06 Classification of text sentiment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification of text sentiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Wikishop online store is launching a new service. Now users can edit and add product descriptions, just like in wiki communities. That is, clients propose their own edits and comment on the changes of others. The store needs a tool that will look for toxic comments and send them for moderation.\n",
    "\n",
    "**Task:**\n",
    "- Train the model to classify comments into positive and negative.\n",
    "\n",
    "**Important criteria:**\n",
    "- The value of the F1 metric on the test sample must be at least 0.75.\n",
    "\n",
    "**Work plan:**\n",
    "- Loading and preparing data\n",
    "- Feature preparation and model training\n",
    "- Summing up the work\n",
    "\n",
    "**Data description - file `toxic_comments.csv`**\n",
    "\n",
    "- `text` - text\n",
    "- `toxic` — text toxicity (`0` - positive; `1` - negative) (***target***)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and preparing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --upgrade scikit-learn\n",
    "%pip install nltk\n",
    "%pip install lightgbm\n",
    "%pip install spacy\n",
    "%pip install wordcloud\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\dmitr\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\dmitr\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\dmitr\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np \n",
    "import warnings\n",
    "\n",
    "import re\n",
    "import spacy\n",
    "from spacy.lang.en import English\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from tqdm.notebook import tqdm\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "\n",
    "# Loading required NLTK resources\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import f1_score, roc_auc_score, confusion_matrix, accuracy_score, roc_curve, auc as sklearn_auc\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from lightgbm import LGBMClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option('display.max_colwidth', None) \n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_STATE = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving the file to a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    df = pd.read_csv('/datasets/toxic_comments.csv', index_col=0)\n",
    "except:\n",
    "    df = pd.read_csv('https://code.s3.yandex.net/datasets/toxic_comments.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at general information about the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 159292 entries, 0 to 159450\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count   Dtype \n",
      "---  ------  --------------   ----- \n",
      " 0   text    159292 non-null  object\n",
      " 1   toxic   159292 non-null  int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 3.6+ MB\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>toxic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49576</th>\n",
       "      <td>no change in hysteresis loss</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4647</th>\n",
       "      <td>niggah \\n\\nhey fool whyd you delete my article?</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66815</th>\n",
       "      <td>Please no Wiki-Weirdo posts !</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65970</th>\n",
       "      <td>japanase translator needed\\nthe official timeline is explained here.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33879</th>\n",
       "      <td>Hello, .  This message is being sent to inform you that there currently is a discussion at Wikipedia:Administrators' noticeboard/Incidents regarding an issue with which you may have been involved.     Thank you.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                                                                                                                                                                                                      text  \\\n",
       "49576                                                                                                                                                                                         no change in hysteresis loss   \n",
       "4647                                                                                                                                                                       niggah \\n\\nhey fool whyd you delete my article?   \n",
       "66815                                                                                                                                                                                        Please no Wiki-Weirdo posts !   \n",
       "65970                                                                                                                                                 japanase translator needed\\nthe official timeline is explained here.   \n",
       "33879  Hello, .  This message is being sent to inform you that there currently is a discussion at Wikipedia:Administrators' noticeboard/Incidents regarding an issue with which you may have been involved.     Thank you.   \n",
       "\n",
       "       toxic  \n",
       "49576      0  \n",
       "4647       1  \n",
       "66815      0  \n",
       "65970      0  \n",
       "33879      0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the number of gaps in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text     0\n",
       "toxic    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the number of complete duplicates in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the balance of classes in the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_graph = df.copy()\n",
    "data_graph['length_data'] = data_graph.text.apply(lambda x: len(x.split()))\n",
    "toxic=data_graph.query('toxic == 1')\n",
    "not_toxic=data_graph.query('toxic == 0')\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "sns.histplot(data=toxic, bins=100, alpha=0.8, edgecolor=\"gray\",color='red', x='length_data')\n",
    "sns.histplot(data=not_toxic, bins=100, alpha=0.4, edgecolor=\"gray\",  color='green', x='length_data')\n",
    "plt.title('Relative frequency of comments length')\n",
    "plt.legend(['Toxic','Not toxic'])\n",
    "plt.xlabel('Words in comment')\n",
    "plt.ylabel('Quantity')\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "toxic\n",
      "0    143106\n",
      "1     16186\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.toxic.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "toxic\n",
      "Not toxic    143106\n",
      "Toxic         16186\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Count')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2MAAADdCAYAAAA2GaqRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAfLklEQVR4nO3de1RVZR7/8c8BNC+pI65Ma2xy6aCCXEXUJSqgmcvsYmlWXprwljZTLnEE06nIzNLyfuuC45TOTxtFG7PGbJVZv7yATk7lBVGotNRSwRsicJ7fHy3PzxMoICefA+f9Wsu1OM/e53se9reHw6e9OdthjDECAAAAAFxXfrYnAAAAAAC+iDAGAAAAABYQxgAAAADAAsIYAAAAAFhAGAMAAAAACwhjAAAAAGABYQwAAAAALCCMAQAAAIAFAbYnUFMYY+R0cv9sb+Tn56A3Xoz+eC96473ojfeiN96L3nivmtYbPz+HHA5HhfYljHmIw+HQ6dPnVVzstD0VXCYgwE+NG9enN16K/ngveuO96I33ojfei954r5rYm8DA+vL3r1gY4zJFAAAAALCAMAYAAAAAFhDGAAAAAMACwhgAAAAAWEAYAwAAAAAL+DRFD/L3J9t6m0s9oTfeif54L3rjveiN96I33oveeC9P9MbprL63mHIYY6rnzL2MMabC9xMAAAAA4BklJU7l5Z33mkD2y0fbVyxccmbMQxwOhxb+n/+rI8fzbU8FAAAA8Am3Nm2kJx7uWm1vHE0Y86Ajx/OVe+SU7WkAAAAAqAa4cBYAAAAALCCMAQAAAIAFhDEAAAAAsIAwBgAAAAAWEMYAAAAAwALCGAAAAABYQBgDAAAAAAsIYwAAAABgAWEMAAAAACwgjAEAAACABYQxAAAAALCAMAYAAAAAFhDGAAAAAMACwhgAAAAAWEAYAwAAAAALCGMAAAAAYAFhDAAAAAAsIIwBAAAAgAWEMQAAAACwgDAGAAAAABYQxgAAAADAAsIYAAAAAFhAGAMAAAAACyoVxhISEpSQkKCzZ8+W2paSkqKhQ4dWuJYxRmvXrtWJEyeuuM+BAwe0efPmykzxqhISEjR//nyP1QMAAACAa1XpM2NHjhzRjBkzqvzCGRkZSklJUUFBwRX3GT16tL766qsqv9Ylq1evVmJiosfqAQAAAMC1qnQYa9GihVatWqUvvviiSi9sjKnS869FYGCg6tevf91fFwAAAAB+rdJh7J577lGXLl00efLkMi9XvCQvL0+pqanq0aOHwsLC9NBDD2n79u2SpO3bt2vYsGGSpJ49eyo9Pb3U8xMSEnTkyBEtWLDAdfnj1WoWFRWpf//+6t+/v4qLiyVJ+/fvV2hoqJYuXeqqefllip999pkGDRqk8PBwde/eXbNnz1ZJSUllDwkAAAAAVFqlw5jD4dC0adOUn5+vl19+ucx9SkpKlJiYqMzMTM2cOVPp6ekKCgrS8OHD9b///U+RkZGuUPSvf/1Lffv2LVVj9erVatasmRITEzV//vxya9aqVUszZ87UwYMHlZaWposXL2rChAnq1KmTHnvssVL1//vf/2rUqFHq0KGD0tPT9cILL2jlypVatGhRZQ8JAAAAAFTaNX2a4q233qrk5GS98847+vzzz0tt//zzz/XNN9/o1VdfVUxMjFq3bq3U1FT98Y9/VFpammrXrq1GjRpJ+uXSwTp16pSqERgYKH9/f9WrV0+/+93vyq0pSa1bt9b48eO1cOFCTZo0SSdPntTLL78sh8NRqv7bb7+t8PBwTZw4Ua1atVL37t31/PPPq0mTJtdySAAAAACgUq75o+0HDRqkrl27asqUKaUuV8zKylKDBg0UFBTkGnM4HIqOjlZWVtY1vV5Faz766KMKDw/Xe++9p9TU1CuGq6ysLIWHh7uN3XnnnXrkkUeuaX4AAAAAUBlVus/YCy+8oDNnzmj69Olu41f6cA5jjAICAq7ptSpa8/Tp0/ruu+8UEBBQ5lm7S651HgAAAADgCVUKY7fccotSUlK0evVqZWZmusbbtGmjM2fOuJ2xMsZo586dat26tSSVeeng1VSkpiSlpqaqbt26WrhwoVauXKktW7aUWa9Vq1alPjb/H//4hwYOHFipeQEAAADAtahSGJOkgQMHKjY2Vt9//71rLDY2Vu3atVNSUpJ27NihgwcP6vnnn1dWVpYeffRRSVK9evUkSfv27dO5c+fKrF2/fn3l5ubq559/rlDN9957Tx988IFefPFFxcXFacCAAZo8ebLy8vJK1R4xYoS+/PJLzZ07V7m5ufr000+1aNEixcXFVfWQAAAAAEC5qhzGpF8uV2zQoIHrsb+/v5YuXarg4GD9+c9/1gMPPKADBw5o2bJlioiIkCQFBQWpR48eGjdunFatWlVm3aFDh2rz5s1KTEwst+axY8f0/PPPa8iQIYqKipIkJScny+Fw6Nlnny1Vu127dlq4cKE2b96sfv36KTU1VcOGDdOYMWM8cUgAAAAA4Kocxsbdl2uop+e+r9wjp2xPAwAAAPAJt9/aWC8+1VenTp1TcbHT9nQkSYGB9eXvX7FzXh45MwYAAAAAqBzCGAAAAABYQBgDAAAAAAsIYwAAAABgAWEMAAAAACwgjAEAAACABYQxAAAAALCAMAYAAAAAFhDGAAAAAMACwhgAAAAAWEAYAwAAAAALCGMAAAAAYAFhDAAAAAAsIIwBAAAAgAWEMQAAAACwgDAGAAAAABYQxgAAAADAAsIYAAAAAFhAGAMAAAAACwhjAAAAAGABYQwAAAAALCCMAQAAAIAFhDEAAAAAsCDA9gRqklubNrI9BQAAAMBnVPffvx3GGGN7EjWBMUYOh8P2NAAAAACfUlLiVF7eeTmd3hFrAgPry9+/YhcgcmbMQxwOh06fLlBJidP2VHAZf38/NWxYl954KfrjveiN96I33oveeC9647080Run03hNEKsswpgHlZQ4VVzMAvdG9Ma70R/vRW+8F73xXvTGe9Eb7+WrveEDPAAAAADAAsIYAAAAAFhAGAMAAAAACwhjAAAAAGABYQwAAAAALCCMAQAAAIAFhDEAAAAAsIAwBgAAAAAWEMYAAAAAwALCGAAAAABYQBgDAAAAAAsCbE+gJvH3J9t6itNp5HQa29MAAAAAfjOEMQ8xxqhhw7q2p1FjOJ0lOnWqgEAGAACAGosw5iEOh0M5772hghM/2p5KtVe3SXO17DdSfn4OwhgAAABqLMKYBxWc+FEFx76zPQ0AAAAA1QB/5AQAAAAAFhDGAAAAAMACwhgAAAAAWEAYAwAAAAALCGMAAAAAYAFhDAAAAAAsIIwBAAAAgAWEMQAAAACwgDAGAAAAABYQxgAAAADAAsIYAAAAAFhAGAMAAAAACwhjAAAAAGABYQwAAAAALCCMAQAAAIAFhDEAAAAAsIAwBgAAAAAWEMYAAAAAwALCGAAAAABYQBgDAAAAAAsIYwAAAABgAWEMAAAAACwgjAEAAACABQG2J3C5lJQUrV279qr77N+//5pqp6ena9KkSdf8fAAAAADwJK8KY5MnT1ZSUpLrcWxsrJ5++mn17du3yrX79u2rbt26VbkOAAAAAHiCV4WxBg0aqEGDBqXGbrrppirXrlOnjurUqVPlOgAAAADgCdXqb8Y2b96sBx98UJGRkYqNjdX06dN14cIFSdLGjRvVpk0bbdy40bV/UlKS4uPjlZ+fr/T0dLVp08a17dy5c5o6dapiY2MVGRmpIUOG6Ouvv77u3xMAAAAA31RtwtimTZs0ZswYxcXFKT09XampqXr//fc1fvx4SdKdd96pe++9V1OnTlV+fr7ee+89ffDBB5o5c6YaNWpUqt64ceO0ZcsWTZ8+XevWrVOLFi2UmJio/Pz86/2tAQAAAPBBXnWZ4tW8/vrruuOOOzR27FhJUsuWLWWM0RNPPKHs7Gy1bt1azzzzjO6++25NmTJF27Zt09ixYxUdHV2q1qFDh7RlyxalpaUpNjZWkvTcc8+pYcOGOnXqVJnhDQAAAAA8qdqcGcvKylJUVJTbWExMjGubJN14442aPn26PvzwQ7Vo0UJjxoy5Yi1JioiIcI3dcMMNmjRpkm6//XbPTx4AAAAAfqXahDFjTKkxp9MpSQoI+P8n+L7++msFBAQoJydHP/zwQ5m1Lt8fAAAAAGyoNmGsTZs22rVrl9tYZmamJKlVq1aSpH379mnu3LlKTU1VSEiIJk6c6Apsl7u0/1dffeUaKy4uVkJCgv7zn//8Vt8CAAAAALhUmzA2YsQIffjhh1q0aJFycnL0ySefaOrUqYqPj1erVq108eJFTZw4UTExMRowYIBeeOEF7dmzR2+88UapWi1btlTv3r2Vmpqqbdu2KScnR3/7299UWFjouvQRAAAAAH5L1eZ6vTvvvFOzZs3S4sWLtWjRIgUGBqpfv3568sknJUmzZ8/W4cOH9dprr0mSbr/9dj355JOaPXu2unfvXqreiy++qBkzZuipp57SxYsXFR4errS0NAUGBl7X7wsAAACAb3KYsv4YC9dkzz+eV8Gx72xPo9qre/NtCn70GZ06dU7FxaUvM62MgAA/NW5c3yO14Hn0x3vRG+9Fb7wXvfFe9MZ71cTeBAbWl79/xS5ArDaXKQIAAABATUIYAwAAAAALCGMAAAAAYAFhDAAAAAAsIIwBAAAAgAWEMQAAAACwgDAGAAAAABYQxgAAAADAAsIYAAAAAFhAGAMAAAAACwhjAAAAAGABYQwAAAAALCCMAQAAAIAFhDEAAAAAsIAwBgAAAAAWEMYAAAAAwALCGAAAAABYQBgDAAAAAAsIYwAAAABgAWEMAAAAACwgjAEAAACABYQxAAAAALCAMAYAAAAAFgTYnkBNUrdJc9tTqBE4jgAAAPAFhDEPMcaoZb+RtqdRYzidJXI6je1pAAAAAL8ZwpiHOBwOnT5doJISp+2p1AhOpyGMAQAAoEYjjHlQSYlTxcWEMQAAAADl4wM8AAAAAMACwhgAAAAAWEAYAwAAAAALHMYYPiXBQ/jwDu/k7+9Hb7wY/fFe9MZ70RvvRW+8F73xXjWtN35+DjkcjgrtSxgDAAAAAAu4TBEAAAAALCCMAQAAAIAFhDEAAAAAsIAwBgAAAAAWEMYAAAAAwALCGAAAAABYQBgDAAAAAAsIYwAAAABgAWEMAAAAACwgjAEAAACABYQxAAAAALCAMAYAAAAAFhDGAAAAAMACwlgVOZ1OzZs3T926dVNERIRGjhyp77//3va0qr28vDw988wz6t69u6KiovTwww8rMzPTtX3r1q26//77FR4erj59+mjDhg1uzy8sLFRqaqq6dOmiyMhIJSUl6eTJk277eKKGr8vJyVFkZKTS09NdY3v37tWQIUMUERGhhIQEvfXWW27Pqcia8UQNX7Vu3Tr17dtXoaGhuuuuu/TBBx+4th0+fFijR49WVFSUYmNjNWfOHJWUlLg9f8WKFerZs6fCwsL0yCOPaM+ePW7bPVHDFxUXF2vu3LmKj49XZGSkBg8erC+//NK1nXVjx2uvvaahQ4e6jXlLL8qrUdOV1ZuPP/5YDzzwgCIjI5WQkKCXX35ZFy5ccG2/Xu/95dXwBWX153JTpkxRQkKC2xhr5woMqmT+/PmmU6dO5pNPPjF79+41iYmJpnfv3qawsND21Kq1xx57zPTr189kZGSYQ4cOmdTUVBMWFmYOHjxosrOzTWhoqJk1a5bJzs42b775pgkODjZffPGF6/kpKSmmV69eJiMjw+zevdvcd999ZvDgwa7tnqjh6y5evGjuv/9+ExQUZNasWWOMMebkyZOmU6dOZtKkSSY7O9usXr3ahIaGmtWrV7ueV96a8UQNX7Vu3ToTHBxsli9fbr799luzaNEi07ZtW7Nr1y5z8eJF07t3bzNq1Cizf/9+s2nTJhMTE2Pmzp3ren56eroJCwsz7777rjlw4ID561//amJiYsyJEyeMMcYjNXzVvHnzTNeuXc1nn31mcnNzzeTJk02HDh3MsWPHWDeWLF++3LRt29YMGTLENeYtvahIjZqsrN5kZGSYdu3amcWLF5ucnByzefNm0717d5OSkuLa53q891ekRk1XVn8ut2nTJhMUFGTi4+Pdxlk7ZSOMVUFhYaGJjIw0K1ascI3l5+ebsLAws379eoszq95yc3NNUFCQyczMdI05nU7Tq1cvM2fOHPO3v/3NDBgwwO0548ePN4mJicYYY44ePWratm1rNm/e7Np+6NAhExQUZHbt2mWMMR6p4eteffVVM2zYMLcwtmTJEhMbG2uKiorc9uvdu7cxpmJrxhM1fJHT6TTx8fHmpZdechtPTEw0S5YsMevXrzft27c3eXl5rm0rV640UVFRrjex3r17mxkzZri2FxUVmR49epglS5YYY4xHaviqe+65x0yfPt31+MyZMyYoKMhs3LiRdXOdHT161IwePdpERESYPn36uP1C6S29KK9GTXW13iQlJZk//elPbvuvXbvWhISEmMLCwuv23l9ejZrsav255NixY6Zz585myJAhbmGMtXNlXKZYBfv27dO5c+fUpUsX11jDhg0VHBysjIwMizOr3ho3bqzXX39doaGhrjGHwyGHw6HTp08rMzPT7ZhLUufOnbVz504ZY7Rz507X2CUtW7bUzTff7OqLJ2r4soyMDK1atUovvfSS23hmZqZiYmIUEBDgGuvcubNyc3P1888/V2jNeKKGL8rJydGRI0d09913u42npaVp9OjRyszMVEhIiBo1auTa1rlzZ509e1Z79+7ViRMnlJub63ZcAwICFB0d7dabqtbwVU2aNNEnn3yiw4cPq6SkRKtWrVLt2rXVtm1b1s119s0336hWrVr697//rfDwcLdt3tKL8mrUVFfrTWJiopKTk93G/Pz8VFRUpLNnz1639/7yatRkV+uPJBljlJKSonvvvVcxMTFu21g7V0YYq4KjR49Kkpo3b+423rRpU9c2VF7Dhg3Vo0cP1a5d2zW2ceNGffvtt+rWrZuOHj2qZs2auT2nadOmKigo0KlTp3Ts2DE1btxYN9xwQ6l9LvXFEzV81enTpzVx4kRNmTKl1H/7VzqukvTjjz9WaM14ooYvysnJkSSdP39ew4cPV5cuXTRw4EB9/PHHkuiNbZMnT1atWrXUs2dPhYaGavbs2Zo3b55uu+02enOdJSQkaP78+WrRokWpbd7Si/Jq1FRX601wcLDatm3relxUVKRly5apffv2CgwMvG7v/eXVqMmu1h9JWrZsmX766SeNHz++1DbWzpURxqqgoKBAktxCgyTdcMMNKiwstDGlGmnXrl2aNGmSevfurbi4OF24cKHUMb/0+OLFiyooKCi1XXLviydq+KrnnntOkZGRpc7ASGUf10tvaoWFhRVaM56o4YvOnj0rSUpOTla/fv20dOlSde3aVWPHjtXWrVvpjWXZ2dlq0KCBFi5cqFWrVun+++/XhAkTtHfvXnrjRbylF+XV8HXFxcWaOHGiDhw4oGeffVaSrtt7f3k1fNW+ffu0YMECzZw5s8xjyNq5soDyd8GV1KlTR9Ivi+/S19Ivza5bt66tadUoH330kSZMmKCoqCi98sorkn5ZVL/+gXfpcd26dVWnTp0yfyBe3hdP1PBF69atU2ZmptavX1/m9rKO26UffvXq1avQmvFEDV9Uq1YtSdLw4cPVv39/SVK7du20Z88e/f3vf6/Ucf31PtfSmyvV8EU//vijkpKStGzZMkVHR0uSQkNDlZ2drfnz57NuvIi39KK8Gr7s7NmzGjdunHbs2KEFCxYoLCxMUtnHTPL8e395NXxRYWGhJkyYoDFjxridvbwca+fKODNWBZdOkx4/ftxt/Pjx47r55pttTKlGWb58uf7yl78oPj5eS5Yscf2fjebNm5d5zOvVq6cGDRqoWbNmysvLK7UYL++LJ2r4ojVr1ujEiROKi4tTZGSkIiMjJUnPPvusRowYoWbNmpV5XCXp5ptvrtCa8UQNX3Tpew8KCnIbb926tQ4fPkxvLNq9e7eKiorc/g5WksLDw/Xtt9/SGy/iLb0or4avOn78uOu2EGlpaerRo4dr2/V67y+vhi/avXu3Dhw4oAULFrh+N3jttdf0ww8/KDIyUpmZmaydqyCMVUHbtm114403avv27a6x06dPa8+ePerYsaPFmVV///znPzV16lQNHjxYs2bNcjvlHB0drR07drjtv23bNkVFRcnPz08dOnSQ0+l0/SGu9Mvf0xw7dszVF0/U8EWvvPKK3n//fa1bt871T5KefPJJTZs2TR07dtTOnTvd7ju1bds2tWzZUk2aNKnQmvFEDV8UEhKi+vXra/fu3W7jWVlZuu2229SxY0ft2bPHdTmj9MtxrV+/vtq2basmTZqoZcuWbse1uLhYmZmZbr2pag1fdOnvF/bv3+82npWVpdtvv51140W8pRfl1fBF+fn5evTRR3Xy5EmtWLGi1H+31+u9v7wavigsLEwffvih3n33XdfvBg899JCaNm2qdevWqX379qydq7H6WY41wKxZs0xMTIz56KOP3O53cPHiRdtTq7YOHTpkQkJCzBNPPGGOHz/u9u/06dMmKyvLhISEmJkzZ5rs7GyTlpZW6h4f48ePNwkJCWbbtm2u+4Rc/hGsnqiBX1z+0fY///yz6dixo0lOTjYHDhwwa9asMaGhoSY9Pd21f3lrxhM1fNXChQtNZGSkWb9+vdt9xrZt22YuXLhgevXqZYYPH2727t3rukfY/PnzXc9ftWqVCQsLM+np6a57hHXq1Ml1jzBP1PBFJSUl5uGHHzZ9+vQxW7duNTk5OWb27NmmXbt25ssvv2TdWJScnOz2c91belGRGjXdr3uTnJxsQkJCzNatW0v9blBcXGyMuT7v/RWp4Qt+3Z9fmzdvXqn7jLF2ykYYq6Li4mIzY8YM07lzZxMREWFGjhxpvv/+e9vTqtYWL15sgoKCyvyXnJxsjDHm008/Nf369TPt27c3ffr0MRs2bHCrce7cOTN58mQTHR1toqOjzfjx483Jkyfd9vFEDbiHMWOM2b17t3nwwQdN+/btTXx8vHn77bfd9q/ImvFEDV+1dOlSk5CQYEJCQsw999xjNm3a5NqWm5trHnvsMRMaGmpiY2PNnDlzTElJidvz33zzTdO9e3cTFhZmHnnkEbNnzx637Z6o4Yvy8vLMc889Z+Li4kxkZKQZNGiQ2b59u2s768aOsn6h9JZelFejpru8N8XFxSY0NPSKvxtcOnbX672/vBq+4FrCGGunbA5javhNEQAAAADAC/nmxa0AAAAAYBlhDAAAAAAsIIwBAAAAgAWEMQAAAACwgDAGAAAAABYQxgAAAADAAsIYAAAAAFhAGAMAwItxO1AAqLkCbE8AAABv8NVXX+mtt95SRkaGTp48qaZNm6pLly4aNWqUWrRoYWVOixYtUu3atTVixAgrrw8A+G1xZgwA4PNWrFihhx56SCdOnFBSUpLeeOMNjRo1Sjt27NCAAQO0b98+K/OaO3euCgoKrLw2AOC3x5kxAIBP27lzp6ZNm6bBgwdr8uTJrvFOnTqpV69euu+++/T0008rPT3d4iwBADURZ8YAAD4tLS1NDRo00Pjx40ttCwwMVEpKinr27Knz58+rpKREK1as0N13362wsDDFxcXplVdeUWFhoes5Q4cO1dChQ93qbN++XW3atNH27dslSenp6QoODtbu3bs1aNAghYaGKj4+Xmlpaa7ntGnTRpK0YMEC19cAgJqFMAYA8FnGGH3++efq0qWL6tatW+Y+ffv21RNPPKF69erpmWee0fTp09WrVy8tXrxYgwcP1vLlyzV27NhKf9CG0+nUuHHj1LdvX73++uuKiorSjBkz9Nlnn0mSVq1aJUkaMGCA62sAQM3CZYoAAJ916tQpFRYW6ve//325+2ZnZ2v16tVKSkrSqFGjJEldu3ZV06ZNNXHiRG3ZskU9evSo8GsbYzR27FgNHDhQktShQwdt2rRJmzdvVrdu3RQRESFJatasmetrAEDNwpkxAIDP8vf3lySVlJSUu++OHTskSXfddZfb+F133SV/f3/XJYiVERkZ6fq6du3aCgwM1Pnz5ytdBwBQPXFmDADgsxo1aqT69evrhx9+uOI+58+fV1FRkfLz8yVJN910k9v2gIAANW7cWGfOnKn069epU8ftsZ+fH/cVAwAfwpkxAIBPi42N1fbt290+hONy77zzjjp37ux6/NNPP7ltLyoq0qlTp9S4cWPX2K/PtHG2CwBQFsIYAMCnJSYmKi8vT3PmzCm17aefftLSpUvVunVr3XHHHZKkDRs2uO2zYcMGlZSUqEOHDpKkG2+8UUePHnXbZ+fOndc0Nz8/3qYBoCbjMkUAgE+LiIjQU089pTlz5ujgwYO677771LhxYx04cEBpaWkqLCzUnDlz1KpVK/Xv31/z5s1TQUGBOnbsqL1792rBggXq1KmTunXrJkmKj4/Xxx9/rOnTpyshIUGZmZlat27dNc2tYcOG2rVrlzIyMhQdHS2Hw+HB7xwAYBthDADg88aMGaPg4GCtWLFCL774ovLz89W8eXPFxcXp8ccfV/PmzSVJ06ZN0x/+8AetWbNGb7zxhpo2baphw4Zp7NixrrNYDzzwgL777jutXbtWK1euVMeOHTVv3jw9/PDDlZ7X448/rkWLFmnkyJF6//33dcstt3j0+wYA2OUw/KUwAAAAAFx3XIwOAAAAABYQxgAAAADAAsIYAAAAAFhAGAMAAAAACwhjAAAAAGABYQwAAAAALCCMAQAAAIAFhDEAAAAAsIAwBgAAAAAWEMYAAAAAwALCGAAAAABY8P8AiM/xwtJINVMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_graph = df['toxic'].apply(lambda x: 'Toxic' if x == 1 else 'Not toxic').value_counts()\n",
    "print(data_graph)\n",
    "plt.figure(figsize=(10, 2))\n",
    "sns.barplot(x=data_graph.values, y=data_graph.index, orient='h')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The classes are unbalanced, so we will have to take this into account in the future"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature preparation and model training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's write a function to preprocess our text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_sm\", disable = ['parser','ner'])\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "def process_text(text):\n",
    "    text = re.sub(r'<.*?>', '', text) # Removing HTML tags\n",
    "    text = re.sub(r'[^\\w\\s]', '', text)  # Remove punctuation\n",
    "    text = text.lower()  # Cast to lowercase\n",
    "    text = ' '.join(text.split())   # Remove extra spaces and spaces at the beginning and end of a line\n",
    "    \n",
    "\n",
    "    # Using spaCy for tokenization and lemmatization, eliminating stop words\n",
    "    doc = nlp(text)\n",
    "    lemmas = [token.lemma_ for token in doc if token.text not in stop_words]\n",
    "\n",
    "    return ' '.join(lemmas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22411e1e110a4c1299be605e36ca90d8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/159292 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                                                                                                                                                                                                                                                                                           text  \\\n",
      "0                                                                                                                                                                                                explanation edit make username hardcore metallica fan revert not vandalism closure gas vote new york dolls fac please not remove template talk page since retired now892053827   \n",
      "1                                                                                                                                                                                                                                                                                              daww match background colour seemingly stuck thank talk 2151 january 11 2016 utc   \n",
      "2                                                                                                                                                                                                                                           hey man really try edit war guy constantly remove relevant information talk edit instead talk page seem care formatting actual info   \n",
      "3  can not make real suggestion improvement wonder section statistic later subsection type accident think reference may need tidying exact format ie date format etc later noone else first preference format style reference want please let know appear backlog article review guess may delay reviewer turn list relevant form eg wikipediagood_article_nominationstransport   \n",
      "4                                                                                                                                                                                                                                                                                                                                                 sir hero chance remember page   \n",
      "\n",
      "   toxic  \n",
      "0      0  \n",
      "1      0  \n",
      "2      0  \n",
      "3      0  \n",
      "4      0  \n",
      "CPU times: total: 8min 47s\n",
      "Wall time: 9min 42s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "df['text'] = df['text'].progress_apply(process_text)\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of curiosity, let's look at frequently occurring words in different comment groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_words = df[df['toxic'] == 0]\n",
    "toxic_words = df[df['toxic'] == 1]\n",
    "\n",
    "normal_words = \" \".join(word for word in normal_words.text)\n",
    "toxic_words = \" \".join(word for word in toxic_words.text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "wordcloud_normal = WordCloud(max_font_size=40, max_words=50, background_color=\"black\").generate(normal_words)\n",
    "wordcloud_toxic = WordCloud(max_font_size=40, max_words=50, background_color=\"black\").generate(toxic_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x26453d7dc10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Toxic words')\n",
    "plt.imshow(wordcloud_toxic, interpolation='bilinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2643c788690>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Normal words')\n",
    "plt.imshow(wordcloud_normal, interpolation='bilinear')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dividing the data into samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df['text'],\n",
    "                                                    df['toxic'], \n",
    "                                                    test_size=0.25, \n",
    "                                                    random_state=RANDOM_STATE,\n",
    "\n",
    "                                                    # Stratify samples by target variable\n",
    "                                                    stratify=df['toxic'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a pipeline for our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_lg = Pipeline([\n",
    "    ('vct', TfidfVectorizer(stop_words=list(stop_words))),\n",
    "    ('model', LogisticRegression(class_weight='balanced', random_state=RANDOM_STATE))\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a grid of parameters for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid_lg = {\n",
    "    'model__C': [0.1, 1, 2, 10]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We look at the model metrics on the cross-validation sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 5.16 s\n",
      "Wall time: 25.7 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[(&#x27;vct&#x27;,\n",
       "                                              TfidfVectorizer(stop_words=[&#x27;any&#x27;,\n",
       "                                                                          &#x27;our&#x27;,\n",
       "                                                                          &#x27;had&#x27;,\n",
       "                                                                          &#x27;wasn&#x27;,\n",
       "                                                                          &#x27;from&#x27;,\n",
       "                                                                          &#x27;here&#x27;,\n",
       "                                                                          &#x27;few&#x27;,\n",
       "                                                                          &#x27;has&#x27;,\n",
       "                                                                          &#x27;too&#x27;,\n",
       "                                                                          &#x27;then&#x27;,\n",
       "                                                                          &quot;aren&#x27;t&quot;,\n",
       "                                                                          &quot;couldn&#x27;t&quot;,\n",
       "                                                                          &#x27;i&#x27;,\n",
       "                                                                          &#x27;some&#x27;,\n",
       "                                                                          &#x27;itself&#x27;,\n",
       "                                                                          &quot;you&#x27;d&quot;,\n",
       "                                                                          &#x27;t&#x27;,\n",
       "                                                                          &#x27;myself&#x27;,\n",
       "                                                                          &#x27;were&#x27;,\n",
       "                                                                          &#x27;against&#x27;,\n",
       "                                                                          &#x27;weren&#x27;,\n",
       "                                                                          &#x27;me&#x27;,\n",
       "                                                                          &#x27;hers&#x27;,\n",
       "                                                                          &#x27;the&#x27;,\n",
       "                                                                          &#x27;o&#x27;,\n",
       "                                                                          &#x27;did&#x27;,\n",
       "                                                                          &#x27;after&#x27;,\n",
       "                                                                          &quot;that&#x27;ll&quot;,\n",
       "                                                                          &#x27;up&#x27;,\n",
       "                                                                          &#x27;as&#x27;, ...])),\n",
       "                                             (&#x27;model&#x27;,\n",
       "                                              LogisticRegression(class_weight=&#x27;balanced&#x27;,\n",
       "                                                                 random_state=42))]),\n",
       "                   n_jobs=-1, param_distributions={&#x27;model__C&#x27;: [0.1, 1, 2, 10]},\n",
       "                   random_state=42, scoring=&#x27;f1&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[(&#x27;vct&#x27;,\n",
       "                                              TfidfVectorizer(stop_words=[&#x27;any&#x27;,\n",
       "                                                                          &#x27;our&#x27;,\n",
       "                                                                          &#x27;had&#x27;,\n",
       "                                                                          &#x27;wasn&#x27;,\n",
       "                                                                          &#x27;from&#x27;,\n",
       "                                                                          &#x27;here&#x27;,\n",
       "                                                                          &#x27;few&#x27;,\n",
       "                                                                          &#x27;has&#x27;,\n",
       "                                                                          &#x27;too&#x27;,\n",
       "                                                                          &#x27;then&#x27;,\n",
       "                                                                          &quot;aren&#x27;t&quot;,\n",
       "                                                                          &quot;couldn&#x27;t&quot;,\n",
       "                                                                          &#x27;i&#x27;,\n",
       "                                                                          &#x27;some&#x27;,\n",
       "                                                                          &#x27;itself&#x27;,\n",
       "                                                                          &quot;you&#x27;d&quot;,\n",
       "                                                                          &#x27;t&#x27;,\n",
       "                                                                          &#x27;myself&#x27;,\n",
       "                                                                          &#x27;were&#x27;,\n",
       "                                                                          &#x27;against&#x27;,\n",
       "                                                                          &#x27;weren&#x27;,\n",
       "                                                                          &#x27;me&#x27;,\n",
       "                                                                          &#x27;hers&#x27;,\n",
       "                                                                          &#x27;the&#x27;,\n",
       "                                                                          &#x27;o&#x27;,\n",
       "                                                                          &#x27;did&#x27;,\n",
       "                                                                          &#x27;after&#x27;,\n",
       "                                                                          &quot;that&#x27;ll&quot;,\n",
       "                                                                          &#x27;up&#x27;,\n",
       "                                                                          &#x27;as&#x27;, ...])),\n",
       "                                             (&#x27;model&#x27;,\n",
       "                                              LogisticRegression(class_weight=&#x27;balanced&#x27;,\n",
       "                                                                 random_state=42))]),\n",
       "                   n_jobs=-1, param_distributions={&#x27;model__C&#x27;: [0.1, 1, 2, 10]},\n",
       "                   random_state=42, scoring=&#x27;f1&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;vct&#x27;,\n",
       "                 TfidfVectorizer(stop_words=[&#x27;any&#x27;, &#x27;our&#x27;, &#x27;had&#x27;, &#x27;wasn&#x27;,\n",
       "                                             &#x27;from&#x27;, &#x27;here&#x27;, &#x27;few&#x27;, &#x27;has&#x27;,\n",
       "                                             &#x27;too&#x27;, &#x27;then&#x27;, &quot;aren&#x27;t&quot;,\n",
       "                                             &quot;couldn&#x27;t&quot;, &#x27;i&#x27;, &#x27;some&#x27;, &#x27;itself&#x27;,\n",
       "                                             &quot;you&#x27;d&quot;, &#x27;t&#x27;, &#x27;myself&#x27;, &#x27;were&#x27;,\n",
       "                                             &#x27;against&#x27;, &#x27;weren&#x27;, &#x27;me&#x27;, &#x27;hers&#x27;,\n",
       "                                             &#x27;the&#x27;, &#x27;o&#x27;, &#x27;did&#x27;, &#x27;after&#x27;,\n",
       "                                             &quot;that&#x27;ll&quot;, &#x27;up&#x27;, &#x27;as&#x27;, ...])),\n",
       "                (&#x27;model&#x27;,\n",
       "                 LogisticRegression(class_weight=&#x27;balanced&#x27;, random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer(stop_words=[&#x27;any&#x27;, &#x27;our&#x27;, &#x27;had&#x27;, &#x27;wasn&#x27;, &#x27;from&#x27;, &#x27;here&#x27;, &#x27;few&#x27;,\n",
       "                            &#x27;has&#x27;, &#x27;too&#x27;, &#x27;then&#x27;, &quot;aren&#x27;t&quot;, &quot;couldn&#x27;t&quot;, &#x27;i&#x27;,\n",
       "                            &#x27;some&#x27;, &#x27;itself&#x27;, &quot;you&#x27;d&quot;, &#x27;t&#x27;, &#x27;myself&#x27;, &#x27;were&#x27;,\n",
       "                            &#x27;against&#x27;, &#x27;weren&#x27;, &#x27;me&#x27;, &#x27;hers&#x27;, &#x27;the&#x27;, &#x27;o&#x27;, &#x27;did&#x27;,\n",
       "                            &#x27;after&#x27;, &quot;that&#x27;ll&quot;, &#x27;up&#x27;, &#x27;as&#x27;, ...])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(class_weight=&#x27;balanced&#x27;, random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[('vct',\n",
       "                                              TfidfVectorizer(stop_words=['any',\n",
       "                                                                          'our',\n",
       "                                                                          'had',\n",
       "                                                                          'wasn',\n",
       "                                                                          'from',\n",
       "                                                                          'here',\n",
       "                                                                          'few',\n",
       "                                                                          'has',\n",
       "                                                                          'too',\n",
       "                                                                          'then',\n",
       "                                                                          \"aren't\",\n",
       "                                                                          \"couldn't\",\n",
       "                                                                          'i',\n",
       "                                                                          'some',\n",
       "                                                                          'itself',\n",
       "                                                                          \"you'd\",\n",
       "                                                                          't',\n",
       "                                                                          'myself',\n",
       "                                                                          'were',\n",
       "                                                                          'against',\n",
       "                                                                          'weren',\n",
       "                                                                          'me',\n",
       "                                                                          'hers',\n",
       "                                                                          'the',\n",
       "                                                                          'o',\n",
       "                                                                          'did',\n",
       "                                                                          'after',\n",
       "                                                                          \"that'll\",\n",
       "                                                                          'up',\n",
       "                                                                          'as', ...])),\n",
       "                                             ('model',\n",
       "                                              LogisticRegression(class_weight='balanced',\n",
       "                                                                 random_state=42))]),\n",
       "                   n_jobs=-1, param_distributions={'model__C': [0.1, 1, 2, 10]},\n",
       "                   random_state=42, scoring='f1')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "randomized_search_lg = RandomizedSearchCV(pipe_lg,\n",
    "                                        param_grid_lg, \n",
    "                                          cv=5,\n",
    "                                        scoring='f1',\n",
    "                                        n_jobs=-1, \n",
    "                                        random_state=RANDOM_STATE)\n",
    "randomized_search_lg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We display the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>params</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.460311</td>\n",
       "      <td>0.676140</td>\n",
       "      <td>{'model__C': 10}</td>\n",
       "      <td>0.762700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.830322</td>\n",
       "      <td>1.284302</td>\n",
       "      <td>{'model__C': 2}</td>\n",
       "      <td>0.756164</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.860288</td>\n",
       "      <td>1.833395</td>\n",
       "      <td>{'model__C': 1}</td>\n",
       "      <td>0.749791</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.952426</td>\n",
       "      <td>2.127121</td>\n",
       "      <td>{'model__C': 0.1}</td>\n",
       "      <td>0.704219</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  mean_score_time             params  mean_test_score  \\\n",
       "3      14.460311         0.676140   {'model__C': 10}         0.762700   \n",
       "2      13.830322         1.284302    {'model__C': 2}         0.756164   \n",
       "1      11.860288         1.833395    {'model__C': 1}         0.749791   \n",
       "0       8.952426         2.127121  {'model__C': 0.1}         0.704219   \n",
       "\n",
       "   rank_test_score  \n",
       "3                1  \n",
       "2                2  \n",
       "1                3  \n",
       "0                4  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:\n",
      "{'model__C': 10}\n",
      "F1 Score: 0.76\n"
     ]
    }
   ],
   "source": [
    "results_lg = pd.DataFrame(randomized_search_lg.cv_results_)\n",
    "linear_model_metric = round(randomized_search_lg.best_score_, 2)\n",
    "# ['mean_fit_time', 'mean_score_time', 'param_C', 'mean_test_score', 'rank_test_score']\n",
    "display(results_lg[['mean_fit_time', 'mean_score_time', 'params', 'mean_test_score', 'rank_test_score']]\n",
    "        .sort_values(by='mean_test_score', ascending=False)\n",
    "        .head(5))\n",
    "\n",
    "print(f'Best parameters:\\n{randomized_search_lg.best_params_}') \n",
    "print('F1 Score:', linear_model_metric)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model with parameter `C` = 10 has F1 metric = 0.76, which is a pretty good result. This model trains in 13 seconds. Let's look at how other models cope with this task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a pipeline for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_dt = Pipeline([\n",
    "    ('vct', TfidfVectorizer(stop_words=list(stop_words))),\n",
    "    ('model', DecisionTreeClassifier(class_weight='balanced', random_state=RANDOM_STATE))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a grid of parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid_dt = {\n",
    "    'model__max_depth': [None, 10, 40],\n",
    "    'model__min_samples_split': [2, 5, 10],\n",
    "    'model__min_samples_leaf': [1, 2, 5]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We train the model on a cross-validation sample, selecting the best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 38.7 s\n",
      "Wall time: 3min 31s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[(&#x27;vct&#x27;,\n",
       "                                              TfidfVectorizer(stop_words=[&#x27;any&#x27;,\n",
       "                                                                          &#x27;our&#x27;,\n",
       "                                                                          &#x27;had&#x27;,\n",
       "                                                                          &#x27;wasn&#x27;,\n",
       "                                                                          &#x27;from&#x27;,\n",
       "                                                                          &#x27;here&#x27;,\n",
       "                                                                          &#x27;few&#x27;,\n",
       "                                                                          &#x27;has&#x27;,\n",
       "                                                                          &#x27;too&#x27;,\n",
       "                                                                          &#x27;then&#x27;,\n",
       "                                                                          &quot;aren&#x27;t&quot;,\n",
       "                                                                          &quot;couldn&#x27;t&quot;,\n",
       "                                                                          &#x27;i&#x27;,\n",
       "                                                                          &#x27;some&#x27;,\n",
       "                                                                          &#x27;itself&#x27;,\n",
       "                                                                          &quot;you&#x27;d&quot;,\n",
       "                                                                          &#x27;t&#x27;,\n",
       "                                                                          &#x27;myself&#x27;,\n",
       "                                                                          &#x27;were&#x27;,\n",
       "                                                                          &#x27;against&#x27;,\n",
       "                                                                          &#x27;weren&#x27;,\n",
       "                                                                          &#x27;me&#x27;,\n",
       "                                                                          &#x27;hers&#x27;,\n",
       "                                                                          &#x27;the&#x27;,\n",
       "                                                                          &#x27;o&#x27;,\n",
       "                                                                          &#x27;did&#x27;,\n",
       "                                                                          &#x27;after&#x27;,\n",
       "                                                                          &quot;that&#x27;ll&quot;,\n",
       "                                                                          &#x27;up&#x27;,\n",
       "                                                                          &#x27;as&#x27;, ...])),\n",
       "                                             (&#x27;model&#x27;,\n",
       "                                              DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;,\n",
       "                                                                     random_state=42))]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;model__max_depth&#x27;: [None, 10, 40],\n",
       "                                        &#x27;model__min_samples_leaf&#x27;: [1, 2, 5],\n",
       "                                        &#x27;model__min_samples_split&#x27;: [2, 5, 10]},\n",
       "                   random_state=42, scoring=&#x27;f1&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[(&#x27;vct&#x27;,\n",
       "                                              TfidfVectorizer(stop_words=[&#x27;any&#x27;,\n",
       "                                                                          &#x27;our&#x27;,\n",
       "                                                                          &#x27;had&#x27;,\n",
       "                                                                          &#x27;wasn&#x27;,\n",
       "                                                                          &#x27;from&#x27;,\n",
       "                                                                          &#x27;here&#x27;,\n",
       "                                                                          &#x27;few&#x27;,\n",
       "                                                                          &#x27;has&#x27;,\n",
       "                                                                          &#x27;too&#x27;,\n",
       "                                                                          &#x27;then&#x27;,\n",
       "                                                                          &quot;aren&#x27;t&quot;,\n",
       "                                                                          &quot;couldn&#x27;t&quot;,\n",
       "                                                                          &#x27;i&#x27;,\n",
       "                                                                          &#x27;some&#x27;,\n",
       "                                                                          &#x27;itself&#x27;,\n",
       "                                                                          &quot;you&#x27;d&quot;,\n",
       "                                                                          &#x27;t&#x27;,\n",
       "                                                                          &#x27;myself&#x27;,\n",
       "                                                                          &#x27;were&#x27;,\n",
       "                                                                          &#x27;against&#x27;,\n",
       "                                                                          &#x27;weren&#x27;,\n",
       "                                                                          &#x27;me&#x27;,\n",
       "                                                                          &#x27;hers&#x27;,\n",
       "                                                                          &#x27;the&#x27;,\n",
       "                                                                          &#x27;o&#x27;,\n",
       "                                                                          &#x27;did&#x27;,\n",
       "                                                                          &#x27;after&#x27;,\n",
       "                                                                          &quot;that&#x27;ll&quot;,\n",
       "                                                                          &#x27;up&#x27;,\n",
       "                                                                          &#x27;as&#x27;, ...])),\n",
       "                                             (&#x27;model&#x27;,\n",
       "                                              DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;,\n",
       "                                                                     random_state=42))]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;model__max_depth&#x27;: [None, 10, 40],\n",
       "                                        &#x27;model__min_samples_leaf&#x27;: [1, 2, 5],\n",
       "                                        &#x27;model__min_samples_split&#x27;: [2, 5, 10]},\n",
       "                   random_state=42, scoring=&#x27;f1&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;vct&#x27;,\n",
       "                 TfidfVectorizer(stop_words=[&#x27;any&#x27;, &#x27;our&#x27;, &#x27;had&#x27;, &#x27;wasn&#x27;,\n",
       "                                             &#x27;from&#x27;, &#x27;here&#x27;, &#x27;few&#x27;, &#x27;has&#x27;,\n",
       "                                             &#x27;too&#x27;, &#x27;then&#x27;, &quot;aren&#x27;t&quot;,\n",
       "                                             &quot;couldn&#x27;t&quot;, &#x27;i&#x27;, &#x27;some&#x27;, &#x27;itself&#x27;,\n",
       "                                             &quot;you&#x27;d&quot;, &#x27;t&#x27;, &#x27;myself&#x27;, &#x27;were&#x27;,\n",
       "                                             &#x27;against&#x27;, &#x27;weren&#x27;, &#x27;me&#x27;, &#x27;hers&#x27;,\n",
       "                                             &#x27;the&#x27;, &#x27;o&#x27;, &#x27;did&#x27;, &#x27;after&#x27;,\n",
       "                                             &quot;that&#x27;ll&quot;, &#x27;up&#x27;, &#x27;as&#x27;, ...])),\n",
       "                (&#x27;model&#x27;,\n",
       "                 DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;,\n",
       "                                        random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer(stop_words=[&#x27;any&#x27;, &#x27;our&#x27;, &#x27;had&#x27;, &#x27;wasn&#x27;, &#x27;from&#x27;, &#x27;here&#x27;, &#x27;few&#x27;,\n",
       "                            &#x27;has&#x27;, &#x27;too&#x27;, &#x27;then&#x27;, &quot;aren&#x27;t&quot;, &quot;couldn&#x27;t&quot;, &#x27;i&#x27;,\n",
       "                            &#x27;some&#x27;, &#x27;itself&#x27;, &quot;you&#x27;d&quot;, &#x27;t&#x27;, &#x27;myself&#x27;, &#x27;were&#x27;,\n",
       "                            &#x27;against&#x27;, &#x27;weren&#x27;, &#x27;me&#x27;, &#x27;hers&#x27;, &#x27;the&#x27;, &#x27;o&#x27;, &#x27;did&#x27;,\n",
       "                            &#x27;after&#x27;, &quot;that&#x27;ll&quot;, &#x27;up&#x27;, &#x27;as&#x27;, ...])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;, random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[('vct',\n",
       "                                              TfidfVectorizer(stop_words=['any',\n",
       "                                                                          'our',\n",
       "                                                                          'had',\n",
       "                                                                          'wasn',\n",
       "                                                                          'from',\n",
       "                                                                          'here',\n",
       "                                                                          'few',\n",
       "                                                                          'has',\n",
       "                                                                          'too',\n",
       "                                                                          'then',\n",
       "                                                                          \"aren't\",\n",
       "                                                                          \"couldn't\",\n",
       "                                                                          'i',\n",
       "                                                                          'some',\n",
       "                                                                          'itself',\n",
       "                                                                          \"you'd\",\n",
       "                                                                          't',\n",
       "                                                                          'myself',\n",
       "                                                                          'were',\n",
       "                                                                          'against',\n",
       "                                                                          'weren',\n",
       "                                                                          'me',\n",
       "                                                                          'hers',\n",
       "                                                                          'the',\n",
       "                                                                          'o',\n",
       "                                                                          'did',\n",
       "                                                                          'after',\n",
       "                                                                          \"that'll\",\n",
       "                                                                          'up',\n",
       "                                                                          'as', ...])),\n",
       "                                             ('model',\n",
       "                                              DecisionTreeClassifier(class_weight='balanced',\n",
       "                                                                     random_state=42))]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'model__max_depth': [None, 10, 40],\n",
       "                                        'model__min_samples_leaf': [1, 2, 5],\n",
       "                                        'model__min_samples_split': [2, 5, 10]},\n",
       "                   random_state=42, scoring='f1')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "randomized_search_dt = RandomizedSearchCV(pipe_dt,\n",
    "                                        param_grid_dt,\n",
    "                                        cv=5,\n",
    "                                        scoring='f1',\n",
    "                                        n_jobs=-1,  \n",
    "                                        random_state=RANDOM_STATE)\n",
    "\n",
    "randomized_search_dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>params</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75.704971</td>\n",
       "      <td>1.416909</td>\n",
       "      <td>{'model__min_samples_split': 2, 'model__min_samples_leaf': 2, 'model__max_depth': 40}</td>\n",
       "      <td>0.640839</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>51.764044</td>\n",
       "      <td>0.998939</td>\n",
       "      <td>{'model__min_samples_split': 2, 'model__min_samples_leaf': 5, 'model__max_depth': 40}</td>\n",
       "      <td>0.627875</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>156.257768</td>\n",
       "      <td>0.514084</td>\n",
       "      <td>{'model__min_samples_split': 2, 'model__min_samples_leaf': 1, 'model__max_depth': None}</td>\n",
       "      <td>0.625558</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20.787771</td>\n",
       "      <td>1.450902</td>\n",
       "      <td>{'model__min_samples_split': 5, 'model__min_samples_leaf': 5, 'model__max_depth': 10}</td>\n",
       "      <td>0.575176</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20.698866</td>\n",
       "      <td>1.455183</td>\n",
       "      <td>{'model__min_samples_split': 10, 'model__min_samples_leaf': 5, 'model__max_depth': 10}</td>\n",
       "      <td>0.575176</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  mean_score_time  \\\n",
       "3      75.704971         1.416909   \n",
       "9      51.764044         0.998939   \n",
       "4     156.257768         0.514084   \n",
       "6      20.787771         1.450902   \n",
       "7      20.698866         1.455183   \n",
       "\n",
       "                                                                                    params  \\\n",
       "3    {'model__min_samples_split': 2, 'model__min_samples_leaf': 2, 'model__max_depth': 40}   \n",
       "9    {'model__min_samples_split': 2, 'model__min_samples_leaf': 5, 'model__max_depth': 40}   \n",
       "4  {'model__min_samples_split': 2, 'model__min_samples_leaf': 1, 'model__max_depth': None}   \n",
       "6    {'model__min_samples_split': 5, 'model__min_samples_leaf': 5, 'model__max_depth': 10}   \n",
       "7   {'model__min_samples_split': 10, 'model__min_samples_leaf': 5, 'model__max_depth': 10}   \n",
       "\n",
       "   mean_test_score  rank_test_score  \n",
       "3         0.640839                1  \n",
       "9         0.627875                2  \n",
       "4         0.625558                3  \n",
       "6         0.575176                4  \n",
       "7         0.575176                4  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:\n",
      "{'model__min_samples_split': 2, 'model__min_samples_leaf': 2, 'model__max_depth': 40}\n",
      "Best F1 Score: 0.64\n"
     ]
    }
   ],
   "source": [
    "results_dt = pd.DataFrame(randomized_search_dt.cv_results_)\n",
    "dt_model_metric = round(randomized_search_dt.best_score_, 2)\n",
    "\n",
    "display(results_dt[['mean_fit_time', 'mean_score_time', 'params', 'mean_test_score', 'rank_test_score']]\n",
    "        .sort_values(by='mean_test_score', ascending=False)\n",
    "        .head(5))\n",
    "\n",
    "print(f'Best parameters:\\n{randomized_search_dt.best_params_}')   \n",
    "print('Best F1 Score:', dt_model_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best metric for the model is F1 = `0.64`, and the model trains in 76 seconds. A decision tree is definitely not suitable for our task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LightGBM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new pipeline for our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_lgbm = Pipeline([\n",
    "    ('vct', TfidfVectorizer(stop_words=list(stop_words))),\n",
    "    ('model', LGBMClassifier(class_weight='balanced', random_state=RANDOM_STATE))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a grid of parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid_lgbm = {\n",
    "    'model__max_depth': [-1, 10, 20],\n",
    "    'model__learning_rate': [0.01, 0.1, 0.2],\n",
    "    'model__num_leaves': [30, 50, 70]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 12140, number of negative: 107329\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.737838 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 511469\n",
      "[LightGBM] [Info] Number of data points in the train set: 119469, number of used features: 9609\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n",
      "[LightGBM] [Info] Start training from score -0.000000\n",
      "CPU times: total: 1min 24s\n",
      "Wall time: 9min 32s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[(&#x27;vct&#x27;,\n",
       "                                              TfidfVectorizer(stop_words=[&#x27;any&#x27;,\n",
       "                                                                          &#x27;our&#x27;,\n",
       "                                                                          &#x27;had&#x27;,\n",
       "                                                                          &#x27;wasn&#x27;,\n",
       "                                                                          &#x27;from&#x27;,\n",
       "                                                                          &#x27;here&#x27;,\n",
       "                                                                          &#x27;few&#x27;,\n",
       "                                                                          &#x27;has&#x27;,\n",
       "                                                                          &#x27;too&#x27;,\n",
       "                                                                          &#x27;then&#x27;,\n",
       "                                                                          &quot;aren&#x27;t&quot;,\n",
       "                                                                          &quot;couldn&#x27;t&quot;,\n",
       "                                                                          &#x27;i&#x27;,\n",
       "                                                                          &#x27;some&#x27;,\n",
       "                                                                          &#x27;itself&#x27;,\n",
       "                                                                          &quot;you&#x27;d&quot;,\n",
       "                                                                          &#x27;t&#x27;,\n",
       "                                                                          &#x27;myself&#x27;,\n",
       "                                                                          &#x27;were&#x27;,\n",
       "                                                                          &#x27;against&#x27;,\n",
       "                                                                          &#x27;weren&#x27;,\n",
       "                                                                          &#x27;me&#x27;,\n",
       "                                                                          &#x27;hers&#x27;,\n",
       "                                                                          &#x27;the&#x27;,\n",
       "                                                                          &#x27;o&#x27;,\n",
       "                                                                          &#x27;did&#x27;,\n",
       "                                                                          &#x27;after&#x27;,\n",
       "                                                                          &quot;that&#x27;ll&quot;,\n",
       "                                                                          &#x27;up&#x27;,\n",
       "                                                                          &#x27;as&#x27;, ...])),\n",
       "                                             (&#x27;model&#x27;,\n",
       "                                              LGBMClassifier(class_weight=&#x27;balanced&#x27;,\n",
       "                                                             random_state=42))]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;model__learning_rate&#x27;: [0.01, 0.1,\n",
       "                                                                 0.2],\n",
       "                                        &#x27;model__max_depth&#x27;: [-1, 10, 20],\n",
       "                                        &#x27;model__num_leaves&#x27;: [30, 50, 70]},\n",
       "                   random_state=42, scoring=&#x27;f1&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[(&#x27;vct&#x27;,\n",
       "                                              TfidfVectorizer(stop_words=[&#x27;any&#x27;,\n",
       "                                                                          &#x27;our&#x27;,\n",
       "                                                                          &#x27;had&#x27;,\n",
       "                                                                          &#x27;wasn&#x27;,\n",
       "                                                                          &#x27;from&#x27;,\n",
       "                                                                          &#x27;here&#x27;,\n",
       "                                                                          &#x27;few&#x27;,\n",
       "                                                                          &#x27;has&#x27;,\n",
       "                                                                          &#x27;too&#x27;,\n",
       "                                                                          &#x27;then&#x27;,\n",
       "                                                                          &quot;aren&#x27;t&quot;,\n",
       "                                                                          &quot;couldn&#x27;t&quot;,\n",
       "                                                                          &#x27;i&#x27;,\n",
       "                                                                          &#x27;some&#x27;,\n",
       "                                                                          &#x27;itself&#x27;,\n",
       "                                                                          &quot;you&#x27;d&quot;,\n",
       "                                                                          &#x27;t&#x27;,\n",
       "                                                                          &#x27;myself&#x27;,\n",
       "                                                                          &#x27;were&#x27;,\n",
       "                                                                          &#x27;against&#x27;,\n",
       "                                                                          &#x27;weren&#x27;,\n",
       "                                                                          &#x27;me&#x27;,\n",
       "                                                                          &#x27;hers&#x27;,\n",
       "                                                                          &#x27;the&#x27;,\n",
       "                                                                          &#x27;o&#x27;,\n",
       "                                                                          &#x27;did&#x27;,\n",
       "                                                                          &#x27;after&#x27;,\n",
       "                                                                          &quot;that&#x27;ll&quot;,\n",
       "                                                                          &#x27;up&#x27;,\n",
       "                                                                          &#x27;as&#x27;, ...])),\n",
       "                                             (&#x27;model&#x27;,\n",
       "                                              LGBMClassifier(class_weight=&#x27;balanced&#x27;,\n",
       "                                                             random_state=42))]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;model__learning_rate&#x27;: [0.01, 0.1,\n",
       "                                                                 0.2],\n",
       "                                        &#x27;model__max_depth&#x27;: [-1, 10, 20],\n",
       "                                        &#x27;model__num_leaves&#x27;: [30, 50, 70]},\n",
       "                   random_state=42, scoring=&#x27;f1&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;vct&#x27;,\n",
       "                 TfidfVectorizer(stop_words=[&#x27;any&#x27;, &#x27;our&#x27;, &#x27;had&#x27;, &#x27;wasn&#x27;,\n",
       "                                             &#x27;from&#x27;, &#x27;here&#x27;, &#x27;few&#x27;, &#x27;has&#x27;,\n",
       "                                             &#x27;too&#x27;, &#x27;then&#x27;, &quot;aren&#x27;t&quot;,\n",
       "                                             &quot;couldn&#x27;t&quot;, &#x27;i&#x27;, &#x27;some&#x27;, &#x27;itself&#x27;,\n",
       "                                             &quot;you&#x27;d&quot;, &#x27;t&#x27;, &#x27;myself&#x27;, &#x27;were&#x27;,\n",
       "                                             &#x27;against&#x27;, &#x27;weren&#x27;, &#x27;me&#x27;, &#x27;hers&#x27;,\n",
       "                                             &#x27;the&#x27;, &#x27;o&#x27;, &#x27;did&#x27;, &#x27;after&#x27;,\n",
       "                                             &quot;that&#x27;ll&quot;, &#x27;up&#x27;, &#x27;as&#x27;, ...])),\n",
       "                (&#x27;model&#x27;,\n",
       "                 LGBMClassifier(class_weight=&#x27;balanced&#x27;, random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer(stop_words=[&#x27;any&#x27;, &#x27;our&#x27;, &#x27;had&#x27;, &#x27;wasn&#x27;, &#x27;from&#x27;, &#x27;here&#x27;, &#x27;few&#x27;,\n",
       "                            &#x27;has&#x27;, &#x27;too&#x27;, &#x27;then&#x27;, &quot;aren&#x27;t&quot;, &quot;couldn&#x27;t&quot;, &#x27;i&#x27;,\n",
       "                            &#x27;some&#x27;, &#x27;itself&#x27;, &quot;you&#x27;d&quot;, &#x27;t&#x27;, &#x27;myself&#x27;, &#x27;were&#x27;,\n",
       "                            &#x27;against&#x27;, &#x27;weren&#x27;, &#x27;me&#x27;, &#x27;hers&#x27;, &#x27;the&#x27;, &#x27;o&#x27;, &#x27;did&#x27;,\n",
       "                            &#x27;after&#x27;, &quot;that&#x27;ll&quot;, &#x27;up&#x27;, &#x27;as&#x27;, ...])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">LGBMClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>LGBMClassifier(class_weight=&#x27;balanced&#x27;, random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[('vct',\n",
       "                                              TfidfVectorizer(stop_words=['any',\n",
       "                                                                          'our',\n",
       "                                                                          'had',\n",
       "                                                                          'wasn',\n",
       "                                                                          'from',\n",
       "                                                                          'here',\n",
       "                                                                          'few',\n",
       "                                                                          'has',\n",
       "                                                                          'too',\n",
       "                                                                          'then',\n",
       "                                                                          \"aren't\",\n",
       "                                                                          \"couldn't\",\n",
       "                                                                          'i',\n",
       "                                                                          'some',\n",
       "                                                                          'itself',\n",
       "                                                                          \"you'd\",\n",
       "                                                                          't',\n",
       "                                                                          'myself',\n",
       "                                                                          'were',\n",
       "                                                                          'against',\n",
       "                                                                          'weren',\n",
       "                                                                          'me',\n",
       "                                                                          'hers',\n",
       "                                                                          'the',\n",
       "                                                                          'o',\n",
       "                                                                          'did',\n",
       "                                                                          'after',\n",
       "                                                                          \"that'll\",\n",
       "                                                                          'up',\n",
       "                                                                          'as', ...])),\n",
       "                                             ('model',\n",
       "                                              LGBMClassifier(class_weight='balanced',\n",
       "                                                             random_state=42))]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'model__learning_rate': [0.01, 0.1,\n",
       "                                                                 0.2],\n",
       "                                        'model__max_depth': [-1, 10, 20],\n",
       "                                        'model__num_leaves': [30, 50, 70]},\n",
       "                   random_state=42, scoring='f1')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "randomized_search_lgbm = RandomizedSearchCV(pipe_lgbm,\n",
    "                                       param_grid_lgbm,\n",
    "                                       cv=5,\n",
    "                                       n_jobs=-1,\n",
    "                                       scoring='f1',\n",
    "                                       random_state=RANDOM_STATE)\n",
    "\n",
    "randomized_search_lgbm.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>params</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>112.579508</td>\n",
       "      <td>1.203389</td>\n",
       "      <td>{'model__num_leaves': 30, 'model__max_depth': 20, 'model__learning_rate': 0.2}</td>\n",
       "      <td>0.751528</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160.214839</td>\n",
       "      <td>1.907401</td>\n",
       "      <td>{'model__num_leaves': 30, 'model__max_depth': 10, 'model__learning_rate': 0.2}</td>\n",
       "      <td>0.746894</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>298.404439</td>\n",
       "      <td>2.213732</td>\n",
       "      <td>{'model__num_leaves': 70, 'model__max_depth': -1, 'model__learning_rate': 0.1}</td>\n",
       "      <td>0.746232</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>268.213631</td>\n",
       "      <td>2.281732</td>\n",
       "      <td>{'model__num_leaves': 70, 'model__max_depth': 20, 'model__learning_rate': 0.1}</td>\n",
       "      <td>0.745392</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>239.679435</td>\n",
       "      <td>2.109636</td>\n",
       "      <td>{'model__num_leaves': 50, 'model__max_depth': 20, 'model__learning_rate': 0.1}</td>\n",
       "      <td>0.744314</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  mean_score_time  \\\n",
       "9     112.579508         1.203389   \n",
       "3     160.214839         1.907401   \n",
       "5     298.404439         2.213732   \n",
       "7     268.213631         2.281732   \n",
       "6     239.679435         2.109636   \n",
       "\n",
       "                                                                           params  \\\n",
       "9  {'model__num_leaves': 30, 'model__max_depth': 20, 'model__learning_rate': 0.2}   \n",
       "3  {'model__num_leaves': 30, 'model__max_depth': 10, 'model__learning_rate': 0.2}   \n",
       "5  {'model__num_leaves': 70, 'model__max_depth': -1, 'model__learning_rate': 0.1}   \n",
       "7  {'model__num_leaves': 70, 'model__max_depth': 20, 'model__learning_rate': 0.1}   \n",
       "6  {'model__num_leaves': 50, 'model__max_depth': 20, 'model__learning_rate': 0.1}   \n",
       "\n",
       "   mean_test_score  rank_test_score  \n",
       "9         0.751528                1  \n",
       "3         0.746894                2  \n",
       "5         0.746232                3  \n",
       "7         0.745392                4  \n",
       "6         0.744314                5  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:\n",
      "{'model__num_leaves': 30, 'model__max_depth': 20, 'model__learning_rate': 0.2}\n",
      "Best F1 Score: 0.75\n"
     ]
    }
   ],
   "source": [
    "results_lgbm = pd.DataFrame(randomized_search_lgbm.cv_results_)\n",
    "lgbm_model_metric = round(randomized_search_lgbm.best_score_, 2)\n",
    "\n",
    "display(results_lgbm[['mean_fit_time', 'mean_score_time', 'params', 'mean_test_score', 'rank_test_score']]\n",
    "        .sort_values(by='mean_test_score', ascending=False)\n",
    "        .head(5))\n",
    "\n",
    "print(f'Best parameters:\\n{randomized_search_lgbm.best_params_}')\n",
    "print('Best F1 Score:', lgbm_model_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model has a good metric F1 = `0.75`, but at the same time it trains in 101 seconds, which is much more than the logical regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the best model turned out to be `LogisticRegression` with parameter `C`=10. Let's see how the model behaves on the test sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Roc-Auc: 0.97\n",
      "Accuracy: 0.95\n",
      "F1 score: 0.77\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = randomized_search_lg.best_estimator_\n",
    "y_pred = model.predict(X_test)\n",
    "y_proba = model.predict_proba(X_test)[:,1]\n",
    "\n",
    "# Metrics\n",
    "auc = roc_auc_score(y_test, y_proba)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "print(\"Roc-Auc:\", round(auc, 2))\n",
    "print(\"Accuracy:\", round(accuracy, 2))\n",
    "print(\"F1 score:\", round(f1, 2))\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
    "\n",
    "# Confusion matrix\n",
    "ax[0] = sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt=\"d\", ax=ax[0]) \n",
    "ax[0].set_title('Confusion matrix')\n",
    "ax[0].set_xlabel('Predict')\n",
    "ax[0].set_ylabel('Real')\n",
    "\n",
    "# Roc curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_proba)\n",
    "roc_auc = sklearn_auc(fpr, tpr)\n",
    "\n",
    "ax[1].plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "ax[1].plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "ax[1].set_xlim([0.0, 1.0])\n",
    "ax[1].set_ylim([0.0, 1.05])\n",
    "ax[1].set_xlabel('False Positive Rate')\n",
    "ax[1].set_ylabel('True Positive Rate')\n",
    "ax[1].set_title('ROC-curve')\n",
    "ax[1].legend(loc=\"lower right\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `ROC-AUC (0.97)`: The ROC-AUC value is very high, indicating the excellent ability of the model to distinguish between positive and negative classes. This suggests that the model has good prediction quality.\n",
    "\n",
    "- `Accuracy (0.95)`: The model correctly predicts 95% of cases. This means that in most cases the model makes the right decision.\n",
    "\n",
    "- `F1-measure (0.77)`: Although the F1-measure value is slightly lower than other indicators, it is still quite high and passes the customer's criterion. This indicates a good balance between model accuracy and recall, especially in the context of class imbalance.\n",
    "\n",
    "- `Confusion Matrix`:\n",
    "\n",
    "   - `True Negatives`: The model correctly predicted 34520 negative cases.\n",
    "   - `False Positives`: The model incorrectly predicted 1257 cases as positive when they were negative.\n",
    "   - `False Negatives`: The model missed 743 positive cases, classifying them as negative.\n",
    "   - `True Positives`: The model correctly identified 3303 positive cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DummyClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just in case, it’s worth checking how the constant model works to make sure that our model is really useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score: 0.18445827349609065\n",
      "Accuracy: 0.10159957813323958\n"
     ]
    }
   ],
   "source": [
    "dummy_model = DummyClassifier(strategy='constant', constant=1)\n",
    "dummy_model.fit(X_train, y_train)\n",
    "y_pred = dummy_model.predict(X_test)\n",
    "\n",
    "print(\"F1 score:\", f1_score(y_test, y_pred))\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model really gets the job done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Description of tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our task was to train the model to determine the sentiment of comments.\n",
    "\n",
    "Criteria that are important to the customer:\n",
    "- F1-measure not less than `0.75`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Description of work stages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. We have downloaded the data\n",
    "2. Make sure that there are no gaps/duplicates in the file\n",
    "3. Class imbalance was identified\n",
    "4. Processed the text, clearing it of unnecessary elements, lemmatizing, tokenizing\n",
    "5. Converted all comments into vectors\n",
    "6. Trained three models (`LogisticRegression`, `DecisionTreeClassifier`, `LGBMClassifier`) on the data and determined the best one (based on the customer’s criteria)"
   ]
  },
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    "### Final conclusions"
   ]
  },
  {
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   "source": [
    "We tested three models:\n",
    "\n",
    "- `LogisticRegression` (`C` = 10) - the F1 metric of the model was 0.76, which satisfies the customer’s criterion; the training time of such a model was 12.4 seconds, and the prediction speed was 0.87 seconds\n",
    "\n",
    "- `DecisionTreeClassifier` (`min_samples_split` = 2, `min_samples_leaf` = 2, `max_depth` = 40) - the F1 metric of the model is 0.64, which does not meet the customer’s criteria; training time - 76 seconds, prediction speed - 1.61 seconds\n",
    "\n",
    "- `LGBMClassifier` (`num_leaves` = 30, `max_depth` = 20, `learning_rate` = 0.2) - F1-measure was 0.75, which passes the expected threshold; training time of this model is 101.8, prediction speed is 1.46 seconds\n",
    "\n",
    "\n",
    "`LogisticRegression` with parameter `C` = 10 is the best of the studied models, since it has the best F1 metric in the minimum training and prediction time. Small training time and fast prediction speed can be very important in the context of an online store, where the number of users can be large.\n",
    "We did not try to improve the metrics of the remaining models, since they trained on data for too long, which is already a big disadvantage compared to the logistic regression model."
   ]
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