mda_graph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import scipy\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "from numpy.random import randn\n",
    "from scipy.stats import anderson\n",
    "\n",
    "from scipy.stats import boxcox\n",
    "from scipy.special import inv_boxcox\n",
    "\n",
    "from numpy.random import seed\n",
    "from numpy.random import randn\n",
    "from scipy.stats import shapiro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "inspector_results = pd.read_csv(\"mda_dataset.csv\")\n",
    "l2_results = pd.read_csv(\"l2_result_graph.csv\")\n",
    "taassc_results = pd.read_csv(\"result_taassc_graph.csv\")\n",
    "errors_data = pd.read_excel(\"dannye_novye.xlsx\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Преобразуем значения разных метрик для последующего слияния в единый датафрейм:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>text_name</th>\n",
       "      <th>errors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>exam2014/AAl_11_1.txt</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>exam2014/AAl_20_1.txt</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               text_name  errors\n",
       "0  exam2014/AAl_11_1.txt     1.0\n",
       "1  exam2014/AAl_20_1.txt     1.0"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors_data = errors_data.iloc[:-2]\n",
    "errors_data = errors_data[[\"text_name\", \"errors\"]]\n",
    "errors_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_num_words(text):\n",
    "    if len(text.split()) >= 60:\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "    \n",
    "inspector_results = inspector_results[inspector_results[\"type\"] == 1]\n",
    "del inspector_results[\"type\"]\n",
    "filter_arr = [filter_num_words(val) for val in inspector_results[\"text\"].values]\n",
    "inspector_results = inspector_results[filter_arr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>av_depth</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>min_depth</th>\n",
       "      <th>num_acl</th>\n",
       "      <th>num_rel_cl</th>\n",
       "      <th>num_advcl</th>\n",
       "      <th>num_sent</th>\n",
       "      <th>num_tok</th>\n",
       "      <th>av_tok_before_root</th>\n",
       "      <th>av_len_sent</th>\n",
       "      <th>...</th>\n",
       "      <th>punct_mistakes_pp</th>\n",
       "      <th>punct_mistakes_because</th>\n",
       "      <th>punct_mistakes_but</th>\n",
       "      <th>punct_mistakes_compare</th>\n",
       "      <th>million_mistake</th>\n",
       "      <th>side_mistake</th>\n",
       "      <th>name</th>\n",
       "      <th>text</th>\n",
       "      <th>class</th>\n",
       "      <th>sum_punct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.56</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>244</td>\n",
       "      <td>4.39</td>\n",
       "      <td>13.56</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>./data/exam/exam2017/OBy_146_1</td>\n",
       "      <td>This two line graphs illustrates monthly avera...</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.43</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>175</td>\n",
       "      <td>6.29</td>\n",
       "      <td>25.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>./data/exam/exam2017/EGe_15_1</td>\n",
       "      <td>﻿We have two graphics, which show us the popul...</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   av_depth  max_depth  min_depth  num_acl  num_rel_cl  num_advcl  num_sent  \\\n",
       "0      3.56          6          2        2           0          1        18   \n",
       "1      5.43         10          4        1           3          1         7   \n",
       "\n",
       "   num_tok  av_tok_before_root  av_len_sent  ...  punct_mistakes_pp  \\\n",
       "0      244                4.39        13.56  ...                  0   \n",
       "1      175                6.29        25.00  ...                  0   \n",
       "\n",
       "   punct_mistakes_because  punct_mistakes_but  punct_mistakes_compare  \\\n",
       "0                       0                   0                       0   \n",
       "1                       0                   0                       0   \n",
       "\n",
       "   million_mistake  side_mistake                            name  \\\n",
       "0                0             0  ./data/exam/exam2017/OBy_146_1   \n",
       "1                0             0   ./data/exam/exam2017/EGe_15_1   \n",
       "\n",
       "                                                text  class  sum_punct  \n",
       "0  This two line graphs illustrates monthly avera...     12          0  \n",
       "1  ﻿We have two graphics, which show us the popul...     13          0  \n",
       "\n",
       "[2 rows x 76 columns]"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inspector_results.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_name(x):\n",
    "    if x[20] == \"_\":\n",
    "        x = x[:20] + x[22:]\n",
    "    return x[12:] + \".txt\"\n",
    "    \n",
    "    \n",
    "inspector_results[\"text_name\"] = inspector_results[\"name\"].apply(fix_name)\n",
    "# inspector_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "taassc_results[\"FILENAME\"] = taassc_results[\"filename\"]\n",
    "del taassc_results[\"filename\"]\n",
    "# taassc_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_name(x):\n",
    "    if x[20] == \"_\":\n",
    "        x = x[:20] + x[22:]\n",
    "    return x[12:] + \".txt\"\n",
    "    \n",
    "    \n",
    "inspector_results[\"text_name\"] = inspector_results[\"name\"].apply(fix_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "inspector_results[\"FILENAME\"] = [str(i) + \".txt\" for i in list(range(len(inspector_results)))]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
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       "      <th>DC/C</th>\n",
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       "      <th>CP/C</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>159.txt</td>\n",
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       "      <td>11</td>\n",
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       "      <td>6</td>\n",
       "      <td>7</td>\n",
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       "      <td>1.7500</td>\n",
       "      <td>0.3810</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>1.0909</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.5833</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>3.0833</td>\n",
       "      <td>1.7619</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>158.txt</td>\n",
       "      <td>198</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.7273</td>\n",
       "      <td>1.4545</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.3636</td>\n",
       "      <td>1.3750</td>\n",
       "      <td>0.3636</td>\n",
       "      <td>0.4545</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>2.1818</td>\n",
       "      <td>1.5000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>156.txt</td>\n",
       "      <td>193</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4545</td>\n",
       "      <td>1.3636</td>\n",
       "      <td>0.1333</td>\n",
       "      <td>0.1818</td>\n",
       "      <td>1.1000</td>\n",
       "      <td>0.1818</td>\n",
       "      <td>0.5455</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.4667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>157.txt</td>\n",
       "      <td>223</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6667</td>\n",
       "      <td>1.4000</td>\n",
       "      <td>0.2381</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>1.2500</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.2857</td>\n",
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       "      <td>1.3810</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4375</td>\n",
       "      <td>1.1875</td>\n",
       "      <td>0.1579</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>1.2308</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.2632</td>\n",
       "      <td>1.0625</td>\n",
       "      <td>0.8947</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 25 columns</p>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0 FILENAME    W   S  VP   C   T  DC  CT  CP  ...    VP/T     C/T  \\\n",
       "0           0  159.txt  272  11  25  21  12   8   6   7  ...  2.0833  1.7500   \n",
       "1           1  158.txt  198   8  19  16  11   4   4   5  ...  1.7273  1.4545   \n",
       "2           2  156.txt  193  10  16  15  11   2   2   6  ...  1.4545  1.3636   \n",
       "3           3  157.txt  223  12  25  21  15   5   5   6  ...  1.6667  1.4000   \n",
       "4           4  155.txt  186  13  23  19  16   3   3   5  ...  1.4375  1.1875   \n",
       "\n",
       "     DC/C    DC/T     T/S    CT/T    CP/T    CP/C    CN/T    CN/C  \n",
       "0  0.3810  0.6667  1.0909  0.5000  0.5833  0.3333  3.0833  1.7619  \n",
       "1  0.2500  0.3636  1.3750  0.3636  0.4545  0.3125  2.1818  1.5000  \n",
       "2  0.1333  0.1818  1.1000  0.1818  0.5455  0.4000  2.0000  1.4667  \n",
       "3  0.2381  0.3333  1.2500  0.3333  0.4000  0.2857  1.9333  1.3810  \n",
       "4  0.1579  0.1875  1.2308  0.1875  0.3125  0.2632  1.0625  0.8947  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l2_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data = inspector_results.merge(errors_data, on=\"text_name\")\n",
    "\n",
    "for col_name in taassc_results.columns:\n",
    "    full_data[col_name] = taassc_results[col_name]\n",
    "    \n",
    "for col_name in l2_results.columns:\n",
    "    full_data[col_name] = l2_results[col_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>av_depth</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>min_depth</th>\n",
       "      <th>num_acl</th>\n",
       "      <th>num_rel_cl</th>\n",
       "      <th>num_advcl</th>\n",
       "      <th>num_sent</th>\n",
       "      <th>num_tok</th>\n",
       "      <th>av_tok_before_root</th>\n",
       "      <th>av_len_sent</th>\n",
       "      <th>...</th>\n",
       "      <th>VP/T</th>\n",
       "      <th>C/T</th>\n",
       "      <th>DC/C</th>\n",
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       "      <th>T/S</th>\n",
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       "      <th>CP/C</th>\n",
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       "      <th>CN/C</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>3.91</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.5833</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>3.0833</td>\n",
       "      <td>1.7619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.22</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>301</td>\n",
       "      <td>4.22</td>\n",
       "      <td>33.44</td>\n",
       "      <td>...</td>\n",
       "      <td>1.7273</td>\n",
       "      <td>1.4545</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.3636</td>\n",
       "      <td>1.3750</td>\n",
       "      <td>0.3636</td>\n",
       "      <td>0.4545</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>2.1818</td>\n",
       "      <td>1.5000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 458 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   av_depth  max_depth  min_depth  num_acl  num_rel_cl  num_advcl  num_sent  \\\n",
       "0      3.91          6          2        5           1          1        11   \n",
       "1      7.22         14          3        7           3          8         9   \n",
       "\n",
       "   num_tok  av_tok_before_root  av_len_sent  ...    VP/T     C/T   DC/C  \\\n",
       "0      196                5.64        17.82  ...  2.0833  1.7500  0.381   \n",
       "1      301                4.22        33.44  ...  1.7273  1.4545  0.250   \n",
       "\n",
       "     DC/T     T/S    CT/T    CP/T    CP/C    CN/T    CN/C  \n",
       "0  0.6667  1.0909  0.5000  0.5833  0.3333  3.0833  1.7619  \n",
       "1  0.3636  1.3750  0.3636  0.4545  0.3125  2.1818  1.5000  \n",
       "\n",
       "[2 rows x 458 columns]"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Выделим только необходимые метрики из каждого инструмента:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "226"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"av_depth max_depth min_depth num_acl num_advcl num_sent num_tok av_tok_before_root av_len_sent num_cl num_tu num_compl_tu num_coord num_adj_noun num_part_noun num_noun_inf pos_sim_nei lemma_sim_nei pos_sim_all lemma_sim_all\"\n",
    "cols = [st for st in s.split()] + [\"class\"]\n",
    "\n",
    "l2_must_have = ['MLS',\n",
    "       'MLT', 'MLC', 'C/S', 'VP/T', 'C/T', 'DC/C', 'DC/T', 'T/S', 'CT/T',\n",
    "       'CP/T', 'CP/C', 'CN/T', 'CN/C']\n",
    "\n",
    "taassc_must_have = \"\"\"acad_av_lemma_freq\n",
    "acad_av_construction_freq\n",
    "acad_av_lemma_construction_freq\n",
    "acad_av_approx_collexeme\n",
    "acad_av_faith_verb_cue\n",
    "acad_av_faith_const_cue\n",
    "acad_av_delta_p_verb_cue\n",
    "acad_av_delta_p_const_cue\n",
    "acad_av_lemma_freq_log\n",
    "acad_av_construction_freq_log\n",
    "acad_av_lemma_construction_freq_log\n",
    "acad_av_lemma_freq_type\n",
    "acad_av_construction_freq_type\n",
    "acad_av_lemma_construction_freq_type\n",
    "acad_av_approx_collexeme_type\n",
    "acad_av_faith_verb_cue_type\n",
    "acad_av_faith_const_cue_type\n",
    "acad_av_delta_p_verb_cue_type\n",
    "acad_av_delta_p_const_cue_type\n",
    "acad_collexeme_ratio\n",
    "acad_collexeme_ratio_type\n",
    "acad_lemma_ttr\n",
    "acad_construction_ttr\n",
    "acad_lemma_construction_ttr\n",
    "acad_lemma_attested\n",
    "acad_construction_attested\n",
    "acad_lemma_construction_attested\n",
    "news_av_lemma_freq\n",
    "news_av_construction_freq\n",
    "news_av_lemma_construction_freq\n",
    "news_av_approx_collexeme\n",
    "news_av_faith_verb_cue\n",
    "news_av_faith_const_cue\n",
    "news_av_delta_p_verb_cue\n",
    "news_av_delta_p_const_cue\n",
    "news_av_lemma_freq_log\n",
    "news_av_construction_freq_log\n",
    "news_av_lemma_construction_freq_log\n",
    "news_av_lemma_freq_type\n",
    "news_av_construction_freq_type\n",
    "news_av_lemma_construction_freq_type\n",
    "news_av_approx_collexeme_type\n",
    "news_av_faith_verb_cue_type\n",
    "news_av_faith_const_cue_type\n",
    "news_av_delta_p_verb_cue_type\n",
    "news_av_delta_p_const_cue_type\n",
    "news_collexeme_ratio\n",
    "news_collexeme_ratio_type\n",
    "news_lemma_ttr\n",
    "news_construction_ttr\n",
    "news_lemma_construction_ttr\n",
    "news_lemma_attested\n",
    "news_construction_attested\n",
    "news_lemma_construction_attested\n",
    "mag_av_lemma_freq\n",
    "mag_av_construction_freq\n",
    "mag_av_lemma_construction_freq\n",
    "mag_av_approx_collexeme\n",
    "mag_av_faith_verb_cue\n",
    "mag_av_faith_const_cue\n",
    "mag_av_delta_p_verb_cue\n",
    "mag_av_delta_p_const_cue\n",
    "mag_av_lemma_freq_log\n",
    "mag_av_construction_freq_log\n",
    "mag_av_lemma_construction_freq_log\n",
    "mag_av_lemma_freq_type\n",
    "mag_av_construction_freq_type\n",
    "mag_av_lemma_construction_freq_type\n",
    "mag_av_approx_collexeme_type\n",
    "mag_av_faith_verb_cue_type\n",
    "mag_av_faith_const_cue_type\n",
    "mag_av_delta_p_verb_cue_type\n",
    "mag_av_delta_p_const_cue_type\n",
    "mag_collexeme_ratio\n",
    "mag_collexeme_ratio_type\n",
    "mag_lemma_ttr\n",
    "mag_construction_ttr\n",
    "mag_lemma_construction_ttr\n",
    "mag_lemma_attested\n",
    "mag_construction_attested\n",
    "mag_lemma_construction_attested\n",
    "fic_av_lemma_freq\n",
    "fic_av_construction_freq\n",
    "fic_av_lemma_construction_freq\n",
    "fic_av_approx_collexeme\n",
    "fic_av_faith_verb_cue\n",
    "fic_av_faith_const_cue\n",
    "fic_av_delta_p_verb_cue\n",
    "fic_av_delta_p_const_cue\n",
    "fic_av_lemma_freq_log\n",
    "fic_av_construction_freq_log\n",
    "fic_av_lemma_construction_freq_log\n",
    "fic_av_lemma_freq_type\n",
    "fic_av_construction_freq_type\n",
    "fic_av_lemma_construction_freq_type\n",
    "fic_av_approx_collexeme_type\n",
    "fic_av_faith_verb_cue_type\n",
    "fic_av_faith_const_cue_type\n",
    "fic_av_delta_p_verb_cue_type\n",
    "fic_av_delta_p_const_cue_type\n",
    "fic_collexeme_ratio\n",
    "fic_collexeme_ratio_type\n",
    "fic_lemma_ttr\n",
    "fic_construction_ttr\n",
    "fic_lemma_construction_ttr\n",
    "fic_lemma_attested\n",
    "fic_construction_attested\n",
    "fic_lemma_construction_attested\n",
    "all_av_lemma_freq\n",
    "all_av_construction_freq\n",
    "all_av_lemma_construction_freq\n",
    "all_av_approx_collexeme\n",
    "all_av_faith_verb_cue\n",
    "all_av_faith_const_cue\n",
    "all_av_delta_p_verb_cue\n",
    "all_av_delta_p_const_cue\n",
    "all_av_lemma_freq_log\n",
    "all_av_construction_freq_log\n",
    "all_av_lemma_construction_freq_log\n",
    "all_av_lemma_freq_type\n",
    "all_av_construction_freq_type\n",
    "all_av_lemma_construction_freq_type\n",
    "all_av_approx_collexeme_type\n",
    "all_av_faith_verb_cue_type\n",
    "all_av_faith_const_cue_type\n",
    "all_av_delta_p_verb_cue_type\n",
    "all_av_delta_p_const_cue_type\n",
    "all_collexeme_ratio\n",
    "all_collexeme_ratio_type\n",
    "all_lemma_ttr\n",
    "all_construction_ttr\n",
    "all_lemma_construction_ttr\n",
    "all_lemma_attested\n",
    "all_construction_attested\n",
    "all_lemma_construction_attested\n",
    "all_av_lemma_freq_stdev\n",
    "all_av_construction_freq_stdev\n",
    "all_av_lemma_construction_freq_stdev\n",
    "all_av_approx_collexeme_stdev\n",
    "all_av_faith_verb_cue_stdev\n",
    "all_av_faith_const_cue_stdev\n",
    "all_av_delta_p_verb_cue_stdev\n",
    "all_av_delta_p_const_cue_stdev\n",
    "acad_av_lemma_freq_stdev\n",
    "acad_av_construction_freq_stdev\n",
    "acad_av_lemma_construction_freq_stdev\n",
    "acad_av_approx_collexeme_stdev\n",
    "acad_av_faith_verb_cue_stdev\n",
    "acad_av_faith_const_cue_stdev\n",
    "acad_av_delta_p_verb_cue_stdev\n",
    "acad_av_delta_p_const_cue_stdev\n",
    "news_av_lemma_freq_stdev\n",
    "news_av_construction_freq_stdev\n",
    "news_av_lemma_construction_freq_stdev\n",
    "news_av_approx_collexeme_stdev\n",
    "news_av_faith_verb_cue_stdev\n",
    "news_av_faith_const_cue_stdev\n",
    "news_av_delta_p_verb_cue_stdev\n",
    "news_av_delta_p_const_cue_stdev\n",
    "mag_av_lemma_freq_stdev\n",
    "mag_av_construction_freq_stdev\n",
    "mag_av_lemma_construction_freq_stdev\n",
    "mag_av_approx_collexeme_stdev\n",
    "mag_av_faith_verb_cue_stdev\n",
    "mag_av_faith_const_cue_stdev\n",
    "mag_av_delta_p_verb_cue_stdev\n",
    "mag_av_delta_p_const_cue_stdev\n",
    "fic_av_lemma_freq_stdev\n",
    "fic_av_construction_freq_stdev\n",
    "fic_av_lemma_construction_freq_stdev\n",
    "fic_av_approx_collexeme_stdev\n",
    "fic_av_faith_verb_cue_stdev\n",
    "fic_av_faith_const_cue_stdev\n",
    "fic_av_delta_p_verb_cue_stdev\n",
    "fic_av_delta_p_const_cue_stdev\n",
    "all_av_lemma_freq_log_stdev\n",
    "all_av_construction_freq_log_stdev\n",
    "all_av_lemma_construction_freq_log_stdev\n",
    "acad_av_lemma_freq_log_stdev\n",
    "acad_av_construction_freq_log_stdev\n",
    "acad_av_lemma_construction_freq_log_stdev\n",
    "news_av_lemma_freq_log_stdev\n",
    "news_av_construction_freq_log_stdev\n",
    "news_av_lemma_construction_freq_log_stdev\n",
    "mag_av_lemma_freq_log_stdev\n",
    "mag_av_construction_freq_log_stdev\n",
    "mag_av_lemma_construction_freq_log_stdev\n",
    "fic_av_lemma_freq_log_stdev\n",
    "fic_av_construction_freq_log_stdev\n",
    "fic_av_lemma_construction_freq_log_stdev\"\"\"\n",
    "\n",
    "taassc_must_have = taassc_must_have.split(\"\\n\")\n",
    "\n",
    "\n",
    "must_have_cols = cols + l2_must_have + list(taassc_must_have) + [\"errors\"]\n",
    "len(must_have_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col_name in full_data.columns:\n",
    "    if col_name not in must_have_cols:\n",
    "        del full_data[col_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Посмотрим, сколько эссе содержится в каждом классе"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(5, 2),\n",
       " (7, 3),\n",
       " (8, 6),\n",
       " (9, 14),\n",
       " (10, 48),\n",
       " (11, 110),\n",
       " (12, 155),\n",
       " (13, 88),\n",
       " (14, 31),\n",
       " (15, 8),\n",
       " (17, 1)]"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(set(full_data[\"class\"].values))\n",
    "[(i, len(full_data[full_data[\"class\"] == i])) for i in \n",
    "     set(full_data[\"class\"].values)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 14.0, 15.0, 19.0}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(1.0, 276),\n",
       " (4.0, 70),\n",
       " (5.0, 53),\n",
       " (6.0, 19),\n",
       " (7.0, 22),\n",
       " (8.0, 10),\n",
       " (9.0, 5),\n",
       " (10.0, 3),\n",
       " (11.0, 4),\n",
       " (12.0, 1),\n",
       " (14.0, 1),\n",
       " (15.0, 1),\n",
       " (19.0, 1)]"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(set(full_data[\"errors\"].values))\n",
    "[(i, len(full_data[full_data[\"errors\"] == i])) for i in \n",
    "     set(full_data[\"errors\"].values)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Отбросим группы, в которых слишком мало данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data_error = full_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data_error = full_data_error[full_data[\"errors\"] <= 11]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data = full_data[full_data[\"class\"] != 3]\n",
    "full_data = full_data[full_data[\"class\"] != 4]\n",
    "full_data = full_data[full_data[\"class\"] != 5]\n",
    "full_data = full_data[full_data[\"class\"] != 6]\n",
    "full_data = full_data[full_data[\"class\"] != 7]\n",
    "full_data = full_data[full_data[\"class\"] != 8]\n",
    "full_data = full_data[full_data[\"class\"] != 15]\n",
    "full_data = full_data[full_data[\"class\"] != 16]\n",
    "full_data = full_data[full_data[\"class\"] != 17]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Удалим мультиколлинеарные переменные, то есть для тех независимых переменных, у которых абсолютное значение коллинеарности по Спирмену больше, чем 0.7 возьмём ту метрику, которая показывает максимальный коэффициент корреляции с зависимой переменной"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Сначала удалим коррелирующие переменные для зависимого столбца 'class':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_data = full_data\n",
    "del filtered_data[\"errors\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "while True:\n",
    "    class_col_index = [i for i, j in enumerate(filtered_data.columns) if j == \"class\"][0]\n",
    "    corr_mat = filtered_data.corr(\"spearman\").values\n",
    "    col_to_del = set()\n",
    "    \n",
    "    \n",
    "    for i, col_name in enumerate(filtered_data.columns):\n",
    "        buff_arr = []\n",
    "        for j, col_name_2 in enumerate(filtered_data.columns):\n",
    "            if i == j:\n",
    "                continue\n",
    "            if corr_mat[i][j] > 0.7:\n",
    "                buff_arr.append((j, col_name_2))\n",
    "        if len(buff_arr) > 0:\n",
    "            buff_arr.append((i, col_name))\n",
    "            break\n",
    "    \n",
    "    if len(buff_arr) == 0:\n",
    "        break\n",
    "    max_val = 0.0\n",
    "    max_col = None\n",
    "    for j, col in buff_arr:\n",
    "        if corr_mat[class_col_index][j] > max_val:\n",
    "            max_val = corr_mat[class_col_index][j]\n",
    "            max_col = col\n",
    "    col_to_del = col_to_del.union(set([i for _, i in buff_arr]))\n",
    "    col_to_del -= set([max_col])\n",
    "    \n",
    "    for col in col_to_del:\n",
    "        del filtered_data[col]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Рассмотрим матрицу корреляции Спирмена, чтобы убедиться в отсутствии мультиколлинеарности между независимыми переменными"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1512x1224 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(21, 17))\n",
    "plt.matshow(filtered_data.corr(\"spearman\"), fignum=f.number)\n",
    "plt.xticks(range(filtered_data.select_dtypes(['number']).shape[1]),\n",
    "           filtered_data.select_dtypes(['number']).columns, fontsize=14, rotation=90)\n",
    "plt.yticks(range(filtered_data.select_dtypes(['number']).shape[1]),\n",
    "           filtered_data.select_dtypes(['number']).columns, fontsize=14)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize=14)\n",
    "plt.title(' ', fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "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>max_depth</th>\n",
       "      <th>min_depth</th>\n",
       "      <th>num_acl</th>\n",
       "      <th>num_advcl</th>\n",
       "      <th>num_sent</th>\n",
       "      <th>num_tok</th>\n",
       "      <th>av_tok_before_root</th>\n",
       "      <th>av_len_sent</th>\n",
       "      <th>num_cl</th>\n",
       "      <th>num_compl_tu</th>\n",
       "      <th>...</th>\n",
       "      <th>mag_collexeme_ratio_type</th>\n",
       "      <th>fic_construction_ttr</th>\n",
       "      <th>fic_lemma_attested</th>\n",
       "      <th>fic_lemma_construction_attested</th>\n",
       "      <th>all_av_construction_freq_log</th>\n",
       "      <th>fic_av_approx_collexeme_stdev</th>\n",
       "      <th>MLC</th>\n",
       "      <th>T/S</th>\n",
       "      <th>CT/T</th>\n",
       "      <th>CN/C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>196</td>\n",
       "      <td>5.64</td>\n",
       "      <td>17.82</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>4.913453</td>\n",
       "      <td>10214.956217</td>\n",
       "      <td>12.9524</td>\n",
       "      <td>1.0909</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>1.7619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>301</td>\n",
       "      <td>4.22</td>\n",
       "      <td>33.44</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.703704</td>\n",
       "      <td>4.919330</td>\n",
       "      <td>3311.645218</td>\n",
       "      <td>12.3750</td>\n",
       "      <td>1.3750</td>\n",
       "      <td>0.3636</td>\n",
       "      <td>1.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>174</td>\n",
       "      <td>5.88</td>\n",
       "      <td>21.75</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>4.847445</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.8667</td>\n",
       "      <td>1.1000</td>\n",
       "      <td>0.1818</td>\n",
       "      <td>1.4667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>241</td>\n",
       "      <td>6.70</td>\n",
       "      <td>24.10</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>3.188643</td>\n",
       "      <td>11890.900293</td>\n",
       "      <td>10.6190</td>\n",
       "      <td>1.2500</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>1.3810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>230</td>\n",
       "      <td>6.36</td>\n",
       "      <td>20.91</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>4.921985</td>\n",
       "      <td>14850.312468</td>\n",
       "      <td>9.7895</td>\n",
       "      <td>1.2308</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.8947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>461</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>10</td>\n",
       "      <td>203</td>\n",
       "      <td>4.20</td>\n",
       "      <td>20.30</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
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       "      <td>6.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>4.915644</td>\n",
       "      <td>8435.715788</td>\n",
       "      <td>8.9000</td>\n",
       "      <td>1.0909</td>\n",
       "      <td>0.4167</td>\n",
       "      <td>1.4000</td>\n",
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       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>179</td>\n",
       "      <td>4.90</td>\n",
       "      <td>17.90</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>4.570030</td>\n",
       "      <td>9096.162792</td>\n",
       "      <td>11.7692</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>1.9231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>181</td>\n",
       "      <td>3.67</td>\n",
       "      <td>20.11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>5.007290</td>\n",
       "      <td>16851.614184</td>\n",
       "      <td>10.2308</td>\n",
       "      <td>1.2000</td>\n",
       "      <td>0.8333</td>\n",
       "      <td>1.1538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>202</td>\n",
       "      <td>5.54</td>\n",
       "      <td>15.54</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>4.785854</td>\n",
       "      <td>6210.145215</td>\n",
       "      <td>11.2857</td>\n",
       "      <td>0.9333</td>\n",
       "      <td>0.3571</td>\n",
       "      <td>1.6190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>210</td>\n",
       "      <td>3.67</td>\n",
       "      <td>17.50</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>4.664516</td>\n",
       "      <td>6331.431625</td>\n",
       "      <td>8.2222</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.2308</td>\n",
       "      <td>0.8889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>446 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     max_depth  min_depth  num_acl  num_advcl  num_sent  num_tok  \\\n",
       "0            6          2        5          1        11      196   \n",
       "1           14          3        7          8         9      301   \n",
       "2            7          3        5          3         8      174   \n",
       "3            6          2        1          2        10      241   \n",
       "4            4          2        0          4        11      230   \n",
       "..         ...        ...      ...        ...       ...      ...   \n",
       "461          7          2        0          2        10      203   \n",
       "462          6          3        4          1        10      179   \n",
       "463          7          3        3          2         9      181   \n",
       "464          6          2        0          2        13      202   \n",
       "465          5          3        3          1        12      210   \n",
       "\n",
       "     av_tok_before_root  av_len_sent  num_cl  num_compl_tu  ...  \\\n",
       "0                  5.64        17.82      18             3  ...   \n",
       "1                  4.22        33.44      36            11  ...   \n",
       "2                  5.88        21.75      13             3  ...   \n",
       "3                  6.70        24.10      16             1  ...   \n",
       "4                  6.36        20.91      17             3  ...   \n",
       "..                  ...          ...     ...           ...  ...   \n",
       "461                4.20        20.30      19             5  ...   \n",
       "462                4.90        17.90      13             1  ...   \n",
       "463                3.67        20.11       9             2  ...   \n",
       "464                5.54        15.54      16             2  ...   \n",
       "465                3.67        17.50      14             0  ...   \n",
       "\n",
       "     mag_collexeme_ratio_type  fic_construction_ttr  fic_lemma_attested  \\\n",
       "0                    5.000000              0.857143            1.000000   \n",
       "1                    3.333333              0.708333            0.962963   \n",
       "2                    0.000000              0.666667            1.000000   \n",
       "3                    3.000000              1.000000            1.000000   \n",
       "4                    3.000000              0.888889            1.000000   \n",
       "..                        ...                   ...                 ...   \n",
       "461                  6.000000              0.666667            1.000000   \n",
       "462                  0.000000              1.000000            1.000000   \n",
       "463                  1.333333              0.600000            1.000000   \n",
       "464                  4.000000              0.736842            1.000000   \n",
       "465                  1.500000              1.000000            1.000000   \n",
       "\n",
       "     fic_lemma_construction_attested  all_av_construction_freq_log  \\\n",
       "0                           0.722222                      4.913453   \n",
       "1                           0.703704                      4.919330   \n",
       "2                           0.600000                      4.847445   \n",
       "3                           0.312500                      3.188643   \n",
       "4                           0.636364                      4.921985   \n",
       "..                               ...                           ...   \n",
       "461                         0.500000                      4.915644   \n",
       "462                         0.692308                      4.570030   \n",
       "463                         0.764706                      5.007290   \n",
       "464                         0.652174                      4.785854   \n",
       "465                         0.857143                      4.664516   \n",
       "\n",
       "     fic_av_approx_collexeme_stdev      MLC     T/S    CT/T    CN/C  \n",
       "0                     10214.956217  12.9524  1.0909  0.5000  1.7619  \n",
       "1                      3311.645218  12.3750  1.3750  0.3636  1.5000  \n",
       "2                         0.000000  12.8667  1.1000  0.1818  1.4667  \n",
       "3                     11890.900293  10.6190  1.2500  0.3333  1.3810  \n",
       "4                     14850.312468   9.7895  1.2308  0.1875  0.8947  \n",
       "..                             ...      ...     ...     ...     ...  \n",
       "461                    8435.715788   8.9000  1.0909  0.4167  1.4000  \n",
       "462                    9096.162792  11.7692  1.0000  0.5000  1.9231  \n",
       "463                   16851.614184  10.2308  1.2000  0.8333  1.1538  \n",
       "464                    6210.145215  11.2857  0.9333  0.3571  1.6190  \n",
       "465                    6331.431625   8.2222  1.0000  0.2308  0.8889  \n",
       "\n",
       "[446 rows x 27 columns]"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Отсортируем значения коэффициента корреляции с зависимой переменной для оценки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_col_index = [i for i, j in enumerate(filtered_data.columns) if j == \"class\"][0]\n",
    "\n",
    "\n",
    "corr_mat = filtered_data.corr(\"spearman\").values[class_col_index, :]\n",
    "corr_mat_kend = filtered_data.corr(\"kendall\").values[class_col_index, :]\n",
    "res_dict = {}\n",
    "for i, colname in enumerate(filtered_data.corr(\"spearman\").columns):\n",
    "    res_dict[colname] = (round(corr_mat[i], 3), round(corr_mat_kend[i], 3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t (Spearman, Kendal)\n",
      "\n",
      "class :   (1.0, 1.0)\n",
      "num_tok :   (0.392, 0.303)\n",
      "av_len_sent :   (0.312, 0.235)\n",
      "num_advcl :   (0.29, 0.235)\n",
      "num_acl :   (0.267, 0.209)\n",
      "max_depth :   (0.219, 0.176)\n",
      "num_compl_tu :   (0.194, 0.152)\n",
      "num_adj_noun :   (0.175, 0.134)\n",
      "num_cl :   (0.17, 0.131)\n",
      "min_depth :   (0.155, 0.131)\n",
      "av_tok_before_root :   (0.132, 0.098)\n",
      "fic_construction_ttr :   (0.116, 0.09)\n",
      "CN/C :   (0.111, 0.081)\n",
      "all_av_construction_freq_log :   (-0.088, -0.066)\n",
      "fic_lemma_attested :   (0.085, 0.072)\n",
      "news_lemma_ttr :   (0.057, 0.042)\n",
      "T/S :   (-0.057, -0.045)\n",
      "CT/T :   (0.054, 0.04)\n",
      "num_noun_inf :   (0.051, 0.045)\n",
      "fic_lemma_construction_attested :   (-0.032, -0.024)\n",
      "news_av_faith_const_cue_type :   (0.028, 0.022)\n",
      "MLC :   (0.028, 0.019)\n",
      "fic_av_approx_collexeme_stdev :   (-0.026, -0.019)\n",
      "num_sent :   (0.014, 0.011)\n",
      "num_coord :   (-0.008, -0.006)\n",
      "acad_collexeme_ratio :   (0.003, 0.003)\n",
      "mag_collexeme_ratio_type :   (0.001, 0.001)\n"
     ]
    }
   ],
   "source": [
    "print(\"\\t (Spearman, Kendal)\\n\")\n",
    "\n",
    "for col, val in sorted(res_dict.items(), key=lambda x: -np.abs(x[1][0])):\n",
    "    print(col,\":  \", val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colname:  Spearman p-value:   Kendall p-value:\n",
      "max_depth        0.0        0.0\n",
      "min_depth        0.001        0.0011\n",
      "num_acl        0.0        0.0\n",
      "num_advcl        0.0        0.0\n",
      "num_sent        0.7624        0.7694\n",
      "num_tok        0.0        0.0\n",
      "av_tok_before_root        0.0051        0.0051\n",
      "av_len_sent        0.0        0.0\n",
      "num_cl        0.0003        0.0003\n",
      "num_compl_tu        0.0        0.0\n",
      "num_coord        0.8626        0.8658\n",
      "num_adj_noun        0.0002        0.0002\n",
      "num_noun_inf        0.2796        0.2756\n",
      "class        0.0        0.0\n",
      "acad_collexeme_ratio        0.948        0.9404\n",
      "news_av_faith_const_cue_type        0.55        0.5375\n",
      "news_lemma_ttr        0.2308        0.2304\n",
      "mag_collexeme_ratio_type        0.9801        0.9828\n",
      "fic_construction_ttr        0.0145        0.0128\n",
      "fic_lemma_attested        0.072        0.073\n",
      "fic_lemma_construction_attested        0.5006        0.4984\n",
      "all_av_construction_freq_log        0.0619        0.061\n",
      "fic_av_approx_collexeme_stdev        0.5783        0.5909\n",
      "MLC        0.5615        0.5891\n",
      "T/S        0.2328        0.2334\n",
      "CT/T        0.2562        0.2549\n",
      "CN/C        0.019        0.0212\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "print(\"Colname:  Spearman p-value:   Kendall p-value:\")\n",
    "for col in filtered_data.columns:\n",
    "    val = filtered_data[col].values\n",
    "    print(col, \"      \", round(stats.spearmanr(val, filtered_data[\"class\"].values)[1], 4), \"      \", \n",
    "    round(stats.kendalltau(val, filtered_data[\"class\"].values)[1], 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Сделаем те же самые шаги для выявления корреляции с количеством синтаксических ошибок:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удалим линейно зависимые переменные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_data = full_data_error.copy()\n",
    "del filtered_data[\"class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "while True:\n",
    "    class_col_index = [i for i, j in enumerate(filtered_data.columns) if j == \"errors\"][0]\n",
    "    corr_mat = filtered_data.corr(\"spearman\").values\n",
    "    col_to_del = set()\n",
    "    \n",
    "    for i, col_name in enumerate(filtered_data.columns):\n",
    "        buff_arr = []\n",
    "        for j, col_name_2 in enumerate(filtered_data.columns):\n",
    "            if i == j:\n",
    "                continue\n",
    "            if corr_mat[i][j] > 0.7:\n",
    "                buff_arr.append((j, col_name_2))\n",
    "        if len(buff_arr) > 0:\n",
    "            buff_arr.append((i, col_name))\n",
    "            break\n",
    "    \n",
    "    if len(buff_arr) == 0:\n",
    "        break\n",
    "    max_val = 0.0\n",
    "    max_col = None\n",
    "    for j, col in buff_arr:\n",
    "        if corr_mat[class_col_index][j] > max_val:\n",
    "            max_val = corr_mat[class_col_index][j]\n",
    "            max_col = col\n",
    "    col_to_del = col_to_del.union(set([i for _, i in buff_arr]))\n",
    "#     print(col_to_del)\n",
    "    col_to_del -= set([max_col])\n",
    "    \n",
    "    for col in col_to_del:\n",
    "        del filtered_data[col]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Посмотрим матрицу корреляции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1512x1224 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(21, 17))\n",
    "plt.matshow(filtered_data.corr(), fignum=f.number)\n",
    "plt.xticks(range(filtered_data.select_dtypes(['number']).shape[1]),\n",
    "           filtered_data.select_dtypes(['number']).columns, fontsize=14, rotation=90)\n",
    "plt.yticks(range(filtered_data.select_dtypes(['number']).shape[1]),\n",
    "           filtered_data.select_dtypes(['number']).columns, fontsize=14)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize=14)\n",
    "plt.title(' ', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Отсортируем значения коэффициента корреляции с зависимой переменной для количества синтаксических ошибок:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_col_index = [i for i, j in enumerate(filtered_data.columns) if j == \"errors\"][0]\n",
    "\n",
    "\n",
    "corr_mat = filtered_data.corr(\"spearman\").values[class_col_index, :]\n",
    "corr_mat_kend = filtered_data.corr(\"kendall\").values[class_col_index, :]\n",
    "res_dict = {}\n",
    "for i, colname in enumerate(filtered_data.corr(\"spearman\").columns):\n",
    "    res_dict[colname] = (round(corr_mat[i], 3), round(corr_mat_kend[i], 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t (Spearman, Kendal)\n",
      "\n",
      "errors :   (1.0, 1.0)\n",
      "CP/T :   (0.117, 0.09)\n",
      "all_lemma_construction_attested :   (0.09, 0.069)\n",
      "VP/T :   (0.088, 0.068)\n",
      "fic_av_faith_verb_cue_stdev :   (0.081, 0.061)\n",
      "fic_av_approx_collexeme :   (0.072, 0.052)\n",
      "MLT :   (0.072, 0.054)\n",
      "fic_collexeme_ratio_type :   (0.07, 0.056)\n",
      "av_tok_before_root :   (-0.067, -0.051)\n",
      "num_tok :   (-0.063, -0.048)\n",
      "news_collexeme_ratio :   (0.06, 0.048)\n",
      "num_adj_noun :   (-0.057, -0.044)\n",
      "fic_av_faith_const_cue :   (0.055, 0.039)\n",
      "news_av_construction_freq_type :   (0.054, 0.04)\n",
      "MLC :   (0.046, 0.034)\n",
      "acad_lemma_attested :   (0.04, 0.034)\n",
      "T/S :   (-0.037, -0.03)\n",
      "min_depth :   (-0.036, -0.031)\n",
      "CN/C :   (0.031, 0.023)\n",
      "fic_av_lemma_freq_log :   (0.022, 0.017)\n",
      "all_collexeme_ratio_type :   (0.022, 0.017)\n",
      "num_noun_inf :   (0.021, 0.019)\n",
      "num_coord :   (0.017, 0.013)\n",
      "news_av_lemma_construction_freq_log :   (0.013, 0.011)\n",
      "max_depth :   (-0.012, -0.01)\n",
      "fic_lemma_construction_ttr :   (0.01, 0.009)\n",
      "num_tu :   (0.009, 0.007)\n",
      "pos_sim_nei :   (0.005, 0.003)\n",
      "num_sent :   (0.002, 0.002)\n"
     ]
    }
   ],
   "source": [
    "print(\"\\t (Spearman, Kendal)\\n\")\n",
    "\n",
    "for col, val in sorted(res_dict.items(), key=lambda x: -np.abs(x[1][0])):\n",
    "    print(col,\":  \", val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colname:  Spearman p-value:   Kendall p-value:\n",
      "max_depth        0.797        0.7987\n",
      "min_depth        0.4463        0.4496\n",
      "num_sent        0.96        0.9644\n",
      "num_tok        0.1776        0.1791\n",
      "av_tok_before_root        0.1491        0.1512\n",
      "num_tu        0.8467        0.8492\n",
      "num_coord        0.723        0.7304\n",
      "num_adj_noun        0.2233        0.2337\n",
      "num_noun_inf        0.656        0.655\n",
      "pos_sim_nei        0.9166        0.9355\n",
      "errors        0.0        0.0\n",
      "acad_lemma_attested        0.3937        0.3963\n",
      "news_av_lemma_construction_freq_log        0.779        0.7665\n",
      "news_av_construction_freq_type        0.2485        0.2563\n",
      "news_collexeme_ratio        0.1978        0.1996\n",
      "fic_av_approx_collexeme        0.123        0.1428\n",
      "fic_av_faith_const_cue        0.2399        0.2712\n",
      "fic_av_lemma_freq_log        0.6325        0.6377\n",
      "fic_collexeme_ratio_type        0.1308        0.1343\n",
      "fic_lemma_construction_ttr        0.8237        0.82\n",
      "all_collexeme_ratio_type        0.6441        0.6532\n",
      "all_lemma_construction_attested        0.0545        0.0531\n",
      "fic_av_faith_verb_cue_stdev        0.0837        0.0871\n",
      "MLT        0.1234        0.1252\n",
      "MLC        0.3281        0.3367\n",
      "VP/T        0.0587        0.0583\n",
      "T/S        0.4287        0.4313\n",
      "CP/T        0.0115        0.012\n",
      "CN/C        0.5032        0.5158\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "print(\"Colname:  Spearman p-value:   Kendall p-value:\")\n",
    "for col in filtered_data.columns:\n",
    "    val = filtered_data[col].values\n",
    "    print(col, \"      \", round(stats.spearmanr(val, filtered_data[\"errors\"].values)[1], 4), \"      \", \n",
    "    round(stats.kendalltau(val, filtered_data[\"errors\"].values)[1], 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sorted_class' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-307-9a1e6af5df21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msorted_class\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minspector_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minspector_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"class\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pos_sim_all\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'sorted_class' is not defined"
     ]
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "\n",
    "# fig, ax = plt.subplots()\n",
    "# figure(figsize=(8, 6), dpi=80)\n",
    "\n",
    "\n",
    "res = []\n",
    "for i in sorted_class:\n",
    "    res.append(inspector_results[inspector_results[\"class\"] == i][\"pos_sim_all\"].mean())\n",
    "    \n",
    "\n",
    "# plt.errorbar(list(set(inspector_results[\"class\"].values)),\n",
    "#             res,\n",
    "#             label='pos_sim_all')\n",
    "\n",
    "plt.errorbar(list(set(inspector_results[\"class\"].values)),\n",
    "            inspector_results.groupby('class')[\"pos_sim_all\"].mean().values,\n",
    "            label='pos_sim_all')\n",
    "\n",
    "plt.errorbar(list(set(inspector_results[\"class\"].values)),\n",
    "            inspector_results.groupby('class')[\"av_len_sent\"].mean().values,\n",
    "            label='av_len_sent')\n",
    "\n",
    "plt.errorbar(list(set(inspector_results[\"class\"].values)),\n",
    "            inspector_results.groupby('class')[\"av_depth\"].mean().values,\n",
    "            label='av_depth')\n",
    "\n",
    "plt.errorbar(list(set(inspector_results[\"class\"].values)),\n",
    "            inspector_results.groupby('class')[\"av_tok_before_root\"].mean().values,\n",
    "            label='av_tok_before_root')\n",
    "\n",
    "plt.errorbar(list(set(inspector_results[\"class\"].values)),\n",
    "            inspector_results.groupby('class')[\"det_pobj_deps_NN_struct\"].mean().values,\n",
    "            label='det_pobj_deps_NN_struct')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(2, 2, figsize=(14, 8))\n",
    "\n",
    "# fig.figure(figsize=(8, 6), dpi=80)\n",
    "fig.suptitle('Top metric mean value')\n",
    "\n",
    "axs[0][0].plot(list(set(inspector_results[\"class\"].values)),\n",
    "            inspector_results.groupby('class')[\"pos_sim_all\"].mean().values)\n",
    "axs[0, 0].set_title(\"pos_sim_all\")\n",
    "\n",
    "axs[0][1].plot(list(set(inspector_results[\"class\"].values)),\n",
    "            inspector_results.groupby('class')[\"av_len_sent\"].mean().values)\n",
    "axs[0, 1].set_title(\"av_len_sent\")\n",
    "\n",
    "axs[1][0].plot(list(set(inspector_results[\"class\"].values)),\n",
    "            inspector_results.groupby('class')[\"av_depth\"].mean().values)\n",
    "axs[1, 0].set_title(\"av_depth\")\n",
    "\n",
    "axs[1][1].plot(list(set(inspector_results[\"class\"].values)),\n",
    "            inspector_results.groupby('class')[\"av_tok_before_root\"].mean().values)\n",
    "axs[1, 1].set_title(\"av_tok_before_root\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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