preprocessing.ipynb

{
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   "id": "d17a2e96",
   "metadata": {
    "id": "d17a2e96"
   },
   "outputs": [],
   "source": [
    "import re\n",
    "from typing import List\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from termcolor import colored\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.probability import FreqDist\n",
    "from wordcloud import WordCloud\n",
    "import PIL\n",
    "from PIL import Image\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from imblearn.pipeline import make_pipeline\n",
    "from imblearn.over_sampling import SMOTE, RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from packaging.version import Version"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Dzf2B9X5yNh2",
   "metadata": {
    "id": "Dzf2B9X5yNh2"
   },
   "source": [
    "Problem prilikom koriscenja Google Colab-a sa verzijom Pillow biblioteke. Za projekat se koristi verzija $ \\le $ `9.5.0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9Vo0AxAyCUa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "a9Vo0AxAyCUa",
    "outputId": "7cc3c821-4c5c-4f98-bc72-452ce98dee9a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting Pillow==9.5.0\n",
      "  Downloading Pillow-9.5.0-cp310-cp310-manylinux_2_28_x86_64.whl (3.4 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: Pillow\n",
      "  Attempting uninstall: Pillow\n",
      "    Found existing installation: Pillow 10.0.0\n",
      "    Uninstalling Pillow-10.0.0:\n",
      "      Successfully uninstalled Pillow-10.0.0\n",
      "Successfully installed Pillow-9.5.0\n"
     ]
    },
    {
     "data": {
      "application/vnd.colab-display-data+json": {
       "pip_warning": {
        "packages": [
         "PIL"
        ]
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if Version(PIL.__version__) > Version('9.5.0'):\n",
    "    !pip3 install Pillow==9.5.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "BxemcYiXy7Ho",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BxemcYiXy7Ho",
    "outputId": "d9fd232e-c567-4e2f-a419-39968e0f9672"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.5.0\n"
     ]
    }
   ],
   "source": [
    "print(PIL.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2038baea",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2038baea",
    "outputId": "a5296cf9-3b61-4b0b-b5b6-461191a635bd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/filip/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /home/filip/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /home/filip/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "636f56f9",
   "metadata": {
    "id": "636f56f9"
   },
   "source": [
    "## Ucitavanje podataka\n",
    "-  Podaci se nalaze u direktorijumu `dataset`  \n",
    "-  Iako su na prvi pogled podaci podeljeni na trening i test skup, test skup ne sadrzi izlaznu promenljivu i na njemu ne mozemo testirati koliko dobro nas model radi pa je potrebno podeliti `train.csv` na trening i test skup. Ali ce to biti odradjeno tek nakon pretprocesiranja teksta\n",
    "<!-- -  Podaci su vec podeljeni na train i test skup tako da nema potrebe koristiti neku od ugradjenih funkcija (npr. `train_test_split` iz `sklearn.model_selection` paketa)   -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cafe31fc",
   "metadata": {
    "id": "cafe31fc"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('dataset/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "F6guP0aXlhF8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F6guP0aXlhF8",
    "outputId": "b542a38e-b65f-4746-a104-3a927ea9e2d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Broj instanci: 31,962\n",
      "Broj atributa: 3 (id, label, tweet)\n"
     ]
    }
   ],
   "source": [
    "(num_instances, num_attrs) = data.shape\n",
    "\n",
    "print(f'Broj instanci: {num_instances:,}')\n",
    "print(f'Broj atributa: {num_attrs} ({\", \".join(data.columns.values)})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "35fcbd0e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "35fcbd0e",
    "outputId": "cff2ef02-7b3d-42a7-a912-d4078ac3c9d7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['label'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "439b3c73",
   "metadata": {
    "id": "439b3c73"
   },
   "source": [
    "Kako imamo 2 moguce vrednosti izlaznog atributa (tweet sadrzi govor mrznje odnosno tweet ne sadrzi govor mrznje), rec je o binarnoj klasifikaciji. Naravno ulazni atribut predstavlja `tweet`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "669c40eb",
   "metadata": {
    "id": "669c40eb"
   },
   "source": [
    "## Obrada train podataka"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c0467b26",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "c0467b26",
    "outputId": "9041883b-4cde-4419-b7d1-40ac256f4eba"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>@user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction.   #run</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't use cause they don't offer wheelchair vans in pdx.    #disapointed #getthanked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>#model   i love u take with u all the time in urð±!!! ðððð",
       "ð¦ð¦ð¦</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>[2/2] huge fan fare and big talking before they leave. chaos and pay disputes when they get there. #allshowandnogo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>@user camping tomorrow @user @user @user @user @user @user @user dannyâ¦</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>the next school year is the year for exams.ð¯ can't think about that ð­ #school #exams   #hate #imagine #actorslife #revolutionschool #girl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>we won!!! love the land!!! #allin #cavs #champions #cleveland #clevelandcavaliers  â¦</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>@user @user welcome here !  i'm   it's so #gr8 !</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  label  \\\n",
       "0   1      0   \n",
       "1   2      0   \n",
       "2   3      0   \n",
       "3   4      0   \n",
       "4   5      0   \n",
       "5   6      0   \n",
       "6   7      0   \n",
       "7   8      0   \n",
       "8   9      0   \n",
       "9  10      0   \n",
       "\n",
       "                                                                                                                                             tweet  \n",
       "0                                            @user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction.   #run  \n",
       "1                       @user @user thanks for #lyft credit i can't use cause they don't offer wheelchair vans in pdx.    #disapointed #getthanked  \n",
       "2                                                                                                                              bihday your majesty  \n",
       "3                                                           #model   i love u take with u all the time in urð±!!! ðððð\n",
       "ð¦ð¦ð¦    \n",
       "4                                                                                                           factsguide: society now    #motivation  \n",
       "5                             [2/2] huge fan fare and big talking before they leave. chaos and pay disputes when they get there. #allshowandnogo    \n",
       "6                                                                        @user camping tomorrow @user @user @user @user @user @user @user dannyâ¦  \n",
       "7  the next school year is the year for exams.ð¯ can't think about that ð­ #school #exams   #hate #imagine #actorslife #revolutionschool #girl  \n",
       "8                                                          we won!!! love the land!!! #allin #cavs #champions #cleveland #clevelandcavaliers  â¦   \n",
       "9                                                                                                @user @user welcome here !  i'm   it's so #gr8 !   "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', 1000)\n",
    "\n",
    "data.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dfea42e",
   "metadata": {
    "id": "1dfea42e"
   },
   "source": [
    "Kako je atributan `id` irelevantan mozemo ga izbaciti iz DataFrame-a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30ead904",
   "metadata": {
    "colab": {
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     "height": 363
    },
    "id": "30ead904",
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   "outputs": [
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>@user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction.   #run</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't use cause they don't offer wheelchair vans in pdx.    #disapointed #getthanked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>#model   i love u take with u all the time in urð±!!! ðððð",
       "ð¦ð¦ð¦</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>[2/2] huge fan fare and big talking before they leave. chaos and pay disputes when they get there. #allshowandnogo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>@user camping tomorrow @user @user @user @user @user @user @user dannyâ¦</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>the next school year is the year for exams.ð¯ can't think about that ð­ #school #exams   #hate #imagine #actorslife #revolutionschool #girl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>we won!!! love the land!!! #allin #cavs #champions #cleveland #clevelandcavaliers  â¦</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>@user @user welcome here !  i'm   it's so #gr8 !</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  \\\n",
       "0      0   \n",
       "1      0   \n",
       "2      0   \n",
       "3      0   \n",
       "4      0   \n",
       "5      0   \n",
       "6      0   \n",
       "7      0   \n",
       "8      0   \n",
       "9      0   \n",
       "\n",
       "                                                                                                                                             tweet  \n",
       "0                                            @user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction.   #run  \n",
       "1                       @user @user thanks for #lyft credit i can't use cause they don't offer wheelchair vans in pdx.    #disapointed #getthanked  \n",
       "2                                                                                                                              bihday your majesty  \n",
       "3                                                           #model   i love u take with u all the time in urð±!!! ðððð\n",
       "ð¦ð¦ð¦    \n",
       "4                                                                                                           factsguide: society now    #motivation  \n",
       "5                             [2/2] huge fan fare and big talking before they leave. chaos and pay disputes when they get there. #allshowandnogo    \n",
       "6                                                                        @user camping tomorrow @user @user @user @user @user @user @user dannyâ¦  \n",
       "7  the next school year is the year for exams.ð¯ can't think about that ð­ #school #exams   #hate #imagine #actorslife #revolutionschool #girl  \n",
       "8                                                          we won!!! love the land!!! #allin #cavs #champions #cleveland #clevelandcavaliers  â¦   \n",
       "9                                                                                                @user @user welcome here !  i'm   it's so #gr8 !   "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['id'], axis=1, inplace=True)\n",
    "\n",
    "data.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eb4518e",
   "metadata": {
    "id": "0eb4518e"
   },
   "source": [
    "### Obrada null vrednosti (nedostajucih vrednosti)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6561726c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6561726c",
    "outputId": "c72746e8-5992-40ba-c553-040f4a1bfae1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nema null vrednosti\n"
     ]
    }
   ],
   "source": [
    "null_values = data.isna().any().any()\n",
    "\n",
    "print('Nema null vrednosti') if not null_values else print(data.isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5b71186",
   "metadata": {
    "id": "b5b71186"
   },
   "source": [
    "Iako nema `NaN` vrednost to ne znaci da nas skup podataka zaista nema nedostajucih vrednosti, u nasem slucaju prazan tekst odnosno prazan tweet je takodje nedostajuca vrednost (iako je nemoguce objaviti prazan tweet). Nakon obrade teksta je moguce dobiti prazan tweet u slucaju da je sadrzaj tweet-a neka od Stopwords, emoji i sl. o cemu ce biti rec u nastavku"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cbbfc45d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cbbfc45d",
    "outputId": "99c88bb6-e3c2-4fd6-b954-e8bff5296dba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(len(data.loc[data['tweet'] == '']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ccdb064",
   "metadata": {
    "id": "0ccdb064"
   },
   "source": [
    "#### Neke od metoda za obradjivanje null vrednosti\n",
    "Kako nas skup ne sadrzi null vrednosti nema potrebe za daljom analizom nedostajucih podataka  \n",
    "U slucaju da su postojale null vrednosti one su mogle biti obradjene na neki od sledecih nacina:  \n",
    "-  **`Izbacivanjem`** (*train_data.dropna()*, pri cemu moramo voditi racuna da ne izbacimo previse podataka)  \n",
    "\n",
    "-  **`Popunjavanjem default vrednostima`** (*train_data.fillna(def)*, ovde se postavlja pitanje sta izabrati kao default vrednost, u slucaju da nemamo vrednost za *label* atribut ako izaberemo 0 tweet-ovi koji sadrze govor mrznje mogu biti okarakaterisani kao tweet-ovi koji ne sadrze govor mrznje, slicno za 1)  \n",
    "\n",
    "- **`Popunjavanje unapred / unazad`** (*train_data['label'].ffill()*, odnosno *train_data['label'].bfill()*)\n",
    "    -  *ffill()* popunjava null vrednost na osnovu vrednosti koja se nalazi iznad nje\n",
    "    -  *bfill()* popunjava null vrednost na osnovu vrednosti koja se nalazi ispod nje\n",
    "- **...**\n",
    "\n",
    "Nakon popunjavanja nedostajucih vrednosti mozemo iskoristiti metod `hist()` kako bi proverili koliko smo zaista uticali na nas set podataka  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tq5P2VYUaaHH",
   "metadata": {
    "id": "tq5P2VYUaaHH"
   },
   "source": [
    "Provera da li nas skup sadrzi duplirane podatke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "nm3OJ-o3ab4d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nm3OJ-o3ab4d",
    "outputId": "2b45fee7-0c3f-4872-9c70-2b56ff2a5fd6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ima 2432 duplikata\n"
     ]
    }
   ],
   "source": [
    "print(f'Ima {len(data.loc[data[\"tweet\"].duplicated() == True])} duplikata') if data.duplicated().any() else print('Nema duplikata')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "A7tHMDairVzz",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A7tHMDairVzz",
    "outputId": "a20007f6-66b7-46d8-e6a9-3e6bf860fad0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29530, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop_duplicates(keep='first')\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rVtWVLixaaPQ",
   "metadata": {
    "id": "rVtWVLixaaPQ"
   },
   "source": [
    "Naredni korak predstavlja obrada tweet-ova na taj nacin da ne sadrze `stopwords` ('I', 'myself', 'your', 'like' ...), `znakove interpunkcije` (',', '!', '.' ...), `emojis` (':)', ';)', ...) i sl. sto ne moze znacajnije uticati na tekst.\n",
    "Takodje bice izvrsena lematizacija koja reci svodi na njihov osnovni oblik (npr. [`improve`, `improving`, `improved`] i sl. ce se svesti na `improve`) (Pored lematizacije postoji i stemming pri cemu se uklanjaju poslednji karakteri koji vode ka pogresnom zakljucku i spelovanju)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "778c3f04",
   "metadata": {
    "id": "778c3f04",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "stop_words = set(stopwords.words('english'))\n",
    "stop_words.add('user')\n",
    "\n",
    "lemma = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "084e3088",
   "metadata": {
    "id": "084e3088"
   },
   "outputs": [],
   "source": [
    "def preprocessing_tweet(tweet: str) -> str:\n",
    "    new_tweet = tweet.lower()\n",
    "    new_tweet = re.sub(r'[^a-zA-Z]', ' ', new_tweet)   # Zameni sve sto nije slovo sa ' '\n",
    "    new_tweet = [word for word in new_tweet.split() if word not in stop_words]\n",
    "    new_tweet = ' '.join([lemma.lemmatize(word) for word in new_tweet])\n",
    "\n",
    "    return new_tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5221f235",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "5221f235",
    "outputId": "df79609f-8a98-4d5f-f3ff-3907bafa20ab"
   },
   "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>label</th>\n",
       "      <th>tweet</th>\n",
       "      <th>tweet_cleaned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>@user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction.   #run</td>\n",
       "      <td>father dysfunctional selfish drag kid dysfunction run</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't use cause they don't offer wheelchair vans in pdx.    #disapointed #getthanked</td>\n",
       "      <td>thanks lyft credit use cause offer wheelchair van pdx disapointed getthanked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "      <td>bihday majesty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>#model   i love u take with u all the time in urð±!!! ðððð",
       "ð¦ð¦ð¦</td>\n",
       "      <td>model love u take u time ur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "      <td>factsguide society motivation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>[2/2] huge fan fare and big talking before they leave. chaos and pay disputes when they get there. #allshowandnogo</td>\n",
       "      <td>huge fan fare big talking leave chaos pay dispute get allshowandnogo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>@user camping tomorrow @user @user @user @user @user @user @user dannyâ¦</td>\n",
       "      <td>camping tomorrow danny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>the next school year is the year for exams.ð¯ can't think about that ð­ #school #exams   #hate #imagine #actorslife #revolutionschool #girl</td>\n",
       "      <td>next school year year exam think school exam hate imagine actorslife revolutionschool girl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>we won!!! love the land!!! #allin #cavs #champions #cleveland #clevelandcavaliers  â¦</td>\n",
       "      <td>love land allin cavs champion cleveland clevelandcavaliers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>@user @user welcome here !  i'm   it's so #gr8 !</td>\n",
       "      <td>welcome gr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  \\\n",
       "0      0   \n",
       "1      0   \n",
       "2      0   \n",
       "3      0   \n",
       "4      0   \n",
       "5      0   \n",
       "6      0   \n",
       "7      0   \n",
       "8      0   \n",
       "9      0   \n",
       "\n",
       "                                                                                                                                             tweet  \\\n",
       "0                                            @user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction.   #run   \n",
       "1                       @user @user thanks for #lyft credit i can't use cause they don't offer wheelchair vans in pdx.    #disapointed #getthanked   \n",
       "2                                                                                                                              bihday your majesty   \n",
       "3                                                           #model   i love u take with u all the time in urð±!!! ðððð\n",
       "ð¦ð¦ð¦     \n",
       "4                                                                                                           factsguide: society now    #motivation   \n",
       "5                             [2/2] huge fan fare and big talking before they leave. chaos and pay disputes when they get there. #allshowandnogo     \n",
       "6                                                                        @user camping tomorrow @user @user @user @user @user @user @user dannyâ¦   \n",
       "7  the next school year is the year for exams.ð¯ can't think about that ð­ #school #exams   #hate #imagine #actorslife #revolutionschool #girl   \n",
       "8                                                          we won!!! love the land!!! #allin #cavs #champions #cleveland #clevelandcavaliers  â¦    \n",
       "9                                                                                                @user @user welcome here !  i'm   it's so #gr8 !    \n",
       "\n",
       "                                                                                tweet_cleaned  \n",
       "0                                       father dysfunctional selfish drag kid dysfunction run  \n",
       "1                thanks lyft credit use cause offer wheelchair van pdx disapointed getthanked  \n",
       "2                                                                              bihday majesty  \n",
       "3                                                                 model love u take u time ur  \n",
       "4                                                               factsguide society motivation  \n",
       "5                        huge fan fare big talking leave chaos pay dispute get allshowandnogo  \n",
       "6                                                                      camping tomorrow danny  \n",
       "7  next school year year exam think school exam hate imagine actorslife revolutionschool girl  \n",
       "8                                  love land allin cavs champion cleveland clevelandcavaliers  \n",
       "9                                                                                  welcome gr  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['tweet_cleaned'] = data['tweet'].apply(preprocessing_tweet)\n",
    "\n",
    "data.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea5b633",
   "metadata": {
    "id": "3ea5b633"
   },
   "source": [
    "Nakon obrade teksta moze se desiti da je `tweet_cleaned` prazna niska, u slucaju da se tweet sastoji samo od taga, stopwords i sl. Zato je potrebno utvrditi koliko takvih instanci postoji i na koji nacin ih obraditi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f4f6362",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4f4f6362",
    "outputId": "3c7c20ca-cf9c-4a03-9c7a-f953ad3d7bee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "empty_tweets = data.loc[data['tweet_cleaned'] == '']\n",
    "empty_tweets.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c8216fd",
   "metadata": {
    "id": "0c8216fd"
   },
   "source": [
    "Kako je broj instanci neuporedivo manji od ukupnog broja instanci skupa podataka za treniranje mozemo ih izbaciti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "893b15c3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "893b15c3",
    "outputId": "a79f2d68-d2d6-4620-9965-c08378295954"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29493, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.loc[data['tweet_cleaned'] != '']\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "uYdqwOFMljtZ",
   "metadata": {
    "id": "uYdqwOFMljtZ"
   },
   "source": [
    "Dodajemo kolonu `length` koja ce nam biti potrebna za odredjivanje autlajera"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "UancplhIWj4b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 354
    },
    "id": "UancplhIWj4b",
    "outputId": "cc52b257-4171-4248-c623-916b5642b6c7"
   },
   "outputs": [
    {
     "data": {
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       "    }\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>label</th>\n",
       "      <th>tweet</th>\n",
       "      <th>tweet_cleaned</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>@user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction.   #run</td>\n",
       "      <td>father dysfunctional selfish drag kid dysfunction run</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't use cause they don't offer wheelchair vans in pdx.    #disapointed #getthanked</td>\n",
       "      <td>thanks lyft credit use cause offer wheelchair van pdx disapointed getthanked</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "      <td>bihday majesty</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>#model   i love u take with u all the time in urð±!!! ðððð",
       "ð¦ð¦ð¦</td>\n",
       "      <td>model love u take u time ur</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "      <td>factsguide society motivation</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  \\\n",
       "0      0   \n",
       "1      0   \n",
       "2      0   \n",
       "3      0   \n",
       "4      0   \n",
       "\n",
       "                                                                                                                        tweet  \\\n",
       "0                       @user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction.   #run   \n",
       "1  @user @user thanks for #lyft credit i can't use cause they don't offer wheelchair vans in pdx.    #disapointed #getthanked   \n",
       "2                                                                                                         bihday your majesty   \n",
       "3                                      #model   i love u take with u all the time in urð±!!! ðððð\n",
       "ð¦ð¦ð¦     \n",
       "4                                                                                      factsguide: society now    #motivation   \n",
       "\n",
       "                                                                  tweet_cleaned  \\\n",
       "0                         father dysfunctional selfish drag kid dysfunction run   \n",
       "1  thanks lyft credit use cause offer wheelchair van pdx disapointed getthanked   \n",
       "2                                                                bihday majesty   \n",
       "3                                                   model love u take u time ur   \n",
       "4                                                 factsguide society motivation   \n",
       "\n",
       "   length  \n",
       "0      53  \n",
       "1      76  \n",
       "2      14  \n",
       "3      27  \n",
       "4      29  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['length'] = data['tweet_cleaned'].str.len()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mzE_MO6dRNKy",
   "metadata": {
    "id": "mzE_MO6dRNKy"
   },
   "source": [
    "## Podela na ulaznu odnosno izlaznu promenljivu i podela na trening i test skup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cabfd6c",
   "metadata": {
    "id": "4cabfd6c"
   },
   "source": [
    "Podela trening skupa na ulaznu (`X`) odnosno izlaznu promenljivu (`Y`). Na ulazu dobijamo tweet a na izlazu se dobija odgovor na pitanje da li tweet sadrzi govor mrznje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "scALGG44NBUg",
   "metadata": {
    "id": "scALGG44NBUg"
   },
   "outputs": [],
   "source": [
    "X = data.drop(['label', 'tweet'], axis=1)\n",
    "Y = data['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "BOTEGMCsNS9W",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "BOTEGMCsNS9W",
    "outputId": "e1bc7526-49da-46a7-b6c3-95b2c5747a04"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_cleaned</th>\n",
       "      <th>length</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>thanks lyft credit use cause offer wheelchair van pdx disapointed getthanked</td>\n",
       "      <td>76</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bihday majesty</td>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>model love u take u time ur</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>factsguide society motivation</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  tweet_cleaned  \\\n",
       "0                         father dysfunctional selfish drag kid dysfunction run   \n",
       "1  thanks lyft credit use cause offer wheelchair van pdx disapointed getthanked   \n",
       "2                                                                bihday majesty   \n",
       "3                                                   model love u take u time ur   \n",
       "4                                                 factsguide society motivation   \n",
       "\n",
       "   length  \n",
       "0      53  \n",
       "1      76  \n",
       "2      14  \n",
       "3      27  \n",
       "4      29  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bWY8zq2pNTIF",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bWY8zq2pNTIF",
    "outputId": "b0247d3d-f7cd-4677-e63e-5d7f837741cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "oWlcCIeLJJNj",
   "metadata": {
    "id": "oWlcCIeLJJNj"
   },
   "source": [
    "Sledi podela na Train i Test skup podataka"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "upE2KyhaJImD",
   "metadata": {
    "id": "upE2KyhaJImD"
   },
   "outputs": [],
   "source": [
    "(X_train, X_test, Y_train, Y_test) = train_test_split(X, Y, stratify=Y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "91h3gk_UOLzA",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "91h3gk_UOLzA",
    "outputId": "ed2c4e49-fadd-4bff-c090-37d9b5a31cda"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Broj instanci trening skupa: 20645\n",
      "Broj instanci test skupa: 8848\n"
     ]
    }
   ],
   "source": [
    "print(f'Broj instanci trening skupa: {X_train.shape[0]}')\n",
    "print(f'Broj instanci test skupa: {X_test.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc89a2a6",
   "metadata": {
    "id": "cc89a2a6"
   },
   "source": [
    "### Autlajeri\n",
    "U slucaju obrade teksta duzina samog teksta moze uticati na sam model. Naime ukoliko se u nekom duzem tekstu nadju neke reci koje nisu znacajne za kategorizaciju tweet-a one mogu uticati na preprilagodjavanje modela. Nakon ociscenih tweet-ova dodata je kolona sa duzinom svakog tweet-a. Zatim se odredjuje Box plot na osnovu koga mozemo videti koje instance predstavljaju autlajere nakon cega slediti izbacivanje datih autlajera. Treba napomenuti da se obrada outliera vrsi nakon podele na train i test skup podataka kako se test skup podataka ne bi kompromitovao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "EvTtRjRVXrg9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "EvTtRjRVXrg9",
    "outputId": "1970ac57-9cbc-4c6c-d053-c494f9c28df4"
   },
   "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>tweet_cleaned</th>\n",
       "      <th>length</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22001</th>\n",
       "      <td>wait stage look awesome see pm cmafest drinkingwithdolly flockon</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18793</th>\n",
       "      <td>bbc news orlando gay nightclub shooting killed suspect omar mateen love love get smh</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14980</th>\n",
       "      <td>friend netflix</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10457</th>\n",
       "      <td>ceain area yeah seen video seeing little kid cry</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29761</th>\n",
       "      <td>thnx pilot nick amazing experience grandcanyon afternoon</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                              tweet_cleaned  \\\n",
       "22001                      wait stage look awesome see pm cmafest drinkingwithdolly flockon   \n",
       "18793  bbc news orlando gay nightclub shooting killed suspect omar mateen love love get smh   \n",
       "14980                                                                        friend netflix   \n",
       "10457                                      ceain area yeah seen video seeing little kid cry   \n",
       "29761                              thnx pilot nick amazing experience grandcanyon afternoon   \n",
       "\n",
       "       length  label  \n",
       "22001      64      0  \n",
       "18793      84      0  \n",
       "14980      14      0  \n",
       "10457      48      0  \n",
       "29761      56      0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.concat([X_train, Y_train], axis=1)\n",
    "\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b79f8cd1",
   "metadata": {
    "id": "b79f8cd1"
   },
   "outputs": [],
   "source": [
    "hatred_tweets = train_data.loc[data['label'] == 1]['tweet_cleaned']\n",
    "hatred_tweets_len = hatred_tweets.str.len()\n",
    "non_hatred_tweets = train_data.loc[data['label'] == 0]['tweet_cleaned']\n",
    "non_hatred_tweets_len = non_hatred_tweets.str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "23014b33",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 408
    },
    "id": "23014b33",
    "outputId": "de198d98-80be-48c5-ddee-313de1669439"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Non-Hatred tweets length')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(hatred_Q1, hatred_Q3) = hatred_tweets_len.quantile([0.25, 0.75])\n",
    "hatred_IQR = hatred_Q3 - hatred_Q1\n",
    "hatred_min = hatred_Q1 - (1.5*hatred_IQR)\n",
    "hatred_max = hatred_Q3 + (1.5*hatred_IQR)\n",
    "\n",
    "(non_hatred_Q1, non_hatred_Q3) = non_hatred_tweets_len.quantile([0.25, 0.75])\n",
    "non_hatred_IQR = non_hatred_Q3 - non_hatred_Q1\n",
    "non_hatred_min = non_hatred_Q1 - (1.5*non_hatred_IQR)\n",
    "non_hatred_max = non_hatred_Q3 + (1.5*non_hatred_IQR)\n",
    "\n",
    "(fig, axes) = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n",
    "plt.clf()\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.boxplot(hatred_tweets_len)\n",
    "plt.title('Hatred tweets length')\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.boxplot(non_hatred_tweets_len)\n",
    "plt.title('Non-Hatred tweets length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "485e7c3c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 98
    },
    "id": "485e7c3c",
    "outputId": "5f21f021-40b8-44be-9653-a937e5562236",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ukupno ima 1 tweet / tweet-ova sa govorom mrznje koji su autlajeri\n"
     ]
    },
    {
     "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>tweet_cleaned</th>\n",
       "      <th>length</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2784</th>\n",
       "      <td>happy newyear sameshitnewyear corruption trump terrorattack fundamentalism violence isi crisis hatred murder terrorism</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                               tweet_cleaned  \\\n",
       "2784  happy newyear sameshitnewyear corruption trump terrorattack fundamentalism violence isi crisis hatred murder terrorism   \n",
       "\n",
       "      length  label  \n",
       "2784     118      1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hatred_outliers = train_data.loc[\n",
    "    (train_data['label'] == 1) &\n",
    "    ((train_data['length'] > hatred_max) |\n",
    "    (train_data['length'] < hatred_min))\n",
    "]\n",
    "\n",
    "print(f'Ukupno ima {hatred_outliers.shape[0]} tweet / tweet-ova sa govorom mrznje koji su autlajeri')\n",
    "hatred_outliers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7a82e383",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 161
    },
    "id": "7a82e383",
    "outputId": "6cb5341a-127c-4375-a599-10449884a80b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ukupno ima 3 tweet /  tweet-ova bez govora mrznje koji su autlajeri\n"
     ]
    },
    {
     "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>tweet_cleaned</th>\n",
       "      <th>length</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26600</th>\n",
       "      <td>auba wil go city vardy x liverpool morata wil stay juve n gud old giroud wil stil sta arsenal next season pathetic wenger</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18958</th>\n",
       "      <td>epic video blm zombie v trump kid youcanalwaystelldemocraticvoterstheyrloud amp neverhavecorrectfactsthisolderblackmanispitiful</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5846</th>\n",
       "      <td>controversy trifecta isi inspired mass shooting perpetrated legally purchased assault weapon aimed gay victim loveislove</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                         tweet_cleaned  \\\n",
       "26600        auba wil go city vardy x liverpool morata wil stay juve n gud old giroud wil stil sta arsenal next season pathetic wenger   \n",
       "18958  epic video blm zombie v trump kid youcanalwaystelldemocraticvoterstheyrloud amp neverhavecorrectfactsthisolderblackmanispitiful   \n",
       "5846          controversy trifecta isi inspired mass shooting perpetrated legally purchased assault weapon aimed gay victim loveislove   \n",
       "\n",
       "       length  label  \n",
       "26600     121      0  \n",
       "18958     127      0  \n",
       "5846      120      0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_hatred_outliers = train_data.loc[\n",
    "    (train_data['label'] == 0) &\n",
    "    ((train_data['length'] > non_hatred_max) |\n",
    "    (train_data['length'] < non_hatred_min))\n",
    "]\n",
    "\n",
    "print(f'Ukupno ima {non_hatred_outliers.shape[0]} tweet /  tweet-ova bez govora mrznje koji su autlajeri')\n",
    "non_hatred_outliers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "14ee3d3c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "14ee3d3c",
    "outputId": "f3ca44fb-cdb0-4151-d569-84fc1e5d6b56"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20641, 3)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.drop(hatred_outliers.index, inplace=True)\n",
    "train_data.drop(non_hatred_outliers.index, inplace=True)\n",
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ht3TzosZkAiQ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "ht3TzosZkAiQ",
    "outputId": "fc8f3ef3-baa3-438f-951e-86f9724db195"
   },
   "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>tweet_cleaned</th>\n",
       "      <th>length</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22001</th>\n",
       "      <td>wait stage look awesome see pm cmafest drinkingwithdolly flockon</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18793</th>\n",
       "      <td>bbc news orlando gay nightclub shooting killed suspect omar mateen love love get smh</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14980</th>\n",
       "      <td>friend netflix</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10457</th>\n",
       "      <td>ceain area yeah seen video seeing little kid cry</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29761</th>\n",
       "      <td>thnx pilot nick amazing experience grandcanyon afternoon</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                              tweet_cleaned  \\\n",
       "22001                      wait stage look awesome see pm cmafest drinkingwithdolly flockon   \n",
       "18793  bbc news orlando gay nightclub shooting killed suspect omar mateen love love get smh   \n",
       "14980                                                                        friend netflix   \n",
       "10457                                      ceain area yeah seen video seeing little kid cry   \n",
       "29761                              thnx pilot nick amazing experience grandcanyon afternoon   \n",
       "\n",
       "       length  label  \n",
       "22001      64      0  \n",
       "18793      84      0  \n",
       "14980      14      0  \n",
       "10457      48      0  \n",
       "29761      56      0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "932d684c",
   "metadata": {
    "id": "932d684c"
   },
   "source": [
    "## Vizuelni prikaz train podataka"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "W6p7QKXRj0Ub",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "W6p7QKXRj0Ub",
    "outputId": "0058ee5a-cb6e-4840-a4e6-1c3c0ecdb917"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22001                        wait stage look awesome see pm cmafest drinkingwithdolly flockon\n",
       "18793    bbc news orlando gay nightclub shooting killed suspect omar mateen love love get smh\n",
       "14980                                                                          friend netflix\n",
       "10457                                        ceain area yeah seen video seeing little kid cry\n",
       "29761                                thnx pilot nick amazing experience grandcanyon afternoon\n",
       "Name: tweet_cleaned, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = X_train['tweet_cleaned']\n",
    "X_test = X_test['tweet_cleaned']\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8d488707",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 500
    },
    "id": "8d488707",
    "outputId": "5cec929c-8526-489c-c8c6-4ac46ea91b9f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_tweets_img = np.array(Image.open('images/all_tweets.png'))\n",
    "\n",
    "all_tweets = ' '.join(X_train.tolist())\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "wordcloud = WordCloud(\n",
    "    stopwords=stop_words,\n",
    "    max_words=100,\n",
    "    mask=all_tweets_img,\n",
    ").generate(all_tweets)\n",
    "\n",
    "plt.title('Najzastupljenije reci u trening podacima')\n",
    "plt.axis('off')\n",
    "plt.imshow(wordcloud)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cfa51286",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 502
    },
    "id": "cfa51286",
    "outputId": "2c0cf34d-ca38-4639-e3cb-36d5d242d272",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_words = FreqDist(\n",
    "    ' '.join(X_train).split()\n",
    ").most_common(10)\n",
    "\n",
    "all_words_data = pd.DataFrame(all_words, columns=['words', 'frequency'])\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "plt.bar(\n",
    "    all_words_data['words'].tolist(),\n",
    "    all_words_data['frequency'].tolist(),\n",
    "    width=0.5,\n",
    "    color='green'\n",
    ")\n",
    "\n",
    "plt.title('Najzastupljenije reci u trening podacima', fontsize=20)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "742b0cde",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 500
    },
    "id": "742b0cde",
    "outputId": "ec19bc90-1319-40fa-a669-707fecd0d9d5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hatred_tweets_img = np.array(Image.open('images/hatred_tweets.png'))\n",
    "hatred_tweets_data = train_data.loc[train_data['label'] == 1]\n",
    "\n",
    "hatred_tweets = ' '.join(hatred_tweets_data['tweet_cleaned'].tolist())\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "wordcloud = WordCloud(\n",
    "    stopwords=stop_words,\n",
    "    max_words=100,\n",
    "    mask=hatred_tweets_img,\n",
    ").generate(hatred_tweets)\n",
    "\n",
    "plt.title('Najzastupljenije reci u tweet-ovima koji sadrze elemente mrznje')\n",
    "plt.axis('off')\n",
    "plt.imshow(wordcloud)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3d1be40f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 506
    },
    "id": "3d1be40f",
    "outputId": "f56d5307-c43f-448c-b61f-d44d7d0e3a2c",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xNTXVOfKqfZ8iRYqk25kSEZk1a5ZyVMaQOkT0t9WlS5dEo9GIu7u73p0SEZFmzZoJANmyZYve8a9Lu4JftWqV3mk6duwoAOSrr77SO/7EiRPpdlyjo6OVo5unTp0yqJb3Kfxoz+LUrVtXGXbz5k2l/vbt2wsAiYqKUsbrO1uU1e903LhxAqSe3dDn7t27YmZmJoUKFdJZH2RH+NGu5/SdbRURad68uQCQfv366QyvXbu23g2wiEhQUJAAkIULF+oMf1v4yZ8/f7odHRGRli1bCpB6RlNf2NLunL2+U/omf/75pwCQ9u3bG/waEd3f37lz55Sd3rRHbrW0VwlYW1vr/Ha0Tp06JUDqkf+0O4jZGX7+97//CZB6pj07aEP/okWLdIZrr1QYN26c3tdpA+Kbws/roelNwsPDJX/+/JIvXz7ZvHmzzrisLk9vog2xv//+u97x2nZu27atznB9657k5GRxd3d/4/p0zpw5AkA5e6ClbavX219EZMOGDcr4bdu2pRv/9ddf693Jzco2Ie1na9Omjd7XNWnSRO+26G3rrZzeNuprm7lz5wqQevZb3wGTYcOG6Q3M2s/i6OiYqTMn2n01FxcX+fvvv/XOM7v2QbK6L5Rb8Z6fbFKmTBn07t0bCQkJmDx5ssGvO3r0KGbPno0hQ4agV69e6NmzJwYPHgxzc3M8fPgQT548yXQtIoKePXti//79CAgIwMqVK/Xe3/D8+XOsW7cOEydORP/+/dGzZ0/07NkT69evBwBcvXpVZ/pq1aohJiYG3bt3R3h4eLZe655V3bt31zu8R48eAIDDhw8jOTk5y/Pftm0bAKBjx456x+fPnx9+fn5ISkrCyZMn041v0KCB3k4wvL29AaTee/BfbN++HSKCpk2bwtbWVu802mt3jx49mun5t23bNt2wlJQU7NixA0DG7eLn54f8+fPjzJkzePnyJQBg3759ePXqFapUqYIqVapkupbcrlatWrCyskJYWJhy7bP23oaGDRsiMDBQZ9jTp08RHh4OBwcH+Pn5KfPJ6nf6tt9q4cKFUapUKTx8+BDXr1//D59UV1JSEo4cOQIAGd4n1KdPHwCpv4G0evXqBQDpnoP28OFDbNu2DRYWFujSpUum6qlSpQqcnZ3TDS9VqhSA1Hu2LC0tMxx/7969dOMSEhKwZcsWTJkyBQMHDlTW1T/88AOA9OtKQ/3555/w9/fHgwcP8PPPP+vddmjvd2zSpAlcXFzSja9SpQoqVaqElJQU5b607Fa5cmXky5cPP/74IxYtWoT79+8b9LpHjx5h1apVGDt2LPr166dsY7R1pm23pKQkHD58GADQtWtXvfPLaH2v5ezsjNq1axtU2+3bt/HRRx/h+fPnWLhwIVq0aKEzPruXp3///RcnTpyAlZVVuvfSysy6+syZM7h37x5KlCiR4fr0bfNr1qxZumHa5cDU1BSNGjXKcHza5SSr24S0MmqTrG4rc3Lb+La28fT0RPny5TMcr28dA6Q+F9LQDn62bduGwYMHw8bGBlu3boWXl5fe6bJrH+S/7gvlNuztLRtNmzYNq1evxpo1azB69GhUrFgxw2kfPHiAtm3bKiv7jMTGxqJAgQKZqmPChAn45ZdfUK5cOWzatAkWFhbpptmyZQt69eqFR48evfG90/ruu+/QvHlz/Pzzz/j555+VGz/r16+Pbt26GaUnuowWeO3w+Ph4PHr0SO/OkCH+/vtvAEC3bt3QrVu3N0778OHDdMMyahM7OzsA0LsRyEp9y5cvx/LlyzNd35s4OzvrXWk+evRI+W14eHi8dT6PHj1C4cKFcfv2bQB5t9c6CwsL+Pv7Y/fu3Th06BAaN26MPXv2QKPRIDAwULnRdc+ePQgODkZoaChSUlJQr149mJj833GorH6n2tcZsvP38OFDlC5d2qDPNWvWLFy5ciXd8K+++goFCxbEo0ePlN9xRsujtnex1ze0HTp0wMcff4w9e/bg7t27KFKkCIDUm7ATExPRsWPHTK//Mlrm8ufP/8bx2h2k15fJY8eOoWPHjspN4Pq8vq40VPPmzZGUlITVq1cjODhY7zTaNsuobYHU9j137tx/Ppjypvl/8803GDNmDIYOHYqhQ4eiaNGiqFGjBpo3b4727dvD3Nxc5zVLly5FSEiI3hu8tdK2myG/oze1AQC9nTvo8+TJEzRt2hRRUVEYP3683sdUZPfyFBERARFBfHy83m3y6/N7G219N2/efGvnLRnNT9+yoF1O3NzcYGqafhdR33KS1W3C22oBsr6tzMltY0Ztk9V1jJahv9/w8HAlhPz22286B89el13t+l/3hXIbhp9s5ObmpnR9PWHCBCUp69O3b18cPnwYNWrUwPTp01GpUiUUKFBAeSaDu7s77t+/n66Xk7f5/vvvMXv2bLi5uWH79u16dxz++ecfdOzYEfHx8Rg7diyCg4NRrFgx5M+fHyYmJti1axcaN26c7r29vb1x9epV7Nq1C6GhoTh69CgOHTqE0NBQfPrpp1i+fHmGR+yyKjvOLmW2DfW9f0ZHXdMqWrRoumFpd2pzgrY+Hx8fVKpU6Y3TVq9ePVPztrKyeuN7Av93hu1N3rahf9dy8oxlYGAgdu/ejd27d6NRo0YIDQ1FhQoVlN+Ol5eXcuZH+1/tGaHX68vsd6p9Xbt27WBjY/PG1zk5ORn8mXbu3Kn3jMK0adP+c5fTNjY26NChA3788UesWrUKEydOBPB/Z4K0Z4Yy423LXGaWyRcvXiAoKAjR0dHo1asXBg0ahJIlS8LOzg758uXDtWvXUKZMmSyvY3r06IHly5dj8uTJqFmz5lt37o1p2LBh6NChAzZv3ozDhw/j8OHD+PXXX/Hrr79i6tSpOHToENzc3ACk7pwNGDAA+fLlw+zZs9GiRQt4enrC2toaGo0GS5YswYABA/7TulmfjNZZaSUkJCAoKAiXL19GcHAwZsyYoXe67F6etPPLnz+/3jPqmaWdn6urKxo3bvzGaTNaTt+0LGRmOcmObUJ2bytzctuYneuYtAz5/d66dQsfffQR4uLisGTJEnz00Uc5Usvr/uu+UG7D8JPNxo0bhyVLlmD79u06XT+nFRcXh+3bt8PExATbt2+Hg4NDuvFRUVGZfu8tW7Zg2LBhsLW1xbZt2zL8AW7ZsgXx8fFo3bo1Zs+enW78m07hm5qaolmzZsrp8tjYWMydOxfTp0/HgAED0Lp167duKNIyMzNDYmIinj17pvfUtPZsQUYiIiL0Dtd24WppaZmpHb3XeXh44MqVK+jTpw/atWuX5fnkFO1Rtlq1amHhwoXv5D0LFiwIKysrxMfHK0f/DaE9AqXvLEJ20h6Bfvbsmd7xb/tN/RdpL207c+YMHj16pLMzEBgYiKVLl+LKlSsZhp+sfqceHh64fv06xo0b98YjgZn1ejfzr3NycoKFhQUSEhLw999/6z3jrT1qqO+hmr169cKPP/6IFStWYOLEiTh9+jTOnz+PIkWKoGHDhtnyGbLq4MGDiI6ORuXKlfHjjz+mG/9fLx9cunQp8ufPj/nz56N27drYs2dPujOj2jbTtqE+b2rf7OTi4oJ+/fqhX79+AFKX5d69eyMsLAzjx4/HypUrAQDr1q2DiGDYsGEYO3Zsuvnoa7e0v6Nbt27hgw8+SDdNZrrm1kdE0KNHDxw8eBD16tXDjz/+mOFZk+xenrTLtUajwY8//vifd0q183Nyckp32ei7ltVtQk4yxrYxpz1+/BhNmzZFdHQ0PvnkE2U5fBdy+75QZvGen2xmb2+vHL3Ut9IHgJiYGCQnJ8POzi5d8AFSL/nI7BGxkydPolOnTtBoNFi3bh18fX0znFbbD72+cCQiWLt2rcHva2dnh2nTpsHBwQEvXrzAtWvXlHHandA3PQtBu7G+fPlyunHnz59/az/8GT07YNWqVQAAf39/vaenDdW0aVMAeOOzj4xJW9/mzZv/8yV0hsqXL5+yU5qZdqlfvz7Mzc0RHh6O06dP51R5OjuLr169Sjf+TWdk38SQ37Ovry+cnJxw/vx5ZTlKuwOvDTrLly/H9evX4eHhke5ymax+p1n9rRryud7E1NQU/v7+ANLfu6OlDQ716tVLN87f3x+lS5fG9evXceTIEfz0008AUo8g5/SZ07fRriszunTkTc+zMoRGo8G8efMwadIk/PPPPwgICMDZs2d1ptHel7Bz505ER0enm8eZM2dw9uxZmJiYICAg4D/Vk1lly5bFuHHjAECn7jdtY16+fKncV5qWqakpatWqBQBYs2aN3vfL7PPbXjd27Fj89ttvKF++PDZu3JjuUr20snvd7+7ujooVK+LZs2fYuXPnf55f1apVUbBgQVy6dAkXL17MhgqzLqvbhP/ibestY2wbc1JCQgJatWqFK1euoHv37vj888/f6fvn9n2hzGL4yQFDhgyBp6cnjh8/rvchYy4uLihQoACePn2abmV+7NgxTJgwIVPv9/fff6N58+Z48eIFlixZ8tZT4Nob3X7//XedG1eTk5MxZcoUvTf/vXjxAnPnztV7LeehQ4eUhzxqr9kHoPz/m1bM2p3B6dOnIyEhQRl+69Yt9OjR460hMDw8HHPmzNEZdvjwYSxatAgAdB6OmBX9+/dH0aJFsW7dOowbN07v2YSoqCgsXbr0P71PVvn6+qJt27aIjIxEmzZt9B4ZjYuLw5o1a/TuOGXV1KlTYW5ujjFjxmDlypV6LyX766+/sGHDBuXfzs7OGDRoEACgffv2+Ouvv3SmFxGEhoYiJibmP9VWtGhRlCpVCk+fPk13ZnP//v2YMmVKluZryO9Zo9Ggfv36EBEsWrQI5ubmOjukDRo0gEajUY5Evn7WB8j6dzpmzBg4ODhg7ty5+Prrr/UGv4iIiHQ77IZ8rrcZNWoUgNTLbvfu3aszbsWKFdi8eTPMzMwwfPhwva/XXt62ePFiJTRm5SGr2U27rty7dy8uXbqkM27JkiX47bffsuV9PvvsM8yZMwcPHz5EvXr1dLYb/v7+qF69OuLj4zFgwACdBwn++++/GDBgAACgU6dOBt1vkRWhoaHYvn07EhMTdYaLCLZu3QpAN+ho223lypU668yXL19i8ODBGZ6xHzFiBADg22+/TbcdmjNnzn86aLJw4UJ89dVXKFy4MHbs2PHWG8uzujy9iXaHtVevXtiyZUu68SKC48ePY9euXW+dl5mZGaZOnQoRQevWrfXeP5ycnIzQ0NA3Pkg4u2Rlm/BfvG29ZaxtY04QEXTr1g2HDx9GYGAgli1b9s5ryO37QpnFy95ygIWFBT799FP07NlT7xNv8+XLhylTpiAkJATdu3fHokWLULx4cdy5cwdHjx5F165dcfDgQYMvz/niiy/w4MEDFCpUCAcOHMiwxx/t6egWLVqgSpUqCA8PR+nSpVGnTh3Y2Njg+PHjuHfvHsaNG5dup/HVq1cYNWoUxowZgwoVKqBUqVIwMzPDrVu3lBXrJ598gkKFCimvady4MWxsbLBp0yb4+/ujVKlSyJcvH2rVqqXs7EycOBG///47tm/fjtKlS6Nq1ap4+PAhTp48iVq1aqFmzZpv7Inl448/xoQJE7Bq1SpUrFgR9+7dw6FDh5CSkoLhw4fr7c0mM2xsbLBt2zY0b94cc+bMwZIlS1CxYkUUKVJEOdN1+fJlODs7v9NT0Gn99NNPePr0KXbs2IEyZcqgUqVK8PLygojg1q1bOHfuHF69eoXLly+/9VpdQ1WuXBmrV69Wem+aNGkSypUrh0KFCuHx48e4cOEC7t69i44dO6JNmzbK6+bMmYOIiAhs3rwZlSpVQvXq1eHl5YV///0XFy9exD///IOIiAiDe7zJyKxZs9CuXTtMmTIFGzZsQKlSpfD333/j9OnTmDx5Mj799NNMz7Nt27b46aefMHbsWOzZswfOzs7QaDTo3bs3atasqUwXGBiIdevW4eXLl6hXr55OpxFOTk7w8fHBmTNnlGn1ycp3WqRIEfzxxx9o27YtRo8ejTlz5qB8+fJwc3NDTEwMLl++jJs3b6J69eo69+YZspy+TdOmTTFp0iR8/vnnaNiwIWrVqgVPT09cuXIFp0+fRr58+bB48WK9lzIBqb14TZo0SdmRDAgIQMmSJQ1675zk6+uLVq1a4Y8//oCvr6/ylPizZ8/i6tWrmDhxIr744otsea8xY8bA1tYWgwcPRsOGDbF582bUr18fALB27VrUr18ff/zxB7y8vBAQEIDExETs27cPsbGxqFy5co5e2nP+/HmEhITAzs4OlStXhru7O+Lj43H69Gncvn0b9vb2OstUr169MH/+fJw5cwZeXl6oXbs28uXLh0OHDiE+Ph7Dhw/H/Pnz071PixYtMGTIECxatAi1a9dGQEAA3NzccP78eVy+fDnD1xlCG7w9PT0xadIkvdOULVsW48ePB5D15elNWrRogfnz52PUqFFo2bIlSpYsiTJlysDe3h4PHz7EuXPn8ODBA4wbN05vb2KvGzp0KO7cuYMvv/wStWvXxgcffICSJUvCysoKUVFROHv2LJ4+fYrvv/8eH374oYEtlTVZ3SZklSHrLWNsG3PC4cOHsW7dOgCpnSZktK8RFBSEoKCgHKnhfdgXypR316t23vL6c35el5ycrDw3Ahk852fTpk1Ss2ZNcXBwkPz584ufn5989913kpKSkuFzGLR94qd9CFqPHj2U93nTX9p5PXv2TCZOnChlypQRS0tLcXZ2lqCgIDl16pTeft4TExNl8eLF0rlzZylbtqzY29uLlZWVlChRQtq2bSt79+7V2w4HDx6UwMBAKVCggPJU69ef2XLp0iVp06aNFChQQCwsLKRMmTLy+eefy6tXr976nJ99+/bJ3r17pUGDBkpNfn5+smLFCr31vO05P2mf0ZJWbGyszJkzR2rUqCEODg5iZmYmbm5uUrVqVRkzZowcPXrUoPd5/f0yel5FZh5yKpL6e1u7dq00a9ZMXFxcxMzMTJycnKR8+fLSq1cv2bhxo8FPIn/T80H0TRsSEiLly5cXGxsbsbS0lKJFi0rdunVl1qxZcuPGjXSvSUlJkbVr10qjRo3EyclJzMzMxNXVVWrXri1ffvmlzjNYsvKcH61t27ZJrVq1xNraWmxsbOTDDz+U3377TUSy9pwfEZGlS5dK5cqVxdraOsNlW/tsHyD9w0tFRMaMGaM8C0Lfc1u0svqdRkdHy+TJk6Vy5crKQw6LFCkiNWvWlKlTp8r58+fTvcaQ5dQQO3bskGbNmomTk5OYmpqKq6urtG/fPt0Dk/XRPnMjo/Wl1tue85NR3W9bJrXr0dff+9WrV/Lll19KhQoVxNraWhwdHaVRo0aya9euTC0rab3p97d69WoxNTUVS0tLneePPHr0SCZMmCDe3t5iaWkp1tbW4uvrK7NmzZIXL16km4+2tmLFiqUbl9nn/Ny4cUOmTZsmDRo0EE9PT7G0tJQCBQpIxYoVZfz48ekeQCmS+kDMwYMHS4kSJcTCwkLc3d2la9eucv369bd+Vz/++KNUqVJFLC0txd7eXgIDA2Xfvn1ZWm9qGbKN1Pf6rCxPb3PhwgXp37+/lCpVSvkuixcvLo0bN5YFCxbIP//8ozP9254xduTIEQkODpaiRYuKhYWF2NraSunSpSUoKEiWLVuW7rkrb/r9ve03/ba2zuw24W2f7U3LrSHrrXe5bXxb22T0uzd0f+Ftf2lf/1/3QbJrXyi30ohkc3crlKOqVauGkydP4sSJE6hataqxyzGaunXr4sCBA9i3b59yTfx/sX37dnz00Udo1qxZlu8JyUkvXryAjY0NrK2t39h1LBERAFy6dAkffPABypUrZ/R7Qojo/ZDb94WyC+/5eY/ExMTgypUrMDExUR6WRdlDe+lemTJljFyJfrm9PiLKXbjOIKLMUst6g/f8vAeuXLmCadOm4ciRI3j27BnatGmjt5c4yrxZs2Zhz549CA0NRb58+d76BPF3bdOmTVi5ciV2794NIGvPPSEi9Vi2bBk2bdqk3DTPdQYRvU1u3xfKbjzz8x6IiorC//73PyQmJqJv3756nzdBWbNz504cOXIEVapUwYYNG+Dj42PsknScPXsWW7duhaurK2bNmoWhQ4cauyQiysUOHz6M3bt3o3Tp0li2bBlatGhh7JKIKJfL7ftC2Y33/BARERERkSrwzA8REREREakCww8REREREanCe9nhQUpKCu7duwdbW1toNBpjl0NEREREREYiInj27Bnc3d1hYvLmczvvZfi5d+8ePDw8jF0GERERERHlEpGRkShSpMgbp3kvw4+trS2A1A9oZ2dn5GqIiIiIiMhYYmNj4eHhoWSEN3kvw4/2Ujc7OzuGHyIiIiIiMuh2GHZ4QEREREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqmBq7ALyBI3G2BXoJ2LsCoiIiIiIcg2e+SEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlXIdPg5ePAgWrRoAXd3d2g0GmzatCndNJcvX0bLli1hb28PGxsbVK1aFXfu3FHGv3z5EkOGDIGTkxPy58+Ptm3bIjo6+j99ECIiIiIiojfJdPiJi4tDpUqVsGjRIr3jb968CX9/f5QtWxb79+/H+fPnMXnyZFhaWirThISEYMuWLVi3bh0OHDiAe/fuoU2bNln/FERERERERG+hEcl6f8gajQYbN25EUFCQMqxTp04wMzPDzz//rPc1MTExKFSoENauXYt27doBAK5cuQJvb2+EhYXhww8/fOv7xsbGwt7eHjExMbCzs8tq+dmHXV0TERERERlFZrJBtt7zk5KSgm3btqF06dJo3LgxnJ2dUb16dZ1L48LDw5GYmIjAwEBlWNmyZeHp6YmwsDC9801ISEBsbKzOHxERERERUWZka/h58OABnj9/jlmzZqFJkybYtWsXWrdujTZt2uDAgQMAgKioKJibm8PBwUHntS4uLoiKitI735kzZ8Le3l758/DwyM6yiYiIiIhIBbL9zA8AtGrVCiEhIfDx8cH48ePRvHlzLF68OMvznTBhAmJiYpS/yMjI7CqZiIiIiIhUwjQ7Z1awYEGYmpqiXLlyOsO9vb1x+PBhAICrqytevXqFp0+f6pz9iY6Ohqurq975WlhYwMLCIjtLJSIiIiIilcnWMz/m5uaoWrUqrl69qjP82rVrKFq0KACgSpUqMDMzw969e5XxV69exZ07d1CjRo3sLIeIiIiIiEiR6TM/z58/x40bN5R/R0RE4OzZs3B0dISnpyfGjBmDjh07IiAgAPXq1cPOnTuxZcsW7N+/HwBgb2+PPn36YOTIkXB0dISdnR2GDRuGGjVqGNTTGxERERERUVZkuqvr/fv3o169eumG9+jRAytWrAAA/Pjjj5g5cybu3r2LMmXKYPr06WjVqpUy7cuXLzFq1Cj88ssvSEhIQOPGjfHdd99leNnb69jVtYHY1TURERER5XGZyQb/6Tk/xsLwY6D376slIiIiIsoUoz3nh4iIiIiIKLdi+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlXIdPg5ePAgWrRoAXd3d2g0GmzatCnDaQcOHAiNRoN58+bpDH/8+DGCg4NhZ2cHBwcH9OnTB8+fP89sKURERERERAbLdPiJi4tDpUqVsGjRojdOt3HjRhw7dgzu7u7pxgUHB+PixYvYvXs3tm7dioMHD6J///6ZLYWIiIiIiMhgppl9QdOmTdG0adM3TvPPP/9g2LBh+PPPP/HRRx/pjLt8+TJ27tyJkydPws/PDwDw7bffolmzZvjqq6/0hiUiIiIiIqL/Ktvv+UlJSUG3bt0wZswYfPDBB+nGh4WFwcHBQQk+ABAYGAgTExMcP35c7zwTEhIQGxur80dERERERJQZ2R5+Zs+eDVNTU3z88cd6x0dFRcHZ2VlnmKmpKRwdHREVFaX3NTNnzoS9vb3y5+Hhkd1lExERERFRHpet4Sc8PBzz58/HihUroNFosm2+EyZMQExMjPIXGRmZbfMmIiIiIiJ1yNbwc+jQITx48ACenp4wNTWFqakpbt++jVGjRqFYsWIAAFdXVzx48EDndUlJSXj8+DFcXV31ztfCwgJ2dnY6f0RERERERJmR6Q4P3qRbt24IDAzUGda4cWN069YNvXr1AgDUqFEDT58+RXh4OKpUqQIACA0NRUpKCqpXr56d5RARERERESkyHX6eP3+OGzduKP+OiIjA2bNn4ejoCE9PTzg5OelMb2ZmBldXV5QpUwYA4O3tjSZNmqBfv35YvHgxEhMTMXToUHTq1Ik9vRERERERUY7J9GVvp06dgq+vL3x9fQEAI0eOhK+vL6ZMmWLwPNasWYOyZcuiQYMGaNasGfz9/bFkyZLMlkJERERERGQwjYiIsYvIrNjYWNjb2yMmJiZ33P+TjZ07ZKv376slIiIiIsqUzGSDbO/qmoiIiIiIKDdi+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlUwNXYBpDIajbErSE/E2BUQERER0TvAMz9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKDD9ERERERKQKmQ4/Bw8eRIsWLeDu7g6NRoNNmzYp4xITEzFu3DhUqFABNjY2cHd3R/fu3XHv3j2deTx+/BjBwcGws7ODg4MD+vTpg+fPn//nD0NERERERJSRTIefuLg4VKpUCYsWLUo37sWLFzh9+jQmT56M06dPY8OGDbh69SpatmypM11wcDAuXryI3bt3Y+vWrTh48CD69++f9U9BRERERET0FhoRkSy/WKPBxo0bERQUlOE0J0+eRLVq1XD79m14enri8uXLKFeuHE6ePAk/Pz8AwM6dO9GsWTPcvXsX7u7ub33f2NhY2NvbIyYmBnZ2dlktP/toNMauQL+sf7U5Jze2VW5sJyIiIiIySGayQY7f8xMTEwONRgMHBwcAQFhYGBwcHJTgAwCBgYEwMTHB8ePH9c4jISEBsbGxOn9ERERERESZkaPh5+XLlxg3bhw6d+6spLCoqCg4OzvrTGdqagpHR0dERUXpnc/MmTNhb2+v/Hl4eORk2URERERElAflWPhJTExEhw4dICL4/vvv/9O8JkyYgJiYGOUvMjIym6okIiIiIiK1MM2JmWqDz+3btxEaGqpz7Z2rqysePHigM31SUhIeP34MV1dXvfOzsLCAhYVFTpRKREREREQqke1nfrTB5/r169izZw+cnJx0xteoUQNPnz5FeHi4Miw0NBQpKSmoXr16dpdDREREREQEIAtnfp4/f44bN24o/46IiMDZs2fh6OgINzc3tGvXDqdPn8bWrVuRnJys3Mfj6OgIc3NzeHt7o0mTJujXrx8WL16MxMREDB06FJ06dTKopzciIiIiIqKsyHRX1/v370e9evXSDe/RowemTZsGLy8vva/bt28f6tatCyD1IadDhw7Fli1bYGJigrZt22LBggXInz+/QTWwq2sD5cYunHNjW+XGdiIiIiIig2QmG/yn5/wYC8OPgXLjV5sb2yo3thMRERERGSRXPeeHiIiIiIgoN2D4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVWD4ISIiIiIiVch0+Dl48CBatGgBd3d3aDQabNq0SWe8iGDKlClwc3ODlZUVAgMDcf36dZ1pHj9+jODgYNjZ2cHBwQF9+vTB8+fP/9MHIcpTNJrc90dERET0nst0+ImLi0OlSpWwaNEivePnzJmDBQsWYPHixTh+/DhsbGzQuHFjvHz5UpkmODgYFy9exO7du7F161YcPHgQ/fv3z/qnICIiIiIieguNiEiWX6zRYOPGjQgKCgKQetbH3d0do0aNwujRowEAMTExcHFxwYoVK9CpUydcvnwZ5cqVw8mTJ+Hn5wcA2LlzJ5o1a4a7d+/C3d39re8bGxsLe3t7xMTEwM7OLqvlZ5/celQ8619tzsmNbcV2MkxubCciIiJSvcxkg2y95yciIgJRUVEIDAxUhtnb26N69eoICwsDAISFhcHBwUEJPgAQGBgIExMTHD9+XO98ExISEBsbq/NHRERERESUGdkafqKiogAALi4uOsNdXFyUcVFRUXB2dtYZb2pqCkdHR2Wa182cORP29vbKn4eHR3aWTUREREREKvBe9PY2YcIExMTEKH+RkZHGLomIiIiIiN4z2Rp+XF1dAQDR0dE6w6Ojo5Vxrq6uePDggc74pKQkPH78WJnmdRYWFrCzs9P5IyIiIiIiyoxsDT9eXl5wdXXF3r17lWGxsbE4fvw4atSoAQCoUaMGnj59ivDwcGWa0NBQpKSkoHr16tlZDhERERERkcI0sy94/vw5bty4ofw7IiICZ8+ehaOjIzw9PTFixAh8/vnnKFWqFLy8vDB58mS4u7srPcJ5e3ujSZMm6NevHxYvXozExEQMHToUnTp1MqinNyIiIiIioqzIdPg5deoU6tWrp/x75MiRAIAePXpgxYoVGDt2LOLi4tC/f388ffoU/v7+2LlzJywtLZXXrFmzBkOHDkWDBg1gYmKCtm3bYsGCBdnwcYiIiIiIiPT7T8/5MRY+58dAufGrzY1txXYyTG5sJyIiIlI9oz3nh4iIiIiIKLdi+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlUwNXYBRERZptEYu4L0RIxdAREREWWAZ36IiIiIiEgVeOaHiCiv4xkyIiIiADzzQ0REREREKsHwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqsDwQ0REREREqpDt4Sc5ORmTJ0+Gl5cXrKysUKJECXz22WcQEWUaEcGUKVPg5uYGKysrBAYG4vr169ldChERERERkSLbw8/s2bPx/fffY+HChbh8+TJmz56NOXPm4Ntvv1WmmTNnDhYsWIDFixfj+PHjsLGxQePGjfHy5cvsLoeIiIiIiAgAoJG0p2SyQfPmzeHi4oLly5crw9q2bQsrKyusXr0aIgJ3d3eMGjUKo0ePBgDExMTAxcUFK1asQKdOnd76HrGxsbC3t0dMTAzs7Oyys/ys0WiMXYF+2fvVZo/c2FZsJ8OwnQzDdjJMbmwnIiJ6L2UmG2T7mZ+aNWti7969uHbtGgDg3LlzOHz4MJo2bQoAiIiIQFRUFAIDA5XX2Nvbo3r16ggLC9M7z4SEBMTGxur8ERERERERZYZpds9w/PjxiI2NRdmyZZEvXz4kJyfjiy++QHBwMAAgKioKAODi4qLzOhcXF2Xc62bOnInp06dnd6lERERERKQi2X7m53//+x/WrFmDtWvX4vTp01i5ciW++uorrFy5MsvznDBhAmJiYpS/yMjIbKyYiIiIiIjUINvP/IwZMwbjx49X7t2pUKECbt++jZkzZ6JHjx5wdXUFAERHR8PNzU15XXR0NHx8fPTO08LCAhYWFtldKhERERERqUi2n/l58eIFTEx0Z5svXz6kpKQAALy8vODq6oq9e/cq42NjY3H8+HHUqFEju8shIiIiIiICkANnflq0aIEvvvgCnp6e+OCDD3DmzBnMnTsXvXv3BgBoNBqMGDECn3/+OUqVKgUvLy9MnjwZ7u7uCAoKyu5yiIiIiIiIAORA+Pn2228xefJkDB48GA8ePIC7uzsGDBiAKVOmKNOMHTsWcXFx6N+/P54+fQp/f3/s3LkTlpaW2V0OERERERERgBx4zs+7wOf8GCg3frW5sa3YToZhOxmG7WSY3NhORET0XjLqc36IiIiIiIhyI4YfIiIiIiJSBYYfIiIiIiJSBYYfIiIiIiJSBYYfIiIiIiJSBYYfIiIiIiJSBYYfIiIiIiJShWx/yCkREdF7ic9DIiLK83jmh4iIiIiIVIHhh4iIiIiIVIHhh4iIiIiIVIH3/BAREZHhcuO9UQDvjyIig/DMDxERERERqQLDDxERERERqQLDDxERERERqQLDDxERERERqQLDDxERERERqQLDDxERERERqQLDDxERERERqQLDDxERERERqQLDDxERERERqQLDDxERERERqYKpsQsgIiIiypM0GmNXkJ6IsSsgMiqe+SEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIiIlXIkfDzzz//oGvXrnBycoKVlRUqVKiAU6dOKeNFBFOmTIGbmxusrKwQGBiI69ev50QpREREREREAHIg/Dx58gS1atWCmZkZduzYgUuXLuHrr79GgQIFlGnmzJmDBQsWYPHixTh+/DhsbGzQuHFjvHz5MrvLISIiIiIiAgBoRESyc4bjx4/HkSNHcOjQIb3jRQTu7u4YNWoURo8eDQCIiYmBi4sLVqxYgU6dOr31PWJjY2Fvb4+YmBjY2dllZ/lZo9EYuwL9sverzR65sa3YToZhOxmG7WQYtpNh2E6GY1sZJje2E9F/lJlskO1nfjZv3gw/Pz+0b98ezs7O8PX1xdKlS5XxERERiIqKQmBgoDLM3t4e1atXR1hYmN55JiQkIDY2VuePiIiIiIgoM7I9/Pz999/4/vvvUapUKfz5558YNGgQPv74Y6xcuRIAEBUVBQBwcXHReZ2Li4sy7nUzZ86Evb298ufh4ZHdZRMRERGRMWg0ue+P8qxsDz8pKSmoXLkyZsyYAV9fX/Tv3x/9+vXD4sWLszzPCRMmICYmRvmLjIzMxoqJiIiIiEgNsj38uLm5oVy5cjrDvL29cefOHQCAq6srACA6OlpnmujoaGXc6ywsLGBnZ6fzR0RERERElBnZHn5q1aqFq1ev6gy7du0aihYtCgDw8vKCq6sr9u7dq4yPjY3F8ePHUaNGjewuh4iIiIiICABgmt0zDAkJQc2aNTFjxgx06NABJ06cwJIlS7BkyRIAgEajwYgRI/D555+jVKlS8PLywuTJk+Hu7o6goKDsLoeIiIiI6P2XG+9Feg97D8z28FO1alVs3LgREyZMwKeffgovLy/MmzcPwcHByjRjx45FXFwc+vfvj6dPn8Lf3x87d+6EpaVldpdDREREREQEIAee8/Mu8Dk/BsqNX21ubCu2k2HYToZhOxmG7WQYtpPh2FaGYTsZhu1kmFzSTkZ9zg8REREREVFuxPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqwPBDRERERESqkOPhZ9asWdBoNBgxYoQy7OXLlxgyZAicnJyQP39+tG3bFtHR0TldChERERERqViOhp+TJ0/ihx9+QMWKFXWGh4SEYMuWLVi3bh0OHDiAe/fuoU2bNjlZChERERERqVyOhZ/nz58jODgYS5cuRYECBZThMTExWL58OebOnYv69eujSpUq+Omnn3D06FEcO3Ysp8ohIiIiIiKVy7HwM2TIEHz00UcIDAzUGR4eHo7ExESd4WXLloWnpyfCwsJyqhwiIiIiIlI505yY6a+//orTp0/j5MmT6cZFRUXB3NwcDg4OOsNdXFwQFRWld34JCQlISEhQ/h0bG5ut9RIRERERUd6X7Wd+IiMjMXz4cKxZswaWlpbZMs+ZM2fC3t5e+fPw8MiW+RIRERERkXpke/gJDw/HgwcPULlyZZiamsLU1BQHDhzAggULYGpqChcXF7x69QpPnz7VeV10dDRcXV31znPChAmIiYlR/iIjI7O7bCIiIiIiyuOy/bK3Bg0a4MKFCzrDevXqhbJly2LcuHHw8PCAmZkZ9u7di7Zt2wIArl69ijt37qBGjRp652lhYQELC4vsLpWIiIiIiFQk28OPra0typcvrzPMxsYGTk5OyvA+ffpg5MiRcHR0hJ2dHYYNG4YaNWrgww8/zO5yiIiIiIiIAORQhwdv880338DExARt27ZFQkICGjdujO+++84YpRARERERkUpoRESMXURmxcbGwt7eHjExMbCzszN2OYBGY+wK9MuNX21ubCu2k2HYToZhOxmG7WQYtpPh2FaGYTsZhu1kmFzSTpnJBjn2nB8iIiIiIqLchOGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUgeGHiIiIiIhUIdvDz8yZM1G1alXY2trC2dkZQUFBuHr1qs40L1++xJAhQ+Dk5IT8+fOjbdu2iI6Ozu5SiIiIiIiIFNkefg4cOIAhQ4bg2LFj2L17NxITE9GoUSPExcUp04SEhGDLli1Yt24dDhw4gHv37qFNmzbZXQoREREREZFCIyKSk2/w8OFDODs748CBAwgICEBMTAwKFSqEtWvXol27dgCAK1euwNvbG2FhYfjwww/fOs/Y2FjY29sjJiYGdnZ2OVm+YTQaY1egX85+tVmTG9uK7WQYtpNh2E6GYTsZhu1kOLaVYdhOhmE7GSaXtFNmskGO3/MTExMDAHB0dAQAhIeHIzExEYGBgco0ZcuWhaenJ8LCwvTOIyEhAbGxsTp/REREREREmZGj4SclJQUjRoxArVq1UL58eQBAVFQUzM3N4eDgoDOti4sLoqKi9M5n5syZsLe3V/48PDxysmwiIiIiIsqDcjT8DBkyBH/99Rd+/fXX/zSfCRMmICYmRvmLjIzMpgqJiIiIiEgtTHNqxkOHDsXWrVtx8OBBFClSRBnu6uqKV69e4enTpzpnf6Kjo+Hq6qp3XhYWFrCwsMipUomIiIiISAWy/cyPiGDo0KHYuHEjQkND4eXlpTO+SpUqMDMzw969e5VhV69exZ07d1CjRo3sLoeIiIiIiAhADpz5GTJkCNauXYs//vgDtra2yn089vb2sLKygr29Pfr06YORI0fC0dERdnZ2GDZsGGrUqGFQT29ERERERERZke3h5/vvvwcA1K1bV2f4Tz/9hJ49ewIAvvnmG5iYmKBt27ZISEhA48aN8d1332V3KURERERERIocf85PTuBzfgyUG7/a3NhWbCfDsJ0Mw3YyDNvJMGwnw7GtDMN2MgzbyTC5pJ1y1XN+iIiIiIiIcgOGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWGHyIiIiIiUgWjhp9FixahWLFisLS0RPXq1XHixAljlkNERERERHmY0cLPb7/9hpEjR2Lq1Kk4ffo0KlWqhMaNG+PBgwfGKomIiIiIiPIwo4WfuXPnol+/fujVqxfKlSuHxYsXw9raGj/++KOxSiIiIiIiojzM1Bhv+urVK4SHh2PChAnKMBMTEwQGBiIsLCzd9AkJCUhISFD+HRMTAwCIjY3N+WLfZ2wfw7CdDMN2MgzbyTBsJ8OwnQzHtjIM28kwbCfD5JJ20mYCEXnrtEYJP//++y+Sk5Ph4uKiM9zFxQVXrlxJN/3MmTMxffr0dMM9PDxyrMY8wd7e2BW8H9hOhmE7GYbtZBi2k2HYToZjWxmG7WQYtpNhclk7PXv2DPZvqcko4SezJkyYgJEjRyr/TklJwePHj+Hk5ASNRmPEyrJXbGwsPDw8EBkZCTs7O2OXk2uxnQzDdjIM28lwbCvDsJ0Mw3YyDNvJMGwnw+TVdhIRPHv2DO7u7m+d1ijhp2DBgsiXLx+io6N1hkdHR8PV1TXd9BYWFrCwsNAZ5uDgkJMlGpWdnV2e+kHmFLaTYdhOhmE7GY5tZRi2k2HYToZhOxmG7WSYvNhObzvjo2WUDg/Mzc1RpUoV7N27VxmWkpKCvXv3okaNGsYoiYiIiIiI8jijXfY2cuRI9OjRA35+fqhWrRrmzZuHuLg49OrVy1glERERERFRHma08NOxY0c8fPgQU6ZMQVRUFHx8fLBz5850nSCoiYWFBaZOnZruEj/SxXYyDNvJMGwnw7GtDMN2MgzbyTBsJ8OwnQzDdgI0YkifcERERERERO85oz3klIiIiIiI6F1i+CEiIiIiIlVg+CEiIiIiIlVg+CEiIiIi1eLt7+rC8ENEREREqnX79m1jl0DvEMMPEREREanSH3/8AX9/f+zatcvYpdA7wvDznuApWcOxrQzHtqLsxN9TqhcvXhi7BFKBlJQUY5eQJ9jb26NWrVoYN24cdu/ebexy6B1g+HlPaDQanX9zJ0O/lJQUnbZKTk42YjW5l/b38/rvigz3+m9LzcukdidMo9EgNDQU+/btM3JFxnP48GGMHj0aFy9eNHYp7x01L0NZYWJign/++Qd79+4FAPz666+YNm2acYt6j/zyyy8AgLp162LUqFHw9vbGyJEjGYBUgOHnPbBgwQJ07NgR/fv3x/fffw+AO60ZMTFJ/Ul/9dVX6Ny5MwIDA/Hzzz/j1q1bxi0sFxERaDQa7N+/HyEhIejevTvmzp2Lly9fGru090ZoaChCQkIwadIkHDt2DEDqMqm2nbeTJ08iISEBJiYmSEpKwsuXL9G/f388e/bM2KUZzfXr17F582YsWbIEly9fNnY57wXtcvP48WPExcXh33//1RlO6YkI4uPjMWDAAHz11VeYMmUKunTpAk9PT2OX9l44efIkJk+erNzrU61aNQwePBgVKlRQXQDSLmfXrl3DsWPHcPz4cTx58sTIVeUwoVxt2rRpYmtrK7169ZLWrVuLlZWVdOnSRRISEkREJCUlxcgV5g7JycnK/0+aNEkcHR1l0KBB0rlzZylQoID07t1bTp06ZcQKc5cNGzaIvb29dO/eXT799FPJly+fdOrUSR48eGDs0nK9P//8U/Llyyft2rUTZ2dnCQgIkAULFijj1bJMbt26VUqXLi1z585V1kfPnj2TYsWKyaFDh4xcnXGtXLlSypQpI0OGDJFLly4Zu5xcTbu8bNmyRerVqye+vr7i4+Mjv/76q5Erez/89ddfUr58edFoNDJmzBhluFrWQ1n1/Plzefz4sYiInD17Vhl+6NAh6dy5s5QvX1527dplrPLeGe3vZP369VKkSBGpWrWquLm5SVBQkGzcuNG4xeUghp9c7Pjx49KzZ08JDQ0VEZGkpCTZv3+/FCxYUHr37m3k6nKnf/75R0JCQmT//v3KsA0bNoiPj48MGjRIYmNjVb9RuHPnjnh7eys77M+ePRNHR0cZMWKEkSvL/SIjIyUkJES+//57ERGJjo6Wnj17Sq1atWTevHnKdGr4jT1//ly6desmNWvWlHnz5snLly9FRKRChQpy/fp1ERF59eqV0hZpD1DkNTdv3pR//vlHZ9hPP/0kZcuWlUGDBsm1a9eMVNn7YevWrWJpaSnffPONHDp0SD7++GPRaDRy4sQJY5eWa2mXq9jYWPHx8ZHSpUtLmzZtZMeOHco0eXmZyy53796VQoUKSadOnZRhagtAR44ckQIFCsh3330nIiLr1q0TExMTWbhwoZEryzkMP7nU+vXrxdfXV0qUKKHsSKQ9Qubg4CB79uwxZom5zrp160Sj0UjhwoXl8OHD6cZZWFjIyZMnjVSd8by+I37t2jWpUqWKiIhERESIu7u79O/fXxl//Pjxd1rf++LUqVPSrFkz8fX11Tmzcf/+fenVq5fUrFlT5wxQXpaUlCQiInFxcdKzZ0+pXr26zJ8/X+7cuSM+Pj5y8+bNdK/Jq4Hw8ePH4ubmJhMnTpR79+7pjFu2bJmYmZnJsGHD5Pz580aqMHdLTEyUTp06yfTp00VE5Pbt21KiRAmddZJI3v39ZIW2La5evSrJyckSHx8vp0+fltq1a0uLFi10AhC9WWxsrCxdulSKFCkivXr1UoZrA5Cvr69s27bNiBXmHG04njVrlrRp00ZEUvcJihcvLgMGDFCmi4qKMkp9OYn3/ORSdnZ2cHNzw+3bt3H06FEA/3efT4UKFWBra4tHjx4Zs8Rcp1q1aujZsyfu3buHe/fuAQASExMBAO3atYOnpyfCwsKMWeI7o70B/dWrV8rv5v79+wBS74t6/Pgx1q9fjwYNGqB58+ZYtGgRAODChQsYP348zp49a5S6czM7OzvEx8fjypUrOr8jV1dXzJo1Cx988AEWL16MxYsXG7HKdyNfvnwAAGtrayxcuBDlypXDunXrsGjRIty6dQufffYZPv74Y4wfPx7jx4/HoEGDMH/+/Dx5D0eBAgWwdu1arF27FkuXLlXWPQDQp08fVKxYEatWrcLq1avx6tUrI1aae6T9HSQmJuL06dOoWrUqYmJiULNmTTRo0EBZjn744QdcvnyZ97n+f/L/79n8448/0LRpU8yePRsmJibw9fXF7Nmz8fTpUyxevBg7duwAAEyePBmfffaZkavOPV5fB9na2qJTp074/PPPsW3bNvTu3RsA4O/vjyFDhqBw4cL44osv3vseHNP2DKjdL0pISACQ2jtluXLl8OLFC/j7+6Nhw4bK/eVbtmzB1q1b8949wcbNXvQmR48elWbNmomfn59s2LBBGf7s2TMpWbKk/PTTT8YrzsgyOp1/9+5dadeundjb28uxY8eU4Y8ePRIvLy9VtdmtW7dkzJgxEhsbK7///rvY2dnJrVu3JD4+Xrp16yb58+eXoKAgnddMmDBBatSoIffv3zdS1blbRESEfPTRR1KrVi355ZdfdMbdv39fhg4dKhEREcYp7h3RHnUOCwuTP//8U0RSzwD16NFDSpYsKZ6entKyZUvp3r27BAcHS+vWraVp06Zy8eJFY5ad4w4dOiRFihSRTz/9VDkDFBcXJwMHDpQZM2bI33//beQKc5ddu3Yp6+jBgwfLoEGDpHDhwjJw4EBJTEwUkdSj8p07d5YFCxao/hKutGe+/vjjD7G0tJTvv/8+3VnWo0ePSoMGDcTHx0fq1asnFhYWPJv//2nb8PDhw/LVV1/J8OHD5dChQ/Ls2TNJSUmRFStWiLOzs84ZoLCwMLl7966xSs5WEREREh0dLSIiGzdulE8//VREUs9QW1hYSMGCBWXkyJHKmX0Rkd69e0u/fv3kxYsXRqk5pzD85CJr1qyR7777TtauXasM27dvn7Rq1UqKFSsmU6ZMkYULF0rLli2lTJkyOj9QNUm7EVi9erV89dVXOm0WFRUlQUFBYmtrKxMmTJD58+fLRx99JBUqVFA2qmqwZMkSKV26tDRr1kwsLCxk1apVyrjt27dLlSpVpFmzZrJx40bZtWuXjBgxQuzt7eXcuXNGrDp3uXv3rly4cEH+/fdfZeV/9epVadq0qdSvXz9dAMrrO2jaZe/333+XQoUKyZAhQ+TWrVsiIvLixQvp06eP1K5dW+bPn6/Ky5QOHTokxYoVk6FDh8ratWvlk08+kXLlyklMTIyxSzOqq1evKp1iJCcny5MnT6RcuXLK/RTz58+XQoUKSUBAgDx58kREUn9rEyZMkBIlSqg6OO7atUtiY2OVfz9+/Fjq168vX375pYiIxMfHy4MHD2Tp0qVKpz7nz5+Xr776Sj7++GN2uPGa33//XWxsbKR+/fri6+srtra2MmLECLl586YSgIoUKSLt2rUzdqnZ6sWLF9KsWTMpUqSILF26VDQajaxZs0YZ369fP7G0tFQ6f4iJiZHx48eLi4uLXL582Vhl5xiGn1xi/Pjx4ujoKN7e3lK6dGlp1aqVMu7gwYPStGlTsbKyksDAQFm0aJESfNQagEREJk+eLNbW1hIQECAajUa6d+8ukZGRIpIagDp37iwajUa6dOkiq1atkvj4eBGRPB2AvvnmG52zW8OGDRONRiMNGjRId93uunXrpGPHjmJjYyOVKlWSgIAABp80Nm7cKOXKlRN3d3epXLmyzj0dV65ckaZNm0qjRo1kxYoVRq703Tp06JDY2trKTz/9JM+ePROR/wtF2nuAatasKV988YXSCYKagtDJkyeldu3a4uHhIeXKlZPw8HBjl2RUGzduFI1GI7///ru8evVKRFJ3xEqUKCEHDx5UphsxYoSUKlVKgoKCJCQkRDp16iQFChSQM2fOGKly49uwYYPUrl1bOVovIvLw4UMpWbKkfPfdd5KQkCDjxo2TWrVqiaOjo1hZWcnvv/+uTKum5c4QN27cEC8vL1m2bJlyoGrp0qVSqVIlGTlypMTHx0tsbKz88MMPUqZMmXT38L3PUlJS5OLFi1K6dGkxMzNT7k/VLpNnzpyR5s2bi7m5uVSuXFlq1aolRYoUkdOnTxuz7BzD8GNkKSkpEhsbK61bt5bz58/Lv//+K7///ruULVtWGjRooEx3+PBhad26tTRv3lw2b94sIqlH0NS0ctOurFJSUuTZs2cSFBQkR48elaSkJDl+/LjY2NhI27Zt5c6dOyKS2vNbcHCwFChQQDmaod0Zy4uio6NlwIABOj1Lffrpp9KnTx/x8/OTwYMHp+t16tWrV3Lr1i158uSJztFFtduxY4fY2trK3Llz5fHjxzJ27Fhxc3OTXr16KQH76tWrUrNmTWnVqpWq2m7OnDnSunVrSUxM1HsQJi4uTtq1ayeBgYFKV7JqExsbK3fu3GHX8f9fhw4dxMnJSTZs2CDx8fHy4sUL8fb2lmvXrulswxYuXCi9e/eWevXqyYgRI3jWQkS55OrGjRvKWbExY8aIvb29ODg4SKtWrWTRokUiItK8efM8d8YiO/3111/i6emZ7jLAH374QfLnz68cqHj+/Lk8ffrUGCXmqHv37knJkiWlaNGiUr58+XQHRJOSkmTt2rXy5Zdfys8//6yc1c+LGH6M7Pbt23L+/Hlp2rSp0lVqQkKCbN26VcqUKSOBgYHKtHv37pWgoCBp0KCBrFu3zlglG0Xay4muX78uZ8+elY8//lgePnyoDD9z5ozY2NhIu3btlAAUFRUlrVu3FmdnZ1U850cb7o4cOaJzKeC8efPE19dXBg8erPQeKJK6A0+6Hjx4II0bN5YZM2aISOqRVk9PT6lRo4ZUqFBBevXqpRwRvH79uvJbU4vg4GD58MMPlX+nXTa14TouLi5PHTWlzFuzZo3O81M6d+4s9vb2sm7dOvn777+lfPnyeeZeipyQ9oDCxYsXxdfXVyZPnizPnz+Xly9fys6dO+WXX36Rly9fKtP26NFDxowZk+cvv82qsLAwcXR0VO41S3sfS+nSpeWzzz4zVmnvxMuXLyUyMlLCw8PF399fvL29lQCkvSJGLQfUGX6MaPz48VK4cGGpUqWKFClSROc5EdoAVK5cOalUqZIy/OjRo1K/fn1p0aKFcsmJmowePVqKFy8udnZ24uDgINu3b9cZf+bMGbGzs5N69eoplwrcv39fGjRoIF5eXnn6zI9I6oorLi5OunXrJuXLl5fFixcr4+bPny+VK1eWgQMHyqlTp2T69Oni5OSk+vsRXpeSkiK//vqrXLx4UR48eCBly5ZVuv3s37+/2NnZSZs2bZQzQHmddmOo/e/y5culfPnysm/fPmWapKQkefTokXTv3l1nOKnTzZs3pWzZsumOHHfu3FkKFSokS5YskWLFikn79u1l6tSpMnv2bJk4caKMGjVKVq9eLSkpKarZCcvI65+/f//+Uq1aNfniiy+UM0BakZGRMmnSJClQoECe71jEUNr2O3bsmKxfv14Z3qJFCylRooQ8f/5cGfbixQupWrWqLF++/J3XmZO0bXD//n159OiRckAqOTlZDh06JP7+/vLBBx8oZ6i//vpr+eKLL3Sez5ZXMfwYybp166Ro0aKyevVq+fLLL6V48eJSs2ZN5fpLkdQA9Pvvv0vnzp117lM5duyY6na8RFIfhFe2bFn59ddfZfXq1VK4cGFp1apVulPYJ06ckAYNGugcOYuKilJNm4mknt7v06eP1KhRQ3kgp4jIokWLpFq1alK8eHHx8PBgL0AZiIuLE5HUjUHTpk3l0aNHIiLy3XffSbly5aR9+/bpHmqZ12iXPe1BFu266fTp01K+fHnp0qWL7N69W0RSLxOZNm2aeHp6qvrmdEpdT6e93O/s2bM69z117NhRNBqNVKpUSRo3bixdu3aVTp06ScOGDaV58+Z8HpLo9kqW9h6e4cOHS5UqVeTzzz9XDlrt3btXgoODpXjx4qq+PyqttB2zuLm5yahRo+TKlSsikrpt9PPzk2LFismePXtkz549MmnSJHFycpIbN24Ys+xspW2DzZs3y4cffihly5aVKlWqyOrVq0UkNQAdPnxYAgICxMHBQYKDg0Wj0ajmvl+GHyNYu3atzJ8/X7lONykpSU6cOCElS5YUf39/nQCUNvSkHa4227dvl/79+8ucOXOUYSdOnJBSpUpJmzZtMtyJz8udG4ikBmTtSu7hw4fy/PlzpWOHv/76S3r06JEuAIWHh8uBAwfk9u3bRqk5Nzp79qyEhobK0aNHdYaPGzdOKleurFz/PXr0aJk1a5YShvIq7W9q586d0qJFC6lfv760a9dOOXK4b98+qVGjhpQrV07Kli0rdevWFScnpzx7cywZJioqSooWLSq9evWSc+fOSUJCgri7u0uHDh10dsz79Okjtra2OkfkKVXaHfeCBQvKoEGDdHbK0waguLg4iY6Olt9++y3Pd7GfWXv27BFra2tZsmRJurMYERER0rZtW3F2dpYSJUpIhQoV8uS6a8uWLWJjYyNff/21hIaGysiRI0Wj0ciSJUtEJPW3du3aNRk/frz07t1bVWcNGX7esdjYWHF3dxeNRiNjx45VhqekpMjJkyelZMmSEhAQoHQLqlbalVVycrLcvXtXfHx8xMLCIt1Tv0+ePCmlS5eW9u3b6/QclNf9+OOPOpc9btq0ScqXLy9Vq1aVoKAg5TreCxcuKAHohx9+MFa5udrGjRvF2tpaypQpI6ampjJ16lTlQMPSpUulatWq0rJlS+nSpYvY2Njk6fuk0u4kbNq0SfLnzy8TJkyQhQsXSkBAgBQvXlz5/JcuXZKtW7fKiBEjZPHixTr3kpF6hYeHS7Vq1aRv377y5MkT2bdvnxQvXlx69uyps4PZoUMHKViwoKxdu1Y5YEOpwsLCxN7eXn788UelbdIum8OHD5dq1arJpEmTdC7fotR2SkxMlMGDB8vgwYNFROTJkydy9OhRGTp0qAwZMkS5T/PChQty+/ZtnXuH84o7d+5IgwYNZP78+SKS2gFUsWLFxMfHRzQajXLwXUttB9cZfozgzp078uGHH0q5cuXSXSJy6tQpyZ8/vwwaNMhI1eUu2hX/qVOnpH79+lKxYkX5448/dKY5deqU2NnZycSJE41R4jt3+/ZtKV68uPj4+EhCQoJERkaKjY2NzJw5U6ZMmSIBAQFSuHBh5bKsCxcuSJ8+fcTb21t13TK/SUpKijx//lwaNGggy5cvl2vXrsmqVavE3Nxchg0bJiKp4XvGjBnSoUOHPH1JzusPtb1y5Yr4+voqG8g7d+6Ip6enFChQQJydnfPkcx8o+5w+fVp8fHykd+/e8vjxYzl8+LB4eHikC0AfffSRFC1aVJX3r77JokWL5KOPPpKEhATl6oXXH2vRp08fqVOnjvz777/GKDHXGz58uHh7e8uJEyekS5cu0rBhQwkICJAyZcpItWrVjF1ejrt3755MmTJF7t+/L/fu3RNvb2/p37+/PH78WLn0VNvdtRox/Lwju3fvlo0bNyo77pGRkcqR+rS9RaWkpMiVK1dU/fwerZUrV0rz5s2VS4xOnDghdevWlY8++ki2bt2qM62a2iwxMVH27t0rfn5+4ufnJ5s3b9bppebixYsSEBAgbm5uSgA6e/asDB48mJdGpPHs2TN5+vSpjB07Vqdnsk2bNom5ubkMGTJEZ/q82lnGokWLpFmzZnLy5Ell2IkTJ5QnfUdGRkqpUqWkb9++cunSJSldurSUKVOGAYjeKG0AevLkiU4ASnsJHHt8S2/QoEHywQcfKP9O23tb2kuTXu+qWI0SExN1rhTRCg0NlYYNG4q5ubl06tRJtmzZIiKpjzGoUqVKngqNKSkpyv7Pv//+q5wN1PZm98knn0izZs2UjjImTJggRYoUEUdHxzx/CXdGGH7eAW2vbr6+vmJpaSk9evSQyMhIuXPnjnzwwQdSrVo1vTfjq2VnXp+UlBT56quvpGrVqtK1a1fleSFhYWFKANq2bVu61+X1Nku7ct+xY4c0adJEzMzMlDMVWtoA5Onpqfy21H4pZVobN24Uf39/qVy5sri5uSldn2pt2rRJbGxspFevXnk29GiFhoaKh4eHBAcH63QHr728rWfPntKuXTvl9xMUFCQajUZKlizJ3xS9kb4zQMWLF5e2bdsqN1bn9V6lDPF6j4q//fabeHt7y9atW5VhSUlJ8uzZMwkODlZ25NVM24GB1r59+yQkJESmT5+unKFPTk7W6W5dROTjjz+W+vXr54nLBbdt26bz+TZs2CC1atWSUqVKydSpU5WORoKCgiQ4OFiZbsSIEfLTTz+puqdXhp8cNnv2bHFzc1NuyP/2229Fo9EoXeVGRkZKxYoVpVixYjpPcVYbfc8lSExMlO+//15q1KghnTt3VgLQsWPHpH79+lK9evV0N6irxfnz56V///6yfv165V4MbQ9lWpcuXZJKlSpJ2bJlJSkpic9++P9OnjwphQoVksGDB8u4cePEwsJCunTpku4I9G+//SYuLi55+uiq9jdx5MgRKV68uHTq1ElOnDihjH/+/LnUrFlT5/KIgQMHytatW/kcHzLI62eA9u3bJ+XLl8/zvSUaQhtsnj59KomJico6/Pr16+Ln5yetWrWSjRs3KtNMnTpVihQpkqd6JcuKtWvXSrVq1ZTnHe7Zs0fy5csnQUFB4uTkJAEBATqPeRBJ/R0OHz5cHBwc8kSPZlFRUeLl5SW9evWSGzduyOXLl8XBwUE+++wzGT58uFSuXFnatGkj4eHhsnz5cjE3N5fJkydLz549pWDBgukeeK42DD856J9//pEePXrIr7/+KiIi69evlwIFCsjkyZPF3t5e2rRpIxERERIRESFdu3bN82ctDLFnzx6dI+1JSUmyaNEiqVmzpnTt2lU5bXvw4EEZOnSoanfo586dK5UrV5aTJ0/KkSNH5IMPPpAqVaqkO5p15cqVPP2U5sy6du2azJ49W+cywf3794uZmZn07NkzXQDK6/cipL1c4sCBA1K8eHHp0KGDzhmgpk2bire3t4SGhsqwYcPEw8ODPQVSppw+fVr8/PykQ4cO8vTpU52HS6qVNvhs375dGjVqJDVq1JCmTZvKX3/9JSKplyrXq1dPvL29pUiRIlK7dm0pVKhQnuyVLLPOnz8v9evXl0aNGskvv/wiw4cPV3o0jYyMlODgYPH391fuWbx48aIMGzZMqlWrlieCj1Z4eLj4+fnJ0KFD5bPPPtPZrm3dulXq1asnQUFB8ttvv8mcOXOkQoUKUq9ePXaJLgw/OSo+Pl42bNggT548kZMnT0qxYsWUnje+/vpr0Wg0Og/jFMn7l229Lu3n3bdvn3h7e0tISIjO5TQJCQkyY8YMKViwoPTt2zfdtbpqCEDaDWXanQZ/f38JDAwUkdTLAX19fcXPzy/dGSBKbb9Hjx6Jh4eHWFpaputQRBuA+vbtq3MPXl6VNvQ8fPhQufzh/PnzUrx4cWnfvr1yBujMmTNSs2ZN8fDwkHLlynHni7LkxIkTEhAQwDOGaWzcuFFsbGxk2rRpsmLFCmnRooUULFhQwsLCRCS1c5v9+/fLlClTZNWqVXLz5k0jV2x82u39pUuXpGHDhtKqVSv58MMPlTYTSe2gpWvXruLv7y/Lli0TkdSzaXnx6hpt74pFixaVcePG6YzbsmWL1K9fX9q3by+HDx8WEckTl/tlB4afHKbtPnDmzJny0UcfKc8L+fbbb6Vr167SpEkTVey8v83y5cvl8OHDMm7cOKlVq5aMHj1aJwA9fPhQihcvLi4uLjJ58mQRUd+14jt37pSuXbvKn3/+KSL/1+vbrFmzRCT1yH21atWkZMmSDEBppP2dhIaGSokSJaRmzZo6N/iLpLafRqORIUOG5NmDEK9fI75+/XqpXr26FC9eXFq0aCE7duyQmzdvSvHixaVdu3bKtCkpKXL58mXV3hxL2YNdWv+fW7duSa1atZRLSiMjI6Vo0aLi5uYmNjY2cuTIESNXmHtp95n++usvady4sZiZmSkHlrXu3r0rPXv2VEUvp+fOnRMvLy+pVauWcuZQa+vWreLj4yNdunTJ8/evZgbDTw7T7nj16tVL/P39JSYmRuLj46V58+bK5XAi6jh7kVbaz6u9D+rWrVsSGxsrkydPlurVq8uYMWOUaSIiIqRbt26yevVq1bWVSOrvqF+/fqLRaMTR0VGmTp0qf//9t3zxxRfSrl07OX/+vKSkpMjOnTulbt266bpQVzNtkNH+bvbu3SvFihWTrl27pjuLceTIEbl06dI7r/FdSHuN+M2bN+XixYtia2srn3/+ucyaNUsGDhwopqamsmLFCiUAderUSeeIKhH9d1u2bJGRI0fKxIkT5cWLFxIZGSmlS5eWvn37yt9//y1+fn7i5uamHK2njF29elUaNWokAQEB6R6ae/v2bRkwYIAqejk9d+6c+Pj4SP/+/dMFoD///JOXv79GIyICynHHjh1DQEAAypQpg4SEBFhaWuL06dMwNTU1dmnvXEpKCkxMTAAAhw8fxsWLF1GgQAF06NABABAXF4fZs2dj165dcHV1RY8ePfDdd9/B3t4e69atg0aj0ZlHXiUi0Gg0yr9PnDiBefPmoXz58ti4cSP8/PyQlJSEI0eOoG/fvhg5ciQSExORmJgIa2trI1aee+zduxcbN27E06dPUa5cOfTt2xfOzs7YtWsXBgwYgFq1amHMmDGoVKmSsUt9J06fPo0BAwagevXqcHBwQEJCAr788ksAQGxsLFatWoWRI0dix44dcHZ2RkBAANq2bYuFCxfC0tLSyNUTvf/Cw8PRuHFjfP/996hSpQqKFy+OQYMGITo6GmvWrIGVlRW6deuGX375BS4uLrhx4wasrKyMXbbRabeH9+/fx9OnT+Hm5gYrKytYWFjgr7/+wsiRIwEAAwcORJs2bZTXJScnI1++fMYq+506c+YM+vbti8qVKyMkJATlypUzdkm5Vt7ee8xFPvzwQxw7dgytWrVC3759leCTlJRk7NLeKRFRQsuJEycQEBCAQYMG4cWLFwBSV1Q2NjYYP348+vbtiwcPHmD8+PEwMTHBL7/8Ao1GozOPvEyj0SA0NBTLli0DAPj5+cHJyQk3b95EaGgoKlasCAC4cuUKRo8ejbCwMJiZmTH4/H+bNm1C8+bNkZCQgAcPHmDjxo2oVq0a7ty5g0aNGmHJkiU4ceIEpkyZggsXLhi73HeicuXK+OGHH3Dy5EmsXr0a8fHxyjg7Ozt069YNwcHBWLZsGSpUqIBt27Zh/PjxDD5E2eDGjRvYvHkz+vbti/bt28PLywuvXr3C5cuXUaVKFSXk2NraYv369Thz5gyDD/4v+GzatAmNGjVC/fr10bhxY8yYMQMPHz5E+fLlMXfuXADAsmXLsHbtWuW1agk+AODr64tly5bh/Pnz+Oyzz3DlyhVjl5R7GfGsk+ppn9ysFqGhobJ69WoRSe0ut3fv3vLTTz+Jo6Oj9OvXT5lO2y4pKSmSkJAgt27dUi4fVFObJSUlyYwZM0Sj0Ui3bt3k8OHDkpKSIpUrV5ZPP/1URERiYmJk6NChUrhwYbl+/bqRK849Hjx4IJUqVZI5c+Yowy5cuCCNGjUSLy8vefDggYik3kdVqVIl1XW7e+7cOSlWrJiULVs2Xc8/EydOlIoVK/L+DKJsFBMTI35+flKoUCEJCQnRGde9e3dxd3eX9evXy+DBg8Xd3Z2XLr9m+/btYmtrK19//bVER0dLSEiIuLu7S79+/ZTHEfz1119SrVo1ad26tcTGxhq5YuM5ceKE1KlTh52LvAHDD70TsbGx0rBhQ6lTp460aNFC7O3t5eLFi5KSkiLLly8XMzMzmTRpkjK9vvt61Hivj0jqjmqjRo2kZs2aMnz4cNmxY4e0atVK54ZYbRfglBqQnzx5IoUKFZJdu3Ypw5OSkuTs2bNSuXJl+fbbb5Xfk1o7hzh//rxUqFBBevbsqdMJQv/+/SUwMJC9AhFls9OnT0upUqXEx8dHp8vlCxcuSMuWLaVo0aLi6+ur+h4VX++MJjo6WgIDA5XOfR49eiSenp5SvXp1KV++vPTv3185oHXp0iV2xS/sXORtGH7onXn06JGUKVNGNBqNzJw5UxkeHx8vy5YtE1NTU6UnN9IVFRUlq1atEh8fH7GxsREvLy/55JNPjF1WrnPq1CkZMmSIPHjwQKpXry4TJ07UGZ+SkiLVqlWToUOH6gxTq9OnT0v58uWlePHi0rNnTxkwYIA4OTnxORBEOeTcuXNSsWJF6du3r1y4cEEZnpKSIrdv31b9gaywsDDRaDQyb948ZVhSUpL8/PPPcunSJXnw4IGUKVNGBg4cKCIiPXr0EAcHB+nYsWOefiA1Za+8f+ME5RomJiYoUaIEateujdDQUKxevRoAYGlpiS5duuCHH37Al19+ieHDhxu50tzHxcUF3bp1w4kTJ9C/f3/cvXsXS5cuxbNnz4xdWq5y+PBhHDhwALdv34a/vz92796NDRs2KOM1Gg0KFy4MBwcHSOrBH51OJdTG19cXa9euhYmJCfbu3YtixYohPDwcPj4+xi6NKE+qWLEiVqxYgdOnT2P+/Pm4ePEigNR1k6enJxwcHIxboJH5+PhgxowZGDNmDL799lsAqffttGnTBt7e3lixYgVKliyJL774AkDqfYxubm5ISkpCcnKyMUun9wh7e6N3LioqCn369EF8fDz69OmD4OBgAEBiYiLmzZuH7du3IzQ0VNU7pfqk3VHfs2cPSpUqhaJFixq5qtwhPj5euTG4du3acHR0xPr169GhQwfcvXsXNWvWRK1atXDw4EGsWrUKx48fR9myZY1cde4RHh6OCRMmYM2aNShUqJCxyyHK886cOYOBAweiePHimDp1qurXRytWrED9+vXh6emJhIQEzJ8/H+PHj8f8+fMxbNgwZbrRo0fj0KFDCA0NhY2NDUaPHg1nZ2f07dsXjo6ORvwE9D5h+CGjiIiIwLBhw/Dq1St07twZ3bt3R+PGjeHj44Mvv/xS6dWNAUgX2yS9P//8E6tXr0a3bt3QqFEj3LlzB3Xq1MGYMWPQu3dvfPrpp9i3bx8ePXoEV1dXLFiwgGc29Hj58iV7dSN6h06ePIkxY8bgl19+gZubm7HLMZpnz56hVKlScHd3x+bNm1GkSBG8fPkSCxYsSBeAvvvuO6xcuRIeHh6wtrbGhg0bcObMGZQqVcrIn4LeJww/ZDQREREYPXo0Ll++jJcvX8LGxgbh4eEwNzfnTj4ZREQwYMAALFu2DAUKFMCwYcPQo0cP/PLLLwgPD8fs2bNRsmRJpKSk4NGjR7C2toaNjY2xyyYiAsCDDlqRkZFo1qwZrKyssGHDhnQBaN68efj444+RkJCAmTNn4sKFC4iPj8fs2bNRoUIFY5dP7xmGHzKq+/fvIzw8HNHR0ejRo4fy7CM1PvyVsuZND3/t168fQkJCjF0iERG9xd27d9GwYUPY2toqASjtJXBff/01QkJClIecMzhSVjH8UK6ipqcxU9aFhobi77//Rt++fZGSkoLhw4fjxYsXmDdvHlavXo3Tp09j+fLlAICwsDBUr17dyBUTEdHb3L17F4GBgbC1tcXGjRt1AtDkyZMxffp0jB8/3thl0nuOh9cpV2HwobdJTk7G8ePH8cknn+DgwYMYMGAAFixYAD8/P8ybNw+TJ09GbGwsLC0tsXHjRjg5ORm7ZCIieo328varV6/i2bNniI+PR+3atbFnzx40adIErVu3VgKQ9gDXl19+iQEDBqBAgQLGLp/eYzzzQ0TvpfPnz2PMmDF4/vw5qlatiiZNmmDx4sUYO3YsatasCQB4+vSp6ruOJSLKbbTBZ9OmTQgJCYGVlRVu3bqFjh07YsaMGUhKSkLTpk1hZWWFTZs2oXDhwkhISMDz5895QIv+Mz7nh4jeSxUrVsSqVaswcOBAHDhwAO3atcP58+exfft2ZRoGHyKi3Eej0WDXrl3o1asXJkyYgLNnz2L9+vVYuXIlQkJCoNFosGPHDiQlJaFOnTq4d+8eLCwsGHwoW/DMDxG99xITEzFu3DgsXLgQBQoUwI0bN2Bra2vssoiISI/Y2FiMGTMGhQsXxpQpUxAREYGGDRvC19cXu3fvRp06dbBgwQIAQOfOnbFmzRp4eXkZuWrKKxh+iOi9xoe/EhG9X169eoU//vgDlStXRoECBRAYGIjKlStj2bJl+OWXXxAcHIwmTZpg6dKlcHFxYQ+wlK34ayKi91raB+IGBgYauxwiInoLc3NztGjRApaWlli9ejUsLS0xbdo0AKnr9Dp16uDSpUtITk5m8KFsx3t+iOi9xwfiEhG9X7TP6ImIiMCzZ8+UB1CfO3cObdu2xfXr1+Hp6WnMEimP4mVvRERERGQUZ86cQY0aNeDn5wdLS0ucPHkShw4dQsWKFY1dGuVRPPNDREREREbh6+uLffv2wcvLC2XLlsXRo0cZfChH8cwPERERERlVSkoKNBoNL2OmHMfwQ0REREREqsDL3oiIiIiISBUYfoiIiIiISBUYfoiIiIiISBUYfoiIiIiISBUYfoiIiIiISBUYfoiIiIiISBUYfoiIiIiISBUYfoiIiIiISBUYfoiIiIiISBUYfoiIiIiISBUYfoiIiIiISBX+HzxH7iaO52SBAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hatred_words = FreqDist(\n",
    "    ' '.join(train_data.loc[train_data['label'] == 1]['tweet_cleaned']).split()\n",
    ").most_common(10)\n",
    "\n",
    "hatred_df = pd.DataFrame(hatred_words, columns=['words', 'frequency'])\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "plt.bar(\n",
    "    hatred_df['words'].tolist(),\n",
    "    hatred_df['frequency'].tolist(),\n",
    "    width=0.5,\n",
    "    color='red'\n",
    ")\n",
    "\n",
    "plt.title('Najzastupljenije reci u tweet-ovima koji sadrze elemente mrznje', fontsize=16)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d1ff705e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 500
    },
    "id": "d1ff705e",
    "outputId": "b28d194a-daff-4f7e-dfc9-634bbf6a5de8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "non_hatred_tweets_img = np.array(Image.open('images/non-hatred_tweets.png'))\n",
    "non_hatred_tweets_data = train_data.loc[train_data['label'] == 0]\n",
    "\n",
    "non_hatred_tweets = ' '.join(non_hatred_tweets_data['tweet_cleaned'].tolist())\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "wordcloud = WordCloud(\n",
    "    stopwords=stop_words,\n",
    "    max_words=100,\n",
    "    mask=non_hatred_tweets_img,\n",
    ").generate(non_hatred_tweets)\n",
    "\n",
    "plt.title('Najzastupljenije reci u tweet-ovima koji ne sadrze elemente mrznje')\n",
    "plt.axis('off')\n",
    "plt.imshow(wordcloud)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "26acc81b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 496
    },
    "id": "26acc81b",
    "outputId": "4bdf8bf5-9020-453e-ef9d-c3022dd70400"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "non_hatred_words = FreqDist(\n",
    "    ' '.join(train_data.loc[train_data['label'] == 0]['tweet_cleaned']).split()\n",
    ").most_common(10)\n",
    "\n",
    "non_hatred_df = pd.DataFrame(non_hatred_words, columns=['words', 'frequency'])\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "plt.bar(\n",
    "    non_hatred_df['words'].tolist(),\n",
    "    non_hatred_df['frequency'].tolist(),\n",
    "    width=0.5,\n",
    "    color='green'\n",
    ")\n",
    "\n",
    "plt.title('Najzastupljenije reci u tweet-ovima koji ne sadrze elemente mrznje', fontsize=16)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1e09a11",
   "metadata": {
    "id": "b1e09a11"
   },
   "source": [
    "### Hatred vs Non-Hatred klase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8af2f5b1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8af2f5b1",
    "outputId": "3447e42b-b49b-47b0-87a9-bd9f4a393a84"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mBroj instanci odnosno broj tweet-ova koji su okaraksterisani kao govor mrznje: 1,405 (6.81%)\u001b[0m\n",
      "\u001b[32mBroj instanci odnosno broj tweet-ova koji nisu okarakterisani kao govor mrznje: 19,236 (93.19%)\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "(non_hatred, hatred) = train_data['label'].value_counts()\n",
    "hatred_perc = hatred / (train_data.shape[0]) * 100\n",
    "non_hatred_perc = non_hatred / (train_data.shape[0]) * 100\n",
    "\n",
    "print(colored(\n",
    "    'Broj instanci odnosno broj tweet-ova koji su okaraksterisani kao govor mrznje: '\\\n",
    "    f'{hatred:,} ({hatred_perc:.2f}%)',\n",
    "    'red')\n",
    ")\n",
    "print(colored(\n",
    "    'Broj instanci odnosno broj tweet-ova koji nisu okarakterisani kao govor mrznje: '\\\n",
    "    f'{non_hatred:,} ({non_hatred_perc:.2f}%)',\n",
    "    'green')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ad30fe1f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 454
    },
    "id": "ad30fe1f",
    "outputId": "214531d4-b3a8-4e7e-97e8-bcba232ffd61",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Hatred', 'Non-hatred'\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.suptitle(t='Tweets', fontsize=16)\n",
    "\n",
    "ax.pie(\n",
    "    x=[hatred, non_hatred],\n",
    "    explode=(0.25, 0),\n",
    "    labels=labels,\n",
    "    colors=['Red', 'Green'],\n",
    "    autopct='%.2f',\n",
    "    shadow=True,\n",
    "    startangle=15\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c340ac28",
   "metadata": {
    "id": "c340ac28"
   },
   "source": [
    "Iz prilozenog se moze videti da imamo problem sa nebalansiranim klasama gde su tweet-ovi koji su okarakterisani kao tweet-ovi koji ne sadrze govor mrznje zastupljeniji u nasem trening skupu (odnos 93.19% / 6.81%). Problem moze biti da se nas model preprilagodi podacima i da daje jako dobar accuracy ali da na nepoznatim podacima daje lose rezultate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb0df0a",
   "metadata": {
    "id": "4bb0df0a"
   },
   "source": [
    "# Problem sa nebalansiranim klasama"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a2d71c8",
   "metadata": {
    "id": "5a2d71c8"
   },
   "source": [
    "Kako nas trening skup sadrzi nebalansirane klase (odnos 93% - 7% u korist tweet-ova koji ne sadrze govor mrznje) moramo primeniti neku od tehnika rada sa nebalansiranim klasama.  \n",
    "- Oversampling (Prilagodjavanje manjeg skupa vecem)\n",
    "    - RandomOverSampler\n",
    "    - SMOTE\n",
    "- Undersampling (Prilagodjavanje veceg skupa manjem)\n",
    "    - RandomUnderSampling\n",
    "    - NearMiss\n",
    "    - CondenseNearestNeighbour\n",
    "- Combination Oversampling & Undersampling (Kombinacija prethodna dva)\n",
    "    - SMOTEENN\n",
    "- Imbalanced Ensemble\n",
    "    - BalancedRandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "xVIPUHxIY6D6",
   "metadata": {
    "id": "xVIPUHxIY6D6"
   },
   "source": [
    "Pre primene nekih od modela za rad sa podacima potrebno je izvrsiti transformaciju nasih podataka iz tekstualne u numericku kategoriju koristecu `TfidVectorizer` klasu.  \n",
    "`TF-IDF` (`T`erm `F`requency - `I`nverse `D`ocument `F`requency) matrica koristi frekvenciju pojavljivanja reci da bi odredila koliko je data rec relevantna u datom dokumentu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "AHIpkxt0ego2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AHIpkxt0ego2",
    "outputId": "e53a82a9-57fe-4754-b105-216ebae0a3f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_idf_vectorizer = TfidfVectorizer(max_features=3000)\n",
    "tf_idf_vectorizer.fit(X_train)\n",
    "\n",
    "X_train_tf_idf = tf_idf_vectorizer.transform(X_train)\n",
    "X_test_tf_idf = tf_idf_vectorizer.transform(X_test)\n",
    "\n",
    "X_train_tf_idf.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "_WHrsmd8EReb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_WHrsmd8EReb",
    "outputId": "597955c6-fb48-4e86-a183-d01cc9ee7d63"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20645, 3000)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_tf_idf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weY4EdCdm6X7",
   "metadata": {
    "id": "weY4EdCdm6X7"
   },
   "source": [
    "Standardni model koji bi koristili da nemamo problem sa nebalansiranima klasama. (Stabla odlucivanja)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "be379bCS7oJu",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "be379bCS7oJu",
    "outputId": "b29e9904-e6a5-4993-e27b-20b88b77fcd4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=6, min_samples_split=50)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dtc = DecisionTreeClassifier(max_depth=6, min_samples_split=50, criterion='gini')\n",
    "\n",
    "model_dtc.fit(X_train_tf_idf, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "IyNV08Zc7_kt",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IyNV08Zc7_kt",
    "outputId": "3c292143-a18d-4eed-dbc8-ec3217037dd8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9424728752260397\n"
     ]
    }
   ],
   "source": [
    "print(f'{model_dtc.score(X_test_tf_idf, Y_test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "uzcOgm1SnQz2",
   "metadata": {
    "id": "uzcOgm1SnQz2"
   },
   "source": [
    "Accuracy naseg modela je 0.94, medjutim to nije prava slika modela s obzirom da nase klase nisu balansirane.  \n",
    "Accuracy se racuna po formuli: $ \\frac{TN + TP}{TP + FP + TN + FN} $, gde su parametri instance koje:  \n",
    "-  TN (True Negative) - Model predvidja da su negativne i one su negativne\n",
    "-  TP (True Positive) - Model predvidja da su pozitivne i one su pozitivne\n",
    "-  FN (False Negative) - Model predvidja da su negativne ali su one pozitivne\n",
    "-  FP (False Positive) - Model predvidja da su positivne ali su one negativne  \n",
    "\n",
    "Iz date formule se moze primetiti da losi rezultati u manjoj klasi ne uticu drasticno na konacan rezultat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "XE-BlzNxlKqn",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XE-BlzNxlKqn",
    "outputId": "8793ec74-6b7e-40aa-9a7a-1f2036a8563f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True positive: 126\n",
      "False positive: 32\n",
      "True negative: 8213\n",
      "False negative: 477\n",
      "Accuracy: 0.9424728752260397\n"
     ]
    }
   ],
   "source": [
    "Y_pred = model_dtc.predict(X_test_tf_idf)\n",
    "\n",
    "CM = confusion_matrix(Y_test, Y_pred)\n",
    "\n",
    "(TN, FN, TP, FP) = CM[0][0], CM[1][0], CM[1][1], CM[0][1]\n",
    "\n",
    "print(f'True positive: {TP}')\n",
    "print(f'False positive: {FP}')\n",
    "print(f'True negative: {TN}')\n",
    "print(f'False negative: {FN}')\n",
    "\n",
    "print(f'Accuracy: {(TN + TP) / (TP + FP + TN + FN)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9oPyTKPu0tzh",
   "metadata": {
    "id": "9oPyTKPu0tzh"
   },
   "source": [
    "Zato koristimo funkciju `classification_report_imbalanced` koja nam daje bolju sliku modela"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "R1Tkka7IWvL-",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R1Tkka7IWvL-",
    "outputId": "b922258f-1cad-426f-bc5b-fa5bf0837db9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.95      1.00      0.21      0.97      0.46      0.22      8245\n",
      "          1       0.80      0.21      1.00      0.33      0.46      0.19       603\n",
      "\n",
      "avg / total       0.94      0.94      0.26      0.93      0.46      0.22      8848\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report_imbalanced(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "_481W-8i01o8",
   "metadata": {
    "id": "_481W-8i01o8"
   },
   "source": [
    "U tabeli se moze videti da nebalansiranost klasa dolazi do izrazaja pa je sledeci korak obrada nebalansiranih klasa"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ZbOrCJ5dw2I",
   "metadata": {
    "id": "5ZbOrCJ5dw2I"
   },
   "source": [
    "SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "zFL5cFJHXH9k",
   "metadata": {
    "id": "zFL5cFJHXH9k"
   },
   "outputs": [],
   "source": [
    "imbalanced_model_smote = make_pipeline(TfidfVectorizer(), SMOTE(), MultinomialNB())\n",
    "\n",
    "imbalanced_model_smote.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred_smote = imbalanced_model_smote.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "mjsUhlluXIBK",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mjsUhlluXIBK",
    "outputId": "bc464983-7064-46fe-d472-8d2cf4d56db8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.99      0.91      0.85      0.95      0.88      0.78      8245\n",
      "          1       0.41      0.85      0.91      0.55      0.88      0.77       603\n",
      "\n",
      "avg / total       0.95      0.91      0.85      0.92      0.88      0.77      8848\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report_imbalanced(Y_test, Y_pred_smote))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "EvsgbhwTdzFd",
   "metadata": {
    "id": "EvsgbhwTdzFd"
   },
   "source": [
    "RandomOverSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "kuPyAxrXd1bi",
   "metadata": {
    "id": "kuPyAxrXd1bi"
   },
   "outputs": [],
   "source": [
    "imbalanced_model_ros = make_pipeline(TfidfVectorizer(), RandomOverSampler(), MultinomialNB())\n",
    "\n",
    "imbalanced_model_ros.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred_ros = imbalanced_model_ros.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "oNuMRMFPd1hx",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oNuMRMFPd1hx",
    "outputId": "80988124-11b1-41e9-ac62-f8dd2d86efa8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.99      0.91      0.86      0.95      0.88      0.78      8245\n",
      "          1       0.40      0.86      0.91      0.55      0.88      0.77       603\n",
      "\n",
      "avg / total       0.95      0.90      0.86      0.92      0.88      0.78      8848\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report_imbalanced(Y_test, Y_pred_ros))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cmgCN7lUNoSu",
   "metadata": {
    "id": "cmgCN7lUNoSu"
   },
   "source": [
    "Kako RandomOverSampler i SMOTE daju jako slicne rezultate koristicemo RandomOverSampler (laksi rad sa tekstualnim podacima)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c2YLDDPTNxVb",
   "metadata": {
    "id": "c2YLDDPTNxVb"
   },
   "outputs": [],
   "source": [
    "ros = RandomOverSampler(random_state=0, sampling_strategy='minority')\n",
    "\n",
    "(X_resampled_ros, Y_resampled_ros) = ros.fit_resample(np.array(X_train).reshape(-1, 1), Y_train)\n",
    "\n",
    "X_resampled_ros_list = [item for row in X_resampled_ros for item in row]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dcCUioHAN9Fb",
   "metadata": {
    "id": "dcCUioHAN9Fb"
   },
   "outputs": [],
   "source": [
    "X_resampled_ros_df = pd.Series(X_resampled_ros_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ZCgZne_V-nCH",
   "metadata": {
    "id": "ZCgZne_V-nCH"
   },
   "source": [
    "## Cuvamo novi set podataka za potrebe algoritama klasifikcije"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "97ti1blJBW23",
   "metadata": {
    "id": "97ti1blJBW23"
   },
   "outputs": [],
   "source": [
    "tf_idf_vectorizer_classification = TfidfVectorizer(max_features=3000)\n",
    "tf_idf_vectorizer_classification.fit(X_resampled_ros_df)\n",
    "\n",
    "X_resampled_ros_tf_idf = tf_idf_vectorizer_classification.transform(X_resampled_ros_df)\n",
    "X_test_ros_tf_idf = tf_idf_vectorizer_classification.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "RFFs-dwX-uNK",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 236
    },
    "id": "RFFs-dwX-uNK",
    "outputId": "5aff4c13-9774-40cc-b144-ca3c457304b4"
   },
   "outputs": [
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       "   able  ableism  abou  absence  absolutely  absurd  abt  abuse  abusive  \\\n",
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       "\n",
       "   acceptable  ...  yup  yur  zero  zille  zionazi  zionazis  zionism  \\\n",
       "0         0.0  ...  0.0  0.0   0.0    0.0      0.0       0.0      0.0   \n",
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       "4         0.0  ...  0.0  0.0   0.0    0.0      0.0       0.0      0.0   \n",
       "\n",
       "   zionist  zuma  label  \n",
       "0      0.0   0.0      0  \n",
       "1      0.0   0.0      0  \n",
       "2      0.0   0.0      0  \n",
       "3      0.0   0.0      0  \n",
       "4      0.0   0.0      0  \n",
       "\n",
       "[5 rows x 3001 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_tf_idf_ros_df = pd.concat(\n",
    "    [\n",
    "        pd.DataFrame(\n",
    "            X_resampled_ros_tf_idf.toarray(),\n",
    "            columns=tf_idf_vectorizer_classification.get_feature_names_out()\n",
    "        ),\n",
    "        Y_resampled_ros\n",
    "    ],\n",
    "    axis=1\n",
    ")\n",
    "\n",
    "train_tf_idf_ros_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9SAfUlY9ahHm",
   "metadata": {
    "id": "9SAfUlY9ahHm"
   },
   "source": [
    "Prilikom konkatanacije `Series` i `DataFrame-a` dolazi do spajanja na osnovu index-a, kako se prilikom transformacije iz tekstualne u numericku kategoriju dobija niz a kasnije taj niz koristi za kreiranje novog DataFrame-a indeksi ce biti resetovani, pa je to isto potrebno uraditi za Y_test podatke kako bi podaci bili ispravno upareni"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "QBA9ssJmaVpT",
   "metadata": {
    "id": "QBA9ssJmaVpT"
   },
   "outputs": [],
   "source": [
    "Y_test.index = range(0, Y_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f40AEwM2JO1K",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253
    },
    "id": "f40AEwM2JO1K",
    "outputId": "bbfb0590-e6e1-452f-ff66-619b473bb67d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8848, 3001)\n"
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    },
    {
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       "   able  ableism  abou  absence  absolutely  absurd  abt  abuse  abusive  \\\n",
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       "\n",
       "   zionist  zuma  label  \n",
       "0      0.0   0.0      0  \n",
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       "\n",
       "[5 rows x 3001 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_tf_idf_ros_df = pd.concat(\n",
    "    [\n",
    "        pd.DataFrame(\n",
    "            X_test_ros_tf_idf.toarray(),\n",
    "            columns=tf_idf_vectorizer_classification.get_feature_names_out()\n",
    "        ),\n",
    "        Y_test\n",
    "    ],\n",
    "    axis=1\n",
    ")\n",
    "\n",
    "print(test_tf_idf_ros_df.shape)\n",
    "test_tf_idf_ros_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ZwsuqZ4cBPcC",
   "metadata": {
    "id": "ZwsuqZ4cBPcC"
   },
   "outputs": [],
   "source": [
    "train_tf_idf_ros_df.to_csv('dataset/classification/train.csv', index=False)\n",
    "test_tf_idf_ros_df.to_csv('dataset/classification/test.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd380db0",
   "metadata": {},
   "source": [
    "## Cuvamo novi set podataka za potrebe algoritama klasterovanja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "98f7ce30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38478                              tag dwayne johnson provocative statement scared agent like shame\n",
       "38479                                                                 excited use oneblade phillips\n",
       "38480                       thanks autism film plan hotting amp andrew hard work coming together xx\n",
       "38481                                   right enjoying every single day yong forever th anniversary\n",
       "38482                                                                                    thanks jan\n",
       "                                                    ...                                            \n",
       "47321                        prayer victim orlando shooting victim especially lgbt community tragic\n",
       "47322    top food item seasonal affective disorder patient must avoid peh busselton hoba wellington\n",
       "47323          bit colour brighten grey morning kidsroom scandi fermliving dollshouse retrotoys toy\n",
       "47324                           chick fil heard boy say let pretend terrorist attack signofthetimes\n",
       "47325                                                        simple sincere smile make u others ali\n",
       "Name: tweet_cleaned, Length: 8848, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.index = range(X_resampled_ros_df.shape[0], X_resampled_ros_df.shape[0] + X_test.shape[0])\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fe79d4ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                  wait stage look awesome see pm cmafest drinkingwithdolly flockon\n",
       "1              bbc news orlando gay nightclub shooting killed suspect omar mateen love love get smh\n",
       "2                                                                                    friend netflix\n",
       "3                                                  ceain area yeah seen video seeing little kid cry\n",
       "4                                          thnx pilot nick amazing experience grandcanyon afternoon\n",
       "                                                    ...                                            \n",
       "47321                        prayer victim orlando shooting victim especially lgbt community tragic\n",
       "47322    top food item seasonal affective disorder patient must avoid peh busselton hoba wellington\n",
       "47323          bit colour brighten grey morning kidsroom scandi fermliving dollshouse retrotoys toy\n",
       "47324                           chick fil heard boy say let pretend terrorist attack signofthetimes\n",
       "47325                                                        simple sincere smile make u others ali\n",
       "Length: 47326, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined = pd.concat(\n",
    "    [X_resampled_ros_df, X_test]\n",
    ")\n",
    "df_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c8e5493f",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_idf_vectorizer_clustering = TfidfVectorizer(max_features=3000)\n",
    "tf_idf_vectorizer_clustering.fit(df_combined)\n",
    "\n",
    "clustering_ros_tf_idf = tf_idf_vectorizer_clustering.transform(df_combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0778d013",
   "metadata": {},
   "outputs": [
    {
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       "<p>47326 rows × 3000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       able  ableism  abou  absolutely  abt  abuse  abusive  accept  \\\n",
       "0       0.0      0.0   0.0         0.0  0.0    0.0      0.0     0.0   \n",
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       "...     ...      ...   ...         ...  ...    ...      ...     ...   \n",
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       "\n",
       "       acceptable  accepting  ...  yummy  yup  yur  zero  zille  zionazi  \\\n",
       "0             0.0        0.0  ...    0.0  0.0  0.0   0.0    0.0      0.0   \n",
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       "4             0.0        0.0  ...    0.0  0.0  0.0   0.0    0.0      0.0   \n",
       "...           ...        ...  ...    ...  ...  ...   ...    ...      ...   \n",
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       "47325         0.0        0.0  ...    0.0  0.0  0.0   0.0    0.0      0.0   \n",
       "\n",
       "       zionazis  zionism  zionist  zuma  \n",
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       "...         ...      ...      ...   ...  \n",
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       "\n",
       "[47326 rows x 3000 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_idf_ros_df = pd.DataFrame(\n",
    "    clustering_ros_tf_idf.toarray(), \n",
    "    columns=tf_idf_vectorizer_clustering.get_feature_names_out()\n",
    ")\n",
    "\n",
    "tf_idf_ros_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d129c61f",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_idf_ros_df.to_csv('dataset/clustering/clustering.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b96bb08",
   "metadata": {},
   "source": [
    "## Cuvamo novi set podataka za potrebe pravila pridruzivanja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0fc7d8de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22001                        wait stage look awesome see pm cmafest drinkingwithdolly flockon\n",
       "18793    bbc news orlando gay nightclub shooting killed suspect omar mateen love love get smh\n",
       "14980                                                                          friend netflix\n",
       "10457                                        ceain area yeah seen video seeing little kid cry\n",
       "29761                                thnx pilot nick amazing experience grandcanyon afternoon\n",
       "                                                 ...                                         \n",
       "19554                                                     downtime lunchtime unique inclusive\n",
       "15531                                                               going mexico day vacation\n",
       "240                                                sanjapan animeconvention see guy september\n",
       "24219              washtimes anybody dropped ball gop passing law keep gun mateens crazy hand\n",
       "31490                  good news job interview thursday excited mean sta internship next year\n",
       "Name: tweet_cleaned, Length: 20645, dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association_rules_data = X_train.copy()\n",
    "association_rules_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f4b02598",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer_association_rules = TfidfVectorizer(max_features=3000)\n",
    "vectorizer_association_rules.fit(association_rules_data)\n",
    "\n",
    "vectorizer_association_rules_t = tf_idf_vectorizer_clustering.transform(association_rules_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "0fe19351",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 82,
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    }
   ],
   "source": [
    "matrix = np.where(vectorizer_association_rules_t.toarray() > 0, 1, 0)\n",
    "association_rules_df = pd.DataFrame(\n",
    "    matrix, \n",
    "    columns=vectorizer_association_rules.get_feature_names_out()\n",
    ")\n",
    "\n",
    "association_rules_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5c1478a9",
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     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association_rules_Y = Y_train.copy()\n",
    "association_rules_Y.index = range(0, association_rules_Y.shape[0])\n",
    "\n",
    "association_rules_df['label'] = association_rules_Y\n",
    "association_rules_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "421bf500",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_11727/1749358847.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  association_rules_non_hatred.drop('label', axis=1, inplace=True)\n"
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       "  </tbody>\n",
       "</table>\n",
       "<p>19239 rows × 3000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       aap  able  absolutely  abt  abuse  accept  acceptable  accepted  \\\n",
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       "...    ...   ...         ...  ...    ...     ...         ...       ...   \n",
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       "20641    0     0           0    0      0       0           0         0   \n",
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       "20643    0     0           0    0      0       0           0         0   \n",
       "20644    0     0           0    0      0       0           0         0   \n",
       "\n",
       "       access  accessory  ...  young  youth  youtube  youtuber  yoy  yr  yum  \\\n",
       "0           0          0  ...      0      0        0         0    0   0    0   \n",
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       "3           0          0  ...      0      0        0         0    0   0    0   \n",
       "4           0          0  ...      0      0        0         0    0   0    0   \n",
       "...       ...        ...  ...    ...    ...      ...       ...  ...  ..  ...   \n",
       "20640       0          0  ...      0      0        0         0    0   0    0   \n",
       "20641       0          0  ...      0      0        0         0    0   0    0   \n",
       "20642       0          0  ...      0      0        0         0    0   0    0   \n",
       "20643       0          0  ...      0      0        0         0    0   0    0   \n",
       "20644       0          0  ...      0      0        0         0    0   0    0   \n",
       "\n",
       "       yummy  zelda  zen  \n",
       "0          0      0    0  \n",
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       "3          0      0    0  \n",
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       "...      ...    ...  ...  \n",
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       "\n",
       "[19239 rows x 3000 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association_rules_non_hatred = association_rules_df.loc[association_rules_df['label'] == 0]\n",
    "association_rules_non_hatred.drop('label', axis=1, inplace=True)\n",
    "association_rules_non_hatred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "647170ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_11727/1987741181.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  association_rules_hatred.drop('label', axis=1, inplace=True)\n"
     ]
    },
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20614</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20630</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1406 rows × 3000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       aap  able  absolutely  abt  abuse  accept  acceptable  accepted  \\\n",
       "14       0     0           0    0      0       0           0         0   \n",
       "33       0     0           0    0      0       0           0         0   \n",
       "72       0     0           0    0      0       0           0         0   \n",
       "93       0     0           0    0      0       0           0         0   \n",
       "99       0     0           0    0      0       0           0         0   \n",
       "...    ...   ...         ...  ...    ...     ...         ...       ...   \n",
       "20588    0     0           0    0      0       0           0         0   \n",
       "20598    0     0           0    0      0       0           0         0   \n",
       "20606    0     0           0    0      0       0           0         0   \n",
       "20614    0     0           0    0      0       0           0         0   \n",
       "20630    0     0           0    0      0       0           0         0   \n",
       "\n",
       "       access  accessory  ...  young  youth  youtube  youtuber  yoy  yr  yum  \\\n",
       "14          0          0  ...      0      0        0         0    0   0    0   \n",
       "33          0          0  ...      0      0        0         0    0   0    0   \n",
       "72          0          0  ...      0      0        0         0    0   0    0   \n",
       "93          0          0  ...      0      0        0         0    0   0    0   \n",
       "99          0          0  ...      0      0        0         0    0   0    0   \n",
       "...       ...        ...  ...    ...    ...      ...       ...  ...  ..  ...   \n",
       "20588       0          0  ...      0      0        0         0    0   0    0   \n",
       "20598       0          0  ...      0      0        0         0    0   0    0   \n",
       "20606       0          0  ...      0      0        0         0    0   0    0   \n",
       "20614       0          0  ...      0      0        0         0    0   0    0   \n",
       "20630       0          0  ...      0      0        0         0    0   0    0   \n",
       "\n",
       "       yummy  zelda  zen  \n",
       "14         0      0    0  \n",
       "33         0      0    0  \n",
       "72         0      0    0  \n",
       "93         0      0    0  \n",
       "99         0      0    0  \n",
       "...      ...    ...  ...  \n",
       "20588      0      0    0  \n",
       "20598      0      0    0  \n",
       "20606      0      0    0  \n",
       "20614      0      0    0  \n",
       "20630      0      0    0  \n",
       "\n",
       "[1406 rows x 3000 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association_rules_hatred = association_rules_df.loc[association_rules_df['label'] == 1]\n",
    "association_rules_hatred.drop('label', axis=1, inplace=True)\n",
    "association_rules_hatred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b5fd6bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "association_rules_df.to_csv('dataset/association_rules/association_rules.csv', index=False)\n",
    "association_rules_non_hatred.to_csv('dataset/association_rules/non_hatred.csv', index=False)\n",
    "association_rules_hatred.to_csv('dataset/association_rules/hatred.csv', index=False)"
   ]
  }
 ],
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