class2_advanced_ensemble_techniques.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboost\n",
      "  Downloading xgboost-2.0.0-py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.macosx_12_0_x86_64.whl (2.2 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.11/site-packages (from xgboost) (1.25.0)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.11/site-packages (from xgboost) (1.11.0)\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-2.0.0\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\teval-rmse:72.93089\ttrain-rmse:77.56304\n",
      "[1]\teval-rmse:72.64378\ttrain-rmse:77.18040\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\teval-rmse:72.36109\ttrain-rmse:76.80327\n",
      "[3]\teval-rmse:72.08278\ttrain-rmse:76.43160\n",
      "[4]\teval-rmse:71.80880\ttrain-rmse:76.06532\n",
      "[5]\teval-rmse:71.53908\ttrain-rmse:75.70437\n",
      "[6]\teval-rmse:71.25887\ttrain-rmse:75.33864\n",
      "[7]\teval-rmse:70.99426\ttrain-rmse:74.97820\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/site-packages/xgboost/core.py:726: FutureWarning: Pass `evals` as keyword args.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load data and split\n",
    "data = load_diabetes()\n",
    "X = data.data\n",
    "y = data.target\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Convert data to DMatrix format for XGBoost\n",
    "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
    "dvalid = xgb.DMatrix(X_valid, label=y_valid)\n",
    "\n",
    "# Parameters for XGBoost\n",
    "param = {'max_depth': 3, 'eta': 0.01, 'objective': 'reg:squarederror'}\n",
    "\n",
    "# Train the model with early stopping\n",
    "num_round = 1000\n",
    "evallist = [(dvalid, 'eval'), (dtrain, 'train')]\n",
    "evals_result = {}  # Initialize an empty dictionary to store results\n",
    "bst = xgb.train(param, dtrain, num_round, evallist, early_stopping_rounds=10, evals_result=evals_result)\n",
    "\n",
    "# Plotting the learning curves\n",
    "epochs = len(evals_result['eval']['rmse'])\n",
    "x_axis = range(0, epochs)\n",
    "plt.figure()\n",
    "plt.plot(x_axis, evals_result['eval']['rmse'], label='Validation')\n",
    "plt.plot(x_axis, evals_result['train']['rmse'], label='Train')\n",
    "plt.legend()\n",
    "plt.ylabel('RMSE')\n",
    "plt.xlabel('Iterations')\n",
    "plt.title('XGBoost Learning Curves with Early Stopping')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['eval', 'train'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "evals_result.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df = pd.DataFrame([evals_result['eval']['rmse'],evals_result['train']['rmse']]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df['diff'] = results_df[0] - results_df[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>71.808797</td>\n",
       "      <td>76.065323</td>\n",
       "      <td>-4.256526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>60.292083</td>\n",
       "      <td>60.532969</td>\n",
       "      <td>-0.240886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>60.173519</td>\n",
       "      <td>60.360002</td>\n",
       "      <td>-0.186483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>60.064582</td>\n",
       "      <td>60.194993</td>\n",
       "      <td>-0.130410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>59.941553</td>\n",
       "      <td>60.024619</td>\n",
       "      <td>-0.083066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>59.828974</td>\n",
       "      <td>59.858361</td>\n",
       "      <td>-0.029386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0          1      diff\n",
       "0   72.930889  77.563037 -4.632147\n",
       "1   72.643776  77.180398 -4.536622\n",
       "2   72.361093  76.803274 -4.442181\n",
       "3   72.082784  76.431603 -4.348818\n",
       "4   71.808797  76.065323 -4.256526\n",
       "..        ...        ...       ...\n",
       "64  60.292083  60.532969 -0.240886\n",
       "65  60.173519  60.360002 -0.186483\n",
       "66  60.064582  60.194993 -0.130410\n",
       "67  59.941553  60.024619 -0.083066\n",
       "68  59.828974  59.858361 -0.029386\n",
       "\n",
       "[69 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df[results_df['diff'] < 0.01]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "bst.best_ntree_limit = 67\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = bst.predict(dvalid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([159.18893 , 206.27591 , 175.22429 , 295.9157  , 125.61823 ,\n",
       "       128.06154 , 257.15338 , 211.338   , 149.00725 , 154.41249 ,\n",
       "       104.102036, 196.0869  ,  91.59793 , 239.47511 , 118.52005 ,\n",
       "       111.4021  , 229.35774 , 256.7412  , 185.72925 , 235.24536 ,\n",
       "       186.84236 ,  96.88423 ,  70.414986, 199.52876 , 152.07    ,\n",
       "       187.21767 , 204.85905 , 196.00319 ,  72.606544, 108.55862 ,\n",
       "       180.78674 , 118.76349 , 146.50636 , 183.84616 , 149.25348 ,\n",
       "       187.11473 , 112.96919 , 114.76101 , 184.28674 ,  68.52865 ,\n",
       "        67.98585 ,  83.91956 , 174.2202  , 185.96986 , 180.46988 ,\n",
       "        79.47412 , 100.26168 , 112.16302 ,  73.11136 , 164.4933  ,\n",
       "       124.60371 ,  82.27981 , 140.3247  , 100.12516 , 195.36021 ,\n",
       "       146.1376  ,  96.57848 , 232.90302 ,  89.81601 ,  89.82054 ,\n",
       "       174.07098 , 185.5151  , 127.16104 ,  85.30461 , 122.92981 ,\n",
       "       234.15456 , 162.96346 , 181.43661 , 151.00249 , 112.90678 ,\n",
       "       161.7822  , 192.78462 , 239.39641 ,  97.16356 ,  87.35134 ,\n",
       "       204.82877 , 211.18504 , 179.95682 , 182.38759 , 260.19574 ,\n",
       "        96.240685, 120.91771 ,  69.728035,  72.20921 ,  80.415306,\n",
       "        69.942375,  80.93848 ,  88.69137 , 165.91129 ], dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best iteration: 999\n"
     ]
    }
   ],
   "source": [
    "best_iteration = bst.best_iteration\n",
    "print(f\"Best iteration: {best_iteration}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost with Additional Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\teval-rmse:72.91122\ttrain-rmse:77.62505\n",
      "[1]\teval-rmse:72.68601\ttrain-rmse:77.30581\n",
      "[2]\teval-rmse:72.38099\ttrain-rmse:76.92980\n",
      "[3]\teval-rmse:72.13152\ttrain-rmse:76.63108\n",
      "[4]\teval-rmse:71.91308\ttrain-rmse:76.27968\n",
      "[5]\teval-rmse:71.65499\ttrain-rmse:75.93592\n",
      "[6]\teval-rmse:71.38906\ttrain-rmse:75.59921\n",
      "[7]\teval-rmse:71.12733\ttrain-rmse:75.29746\n",
      "[8]\teval-rmse:70.83051\ttrain-rmse:74.96197\n",
      "[9]\teval-rmse:70.60977\ttrain-rmse:74.65740\n",
      "[10]\teval-rmse:70.41571\ttrain-rmse:74.32797\n",
      "[11]\teval-rmse:70.13324\ttrain-rmse:73.98002\n",
      "[12]\teval-rmse:69.93475\ttrain-rmse:73.67220\n",
      "[13]\teval-rmse:69.65156\ttrain-rmse:73.34252\n",
      "[14]\teval-rmse:69.45741\ttrain-rmse:73.05109\n",
      "[15]\teval-rmse:69.20270\ttrain-rmse:72.72354\n",
      "[16]\teval-rmse:68.95054\ttrain-rmse:72.46135\n",
      "[17]\teval-rmse:68.75625\ttrain-rmse:72.16616\n",
      "[18]\teval-rmse:68.50591\ttrain-rmse:71.86140\n",
      "[19]\teval-rmse:68.23838\ttrain-rmse:71.53546\n",
      "[20]\teval-rmse:67.97588\ttrain-rmse:71.22011\n",
      "[21]\teval-rmse:67.74984\ttrain-rmse:70.90573\n",
      "[22]\teval-rmse:67.48982\ttrain-rmse:70.63607\n",
      "[23]\teval-rmse:67.24560\ttrain-rmse:70.37375\n",
      "[24]\teval-rmse:67.07200\ttrain-rmse:70.16951\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/site-packages/xgboost/core.py:726: FutureWarning: Pass `evals` as keyword args.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[25]\teval-rmse:66.90446\ttrain-rmse:69.90887\n",
      "[26]\teval-rmse:66.71168\ttrain-rmse:69.62072\n",
      "[27]\teval-rmse:66.49487\ttrain-rmse:69.35200\n",
      "[28]\teval-rmse:66.28678\ttrain-rmse:69.07537\n",
      "[29]\teval-rmse:66.11538\ttrain-rmse:68.79984\n",
      "[30]\teval-rmse:65.89030\ttrain-rmse:68.53135\n",
      "[31]\teval-rmse:65.68578\ttrain-rmse:68.27290\n",
      "[32]\teval-rmse:65.47228\ttrain-rmse:68.00516\n",
      "[33]\teval-rmse:65.28551\ttrain-rmse:67.75740\n",
      "[34]\teval-rmse:65.08332\ttrain-rmse:67.48967\n",
      "[35]\teval-rmse:64.88273\ttrain-rmse:67.24296\n",
      "[36]\teval-rmse:64.66088\ttrain-rmse:66.96934\n",
      "[37]\teval-rmse:64.44432\ttrain-rmse:66.69464\n",
      "[38]\teval-rmse:64.24504\ttrain-rmse:66.43612\n",
      "[39]\teval-rmse:63.99756\ttrain-rmse:66.17684\n",
      "[40]\teval-rmse:63.82602\ttrain-rmse:65.92251\n",
      "[41]\teval-rmse:63.67260\ttrain-rmse:65.66036\n",
      "[42]\teval-rmse:63.47818\ttrain-rmse:65.44481\n",
      "[43]\teval-rmse:63.31670\ttrain-rmse:65.20631\n",
      "[44]\teval-rmse:63.11990\ttrain-rmse:64.96102\n",
      "[45]\teval-rmse:62.97047\ttrain-rmse:64.74079\n",
      "[46]\teval-rmse:62.84142\ttrain-rmse:64.51103\n",
      "[47]\teval-rmse:62.66553\ttrain-rmse:64.26754\n",
      "[48]\teval-rmse:62.56660\ttrain-rmse:64.10230\n",
      "[49]\teval-rmse:62.37388\ttrain-rmse:63.88074\n",
      "[50]\teval-rmse:62.22211\ttrain-rmse:63.66151\n",
      "[51]\teval-rmse:62.08877\ttrain-rmse:63.45603\n",
      "[52]\teval-rmse:61.97002\ttrain-rmse:63.27090\n",
      "[53]\teval-rmse:61.82710\ttrain-rmse:63.09684\n",
      "[54]\teval-rmse:61.69901\ttrain-rmse:62.88247\n",
      "[55]\teval-rmse:61.59903\ttrain-rmse:62.69462\n",
      "[56]\teval-rmse:61.44636\ttrain-rmse:62.48868\n",
      "[57]\teval-rmse:61.30930\ttrain-rmse:62.31568\n",
      "[58]\teval-rmse:61.15210\ttrain-rmse:62.12663\n",
      "[59]\teval-rmse:61.01481\ttrain-rmse:61.93959\n",
      "[60]\teval-rmse:60.86626\ttrain-rmse:61.76634\n",
      "[61]\teval-rmse:60.69332\ttrain-rmse:61.57321\n",
      "[62]\teval-rmse:60.57458\ttrain-rmse:61.38657\n",
      "[63]\teval-rmse:60.49129\ttrain-rmse:61.22796\n",
      "[64]\teval-rmse:60.35520\ttrain-rmse:61.04282\n",
      "[65]\teval-rmse:60.21861\ttrain-rmse:60.87372\n",
      "[66]\teval-rmse:60.10972\ttrain-rmse:60.70456\n",
      "[67]\teval-rmse:59.99852\ttrain-rmse:60.53593\n",
      "[68]\teval-rmse:59.85645\ttrain-rmse:60.36407\n",
      "[69]\teval-rmse:59.78206\ttrain-rmse:60.20371\n",
      "[70]\teval-rmse:59.69526\ttrain-rmse:60.05257\n",
      "[71]\teval-rmse:59.54158\ttrain-rmse:59.89911\n",
      "[72]\teval-rmse:59.42737\ttrain-rmse:59.74532\n",
      "[73]\teval-rmse:59.30579\ttrain-rmse:59.56893\n",
      "[74]\teval-rmse:59.19405\ttrain-rmse:59.40292\n",
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      "[76]\teval-rmse:58.99159\ttrain-rmse:59.09616\n",
      "[77]\teval-rmse:58.88022\ttrain-rmse:58.94545\n",
      "[78]\teval-rmse:58.74759\ttrain-rmse:58.78192\n",
      "[79]\teval-rmse:58.64728\ttrain-rmse:58.63316\n",
      "[80]\teval-rmse:58.50385\ttrain-rmse:58.48088\n",
      "[81]\teval-rmse:58.41252\ttrain-rmse:58.32955\n",
      "[82]\teval-rmse:58.29694\ttrain-rmse:58.17969\n",
      "[83]\teval-rmse:58.20350\ttrain-rmse:58.04516\n",
      "[84]\teval-rmse:58.11375\ttrain-rmse:57.90065\n",
      "[85]\teval-rmse:58.03322\ttrain-rmse:57.76009\n",
      "[86]\teval-rmse:57.94820\ttrain-rmse:57.62477\n",
      "[87]\teval-rmse:57.83140\ttrain-rmse:57.47651\n",
      "[88]\teval-rmse:57.69515\ttrain-rmse:57.31467\n",
      "[89]\teval-rmse:57.59545\ttrain-rmse:57.18258\n",
      "[90]\teval-rmse:57.49851\ttrain-rmse:57.04352\n",
      "[91]\teval-rmse:57.36734\ttrain-rmse:56.93179\n",
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      "[94]\teval-rmse:57.10297\ttrain-rmse:56.55990\n",
      "[95]\teval-rmse:57.00443\ttrain-rmse:56.44961\n",
      "[96]\teval-rmse:56.93312\ttrain-rmse:56.32571\n",
      "[97]\teval-rmse:56.84326\ttrain-rmse:56.20178\n",
      "[98]\teval-rmse:56.78141\ttrain-rmse:56.07292\n",
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      "[123]\teval-rmse:55.37008\ttrain-rmse:53.25911\n",
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      "[161]\teval-rmse:54.04757\ttrain-rmse:50.16078\n",
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      "[995]\teval-rmse:54.05787\ttrain-rmse:30.70962\n",
      "[996]\teval-rmse:54.05535\ttrain-rmse:30.68410\n",
      "[997]\teval-rmse:54.04823\ttrain-rmse:30.66950\n",
      "[998]\teval-rmse:54.06522\ttrain-rmse:30.65692\n",
      "[999]\teval-rmse:54.04842\ttrain-rmse:30.64172\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load data and split\n",
    "data = load_diabetes()\n",
    "X = data.data\n",
    "y = data.target\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Convert data to DMatrix format for XGBoost\n",
    "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
    "dvalid = xgb.DMatrix(X_valid, label=y_valid)\n",
    "\n",
    "# Parameters for XGBoost with added regularization and other hyperparameters\n",
    "param = {\n",
    "    'max_depth': 3,\n",
    "    'eta': 0.01,\n",
    "    'objective': 'reg:squarederror',\n",
    "    'subsample': 0.7,\n",
    "    'colsample_bytree': 0.7,\n",
    "    'alpha': 0.1,\n",
    "    'lambda': 1\n",
    "}\n",
    "\n",
    "# Train the model with early stopping\n",
    "num_round = 1000\n",
    "evallist = [(dvalid, 'eval'), (dtrain, 'train')]\n",
    "evals_result = {}  # Initialize an empty dictionary to store results\n",
    "bst = xgb.train(param, dtrain, num_round, evallist, early_stopping_rounds=50, evals_result=evals_result)\n",
    "\n",
    "# Plotting the learning curves\n",
    "epochs = len(evals_result['eval']['rmse'])\n",
    "x_axis = range(0, epochs)\n",
    "plt.figure()\n",
    "plt.plot(x_axis, evals_result['eval']['rmse'], label='Validation')\n",
    "plt.plot(x_axis, evals_result['train']['rmse'], label='Train')\n",
    "plt.legend()\n",
    "plt.ylabel('RMSE')\n",
    "plt.xlabel('Iterations')\n",
    "plt.title('XGBoost Learning Curves with Early Stopping')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Early Stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\teval-rmse:72.93089\ttrain-rmse:77.56304\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\teval-rmse:72.93089\ttrain-rmse:77.56304\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/site-packages/xgboost/core.py:726: FutureWarning: Pass `evals` as keyword args.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Stopping early at iteration 68 due to difference of 0.08 between train-rmse and eval-rmse.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load data and split\n",
    "data = load_diabetes()\n",
    "X = data.data\n",
    "y = data.target\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Convert data to DMatrix format for XGBoost\n",
    "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
    "dvalid = xgb.DMatrix(X_valid, label=y_valid)\n",
    "\n",
    "# Parameters for XGBoost\n",
    "param = {\n",
    "    'max_depth': 3,\n",
    "    'eta': 0.01,\n",
    "    'objective': 'reg:squarederror'\n",
    "}\n",
    "\n",
    "num_round = 1000\n",
    "evallist = [(dvalid, 'eval'), (dtrain, 'train')]\n",
    "evals_result = {}\n",
    "threshold_difference = 0.1  # example threshold\n",
    "\n",
    "for i in range(1, num_round + 1):\n",
    "    bst = xgb.train(param, dtrain, i, evallist, evals_result=evals_result)\n",
    "    diff = abs(evals_result['train']['rmse'][-1] - evals_result['eval']['rmse'][-1])\n",
    "    \n",
    "    if diff < threshold_difference:\n",
    "        print(f\"Stopping early at iteration {i} due to difference of {diff:.2f} between train-rmse and eval-rmse.\")\n",
    "        break\n",
    "\n",
    "# Plotting the learning curves\n",
    "epochs = len(evals_result['eval']['rmse'])\n",
    "x_axis = range(0, epochs)\n",
    "plt.figure()\n",
    "plt.plot(x_axis, evals_result['eval']['rmse'], label='Validation')\n",
    "plt.plot(x_axis, evals_result['train']['rmse'], label='Train')\n",
    "plt.legend()\n",
    "plt.ylabel('RMSE')\n",
    "plt.xlabel('Iterations')\n",
    "plt.title('XGBoost Learning Curves with Custom Early Stopping')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LightGBM example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running `brew update --auto-update`...\n",
      "\u0007\u001b[34m==>\u001b[0m \u001b[1mHomebrew collects anonymous analytics.\u001b[0m\n",
      "\u001b[1mRead the analytics documentation (and how to opt-out) here:\n",
      "  \u001b[4mhttps://docs.brew.sh/Analytics\u001b[24m\u001b[0m\n",
      "No analytics have been recorded yet (nor will be during this `brew` run).\n",
      "\n",
      "\u001b[34m==>\u001b[0m \u001b[1mhomebrew/core is old and unneeded, untapping to save space...\u001b[0m\n",
      "Untapping homebrew/core...\n",
      "Untapped 3 commands and 6715 formulae (7,116 files, 710.7MB).\n",
      "\u001b[34m==>\u001b[0m \u001b[1mhomebrew/cask is old and unneeded, untapping to save space...\u001b[0m\n",
      "Untapping homebrew/cask...\n",
      "Untapped 4237 casks (4,352 files, 374.4MB).\n",
      "\u001b[34m==>\u001b[0m \u001b[1mDownloading https://formulae.brew.sh/api/formula_tap_migrations.jws.json\u001b[0m\n",
      "######################################################################### 100.0%\n",
      "\u001b[34m==>\u001b[0m \u001b[1mAuto-updated Homebrew!\u001b[0m\n",
      "Updated 2 taps (homebrew/core and homebrew/cask).\n",
      "\u001b[34m==>\u001b[0m \u001b[1mNew Formulae\u001b[0m\n",
      "adb-enhanced                             mariadb@11.0\n",
      "aerleon                                  massdriver\n",
      "alass                                    medusa\n",
      "apify-cli                                meson-python\n",
      "apprise                                  mfem\n",
      "arm-none-eabi-binutils                   mgis\n",
      "arm-none-eabi-gcc                        mjml\n",
      "arm-none-eabi-gdb                        modsecurity\n",
      "asn                                      mtbl\n",
      "asnmap                                   mvfst\n",
      "bacon                                    mvt\n",
      "bazel-diff                               mysql-client@8.0\n",
      "bazel-remote                             mysql@8.0\n",
      "bbot                                     neonctl\n",
      "bfs                                      notation\n",
      "bilix                                    ntbtls\n",
      "biome                                    ollama\n",
      "blades                                   orbiton\n",
      "blink                                    orcania\n",
      "botan@2                                  pgrok\n",
      "cargo-all-features                       pixi\n",
      "cargo-auditable                          plog\n",
      "cargo-binstall                           pop\n",
      "cargo-binutils                           postgresql@16\n",
      "cargo-deps                               powerlevel10k\n",
      "cargo-docset                             prettierd\n",
      "cbonsai                                  proxify\n",
      "cdi                                      proxygen\n",
      "cdxgen                                   pwned\n",
      "chaoskube                                pylyzer\n",
      "checkdmarc                               pypy3.10\n",
      "cloud-sql-proxy                          pypy3.9\n",
      "cloudfox                                 pyspelling\n",
      "cloudlist                                pystring\n",
      "codelimit                                python-certifi\n",
      "coder                                    python-click\n",
      "colmap                                   python-cryptography\n",
      "couchbase-shell                          python-flit-core\n",
      "counts                                   python-lxml\n",
      "cppinsights                              python-markupsafe\n",
      "crabz                                    python-mutagen\n",
      "cryptopp                                 python-packaging\n",
      "crystalline                              python-pycurl\n",
      "ctpv                                     python-pyparsing\n",
      "cyclonedx-gomod                          python-pyproject-hooks\n",
      "cyclonedx-python                         python-pytz\n",
      "cycode                                   python-toml\n",
      "czkawka                                  quictls\n",
      "dnsrobocert                              recoverpy\n",
      "dolphie                                  rio\n",
      "dovi_tool                                riscv64-elf-binutils\n",
      "driftwood                                riscv64-elf-gcc\n",
      "dysk                                     riscv64-elf-gdb\n",
      "ebook2cw                                 risor\n",
      "erg                                      roadrunner\n",
      "erlang@25                                rpmspectool\n",
      "espflash                                 ruff-lsp\n",
      "eza                                      runme\n",
      "falco                                    s3scanner\n",
      "falcoctl                                 sbom-tool\n",
      "feishu2md                                scrapy\n",
      "fuc                                      sh4d0wup\n",
      "fw                                       shub\n",
      "fwupd                                    shuffledns\n",
      "gickup                                   sickchill\n",
      "gismo                                    solr@8.11\n",
      "gitea                                    spacer\n",
      "go@1.20                                  sqlpage\n",
      "goread                                   strip-nondeterminism\n",
      "gotestsum                                surelog\n",
      "gotestwaf                                talhelper\n",
      "govulncheck                              terraform-graph-beautifier\n",
      "hex                                      terragrunt-atlantis-config\n",
      "hoverfly                                 tf-profile\n",
      "hyfetch                                  toxiproxy\n",
      "img2pdf                                  tpm\n",
      "imgdiet                                  trufflehog\n",
      "imgdiff                                  trzsz-ssh\n",
      "ittapi                                   tzdiff\n",
      "jr                                       udp2raw-multiplatform\n",
      "json2ts                                  uffizzi\n",
      "katana                                   uhdm\n",
      "killport                                 uncover\n",
      "kor                                      urlfinder\n",
      "kosli-cli                                vulkan-utility-libraries\n",
      "ldid-procursus                           vunnel\n",
      "legitify                                 web-ext\n",
      "libdivsufsort                            webpod\n",
      "libgit2@1.6                              wget2\n",
      "libimobiledevice-glue                    woodpecker-cli\n",
      "libjcat                                  woof-doom\n",
      "libpanel                                 wpscan\n",
      "libshumate                               wtfis\n",
      "libversion                               xe\n",
      "libxmlb                                  xlsxio\n",
      "llm                                      yazi\n",
      "lr                                       yder\n",
      "lune                                     yyjson\n",
      "lxi-tools                                zchunk\n",
      "mailpit                                  zrok\n",
      "\u001b[34m==>\u001b[0m \u001b[1mNew Casks\u001b[0m\n",
      "4k-video-downloaderplus    git-credential-manager     reqable\n",
      "aifun                      glaze                      rio\n",
      "akuity                     herd                       rippling\n",
      "apidog                     hhkb                       ripx\n",
      "applite                    hovrly                     rustrover\n",
      "audiocupcake               hypercal                   score\n",
      "ava                        iem-plugin-suite           screen-studio\n",
      "bepo                       json-viewer                sfm\n",
      "browser-deputy             jukebox                    shattered-pixel-dungeon\n",
      "chainner                   keyclu                     showmeyourhotkeys\n",
      "chatall                    kreya                      simple-web-server\n",
      "cilicon                    lm-studio                  sparkplate\n",
      "clickhouse                 luniistore                 spundle\n",
      "clop                       maa                        stash\n",
      "cloudnet                   meld-studio                ths\n",
      "crystalfetch               monotype                   twelite-stage\n",
      "dockx                      mutedeck                   updf\n",
      "drata-agent                mycard                     viso\n",
      "draw-things                picoscope                  voicepeak\n",
      "elektron-overbridge        pieces                     wetype\n",
      "elektron-transfer          pieces-os                  whisky\n",
      "energiza                   piphero                    wiso-steuer-2022\n",
      "ente                       playdate-mirror            wiso-steuer-2023\n",
      "fedistar                   poe                        xiaomi-cloud\n",
      "flexoptix                  proxy-audio-device         zspace\n",
      "floorp                     recipeui                   zui\n",
      "font-finagler              replay\n",
      "\n",
      "You have \u001b[1m50\u001b[0m outdated formulae and \u001b[1m1\u001b[0m outdated cask installed.\n",
      "\u001b[34m==>\u001b[0m \u001b[1mDownloading https://formulae.brew.sh/api/cask_tap_migrations.jws.json\u001b[0m\n",
      "######################################################################### 100.0%\n",
      "\n",
      "\u001b[34m==>\u001b[0m \u001b[1mDownloading https://ghcr.io/v2/homebrew/core/libomp/manifests/17.0.1\u001b[0m\n",
      "######################################################################### 100.0%\n",
      "\u001b[32m==>\u001b[0m \u001b[1mFetching \u001b[32mlibomp\u001b[39m\u001b[0m\n",
      "\u001b[34m==>\u001b[0m \u001b[1mDownloading https://ghcr.io/v2/homebrew/core/libomp/blobs/sha256:5e794b1f3d6\u001b[0m\n",
      "######################################################################### 100.0%\n",
      "\u001b[34m==>\u001b[0m \u001b[1mPouring libomp--17.0.1.ventura.bottle.tar.gz\u001b[0m\n",
      "\u001b[34m==>\u001b[0m \u001b[1mCaveats\u001b[0m\n",
      "libomp is keg-only, which means it was not symlinked into /usr/local,\n",
      "because it can override GCC headers and result in broken builds.\n",
      "\n",
      "For compilers to find libomp you may need to set:\n",
      "  export LDFLAGS=\"-L/usr/local/opt/libomp/lib\"\n",
      "  export CPPFLAGS=\"-I/usr/local/opt/libomp/include\"\n",
      "\u001b[34m==>\u001b[0m \u001b[1mSummary\u001b[0m\n",
      "🍺  /usr/local/Cellar/libomp/17.0.1: 7 files, 1.7MB\n",
      "\u001b[34m==>\u001b[0m \u001b[1mRunning `brew cleanup libomp`...\u001b[0m\n",
      "Disable this behaviour by setting HOMEBREW_NO_INSTALL_CLEANUP.\n",
      "Hide these hints with HOMEBREW_NO_ENV_HINTS (see `man brew`).\n",
      "\u001b[32m==>\u001b[0m \u001b[1m`brew cleanup` has not been run in the last 30 days, running now...\u001b[0m\n",
      "Disable this behaviour by setting HOMEBREW_NO_INSTALL_CLEANUP.\n",
      "Hide these hints with HOMEBREW_NO_ENV_HINTS (see `man brew`).\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/aom--3.6.1... (4.6MB)\n",
      "Removing: /usr/local/Cellar/bdw-gc/8.0.6... (69 files, 1.6MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/ca-certificates--2023-05-30... (122.1KB)\n",
      "Removing: /usr/local/Cellar/dav1d/0.9.2... (15 files, 2.0MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/flac--1.4.2... (1.2MB)\n",
      "Removing: /usr/local/Cellar/fontconfig/2.13.1... (531 files, 3.4MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/freetype--2.13.0_1... (943.5KB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/frei0r--1.8.0... (484.6KB)\n",
      "Removing: /usr/local/Cellar/fribidi/1.0.11... (67 files, 525.7KB)\n",
      "Removing: /usr/local/Cellar/fribidi/1.0.9... (67 files, 608.6KB)\n",
      "Removing: /usr/local/Cellar/gdbm/1.18.1... (20 files, 602.8KB)\n",
      "Removing: /usr/local/Cellar/gdbm/1.22... (24 files, 957.9KB)\n",
      "Removing: /usr/local/Cellar/gettext/0.21... (1,953 files, 19.8MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/glib--2.76.3... (6.3MB)\n",
      "Removing: /usr/local/Cellar/gmp/6.2.0... (20 files, 3.2MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/gnutls--3.8.0... (3.0MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/gobject-introspection--1.76.1... (1.8MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/harfbuzz--7.3.0_1... (2.4MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/highway--1.0.4... (1017.9KB)\n",
      "Removing: /usr/local/Cellar/icu4c/66.1... (258 files, 70.4MB)\n",
      "Removing: /usr/local/Cellar/icu4c/69.1... (259 files, 72.8MB)\n",
      "Removing: /usr/local/Cellar/imath/3.1.3... (49 files, 920.2KB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/jpeg-turbo--2.1.5.1... (947.8KB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/leptonica--1.82.0_2... (2.5MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/libarchive--3.6.2_1... (1.5MB)\n",
      "Removing: /usr/local/Cellar/libbluray/1.2.0... (21 files, 1.5MB)\n",
      "Removing: /usr/local/Cellar/libbluray/1.3.0... (21 files, 939KB)\n",
      "Removing: /usr/local/Cellar/libidn2/2.3.2... (77 files, 846.8KB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/libnghttp2--1.54.0... (212.8KB)\n",
      "Removing: /usr/local/Cellar/libogg/1.3.4... (97 files, 476.9KB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/libpng--1.6.39... (442.9KB)\n",
      "Removing: /usr/local/Cellar/librist/0.2.6... (20 files, 753.5KB)\n",
      "Removing: /usr/local/Cellar/libsamplerate/0.1.9_1... (28 files, 3MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/libsndfile--1.2.0_1... (355.4KB)\n",
      "Removing: /usr/local/Cellar/libtasn1/4.16.0... (60 files, 404.5KB)\n",
      "Removing: /usr/local/Cellar/libtasn1/4.18.0... (61 files, 650.5KB)\n",
      "Removing: /usr/local/Cellar/libtiff/4.1.0... (247 files, 3.7MB)\n",
      "Removing: /usr/local/Cellar/libtiff/4.3.0... (249 files, 4.4MB)\n",
      "Removing: /usr/local/Cellar/libtool/2.4.6_4... (75 files, 3.7MB)\n",
      "Removing: /usr/local/Cellar/libunistring/0.9.10... (54 files, 4.4MB)\n",
      "Removing: /usr/local/Cellar/libvidstab/1.1.0... (25 files, 135.9KB)\n",
      "Removing: /usr/local/Cellar/libvmaf/2.3.0_1... (234 files, 7.5MB)\n",
      "Removing: /usr/local/Cellar/libvorbis/1.3.6... (157 files, 2.3MB)\n",
      "Removing: /usr/local/Cellar/libvpx/1.11.0... (20 files, 5.2MB)\n",
      "Removing: /usr/local/Cellar/libvpx/1.8.2... (17 files, 1.8MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/libx11--1.8.4... (2.2MB)\n",
      "Removing: /usr/local/Cellar/libxau/1.0.9... (20 files, 117.8KB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/libxcb--1.15_1... (921.9KB)\n",
      "Removing: /usr/local/Cellar/libxdmcp/1.1.3... (11 files, 125.4KB)\n",
      "Removing: /usr/local/Cellar/libxext/1.3.4... (87 files, 421.0KB)\n",
      "Removing: /usr/local/Cellar/libxrender/0.9.10... (12 files, 170KB)\n",
      "Removing: /usr/local/Cellar/little-cms2/2.12... (21 files, 1.3MB)\n",
      "Removing: /usr/local/Cellar/little-cms2/2.9... (18 files, 1MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/mbedtls--3.4.0... (3MB)\n",
      "Removing: /usr/local/Cellar/nettle/3.4.1... (85 files, 2.1MB)\n",
      "Removing: /usr/local/Cellar/nettle/3.7.3... (89 files, 2.7MB)\n",
      "Removing: /usr/local/Cellar/opencore-amr/0.1.5... (17 files, 645.5KB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/openexr--3.1.8_1... (1.8MB)\n",
      "Removing: /usr/local/Cellar/openjpeg/2.3.1... (516 files, 13.9MB)\n",
      "Removing: /usr/local/Cellar/openjpeg/2.4.0... (522 files, 13.3MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/openssl@1.1--1.1.1u... (5.2MB)\n",
      "Removing: /usr/local/Cellar/opus/1.3.1... (17 files, 987.1KB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/p11-kit--0.24.1_1... (870.8KB)\n",
      "Removing: /usr/local/Cellar/pcre/8.44... (204 files, 5.5MB)\n",
      "Removing: /usr/local/Cellar/pcre2/10.39... (230 files, 6.2MB)\n",
      "Removing: /usr/local/Cellar/pixman/0.40.0... (14 files, 1.3MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/python@3.11--3.11.4... (15MB)\n",
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      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/python@3.9--3.9.17... (14.3MB)\n",
      "Removing: /usr/local/Cellar/rav1e/0.5.1... (13 files, 118.9MB)\n",
      "Removing: /usr/local/Cellar/readline/8.0.4... (48 files, 1.5MB)\n",
      "Removing: /usr/local/Cellar/readline/8.1.1... (48 files, 1.6MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/rubberband--3.2.1... (580.5KB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/sdl2--2.26.5... (1.8MB)\n",
      "Removing: /usr/local/Cellar/snappy/1.1.8... (18 files, 122.6KB)\n",
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      "Removing: /usr/local/Cellar/speex/1.2.0... (21 files, 694.5KB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/sqlite--3.42.0... (2.1MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/srt--1.5.1... (1.6MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/svt-av1--1.6.0... (2.7MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/tesseract--5.3.1_1... (12.6MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/unbound--1.17.1... (2.8MB)\n",
      "Removing: /Users/delinaivanova/Library/Caches/Homebrew/webp--1.3.0_1... (922KB)\n",
      "Removing: /usr/local/Cellar/x264/r2917_1... (11 files, 5.9MB)\n",
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      "Removing: /usr/local/Cellar/x265/3.3... (11 files, 35.4MB)\n",
      "Removing: /usr/local/Cellar/xorgproto/2021.5... (264 files, 3.9MB)\n",
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      "Removing: /Users/delinaivanova/Library/Logs/Homebrew/freetype... (64B)\n",
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      "Removing: /Users/delinaivanova/Library/Logs/Homebrew/libbluray... (64B)\n",
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     ]
    }
   ],
   "source": [
    "!brew install libomp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting lightgbm\n",
      "  Downloading lightgbm-4.1.0-py3-none-macosx_10_15_x86_64.macosx_11_6_x86_64.macosx_12_5_x86_64.whl (1.7 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m:00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.11/site-packages (from lightgbm) (1.25.0)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.11/site-packages (from lightgbm) (1.11.0)\n",
      "Installing collected packages: lightgbm\n",
      "Successfully installed lightgbm-4.1.0\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install lightgbm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LIGHT GBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on validation set: 56.60\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.model_selection import train_test_split\n",
    "import lightgbm as lgb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load data\n",
    "data = load_diabetes()\n",
    "X = data.data\n",
    "y = data.target\n",
    "\n",
    "# Split data into training and validation sets\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Define the datasets for LightGBM\n",
    "train_data = lgb.Dataset(X_train, label=y_train)\n",
    "valid_data = lgb.Dataset(X_valid, label=y_valid, reference=train_data)\n",
    "\n",
    "# Define the parameters\n",
    "params = {\n",
    "    'objective': 'regression',\n",
    "    'metric': 'rmse',\n",
    "    'verbosity': -1\n",
    "}\n",
    "\n",
    "# Predict on the validation set\n",
    "y_pred = bst.predict(X_valid, num_iteration=bst.best_iteration)\n",
    "\n",
    "# Calculate RMSE (Root Mean Squared Error)\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_valid, y_pred))\n",
    "print(f\"RMSE on validation set: {rmse:.2f}\")\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting imblearn\n",
      "  Downloading imblearn-0.0-py2.py3-none-any.whl (1.9 kB)\n",
      "Collecting imbalanced-learn (from imblearn)\n",
      "  Downloading imbalanced_learn-0.11.0-py3-none-any.whl (235 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m235.6/235.6 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.11/site-packages (from imbalanced-learn->imblearn) (1.25.0)\n",
      "Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.11/site-packages (from imbalanced-learn->imblearn) (1.11.0)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.11/site-packages (from imbalanced-learn->imblearn) (1.2.2)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.11/site-packages (from imbalanced-learn->imblearn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.11/site-packages (from imbalanced-learn->imblearn) (3.1.0)\n",
      "Installing collected packages: imbalanced-learn, imblearn\n",
      "Successfully installed imbalanced-learn-0.11.0 imblearn-0.0\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       249\n",
      "           1       1.00      1.00      1.00         1\n",
      "\n",
      "    accuracy                           1.00       250\n",
      "   macro avg       1.00      1.00      1.00       250\n",
      "weighted avg       1.00      1.00      1.00       250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Create an imbalanced dataset\n",
    "X, y = make_classification(n_samples=1000, n_features=20, n_informative=2, n_redundant=10,\n",
    "                           n_clusters_per_class=1, weights=[0.99], flip_y=0, random_state=42)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
    "\n",
    "# Apply SMOTE to the training data\n",
    "smote = SMOTE()\n",
    "X_resampled, y_resampled = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "# Train and evaluate a model\n",
    "clf = RandomForestClassifier()\n",
    "clf.fit(X_resampled, y_resampled)\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "rus = RandomUnderSampler()\n",
    "X_resampled, y_resampled = rus.fit_resample(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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