Ensemble-models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import polars as pl\n",
    "\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.base import clone\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.pipeline import Pipeline\n",
    "\n",
    "import lightgbm as lgb\n",
    "import catboost as cb\n",
    "import xgboost as xgb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/charles/mc-it/sml/SML-A3\n"
     ]
    }
   ],
   "source": [
    "root = os.getcwd()\n",
    "data_root = \"\"\n",
    "print(root)\n",
    "if root.startswith(\"/kaggle\"):\n",
    "    print(\"True\")\n",
    "    # Running on kaggle notebook\n",
    "    data_root  = \"/kaggle/input/isic-2024-challenge/\"\n",
    "else:\n",
    "    data_root = \"isic-2024-challenge/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train_path =  data_root + 'train-metadata.csv'\n",
    "test_path = data_root + \"test-metadata.csv\"\n",
    "subm_path = data_root + \"sample_submission.csv\"\n",
    "\n",
    "id_col = 'isic_id'\n",
    "target_col = 'target'\n",
    "group_col = 'patient_id'\n",
    "\n",
    "err = 1e-5\n",
    "sampling_ratio = 0.01\n",
    "seed = 42\n",
    "\n",
    "num_cols = [\n",
    "    'age_approx',                        \n",
    "    'clin_size_long_diam_mm',            \n",
    "    'tbp_lv_A',                          \n",
    "    'tbp_lv_Aext',                      \n",
    "    'tbp_lv_B',                          \n",
    "    'tbp_lv_Bext',                   \n",
    "    'tbp_lv_C',                         \n",
    "    'tbp_lv_Cext',                      \n",
    "    'tbp_lv_H',                         \n",
    "    'tbp_lv_Hext',                      \n",
    "    'tbp_lv_L',                         \n",
    "    'tbp_lv_Lext',                      \n",
    "    'tbp_lv_areaMM2',                    \n",
    "    'tbp_lv_area_perim_ratio',           \n",
    "    'tbp_lv_color_std_mean',             \n",
    "    'tbp_lv_deltaA',                     \n",
    "    'tbp_lv_deltaB',                     \n",
    "    'tbp_lv_deltaL',                     \n",
    "    'tbp_lv_deltaLB',                    \n",
    "    'tbp_lv_deltaLBnorm',               \n",
    "    'tbp_lv_eccentricity',               \n",
    "    'tbp_lv_minorAxisMM',                \n",
    "    'tbp_lv_nevi_confidence',           \n",
    "    'tbp_lv_norm_border',               \n",
    "    'tbp_lv_norm_color',                \n",
    "    'tbp_lv_perimeterMM',                \n",
    "    'tbp_lv_radial_color_std_max',      \n",
    "    'tbp_lv_stdL',                      \n",
    "    'tbp_lv_stdLExt',                    \n",
    "    'tbp_lv_symm_2axis',                 \n",
    "    'tbp_lv_symm_2axis_angle',           \n",
    "    'tbp_lv_x',                         \n",
    "    'tbp_lv_y',                          \n",
    "    'tbp_lv_z',                          \n",
    "]\n",
    "new_num_cols = [\n",
    "\n",
    "    \n",
    "]\n",
    "cat_cols = ['sex', 'anatom_site_general', 'tbp_tile_type', 'tbp_lv_location', 'tbp_lv_location_simple', 'attribution']\n",
    "norm_cols = [f'{col}_patient_norm' for col in num_cols ]\n",
    "special_cols = ['count_per_patient']\n",
    "feature_cols = num_cols + cat_cols + norm_cols + special_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(path):\n",
    "    return (\n",
    "        pl.read_csv(path)\n",
    "        # Cast age_approx to float, replace 'NA' with NaN, and then cast to float\n",
    "        .with_columns(\n",
    "            pl.col('age_approx').cast(pl.String).replace('NA', np.nan).cast(pl.Float64),\n",
    "        )\n",
    "        .with_columns(\n",
    "            pl.col(pl.Float64).fill_nan(pl.col(pl.Float64).median()),\n",
    "        )\n",
    "        # Feature engineering\n",
    "        .with_columns(\n",
    "            # Using Polars' over function to calculate the mean and std for each patient\n",
    "            ((pl.col(col) - pl.col(col).mean().over('patient_id')) / (pl.col(col).std().over('patient_id') + err)).alias(f'{col}_patient_norm') for col in (num_cols + new_num_cols)\n",
    "        )\n",
    "        # Count the number of lesions per patient\n",
    "        .with_columns(\n",
    "            count_per_patient = pl.col('isic_id').count().over('patient_id'),\n",
    "        )\n",
    "        # Cast categorical columns to categorical type\n",
    "        .with_columns(\n",
    "            pl.col(cat_cols).cast(pl.Categorical),\n",
    "        )\n",
    "        .to_pandas()\n",
    "        .set_index(id_col)\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(df_train, df_test):\n",
    "    global cat_cols\n",
    "    \n",
    "    encoder = OneHotEncoder(sparse_output=False, dtype=np.int32, handle_unknown='ignore')\n",
    "    encoder.fit(df_train[cat_cols])\n",
    "    \n",
    "    new_cat_cols = [f'onehot_{i}' for i in range(len(encoder.get_feature_names_out()))]\n",
    "\n",
    "    df_train[new_cat_cols] = encoder.transform(df_train[cat_cols])\n",
    "    df_train[new_cat_cols] = df_train[new_cat_cols].astype('category')\n",
    "\n",
    "    df_test[new_cat_cols] = encoder.transform(df_test[cat_cols])\n",
    "    df_test[new_cat_cols] = df_test[new_cat_cols].astype('category')\n",
    "\n",
    "    for col in cat_cols:\n",
    "        feature_cols.remove(col)\n",
    "\n",
    "    feature_cols.extend(new_cat_cols)\n",
    "    cat_cols = new_cat_cols\n",
    "    \n",
    "    return df_train, df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_metric(estimator, X, y_true):\n",
    "    y_hat = estimator.predict_proba(X)[:, 1]\n",
    "    min_tpr = 0.80\n",
    "    max_fpr = abs(1 - min_tpr)\n",
    "    \n",
    "    v_gt = abs(y_true - 1)\n",
    "    v_pred = np.array([1.0 - x for x in y_hat])\n",
    "    \n",
    "    partial_auc_scaled = roc_auc_score(v_gt, v_pred, max_fpr=max_fpr)\n",
    "    partial_auc = 0.5 * max_fpr**2 + (max_fpr - 0.5 * max_fpr**2) / (1.0 - 0.5) * (partial_auc_scaled - 0.5)\n",
    "    print(\"partial_auc: \", partial_auc)\n",
    "\n",
    "    \n",
    "    return partial_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = read_data(train_path)\n",
    "df_test = read_data(test_path)\n",
    "df_subm = pd.read_csv(subm_path, index_col=id_col)\n",
    "\n",
    "df_train, df_test = preprocess(df_train, df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cb_params = {\n",
    "    'loss_function':     'Logloss',\n",
    "    'iterations':        250,\n",
    "    'verbose':           False,\n",
    "    'random_state':      seed,\n",
    "    'learning_rate':     0.2, \n",
    "    'cat_features':      cat_cols,\n",
    "}\n",
    "\n",
    "cb_model = Pipeline([\n",
    "    ('sampler_1', RandomOverSampler(sampling_strategy= 0.003 , random_state=seed)),\n",
    "    ('sampler_2', RandomUnderSampler(sampling_strategy=sampling_ratio, random_state=seed)),\n",
    "    ('classifier', cb.CatBoostClassifier(**cb_params)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_params = {\n",
    "    'enable_categorical': True,\n",
    "    'tree_method':        'hist',\n",
    "    'random_state':       seed,\n",
    "    'learning_rate':      0.08, \n",
    "}\n",
    "\n",
    "xgb_model = Pipeline([\n",
    "    ('sampler_1', RandomOverSampler(sampling_strategy= 0.003 , random_state=seed)),\n",
    "    ('sampler_2', RandomUnderSampler(sampling_strategy=sampling_ratio, random_state=seed)),\n",
    "    ('classifier', xgb.XGBClassifier(**xgb_params)),\n",
    "])\n",
    "\n",
    "lgb_params = {\n",
    "    'objective': 'binary',\n",
    "    'metric': 'auc',\n",
    "    'learning_rate': 0.05,\n",
    "    'max_depth': 6,\n",
    "    'random_state': seed,\n",
    "    'verbose': -1,\n",
    "}\n",
    "\n",
    "lgb_model = Pipeline([\n",
    "    ('sampler_1', RandomOverSampler(sampling_strategy= 0.003 , random_state=seed)),\n",
    "    ('sampler_2', RandomUnderSampler(sampling_strategy=sampling_ratio, random_state=seed)),\n",
    "    ('classifier', lgb.LGBMClassifier(**lgb_params)),\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator = VotingClassifier([\n",
    "    ('lgb', lgb_model), ('cb', cb_model), ('xgb', xgb_model),\n",
    "], voting='soft') \n",
    "# Create a stratified group K-fold cross-validation\n",
    "from collections import defaultdict\n",
    "from sklearn.base import clone\n",
    "def stratified_group_k_fold(X, y, groups, n_splits=20, shuffle=True, random_state=None):\n",
    "    # Create a dictionary mapping groups to their indices\n",
    "    group_dict = defaultdict(list)\n",
    "    for idx, group in enumerate(groups):\n",
    "        group_dict[group].append(idx)\n",
    "\n",
    "    # Randomize the groups\n",
    "    if shuffle:\n",
    "        rng = np.random.default_rng(random_state)\n",
    "        group_keys = list(group_dict.keys())\n",
    "        rng.shuffle(group_keys)\n",
    "    else:\n",
    "        group_keys = list(group_dict.keys())\n",
    "\n",
    "    # Stratify by class\n",
    "    unique_classes, class_counts = np.unique(y, return_counts=True)\n",
    "    splits = [[] for _ in range(n_splits)]\n",
    "    class_splits = {cls: [[] for _ in range(n_splits)] for cls in unique_classes}\n",
    "\n",
    "    for cls in unique_classes:\n",
    "        # Get all groups for the current class\n",
    "        cls_indices = np.where(y == cls)[0]\n",
    "        cls_groups = np.unique(groups[cls_indices])\n",
    "\n",
    "        for group in cls_groups:\n",
    "            group_indices = group_dict[group]   \n",
    "\n",
    "            # Assign groups to different folds, ensuring uniform distribution of classes\n",
    "            assigned_split = min(range(n_splits), key=lambda x: len(class_splits[cls][x]))\n",
    "            class_splits[cls][assigned_split].extend(group_indices)\n",
    "            splits[assigned_split].extend(group_indices)\n",
    "\n",
    "    return splits\n",
    "\n",
    "\n",
    "def custom_cross_val_score(estimator, X, y, groups, splits, scoring):\n",
    "    scores = []\n",
    "    for i, test_indices in enumerate(splits):\n",
    "        train_indices = [idx for j, s in enumerate(splits) if j != i for idx in s]\n",
    "\n",
    "        X_train, X_test = X.iloc[train_indices], X.iloc[test_indices]\n",
    "        y_train, y_test = y.iloc[train_indices], y.iloc[test_indices]\n",
    "\n",
    "        # Clone the model to avoid changing the original model during training\n",
    "        model = clone(estimator)\n",
    "        model.fit(X_train, y_train)\n",
    "\n",
    "        # Calculate the score\n",
    "        score = scoring(model, X_test, y_test)\n",
    "        scores.append(score)\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2431/3296131715.py:29: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  cls_groups = np.unique(groups[cls_indices])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "partial_auc:  0.196044263165103\n",
      "Precision: 0.6363636363636364\n",
      "Recall: 0.65625\n",
      "partial_auc:  0.18735284103786817\n",
      "Precision: 0.7105263157894737\n",
      "Recall: 0.7297297297297297\n",
      "partial_auc:  0.1954008986343935\n",
      "Precision: 0.8333333333333334\n",
      "Recall: 0.6097560975609756\n",
      "partial_auc:  0.1996768674432517\n",
      "Precision: 0.7586206896551724\n",
      "Recall: 0.6111111111111112\n",
      "partial_auc:  0.19422243585003326\n",
      "Precision: 0.723404255319149\n",
      "Recall: 0.68\n",
      "partial_auc:  0.19976237702761832\n",
      "Precision: 0.6896551724137931\n",
      "Recall: 0.6896551724137931\n",
      "partial_auc:  0.19808864905242865\n",
      "Precision: 0.7714285714285715\n",
      "Recall: 0.6428571428571429\n",
      "partial_auc:  0.19941910324062764\n",
      "Precision: 0.7021276595744681\n",
      "Recall: 0.7021276595744681\n",
      "partial_auc:  0.19856522569261553\n",
      "Precision: 0.6458333333333334\n",
      "Recall: 0.7045454545454546\n",
      "partial_auc:  0.19925222456491434\n",
      "Precision: 0.7631578947368421\n",
      "Recall: 0.725\n",
      "partial_auc:  0.19755565255347007\n",
      "Precision: 0.5862068965517241\n",
      "Recall: 0.5666666666666667\n",
      "partial_auc:  0.19856364641545443\n",
      "Precision: 0.7837837837837838\n",
      "Recall: 0.6904761904761905\n",
      "partial_auc:  0.19325986681878654\n",
      "Precision: 0.6153846153846154\n",
      "Recall: 0.48484848484848486\n",
      "partial_auc:  0.19665573223618363\n",
      "Precision: 0.7058823529411765\n",
      "Recall: 0.6\n",
      "partial_auc:  0.17328166206453705\n",
      "Precision: 0.6956521739130435\n",
      "Recall: 0.38095238095238093\n",
      "partial_auc:  0.19923195327527396\n",
      "Precision: 0.8157894736842105\n",
      "Recall: 0.6326530612244898\n",
      "partial_auc:  0.1989824314645911\n",
      "Precision: 0.6875\n",
      "Recall: 0.6470588235294118\n",
      "partial_auc:  0.19907563985374763\n",
      "Precision: 0.7777777777777778\n",
      "Recall: 0.65625\n",
      "partial_auc:  0.19807269653423495\n",
      "Precision: 0.8292682926829268\n",
      "Recall: 0.68\n",
      "partial_auc:  0.1997023209364407\n",
      "Precision: 0.8148148148148148\n",
      "Recall: 0.6285714285714286\n",
      "Mean pAucscore: 0.1961\n",
      "Mean recall: 0.6359\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc, recall_score, precision_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def custom_cross_val_score(estimator, X, y, groups, splits, scoring):\n",
    "    scores = []\n",
    "    all_fpr = []\n",
    "    all_tpr = []\n",
    "    recalls = []\n",
    "    precisions = []\n",
    "    for i, test_indices in enumerate(splits):\n",
    "        \n",
    "        train_indices = [idx for j, s in enumerate(splits) if j != i for idx in s]\n",
    "        \n",
    "        X_train, X_test = X.iloc[train_indices], X.iloc[test_indices]\n",
    "        y_train, y_test = y.iloc[train_indices], y.iloc[test_indices]\n",
    "\n",
    "        model = clone(estimator)\n",
    "        model.fit(X_train, y_train)\n",
    "\n",
    "        score = scoring(model, X_test, y_test)\n",
    "        scores.append(score)\n",
    "\n",
    "        y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
    "        y_pred = model.predict(X_test)  # Predict the labels\n",
    "        fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "        all_fpr.append(fpr)\n",
    "        all_tpr.append(tpr)\n",
    "        recalls.append(recall_score(y_test, y_pred))\n",
    "        precisions.append(precision_score(y_test, y_pred))\n",
    "        print(f\"Precision: {precisions[-1]}\")\n",
    "        print(f\"Recall: {recalls[-1]}\")\n",
    "    return scores, all_fpr, all_tpr, recalls, precisions\n",
    "\n",
    "X = df_train[feature_cols]\n",
    "y = df_train[target_col]\n",
    "groups = df_train[group_col]\n",
    "\n",
    "splits= stratified_group_k_fold(X, y, groups, n_splits=20, shuffle=True, random_state=seed)\n",
    "\n",
    "\n",
    "val_score, all_fpr, all_tpr, recalls, precisions = custom_cross_val_score(\n",
    "    estimator=estimator,\n",
    "    X=X,\n",
    "    y=y,\n",
    "    groups=groups,\n",
    "    splits=splits,  \n",
    "    scoring=custom_metric,\n",
    ")\n",
    "\n",
    "mean_score = np.mean(val_score)\n",
    "mean_recall = np.mean(recalls)\n",
    "mean_precision = np.mean(precisions)\n",
    "print(f\"Mean pAucscore: {mean_score:.4f}\")\n",
    "print(f\"Mean recall: {mean_recall:.4f}\")\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "mean_fpr = np.linspace(0, 1, 100)\n",
    "tprs = []\n",
    "aucs = []\n",
    "for i, (fpr, tpr) in enumerate(zip(all_fpr, all_tpr)):\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    aucs.append(roc_auc)\n",
    "    # Using Numpy's interp function to interpolate the tpr values\n",
    "    interp_tpr = np.interp(mean_fpr, fpr, tpr)\n",
    "    interp_tpr[0] = 0.0\n",
    "    tprs.append(interp_tpr)\n",
    "    plt.plot(fpr, tpr, lw=1, alpha=0.3,\n",
    "             label=f'ROC fold {i+1} (AUC = {roc_auc:.2f})')\n",
    "\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n",
    "         label='Chance', alpha=.8)\n",
    "\n",
    "mean_tpr = np.mean(tprs, axis=0)\n",
    "mean_tpr[-1] = 1.0\n",
    "mean_auc = auc(mean_fpr, mean_tpr)\n",
    "std_auc = np.std(aucs)\n",
    "plt.plot(mean_fpr, mean_tpr, color='b',\n",
    "         label=f'Mean ROC (AUC = {mean_auc:.2f} ± {std_auc:.2f})',\n",
    "         lw=2, alpha=.8)\n",
    "\n",
    "plt.xlim([-0.05, 1.05])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\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-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 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-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;lgb&#x27;,\n",
       "                              Pipeline(steps=[(&#x27;sampler_1&#x27;,\n",
       "                                               RandomOverSampler(random_state=42,\n",
       "                                                                 sampling_strategy=0.003)),\n",
       "                                              (&#x27;sampler_2&#x27;,\n",
       "                                               RandomUnderSampler(random_state=42,\n",
       "                                                                  sampling_strategy=0.01)),\n",
       "                                              (&#x27;classifier&#x27;,\n",
       "                                               LGBMClassifier(learning_rate=0.05,\n",
       "                                                              max_depth=6,\n",
       "                                                              metric=&#x27;auc&#x27;,\n",
       "                                                              objective=&#x27;binary&#x27;,\n",
       "                                                              random_state=42,\n",
       "                                                              verbose=-1))])),\n",
       "                             (&#x27;cb&#x27;,\n",
       "                              Pipeline(steps=[(&#x27;samp...\n",
       "                                                             importance_type=None,\n",
       "                                                             interaction_constraints=None,\n",
       "                                                             learning_rate=0.08,\n",
       "                                                             max_bin=None,\n",
       "                                                             max_cat_threshold=None,\n",
       "                                                             max_cat_to_onehot=None,\n",
       "                                                             max_delta_step=None,\n",
       "                                                             max_depth=None,\n",
       "                                                             max_leaves=None,\n",
       "                                                             min_child_weight=None,\n",
       "                                                             missing=nan,\n",
       "                                                             monotone_constraints=None,\n",
       "                                                             multi_strategy=None,\n",
       "                                                             n_estimators=None,\n",
       "                                                             n_jobs=None,\n",
       "                                                             num_parallel_tree=None,\n",
       "                                                             random_state=42, ...))]))],\n",
       "                 voting=&#x27;soft&#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 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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;VotingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.VotingClassifier.html\">?<span>Documentation for VotingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>VotingClassifier(estimators=[(&#x27;lgb&#x27;,\n",
       "                              Pipeline(steps=[(&#x27;sampler_1&#x27;,\n",
       "                                               RandomOverSampler(random_state=42,\n",
       "                                                                 sampling_strategy=0.003)),\n",
       "                                              (&#x27;sampler_2&#x27;,\n",
       "                                               RandomUnderSampler(random_state=42,\n",
       "                                                                  sampling_strategy=0.01)),\n",
       "                                              (&#x27;classifier&#x27;,\n",
       "                                               LGBMClassifier(learning_rate=0.05,\n",
       "                                                              max_depth=6,\n",
       "                                                              metric=&#x27;auc&#x27;,\n",
       "                                                              objective=&#x27;binary&#x27;,\n",
       "                                                              random_state=42,\n",
       "                                                              verbose=-1))])),\n",
       "                             (&#x27;cb&#x27;,\n",
       "                              Pipeline(steps=[(&#x27;samp...\n",
       "                                                             importance_type=None,\n",
       "                                                             interaction_constraints=None,\n",
       "                                                             learning_rate=0.08,\n",
       "                                                             max_bin=None,\n",
       "                                                             max_cat_threshold=None,\n",
       "                                                             max_cat_to_onehot=None,\n",
       "                                                             max_delta_step=None,\n",
       "                                                             max_depth=None,\n",
       "                                                             max_leaves=None,\n",
       "                                                             min_child_weight=None,\n",
       "                                                             missing=nan,\n",
       "                                                             monotone_constraints=None,\n",
       "                                                             multi_strategy=None,\n",
       "                                                             n_estimators=None,\n",
       "                                                             n_jobs=None,\n",
       "                                                             num_parallel_tree=None,\n",
       "                                                             random_state=42, ...))]))],\n",
       "                 voting=&#x27;soft&#x27;)</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\"><label>lgb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">RandomOverSampler</label><div class=\"sk-toggleable__content fitted\"><pre>RandomOverSampler(random_state=42, sampling_strategy=0.003)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">RandomUnderSampler</label><div class=\"sk-toggleable__content fitted\"><pre>RandomUnderSampler(random_state=42, sampling_strategy=0.01)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">LGBMClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>LGBMClassifier(learning_rate=0.05, max_depth=6, metric=&#x27;auc&#x27;,\n",
       "               objective=&#x27;binary&#x27;, random_state=42, verbose=-1)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>cb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">RandomOverSampler</label><div class=\"sk-toggleable__content fitted\"><pre>RandomOverSampler(random_state=42, sampling_strategy=0.003)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">RandomUnderSampler</label><div class=\"sk-toggleable__content fitted\"><pre>RandomUnderSampler(random_state=42, sampling_strategy=0.01)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">CatBoostClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x7fe7f419a450&gt;</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>xgb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">RandomOverSampler</label><div class=\"sk-toggleable__content fitted\"><pre>RandomOverSampler(random_state=42, sampling_strategy=0.003)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">RandomUnderSampler</label><div class=\"sk-toggleable__content fitted\"><pre>RandomUnderSampler(random_state=42, sampling_strategy=0.01)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" 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=True, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.08, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingClassifier(estimators=[('lgb',\n",
       "                              Pipeline(steps=[('sampler_1',\n",
       "                                               RandomOverSampler(random_state=42,\n",
       "                                                                 sampling_strategy=0.003)),\n",
       "                                              ('sampler_2',\n",
       "                                               RandomUnderSampler(random_state=42,\n",
       "                                                                  sampling_strategy=0.01)),\n",
       "                                              ('classifier',\n",
       "                                               LGBMClassifier(learning_rate=0.05,\n",
       "                                                              max_depth=6,\n",
       "                                                              metric='auc',\n",
       "                                                              objective='binary',\n",
       "                                                              random_state=42,\n",
       "                                                              verbose=-1))])),\n",
       "                             ('cb',\n",
       "                              Pipeline(steps=[('samp...\n",
       "                                                             importance_type=None,\n",
       "                                                             interaction_constraints=None,\n",
       "                                                             learning_rate=0.08,\n",
       "                                                             max_bin=None,\n",
       "                                                             max_cat_threshold=None,\n",
       "                                                             max_cat_to_onehot=None,\n",
       "                                                             max_delta_step=None,\n",
       "                                                             max_depth=None,\n",
       "                                                             max_leaves=None,\n",
       "                                                             min_child_weight=None,\n",
       "                                                             missing=nan,\n",
       "                                                             monotone_constraints=None,\n",
       "                                                             multi_strategy=None,\n",
       "                                                             n_estimators=None,\n",
       "                                                             n_jobs=None,\n",
       "                                                             num_parallel_tree=None,\n",
       "                                                             random_state=42, ...))]))],\n",
       "                 voting='soft')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = df_train[feature_cols],   df_train[target_col]\n",
    "\n",
    "estimator.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isic_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ISIC_0015657</th>\n",
       "      <td>0.687896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISIC_0015729</th>\n",
       "      <td>0.331432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISIC_0015740</th>\n",
       "      <td>0.865464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                target\n",
       "isic_id               \n",
       "ISIC_0015657  0.687896\n",
       "ISIC_0015729  0.331432\n",
       "ISIC_0015740  0.865464"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subm['target'] = estimator.predict_proba(df_test[feature_cols])[:, 1]\n",
    "\n",
    "df_subm.to_csv('submission.csv')\n",
    "df_subm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean precision: 0.7273\n",
      "[0.65625, 0.7297297297297297, 0.6097560975609756, 0.6111111111111112, 0.68, 0.6896551724137931, 0.6428571428571429, 0.7021276595744681, 0.7045454545454546, 0.725, 0.5666666666666667, 0.6904761904761905, 0.48484848484848486, 0.6, 0.38095238095238093, 0.6326530612244898, 0.6470588235294118, 0.65625, 0.68, 0.6285714285714286]\n",
      "[0.6363636363636364, 0.7105263157894737, 0.8333333333333334, 0.7586206896551724, 0.723404255319149, 0.6896551724137931, 0.7714285714285715, 0.7021276595744681, 0.6458333333333334, 0.7631578947368421, 0.5862068965517241, 0.7837837837837838, 0.6153846153846154, 0.7058823529411765, 0.6956521739130435, 0.8157894736842105, 0.6875, 0.7777777777777778, 0.8292682926829268, 0.8148148148148148]\n",
      "[0.196044263165103, 0.18735284103786817, 0.1954008986343935, 0.1996768674432517, 0.19422243585003326, 0.19976237702761832, 0.19808864905242865, 0.19941910324062764, 0.19856522569261553, 0.19925222456491434, 0.19755565255347007, 0.19856364641545443, 0.19325986681878654, 0.19665573223618363, 0.17328166206453705, 0.19923195327527396, 0.1989824314645911, 0.19907563985374763, 0.19807269653423495, 0.1997023209364407]\n"
     ]
    }
   ],
   "source": [
    "print(f\"Mean precision: {mean_precision:.4f}\")\n",
    "print(recalls)\n",
    "print(precisions)\n",
    "print(val_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recalls: [0.65625, 0.7297297297297297, 0.6097560975609756, 0.6111111111111112, 0.68, 0.6896551724137931, 0.6428571428571429, 0.7021276595744681, 0.7045454545454546, 0.725, 0.5666666666666667, 0.6904761904761905, 0.48484848484848486, 0.6, 0.38095238095238093, 0.6326530612244898, 0.6470588235294118, 0.65625, 0.68, 0.6285714285714286]\n",
      "Val Scores: [0.196044263165103, 0.18735284103786817, 0.1954008986343935, 0.1996768674432517, 0.19422243585003326, 0.19976237702761832, 0.19808864905242865, 0.19941910324062764, 0.19856522569261553, 0.19925222456491434, 0.19755565255347007, 0.19856364641545443, 0.19325986681878654, 0.19665573223618363, 0.17328166206453705, 0.19923195327527396, 0.1989824314645911, 0.19907563985374763, 0.19807269653423495, 0.1997023209364407]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a figure with two subplots side by side\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 6))\n",
    "\n",
    "# Plot recalls\n",
    "ax1.plot(range(1, len(recalls) + 1), recalls, marker='o', linestyle='-', color='blue')\n",
    "ax1.set_xlabel('Fold')\n",
    "ax1.set_ylabel('Recall')\n",
    "ax1.set_title('Recalls Across Folds')\n",
    "ax1.grid(True, linestyle='--', alpha=0.7)\n",
    "ax1.text(0.02, 0.98, f'Mean Recall: {np.mean(recalls):.4f}', transform=ax1.transAxes, verticalalignment='top')\n",
    "\n",
    "# Plot val_scores\n",
    "ax2.plot(range(1, len(val_score) + 1), val_score, marker='s', linestyle='-', color='orange')\n",
    "ax2.set_xlabel('Fold')\n",
    "ax2.set_ylabel('Validation Score')\n",
    "ax2.set_title('Validation Scores Across Folds')\n",
    "ax2.grid(True, linestyle='--', alpha=0.7)\n",
    "ax2.text(0.02, 0.98, f'Mean Val Score: {np.mean(val_score):.4f}', transform=ax2.transAxes, verticalalignment='top')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print out the values for reference\n",
    "print(\"Recalls:\", recalls)\n",
    "print(\"Val Scores:\", val_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pAUC Scores: [0.196044263165103, 0.18735284103786817, 0.1954008986343935, 0.1996768674432517, 0.19422243585003326, 0.19976237702761832, 0.19808864905242865, 0.19941910324062764, 0.19856522569261553, 0.19925222456491434, 0.19755565255347007, 0.19856364641545443, 0.19325986681878654, 0.19665573223618363, 0.17328166206453705, 0.19923195327527396, 0.1989824314645911, 0.19907563985374763, 0.19807269653423495, 0.1997023209364407]\n",
      "Mean pAUC Score: 0.1961\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Assuming val_score contains your pAUC scores\n",
    "pAUC_scores = val_score\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot pAUC scores\n",
    "plt.plot(range(1, len(pAUC_scores) + 1), pAUC_scores, marker='o', linestyle='-', color='purple')\n",
    "plt.fill_between(range(1, len(pAUC_scores) + 1), pAUC_scores, alpha=0.2, color='purple')\n",
    "\n",
    "plt.xlabel('Fold')\n",
    "plt.ylabel('pAUC Score')\n",
    "plt.title('Partial AUC Scores Across Folds')\n",
    "plt.grid(True, linestyle='--', alpha=0.7)\n",
    "\n",
    "# Add text annotation for mean pAUC score\n",
    "mean_pAUC = np.mean(pAUC_scores)\n",
    "plt.text(0.02, 0.98, f'Mean pAUC Score: {mean_pAUC:.4f}', \n",
    "         transform=plt.gca().transAxes, verticalalignment='top')\n",
    "\n",
    "# Add horizontal line for mean pAUC score\n",
    "plt.axhline(y=mean_pAUC, color='r', linestyle='--', label='Mean pAUC')\n",
    "\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print out the values for reference\n",
    "print(\"pAUC Scores:\", pAUC_scores)\n",
    "print(f\"Mean pAUC Score: {mean_pAUC:.4f}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "charles",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}