21f3001823-notebook-t12024.ipynb

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    {
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     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/recipe-for-rating-predict-food-ratings-using-ml/sample.csv\n",
      "/kaggle/input/recipe-for-rating-predict-food-ratings-using-ml/train.csv\n",
      "/kaggle/input/recipe-for-rating-predict-food-ratings-using-ml/test.csv\n"
     ]
    }
   ],
   "source": [
    "#Importing required libraries and functions\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn as sk\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import resample\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.feature_selection import SelectPercentile, chi2\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from imblearn.ensemble import BalancedBaggingClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "from scipy.stats import uniform, randint\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8f44b21",
   "metadata": {
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    "tags": []
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   "source": [
    "# Loading the Dataset\n",
    "* Loading train and test dataset\n",
    "* Seeing if their are any NaN values in the dataset\n",
    "* Dealing with the NaN values accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f238ef73",
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   "source": [
    "# Loading the train and test dataset\n",
    "train_dataset = pd.read_csv(\"/kaggle/input/recipe-for-rating-predict-food-ratings-using-ml/train.csv\")\n",
    "test_dataset = pd.read_csv(\"/kaggle/input/recipe-for-rating-predict-food-ratings-using-ml/test.csv\")\n",
    "# Setting a random state to be used everywhere\n",
    "RANDOM_STATE = 6443"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0981e6c9",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13636, 15)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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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>ID</th>\n",
       "      <th>RecipeNumber</th>\n",
       "      <th>RecipeCode</th>\n",
       "      <th>RecipeName</th>\n",
       "      <th>CommentID</th>\n",
       "      <th>UserID</th>\n",
       "      <th>UserName</th>\n",
       "      <th>UserReputation</th>\n",
       "      <th>CreationTimestamp</th>\n",
       "      <th>ReplyCount</th>\n",
       "      <th>ThumbsUpCount</th>\n",
       "      <th>ThumbsDownCount</th>\n",
       "      <th>Rating</th>\n",
       "      <th>BestScore</th>\n",
       "      <th>Recipe_Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>71</td>\n",
       "      <td>12259</td>\n",
       "      <td>Banana Bars with Cream Cheese Frosting</td>\n",
       "      <td>sp_aUSaElGf_12259_c_149978</td>\n",
       "      <td>u_1oKVaBmiEKwAFyiHrDLoWpbel0J</td>\n",
       "      <td>misscoffeepot</td>\n",
       "      <td>20</td>\n",
       "      <td>1622717078</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>great recipe! I have made them just as written...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>8202</td>\n",
       "      <td>Simple Taco Soup</td>\n",
       "      <td>sp_aUSaElGf_8202_c_310332</td>\n",
       "      <td>u_1oKZeRIXYzGNhGW8OMR3O4lX1ih</td>\n",
       "      <td>MichelleMalone</td>\n",
       "      <td>1</td>\n",
       "      <td>1622717852</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>This is an easy and quick recipe that is great...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>458</td>\n",
       "      <td>3</td>\n",
       "      <td>2832</td>\n",
       "      <td>Cheeseburger Soup</td>\n",
       "      <td>sp_aUSaElGf_2832_c_206522</td>\n",
       "      <td>u_1oKYHUtdaD48KJCDYq5wfpAUxWz</td>\n",
       "      <td>jeannem32</td>\n",
       "      <td>1</td>\n",
       "      <td>1622717357</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>I think I was looking for something extra spec...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>50</td>\n",
       "      <td>100276</td>\n",
       "      <td>Grilled Huli Huli Chicken</td>\n",
       "      <td>sp_aUSaElGf_100276_c_434088</td>\n",
       "      <td>u_1oKZCQcKQZdQhDVBS7oISc216VA</td>\n",
       "      <td>ds422</td>\n",
       "      <td>1</td>\n",
       "      <td>1622648899</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>This is our favorite grilled chicken recipe.  ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>19731</td>\n",
       "      <td>Cauliflower Soup</td>\n",
       "      <td>sp_aUSaElGf_19731_c_387011</td>\n",
       "      <td>u_1oKd4sudZQPgu6YALiC7bW7mKMs</td>\n",
       "      <td>SusanHoward</td>\n",
       "      <td>1</td>\n",
       "      <td>1622718260</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>100</td>\n",
       "      <td>Great basic recipe. I added 2 teaspoons Tuscan...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID  RecipeNumber  RecipeCode                              RecipeName  \\\n",
       "0   70            71       12259  Banana Bars with Cream Cheese Frosting   \n",
       "1   72            88        8202                        Simple Taco Soup   \n",
       "2  458             3        2832                       Cheeseburger Soup   \n",
       "3    7            50      100276               Grilled Huli Huli Chicken   \n",
       "4   60            12       19731                        Cauliflower Soup   \n",
       "\n",
       "                     CommentID                         UserID        UserName  \\\n",
       "0   sp_aUSaElGf_12259_c_149978  u_1oKVaBmiEKwAFyiHrDLoWpbel0J   misscoffeepot   \n",
       "1    sp_aUSaElGf_8202_c_310332  u_1oKZeRIXYzGNhGW8OMR3O4lX1ih  MichelleMalone   \n",
       "2    sp_aUSaElGf_2832_c_206522  u_1oKYHUtdaD48KJCDYq5wfpAUxWz       jeannem32   \n",
       "3  sp_aUSaElGf_100276_c_434088  u_1oKZCQcKQZdQhDVBS7oISc216VA           ds422   \n",
       "4   sp_aUSaElGf_19731_c_387011  u_1oKd4sudZQPgu6YALiC7bW7mKMs     SusanHoward   \n",
       "\n",
       "   UserReputation  CreationTimestamp  ReplyCount  ThumbsUpCount  \\\n",
       "0              20         1622717078           0              0   \n",
       "1               1         1622717852           0              0   \n",
       "2               1         1622717357           0              0   \n",
       "3               1         1622648899           0              0   \n",
       "4               1         1622718260           0              0   \n",
       "\n",
       "   ThumbsDownCount  Rating  BestScore  \\\n",
       "0                0       5        100   \n",
       "1                1       5        100   \n",
       "2                0       3        100   \n",
       "3                0       5        100   \n",
       "4                0       4        100   \n",
       "\n",
       "                                       Recipe_Review  \n",
       "0  great recipe! I have made them just as written...  \n",
       "1  This is an easy and quick recipe that is great...  \n",
       "2  I think I was looking for something extra spec...  \n",
       "3  This is our favorite grilled chicken recipe.  ...  \n",
       "4  Great basic recipe. I added 2 teaspoons Tuscan...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Previewing the dataset\n",
    "print(train_dataset.shape)\n",
    "train_dataset.head()"
   ]
  },
  {
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     "text": [
      "(4546, 14)\n"
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       "      <th>RecipeName</th>\n",
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       "      <th>Recipe_Review</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>86</td>\n",
       "      <td>45</td>\n",
       "      <td>41384</td>\n",
       "      <td>Black Bean ‘n’ Pumpkin Chili</td>\n",
       "      <td>sp_aUSaElGf_41384_c_341929</td>\n",
       "      <td>u_1oKc4U6bh65cEli8Z8eTIf98Vpr</td>\n",
       "      <td>kalloween</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>This was so good!!  The pumpkin was perfect as...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102</td>\n",
       "      <td>53</td>\n",
       "      <td>3143</td>\n",
       "      <td>Pumpkin Bars</td>\n",
       "      <td>sp_aUSaElGf_3143_c_274545</td>\n",
       "      <td>u_1oKW5seFLQMSwAeOfBVmwJizy5D</td>\n",
       "      <td>chocolate desert</td>\n",
       "      <td>1</td>\n",
       "      <td>1622717685</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>So good!   Could not stop eating them, so bad....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>156</td>\n",
       "      <td>53</td>\n",
       "      <td>3143</td>\n",
       "      <td>Pumpkin Bars</td>\n",
       "      <td>sp_aUSaElGf_3143_c_398512</td>\n",
       "      <td>u_1oKZ6GUIAIol8xe6oEI7t1Mpxdz</td>\n",
       "      <td>Vantreesta</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>I use a small ladle to fill muffin cups.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>89</td>\n",
       "      <td>1324</td>\n",
       "      <td>Porcupine Meatballs</td>\n",
       "      <td>sp_aUSaElGf_1324_c_431662</td>\n",
       "      <td>u_1oKWCP33NVk4Osfpu4VJuIckEF0</td>\n",
       "      <td>Roberta L.Beauchamp</td>\n",
       "      <td>1</td>\n",
       "      <td>1622648872</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>212</td>\n",
       "      <td>I made this and followed the instructions as d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>70</td>\n",
       "      <td>32264</td>\n",
       "      <td>Easy Chicken Enchiladas</td>\n",
       "      <td>sp_aUSaElGf_32264_c_423935</td>\n",
       "      <td>u_1oKVhIJMuUlrSCVkbO1jheVTol8</td>\n",
       "      <td>fhquilting</td>\n",
       "      <td>1</td>\n",
       "      <td>1622718460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>Very Good!  I used corn tortillas.  Used fresh...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID  RecipeNumber  RecipeCode                    RecipeName  \\\n",
       "0   86            45       41384  Black Bean ‘n’ Pumpkin Chili   \n",
       "1  102            53        3143                  Pumpkin Bars   \n",
       "2  156            53        3143                  Pumpkin Bars   \n",
       "3   18            89        1324           Porcupine Meatballs   \n",
       "4   27            70       32264       Easy Chicken Enchiladas   \n",
       "\n",
       "                    CommentID                         UserID  \\\n",
       "0  sp_aUSaElGf_41384_c_341929  u_1oKc4U6bh65cEli8Z8eTIf98Vpr   \n",
       "1   sp_aUSaElGf_3143_c_274545  u_1oKW5seFLQMSwAeOfBVmwJizy5D   \n",
       "2   sp_aUSaElGf_3143_c_398512  u_1oKZ6GUIAIol8xe6oEI7t1Mpxdz   \n",
       "3   sp_aUSaElGf_1324_c_431662  u_1oKWCP33NVk4Osfpu4VJuIckEF0   \n",
       "4  sp_aUSaElGf_32264_c_423935  u_1oKVhIJMuUlrSCVkbO1jheVTol8   \n",
       "\n",
       "              UserName  UserReputation  CreationTimestamp  ReplyCount  \\\n",
       "0            kalloween               1         1622718043           0   \n",
       "1     chocolate desert               1         1622717685           0   \n",
       "2           Vantreesta               1         1622718307           0   \n",
       "3  Roberta L.Beauchamp               1         1622648872           0   \n",
       "4           fhquilting               1         1622718460           0   \n",
       "\n",
       "   ThumbsUpCount  ThumbsDownCount  BestScore  \\\n",
       "0              0                0        100   \n",
       "1              0                1        100   \n",
       "2              0                0        100   \n",
       "3              4                3        212   \n",
       "4              0                1        100   \n",
       "\n",
       "                                       Recipe_Review  \n",
       "0  This was so good!!  The pumpkin was perfect as...  \n",
       "1  So good!   Could not stop eating them, so bad....  \n",
       "2           I use a small ladle to fill muffin cups.  \n",
       "3  I made this and followed the instructions as d...  \n",
       "4  Very Good!  I used corn tortillas.  Used fresh...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Previewing the test dataset\n",
    "print(test_dataset.shape)\n",
    "test_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b658515",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:33:43.710022Z",
     "iopub.status.busy": "2024-03-12T08:33:43.709702Z",
     "iopub.status.idle": "2024-03-12T08:33:43.728367Z",
     "shell.execute_reply": "2024-03-12T08:33:43.727217Z"
    },
    "papermill": {
     "duration": 0.036907,
     "end_time": "2024-03-12T08:33:43.730320",
     "exception": false,
     "start_time": "2024-03-12T08:33:43.693413",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID: [False]\n",
      "RecipeNumber: [False]\n",
      "RecipeCode: [False]\n",
      "RecipeName: [False]\n",
      "CommentID: [False]\n",
      "UserID: [False]\n",
      "UserName: [False]\n",
      "UserReputation: [False]\n",
      "CreationTimestamp: [False]\n",
      "ReplyCount: [False]\n",
      "ThumbsUpCount: [False]\n",
      "ThumbsDownCount: [False]\n",
      "Rating: [False]\n",
      "BestScore: [False]\n",
      "Recipe_Review: [False  True]\n"
     ]
    }
   ],
   "source": [
    "# Checking for NaN values and seeing which columns have NaN values\n",
    "for i in train_dataset.columns:\n",
    "    print(f\"{i}: {train_dataset[i].isna().unique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "566ac9ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:33:43.764682Z",
     "iopub.status.busy": "2024-03-12T08:33:43.764385Z",
     "iopub.status.idle": "2024-03-12T08:33:43.780702Z",
     "shell.execute_reply": "2024-03-12T08:33:43.780030Z"
    },
    "papermill": {
     "duration": 0.036312,
     "end_time": "2024-03-12T08:33:43.782512",
     "exception": false,
     "start_time": "2024-03-12T08:33:43.746200",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Recipie_Review contains rows with nan values\n",
    "#print(train_dataset[train_dataset[\"Recipe_Review\"].isna() == True])\n",
    "# Since, there are only 2 rows we drop them\n",
    "train_dataset = train_dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d047829a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:33:43.814632Z",
     "iopub.status.busy": "2024-03-12T08:33:43.814344Z",
     "iopub.status.idle": "2024-03-12T08:33:43.820570Z",
     "shell.execute_reply": "2024-03-12T08:33:43.819809Z"
    },
    "papermill": {
     "duration": 0.024366,
     "end_time": "2024-03-12T08:33:43.822522",
     "exception": false,
     "start_time": "2024-03-12T08:33:43.798156",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirming that nan values are removed\n",
    "train_dataset[\"Rating\"].isna().unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01edfb42",
   "metadata": {
    "papermill": {
     "duration": 0.015429,
     "end_time": "2024-03-12T08:33:43.853558",
     "exception": false,
     "start_time": "2024-03-12T08:33:43.838129",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Exploration\n",
    "* Checking the distribution of the **target** variable\n",
    "* Visualizing categorical variables\n",
    "* Visualizing Numerical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "200818cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:33:43.886462Z",
     "iopub.status.busy": "2024-03-12T08:33:43.886128Z",
     "iopub.status.idle": "2024-03-12T08:33:43.916540Z",
     "shell.execute_reply": "2024-03-12T08:33:43.915531Z"
    },
    "papermill": {
     "duration": 0.049257,
     "end_time": "2024-03-12T08:33:43.918582",
     "exception": false,
     "start_time": "2024-03-12T08:33:43.869325",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID: 701\n",
      "RecipeNumber: 100\n",
      "RecipeCode: 100\n",
      "RecipeName: 100\n",
      "CommentID: 13634\n",
      "UserID: 10782\n",
      "UserName: 10619\n",
      "UserReputation: 20\n",
      "CreationTimestamp: 2422\n",
      "ReplyCount: 4\n",
      "ThumbsUpCount: 57\n",
      "ThumbsDownCount: 51\n",
      "Rating: 6\n",
      "BestScore: 525\n",
      "Recipe_Review: 13370\n"
     ]
    }
   ],
   "source": [
    "# Checking the number of unique values in each column\n",
    "for i in train_dataset.columns:\n",
    "    print(f\"{i}: {train_dataset[i].nunique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "79ba411e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:33:43.992331Z",
     "iopub.status.busy": "2024-03-12T08:33:43.991997Z",
     "iopub.status.idle": "2024-03-12T08:33:44.200155Z",
     "shell.execute_reply": "2024-03-12T08:33:44.199313Z"
    },
    "papermill": {
     "duration": 0.267807,
     "end_time": "2024-03-12T08:33:44.202272",
     "exception": false,
     "start_time": "2024-03-12T08:33:43.934465",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dataset is biased towards Rating 5\n",
    "ax = sns.countplot(x = \"Rating\", data = train_dataset, palette='Set3')\n",
    "ax.set_title(\"Rating Distrbution\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3e611552",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:33:44.236735Z",
     "iopub.status.busy": "2024-03-12T08:33:44.236418Z",
     "iopub.status.idle": "2024-03-12T08:33:44.245881Z",
     "shell.execute_reply": "2024-03-12T08:33:44.245005Z"
    },
    "papermill": {
     "duration": 0.028821,
     "end_time": "2024-03-12T08:33:44.247977",
     "exception": false,
     "start_time": "2024-03-12T08:33:44.219156",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 9.33% target values with 0\n",
      "There are 1.54% target values with 1\n",
      "There are 1.28% target values with 2\n",
      "There are 2.7% target values with 3\n",
      "There are 9.1% target values with 4\n",
      "There are 76.05% target values with 5\n"
     ]
    }
   ],
   "source": [
    "# Checking the percentages\n",
    "# 1 and 2 are about 1.3% each\n",
    "# 0 and 4 are about 9.1% each\n",
    "# 5 has the highest 76.05%\n",
    "counts = train_dataset[\"Rating\"].value_counts()\n",
    "for i in range(6):\n",
    "    val = 100 * counts[i]/train_dataset.shape[0]\n",
    "    val = round(val, 2)\n",
    "    print(f\"There are {val}% target values with {i}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "310f54bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:33:44.282370Z",
     "iopub.status.busy": "2024-03-12T08:33:44.282087Z",
     "iopub.status.idle": "2024-03-12T08:33:50.964749Z",
     "shell.execute_reply": "2024-03-12T08:33:50.963858Z"
    },
    "papermill": {
     "duration": 6.702382,
     "end_time": "2024-03-12T08:33:50.966950",
     "exception": false,
     "start_time": "2024-03-12T08:33:44.264568",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Everything matches\n"
     ]
    }
   ],
   "source": [
    "# Firstly we see that RecipeNumber, RecipeCode, and RecipeName seem to be similar\n",
    "# We see their is only possibility for RecipeCode and RecipeName for every RecipeNumber\n",
    "# We choose to drop RecipeCode\n",
    "# We keep RecipeNumber as some recipes can get more rating than others\n",
    "# We add RecipeName to Recipe_review to add more textual for the Vectorizer\n",
    "flag = True\n",
    "for i in train_dataset[\"RecipeNumber\"]:\n",
    "    test = len(train_dataset[\"RecipeCode\"][train_dataset[\"RecipeNumber\"] == i].unique())\n",
    "    test2 = len(train_dataset[\"RecipeName\"][train_dataset[\"RecipeNumber\"] == i].unique())\n",
    "    if test != 1 or test2 != 1:\n",
    "        flag = False\n",
    "        print(f\"{i} has more than one value\")\n",
    "if flag: print(\"Everything matches\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a697669",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:33:51.001260Z",
     "iopub.status.busy": "2024-03-12T08:33:51.000944Z",
     "iopub.status.idle": "2024-03-12T08:34:20.183960Z",
     "shell.execute_reply": "2024-03-12T08:34:20.182953Z"
    },
    "papermill": {
     "duration": 29.202529,
     "end_time": "2024-03-12T08:34:20.186151",
     "exception": false,
     "start_time": "2024-03-12T08:33:50.983622",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Users seem to have multiple UserIDs\n"
     ]
    }
   ],
   "source": [
    "# CommentID is unique for every row and can be dropped\n",
    "# UserID and UserName also seem to be similar\n",
    "# Since each user has multiple IDs, it would be better to drop UserID column\n",
    "# and keep UserName column. As it can be used in case some users always give a\n",
    "# higher rating\n",
    "flag = True\n",
    "for i in train_dataset[\"UserName\"]:\n",
    "    test = train_dataset[\"UserID\"][train_dataset[\"UserName\"] == i].unique()\n",
    "    if len(test) != 1:\n",
    "        flag = False\n",
    "        #print(f\"{i} has more than one value, they are {test}\")\n",
    "if flag: \n",
    "    print(\"Everything matches\")\n",
    "else:\n",
    "    print(\"Users seem to have multiple UserIDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b2daf276",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:20.220370Z",
     "iopub.status.busy": "2024-03-12T08:34:20.220074Z",
     "iopub.status.idle": "2024-03-12T08:34:20.578039Z",
     "shell.execute_reply": "2024-03-12T08:34:20.577191Z"
    },
    "papermill": {
     "duration": 0.377237,
     "end_time": "2024-03-12T08:34:20.580125",
     "exception": false,
     "start_time": "2024-03-12T08:34:20.202888",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{5: [60, 510, 110, 130, 150, 300], 4: [160, 220]}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UserReputation has 20 unique values\n",
    "# Based on the count plot it is biased towards a reputation of 1\n",
    "reputations = train_dataset[\"UserReputation\"].unique()\n",
    "merge = {}\n",
    "for i in reputations:\n",
    "    val = train_dataset['Rating'][train_dataset['UserReputation'] == i].unique()\n",
    "    if len(val) == 1:\n",
    "        if val[0] not in merge: merge[val[0]] = []\n",
    "        merge[val[0]].append(i)\n",
    "print(merge)\n",
    "# Some Reputations only correspond to a single rating, we can merge them\n",
    "# Specifically, we can merge 60, 510, 110, 130, 150, 300 into a single label\n",
    "# and 160, 220 into a single label\n",
    "ax = sns.countplot(x = \"UserReputation\", data = train_dataset, palette = 'Set3')\n",
    "ax.set_title(\"UserReputation Distrbution\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9f0497c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:20.615870Z",
     "iopub.status.busy": "2024-03-12T08:34:20.615539Z",
     "iopub.status.idle": "2024-03-12T08:34:20.863000Z",
     "shell.execute_reply": "2024-03-12T08:34:20.862124Z"
    },
    "papermill": {
     "duration": 0.267523,
     "end_time": "2024-03-12T08:34:20.865152",
     "exception": false,
     "start_time": "2024-03-12T08:34:20.597629",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: [5 3 4 0 2 1]\n",
      "1: [0 4 5 1 2 3]\n",
      "2: [3 4 5 0]\n",
      "3: [0 5 2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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3v/9dKSkpKikpqXU550o6/wNyyZIlatu2rTkSVPPypl0ZGRn6+uuvaz1GpVpUVJSGDBlie3/Z2dnat2+fAgMD1apVKzVu3FiVlZW1/m7UPJfzz2vlypXauXOn/Pz81KZNm1qT8i+V2+3W/PnzFRkZqeLiYsu3SwMCAiyB/PybXWZmZio7O1uVlZW17ml2vsaNGysgIMC89Lht2zYVFRUpMDBQ/v7+th5QPWjQIH344Yfm+ocffqg2bdrIx8en1s9w4MCBF90fbmyMNAE23XnnnZb19evXm/NvIiIiNHLkSMs3tQoLC7Vv3z7z7uDVfugySMeOHRUaGqpvv/1Whw8ftgSGLl262LrdwPluuummWpfhYmNjL3jJLTQ0VE8++aTltgqGYejo0aPKycnRoUOHzA+7mufi7++v0aNHW0ZQiouLtX//fktgioyM1MiRIy3HvPXWW2tNOHe73aqqqlLnzp0vaXTtUtx8882Wb4uVlpYqJydH2dnZlm9dXYqjR48qOztb+/fvtwQmh8Ohnj176rHHHqvXDTbLysqUl5ennJwc7d+/3zKHqV27duratau5HhERYZl4X15ern379ik7O/sH7xNmV/fu3VVWVqYDBw5YApPD4dB9991nGV0KDQ2t9R+F6i9PNG/eXNHR0Rc8jsPhUPfu3c11wzCUm5ur7Oxsy+XOH9KlSxcNGTLE/LtqGIaOHDlS6/dr4MCBlmMBdWGkCbCpc+fOCgsLM+fmfPfdd8rOzjbvfhwTE6M2bdooIyNDBw4c0MmTJ3X27Fn5+/srJCRErVu3VqdOnSxzQc7XpEkTPfDAA1qzZo327t2r06dPy+l0mo9Rqe9E3b59+1ouc11sBKV58+Z68skntW/fPu3Zs8d8fEdlZaUCAwPVsmVLRUdH1/owDA8P1y9+8QvzMSrfffedOSJV/RiVuLi4WvNcGjVqpLFjxyo1NVX79u1TeXm5QkNDddttt6lv376aN29evc77Yvz9/fXYY48pNTVVhw8frjWfqr4cDocaNWqkxo0bKyQkRG3atFHPnj1tfxOxpoEDB6p169Y6fPiwiouLdfr0afN9qH5Qba9evSwTkx0Oh0aPHq0vv/xSubm5On369BV9uHLPnj0VFxenr776SkePHrU8RqWu+0Hdd999Cg4O1s6dO+V2u9W0aVPFxMRo0KBBWrVq1Q8ea8iQIWrUqJH27NljBulLFR8fr+joaG3evFmHDh0yR0ObNm2qdu3aqU+fPrW+BAHUxWHwmHLAo85/vpWdSw6Xat26dea8k9DQUCUnJ3vNt8oAoKFgpAm4TuXl5ZmjE9u2bTO39+vXj8AEAPVAaAKuU/v376/1Tb02bdqoV69eHqoIABo2QhNwnXM4HGrWrJm6du2qQYMGXfAhrACAH8acJgAAABu45QAAAIANhCYAAAAbPDqnKS0tTa+99poyMzN17NgxLVu27IJft/7FL36ht99+W6+//romTpxobi8sLNTTTz+tzz77TD4+Pho5cqTmzp1rubnajh07NGHCBGVkZKhVq1Z6+umnNXXqVMv+P/roI73wwgs6ePCgOnXqpFdffVXDhg2zfS5VVVU6evSomjVrxjeTAABoIAzD0KlTpxQZGXnxBzEbHvTPf/7TmDFjhvHJJ58Ykoxly5bV2e+TTz4x4uLijMjISOP111+3tA0dOtSIi4szNm7caHz11VdGx44djUceecRsd7lcRnh4uDFmzBhj165dxv/9v//XCAoKMt5++22zz/r16w1fX19j9uzZxp49e4znn3/eaNSokbFz507b55KXl2dIYmFhYWFhYWmAS15e3kU/671mIrjD4ahzpOnbb79Vv3799Pnnn2v48OGaOHGiOdJUfTfmjIwM8/ESq1at0rBhw3TkyBFFRkZq/vz5mjFjhvLz881HXDz33HNavny5eRv+hx9+WKWlpVqxYoV53P79+6tHjx5asGCBrfpdLpdCQkKUl5dnecI3AADwXm63W1FRUSouLr7og7S9+pYDVVVVevTRRzVlypQ6Hy6anp6ukJAQy/O4EhIS5OPjo02bNun+++9Xenq6BgwYYHkmWGJiol599VUVFRWpefPmSk9P16RJkyz7TkxM1PLly23XWn1JLjg4mNAEAEADY2dqjVeHpldffVV+fn761a9+VWd7fn6+5aGUkuTn56fQ0FDzYZv5+fm1HggZHh5utjVv3lz5+fnmtpp9fuiBneXl5ZYHZlY/ywgAAFyfvPbbc5mZmZo7d64WLlzolROrZ82aJafTaS5RUVGeLgkAAFxFXhuavvrqKx0/flzt2rWTn5+f/Pz8dOjQIU2ePNl8inZERIT5xPlqlZWVKiwsVEREhNmnoKDA0qd6/WJ9qtvrMn36dLlcLnPJy8u7rPMFAADezWtD06OPPqodO3YoKyvLXCIjIzVlyhR9/vnnkqT4+HgVFxcrMzPTfN3q1atVVVWlfv36mX3S0tJUUVFh9klJSVGXLl3UvHlzs09qaqrl+CkpKYqPj79gfQEBAeb8JeYxAQBw/fPonKaSkhJ988035npubq6ysrIUGhqqdu3aqUWLFpb+jRo1UkREhLp06SJJiomJ0dChQzVu3DgtWLBAFRUVSk5O1qhRoxQZGSlJGj16tF5++WUlJSVp2rRp2rVrl+bOnavXX3/d3O8zzzyjgQMHas6cORo+fLg+/PBDbdmyRe+88841+CkAAIAGwfaNiK6CNWvW1HmvhLFjx9bZv3379rXu03Ty5EnjkUceMZo2bWoEBwcbjz/+uHHq1ClLn+3btxt33nmnERAQYLRp08b4wx/+UGvfS5cuNTp37mz4+/sbt9xyi7Fy5cpLOheXy2VIMlwu1yW9DgAAeM6lfH57zX2aGjq32y2n0ymXy8WlOgAAGohL+fz22jlNAAAA3oTQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANjg0ceoQJqfkebpEvC/nuozwNMlAAC8GCNNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADZ4NDSlpaXpvvvuU2RkpBwOh5YvX262VVRUaNq0aerWrZuaNGmiyMhIPfbYYzp69KhlH4WFhRozZoyCg4MVEhKipKQklZSUWPrs2LFDd911lwIDAxUVFaXZs2fXquWjjz5S165dFRgYqG7duumf//znVTlnAADQMHk0NJWWliouLk5vvfVWrbbTp09r69ateuGFF7R161Z98sknysnJ0X/8x39Y+o0ZM0a7d+9WSkqKVqxYobS0NI0fP95sd7vdGjJkiNq3b6/MzEy99tpreumll/TOO++YfTZs2KBHHnlESUlJ2rZtm0aMGKERI0Zo165dV+/kAQBAg+IwDMPwdBGS5HA4tGzZMo0YMeKCfTIyMtS3b18dOnRI7dq1U3Z2tmJjY5WRkaHevXtLklatWqVhw4bpyJEjioyM1Pz58zVjxgzl5+fL399fkvTcc89p+fLl2rt3ryTp4YcfVmlpqVasWGEeq3///urRo4cWLFhgq3632y2n0ymXy6Xg4GDb5z0/I812X1xdT/UZ4OkSAADX2KV8fjeoOU0ul0sOh0MhISGSpPT0dIWEhJiBSZISEhLk4+OjTZs2mX0GDBhgBiZJSkxMVE5OjoqKisw+CQkJlmMlJiYqPT39grWUl5fL7XZbFgAAcP1qMKGprKxM06ZN0yOPPGImwfz8fIWFhVn6+fn5KTQ0VPn5+Waf8PBwS5/q9Yv1qW6vy6xZs+R0Os0lKirq8k4QAAB4tQYRmioqKvTQQw/JMAzNnz/f0+VIkqZPny6Xy2UueXl5ni4JAABcRX6eLuBiqgPToUOHtHr1asv1xoiICB0/ftzSv7KyUoWFhYqIiDD7FBQUWPpUr1+sT3V7XQICAhQQEFD/EwMAAA2KV480VQemffv26csvv1SLFi0s7fHx8SouLlZmZqa5bfXq1aqqqlK/fv3MPmlpaaqoqDD7pKSkqEuXLmrevLnZJzU11bLvlJQUxcfHX61TAwAADYxHQ1NJSYmysrKUlZUlScrNzVVWVpYOHz6siooK/fSnP9WWLVu0aNEinTt3Tvn5+crPz9fZs2clSTExMRo6dKjGjRunzZs3a/369UpOTtaoUaMUGRkpSRo9erT8/f2VlJSk3bt3a8mSJZo7d64mTZpk1vHMM89o1apVmjNnjvbu3auXXnpJW7ZsUXJy8jX/mQAAAO/k0VsOrF27VoMHD661fezYsXrppZcUHR1d5+vWrFmjQYMGSfr+5pbJycn67LPP5OPjo5EjR2revHlq2rSp2X/Hjh2aMGGCMjIy1LJlSz399NOaNm2aZZ8fffSRnn/+eR08eFCdOnXS7NmzNWzYMNvnwi0HGj5uOQAAN55L+fz2mvs0NXSEpoaP0AQAN57r9j5NAAAAnkJoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADY4NHQlJaWpvvuu0+RkZFyOBxavny5pd0wDM2cOVOtW7dWUFCQEhIStG/fPkufwsJCjRkzRsHBwQoJCVFSUpJKSkosfXbs2KG77rpLgYGBioqK0uzZs2vV8tFHH6lr164KDAxUt27d9M9//vOKny8AAGi4PBqaSktLFRcXp7feeqvO9tmzZ2vevHlasGCBNm3apCZNmigxMVFlZWVmnzFjxmj37t1KSUnRihUrlJaWpvHjx5vtbrdbQ4YMUfv27ZWZmanXXntNL730kt555x2zz4YNG/TII48oKSlJ27Zt04gRIzRixAjt2rXr6p08AABoUByGYRieLkKSHA6Hli1bphEjRkj6fpQpMjJSkydP1q9//WtJksvlUnh4uBYuXKhRo0YpOztbsbGxysjIUO/evSVJq1at0rBhw3TkyBFFRkZq/vz5mjFjhvLz8+Xv7y9Jeu6557R8+XLt3btXkvTwww+rtLRUK1asMOvp37+/evTooQULFtiq3+12y+l0yuVyKTg42PZ5z89Is90XV9dTfQZ4ugQAwDV2KZ/fXjunKTc3V/n5+UpISDC3OZ1O9evXT+np6ZKk9PR0hYSEmIFJkhISEuTj46NNmzaZfQYMGGAGJklKTExUTk6OioqKzD41j1Pdp/o4dSkvL5fb7bYsAADg+uW1oSk/P1+SFB4ebtkeHh5utuXn5yssLMzS7ufnp9DQUEufuvZR8xgX6lPdXpdZs2bJ6XSaS1RU1KWeIgAAaEC8NjR5u+nTp8vlcplLXl6ep0sCAABXkdeGpoiICElSQUGBZXtBQYHZFhERoePHj1vaKysrVVhYaOlT1z5qHuNCfarb6xIQEKDg4GDLAgAArl9eG5qio6MVERGh1NRUc5vb7damTZsUHx8vSYqPj1dxcbEyMzPNPqtXr1ZVVZX69etn9klLS1NFRYXZJyUlRV26dFHz5s3NPjWPU92n+jgAAAAeDU0lJSXKyspSVlaWpO8nf2dlZenw4cNyOByaOHGifvvb3+rTTz/Vzp079dhjjykyMtL8hl1MTIyGDh2qcePGafPmzVq/fr2Sk5M1atQoRUZGSpJGjx4tf39/JSUlaffu3VqyZInmzp2rSZMmmXU888wzWrVqlebMmaO9e/fqpZde0pYtW5ScnHytfyQAAMBL+Xny4Fu2bNHgwYPN9eogM3bsWC1cuFBTp05VaWmpxo8fr+LiYt15551atWqVAgMDzdcsWrRIycnJuueee+Tj46ORI0dq3rx5ZrvT6dQXX3yhCRMmqFevXmrZsqVmzpxpuZfT7bffrsWLF+v555/Xf/3Xf6lTp05avny5br311mvwUwAAAA2B19ynqaHjPk0NH/dpAoAbz3VxnyYAAABvQmgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANjg1aHp3LlzeuGFFxQdHa2goCDdfPPN+s1vfiPDMMw+hmFo5syZat26tYKCgpSQkKB9+/ZZ9lNYWKgxY8YoODhYISEhSkpKUklJiaXPjh07dNdddykwMFBRUVGaPXv2NTlHAADQMHh1aHr11Vc1f/58vfnmm8rOztarr76q2bNn609/+pPZZ/bs2Zo3b54WLFigTZs2qUmTJkpMTFRZWZnZZ8yYMdq9e7dSUlK0YsUKpaWlafz48Wa72+3WkCFD1L59e2VmZuq1117TSy+9pHfeeeeani8AAPBeDqPmsI2X+fGPf6zw8HC9++675raRI0cqKChIH3zwgQzDUGRkpCZPnqxf//rXkiSXy6Xw8HAtXLhQo0aNUnZ2tmJjY5WRkaHevXtLklatWqVhw4bpyJEjioyM1Pz58zVjxgzl5+fL399fkvTcc89p+fLl2rt3r61a3W63nE6nXC6XgoODbZ/j/Iw0231xdT3VZ4CnSwAAXGOX8vnt1SNNt99+u1JTU/X1119LkrZv365///vf+tGPfiRJys3NVX5+vhISEszXOJ1O9evXT+np6ZKk9PR0hYSEmIFJkhISEuTj46NNmzaZfQYMGGAGJklKTExUTk6OioqKrvp5AgAA7+fn6QJ+yHPPPSe3262uXbvK19dX586d0+9+9zuNGTNGkpSfny9JCg8Pt7wuPDzcbMvPz1dYWJil3c/PT6GhoZY+0dHRtfZR3da8efNatZWXl6u8vNxcd7vdl3OqAADAy3n1SNPSpUu1aNEiLV68WFu3btX777+v//N//o/ef/99T5emWbNmyel0mktUVJSnSwIAAFeRV4emKVOm6LnnntOoUaPUrVs3Pfroo3r22Wc1a9YsSVJERIQkqaCgwPK6goICsy0iIkLHjx+3tFdWVqqwsNDSp6591DzG+aZPny6Xy2UueXl5l3m2AADAm3l1aDp9+rR8fKwl+vr6qqqqSpIUHR2tiIgIpaammu1ut1ubNm1SfHy8JCk+Pl7FxcXKzMw0+6xevVpVVVXq16+f2SctLU0VFRVmn5SUFHXp0qXOS3OSFBAQoODgYMsCAACuX14dmu677z797ne/08qVK3Xw4EEtW7ZMf/zjH3X//fdLkhwOhyZOnKjf/va3+vTTT7Vz50499thjioyM1IgRIyRJMTExGjp0qMaNG6fNmzdr/fr1Sk5O1qhRoxQZGSlJGj16tPz9/ZWUlKTdu3dryZIlmjt3riZNmuSpUwcAAF7GqyeC/+lPf9ILL7ygX/7ylzp+/LgiIyP1n//5n5o5c6bZZ+rUqSotLdX48eNVXFysO++8U6tWrVJgYKDZZ9GiRUpOTtY999wjHx8fjRw5UvPmzTPbnU6nvvjiC02YMEG9evVSy5YtNXPmTMu9nAAAwI3Nq+/T1JBwn6aGj/s0AcCN57q5TxMAAIC3qFdouvvuu1VcXFxru9vt1t133325NQEAAHideoWmtWvX6uzZs7W2l5WV6auvvrrsogAAALzNJU0E37Fjh/nnPXv2mHfUlqRz585p1apVatOmzZWrDgAAwEtcUmjq0aOHHA6HHA5HnZfhgoKC9Kc//emKFQcAAOAtLik05ebmyjAM3XTTTdq8ebNatWpltvn7+yssLEy+vr5XvEgAAABPu6TQ1L59e0ky78gNAABwo6j3zS337dunNWvW6Pjx47VCVM2bTwIAAFwP6hWa/vKXv+ipp55Sy5YtFRERIYfDYbY5HA5CEwAAuO7UKzT99re/1e9+9ztNmzbtStcDAADglep1n6aioiI9+OCDV7oWAAAAr1Wv0PTggw/qiy++uNK1AAAAeK16XZ7r2LGjXnjhBW3cuFHdunVTo0aNLO2/+tWvrkhxAAAA3sJhGIZxqS+Kjo6+8A4dDh04cOCyimqILuUpyTXNz0i7ilXhUjzVZ4CnSwAAXGOX8vldr5Gm3NzcehUGAADQUNVrThMAAMCNpl4jTU888cQPtv/1r3+tVzEAAADeql6hqaioyLJeUVGhXbt2qbi4uM4H+QIAADR09QpNy5Ytq7WtqqpKTz31lG6++ebLLgoAAMDbXLE5TT4+Ppo0aZJef/31K7VLAAAAr3FFJ4Lv379flZWVV3KXAAAAXqFel+cmTZpkWTcMQ8eOHdPKlSs1duzYK1IYAACAN6lXaNq2bZtl3cfHR61atdKcOXMu+s06AACAhqheoWnNmjVXug4AAACvVq/QVO3EiRPKycmRJHXp0kWtWrW6IkUBAAB4m3pNBC8tLdUTTzyh1q1ba8CAARowYIAiIyOVlJSk06dPX+kaAQAAPK5eoWnSpElat26dPvvsMxUXF6u4uFj/+Mc/tG7dOk2ePPlK1wgAAOBx9bo89/e//10ff/yxBg0aZG4bNmyYgoKC9NBDD2n+/PlXqj4AAACvUK+RptOnTys8PLzW9rCwMC7PAQCA61K9QlN8fLxefPFFlZWVmdvOnDmjl19+WfHx8VesOAAAAG9Rr8tzb7zxhoYOHaq2bdsqLi5OkrR9+3YFBAToiy++uKIFAgAAeIN6haZu3bpp3759WrRokfbu3StJeuSRRzRmzBgFBQVd0QIBAAC8Qb1C06xZsxQeHq5x48ZZtv/1r3/ViRMnNG3atCtSHAAAgLeo15ymt99+W127dq21/ZZbbtGCBQsuuygAAABvU6/QlJ+fr9atW9fa3qpVKx07duyyiwIAAPA29QpNUVFRWr9+fa3t69evV2Rk5GUXBQAA4G3qNadp3LhxmjhxoioqKnT33XdLklJTUzV16lTuCA4AAK5L9QpNU6ZM0cmTJ/XLX/5SZ8+elSQFBgZq2rRpmj59+hUtEAAAwBvUKzQ5HA69+uqreuGFF5Sdna2goCB16tRJAQEBV7o+AAAAr1Cv0FStadOm6tOnz5WqBQAAwGvVayI4AADAjYbQBAAAYIPXh6Zvv/1WP/vZz9SiRQsFBQWpW7du2rJli9luGIZmzpyp1q1bKygoSAkJCdq3b59lH4WFhRozZoyCg4MVEhKipKQklZSUWPrs2LFDd911lwIDAxUVFaXZs2dfk/MDAAANg1eHpqKiIt1xxx1q1KiR/vWvf2nPnj2aM2eOmjdvbvaZPXu25s2bpwULFmjTpk1q0qSJEhMTVVZWZvYZM2aMdu/erZSUFK1YsUJpaWkaP3682e52uzVkyBC1b99emZmZeu211/TSSy/pnXfeuabnCwAAvJfDMAzD00VcyHPPPaf169frq6++qrPdMAxFRkZq8uTJ+vWvfy1JcrlcCg8P18KFCzVq1ChlZ2crNjZWGRkZ6t27tyRp1apVGjZsmI4cOaLIyEjNnz9fM2bMUH5+vvz9/c1jL1++3Hwg8cW43W45nU65XC4FBwfbPsf5GWm2++LqeqrPAE+XAAC4xi7l89urR5o+/fRT9e7dWw8++KDCwsLUs2dP/eUvfzHbc3NzlZ+fr4SEBHOb0+lUv379lJ6eLklKT09XSEiIGZgkKSEhQT4+Ptq0aZPZZ8CAAWZgkqTExETl5OSoqKioztrKy8vldrstCwAAuH55dWg6cOCA5s+fr06dOunzzz/XU089pV/96ld6//33JX3/DDxJCg8Pt7wuPDzcbMvPz1dYWJil3c/PT6GhoZY+de2j5jHON2vWLDmdTnOJioq6zLMFAADezKtDU1VVlW677Tb9/ve/V8+ePTV+/HiNGzdOCxYs8HRpmj59ulwul7nk5eV5uiQAAHAVeXVoat26tWJjYy3bYmJidPjwYUlSRESEJKmgoMDSp6CgwGyLiIjQ8ePHLe2VlZUqLCy09KlrHzWPcb6AgAAFBwdbFgAAcP3y6tB0xx13KCcnx7Lt66+/Vvv27SVJ0dHRioiIUGpqqtnudru1adMmxcfHS5Li4+NVXFyszMxMs8/q1atVVVWlfv36mX3S0tJUUVFh9klJSVGXLl0s39QDAAA3Lq8OTc8++6w2btyo3//+9/rmm2+0ePFivfPOO5owYYKk75+BN3HiRP32t7/Vp59+qp07d+qxxx5TZGSkRowYIen7kamhQ4dq3Lhx2rx5s9avX6/k5GSNGjVKkZGRkqTRo0fL399fSUlJ2r17t5YsWaK5c+dq0qRJnjp1AADgZS7r2XNXW58+fbRs2TJNnz5dr7zyiqKjo/XGG29ozJgxZp+pU6eqtLRU48ePV3Fxse68806tWrVKgYGBZp9FixYpOTlZ99xzj3x8fDRy5EjNmzfPbHc6nfriiy80YcIE9erVSy1bttTMmTMt93ICAAA3Nq++T1NDwn2aGj7u0wQAN57r5j5NAAAA3oLQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwoUGFpj/84Q9yOByaOHGiua2srEwTJkxQixYt1LRpU40cOVIFBQWW1x0+fFjDhw9X48aNFRYWpilTpqiystLSZ+3atbrtttsUEBCgjh07auHChdfgjAAAQEPRYEJTRkaG3n77bXXv3t2y/dlnn9Vnn32mjz76SOvWrdPRo0f1wAMPmO3nzp3T8OHDdfbsWW3YsEHvv/++Fi5cqJkzZ5p9cnNzNXz4cA0ePFhZWVmaOHGinnzySX3++efX7PwAAIB3axChqaSkRGPGjNFf/vIXNW/e3Nzucrn07rvv6o9//KPuvvtu9erVS++99542bNigjRs3SpK++OIL7dmzRx988IF69OihH/3oR/rNb36jt956S2fPnpUkLViwQNHR0ZozZ45iYmKUnJysn/70p3r99dc9cr4AAMD7NIjQNGHCBA0fPlwJCQmW7ZmZmaqoqLBs79q1q9q1a6f09HRJUnp6urp166bw8HCzT2Jiotxut3bv3m32OX/fiYmJ5j7qUl5eLrfbbVkAAMD1y8/TBVzMhx9+qK1btyojI6NWW35+vvz9/RUSEmLZHh4ervz8fLNPzcBU3V7d9kN93G63zpw5o6CgoFrHnjVrll5++eV6nxcAAGhYvHqkKS8vT88884wWLVqkwMBAT5djMX36dLlcLnPJy8vzdEkAAOAq8urQlJmZqePHj+u2226Tn5+f/Pz8tG7dOs2bN09+fn4KDw/X2bNnVVxcbHldQUGBIiIiJEkRERG1vk1XvX6xPsHBwXWOMklSQECAgoODLQsAALh+eXVouueee7Rz505lZWWZS+/evTVmzBjzz40aNVJqaqr5mpycHB0+fFjx8fGSpPj4eO3cuVPHjx83+6SkpCg4OFixsbFmn5r7qO5TvQ8AAACvntPUrFkz3XrrrZZtTZo0UYsWLcztSUlJmjRpkkJDQxUcHKynn35a8fHx6t+/vyRpyJAhio2N1aOPPqrZs2crPz9fzz//vCZMmKCAgABJ0i9+8Qu9+eabmjp1qp544gmtXr1aS5cu1cqVK6/tCQMAAK/l1aHJjtdff10+Pj4aOXKkysvLlZiYqD//+c9mu6+vr1asWKGnnnpK8fHxatKkicaOHatXXnnF7BMdHa2VK1fq2Wef1dy5c9W2bVv993//txITEz1xSgAAwAs5DMMwPF3E9cDtdsvpdMrlcl3S/Kb5GWlXsSpciqf6DPB0CQCAa+xSPr+9ek4TAACAtyA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABs8OrQNGvWLPXp00fNmjVTWFiYRowYoZycHEufsrIyTZgwQS1atFDTpk01cuRIFRQUWPocPnxYw4cPV+PGjRUWFqYpU6aosrLS0mft2rW67bbbFBAQoI4dO2rhwoVX+/QAAEAD4tWhad26dZowYYI2btyolJQUVVRUaMiQISotLTX7PPvss/rss8/00Ucfad26dTp69KgeeOABs/3cuXMaPny4zp49qw0bNuj999/XwoULNXPmTLNPbm6uhg8frsGDBysrK0sTJ07Uk08+qc8///yani8AAPBeDsMwDE8XYdeJEycUFhamdevWacCAAXK5XGrVqpUWL16sn/70p5KkvXv3KiYmRunp6erfv7/+9a9/6cc//rGOHj2q8PBwSdKCBQs0bdo0nThxQv7+/po2bZpWrlypXbt2mccaNWqUiouLtWrVKlu1ud1uOZ1OuVwuBQcH2z6n+Rlpl/ATwNX0VJ8Bni4BAHCNXcrnt1ePNJ3P5XJJkkJDQyVJmZmZqqioUEJCgtmna9euateundLT0yVJ6enp6tatmxmYJCkxMVFut1u7d+82+9TcR3Wf6n3Upby8XG6327IAAIDrV4MJTVVVVZo4caLuuOMO3XrrrZKk/Px8+fv7KyQkxNI3PDxc+fn5Zp+agam6vbrth/q43W6dOXOmznpmzZolp9NpLlFRUZd9jgAAwHs1mNA0YcIE7dq1Sx9++KGnS5EkTZ8+XS6Xy1zy8vI8XRIAALiK/DxdgB3JyclasWKF0tLS1LZtW3N7RESEzp49q+LiYstoU0FBgSIiIsw+mzdvtuyv+tt1Nfuc/427goICBQcHKygoqM6aAgICFBAQcNnnBgAAGgavHmkyDEPJyclatmyZVq9erejoaEt7r1691KhRI6WmpprbcnJydPjwYcXHx0uS4uPjtXPnTh0/ftzsk5KSouDgYMXGxpp9au6juk/1PgAAALx6pGnChAlavHix/vGPf6hZs2bmHCSn06mgoCA5nU4lJSVp0qRJCg0NVXBwsJ5++mnFx8erf//+kqQhQ4YoNjZWjz76qGbPnq38/Hw9//zzmjBhgjlS9Itf/EJvvvmmpk6dqieeeEKrV6/W0qVLtXLlSo+dOwAA8C5ePdI0f/58uVwuDRo0SK1btzaXJUuWmH1ef/11/fjHP9bIkSM1YMAARURE6JNPPjHbfX19tWLFCvn6+io+Pl4/+9nP9Nhjj+mVV14x+0RHR2vlypVKSUlRXFyc5syZo//+7/9WYmLiNT1fAADgvRrUfZq8Gfdpavi4TxMA3Hiu2/s0AQAAeAqhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6HpPG+99ZY6dOigwMBA9evXT5s3b/Z0SQAAwAv4eboAb7JkyRJNmjRJCxYsUL9+/fTGG28oMTFROTk5CgsL83R5uA6cOZPq6RLwv4KC7vF0CQAaGEaaavjjH/+ocePG6fHHH1dsbKwWLFigxo0b669//aunSwMAAB5GaPpfZ8+eVWZmphISEsxtPj4+SkhIUHp6ugcrAwAA3oDLc//ru+++07lz5xQeHm7ZHh4err1799bqX15ervLycnPd5XJJktxu9yUd90xJaT2qxdVwqe9dfZw5w/vtLSoqrv77vT4t56ofA/bcMaCLp0uAl6r+t98wjIv2JTTV06xZs/Tyyy/X2h4VFeWBanAlTPZ0AQAAjzl16pScTucP9iE0/a+WLVvK19dXBQUFlu0FBQWKiIio1X/69OmaNGmSuV5VVaXCwkK1aNFCDofjqtfrLdxut6KiopSXl6fg4GBPl4OrjPf7xsL7fWO5Ud9vwzB06tQpRUZGXrQvoel/+fv7q1evXkpNTdWIESMkfR+EUlNTlZycXKt/QECAAgICLNtCQkKuQaXeKTg4+Ib6JbvR8X7fWHi/byw34vt9sRGmaoSmGiZNmqSxY8eqd+/e6tu3r9544w2Vlpbq8ccf93RpAADAwwhNNTz88MM6ceKEZs6cqfz8fPXo0UOrVq2qNTkcAADceAhN50lOTq7zchzqFhAQoBdffLHWpUpcn3i/byy83zcW3u+Lcxh2vmMHAABwg+PmlgAAADYQmgAAAGwgNAEAANhAaAIAALCB0ITL8tZbb6lDhw4KDAxUv379tHnzZk+XhKsgLS1N9913nyIjI+VwOLR8+XJPl4SraNasWerTp4+aNWumsLAwjRgxQjk5PEfvejV//nx1797dvKllfHy8/vWvf3m6LK9EaEK9LVmyRJMmTdKLL76orVu3Ki4uTomJiTp+/LinS8MVVlpaqri4OL311lueLgXXwLp16zRhwgRt3LhRKSkpqqio0JAhQ1RaygOnr0dt27bVH/7wB2VmZmrLli26++679ZOf/ES7d+/2dGleh1sOoN769eunPn366M0335T0/WNnoqKi9PTTT+u5557zcHW4WhwOh5YtW2Y+bgjXvxMnTigsLEzr1q3TgAEDPF0OroHQ0FC99tprSkpK8nQpXoWRJtTL2bNnlZmZqYSEBHObj4+PEhISlJ6e7sHKAFxpLpdL0vcfpLi+nTt3Th9++KFKS0sVHx/v6XK8DncER7189913OnfuXK1HzISHh2vv3r0eqgrAlVZVVaWJEyfqjjvu0K233urpcnCV7Ny5U/Hx8SorK1PTpk21bNkyxcbGerosr0NoAgBc0IQJE7Rr1y79+9//9nQpuIq6dOmirKwsuVwuffzxxxo7dqzWrVtHcDoPoQn10rJlS/n6+qqgoMCyvaCgQBERER6qCsCVlJycrBUrVigtLU1t27b1dDm4ivz9/dWxY0dJUq9evZSRkaG5c+fq7bff9nBl3oU5TagXf39/9erVS6mpqea2qqoqpaamch0caOAMw1BycrKWLVum1atXKzo62tMl4RqrqqpSeXm5p8vwOow0od4mTZqksWPHqnfv3urbt6/eeOMNlZaW6vHHH/d0abjCSkpK9M0335jrubm5ysrKUmhoqNq1a+fBynA1TJgwQYsXL9Y//vEPNWvWTPn5+ZIkp9OpoKAgD1eHK2369On60Y9+pHbt2unUqVNavHix1q5dq88//9zTpXkdbjmAy/Lmm2/qtddeU35+vnr06KF58+apX79+ni4LV9jatWs1ePDgWtvHjh2rhQsXXvuCcFU5HI46t7/33nv6+c9/fm2LwVWXlJSk1NRUHTt2TE6nU927d9e0adN07733ero0r0NoAgAAsIE5TQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAN5yFCxcqJCTE02UAaGAITQC8ys9//nM5HA45HA41atRI0dHRmjp1qsrKyjxdmv7+979r0KBBcjqdatq0qbp3765XXnlFhYWF17SOl156ST169LimxwRAaALghYYOHapjx47pwIEDev311/X222/rxRdf9GhNM2bM0MMPP6w+ffroX//6l3bt2qU5c+Zo+/bt+p//+R+P1gbg2iA0AfA6AQEBioiIUFRUlEaMGKGEhASlpKRI+v7p67NmzVJ0dLSCgoIUFxenjz/+2Hzt2rVr5XA4tHLlSnXv3l2BgYHq37+/du3aVeexDh48KB8fH23ZssWy/Y033lD79u1VVVWlzZs36/e//73mzJmj1157Tbfffrs6dOige++9V3//+981duxY83Xz58/XzTffLH9/f3Xp0sUSqA4ePCiHw6GsrCxzW3FxsRwOh9auXWupPzU1Vb1791bjxo11++23KycnR9L3lxZffvllbd++3RyR4/l/wLVBaALg1Xbt2qUNGzbI399fkjRr1iz97W9/04IFC7R79249++yz+tnPfqZ169ZZXjdlyhTNmTNHGRkZatWqle677z5VVFTU2n+HDh2UkJCg9957z7K9+uG0Pj4+WrRokZo2bapf/vKXddZYPT9q2bJleuaZZzR58mTt2rVL//mf/6nHH39ca9asueTznjFjhubMmaMtW7bIz89PTzzxhCTp4Ycf1uTJk3XLLbfo2LFjOnbsmB5++OFL3j+AejAAwIuMHTvW8PX1NZo0aWIEBAQYkgwfHx/j448/NsrKyozGjRsbGzZssLwmKSnJeOSRRwzDMIw1a9YYkowPP/zQbD958qQRFBRkLFmyxDAMw3jvvfcMp9Npti9ZssRo3ry5UVZWZhiGYWRmZhoOh8PIzc01DMMwfvSjHxndu3e/aO233367MW7cOMu2Bx980Bg2bJhhGIaRm5trSDK2bdtmthcVFRmSjDVr1ljq//LLL80+K1euNCQZZ86cMQzDMF588UUjLi7uovUAuLIYaQLgdQYPHqysrCxt2rRJY8eO1eOPP66RI0fqm2++0enTp3XvvfeqadOm5vK3v/1N+/fvt+wjPj7e/HNoaKi6dOmi7OzsOo83YsQI+fr6atmyZZK+vwQ2ePBgdejQQZJkGIaturOzs3XHHXdYtt1xxx0XPO4P6d69u/nn1q1bS5KOHz9+yfsBcOX4eboAADhfkyZN1LFjR0nSX//6V8XFxendd9/VrbfeKklauXKl2rRpY3lNQEBAvY/n7++vxx57TO+9954eeOABLV68WHPnzjXbO3furH//+9+qqKhQo0aN6n0cH5/v/59aM4TVdclQkuU4DodD0vfzuQB4DiNNALyaj4+P/uu//kvPP/+8YmNjFRAQoMOHD6tjx46WJSoqyvK6jRs3mn8uKirS119/rZiYmAse58knn9SXX36pP//5z6qsrNQDDzxgto0ePVolJSX685//XOdri4uLJUkxMTFav369pW39+vWKjY2VJLVq1UqSdOzYMbO95qRwu/z9/XXu3LlLfh2Ay8NIEwCv9+CDD2rKlCl6++239etf/1rPPvusqqqqdOedd8rlcmn9+vUKDg62fIvtlVdeUYsWLRQeHq4ZM2aoZcuWGjFixAWPERMTo/79+2vatGl64oknFBQUZLb169dPU6dO1eTJk/Xtt9/q/vvvV2RkpL755hstWLBAd955p5555hlNmTJFDz30kHr27KmEhAR99tln+uSTT/Tll19KkoKCgtS/f3/94Q9/UHR0tI4fP67nn3/+kn8eHTp0UG5urrKystS2bVs1a9bsskbaANjk6UlVAFDT2LFjjZ/85Ce1ts+aNcto1aqVUVJSYrzxxhtGly5djEaNGhmtWrUyEhMTjXXr1hmG8f8nUn/22WfGLbfcYvj7+xt9+/Y1tm/fbu7r/Ing1d59911DkrF58+Y6a1uyZIkxYMAAo1mzZkaTJk2M7t27G6+88opRVFRk9vnzn/9s3HTTTUajRo2Mzp07G3/7298s+9izZ48RHx9vBAUFGT169DC++OKLOieC19zntm3bDEnmxPSysjJj5MiRRkhIiCHJeO+99y76cwVw+RyGYXOGIwA0AGvXrtXgwYNVVFR0yY9K+c1vfqOPPvpIO3bsuDrFAWjQmNME4IZXUlKiXbt26c0339TTTz/t6XIAeClCE4AbXnJysnr16qVBgwaZN5EEgPNxeQ4AAMAGRpoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbPh/WWgqDleX8JgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ReplyCount is heavily biased towards 0, with very low values for others\n",
    "replys = train_dataset[\"ReplyCount\"].unique()\n",
    "for i in replys:\n",
    "    val = train_dataset['Rating'][train_dataset['ReplyCount'] == i].unique()\n",
    "    print(f\"{i}: {val}\")\n",
    "ax = sns.countplot(x = \"ReplyCount\", data = train_dataset, palette='Set3')\n",
    "ax.set_title(\"ReplyCount Distrbution\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e5c53e03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:20.903862Z",
     "iopub.status.busy": "2024-03-12T08:34:20.903346Z",
     "iopub.status.idle": "2024-03-12T08:34:21.205604Z",
     "shell.execute_reply": "2024-03-12T08:34:21.204735Z"
    },
    "papermill": {
     "duration": 0.324919,
     "end_time": "2024-03-12T08:34:21.207808",
     "exception": false,
     "start_time": "2024-03-12T08:34:20.882889",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the correlation table to gain more insights\n",
    "# Both ThumbUp and ThumbsDown seem to be correlated to BestScore\n",
    "# Most probably some combination of both is being used to calculate BestScore\n",
    "numerical = [\"ThumbsUpCount\", \"ThumbsDownCount\", \"BestScore\"]\n",
    "ax = sns.heatmap(train_dataset[numerical].corr(), annot = True)\n",
    "ax.set_title(\"Correlation Heatmap\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "09c85131",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:21.246429Z",
     "iopub.status.busy": "2024-03-12T08:34:21.245839Z",
     "iopub.status.idle": "2024-03-12T08:34:29.762999Z",
     "shell.execute_reply": "2024-03-12T08:34:29.762059Z"
    },
    "papermill": {
     "duration": 8.541183,
     "end_time": "2024-03-12T08:34:29.767158",
     "exception": false,
     "start_time": "2024-03-12T08:34:21.225975",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 808.875x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making pairplot with different numerical variables to see how they are distributed and to see how they could be related\n",
    "ax = sns.pairplot(train_dataset, hue = \"Rating\", vars = numerical, palette = 'Set3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51c39a21",
   "metadata": {
    "papermill": {
     "duration": 0.02048,
     "end_time": "2024-03-12T08:34:29.808916",
     "exception": false,
     "start_time": "2024-03-12T08:34:29.788436",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Result from EDA\n",
    "* **RecipeNumer, RecipieCode, RecipieName** follow the same pattern only 1 needed\n",
    "* We pick **RecipeNumber** as it represents the ranking of the recipies on the top 100 recipies list, we take it as a numerical variable.\n",
    "* We also take **RecipeName**, but we merge it with **Recipe_review** to give it more textual data.\n",
    "* As there are only 20 values in **UserReputation**, we consider it to be categorical variable, we also merge few categories into one as they correspond to only one **Rating**\n",
    "* **ReplyCount** also only has 4 values, we take it as categorical variable\n",
    "* **BestScore** contains the ranking of the comments, we take it as numerical variable as there are 525 unique values.\n",
    "* **Recipie_Review** we take as text data and perform sentiment analysis on it.\n",
    "* **CommentID** has no patterns as such it does not give any information\n",
    "* **UserID** and **UserName** also has no benefit as they just specify which user wrote the comment, but we may be able to use **UserName**.\n",
    "* **ThumbsUpCount** and **ThumbsDownCount** will be taken as numerical variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a77623fc",
   "metadata": {
    "papermill": {
     "duration": 0.020167,
     "end_time": "2024-03-12T08:34:29.849638",
     "exception": false,
     "start_time": "2024-03-12T08:34:29.829471",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Feature Extraction\n",
    "* Dropping unecessary columns\n",
    "* Merging columns\n",
    "* Splitting into train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6bb41acd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:29.894843Z",
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     "shell.execute_reply": "2024-03-12T08:34:29.915070Z"
    },
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     "end_time": "2024-03-12T08:34:29.918388",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Downsampling and Upsampling to try and reduce imbalance\n",
    "# There are two ways to upsample data, either copy rows or create synthetic data using SMOTE\n",
    "# We will first be testing by copying rows\n",
    "#resampled_data = train_dataset.copy()\n",
    "FACTOR = 3\n",
    "rating_0 = train_dataset[train_dataset[\"Rating\"] == 0]\n",
    "rating_1 = train_dataset[train_dataset[\"Rating\"] == 1]\n",
    "# Increasing 1,2,3 by a factor of 5\n",
    "rating_1_up = resample(rating_1, replace=True, n_samples= int(FACTOR * rating_1.shape[0]), random_state = RANDOM_STATE)\n",
    "rating_2 = train_dataset[train_dataset[\"Rating\"] == 2]\n",
    "rating_2_up = resample(rating_2, replace=True, n_samples= int(FACTOR * rating_2.shape[0]), random_state = RANDOM_STATE)\n",
    "rating_3 = train_dataset[train_dataset[\"Rating\"] == 3]\n",
    "rating_3_up = resample(rating_3, replace=True, n_samples= int(FACTOR * rating_3.shape[0]), random_state = RANDOM_STATE)\n",
    "rating_4 = train_dataset[train_dataset[\"Rating\"] == 4]\n",
    "rating_5 = train_dataset[train_dataset[\"Rating\"] == 5]\n",
    "# Decreasing 5 by a factor of 5\n",
    "rating_5_down = resample(rating_5, n_samples = int(rating_5.shape[0] / FACTOR), random_state = RANDOM_STATE)\n",
    "resampled_data = pd.concat([rating_0, rating_1_up, rating_2_up, rating_3_up, rating_4, rating_5_down])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "da8cdb8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:29.966107Z",
     "iopub.status.busy": "2024-03-12T08:34:29.965801Z",
     "iopub.status.idle": "2024-03-12T08:34:29.981974Z",
     "shell.execute_reply": "2024-03-12T08:34:29.981033Z"
    },
    "papermill": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Instead of downsampling and oversamplung, we can try to make two different models\n",
    "# One which classifies rating into 5 and not 5\n",
    "# and another which classifies 0, 1, 2, 3, 4\n",
    "rating_5 = train_dataset[train_dataset[\"Rating\"] == 5]\n",
    "rating_not5 = train_dataset[train_dataset[\"Rating\"] != 5]\n",
    "rating_5 = rating_5.assign(Rating = 1)\n",
    "rating_alt = rating_not5.assign(Rating = 0)\n",
    "train_model1 = pd.concat([rating_5, rating_alt])\n",
    "train_model2 = rating_not5.copy()\n",
    "X1 = train_model1.drop(\"Rating\", axis = 1)\n",
    "y1 = train_model1[\"Rating\"]\n",
    "X2 = train_model2.drop(\"Rating\", axis = 1)\n",
    "y2 = train_model2[\"Rating\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1e1fc5c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:30.027038Z",
     "iopub.status.busy": "2024-03-12T08:34:30.026741Z",
     "iopub.status.idle": "2024-03-12T08:34:30.033339Z",
     "shell.execute_reply": "2024-03-12T08:34:30.032410Z"
    },
    "papermill": {
     "duration": 0.030363,
     "end_time": "2024-03-12T08:34:30.035358",
     "exception": false,
     "start_time": "2024-03-12T08:34:30.004995",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Splitting train dataset into X and y\n",
    "#X = train_dataset.drop(\"Rating\", axis = 1)\n",
    "#y = train_dataset[\"Rating\"]\n",
    "X = resampled_data.drop(\"Rating\", axis = 1)\n",
    "y = resampled_data[\"Rating\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "87194602",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:30.078631Z",
     "iopub.status.busy": "2024-03-12T08:34:30.078291Z",
     "iopub.status.idle": "2024-03-12T08:34:30.097031Z",
     "shell.execute_reply": "2024-03-12T08:34:30.096147Z"
    },
    "papermill": {
     "duration": 0.042781,
     "end_time": "2024-03-12T08:34:30.098917",
     "exception": false,
     "start_time": "2024-03-12T08:34:30.056136",
     "status": "completed"
    },
    "tags": []
   },
   "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>RecipeNumber</th>\n",
       "      <th>UserReputation</th>\n",
       "      <th>ReplyCount</th>\n",
       "      <th>ThumbsUpCount</th>\n",
       "      <th>ThumbsDownCount</th>\n",
       "      <th>BestScore</th>\n",
       "      <th>Recipe_Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>136</td>\n",
       "      <td>First-Place Coconut Macaroons. I made this. .....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>193</td>\n",
       "      <td>Chicken Wild Rice Soup. I&amp;#39;m going to try t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>Creamy White Chili. I omitted the green chilis...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>485</td>\n",
       "      <td>Chicken and Dumplings. At end of instructions,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>Porcupine Meatballs. I make them all the time ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RecipeNumber  UserReputation  ReplyCount  ThumbsUpCount  ThumbsDownCount  \\\n",
       "10            86               1           0              3               10   \n",
       "17            67               1           0              1                0   \n",
       "20             1               1           0              0                0   \n",
       "23            63               1           0             23                0   \n",
       "24            89               1           0              0                0   \n",
       "\n",
       "    BestScore                                      Recipe_Review  \n",
       "10        136  First-Place Coconut Macaroons. I made this. .....  \n",
       "17        193  Chicken Wild Rice Soup. I&#39;m going to try t...  \n",
       "20        100  Creamy White Chili. I omitted the green chilis...  \n",
       "23        485  Chicken and Dumplings. At end of instructions,...  \n",
       "24        100  Porcupine Meatballs. I make them all the time ...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping columns, and merging columns\n",
    "X[\"Recipe_Review\"] = X[\"RecipeName\"] + \". \" + X[\"Recipe_Review\"]\n",
    "remove = [\"ID\", \"CommentID\", \"UserID\", \"UserName\", \"CreationTimestamp\", \"RecipeCode\", \"RecipeName\"]\n",
    "X = X.drop(remove, axis = 1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "33acccc9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:30.142089Z",
     "iopub.status.busy": "2024-03-12T08:34:30.141806Z",
     "iopub.status.idle": "2024-03-12T08:34:30.157933Z",
     "shell.execute_reply": "2024-03-12T08:34:30.157061Z"
    },
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     "exception": false,
     "start_time": "2024-03-12T08:34:30.120421",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Performing the same transformations for X1 and X2\n",
    "X1[\"Recipe_Review\"] = X1[\"RecipeName\"] + \". \" + X1[\"Recipe_Review\"]\n",
    "X1 = X1.drop(remove, axis = 1)\n",
    "X2[\"Recipe_Review\"] = X2[\"RecipeName\"] + \". \" + X2[\"Recipe_Review\"]\n",
    "X2 = X2.drop(remove, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bd214206",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:30.203021Z",
     "iopub.status.busy": "2024-03-12T08:34:30.202726Z",
     "iopub.status.idle": "2024-03-12T08:34:30.217873Z",
     "shell.execute_reply": "2024-03-12T08:34:30.217182Z"
    },
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     "duration": 0.038881,
     "end_time": "2024-03-12T08:34:30.219930",
     "exception": false,
     "start_time": "2024-03-12T08:34:30.181049",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Merging a few categories of UserReputation\n",
    "five = [510, 110, 130, 150, 300]\n",
    "five_change = 60\n",
    "four = [220]\n",
    "four_change = 160\n",
    "user_rep = X[\"UserReputation\"].values\n",
    "for i in range(len(user_rep)):\n",
    "    if user_rep[i] in five:\n",
    "        user_rep[i] = five_change\n",
    "    elif user_rep[i] in four:\n",
    "        user_rep[i] = four_change\n",
    "X.loc[:, \"UserReputation\"] = user_rep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5fcabc61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:30.265594Z",
     "iopub.status.busy": "2024-03-12T08:34:30.265245Z",
     "iopub.status.idle": "2024-03-12T08:34:30.292203Z",
     "shell.execute_reply": "2024-03-12T08:34:30.291443Z"
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     "start_time": "2024-03-12T08:34:30.241649",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Merging a few categories of UserReputation\n",
    "five = [510, 110, 130, 150, 300]\n",
    "five_change = 60\n",
    "four = [220]\n",
    "four_change = 160\n",
    "user_rep = X1[\"UserReputation\"].values\n",
    "for i in range(len(user_rep)):\n",
    "    if user_rep[i] in five:\n",
    "        user_rep[i] = five_change\n",
    "    elif user_rep[i] in four:\n",
    "        user_rep[i] = four_change\n",
    "X1.loc[:, \"UserReputation\"] = user_rep\n",
    "user_rep = X2[\"UserReputation\"].values\n",
    "for i in range(len(user_rep)):\n",
    "    if user_rep[i] in five:\n",
    "        user_rep[i] = five_change\n",
    "    elif user_rep[i] in four:\n",
    "        user_rep[i] = four_change\n",
    "X2.loc[:, \"UserReputation\"] = user_rep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ad53f3b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:30.336910Z",
     "iopub.status.busy": "2024-03-12T08:34:30.336609Z",
     "iopub.status.idle": "2024-03-12T08:34:30.351145Z",
     "shell.execute_reply": "2024-03-12T08:34:30.350299Z"
    },
    "papermill": {
     "duration": 0.038284,
     "end_time": "2024-03-12T08:34:30.353323",
     "exception": false,
     "start_time": "2024-03-12T08:34:30.315039",
     "status": "completed"
    },
    "tags": []
   },
   "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>RecipeNumber</th>\n",
       "      <th>UserReputation</th>\n",
       "      <th>ReplyCount</th>\n",
       "      <th>ThumbsUpCount</th>\n",
       "      <th>ThumbsDownCount</th>\n",
       "      <th>BestScore</th>\n",
       "      <th>Recipe_Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>Black Bean ‘n’ Pumpkin Chili. This was so good...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>Pumpkin Bars. So good!   Could not stop eating...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>Pumpkin Bars. I use a small ladle to fill muff...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>212</td>\n",
       "      <td>Porcupine Meatballs. I made this and followed ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>Easy Chicken Enchiladas. Very Good!  I used co...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RecipeNumber  UserReputation  ReplyCount  ThumbsUpCount  ThumbsDownCount  \\\n",
       "0            45               1           0              0                0   \n",
       "1            53               1           0              0                1   \n",
       "2            53               1           0              0                0   \n",
       "3            89               1           0              4                3   \n",
       "4            70               1           0              0                1   \n",
       "\n",
       "   BestScore                                      Recipe_Review  \n",
       "0        100  Black Bean ‘n’ Pumpkin Chili. This was so good...  \n",
       "1        100  Pumpkin Bars. So good!   Could not stop eating...  \n",
       "2        100  Pumpkin Bars. I use a small ladle to fill muff...  \n",
       "3        212  Porcupine Meatballs. I made this and followed ...  \n",
       "4        100  Easy Chicken Enchiladas. Very Good!  I used co...  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Performing same operations on test_dataset so we don't have to do it again\n",
    "test_dataset[\"Recipe_Review\"] = test_dataset[\"RecipeName\"] + \". \" + test_dataset[\"Recipe_Review\"]\n",
    "test_dataset = test_dataset.drop(remove, axis = 1)\n",
    "test_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "546bcec6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:30.396769Z",
     "iopub.status.busy": "2024-03-12T08:34:30.396459Z",
     "iopub.status.idle": "2024-03-12T08:34:30.407562Z",
     "shell.execute_reply": "2024-03-12T08:34:30.406734Z"
    },
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     "start_time": "2024-03-12T08:34:30.374508",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Changing the same categories of UserReputation in test dataset\n",
    "five = [510, 110, 130, 150, 300]\n",
    "five_change = 60\n",
    "four = [220]\n",
    "four_change = 160\n",
    "user_rep = test_dataset[\"UserReputation\"].values\n",
    "for i in range(len(user_rep)):\n",
    "    if user_rep[i] in five:\n",
    "        user_rep[i] = five_change\n",
    "    elif user_rep[i] in four:\n",
    "        user_rep[i] = four_change\n",
    "test_dataset.loc[:, \"UserReputation\"] = user_rep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8967f3f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:30.453763Z",
     "iopub.status.busy": "2024-03-12T08:34:30.453380Z",
     "iopub.status.idle": "2024-03-12T08:34:30.478994Z",
     "shell.execute_reply": "2024-03-12T08:34:30.478096Z"
    },
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     "start_time": "2024-03-12T08:34:30.430772",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Performing a train test split using stratify, such that we have equal representation\n",
    "# of all Ratings in both train and test\n",
    "trainX, testX, trainY, testY = train_test_split(X, y, test_size = 0.1, stratify = y, random_state = RANDOM_STATE)\n",
    "trainX1, testX1, trainY1, testY1 = train_test_split(X1, y1, test_size = 0.1, stratify = y1, random_state = RANDOM_STATE)\n",
    "trainX2, testX2, trainY2, testY2 = train_test_split(X2, y2, test_size = 0.1, stratify = y2, random_state = RANDOM_STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "31da0fd9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:30.524048Z",
     "iopub.status.busy": "2024-03-12T08:34:30.523734Z",
     "iopub.status.idle": "2024-03-12T08:34:30.529560Z",
     "shell.execute_reply": "2024-03-12T08:34:30.528696Z"
    },
    "papermill": {
     "duration": 0.029577,
     "end_time": "2024-03-12T08:34:30.531578",
     "exception": false,
     "start_time": "2024-03-12T08:34:30.502001",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7402, 7) (7402,)\n",
      "(823, 7) (823,)\n",
      "(12270, 7) (12270,)\n",
      "(1364, 7) (1364,)\n",
      "(2938, 7) (2938,)\n",
      "(327, 7) (327,)\n"
     ]
    }
   ],
   "source": [
    "print(trainX.shape, trainY.shape)\n",
    "print(testX.shape, testY.shape)\n",
    "print(trainX1.shape, trainY1.shape)\n",
    "print(testX1.shape, testY1.shape)\n",
    "print(trainX2.shape, trainY2.shape)\n",
    "print(testX2.shape, testY2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49936dd8",
   "metadata": {
    "papermill": {
     "duration": 0.020699,
     "end_time": "2024-03-12T08:34:30.573122",
     "exception": false,
     "start_time": "2024-03-12T08:34:30.552423",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Defining Models and Pipelines\n",
    "* Define Transformers and Pipelines\n",
    "* Add Classifier to the pipeline\n",
    "* Define hyperparameters to search for in Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "03ffdb70",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:30.615913Z",
     "iopub.status.busy": "2024-03-12T08:34:30.615597Z",
     "iopub.status.idle": "2024-03-12T08:34:30.622179Z",
     "shell.execute_reply": "2024-03-12T08:34:30.621412Z"
    },
    "papermill": {
     "duration": 0.030149,
     "end_time": "2024-03-12T08:34:30.624152",
     "exception": false,
     "start_time": "2024-03-12T08:34:30.594003",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Defining Transformers for every type of variable\n",
    "# and merging them into a single Pipeline\n",
    "numeric_features = [\"RecipeNumber\", \"BestScore\", \"ThumbsUpCount\", \"ThumbsDownCount\"]\n",
    "numeric_transformer = Pipeline(\n",
    "    steps=[(\"scaler\", StandardScaler())]\n",
    ")\n",
    "\n",
    "categorical_features = [\"UserReputation\", \"ReplyCount\"]\n",
    "categorical_transformer = Pipeline(\n",
    "    steps=[\n",
    "        (\"encoder\", OneHotEncoder(handle_unknown=\"ignore\")),\n",
    "        #(\"selector\", SelectPercentile(chi2, percentile=80)),\n",
    "    ]\n",
    ")\n",
    "\n",
    "text_features = \"Recipe_Review\"\n",
    "text_transformer = Pipeline(\n",
    "steps=[#(\"text_preprocess\", CountVectorizer()),\n",
    "       ('tfidf', TfidfVectorizer(use_idf = True, stop_words = 'english')),\n",
    "      ])\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"num\", numeric_transformer, numeric_features),\n",
    "        (\"cat\", categorical_transformer, categorical_features),\n",
    "        (\"text\", text_transformer, text_features),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a3d098d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:30.667198Z",
     "iopub.status.busy": "2024-03-12T08:34:30.666918Z",
     "iopub.status.idle": "2024-03-12T08:34:30.676296Z",
     "shell.execute_reply": "2024-03-12T08:34:30.675635Z"
    },
    "papermill": {
     "duration": 0.033221,
     "end_time": "2024-03-12T08:34:30.678187",
     "exception": false,
     "start_time": "2024-03-12T08:34:30.644966",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Adding different Classifiers at the end of Preprocess pipeline\n",
    "log_pipeline = Pipeline(\n",
    "    steps=[(\"preprocessor\", preprocessor), (\"log\", LogisticRegression(solver = 'liblinear', class_weight=\"balanced\", random_state = RANDOM_STATE))]\n",
    ")\n",
    "random_forest_pipeline = Pipeline(\n",
    "    steps=[(\"preprocessor\", preprocessor), (\"rfr\", RandomForestClassifier(class_weight=\"balanced\", random_state = RANDOM_STATE))]\n",
    ")\n",
    "SVM_pipeline = Pipeline(\n",
    "    steps=[(\"preprocessor\", preprocessor), (\"svm\", SVC(class_weight=\"balanced\", random_state = RANDOM_STATE))]\n",
    ")\n",
    "KNN_pipeline = Pipeline(\n",
    "    steps=[(\"preprocessor\", preprocessor), (\"knn\", KNeighborsClassifier())]\n",
    ")\n",
    "AdaBoost_pipeline = Pipeline(\n",
    "    steps=[(\"preprocessor\", preprocessor), (\"ada\", AdaBoostClassifier(DecisionTreeClassifier(max_depth = 100, class_weight=\"balanced\", random_state = RANDOM_STATE)))]\n",
    ")\n",
    "BlanBag_pipeline = Pipeline(\n",
    "    steps=[(\"preprocessor\", preprocessor), (\"blb\", BalancedBaggingClassifier(estimator=DecisionTreeClassifier(random_state = RANDOM_STATE), sampling_strategy='not majority',replacement=False, random_state = RANDOM_STATE))]\n",
    ")\n",
    "MLP_pipeline = Pipeline(\n",
    "    steps=[(\"preprocessor\", preprocessor), (\"mlp\", MLPClassifier(random_state = RANDOM_STATE))]\n",
    ")\n",
    "models = [log_pipeline, random_forest_pipeline, SVM_pipeline, KNN_pipeline, AdaBoost_pipeline, BlanBag_pipeline, MLP_pipeline]\n",
    "model_name = [\"LogReg\", \"RandForest\", \"SVM\", \"KNN\", \"AdaBoost\", \"BalBag\", \"MLP\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e5512e6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:34:30.721134Z",
     "iopub.status.busy": "2024-03-12T08:34:30.720859Z",
     "iopub.status.idle": "2024-03-12T08:40:10.688593Z",
     "shell.execute_reply": "2024-03-12T08:40:10.687770Z"
    },
    "papermill": {
     "duration": 340.014733,
     "end_time": "2024-03-12T08:40:10.713786",
     "exception": false,
     "start_time": "2024-03-12T08:34:30.699053",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogReg, Accuracy: 0.67, Precision: 0.65, F1 Score:0.66\n",
      "RandForest, Accuracy: 0.72, Precision: 0.74, F1 Score:0.68\n",
      "SVM, Accuracy: 0.58, Precision: 0.63, F1 Score:0.60\n",
      "KNN, Accuracy: 0.53, Precision: 0.50, F1 Score:0.50\n",
      "AdaBoost, Accuracy: 0.71, Precision: 0.70, F1 Score:0.69\n",
      "BalBag, Accuracy: 0.68, Precision: 0.66, F1 Score:0.61\n",
      "MLP, Accuracy: 0.69, Precision: 0.67, F1 Score:0.68\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Training Base models and comparing their performace\n",
    "acc = []\n",
    "prec = []\n",
    "f1_score = []\n",
    "for i in range(len(models)):\n",
    "    models[i].fit(trainX, trainY)\n",
    "    acc.append(models[i].score(testX, testY))\n",
    "    preds = models[i].predict(testX)\n",
    "    p, _, f1, _ = precision_recall_fscore_support(testY, preds, average = \"weighted\")\n",
    "    prec.append(p)\n",
    "    f1_score.append(f1)\n",
    "    print(f\"{model_name[i]}, Accuracy: {acc[i]:.2f}, Precision: {prec[i]:.2f}, F1 Score:{f1_score[i]:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "59cddfc9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:40:10.758653Z",
     "iopub.status.busy": "2024-03-12T08:40:10.757951Z",
     "iopub.status.idle": "2024-03-12T08:40:11.044375Z",
     "shell.execute_reply": "2024-03-12T08:40:11.043467Z"
    },
    "papermill": {
     "duration": 0.310958,
     "end_time": "2024-03-12T08:40:11.046461",
     "exception": false,
     "start_time": "2024-03-12T08:40:10.735503",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.67 0.72 0.58 0.53 0.71 0.68 0.69]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
     "data": {
      "image/png": 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w4EAcOXIEQO1E27i4OLi6uqKyshJFRUXicxgbG2PIkCF613a/CgoK8Pnnn0OtVqO4uFgyx8bGxkacSNvwUM8vv/yC06dPQxAE5OTkNDqc2Rp17t69GyqVCo6OjrCysoJCoUBBQYGk5ro5M0qlEsOGDRMPT2q1WsTHx6Nz5864ffu2JPAqFAoMGzasVeunhwtHbki26gJCnfpnWzTUo0cPyf26cGJlZYUXXnhBMlyu0Whw6dIlnDt3TuchrrFjx4qnUtcpLCzE2bNnkZ+f3+gMJTs7O/HDuM6NGzdQVVUFW1vbO34B3L167LHHJIdbrly5gnPnzqGiouKOV31uzvbXsbe3b/SleO7u7pKzbu6VQqHApEmTEBQUJBn1KS0txblz53D+/HnJ5Of6fYKDgyVndFVVVeHPP/+UBBsTExNMnDhRPBuqLQUEBKCyshIXLlyQhAQjIyNMmDBBHM2ws7ND//79xccFQUBWVhYuXboEKysrnd8M3Bq0Wi3y8/Nx7tw5nD17VlKzpaWl5FBZYGCg5BuEtVotLl26JAk2SqUSTz31VKOQT3Q/OHJDstXwkNSdwo2Pj0+ja009/vjjUCgU6Ny5M1577TWkpqbi7NmzKC4ubnT5gfqjFsbGxpg8eTIyMzMll19QqVTi5RcazsMZO3YsrK2tcfLkSZSVlcHKygo9e/bEiBEjxP9874e7uzvCw8Oxd+9eXL58GQqFAmq1GkOHDoWDg8MdD03pu/31BQQESObzNGfUpo5SqcQTTzwBf39/pKWliae037p1S5xf07lzZ/j6+koOL6lUKkycOBG9e/dGenq65PdR//IL+nx/TksKCgqCp6cnDh06hIKCAiiVSri7u+Oxxx5r9KWPTz31FGxtbXHs2DFcv34dFhYW8PLywuOPP47ff/+9Vev09fWFQqHApUuXUFJSgsrKSmg0GpiYmIj7cdCgQY0Os4aEhKBHjx5ITU1Fdna2zssv8AKd1NIUAi+fS0St5Pjx49ixYweA2nk40dHRTV6/62Fxt2uJEdH948gNEbWooqIinD9/HhUVFeLkaKD2C/Qe9mBDRG2D4YaIWlReXh52794taevYsSMnjBJRm2G4IaJWY2VlhW7duuGxxx67p4szEhG1BM65ISIiIlnhqeBEREQkKww3REREJCsP3Zybmpoa5OXloUOHDs2+vgoRERG1LUEQUF5eDhcXl7teA+6hCzd5eXlwc3MzdBlERETUDDk5OejcufMd+zx04abua+RzcnLu6erGREREZHhlZWVwc3O7p6vHP3Thpu5QlLW1NcMNERHRA+ZeppRwQjERERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREcmKkaELoAfHzZuJhi6h1ZmbP27oEoiI6D5x5IaIiIhkheGGiIiIZIXhhoiIiGSFc26IiAzkkx0phi6hVc0eH2DoEughxZEbIiIikhWGGyIiIpKVdhFu1qxZAw8PD5iZmSEwMBApKU0P1Y4YMQIKhaLRbcyYMW1YMREREbVXBg83CQkJiI6ORkxMDNLS0tC3b1+EhISgqKhIZ//t27cjPz9fvJ06dQoqlQqTJk1q48qJiIioPTJ4uFmxYgVmzJiBiIgI+Pr6Ii4uDhYWFoiPj9fZ387ODmq1Wrzt3r0bFhYWDDdEREQEwMDhpqqqCqmpqQgODhbblEolgoODkZycfE/rWLduHZ5//nlYWlrqfFyj0aCsrExyIyIiIvkyaLgpKSmBVquFk5OTpN3JyQkFBQV3XT4lJQWnTp3CSy+91GSf2NhY2NjYiDc3N7f7rpuIiIjaL4Mflrof69atQ+/evREQ0PR3KcybNw+lpaXiLScnpw0rJCIiorZm0C/xc3BwgEqlQmFhoaS9sLAQarX6jstWVlZi8+bNWLhw4R37mZqawtTU9L5rJSIiogeDQUduTExMMGDAACQm/u9q0zU1NUhMTERQUNAdl92yZQs0Gg1efPHF1i6TiIiIHiAGv/xCdHQ0wsPD4e/vj4CAAKxatQqVlZWIiIgAAISFhcHV1RWxsbGS5datW4fx48fD3t7eEGUTEREZxI70eYYuoVWN94u9e6e7MHi4CQ0NRXFxMebPn4+CggL4+flh165d4iTj7OxsKJXSAabMzEwcPHgQ//nPfwxRMhEREbVjBg83ABAVFYWoqCidjyUlJTVq6969OwRBaOWqiIiI6EH0QJ8tRURERNQQww0RERHJCsMNERERyQrDDREREclKu5hQTEREVF/xj28buoRW1emppYYuQdY4ckNERESywnBDREREssJwQ0RERLLCcENERESywgnFDXzx3/2GLqHVvTpwmKFLICIiajUcuSEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIlkxeLhZs2YNPDw8YGZmhsDAQKSkpNyx//Xr1/H666/D2dkZpqam8PHxwc8//9xG1RIREVF7Z2TIJ09ISEB0dDTi4uIQGBiIVatWISQkBJmZmXB0dGzUv6qqCiNHjoSjoyO2bt0KV1dXXLp0CR07dmz74omIiKhdMmi4WbFiBWbMmIGIiAgAQFxcHH766SfEx8dj7ty5jfrHx8fj6tWrOHz4MIyNjQEAHh4ebVkyERERtXMGOyxVVVWF1NRUBAcH/68YpRLBwcFITk7WuczOnTsRFBSE119/HU5OTvjLX/6CxYsXQ6vVtlXZRERE1M4ZbOSmpKQEWq0WTk5OknYnJyecOXNG5zIXL17Eb7/9hilTpuDnn3/G+fPn8dprr6G6uhoxMTE6l9FoNNBoNOL9srKyltsIIiIiancMPqFYHzU1NXB0dMSXX36JAQMGIDQ0FO+++y7i4uKaXCY2NhY2Njbizc3NrQ0rJiIiorZmsHDj4OAAlUqFwsJCSXthYSHUarXOZZydneHj4wOVSiW29ezZEwUFBaiqqtK5zLx581BaWirecnJyWm4jiIiIqN0xWLgxMTHBgAEDkJiYKLbV1NQgMTERQUFBOpcZMmQIzp8/j5qaGrHt7NmzcHZ2homJic5lTE1NYW1tLbkRERGRfBn0sFR0dDTWrl2LjRs3IiMjA6+++ioqKyvFs6fCwsIwb948sf+rr76Kq1evYtasWTh79ix++uknLF68GK+//rqhNoGIiIjaGYOeCh4aGori4mLMnz8fBQUF8PPzw65du8RJxtnZ2VAq/5e/3Nzc8Ouvv+L//u//0KdPH7i6umLWrFmYM2eOoTaBiIiI2hmDhhsAiIqKQlRUlM7HkpKSGrUFBQXhyJEjrVwVERERPageqLOliIiIiO6G4YaIiIhkxeCHpYjkYP/eDEOX0OqGPdrT0CUQEd0TjtwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrBgZugAikrfilR8auoRW1+n/3jN0CURUD0duiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVtpFuFmzZg08PDxgZmaGwMBApKSkNNl3w4YNUCgUkpuZmVkbVktERETtmcHDTUJCAqKjoxETE4O0tDT07dsXISEhKCoqanIZa2tr5Ofni7dLly61YcVERETUnhk83KxYsQIzZsxAREQEfH19ERcXBwsLC8THxze5jEKhgFqtFm9OTk5tWDERERG1ZwYNN1VVVUhNTUVwcLDYplQqERwcjOTk5CaXq6iogLu7O9zc3DBu3Dj88ccfTfbVaDQoKyuT3IiIiEi+DBpuSkpKoNVqG428ODk5oaCgQOcy3bt3R3x8PP7973/j66+/Rk1NDQYPHozc3Fyd/WNjY2FjYyPe3NzcWnw7iIiIqP0w+GEpfQUFBSEsLAx+fn4YPnw4tm/fjk6dOuEf//iHzv7z5s1DaWmpeMvJyWnjiomIiKgtGRnyyR0cHKBSqVBYWChpLywshFqtvqd1GBsbo1+/fjh//rzOx01NTWFqanrftRIREdGDwaAjNyYmJhgwYAASExPFtpqaGiQmJiIoKOie1qHVanHy5Ek4Ozu3VplERET0ADHoyA0AREdHIzw8HP7+/ggICMCqVatQWVmJiIgIAEBYWBhcXV0RGxsLAFi4cCEGDRoELy8vXL9+HZ988gkuXbqEl156yZCbQURERO2EwcNNaGgoiouLMX/+fBQUFMDPzw+7du0SJxlnZ2dDqfzfANO1a9cwY8YMFBQUwNbWFgMGDMDhw4fh6+trqE0gIiKidsTg4QYAoqKiEBUVpfOxpKQkyf2VK1di5cqVbVAVERERPYgeuLOliIiIiO6E4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkRe9w4+HhgYULFyI7O7s16iEiIiK6L3qHm7/97W/Yvn07PD09MXLkSGzevBkajaY1aiMiIiLSW7PCTXp6OlJSUtCzZ0+88cYbcHZ2RlRUFNLS0lqjRiIiIqJ71uw5N/3798fq1auRl5eHmJgYfPXVVxg4cCD8/PwQHx8PQRBask4iIiKie2LU3AWrq6vx/fffY/369di9ezcGDRqEyMhI5Obm4p133sGePXvwzTfftGStRERERHeld7hJS0vD+vXr8e2330KpVCIsLAwrV65Ejx49xD4TJkzAwIEDW7RQIiIionuhd7gZOHAgRo4ciS+++ALjx4+HsbFxoz5du3bF888/3yIFEhEREelD73Bz8eJFuLu737GPpaUl1q9f3+yiiIiIiJpL7wnFRUVFOHr0aKP2o0eP4vfff2+RooiIiIiaS+9w8/rrryMnJ6dR++XLl/H666+3SFFEREREzaV3uDl9+jT69+/fqL1fv344ffp0ixRFRERE1Fx6hxtTU1MUFhY2as/Pz4eRUbPPLCciIiJqEXqHmyeeeALz5s1DaWmp2Hb9+nW88847GDlyZIsWR0RERKQvvYdali1bhmHDhsHd3R39+vUDAKSnp8PJyQn/+te/WrxAIiIiIn3oHW5cXV1x4sQJbNq0CcePH4e5uTkiIiIwefJknd95Q0RERNSWmnVtKUtLS7z88stYs2YNli1bhrCwsPsKNmvWrIGHhwfMzMwQGBiIlJSUe1pu8+bNUCgUGD9+fLOfm4iIiOSl2TOAT58+jezsbFRVVUnan376ab3Wk5CQgOjoaMTFxSEwMBCrVq1CSEgIMjMz4ejo2ORyWVlZeOuttzB06NBm1U9ERETy1KxvKJ4wYQJOnjwJhUIhXv1boVAAALRarV7rW7FiBWbMmIGIiAgAQFxcHH766SfEx8dj7ty5OpfRarWYMmUKPvjgAxw4cADXr1/XdzOIiIhIpvQ+LDVr1ix07doVRUVFsLCwwB9//IH9+/fD398fSUlJeq2rqqoKqampCA4O/l9BSiWCg4ORnJzc5HILFy6Eo6MjIiMj7/ocGo0GZWVlkhsRERHJl97hJjk5GQsXLoSDgwOUSiWUSiUeeeQRxMbGYubMmXqtq6SkBFqtFk5OTpJ2JycnFBQU6Fzm4MGDWLduHdauXXtPzxEbGwsbGxvx5ubmpleNRERE9GDRO9xotVp06NABAODg4IC8vDwAgLu7OzIzM1u2ugbKy8sxdepUrF27Fg4ODve0TN138tTddF06goiIiORD7zk3f/nLX3D8+HF07doVgYGBWLp0KUxMTPDll1/C09NTr3U5ODhApVI1+sbjwsJCqNXqRv0vXLiArKwsjB07Vmyrqamp3RAjI2RmZqJbt26SZUxNTWFqaqpXXURERPTg0nvk5r333hMDxcKFC/Hnn39i6NCh+Pnnn7F69Wq91mViYoIBAwYgMTFRbKupqUFiYiKCgoIa9e/RowdOnjyJ9PR08fb000/j0UcfRXp6Og85ERERkf4jNyEhIeLPXl5eOHPmDK5evQpbW1vxjCl9REdHIzw8HP7+/ggICMCqVatQWVkpnj0VFhYGV1dXxMbGwszMDH/5y18ky3fs2BEAGrUTERHRw0mvcFNdXQ1zc3Okp6dLwoSdnV2zCwgNDUVxcTHmz5+PgoIC+Pn5YdeuXeIk4+zsbCiVzfquQSIiInoI6RVujI2N0aVLF72/y+ZuoqKiEBUVpfOxu51evmHDhhathYiIiB5seg+JvPvuu3jnnXdw9erV1qiHiIiI6L7oPefm888/x/nz5+Hi4gJ3d3dYWlpKHk9LS2ux4oiIiIj0pXe44UUqiYiIqD3TO9zExMS0Rh1ERERELYKnIREREZGs6D1yo1Qq7/h9Ni19JhURERGRPvQON99//73kfnV1NY4dO4aNGzfigw8+aLHCiIiIiJpD73Azbty4Rm0TJ05Er169kJCQgMjIyBYpjIiIiKg5WmzOzaBBgyTXiCIiIiIyhBYJNzdv3sTq1avh6uraEqsjIiIiaja9D0s1vECmIAgoLy+HhYUFvv766xYtjoiIiEhfeoeblStXSsKNUqlEp06dEBgYCFtb2xYtjoiIiEhfeoebadOmtUIZRERERC1D7zk369evx5YtWxq1b9myBRs3bmyRooiIiIiaS+9wExsbCwcHh0btjo6OWLx4cYsURURERNRceoeb7OxsdO3atVG7u7s7srOzW6QoIiIioubSO9w4OjrixIkTjdqPHz8Oe3v7FimKiIiIqLn0DjeTJ0/GzJkzsXfvXmi1Wmi1Wvz222+YNWsWnn/++daokYiIiOie6X221KJFi5CVlYXHH38cRka1i9fU1CAsLIxzboiIiMjg9A43JiYmSEhIwIcffoj09HSYm5ujd+/ecHd3b436iIiIiPSid7ip4+3tDW9v75ashYiIiOi+6T3n5tlnn8XHH3/cqH3p0qWYNGlSixRFRERE1Fx6h5v9+/dj9OjRjdqffPJJ7N+/v0WKIiIiImouvcNNRUUFTExMGrUbGxujrKysRYoiIiIiai69w03v3r2RkJDQqH3z5s3w9fVtkaKIiIiImkvvCcXvv/8+nnnmGVy4cAGPPfYYACAxMRHffPMNtm7d2uIFEhEREelD73AzduxY7NixA4sXL8bWrVthbm6Ovn374rfffoOdnV1r1EhERER0z5p1KviYMWMwZswYAEBZWRm+/fZbvPXWW0hNTYVWq23RAomIiIj0ofecmzr79+9HeHg4XFxcsHz5cjz22GM4cuRIS9ZGREREpDe9Rm4KCgqwYcMGrFu3DmVlZXjuueeg0WiwY8cOTiYmIiKiduGeR27Gjh2L7t2748SJE1i1ahXy8vLw2WeftWZtRERERHq755GbX375BTNnzsSrr77Kyy4QERFRu3XPIzcHDx5EeXk5BgwYgMDAQHz++ecoKSlpzdqIiIiI9HbP4WbQoEFYu3Yt8vPz8de//hWbN2+Gi4sLampqsHv3bpSXl7dmnURERET3RO+zpSwtLTF9+nQcPHgQJ0+exJtvvoklS5bA0dERTz/9dGvUSERERHTPmn0qOAB0794dS5cuRW5uLr799tuWqomIiIio2e4r3NRRqVQYP348du7c2RKrIyIiImq2Fgk392vNmjXw8PCAmZkZAgMDkZKS0mTf7du3w9/fHx07doSlpSX8/Pzwr3/9qw2rJSIiovbM4OEmISEB0dHRiImJQVpaGvr27YuQkBAUFRXp7G9nZ4d3330XycnJOHHiBCIiIhAREYFff/21jSsnIiKi9sjg4WbFihWYMWMGIiIi4Ovri7i4OFhYWCA+Pl5n/xEjRmDChAno2bMnunXrhlmzZqFPnz44ePBgG1dORERE7ZFBw01VVRVSU1MRHBwstimVSgQHByM5OfmuywuCgMTERGRmZmLYsGGtWSoRERE9IJp1VfCWUlJSAq1WCycnJ0m7k5MTzpw50+RypaWlcHV1hUajgUqlwt///neMHDlSZ1+NRgONRiPeLysra5niiYiIqF0yaLhprg4dOiA9PR0VFRVITExEdHQ0PD09MWLEiEZ9Y2Nj8cEHH7R9kURERGQQBg03Dg4OUKlUKCwslLQXFhZCrVY3uZxSqYSXlxcAwM/PDxkZGYiNjdUZbubNm4fo6GjxfllZGdzc3FpmA4iIiKjdMeicGxMTEwwYMACJiYliW01NDRITExEUFHTP66mpqZEceqrP1NQU1tbWkhsRERHJl8EPS0VHRyM8PBz+/v4ICAjAqlWrUFlZiYiICABAWFgYXF1dERsbC6D2MJO/vz+6desGjUaDn3/+Gf/617/wxRdfGHIziIiIqJ0weLgJDQ1FcXEx5s+fj4KCAvj5+WHXrl3iJOPs7Gwolf8bYKqsrMRrr72G3NxcmJubo0ePHvj6668RGhpqqE0gIiKidsTg4QYAoqKiEBUVpfOxpKQkyf0PP/wQH374YRtURURERA8ig3+JHxEREVFLYrghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWkX4WbNmjXw8PCAmZkZAgMDkZKS0mTftWvXYujQobC1tYWtrS2Cg4Pv2J+IiIgeLgYPNwkJCYiOjkZMTAzS0tLQt29fhISEoKioSGf/pKQkTJ48GXv37kVycjLc3NzwxBNP4PLly21cOREREbVHBg83K1aswIwZMxAREQFfX1/ExcXBwsIC8fHxOvtv2rQJr732Gvz8/NCjRw989dVXqKmpQWJiYhtXTkRERO2RQcNNVVUVUlNTERwcLLYplUoEBwcjOTn5ntZx48YNVFdXw87OTufjGo0GZWVlkhsRERHJl0HDTUlJCbRaLZycnCTtTk5OKCgouKd1zJkzBy4uLpKAVF9sbCxsbGzEm5ub233XTURERO2XwQ9L3Y8lS5Zg8+bN+P7772FmZqazz7x581BaWirecnJy2rhKIiIiaktGhnxyBwcHqFQqFBYWStoLCwuhVqvvuOyyZcuwZMkS7NmzB3369Gmyn6mpKUxNTVukXiIiImr/DDpyY2JiggEDBkgmA9dNDg4KCmpyuaVLl2LRokXYtWsX/P3926JUIiIiekAYdOQGAKKjoxEeHg5/f38EBARg1apVqKysREREBAAgLCwMrq6uiI2NBQB8/PHHmD9/Pr755ht4eHiIc3OsrKxgZWVlsO0gIiKi9sHg4SY0NBTFxcWYP38+CgoK4Ofnh127domTjLOzs6FU/m+A6YsvvkBVVRUmTpwoWU9MTAwWLFjQlqUTERFRO2TwcAMAUVFRiIqK0vlYUlKS5H5WVlbrF0REREQPrAf6bCkiIiKihhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYMHm7WrFkDDw8PmJmZITAwECkpKU32/eOPP/Dss8/Cw8MDCoUCq1atartCiYiI6IFg0HCTkJCA6OhoxMTEIC0tDX379kVISAiKiop09r9x4wY8PT2xZMkSqNXqNq6WiIiIHgQGDTcrVqzAjBkzEBERAV9fX8TFxcHCwgLx8fE6+w8cOBCffPIJnn/+eZiamrZxtURERPQgMFi4qaqqQmpqKoKDg/9XjFKJ4OBgJCcnt9jzaDQalJWVSW5EREQkXwYLNyUlJdBqtXBycpK0Ozk5oaCgoMWeJzY2FjY2NuLNzc2txdZNRERE7Y/BJxS3tnnz5qG0tFS85eTkGLokIiIiakVGhnpiBwcHqFQqFBYWStoLCwtbdLKwqakp5+cQERE9RAw2cmNiYoIBAwYgMTFRbKupqUFiYiKCgoIMVRYRERE94Aw2cgMA0dHRCA8Ph7+/PwICArBq1SpUVlYiIiICABAWFgZXV1fExsYCqJ2EfPr0afHny5cvIz09HVZWVvDy8jLYdhAREVH7YdBwExoaiuLiYsyfPx8FBQXw8/PDrl27xEnG2dnZUCr/N7iUl5eHfv36ifeXLVuGZcuWYfjw4UhKSmrr8omIiKgdMmi4AYCoqChERUXpfKxhYPHw8IAgCG1QFRERET2oZH+2FBERET1cGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhW2kW4WbNmDTw8PGBmZobAwECkpKTcsf+WLVvQo0cPmJmZoXfv3vj555/bqFIiIiJq7wwebhISEhAdHY2YmBikpaWhb9++CAkJQVFRkc7+hw8fxuTJkxEZGYljx45h/PjxGD9+PE6dOtXGlRMREVF7ZPBws2LFCsyYMQMRERHw9fVFXFwcLCwsEB8fr7P/p59+ilGjRmH27Nno2bMnFi1ahP79++Pzzz9v48qJiIioPTJouKmqqkJqaiqCg4PFNqVSieDgYCQnJ+tcJjk5WdIfAEJCQprsT0RERA8XI0M+eUlJCbRaLZycnCTtTk5OOHPmjM5lCgoKdPYvKCjQ2V+j0UCj0Yj3S0tLAQBlZWU6+9+sqLzn+h9UTW373dy8Kf99U13dvH1TWVnRwpW0P8193ZTfutXClbQ/ps3cN7duyPt109zXDACU39DcvdMDrLmvGQC4USHvfdPU66auXRCEu67DoOGmLcTGxuKDDz5o1O7m5maAatqHNw1dAJHcvPORoStol+YbuoB2bbWhC2jHVt7x0fLyctjY2Nyxj0HDjYODA1QqFQoLCyXthYWFUKvVOpdRq9V69Z83bx6io6PF+zU1Nbh69Srs7e2hUCjucwvuX1lZGdzc3JCTkwNra2tDl9OucN/oxv3SNO6bpnHfNI37pmntad8IgoDy8nK4uLjcta9Bw42JiQkGDBiAxMREjB8/HkBt+EhMTERUVJTOZYKCgpCYmIi//e1vYtvu3bsRFBSks7+pqSlMTU0lbR07dmyJ8luUtbW1wV847RX3jW7cL03jvmka903TuG+a1l72zd1GbOoY/LBUdHQ0wsPD4e/vj4CAAKxatQqVlZWIiIgAAISFhcHV1RWxsbEAgFmzZmH48OFYvnw5xowZg82bN+P333/Hl19+acjNICIionbC4OEmNDQUxcXFmD9/PgoKCuDn54ddu3aJk4azs7OhVP7vpK7Bgwfjm2++wXvvvYd33nkH3t7e2LFjB/7yl78YahOIiIioHTF4uAGAqKioJg9DJSUlNWqbNGkSJk2a1MpVtQ1TU1PExMQ0OnRG3DdN4X5pGvdN07hvmsZ907QHdd8ohHs5p4qIiIjoAWHwbygmIiIiakkMN0RERCQrDDdEREQkKww3ZHAKhQI7duwwdBlE7dKCBQvg5+dn6DJkg/vz4cBwcxfTpk0Tv2CwNYwYMQIKhQIKhQJmZmbw8fFBbGzsPV07oyVNmzZNrMPY2Bhdu3bF22+/jVsGuC5QXR31b4888kib19GwptYKYMXFxXj11VfRpUsXmJqaQq1WIyQkBPv27YODgwOWLFmic7lFixbByckJ1dXV2LBhAxQKBXr27Nmo35YtW6BQKODh4dEq9bcmXX9/W7duhZmZGZYvXy6+bhvuox07dki+gTwpKQkKhQK9evWCVquV9O3YsSM2bNjQWpugU3JyMlQqFcaMGdMq6/fw8BD/dlQqFVxcXBAZGYlr1661yvPpUrfPr1+/3mLrrP8+pVAoYG9vj1GjRuHEiRPNXueCBQsk67SxscHQoUOxb9++Fqu7Panbh6+88kqjx15//XUoFApMmzZN7Hunz7/6rzNLS0v0798fW7ZsaaXK9cNw0w7MmDED+fn5yMzMxLx58zB//nzExcW1eR2jRo1Cfn4+Ll68iJUrV+If//gHYmJi2rwOAFi/fj3y8/PF286dO5u9rurq6hasrOU9++yzOHbsGDZu3IizZ89i586dGDFiBEpLS/Hiiy9i/fr1jZYRBAEbNmxAWFgYjI2NAQCWlpYoKipCcnKypO+6devQpUuXNtmW1vbVV19hypQp+OKLL/Dmm7VXSTMzM8PHH398Tx/cFy9exD//+c/WLvOu1q1bhzfeeAP79+9HXl5eqzzHwoULkZ+fj+zsbGzatAn79+/HzJkzW+W52lLd+1R+fj4SExNhZGSEp5566r7W2atXL3GdycnJ8Pb2xlNPPSVeaFlu3NzcsHnzZty8eVNsu3XrFr755hu93yvqXmfHjh3DwIEDERoaisOHD7d0yXpjuLkP+/btQ0BAAExNTeHs7Iy5c+fi9u3b4uPl5eWYMmUKLC0t4ezsjJUrV2LEiBGSS0cAgIWFBdRqNdzd3REREYE+ffpg9+7d4uMajQZvvfUWXF1dYWlpicDAwEbf/7N27Vq4ubnBwsICEyZMwIoVK/S+zETdqIGbmxvGjx+P4OBgsY4rV65g8uTJcHV1hYWFBXr37o1vv/1WsvyIESMwc+ZMvP3227Czs4NarcaCBQskfc6dO4dhw4bBzMwMvr6+ku2sr2PHjlCr1eLNzs4OQO3lORYuXIjOnTvD1NRU/NLHOllZWVAoFEhISMDw4cNhZmaGTZs2Aaj9YOzZsyfMzMzQo0cP/P3vfxeXq6qqQlRUFJydnWFmZgZ3d3fxW7HrRjwmTJjQ4iMg169fx4EDB/Dxxx/j0Ucfhbu7OwICAjBv3jw8/fTTiIyMxNmzZ3Hw4EHJcvv27cPFixcRGRkpthkZGeGFF15AfHy82Jabm4ukpCS88MILLVazoSxduhRvvPEGNm/eLH6DOQAEBwdDrVaLv687eeONNxATEwONxnBXVa6oqEBCQgJeffVVjBkzptGo0ZIlS+Dk5IQOHTogMjKy0ejpf//7X4wcORIODg6wsbHB8OHDkZaW1uh5OnToALVaDVdXVzz66KMIDw9v1G/btm3o1asXTE1N4eHhgeXLl0sev3btGsLCwmBrawsLCws8+eSTOHfunPj4pUuXMHbsWNja2sLS0hK9evXCzz//jKysLDz66KMAAFtbW8lowP2qe59Sq9Xw8/PD3LlzkZOTg+LiYgDAnDlz4OPjAwsLC3h6euL999+/6z84RkZG4jp9fX2xcOFCVFRU4OzZs2KfFStWoHfv3rC0tISbmxtee+01VFRIr+reEu/DbaF///5wc3PD9u3bxbbt27ejS5cu6Nevn17rqnud+fj4YM2aNTA3N8cPP/zQ0iXrjeGmmS5fvozRo0dj4MCBOH78OL744gusW7cOH374odgnOjoahw4dws6dO7F7924cOHBA55tQHUEQcODAAZw5cwYmJiZie1RUFJKTk7F582acOHECkyZNwqhRo8Q3mUOHDuGVV17BrFmzkJ6ejpEjR+Kjj+7vKsWnTp3C4cOHxTpu3bqFAQMG4KeffsKpU6fw8ssvY+rUqUhJSZEst3HjRlhaWuLo0aNYunQpFi5cKAaYmpoaPPPMMzAxMcHRo0cRFxeHOXPm6FXXp59+iuXLl2PZsmU4ceIEQkJC8PTTT0vecAFg7ty5mDVrFjIyMhASEoJNmzZh/vz5+Oijj5CRkYHFixfj/fffx8aNGwEAq1evxs6dO/Hdd98hMzMTmzZtEkPMf//7XwD/G02qu98SrKysYGVlhR07duj8wO3duzcGDhwoCSx1tQwePBg9evSQtE+fPh3fffcdbty4AQDYsGEDRo0aJX7j94Nqzpw5WLRoEX788UdMmDBB8phKpcLixYvx2WefITc3947r+dvf/obbt2/js88+a81y7+i7775Djx490L17d7z44ouIj48XD0N/9913WLBgARYvXozff/8dzs7OkhAO1P7TFB4ejoMHD+LIkSPw9vbG6NGjUV5e3uRzXr58GT/88AMCAwPFttTUVDz33HN4/vnncfLkSSxYsADvv/++JGxNmzYNv//+O3bu3Ink5GQIgoDRo0eLYeH111+HRqPB/v37cfLkSXz88cewsrKCm5sbtm3bBgDIzMxEfn4+Pv3005bahaKKigp8/fXX8PLygr29PYDaD9sNGzbg9OnT+PTTT7F27VqsXHnnq0zXp9FosH79enTs2BHdu3cX25VKJVavXo0//vgDGzduxG+//Ya3335bfLw13odb0/Tp0yWjwvHx8ZJ/GprDyMgIxsbGqKqqut/y7p9AdxQeHi6MGzeuUfs777wjdO/eXaipqRHb1qxZI1hZWQlarVYoKysTjI2NhS1btoiPX79+XbCwsBBmzZoltg0fPlwwNjYWLC0tBWNjYwGAYGZmJhw6dEgQBEG4dOmSoFKphMuXL0ue//HHHxfmzZsnCIIghIaGCmPGjJE8PmXKFMHGxkav7VSpVIKlpaVgamoqABCUSqWwdevWJpcZM2aM8Oabb0q25ZFHHpH0GThwoDBnzhxBEATh119/FYyMjCTb8ssvvwgAhO+//15sq9sHlpaW4q3ucRcXF+Gjjz5q9ByvvfaaIAiC8OeffwoAhFWrVkn6dOvWTfjmm28kbYsWLRKCgoIEQRCEN954Q3jsscckv8/6GtbYkrZu3SrY2toKZmZmwuDBg4V58+YJx48fFx+Pi4sTrKyshPLyckEQBKGsrEywsLAQvvrqK7HP+vXrxd+3n5+fsHHjRqGmpkbo1q2b8O9//1tYuXKl4O7u3ir1t6bw8HDBxMREACAkJibqfLzu73PQoEHC9OnTBUEQhO+//16o//a2d+9eAYBw7do1IS4uTrCzsxOuX78uCIIg2NjYCOvXr2/1bakzePBg8fVZXV0tODg4CHv37hUEQRCCgoLE13KdwMBAoW/fvk2uT6vVCh06dBB++OEHsc3d3V0wMTERLC0tBTMzMwGAEBgYKFy7dk3s88ILLwgjR46UrGv27NmCr6+vIAiCcPbsWQGA+F4kCIJQUlIimJubC999950gCILQu3dvYcGCBTrrqr/PW0r99ylLS0sBgODs7CykpqY2ucwnn3wiDBgwQLwfExMj2Z8xMTGCUqkU16lQKARra2vhl19+uWMtW7ZsEezt7cX7LfE+3Bbq/maKiooEU1NTISsrS8jKyhLMzMyE4uJiYdy4cUJ4eLikb1Pc3d2FlStXCoIgCBqNRli8eLEAQPjxxx9bf0PugiM3zZSRkYGgoCDJpMUhQ4agoqICubm5uHjxIqqrqxEQECA+bmNjI/lPoM6UKVOQnp6OQ4cO4cknn8S7776LwYMHAwBOnjwJrVYLHx8f8b98Kysr7Nu3DxcuXABQ+59R/ecB0Oj+vXj00UeRnp6Oo0ePIjw8HBEREXj22WcBAFqtFosWLULv3r1hZ2cHKysr/Prrr8jOzpaso0+fPpL7zs7OKCoqEveZm5ub5HL1TV3NfeXKlUhPTxdvI0eORFlZGfLy8jBkyBBJ3yFDhiAjI0PS5u/vL/5cWVmJCxcuIDIyUrIPP/zwQ3EfTps2Denp6ejevTtmzpyJ//znP/rsuvvy7LPPIi8vDzt37sSoUaOQlJSE/v37i/9BT548GVqtFt999x0AICEhAUqlEqGhoTrXV/cf2b59+1BZWYnRo0e31aa0ij59+sDDwwMxMTGNDgPU9/HHH2Pjxo2NXgsNRUZGwt7eHh9//HFLl3pXmZmZSElJweTJkwHU/qcbGhqKdevWAaj9G6k/ugI0/hspLCzEjBkz4O3tDRsbG1hbW6OioqLR3+Ls2bORnp6OEydOIDExEQAwZswYcUJ1RkaGzr+lc+fOQavVIiMjA0ZGRpJ67O3t0b17d3Efz5w5Ex9++CGGDBmCmJiY+5rYe6/q3qfS09ORkpKCkJAQPPnkk7h06RKA2r+PIUOGQK1Ww8rKCu+9916jfdNQ9+7dxXWmpqbi1VdfxaRJk/D777+Lffbs2YPHH38crq6u6NChA6ZOnYorV66Io6Qt9T7cVjp16iQeFl2/fj3GjBkDBwcHvdczZ84cWFlZwcLCAh9//DGWLFnSahPl9cFw0w7Y2NjAy8sLAwcOxHfffYfPP/8ce/bsAVA77KpSqZCamir5sM/IyGjxYV5LS0t4eXmhb9++iI+Px9GjR8U33U8++QSffvop5syZg7179yI9PR0hISGNhh/rJrfWUSgUqKmp0bsWtVoNLy8v8WZpaan3ttSp+0Bcu3atZB+eOnUKR44cAVB7DPrPP//EokWLcPPmTTz33HOYOHGi3nU3l5mZGUaOHIn3338fhw8fxrRp08TJ3NbW1pg4caI4hLx+/Xo899xzsLKy0rmuKVOm4MiRI1iwYAGmTp0KI6N2cQm5ZnN1dUVSUhIuX76MUaNGNXn4ZdiwYQgJCcG8efPuuD4jIyN89NFH+PTTT1ttMm9T1q1bh9u3b8PFxQVGRkYwMjLCF198gW3btt3z5NXw8HCkp6fj008/xeHDh5Geng57e/tGf4sODg7w8vKCt7c3HnvsMaxatQqHDx/G3r17W2x7XnrpJVy8eBFTp07FyZMn4e/v3+qH/Orep+reM7/66itUVlZi7dq1SE5OxpQpUzB69Gj8+OOPOHbsGN599927HiYxMTER19mvXz8sWbIErq6uWLVqFYDauXxPPfUU+vTpg23btiE1NRVr1qwBgPZxCKaZpk+fjg0bNmDjxo2YPn16s9ZRF6Jzc3Nx7do1vacatBaGm2bq2bOneAy6zqFDh9ChQwd07twZnp6eMDY2lszPKC0tlUxQ08XKygqzZs3CW2+9BUEQ0K9fP2i1WhQVFUk+7L28vKBWqwHU/tfRcB7I/c4LUSqVeOedd/Dee+/h5s2bOHToEMaNG4cXX3wRffv2haen5123paGePXsiJycH+fn5YltduLgX1tbWcHFxwaFDhyTthw4dgq+vb5PLOTk5wcXFBRcvXmy0D7t27SpZf2hoKNauXYuEhARs27YNV69eBVAb2hqeQtyafH19UVlZKd6PjIzEwYMH8eOPP+Lw4cOSicQN2dnZ4emnn8a+ffua/YbV3ri7u2Pfvn0oKCi4Y8BZsmQJfvjhh0ZnjDU0adIk9OrVCx988EFrlKvT7du38c9//hPLly+XhOzjx4/DxcUF3377LXr27ImjR49Klmv4N3Lo0CHMnDkTo0ePFicDl5SU3PX5VSoVAIhnyPTs2VPn35KPjw9UKhV69uyJ27dvS+q5cuUKMjMzJX9vbm5ueOWVV7B9+3a8+eabWLt2LQCI8/Va++9GoVBAqVTi5s2bOHz4MNzd3fHuu+/C398f3t7e4oiOvlQqlbivUlNTUVNTg+XLl2PQoEHw8fFpFIxb4324tY0aNQpVVVWorq5GSEhIs9ZRF6LVarXkSIahPdj/0rWR0tJSpKenS9pefvllrFq1Cm+88QaioqKQmZmJmJgYREdHQ6lUokOHDggPD8fs2bNhZ2cHR0dHxMTEQKlU3vUF8Ne//hWLFi3Ctm3bMHHiREyZMgVhYWFYvnw5+vXrh+LiYiQmJqJPnz4YM2YM3njjDQwbNgwrVqzA2LFj8dtvv+GXX3657xfapEmTMHv2bKxZswbe3t7YunUrDh8+DFtbW6xYsQKFhYV3DBUNBQcHw8fHB+Hh4fjkk09QVlaGd999V6+aZs+ejZiYGHTr1g1+fn5Yv3490tPTxTOimvLBBx9g5syZsLGxwahRo6DRaPD777/j2rVriI6OxooVK+Ds7Ix+/fpBqVRiy5YtUKvV4pkOHh4eSExMxJAhQ2BqagpbW1u96m7KlStXMGnSJEyfPh19+vRBhw4d8Pvvv2Pp0qUYN26c2G/YsGHw8vJCWFgYevToIR62bMqGDRvw97//XZxkKQdubm5ISkrCo48+ipCQEMlZcnV69+6NKVOmYPXq1Xdd35IlS5r9ht4cP/74I65du4bIyEjY2NhIHnv22Wexbt06vPXWW5g2bRr8/f0xZMgQbNq0CX/88Qc8PT3Fvt7e3vjXv/4Ff39/lJWVYfbs2TA3N2/0fOXl5SgoKIAgCMjJycHbb7+NTp06ia+dN998EwMHDsSiRYsQGhqK5ORkfP755+IEZm9vb4wbNw4zZszAP/7xD3To0AFz586Fq6ur+Nr829/+hieffBI+Pj64du0a9u7dK37Xkru7OxQKBX788UeMHj0a5ubmTY426kOj0aCgoABA7dlcn3/+OSoqKjB27FiUlZUhOzsbmzdvxsCBA/HTTz/h+++/v+s6b9++La6zvLwcCQkJOH36tDgK4eXlherqanz22WcYO3YsDh061OjrOlrrfbg1qVQq8RBjXfhtSNfnn729Pdzc3Fq7vPtj4Dk/7V54eLgAoNEtMjJSSEpKEgYOHCiYmJgIarVamDNnjlBdXS0uW1ZWJrzwwguChYWFoFarhRUrVggBAQHC3LlzxT7Dhw+XTDCu89e//lXo1auXoNVqhaqqKmH+/PmCh4eHYGxsLDg7OwsTJkwQTpw4Ifb/8ssvBVdXV8Hc3FwYP3688OGHHwpqtVqv7dQ1cSw2Nlbo1KmTkJubK4wbN06wsrISHB0dhffee08ICwuTLKNrW+pPThMEQcjMzBQeeeQRwcTERPDx8RF27dqlc0JxU5N3tVqtsGDBAsHV1VUwNjYW+vbtK5n4Vzeh+NixY42W3bRpk+Dn5yeYmJgItra2wrBhw4Tt27cLglC7//z8/ARLS0vB2tpaePzxx4W0tDRx2Z07dwpeXl6CkZFRi07MvXXrljB37lyhf//+go2NjWBhYSF0795deO+994QbN25I+tZN1lu6dGmj9dSfUKzLgzyhuOHrMjc3V/D29hYGDRokTJgwodHjf/75pzgJuU5Tk1ufeOIJAUCbTCh+6qmnhNGjR+t87OjRowIA4fjx48JHH30kODg4CFZWVkJ4eLjw9ttvSybApqWlCf7+/oKZmZng7e0tbNmyRTKxUxBqJ3rWf7/q1KmTMHr06EZ/F1u3bhV8fX0FY2NjoUuXLsInn3wiefzq1avC1KlTBRsbG8Hc3FwICQkRzp49Kz4eFRUldOvWTTA1NRU6deokTJ06VSgpKREfX7hwoaBWqwWFQiF5H2iuhu/HHTp0EAYOHCg58WH27NmCvb29YGVlJYSGhgorV66U/G3omlBcf50WFhZC7969hS+++ELy3CtWrBCcnZ3F/fDPf/6z0Wvqft+H28LdJgk3nFDc1OefIAiNXnftiUIQ2vircB9ilZWVcHV1xfLly+94WKElzJgxA2fOnMGBAwda9XmIiEg3vg8bDg9LtaJjx47hzJkzCAgIQGlpKRYuXAgAksMNLWXZsmUYOXIkLC0t8csvv2Djxo2Nvh+DiIhaD9+H2w+Gm1a2bNkyZGZmwsTEBAMGDMCBAweadbrd3aSkpGDp0qUoLy+Hp6cnVq9ejZdeeqnFn4eIiHTj+3D7wcNSREREJCs8FZyIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiGQnKSkJCoUC169fv+dlPDw8xGsJEdGDjeGGiNrctGnToFAo8MorrzR67PXXX4dCocC0adPavjAikgWGGyIyCDc3N2zevFm8OCEA3Lp1C9988w26dOliwMqI6EHHcENEBtG/f3+4ublh+/btYtv27dvRpUsX9OvXT2zTaDSYOXMmHB0dYWZmhkceeaTR1ZZ//vln+Pj4wNzcHI8++iiysrIaPd/BgwcxdOhQmJubw83NDTNnzpRceb0+QRCwYMECdOnSBaampnBxccHMmTNbZsOJqNUx3BCRwUyfPh3r168X78fHxyMiIkLS5+2338a2bduwceNGpKWlwcvLCyEhIbh69SoAICcnB8888wzGjh2L9PR0vPTSS5g7d65kHRcuXMCoUaPw7LPP4sSJE0hISMDBgwcRFRWls65t27Zh5cqV+Mc//oFz585hx44d6N27dwtvPRG1GoNetpOIHkp1VyYuKioSTE1NhaysLCErK0swMzMTiouLxSsTV1RUCMbGxsKmTZvEZauqqgQXFxfx6ujz5s0TfH19JeufM2eO5IrNkZGRwssvvyzpc+DAAUGpVAo3b94UBEF6hePly5cLPj4+QlVVVSvtASJqTRy5ISKD6dSpE8aMGYMNGzZg/fr1GDNmjOTaaxcuXEB1dTWGDBkithkbGyMgIAAZGRkAgIyMDAQGBkrWGxQUJLl//PhxbNiwAVZWVuItJCQENTU1+PPPPxvVNWnSJNy8eROenp6YMWMGvv/+e9y+fbslN52IWhEvnElEBjV9+nTx8NCaNWta5TkqKirw17/+Vee8GV2Tl93c3JCZmYk9e/Zg9+7deO211/DJJ59g3759MDY2bpUaiajlcOSGiAxq1KhRqKqqQnV1NUJCQiSPdevWDSYmJjh06JDYVl1djf/+97/w9fUFAPTs2RMpKSmS5Y4cOSK5379/f5w+fRpeXl6NbiYmJjrrMjc3x9ixY7F69WokJSUhOTkZJ0+ebIlNJqJWxpEbIjIolUolHmJSqVSSxywtLfHqq69i9uzZsLOzQ5cuXbB06VLcuHEDkZGRAIBXXnkFy5cvx+zZs/HSSy8hNTUVGzZskKxnzpw5GDRoEKKiovDSSy/B0tISp0+fxu7du/H55583qmnDhg3QarUIDAyEhYUFvv76a5ibm8Pd3b11dgIRtSiO3BCRwVlbW8Pa2lrnY0uWLMGzzz6LqVOnon///jh//jx+/fVX2NraAqg9rLRt2zbs2LEDffv2RVxcHBYvXixZR58+fbBv3z6cPXsWQ4cORb9+/TB//ny4uLjofM6OHTti7dq1GDJkCPr06YM9e/bghx9+gL29fctuOBG1CoUgCIKhiyAiIiJqKRy5ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWfl/NAHMrRxeenAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing Accuracy of all models\n",
    "print(np.round(acc, 2))\n",
    "ax = sns.barplot(x = model_name, y = acc, palette = \"Set3\")\n",
    "ax.set(xlabel = \"Models\", ylabel = \"Accuracy\")\n",
    "ax.set_title(\"Accuracy Comparison\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c1b9aa4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:40:11.092804Z",
     "iopub.status.busy": "2024-03-12T08:40:11.092471Z",
     "iopub.status.idle": "2024-03-12T08:40:11.377738Z",
     "shell.execute_reply": "2024-03-12T08:40:11.376871Z"
    },
    "papermill": {
     "duration": 0.310634,
     "end_time": "2024-03-12T08:40:11.379823",
     "exception": false,
     "start_time": "2024-03-12T08:40:11.069189",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.65 0.74 0.63 0.5  0.7  0.66 0.67]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing Precision of different models\n",
    "print(np.round(prec,2))\n",
    "ax = sns.barplot(x = model_name, y = prec, palette = \"Set3\")\n",
    "ax.set(xlabel = \"Models\", ylabel = \"Precision\")\n",
    "ax.set_title(\"Precision Comparison\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "48310b84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:40:11.428678Z",
     "iopub.status.busy": "2024-03-12T08:40:11.428369Z",
     "iopub.status.idle": "2024-03-12T08:40:11.718085Z",
     "shell.execute_reply": "2024-03-12T08:40:11.717245Z"
    },
    "papermill": {
     "duration": 0.317368,
     "end_time": "2024-03-12T08:40:11.720316",
     "exception": false,
     "start_time": "2024-03-12T08:40:11.402948",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.66 0.68 0.6  0.5  0.69 0.61 0.68]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing F1 Score for all models\n",
    "print(np.round(f1_score,2))\n",
    "ax = sns.barplot(x = model_name, y = f1_score, palette = \"Set3\")\n",
    "ax.set(xlabel = \"Models\", ylabel = \"F1 Score\")\n",
    "ax.set_title(\"F1 Score Comparison\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a54f04da",
   "metadata": {
    "papermill": {
     "duration": 0.023447,
     "end_time": "2024-03-12T08:40:11.768023",
     "exception": false,
     "start_time": "2024-03-12T08:40:11.744576",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Base Model Comparison\n",
    "Since, Random Forest, AdaBoost, Balanced Bagging and Multi Layered Perceptron seem to work best for all metrics we will tune their hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7d06c1f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:40:11.816901Z",
     "iopub.status.busy": "2024-03-12T08:40:11.816527Z",
     "iopub.status.idle": "2024-03-12T08:40:11.834080Z",
     "shell.execute_reply": "2024-03-12T08:40:11.833248Z"
    },
    "papermill": {
     "duration": 0.044402,
     "end_time": "2024-03-12T08:40:11.835971",
     "exception": false,
     "start_time": "2024-03-12T08:40:11.791569",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Making dictionaries for hyperparameter tuning\n",
    "#tfidf = {'preprocessor__text__tfidf__ngram_range': [(1,1), (1,2)], 'preprocessor__text__tfidf__stop_words': [None, 'english'], 'preprocessor__text__tfidf__use_idf': [True, False]}\n",
    "#rand_log = {'log__penalty': [\"l1\", \"l2\"], 'log__C': uniform(loc=0, scale=4), \"log__max_iter\": randint(low = 100, high = 3000)}\n",
    "#rand_svm = {'svm__C': uniform(loc = 0, scale = 4), 'svm__gamma': ['scale','auto'], 'svm__tol': uniform(loc = 1e-5, scale = 1e-3), 'svm__degree': [3, 4, 5]}\n",
    "#rand_knn = {'knn__n_neighbors': randint(low = 1, high = 1000)}\n",
    "#rand_search_log = RandomizedSearchCV(log_pipeline, rand_log, refit = True, verbose = 1, n_jobs = -1)\n",
    "#rand_search_svm = RandomizedSearchCV(SVM_pipeline, rand_svm, refit = True, verbose = 1, n_jobs = -1)\n",
    "#rand_search_knn = RandomizedSearchCV(KNN_pipeline, rand_knn, refit = True, verbose = 1, n_jobs = -1)\n",
    "rand_forest = {'rfr__n_estimators': randint(low = 10, high = 200), 'rfr__criterion':[\"gini\", \"entropy\",\"log_loss\"], 'rfr__max_depth': randint(low = 1, high = 100)}\n",
    "rand_ada = {'ada__n_estimators': randint(low = 50, high = 150), 'ada__learning_rate': uniform()}\n",
    "rand_blb = {'blb__n_estimators': randint(low = 50, high = 150), 'blb__max_samples': uniform(), 'blb__max_features':uniform()}\n",
    "rand_mlp = {'mlp__hidden_layer_sizes': [[100,50,30], [80,40,20]], 'mlp__activation': ['identity', 'logistic', 'tanh', 'relu'], 'mlp__learning_rate':['constant', 'invscaling', 'adaptive'], 'mlp__max_iter': randint(100,4000), 'mlp__learning_rate_init':uniform(loc = 0.0001, scale = 0.01)}\n",
    "\n",
    "rand_search_rfr = RandomizedSearchCV(random_forest_pipeline, rand_forest, refit = True, verbose = 1, n_jobs = -1, random_state = RANDOM_STATE)\n",
    "rand_search_ada = RandomizedSearchCV(AdaBoost_pipeline, rand_ada, refit = True, verbose = 1, n_jobs = -1, random_state = RANDOM_STATE)\n",
    "rand_search_blb = RandomizedSearchCV(BlanBag_pipeline, rand_blb, refit = True, verbose = 1, n_jobs = -1, random_state = RANDOM_STATE)\n",
    "rand_search_mlp = RandomizedSearchCV(MLP_pipeline, rand_mlp, refit = True, verbose = 1, n_jobs = -1, random_state = RANDOM_STATE)\n",
    "model_name = [\"RandForest\", \"AdaBoost\", \"BalBag\", \"MLP\"]\n",
    "acc = []\n",
    "prec = []\n",
    "f1_score = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e25ec064",
   "metadata": {
    "papermill": {
     "duration": 0.023797,
     "end_time": "2024-03-12T08:40:11.884148",
     "exception": false,
     "start_time": "2024-03-12T08:40:11.860351",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Training Models\n",
    "* Random Forest Classifier\n",
    "* AdaBoost Classifier\n",
    "* BalancedBagging Classifier\n",
    "* MLP Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3e215f8e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:40:11.933100Z",
     "iopub.status.busy": "2024-03-12T08:40:11.932792Z",
     "iopub.status.idle": "2024-03-12T08:40:11.936701Z",
     "shell.execute_reply": "2024-03-12T08:40:11.935829Z"
    },
    "papermill": {
     "duration": 0.03057,
     "end_time": "2024-03-12T08:40:11.938639",
     "exception": false,
     "start_time": "2024-03-12T08:40:11.908069",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#This is the Dummy Classifier\n",
    "#dummy = DummyClassifier()\n",
    "#dummy.fit(train_dataset.drop(\"Rating\", axis = 1), train_dataset[\"Rating\"])\n",
    "#result = dummy.predict(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1ee3e71c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:40:11.987746Z",
     "iopub.status.busy": "2024-03-12T08:40:11.987424Z",
     "iopub.status.idle": "2024-03-12T08:40:11.991445Z",
     "shell.execute_reply": "2024-03-12T08:40:11.990590Z"
    },
    "papermill": {
     "duration": 0.030889,
     "end_time": "2024-03-12T08:40:11.993348",
     "exception": false,
     "start_time": "2024-03-12T08:40:11.962459",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Performing Random Search with Logistic Regression\n",
    "#rand_search_log.fit(trainX, trainY)\n",
    "#print(\"model score: %.3f\" % rand_search_log.score(testX, testY))\n",
    "#print(rand_search_log.best_params_)\n",
    "# Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
    "# model score: 0.708\n",
    "#{'log__C': 3.423475552794303, 'log__max_iter': 1014, 'log__penalty': 'l2'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ad56f896",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:40:12.041627Z",
     "iopub.status.busy": "2024-03-12T08:40:12.041355Z",
     "iopub.status.idle": "2024-03-12T08:40:12.045335Z",
     "shell.execute_reply": "2024-03-12T08:40:12.044508Z"
    },
    "papermill": {
     "duration": 0.030426,
     "end_time": "2024-03-12T08:40:12.047194",
     "exception": false,
     "start_time": "2024-03-12T08:40:12.016768",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Performing Random Search with SVM Classifier\n",
    "#rand_search_svm.fit(trainX, trainY)\n",
    "#print(\"model score: %.3f\" % rand_search_svm.score(testX, testY))\n",
    "#print(rand_search_svm.best_params_)\n",
    "# Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
    "# model score: 0.678\n",
    "#{'svm__C': 3.698170133401691, 'svm__degree': 4, 'svm__gamma': 'scale', 'svm__tol': 0.0008850143687158826}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c08f9399",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:40:12.096014Z",
     "iopub.status.busy": "2024-03-12T08:40:12.095748Z",
     "iopub.status.idle": "2024-03-12T08:40:12.099590Z",
     "shell.execute_reply": "2024-03-12T08:40:12.098839Z"
    },
    "papermill": {
     "duration": 0.030482,
     "end_time": "2024-03-12T08:40:12.101546",
     "exception": false,
     "start_time": "2024-03-12T08:40:12.071064",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Performing Random Search with KNN Classifier\n",
    "#rand_search_knn.fit(trainX, trainY)\n",
    "#print(\"model score: %.3f\" % rand_search_knn.score(testX, testY))\n",
    "#print(rand_search_knn.best_params_)\n",
    "# Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
    "# model score: 0.544\n",
    "#{'knn__n_neighbors': 16}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5dc2fa0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:40:12.151265Z",
     "iopub.status.busy": "2024-03-12T08:40:12.150978Z",
     "iopub.status.idle": "2024-03-12T08:42:30.946355Z",
     "shell.execute_reply": "2024-03-12T08:42:30.945104Z"
    },
    "papermill": {
     "duration": 138.847212,
     "end_time": "2024-03-12T08:42:30.973022",
     "exception": false,
     "start_time": "2024-03-12T08:40:12.125810",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n",
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n",
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n",
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandForest, Accuracy: 0.74, Precision: 0.75, F1 Score:0.72\n",
      "{'rfr__criterion': 'gini', 'rfr__max_depth': 75, 'rfr__n_estimators': 163}\n"
     ]
    }
   ],
   "source": [
    "# Performing Random Search with Random Forest Classifier\n",
    "rand_search_rfr.fit(trainX, trainY)\n",
    "acc.append(rand_search_rfr.score(testX, testY))\n",
    "preds = rand_search_rfr.predict(testX)\n",
    "p, _, f1, _ = precision_recall_fscore_support(testY, preds, average = \"weighted\")\n",
    "prec.append(p)\n",
    "f1_score.append(f1)\n",
    "print(f\"{model_name[0]}, Accuracy: {acc[-1]:.2f}, Precision: {prec[-1]:.2f}, F1 Score:{f1_score[-1]:.2f}\")\n",
    "print(rand_search_rfr.best_params_)\n",
    "# Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
    "# RandForest, Accuracy: 0.74, Precision: 0.83, F1 Score:0.75\n",
    "#{'rfr__criterion': 'gini', 'rfr__max_depth': 75, 'rfr__n_estimators': 163}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6f5e0ec2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T08:42:31.023480Z",
     "iopub.status.busy": "2024-03-12T08:42:31.022640Z",
     "iopub.status.idle": "2024-03-12T09:23:40.981886Z",
     "shell.execute_reply": "2024-03-12T09:23:40.980904Z"
    },
    "papermill": {
     "duration": 2470.016581,
     "end_time": "2024-03-12T09:23:41.013877",
     "exception": false,
     "start_time": "2024-03-12T08:42:30.997296",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "AdaBoost, Accuracy: 0.71, Precision: 0.69, F1 Score:0.69\n",
      "{'ada__learning_rate': 0.8563404676678662, 'ada__n_estimators': 91}\n"
     ]
    }
   ],
   "source": [
    "# Performing Random Search with AdaBoost Classifier\n",
    "rand_search_ada.fit(trainX, trainY)\n",
    "acc.append(rand_search_ada.score(testX, testY))\n",
    "preds = rand_search_ada.predict(testX)\n",
    "p, _, f1, _ = precision_recall_fscore_support(testY, preds, average = \"weighted\")\n",
    "prec.append(p)\n",
    "f1_score.append(f1)\n",
    "print(f\"{model_name[1]}, Accuracy: {acc[-1]:.2f}, Precision: {prec[-1]:.2f}, F1 Score:{f1_score[-1]:.2f}\")\n",
    "print(rand_search_ada.best_params_)\n",
    "# Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
    "# AdaBoost, Accuracy: 0.72, Precision: 0.78, F1 Score:0.73\n",
    "#{'ada__learning_rate': 0.7856086427271983, 'ada__n_estimators': 59}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "51721a25",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T09:23:41.066476Z",
     "iopub.status.busy": "2024-03-12T09:23:41.066119Z",
     "iopub.status.idle": "2024-03-12T09:31:15.641590Z",
     "shell.execute_reply": "2024-03-12T09:31:15.640543Z"
    },
    "papermill": {
     "duration": 454.628985,
     "end_time": "2024-03-12T09:31:15.667417",
     "exception": false,
     "start_time": "2024-03-12T09:23:41.038432",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BalBag, Accuracy: 0.69, Precision: 0.76, F1 Score:0.60\n",
      "{'blb__max_features': 0.6595388625674403, 'blb__max_samples': 0.905883075843612, 'blb__n_estimators': 89}\n"
     ]
    }
   ],
   "source": [
    "# Performing Random Search with BalancedBagging Classifier\n",
    "rand_search_blb.fit(trainX, trainY)\n",
    "acc.append(rand_search_blb.score(testX, testY))\n",
    "preds = rand_search_blb.predict(testX)\n",
    "p, _, f1, _ = precision_recall_fscore_support(testY, preds, average = \"weighted\")\n",
    "prec.append(p)\n",
    "f1_score.append(f1)\n",
    "print(f\"{model_name[2]}, Accuracy: {acc[-1]:.2f}, Precision: {prec[-1]:.2f}, F1 Score:{f1_score[-1]:.2f}\")\n",
    "print(rand_search_blb.best_params_)\n",
    "# Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
    "# BalBag, Accuracy: 0.69, Precision: 0.85, F1 Score:0.63\n",
    "#{'blb__max_features': 0.6595388625674403, 'blb__max_samples': 0.905883075843612, 'blb__n_estimators': 89}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b5831e38",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T09:31:15.717371Z",
     "iopub.status.busy": "2024-03-12T09:31:15.717042Z",
     "iopub.status.idle": "2024-03-12T09:54:21.299015Z",
     "shell.execute_reply": "2024-03-12T09:54:21.297943Z"
    },
    "papermill": {
     "duration": 1385.741309,
     "end_time": "2024-03-12T09:54:21.432984",
     "exception": false,
     "start_time": "2024-03-12T09:31:15.691675",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (333) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (333) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (333) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (333) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP, Accuracy: 0.67, Precision: 0.65, F1 Score:0.66\n",
      "{'mlp__activation': 'tanh', 'mlp__hidden_layer_sizes': [100, 50, 30], 'mlp__learning_rate': 'adaptive', 'mlp__learning_rate_init': 0.007554238267260179, 'mlp__max_iter': 362}\n"
     ]
    }
   ],
   "source": [
    "# Performing Random Search with MLP Classifier\n",
    "rand_search_mlp.fit(trainX, trainY)\n",
    "acc.append(rand_search_mlp.score(testX, testY))\n",
    "preds = rand_search_mlp.predict(testX)\n",
    "p, _, f1, _ = precision_recall_fscore_support(testY, preds, average = \"weighted\")\n",
    "prec.append(p)\n",
    "f1_score.append(f1)\n",
    "print(f\"{model_name[3]}, Accuracy: {acc[-1]:.2f}, Precision: {prec[-1]:.2f}, F1 Score:{f1_score[-1]:.2f}\")\n",
    "print(rand_search_mlp.best_params_)\n",
    "# Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
    "# MLP, Accuracy: 0.70, Precision: 0.71, F1 Score:0.71\n",
    "#{'mlp__activation': 'relu', 'mlp__hidden_layer_sizes': [80, 40, 20], 'mlp__learning_rate': 'adaptive', 'mlp__learning_rate_init': 0.0055911194870163555, 'mlp__max_iter': 1176}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e0597737",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T09:54:21.484446Z",
     "iopub.status.busy": "2024-03-12T09:54:21.484092Z",
     "iopub.status.idle": "2024-03-12T09:54:21.747483Z",
     "shell.execute_reply": "2024-03-12T09:54:21.746606Z"
    },
    "papermill": {
     "duration": 0.291662,
     "end_time": "2024-03-12T09:54:21.749589",
     "exception": false,
     "start_time": "2024-03-12T09:54:21.457927",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.74 0.71 0.69 0.67]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing Accuracy of all models\n",
    "print(np.round(acc, 2))\n",
    "ax = sns.barplot(x = model_name, y = acc, palette = \"Set3\")\n",
    "ax.set(xlabel = \"Models\", ylabel = \"Accuracy\")\n",
    "ax.set_title(\"Accuracy Comparison\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "be0443f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T09:54:21.802063Z",
     "iopub.status.busy": "2024-03-12T09:54:21.801778Z",
     "iopub.status.idle": "2024-03-12T09:54:22.048708Z",
     "shell.execute_reply": "2024-03-12T09:54:22.047844Z"
    },
    "papermill": {
     "duration": 0.275186,
     "end_time": "2024-03-12T09:54:22.050510",
     "exception": false,
     "start_time": "2024-03-12T09:54:21.775324",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.75 0.69 0.76 0.65]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
     "data": {
      "image/png": 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FBQUpPz/f4TWz2+3mbx21bNlSjRo1Mp//TZs2KSMjQ+7u7srMzKyz057lavI+u+2227Ro0SJzftGiRQoODpaLi0uFMWr9+/ev0/px7SDcAFfozjvvVFFRkbZv3y7p3IduVQOLzz8V0K5dOw0ZMkSff/65+Y28sLCw0t80uVxlZWU6cuRIhdNG0rkelsv98AgKCtK9996r//znP+YH6flXvZTr3r37RQdi17aBAwfK19fX4caZRUVF+umnnyqse/5zHRkZqfz8fPOy/dLS0gq/sePi4qLBgwdX+EXg+tCzZ0+lpqbq559/dmi32WwaMmSIOebL3d1d/fr105dffmmuU/5es9vtuuWWWyr9ocLaVp33WceOHXXXXXdp9erVMgzD7Mm8UP/+/dW1a9c6rRvXDsINcIVcXFzMsTbbtm0zf9q/rKxMXl5eat68uVq3bq3w8HCHLnrp3G/nhIaGasuWLdq/f7/y8vJ09uxZeXt7q0WLFpd9KXibNm109913KyMjQ9nZ2SoqKtLp06fVqFEjNWnSRKGhobr55pur1eV/4403Kjg4WJs3b9bPP/+s/Px88zGFhITopptucjh9VV9uueUWde3a1QwDR48e1alTp+Tq6ipfX18FBQWpU6dO6tSpk8N2MTEx6tSpk1JTU5WRkVHp7Rcqu5qnPtxwww3q0qWLvvrqK/3yyy8qKyszb79w4e8A9e3bV97e3tq8ebNyc3Nlt9sVGhqqO++8U5mZmXVaZ03fZ1FRUbr++uuVkpKiAwcOmKfgfHx81Lp1a/Xq1euivzEFVJfN4Na5AFCvLue+XgBqjgHFAADAUgg3AADAUgg3AADAUhhzAwAALIWeGwAAYCmEGwAAYCnX3O/clP/ya+PGja/oPj4AAKD+lN/LLCgo6JI3Tr3mws2hQ4fq5T44AACg9mVmZl7yRx+vuXBTfrffzMxM8067AACgYSsoKFBISIj5OX4x11y4KT8V5evrS7gBAOAqczlDShhQDAAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALKWRswsAAGdbv3a3s0vA/+l3e2dnlwALoOcGAABYCuEGAABYCqelLtNbW9Y7uwT8n8d79XN2CQCABoyeGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCkNItzMmjVLoaGh8vDwUGRkpFJSUqpc97bbbpPNZqswDR48uB4rBgAADZXTw83ixYuVkJCgyZMna+vWrerWrZtiYmJ05MiRStdfunSpDh8+bE47d+6Uq6ur7r///nquHAAANERODzfTpk3TmDFjFBcXp/DwcM2ePVteXl6aN29epes3a9ZMgYGB5rR69Wp5eXkRbgAAgCQnh5uSkhKlpqYqOjrabHNxcVF0dLSSk5Mvax9z587Vb3/7W3l7e1e6vLi4WAUFBQ4TAACwLqeGm9zcXJWWliogIMChPSAgQFlZWZfcPiUlRTt37tSjjz5a5TqJiYny8/Mzp5CQkCuuGwAANFxOPy11JebOnasuXbqod+/eVa4zceJEHT9+3JwyMzPrsUIAAFDfGjnz4P7+/nJ1dVV2drZDe3Z2tgIDAy+6bVFRkRYtWqQXXnjhouvZ7XbZ7fYrrhUAAFwdnBpu3N3d1aNHDyUlJWnYsGGSpLKyMiUlJSk+Pv6i23744YcqLi7Www8/XA+V4lpz6lSSs0vA//H0vNPZJQC4yjg13EhSQkKCYmNj1bNnT/Xu3VszZsxQUVGR4uLiJEkjR45UcHCwEhMTHbabO3euhg0bpubNmzujbAAA0EA5PdwMHz5cOTk5mjRpkrKyshQREaGVK1eag4wzMjLk4uI4NGjPnj3asGGDvvjiC2eUDAAAGjCnhxtJio+Pr/I01Lp16yq0dezYUYZh1HFVAADganRVXy0FAABwIcINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwlEbOLgAAgPqUM/0lZ5eA/9Pij8/VyX7puQEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJbi9HAza9YshYaGysPDQ5GRkUpJSbno+vn5+Ro7dqxatWolu92uDh066PPPP6+nagEAQEPXyJkHX7x4sRISEjR79mxFRkZqxowZiomJ0Z49e9SyZcsK65eUlGjAgAFq2bKlPvroIwUHB+vAgQNq0qRJ/RcPAAAaJKeGm2nTpmnMmDGKi4uTJM2ePVufffaZ5s2bp2effbbC+vPmzdOxY8e0adMmubm5SZJCQ0Prs2QAANDAOe20VElJiVJTUxUdHf3fYlxcFB0dreTk5Eq3WbZsmaKiojR27FgFBAToxhtv1CuvvKLS0tIqj1NcXKyCggKHCQAAWJfTwk1ubq5KS0sVEBDg0B4QEKCsrKxKt/n555/10UcfqbS0VJ9//rn++te/aurUqXrppZeqPE5iYqL8/PzMKSQkpFYfBwAAaFicPqC4OsrKytSyZUu988476tGjh4YPH66//OUvmj17dpXbTJw4UcePHzenzMzMeqwYAADUN6eNufH395erq6uys7Md2rOzsxUYGFjpNq1atZKbm5tcXV3Nts6dOysrK0slJSVyd3evsI3dbpfdbq/d4gEAQIPltJ4bd3d39ejRQ0lJSWZbWVmZkpKSFBUVVek2ffr00U8//aSysjKzbe/evWrVqlWlwQYAAFx7nHpaKiEhQXPmzNG7776r3bt36/HHH1dRUZF59dTIkSM1ceJEc/3HH39cx44d0/jx47V371599tlneuWVVzR27FhnPQQAANDAOPVS8OHDhysnJ0eTJk1SVlaWIiIitHLlSnOQcUZGhlxc/pu/QkJCtGrVKv3xj39U165dFRwcrPHjx+uZZ55x1kMAAAANjFPDjSTFx8crPj6+0mXr1q2r0BYVFaVvvvmmjqsCAABXq6vqaikAAIBLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLaRDhZtasWQoNDZWHh4ciIyOVkpJS5boLFiyQzWZzmDw8POqxWgAA0JA5PdwsXrxYCQkJmjx5srZu3apu3bopJiZGR44cqXIbX19fHT582JwOHDhQjxUDAICGzOnhZtq0aRozZozi4uIUHh6u2bNny8vLS/PmzatyG5vNpsDAQHMKCAiox4oBAEBD5tRwU1JSotTUVEVHR5ttLi4uio6OVnJycpXbFRYWqk2bNgoJCdHQoUP1/fff10e5AADgKuDUcJObm6vS0tIKPS8BAQHKysqqdJuOHTtq3rx5+s9//qP33ntPZWVluuWWW3Tw4MFK1y8uLlZBQYHDBAAArMvpp6WqKyoqSiNHjlRERIT69++vpUuXqkWLFnr77bcrXT8xMVF+fn7mFBISUs8VAwCA+uTUcOPv7y9XV1dlZ2c7tGdnZyswMPCy9uHm5qbu3bvrp59+qnT5xIkTdfz4cXPKzMy84roBAEDD5dRw4+7urh49eigpKclsKysrU1JSkqKioi5rH6Wlpfruu+/UqlWrSpfb7Xb5+vo6TAAAwLoaObuAhIQExcbGqmfPnurdu7dmzJihoqIixcXFSZJGjhyp4OBgJSYmSpJeeOEF3XzzzQoLC1N+fr5ef/11HThwQI8++qgzHwYAAGggnB5uhg8frpycHE2aNElZWVmKiIjQypUrzUHGGRkZcnH5bwdTXl6exowZo6ysLDVt2lQ9evTQpk2bFB4e7qyHAAAAGhCnhxtJio+PV3x8fKXL1q1b5zA/ffp0TZ8+vR6qAgAAV6Or7mopAACAiyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAAS6nRXcFLS0u1YMECJSUl6ciRIyorK3NY/uWXX9ZKcQAAANVVo3Azfvx4LViwQIMHD9aNN94om81W23UBAADUSI3CzaJFi/TBBx9o0KBBtV0PAADAFanRmBt3d3eFhYXVdi0AAABXrEbh5qmnntLMmTNlGEZt1wMAAHBFanRaasOGDVq7dq1WrFihG264QW5ubg7Lly5dWivFAQAAVFeNwk2TJk1077331nYtAAAAV6xG4Wb+/Pm1XQcAAECtqFG4KZeTk6M9e/ZIkjp27KgWLVrUSlEAAAA1VaMBxUVFRXrkkUfUqlUr9evXT/369VNQUJBGjx6tkydP1naNAAAAl61G4SYhIUFfffWVli9frvz8fOXn5+s///mPvvrqKz311FO1XSMAAMBlq9FpqSVLluijjz7SbbfdZrYNGjRInp6eeuCBB/TWW2/VVn0AAADVUqOem5MnTyogIKBCe8uWLTktBQAAnKpG4SYqKkqTJ0/W6dOnzbZTp07p+eefV1RUVK0VBwAAUF01Oi01c+ZMxcTE6LrrrlO3bt0kSdu3b5eHh4dWrVpVqwUCAABUR43CzY033qgff/xRCxcu1A8//CBJevDBB/XQQw/J09OzVgsEAACojhr/zo2Xl5fGjBlTm7UAAABcscsON8uWLdPdd98tNzc3LVu27KLr/upXv7riwgAAAGrissPNsGHDlJWVpZYtW2rYsGFVrmez2VRaWlobtQEAAFTbZYebsrKySv8NAADQkNToUvDK5Ofn19auAAAAaqxG4ea1117T4sWLzfn7779fzZo1U3BwsLZv315rxQEAAFRXjcLN7NmzFRISIklavXq11qxZo5UrV+ruu+/WhAkTarVAAACA6qhRuMnKyjLDzaeffqoHHnhAd911l55++mlt2bKl2vubNWuWQkND5eHhocjISKWkpFzWdosWLZLNZrvoAGcAAHBtqVG4adq0qTIzMyVJK1euVHR0tCTJMIxqXym1ePFiJSQkaPLkydq6dau6deummJgYHTly5KLbpaen609/+pP69u1bk4cAAAAsqkbh5te//rV+97vfacCAATp69KjuvvtuSdK2bdsUFhZWrX1NmzZNY8aMUVxcnMLDwzV79mx5eXlp3rx5VW5TWlqqhx56SM8//7zatm1bk4cAAAAsqkbhZvr06YqPj1d4eLhWr14tHx8fSdLhw4f1xBNPXPZ+SkpKlJqaavb8SJKLi4uio6OVnJxc5XYvvPCCWrZsqdGjR1/yGMXFxSooKHCYAACAddXo9gtubm7605/+VKH9j3/8Y7X2k5ubq9LSUgUEBDi0BwQEmPesutCGDRs0d+5cpaWlXdYxEhMT9fzzz1erLgAAcPW6qm6/cOLECY0YMUJz5syRv7//ZW0zceJEJSQkmPMFBQXmYGgAAGA9Tr39gr+/v1xdXZWdne3Qnp2drcDAwArr79u3T+np6RoyZIjZVv5ryY0aNdKePXvUrl07h23sdrvsdvtl1QMAAK5+lz3mpqysTC1btjT/XdVUnaul3N3d1aNHDyUlJTkcJykpSVFRURXW79Spk7777julpaWZ069+9SvdfvvtSktLo0cGAADUbMxNbUpISFBsbKx69uyp3r17a8aMGSoqKlJcXJwkaeTIkQoODlZiYqI8PDx04403OmzfpEkTSarQDgAArk01Cjfjxo1TWFiYxo0b59D+5ptv6qefftKMGTMue1/Dhw9XTk6OJk2apKysLEVERGjlypXmIOOMjAy5uNTaLbAAAIDF1SjcLFmypNJBxbfccoteffXVaoUbSYqPj1d8fHyly9atW3fRbRcsWFCtYwEAAGurUZfI0aNH5efnV6Hd19dXubm5V1wUAABATdUo3ISFhWnlypUV2lesWMEvBgMAAKeq0WmphIQExcfHKycnR3fccYckKSkpSVOnTq32KSkAAIDaVKNw88gjj6i4uFgvv/yyXnzxRUlSaGio3nrrLY0cObJWCwQAAKiOGl8K/vjjj+vxxx9XTk6OPD09zftLAQAAOFONr7E+e/as1qxZo6VLl8owDEnSoUOHVFhYWGvFAQAAVFeNem4OHDiggQMHKiMjQ8XFxRowYIAaN26s1157TcXFxZo9e3Zt1wkAAHBZatRzM378ePXs2VN5eXny9PQ02++9916HWykAAADUtxr13Hz99dfatGmT3N3dHdpDQ0P1yy+/1EphAAAANVGjnpuqbpB58OBBNW7c+IqLAgAAqKkahZu77rrL4fdsbDabCgsLNXnyZA0aNKi2agMAAKi2Gp2WeuONNzRw4ECFh4fr9OnT+t3vfqcff/xR/v7++ve//13bNQIAAFy2GoWbkJAQbd++XYsXL9b27dtVWFio0aNH66GHHnIYYAwAAFDfqh1uzpw5o06dOunTTz/VQw89pIceeqgu6gIAAKiRao+5cXNz0+nTp+uiFgAAgCtWowHFY8eO1WuvvaazZ8/Wdj0AAABXpEZjbrZs2aKkpCR98cUX6tKli7y9vR2WL126tFaKAwAAqK4ahZsmTZrovvvuq+1aAAAArli1wk1ZWZlef/117d27VyUlJbrjjjs0ZcoUrpACAAANRrXG3Lz88sv685//LB8fHwUHB+vvf/+7xo4dW1e1AQAAVFu1ws0///lP/c///I9WrVqlTz75RMuXL9fChQtVVlZWV/UBAABUS7XCTUZGhsPtFaKjo2Wz2XTo0KFaLwwAAKAmqhVuzp49Kw8PD4c2Nzc3nTlzplaLAgAAqKlqDSg2DEOjRo2S3W43206fPq3HHnvM4XJwLgUHAADOUq1wExsbW6Ht4YcfrrViAAAArlS1ws38+fPrqg4AAIBaUaPbLwAAADRUhBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGApDSLczJo1S6GhofLw8FBkZKRSUlKqXHfp0qXq2bOnmjRpIm9vb0VEROhf//pXPVYLAAAaMqeHm8WLFyshIUGTJ0/W1q1b1a1bN8XExOjIkSOVrt+sWTP95S9/UXJysnbs2KG4uDjFxcVp1apV9Vw5AABoiJwebqZNm6YxY8YoLi5O4eHhmj17try8vDRv3rxK17/tttt07733qnPnzmrXrp3Gjx+vrl27asOGDfVcOQAAaIicGm5KSkqUmpqq6Ohos83FxUXR0dFKTk6+5PaGYSgpKUl79uxRv379Kl2nuLhYBQUFDhMAALAup4ab3NxclZaWKiAgwKE9ICBAWVlZVW53/Phx+fj4yN3dXYMHD9Y//vEPDRgwoNJ1ExMT5efnZ04hISG1+hgAAEDD4vTTUjXRuHFjpaWlacuWLXr55ZeVkJCgdevWVbruxIkTdfz4cXPKzMys32IBAEC9auTMg/v7+8vV1VXZ2dkO7dnZ2QoMDKxyOxcXF4WFhUmSIiIitHv3biUmJuq2226rsK7dbpfdbq/VugEAQMPl1J4bd3d39ejRQ0lJSWZbWVmZkpKSFBUVddn7KSsrU3FxcV2UCAAArjJO7bmRpISEBMXGxqpnz57q3bu3ZsyYoaKiIsXFxUmSRo4cqeDgYCUmJko6N4amZ8+eateunYqLi/X555/rX//6l9566y1nPgwAANBAOD3cDB8+XDk5OZo0aZKysrIUERGhlStXmoOMMzIy5OLy3w6moqIiPfHEEzp48KA8PT3VqVMnvffeexo+fLizHgIAAGhAnB5uJCk+Pl7x8fGVLrtwoPBLL72kl156qR6qAgAAV6Or8mopAACAqhBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApTSIcDNr1iyFhobKw8NDkZGRSklJqXLdOXPmqG/fvmratKmaNm2q6Ojoi64PAACuLU4PN4sXL1ZCQoImT56srVu3qlu3boqJidGRI0cqXX/dunV68MEHtXbtWiUnJyskJER33XWXfvnll3quHAAANERODzfTpk3TmDFjFBcXp/DwcM2ePVteXl6aN29epesvXLhQTzzxhCIiItSpUyf97//+r8rKypSUlFTPlQMAgIbIqeGmpKREqampio6ONttcXFwUHR2t5OTky9rHyZMndebMGTVr1qzS5cXFxSooKHCYAACAdTk13OTm5qq0tFQBAQEO7QEBAcrKyrqsfTzzzDMKCgpyCEjnS0xMlJ+fnzmFhIRccd0AAKDhcvppqSvx6quvatGiRfr444/l4eFR6ToTJ07U8ePHzSkzM7OeqwQAAPWpkTMP7u/vL1dXV2VnZzu0Z2dnKzAw8KLbvvHGG3r11Ve1Zs0ade3atcr17Ha77HZ7rdQLAAAaPqf23Li7u6tHjx4Og4HLBwdHRUVVud3f/vY3vfjii1q5cqV69uxZH6UCAICrhFN7biQpISFBsbGx6tmzp3r37q0ZM2aoqKhIcXFxkqSRI0cqODhYiYmJkqTXXntNkyZN0vvvv6/Q0FBzbI6Pj498fHyc9jgAAEDD4PRwM3z4cOXk5GjSpEnKyspSRESEVq5caQ4yzsjIkIvLfzuY3nrrLZWUlOg3v/mNw34mT56sKVOm1GfpAACgAXJ6uJGk+Ph4xcfHV7ps3bp1DvPp6el1XxAAALhqXdVXSwEAAFyIcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACzF6eFm1qxZCg0NlYeHhyIjI5WSklLlut9//73uu+8+hYaGymazacaMGfVXKAAAuCo4NdwsXrxYCQkJmjx5srZu3apu3bopJiZGR44cqXT9kydPqm3btnr11VcVGBhYz9UCAICrgVPDzbRp0zRmzBjFxcUpPDxcs2fPlpeXl+bNm1fp+r169dLrr7+u3/72t7Lb7fVcLQAAuBo4LdyUlJQoNTVV0dHR/y3GxUXR0dFKTk6uteMUFxeroKDAYQIAANbltHCTm5ur0tJSBQQEOLQHBAQoKyur1o6TmJgoPz8/cwoJCam1fQMAgIbH6QOK69rEiRN1/Phxc8rMzHR2SQAAoA41ctaB/f395erqquzsbIf27OzsWh0sbLfbGZ8DAMA1xGk9N+7u7urRo4eSkpLMtrKyMiUlJSkqKspZZQEAgKuc03puJCkhIUGxsbHq2bOnevfurRkzZqioqEhxcXGSpJEjRyo4OFiJiYmSzg1C3rVrl/nvX375RWlpafLx8VFYWJjTHgcAAGg4nBpuhg8frpycHE2aNElZWVmKiIjQypUrzUHGGRkZcnH5b+fSoUOH1L17d3P+jTfe0BtvvKH+/ftr3bp19V0+AABogJwabiQpPj5e8fHxlS67MLCEhobKMIx6qAoAAFytLH+1FAAAuLYQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKU0iHAza9YshYaGysPDQ5GRkUpJSbno+h9++KE6deokDw8PdenSRZ9//nk9VQoAABo6p4ebxYsXKyEhQZMnT9bWrVvVrVs3xcTE6MiRI5Wuv2nTJj344IMaPXq0tm3bpmHDhmnYsGHauXNnPVcOAAAaIqeHm2nTpmnMmDGKi4tTeHi4Zs+eLS8vL82bN6/S9WfOnKmBAwdqwoQJ6ty5s1588UXddNNNevPNN+u5cgAA0BA5NdyUlJQoNTVV0dHRZpuLi4uio6OVnJxc6TbJyckO60tSTExMlesDAIBrSyNnHjw3N1elpaUKCAhwaA8ICNAPP/xQ6TZZWVmVrp+VlVXp+sXFxSouLjbnjx8/LkkqKCioVq2nCouqtT7qTnVfu5o4dYrXu6E4c6buX++iosI6PwYuT338/z5x+nSdHwOXx16N17v8vWEYxiXXdWq4qQ+JiYl6/vnnK7SHhIQ4oRrUhqecXQAAoHb8+eVqb3LixAn5+flddB2nhht/f3+5uroqOzvboT07O1uBgYGVbhMYGFit9SdOnKiEhARzvqysTMeOHVPz5s1ls9mu8BFcPQoKChQSEqLMzEz5+vo6uxzUMV7vawuv97XlWn29DcPQiRMnFBQUdMl1nRpu3N3d1aNHDyUlJWnYsGGSzoWPpKQkxcfHV7pNVFSUkpKS9Ic//MFsW716taKioipd3263y263O7Q1adKkNsq/Kvn6+l5T/xmudbze1xZe72vLtfh6X6rHppzTT0slJCQoNjZWPXv2VO/evTVjxgwVFRUpLi5OkjRy5EgFBwcrMTFRkjR+/Hj1799fU6dO1eDBg7Vo0SJ9++23euedd5z5MAAAQAPh9HAzfPhw5eTkaNKkScrKylJERIRWrlxpDhrOyMiQi8t/L+q65ZZb9P777+u5557Tn//8Z7Vv316ffPKJbrzxRmc9BAAA0IA4PdxIUnx8fJWnodatW1eh7f7779f9999fx1VZi91u1+TJkyucooM18XpfW3i9ry283pdmMy7nmioAAICrhNN/oRgAAKA2EW4AAIClEG4AAIClEG4syGaz6ZNPPnF2GaiBKVOmKCIiwtlloAHjPQJcGuGmjowaNUo2m002m01ubm66/vrr9fTTT+u0E+5pUl7H+dOtt95a73VcWNO1EsCSk5Pl6uqqwYMH18n+Q0NDzdfV1dVVQUFBGj16tPLy8urkeJVZt26dbDab8vPz6+2YV6Pz/y7YbDY1b95cAwcO1I4dO2q8zylTpjjs08/PT3379tVXX31Vi5WjrpW/Nx577LEKy8aOHSubzaZRo0aZ65b/8G1lzv+b4O3trZtuukkffvhhHVXeMBFu6tDAgQN1+PBh/fzzz5o+fbrefvttTZ482Sm1zJ8/X4cPHzanZcuW1XhfZ86cqcXKrG/u3Ll68skntX79eh06dKhOjvHCCy/o8OHDysjI0MKFC7V+/XqNGzeuTo6FK1P+d+Hw4cNKSkpSo0aNdM8991zRPm+44QZzn8nJyWrfvr3uuece80bBuDqEhIRo0aJFOnXqlNl2+vRpvf/++2rdunW19lX+N2Hbtm3q1auXhg8frk2bNtV2yQ0W4aYO2e12BQYGKiQkRMOGDVN0dLRWr14tSTp69KgefPBBBQcHy8vLS126dNG///1vh+1vu+02jRs3Tk8//bSaNWumwMBATZkyxWGdH3/8Uf369ZOHh4fCw8PN/V+oSZMmCgwMNKdmzZpJOne7ixdeeEHXXXed7Ha7+SOK5dLT02Wz2bR48WL1799fHh4eWrhwoSTpf//3f9W5c2d5eHioU6dO+p//+R9zu5KSEsXHx6tVq1by8PBQmzZtzF+ZDg0NlSTde++9stls5rwVFRYWavHixXr88cc1ePBgLViwwGH5q6++qoCAADVu3FijR4+u0LO3ZcsWDRgwQP7+/vLz81P//v21devWCsdp3LixAgMDFRwcrNtvv12xsbEV1luyZIluuOEG2e12hYaGaurUqQ7L8/LyNHLkSDVt2lReXl66++679eOPP5rLDxw4oCFDhqhp06by9vbWDTfcoM8//1zp6em6/fbbJUlNmzZ1+IaJisr/LgQGBioiIkLPPvusMjMzlZOTI0l65pln1KFDB3l5ealt27b661//eskvFI0aNTL3GR4erhdeeEGFhYXau3evuc60adPUpUsXeXt7KyQkRE888YQKCx3vhj5nzhyFhITIy8tL9957r6ZNm3ZN366mvt10000KCQnR0qVLzbalS5eqdevW6t69e7X2Vf43oUOHDpo1a5Y8PT21fPny2i65wSLc1JOdO3dq06ZNcnd3l3Qujffo0UOfffaZdu7cqd///vcaMWKEUlJSHLZ799135e3trc2bN+tvf/ubXnjhBTPAlJWV6de//rXc3d21efNmzZ49W88880y16po5c6amTp2qN954Qzt27FBMTIx+9atfOXyoSdKzzz6r8ePHa/fu3YqJidHChQs1adIkvfzyy9q9e7deeeUV/fWvf9W7774rSfr73/+uZcuW6YMPPtCePXu0cOFCM8Rs2bJF0n97k8rnreiDDz5Qp06d1LFjRz388MOaN2+eyn9a6oMPPtCUKVP0yiuv6Ntvv1WrVq0cAqJ07u63sbGx2rBhg7755hu1b99egwYN0okTJ6o85i+//KLly5crMjLSbEtNTdUDDzyg3/72t/ruu+80ZcoU/fWvf3UIW6NGjdK3336rZcuWKTk5WYZhaNCgQeYH69ixY1VcXKz169fru+++02uvvSYfHx+FhIRoyZIlkqQ9e/bo8OHDmjlzZm09hZZWWFio9957T2FhYWrevLmkcx9KCxYs0K5duzRz5kzNmTNH06dPv+x9FhcXa/78+WrSpIk6duxotru4uOjvf/+7vv/+e7377rv68ssv9fTTT5vLN27cqMcee0zjx49XWlqaBgwYoJdfrv4dm3FlHnnkEc2fP9+cnzdvnnk7oppq1KiR3NzcVFJScqXlXT0M1InY2FjD1dXV8Pb2Nux2uyHJcHFxMT766KMqtxk8eLDx1FNPmfP9+/c3br31Vod1evXqZTzzzDOGYRjGqlWrjEaNGhm//PKLuXzFihWGJOPjjz822yQZHh4ehre3tzmVLw8KCjJefvnlCsd44oknDMMwjP379xuSjBkzZjis065dO+P99993aHvxxReNqKgowzAM48knnzTuuOMOo6ysrNLHemGNVnXLLbeYz92ZM2cMf39/Y+3atYZhGEZUVJT5PJeLjIw0unXrVuX+SktLjcaNGxvLly8329q0aWO4u7sb3t7ehoeHhyHJiIyMNPLy8sx1fve73xkDBgxw2NeECROM8PBwwzAMY+/evYYkY+PGjeby3Nxcw9PT0/jggw8MwzCMLl26GFOmTKm0rrVr1xqSHI6Jis7/u+Dt7W1IMlq1amWkpqZWuc3rr79u9OjRw5yfPHmyw3tk8uTJhouLi7lPm81m+Pr6GitWrLhoLR9++KHRvHlzc3748OHG4MGDHdZ56KGHDD8/v+o9SNRIbGysMXToUOPIkSOG3W430tPTjfT0dMPDw8PIyckxhg4dasTGxjqsW5U2bdoY06dPNwzDMIqLi41XXnnFkGR8+umndf9AGgh6burQ7bffrrS0NG3evFmxsbGKi4vTfffdJ0kqLS3Viy++qC5duqhZs2by8fHRqlWrlJGR4bCPrl27Osy3atVKR44ckSTt3r1bISEhDrd/r+ru6NOnT1daWpo5DRgwQAUFBTp06JD69OnjsG6fPn20e/duh7aePXua/y4qKtK+ffs0evRo+fj4mNNLL72kffv2STrXC5CWlqaOHTtq3Lhx+uKLL6rz1FnCnj17lJKSogcffFDSuW9Pw4cP19y5cyWde/3O712RKr5+2dnZGjNmjNq3by8/Pz/5+vqqsLCwwvtkwoQJSktL044dO5SUlCRJGjx4sEpLS81jVfY6//jjjyotLdXu3bvVqFEjh3qaN2+ujh07mu+FcePG6aWXXlKfPn00efLkKxoEey0r/7uQlpamlJQUxcTE6O6779aBAwckSYsXL1afPn0UGBgoHx8fPffccxVe7wt17NjR3Gdqaqoef/xx3X///fr222/NddasWaM777xTwcHBaty4sUaMGKGjR4/q5MmTks69X3v37u2w3wvnUfdatGhhnsKeP3++Bg8eLH9//2rv55lnnpGPj4+8vLz02muv6dVXX62zixoaogZxbymr8vb2VlhYmKRzXYvdunXT3LlzNXr0aL3++uuaOXOmZsyYYZ4H/8Mf/lCh29DNzc1h3mazqaysrNq1BAYGmrWUKygoqNZjKVd+nn7OnDkVPpxdXV0lnTt3vH//fq1YsUJr1qzRAw88oOjoaH300UfVrv1qNXfuXJ09e9YhfBqGIbvdrjfffPOy9hEbG6ujR49q5syZatOmjex2u6Kioiq8T/z9/c3Xt3379poxY4aioqK0du1aRUdH18rjefTRRxUTE6PPPvtMX3zxhRITEzV16lQ9+eSTtbL/a8X5fxekc2PX/Pz8NGfOHA0ePFgPPfSQnn/+ecXExMjPz0+LFi2qMD7qQu7u7g777N69uz755BPNmDFD7733ntLT03XPPffo8ccf18svv6xmzZppw4YNGj16tEpKSuTl5VVnjxfV98gjj5j3W5w1a1aN9jFhwgSNGjVKPj4+CggIkM1mq80SGzx6buqJi4uL/vznP+u5557TqVOntHHjRg0dOlQPP/ywunXrprZt2zoM/rscnTt3VmZmpg4fPmy2ffPNN5e9va+vr4KCgrRx40aH9o0bNyo8PLzK7QICAhQUFKSff/5ZYWFhDtP111/vsP/hw4drzpw5Wrx4sZYsWaJjx45JOhfaynsVrOjs2bP65z//qalTpzr0mG3fvl1BQUH697//rc6dO2vz5s0O2134+m3cuFHjxo3ToEGDzMHAubm5lzx+ecgsv+qic+fOlb7OHTp0kKurqzp37qyzZ8861HP06FHt2bPH4b0QEhKixx57TEuXLtVTTz2lOXPmSJI5lszKr2ldsdlscnFx0alTp7Rp0ya1adNGf/nLX9SzZ0+1b9/e7NGpLldXV/P1T01NVVlZmaZOnaqbb75ZHTp0qHDlXseOHSuMf7PyeLiGbODAgSopKdGZM2cUExNTo32Uf+EJDAy85oKNRM9Nvbr//vs1YcIEzZo1S+3bt9dHH32kTZs2qWnTppo2bZqys7MvGiouFB0drQ4dOig2Nlavv/66CgoK9Je//KVaNU2YMEGTJ09Wu3btFBERofnz5ystLc28Iqoqzz//vMaNGyc/Pz8NHDhQxcXF+vbbb5WXl6eEhARNmzZNrVq1Uvfu3eXi4qIPP/xQgYGB5pUXoaGhSkpKUp8+fWS329W0adNq1d3Qffrpp8rLy9Po0aPl5+fnsOy+++7T3Llz9ac//UmjRo1Sz5491adPHy1cuFDff/+92rZta67bvn17/etf/1LPnj1VUFCgCRMmyNPTs8LxTpw4oaysLBmGoczMTD399NNq0aKFbrnlFknSU089pV69eunFF1/U8OHDlZycrDfffNMcwNy+fXsNHTpUY8aM0dtvv63GjRvr2WefVXBwsIYOHSpJ+sMf/qC7775bHTp0UF5entauXavOnTtLktq0aSObzaZPP/1UgwYNkqenp3x8fOrkub3aFRcXKysrS9K5K9TefPNNFRYWasiQISooKFBGRoYWLVqkXr166bPPPtPHH398yX2ePXvW3OeJEye0ePFi7dq1y7zAICwsTGfOnNE//vEPDRkyRBs3btTs2bMd9vHkk0+qX79+mjZtmoYMGaIvv/xSK1asuCY/GJ3N1dXVPB1c/kXlQsePH1daWppDW/PmzRUSElLX5V0dnD3ox6qqGvCVmJhotGjRwjh48KAxdOhQw8fHx2jZsqXx3HPPGSNHjnTYpn///sb48eMdtj9/UJlhGMaePXuMW2+91XB3dzc6dOhgrFy5stIBxVUN3i0tLTWmTJliBAcHG25ubka3bt0cBiKWDyjetm1bhW0XLlxoREREGO7u7kbTpk2Nfv36GUuXLjUMwzDeeecdIyIiwvD29jZ8fX2NO++809i6dau57bJly4ywsDCjUaNGRps2bap6Gq9a99xzjzFo0KBKl23evNmQZGzfvt14+eWXDX9/f8PHx8eIjY01nn76aYfBolu3bjV69uxpeHh4GO3btzc+/PBDh8GChnFu8KAkc2rRooUxaNCgCq/ZRx99ZISHhxtubm5G69atjddff91h+bFjx4wRI0YYfn5+hqenpxETE2Ps3bvXXB4fH2+0a9fOsNvtRosWLYwRI0YYubm55vIXXnjBCAwMNGw2m8N7FP8VGxvr8Fo1btzY6NWrl8OFBhMmTDCaN29u+Pj4GMOHDzemT5/uMKi3sgHF5+/Ty8vL6NKli/HWW285HHvatGlGq1atzNf2n//8Z4VB4O+8844RHBxseHp6GsOGDTNeeuklIzAwsK6eDpznUoOELxxQfP5rXj6NHj3aMAyjwt+Ia5HNMP7vulQAAM4zZswY/fDDD/r666+dXQpQLZyWAgBIkt544w0NGDBA3t7eWrFihd59990Kv70EXA3ouQEASJIeeOABrVu3TidOnFDbtm315JNPVnqvI6ChI9wAAABL4VJwAABgKYQbAABgKYQbAABgKYQbAABgKYQbAJazbt062Ww25efnX/Y2oaGhmjFjRp3VBKD+EG4A1LtRo0bJZrNVepnx2LFjZbPZNGrUqPovDIAlEG4AOEVISIgWLVpk3txRkk6fPq33339frVu3dmJlAK52hBsATnHTTTcpJCRES5cuNduWLl2q1q1bq3v37mZbcXGxxo0bp5YtW8rDw0O33nprhbtVf/755+rQoYM8PT11++23Kz09vcLxNmzYoL59+8rT01MhISEaN26cioqKKq3NMAxNmTJFrVu3lt1uV1BQkMaNG1c7DxxAnSPcAHCaRx55RPPnzzfn582bp7i4OId1nn76aS1ZskTvvvuutm7dqrCwMMXExOjYsWOSpMzMTP3617/WkCFDlJaWpkcffVTPPvuswz727dungQMH6r777tOOHTu0ePFibdiwQfHx8ZXWtWTJEk2fPl1vv/22fvzxR33yySfq0qVLLT96AHXGiTftBHCNKr8D8pEjRwy73W6kp6cb6enphoeHh5GTk2PeAbmwsNBwc3MzFi5caG5bUlJiBAUFGX/7298MwzCMiRMnGuHh4Q77f+aZZxzueD169Gjj97//vcM6X3/9teHi4mKcOnXKMAzHOylPnTrV6NChg1FSUlJHzwCAukTPDQCnadGihQYPHqwFCxZo/vz5Gjx4sPz9/c3l+/bt05kzZ9SnTx+zzc3NTb1799bu3bslSbt371ZkZKTDfqOiohzmt2/frgULFsjHx8ecYmJiVFZWpv3791eo6/7779epU6fUtm1bjRkzRh9//LHOnj1bmw8dQB3iruAAnOqRRx4xTw/NmjWrTo5RWFio//f//l+l42YqG7wcEhKiPXv2aM2aNVq9erWeeOIJvf766/rqq6/k5uZWJzUCqD303ABwqoEDB6qkpERnzpxRTEyMw7J27drJ3d1dGzduNNvOnDmjLVu2KDw8XJLUuXNnpaSkOGz3zTffOMzfdNNN2rVrl8LCwipM7u7uldbl6empIUOG6O9//7vWrVun5ORkfffdd7XxkAHUMXpuADiVq6ureYrJ1dXVYZm3t7cef/xxTZgwQc2aNVPr1q31t7/9TSdPntTo0aMlSY899pimTp2qCRMm6NFHH1VqaqoWLFjgsJ9nnnlGN998s+Lj4/Xoo4/K29tbu3bt0urVq/Xmm29WqGnBggUqLS1VZGSkvLy89N5778nT01Nt2rSpmycBQK2i5waA0/n6+srX17fSZa+++qruu+8+jRgxQjfddJN++uknrVq1Sk2bNpV07rTSkiVL9Mknn6hbt26aPXu2XnnlFYd9dO3aVV999ZX27t2rvn37qnv37po0aZKCgoIqPWaTJk00Z84c9enTR127dtWaNWu0fPlyNW/evHYfOIA6YTMMw3B2EQAAALWFnhsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAp/x9vvkzSpvkdUgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing Precision of different models\n",
    "print(np.round(prec,2))\n",
    "ax = sns.barplot(x = model_name, y = prec, palette = \"Set3\")\n",
    "ax.set(xlabel = \"Models\", ylabel = \"Precision\")\n",
    "ax.set_title(\"Precision Comparison\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "037dc7d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T09:54:22.104440Z",
     "iopub.status.busy": "2024-03-12T09:54:22.104154Z",
     "iopub.status.idle": "2024-03-12T09:54:22.491508Z",
     "shell.execute_reply": "2024-03-12T09:54:22.490623Z"
    },
    "papermill": {
     "duration": 0.416412,
     "end_time": "2024-03-12T09:54:22.493422",
     "exception": false,
     "start_time": "2024-03-12T09:54:22.077010",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.72 0.69 0.6  0.66]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing F1 Score for all models\n",
    "print(np.round(f1_score,2))\n",
    "ax = sns.barplot(x = model_name, y = f1_score, palette = \"Set3\")\n",
    "ax.set(xlabel = \"Models\", ylabel = \"F1 Score\")\n",
    "ax.set_title(\"F1 Score Comparison\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c2ba956",
   "metadata": {
    "papermill": {
     "duration": 0.026287,
     "end_time": "2024-03-12T09:54:22.546987",
     "exception": false,
     "start_time": "2024-03-12T09:54:22.520700",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# HyperParameter Tuning\n",
    "After hyperparameter tuning we see that their is some improvement but not by much<br>\n",
    "Next we tried making two different models, one that classifies rating 5 vs not rating 5(Binary classification task) and the second model that classifies 0,1,2,3,4 ratings.<br>\n",
    "This seems to increase the accuracy by quite a bit<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8c141c6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T09:54:22.602244Z",
     "iopub.status.busy": "2024-03-12T09:54:22.601436Z",
     "iopub.status.idle": "2024-03-12T11:12:36.702083Z",
     "shell.execute_reply": "2024-03-12T11:12:36.700790Z"
    },
    "papermill": {
     "duration": 4694.193809,
     "end_time": "2024-03-12T11:12:36.767157",
     "exception": false,
     "start_time": "2024-03-12T09:54:22.573348",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "Random Forest 1, Accuracy: 0.80, Precision: 0.79, F1 Score:0.78\n",
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest 2, Accuracy: 0.58, Precision: 0.54, F1 Score:0.52\n",
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "AdaBoost 1, Accuracy: 0.78, Precision: 0.76, F1 Score:0.76\n",
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "AdaBoost 2, Accuracy: 0.51, Precision: 0.49, F1 Score:0.49\n",
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "Balanced Bagging 1, Accuracy: 0.80, Precision: 0.79, F1 Score:0.76\n",
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balanced Bagging 2, Accuracy: 0.48, Precision: 0.44, F1 Score:0.41\n",
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "MLP 1, Accuracy: 0.77, Precision: 0.76, F1 Score:0.76\n",
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "MLP 2, Accuracy: 0.49, Precision: 0.49, F1 Score:0.49\n",
      "{'rfr__criterion': 'log_loss', 'rfr__max_depth': 79, 'rfr__n_estimators': 144}\n",
      "{'rfr__criterion': 'entropy', 'rfr__max_depth': 58, 'rfr__n_estimators': 165}\n",
      "{'ada__learning_rate': 0.3508061285265429, 'ada__n_estimators': 84}\n",
      "{'ada__learning_rate': 0.053754179147341485, 'ada__n_estimators': 91}\n",
      "{'blb__max_features': 0.7120688399906325, 'blb__max_samples': 0.8957249814634006, 'blb__n_estimators': 118}\n",
      "{'blb__max_features': 0.880336756228033, 'blb__max_samples': 0.13620402024090184, 'blb__n_estimators': 57}\n",
      "{'mlp__activation': 'relu', 'mlp__hidden_layer_sizes': [80, 40, 20], 'mlp__learning_rate': 'constant', 'mlp__learning_rate_init': 0.0085963311663785, 'mlp__max_iter': 3916}\n",
      "{'mlp__activation': 'relu', 'mlp__hidden_layer_sizes': [80, 40, 20], 'mlp__learning_rate': 'invscaling', 'mlp__learning_rate_init': 0.006262655094110565, 'mlp__max_iter': 1654}\n"
     ]
    }
   ],
   "source": [
    "# We will be using it for both of our models\n",
    "rand_search_rfr1 = RandomizedSearchCV(random_forest_pipeline, rand_forest, refit = True, verbose = 1, n_jobs = -1)\n",
    "rand_search_rfr2 = RandomizedSearchCV(random_forest_pipeline, rand_forest, refit = True, verbose = 1, n_jobs = -1)\n",
    "\n",
    "rand_search_blb1 = RandomizedSearchCV(BlanBag_pipeline, rand_blb, refit = True, verbose = 1, n_jobs = -1)\n",
    "rand_search_blb2 = RandomizedSearchCV(BlanBag_pipeline, rand_blb, refit = True, verbose = 1, n_jobs = -1)\n",
    "\n",
    "rand_search_ada1 = RandomizedSearchCV(AdaBoost_pipeline, rand_ada, refit = True, verbose = 1, n_jobs = -1)\n",
    "rand_search_ada2 = RandomizedSearchCV(AdaBoost_pipeline, rand_ada, refit = True, verbose = 1, n_jobs = -1)\n",
    "\n",
    "rand_search_mlp1 = RandomizedSearchCV(MLP_pipeline, rand_mlp, refit = True, verbose = 1, n_jobs = -1)\n",
    "rand_search_mlp2 = RandomizedSearchCV(MLP_pipeline, rand_mlp, refit = True, verbose = 1, n_jobs = -1)\n",
    "\n",
    "# Fitting all these models with modifies train and test set\n",
    "acc, prec, f1_score, acc1, prec1, f1_score1 = [], [], [], [], [], []\n",
    "\n",
    "rand_search_rfr1.fit(trainX1, trainY1)\n",
    "acc.append(rand_search_rfr1.score(testX1, testY1))\n",
    "preds = rand_search_rfr1.predict(testX1)\n",
    "p, _, f1, _ = precision_recall_fscore_support(testY1, preds, average = \"weighted\")\n",
    "prec.append(p)\n",
    "f1_score.append(f1)\n",
    "print(f\"Random Forest 1, Accuracy: {acc[-1]:.2f}, Precision: {prec[-1]:.2f}, F1 Score:{f1_score[-1]:.2f}\")\n",
    "\n",
    "rand_search_rfr2.fit(trainX2, trainY2)\n",
    "acc1.append(rand_search_rfr2.score(testX2, testY2))\n",
    "preds = rand_search_rfr2.predict(testX2)\n",
    "p, _, f1, _ = precision_recall_fscore_support(testY2, preds, average = \"weighted\")\n",
    "prec1.append(p)\n",
    "f1_score1.append(f1)\n",
    "print(f\"Random Forest 2, Accuracy: {acc1[-1]:.2f}, Precision: {prec1[-1]:.2f}, F1 Score:{f1_score1[-1]:.2f}\")\n",
    "\n",
    "rand_search_ada1.fit(trainX1, trainY1)\n",
    "acc.append(rand_search_ada1.score(testX1, testY1))\n",
    "preds = rand_search_ada1.predict(testX1)\n",
    "p, _, f1, _ = precision_recall_fscore_support(testY1, preds, average = \"weighted\")\n",
    "prec.append(p)\n",
    "f1_score.append(f1)\n",
    "print(f\"AdaBoost 1, Accuracy: {acc[-1]:.2f}, Precision: {prec[-1]:.2f}, F1 Score:{f1_score[-1]:.2f}\")\n",
    "\n",
    "rand_search_ada2.fit(trainX2, trainY2)\n",
    "acc1.append(rand_search_ada2.score(testX2, testY2))\n",
    "preds = rand_search_ada2.predict(testX2)\n",
    "p, _, f1, _ = precision_recall_fscore_support(testY2, preds, average = \"weighted\")\n",
    "prec1.append(p)\n",
    "f1_score1.append(f1)\n",
    "print(f\"AdaBoost 2, Accuracy: {acc1[-1]:.2f}, Precision: {prec1[-1]:.2f}, F1 Score:{f1_score1[-1]:.2f}\")\n",
    "\n",
    "rand_search_blb1.fit(trainX1, trainY1)\n",
    "acc.append(rand_search_blb1.score(testX1, testY1))\n",
    "preds = rand_search_blb1.predict(testX1)\n",
    "p, _, f1, _ = precision_recall_fscore_support(testY1, preds, average = \"weighted\")\n",
    "prec.append(p)\n",
    "f1_score.append(f1)\n",
    "print(f\"Balanced Bagging 1, Accuracy: {acc[-1]:.2f}, Precision: {prec[-1]:.2f}, F1 Score:{f1_score[-1]:.2f}\")\n",
    "\n",
    "rand_search_blb2.fit(trainX2, trainY2)\n",
    "acc1.append(rand_search_blb2.score(testX2, testY2))\n",
    "preds = rand_search_blb2.predict(testX2)\n",
    "p, _, f1, _ = precision_recall_fscore_support(testY2, preds, average = \"weighted\")\n",
    "prec1.append(p)\n",
    "f1_score1.append(f1)\n",
    "print(f\"Balanced Bagging 2, Accuracy: {acc1[-1]:.2f}, Precision: {prec1[-1]:.2f}, F1 Score:{f1_score1[-1]:.2f}\")\n",
    "\n",
    "rand_search_mlp1.fit(trainX1, trainY1)\n",
    "acc.append(rand_search_mlp1.score(testX1, testY1))\n",
    "preds = rand_search_mlp1.predict(testX1)\n",
    "p, _, f1, _ = precision_recall_fscore_support(testY1, preds, average = \"weighted\")\n",
    "prec.append(p)\n",
    "f1_score.append(f1)\n",
    "print(f\"MLP 1, Accuracy: {acc[-1]:.2f}, Precision: {prec[-1]:.2f}, F1 Score:{f1_score[-1]:.2f}\")\n",
    "\n",
    "rand_search_mlp2.fit(trainX2, trainY2)\n",
    "acc1.append(rand_search_mlp2.score(testX2, testY2))\n",
    "preds = rand_search_mlp2.predict(testX2)\n",
    "p, _, f1, _ = precision_recall_fscore_support(testY2, preds, average = \"weighted\")\n",
    "prec1.append(p)\n",
    "f1_score1.append(f1)\n",
    "print(f\"MLP 2, Accuracy: {acc1[-1]:.2f}, Precision: {prec1[-1]:.2f}, F1 Score:{f1_score1[-1]:.2f}\")\n",
    "\n",
    "print(rand_search_rfr1.best_params_)\n",
    "print(rand_search_rfr2.best_params_)\n",
    "print(rand_search_ada1.best_params_)\n",
    "print(rand_search_ada2.best_params_)\n",
    "print(rand_search_blb1.best_params_)\n",
    "print(rand_search_blb2.best_params_)\n",
    "print(rand_search_mlp1.best_params_)\n",
    "print(rand_search_mlp2.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d665f6bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T11:12:36.855467Z",
     "iopub.status.busy": "2024-03-12T11:12:36.855166Z",
     "iopub.status.idle": "2024-03-12T11:12:37.120304Z",
     "shell.execute_reply": "2024-03-12T11:12:37.119451Z"
    },
    "papermill": {
     "duration": 0.295874,
     "end_time": "2024-03-12T11:12:37.122318",
     "exception": false,
     "start_time": "2024-03-12T11:12:36.826444",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8  0.78 0.8  0.77]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
     "data": {
      "image/png": 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fYs0/bNtDVVWV3mnuarUa3bp1g0KhQHl5OSoqKlq1rpZeJzdiY2ODZ555Bvv378eZM2dQXl4uhora2lqcOHECubm5mDlzpmRMTVtoHtIMhc+/wsrKCnfeeaekN8jFxeWGA8G7kuYDnanrYLghg06dOqX3Q1M368I+cuQIRo4cCQDo1q2b+IFRWVmJqqqqG/ZeNA9FBQUFNx0L0HwsRtN66+vrUVxcfMPlm2pp4GNBQYH4t1qtxqxZs8Ttnj9/vsVwY+z+NwoKCsIvv/yC2tpa1NXV4ZtvvhFDm4ODg97p0Tfi6+sLa2tr8VBBUVERTp48ecMzphoPbzQfn1NeXq43X9OxCw4ODm3+wXkz5eXlkkHIzWtsfLwLCwslQbxxjE+j77//vtXh5lbPnrGzs0NERAQiIiKg0+lQWVmJgoICcSC6TqdDVlYWPDw8bvrYN29TKpW3FLraSkhIiCTc3GwgMdAQuhq/IAAN4+kEQZAsV1ZWJlmm8XFp/v+ovLxcsv+GHq9GTf9fWlhY4JVXXmnVoUXqeni2FBlk7OmuzZfx9/cX/xYEAf/9738l4wgaB9Q2vrk1nR9oOJOj6fgJoGHgZdMzQ5of0288Pq/T6bBjxw6jem5a0rQ3yNzcXPwAr6urQ1paWovLGbv/jVQqlWTAcNMzngYNGmTUh2vjOJSmNm/eLBlj0KikpAQpKSliWOvVq5ckPGZmZkrCzJ49eyQ9WsaErrayd+9eMdDW19fjt99+k9zfGI6bH3ppOkC0pKTE4Nl+bens2bM4evSoeGaSUqmEk5MT7rjjDsmhw8ZDIB4eHpLxWcePH5f8Qnh2drZkfFfzs4s6moeHhxik7ezsWjXgXalUSl4zVVVV+P3338XbZWVlkufF0tJSPFTd/EvPb7/9Jo6f0mg0NzzLqelPFtTV1eHnn3/We31oNBocOXKkxXE71DUwspKe2tpa8YwBoGFA5Isvvmhw3pSUFPH07bKyMpSWlsLd3R3Dhw9Hdna22Gtw9uxZfPzxx1Cr1bC0tERJSQmqq6sxf/58WFhYwM3NDQMGDBDf0DQaDdasWQN3d3c4OjqiuroaJSUluP/++8WxCc0HHGdmZiInJwf19fV/6RTXpjw8PMRAdf78eSxfvhzOzs4oLi7W+92Spozd/6ZCQ0MNns7a2rOkmho2bBjy8/PF56i2thYbN24UL78gCAIqKirEsQiDBg0C0DBeZ+jQodi/fz+AhoG2K1asgKenJ2pqaiTfqi0sLDBixAija/urSkpK8Omnn0KtVqO8vFwyFsPR0VEcSNv8UM+2bdtw/PhxCIKAgoICvcOZ7VHnjh07YGZmBjc3N9jZ2UGhUKCkpERSc+OYGaVSiVGjRomHJ7VaLZKTk9GzZ0/U19dLAq9CocCoUaPatf7WiI6ONnqZkSNHIjc3V3z8t23bhoMHD8LKygrnz5+XDPi+6667xP8ngYGBSE1NFR+7vLw8fPLJJ+jevTtKSkpu+KVmyJAh2L9/v/h6//3333H8+HG4u7vD3NwclZWVKC8vh06nM+rswpv54YcfxJ7k5uP3srKyxC8V9vb2iIqKarPt3s7Yc0N6GgNCo6ZnWzTXr18/ye3GcGJnZ4cnnnhC0l2s0Whw7tw5nDp1yuAhrokTJ+r9MmhpaSlyc3NRXFysd4aSs7Oz+GHc6OrVq6itrYWTk1ObHPe/9957JYdbLly4gFOnTuHKlSs3vJLvrex/IxcXF70fxfPx8ZGcddNaCoUCU6ZMQXh4uKTXp7KyEqdOncLp06clgyybzhMRESE5o6u2thZ//vmnJNhYWlri0UcfFc+G6kihoaGoqanBmTNnJCHB3NwcDz30kNib4ezsjCFDhoj3C4KAvLw8nDt3DnZ2djcd/NpWtFotiouLcerUKeTm5kpqtrW1lRwqCwsLk/yCsFarxblz5yTBRqlUYsKECXohv6vw8PCQPE9AQxBsPPur0eDBg3HXXXeJt83NzfHwww9LlquqqsKZM2dQU1Nzw7MJLSws8MQTT0gGX9fU1ODs2bPIzc1FaWmpGLba8jBreXk5CgsLUVhYqHcItLq6WrzPmEPpdGPsuSE9zQ9J3SjcBAQE6F1r6r777oNCoUDPnj3x/PPPIzMzE7m5ueLPnDe9/EDTXgsLCwtMnToVJ0+elFx+wczMTLz8QvMu6YkTJ8LBwQFHjhxBVVUV7Ozs0L9/f9x9993iN9+/wsfHB9HR0di1axcKCwuhUCigVqsxcuRIuLq63vDQlLH731RoaKhkPM+t9No0UiqVuP/++xESEoKsrCzxlPbr16+L42t69uyJwMBAyaECMzMzPProoxgwYACys7Mlz0fTyy+05TdcY4SHh8PPzw979+5FSUkJlEolfHx8cO+99+r96OOECRPg5OSEgwcP4vLly7CxsUGfPn1w3333SS4X0B4CAwOhUChw7tw5VFRUoKamBhqNBpaWluLjOGzYML3DrJGRkejXrx8yMzORn59v8PILXf0CnXfeeSc8PT1x4MAB8dIUOp0ONjY28PLywpAhQwwe8vTx8cGsWbPEyy/odDq4ublh2LBh6Nmz5w1/Fbl79+549tlncfDgQZw4cQJlZWW4fv06zMzMYG9vD3d3d/j5+UnODqWuRyHwMsBEnc6hQ4ewZcsWAA3jcOLi4lq8ftft4mbXEiMiasSeG6JOoqysDKdPn8aVK1fEwdFAwziB2z3YEBEZg+GGqJMoKirCjh07JG3dunXrFANGiYi6EoYbok7Izs4OvXv3xr333tuqizMSEdH/cMwNERERyQpPBSciIiJZYbghIiIiWbntxtzodDoUFRXB3t7+lq8TQ0RERB1LEARUV1fDw8Pjpj+yeNuFm6KiIvEaJURERNS1FBQUoGfPnjec57YLN40/h19QUNCqqzQTERGR6VVVVcHLy0tyWZuW3HbhpvFQlIODA8MNERFRF9OaISUcUExERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLJi8nCzfPly+Pr6wsrKCmFhYcjIyLjh/ElJSejbty+sra3h5eWFv/3tb7h+/XoHVUtERESdnUnDTUpKCuLi4hAfH4+srCwMGjQIkZGRKCsrMzj/+vXr8eqrryI+Ph45OTlYtWoVUlJS8Nprr3Vw5URERNRZmTTcJCYmYvbs2YiJiUFgYCBWrFgBGxsbJCcnG5x/3759GDFiBJ544gn4+vri/vvvx9SpU2/a20NERES3D3NTbbi2thaZmZlYsGCB2KZUKhEREYH09HSDywwfPhxffvklMjIyEBoairNnz+LHH3/EtGnTWtyORqOBRqMRb1dVVd1SvZ/9vvuWlqO299zQUaYugYiIOjGThZuKigpotVq4u7tL2t3d3XHixAmDyzzxxBOoqKjAXXfdBUEQUF9fj2efffaGh6USEhLw1ltvtWntRCQvu3flmLoE+v9G3dPf1CWQDJh8QLEx0tLSsGTJEvzzn/9EVlYWNm/ejB9++AFvv/12i8ssWLAAlZWV4lRQUNCBFRMREVFHM1nPjaurK8zMzFBaWippLy0thVqtNrjMm2++iWnTpmHWrFkAgAEDBqCmpgbPPPMMXn/9dSiV+llNpVJBpVK1/Q4QERFRp2SynhtLS0sEBwcjNTVVbNPpdEhNTUV4eLjBZa5evaoXYMzMzAAAgiC0X7FERETUZZis5wYA4uLiEB0djZCQEISGhiIpKQk1NTWIiYkBAEyfPh2enp5ISEgAAEycOBGJiYkYPHgwwsLCcPr0abz55puYOHGiGHKIiIjo9mbScBMVFYXy8nIsXLgQJSUlCAoKwvbt28VBxvn5+ZKemjfeeAMKhQJvvPEGCgsL0b17d0ycOBHvvvuuqXaBiIiIOhmFcJsdz6mqqoKjoyMqKyvh4ODQ6uV4KnjnwVPBqa3xbKnOg2dLUUuM+fzuUmdLEREREd0Mww0RERHJiknH3BB1Vteupd58JuoQ1tb3mboEIupi2HNDREREssJwQ0RERLLCw1JERHRbKV/2jqlLoP+v+9/eaJf1sueGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkpVOEm+XLl8PX1xdWVlYICwtDRkZGi/PefffdUCgUetP48eM7sGIiIiLqrEweblJSUhAXF4f4+HhkZWVh0KBBiIyMRFlZmcH5N2/ejOLiYnE6evQozMzMMGXKlA6unIiIiDojk4ebxMREzJ49GzExMQgMDMSKFStgY2OD5ORkg/M7OztDrVaL044dO2BjY8NwQ0RERABMHG5qa2uRmZmJiIgIsU2pVCIiIgLp6emtWseqVavw+OOPw9bW1uD9Go0GVVVVkomIiIjky6ThpqKiAlqtFu7u7pJ2d3d3lJSU3HT5jIwMHD16FLNmzWpxnoSEBDg6OoqTl5fXX66biIiIOi+TH5b6K1atWoUBAwYgNDS0xXkWLFiAyspKcSooKOjAComIiKijmZty466urjAzM0NpaamkvbS0FGq1+obL1tTUYMOGDVi8ePEN51OpVFCpVH+5ViIiIuoaTNpzY2lpieDgYKSmpoptOp0OqampCA8Pv+GyGzduhEajwVNPPdXeZRIREVEXYtKeGwCIi4tDdHQ0QkJCEBoaiqSkJNTU1CAmJgYAMH36dHh6eiIhIUGy3KpVqzB58mS4uLiYomwiIiLqpEwebqKiolBeXo6FCxeipKQEQUFB2L59uzjIOD8/H0qltIPp5MmT2LNnD37++WdTlExERESdmMnDDQDExsYiNjbW4H1paWl6bX379oUgCO1cFREREXVFXfpsKSIiIqLmGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFZMHm6WL18OX19fWFlZISwsDBkZGTec//Lly3jhhRfQo0cPqFQqBAQE4Mcff+ygaomIiKizMzflxlNSUhAXF4cVK1YgLCwMSUlJiIyMxMmTJ+Hm5qY3f21tLcaMGQM3Nzds2rQJnp6eOHfuHLp169bxxRMREVGnZNJwk5iYiNmzZyMmJgYAsGLFCvzwww9ITk7Gq6++qjd/cnIyLl68iH379sHCwgIA4Ovr25ElExERUSdnssNStbW1yMzMRERExP+KUSoRERGB9PR0g8ts3boV4eHheOGFF+Du7o4777wTS5YsgVarbXE7Go0GVVVVkomIiIjky2ThpqKiAlqtFu7u7pJ2d3d3lJSUGFzm7Nmz2LRpE7RaLX788Ue8+eabWLp0Kd55550Wt5OQkABHR0dx8vLyatP9ICIios7F5AOKjaHT6eDm5oYvvvgCwcHBiIqKwuuvv44VK1a0uMyCBQtQWVkpTgUFBR1YMREREXU0k425cXV1hZmZGUpLSyXtpaWlUKvVBpfp0aMHLCwsYGZmJrb1798fJSUlqK2thaWlpd4yKpUKKpWqbYsnIiKiTstkPTeWlpYIDg5Gamqq2KbT6ZCamorw8HCDy4wYMQKnT5+GTqcT23Jzc9GjRw+DwYaIiIhuPyY9LBUXF4eVK1di7dq1yMnJwXPPPYeamhrx7Knp06djwYIF4vzPPfccLl68iLlz5yI3Nxc//PADlixZghdeeMFUu0BERESdjElPBY+KikJ5eTkWLlyIkpISBAUFYfv27eIg4/z8fCiV/8tfXl5e+Omnn/C3v/0NAwcOhKenJ+bOnYv58+ebaheIiIiokzFpuAGA2NhYxMbGGrwvLS1Nry08PBz79+9v56qIiIioq+pSZ0sRERER3QzDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREclKpwg3y5cvh6+vL6ysrBAWFoaMjIwW512zZg0UCoVksrKy6sBqiYiIqDMzebhJSUlBXFwc4uPjkZWVhUGDBiEyMhJlZWUtLuPg4IDi4mJxOnfuXAdWTERERJ2ZycNNYmIiZs+ejZiYGAQGBmLFihWwsbFBcnJyi8soFAqo1Wpxcnd378CKiYiIqDMzabipra1FZmYmIiIixDalUomIiAikp6e3uNyVK1fg4+MDLy8vTJo0CceOHeuIcomIiKgLMGm4qaiogFar1et5cXd3R0lJicFl+vbti+TkZPz3v//Fl19+CZ1Oh+HDh+P8+fMG59doNKiqqpJMREREJF8mPyxlrPDwcEyfPh1BQUEYPXo0Nm/ejO7du+Pzzz83OH9CQgIcHR3FycvLq4MrJiIioo5k0nDj6uoKMzMzlJaWStpLS0uhVqtbtQ4LCwsMHjwYp0+fNnj/ggULUFlZKU4FBQV/uW4iIiLqvEwabiwtLREcHIzU1FSxTafTITU1FeHh4a1ah1arxZEjR9CjRw+D96tUKjg4OEgmIiIiki9zUxcQFxeH6OhohISEIDQ0FElJSaipqUFMTAwAYPr06fD09ERCQgIAYPHixRg2bBj69OmDy5cv44MPPsC5c+cwa9YsU+4GERERdRImDzdRUVEoLy/HwoULUVJSgqCgIGzfvl0cZJyfnw+l8n8dTJcuXcLs2bNRUlICJycnBAcHY9++fQgMDDTVLhAREVEnYnS48fX1xdNPP40ZM2bA29u7TYqIjY1FbGyswfvS0tIkt5ctW4Zly5a1yXaJiIhIfowec/PSSy9h8+bN8PPzw5gxY7BhwwZoNJr2qI2IiIjIaLcUbrKzs5GRkYH+/fvjxRdfRI8ePRAbG4usrKz2qJGIiIio1W75bKkhQ4bg448/RlFREeLj4/Gvf/0LQ4cORVBQEJKTkyEIQlvWSURERNQqtzyguK6uDt9++y1Wr16NHTt2YNiwYZg5cybOnz+P1157DTt37sT69evbslYiIiKimzI63GRlZWH16tX46quvoFQqMX36dCxbtgz9+vUT53nooYcwdOjQNi2UiIiIqDWMDjdDhw7FmDFj8Nlnn2Hy5MmwsLDQm6dXr154/PHH26RAIiIiImMYHW7Onj0LHx+fG85ja2uL1atX33JRRERERLfK6AHFZWVlOHDggF77gQMH8Mcff7RJUURERES3yuhw88ILLxi8+GRhYSFeeOGFNimKiIiI6FYZHW6OHz+OIUOG6LUPHjwYx48fb5OiiIiIiG6V0eFGpVKhtLRUr724uBjm5ia/VBURERHd5owON/fffz8WLFiAyspKse3y5ct47bXXMGbMmDYtjoiIiMhYRne1fPjhhxg1ahR8fHwwePBgAEB2djbc3d3xn//8p80LJCIiIjKG0eHG09MThw8fxrp163Do0CFYW1sjJiYGU6dONfibN0REREQd6ZYGydja2uKZZ55p61qIiIiI/rJbHgF8/Phx5Ofno7a2VtL+4IMP/uWiiIiIiG7VLf1C8UMPPYQjR45AoVCIV/9WKBQAAK1W27YVEhERERnB6LOl5s6di169eqGsrAw2NjY4duwYdu/ejZCQEKSlpbVDiUREREStZ3TPTXp6On755Re4urpCqVRCqVTirrvuQkJCAubMmYODBw+2R51ERERErWJ0z41Wq4W9vT0AwNXVFUVFRQAAHx8fnDx5sm2rIyIiIjKS0T03d955Jw4dOoRevXohLCwM77//PiwtLfHFF1/Az8+vPWokIiIiajWjw80bb7yBmpoaAMDixYsxYcIEjBw5Ei4uLkhJSWnzAomIiIiMYXS4iYyMFP/u06cPTpw4gYsXL8LJyUk8Y4qIiIjIVIwac1NXVwdzc3McPXpU0u7s7MxgQ0RERJ2CUeHGwsIC3t7e/C0bIiIi6rSMPlvq9ddfx2uvvYaLFy+2Rz1EREREf4nRY24+/fRTnD59Gh4eHvDx8YGtra3k/qysrDYrjoiIiMhYRoebyZMnt0MZRERERG3D6HATHx/fHnUQERERtQmjx9wQERERdWZG99wolcobnvbNM6mIiIjIlIwON99++63kdl1dHQ4ePIi1a9firbfearPCiIiIiG6F0eFm0qRJem2PPvoo7rjjDqSkpGDmzJltUhgRERHRrWizMTfDhg1DamrqLS27fPly+Pr6wsrKCmFhYcjIyGjVchs2bIBCoeAZXERERCRqk3Bz7do1fPzxx/D09DR62ZSUFMTFxSE+Ph5ZWVkYNGgQIiMjUVZWdsPl8vLy8Pe//x0jR4681bKJiIhIhowON05OTnB2dhYnJycn2NvbIzk5GR988IHRBSQmJmL27NmIiYlBYGAgVqxYARsbGyQnJ7e4jFarxZNPPom33noLfn5+Rm+TiIiI5MvoMTfLli2TnC2lVCrRvXt3hIWFwcnJyah11dbWIjMzEwsWLJCsLyIiAunp6S0ut3jxYri5uWHmzJn47bffbrgNjUYDjUYj3q6qqjKqRiIiIupajA43M2bMaLONV1RUQKvVwt3dXdLu7u6OEydOGFxmz549WLVqFbKzs1u1jYSEBJ7FRUREdBsx+rDU6tWrsXHjRr32jRs3Yu3atW1SVEuqq6sxbdo0rFy5Eq6urq1aZsGCBaisrBSngoKCdq2RiIiITMvonpuEhAR8/vnneu1ubm545plnEB0d3ep1ubq6wszMDKWlpZL20tJSqNVqvfnPnDmDvLw8TJw4UWzT6XQAAHNzc5w8eRK9e/eWLKNSqaBSqVpdExEREXVtRvfc5Ofno1evXnrtPj4+yM/PN2pdlpaWCA4OlpxCrtPpkJqaivDwcL35+/XrhyNHjiA7O1ucHnzwQdxzzz3Izs6Gl5eXsbtDREREMmN0z42bmxsOHz4MX19fSfuhQ4fg4uJidAFxcXGIjo5GSEgIQkNDkZSUhJqaGsTExAAApk+fDk9PTyQkJMDKygp33nmnZPlu3boBgF47ERER3Z6MDjdTp07FnDlzYG9vj1GjRgEAfv31V8ydOxePP/640QVERUWhvLwcCxcuRElJCYKCgrB9+3ZxkHF+fj6USl7fk4iIiFrH6HDz9ttvIy8vD/fddx/MzRsW1+l0mD59OpYsWXJLRcTGxiI2NtbgfWlpaTdcds2aNbe0TSIiIpIno8ONpaUlUlJS8M477yA7OxvW1tYYMGAAfHx82qM+IiIiIqMYHW4a+fv7w9/fvy1rISIiIvrLjB7M8sgjj+Af//iHXvv777+PKVOmtElRRERERLfK6HCze/dujBs3Tq/9gQcewO7du9ukKCIiIqJbZXS4uXLlCiwtLfXaLSwseN0mIiIiMjmjw82AAQOQkpKi175hwwYEBga2SVFEREREt8roAcVvvvkmHn74YZw5cwb33nsvACA1NRXr16/Hpk2b2rxAIiIiImMYHW4mTpyILVu2YMmSJdi0aROsra0xaNAg/PLLL3B2dm6PGomIiIha7ZZOBR8/fjzGjx8PAKiqqsJXX32Fv//978jMzIRWq23TAomIiIiMccvXNdi9ezeio6Ph4eGBpUuX4t5778X+/fvbsjYiIiIioxnVc1NSUoI1a9Zg1apVqKqqwmOPPQaNRoMtW7ZwMDERERF1Cq3uuZk4cSL69u2Lw4cPIykpCUVFRfjkk0/aszYiIiIio7W652bbtm2YM2cOnnvuOV52gYiIiDqtVvfc7NmzB9XV1QgODkZYWBg+/fRTVFRUtGdtREREREZrdbgZNmwYVq5cieLiYvzf//0fNmzYAA8PD+h0OuzYsQPV1dXtWScRERFRqxh9tpStrS2efvpp7NmzB0eOHMHLL7+M9957D25ubnjwwQfbo0YiIiKiVrvlU8EBoG/fvnj//fdx/vx5fPXVV21VExEREdEt+0vhppGZmRkmT56MrVu3tsXqiIiIiG5Zm4QbIiIios6C4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZKVThJvly5fD19cXVlZWCAsLQ0ZGRovzbt68GSEhIejWrRtsbW0RFBSE//znPx1YLREREXVmJg83KSkpiIuLQ3x8PLKysjBo0CBERkairKzM4PzOzs54/fXXkZ6ejsOHDyMmJgYxMTH46aefOrhyIiIi6oxMHm4SExMxe/ZsxMTEIDAwECtWrICNjQ2Sk5MNzn/33XfjoYceQv/+/dG7d2/MnTsXAwcOxJ49ezq4ciIiIuqMTBpuamtrkZmZiYiICLFNqVQiIiIC6enpN11eEASkpqbi5MmTGDVqlMF5NBoNqqqqJBMRERHJl0nDTUVFBbRaLdzd3SXt7u7uKCkpaXG5yspK2NnZwdLSEuPHj8cnn3yCMWPGGJw3ISEBjo6O4uTl5dWm+0BERESdi8kPS90Ke3t7ZGdn4/fff8e7776LuLg4pKWlGZx3wYIFqKysFKeCgoKOLZaIiIg6lLkpN+7q6gozMzOUlpZK2ktLS6FWq1tcTqlUok+fPgCAoKAg5OTkICEhAXfffbfevCqVCiqVqk3rJiIios7LpD03lpaWCA4ORmpqqtim0+mQmpqK8PDwVq9Hp9NBo9G0R4lERETUxZi05wYA4uLiEB0djZCQEISGhiIpKQk1NTWIiYkBAEyfPh2enp5ISEgA0DCGJiQkBL1794ZGo8GPP/6I//znP/jss89MuRtERETUSZg83ERFRaG8vBwLFy5ESUkJgoKCsH37dnGQcX5+PpTK/3Uw1dTU4Pnnn8f58+dhbW2Nfv364csvv0RUVJSpdoGIiIg6EZOHGwCIjY1FbGyswfuaDxR+55138M4773RAVURERNQVdcmzpYiIiIhawnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLSKcLN8uXL4evrCysrK4SFhSEjI6PFeVeuXImRI0fCyckJTk5OiIiIuOH8REREdHsxebhJSUlBXFwc4uPjkZWVhUGDBiEyMhJlZWUG509LS8PUqVOxa9cupKenw8vLC/fffz8KCws7uHIiIiLqjEwebhITEzF79mzExMQgMDAQK1asgI2NDZKTkw3Ov27dOjz//PMICgpCv3798K9//Qs6nQ6pqakdXDkRERF1RiYNN7W1tcjMzERERITYplQqERERgfT09Fat4+rVq6irq4Ozs7PB+zUaDaqqqiQTERERyZdJw01FRQW0Wi3c3d0l7e7u7igpKWnVOubPnw8PDw9JQGoqISEBjo6O4uTl5fWX6yYiIqLOy+SHpf6K9957Dxs2bMC3334LKysrg/MsWLAAlZWV4lRQUNDBVRIREVFHMjflxl1dXWFmZobS0lJJe2lpKdRq9Q2X/fDDD/Hee+9h586dGDhwYIvzqVQqqFSqNqmXiIiIOj+T9txYWloiODhYMhi4cXBweHh4i8u9//77ePvtt7F9+3aEhIR0RKlERETURZi05wYA4uLiEB0djZCQEISGhiIpKQk1NTWIiYkBAEyfPh2enp5ISEgAAPzjH//AwoULsX79evj6+opjc+zs7GBnZ2ey/SAiIqLOweThJioqCuXl5Vi4cCFKSkoQFBSE7du3i4OM8/PzoVT+r4Pps88+Q21tLR599FHJeuLj47Fo0aKOLJ2IiIg6IZOHGwCIjY1FbGyswfvS0tIkt/Py8tq/ICIiIuqyuvTZUkRERETNMdwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkayYPNwsX74cvr6+sLKyQlhYGDIyMlqc99ixY3jkkUfg6+sLhUKBpKSkjiuUiIiIugSThpuUlBTExcUhPj4eWVlZGDRoECIjI1FWVmZw/qtXr8LPzw/vvfce1Gp1B1dLREREXYFJw01iYiJmz56NmJgYBAYGYsWKFbCxsUFycrLB+YcOHYoPPvgAjz/+OFQqVQdXS0RERF2BycJNbW0tMjMzERER8b9ilEpEREQgPT29zbaj0WhQVVUlmYiIiEi+TBZuKioqoNVq4e7uLml3d3dHSUlJm20nISEBjo6O4uTl5dVm6yYiIqLOx+QDitvbggULUFlZKU4FBQWmLomIiIjakbmpNuzq6gozMzOUlpZK2ktLS9t0sLBKpeL4HCIiotuIyXpuLC0tERwcjNTUVLFNp9MhNTUV4eHhpiqLiIiIujiT9dwAQFxcHKKjoxESEoLQ0FAkJSWhpqYGMTExAIDp06fD09MTCQkJABoGIR8/flz8u7CwENnZ2bCzs0OfPn1Mth9ERETUeZg03ERFRaG8vBwLFy5ESUkJgoKCsH37dnGQcX5+PpTK/3UuFRUVYfDgweLtDz/8EB9++CFGjx6NtLS0ji6fiIiIOiGThhsAiI2NRWxsrMH7mgcWX19fCILQAVURERFRVyX7s6WIiIjo9sJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLSKcLN8uXL4evrCysrK4SFhSEjI+OG82/cuBH9+vWDlZUVBgwYgB9//LGDKiUiIqLOzuThJiUlBXFxcYiPj0dWVhYGDRqEyMhIlJWVGZx/3759mDp1KmbOnImDBw9i8uTJmDx5Mo4ePdrBlRMREVFnZPJwk5iYiNmzZyMmJgaBgYFYsWIFbGxskJycbHD+jz76CGPHjsW8efPQv39/vP322xgyZAg+/fTTDq6ciIiIOiOThpva2lpkZmYiIiJCbFMqlYiIiEB6errBZdLT0yXzA0BkZGSL8xMREdHtxdyUG6+oqIBWq4W7u7uk3d3dHSdOnDC4TElJicH5S0pKDM6v0Wig0WjE25WVlQCAqqoqo2q9dqXGqPmp/Rj73N2Ka9f4fHcWdXXt/3zX1Fxp921Q63TE/+/q69fbfRvUOiojnu/G14YgCDed16ThpiMkJCTgrbfe0mv38vIyQTXUFl42dQFERNQ2XnvX6EWqq6vh6Oh4w3lMGm5cXV1hZmaG0tJSSXtpaSnUarXBZdRqtVHzL1iwAHFxceJtnU6HixcvwsXFBQqF4i/uQddRVVUFLy8vFBQUwMHBwdTlUDvj83174fN9e7ldn29BEFBdXQ0PD4+bzmvScGNpaYng4GCkpqZi8uTJABrCR2pqKmJjYw0uEx4ejtTUVLz00kti244dOxAeHm5wfpVKBZVKJWnr1q1bW5TfJTk4ONxW/xlud3y+by98vm8vt+PzfbMem0YmPywVFxeH6OhohISEIDQ0FElJSaipqUFMTAwAYPr06fD09ERCQgIAYO7cuRg9ejSWLl2K8ePHY8OGDfjjjz/wxRdfmHI3iIiIqJMwebiJiopCeXk5Fi5ciJKSEgQFBWH79u3ioOH8/Hwolf87qWv48OFYv3493njjDbz22mvw9/fHli1bcOedd5pqF4iIiKgTMXm4AYDY2NgWD0OlpaXptU2ZMgVTpkxp56rkRaVSIT4+Xu8QHckTn+/bC5/v2wuf75tTCK05p4qIiIioizD5LxQTERERtSWGGyIiIpIVhhsiIiKSFYYbGVIoFNiyZYupy6BbsGjRIgQFBZm6DOrE+BohujmGm3YyY8YMKBQKKBQKWFhYoFevXnjllVdw3QTXNGmso+l01113dXgdzWu6XQJYeno6zMzMMH78+HZZv6+vr/i8mpmZwcPDAzNnzsSlS5faZXuGpKWlQaFQ4PLlyx22za6o6fuCQqGAi4sLxo4di8OHD9/yOhctWiRZp6OjI0aOHIlff/21DSun9tb42nj22Wf17nvhhRegUCgwY8YMcd7GH741pOl7gq2tLYYMGYKNGze2U+WdE8NNOxo7diyKi4tx9uxZLFu2DJ9//jni4+NNUsvq1atRXFwsTlu3br3lddXV1bVhZfK3atUqvPjii9i9ezeKioraZRuLFy9GcXEx8vPzsW7dOuzevRtz5sxpl23RX9P4vlBcXIzU1FSYm5tjwoQJf2mdd9xxh7jO9PR0+Pv7Y8KECeKFgqlr8PLywoYNG3Dt2jWx7fr161i/fj28vb2NWlfje8LBgwcxdOhQREVFYd++fW1dcqfFcNOOVCoV1Go1vLy8MHnyZERERGDHjh0AgAsXLmDq1Knw9PSEjY0NBgwYgK+++kqy/N133405c+bglVdegbOzM9RqNRYtWiSZ59SpUxg1ahSsrKwQGBgorr+5bt26Qa1Wi5OzszOAhstdLF68GD179oRKpRJ/RLFRXl4eFAoFUlJSMHr0aFhZWWHdunUAgH/961/o378/rKys0K9fP/zzn/8Ul6utrUVsbCx69OgBKysr+Pj4iL8y7evrCwB46KGHoFAoxNtydOXKFaSkpOC5557D+PHjsWbNGsn97733Htzd3WFvb4+ZM2fq9ez9/vvvGDNmDFxdXeHo6IjRo0cjKytLbzv29vZQq9Xw9PTEPffcg+joaL35vvnmG9xxxx1QqVTw9fXF0qVLJfdfunQJ06dPh5OTE2xsbPDAAw/g1KlT4v3nzp3DxIkT4eTkBFtbW9xxxx348ccfkZeXh3vuuQcA4OTkJPmGSfoa3xfUajWCgoLw6quvoqCgAOXl5QCA+fPnIyAgADY2NvDz88Obb7550y8U5ubm4joDAwOxePFiXLlyBbm5ueI8iYmJGDBgAGxtbeHl5YXnn38eV65Ir4a+cuVKeHl5wcbGBg899BASExNv68vVdLQhQ4bAy8sLmzdvFts2b94Mb29vDB482Kh1Nb4nBAQEYPny5bC2tsZ3333X1iV3Wgw3HeTo0aPYt28fLC0tATSk8eDgYPzwww84evQonnnmGUybNg0ZGRmS5dauXQtbW1scOHAA77//PhYvXiwGGJ1Oh4cffhiWlpY4cOAAVqxYgfnz5xtV10cffYSlS5fiww8/xOHDhxEZGYkHH3xQ8qEGAK+++irmzp2LnJwcREZGYt26dVi4cCHeffdd5OTkYMmSJXjzzTexdu1aAMDHH3+MrVu34uuvv8bJkyexbt06McT8/vvvAP7Xm9R4W46+/vpr9OvXD3379sVTTz2F5ORkNP601Ndff41FixZhyZIl+OOPP9CjRw9JQAQarn4bHR2NPXv2YP/+/fD398e4ceNQXV3d4jYLCwvx3XffISwsTGzLzMzEY489hscffxxHjhzBokWL8Oabb0rC1owZM/DHH39g69atSE9PhyAIGDdunPjB+sILL0Cj0WD37t04cuQI/vGPf8DOzg5eXl745ptvAAAnT55EcXExPvroo7Z6CGXtypUr+PLLL9GnTx+4uLgAaPhQWrNmDY4fP46PPvoIK1euxLJly1q9To1Gg9WrV6Nbt27o27ev2K5UKvHxxx/j2LFjWLt2LX755Re88sor4v179+7Fs88+i7lz5yI7OxtjxozBu+8af8Vm+muefvpprF69WrydnJwsXo7oVpmbm8PCwgK1tbV/tbyuQ6B2ER0dLZiZmQm2traCSqUSAAhKpVLYtGlTi8uMHz9eePnll8Xbo0ePFu666y7JPEOHDhXmz58vCIIg/PTTT4K5ublQWFgo3r9t2zYBgPDtt9+KbQAEKysrwdbWVpwa7/fw8BDeffddvW08//zzgiAIwp9//ikAEJKSkiTz9O7dW1i/fr2k7e233xbCw8MFQRCEF198Ubj33nsFnU5ncF+b1yhXw4cPFx+7uro6wdXVVdi1a5cgCIIQHh4uPs6NwsLChEGDBrW4Pq1WK9jb2wvfffed2Obj4yNYWloKtra2gpWVlQBACAsLEy5duiTO88QTTwhjxoyRrGvevHlCYGCgIAiCkJubKwAQ9u7dK95fUVEhWFtbC19//bUgCIIwYMAAYdGiRQbr2rVrlwBAsk3S1/R9wdbWVgAg9OjRQ8jMzGxxmQ8++EAIDg4Wb8fHx0teI/Hx8YJSqRTXqVAoBAcHB2Hbtm03rGXjxo2Ci4uLeDsqKkoYP368ZJ4nn3xScHR0NG4n6ZZER0cLkyZNEsrKygSVSiXk5eUJeXl5gpWVlVBeXi5MmjRJiI6OlszbEh8fH2HZsmWCIAiCRqMRlixZIgAQvv/++/bfkU6CPTft6J577kF2djYOHDiA6OhoxMTE4JFHHgEAaLVavP322xgwYACcnZ1hZ2eHn376Cfn5+ZJ1DBw4UHK7R48eKCsrAwDk5OTAy8tLcvn3lq6OvmzZMmRnZ4vTmDFjUFVVhaKiIowYMUIy74gRI5CTkyNpCwkJEf+uqanBmTNnMHPmTNjZ2YnTO++8gzNnzgBo6AXIzs5G3759MWfOHPz888/GPHSycPLkSWRkZGDq1KkAGr49RUVFYdWqVQAanr+mvSuA/vNXWlqK2bNnw9/fH46OjnBwcMCVK1f0Xifz5s1DdnY2Dh8+jNTUVADA+PHjodVqxW0Zep5PnToFrVaLnJwcmJubS+pxcXFB3759xdfCnDlz8M4772DEiBGIj4//S4Ngb2eN7wvZ2dnIyMhAZGQkHnjgAZw7dw4AkJKSghEjRkCtVsPOzg5vvPGG3vPdXN++fcV1ZmZm4rnnnsOUKVPwxx9/iPPs3LkT9913Hzw9PWFvb49p06bhwoULuHr1KoCG12toaKhkvc1vU/vr3r27eAh79erVGD9+PFxdXY1ez/z582FnZwcbGxv84x//wHvvvdduJzV0Rp3i2lJyZWtriz59+gBo6FocNGgQVq1ahZkzZ+KDDz7ARx99hKSkJPE4+EsvvaTXbWhhYSG5rVAooNPpjK5FrVaLtTSqqqoyal8aNR6nX7lypd6Hs5mZGYCGY8d//vkntm3bhp07d+Kxxx5DREQENm3aZHTtXdWqVatQX18vCZ+CIEClUuHTTz9t1Tqio6Nx4cIFfPTRR/Dx8YFKpUJ4eLje68TV1VV8fv39/ZGUlITw8HDs2rULERERbbI/s2bNQmRkJH744Qf8/PPPSEhIwNKlS/Hiiy+2yfpvF03fF4CGsWuOjo5YuXIlxo8fjyeffBJvvfUWIiMj4ejoiA0bNuiNj2rO0tJSss7Bgwdjy5YtSEpKwpdffom8vDxMmDABzz33HN599104Oztjz549mDlzJmpra2FjY9Nu+0vGe/rpp8XrLS5fvvyW1jFv3jzMmDEDdnZ2cHd3h0KhaMsSOz323HQQpVKJ1157DW+88QauXbuGvXv3YtKkSXjqqacwaNAg+Pn5SQb/tUb//v1RUFCA4uJisW3//v2tXt7BwQEeHh7Yu3evpH3v3r0IDAxscTl3d3d4eHjg7Nmz6NOnj2Tq1auXZP1RUVFYuXIlUlJS8M033+DixYsAGkJbY6+CHNXX1+Pf//43li5dKukxO3ToEDw8PPDVV1+hf//+OHDggGS55s/f3r17MWfOHIwbN04cDFxRUXHT7TeGzMazLvr372/weQ4ICICZmRn69++P+vp6ST0XLlzAyZMnJa8FLy8vPPvss9i8eTNefvllrFy5EgDEsWRyfk7bi0KhgFKpxLVr17Bv3z74+Pjg9ddfR0hICPz9/cUeHWOZmZmJz39mZiZ0Oh2WLl2KYcOGISAgQO/Mvb59++qNf5PzeLjObOzYsaitrUVdXR0iIyNvaR2NX3jUavVtF2wA9tx0qClTpmDevHlYvnw5/P39sWnTJuzbtw9OTk5ITExEaWnpDUNFcxEREQgICEB0dDQ++OADVFVV4fXXXzeqpnnz5iE+Ph69e/dGUFAQVq9ejezsbPGMqJa89dZbmDNnDhwdHTF27FhoNBr88ccfuHTpEuLi4pCYmIgePXpg8ODBUCqV2LhxI9RqtXjmha+vL1JTUzFixAioVCo4OTkZVXdn9/333+PSpUuYOXMmHB0dJfc98sgjWLVqFf7+979jxowZCAkJwYgRI7Bu3TocO3YMfn5+4rz+/v74z3/+g5CQEFRVVWHevHmwtrbW2151dTVKSkogCAIKCgrwyiuvoHv37hg+fDgA4OWXX8bQoUPx9ttvIyoqCunp6fj000/FAcz+/v6YNGkSZs+ejc8//xz29vZ49dVX4enpiUmTJgEAXnrpJTzwwAMICAjApUuXsGvXLvTv3x8A4OPjA4VCge+//x7jxo2DtbU17Ozs2uWx7eo0Gg1KSkoANJyh9umnn+LKlSuYOHEiqqqqkJ+fjw0bNmDo0KH44Ycf8O233950nfX19eI6q6urkZKSguPHj4snGPTp0wd1dXX45JNPMHHiROzduxcrVqyQrOPFF1/EqFGjkJiYiIkTJ+KXX37Btm3bbssPRlMzMzMTDwc3flFprrKyEtnZ2ZI2FxcXeHl5tXd5XYOpB/3IVUsDvhISEoTu3bsL58+fFyZNmiTY2dkJbm5uwhtvvCFMnz5dsszo0aOFuXPnSpZvOqhMEATh5MmTwl133SVYWloKAQEBwvbt2w0OKG5p8K5WqxUWLVokeHp6ChYWFsKgQYMkAxEbBxQfPHhQb9l169YJQUFBgqWlpeDk5CSMGjVK2Lx5syAIgvDFF18IQUFBgq2treDg4CDcd999QlZWlrjs1q1bhT59+gjm5uaCj49PSw9jlzVhwgRh3LhxBu87cOCAAEA4dOiQ8O677wqurq6CnZ2dEB0dLbzyyiuSwaJZWVlCSEiIYGVlJfj7+wsbN26UDBYUhIbBgwDEqXv37sK4ceP0nrNNmzYJgYGBgoWFheDt7S188MEHkvsvXrwoTJs2TXB0dBSsra2FyMhIITc3V7w/NjZW6N27t6BSqYTu3bsL06ZNEyoqKsT7Fy9eLKjVakGhUEheo/Q/0dHRkufK3t5eGDp0qOREg3nz5gkuLi6CnZ2dEBUVJSxbtkwyqNfQgOKm67SxsREGDBggfPbZZ5JtJyYmCj169BCf23//+996g8C/+OILwdPTU7C2thYmT54svPPOO4JarW6vh4OauNkg4eYDips+543TzJkzBUEQ9N4jbkcKQfj/56USERE1MXv2bJw4cQK//fabqUshMgoPSxEREQDgww8/xJgxY2Bra4tt27Zh7dq1er+9RNQVsOeGiIgAAI899hjS0tJQXV0NPz8/vPjiiwavdUTU2THcEBERkazwVHAiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIpKdtLQ0KBQKXL58udXL+Pr6Iikpqd1qIqKOw3BDRB1uxowZUCgUBk8zfuGFF6BQKDBjxoyOL4yIZIHhhohMwsvLCxs2bBAv7ggA169fx/r16+Ht7W3Cyoioq2O4ISKTGDJkCLy8vLB582axbfPmzfD29sbgwYPFNo1Ggzlz5sDNzQ1WVla466679K5W/eOPPyIgIADW1ta45557kJeXp7e9PXv2YOTIkbC2toaXlxfmzJmDmpoag7UJgoBFixbB29sbKpUKHh4emDNnTtvsOBG1O4YbIjKZp59+GqtXrxZvJycnIyYmRjLPK6+8gm+++QZr165FVlYW+vTpg8jISFy8eBEAUFBQgIcffhgTJ05EdnY2Zs2ahVdffVWyjjNnzmDs2LF45JFHcPjwYaSkpGDPnj2IjY01WNc333yDZcuW4fPPP8epU6ewZcsWDBgwoI33nojajQkv2klEt6nGKyCXlZUJKpVKyMvLE/Ly8gQrKyuhvLxcvALylStXBAsLC2HdunXisrW1tYKHh4fw/vvvC4IgCAsWLBACAwMl658/f77kitczZ84UnnnmGck8v/32m6BUKoVr164JgiC9kvLSpUuFgIAAoba2tp0eASJqT+y5ISKT6d69O8aPH481a9Zg9erVGD9+PFxdXcX7z5w5g7q6OowYMUJss7CwQGhoKHJycgAAOTk5CAsLk6w3PDxccvvQoUNYs2YN7OzsxCkyMhI6nQ5//vmnXl1TpkzBtWvX4Ofnh9mzZ+Pbb79FfX19W+46EbUjXhWciEzq6aefFg8PLV++vF22ceXKFfzf//2fwXEzhgYve3l54eTJk9i5cyd27NiB559/Hh988AF+/fVXWFhYtEuNRNR22HNDRCY1duxY1NbWoq6uDpGRkZL7evfuDUtLS+zdu1dsq6urw++//47AwEAAQP/+/ZGRkSFZbv/+/ZLbQ4YMwfHjx9GnTx+9ydLS0mBd1tbWmDhxIj7++GOkpaUhPT0dR44caYtdJqJ2xp4bIjIpMzMz8RCTmZmZ5D5bW1s899xzmDdvHpydneHt7Y33338fV69excyZMwEAzz77LJYuXYp58+Zh1qxZyMzMxJo1ayTrmT9/PoYNG4bY2FjMmjULtra2OH78OHbs2IFPP/1Ur6Y1a9ZAq9UiLCwMNjY2+PLLL2FtbQ0fH5/2eRCIqE2x54aITM7BwQEODg4G73vvvffwyCOPYNq0aRgyZAhOnz6Nn376CU5OTgAaDit988032LJlCwYNGoQVK1ZgyZIlknUMHDgQv/76K3JzczFy5EgMHjwYCxcuhIeHh8FtduvWDStXrsSIESMwcOBA7Ny5E9999x1cXFzadseJqF0oBEEQTF0EERERUVthzw0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREcnK/wNVNmIhEYfgfAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing Accuracy of all models\n",
    "print(np.round(acc, 2))\n",
    "ax = sns.barplot(x = model_name, y = acc, palette = \"Set3\")\n",
    "ax.set(xlabel = \"Models\", ylabel = \"Accuracy\")\n",
    "ax.set_title(\"Accuracy Comparison Model 1\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2cb3f198",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T11:12:37.180954Z",
     "iopub.status.busy": "2024-03-12T11:12:37.180691Z",
     "iopub.status.idle": "2024-03-12T11:12:37.447306Z",
     "shell.execute_reply": "2024-03-12T11:12:37.446404Z"
    },
    "papermill": {
     "duration": 0.297701,
     "end_time": "2024-03-12T11:12:37.449287",
     "exception": false,
     "start_time": "2024-03-12T11:12:37.151586",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.79 0.76 0.79 0.76]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing Precision of different models\n",
    "print(np.round(prec,2))\n",
    "ax = sns.barplot(x = model_name, y = prec, palette = \"Set3\")\n",
    "ax.set(xlabel = \"Models\", ylabel = \"Precision\")\n",
    "ax.set_title(\"Precision Comparison Model 1\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b3c3f3c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T11:12:37.510566Z",
     "iopub.status.busy": "2024-03-12T11:12:37.510268Z",
     "iopub.status.idle": "2024-03-12T11:12:37.782092Z",
     "shell.execute_reply": "2024-03-12T11:12:37.781195Z"
    },
    "papermill": {
     "duration": 0.304454,
     "end_time": "2024-03-12T11:12:37.784007",
     "exception": false,
     "start_time": "2024-03-12T11:12:37.479553",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.78 0.76 0.76 0.76]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing F1 Score for all models\n",
    "print(np.round(f1_score,2))\n",
    "ax = sns.barplot(x = model_name, y = f1_score, palette = \"Set3\")\n",
    "ax.set(xlabel = \"Models\", ylabel = \"F1 Score\")\n",
    "ax.set_title(\"F1 Score Comparison Model 1\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7c14d92a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T11:12:37.848041Z",
     "iopub.status.busy": "2024-03-12T11:12:37.847722Z",
     "iopub.status.idle": "2024-03-12T11:12:38.104695Z",
     "shell.execute_reply": "2024-03-12T11:12:38.103790Z"
    },
    "papermill": {
     "duration": 0.290997,
     "end_time": "2024-03-12T11:12:38.106571",
     "exception": false,
     "start_time": "2024-03-12T11:12:37.815574",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.58 0.51 0.48 0.49]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing Accuracy of all models\n",
    "print(np.round(acc1, 2))\n",
    "ax = sns.barplot(x = model_name, y = acc1, palette = \"Set3\")\n",
    "ax.set(xlabel = \"Models\", ylabel = \"Accuracy\")\n",
    "ax.set_title(\"Accuracy Comparison Model 2\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0ef7b6f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T11:12:38.169760Z",
     "iopub.status.busy": "2024-03-12T11:12:38.169444Z",
     "iopub.status.idle": "2024-03-12T11:12:38.417430Z",
     "shell.execute_reply": "2024-03-12T11:12:38.416460Z"
    },
    "papermill": {
     "duration": 0.281618,
     "end_time": "2024-03-12T11:12:38.419402",
     "exception": false,
     "start_time": "2024-03-12T11:12:38.137784",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.54 0.49 0.44 0.49]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing Precision of different models\n",
    "print(np.round(prec1,2))\n",
    "ax = sns.barplot(x = model_name, y = prec1, palette = \"Set3\")\n",
    "ax.set(xlabel = \"Models\", ylabel = \"Precision\")\n",
    "ax.set_title(\"Precision Comparison Model 2\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0f137f1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T11:12:38.484330Z",
     "iopub.status.busy": "2024-03-12T11:12:38.483727Z",
     "iopub.status.idle": "2024-03-12T11:12:38.727316Z",
     "shell.execute_reply": "2024-03-12T11:12:38.726383Z"
    },
    "papermill": {
     "duration": 0.278457,
     "end_time": "2024-03-12T11:12:38.729453",
     "exception": false,
     "start_time": "2024-03-12T11:12:38.450996",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.52 0.49 0.41 0.49]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing F1 Score for all models\n",
    "print(np.round(f1_score1,2))\n",
    "ax = sns.barplot(x = model_name, y = f1_score1, palette = \"Set3\")\n",
    "ax.set(xlabel = \"Models\", ylabel = \"F1 Score\")\n",
    "ax.set_title(\"F1 Score Comparison Model 2\", fontdict={'size': 15, 'weight': 'bold', 'color': 'grey'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d3b0bfca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T11:12:38.795813Z",
     "iopub.status.busy": "2024-03-12T11:12:38.795472Z",
     "iopub.status.idle": "2024-03-12T11:12:40.654998Z",
     "shell.execute_reply": "2024-03-12T11:12:40.653582Z"
    },
    "papermill": {
     "duration": 1.896672,
     "end_time": "2024-03-12T11:12:40.658890",
     "exception": false,
     "start_time": "2024-03-12T11:12:38.762218",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Getting the predictions from different classifiers\n",
    "#result_log = rand_search_log.predict(test_dataset)\n",
    "result_rfr = rand_search_rfr.predict(test_dataset)\n",
    "#result_svm = rand_search_svm.predict(test_dataset)\n",
    "#result_knn = rand_search_knn.predict(test_dataset)\n",
    "result_ada = rand_search_ada.predict(test_dataset)\n",
    "result_blb = rand_search_blb.predict(test_dataset)\n",
    "result_mlp = rand_search_mlp.predict(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fa982ce8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T11:12:40.791848Z",
     "iopub.status.busy": "2024-03-12T11:12:40.790982Z",
     "iopub.status.idle": "2024-03-12T11:12:44.203735Z",
     "shell.execute_reply": "2024-03-12T11:12:44.202388Z"
    },
    "papermill": {
     "duration": 3.477501,
     "end_time": "2024-03-12T11:12:44.207250",
     "exception": false,
     "start_time": "2024-03-12T11:12:40.729749",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandForest_RandForest: [5 5 4 ... 5 2 5]\n",
      "RandForest_AdaBoost: [5 5 4 ... 5 2 5]\n",
      "RandForest_BalBag: [5 5 4 ... 5 2 5]\n",
      "RandForest_MLP: [5 5 4 ... 5 2 5]\n",
      "AdaBoost_RandForest: [5 5 5 ... 4 2 5]\n",
      "AdaBoost_AdaBoost: [5 5 5 ... 4 2 5]\n",
      "AdaBoost_BalBag: [5 5 5 ... 4 2 5]\n",
      "AdaBoost_MLP: [5 5 5 ... 4 2 5]\n",
      "BalBag_RandForest: [5 5 5 ... 5 2 5]\n",
      "BalBag_AdaBoost: [5 5 5 ... 5 2 5]\n",
      "BalBag_BalBag: [5 5 5 ... 5 2 5]\n",
      "BalBag_MLP: [5 5 5 ... 5 2 5]\n",
      "MLP_RandForest: [0 4 5 ... 5 2 5]\n",
      "MLP_AdaBoost: [0 4 5 ... 5 2 5]\n",
      "MLP_BalBag: [0 4 5 ... 5 2 5]\n",
      "MLP_MLP: [0 4 5 ... 5 2 5]\n"
     ]
    }
   ],
   "source": [
    "# We will need to format our output a bit for the two model predictions\n",
    "model_1 = [rand_search_rfr1, rand_search_ada1, rand_search_blb1, rand_search_mlp1]\n",
    "model_2 = [rand_search_rfr2, rand_search_ada2, rand_search_blb2, rand_search_mlp2]\n",
    "model_results = {}\n",
    "\n",
    "for i in range(len(model_1)):\n",
    "    result_1_model = model_1[i].predict(test_dataset)\n",
    "    index = []\n",
    "    name = model_name[i]\n",
    "    for i in range(len(result_1_model)):\n",
    "        if result_1_model[i] == 1:\n",
    "            result_1_model[i] = 5\n",
    "        else:\n",
    "            index.append(i)\n",
    "    rest = test_dataset.iloc[index]\n",
    "    for j in range(len(model_2)):\n",
    "        name1 = name + \"_\" + model_name[j]\n",
    "        result_2_model = model_2[j].predict(rest)\n",
    "        for i in range(len(result_2_model)):\n",
    "            result_1_model[index[i]] = result_2_model[i]\n",
    "        model_results[name1] = result_1_model\n",
    "\n",
    "for key in model_results.keys():\n",
    "    print(f\"{key}: {model_results[key]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "35203c28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T11:12:44.337814Z",
     "iopub.status.busy": "2024-03-12T11:12:44.337464Z",
     "iopub.status.idle": "2024-03-12T11:12:44.343722Z",
     "shell.execute_reply": "2024-03-12T11:12:44.342805Z"
    },
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     "exception": false,
     "start_time": "2024-03-12T11:12:44.276918",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 5 0 ... 5 1 5]\n",
      "[5 5 0 ... 0 2 4]\n",
      "[5 5 5 ... 5 1 5]\n",
      "[5 4 4 ... 5 2 1]\n"
     ]
    }
   ],
   "source": [
    "#print(result_log)\n",
    "print(result_rfr)\n",
    "#print(result_svm)\n",
    "#print(result_knn)\n",
    "print(result_ada)\n",
    "print(result_blb)\n",
    "print(result_mlp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4bc2b367",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T11:12:44.411158Z",
     "iopub.status.busy": "2024-03-12T11:12:44.410862Z",
     "iopub.status.idle": "2024-03-12T11:12:44.600674Z",
     "shell.execute_reply": "2024-03-12T11:12:44.599930Z"
    },
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     "exception": false,
     "start_time": "2024-03-12T11:12:44.378086",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Exporting results to a csv file\n",
    "#submission = pd.DataFrame({\"ID\": range(1,test_dataset.shape[0]+1), \"Rating\":result_log})\n",
    "#submission.to_csv(\"submission_log.csv\", index=False)\n",
    "\n",
    "submission = pd.DataFrame({\"ID\": range(1,test_dataset.shape[0]+1), \"Rating\":result_rfr})\n",
    "submission.to_csv(\"submission_rfr.csv\", index=False)\n",
    "\n",
    "#submission = pd.DataFrame({\"ID\": range(1,test_dataset.shape[0]+1), \"Rating\":result_svm})\n",
    "#submission.to_csv(\"submission_svm.csv\", index=False)\n",
    "\n",
    "#submission = pd.DataFrame({\"ID\": range(1,test_dataset.shape[0]+1), \"Rating\":result_knn})\n",
    "#submission.to_csv(\"submission_knn.csv\", index=False)\n",
    "\n",
    "submission = pd.DataFrame({\"ID\": range(1,test_dataset.shape[0]+1), \"Rating\":result_ada})\n",
    "submission.to_csv(\"submission_ada.csv\", index=False)\n",
    "\n",
    "submission = pd.DataFrame({\"ID\": range(1,test_dataset.shape[0]+1), \"Rating\":result_blb})\n",
    "submission.to_csv(\"submission_blb.csv\", index=False)\n",
    "\n",
    "submission = pd.DataFrame({\"ID\": range(1,test_dataset.shape[0]+1), \"Rating\":result_mlp})\n",
    "submission.to_csv(\"submission_mlp.csv\", index=False)\n",
    "\n",
    "# Exporting all results\n",
    "for key in model_results.keys():\n",
    "    submission = pd.DataFrame({\"ID\": range(1,test_dataset.shape[0]+1), \"Rating\":model_results[key]})\n",
    "    submission.to_csv(f\"submission_{key}.csv\", index=False)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "9d6fe43b",
   "metadata": {
    "papermill": {
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    },
    "tags": []
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