Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (2.4%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: DurandinDaniil
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 413 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 5 years ago · Last pushed almost 5 years ago
Metadata Files
Readme Citation

README.md

citation-prediction

Project to predict the number of citations of an article in 5 years (with XGBoost and feature selection)

R2score = 0.88

P.S

Not sure about Win Linux venv compatibility, so add also environment.yml and Google Colab link (with .csv uploading via url): https://colab.research.google.com/drive/1XEJNO3kD1aXHu4KcGZQfFU_sFlhQ2y7H?usp=sharing

Owner

  • Login: DurandinDaniil
  • Kind: user

Citation (citation_predict.ipynb)

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = r'C:\\Users\\duran\\Python Projects\\predict.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, KFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import xgboost as xgb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pub_df = pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>recency</th>\n",
       "      <th>topic_rank</th>\n",
       "      <th>diversity</th>\n",
       "      <th>authors_mean_rank</th>\n",
       "      <th>authors_mean_hindex</th>\n",
       "      <th>authors_mean_gindex</th>\n",
       "      <th>authors_mean_sociality</th>\n",
       "      <th>authors_mean_pagerank</th>\n",
       "      <th>authors_mean_productivity</th>\n",
       "      <th>journal_pagerank</th>\n",
       "      <th>journal_rank</th>\n",
       "      <th>title_len</th>\n",
       "      <th>abstract_len</th>\n",
       "      <th>n_authors</th>\n",
       "      <th>c5</th>\n",
       "      <th>log_authors_mean_sociality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>479.000000</td>\n",
       "      <td>479.000000</td>\n",
       "      <td>479.000000</td>\n",
       "      <td>473.000000</td>\n",
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       "      <td>479.000000</td>\n",
       "      <td>479.000000</td>\n",
       "      <td>479.000000</td>\n",
       "      <td>473.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.419624</td>\n",
       "      <td>10.125261</td>\n",
       "      <td>-0.675481</td>\n",
       "      <td>741.769615</td>\n",
       "      <td>0.844051</td>\n",
       "      <td>0.844668</td>\n",
       "      <td>3.411132</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>146.184091</td>\n",
       "      <td>94.983299</td>\n",
       "      <td>1162.283925</td>\n",
       "      <td>4.369520</td>\n",
       "      <td>6.713987</td>\n",
       "      <td>1.264066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.860415</td>\n",
       "      <td>5.748655</td>\n",
       "      <td>0.606013</td>\n",
       "      <td>581.948922</td>\n",
       "      <td>0.363961</td>\n",
       "      <td>0.364351</td>\n",
       "      <td>2.790555</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>111.398299</td>\n",
       "      <td>38.524238</td>\n",
       "      <td>657.597003</td>\n",
       "      <td>2.800775</td>\n",
       "      <td>12.547968</td>\n",
       "      <td>0.703466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>-1.087146</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>758.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>-0.526332</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>1197.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.386294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>-0.150066</td>\n",
       "      <td>1141.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>216.750000</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>1587.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.791759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>-0.056685</td>\n",
       "      <td>2336.000000</td>\n",
       "      <td>1.125000</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>436.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>3655.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>2.772589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          recency  topic_rank   diversity  authors_mean_rank  \\\n",
       "count  479.000000  479.000000  479.000000         473.000000   \n",
       "mean    13.419624   10.125261   -0.675481         741.769615   \n",
       "std      5.860415    5.748655    0.606013         581.948922   \n",
       "min      5.000000    1.000000   -2.995732           1.000000   \n",
       "25%      8.000000    5.000000   -1.087146         265.000000   \n",
       "50%     13.000000   10.000000   -0.526332         596.000000   \n",
       "75%     18.000000   15.000000   -0.150066        1141.000000   \n",
       "max     25.000000   20.000000   -0.056685        2336.000000   \n",
       "\n",
       "       authors_mean_hindex  authors_mean_gindex  authors_mean_sociality  \\\n",
       "count           473.000000           473.000000              473.000000   \n",
       "mean              0.844051             0.844668                3.411132   \n",
       "std               0.363961             0.364351                2.790555   \n",
       "min               0.000000             0.000000                0.000000   \n",
       "25%               1.000000             1.000000                1.000000   \n",
       "50%               1.000000             1.000000                3.000000   \n",
       "75%               1.000000             1.000000                5.000000   \n",
       "max               1.125000             1.166667               15.000000   \n",
       "\n",
       "       authors_mean_pagerank  authors_mean_productivity  journal_pagerank  \\\n",
       "count                    0.0                        0.0               0.0   \n",
       "mean                     NaN                        NaN               NaN   \n",
       "std                      NaN                        NaN               NaN   \n",
       "min                      NaN                        NaN               NaN   \n",
       "25%                      NaN                        NaN               NaN   \n",
       "50%                      NaN                        NaN               NaN   \n",
       "75%                      NaN                        NaN               NaN   \n",
       "max                      NaN                        NaN               NaN   \n",
       "\n",
       "       journal_rank   title_len  abstract_len   n_authors          c5  \\\n",
       "count    440.000000  479.000000    479.000000  479.000000  479.000000   \n",
       "mean     146.184091   94.983299   1162.283925    4.369520    6.713987   \n",
       "std      111.398299   38.524238    657.597003    2.800775   12.547968   \n",
       "min        1.000000   16.000000      0.000000    1.000000    0.000000   \n",
       "25%       53.750000   68.000000    758.500000    2.000000    0.000000   \n",
       "50%      122.000000   91.000000   1197.000000    4.000000    2.000000   \n",
       "75%      216.750000  116.500000   1587.500000    6.000000    8.000000   \n",
       "max      436.000000  279.000000   3655.000000   16.000000  154.000000   \n",
       "\n",
       "       log_authors_mean_sociality  \n",
       "count                  473.000000  \n",
       "mean                     1.264066  \n",
       "std                      0.703466  \n",
       "min                      0.000000  \n",
       "25%                      0.693147  \n",
       "50%                      1.386294  \n",
       "75%                      1.791759  \n",
       "max                      2.772589  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pub_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "drop columns that contains only \"NaN\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pub_df = pub_df.drop(['authors_mean_pagerank', 'authors_mean_productivity', 'journal_pagerank'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "explore dependence between response and explanatory variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mIqLq48ZcBDAfEVWT4UYkXZ323z8FsAzAsQBuB/CfBb7n+QB2SCn3SSmjAP4A4OMAxqWGUAJAE4DuAl+fiCoPcxERuYETuQhgPiIiM8xFRFRWJlPbTpVS/lJKmZBS/hHJZFWInQDmCiGOEUIIAOcB2AbgWQALU9t8BcDKAl+fiCobcxERuUGxchHAfEREhWMuIqKSCwzz8yYhxF0ABIAJQohgqncaAIKFvKGUcpMQYjmAlwHEAPwdwBIATwJ4WAjx76nYrwt5fSKqSMxFROQGRc9FAPMRERljLiKishquI+m6tP9uBVAPoFcIcQKAVYW+qZTyJgA3ZYXfAnB6oa9JRBWNuYiI3MCRXAQwHxGREeYiIiqrvB1JUsoHAEAI8QCAb0kpD6R+FAZwgsP7RkQEgLmIiNyBuYiI3IC5iIjKzW6NpJlpCQpSyl4AH3Fml4iItJiLiMgNmIuIyA2Yi4ioLOx2JPlSy0ECAIQQx2L4aXFERMXGXEREbsBcRERuwFxERGVhN9HcAeCFVPE1CeBSAD9ybK+IiNSYi4jIDZiLiMgNmIuIqCxsdSRJKR8UQrQCOBfJ1QH+UUq5zdE9IyLKwlxERG7AXEREbsBcRETlYnvoYyopMTERUVkxFxGRGzAXEZEbMBcRUTnYrZFEVFF6+sJo33UAPX3hcu8KkSfwnCEiKi/mYapEPK6JvInF2KjqrGzrwuIVmxH0+RBNJHDbxTOxYPakcu8WkWvxnCEiKi/mYapEPK6JvIsjkqiq9PSFsXjFZgxGEzgcjmEwmsCiFZv5FIRIg+cMEVF5MQ9TJeJxTeRt7EiiqtLZO4CgL/OwD/p86OwdKNMeEbkbzxkiovJiHqZKxOOayNvYkURVpamhDtFEIiMWTSTQ1FBXpj0icjeeM0RE5cU8TJWIxzWRt7EjKQsLvlW2xvoQbrt4JmqDPowOBVAb9OG2i2eisT5U7l0jciWeM5WJbR2RdzAPew9z7PDsHNf8HInci8W207DgW3VYMHsS5jWPR2fvAJoa6nghRjQMnjOVhW0dkfcwD3sHc6x9+Y5rfo5E7laWEUlCiHFCiOVCiNeEEK8KIc4QQhwrhHhaCPFG6n8bSrlPLPhWXRrrQ5g1eRwvxKqcG3ORW/GcqQxs69yL+YiGwzzsfpWQY0udi1THdSV8jkSVrlxT234O4M9Syg8CmAXgVQD/BmC1lPIUAKtT/y4ZFnwjqkquy0VETmJb52rMR0QeVyE5tuy5qEI+R6KKVvKOJCHEGABnA/g1AEgpI1LKAwAuAvBAarMHAHyulPvFgm9E1cWtuYjISWzr3In5iKgyeD3HuiUXef1zJKoG5RiR9D4A+wD8txDi70KI+4QQowAcL6V8FwBS/3tcKXeKhQyJqo4rcxGRk9jWuRbzEVEFqIAc64pcVAGfI1HFK0ex7QCAjwL4hpRykxDi5zAYHimEuBLAlQAwZcqUou4YCxkSVRXX5iIiJ7Gtc6WC8xFzEZG7eDzHuubayOOfI1HFK8eIpE4AnVLKTal/L0cyYe0RQpwIAKn/3av6ZSnlEilli5SyZcKECUXfORYyJKoars5FRE5iW+c6Becj5iIi9/FwjnXVtZGHP0eiilfyjiQp5W4Au4QQH0iFzgOwDcAqAF9Jxb4CYGWp942IqgdzERG5BfMREbkBcxER2VWOqW0A8A0ADwkhagC8BeCfkOzUWiaE+CqAnQAuKdO+EVH1YC4iIrdgPiIiN2AuIqJhlaUjSUrZBqBF8aPzSr0vRFS9mIuIyC2Yj4jIDZiLiMiOctRIqko9fWG07zqAnr5wuXeFiIg5iYiIyEFsZ0eOnyGRe5VraltVWdnWhcUrNiPo8yGaSOC2i2diwexJ5d4tIqpSzElERETOYTs7cvwMidyNI5Ic1tMXxuIVmzEYTeBwOIbBaAKLVmxmzzoRlQVzEhERkXPYzo4cP0Mi96uKjqRyDovs7B1A0Jf5MQd9PnT2DpR8X4iImJPIaZyKQETVTNXO+n2C7ayBQq9V2P4QlU7FT21b2daFRcs3w+8TiCckbl9Y2mGRTQ11iCYSGbFoIoGmhrqS7QMRVYeevjA6ewfQ1FCHxvqQchvmJHISpyIQeZOd9oPsUbWz/eE4tnQdxKzJ48q0V95SyLWK1f4EfAKRuMRNF07D5XOmOr2rRFWrokck9fSFce2j7QjHEjgSiSMcS+CaR9tL2kvdWB/CbRfPRG3Qh9GhAGqDPtx28Uw20kRUVCvbujDv1jX40n2bMO/WNVjV1qXcjjmJnMKpCETeZLf9IHsa60O4Yf60nPjNT25jPrTJ9Folvf3pC8cRiSVw/R+34KGN75R4z4mqR0WPSNrafRDRuMyIReMSW7sP4uxTjyvZfiyYPQnzmsfzSQ8ROSL9AmoQySd4i1Zsxrzm8cp8w5xETrCmIljHIHB0KgKPMSJ3Mm0/yJ4ZE8diVI0f/ZH4UIz50IzJtUpn7wACPpET/8HjW/HpGSfwMydyQEV3JAG5CSV/3DmN9SEmMSJyRCE38MxJVGycNknkPewAdkZTQx3iMuthNvOhMbvXKk0NdYhkDR4AgKCfxzKRUyp6atv0iWMQyPoLA75kvBhMCrqx+BsRmbKbN9x2A898V504bZLIPbzafhTKbe0O82HxWN9tx57D2u+4sT6Emy7MnU4Yl9JzxzKRV1T0iKTG+hB+eulsXLe8HX7hQ1wmcPvCWUVJ4iYFRVl8lIhMmeQN64J1Udb25bhgZb6rbpw2SVR+Xm0/CuXWdof5cOSs71YmJMJxidpgcoSA6ju+fM5UQCanswX9PsSl9NyxTOQlQsrcYYBe0dLSIltbW4fdrtgrUfT0hTHv1jUYjB59glMb9GH94nNzXt9kW6JqJYR4SUrZUu79KJTdXGRXoXmj3KvuMN+R1zEXkdd5tf0oVKW2O17PRcDI85Hqu7Xk+469eiwTuVG+XFTRI5Isxa4FYjKfnHPPichUoXmj3HWPmO+IiMrLq+1HodjuVC7Vd2vJ9x179Vgm8pqq6EgqNpP55JUy97xcTJ4q8AkEVQqv5g2v7nehvJxzvLzvRKTnlTxcrBzklr+XObVw1mdnrXJnfYaq79bixmOaqNqwI6kAJvPJK2HuebmwDhVVK6/mDa/udyG8nHO8vO9ElJ8X8nAxc5Ab/l7m1MINVwPJ+m5VP3fTMU1UjcpWI0kI4QfQCqBLSjlfCHEygIcBHAvgZQBfllJG8r1GuWsBcLSMc1iHqrqUsxaAm3ORV/OGV/fbLi/nHC/veykwF1GlcGsedioHlevvdervKXeNpFLkI7s1kHQjlojIeflyka/UO5PmWwBeTfv3rQDulFKeAqAXwFfLslcGGutDmDV5nK1kZrItHZ0Xnc6aDz2SbYkUXJuLvJo3vLrfdnk553h536uAa3MReY9b87BTOahcf28F51TH85Hqs7Okf4bWd9t8/GhXHtNE1aosHUlCiCYAnwVwX+rfAsC5AJanNnkAwOeK9X49fWG07zqAnr5wsV6SHMY6VFQKpc5F5H522gsv5xwv73slYy6ialFpOUj190Ti3v17gNLlo1LWQOK9IFHxlWtE0s8ALAKGyvA3AjggpYyl/t0JQDm5WAhxpRCiVQjRum/fvmHfaGVbF+bdugZfum8T5t26Bqvauoqw+5Wv3AnXmvNeG/RhdCiA2qBv2DpUdrYlylKyXOR15c4JpWC3vfByzvHyvlc45iKqCiPJQW5sh6y/J+gXQ7F4IoH1HfvLuFcjVpJ8lH4shFKfX23Qh1BA4KpzmovxdwDgvSCRU0pebFsIMR/AXinlS0KIc6ywYlNl8SYp5RIAS4Dk3Nt879XTF8biFZsxGE0MLR25aMVmzGsez4vmPNxSNHDB7EmY1zze1px3k22JgNLmIq9zS05wkml74eWc4+V9r0TMRVRtCslBbm6H5jWPhy/tjI0lvHu/Uep8lH4sjKrx46ktu3Hvsx1YsvYt3Ptcx4i/Z94LEjmnHKu2zQOwQAhxAYBaAGOQ7PkeJ4QIpHq7mwB0j/SNrLm3gzg6bNKac8vkoea2hNtYH7L9vibbEqGEucjL3JYTnFJIe+HlnOPlfa9AzEVUdUxykNvboc7eAdT4/QjHYkMxD99vlDwfWcdCT18Yv3iuA+FYAuFYcb5n3gsSOafkU9uklN+VUjZJKU8C8AUAa6SUlwN4FsDC1GZfAbBypO9VafOwS6GCiwYSZShlLvKyaskJbC+oXJiLiPJzeztUSe1HOfORE99zJX03RG5TzlXbsi0G8B0hRAeSc3F/PdIXZC0Ic0y4RMXPRV5WLTmB7QW5EHMREdzfDlVJ++F4PnLie66S74aoLISU3p1O39LSIltbW4fdrqcvzFoQBla1dWGRS+ehU2USQrwkpWwp934Uym4u8qpqyglsL6obcxGRO3mhHSpm++H1XAQUlo+c+p7ZthMVJl8uKkeNpKrUsecw2nYdwOzJ49B8/Ohy705eLMRKROmczglOXuCZvna11A7iRTUReYmddqinL4yt3YcASEyfONbx3JadR6ul/XCS3e/Z9Od2vhu2i0RmKr4jyQ2rPNz42Ct4cOPOoX9fccYU/PCiD5d0H0yxMSSidE7lBCdztBvyvxvxcyEiL8rXDq1s68I1y9qQqtGMoF/gjktmOZbbmEedM9z3nO9zL/R74fdJZM5NNZKKLn2Vh8PhGAajCSxasRk9feGS7UPHnsMZnUgA8OCGnejYc7hk+2Dp6QujfdeBkv79REQ6heZoO7nMDfnfjUrxubCtIfKWnr4w1m7fh7Xb93ryvO3pC2PR8vahTiQAiMYlrlvuTM5n+1I66e3JcJ/7SK4psn/vuuXtnj0fiEqlokckdfYOQCYya0DJhCzpko9tuw5o46Wc4saediJym0KW5bWby7jkr5rTnwvbGiJvKfVIHid09g7AL3wA4hlxv084kvPZvpRGdnty1TnNeT/3Qr8X1e+FYxL//NuXkZCS7RiRRkWPSBpV40c4ntmRFI5LjKrxF+X17Tx1nT15nFHc7uua4JMTourgtZEgpiu0mOQyt6/yUy5Ofi5ubGu8dk4QlVJPXxjXPVq6kTy6fcg+R03P26aGOsRlIiceT0hHcj7bF+dY333HnsM57ck9z3YgEs/sLEz/3Av9XpL3i7nHz5FI3BXtGJFbVfSIpP5IHLVBHwajR5NDbdCH/kg8z2/ZY/epa/Pxo3HFGVPw4IbMGkm60UhOPM3t7B3Qxqv1yQkL6lGl8eJIEGtZ3uwVWnTnpMnTRtPXrhZOfi6FtjVO5WMvnhNEpfTQpp2IxHNXb3ZqJE821TkqAePztrE+hNsXzsJ3skZW3b7QmZyfnUcj8eRoGRqZ9OMhHIvD5xMZP/cLga98/CT89wtvK9uvxvoQLm1pyrjnurSlKe8xYL2nSK1iXuMXOecER5sRqVV0R5KuB3qkTwzSn7paNzSLVmzGvObxyiTzw4s+jCvmnjTsqm2mr2vXqBp/RmcaAAxGE0UbmeU1Tt5csIOKysGp3FEKJivCmT5t5AqUak59LoW0NU7lYy+fE0Sl0NMXxr3PvqH8mVMjebLfP/scvW55OwCBcMz8vLXyWqlWbbPe76FNO3HPmjfwn399E/c824HbF7LD2lRytb2DWLS8HeGYPPqwKKtD50g0jvvXv40bL5yGGRPH5rRfPX1hLGvtzPidZa2d+NZ5p2pXd7OOQYuUEqGAD+G0YXocbUakVtFT26wnBrVBH0aHAqgN+oZ98mpnOK31VDyd1Vut03z8aCxsmZy3LlIhr2tHfySOkD+zVz/kF0UZmeU1Tk69WNnWhXm3rsGX7tuEebeuwaq2riLsMdHwnModpdJYH8KsyeOGvei3cnooIHBM0I9QQAyb0+2+drVx4nPpj8SR1dTAL6Bta5zMx14/J4ic1tk7gBp/bidvwOfcSJ7s988+R/3CB3/WKBST87axPoSzT52As089Lmf/nZrmeveaNxCJSxyJxBGOJXDNo+2cBmXAunb+59++jHAss+Mo5BcIZjUq4VgCNz+xTfkQxDTvq7avDQZw9SeatfeOnC5NdFRFj0gCzJ682n0y6tTcaCdfV/hERs++8Imi9a57aRSOUwUS+fSbyqma6jUks5gABAAp8m/sMgTChKwAACAASURBVF7KlYUYVePPfoCMuIR2RJKTBWur6ZwgKoTqHAn6gT9986ySLAajev+4TOTk9WKct06MfOzpC+Px9i5Es5JeNC6xtfsgzj71uBG9fjVQjQhKJ3wCdy6chUUrNuNI2gMJXTthmvd12182ZwoumzMlp73mdGmiTBU9Isli58mryZPRQkY62d1PL70u4L1ROE7dXPDpN5WTk+e4m1h5OhxLDD399UoRTK/lykJYdQnT5atL6GRnT7WcE0SFUp0jd1wyu2QrCqve//aFs3D7wuKet06MfLTy+a1/fl2zhbcecpSL6toZAI4J+oe++zPe34hoViHswVhc2U6Y5v1822ffO7pxMQmicqv4EUl2mT4ZdarGhJOvO+3EMcPWaTJRilE4xX6C71ShWT79pnIzzR1eHB3j1SWXq2XEomldQqcLojvR7hFVEl27Uar2Qff+xbwOLna7MdwomoAPmD5xzNC2XmtnS0k9Kk7gtoXJDqTG+hB6+sKQMnPUVyIh0dsfKcr9md3tvXr9QeQkdiSlNDXUYTCW+dRU1+NtsXqs7TBpTExe1y6nVoNzMqk6NYTUic46rhBFTjC9CLWbO7w6PLuQPO0G1XIBWkgedLIgulePcyJTI+mwyG43Sn3eqNqtYl4HF/tBnyqfp7tszhQ01oeYf2xIbzNkQiIcl/D7BK5d3j70eXX2DqAuGMDhcGzo9+ISuODudfiJprB5vuNHda7YOd68ev1B5CR2JKXJ7vHO/nc2uw33yrYuLFq+GX6fQDwhS76ig1NPw50cheP0E3wnOuu4QhQVE1ezUjPN06aceIJcTSMWC8mDTjyU8fpxTmRXoW2F6lxy23lTjHxc7Ad9qnyebllrJ66Ye5KrPkc3s0aOXnD3OgByaKSX9XnpPu9Iamq79ZnaOVYKvR9Lrip3CImEs9cfRF5TFR1JdpKLqse7LhjQPjG223D39IVx7aPtGcX4rnm0vaSNSb7VCkayD06OwvHqE3wnOqhMcSi19zl5Me9UPigF0zxtyqnOOytXXre8HX7hQ1xW9ohFp/Kgyffj1TaEyEShbYXuXHLTeVPMfFzMKXzp174+CByJZo5S8fsEVrV3KzsZmH/Uug8OIiAEImkx67ibNXkcbrt4Jq59tB2RrMLm1jbrOvYPe6wUej9mHYc+iJzFJOxef/C6nCpVyTuShBCTATwI4AQACQBLpJQ/F0IcC+ARACcBeBvApVLK3pG+nxMrsZk03Fu7D5Z9RYdRNf6cudyD0YR2JR0TTo3C8eoT/HI3FhxKbV+pc5GJzt4ByOwnXwlZlIvQQvNBuY9twNujIL282pwbmH4/XmtD3JyPyL0K6fjJdy655bxxIh8XcwrfvObxWPLl03BoIIZrHm1HOHb0M+sPx3Hf82/lLGVfrOtup5XjPu06RSdR+nGXPmopkvZZRxPJz9TOsVLI/VjHnsPKfVPtY76/j9flVKnKsWpbDMA1UsoPAZgL4CohxDQA/wZgtZTyFACrU/8ekUJWYgsFfDimxo9QQF/pP99NXi7dDYP+RqKnL4z2XQeKthJAfySOkD/z/UJ+oV1Jx1T2ygb52P3bTFdeMHltp5R7VSauKGGsZLnI1KgaP8JZFy7huCzKRWgh+cDpY9vJvGCXkysvlmK1OdP8V+58acr0+3HyWHGIa/MRuVNPXxgHB6KIxM06fvKdS245b5xeCXck10tWe/gvD72M7zzajtmTx+Zsc0RRiLumiNfdDivpfdo1y9pyOmpq/MBV5zRnxJqPH42fKFb06z44CF/WPZV1rGS2c+r7rkMDUWVbuLKtK9lxpehEqgv6UOMXuOGz03LOjfT3LPd1udfaefKeko9IklK+C+Dd1H8fFkK8CmASgIsAnJPa7AEAzwFYPJL3Mn1SI63/L8XQv1RMbvKmTxyDgA9I60DPWNEhm2nPtZ1RAk0NdYhnDbGNS1nyJ0ymf5vJaKdy9/i7oa6Am4ake0Epc5Gp7oOD2vhIV55qaqiD8Amkj9EWPqHNB04f28maBUenfN2+cNawecGJlbicfBLv9HRC0/xn+pm7QSHfj5fq1rk5H5F7WNd8W7oO4uYntyHo8yGeSCDoF6gN+G2VGBjuXBrpeVOM0atNDXUYiMYyYgPRWNGuWwu9XlKt2LZph71BOZG4xJaug5g1eVzhO14CpcxFW7sPZdwfWRIS+K+/vom717yBmy6cjsvnTgWQe2yu69iPrz/YmjEiDAAi8Ti2dB3E55dsGGoXb5g/Led+zCeS09tq/P6MttP6niOKnfMLIJ6QqAn4cPOT2zC6NjDUfma3xVed02x0nBVz5He574uoOpRjRNIQIcRJAD4CYBOA41PJy0piynGGQogrhRCtQojWffv25X19kwr7R58YSxyJxhGOSW2v8Wu7DynfTxVvrA/hp5fORiggcEzQj1BA4KeXztYmEJOea5NRAkKIvP8eCTs93oX2ytsZ7VTuHn/AdJSaM9wyJN2LnM5FprZrcowubsL0iXMhT4btPgWznkam593vLGvL+3sr27ow/551+MHj2zD/nnXDjo4yHe2UnquLNQpyJNMJi51bC/nM3aDQkRImI2bdwjQfOZmLyD2sa77L79uI6x/bMnTOxxLJG+J7L/8I1i8+d9ibRTvnUqHnTTFHr5pet5qMvij0eqmzdwABX+HXzzc/uc31uTad89dG6of2sURy9HQkLnH9Y1uw5K9vDv3MOjYBDI30zZaQwE2rtmS0izc/sQ0/WDBjqI2v8Qv4BBCOyZy2U3XdAwBBH+DzCUTiEn3heMbvqNriu9dsRzjrPlR3nBXz3HHDfRFVh7IV2xZC1ANYAeDbUspDdjs2pJRLACwBgJaWlmHL5dtd4cfk6cT+vkj2r+eN2326Y7IPJqMEOnsHkk+q4kef7tQG/EV5Gm63x9vJ0TJuKB7s5FQku5wsfl7JSpWLTOw5rG7sdXFTJk+cTS+4TZ6CqZ5GxhLJ+NmnTsjZ3nR0lOkTOZM6RiavbU0nTM8RdqYTOpFbTT9zN/HSCKNCFZKPnMxF5A6qkTDpavx+jK2rsX1OOHEuFXP0qul1q2muL/R6qamhTlsvxw4vjRIvxbXR9IljEfSLnNpF2W7502sYFQoMjUwC1G2fRfV6QZ8PMyaNxQv/dh46ewdwcCCKqx56OWPxDuv7UV331AR8+Okls/DdP7yScVymP1jL3p9IHAimbgNqg8mOKdVxVuyR35yhQKVSlhFJQoggksnpISnlH1LhPUKIE1M/PxHA3pG+j7XCTzqrwn42k5ulGZppabq4XSb7YDJKwKmRKiY93oXug50nTE4WE7frtd2HjeJOWTB7EtYvPhe//docW08mq12pcpGpT0073ihucWI+vMlIEPOnYLqLx/wd/ul0ea+QkTp26xiZvvbQdMI0dqcTFj+3mn3mpdCx5zCWt+5Cx57h86UXRxjZ5dZ8ROWnGyFhKeSazu6Ib12bkv2zYo5eLXQBHJPRF4VcLzXWh3DThdOG3U7HK6PES5WLGutDuOOSWajxD99J9f3Htw59p7r6YPlYn7113E+fOEZ7jKmue36ycCbOeH+j8ndG1fhxcCCCSDz34ZDVqRVPJPDE1WfmfSCUzi9EwbMaOEOBSqUcq7YJAL8G8KqU8qdpP1oF4CsAfpz635UjfS+TE6mxPoSWqQ1Y19EzFPvY1AZlAxsM+JFdRUmk4irJehSb4fcJxBMSty9UPykxeUJi+rd9bGoDnrfxt5kw6fEu5OmP3SdMhTztL7b9mgsWXdxJTi29XWlKmYtMtZzciLOaGzPO2bOaG9FycqP2d0yeyDpVr8z0KZjqaWTQLzB9Ym7xUsC8s123j7pFFOxub/p3muY/09x66WlNeHDjzqHYpS1N2tc2/cwBZ1fsu/GxVzL2/YozpuCHF324qO/hBW7OR1R+qtwHJB+kxaV0ZPRxvnZC9bN5zeOLNnrVJGeOZPSFyfWSlQchU3VyDPre/QII5lnEx01KnYsWzJ6EcccE8c+/fRlH8ly3B3zJjpV1HfuHjhurPlhNwIf+sP53g/7cqerDHWPp1z2javxD9xTZv3NpSxPm37MOQZ8PCYmc+0NLNA78actufENR11F1fvdH4tjSXVhNLc5QoFIpx9S2eQC+DOAVIURbKvY9JBPTMiHEVwHsBHDJSN/IOpGufbQNAj5I6E+kjj2HMzqRAOD5jh507DmcU8x1VI0/J0nIVDxbT18Y1z7annHRfs2j7drhinYLyZokiY49hzNuSPP9bSZMe7xNhlKbDPM0LR5sys5N1JnN443i5Aoly0WF+M3X5mL1tt34y7Y9+OS043HetBO025qcL04WzzbNCY31IXzx9Ml4cMPRjoQvnj5Zux9W3rsurVC0Lu+ZjlQ02d7pws+mT+OXvdSZEVvW2olvnXeqtlPrjktm4brlm4dity/UX2A6WZi7Y8/hjE4kAHhww05cMfekohVR9xBX5yMqL9U13w3zp2HGxLFF7+Dt6Qtja/chLFqeHKGZ3U709kdw3fJkIeL0n61ffK6t69LhXl91Iz/cgjLZ+TISNxt9kdyngwAEpk8ck/NeVqeXXxT2kFII4Imrz/RKXit5Lpo+cSziifw9cwmZbKezr19CAeDWf5yJbz38d23nnk8A87Kux3v6wpjaOApPXH0m+iNx5THWWB/K6Liyjun1i88d6mCaf8+6jP3J555nO3DZnCkAMHRcW//9nX84Fbc89VrG9jc/sQ1zTjpWu3/5zGsejyVfPg26Y5qoGMqxats66NZgBM4r9vu1vv0ekjk/eYK3vvOe8iK4bdcB5e+37TqQk/j7I/GcpxF+AWXjsrX7YM5c3WhcYmv3QZx9am6dupVtXbgurePrJ5fM1l60221kTf42i53OEyd7vJ0e7WSX3ZEbDaNqlKPUGkbVaF/byaf8NLxS5yJT6cfeyvbuvKOGTM6XQp7eOjGqEkh1grTa7wQB7NcxMh2p2B+JK1fYVG1faM6x+/Tb6afxEoCUiaF2RscqzJ38TJKfw3eWtRVtxb5C2qZK5fZ8ROVnWteokGsMq93xQeQUMQ76fHho007cs+aNnDpBVs7R7WP2anO618/OW3ZyppUvr0l7aBtPJLC+Y//QClz5PoeVbV0ZD3z9Arjz80evvYerT2VHTcBX0lHyI1GOXPTnLbsRyzNNLeATuPHCaeg+OABfVq2mGr8fg9E4jqkJZNQ7yt4m/dhSXdtbI3/SjxcAygdvT1x9JgCg+6C+TpPu77jjL9ux4uVdyf2OxSGlRF0wgHAsjlDAl3FeyITEBXevQ8hvtvIaV2yjUilbse1SMHnaOVszdFAVj8biOb3ecZmM59Ll4tx4T18Y3364LXWjlEwk33o4/0W7nUbW5G8DzJ5AmyzFbZLYnBztZJdpQfOarJvWGr8oWmFIqi6mo4aaGurQH8m8gOqPqJdKNj23ChlVafdcNO0ESa9jZCnWSMVRNX5lEWrdCCanCz+P5Gn8cN/n0c6h5O/pOoecLsxt2jYByXbdTntjYYc9VYL04zjfVJfsDhuTa4zhOkwi8TjufbZDWWw6PedkX5da1zsBn0BfnulHI6nhMq95PNLL0MUSybbh8GAs7+fQ0xfGouWbM9q3uAT+9ZGjOXFr9yH4tNfyyWlTMiERyzOg5kgkUfA0pUr30MZ3cP1jW5Q/8wOADwgFfLhpZXKb7DYpEo+jNuhX1ieypB9b+a6vskcfXXVOc841SnrnTiQexzADqTL0R+L43d+S96Xh2NFrtqMdYLmL9gASkZj90eOqv++65fl/z7RdJbKUpdh2qeR72pmt+fjRuOKMKRmxK86YojyhtnSrl+BWxadPHINA1qcc8CXj2Ta82aOcMrfhzZ6cbS12ius2Hz8aZzZn1lY5q7lR+beZLg29sq0LF9z1PL73x1dwwV3Pa5erNC2GaFLgN/137BZhbd3Rg5/+5XW07tB/tiaFI01WbeOynNXLbjFs06Klvf2RnIuZhEzGs5meW/lGVer09kfwxp7DyvdP19RQh4FoZgfYQFTdAQbkr2OUrbE+hEtbmjJi+WoHdR8cNIq7hfn3qe8cynZoQP396eIWu8WzrZGc6fKN5LzxsVdw/p1rce3yzTj/zrW4ceUreV+/mMspE5WL3ePY2u6yX23E9Y9lLn1+7fLNw56Pnb0D8CtW5jqmxo/aoA9Xf+IU1PhzbxsCPoEb5k9DZ+9ATtuWfr2j60SyXn+4a7x87WfyYV7m9ZZfCPzgiW15r7U6e3NHuADJzqSt3Yewsq0LX3+wFUeiufs+KuRHKODDP3zo+JzFFFR+sGorr/Oy9PSF8YPHt2p/HgcQTyQ7YGKJ3E4kkfr5tY+2I5Z1nSKQvDYPBQSuOqd5KK7qGAz6fNjafSjn2vyeZztyOqjC8WTHzuFwDOGYRELTkzQq5EfQLxDw6R9IZQul6j2NDgVQ4xdDK72l7+dwBbhV14/hWAK/27RTub2uXTVZBIOqV0WPSGo4JmgU/+FFH8aCmROx9o39OPuU8drCtrXZPUN54o31IVx2+pSMkVGXzZmibCz396lvWHRxu6NaevrC2LTjvYzYxh3voacvPKIn0JkjqJJ0I6gKmX5hMtrJ2h87T56/dN/GoXpYd63pwFnNjfjN1+bmbGfypD/fTWj2fnNZzupkMtLPtINlXcd+bVx13piNprE/qhIwL56s6gDTMaljZD5tzmw1M6dHFZqODJ04tnbYtivJ/t85pk7dVurigNn339k7gPpQ5nSE+lBAmQtN6yk5WQuMqFTsHsfDjiaKJXDBXc/jJ5eo80hPXxh/3vJuzvSrUCBZpDhJKlfJSiQkfvj4NtSkRmhc/YlThq5z8y3Rbr3+f33po5g+ceywi68sWr55qIRA9vRq5fVaPIGagA/pg3Wzr7WaGuoQVxQxB5Id5tmjca19vnH+dPT0R3DPmu14astu7X6ni8QlfrdpJ75x3im2tq8Gnb0DCKaOm0JIALGERExx4SABDETj8PsElqx9C/c+14FLW5rwyIudOdMqk8eOzDlWa/w+XHn2+/Dz1dtz7o3StxlQ/PC6T34AF86aCAC44y/bh0Yi5SN8Ak9efSa6Dw7i0EAU1zzalvHz4UYbW3WbVOfpPc++kXP/qWtXDxyJYlX7u0Ox7HacI5jIUtEjkkyXZF/Z1oUv3f83/Pf6t/Gl+/+mfeozseEY2/GevjB+k3WS/mbDTuVTiTOb1VMFVHGTUS1mIwrs32T8Zetu5Qiqv2zNbVRNb4yB5Pcx/551+MHj2zD/nnV5nybbfWLXuqNHWVRdNTLJWg0pnX5Eg/3PjctyVh/TkX4AILKekmb/O914zQW4Lg7YH8FnMqpSd1Gie6K14c0eZUeSbhSmVZ8una4+XWfvAKLZF4uxhPZpnrWaWTrdamZOjyo0PV5ufOwVLPzlRty1pgMLf7kx70idiWPVeUYVN/lMAPPv3yQXmowwBgpbipzIbewex6rtskXiUpmnVrZ14eM/Xo3//OtbOb9z0ayJ+PYjf8fVv/87rv59W84NOJCcIBtOG6Fxx9Pb8fEfJ6/D8q02Vxv04faFs3D2qccNOxLpO8vak4W5YwmEYwn867L2jL9DNTLzpgun53QwZOeXxvoQblowPec9k3lP5Fw7A8Adl8zGp2ecgF881wHTskd3r+ngqKQ0TQ11iEuDuWGGEjJ5v2O10w9u2JlzDFudpdMnjlW2R5+ZcQL8ec6tmFT3ML1vwqih43rFy7uU2wR8yf9LH0289d1DuPI3rfjuH17J6bzS3YOk3wPNv2cdzv9gbg1eq05UuvvX71DuV3onEpDZjpuODKbKVtEdSSZLspvcGDg1Xa1hVA38WcNj/T6hHOZvdpFsf0SByY3DW/v7la+qi5vcGJt8Hybbrn1DPXJDFdethqQ+Jux/boVM2yNvM5lOBCTP79pA5iib2kDuRYDlgyeonwjp4iYa60P46aWzEQoIHBNMDhP/6aWzlcer6c3+Oz3qXKGLJ5e6zozFpXrYuFktu6OrmYUCvqH/u+OSWXkLXKez00lhd2qjyfFi2nnTfVC9j6q4yWcCmH//JrnQtJ4SO+zJ63r6wjg4EM0ZXRBNJEdhpucSXYdNtuw8dbTuXO7NfF3Qjz+2dWlHYuQTjiWvwwDknOM/+l8z8Luvz8X6xecOFcPOlxc3vNmTs6JXPCFzrqMXzJ6E9YvPxW+/NgfrF5+Ly+dOtZVfLp8zFT/63AwE/QJ1wWSe+/6F07X3EGPqgtppgMMRQj9FuxqltwHHBEt/S1oX9OG7n/kQpp04Bp29A7hh/rSM4+WG+dPQtutAzvW99bu1QR++f+GMvNf/qmmXAHDZ6ZOx6XvnY9P3zh86Zuc1j8+4n8k+7lX3IKp7oGde24vsy6Ls9q+nL4wVL9uf7t2268Cw1xucDld9Knpq2/h6dZ0FVdx0lTAnpqt19g4gIKy1cZICqUZHVUh2MOuGaDAWV14kT584RrmimKrjK31p6PQVmlR/26emHY9fPZ/bm/2paccr/za/L/Ppjt+nL0bt1CpUZ58yHnet6ch5v7NPGZ8TMz0m7H5ugPm0PaewEG2pmE2bMjm/AfMVygCz797u1CnTm/2pjerRnbp4fySO2qAvY/pGbVC9Gs7bPUeUr/F2zxHt/svU/7emk+mYfj+A6VQ4+8eL+cpnZlMV7X4mQGHFs+1Os7TqGD64IXPanC53WjWy0rfPVyOLyE3S80U8kUDQL1Ab8COaSODSlibMv2fdUC65Yf40zJg4FjfMn4abn0gWlh6MxRGP567JmH0zmW/q2UA0Djv39gGfUE4t0q3kBiQ7xQ8ORLDrvYFhi4KveW2P8n2tjp7stiz9HLebXy6fOxWfnnFCRqFyVUdR+kPjQUXdJDvYmZ3Juh5+pHUnlr7wjnIUmFMGogn86MltiCaSBb2FAG747DTMmDQ2eRw8kTwOVNcYsUQC379wBi6fOxWv7zmU0dbE4nJo1UBVJ28oIHDNJz8wdDxa/9u+60DeqaCqexDVOZyckteMe5/r0K78muzg8uWM0PrAcaPw+t7ch3mzJ4/Le73x4Ia3805r5/1GZarojqQPnpDbUaKLmzy91I1UUdXeSE5LezXnNVTT1UwKNgOAzBoOmv1vS29/RDkqqrc/MqJVlw4ORG3HTWqbAGbfh8m2J0+oV76fKu7kynGm9VWcSMAr27pw3aPtEEJASqmtnUAjZzKdyJJdODL73+maGupyLjsS0F+wDldvIlt63Zu71nQMW/fIrjPen1xpJ/0exCeScRXd36OKm3ZqZD6ZT1405qupYzf/pr+23Xo91gjH9Atq3QjHkzSdbrq47qmvKm76meiKZOvilh37+oY6KfPlt9OmHouH/7YTAj5IJNAy9VjttuY1sojcQZUvQgHg3ss/golj6zD/nnUZP7v+j1tSozUlvnP+qagJCNzy1Ks5bULQn7xJTj/+hxvJNNyK96GAD188fTIe2vhOzraReO5KbivbutJWjTwqOy8CGKr38uQr6hpEZzaPH/ZaKnsp9/ZdB7TXUVbs80s25FyvhgI+JKTE9xdMR2N9CD19YRQyI+ui2ROZf7KsbOtS1qKy1PiTHZUmq6NZvxdPSPhSMz3SXz/94br1VVsdKjc/uQ1PXH0mbn5ym7bmWPL1ktvOOflYPPJiZlsjkVz5b9wxQUyfOBa3XTwTi1LHaSSewNWfaFa+5vDnY+bqc9Y5oiodctmcKbhszhTtvYPqvWr8Prz9Xu5DuEtbmtB8/GgcOKJeaCPgQ94ahvnO09YdPRkPKdnh5C0VPbVtV6/6ibQqbrLCT2fvAGRWRpMJqRyu2jCqBtmLOfiE+sK6PxJXbqur/1EXzOwHrAsGlPvwP4qaRfniQPLCfvWre7BjX592m+Uvd9qOm0ynAI4Od02fTqOb8tBYH8LHpjZkxD42tUG5rW6lKVXcqZXjTOurOLHyUE9fGP/6SBsicYlwLIFIXOLbj+Sv2UOFMz3+t3YfUnb+6qbCAfY7nuzUm0hnMnVqXcc+5Wvo4o31Ifzs87NR4xcIBXyo8Qv87PPqaXPW9nbzdPPxo3GWzdUqAbO8rprW4Bci76pyqqkZuu2tEY41foGgX6DGL7RTyoIBv3KqdTCg7qTfosmBqrjpFD7d8ZnvuP3SfRsz6jt9+b6Nyu2svBmJA+F4ApE48uZN1kgitxnJqp01fj/G1tWgPxJX1kLqj8QxGE3glj+9hlv//Lqydo9fCNz85DY8tPGdof3ImFqkeLAX8oucunTpwrFEchRJInlznp4X44kE1qctBNHTF8ai5e15p8oFfT7c9/xbOOPHa3D5fRtxwV3PK7ezrpUXLW/PuJa6bvnRtuyhje8Mvc7c/1iNObc8M3Qdlf4ZpFN/9gKxRAI1foGbn9iGVW1deGjTTs2Ykfwe1ZRHqFY9feG8nUhA8iHNvPfnW0BCLRKXqPELQMqc18/XJ5VISKxq77Y1ddGH5Ggc1aJ9cQl8/cGX8PEfrwEArF98Lr5+9vsgpcQvnuvA3P9YjSV/fTPjOMy+91HVULI6ZK37gs/e/Twk1KVD0u9JsvNPY30IN3x2Gmr8Yqhm2TfObc6ZhndM0If5M0/EXavfwOW/3qSsU3ntCnWdpHUd+7F2+96c8/TaR9vRsedwTvv/qTufM7rfsbMCNzmrokckdR/QrKSliPf0hXOWRvzdpp3Kp5cmI4e2dh9UFpPd2n0QZ5+aWQwtGosrt1XV9HCy/oPdVc1MvK4pcP767sM5n4NFApAy+Xfly+cdew7jeUUB7Y49h3NuGrs0NxG6uBNT0EymzDm18tCGN/drihzvx/xZIx+VxCcKmQ5pRu/p4i+/8542nr16IpC/4yl7+3z1JuanVhhJl68jOvt8OHBE/ffo4oDZOdbTF8Zvszq1frtRnad7+sJ44a3Mz/GFt9SrVQJmed109Gg0FlcueKCr1wQArW+/h0ja/rfRJwAAIABJREFU77S+855y1FhTQ13qojHtaasQ2rZgfH2t7bh5O2M2hTPf4gfZ0w9NV7ssZIEHIqeYjEIe7rwbrhbSgGYUxWBMApC4/rEtqA/5EUvIof2YduIYrOvYh1ueejWzE0oAP//CR9Cx9zB+tjq3LEA6CWQUTo4lMq9XOnsHUsuu62/jB6LxoaLfR1dbU0yb8wv86MlXc2o7hWPJldGOHVWD6x/bkvU6GFohUvUZAOrP3srD/ZFk/Lrl7dol34dj1UhVtbXVSLUYULZoAjnX+HYNKGp/DScSl/jl2reUheWzHYkm8N6RiHJqJ3B0lNM1j7bjT988C3eveSP596b+5lv+9BqCfgGfAG68cDpmTByLw4MxAAIQgN/nw40XJqeuWtfTqvuCbDWBZNvY2x9B264DeK8/gjue3p5RekMiOaKqJuBDJC5x04XT8OnpJ+CuNW9k/g1xia890JpxPZIuLoGcgpQptzz1KgJ+X855GolLfPpna5H99by+JzmlLt8Ixf5IsozAtx7+e9HvVclcRXckbXtX/eRVFTdZ9v7Ft9U3eS++/d6IOi427VC/7qYd7+VcVDfWh9AytSHjIlw3CmfOyerh/6q4yYW97qZJFc/XkaRijZhJz83ffqRN2YFiUiOkUzNKTRc3WYLbrqaGOvSFM29w+sLqGxzTmye79veph6fq4iasaVPpDVa1T5kbU6ee2qOL56vvo2LSUfWqJi+++u5BzcWt/c4BkwUOLCY3WCYdoKYdZq/tVo+aeW33oZw8km+Emaoj7KlX3lVsnYyr6jWZLnWv6qTSMSnMbtLOAOZTOPMtfpD9uTQ11A3dBFoOa/KmRdXBRlRqpg+ErFEJi7LyorWt9TO/T6A/XFidnr7w0amqhwdjQ3WKJAQCPom6YAAD0RgSEskVJAusWxP0+bC1+xDG1gWx8a0e5TLp6XQ35NnCMYlnt6tHut61ejvyPn1MSf8MrO8i/bOXEsrOhFhc6u6bbVnz2h52JA1xZ06204lk+enT23H5nClY+sI72m2icYmlL7ytbJut2PV/3JJT6xIAbn5iG9YvPnfo/M9X18zSH47jlqe2YdOOXuXPv7OsDX5fZn2km5/YhjknHZszTT+ekCgsyyQ7jCJx9W/b6eML+nx4aNNO/OK5ZCf2YDSRHBElkbNPz3f0YPW23Rg/utboITYffI9MRU9tGxVU95Op4iY3Yi+9oz4xVfH3+tU35qq4SSdHx57Dyg4f1XST3YfUI7NUcZNVzSY3qGtwqOJjatXfhS7+9NbdyhvGpxWjIxqOCSpfQxe3q5Al2+3IV7MqWyE3T3aGz89QFFrPF7f7utYw5XAsgSOROMKxBK55VD9tqlqY1KUBgHNOVdcI0sVNHNEU4NbFPzX9RNvx40arG2Fd3HSa57Z31Z09qrhpp5ZJXjctWN19UDM6VhM3Wf1MtQJovvhrms57VdyknQHMpmcDwPvGq9sQVbxtp/r70cU7eweUdaw4tY1KrZBplukrkD1x9ZmY2jhqKC9aP/vd1+biR5+bgVCg8Jtxv0/gB49vHcrB0biE3+fD9y74IIRI1mkrtBMJSI4C/PqDrbjsVxvwH396reDXMRFNACYFjPw+gWdf25vx+T5x9Zk5DyIsI60F/eQru6v+msgycax6hKyX+ARw7gePyzsFFACWte4a9rVU51qyM/bg0PW33RUadZ1IQHKgRHZbHfT50LbrQE7ZFBMjSEVKkXgc9z7bkeyET420jCs6kSxX/uYl7bQ41T2MndIhXIkuv4ruSGrRjMRRxd/VPGFWxUdrOj9UcZNViaJxdWJQxU1qkZjcfKlWL9PFTYptf1RTFFUX79inXv5bFd+luRhTxRuO0RSDVcRNl2wH7M3XXdeh7qxTxXU1qnTxlW1dOP3fn8H/+sV6nP7vz2jnFwcD/pxbXgF9XRW7dZpUw5SjcamtTVUtTOrSAMCxmulHurjJqCFVHYx8cZMCyrpCjLp4Z+8AwlnTMMLRhPYGy6T7ZrKms1UXTw4ntxefPnGMsi6RaiVMALjktCajuEmh8Hd61LlAFzdZSdQkVwFm07MBoEsz/VwVf3yzelSXLh6NxZX5O990QqLh2K1zlC7fVLX011PVMHm7px/z71mHy+/biDN+vAYPbXpn6GezJo/D5XOn4osfm1zw3xONSwSy7yilxA0rtxRt9axwLDE0LaxUVLP7/EL98KY/HMdNq7ZmXNv0R+LKJd+LQaRWYyb96F4vORJJ4KW3e/HDi2YM1TRS0U0NG85ANIZ/WvoiLvvVBsy7dQ3Wd+xP1VEa2S189q1lJJ7A7MnjbHVS6cg8p4ydFSAFMmtCXf2JU1Djt/93xiWUDyVXtnXh4z9ejS8u2YiP/3g1VrV1oXVHD659tD3vg8wbH3sF59+5Ftcu34zz71yLG1eqa0HpVEMNp4ruSKoPqTt8VPF3NFNGVPHTsgo754ufMEZ946eKn6bpVFHFdQ28Ki40N5mqeMvJjRhbm3lDObbWr5x+se+w+kJKFTeZTgEAMybaj4/XDEVUxd/UdMCo4ocG1De/urjdorEmHYb/s0297K0q3tMXxrcebkMcyZu3OIBvPqweQTWqxq8cFaWalmgyauTQgPpmXBevFiFNB50u3q0ZnaiLv7lXc1wr4k2akYS6uEkB5YOazhhdfP/hQeVxuP+wuoPBZN9NOpiTr2F/RbjG+hAuO31KRuyyOVO0w6KnNo4yijcfP1qZh1XT2o4dpX5PXXyGYuU3XbxWc6Gqi+ebnq1yJKI+LlRxXSedLm46PZRoOIUufKFbuGNdx/6h15tzyzOY+x+rM147ve3tC8cRiSVw/R+34KGNR6fQPLTxHSzdsDPPu+sF/QKfmz0RR7I78+Myb0Fsi50bCIMZQkWV3Tl2acsk/PCiGdqbeatYuXVt09RQZ7xKmF35ViyuPu6c2mbq52s6cP1jWxBLFO+YD/iSo4ZiiWSnT38kMXSMHh6MITGCDh8B4KYF0zI6S+OJBLa9ewiXZj3gOqu5EbVBn61jVnV7c0zQh9qgD98871TUZvUmhQIiYx8kknWh7r38o1i/+FxcNmdKwR1b1qhP1eySbz7choW/3Jhzz5w+UtRkoRkVu/eEXue6jiQhxKeFEK8LITqEEP82ktfarvmyVXGTp/QHNNPVVHGTi1mTaSERTaZSxccdo35dVbx1Rw8ODmY+sT04GFf2ptZrRmap4qarVj3Rrn7KrIrXabq5VXGTaX4m4x/y1ZbK9u4Bzeg3Rfx949U3mqr4bza8rdxWFTeZamMyLH9MnWYKoybuZsXMRXs0x50u3qPJMbr4a5pcp4pP0nSY6OJGo500Q6J18ae2aGoHaeIm+x7TdNjq4ibTm3v6wlj2Uu7S8rpRCnc+/bpRfPW23co8vHpb7tTe7Hprw8Wzbxrzxc1KZwOdveqRpLr4OM0IUVV8Qr16W11c97B2hA9xy6KYuYj08o02Mp2Gmy19qtr6xediXvP4jNeLJZIPAtNfe2v3wdzRQgBuWrUFHXsOo6cvjB88vtXW+9cojnsBiZVt3bZ+32uyay398e/d+P6qLcPWYPIBQzUob184c9jpSoUI+PTTfd2u2Llo+sQx7rsRdYlEAsrOTJmQuHHVFuWoO7uEAD5wXH3GFNBYIllI/pHWzGubF97swT+f/T5c96kPoD5k1gFa4xe46hPNWPLl0/CZGSfk/DwhgUDW/UXALzC2LojG+hDadvZi+on6khv5hGPJotyq2SU66YsaFLLiOZDsgLrtz68Oe0+oa2+8NorJVeevEMIP4F4AnwEwDcAXhRDTCn09k1E7UnNprIo/p6kjpIqbTFFo71RPc1HFTZ5w64bnquImNZLMRjaY1RR5VVP4VhU3+dw+/n711D1VfEydusaSKm7yuenqDajin5yem3h18b9r6qqo4/ZvEZsa6jCYNR1kMJWgs03XjHbQxd2q2LnIdHSEyZRYADhtimaUpCJu+h2ZxA+H1Z0xurhZVjDbl4BmOLQubnJedvYOIJp1ZRKN6afkbdPUJdLF/6IZiaiK79WMDNXFTc79sGYamC7u96kvMnXxoOa7UMU3vKUe1aSLv7VffW7p4m5V7FyU7rcv7MAl//UCfvvCjmK8nKetbOvCaf/+DC66dz1OU0wLVz1Q8UFoR2xaNwkdew4P3SykL8Xd2Tug7dQGkHovoRxBE0sAF9y9DnevfiP3F3UUxacTceTUEdNR3T+WabBRQaJxaevG+0g0gbvXvIGOPYfx8ju9I66HpJJvVU03cyIXNdaH8H/+v/cVY/c87QTFgAHd4RqOS+XIHxMJieSInKzXkQmZk5fiEvjZ6g58//FtRkXIgeR0vtv/sh1fe+AlfPbudTjvA8chFEiOzAz6BRIJiYFo5vVEfziOLV0H8ck7n8NXH3wJL+1U39t8eFL+DqZIXOLnq7cj3yqR6YJ+gdsunone/giWt+5Cj6YMgOr1rHy/6NE2nH/nWvziubeUv2ndE65s68KcHz2Df/zFesz50dH2ZrhRTI+9vAtfe+BFPPby8PW28ilm2++qjiQApwPokFK+JaWMAHgYwEWFvtjkceppZaq4rsdTFZ9+Qr16W0V8S5f6BFDFj9M8WVXFD/SrbxJUcZOi2Cb7YDJd7aUd6o4WXfyM96mn+aniJvtsMsLHpEDyKM22qviHjlcfP6r489v3KrdVxf/hg8cpt1XFTVdXsrsqlGlNJxcrai4KCPXnpYt3vae+2dXFTaYgmRxTgFmR4w8ppl7li5/ZrO7Y1cWXv6iewqGKm9ZImqJpL1Tx/YcHc24w4lI/Je+0yeoOMF38w5rpWqr49BPVn602btIZp1n5SBf/oCa36eImRf9N2gTArN6fyxU1F1lmff/P+L+rtuHFt3vxf1dtw6zv/3nEO+pV1rTwdNnTwlV1jo5E4/j6g605nU7WFLhL/+sFnH/nWnx+yYacqXCqGl7pookEpk8cg3/R3GRHYgks3fCO7boryg4pTTxbwCfKNkWtHJ55dS/Ov3Mtlm7Qr8I1Et+/cLpXV4dyJBdd/FF1rcBqslv74Ke0Ion8xeQLrZsWiScQjiXw5JbdkDKBL82dAp/Qv9f1j23B9j3qkcwAEAr4cNP84fswH9ywE/90/4u29vG/Lv8oWt9+b6gm0v0vqK83J43LvIa08v1lv9qIZS/ln+589injh9qbmEz+/TGZbG9Wb9uddxTT3FuexreXbcYzr+7Ft5dtxhm3PG3r78pW7LbfbR1JkwCkd7N1pmIF+R/FNABd/K+aZURV8Tc1TzRV8d9uUh+Iqnhbp2alHkX8l2vVvZ2q+O/+pm4MVXGTGhdLNT2Zqvjv/tap2FIfP6SpqaKK/10zIkkVf+zv6pNcFV/xsnrfVPE1r6tvwlXx32u+D1X8NxvVx48q/voedWeNKv7EZvVwdlX8Byu3KLdVxRev2KzcVhd3saLmouffUA9R1cX/qKm9oYv/XtPBooqbHFMA8JO/qFfaUcX/ul3dMayLb9IM3dXFf71efe6o4s+8qh7Vo4s/8YrmnFDETfIIAOzSFJXWxU3aLtO6RHdqvk9V/Nfr1TleF3/mVXUe1MV/pWnHVHHTUX2mHaYuVtRcBCSfRqqmTlbryKTvadqn9LhV5yh7hbRwLHOKW/oUOGs0o7XiUPp2+Wp1BXzJJ+ON9SFs36Nf3KNUvn7WyeXehYrx+ZYmXD53arl3o1BFz0VAcppfdu0c8gbFzNthReLAr9ft0I5IHk5t0IfbF85Ey8mNOP9DE4bd3u4k0rf29+fURFJvdzR3p+f74aarntXciJaTG/EHzb3lf2uuq9a+sR+PvbwLuw9llrZ491DEeGSSE22/285c1SGZ0V8phLhSCNEqhGjdt0/d+WPpG1R/qap4x17NKmGKuEmB0GM1NSBUcZN6FIcGNfU8FPHX3lV3MKjifZq/TRV/7V1NXRZl3KzahkmNGJNi4v2av08Vf7tHfUyo4uGoZlqMIt6puXlUxcdrVsxSxTe9rbkpV8RNpsFtekdzo6qId2nqP+niLlbUXBTQTC/VxU/QjAzTxU2YHFMA0NOnzjWqePdBTZFwTfztHvVxoYsHNdPzVXHTjocDR9S5QRU3yZMAMKhZsUgXN8nZpn/nn7aqO9JU8Z4+TR7WxPccUn9vuviLO9W5RRU3m7oLrNys7ozTxV2sqLkIAFZqVrrTxSud3eNwwexJ+NUVLTm1M9NrBqqmwKm205U9AICbL5qOBbOT9+ev79Y/lS+FUEDgjPc3osaLxcVc6EMF1ntxiWFzEWCej7w4zY+SRtUEcM8XP4KfLJyJe774Eds1xYJ+X0Gr2M2eNAbrF587lB+vOKN4ndx2V5dOH9WcL99bvjRnCpb/n7n4zdf+H3t3HidHXed//P3pniMhCUmYxEAOCJBEl0CIOCIYQARPlAR/IOLKgifuLh67ooCyHuuu+xNYdVdX3WWRBZQfCIkQxAvkWES5AiQh4ZARkEwCIQwhZEIyV39/f1R16O6p6qk+q7r79Xw8kun5TnXXt7qqPl316e9xRNH1WMigDsfMn6abHg6+bgkrD1OLz/6kfTL0Ssqdx3S2pLyvg51zlzjnup1z3dOnF89EnnFkcNY/qPwD3cFNK4PKP/Sm4NcNKj/rmOBmyUHlH3zjvgFLBpcXjqpfrPyUw2YGLhtUfnrItgWVn7Q4+HWDyj9+VPDJHlb+VyHf2ASVnxbyvgWVl7LvPtAdPKVuUPmHlwS/blD5iYv2CVw2qPwTIcdPUPnJi4O/GAoqf1/IvgsqX3pIcH2Dyt/+uuBzMqw8waoai/7mLQeWVP6pt84rqfwTR4ccJwHlpRxTkvTBNwbHmqDyUmKYVFrclUp7H0t97dPfFFzHoPJS4qQkfSQkPoSVn7Q4+JwLKi91O9+9cEbk8qWHhpz7IeWlxFdJOiFkDLig8lJiliQtWxT82mHlCVbVWCRJy0I+g8LKm10px+HCmZOVKRhXKHeA1qAucEHLzZsxSad2j/5sTln+GIinvqHiBh9l62wzXXzKoYHbXEybea0Gzjhy390z1XW2peo60H1nm+mMI/fNW2cScmFh3bYbxJixSCo9HhXOatgRko3oSNvuWQ+Pnjd6JmnUXuHYukOZjI48sEundM/RkQd2RZ6Db8Q5ffXEgzSuPaVSpuL59PHz87qFhs3amnXwzOBu9UFOXBR8LZEr26ooq1i8l6QzjtxX//y+Q/KeE7aeDy+ZO+q4zq7vvYcEf0aFlYepxWd/AsJqnvslzTez/c2sQ9Jpkm4s98VOf/P+gVMon/7m0cmLUpY9/qC99doZ+WPqvHbGBB1/0Ogd2r1/V+iBUcmyF5x48Kidl/LLC/39O/9C7QVnd7t55YVK2bZS6vDJt87X+IIm4ePbTJ986/xRy0rSSYfN0T575reQ2GfPDp102OgkTinvWynbV0odSln2n//PoaPKwspL2bZS3uNS6lvKfv7e6W8cVVasPMFii0VSaftdqt35IpUWP2p5nkulvY+lvnYpdS8ljpRTl1LOuVJfu5T4U8qyUunHeSmvX+p2lnLcJlxVY5FU+n5qdqUch4U3vePaU7u7oRX+vdO/6RrnT3+du5wkXXTKYn3p3a9T2ryb5I606d8+sDhvmaC4JElH7B88wUJbSvrG+w7W37zlAKVz+p2YvJvA7E16dja4zpCb9nPevkB/OP94LV08y5/B7NBIiZi5XeO14m+X6PfnHaevLztk90x1fzj/OH371MUa154qeSa0lHkzKLelTGl7tc7Z18ndls621O66f33ZIbr3S2/TlR89XFd+9I2690tv0xlH5n8xYJKWHbqP2tO2+3VqMVOb5N1UzgsZL7BBVD0WZeXOanj3F48ftZ/OOHJf3f3F43fPevjjjx+h3/79MfrXUxZp+SeP0DlvXxC630zemDqdbSktW7xPYHesJQd0hX5BImnU530lxjq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\n",
      "text/plain": [
       "<Figure size 1440x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_rows = 3 \n",
    "n_cols = 4\n",
    "fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols, figsize=(20, 12))\n",
    "for idx, feature in enumerate(pub_df.columns[pub_df.columns != 'c5']):\n",
    "    pub_df.plot(feature, \"c5\", subplots=True,kind=\"scatter\",ax=axes[idx // 4,idx % 4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "explore correlation between all features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 15))\n",
    "hm = sns.heatmap(pub_df.corr(),\n",
    "                cbar = True,\n",
    "                annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "drop all high correlated features to prevent overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pub_df = pub_df.drop(['authors_mean_gindex', 'journal_rank', 'n_authors', 'log_authors_mean_sociality'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "missing values exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "recency                   0\n",
       "topic_rank                0\n",
       "diversity                 0\n",
       "authors_mean_rank         6\n",
       "authors_mean_hindex       6\n",
       "authors_mean_sociality    6\n",
       "title_len                 0\n",
       "abstract_len              0\n",
       "c5                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pub_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6 values are missing in the same objects, so we can just drop this cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>recency</th>\n",
       "      <th>topic_rank</th>\n",
       "      <th>diversity</th>\n",
       "      <th>authors_mean_rank</th>\n",
       "      <th>authors_mean_hindex</th>\n",
       "      <th>authors_mean_sociality</th>\n",
       "      <th>title_len</th>\n",
       "      <th>abstract_len</th>\n",
       "      <th>c5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>15</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-1.247485</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>22</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-1.045666</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>13</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-2.090505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>25</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-0.317449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>657</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>17</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-2.090505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-0.796709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>218</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     recency  topic_rank  diversity  authors_mean_rank  authors_mean_hindex  \\\n",
       "418       15        16.0  -1.247485                NaN                  NaN   \n",
       "422       22         5.0  -1.045666                NaN                  NaN   \n",
       "423       13        10.0  -2.090505                NaN                  NaN   \n",
       "451       25        17.0  -0.317449                NaN                  NaN   \n",
       "458       17        15.0  -2.090505                NaN                  NaN   \n",
       "464        6        15.0  -0.796709                NaN                  NaN   \n",
       "\n",
       "     authors_mean_sociality  title_len  abstract_len   c5  \n",
       "418                     NaN         46             0  0.0  \n",
       "422                     NaN        119             0  0.0  \n",
       "423                     NaN         51             0  0.0  \n",
       "451                     NaN         64           657  0.0  \n",
       "458                     NaN         38             0  0.0  \n",
       "464                     NaN         22           218  0.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pub_df.loc[pub_df['authors_mean_rank'].isna(), ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pub_df = pub_df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature engineering and model tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set default parameters for regressor and CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = KFold(n_splits=5, random_state=8, shuffle=True)\n",
    "\n",
    "default_params = {  'n_estimators' :50,\n",
    "                    'reg_alpha' : 1,\n",
    "                    'max_depth' : 3,\n",
    "                    'min_child_weight' : 1,\n",
    "                    'gamma' : 0.5} "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create CV pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cv_pipeline(df, params):    \n",
    "    X = np.array(df.loc[:, df.columns != 'c5'].values)\n",
    "    y = np.array(df['c5'].values)\n",
    "\n",
    "\n",
    "    reg_scores = []\n",
    "    features_imp = []\n",
    "    for train_index, test_index in cv.split(y):\n",
    "\n",
    "        regressor = xgb.XGBRegressor()\n",
    "        regressor.set_params(**params)\n",
    "\n",
    "        X_train, X_test =X[train_index], X[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "        \n",
    "        \n",
    "        # test transform based on train parameters, \n",
    "        # its important for preventing data distribution leakage from test\n",
    "        \n",
    "        transformer = StandardScaler().fit(X_train)\n",
    "        X_train = transformer.transform(X_train)\n",
    "        X_test = transformer.transform(X_test)\n",
    "        regressor.fit(X_train, y_train)\n",
    "\n",
    "        reg_scores.append(regressor.score(X_test, y_test))\n",
    "        features_imp.append(regressor.feature_importances_)\n",
    "\n",
    "    \n",
    "    \n",
    "    score = np.mean(reg_scores)\n",
    "    features_importances = dict(zip(df.columns[df.columns != 'c5'], np.mean(features_imp, axis = 0)))\n",
    "    \n",
    "    \n",
    "    return score, features_importances\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_bar(data):\n",
    "    plt.bar(data.keys(), data.values() )\n",
    "    plt.xticks(rotation = 90)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model score with initial feature set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7854656808074802\n"
     ]
    },
    {
     "data": {
      "image/png": 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RzGrZEuhfqHMHcGiRioar+Qbaw21fMjDL895SxbS+DfyEZred4/qO3wFUcTOZZmrnH9C26Lb9XUnPLFvShN1sP1XS5QC2b21bildjIQX6nwOnS+ptzvHbNFfFNbge2Ii+m0Al2f4i8EVJz7D9ndL1TON64BuSqruBBtwi6TG0N73VbOX4k5IFtTeLbwSeUbKOmdi+aeAX4X1Tnbue3dPes+n9P13M2qG+KiyYQLd9aTuUsBPN3ekf1DI1EPgVcEW7krA/mI4sUYykv7L9PuCP27fpk5Sqa4gftx8btx81eQNNz+zHSfov4D+BV5QsSNK3bO8xZHZVTbM1bpL0e4Dbq98jaaZX1uAfgc8DvyHpPTRTeKtYmdyzYG6KArTfKEuYfOPxk8UKakk6ZNhx26es71qg6Ydi+0u11TWOJG0CLLJ9R+laxkG7yO544Hk0v2i+ChxVy32S9qLwuTS1fc12Lb9sgAUU6JJOBR4DXEHfjceKrjar1k7z3NT27aVr6Wnf8v4VzQ3ciZu1tvec8i/Nf01vmu75ksNBkh453fM1rPyt0Th93RbMkAuwHFhW416nkpYCfw8sY3Iw7VisKKCdlXE4zS/AVTSzXt5v+9iSdfX5FPAZmhu4h9Psa7umaEXNjBFohvZ2Ye3+u/tQaNZSn1U0Qy3D+vEYKPb9JulDTB4GmqTwhdfg161Xpyj8dRu0kAL9e8BvUfjG1BQ+QTOf+gPAc4BXM/yHbn1bZvt2SQcDZwFvofnmriXQH2X7pHZRzAXABZIuKFlQbwGMpK8CT+0NtUh6B3B6wdKwvUPJ15/BytIFTGXUr5ukx9u+er7rmc5CCvStgGskXcLkG4/7litpwsNsf02S2pkI75D0TZqQL2kjSRvRTKv8sO17JNX0Dqd3U/snkl4I3AxsU7CeftvRLA/vuZvm/k0V1LREXsrkd4TF3kGMel9G0odsv3G+61lHpwJPnfGsebSQAv0dpQuYxp3tGPWPJB0B/BfwG4VrAvgocAPwXeDCtodKNWPowLvV9Bz/S+BDNAuf/qJsSRNOBS6R9Hmat+UvBorfgAeQ9FqaRVnb0NxTejrwHZoFM7XbvXQB0yj+rnrB3BSFiaZOS22f164S3aCG2QeSdqGZmrUlzQq0zYFjbV9UsKZFNF0fP9t3TDRfs9ILZHo9XI60Xbo74JTaxWt7tA8vtH15yXp6JF1FM75/ke2ntDM33mm7lnUZUxrWtqAWNdS2kNrnHkrTavVj7aGtafpoF9UG08tt/8L2atuvtv3SkmEOYPt+4IiBY64hzAHaJms1DJdN5wqacfPPAz9TJTvvAHfavhNA0kNs/4DmJm6MuYU05PIGYFfgYgDbP5JUfFjD9n2SntaOn9f2dulcSf+LZibJRKOkiqZpfVvSh3lgfZeVK6kx0DjsPtbOiCjeOAxYrWbruS/Q/D++leb+wzgoPqwxjfKdNOvLkPkh6WLbu/W6yknaELisks58x9HcoDqdycFUbOcYAEnD2uS69HTKHknnDznskvPQe1Txzjv9JD2LZlu1s2to7dszVbdFSa+y/S8FSkLS12w/d6ZjJS2kK/QLJL0VeJia/RNfD3ypcE09j6TpdtcfRAaKBnrl09yw/Zzpnpd0SMFVrdU1DpO0eTsNtX+hzFXtn5sCxd95tau5/5kpui2WCHNJDwUeDmzVzg7qvUvYnGaHsWospCv0RcBraDYRFnAO8M8VDnM8gKS/tv33BV734cCbgO1sH9YugNrJdi2b9k6r5E0qVbjzjqQv235R+86rt1Bm4s8a3nlJupimR8oKt/3ZJX3P9hMK1nQUTXO/R9PMQOsF+u3Ax21/uFRtgxZSoG9CczOot2PRBsBDPAY7FpUKJkmfoVlI9CduNml4GPAdV7CN2ihUcNMGZeeddTI4NNoe+67t4vuxSnqj7Q+VrmM6C2nI5Ws0DX96m1w8jKbxTxU7Fs2g1I2gx9g+oNdx0favNdDXtHLFrlZqDm5JLwa+bvu29vGWwLNtF5/1Rd3dFu+XtKXtn8PE4qyDbH+kcF0TFsy0RYbsWEQzLjYOSgXT3e1Vea//82OopGf7iNb7Lx9JH2z//JKkFYMf67ueKby9F+YAbUCVXpXcczjNjLStgdXAU9rHNTi0F+bQbHBBXRu+LKgr9Jp3LJpJqavidwBnA9tK+hTNKr1XFaplXfxHgdc8tf3z/xR47VENu5CrIgts30Kzb2eNFvVPL26Hbavqw7+QxtB3AU5j7Xzb3wYOsL2qXFWjkfRW2/+70Gs/imZpuGhWFt5Soo5hJD0EeCkP7HF/TKmaxoGkk4Gf0+yHaeCNwCNsv6pgTTV3WwRA0rE032sfpan1cOAm239Zsq5+CybQAdpGU9XtWCTpFJom/v1jc8fZ/tPCda0A/o1mxkFVO7ADSDqbZmrgKvq2KbN93JR/aT2RtDvNO5ztaX7Z1DSTZBPg75i8icS7S/4/1hSbqfQUnH46oZ0p9zrWbnDxVZqZcrVskbdwAr1vCt72tg+taQresNkYJWdo9NXwLJp9V18IXEKzIvPLvWXjpZWezjYdST+gaRQ2+MummoVGkjYH7u+/t1SapD+yffpMx2K4hXRT9BM0S3N7G+SuBt5drpxJFrVX5cDEDinFxzRtX9Au6NiRZn/MlwM/LVvVJN+W9MTSRUzhNttfsf1T2z/rfZQuCkDSE9XsXH8VcLWkVZJq+cX41yMeW+8kLZV0hqRrJF3f+yhdV7/iobEe1TwF7ziacDqjffxHwHsK1jOhneWyD82V+lOB4m99++wBvKpdKHMXa4c1irVzkNRbL3B+O+b6OSYvLCreZ4amQd2bbJ8PIOnZNL+wi03hlbQ38IfA1pL+se+pzYEqGsJR70Y0ExZSoFc7Bc/2JyWtpFn6L+Altq8pXFZvYdFuNDNdTgC+0XZhrMXepQsYYnD8fnnf56aOnuOb9MIcwPY32nH1km6m2bVoX5phqp47qKfHfa0b0UxYEGPo7ZX4K2mW/i+juZmxO/Aq298oWNew3hoTSnc1lLQXcG5NN32GUdM1s3/nnR8XLGckJfvMqNl04zLWTrF8BbDc9v4l6uknaaNaJisMkvQfwO/TtOH+Ok0bgPfarqb18IIIdABJq2j6uFQzBW+K3ho9xWZESNrT9tclvWTY8y7cBbJH0r40V8SPphnb3x74vu3HFy1sBIX7zDwCeCfNkJWAC2g2uLi1RD1tTZ+1/XI1m288IJRKDqP1qMKNaAYtpCGXi4AdbZ9ZupAe2y9q/6ytq+Ezaa5A9mFIEycKd4Hs8y6aX9DnuWmJ/BzgoMI1jarY2Gsb3EfCxOKYTWyX3lrwqPbP7wNv7jsu4H3rv5zJtHYjmjfTtA95deGShlpIgf4c4HWSbqTpOV78Blq/9mp4D5rA/Gbhvhp3SHoT8D0mv3Oo7e3cPbZ/JmmRpEW2z5f0D6WLGlGxr6WkT9MsirmPZrx6C0nvt31sqZps/6T99LHt+PQENVvkFeW6N6KZsJACvcYbaABI+gjwWJpFPACHS3q+7VI9LDZt/9yJZu/JL9KE+j5AsZ3hh/i5pE2BbwKfkvRT6pkRMZOSsyOWtfduDgbOAt5CE+zFAl3Sn9HsUbCjpCv7ntqMMi0chrkc+KKkqjai6bdgxtBrJulq4Al9PSIWAVeVHguW9FXgpW430pa0GXC67b1K1tXTzsz4Nc16ioNpdt75VC3zvacj6cO2j5j5zHl57atpml59Gviw7QtUuEWtpC2ARwB/Dxzd99QdpScH9Ej6xJDDLr2iu99CukKv2bXAdkDvrea2wJVTn77ebMfkfRLvpullUQXbv5S0PbDU9intauANStcFM/eZKRXmrY8BNwDfBS5sv4ZFx9Db7o+3Ufc9kH+2PendQtvioRq5Qq+ApAtohjYuaQ/tAnwH+BWA7SK720v6G5rVoZ+nGfN9MfAZF9g9aRhJhwKHAY+0/Zi2ncNHXcEejzX3mRnUTuvdwPa97eNiUyprNmxmUsnZSsMk0CvQ9kyZku0L1lctg9qVj7/fPrzQ9uWlahkk6QpgV+Bir93d5irbxdsB1NxnZia1hVRpkp5Bs4r2z2lWifZsDry45FDVoAy5VKAdw/xNmitzgEtsV9EzpV2qXsNy9WHusn13r4ODpA2pZybOtyU90fZVM59anaqWs1dgY5qJAhvS3KTtuZ1m/9Nq5Aq9ApJeTjPD4Bs0P0y/D7zZ9hnT/b2FTtL7aPp6/wlNT+/XA9fY/puihQGSrqGZuVRNn5lR5Qp9OEnb96ZUthMXNq1g/v4kCfQKSPou8PzeVbmkxTSLZap5K1ej9ofqNTQrgAWcQ3Pjqvg3dXuj8QEG51jXSBW0bq7RsPn7QNH5+4My5FKHRQNDLD9jYbU2Xidto7CPtx9V6buSm9RnZkzUMu+7NtXN3x+UQK/DVySdw9qFRQfQfMPENCS9iGb5/+CuQJsXLYyp+8wAxfvMVD6lsmYbqdn1bH+a+fv3SCr+brBfrgLrYJq5wU8CnkzTmzpm9kHgEOBRtje3vVkNYd7q9Zn5Ydur57nUc+X7RWA/mlW1v+z7iOn15u9vQiXz9wdlDL0CU8xvvXIcbqCVJOl84LmV9WgHQNJK28vb+yM7275f0iW2d62gtrGdUlkbSRv25u/XIEMuBY1J/4qa/RVwVrswq39XoPeXK2lCzX1mxnlKZTGSHkWzmUWvid63gGNo7nlVIVfoBY1D/4qatb1mfkGzN+bEVbrtdxYrqlVzn5lxnlJZkqRzaZrT/Wt76GDg2bafV66qyRLoMbZ6wxql65hKX5+Z83p9ZnqNziqo6wHGYUplSZJW2X7awLGqvgdzUzTG2XmSXlC6iGHaPjNn0NxIA9gaKNnjfoLtG9vw/jXN0EHvI6Z3vqQDe/332wWB1WyYA7lCjzEm6Q6aGQd3AfdQ17TFmvvMjO3WfSW032e9jV42YW2ztQ2AX9Tw/daTm6IxtmxvNt3zkh5v++r1Vc+AmvvMjPPWfetd//eZmg3dl1LpYrEMuUSXnTrzKfPmAklvBR4m6fnA6cCXCtbT75725uzE1n00G17ENCS9lmZD7bOBd7R/vq1kTYMS6NFlJbsGHg2soZmB8zqalb9/W7CefoNTKo+nnimVNTuKpiPqjbafA+wM3FK2pMkyhh6dla6Bw9U8pbJmki61vUt7f2Q323dJusJ2Ne9uMoYeMQ9q7jNT89Z9lVstaUua2UrnSroVuLlwTZPkCj06S9JFtp9e6LWvA15Cs9l3VT9kNW/dNy7aXca2AM62ffdM568vGUOPsSVp93b4AEmvkPT+/kUzpcK8dRPwvdrCvPUGYHfaxlK2fwT8RtGKxoztC2yvqCnMIUMuMd7+CXiypCfT9HU5CfgkMO0eretJzX1map5SGbOQK/QYZ/e2V8D7AcfbPp7Jez6W9B7gVzTzlTfr+6hBzVMqYxYyhh5jq736PRt4NfBMmmmCV1SyGrOqHh/9at66L2YngR5jS9JvAX8MXGr7m5K2o+l+98nCpSHpvcDXbX+1dC2xcCTQYyxJ2gA4p6bWpf0q7zNT7ZTKmJ3cFI2xZPs+Sb+StIXt20rXM6jyPjMfpNIplTE7CfQYZ3cCV7UbD0zsiWn7yHIljexUoNQq1pqnVMYsJNBjnJ1JZf2oH4SSfWZqnlIZs5BAj7HVLlvfGPid9tC1tu8pWdODUPLq+D00W/c9FNi4YB0xxxLoMbYkPRs4BbiB5op3W0mH2L6wZF1j4JG2q9zpKWYngR7j7DjgBbavBZD0O8C/AU+b9m/VoeSS8fMkvSBTKrsn0xZjbEm6cnCn+mHHSpC0O80ip19KegXNDdDja9iIueYplTE7CfQYW5JOphmL7u1MdDCwoe1Xl6uqIelK4MnAk2jqOwl4ie0a+sxMq/CUypiF9HKJcfZnwNXAkTS7yVwDHF60orVq7jMzk5Jb98Us5Ao9Yh7U3GdmJpIut71z6TriwcsVeoytth/6uZJ+KOn63kfpuloH0IxRv8b2fwNbA8eWLWlkucobU7lCj7El6QfAXwCrgPt6x0vvjVl7n5mZZC/W8ZVpizHObrP9ldJFDKq9z8wIqtqFJ0aXK/QYO5J6V48vp9nc+HNMXsJ+WYm6+kn6LPB0oBLrA2cAAAOcSURBVLo+MzVPqYzZSaDH2JF0/jRP2/ae662YKUg6ZNhx26es71oGjfOUypheAj3GlqQdbV8/07FSau0z0xsjl/Q24L9sn5Rx827ILJcYZ2cMOXb6eq9iiLbPzI+AE4CPAD+U9MyiRa11h6S/Bl4BnNnexN2ocE0xB3JTNMaOpMcBjwe2kPSSvqc2p+kgWIOa+8wcQLN132ts/3e7dd+4TKmMaSTQYxztBLwI2BLYp+/4HcChRSp6oI16YQ5g+4eSil8Ft1fj/9o/pdL2j4Hi+7DG7GUMPcaWpGfY/k7pOoapvM/MCuCVYzqlMqaRQI+xJekTDFnVaPtPC5QziaSHAG8A9qDpZngh8BHbd037F9eDmqdUxuwk0GNsSXpp38OHAi8Gbk4wTa/mKZUxOwn06AxJi4DzKpmHvjvwDmB7+u5V2d6xVE39ap1SGbOTm6LRJUuB7UoX0TqJIX1mapCt+7orgR5jq915p/cW08D/0OxoX4Mq+8y0ap5SGbOQQI+xZXszSY+kuTLvzT8vOobY12fmfEnHUmGfGSqdUhmzlzH0GFuSXkuzU9E2wBU0Mze+U3IMfUz6zFQ7pTJmJ4EeY0vSVcAuwEW2n9KuIH2n7QMKl1Z1n5map1TG7CTQY2xJutT2LpKuAHazfZekK2w/pYLaHtDsStIq2xmnjnmTMfQYZ6slbQl8AThX0q3AzSULGoc+M7VPqYx1lyv06ARJzwK2AM62XWzHHUn7AfsD+wIr+p66AzjN9reLFNan1q37YvYS6BHzoPI+Mxfb3q10HTH3EugR86DGPjPjsHVfzE7G0CPmx5f7Pp/oM1Oolp7jBh4v7/vcQPEplTE7uUKPWA8q6zNT7ZTKmJ1sQRexftTUZ6barftidjLkEjEPauwzMw5TKmN2EugR86DGPjOMx9Z9MQsZQ4+YBzX2mempeUplzE4CPWIeVN5nproplTE3MuQSMT/utH2nJCQ9xPYPJO1UuqhWjVMqYw4k0CPmR3V9Znps/3v/Y0n/BpxXqJyYQxlyiZhntfSZmUr7zuFM248tXUvMTq7QI+aZ7QtK19CvximVMTcS6BELTKVTKmMOJNAjFpipplSSXi5jL0v/Ixaeo2imVN5o+znAzsCasiXFXEigRyw8d9q+E5iYUkmzijTGXIZcIhaeaqdUxuxk2mLEAlb7lMp4cBLoEREdkTH0iIiOSKBHRHREAj0ioiMS6BERHZFAj4joiP8P/hudPq1sXQYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "score, importances =  cv_pipeline(pub_df, default_params)\n",
    "print(score)\n",
    "plot_bar(importances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate new features (based on previous data analisys and feature importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pub_df['fresh_and_actual'] = np.log(pub_df['recency'] + pub_df['topic_rank'])\n",
    "pub_df['auth_author_and_fresh'] = np.log(pub_df['authors_mean_rank'] + pub_df['recency'])\n",
    "# drop old feature\n",
    "pub_df = pub_df.drop(['authors_mean_rank'], axis =1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Score with generated features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8430999858942698\n"
     ]
    },
    {
     "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": [
    "score, importances =  cv_pipeline(pub_df, default_params)\n",
    "print(score)\n",
    "plot_bar(importances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "feature selection (Recursive Feature Elimination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_selection(df, params):\n",
    "    \n",
    "    best_score = 0\n",
    "    \n",
    "    for _ in range(df.shape[1]-1):\n",
    "        \n",
    "        score, importances = cv_pipeline(df, params)\n",
    "        if score>= best_score:\n",
    "            less_imp_feature = min(importances, key = importances.get)\n",
    "            print('score: {}, \\n delete feature: {}'.format(score, less_imp_feature))\n",
    "            df = df.drop([less_imp_feature], axis = 1)\n",
    "            best_score = score\n",
    "            best_importances = importances\n",
    "        else:\n",
    "            return best_score, best_importances\n",
    "        \n",
    "    return best_score, best_importances\n",
    "            \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Score with selected features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: 0.8430999858942698, \n",
      " delete feature: title_len\n",
      "score: 0.8489119637996391, \n",
      " delete feature: abstract_len\n",
      "score: 0.8490796830593087, \n",
      " delete feature: diversity\n",
      "score: 0.8507290292491705, \n",
      " delete feature: fresh_and_actual\n",
      "0.8507290292491705\n"
     ]
    },
    {
     "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": [
    "score, importances =  feature_selection(pub_df, default_params)\n",
    "\n",
    "print(score)\n",
    "plot_bar(importances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_grid = {'min_child_weight':[1,2,3],\n",
    "          'gamma':[i/10.0 for i in range(0,7)],\n",
    "          'max_depth': [3,4,5,6,7],\n",
    "          'eta' : [i/20.0 for i in range(4,8)],\n",
    "          'reg_alpha' : [1.5,2, 2.5,3]\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=5, random_state=8, shuffle=True),\n",
       "             error_score=nan,\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, gamma=None,\n",
       "                                    gpu_id=None, importance_type='gain',\n",
       "                                    interaction_constraints=None,\n",
       "                                    learning_rate=None, max_delta_step=None,\n",
       "                                    max_depth=None, min_child_weight=None,\n",
       "                                    mi...\n",
       "                                    reg_lambda=None, scale_pos_weight=None,\n",
       "                                    subsample=None, tree_method=None,\n",
       "                                    validate_parameters=None, verbosity=None),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'eta': [0.2, 0.25, 0.3, 0.35],\n",
       "                         'gamma': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6],\n",
       "                         'max_depth': [3, 4, 5, 6, 7],\n",
       "                         'min_child_weight': [1, 2, 3],\n",
       "                         'reg_alpha': [1.5, 2, 2.5, 3]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array(pub_df.loc[:, importances.keys()].values)\n",
    "y = np.array(pub_df['c5'].values)\n",
    "\n",
    "transformer = StandardScaler().fit(X)\n",
    "X = transformer.transform(X)\n",
    "\n",
    "regressor = xgb.XGBRegressor() \n",
    "\n",
    "grid = GridSearchCV(regressor, params_grid, cv = cv)\n",
    "grid.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'eta': 0.3,\n",
       " 'gamma': 0.5,\n",
       " 'max_depth': 6,\n",
       " 'min_child_weight': 1,\n",
       " 'reg_alpha': 2}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8830090955339343,\n",
       " {'recency': 0.35134962,\n",
       "  'topic_rank': 0.015220876,\n",
       "  'authors_mean_hindex': 0.060827862,\n",
       "  'authors_mean_sociality': 0.025594328,\n",
       "  'fresh_and_actual': 0.019010726,\n",
       "  'auth_author_and_fresh': 0.52799666})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_pipeline(pub_df.loc[:, [*importances.keys()] + ['c5']], grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "- data don't have heavy correlation between response and explanatory variables\n",
    "- data contain a lot of insignificant features\n",
    "- generated features, based on most important features significantly improve validation score for most of cases\n",
    "- the most important features in initial data is \"authors_mean_rank\" and \"recency\" "
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

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Dependencies

requirements.txt pypi
  • Jinja2 ==2.11.3
  • MarkupSafe ==1.1.1
  • Pillow ==8.1.2
  • Pygments ==2.8.1
  • QtPy ==1.9.0
  • Send2Trash ==1.5.0
  • argon2-cffi ==20.1.0
  • async-generator ==1.10
  • attrs ==20.3.0
  • backcall ==0.2.0
  • bleach ==3.3.0
  • certifi ==2020.12.5
  • cffi ==1.14.5
  • colorama ==0.4.4
  • cycler ==0.10.0
  • decorator ==4.4.2
  • defusedxml ==0.7.1
  • entrypoints ==0.3
  • ipykernel ==5.3.4
  • ipython ==7.21.0
  • ipython-genutils ==0.2.0
  • ipywidgets ==7.6.3
  • jedi ==0.17.2
  • joblib ==1.0.1
  • jsonschema ==3.2.0
  • jupyter ==1.0.0
  • jupyter-client ==6.1.7
  • jupyter-console ==6.2.0
  • jupyter-core ==4.7.1
  • jupyterlab-pygments ==0.1.2
  • jupyterlab-widgets ==1.0.0
  • kiwisolver ==1.3.1
  • matplotlib ==3.3.4
  • mistune ==0.8.4
  • nbclient ==0.5.3
  • nbconvert ==6.0.7
  • nbformat ==5.1.2
  • nest-asyncio ==1.5.1
  • notebook ==6.2.0
  • numpy ==1.20.1
  • packaging ==20.9
  • pandas ==1.2.3
  • pandocfilters ==1.4.3
  • parso ==0.7.0
  • pickleshare ==0.7.5
  • prometheus-client ==0.9.0
  • prompt-toolkit ==3.0.8
  • pycparser ==2.20
  • pyparsing ==2.4.7
  • pyrsistent ==0.17.3
  • python-dateutil ==2.8.1
  • pytz ==2021.1
  • pywinpty ==0.5.7
  • pyzmq ==20.0.0
  • qtconsole ==5.0.2
  • scikit-learn ==0.24.1
  • scipy ==1.6.1
  • seaborn ==0.11.1
  • six ==1.15.0
  • sklearn ==0.0
  • terminado ==0.9.2
  • testpath ==0.4.4
  • threadpoolctl ==2.1.0
  • tornado ==6.1
  • traitlets ==5.0.5
  • wcwidth ==0.2.5
  • webencodings ==0.5.1
  • widgetsnbextension ==3.5.1
  • wincertstore ==0.2
  • xgboost ==1.3.3
environment.yml conda
  • backcall 0.2.0
  • ca-certificates 2021.1.19
  • certifi 2020.12.5
  • colorama 0.4.4
  • decorator 4.4.2
  • ipykernel 5.3.4
  • ipython 7.21.0
  • ipython_genutils 0.2.0
  • jedi 0.17.2
  • jupyter_client 6.1.7
  • jupyter_core 4.7.1
  • libsodium 1.0.18
  • openssl 1.1.1j
  • parso 0.7.0
  • pickleshare 0.7.5
  • pip 21.0.1
  • prompt-toolkit 3.0.8
  • pygments 2.8.1
  • python 3.9.2
  • python-dateutil 2.8.1
  • pywin32 228
  • pyzmq 20.0.0
  • setuptools 52.0.0
  • six 1.15.0
  • sqlite 3.33.0
  • tornado 6.1
  • traitlets 5.0.5
  • tzdata 2020f
  • vc 14.2
  • vs2015_runtime 14.27.29016
  • wcwidth 0.2.5
  • wheel 0.36.2
  • wincertstore 0.2
  • zeromq 4.3.3
  • zlib 1.2.11