02.17_Interaction_Features_Example.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import sklearn.preprocessing as preproc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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     "text": [
      "C:\\Users\\Lemon\\Anaconda3\\envs\\mfe\\lib\\site-packages\\ipykernel\\__main__.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data/UCI_Online_News_Popularity/OnlineNewsPopularity/OnlineNewsPopularity.csv', delimiter=', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    {
     "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": 3,
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   "source": [
    "df.columns"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "features = ['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']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    {
     "data": {
      "text/plain": [
       "(39644, 170)"
      ]
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "X = df[features]\n",
    "y = df[['shares']]\n",
    "X2 = preproc.PolynomialFeatures(include_bias=False).fit_transform(X)\n",
    "X2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
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   "outputs": [],
   "source": [
    "X1_train, X1_test, X2_train, X2_test, y_train, y_test = train_test_split(X, X2, y, \n",
    "                                                                         test_size=0.3,\n",
    "                                                                         random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
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       "(11894, 1)"
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     "execution_count": 17,
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   "source": [
    "y_test.shape"
   ]
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   "cell_type": "code",
   "execution_count": 13,
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   "source": [
    "def evaluate_feature(X_train, X_test, y_train, y_test):\n",
    "    '''Fit a linear regression model on the training set and score on the test set'''\n",
    "    model = linear_model.LinearRegression().fit(X_train, y_train)\n",
    "    r_score = model.score(X_test, y_test)\n",
    "    return (model, r_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared score with singleton features: 0.00924\n"
     ]
    }
   ],
   "source": [
    "(m1, r1) = evaluate_feature(X1_train, X1_test, y_train, y_test)\n",
    "print(\"R-squared score with singleton features: %0.5f\" % r1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared score with pairwise features: 0.0113276523\n"
     ]
    }
   ],
   "source": [
    "(m2, r2) = evaluate_feature(X2_train, X2_test, y_train, y_test)\n",
    "print(\"R-squared score with pairwise features: %0.10f\" % r2)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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