AI Domestic violence support .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import clear_output\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "import string\n",
    "from colorama import Fore, Back, Style\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, classification_report, confusion_matrix, f1_score, accuracy_score, ConfusionMatrixDisplay\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from catboost import CatBoostClassifier\n",
    "from sentence_transformers import SentenceTransformer\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install scikit-learn-intelex\n",
    "clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
     ]
    }
   ],
   "source": [
    "from sklearnex import patch_sklearn\n",
    "patch_sklearn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install catboost\n",
    "!pip install nltk\n",
    "!pip install scikit-learn\n",
    "!pip install sentence-transformers\n",
    "\n",
    "clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /home/udb8ca7cfe0f27bea15b16def2ddc1b1/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     /home/udb8ca7cfe0f27bea15b16def2ddc1b1/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XywEI6Qgzkp8"
   },
   "source": [
    "### Reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73
    },
    "id": "At57PlhaiYdo",
    "outputId": "e638c8e0-38e2-462e-cd63-7a1d43a3e475"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('text_des_domestic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "VQaeZyw09tl8",
    "outputId": "63df4a91-bbfb-4a72-df61-88e9aed622de"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>Description</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>My partner got really mad last night and start...</td>\n",
       "      <td>physical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I've been going through physical abuse for a f...</td>\n",
       "      <td>physical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>There's a pattern of violence in my relationsh...</td>\n",
       "      <td>physical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I have injuries on my arms and legs from the a...</td>\n",
       "      <td>physical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>My partner has been hitting me during argument...</td>\n",
       "      <td>physical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Description     Label\n",
       "0  My partner got really mad last night and start...  physical\n",
       "1  I've been going through physical abuse for a f...  physical\n",
       "2  There's a pattern of violence in my relationsh...  physical\n",
       "3  I have injuries on my arms and legs from the a...  physical\n",
       "4  My partner has been hitting me during argument...  physical"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6BDBBtFt0TmT"
   },
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = set(stopwords.words('english'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "bhaFwkpfN0Db"
   },
   "outputs": [],
   "source": [
    "# preprocessing functions\n",
    "\n",
    "lemma = WordNetLemmatizer()\n",
    "\n",
    "def text_preprocessing(text):\n",
    "    text = str(text)\n",
    "    text = text.lower()\n",
    "    text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text)\n",
    "    text= re.sub('<.*?>', ' ', text)\n",
    "    return text\n",
    "\n",
    "\n",
    "def drop_stopwords(text):\n",
    "    dropped = [word for word in text.split() if word not in stop_words]\n",
    "\n",
    "    tokens = [lemma.lemmatize(word) for word in dropped]\n",
    "\n",
    "    final_text = ' '.join(tokens)\n",
    "    return final_text\n",
    "\n",
    "# applying preprocessing functions\n",
    "data_processed = data.copy()\n",
    "data_processed['Description'] = data_processed['Description'].apply(text_preprocessing).apply(drop_stopwords)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "VBrgPB_gR8KA"
   },
   "outputs": [],
   "source": [
    "# label encoding\n",
    "LE = LabelEncoder()\n",
    "data_processed['encoded_label'] = LE.fit_transform(data_processed['Label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "a4v_kXw4SIMu"
   },
   "outputs": [],
   "source": [
    "# a function for calculating and plotting predictions\n",
    "def plot_prediction_results(model, X_data, y_data, target_names = list(LE.classes_)):\n",
    "    pred = model.predict(X_data)\n",
    "\n",
    "    mse = mean_squared_error(y_data, pred)\n",
    "    f1 = f1_score(y_data, pred, average = 'weighted')\n",
    "    acc = accuracy_score(y_data, pred)\n",
    "    cm = confusion_matrix(y_data, pred)\n",
    "\n",
    "    clf = classification_report(y_data, pred, target_names = target_names)\n",
    "\n",
    "    print('Mean Squared Error :\\t{0:.5f}'.format(mse))\n",
    "    print('Weighted F1 Score :\\t{0:.3f}'.format(f1))\n",
    "    print('Accuracy Score :\\t{0:.3f} %'.format(acc*100))\n",
    "    print('--'*35)\n",
    "\n",
    "    print('\\n Classification Report:\\n',clf)\n",
    "    print('--'*35)\n",
    "\n",
    "    print('\\n Confusion Matrix: \\n')\n",
    "\n",
    "    cmd = ConfusionMatrixDisplay(cm, display_labels = target_names)\n",
    "    fig, axs = plt.subplots(figsize=(4,4))\n",
    "    cmd.plot(ax=axs, cmap=\"bone\", xticks_rotation=\"vertical\", colorbar = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/"
    },
    "id": "aMMaPCVwSsfP",
    "outputId": "1fad0bb2-d805-4f32-8005-625345ca9d45"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148, 3)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_processed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "eX2ni8DsSXi-",
    "outputId": "3f586b54-6aa4-4354-8a52-6f1a98a588c7"
   },
   "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>Description</th>\n",
       "      <th>Label</th>\n",
       "      <th>encoded_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>partner got really mad last night started yell...</td>\n",
       "      <td>physical</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>going physical abuse week partner hold hit bel...</td>\n",
       "      <td>physical</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pattern violence relationship partner push sho...</td>\n",
       "      <td>physical</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>injury arm leg abuse partner us force control ...</td>\n",
       "      <td>physical</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>partner hitting argument visible injury living...</td>\n",
       "      <td>physical</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Description     Label  encoded_label\n",
       "0  partner got really mad last night started yell...  physical              3\n",
       "1  going physical abuse week partner hold hit bel...  physical              3\n",
       "2  pattern violence relationship partner push sho...  physical              3\n",
       "3  injury arm leg abuse partner us force control ...  physical              3\n",
       "4  partner hitting argument visible injury living...  physical              3"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data after preprocessing operations\n",
    "data_processed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "lv_cYe2r9Kcr"
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf_vectorizer = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "emmedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')\n",
    "SEED = 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NEDDGYEK1uUz"
   },
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hvSUcosNSaOQ",
    "outputId": "17972f68-536a-4128-e3e1-bbfe89c5acac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train sample size\t 118\n",
      "test sample size\t 30\n"
     ]
    }
   ],
   "source": [
    "# train and test splitting\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "type_train_data, type_test_data = train_test_split(data_processed, test_size = 0.2, random_state = SEED, shuffle = True)\n",
    "type_train_data = type_train_data.reset_index(drop = True)\n",
    "type_test_data = type_test_data.reset_index(drop = True)\n",
    "\n",
    "print('train sample size\\t', type_train_data.shape[0])\n",
    "print('test sample size\\t', type_test_data.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dcb567262dfe493eae7a04bb46045fa9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db64c3bf8a334999b7d7a418855c9867",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_embeddings = emmedding_model.encode(list(type_train_data['Description'].values), show_progress_bar = True)\n",
    "test_embeddings = emmedding_model.encode(list(type_test_data['Description'].values), show_progress_bar = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using oneDAL for PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "before PCA, train feature size\t 384\n",
      "\n",
      "after PCA, train feature size\t 30\n",
      "CPU times: user 431 ms, sys: 561 ms, total: 992 ms\n",
      "Wall time: 13.1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearnex.decomposition import PCA\n",
    "pca= PCA(n_components=30)\n",
    "train_pca = pca.fit_transform(train_embeddings)\n",
    "test_pca = pca.transform(test_embeddings)\n",
    "print('\\nbefore PCA, train feature size\\t', train_embeddings.shape[1])\n",
    "print('\\nafter PCA, train feature size\\t', train_pca.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 257 ms, sys: 394 ms, total: 651 ms\n",
      "Wall time: 10.2 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=50)\n",
    "train_pca_ = pca.fit_transform(train_embeddings)\n",
    "test_pca_ = pca.transform(test_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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66SWnWm4/LZTdkpOTtXz5crVp00alSpWytxctWlTPPPOM1q1bp8TERIdpevXq5VBT/fr1lZycrKNHj963OiWpa9eu9o7AklSrVi0ZY5w61NWqVUvHjx/XrVu3JP15iiYlJUUdOnRw+AyCg4NVunRpxcXFpbvM1Nee2XXw+++/lyTFxsY6tA8cOFDSn52Gb1e+fHnVrl3boXbpz3U2dT26vf3QoUNOy7z9Kp3UU0M3btzQypUr7e3e3t72vy9evKiEhATVr1/f6TSQJEVHRzus5+nJly+fNm3apJMnT6Y5fOvWrTp79qz69OkjLy8ve3vLli1Vrlw5p/dCkl588UWH5/Xr10/zNd/pjz/+UP78+dMdntZ8//jjD6d1Oz3nzp3TmjVr1KNHD4fPRfrf9unKttSzZ0+HbSl1ne7Zs6e9zc3NTdWrV0/zfWjTpo19Xyn9eaVmrVq17OvhqVOntHPnTnXr1k0FChSwj1e5cmU98sgj9vFud7f3Kqvbk5+fn7p06WJ/7uHhoZo1a2bqcw0MDJQkLVu2LMNTgePHj09zf33nY/DgwU7Tli9fXitWrNCiRYs0ePBg+fr6Ol21dO3aNXl6ejpNm7pe33nVYuq6eP78+bu+xgctd3bVt7ipU6eqTJkycnd3V5EiRVS2bFmHnuIBAQG6dOlSpue3fPlynTt3TjVr1tSBAwfs7Y0aNdL8+fM1btw4h/mnp3Tp0mratOldx6tbt6569+6tqVOnqlmzZg7/VM+dO6f4+HjNmDEj3Stazp49K+nPS3vLli37wK8IOXfunK5evaqyZcs6DYuKilJKSoqOHz/ucKXWnTvy1I324sWL97XWO5ebupMLDQ11ak9JSVFCQoIKFiyo/fv3yxij0qVLpznf28PRnQICAiQp0+vg0aNHlSdPHkVGRjq0BwcHK1++fE5hLyuvSXJ+j/PkyePwT1OSypQpI0kOl7IvXrxYo0aN0s6dOx366qQVksPDw9N9fbd7++23FRMTo9DQUFWrVk0tWrRQ165d7fWkvta01q1y5co5XUno5eWloKAgh7b8+fNner0yGVxUmtE6m/oZZyT1n27FihXTHSc7tqWMPv+03oe01ukyZcro3//+t6SMP4OoqCgtW7ZMV65cka+vb7o13fleZXV7Kl68uNN6lj9/fv38889pTn+78PBwxcbGasKECZo7d67q16+vJ554Ql26dLG/V5LsV7K6IiAgwL6vb926tebNm6fWrVtr+/btqlKliqQ/vwik1e8u9RLr278oSP9bF+/nl1BXEWTug5o1a6p69erpDi9Xrpx27typGzduZOpy3NSjLh06dEhz+OrVq9WoUSPXik1DUlKSvXPmwYMHdfXqVftl4ykpKZKkLl26KCYmJs3pK1eunOllpbdRZKYzZHZyc3NLsz2jfyT3c7l3qyclJUU2m00//PBDmuP6+fmlu8yAgACFhISk2YEyI5ndgbn6mrJi7dq1euKJJ9SgQQN98MEHKlq0qPLmzatZs2al2Qn+zp1yejp06KD69etr4cKFWr58ud555x2NGzdO33zzjZo3b57lOtN7zZlRsGDBDANPTq2zd5OVz/9B1Zrd29O9vvfjx49Xt27d9O2332r58uXq37+/xowZo40bN6p48eKSpAsXLujGjRt3nZe3t7dDAErLk08+qWeffVZffPGFPcgULVrUqYO69OcRL0kKCQlxaE9dFwsVKnT3F/iAEWRywOOPP64NGzZowYIFevrppzMc98qVK/r222/VsWNHtW/f3ml4//79NXfu3GwNMkOHDtWePXv07rvv6h//+Idee+01TZ48WZIUFBQkf39/JScn3/XoTkREhDZt2qSbN2+me4Qg9ZvRnTcATOuUTmb/kQYFBcnHx0d79+51Gvbbb78pT548Tt8OrSYiIkLGGIWHh9uPVmRFq1atNGPGDG3YsMHhNFBaSpYsqZSUFO3fv19RUVH29jNnzig+Pl4lS5bM8vIzkpKSokOHDjm8rn379kn639UXCxYskJeXl5YtW+ZweHzWrFn3vPyiRYuqT58+6tOnj86ePauHH35Yo0ePVvPmze2vde/evfarEVPt3bs3W9+LcuXK2a90vB9SjzJlFGhzYlvav3+/U9u+ffvsn/3tn0FaNRUqVMjhaExm3Ov2lJa77a8qVaqkSpUq6fXXX9f69etVt25dTZ8+XaNGjZL0Z/hYvXr1XZcTExNjvxoqPUlJSfYjuqmqVq2quLg4JSYmOhzB27Rpk3347VLXxdv3AbkFfWRywIsvvqiiRYtq4MCB9h307c6ePWtfmRcuXKgrV66ob9++at++vdOjVatWWrBgQZqHCF2xadMmvfvuu3rllVc0cOBAvfrqq5oyZYp9g3Jzc1O7du20YMGCNHeAt1+y3K5dO50/f15TpkxxGi/1m0vJkiXl5uamNWvWOAxP63bYqTun9O56nMrNzU2PPvqovv32W4dTEWfOnNG8efNUr169TB16z82efPJJubm5afjw4U7fAo0x+uOPPzKcPvW8+XPPPZfmXacPHjyo9957T5LsN1GcNGmSwzgTJkyQ9Gf/kOx2+zpjjNGUKVOUN29eNWnSRNKfn7HNZnM4cnfkyBEtWrTI5WUmJyc7Xf5auHBhhYSE2Lev6tWrq3Dhwpo+fbrDNvfDDz9oz5492fpe1K5dW7t37862bftOQUFBatCggWbOnKljx445DEtdp3JiW1q0aJHDkYLNmzdr06ZN9iNiRYsWVdWqVTVnzhyHfcHu3bu1fPnyNG/6eTf3uj2lJb39VWJior2vW6pKlSopT548Dp+1K31k4uPjdfPmTadaPv74Y0lyOFPQvn17JScnO3QRSEpK0qxZs1SrVi2ngLpt2zbZbLa7fvHJCRyRyQH58+fXwoUL1aJFC1WtWtXhzr7bt2/X/Pnz7SvL3LlzVbBgQdWpUyfNeT3xxBP66KOPtGTJEj355JMZLnf79u36/PPPndojIiJUu3ZtXb9+XTExMSpdurT9t12GDx+u//u//1P37t31yy+/yNfXV2PHjlVcXJxq1aql559/XuXLl9eFCxe0fft2rVy50n6fga5du+rTTz9VbGysNm/erPr16+vKlStauXKl+vTpo9atWyswMFBPPfWU3n//fdlsNkVERGjx4sX2fja3S32P+vfvr2bNmsnNzU2dOnVK87WOGjVKK1asUL169dSnTx+5u7vrww8/VFJSksP9HqwqIiJCo0aN0pAhQ3TkyBG1adNG/v7+Onz4sBYuXKhevXpp0KBBGU4/b948dezYUVFRUQ539l2/fr2++uor+91Iq1SpopiYGM2YMUPx8fGKjo7W5s2bNWfOHLVp0yZbjwZKf/YrWbp0qWJiYlSrVi398MMPWrJkif75z3/a+5u0bNlSEyZM0GOPPaZnnnlGZ8+e1dSpUxUZGZmpfgppuXTpkooXL6727durSpUq8vPz08qVK7VlyxaNHz9e0p99JcaNG6fu3bsrOjpaTz/9tM6cOaP33ntPYWFhGjBgQLa9D61bt9bIkSO1evVqPfroo9k239tNnjxZ9erV08MPP6xevXopPDxcR44c0ZIlS+y31n/Q21JkZKTq1aun3r17KykpSZMmTVLBggUd/mG/8847at68uWrXrq2ePXvq2rVrev/99xUYGOjSz5jc6/aU3jzz5cun6dOny9/fX76+vqpVq5Z27dqlfv366amnnlKZMmV069YtffbZZ/Yvialc6SOzatUq9e/fX+3bt1fp0qV148YNrV27Vt98842qV6/u0EG5Vq1aeuqppzRkyBCdPXtWkZGRmjNnjo4cOaJPPvnEad4rVqxQ3bp1VbBgwSzXdd89uAuk/vrSu49Mek6ePGkGDBhgypQpY7y8vIyPj4+pVq2aGT16tElISDBnzpwx7u7u5tlnn013HlevXjU+Pj6mbdu26Y5zt8uvUy/fS73keNOmTQ7Tb9261bi7u5vevXvb286cOWP69u1rQkNDTd68eU1wcLBp0qSJmTFjhlN9//rXv0x4eLh9vPbt2ztcynnu3DnTrl074+PjY/Lnz29eeOEFs3v3bqdLF2/dumVeeuklExQUZGw2m8Ol2LrjMlpj/rwzarNmzYyfn5/x8fExjRo1crgs2Jj0P7P0LgvPiCuXX995CWZ69aReRnr75cfGGLNgwQJTr1494+vra3x9fU25cuVM3759zd69ezNV8759+8zzzz9vwsLCjIeHh/H39zd169Y177//vrl+/bp9vJs3b5rhw4fbP8fQ0FAzZMgQh3GM+fPy65YtWzotR5LTZc1p3XE6JibG+Pr6moMHD5pHH33U+Pj4mCJFipihQ4c63G/GGGM++eQTU7p0aePp6WnKlStnZs2aZX+f7rbs24elrjdJSUnm1VdfNVWqVDH+/v7G19fXVKlSJc17vnz55ZfmoYceMp6enqZAgQKmc+fODpcM3/5a7pRWjempXLmy6dmzZ5rT37kupK47mb3jd6rdu3ebtm3bmnz58hkvLy9TtmxZp3v23Mu2lF69d74/t68P48ePN6GhocbT09PUr1/f7Nq1y6nulStXmrp16xpvb28TEBBgHn/8cfPrr79matnpvVeZ2Z6io6NNhQoVnOqJiYlx2v6//fZbU758eePu7m7fnx06dMj06NHDREREGC8vL1OgQAHTqFGjbLmD84EDB0zXrl1NqVKljLe3t/0+RkOHDjWXL192Gv/atWtm0KBBJjg42Hh6epoaNWqYpUuXOo0XHx9vPDw8zMcff3zPNd4P/NYSgFyjW7du+vrrr9P8gbu/o88++0x9+/bVsWPHlC9fvpwu575K/U24d955J8tHP3B/TZo0SW+//bYOHjyY6Y7zDxJ9ZAAgl+rcubNKlChh/3kO4EG7efOmJkyYoNdffz1XhhiJPjIAkGvlyZMny5fJS1JCQoLTDc3uFBwc7GpZ+BvJmzevU2fw3IYgAwB/MS+//LLmzJmT4Tj0KsBfBX1kAOAv5tdff033ZxZSZeYu34AVEGQAAIBl0dkXAABY1l++j0xKSopOnjwpf3//XPljVwAAwJkxRpcuXVJISEiGP4z8lw8yJ0+etPzv6gAA8Hd1/Phx+49ppuUvH2T8/f0l/flGWP33dQAA+LtITExUaGio/f94ev7yQSb1dFJAQABBBgAAi7lbtxA6+wIAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMtyz+kCACC3C3ttSU6XAORaR8a2zNHlc0QGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYVo7+RMGaNWv0zjvvaNu2bTp16pQWLlyoNm3a2IcbYzR06FB99NFHio+PV926dTVt2jSVLl0654q+DbctBzKW07cuB/DXl6NHZK5cuaIqVapo6tSpaQ5/++23NXnyZE2fPl2bNm2Sr6+vmjVrpuvXrz/gSgEAQG6Uo0dkmjdvrubNm6c5zBijSZMm6fXXX1fr1q0lSZ9++qmKFCmiRYsWqVOnTg+yVAAAkAvl2j4yhw8f1unTp9W0aVN7W2BgoGrVqqUNGzakO11SUpISExMdHgAA4K8p1waZ06dPS5KKFCni0F6kSBH7sLSMGTNGgYGB9kdoaOh9rRMAAOScXBtkXDVkyBAlJCTYH8ePH8/pkgAAwH2Sa4NMcHCwJOnMmTMO7WfOnLEPS4unp6cCAgIcHgAA4K8p1waZ8PBwBQcH68cff7S3JSYmatOmTapdu3YOVgYAAHKLHL1q6fLlyzpw4ID9+eHDh7Vz504VKFBAJUqU0CuvvKJRo0apdOnSCg8P1xtvvKGQkBCHe80AAIC/rxwNMlu3blWjRo3sz2NjYyVJMTExmj17tgYPHqwrV66oV69eio+PV7169bR06VJ5eXnlVMkAACAXydEg07BhQxlj0h1us9k0YsQIjRgx4gFWBQAArCLX9pEBAAC4G4IMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwrFwdZJKTk/XGG28oPDxc3t7eioiI0MiRI2WMyenSAABALuCe0wVkZNy4cZo2bZrmzJmjChUqaOvWrerevbsCAwPVv3//nC4PAADksFwdZNavX6/WrVurZcuWkqSwsDDNnz9fmzdvzuHKAABAbpCrTy3VqVNHP/74o/bt2ydJ2rVrl9atW6fmzZunO01SUpISExMdHgAA4K8pVx+Ree2115SYmKhy5crJzc1NycnJGj16tDp37pzuNGPGjNHw4cMfYJUAACCn5OojMv/+9781d+5czZs3T9u3b9ecOXP07rvvas6cOelOM2TIECUkJNgfx48ff4AVAwCABylXH5F59dVX9dprr6lTp06SpEqVKuno0aMaM2aMYmJi0pzG09NTnp6eD7JMAACQQ3L1EZmrV68qTx7HEt3c3JSSkpJDFQEAgNwkVx+RefzxxzV69GiVKFFCFSpU0I4dOzRhwgT16NEjp0sDAAC5QK4OMu+//77eeOMN9enTR2fPnlVISIheeOEFvfnmmzldGgAAyAVydZDx9/fXpEmTNGnSpJwuBQAA5EK5uo8MAABARggyAADAsggyAADAsggyAADAsggyAADAsggyAADAsggyAADAsggyAADAsly+Id7Nmzd1+vRpXb16VUFBQSpQoEB21gUAAHBXWToic+nSJU2bNk3R0dEKCAhQWFiYoqKiFBQUpJIlS+r555/Xli1b7letAAAADjIdZCZMmKCwsDDNmjVLTZs21aJFi7Rz507t27dPGzZs0NChQ3Xr1i09+uijeuyxx7R///77WTcAAEDmTy1t2bJFa9asUYUKFdIcXrNmTfXo0UPTp0/XrFmztHbtWpUuXTrbCgUAALhTpoPM/PnzMzWep6enXnzxRZcLAgAAyKxsuWopMTFRixYt0p49e7JjdgAAAJniUpDp0KGDpkyZIkm6du2aqlevrg4dOqhy5cpasGBBthYIAACQHpeCzJo1a1S/fn1J0sKFC2WMUXx8vCZPnqxRo0Zla4EAAADpcSnIJCQk2O8bs3TpUrVr104+Pj5q2bIlVysBAIAHxqUgExoaqg0bNujKlStaunSpHn30UUnSxYsX5eXlla0FAgAApMelO/u+8sor6ty5s/z8/FSyZEk1bNhQ0p+nnCpVqpSd9QEAAKTLpSDTp08f1axZU8ePH9cjjzyiPHn+PLBTqlQp+sgAAIAHxuXfWqpevbqqV6/u0NayZct7LggAACCzXAoyxhh9/fXXiouL09mzZ5WSkuIw/JtvvsmW4gAAADLich+ZDz/8UI0aNVKRIkVks9myuy4AAIC7cinIfPbZZ/rmm2/UokWL7K4HAAAg01y6/DowMFClSpXK7loAAACyxKUgM2zYMA0fPlzXrl3L7noAAAAyzaVTSx06dND8+fNVuHBhhYWFKW/evA7Dt2/fni3FAQAAZMSlIBMTE6Nt27apS5cudPYFAAA5xqUgs2TJEi1btkz16tXL7noAAAAyzeXfWgoICMjuWgAAALLEpSAzfvx4DR48WEeOHMnmcgAAADLPpVNLXbp00dWrVxURESEfHx+nzr4XLlzIluIAAAAy4lKQmTRpUjaXAQAAkHUuX7UEAACQ0zLdR+bKlStZmnFWxwcAAMiqTAeZyMhIjR07VqdOnUp3HGOMVqxYoebNm2vy5MnZUiAAAEB6Mn1qadWqVfrnP/+pYcOGqUqVKqpevbpCQkLk5eWlixcv6tdff9WGDRvk7u6uIUOG6IUXXrifdQMAAGQ+yJQtW1YLFizQsWPH9NVXX2nt2rVav369rl27pkKFCumhhx7SRx99pObNm8vNze1+1gwAACDJhc6+JUqU0MCBAzVw4MD7UQ8AAECmuXRDPAAAgNyAIAMAACyLIAMAACyLIAMAACyLIAMAACzL5SCzdu1adenSRbVr19aJEyckSZ999pnWrVuXbcUBAABkxKUgs2DBAjVr1kze3t7asWOHkpKSJEkJCQl66623srVAAACA9LgUZEaNGqXp06fro48+Ut68ee3tdevW1fbt27OtOAAAgIy4FGT27t2rBg0aOLUHBgYqPj7+XmsCAADIFJeCTHBwsA4cOODUvm7dOpUqVeqeiwIAAMgMl4LM888/r5dfflmbNm2SzWbTyZMnNXfuXA0aNEi9e/fO7hoBAADSlOXfWpKk1157TSkpKWrSpImuXr2qBg0ayNPTU4MGDdJLL72U3TUCAACkyaUgY7PZ9K9//UuvvvqqDhw4oMuXL6t8+fLy8/PL7voAAADS5VKQSeXh4aHy5ctnVy0AAABZ4lKQuX79ut5//33FxcXp7NmzSklJcRjOJdgAAOBBcCnI9OzZU8uXL1f79u1Vs2ZN2Wy27K4LAADgrlwKMosXL9b333+vunXrZnc9AAAAmebS5dfFihWTv79/dteSphMnTqhLly4qWLCgvL29ValSJW3duvWBLBsAAORuLgWZ8ePH6x//+IeOHj2a3fU4uHjxourWrau8efPqhx9+0K+//qrx48crf/7893W5AADAGlw6tVS9enVdv35dpUqVko+Pj8PvLUnShQsXsqW4cePGKTQ0VLNmzbK3hYeHZ8u8AQCA9bkUZJ5++mmdOHFCb731looUKXLfOvt+9913atasmZ566imtXr1axYoVU58+ffT888+nO01SUpL917glKTEx8b7UBgAAcp5LQWb9+vXasGGDqlSpkt31ODh06JCmTZum2NhY/fOf/9SWLVvUv39/eXh4KCYmJs1pxowZo+HDh9/XugAAQO7gUh+ZcuXK6dq1a9ldi5OUlBQ9/PDDeuutt/TQQw+pV69eev755zV9+vR0pxkyZIgSEhLsj+PHj9/3OgEAQM5wKciMHTtWAwcO1KpVq/THH38oMTHR4ZFdihYt6nTn4KioKB07dizdaTw9PRUQEODwAAAAf00unVp67LHHJElNmjRxaDfGyGazKTk5+d4rk1S3bl3t3bvXoW3fvn0qWbJktswfAABYm0tBJi4uLrvrSNOAAQNUp04dvfXWW+rQoYM2b96sGTNmaMaMGQ9k+QAAIHdzKchER0dndx1pqlGjhhYuXKghQ4ZoxIgRCg8P16RJk9S5c+cHsnwAAJC7ZTrI/Pzzz6pYsaLy5Mmjn3/+OcNxK1eufM+FpWrVqpVatWqVbfMDAAB/HZkOMlWrVtXp06dVuHBhVa1aVTabTcYYp/Gys48MAABARjIdZA4fPqygoCD73wAAADkt00GmZMmScnNz06lTp7hqCAAA5ApZuo9MWqeSAAAAcopLN8QDAADIDbJ8+fXHH38sPz+/DMfp37+/ywUBAABkVpaDzPTp0+Xm5pbucJvNRpABAAAPRJaDzNatW1W4cOH7UQsAAECWZKmPjM1mu191AAAAZBlXLQEAAMvKUpAZOnToXTv6AgAAPChZ6iMzdOjQ+1UHAABAlnEfGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkuBZkzZ87o2WefVUhIiNzd3eXm5ubwAAAAeBCyfGdfSerWrZuOHTumN954Q0WLFuVGeQAAIEe4FGTWrVuntWvXqmrVqtlcDgAAQOa5dGopNDSUu/wCAIAc51KQmTRpkl577TUdOXIkm8sBAADIPJdOLXXs2FFXr15VRESEfHx8lDdvXofhFy5cyJbiAAAAMuJSkJk0aVI2lwEAAJB1LgWZmJiY7K4DAAAgy1wKMpKUnJysRYsWac+ePZKkChUq6IknnuA+MgAA4IFxKcgcOHBALVq00IkTJ1S2bFlJ0pgxYxQaGqolS5YoIiIiW4sEAABIi0tXLfXv318RERE6fvy4tm/fru3bt+vYsWMKDw9X//79s7tGAACANLl0RGb16tXauHGjChQoYG8rWLCgxo4dq7p162ZbcQAAABlx6YiMp6enLl265NR++fJleXh43HNRAAAAmeFSkGnVqpV69eqlTZs2yRgjY4w2btyoF198UU888UR21wgAAJAml4LM5MmTFRERodq1a8vLy0teXl6qW7euIiMj9d5772V3jQAAAGlyqY9Mvnz59O2332r//v367bffJElRUVGKjIzM1uIAAAAy4vJ9ZCSpdOnSKl26dHbVAgAAkCWZDjKxsbEaOXKkfH19FRsbm+G4EyZMuOfCAAAA7ibTQWbHjh26efOm/W8AAICclukgExcXl+bfAAAAOcWlq5Z69OiR5n1krly5oh49etxzUQAAAJnhUpCZM2eOrl275tR+7do1ffrpp/dcFAAAQGZk6aqlxMRE+w3wLl26JC8vL/uw5ORkff/99ypcuHC2FwkAAJCWLAWZfPnyyWazyWazqUyZMk7DbTabhg8fnm3FAQAAZCRLQSYuLk7GGDVu3FgLFixw+NFIDw8PlSxZUiEhIdleJAAAQFqyFGSio6MlSYcPH1aJEiVks9nuS1EAAACZ4dKdfY8ePaqjR4+mO7xBgwYuFwQAAJBZLgWZhg0bOrXdfnQmOTnZ5YIAAAAyy6XLry9evOjwOHv2rJYuXaoaNWpo+fLl2V0jAABAmlw6IhMYGOjU9sgjj8jDw0OxsbHatm3bPRcGAABwNy4dkUlPkSJFtHfv3uycJQAAQLpcOiLz888/Ozw3xujUqVMaO3asqlatmh11AQAA3JVLQaZq1aqy2Wwyxji0/7//9/80c+bMbCkMAADgblwKMocPH3Z4nidPHgUFBTn8ZAEAAMD95lKQKVmyZHbXAQAAkGUudfbt37+/Jk+e7NQ+ZcoUvfLKK/daEwAAQKa4FGQWLFigunXrOrXXqVNHX3/99T0XBQAAkBkuBZk//vgjzXvJBAQE6Pz58/dcFAAAQGa4FGQiIyO1dOlSp/YffvhBpUqVuueiAAAAMsOlzr6xsbHq16+fzp07p8aNG0uSfvzxR40fP16TJk3KzvoAAADS5VKQ6dGjh5KSkjR69GiNHDlSkhQWFqZp06apa9eu2VogAABAelz+iYLevXvr999/15kzZ5SYmKhDhw7d9xAzduxY2Ww2rowCAACS7iHI3Lp1SytXrtQ333xjv8PvyZMndfny5Wwr7nZbtmzRhx9+qMqVK9+X+QMAAOtxKcgcPXpUlSpVUuvWrdW3b1+dO3dOkjRu3DgNGjQoWwuUpMuXL6tz58766KOPlD9//gzHTUpKUmJiosMDAAD8NbkUZF5++WVVr15dFy9elLe3t729bdu2+vHHH7OtuFR9+/ZVy5Yt1bRp07uOO2bMGAUGBtofoaGh2V4PAADIHVzq7Lt27VqtX79eHh4eDu1hYWE6ceJEthSW6osvvtD27du1ZcuWTI0/ZMgQxcbG2p8nJiYSZgAA+ItyKcikpKQoOTnZqf3333+Xv7//PReV6vjx43r55Ze1YsWKTP8gpaenpzw9PbOtBgAAkHu5dGrp0UcfdbhfjM1m0+XLlzV06FC1aNEiu2rTtm3bdPbsWT388MNyd3eXu7u7Vq9ercmTJ8vd3T3NMAUAAP4+XDoiM378eDVr1kzly5fX9evX9cwzz2j//v0qVKiQ5s+fn23FNWnSRL/88otDW/fu3VWuXDn94x//kJubW7YtCwAAWI9LQaZ48eLatWuXvvzyS+3atUuXL19Wz5491blzZ4fOv/fK399fFStWdGjz9fVVwYIFndoBAMDfj0tB5ty5cwoKClLnzp3VuXNnh2G//PKLKlWqlC3FAQAAZMSlPjKVKlXSkiVLnNrfffdd1axZ856LysiqVav4PScAACDJxSATGxurdu3aqXfv3rp27ZpOnDihJk2a6O2339a8efOyu0YAAIA0uRRkBg8erA0bNmjt2rWqXLmyKleuLE9PT/38889q27ZtdtcIAACQJpd/aykyMlIVK1bUkSNHlJiYqI4dOyo4ODg7awMAAMiQS0Hmp59+UuXKlbV//379/PPPmjZtml566SV17NhRFy9ezO4aAQAA0uRSkGncuLE6duyojRs3KioqSs8995x27NihY8eOccUSAAB4YFy6/Hr58uWKjo52aIuIiNBPP/2k0aNHZ0thAAAAd+PSEZk7Q4x9Znny6I033ringgAAADIrS0GmRYsWSkhIsD8fO3as4uPj7c//+OMPlS9fPtuKAwAAyEiWgsyyZcuUlJRkf/7WW2/pwoUL9ue3bt3S3r17s686AACADGQpyBhjMnwOAADwILl8HxkAAICclqUgY7PZZLPZnNoAAAByQpYuvzbGqFu3bvL09JQkXb9+XS+++KJ8fX0lyaH/DAAAwP2WpSATExPj8LxLly5O43Tt2vXeKgIAAMikLAWZWbNm3a86AAAAsozOvgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLJydZAZM2aMatSoIX9/fxUuXFht2rTR3r17c7osAACQS+TqILN69Wr17dtXGzdu1IoVK3Tz5k09+uijunLlSk6XBgAAcgH3nC4gI0uXLnV4Pnv2bBUuXFjbtm1TgwYNcqgqAACQW+TqIHOnhIQESVKBAgXSHScpKUlJSUn254mJife9LgAAkDNy9aml26WkpOiVV15R3bp1VbFixXTHGzNmjAIDA+2P0NDQB1glAAB4kCwTZPr27avdu3friy++yHC8IUOGKCEhwf44fvz4A6oQAAA8aJY4tdSvXz8tXrxYa9asUfHixTMc19PTU56eng+oMgAAkJNydZAxxuill17SwoULtWrVKoWHh+d0SQAAIBfJ1UGmb9++mjdvnr799lv5+/vr9OnTkqTAwEB5e3vncHUAACCn5eo+MtOmTVNCQoIaNmyookWL2h9ffvllTpcGAABygVx9RMYYk9MlAACAXCxXH5EBAADICEEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYliWCzNSpUxUWFiYvLy/VqlVLmzdvzumSAABALpDrg8yXX36p2NhYDR06VNu3b1eVKlXUrFkznT17NqdLAwAAOSzXB5kJEybo+eefV/fu3VW+fHlNnz5dPj4+mjlzZk6XBgAAcph7TheQkRs3bmjbtm0aMmSIvS1Pnjxq2rSpNmzYkOY0SUlJSkpKsj9PSEiQJCUmJmZ7fSlJV7N9nsBfyf3Y7nIC2zqQvvu1nafO1xiT4Xi5OsicP39eycnJKlKkiEN7kSJF9Ntvv6U5zZgxYzR8+HCn9tDQ0PtSI4D0BU7K6QoA3G/3ezu/dOmSAgMD0x2eq4OMK4YMGaLY2Fj785SUFF24cEEFCxaUzWbLwcpwvyUmJio0NFTHjx9XQEBATpcD4D5gO//7MMbo0qVLCgkJyXC8XB1kChUqJDc3N505c8ah/cyZMwoODk5zGk9PT3l6ejq05cuX736ViFwoICCAHRzwF8d2/veQ0ZGYVLm6s6+Hh4eqVaumH3/80d6WkpKiH3/8UbVr187BygAAQG6Qq4/ISFJsbKxiYmJUvXp11axZU5MmTdKVK1fUvXv3nC4NAADksFwfZDp27Khz587pzTff1OnTp1W1alUtXbrUqQMw4OnpqaFDhzqdWgTw18F2jjvZzN2uawIAAMilcnUfGQAAgIwQZAAAgGURZAAAgGURZAAAgGURZJAlq1atks1mU3x8fIbjhYWFadKkSQ+kJgA5Z/bs2Zm66ajNZtOiRYvuez34+yHI/E1Nnz5d/v7+unXrlr3t8uXLyps3rxo2bOgwbmp4OXjwoOrUqaNTp07Z77aY2Z3Y/ZJbA1OzZs3k5uamLVu2OA3r1q2bbDabbDabPDw8FBkZqREjRtg/i8yGRSA36Nixo/bt22d/PmzYMFWtWjXnCroHL7zwgtzc3PTVV185DRs2bJh9u3V3d1dYWJgGDBigy5cvS5KOHDkim82mnTt3PuCqQZD5m2rUqJEuX76srVu32tvWrl2r4OBgbdq0SdevX7e3x8XFqUSJEoqIiJCHh4eCg4P53aoMHDt2TOvXr1e/fv00c+bMNMd57LHHdOrUKe3fv18DBw7UsGHD9M477zzgSoF75+3trcKFC+d0Gffs6tWr+uKLLzR48OB0t9sKFSro1KlTOnLkiMaNG6cZM2Zo4MCBD7hS3Ikg8zdVtmxZFS1aVKtWrbK3rVq1Sq1bt1Z4eLg2btzo0N6oUSP736lHC1atWqXu3bsrISHB/k1l2LBh9umuXr2qHj16yN/fXyVKlNCMGTMcavjll1/UuHFjeXt7q2DBgurVq5f9240kNWzYUK+88orDNG3atFG3bt3sw48ePaoBAwbYl5+eY8eOqXXr1vLz81NAQIA6dOjg8Bteqd8iP/vsM4WFhSkwMFCdOnXSpUuX7OOkpKRozJgxCg8Pl7e3t6pUqaKvv/7aaVmzZs1Sq1at1Lt3b82fP1/Xrl1zGsfT01PBwcEqWbKkevfuraZNm+q7775Lt37gQVm8eLHy5cun5ORkSdLOnTtls9n02muv2cd57rnn1KVLF0mOR2Vnz56t4cOHa9euXfZtcvbs2fbpzp8/r7Zt28rHx0elS5e+6zp/8eJFde3aVfnz55ePj4+aN2+u/fv324enLnvZsmWKioqSn5+f/UvC7T7++GNFRUXJy8tL5cqV0wcffOC0rK+++krly5fXa6+9pjVr1uj48eNO47i7uys4OFjFixdXx44d1blzZ7bbXIAg8zfWqFEjxcXF2Z/HxcWpYcOGio6Otrdfu3ZNmzZtsgeZ29WpU0eTJk1SQECATp06pVOnTmnQoEH24ePHj1f16tW1Y8cO9enTR71799bevXslSVeuXFGzZs2UP39+bdmyRV999ZVWrlypfv36Zbr+b775RsWLF9eIESPsy09LSkqKWrdurQsXLmj16tVasWKFDh06pI4dOzqMd/DgQS1atEiLFy/W4sWLtXr1ao0dO9Y+fMyYMfr00081ffp0/fe//9WAAQPUpUsXrV692j6OMUazZs1Sly5dVK5cOUVGRqYZdu7k7e2tGzduZPq1A/dL/fr1denSJe3YsUOStHr1ahUqVMjhS8/q1audTkFLf55mGjhwoP3IxalTpxy2s+HDh6tDhw76+eef1aJFC3Xu3FkXLlxIt5Zu3bpp69at+u6777RhwwYZY9SiRQvdvHnTPs7Vq1f17rvv6rPPPtOaNWt07Ngxh/3Q3Llz9eabb2r06NHas2eP3nrrLb3xxhuaM2eOw7I++eQTdenSRYGBgWrevLlDAEsP220uYfC39dFHHxlfX19z8+ZNk5iYaNzd3c3Zs2fNvHnzTIMGDYwxxvz4449Gkjl69Kgxxpi4uDgjyVy8eNEYY8ysWbNMYGCg07xLlixpunTpYn+ekpJiChcubKZNm2aMMWbGjBkmf/785vLly/ZxlixZYvLkyWNOnz5tjDEmOjravPzyyw7zbd26tYmJiXFYzsSJEzN8ncuXLzdubm7m2LFj9rb//ve/RpLZvHmzMcaYoUOHGh8fH5OYmGgf59VXXzW1atUyxhhz/fp14+PjY9avX+8w7549e5qnn37aYVlBQUHm5s2bxhhjJk6caKKjox2miYmJMa1bt7a/LytWrDCenp5m0KBBxhjn9xh40B5++GHzzjvvGGOMadOmjRk9erTx8PAwly5dMr///ruRZPbt22eMcd4HDB061FSpUsVpnpLM66+/bn9++fJlI8n88MMPadawb98+I8n89NNP9rbz588bb29v8+9//9u+bEnmwIED9nGmTp1qihQpYn8eERFh5s2b5zDvkSNHmtq1azssK2/evObcuXPGGGMWLlxowsPDTUpKSrqva+vWraZQoUKmffv2xhhjDh8+bCSZHTt2pPl6cP9wROZvrGHDhrpy5Yq2bNmitWvXqkyZMgoKClJ0dLS9n8yqVatUqlQplShRIsvzr1y5sv1vm82m4OBgnT17VpK0Z88eValSRb6+vvZx6tatq5SUFPtRm+yyZ88ehYaGKjQ01N5Wvnx55cuXT3v27LG3hYWFyd/f3/68aNGi9noPHDigq1ev6pFHHpGfn5/98emnn+rgwYP2aWbOnKmOHTvK3f3PnzF7+umn9dNPPzmMI/15+N7Pz09eXl5q3ry5Onbs6HBaDshJ0dHRWrVqlYwxWrt2rZ588klFRUVp3bp1Wr16tUJCQlS6dOksz/f2fYKvr68CAgLs29id9uzZI3d3d9WqVcveVrBgQZUtW9Zhu/Xx8VFERIT9+e3b7ZUrV3Tw4EH17NnTYbsdNWqU03bbrFkzFSpUSJLUokULJSQk6D//+Y9DTb/88ov8/Pzk7e2tmjVrqnbt2poyZUqW3wdkr1z/o5G4fyIjI1W8eHHFxcXp4sWLio6OliSFhIQoNDRU69evV1xcnBo3buzS/PPmzevw3GazKSUlJdPT58mTR+aOnwK7/ZBydsuo3tS+O0uWLFGxYsUcxkv98boLFy5o4cKFunnzpqZNm2YfnpycrJkzZ2r06NH2tkaNGmnatGny8PBQSEiIPfgAuUHDhg01c+ZM7dq1S3nz5lW5cuXUsGFDrVq1ymFfkVX3uk/I7DxT9xup2+1HH33kEIgkyc3NTdKf2+ecOXN0+vRph+0wdbtt0qSJva1s2bL67rvv5O7urpCQEHl4eNxT7cge7D3/5ho1amTfOb366qv29gYNGuiHH37Q5s2b1bt373Sn9/DwsHcKzIqoqCjNnj1bV65csR+V+emnn5QnTx6VLVtWkhQUFOTQ7yU5OVm7d+926K+TmeVHRUXp+PHjOn78uP2ozK+//qr4+HiVL18+U/WWL19enp6eOnbsWLo78blz56p48eJO98pYvny5xo8frxEjRth3nr6+voqMjMzUsoEHLbWfzMSJE+3re8OGDTV27FhdvHgxwyt1XN0n3CkqKkq3bt3Spk2bVKdOHUnSH3/8ob1792Z6uy1SpIhCQkJ06NAhde7cOc1xvv/+e3ufoNTtU5J2796t7t27Kz4+3t6ZOfV2CchdOLX0N9eoUSOtW7dOO3fudPgHHR0drQ8//FA3btxIs6NvqrCwMF2+fFk//vijzp8/r6tXr2ZquZ07d5aXl5diYmK0e/duxcXF6aWXXtKzzz6rIkWKSJIaN26sJUuWaMmSJfrtt9/Uu3dvp3urhIWFac2aNTpx4oTOnz+f5rKaNm2qSpUqqXPnztq+fbs2b96srl27Kjo6WtWrV89Uvf7+/ho0aJAGDBigOXPm6ODBg9q+fbvef/99e6fBTz75RO3bt1fFihUdHj179tT58+e1dOnSTC0LyGn58+dX5cqVNXfuXHun3gYNGmj79u3at29fhkdkwsLCdPjwYe3cuVPnz59XUlKSSzWULl1arVu31vPPP69169Zp165d6tKli4oVK6bWrVtnej7Dhw/XmDFjNHnyZO3bt0+//PKLZs2apQkTJkj6c7tt2bKlqlSp4rDddujQQfny5dPcuXNdqh8PDkHmb65Ro0a6du2aIiMj7QFC+jPIXLp0yX6Zdnrq1KmjF198UR07dlRQUJDefvvtTC3Xx8dHy5Yt04ULF1SjRg21b99eTZo0cTjf3KNHD8XExNhDR6lSpZxC1YgRI3TkyBFFREQoKCgozWXZbDZ9++23yp8/vxo0aKCmTZuqVKlS+vLLLzNVa6qRI0fqjTfe0JgxYxQVFaXHHntMS5YsUXh4uLZt26Zdu3apXbt2TtMFBgaqSZMm+uSTT7K0PCAnRUdHKzk52R5kChQooPLlyys4ONh+1DQt7dq102OPPaZGjRopKChI8+fPd7mGWbNmqVq1amrVqpVq164tY4y+//57p9NJGXnuuef08ccfa9asWapUqZKio6M1e/ZshYeH68yZM1qyZEma222ePHnUtm1btlsLsJk7OyEAAABYBEdkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZf1/jWZnekcQjVgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "n_components = 30\n",
    "\n",
    "# without Using oneAPI\n",
    "start_time= time.time()\n",
    "pca_normal= PCA(n_components=n_components)\n",
    "train_pca_normal= pca_normal.fit_transform(train_embeddings)\n",
    "test_pca_normal = pca_normal.transform(test_embeddings)\n",
    "normal_execution_time = (time.time() - start_time) * 1000  # Convert to milliseconds\n",
    "\n",
    "\n",
    "# Using oneAPI\n",
    "start_time_onedal = time.time()\n",
    "pca_onedal = PCA(n_components=n_components)\n",
    "train_pca_onedal = pca_onedal.fit_transform(train_embeddings)\n",
    "test_pca_onedal = pca_onedal.transform(test_embeddings)\n",
    "onedal_execution_time = (time.time() - start_time_onedal) * 1000  # Convert to milliseconds\n",
    "\n",
    "# Plotting the comparison graph\n",
    "labels = ['Without oneAPI', 'with oneAPI']\n",
    "execution_times = [normal_execution_time, onedal_execution_time]\n",
    "\n",
    "plt.bar(labels, execution_times)\n",
    "plt.ylabel('Execution Time (ms)')\n",
    "plt.title(f'PCA Execution Time Comparison (n_components={n_components})')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "Va81h3Xke8gm"
   },
   "outputs": [],
   "source": [
    "X_train_pca = train_pca\n",
    "y_train = type_train_data['encoded_label']\n",
    "\n",
    "X_test_pca = test_pca\n",
    "y_test = type_test_data['encoded_label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-i3j-jMb39vb"
   },
   "source": [
    "\n",
    "## Model 1: catboost classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6ROTSmq4WE0U",
    "outputId": "bc9a13fa-b988-4237-d6ea-5eb3d79d92e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.070956\n",
      "0:\tlearn: 1.9179640\ttotal: 6.57ms\tremaining: 6.56s\n",
      "500:\tlearn: 0.0429313\ttotal: 1.21s\tremaining: 1.2s\n",
      "999:\tlearn: 0.0181866\ttotal: 2.38s\tremaining: 0us\n",
      "CPU times: user 6min 58s, sys: 21 s, total: 7min 19s\n",
      "Wall time: 2.49 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x14c8c9bcbad0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# a tuned model\n",
    "cat_model = CatBoostClassifier(verbose = 500)\n",
    "\n",
    "# training\n",
    "cat_model.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 860
    },
    "id": "_jUhvWmvhqEZ",
    "outputId": "459dcb95-93e4-4a2b-983e-0cc4b3b5575c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error :\t1.66667\n",
      "Weighted F1 Score :\t0.942\n",
      "Accuracy Score :\t93.333 %\n",
      "----------------------------------------------------------------------\n",
      "\n",
      " Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "    cultural       1.00      1.00      1.00         6\n",
      "   emotional       1.00      0.60      0.75         5\n",
      "   isolation       1.00      1.00      1.00         3\n",
      "    physical       1.00      1.00      1.00         9\n",
      "      sexual       1.00      1.00      1.00         3\n",
      "    stalking       1.00      1.00      1.00         3\n",
      "      verbal       0.33      1.00      0.50         1\n",
      "\n",
      "    accuracy                           0.93        30\n",
      "   macro avg       0.90      0.94      0.89        30\n",
      "weighted avg       0.98      0.93      0.94        30\n",
      "\n",
      "----------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix: \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prediction_results(cat_model, X_test_pca, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "436Svdg-4FqC"
   },
   "source": [
    "## Model 2: Logistic regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using oneDAL for Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15.7 ms, sys: 4.86 ms, total: 20.5 ms\n",
      "Wall time: 6.81 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;tfidfvectorizer&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;logisticregression&#x27;,\n",
       "                 LogisticRegression(C=2.0, max_iter=500))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;tfidfvectorizer&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;logisticregression&#x27;,\n",
       "                 LogisticRegression(C=2.0, max_iter=500))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=2.0, max_iter=500)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
       "                ('logisticregression',\n",
       "                 LogisticRegression(C=2.0, max_iter=500))])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "%%time\n",
    "from sklearnex.linear_model import LogisticRegression\n",
    "\n",
    "log_reg_intel = make_pipeline(TfidfVectorizer(), LogisticRegression(C=2.0, max_iter = 500))\n",
    "log_reg_intel.fit(type_train_data['Description'].values, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 161
    },
    "id": "9hmUzK7Hi4uF",
    "outputId": "ecca2a05-98a0-4cb4-b2ff-f5c8cae675b3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.96 ms, sys: 8.25 ms, total: 14.2 ms\n",
      "Wall time: 4.74 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;tfidfvectorizer&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;logisticregression&#x27;,\n",
       "                 LogisticRegression(C=2.0, max_iter=500))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;tfidfvectorizer&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;logisticregression&#x27;,\n",
       "                 LogisticRegression(C=2.0, max_iter=500))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=2.0, max_iter=500)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
       "                ('logisticregression',\n",
       "                 LogisticRegression(C=2.0, max_iter=500))])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression as SklearnLogisticRegression\n",
    "\n",
    "log_reg = make_pipeline(TfidfVectorizer(), SklearnLogisticRegression(C=2.0, max_iter = 500))\n",
    "log_reg.fit(type_train_data['Description'].values, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearnex.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression as SklearnLogisticRegression\n",
    "\n",
    "log_reg_sklearnex = make_pipeline(TfidfVectorizer(), LogisticRegression(C=2.0, max_iter=500))\n",
    "log_reg_sklearn = make_pipeline(TfidfVectorizer(), SklearnLogisticRegression(C=2.0, max_iter=500))\n",
    "\n",
    "# Function to measure execution time\n",
    "def measure_time(model, data):\n",
    "    start_time = time.time()\n",
    "    model.fit(data['Description'].values, y_train)\n",
    "    return (time.time() - start_time) * 1000  # Convert to milliseconds\n",
    "\n",
    "# Measure execution times\n",
    "sklearnex_time = [measure_time(log_reg_sklearnex, type_train_data) for _ in range(10)]\n",
    "sklearn_time = [measure_time(log_reg_sklearn, type_train_data) for _ in range(10)]\n",
    "\n",
    "# Plot the comparison graph\n",
    "labels = ['Without OneAPI', 'withOneAPI']\n",
    "times = [sum(sklearnex_time) / len(sklearnex_time), sum(sklearn_time) / len(sklearn_time)]\n",
    "\n",
    "plt.bar(labels, times)\n",
    "plt.ylabel('Average Execution Time (ms)')\n",
    "plt.title('Comparison of Logistic Regression Training Time')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9rglWph-4X25"
   },
   "source": [
    "### Ensemble Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "yIcZRc-ojYZm"
   },
   "outputs": [],
   "source": [
    "# prediction probabilities\n",
    "pred_proba1 = cat_model.predict_proba(X_test_pca)\n",
    "pred_proba2 = log_reg.predict_proba(type_test_data['Description'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "ethtJsg_xugJ"
   },
   "outputs": [],
   "source": [
    "# final ensemble predictions\n",
    "# 32% CatBoostClassifier + 68% Logistic Regression\n",
    "\n",
    "final_test_pred_ = (pred_proba1*0.32 + pred_proba2*0.68)\n",
    "final_test_pred = np.argmax(final_test_pred_, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R26p9_66x1DV",
    "outputId": "456d522e-08d1-4abc-a819-ce396e4bcf5d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error :\t1.70000\n",
      "Weighted F1 Score :\t0.906\n",
      "Accuracy Score :\t90.000 %\n",
      "----------------------------------------------------------------------\n",
      "\n",
      " Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "    cultural       1.00      1.00      1.00         6\n",
      "   emotional       1.00      0.60      0.75         5\n",
      "   isolation       1.00      1.00      1.00         3\n",
      "    physical       0.90      1.00      0.95         9\n",
      "      sexual       1.00      0.67      0.80         3\n",
      "    stalking       1.00      1.00      1.00         3\n",
      "      verbal       0.33      1.00      0.50         1\n",
      "\n",
      "    accuracy                           0.90        30\n",
      "   macro avg       0.89      0.90      0.86        30\n",
      "weighted avg       0.95      0.90      0.91        30\n",
      "\n",
      "----------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, final_test_pred)\n",
    "f1 = f1_score(y_test, final_test_pred, average = 'weighted')\n",
    "acc = accuracy_score(y_test, final_test_pred)\n",
    "cm = confusion_matrix(y_test, final_test_pred)\n",
    "\n",
    "clf = classification_report(y_test, final_test_pred, target_names = list(LE.classes_))\n",
    "\n",
    "print('Mean Squared Error :\\t{0:.5f}'.format(mse))\n",
    "print('Weighted F1 Score :\\t{0:.3f}'.format(f1))\n",
    "print('Accuracy Score :\\t{0:.3f} %'.format(acc*100))\n",
    "print('--'*35)\n",
    "\n",
    "print('\\n Classification Report:\\n',clf)\n",
    "print('--'*35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 460
    },
    "id": "NQ0RntCSx5gX",
    "outputId": "a403f3bd-829e-4152-93a8-5ce9eca5cb85"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x14c7985e0950>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix\n",
    "cmd = ConfusionMatrixDisplay(cm, display_labels = list(LE.classes_))\n",
    "fig, axs = plt.subplots(figsize=(4,4))\n",
    "cmd.plot(ax=axs, cmap=\"bone\", xticks_rotation=\"vertical\", colorbar = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6NEELBRl08rG"
   },
   "source": [
    "## Classifying new input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "ruVZlNFhJUEu"
   },
   "outputs": [],
   "source": [
    "new_data = [\"My partner got really angry last night and started shouting. They hit me in the face a lot, and now I have bruises and a swollen eye.\"]\n",
    "new_data_processed = [text_preprocessing(text) for text in new_data]\n",
    "new_data_processed = [drop_stopwords(text) for text in new_data_processed]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TubNl5gPQS8J",
    "outputId": "5a0d5b80-269d-4f73-ae8f-bc2d7bf421e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Label: physical\n"
     ]
    }
   ],
   "source": [
    "# Predict with Logistic Regression\n",
    "\n",
    "new_pred_log_reg = log_reg.predict(new_data_processed)\n",
    "\n",
    "# Output prediction\n",
    "print(\"Predicted Label:\", LE.inverse_transform(new_pred_log_reg)[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "brB6F4Xy1QQ5"
   },
   "outputs": [],
   "source": [
    "new_data_embeddings = emmedding_model.encode(new_data)\n",
    "new_data_pca = pca.transform(new_data_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "AU147NzUTCrJ"
   },
   "outputs": [],
   "source": [
    "pred_proba1 = cat_model.predict_proba(new_data_pca)\n",
    "pred_proba2 = log_reg.predict_proba(new_data_processed)\n",
    "final_pred_proba = (pred_proba1 * 0.32) + (pred_proba2 * 0.68)\n",
    "final_predictions = np.argmax(final_pred_proba, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eChOg7ynUCE0",
    "outputId": "6a47a31c-93ad-4a69-89a5-bc8e571b87d8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted class: physical\n"
     ]
    }
   ],
   "source": [
    "predicted_class = LE.inverse_transform(final_predictions)[0]\n",
    "print(\"Predicted class:\", predicted_class)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporting pickle file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "TvPBFmxNUqbW"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('cat_model.pkl', 'wb') as cat_model_file:\n",
    "    pickle.dump(cat_model, cat_model_file)\n",
    "\n",
    "with open('log_reg_model.pkl', 'wb') as log_reg_model_file:\n",
    "    pickle.dump(log_reg, log_reg_model_file)\n",
    "\n",
    "weights = {'cat_model_weight': 0.32, 'log_reg_weight': 0.68}\n",
    "\n",
    "with open('ensemble_weights.pkl', 'wb') as weights_file:\n",
    "    pickle.dump(weights, weights_file)\n",
    "\n",
    "pred_proba1 = cat_model.predict_proba(X_test_pca)\n",
    "pred_proba2 = log_reg.predict_proba(type_test_data['Description'].values)\n",
    "final_test_pred_ = (pred_proba1 * weights['cat_model_weight'] + pred_proba2 * weights['log_reg_weight'])\n",
    "\n",
    "with open('ensemble_predictions.pkl', 'wb') as ensemble_pred_file:\n",
    "    pickle.dump(final_test_pred_, ensemble_pred_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sivMps9UvYUq"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "widgets": {
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     "model_module_version": "1.5.0",
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      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
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      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
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