Auto Tagging Generation .ipynb

{
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import nltk \n",
    "import spacy\n",
    "from tqdm import tqdm \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "%matplotlib inline \n",
    "pd.set_option('display.max_colwidth', 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_hdf(\"auto_tagging_data_v2.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Title</th>\n",
       "      <th>Body</th>\n",
       "      <th>Tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>The Two Cultures: statistics vs. machine learning?</td>\n",
       "      <td>&lt;p&gt;Last year, I read a blog post from &lt;a href=\"http://anyall.org/\"&gt;Brendan O'Connor&lt;/a&gt; entitled &lt;a href=\"http://anyall.org/blog/2008/12/statistics-vs-machine-learning-fight/\"&gt;\"Statistics vs. Mach...</td>\n",
       "      <td>[machine-learning]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>Forecasting demographic census</td>\n",
       "      <td>&lt;p&gt;What are some of the ways to forecast demographic census with some validation and calibration techniques?&lt;/p&gt;\\n\\n&lt;p&gt;Some of the concerns:&lt;/p&gt;\\n\\n&lt;ul&gt;\\n&lt;li&gt;Census blocks vary in sizes as rural\\n...</td>\n",
       "      <td>[forecasting]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>Bayesian and frequentist reasoning in plain English</td>\n",
       "      <td>&lt;p&gt;How would you describe in plain English the characteristics that distinguish Bayesian from Frequentist reasoning?&lt;/p&gt;\\n</td>\n",
       "      <td>[bayesian]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31</td>\n",
       "      <td>What is the meaning of p values and t values in statistical tests?</td>\n",
       "      <td>&lt;p&gt;After taking a statistics course and then trying to help fellow students, I noticed one subject that inspires much head-desk banging is interpreting the results of statistical hypothesis tests....</td>\n",
       "      <td>[hypothesis-testing, t-test, p-value, interpretation]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>Examples for teaching: Correlation does not mean causation</td>\n",
       "      <td>&lt;p&gt;There is an old saying: \"Correlation does not mean causation\". When I teach, I tend to use the following standard examples to illustrate this point:&lt;/p&gt;\\n\\n&lt;ol&gt;\\n&lt;li&gt;number of storks and birth ...</td>\n",
       "      <td>[correlation]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id                                                               Title  \\\n",
       "0   6                  The Two Cultures: statistics vs. machine learning?   \n",
       "1  21                                      Forecasting demographic census   \n",
       "2  22                 Bayesian and frequentist reasoning in plain English   \n",
       "3  31  What is the meaning of p values and t values in statistical tests?   \n",
       "4  36          Examples for teaching: Correlation does not mean causation   \n",
       "\n",
       "                                                                                                                                                                                                      Body  \\\n",
       "0  <p>Last year, I read a blog post from <a href=\"http://anyall.org/\">Brendan O'Connor</a> entitled <a href=\"http://anyall.org/blog/2008/12/statistics-vs-machine-learning-fight/\">\"Statistics vs. Mach...   \n",
       "1  <p>What are some of the ways to forecast demographic census with some validation and calibration techniques?</p>\\n\\n<p>Some of the concerns:</p>\\n\\n<ul>\\n<li>Census blocks vary in sizes as rural\\n...   \n",
       "2                                                                               <p>How would you describe in plain English the characteristics that distinguish Bayesian from Frequentist reasoning?</p>\\n   \n",
       "3  <p>After taking a statistics course and then trying to help fellow students, I noticed one subject that inspires much head-desk banging is interpreting the results of statistical hypothesis tests....   \n",
       "4  <p>There is an old saying: \"Correlation does not mean causation\". When I teach, I tend to use the following standard examples to illustrate this point:</p>\\n\\n<ol>\\n<li>number of storks and birth ...   \n",
       "\n",
       "                                                    Tags  \n",
       "0                                     [machine-learning]  \n",
       "1                                          [forecasting]  \n",
       "2                                             [bayesian]  \n",
       "3  [hypothesis-testing, t-test, p-value, interpretation]  \n",
       "4                                          [correlation]  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"text\"] = data[\"Title\"] + \" \" + data[\"Body\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Tags</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>The Two Cultures: statistics vs. machine learning?</td>\n",
       "      <td>&lt;p&gt;Last year, I read a blog post from &lt;a href=\"http://anyall.org/\"&gt;Brendan O'Connor&lt;/a&gt; entitled &lt;a href=\"http://anyall.org/blog/2008/12/statistics-vs-machine-learning-fight/\"&gt;\"Statistics vs. Mach...</td>\n",
       "      <td>[machine-learning]</td>\n",
       "      <td>The Two Cultures: statistics vs. machine learning? &lt;p&gt;Last year, I read a blog post from &lt;a href=\"http://anyall.org/\"&gt;Brendan O'Connor&lt;/a&gt; entitled &lt;a href=\"http://anyall.org/blog/2008/12/statisti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>Forecasting demographic census</td>\n",
       "      <td>&lt;p&gt;What are some of the ways to forecast demographic census with some validation and calibration techniques?&lt;/p&gt;\\n\\n&lt;p&gt;Some of the concerns:&lt;/p&gt;\\n\\n&lt;ul&gt;\\n&lt;li&gt;Census blocks vary in sizes as rural\\n...</td>\n",
       "      <td>[forecasting]</td>\n",
       "      <td>Forecasting demographic census &lt;p&gt;What are some of the ways to forecast demographic census with some validation and calibration techniques?&lt;/p&gt;\\n\\n&lt;p&gt;Some of the concerns:&lt;/p&gt;\\n\\n&lt;ul&gt;\\n&lt;li&gt;Census ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>Bayesian and frequentist reasoning in plain English</td>\n",
       "      <td>&lt;p&gt;How would you describe in plain English the characteristics that distinguish Bayesian from Frequentist reasoning?&lt;/p&gt;\\n</td>\n",
       "      <td>[bayesian]</td>\n",
       "      <td>Bayesian and frequentist reasoning in plain English &lt;p&gt;How would you describe in plain English the characteristics that distinguish Bayesian from Frequentist reasoning?&lt;/p&gt;\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31</td>\n",
       "      <td>What is the meaning of p values and t values in statistical tests?</td>\n",
       "      <td>&lt;p&gt;After taking a statistics course and then trying to help fellow students, I noticed one subject that inspires much head-desk banging is interpreting the results of statistical hypothesis tests....</td>\n",
       "      <td>[hypothesis-testing, t-test, p-value, interpretation]</td>\n",
       "      <td>What is the meaning of p values and t values in statistical tests? &lt;p&gt;After taking a statistics course and then trying to help fellow students, I noticed one subject that inspires much head-desk b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>Examples for teaching: Correlation does not mean causation</td>\n",
       "      <td>&lt;p&gt;There is an old saying: \"Correlation does not mean causation\". When I teach, I tend to use the following standard examples to illustrate this point:&lt;/p&gt;\\n\\n&lt;ol&gt;\\n&lt;li&gt;number of storks and birth ...</td>\n",
       "      <td>[correlation]</td>\n",
       "      <td>Examples for teaching: Correlation does not mean causation &lt;p&gt;There is an old saying: \"Correlation does not mean causation\". When I teach, I tend to use the following standard examples to illustra...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id                                                               Title  \\\n",
       "0   6                  The Two Cultures: statistics vs. machine learning?   \n",
       "1  21                                      Forecasting demographic census   \n",
       "2  22                 Bayesian and frequentist reasoning in plain English   \n",
       "3  31  What is the meaning of p values and t values in statistical tests?   \n",
       "4  36          Examples for teaching: Correlation does not mean causation   \n",
       "\n",
       "                                                                                                                                                                                                      Body  \\\n",
       "0  <p>Last year, I read a blog post from <a href=\"http://anyall.org/\">Brendan O'Connor</a> entitled <a href=\"http://anyall.org/blog/2008/12/statistics-vs-machine-learning-fight/\">\"Statistics vs. Mach...   \n",
       "1  <p>What are some of the ways to forecast demographic census with some validation and calibration techniques?</p>\\n\\n<p>Some of the concerns:</p>\\n\\n<ul>\\n<li>Census blocks vary in sizes as rural\\n...   \n",
       "2                                                                               <p>How would you describe in plain English the characteristics that distinguish Bayesian from Frequentist reasoning?</p>\\n   \n",
       "3  <p>After taking a statistics course and then trying to help fellow students, I noticed one subject that inspires much head-desk banging is interpreting the results of statistical hypothesis tests....   \n",
       "4  <p>There is an old saying: \"Correlation does not mean causation\". When I teach, I tend to use the following standard examples to illustrate this point:</p>\\n\\n<ol>\\n<li>number of storks and birth ...   \n",
       "\n",
       "                                                    Tags  \\\n",
       "0                                     [machine-learning]   \n",
       "1                                          [forecasting]   \n",
       "2                                             [bayesian]   \n",
       "3  [hypothesis-testing, t-test, p-value, interpretation]   \n",
       "4                                          [correlation]   \n",
       "\n",
       "                                                                                                                                                                                                      text  \n",
       "0  The Two Cultures: statistics vs. machine learning? <p>Last year, I read a blog post from <a href=\"http://anyall.org/\">Brendan O'Connor</a> entitled <a href=\"http://anyall.org/blog/2008/12/statisti...  \n",
       "1  Forecasting demographic census <p>What are some of the ways to forecast demographic census with some validation and calibration techniques?</p>\\n\\n<p>Some of the concerns:</p>\\n\\n<ul>\\n<li>Census ...  \n",
       "2                           Bayesian and frequentist reasoning in plain English <p>How would you describe in plain English the characteristics that distinguish Bayesian from Frequentist reasoning?</p>\\n  \n",
       "3  What is the meaning of p values and t values in statistical tests? <p>After taking a statistics course and then trying to help fellow students, I noticed one subject that inspires much head-desk b...  \n",
       "4  Examples for teaching: Correlation does not mean causation <p>There is an old saying: \"Correlation does not mean causation\". When I teach, I tend to use the following standard examples to illustra...  "
      ]
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocessing and cleaning the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    # removing the tags and urls \n",
    "    text= re.sub(r'<.*?>', \"\", text)\n",
    "    #keeping only the alphabets\n",
    "    text=re.sub(r'^[a-zA-z]', \" \", text)\n",
    "    # removing white spaces\n",
    "    text=' '.join(text.split())\n",
    "    \n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"text\"] = data[\"text\"].apply(lambda x:clean_text(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"text\"]= data[\"text\"].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    he two cultures: statistics vs. machine learning? last year, i read a blog post from brendan o'connor entitled \"statistics vs. machine learning, fight!\" that discussed some of the differences betw...\n",
       "1    orecasting demographic census what are some of the ways to forecast demographic census with some validation and calibration techniques? some of the concerns: census blocks vary in sizes as rural a...\n",
       "2                                       ayesian and frequentist reasoning in plain english how would you describe in plain english the characteristics that distinguish bayesian from frequentist reasoning?\n",
       "3    hat is the meaning of p values and t values in statistical tests? after taking a statistics course and then trying to help fellow students, i noticed one subject that inspires much head-desk bangi...\n",
       "4    xamples for teaching: correlation does not mean causation there is an old saying: \"correlation does not mean causation\". when i teach, i tend to use the following standard examples to illustrate t...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"text\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords \n",
    "stop_words=set(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def strip_stopwords(text):\n",
    "    words= [w for w in text.split() if w not in stop_words]\n",
    "    text_clean= \" \".join(words)\n",
    "    \n",
    "    return text_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"text_cleaned\"] = data[\"text\"].apply(lambda x: strip_stopwords(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "      <th>text_cleaned</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>The Two Cultures: statistics vs. machine learning?</td>\n",
       "      <td>&lt;p&gt;Last year, I read a blog post from &lt;a href=\"http://anyall.org/\"&gt;Brendan O'Connor&lt;/a&gt; entitled &lt;a href=\"http://anyall.org/blog/2008/12/statistics-vs-machine-learning-fight/\"&gt;\"Statistics vs. Mach...</td>\n",
       "      <td>[machine-learning]</td>\n",
       "      <td>he two cultures: statistics vs. machine learning? last year, i read a blog post from brendan o'connor entitled \"statistics vs. machine learning, fight!\" that discussed some of the differences betw...</td>\n",
       "      <td>two cultures: statistics vs. machine learning? last year, read blog post brendan o'connor entitled \"statistics vs. machine learning, fight!\" discussed differences two fields. andrew gelman respond...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>Forecasting demographic census</td>\n",
       "      <td>&lt;p&gt;What are some of the ways to forecast demographic census with some validation and calibration techniques?&lt;/p&gt;\\n\\n&lt;p&gt;Some of the concerns:&lt;/p&gt;\\n\\n&lt;ul&gt;\\n&lt;li&gt;Census blocks vary in sizes as rural\\n...</td>\n",
       "      <td>[forecasting]</td>\n",
       "      <td>orecasting demographic census what are some of the ways to forecast demographic census with some validation and calibration techniques? some of the concerns: census blocks vary in sizes as rural a...</td>\n",
       "      <td>orecasting demographic census ways forecast demographic census validation calibration techniques? concerns: census blocks vary sizes rural areas lot larger condensed urban areas. need account area...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>Bayesian and frequentist reasoning in plain English</td>\n",
       "      <td>&lt;p&gt;How would you describe in plain English the characteristics that distinguish Bayesian from Frequentist reasoning?&lt;/p&gt;\\n</td>\n",
       "      <td>[bayesian]</td>\n",
       "      <td>ayesian and frequentist reasoning in plain english how would you describe in plain english the characteristics that distinguish bayesian from frequentist reasoning?</td>\n",
       "      <td>ayesian frequentist reasoning plain english would describe plain english characteristics distinguish bayesian frequentist reasoning?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31</td>\n",
       "      <td>What is the meaning of p values and t values in statistical tests?</td>\n",
       "      <td>&lt;p&gt;After taking a statistics course and then trying to help fellow students, I noticed one subject that inspires much head-desk banging is interpreting the results of statistical hypothesis tests....</td>\n",
       "      <td>[hypothesis-testing, t-test, p-value, interpretation]</td>\n",
       "      <td>hat is the meaning of p values and t values in statistical tests? after taking a statistics course and then trying to help fellow students, i noticed one subject that inspires much head-desk bangi...</td>\n",
       "      <td>hat meaning p values values statistical tests? taking statistics course trying help fellow students, noticed one subject inspires much head-desk banging interpreting results statistical hypothesis...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>Examples for teaching: Correlation does not mean causation</td>\n",
       "      <td>&lt;p&gt;There is an old saying: \"Correlation does not mean causation\". When I teach, I tend to use the following standard examples to illustrate this point:&lt;/p&gt;\\n\\n&lt;ol&gt;\\n&lt;li&gt;number of storks and birth ...</td>\n",
       "      <td>[correlation]</td>\n",
       "      <td>xamples for teaching: correlation does not mean causation there is an old saying: \"correlation does not mean causation\". when i teach, i tend to use the following standard examples to illustrate t...</td>\n",
       "      <td>xamples teaching: correlation mean causation old saying: \"correlation mean causation\". teach, tend use following standard examples illustrate point: number storks birth rate denmark; number priest...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id                                                               Title  \\\n",
       "0   6                  The Two Cultures: statistics vs. machine learning?   \n",
       "1  21                                      Forecasting demographic census   \n",
       "2  22                 Bayesian and frequentist reasoning in plain English   \n",
       "3  31  What is the meaning of p values and t values in statistical tests?   \n",
       "4  36          Examples for teaching: Correlation does not mean causation   \n",
       "\n",
       "                                                                                                                                                                                                      Body  \\\n",
       "0  <p>Last year, I read a blog post from <a href=\"http://anyall.org/\">Brendan O'Connor</a> entitled <a href=\"http://anyall.org/blog/2008/12/statistics-vs-machine-learning-fight/\">\"Statistics vs. Mach...   \n",
       "1  <p>What are some of the ways to forecast demographic census with some validation and calibration techniques?</p>\\n\\n<p>Some of the concerns:</p>\\n\\n<ul>\\n<li>Census blocks vary in sizes as rural\\n...   \n",
       "2                                                                               <p>How would you describe in plain English the characteristics that distinguish Bayesian from Frequentist reasoning?</p>\\n   \n",
       "3  <p>After taking a statistics course and then trying to help fellow students, I noticed one subject that inspires much head-desk banging is interpreting the results of statistical hypothesis tests....   \n",
       "4  <p>There is an old saying: \"Correlation does not mean causation\". When I teach, I tend to use the following standard examples to illustrate this point:</p>\\n\\n<ol>\\n<li>number of storks and birth ...   \n",
       "\n",
       "                                                    Tags  \\\n",
       "0                                     [machine-learning]   \n",
       "1                                          [forecasting]   \n",
       "2                                             [bayesian]   \n",
       "3  [hypothesis-testing, t-test, p-value, interpretation]   \n",
       "4                                          [correlation]   \n",
       "\n",
       "                                                                                                                                                                                                      text  \\\n",
       "0  he two cultures: statistics vs. machine learning? last year, i read a blog post from brendan o'connor entitled \"statistics vs. machine learning, fight!\" that discussed some of the differences betw...   \n",
       "1  orecasting demographic census what are some of the ways to forecast demographic census with some validation and calibration techniques? some of the concerns: census blocks vary in sizes as rural a...   \n",
       "2                                     ayesian and frequentist reasoning in plain english how would you describe in plain english the characteristics that distinguish bayesian from frequentist reasoning?   \n",
       "3  hat is the meaning of p values and t values in statistical tests? after taking a statistics course and then trying to help fellow students, i noticed one subject that inspires much head-desk bangi...   \n",
       "4  xamples for teaching: correlation does not mean causation there is an old saying: \"correlation does not mean causation\". when i teach, i tend to use the following standard examples to illustrate t...   \n",
       "\n",
       "                                                                                                                                                                                              text_cleaned  \n",
       "0  two cultures: statistics vs. machine learning? last year, read blog post brendan o'connor entitled \"statistics vs. machine learning, fight!\" discussed differences two fields. andrew gelman respond...  \n",
       "1  orecasting demographic census ways forecast demographic census validation calibration techniques? concerns: census blocks vary sizes rural areas lot larger condensed urban areas. need account area...  \n",
       "2                                                                     ayesian frequentist reasoning plain english would describe plain english characteristics distinguish bayesian frequentist reasoning?  \n",
       "3  hat meaning p values values statistical tests? taking statistics course trying help fellow students, noticed one subject inspires much head-desk banging interpreting results statistical hypothesis...  \n",
       "4  xamples teaching: correlation mean causation old saying: \"correlation mean causation\". teach, tend use following standard examples illustrate point: number storks birth rate denmark; number priest...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshaping the target variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MultiLabelBinarizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_label= MultiLabelBinarizer()\n",
    "multi_label.fit(data[\"Tags\"])\n",
    "Y= multi_label.transform(data[\"Tags\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76365, 100)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# forming the vector representations using TfidfVectorization and Word2Vec model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TFIDF Vectorization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf= TfidfVectorizer(max_df=0.8, max_features=1000)\n",
    "#Tfidf = tfidf.fit_transform(data.text_cleaned)\n",
    "X_tf = tfidf.fit_transform(data[\"text_cleaned\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76365, 1000)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tf.shape\n",
    "#Tfidf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tfidf_df =  pd.DataFrame.sparse.from_spmatrix(Tfidf, columns=tfidf.get_feature_names(), index= data.text_cleaned.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.263519</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.093569</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.444813</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     perhaps  period  person  phi   pi    please  plot     plots     point  \\\n",
       "0   0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.000000   \n",
       "1   0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.000000   \n",
       "2   0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.000000   \n",
       "3   0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.000000   \n",
       "4   0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.137920   \n",
       "5   0.156849     0.0     0.0  0.0  0.0  0.000000   0.0  0.163612  0.000000   \n",
       "6   0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.000000   \n",
       "7   0.000000     0.0     0.0  0.0  0.0  0.377486   0.0  0.000000  0.000000   \n",
       "8   0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.000000   \n",
       "9   0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.000000   \n",
       "10  0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.000000   \n",
       "11  0.000000     0.0     0.0  0.0  0.0  0.227819   0.0  0.000000  0.000000   \n",
       "12  0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.000000   \n",
       "13  0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.000000   \n",
       "14  0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.000000   \n",
       "15  0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.000000   \n",
       "16  0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.263519   \n",
       "17  0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.000000   \n",
       "18  0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.444813  0.000000   \n",
       "19  0.000000     0.0     0.0  0.0  0.0  0.000000   0.0  0.000000  0.000000   \n",
       "\n",
       "      points  ...   produce  product  project  proportion  proportions  \\\n",
       "0   0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "1   0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "2   0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "3   0.097463  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "4   0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "5   0.000000  ...  0.167238      0.0      0.0         0.0          0.0   \n",
       "6   0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "7   0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "8   0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "9   0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "10  0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "11  0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "12  0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "13  0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "14  0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "15  0.206394  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "16  0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "17  0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "18  0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "19  0.000000  ...  0.000000      0.0      0.0         0.0          0.0   \n",
       "\n",
       "     provide  put  python  question  questions  \n",
       "0   0.000000  0.0     0.0  0.000000   0.000000  \n",
       "1   0.000000  0.0     0.0  0.000000   0.000000  \n",
       "2   0.000000  0.0     0.0  0.000000   0.000000  \n",
       "3   0.000000  0.0     0.0  0.000000   0.000000  \n",
       "4   0.000000  0.0     0.0  0.000000   0.000000  \n",
       "5   0.000000  0.0     0.0  0.000000   0.000000  \n",
       "6   0.000000  0.0     0.0  0.000000   0.000000  \n",
       "7   0.000000  0.0     0.0  0.124486   0.182255  \n",
       "8   0.000000  0.0     0.0  0.000000   0.000000  \n",
       "9   0.000000  0.0     0.0  0.000000   0.000000  \n",
       "10  0.000000  0.0     0.0  0.000000   0.000000  \n",
       "11  0.000000  0.0     0.0  0.000000   0.000000  \n",
       "12  0.000000  0.0     0.0  0.000000   0.000000  \n",
       "13  0.000000  0.0     0.0  0.000000   0.000000  \n",
       "14  0.000000  0.0     0.0  0.038787   0.000000  \n",
       "15  0.000000  0.0     0.0  0.000000   0.100970  \n",
       "16  0.172838  0.0     0.0  0.093569   0.000000  \n",
       "17  0.000000  0.0     0.0  0.000000   0.000000  \n",
       "18  0.000000  0.0     0.0  0.000000   0.000000  \n",
       "19  0.000000  0.0     0.0  0.000000   0.000000  \n",
       "\n",
       "[20 rows x 50 columns]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tfidf_df.iloc[:20, 650:700]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_tf, X_test_tf, y_train_tf, y_test_tf = train_test_split(X_tf, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "#from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import svm\n",
    "# Binary Relevance\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "# Performance metric\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()\n",
    "#svc = svm.SVC(kernel='linear', C=1, probability=True)\n",
    "clf = OneVsRestClassifier(lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneVsRestClassifier(estimator=LogisticRegression())"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train_tf, y_train_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_prob = clf.predict_proba(X_test_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4480622898602018"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set threshold value\n",
    "t = 0.45\n",
    "\n",
    "# convert to integers\n",
    "y = (y_pred_prob >= t).astype(int)\n",
    "f1_score(y_test_tf, y, average=\"micro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 14.856282328291757 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Accuracy:\", accuracy_score(y_test_tf, y)*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Word2Vec model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.utils import simple_preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.text_cleaned.apply(lambda x: simple_preprocess(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "w2v_model = Word2Vec(X, size=300, min_count=2, sg=1, negative= 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vocabulary and its size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocabulary size: 59678\n"
     ]
    }
   ],
   "source": [
    "print('vocabulary size:', len(w2v_model.wv.vocab))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('two', <gensim.models.keyedvectors.Vocab at 0x20d89c543c8>),\n",
       " ('cultures', <gensim.models.keyedvectors.Vocab at 0x20d89967748>),\n",
       " ('statistics', <gensim.models.keyedvectors.Vocab at 0x20d89c25988>),\n",
       " ('vs', <gensim.models.keyedvectors.Vocab at 0x20d89c255c8>),\n",
       " ('machine', <gensim.models.keyedvectors.Vocab at 0x20d89c6fc48>),\n",
       " ('learning', <gensim.models.keyedvectors.Vocab at 0x20d89c6fa08>),\n",
       " ('last', <gensim.models.keyedvectors.Vocab at 0x20d89c6f408>),\n",
       " ('year', <gensim.models.keyedvectors.Vocab at 0x20d89c6f608>),\n",
       " ('read', <gensim.models.keyedvectors.Vocab at 0x20d89c25108>),\n",
       " ('blog', <gensim.models.keyedvectors.Vocab at 0x20d89c6f748>),\n",
       " ('post', <gensim.models.keyedvectors.Vocab at 0x20d89c6ff88>),\n",
       " ('brendan', <gensim.models.keyedvectors.Vocab at 0x20d89c6f3c8>),\n",
       " ('connor', <gensim.models.keyedvectors.Vocab at 0x20d89c6fa48>),\n",
       " ('entitled', <gensim.models.keyedvectors.Vocab at 0x20d89c6f9c8>),\n",
       " ('fight', <gensim.models.keyedvectors.Vocab at 0x20d89c6fdc8>),\n",
       " ('discussed', <gensim.models.keyedvectors.Vocab at 0x20d89c6f248>),\n",
       " ('differences', <gensim.models.keyedvectors.Vocab at 0x20d89abdd08>),\n",
       " ('fields', <gensim.models.keyedvectors.Vocab at 0x20d89c201c8>),\n",
       " ('andrew', <gensim.models.keyedvectors.Vocab at 0x20d89c20108>),\n",
       " ('gelman', <gensim.models.keyedvectors.Vocab at 0x20d89c20b08>)]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(w2v_model.wv.vocab.items())[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finding the contextually similar words for a given word "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('undergraduate', 0.7534754276275635),\n",
       " ('stats', 0.7449577450752258),\n",
       " ('prerequisites', 0.73811274766922),\n",
       " ('craig', 0.7380141019821167),\n",
       " ('coursework', 0.7370071411132812),\n",
       " ('biostatistics', 0.736904501914978),\n",
       " ('brushing', 0.728969931602478),\n",
       " ('syllabus', 0.7274842858314514),\n",
       " ('inferential', 0.7251176834106445),\n",
       " ('discovering', 0.7239354252815247)]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_statistics = w2v_model.wv.most_similar('statistics')\n",
    "words_statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualizaing the word embeddings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate similar word lists\n",
    "words_statistics = w2v_model.wv.most_similar('statistics')\n",
    "words_algorithm = w2v_model.wv.most_similar('algorithm')\n",
    "words_value = w2v_model.wv.most_similar('value')\n",
    "# combine word lists \n",
    "words = words_statistics + words_algorithm + words_value\n",
    "\n",
    "# extract only the word and not the similarity score\n",
    "words = list(map(lambda x: x[0], words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# plots w2v embeddings of a given word list\n",
    "def plot_w2v(word_list):\n",
    "    X = w2v_model[word_list]\n",
    "    \n",
    "    # reduce dimensions to 2\n",
    "    pca = PCA(n_components=2)\n",
    "    result = pca.fit_transform(X)\n",
    "    \n",
    "    # create a scatter plot of the projection\n",
    "    pyplot.scatter(result[:, 0], result[:, 1])\n",
    "    for i, word in enumerate(word_list):\n",
    "        pyplot.annotate(word, xy=(result[i, 0], result[i, 1]))\n",
    "        \n",
    "    # show\n",
    "    pyplot.figure(figsize=(6,15))\n",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\ipykernel_launcher.py:6: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  \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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_w2v(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating the \"X\" from our Word2Vec model developed above "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# returns vector reperesentation of a given word if it is present in vocabulary\n",
    "def get_embedding_w2v(doc_tokens):\n",
    "    embeddings = []\n",
    "    model = w2v_model\n",
    "    # iterate over tokens to extract their vectors    \n",
    "    for tok in doc_tokens:\n",
    "        if tok in model.wv.vocab:\n",
    "            embeddings.append(model.wv.word_vec(tok))\n",
    "    # mean the vectors of individual words to get the vector of the statement\n",
    "    return np.mean(embeddings, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape: (76365, 300)\n"
     ]
    }
   ],
   "source": [
    "X_w2v_model = X.apply(lambda x: get_embedding_w2v(x))\n",
    "X_w2v_model = pd.DataFrame(X_w2v_model.tolist())\n",
    "print('X shape:', X_w2v_model.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_wm, X_test_wm, y_train_wm, y_test_wm = train_test_split(X_w2v_model, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "#from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# Binary Relevance\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "# Performance metric\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()\n",
    "clf = OneVsRestClassifier(lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "E:\\softwares\\envs\\tf\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OneVsRestClassifier(estimator=LogisticRegression())"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train_wm, y_train_wm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_prob = clf.predict_proba(X_test_wm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40506904069767447"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set threshold value\n",
    "t = 0.45\n",
    "\n",
    "# convert to integers\n",
    "y = (y_pred_prob >= t).astype(int)\n",
    "f1_score(y_test_wm, y, average=\"micro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 12.70870163032803 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\", accuracy_score(y_test_wm, y)*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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