cosmos_combo_odTomka.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score,mean_squared_error, classification_report, f1_score, precision_score, roc_auc_score#,plot_roc_curve\n",
    "from sklearn.tree import (\n",
    "    DecisionTreeClassifier,\n",
    "    DecisionTreeRegressor,\n",
    "    plot_tree\n",
    ")\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, ConfusionMatrixDisplay\n",
    "import xgboost as xgb\n",
    "\n",
    "cosmos = pd.read_csv(\"neo.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>est_diameter_min</th>\n",
       "      <th>est_diameter_max</th>\n",
       "      <th>relative_velocity</th>\n",
       "      <th>miss_distance</th>\n",
       "      <th>absolute_magnitude</th>\n",
       "      <th>hazardous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.198271</td>\n",
       "      <td>2.679415</td>\n",
       "      <td>13569.249224</td>\n",
       "      <td>5.483974e+07</td>\n",
       "      <td>16.73</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.265800</td>\n",
       "      <td>0.594347</td>\n",
       "      <td>73588.726663</td>\n",
       "      <td>6.143813e+07</td>\n",
       "      <td>20.00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.722030</td>\n",
       "      <td>1.614507</td>\n",
       "      <td>114258.692129</td>\n",
       "      <td>4.979872e+07</td>\n",
       "      <td>17.83</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.096506</td>\n",
       "      <td>0.215794</td>\n",
       "      <td>24764.303138</td>\n",
       "      <td>2.543497e+07</td>\n",
       "      <td>22.20</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.255009</td>\n",
       "      <td>0.570217</td>\n",
       "      <td>42737.733765</td>\n",
       "      <td>4.627557e+07</td>\n",
       "      <td>20.09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90831</th>\n",
       "      <td>0.026580</td>\n",
       "      <td>0.059435</td>\n",
       "      <td>52078.886692</td>\n",
       "      <td>1.230039e+07</td>\n",
       "      <td>25.00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90832</th>\n",
       "      <td>0.016771</td>\n",
       "      <td>0.037501</td>\n",
       "      <td>46114.605073</td>\n",
       "      <td>5.432121e+07</td>\n",
       "      <td>26.00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90833</th>\n",
       "      <td>0.031956</td>\n",
       "      <td>0.071456</td>\n",
       "      <td>7566.807732</td>\n",
       "      <td>2.840077e+07</td>\n",
       "      <td>24.60</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90834</th>\n",
       "      <td>0.007321</td>\n",
       "      <td>0.016370</td>\n",
       "      <td>69199.154484</td>\n",
       "      <td>6.869206e+07</td>\n",
       "      <td>27.80</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90835</th>\n",
       "      <td>0.039862</td>\n",
       "      <td>0.089133</td>\n",
       "      <td>27024.455553</td>\n",
       "      <td>5.977213e+07</td>\n",
       "      <td>24.12</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90836 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       est_diameter_min  est_diameter_max  relative_velocity  miss_distance  \\\n",
       "0              1.198271          2.679415       13569.249224   5.483974e+07   \n",
       "1              0.265800          0.594347       73588.726663   6.143813e+07   \n",
       "2              0.722030          1.614507      114258.692129   4.979872e+07   \n",
       "3              0.096506          0.215794       24764.303138   2.543497e+07   \n",
       "4              0.255009          0.570217       42737.733765   4.627557e+07   \n",
       "...                 ...               ...                ...            ...   \n",
       "90831          0.026580          0.059435       52078.886692   1.230039e+07   \n",
       "90832          0.016771          0.037501       46114.605073   5.432121e+07   \n",
       "90833          0.031956          0.071456        7566.807732   2.840077e+07   \n",
       "90834          0.007321          0.016370       69199.154484   6.869206e+07   \n",
       "90835          0.039862          0.089133       27024.455553   5.977213e+07   \n",
       "\n",
       "       absolute_magnitude  hazardous  \n",
       "0                   16.73      False  \n",
       "1                   20.00       True  \n",
       "2                   17.83      False  \n",
       "3                   22.20      False  \n",
       "4                   20.09       True  \n",
       "...                   ...        ...  \n",
       "90831               25.00      False  \n",
       "90832               26.00      False  \n",
       "90833               24.60      False  \n",
       "90834               27.80      False  \n",
       "90835               24.12      False  \n",
       "\n",
       "[90836 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosmos = cosmos.drop(['id','name','orbiting_body','sentry_object'], axis=1) #porzucenie kolumn\n",
    "cosmos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cosmos['hazardous'] = cosmos['hazardous'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = cosmos.drop(['hazardous'], axis=1) #stworzenie grup x,y \n",
    "y = cosmos['hazardous']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'setup' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[11], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mimblearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcombine\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SMOTEENN\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# Setup environment\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[43msetup\u001b[49m(\n\u001b[0;32m      5\u001b[0m     data\u001b[38;5;241m=\u001b[39mcosmos,\n\u001b[0;32m      6\u001b[0m     target\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhazardous\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      7\u001b[0m     experiment_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mArmagedon prediction\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      8\u001b[0m     session_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m,  \u001b[38;5;66;03m# set session id for reproducibility\u001b[39;00m\n\u001b[0;32m      9\u001b[0m     train_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.80\u001b[39m,  \u001b[38;5;66;03m# 75% for training, 25% for testing\u001b[39;00m\n\u001b[0;32m     10\u001b[0m     \u001b[38;5;66;03m#categorical_features=['est_diameter_min','OnlineSecurity', 'OnlineBackup', 'InternetService',\u001b[39;00m\n\u001b[0;32m     11\u001b[0m                           \u001b[38;5;66;03m#'DeviceProtection','TechSupport','PaperlessBilling'],  # specify categorical feature columns\u001b[39;00m\n\u001b[0;32m     12\u001b[0m    \u001b[38;5;66;03m# numeric_features=['tenure', 'MonthlyCharges', 'Contract'],  # specify numeric feature columns\u001b[39;00m\n\u001b[0;32m     13\u001b[0m     normalize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,  \u001b[38;5;66;03m# normalize the dataset\u001b[39;00m\n\u001b[0;32m     14\u001b[0m     normalize_method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mminmax\u001b[39m\u001b[38;5;124m'\u001b[39m,  \u001b[38;5;66;03m# normalization method\u001b[39;00m\n\u001b[0;32m     15\u001b[0m     fix_imbalance\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,  \u001b[38;5;66;03m# imbalance method\u001b[39;00m\n\u001b[0;32m     16\u001b[0m     fix_imbalance_method\u001b[38;5;241m=\u001b[39mSMOTEENN() )\u001b[38;5;66;03m# specify imbalance method\u001b[39;00m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'setup' is not defined"
     ]
    }
   ],
   "source": [
    "from imblearn.combine import SMOTEENN\n",
    "\n",
    "# Setup environment\n",
    "s = setup(\n",
    "    data=cosmos,\n",
    "    target='hazardous',\n",
    "    experiment_name = 'Armagedon prediction',\n",
    "    session_id=42,  # set session id for reproducibility\n",
    "    train_size=0.80,  # 75% for training, 25% for testing\n",
    "    #categorical_features=['est_diameter_min','OnlineSecurity', 'OnlineBackup', 'InternetService',\n",
    "                          #'DeviceProtection','TechSupport','PaperlessBilling'],  # specify categorical feature columns\n",
    "   # numeric_features=['tenure', 'MonthlyCharges', 'Contract'],  # specify numeric feature columns\n",
    "    normalize=True,  # normalize the dataset\n",
    "    normalize_method='minmax',  # normalization method\n",
    "    fix_imbalance=True,  # imbalance method\n",
    "    fix_imbalance_method=SMOTEENN() )# specify imbalance method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.1, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.5, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;reg:squarederror&#x27;, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.1, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.5, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;reg:squarederror&#x27;, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.1, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.5, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, objective='reg:squarederror', ...)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = xgb.XGBClassifier(learning_rate = 0.5, reg_lambda=1, gamma = 0.1,\n",
    "                       objective='reg:squarederror',\n",
    "                       n_estimators=100, max_depth=3)\n",
    "\n",
    "reg.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "params={\n",
    "    'learning_rate':[ 0.5],\n",
    "    'gamma':[0.01, 0.1],\n",
    "    'n_estimators':[100],\n",
    "    'max_depth':[3,7]\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search=GridSearchCV(reg, params )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=0.01,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.5, max_bi...\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=3,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=100,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     objective=&#x27;reg:squarederror&#x27;, ...),\n",
       "             param_grid={&#x27;gamma&#x27;: [0.01, 0.1], &#x27;learning_rate&#x27;: [0.5],\n",
       "                         &#x27;max_depth&#x27;: [3, 7], &#x27;n_estimators&#x27;: [100]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=0.01,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.5, max_bi...\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=3,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=100,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     objective=&#x27;reg:squarederror&#x27;, ...),\n",
       "             param_grid={&#x27;gamma&#x27;: [0.01, 0.1], &#x27;learning_rate&#x27;: [0.5],\n",
       "                         &#x27;max_depth&#x27;: [3, 7], &#x27;n_estimators&#x27;: [100]})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.01, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.5, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;reg:squarederror&#x27;, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.01, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.5, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;reg:squarederror&#x27;, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=0.01,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.5, max_bi...\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=3,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=100,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     objective='reg:squarederror', ...),\n",
       "             param_grid={'gamma': [0.01, 0.1], 'learning_rate': [0.5],\n",
       "                         'max_depth': [3, 7], 'n_estimators': [100]})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train_balanced, y_train_balanced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'gamma': 0.1, 'learning_rate': 0.5, 'max_depth': 3, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train = reg.predict(X_train_balanced) #model.predict zwraca przynależność do klasy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1759b7644a0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(y_train_balanced, pred_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_test = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1759a5c3dd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(y_test,pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.99      0.96     16439\n",
      "           1       0.77      0.16      0.26      1729\n",
      "\n",
      "    accuracy                           0.92     18168\n",
      "   macro avg       0.84      0.58      0.61     18168\n",
      "weighted avg       0.90      0.92      0.89     18168\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(classification_report(y_test, pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.54      1.00      0.70      7111\n",
      "           1       0.97      0.17      0.29      7111\n",
      "\n",
      "    accuracy                           0.58     14222\n",
      "   macro avg       0.76      0.58      0.50     14222\n",
      "weighted avg       0.76      0.58      0.50     14222\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(classification_report(y_train_balanced, pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "infoshare_virtual_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}