ICR - Identifying Age-Related Conditions - LightGBM.ipynb

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   "source": [
    "## 1. Setup"
   ]
  },
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, log_loss,\n",
    "    average_precision_score, confusion_matrix, roc_curve, precision_recall_curve,\n",
    ")\n",
    "import lightgbm as lgb"
   ]
  },
  {
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   "execution_count": 2,
   "id": "261ff4ec",
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    "execution": {
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    "tags": []
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   "outputs": [],
   "source": [
    "competition_dataset = Path('/kaggle/input/icr-identify-age-related-conditions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dbc84522",
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Dataset Shape: (617, 58)\n",
      "Test Dataset Shape: (5, 57)\n"
     ]
    }
   ],
   "source": [
    "df_train = pd.read_csv(competition_dataset / 'train.csv')\n",
    "df_test = pd.read_csv(competition_dataset / 'test.csv')\n",
    "\n",
    "print(f'Training Dataset Shape: {df_train.shape}')\n",
    "print(f'Test Dataset Shape: {df_test.shape}')"
   ]
  },
  {
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   },
   "source": [
    "## 2. Metrics"
   ]
  },
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   "source": [
    "def round_probabilities(probabilities, threshold):\n",
    "\n",
    "    \"\"\"\n",
    "    Round probabilities to labels based on the given threshold\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    probabilities : numpy.ndarray of shape (n_samples)\n",
    "        Predicted probabilities\n",
    "\n",
    "    threshold: float\n",
    "        Rounding threshold\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    labels : numpy.ndarray of shape (n_samples)\n",
    "        Rounded probabilities\n",
    "    \"\"\"\n",
    "\n",
    "    labels = np.zeros_like(probabilities, dtype=np.uint8)\n",
    "    labels[probabilities >= threshold] = 1\n",
    "\n",
    "    return labels\n",
    "\n",
    "\n",
    "def specificity_score(y_true, y_pred):\n",
    "\n",
    "    \"\"\"\n",
    "    Calculate specificity score (true-negative rate) of predicted labels\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    y_true: numpy.ndarray of shape (n_samples)\n",
    "        Ground-truth labels\n",
    "\n",
    "    y_pred: numpy.ndarray of shape (n_samples)\n",
    "        Predicted labels\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    score: float\n",
    "        Specificity score between 0 and 1\n",
    "    \"\"\"\n",
    "\n",
    "    tn, fp, _, _ = confusion_matrix(y_true, y_pred).ravel()\n",
    "    score = tn / (tn + fp)\n",
    "\n",
    "    return score\n",
    "\n",
    "\n",
    "def classification_scores(y_true, y_pred, threshold=0.5):\n",
    "\n",
    "    \"\"\"\n",
    "    Calculate binary classification metrics on predicted probabilities and labels\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    y_true: numpy.ndarray of shape (n_samples)\n",
    "        Ground-truth labels\n",
    "\n",
    "    y_pred: numpy.ndarray of shape (n_samples)\n",
    "        Predicted probabilities\n",
    "\n",
    "    threshold: float\n",
    "        Rounding threshold\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    scores: dict\n",
    "        Dictionary of classification scores\n",
    "    \"\"\"\n",
    "\n",
    "    y_pred_labels = round_probabilities(y_pred, threshold=threshold)\n",
    "    scores = {\n",
    "        'log_loss': log_loss(y_true, y_pred),\n",
    "        'accuracy': accuracy_score(y_true, y_pred_labels),\n",
    "        'precision': precision_score(y_true, y_pred_labels),\n",
    "        'recall': recall_score(y_true, y_pred_labels),\n",
    "        'specificity': specificity_score(y_true, y_pred_labels),\n",
    "        'f1': f1_score(y_true, y_pred_labels),\n",
    "        'roc_auc': roc_auc_score(y_true, y_pred),\n",
    "        'average_precision': average_precision_score(y_true, y_pred)\n",
    "    }\n",
    "\n",
    "    return scores\n",
    "\n",
    "\n",
    "def classification_curves(y_true, y_pred):\n",
    "\n",
    "    \"\"\"\n",
    "    Calculate binary classification curves on predicted probabilities\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    y_true: numpy.ndarray of shape (n_samples)\n",
    "        Ground-truth labels\n",
    "\n",
    "    y_pred: numpy.ndarray of shape (n_samples)\n",
    "        Predicted probabilities\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    curves: dict\n",
    "        Dictionary of classification curves\n",
    "    \"\"\"\n",
    "\n",
    "    curves = {\n",
    "        'roc': roc_curve(y_true, y_pred),\n",
    "        'precision_recall': precision_recall_curve(y_true, y_pred),\n",
    "    }\n",
    "\n",
    "    return curves\n"
   ]
  },
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   "source": [
    "## 3. Visualization"
   ]
  },
  {
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   "source": [
    "def visualize_feature_importance(df_feature_importance, path=None):\n",
    "\n",
    "    \"\"\"\n",
    "    Visualize feature importance in descending order\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df_feature_importance: pandas.DataFrame of shape (n_features, n_splits)\n",
    "        Dataframe of feature importance\n",
    "\n",
    "    path: path-like str or None\n",
    "        Path of the output file or None (if path is None, plot is displayed with selected backend)\n",
    "    \"\"\"\n",
    "\n",
    "    n_splits = df_feature_importance.shape[1] - 2\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(24, 20), dpi=100)\n",
    "    ax.barh(\n",
    "        range(len(df_feature_importance)),\n",
    "        df_feature_importance['mean'],\n",
    "        xerr=df_feature_importance['std'],\n",
    "        ecolor='black',\n",
    "        capsize=10,\n",
    "        align='center',\n",
    "    )\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_yticks(range(len(df_feature_importance)))\n",
    "    ax.set_yticklabels([f'{k} ({v:.2f})' for k, v in df_feature_importance['mean'].to_dict().items()])\n",
    "    ax.tick_params(axis='x', labelsize=15, pad=10)\n",
    "    ax.tick_params(axis='y', labelsize=15, pad=10)\n",
    "    ax.set_title(f'Mean and Std Feature Importance of {n_splits} Models', size=20, pad=15)\n",
    "    plt.gca().invert_yaxis()\n",
    "\n",
    "    if path is None:\n",
    "        plt.show()\n",
    "    else:\n",
    "        plt.savefig(path, bbox_inches='tight')\n",
    "        plt.close(fig)\n",
    "\n",
    "\n",
    "def visualize_scores(df_scores, path=None):\n",
    "\n",
    "    \"\"\"\n",
    "    Visualize scores of the models\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df_scores: pandas.DataFrame of shape (n_splits, n_metrics)\n",
    "        Dataframe with multiple scores and metrics\n",
    "\n",
    "    path: path-like str or None\n",
    "        Path of the output file or None (if path is None, plot is displayed with selected backend)\n",
    "    \"\"\"\n",
    "\n",
    "    # Create mean and std of scores for error bars\n",
    "    df_scores = df_scores.T\n",
    "    n_scores = df_scores.shape[1]\n",
    "    column_names = df_scores.columns.to_list()\n",
    "    df_scores['mean'] = df_scores[column_names].mean(axis=1)\n",
    "    df_scores['std'] = df_scores[column_names].std(axis=1).fillna(0)\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(32, 8))\n",
    "    ax.barh(\n",
    "        y=np.arange(df_scores.shape[0]),\n",
    "        width=df_scores['mean'],\n",
    "        xerr=df_scores['std'],\n",
    "        align='center',\n",
    "        ecolor='black',\n",
    "        capsize=10\n",
    "    )\n",
    "    ax.set_yticks(np.arange(df_scores.shape[0]))\n",
    "    ax.set_yticklabels([\n",
    "        f'{metric}\\n{mean:.4f} (±{std:.4f})' for metric, mean, std in zip(\n",
    "            df_scores.index,\n",
    "            df_scores['mean'].values,\n",
    "            df_scores['std'].values\n",
    "        )\n",
    "    ])\n",
    "    ax.set_xlabel('')\n",
    "    ax.tick_params(axis='x', labelsize=15, pad=10)\n",
    "    ax.tick_params(axis='y', labelsize=15, pad=10)\n",
    "    ax.set_title(f'Mean and Std Scores of {n_scores} Model(s)', size=20, pad=15)\n",
    "\n",
    "    if path is None:\n",
    "        plt.show()\n",
    "    else:\n",
    "        plt.savefig(path)\n",
    "        plt.close(fig)\n",
    "\n",
    "        \n",
    "def visualize_roc_curves(roc_curves, path=None):\n",
    "\n",
    "    \"\"\"\n",
    "    Visualize ROC curves of the models\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    roc_curves: array-like of shape (n_models, 3)\n",
    "        List of ROC curves (tuple of false positive rates, true positive rates and thresholds)\n",
    "\n",
    "    path: path-like str or None\n",
    "        Path of the output file or None (if path is None, plot is displayed with selected backend)\n",
    "    \"\"\"\n",
    "\n",
    "    n_scores = len(roc_curves)\n",
    "    tprs_interpolated = []\n",
    "    aucs = []\n",
    "    mean_false_positive_rate = np.linspace(0, 1, 100)\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(16, 16))\n",
    "\n",
    "    # Plot random guess curve\n",
    "    ax.plot([0, 1], [0, 1], linestyle='--', lw=2.5, color='r', alpha=0.75)\n",
    "    # Plot individual ROC curves of multiple models\n",
    "    for fprs, tprs, _ in roc_curves:\n",
    "        tprs_interpolated.append(np.interp(mean_false_positive_rate, fprs, tprs))\n",
    "        tprs_interpolated[-1][0] = 0.0\n",
    "        roc_auc = auc(fprs, tprs)\n",
    "        aucs.append(roc_auc)\n",
    "        ax.plot(fprs, tprs, lw=1, alpha=0.1)\n",
    "\n",
    "    # Plot mean ROC curve of N models\n",
    "    mean_tpr = np.mean(tprs_interpolated, axis=0)\n",
    "    mean_tpr[-1] = 1.0\n",
    "    mean_auc = auc(mean_false_positive_rate, mean_tpr)\n",
    "    std_auc = np.std(aucs)\n",
    "    ax.plot(mean_false_positive_rate, mean_tpr, color='b', label=f'Mean ROC Curve (AUC: {mean_auc:.4f} ±{std_auc:.4f})', lw=2.5, alpha=0.9)\n",
    "\n",
    "    # Plot confidence interval of ROC curves\n",
    "    std_tpr = np.std(tprs_interpolated, axis=0)\n",
    "    tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n",
    "    tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n",
    "    ax.fill_between(mean_false_positive_rate, tprs_lower, tprs_upper, color='grey', alpha=0.2, label='±1 sigma')\n",
    "\n",
    "    ax.set_xlabel('False Positive Rate', size=15, labelpad=12)\n",
    "    ax.set_ylabel('True Positive Rate', size=15, labelpad=12)\n",
    "    ax.tick_params(axis='x', labelsize=15)\n",
    "    ax.tick_params(axis='y', labelsize=15)\n",
    "    ax.set_xlim([-0.05, 1.05])\n",
    "    ax.set_ylim([-0.05, 1.05])\n",
    "    ax.set_title(f'Mean and Std ROC curves of {n_scores} Models', size=20, pad=15)\n",
    "    ax.legend(loc='lower right', prop={'size': 14})\n",
    "\n",
    "    if path is None:\n",
    "        plt.show()\n",
    "    else:\n",
    "        plt.savefig(path)\n",
    "        plt.close(fig)\n"
   ]
  },
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   },
   "source": [
    "## 4. Preprocessing"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "df_train['EJ'] = label_encoder.fit_transform(df_train['EJ'])\n",
    "df_test['EJ'] = label_encoder.transform(df_test['EJ'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1232063c",
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    "tags": []
   },
   "source": [
    "## 5. Training"
   ]
  },
  {
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   "source": [
    "features = [column for column in df_train.columns.tolist() if column != 'Id' and column != 'Class']\n",
    "target = 'Class'\n",
    "\n",
    "n_splits = 5\n",
    "shuffle = True\n",
    "random_state = 42\n",
    "\n",
    "model_parameters = {\n",
    "    'num_leaves': 16,\n",
    "    'learning_rate': 0.01,\n",
    "    'bagging_fraction': 0.9,\n",
    "    'bagging_freq': 1,\n",
    "    'feature_fraction': 0.8,\n",
    "    'feature_fraction_bynode': 1.0,\n",
    "    'min_data_in_leaf': 8,\n",
    "    'min_gain_to_split': 0,\n",
    "    'lambda_l1': 0.,\n",
    "    'lambda_l2': 0.,\n",
    "    'max_bin': 255,\n",
    "    'max_depth': -1,\n",
    "    'boost_from_average': True,\n",
    "    'objective': 'binary',\n",
    "    'metric': 'binary_logloss',\n",
    "    'seed': 42,\n",
    "    'feature_fraction_seed': 42,\n",
    "    'bagging_seed': 42,\n",
    "    'drop_seed': 42,\n",
    "    'data_random_seed': 42,\n",
    "    'boosting_type': 'gbdt',\n",
    "    'verbose': 1,\n",
    "    'n_jobs': -1,\n",
    "}\n",
    "\n",
    "fit_parameters = {\n",
    "    'boosting_rounds': 250,\n",
    "    'log_evaluation': 250,\n",
    "    'early_stopping': 999999,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9494990e",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-05-11T19:33:16.030254Z"
    },
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     "duration": 5.82338,
     "end_time": "2023-05-11T19:33:16.034746",
     "exception": false,
     "start_time": "2023-05-11T19:33:10.211366",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "        Fold 1\n",
      "        Training Shape: (493, 56) - Target Mean: 0.1744\n",
      "        Validation: (124, 56) - Target Mean: 0.1774\n",
      "        \n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 86, number of negative: 407\n",
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001231 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 7352\n",
      "[LightGBM] [Info] Number of data points in the train set: 493, number of used features: 56\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.174442 -> initscore=-1.554466\n",
      "[LightGBM] [Info] Start training from score -1.554466\n",
      "Training until validation scores don't improve for 999999 rounds\n",
      "[250]\ttraining's binary_logloss: 0.0545446\tvalid_1's binary_logloss: 0.176053\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[250]\ttraining's binary_logloss: 0.0545446\tvalid_1's binary_logloss: 0.176053\n",
      "Fold 1 - Validation Scores: {\n",
      "  \"log_loss\": 0.17605267335931557,\n",
      "  \"accuracy\": 0.9112903225806451,\n",
      "  \"precision\": 0.9230769230769231,\n",
      "  \"recall\": 0.5454545454545454,\n",
      "  \"specificity\": 0.9901960784313726,\n",
      "  \"f1\": 0.6857142857142856,\n",
      "  \"roc_auc\": 0.9915329768270944,\n",
      "  \"average_precision\": 0.9460763838406276\n",
      "}\n",
      "\n",
      "        Fold 2\n",
      "        Training Shape: (493, 56) - Target Mean: 0.1744\n",
      "        Validation: (124, 56) - Target Mean: 0.1774\n",
      "        \n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 86, number of negative: 407\n",
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001246 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 7272\n",
      "[LightGBM] [Info] Number of data points in the train set: 493, number of used features: 56\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.174442 -> initscore=-1.554466\n",
      "[LightGBM] [Info] Start training from score -1.554466\n",
      "Training until validation scores don't improve for 999999 rounds\n",
      "[250]\ttraining's binary_logloss: 0.0528482\tvalid_1's binary_logloss: 0.214247\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[250]\ttraining's binary_logloss: 0.0528482\tvalid_1's binary_logloss: 0.214247\n",
      "Fold 2 - Validation Scores: {\n",
      "  \"log_loss\": 0.21424696196725368,\n",
      "  \"accuracy\": 0.9032258064516129,\n",
      "  \"precision\": 0.9166666666666666,\n",
      "  \"recall\": 0.5,\n",
      "  \"specificity\": 0.9901960784313726,\n",
      "  \"f1\": 0.6470588235294118,\n",
      "  \"roc_auc\": 0.9518716577540107,\n",
      "  \"average_precision\": 0.8777905579312044\n",
      "}\n",
      "\n",
      "        Fold 3\n",
      "        Training Shape: (494, 56) - Target Mean: 0.1741\n",
      "        Validation: (123, 56) - Target Mean: 0.1789\n",
      "        \n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 86, number of negative: 408\n",
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001231 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 7283\n",
      "[LightGBM] [Info] Number of data points in the train set: 494, number of used features: 56\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.174089 -> initscore=-1.556920\n",
      "[LightGBM] [Info] Start training from score -1.556920\n",
      "Training until validation scores don't improve for 999999 rounds\n",
      "[250]\ttraining's binary_logloss: 0.0557557\tvalid_1's binary_logloss: 0.174544\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[250]\ttraining's binary_logloss: 0.0557557\tvalid_1's binary_logloss: 0.174544\n",
      "Fold 3 - Validation Scores: {\n",
      "  \"log_loss\": 0.1745438330086142,\n",
      "  \"accuracy\": 0.926829268292683,\n",
      "  \"precision\": 0.9333333333333333,\n",
      "  \"recall\": 0.6363636363636364,\n",
      "  \"specificity\": 0.9900990099009901,\n",
      "  \"f1\": 0.7567567567567568,\n",
      "  \"roc_auc\": 0.9842484248424843,\n",
      "  \"average_precision\": 0.9415054961378491\n",
      "}\n",
      "\n",
      "        Fold 4\n",
      "        Training Shape: (494, 56) - Target Mean: 0.1761\n",
      "        Validation: (123, 56) - Target Mean: 0.1707\n",
      "        \n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 87, number of negative: 407\n",
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001197 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 7271\n",
      "[LightGBM] [Info] Number of data points in the train set: 494, number of used features: 56\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.176113 -> initscore=-1.542905\n",
      "[LightGBM] [Info] Start training from score -1.542905\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "Training until validation scores don't improve for 999999 rounds\n",
      "[250]\ttraining's binary_logloss: 0.0510664\tvalid_1's binary_logloss: 0.26991\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[250]\ttraining's binary_logloss: 0.0510664\tvalid_1's binary_logloss: 0.26991\n",
      "Fold 4 - Validation Scores: {\n",
      "  \"log_loss\": 0.26991034075047055,\n",
      "  \"accuracy\": 0.9024390243902439,\n",
      "  \"precision\": 0.7368421052631579,\n",
      "  \"recall\": 0.6666666666666666,\n",
      "  \"specificity\": 0.9509803921568627,\n",
      "  \"f1\": 0.7,\n",
      "  \"roc_auc\": 0.9094304388422035,\n",
      "  \"average_precision\": 0.8038575856193266\n",
      "}\n",
      "\n",
      "        Fold 5\n",
      "        Training Shape: (494, 56) - Target Mean: 0.1761\n",
      "        Validation: (123, 56) - Target Mean: 0.1707\n",
      "        \n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 87, number of negative: 407\n",
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001384 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 7282\n",
      "[LightGBM] [Info] Number of data points in the train set: 494, number of used features: 56\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.176113 -> initscore=-1.542905\n",
      "[LightGBM] [Info] Start training from score -1.542905\n",
      "Training until validation scores don't improve for 999999 rounds\n",
      "[250]\ttraining's binary_logloss: 0.0541675\tvalid_1's binary_logloss: 0.219959\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[250]\ttraining's binary_logloss: 0.0541675\tvalid_1's binary_logloss: 0.219959\n",
      "Fold 5 - Validation Scores: {\n",
      "  \"log_loss\": 0.21995895319809927,\n",
      "  \"accuracy\": 0.8943089430894309,\n",
      "  \"precision\": 0.7857142857142857,\n",
      "  \"recall\": 0.5238095238095238,\n",
      "  \"specificity\": 0.9705882352941176,\n",
      "  \"f1\": 0.6285714285714286,\n",
      "  \"roc_auc\": 0.9519140989729226,\n",
      "  \"average_precision\": 0.8370252270925149\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "df_test['lightgbm_prediction'] = 0\n",
    "\n",
    "df_feature_importance_gain = pd.DataFrame(\n",
    "    data=np.zeros((len(features), n_splits)),\n",
    "    index=features,\n",
    "    columns=[f'fold_{fold}' for fold in range(1, n_splits + 1)]\n",
    ")\n",
    "df_feature_importance_split = pd.DataFrame(\n",
    "    data=np.zeros((len(features), n_splits)),\n",
    "    index=features,\n",
    "    columns=[f'fold_{fold}' for fold in range(1, n_splits + 1)]\n",
    ")\n",
    "scores = []\n",
    "    \n",
    "skf = StratifiedKFold(n_splits=n_splits, shuffle=shuffle, random_state=random_state)\n",
    "\n",
    "for fold, (training_idx, validation_idx) in enumerate(skf.split(X=df_train, y=df_train['Class']), 1):\n",
    "\n",
    "    print(\n",
    "        f'''\n",
    "        Fold {fold}\n",
    "        Training Shape: ({len(training_idx)}, {len(features)}) - Target Mean: {df_train.loc[training_idx, target].mean():.4f}\n",
    "        Validation: ({len(validation_idx)}, {len(features)}) - Target Mean: {df_train.loc[validation_idx, target].mean():.4f}\n",
    "        '''\n",
    "    )\n",
    "\n",
    "    training_dataset = lgb.Dataset(df_train.loc[training_idx, features], label=df_train.loc[training_idx, target])\n",
    "    validation_dataset = lgb.Dataset(df_train.loc[validation_idx, features], label=df_train.loc[validation_idx, target])\n",
    "\n",
    "    # Set model parameters, train parameters, callbacks and start training\n",
    "    model = lgb.train(\n",
    "        params=model_parameters,\n",
    "        train_set=training_dataset,\n",
    "        valid_sets=[training_dataset, validation_dataset],\n",
    "        num_boost_round=fit_parameters['boosting_rounds'],\n",
    "        callbacks=[\n",
    "            lgb.early_stopping(fit_parameters['early_stopping']),\n",
    "            lgb.log_evaluation(fit_parameters['log_evaluation'])\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    df_feature_importance_gain[f'fold_{fold}'] = model.feature_importance(importance_type='gain')\n",
    "    df_feature_importance_split[f'fold_{fold}'] = model.feature_importance(importance_type='split')\n",
    "    \n",
    "    val_predictions = model.predict(df_train.loc[validation_idx, features])\n",
    "    df_train.loc[validation_idx, 'lightgbm_prediction'] = val_predictions\n",
    "    val_scores = classification_scores(\n",
    "        y_true=df_train.loc[validation_idx, target],\n",
    "        y_pred=df_train.loc[validation_idx, 'lightgbm_prediction'],\n",
    "    )\n",
    "    scores.append(val_scores)\n",
    "    print(f'Fold {fold} - Validation Scores: {json.dumps(val_scores, indent=2)}')\n",
    "    \n",
    "    test_predictions = model.predict(df_test.loc[:, features])\n",
    "    df_test['lightgbm_prediction'] += (test_predictions / n_splits)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7652713",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "## 6. Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e80aa20a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-11T19:33:16.058560Z",
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     "shell.execute_reply": "2023-05-11T19:33:16.083600Z"
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    "papermill": {
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     "exception": false,
     "start_time": "2023-05-11T19:33:16.051275",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 1 - Validation Scores: {\n",
      "  \"log_loss\": 0.17605267335931557,\n",
      "  \"accuracy\": 0.9112903225806451,\n",
      "  \"precision\": 0.9230769230769231,\n",
      "  \"recall\": 0.5454545454545454,\n",
      "  \"specificity\": 0.9901960784313726,\n",
      "  \"f1\": 0.6857142857142856,\n",
      "  \"roc_auc\": 0.9915329768270944,\n",
      "  \"average_precision\": 0.9460763838406276\n",
      "}\n",
      "Fold 2 - Validation Scores: {\n",
      "  \"log_loss\": 0.21424696196725368,\n",
      "  \"accuracy\": 0.9032258064516129,\n",
      "  \"precision\": 0.9166666666666666,\n",
      "  \"recall\": 0.5,\n",
      "  \"specificity\": 0.9901960784313726,\n",
      "  \"f1\": 0.6470588235294118,\n",
      "  \"roc_auc\": 0.9518716577540107,\n",
      "  \"average_precision\": 0.8777905579312044\n",
      "}\n",
      "Fold 3 - Validation Scores: {\n",
      "  \"log_loss\": 0.1745438330086142,\n",
      "  \"accuracy\": 0.926829268292683,\n",
      "  \"precision\": 0.9333333333333333,\n",
      "  \"recall\": 0.6363636363636364,\n",
      "  \"specificity\": 0.9900990099009901,\n",
      "  \"f1\": 0.7567567567567568,\n",
      "  \"roc_auc\": 0.9842484248424843,\n",
      "  \"average_precision\": 0.9415054961378491\n",
      "}\n",
      "Fold 4 - Validation Scores: {\n",
      "  \"log_loss\": 0.26991034075047055,\n",
      "  \"accuracy\": 0.9024390243902439,\n",
      "  \"precision\": 0.7368421052631579,\n",
      "  \"recall\": 0.6666666666666666,\n",
      "  \"specificity\": 0.9509803921568627,\n",
      "  \"f1\": 0.7,\n",
      "  \"roc_auc\": 0.9094304388422035,\n",
      "  \"average_precision\": 0.8038575856193266\n",
      "}\n",
      "Fold 5 - Validation Scores: {\n",
      "  \"log_loss\": 0.21995895319809927,\n",
      "  \"accuracy\": 0.8943089430894309,\n",
      "  \"precision\": 0.7857142857142857,\n",
      "  \"recall\": 0.5238095238095238,\n",
      "  \"specificity\": 0.9705882352941176,\n",
      "  \"f1\": 0.6285714285714286,\n",
      "  \"roc_auc\": 0.9519140989729226,\n",
      "  \"average_precision\": 0.8370252270925149\n",
      "}\n",
      "\n",
      "    LightGBM Mean Validation Scores\n",
      "    {\n",
      "  \"log_loss\": 0.21094255245675067,\n",
      "  \"accuracy\": 0.9076186729609231,\n",
      "  \"precision\": 0.8591266628108734,\n",
      "  \"recall\": 0.5744588744588743,\n",
      "  \"specificity\": 0.9784119588429432,\n",
      "  \"f1\": 0.6836202589143766,\n",
      "  \"roc_auc\": 0.9577995194477431,\n",
      "  \"average_precision\": 0.8812510501243045\n",
      "}\n",
      "    and Standard Deviations\n",
      "    ±{\n",
      "  \"log_loss\": 0.039088313796109345,\n",
      "  \"accuracy\": 0.012306532093436089,\n",
      "  \"precision\": 0.09117292348534908,\n",
      "  \"recall\": 0.0729472554284863,\n",
      "  \"specificity\": 0.017521555384750625,\n",
      "  \"f1\": 0.04997184322809924,\n",
      "  \"roc_auc\": 0.03258387799355351,\n",
      "  \"average_precision\": 0.06283026526478454\n",
      "}\n",
      "    \n",
      "OOF Scores: {\n",
      "  \"log_loss\": 0.21089136044769566,\n",
      "  \"accuracy\": 0.9076175040518638,\n",
      "  \"precision\": 0.8493150684931506,\n",
      "  \"recall\": 0.5740740740740741,\n",
      "  \"specificity\": 0.9783889980353635,\n",
      "  \"f1\": 0.6850828729281768,\n",
      "  \"roc_auc\": 0.9514662009750418,\n",
      "  \"average_precision\": 0.8585662206432995\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Display validation scores\n",
    "df_scores = pd.DataFrame(scores)\n",
    "for fold, scores in df_scores.iterrows():\n",
    "    print(f'Fold {int(fold) + 1} - Validation Scores: {json.dumps(scores.to_dict(), indent=2)}')\n",
    "print(\n",
    "    f'''\n",
    "    LightGBM Mean Validation Scores\n",
    "    {json.dumps(df_scores.mean(axis=0).to_dict(), indent=2)}\n",
    "    and Standard Deviations\n",
    "    ±{json.dumps(df_scores.std(axis=0).to_dict(), indent=2)}\n",
    "    '''\n",
    ")\n",
    "\n",
    "oof_scores = classification_scores(\n",
    "    y_true=df_train.loc[:, target],\n",
    "    y_pred=df_train.loc[:, 'lightgbm_prediction'],\n",
    ")\n",
    "print(f'OOF Scores: {json.dumps(oof_scores, indent=2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "428b333b",
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     "exception": false,
     "start_time": "2023-05-11T19:33:16.093335",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_scores(df_scores=df_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "df4dd664",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-11T19:33:16.556417Z",
     "iopub.status.busy": "2023-05-11T19:33:16.555991Z",
     "iopub.status.idle": "2023-05-11T19:33:18.609203Z",
     "shell.execute_reply": "2023-05-11T19:33:18.607921Z"
    },
    "papermill": {
     "duration": 2.064437,
     "end_time": "2023-05-11T19:33:18.611745",
     "exception": false,
     "start_time": "2023-05-11T19:33:16.547308",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for importance_type, df_feature_importance in zip(['gain', 'split'], [df_feature_importance_gain, df_feature_importance_split]):\n",
    "    df_feature_importance['mean'] = df_feature_importance[[f'fold_{fold}' for fold in range(1, n_splits + 1)]].mean(axis=1)\n",
    "    df_feature_importance['std'] = df_feature_importance[[f'fold_{fold}' for fold in range(1, n_splits + 1)]].std(axis=1).fillna(0)\n",
    "    df_feature_importance.sort_values(by='mean', ascending=False, inplace=True)\n",
    "    visualize_feature_importance(df_feature_importance=df_feature_importance)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb8cfdf2",
   "metadata": {
    "papermill": {
     "duration": 0.01363,
     "end_time": "2023-05-11T19:33:18.638660",
     "exception": false,
     "start_time": "2023-05-11T19:33:18.625030",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 7. Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09b42595",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-11T19:33:18.668014Z",
     "iopub.status.busy": "2023-05-11T19:33:18.667596Z",
     "iopub.status.idle": "2023-05-11T19:33:18.684131Z",
     "shell.execute_reply": "2023-05-11T19:33:18.682763Z"
    },
    "papermill": {
     "duration": 0.034461,
     "end_time": "2023-05-11T19:33:18.686797",
     "exception": false,
     "start_time": "2023-05-11T19:33:18.652336",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_test['class_1'] = df_test['lightgbm_prediction']\n",
    "df_test['class_0'] = 1 - df_test['class_1']\n",
    "df_submission = df_test.loc[:, ['Id', 'class_0', 'class_1']]\n",
    "df_submission.to_csv('submission.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 24.550432,
   "end_time": "2023-05-11T19:33:19.823316",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-05-11T19:32:55.272884",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}