R-squared score with log transform: -0.00114 (+/- 0.00418)\n"
     ]
    }
   ],
   "source": [
    "# Online News Popularity データセットを UCI リポジトリからダウンロードし、\n",
    "# Pandas を使ってデータフレームとして読み込む。\n",
    "df = pd.read_csv('data/news/OnlineNewsPopularity.csv', delimiter=', ', engine='python')\n",
    "\n",
    "# ニュース記事内に含まれる単語数 'n_tokens_content' に対数変換を施す\n",
    "df['log_n_tokens_content'] = np.log10(df['n_tokens_content'] + 1)\n",
    "\n",
    "# 記事のシェア数を予測する2つの線形回帰モデルを学習する。\n",
    "# 1つは元の特徴量であり、もう1つは対数変換をかけた特徴量を使う。\n",
    "m_orig = linear_model.LinearRegression()\n",
    "scores_orig = cross_val_score(m_orig, df[['n_tokens_content']],\n",
    "                              df['shares'], cv=10)\n",
    "m_log = linear_model.LinearRegression()\n",
    "scores_log = cross_val_score(m_log, df[['log_n_tokens_content']],\n",
    "                             df['shares'], cv=10)\n",
    "print('R-squared score without log transform: %0.5f (+/- %0.5f)'\n",
    "      % (scores_orig.mean(), scores_orig.std() * 2))\n",
    "print('R-squared score with log transform: %0.5f (+/- %0.5f)'\n",
    "      % (scores_log.mean(), scores_log.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例2-10. ニュース記事の人気予測問題における入出力間の相関の可視化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Shares')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, (ax1, ax2) = plt.subplots(2,1)\n",
    "ax1.scatter(df['n_tokens_content'], df['shares'])\n",
    "ax1.tick_params(labelsize=14)\n",
    "ax1.set_xlabel('Number of Words in Article', fontsize=14)\n",
    "ax1.set_ylabel('Number of Shares', fontsize=14)\n",
    "\n",
    "ax2.scatter(df['log_n_tokens_content'], df['shares'])\n",
    "ax2.tick_params(labelsize=14)\n",
    "ax2.set_xlabel('Log of the Number of Words in Article', fontsize=14)\n",
    "ax2.set_ylabel('Number of Shares', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例2-11. Yelp評価予測における入力と出力の相関の可視化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Star Rating')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2,1)\n",
    "ax1.scatter(biz_df['review_count'], biz_df['stars'])\n",
    "ax1.tick_params(labelsize=14)\n",
    "ax1.set_xlabel('Review Count', fontsize=14)\n",
    "ax1.set_ylabel('Average Star Rating', fontsize=14)\n",
    "\n",
    "ax2.scatter(biz_df['log_review_count'], biz_df['stars'])\n",
    "ax2.tick_params(labelsize=14)\n",
    "ax2.set_xlabel('Log of Review Count', fontsize=14)\n",
    "ax2.set_ylabel('Average Star Rating', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例2-12. Yelpレビュー件数の Box-Cox 変換"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "# 引き続き biz_df には Yelp のレビューデータが含まれるとする。\n",
    "# Box-Cox 変換は入力がすべて正であることを仮定するため、\n",
    "# まずは最小値を調べてマイナスの値を取らないか確認する。\n",
    "# Check the min to make sure.\n",
    "biz_df['review_count'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5631160899391674"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 引数 lambda に 0 を与えると対数変換になる（定数を足さない）\n",
    "rc_log = stats.boxcox(biz_df['review_count'], lmbda=0)\n",
    "# 引数 lambda に何も与えなければ、scipy 実装では変換後のデータが\n",
    "# 正規分布に最も近づくようにパラメータ lambda が自動的に決定される\n",
    "rc_bc, bc_params = stats.boxcox(biz_df['review_count'])\n",
    "bc_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例2-13. レビュー件数の元データ、対数変換後、Box-Cox変換後のヒストグラムを作成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "biz_df['rc_log'] = rc_log\n",
    "biz_df['rc_bc'] = rc_bc\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3,1)\n",
    "# レビュー件数のヒストグラム\n",
    "biz_df['review_count'].hist(ax=ax1, bins=100)\n",
    "ax1.set_yscale('log')\n",
    "ax1.tick_params(labelsize=14)\n",
    "ax1.set_title('Review Counts Histogram', fontsize=14)\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_ylabel('Occurrence', fontsize=14)\n",
    "\n",
    "# 対数変換後のレビュー件数のヒストグラム\n",
    "biz_df['rc_log'].hist(ax=ax2, bins=100)\n",
    "ax2.set_yscale('log')\n",
    "ax2.tick_params(labelsize=14)\n",
    "ax2.set_title('Log Transformed Counts Histogram', fontsize=14)\n",
    "ax2.set_xlabel('')\n",
    "ax2.set_ylabel('Occurrence', fontsize=14)\n",
    "\n",
    "# 最適な Box-Cox 変換後のレビュー件数のヒストグラム\n",
    "biz_df['rc_bc'].hist(ax=ax3, bins=100)\n",
    "ax3.set_yscale('log')\n",
    "ax3.tick_params(labelsize=14)\n",
    "ax3.set_title('Box-Cox Transformed Counts Histogram', fontsize=14)\n",
    "ax3.set_xlabel('')\n",
    "ax3.set_ylabel('Occurrence', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例2-14. 元データと変換後データの正規分布に対する確率プロット"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Probplot after Box-Cox transform')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, (ax1, ax2, ax3) = plt.subplots(3,1)\n",
    "prob1 = stats.probplot(biz_df['review_count'], dist=stats.norm, plot=ax1)\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_title('Probplot against normal distribution')\n",
    "\n",
    "prob2 = stats.probplot(biz_df['rc_log'], dist=stats.norm, plot=ax2)\n",
    "ax2.set_xlabel('')\n",
    "ax2.set_title('Probplot after log transform')\n",
    "\n",
    "prob3 = stats.probplot(biz_df['rc_bc'], dist=stats.norm, plot=ax3)\n",
    "ax3.set_xlabel('Theoretical quantiles')\n",
    "ax3.set_title('Probplot after Box-Cox transform')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例2-15. 特徴量スケーリングの例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([219., 255., 211., ..., 442., 682., 157.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import sklearn.preprocessing as preproc\n",
    "\n",
    "# Online News Popularity データセットの読み込み\n",
    "df = pd.read_csv('data/news/OnlineNewsPopularity.csv', delimiter=', ', engine='python')\n",
    "\n",
    "# 元データ（記事中の単語数）\n",
    "df['n_tokens_content'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02584376, 0.03009205, 0.02489969, ..., 0.05215955, 0.08048147,\n",
       "       0.01852726])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Min-Max スケーリング\n",
    "df['minmax'] = preproc.minmax_scale(df[['n_tokens_content']])\n",
    "df['minmax'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.69521045, -0.61879381, -0.71219192, ..., -0.2218518 ,\n",
       "        0.28759248, -0.82681689])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 標準化（定義より出力が負になることもある）\n",
    "df['standardized'] = preproc.StandardScaler().fit_transform(df[['n_tokens_content']])\n",
    "df['standardized'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00152439, 0.00177498, 0.00146871, ..., 0.00307663, 0.0047472 ,\n",
       "       0.00109283])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# L2 正規化\n",
    "df['l2_normalized'] = preproc.normalize(df[['n_tokens_content']], axis=0)\n",
    "df['l2_normalized'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例2-16. 元データとスケーリング後のヒストグラムの描画"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(27.125, 0.5, 'Number of articles')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(4,1)\n",
    "fig.tight_layout()\n",
    "df['n_tokens_content'].hist(ax=ax1, bins=100)\n",
    "ax1.tick_params(labelsize=14)\n",
    "ax1.set_xlabel('Article word count', fontsize=14)\n",
    "ax1.set_ylabel('Number of articles', fontsize=14)\n",
    "\n",
    "df['minmax'].hist(ax=ax2, bins=100)\n",
    "ax2.tick_params(labelsize=14)\n",
    "ax2.set_xlabel('Min-max scaled word count', fontsize=14)\n",
    "ax2.set_ylabel('Number of articles', fontsize=14)\n",
    "\n",
    "df['standardized'].hist(ax=ax3, bins=100)\n",
    "ax3.tick_params(labelsize=14)\n",
    "ax3.set_xlabel('Standardized word count', fontsize=14)\n",
    "ax3.set_ylabel('Number of articles', fontsize=14)\n",
    "\n",
    "df['l2_normalized'].hist(ax=ax4, bins=100)\n",
    "ax4.tick_params(labelsize=14)\n",
    "ax4.set_xlabel('L2-normalized word count', fontsize=14)\n",
    "ax4.set_ylabel('Number of articles', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例2-17. 交互作用特徴量を使った予測の例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['url', 'timedelta', 'n_tokens_title', 'n_tokens_content',\n",
       "       'n_unique_tokens', 'n_non_stop_words', 'n_non_stop_unique_tokens',\n",
       "       'num_hrefs', 'num_self_hrefs', 'num_imgs', 'num_videos',\n",
       "       'average_token_length', 'num_keywords', 'data_channel_is_lifestyle',\n",
       "       'data_channel_is_entertainment', 'data_channel_is_bus',\n",
       "       'data_channel_is_socmed', 'data_channel_is_tech',\n",
       "       'data_channel_is_world', 'kw_min_min', 'kw_max_min', 'kw_avg_min',\n",
       "       'kw_min_max', 'kw_max_max', 'kw_avg_max', 'kw_min_avg', 'kw_max_avg',\n",
       "       'kw_avg_avg', 'self_reference_min_shares', 'self_reference_max_shares',\n",
       "       'self_reference_avg_sharess', 'weekday_is_monday', 'weekday_is_tuesday',\n",
       "       'weekday_is_wednesday', 'weekday_is_thursday', 'weekday_is_friday',\n",
       "       'weekday_is_saturday', 'weekday_is_sunday', 'is_weekend', 'LDA_00',\n",
       "       'LDA_01', 'LDA_02', 'LDA_03', 'LDA_04', 'global_subjectivity',\n",
       "       'global_sentiment_polarity', 'global_rate_positive_words',\n",
       "       'global_rate_negative_words', 'rate_positive_words',\n",
       "       'rate_negative_words', 'avg_positive_polarity', 'min_positive_polarity',\n",
       "       'max_positive_polarity', 'avg_negative_polarity',\n",
       "       'min_negative_polarity', 'max_negative_polarity', 'title_subjectivity',\n",
       "       'title_sentiment_polarity', 'abs_title_subjectivity',\n",
       "       'abs_title_sentiment_polarity', 'shares'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "import sklearn.preprocessing as preproc\n",
    "\n",
    "df = pd.read_csv('data/news/OnlineNewsPopularity.csv', delimiter=', ', engine='python')\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39644, 170)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = ['n_tokens_title', 'n_tokens_content', 'n_unique_tokens',\n",
    "            'n_non_stop_words', 'n_non_stop_unique_tokens', 'num_hrefs',\n",
    "            'num_self_hrefs', 'num_imgs', 'num_videos', 'average_token_length',\n",
    "            'num_keywords', 'data_channel_is_lifestyle', 'data_channel_is_entertainment',\n",
    "            'data_channel_is_bus', 'data_channel_is_socmed', 'data_channel_is_tech',\n",
    "            'data_channel_is_world']\n",
    "\n",
    "X = df[features]\n",
    "y = df[['shares']]\n",
    "\n",
    "# ペアワイズ交互作用特徴量を作成する（定数項（bias）は含めない）\n",
    "X2 = preproc.PolynomialFeatures(include_bias=False).fit_transform(X)\n",
    "X2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared score with singleton features: 0.00924\n",
      "R-squared score with pairwise features: 0.0112593685\n"
     ]
    }
   ],
   "source": [
    "# 両方の特徴量セットを訓練データとテストデータに分ける\n",
    "X1_train, X1_test, X2_train, X2_test, y_train, y_test = \\\n",
    "    train_test_split(X, X2, y, test_size=0.3, random_state=123)\n",
    "\n",
    "def evaluate_feature(X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    訓練データに対して線形回帰モデルを適合し\n",
    "    テストデータに対してスコアを算出する\n",
    "    \"\"\"\n",
    "    model = linear_model.LinearRegression().fit(X_train, y_train)\n",
    "    r_score = model.score(X_test, y_test)\n",
    "    return (model, r_score)\n",
    "\n",
    "# それぞれの特徴量に対してモデルを学習し、テストスコアを算出する\n",
    "(m1, r1) = evaluate_feature(X1_train, X1_test, y_train, y_test)\n",
    "(m2, r2) = evaluate_feature(X2_train, X2_test, y_train, y_test)\n",
    "print(\"R-squared score with singleton features: %0.5f\" % r1)\n",
    "print(\"R-squared score with pairwise features: %0.10f\" % r2)"
   ]
  }
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