{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":81933,"databundleVersionId":9643020,"sourceType":"competition"}],"dockerImageVersionId":30804,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:13.381043Z","iopub.execute_input":"2024-12-07T04:58:13.381364Z","iopub.status.idle":"2024-12-07T04:58:16.533986Z","shell.execute_reply.started":"2024-12-07T04:58:13.381335Z","shell.execute_reply":"2024-12-07T04:58:16.533138Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"pip install pytorch-tabnet","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:16.535658Z","iopub.execute_input":"2024-12-07T04:58:16.536052Z","iopub.status.idle":"2024-12-07T04:58:26.428429Z","shell.execute_reply.started":"2024-12-07T04:58:16.536024Z","shell.execute_reply":"2024-12-07T04:58:26.427185Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from pytorch_tabnet.tab_model import TabNetRegressor\nimport torch","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:26.429875Z","iopub.execute_input":"2024-12-07T04:58:26.430172Z","iopub.status.idle":"2024-12-07T04:58:30.537230Z","shell.execute_reply.started":"2024-12-07T04:58:26.430142Z","shell.execute_reply":"2024-12-07T04:58:30.536297Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport os\nimport re\nfrom sklearn.base import clone\nfrom sklearn.metrics import cohen_kappa_score\nfrom sklearn.model_selection import StratifiedKFold\nfrom scipy.optimize import minimize\nfrom concurrent.futures import ThreadPoolExecutor\nfrom tqdm import tqdm\nimport polars as pl\nimport polars.selectors as cs\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MaxNLocator, FormatStrFormatter, PercentFormatter\nimport seaborn as sns\n\nfrom sklearn.preprocessing import StandardScaler\nimport matplotlib.pyplot as plt\nfrom keras.models import Model\nfrom keras.layers import Input, Dense\nfrom keras.optimizers import Adam\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nfrom colorama import Fore, Style\nfrom IPython.display import clear_output\nimport warnings\nfrom lightgbm import LGBMRegressor\nfrom xgboost import XGBRegressor\nfrom catboost import CatBoostRegressor\nfrom sklearn.ensemble import VotingRegressor, RandomForestRegressor, GradientBoostingRegressor\nfrom sklearn.impute import SimpleImputer, KNNImputer\nfrom sklearn.pipeline import Pipeline\nwarnings.filterwarnings('ignore')\npd.options.display.max_columns = None","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:30.538474Z","iopub.execute_input":"2024-12-07T04:58:30.539313Z","iopub.status.idle":"2024-12-07T04:58:46.659822Z","shell.execute_reply.started":"2024-12-07T04:58:30.539271Z","shell.execute_reply":"2024-12-07T04:58:46.658906Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"target_labels = ['None', 'Mild', 'Moderate', 'Severe']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:46.661940Z","iopub.execute_input":"2024-12-07T04:58:46.662580Z","iopub.status.idle":"2024-12-07T04:58:46.666340Z","shell.execute_reply.started":"2024-12-07T04:58:46.662550Z","shell.execute_reply":"2024-12-07T04:58:46.665535Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 檢視資料","metadata":{}},{"cell_type":"code","source":"season_dtype = pl.Enum(['Spring', 'Summer', 'Fall', 'Winter'])\n\ntrain = (\n    pl.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\n    .with_columns(pl.col('^.*Season$').cast(season_dtype))\n)\n\ntest = (\n    pl.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')\n    .with_columns(pl.col('^.*Season$').cast(season_dtype))\n)\n\ntrain\ntest","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:46.667335Z","iopub.execute_input":"2024-12-07T04:58:46.667581Z","iopub.status.idle":"2024-12-07T04:58:46.880056Z","shell.execute_reply.started":"2024-12-07T04:58:46.667557Z","shell.execute_reply":"2024-12-07T04:58:46.879248Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"For a supervised learning, we need the target value, but some (sii) are missing. So we only use the part with valid target value(sii).","metadata":{}},{"cell_type":"markdown","source":"### 分析與可視化缺失值比例","metadata":{}},{"cell_type":"code","source":"#篩選sii 非 NULL值的資料\nsupervised_usable = (\n    train\n    .filter(pl.col('sii').is_not_null())\n)\n\n#計算缺失值\nmissing_count = (\n    supervised_usable\n    .null_count() # 計算每個特徵的缺失值總數\n    .transpose(include_header=True,\n               header_name='feature',\n               column_names=['null_count'])  # 轉置表格，行名改為 'feature'，列名為 'null_count'\n    .sort('null_count', descending=True) # 按缺失值數量降序排序\n    .with_columns((pl.col('null_count') / len(supervised_usable)).alias('null_ratio'))\n)\nplt.figure(figsize=(6, 15))\nplt.title(f'Missing values over the {len(supervised_usable)} samples which have a target')\nplt.barh(np.arange(len(missing_count)), missing_count.get_column('null_ratio'), color='coral', label='missing')\nplt.barh(np.arange(len(missing_count)), \n         1 - missing_count.get_column('null_ratio'),\n         left=missing_count.get_column('null_ratio'),\n         color='darkseagreen', label='available')\nplt.yticks(np.arange(len(missing_count)), missing_count.get_column('feature'))\nplt.gca().xaxis.set_major_formatter(PercentFormatter(xmax=1, decimals=0))\nplt.xlim(0, 1)\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:46.881095Z","iopub.execute_input":"2024-12-07T04:58:46.881315Z","iopub.status.idle":"2024-12-07T04:58:47.916458Z","shell.execute_reply.started":"2024-12-07T04:58:46.881293Z","shell.execute_reply":"2024-12-07T04:58:47.915569Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 檢查兩個特徵的缺失值一致性\n檢查 PCIAT-PCIAT_Total 與 sii 的缺失值是否一致，輸出結果是一個 0 到 1 之間的值，越接近 1 表示一致性越高","metadata":{}},{"cell_type":"code","source":"print(train.select(pl.col('PCIAT-PCIAT_Total').is_null() == pl.col('sii').is_null()).to_series().mean())\n\n(train\n .select(pl.col('PCIAT-PCIAT_Total'))\n .group_by(train.get_column('sii'))\n .agg(pl.col('PCIAT-PCIAT_Total').min().alias('PCIAT-PCIAT_Total min'),\n      pl.col('PCIAT-PCIAT_Total').max().alias('PCIAT-PCIAT_Total max'),\n      pl.col('PCIAT-PCIAT_Total').len().alias('count'))\n .sort('sii')\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T05:21:35.785257Z","iopub.execute_input":"2024-12-07T05:21:35.785636Z","iopub.status.idle":"2024-12-07T05:21:35.823085Z","shell.execute_reply.started":"2024-12-07T05:21:35.785604Z","shell.execute_reply":"2024-12-07T05:21:35.821912Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print('Columns missing in test:')\nprint([f for f in train.columns if f not in test.columns])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:47.959897Z","iopub.execute_input":"2024-12-07T04:58:47.960243Z","iopub.status.idle":"2024-12-07T04:58:47.965293Z","shell.execute_reply.started":"2024-12-07T04:58:47.960206Z","shell.execute_reply":"2024-12-07T04:58:47.964520Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"###　測試數據集沒有PCIAT變數\n1. 隱藏分數的目標項\n* PCIAT-PCIAT_Total 和 sii 是模型需要預測的目標變數。\n* 測試數據集中缺少這些變數，是因為它們是競賽的目標，參賽者應提交模型預測的結果來評估模型的性能。\n* 訓練數據 提供目標變數以便參賽者訓練模型，而測試數據的目標變數通常會被隱藏。\n2. 隱藏分數的組成項\n* 訓練數據中存在的 PCIAT-PCIAT_01 到 PCIAT-PCIAT_20 和 PCIAT-Season 是構成總分 PCIAT-PCIAT_Total 的子項分數。\n* 在測試數據中，這些子項分數可能被隱藏，以防止參賽者直接利用這些特徵來「作弊」，而不是構建一個真正有預測能力的模型。\n* 這樣的設計確保參賽者需要專注於使用其他可用特徵來預測目標變數。","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"### 入學季節分布","metadata":{}},{"cell_type":"code","source":"vc = train.get_column('Basic_Demos-Enroll_Season').value_counts()\nplt.pie(vc.get_column('count'), labels=vc.get_column('Basic_Demos-Enroll_Season'))\nplt.pie(vc.get_column('count'), labels=vc.get_column('Basic_Demos-Enroll_Season'), autopct='%1.1f%%')\nplt.title('Season of enrollment')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T05:21:27.769799Z","iopub.execute_input":"2024-12-07T05:21:27.770183Z","iopub.status.idle":"2024-12-07T05:21:27.810509Z","shell.execute_reply.started":"2024-12-07T05:21:27.770153Z","shell.execute_reply":"2024-12-07T05:21:27.809415Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 性別分布","metadata":{}},{"cell_type":"code","source":"vc = train.get_column('Basic_Demos-Sex').value_counts()\nplt.pie(vc.get_column('count'), labels=['boys', 'girls'])\nplt.title('Sex of participant')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T05:20:58.865562Z","iopub.execute_input":"2024-12-07T05:20:58.866422Z","iopub.status.idle":"2024-12-07T05:20:59.261694Z","shell.execute_reply.started":"2024-12-07T05:20:58.866386Z","shell.execute_reply":"2024-12-07T05:20:59.260490Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 年齡分布","metadata":{}},{"cell_type":"code","source":"_, axs = plt.subplots(2, 1, sharex=True)\nfor sex in range(2):\n    ax = axs.ravel()[sex]\n    vc = train.filter(pl.col('Basic_Demos-Sex') == sex).get_column('Basic_Demos-Age').value_counts()\n    ax.bar(vc.get_column('Basic_Demos-Age'),\n           vc.get_column('count'),\n           color=['blue', 'orange'][sex],\n           label=['boys', 'girls'][sex])\n    ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n    ax.set_ylabel('count')\n    ax.legend()\nplt.suptitle('Age distribution')\naxs.ravel()[1].set_xlabel('years')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:48.242917Z","iopub.execute_input":"2024-12-07T04:58:48.243597Z","iopub.status.idle":"2024-12-07T04:58:48.686481Z","shell.execute_reply.started":"2024-12-07T04:58:48.243544Z","shell.execute_reply":"2024-12-07T04:58:48.685636Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### SII 分布","metadata":{}},{"cell_type":"code","source":"_, axs = plt.subplots(2, 1, sharex=True, sharey=True)\nfor sex in range(2):\n    ax = axs.ravel()[sex]\n    vc = train.filter(pl.col('Basic_Demos-Sex') == sex).get_column('sii').value_counts()\n    ax.bar(vc.get_column('sii'),\n           vc.get_column('count') / vc.get_column('count').sum(),\n           color=['blue', 'coral'][sex],\n           label=['boys', 'girls'][sex])\n    ax.set_xticks(np.arange(4), target_labels)\n    ax.yaxis.set_major_formatter(PercentFormatter(xmax=1, decimals=0))\n    ax.set_ylabel('count')\n    ax.legend()\nplt.suptitle('Target distribution')\naxs.ravel()[1].set_xlabel('Severity Impairment Index (sii)')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:48.687724Z","iopub.execute_input":"2024-12-07T04:58:48.688108Z","iopub.status.idle":"2024-12-07T04:58:48.982195Z","shell.execute_reply.started":"2024-12-07T04:58:48.688069Z","shell.execute_reply":"2024-12-07T04:58:48.981432Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 計算特徵與目標變數 PCIAT-PCIAT_Total 的相關性","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(14, 12))\ncorr_matrix = supervised_usable.select([\n    'PCIAT-PCIAT_Total', 'Basic_Demos-Age', 'Basic_Demos-Sex', 'Physical-BMI', \n    'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n    'Physical-Diastolic_BP', 'Physical-Systolic_BP', 'Physical-HeartRate',\n    'PreInt_EduHx-computerinternet_hoursday', 'SDS-SDS_Total_T', 'PAQ_A-PAQ_A_Total',\n    'PAQ_C-PAQ_C_Total', 'Fitness_Endurance-Max_Stage', 'Fitness_Endurance-Time_Mins','Fitness_Endurance-Time_Sec',\n    'FGC-FGC_CU', 'FGC-FGC_GSND','FGC-FGC_GSD','FGC-FGC_PU','FGC-FGC_SRL','FGC-FGC_SRR','FGC-FGC_TL','BIA-BIA_Activity_Level_num', \n    'BIA-BIA_BMC', 'BIA-BIA_BMI', 'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n    'BIA-BIA_FFMI','BIA-BIA_FMI', 'BIA-BIA_Fat','BIA-BIA_Frame_num','BIA-BIA_ICW','BIA-BIA_LDM','BIA-BIA_LST',\n    'BIA-BIA_SMM','BIA-BIA_TBW'\n    # Add other relevant columns\n]).to_pandas().corr()\n\nsii_corr = corr_matrix['PCIAT-PCIAT_Total'].drop('PCIAT-PCIAT_Total')\nfiltered_corr = sii_corr[(sii_corr > 0.1) | (sii_corr < -0.1)]\n\nprint(filtered_corr)\n\nplt.figure(figsize=(8, 6))\nfiltered_corr.sort_values().plot(kind='barh', color='coral')\nplt.title('Features with Correlation > 0.1 or < -0.1 with PCIAT-PCIAT_Total')\nplt.xlabel('Correlation coefficient')\nplt.ylabel('Features')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:48.985743Z","iopub.execute_input":"2024-12-07T04:58:48.986505Z","iopub.status.idle":"2024-12-07T04:58:49.357172Z","shell.execute_reply.started":"2024-12-07T04:58:48.986474Z","shell.execute_reply":"2024-12-07T04:58:49.356365Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Actigraphy (time series)","metadata":{}},{"cell_type":"code","source":"actigraphy = pl.read_parquet('/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=0417c91e/part-0.parquet')\nactigraphy","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:49.358342Z","iopub.execute_input":"2024-12-07T04:58:49.358735Z","iopub.status.idle":"2024-12-07T04:58:49.444770Z","shell.execute_reply.started":"2024-12-07T04:58:49.358694Z","shell.execute_reply":"2024-12-07T04:58:49.443761Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def analyze_actigraphy(id, only_one_week=False, small=False):\n    actigraphy = pl.read_parquet(f'/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id={id}/part-0.parquet')\n    day = actigraphy.get_column('relative_date_PCIAT') + actigraphy.get_column('time_of_day') / 86400e9\n    sample = train.filter(pl.col('id') == id)\n    age = sample.get_column('Basic_Demos-Age').item()\n    sex = ['boy', 'girl'][sample.get_column('Basic_Demos-Sex').item()]\n    actigraphy = (\n        actigraphy\n        .with_columns(\n            (day.diff() * 86400).alias('diff_seconds'),\n            (np.sqrt(np.square(pl.col('X')) + np.square(pl.col('Y')) + np.square(pl.col('Z'))).alias('norm'))\n        )\n    )\n\n    if only_one_week:\n        start = np.ceil(day.min())\n        mask = (start <= day.to_numpy()) & (day.to_numpy() <= start + 7*3)\n        mask &= ~ actigraphy.get_column('non-wear_flag').cast(bool).to_numpy()\n    else:\n        mask = np.full(len(day), True)\n        \n    if small:\n        timelines = [\n            ('enmo', 'forestgreen'),\n            ('light', 'orange'),\n        ]\n    else:\n        timelines = [\n            ('X', 'm'),\n            ('Y', 'm'),\n            ('Z', 'm'),\n#             ('norm', 'c'),\n            ('enmo', 'forestgreen'),\n            ('anglez', 'lightblue'),\n            ('light', 'orange'),\n            ('non-wear_flag', 'chocolate')\n    #         ('diff_seconds', 'k'),\n        ]\n        \n    _, axs = plt.subplots(len(timelines), 1, sharex=True, figsize=(12, len(timelines) * 1.1 + 0.5))\n    for ax, (feature, color) in zip(axs, timelines):\n        ax.set_facecolor('#eeeeee')\n        ax.scatter(day.to_numpy()[mask],\n                   actigraphy.get_column(feature).to_numpy()[mask],\n                   color=color, label=feature, s=1)\n        ax.legend(loc='upper left', facecolor='#eeeeee')\n        if feature == 'diff_seconds':\n            ax.set_ylim(-0.5, 20.5)\n    axs[-1].set_xlabel('day')\n    axs[-1].xaxis.set_major_locator(MaxNLocator(integer=True))\n    plt.tight_layout()\n    axs[0].set_title(f'id={id}, {sex}, age={age}')\n    plt.show()\n\nanalyze_actigraphy('0417c91e', only_one_week=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:49.446228Z","iopub.execute_input":"2024-12-07T04:58:49.446668Z","iopub.status.idle":"2024-12-07T04:58:51.776286Z","shell.execute_reply.started":"2024-12-07T04:58:49.446626Z","shell.execute_reply":"2024-12-07T04:58:51.775400Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"SEED = 42\nn_splits = 5","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:51.777526Z","iopub.execute_input":"2024-12-07T04:58:51.777909Z","iopub.status.idle":"2024-12-07T04:58:51.782018Z","shell.execute_reply.started":"2024-12-07T04:58:51.777871Z","shell.execute_reply":"2024-12-07T04:58:51.781101Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Feature Engineering","metadata":{}},{"cell_type":"code","source":"def process_file(filename, dirname):\n    df = pd.read_parquet(os.path.join(dirname, filename, 'part-0.parquet'))\n    df.drop('step', axis=1, inplace=True)\n    return df.describe().values.reshape(-1), filename.split('=')[1]\n\ndef load_time_series(dirname) -> pd.DataFrame:\n    ids = os.listdir(dirname)\n    \n    with ThreadPoolExecutor() as executor:\n        results = list(tqdm(executor.map(lambda fname: process_file(fname, dirname), ids), total=len(ids)))\n    \n    stats, indexes = zip(*results)\n    \n    df = pd.DataFrame(stats, columns=[f\"stat_{i}\" for i in range(len(stats[0]))])\n    df['id'] = indexes\n    return df\n\n\nclass AutoEncoder(nn.Module):\n    def __init__(self, input_dim, encoding_dim):\n        super(AutoEncoder, self).__init__()\n        self.encoder = nn.Sequential(\n            nn.Linear(input_dim, encoding_dim*3),\n            nn.ReLU(),\n            nn.Linear(encoding_dim*3, encoding_dim*2),\n            nn.ReLU(),\n            nn.Linear(encoding_dim*2, encoding_dim),\n            nn.ReLU()\n        )\n        self.decoder = nn.Sequential(\n            nn.Linear(encoding_dim, input_dim*2),\n            nn.ReLU(),\n            nn.Linear(input_dim*2, input_dim*3),\n            nn.ReLU(),\n            nn.Linear(input_dim*3, input_dim),\n            nn.Sigmoid()\n        )\n        \n    def forward(self, x):\n        encoded = self.encoder(x)\n        decoded = self.decoder(encoded)\n        return decoded\n\n\ndef perform_autoencoder(df, encoding_dim=50, epochs=50, batch_size=32):\n    scaler = StandardScaler()\n    df_scaled = scaler.fit_transform(df)\n    \n    data_tensor = torch.FloatTensor(df_scaled)\n    \n    input_dim = data_tensor.shape[1]\n    autoencoder = AutoEncoder(input_dim, encoding_dim)\n    \n    criterion = nn.MSELoss()\n    optimizer = optim.Adam(autoencoder.parameters())\n    \n    for epoch in range(epochs):\n        for i in range(0, len(data_tensor), batch_size):\n            batch = data_tensor[i : i + batch_size]\n            optimizer.zero_grad()\n            reconstructed = autoencoder(batch)\n            loss = criterion(reconstructed, batch)\n            loss.backward()\n            optimizer.step()\n            \n        if (epoch + 1) % 10 == 0:\n            print(f'Epoch [{epoch + 1}/{epochs}], Loss: {loss.item():.4f}]')\n                 \n    with torch.no_grad():\n        encoded_data = autoencoder.encoder(data_tensor).numpy()\n        \n    df_encoded = pd.DataFrame(encoded_data, columns=[f'Enc_{i + 1}' for i in range(encoded_data.shape[1])])\n    \n    return df_encoded\n\ndef feature_engineering(df):\n    season_cols = [col for col in df.columns if 'Season' in col]\n    df = df.drop(season_cols, axis=1) \n    df['BMI_Age'] = df['Physical-BMI'] * df['Basic_Demos-Age']\n    df['Internet_Hours_Age'] = df['PreInt_EduHx-computerinternet_hoursday'] * df['Basic_Demos-Age']\n    df['BMI_Internet_Hours'] = df['Physical-BMI'] * df['PreInt_EduHx-computerinternet_hoursday']\n    df['BFP_BMI'] = df['BIA-BIA_Fat'] / df['BIA-BIA_BMI']\n    df['FFMI_BFP'] = df['BIA-BIA_FFMI'] / df['BIA-BIA_Fat']\n    df['FMI_BFP'] = df['BIA-BIA_FMI'] / df['BIA-BIA_Fat']\n    df['LST_TBW'] = df['BIA-BIA_LST'] / df['BIA-BIA_TBW']\n    df['BFP_BMR'] = df['BIA-BIA_Fat'] * df['BIA-BIA_BMR']\n    df['BFP_DEE'] = df['BIA-BIA_Fat'] * df['BIA-BIA_DEE']\n    df['BMR_Weight'] = df['BIA-BIA_BMR'] / df['Physical-Weight']\n    df['DEE_Weight'] = df['BIA-BIA_DEE'] / df['Physical-Weight']\n    df['SMM_Height'] = df['BIA-BIA_SMM'] / df['Physical-Height']\n    df['Muscle_to_Fat'] = df['BIA-BIA_SMM'] / df['BIA-BIA_FMI']\n    df['Hydration_Status'] = df['BIA-BIA_TBW'] / df['Physical-Weight']\n    df['ICW_TBW'] = df['BIA-BIA_ICW'] / df['BIA-BIA_TBW']\n    \n    return df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:51.783189Z","iopub.execute_input":"2024-12-07T04:58:51.783455Z","iopub.status.idle":"2024-12-07T04:58:51.798326Z","shell.execute_reply.started":"2024-12-07T04:58:51.783414Z","shell.execute_reply":"2024-12-07T04:58:51.797543Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\ntest = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')\nsample = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/sample_submission.csv')\n\ntrain_ts = load_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet\")\ntest_ts = load_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_test.parquet\")\n\ndf_train = train_ts.drop('id', axis=1)\ndf_test = test_ts.drop('id', axis=1)\n\ntrain_ts_encoded = perform_autoencoder(df_train, encoding_dim=60, epochs=100, batch_size=32)\ntest_ts_encoded = perform_autoencoder(df_test, encoding_dim=60, epochs=100, batch_size=32)\n\ntime_series_cols = train_ts_encoded.columns.tolist()\ntrain_ts_encoded[\"id\"]=train_ts[\"id\"]\ntest_ts_encoded['id']=test_ts[\"id\"]\n\ntrain = pd.merge(train, train_ts_encoded, how=\"left\", on='id')\ntest = pd.merge(test, test_ts_encoded, how=\"left\", on='id')\n\nimputer = KNNImputer(n_neighbors=5)\nnumeric_cols = train.select_dtypes(include=['float64', 'int64']).columns\nimputed_data = imputer.fit_transform(train[numeric_cols])\ntrain_imputed = pd.DataFrame(imputed_data, columns=numeric_cols)\ntrain_imputed['sii'] = train_imputed['sii'].round().astype(int)\nfor col in train.columns:\n    if col not in numeric_cols:\n        train_imputed[col] = train[col]\n        \ntrain = train_imputed\n\ntrain = feature_engineering(train)\ntrain = train.dropna(thresh=10, axis=0)\ntest = feature_engineering(test)\n\ntrain = train.drop('id', axis=1)\ntest  = test .drop('id', axis=1)   \n\n\nfeaturesCols = ['Basic_Demos-Age', 'Basic_Demos-Sex',\n                'CGAS-CGAS_Score', 'Physical-BMI',\n                'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n                'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n                'Fitness_Endurance-Max_Stage',\n                'Fitness_Endurance-Time_Mins', 'Fitness_Endurance-Time_Sec',\n                'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND',\n                'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone', 'FGC-FGC_PU',\n                'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone', 'FGC-FGC_SRR',\n                'FGC-FGC_SRR_Zone', 'FGC-FGC_TL', 'FGC-FGC_TL_Zone',\n                'BIA-BIA_Activity_Level_num', 'BIA-BIA_BMC', 'BIA-BIA_BMI',\n                'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n                'BIA-BIA_FFMI', 'BIA-BIA_FMI', 'BIA-BIA_Fat', 'BIA-BIA_Frame_num',\n                'BIA-BIA_ICW', 'BIA-BIA_LDM', 'BIA-BIA_LST', 'BIA-BIA_SMM',\n                'BIA-BIA_TBW', 'PAQ_A-PAQ_A_Total',\n                'PAQ_C-PAQ_C_Total', 'SDS-SDS_Total_Raw',\n                'SDS-SDS_Total_T',\n                'PreInt_EduHx-computerinternet_hoursday', 'sii', 'BMI_Age','Internet_Hours_Age','BMI_Internet_Hours',\n                'BFP_BMI', 'FFMI_BFP', 'FMI_BFP', 'LST_TBW', 'BFP_BMR', 'BFP_DEE', 'BMR_Weight', 'DEE_Weight',\n                'SMM_Height', 'Muscle_to_Fat', 'Hydration_Status', 'ICW_TBW']\n\nfeaturesCols += time_series_cols\n\ntrain = train[featuresCols]\ntrain = train.dropna(subset='sii')\n\nfeaturesCols = ['Basic_Demos-Age', 'Basic_Demos-Sex',\n                'CGAS-CGAS_Score', 'Physical-BMI',\n                'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n                'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n                'Fitness_Endurance-Max_Stage',\n                'Fitness_Endurance-Time_Mins', 'Fitness_Endurance-Time_Sec',\n                'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND',\n                'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone', 'FGC-FGC_PU',\n                'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone', 'FGC-FGC_SRR',\n                'FGC-FGC_SRR_Zone', 'FGC-FGC_TL', 'FGC-FGC_TL_Zone',\n                'BIA-BIA_Activity_Level_num', 'BIA-BIA_BMC', 'BIA-BIA_BMI',\n                'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n                'BIA-BIA_FFMI', 'BIA-BIA_FMI', 'BIA-BIA_Fat', 'BIA-BIA_Frame_num',\n                'BIA-BIA_ICW', 'BIA-BIA_LDM', 'BIA-BIA_LST', 'BIA-BIA_SMM',\n                'BIA-BIA_TBW', 'PAQ_A-PAQ_A_Total',\n                'PAQ_C-PAQ_C_Total', 'SDS-SDS_Total_Raw',\n                'SDS-SDS_Total_T',\n                'PreInt_EduHx-computerinternet_hoursday', 'BMI_Age','Internet_Hours_Age','BMI_Internet_Hours',\n                'BFP_BMI', 'FFMI_BFP', 'FMI_BFP', 'LST_TBW', 'BFP_BMR', 'BFP_DEE', 'BMR_Weight', 'DEE_Weight',\n                'SMM_Height', 'Muscle_to_Fat', 'Hydration_Status', 'ICW_TBW']\n\nfeaturesCols += time_series_cols\ntest = test[featuresCols]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T04:58:51.799723Z","iopub.execute_input":"2024-12-07T04:58:51.800157Z","iopub.status.idle":"2024-12-07T05:00:23.532397Z","shell.execute_reply.started":"2024-12-07T04:58:51.800119Z","shell.execute_reply":"2024-12-07T05:00:23.531547Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"if np.any(np.isinf(train)):\n    train = train.replace([np.inf, -np.inf], np.nan)\n\ndef quadratic_weighted_kappa(y_true, y_pred):\n    return cohen_kappa_score(y_true, y_pred, weights='quadratic')\n\ndef threshold_Rounder(oof_non_rounded, thresholds):\n    return np.where(oof_non_rounded < thresholds[0], 0,\n                    np.where(oof_non_rounded < thresholds[1], 1,\n                             np.where(oof_non_rounded < thresholds[2], 2, 3)))\n\ndef evaluate_predictions(thresholds, y_true, oof_non_rounded):\n    rounded_p = threshold_Rounder(oof_non_rounded, thresholds)\n    return -quadratic_weighted_kappa(y_true, rounded_p)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T05:00:23.533658Z","iopub.execute_input":"2024-12-07T05:00:23.534468Z","iopub.status.idle":"2024-12-07T05:00:23.543774Z","shell.execute_reply.started":"2024-12-07T05:00:23.534423Z","shell.execute_reply":"2024-12-07T05:00:23.542894Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Model Training and Evaluation¶","metadata":{}},{"cell_type":"code","source":"def TrainML(model_class, test_data):\n    X = train.drop(['sii'], axis=1)\n    y = train['sii']\n\n    SKF = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=SEED)\n    \n    train_S = []\n    test_S = []\n    \n    oof_non_rounded = np.zeros(len(y), dtype=float) \n    oof_rounded = np.zeros(len(y), dtype=int) \n    test_preds = np.zeros((len(test_data), n_splits))\n\n    for fold, (train_idx, test_idx) in enumerate(tqdm(SKF.split(X, y), desc=\"Training Folds\", total=n_splits)):\n        X_train, X_val = X.iloc[train_idx], X.iloc[test_idx]\n        y_train, y_val = y.iloc[train_idx], y.iloc[test_idx]\n\n        model = clone(model_class)\n        model.fit(X_train, y_train)\n\n        y_train_pred = model.predict(X_train)\n        y_val_pred = model.predict(X_val)\n\n        oof_non_rounded[test_idx] = y_val_pred\n        y_val_pred_rounded = y_val_pred.round(0).astype(int)\n        oof_rounded[test_idx] = y_val_pred_rounded\n\n        train_kappa = quadratic_weighted_kappa(y_train, y_train_pred.round(0).astype(int))\n        val_kappa = quadratic_weighted_kappa(y_val, y_val_pred_rounded)\n\n        train_S.append(train_kappa)\n        test_S.append(val_kappa)\n        \n        test_preds[:, fold] = model.predict(test_data)\n        \n        print(f\"Fold {fold+1} - Train QWK: {train_kappa:.4f}, Validation QWK: {val_kappa:.4f}\")\n        clear_output(wait=True)\n\n    print(f\"Mean Train QWK --> {np.mean(train_S):.4f}\")\n    print(f\"Mean Validation QWK ---> {np.mean(test_S):.4f}\")\n\n    KappaOPtimizer = minimize(evaluate_predictions,\n                              x0=[0.5, 1.5, 2.5], args=(y, oof_non_rounded), \n                              method='Nelder-Mead')\n    assert KappaOPtimizer.success, \"Optimization did not converge.\"\n    \n    oof_tuned = threshold_Rounder(oof_non_rounded, KappaOPtimizer.x)\n    tKappa = quadratic_weighted_kappa(y, oof_tuned)\n\n    print(f\"----> || Optimized QWK SCORE :: {Fore.CYAN}{Style.BRIGHT} {tKappa:.3f}{Style.RESET_ALL}\")\n\n    tpm = test_preds.mean(axis=1)\n    tpTuned = threshold_Rounder(tpm, KappaOPtimizer.x)\n    \n    submission = pd.DataFrame({\n        'id': sample['id'],\n        'sii': tpTuned\n    })\n\n    return submission","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T05:00:23.545000Z","iopub.execute_input":"2024-12-07T05:00:23.545353Z","iopub.status.idle":"2024-12-07T05:00:23.555744Z","shell.execute_reply.started":"2024-12-07T05:00:23.545314Z","shell.execute_reply":"2024-12-07T05:00:23.554901Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Hyperparameter Tuning","metadata":{}},{"cell_type":"code","source":"# Model parameters for LightGBM\nParams = {\n    'learning_rate': 0.046,\n    'max_depth': 12,\n    'num_leaves': 478,\n    'min_data_in_leaf': 13,\n    'feature_fraction': 0.893,\n    'bagging_fraction': 0.784,\n    'bagging_freq': 4,\n    'lambda_l1': 10,  # Increased from 6.59\n    'lambda_l2': 0.01,  # Increased from 2.68e-06\n    'device': 'gpu'\n\n}\n\n\n# XGBoost parameters\nXGB_Params = {\n    'learning_rate': 0.05,\n    'max_depth': 6,\n    'n_estimators': 200,\n    'subsample': 0.8,\n    'colsample_bytree': 0.8,\n    'reg_alpha': 1,  # Increased from 0.1\n    'reg_lambda': 5,  # Increased from 1\n    'random_state': SEED,\n    'tree_method': 'gpu_hist',\n\n}\n\n\nCatBoost_Params = {\n    'learning_rate': 0.05,\n    'depth': 6,\n    'iterations': 200,\n    'random_seed': SEED,\n    'verbose': 0,\n    'l2_leaf_reg': 10,  # Increase this value\n    'task_type': 'GPU'\n\n}","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T05:00:23.556716Z","iopub.execute_input":"2024-12-07T05:00:23.556990Z","iopub.status.idle":"2024-12-07T05:00:23.570627Z","shell.execute_reply.started":"2024-12-07T05:00:23.556965Z","shell.execute_reply":"2024-12-07T05:00:23.569712Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# New: TabNet\n\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import train_test_split\nfrom pytorch_tabnet.callbacks import Callback\nimport os\nimport torch\nfrom pytorch_tabnet.callbacks import Callback\n\nclass TabNetWrapper(BaseEstimator, RegressorMixin):\n    def __init__(self, **kwargs):\n        self.model = TabNetRegressor(**kwargs)\n        self.kwargs = kwargs\n        self.imputer = SimpleImputer(strategy='median')\n        self.best_model_path = 'best_tabnet_model.pt'\n        \n    def fit(self, X, y):\n        # Handle missing values\n        X_imputed = self.imputer.fit_transform(X)\n        \n        if hasattr(y, 'values'):\n            y = y.values\n            \n        # Create internal validation set\n        X_train, X_valid, y_train, y_valid = train_test_split(\n            X_imputed, \n            y, \n            test_size=0.2,\n            random_state=42\n        )\n        \n        # Train TabNet model\n        history = self.model.fit(\n            X_train=X_train,\n            y_train=y_train.reshape(-1, 1),\n            eval_set=[(X_valid, y_valid.reshape(-1, 1))],\n            eval_name=['valid'],\n            eval_metric=['mse'],\n            max_epochs=500,\n            patience=50,\n            batch_size=1024,\n            virtual_batch_size=128,\n            num_workers=0,\n            drop_last=False,\n            callbacks=[\n                TabNetPretrainedModelCheckpoint(\n                    filepath=self.best_model_path,\n                    monitor='valid_mse',\n                    mode='min',\n                    save_best_only=True,\n                    verbose=True\n                )\n            ]\n        )\n        \n        # Load the best model\n        if os.path.exists(self.best_model_path):\n            self.model.load_model(self.best_model_path)\n            os.remove(self.best_model_path)  # Remove temporary file\n        \n        return self\n    \n    def predict(self, X):\n        X_imputed = self.imputer.transform(X)\n        return self.model.predict(X_imputed).flatten()\n    \n    def __deepcopy__(self, memo):\n        # Add deepcopy support for scikit-learn\n        cls = self.__class__\n        result = cls.__new__(cls)\n        memo[id(self)] = result\n        for k, v in self.__dict__.items():\n            setattr(result, k, deepcopy(v, memo))\n        return result\n\n# TabNet hyperparameters\nTabNet_Params = {\n    'n_d': 64,              # Width of the decision prediction layer\n    'n_a': 64,              # Width of the attention embedding for each step\n    'n_steps': 5,           # Number of steps in the architecture\n    'gamma': 1.5,           # Coefficient for feature selection regularization\n    'n_independent': 2,     # Number of independent GLU layer in each GLU block\n    'n_shared': 2,          # Number of shared GLU layer in each GLU block\n    'lambda_sparse': 1e-4,  # Sparsity regularization\n    'optimizer_fn': torch.optim.Adam,\n    'optimizer_params': dict(lr=2e-2, weight_decay=1e-5),\n    'mask_type': 'entmax',\n    'scheduler_params': dict(mode=\"min\", patience=10, min_lr=1e-5, factor=0.5),\n    'scheduler_fn': torch.optim.lr_scheduler.ReduceLROnPlateau,\n    'verbose': 1,\n    'device_name': 'cuda' if torch.cuda.is_available() else 'cpu'\n}\n\nclass TabNetPretrainedModelCheckpoint(Callback):\n    def __init__(self, filepath, monitor='val_loss', mode='min', \n                 save_best_only=True, verbose=1):\n        super().__init__()  # Initialize parent class\n        self.filepath = filepath\n        self.monitor = monitor\n        self.mode = mode\n        self.save_best_only = save_best_only\n        self.verbose = verbose\n        self.best = float('inf') if mode == 'min' else -float('inf')\n        \n    def on_train_begin(self, logs=None):\n        self.model = self.trainer  # Use trainer itself as model\n        \n    def on_epoch_end(self, epoch, logs=None):\n        logs = logs or {}\n        current = logs.get(self.monitor)\n        if current is None:\n            return\n        \n        # Check if current metric is better than best\n        if (self.mode == 'min' and current < self.best) or \\\n           (self.mode == 'max' and current > self.best):\n            if self.verbose:\n                print(f'\\nEpoch {epoch}: {self.monitor} improved from {self.best:.4f} to {current:.4f}')\n            self.best = current\n            if self.save_best_only:\n                self.model.save_model(self.filepath)  # Save the entire model","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T05:00:23.571891Z","iopub.execute_input":"2024-12-07T05:00:23.572196Z","iopub.status.idle":"2024-12-07T05:00:23.588778Z","shell.execute_reply.started":"2024-12-07T05:00:23.572160Z","shell.execute_reply":"2024-12-07T05:00:23.588142Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Ensemble Learning and Submission Preparation","metadata":{}},{"cell_type":"code","source":"# Create model instances\nLight = LGBMRegressor(**Params, random_state=SEED, verbose=-1, n_estimators=300)\nXGB_Model = XGBRegressor(**XGB_Params)\nCatBoost_Model = CatBoostRegressor(**CatBoost_Params)\nTabNet_Model = TabNetWrapper(**TabNet_Params) # New","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T05:00:23.589731Z","iopub.execute_input":"2024-12-07T05:00:23.590020Z","iopub.status.idle":"2024-12-07T05:00:23.612799Z","shell.execute_reply.started":"2024-12-07T05:00:23.589981Z","shell.execute_reply":"2024-12-07T05:00:23.612170Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"voting_model = VotingRegressor(estimators=[\n    ('lightgbm', Light),\n    ('xgboost', XGB_Model),\n    ('catboost', CatBoost_Model),\n    ('tabnet', TabNet_Model)\n])\n\nSubmission1 = TrainML(voting_model, test)\n\nSubmission1","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T05:00:23.613608Z","iopub.execute_input":"2024-12-07T05:00:23.613846Z","iopub.status.idle":"2024-12-07T05:03:23.190682Z","shell.execute_reply.started":"2024-12-07T05:00:23.613822Z","shell.execute_reply":"2024-12-07T05:03:23.189741Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\ntest = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')\nsample = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/sample_submission.csv')\n\nfeaturesCols = ['Basic_Demos-Enroll_Season', 'Basic_Demos-Age', 'Basic_Demos-Sex',\n                'CGAS-Season', 'CGAS-CGAS_Score', 'Physical-Season', 'Physical-BMI',\n                'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n                'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n                'Fitness_Endurance-Season', 'Fitness_Endurance-Max_Stage',\n                'Fitness_Endurance-Time_Mins', 'Fitness_Endurance-Time_Sec',\n                'FGC-Season', 'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND',\n                'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone', 'FGC-FGC_PU',\n                'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone', 'FGC-FGC_SRR',\n                'FGC-FGC_SRR_Zone', 'FGC-FGC_TL', 'FGC-FGC_TL_Zone', 'BIA-Season',\n                'BIA-BIA_Activity_Level_num', 'BIA-BIA_BMC', 'BIA-BIA_BMI',\n                'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n                'BIA-BIA_FFMI', 'BIA-BIA_FMI', 'BIA-BIA_Fat', 'BIA-BIA_Frame_num',\n                'BIA-BIA_ICW', 'BIA-BIA_LDM', 'BIA-BIA_LST', 'BIA-BIA_SMM',\n                'BIA-BIA_TBW', 'PAQ_A-Season', 'PAQ_A-PAQ_A_Total', 'PAQ_C-Season',\n                'PAQ_C-PAQ_C_Total', 'SDS-Season', 'SDS-SDS_Total_Raw',\n                'SDS-SDS_Total_T', 'PreInt_EduHx-Season',\n                'PreInt_EduHx-computerinternet_hoursday', 'sii']\n\ncat_c = ['Basic_Demos-Enroll_Season', 'CGAS-Season', 'Physical-Season', \n          'Fitness_Endurance-Season', 'FGC-Season', 'BIA-Season', \n          'PAQ_A-Season', 'PAQ_C-Season', 'SDS-Season', 'PreInt_EduHx-Season']\n\ntrain_ts = load_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet\")\ntest_ts = load_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_test.parquet\")\n\ntime_series_cols = train_ts.columns.tolist()\ntime_series_cols.remove(\"id\")\n\ntrain = pd.merge(train, train_ts, how=\"left\", on='id')\ntest = pd.merge(test, test_ts, how=\"left\", on='id')\n\ntrain = train.drop('id', axis=1)\ntest = test.drop('id', axis=1)\n\nfeaturesCols += time_series_cols\n\ntrain = train[featuresCols]\ntrain = train.dropna(subset='sii')\n\ndef update(df):\n    global cat_c\n    for c in cat_c: \n        df[c] = df[c].fillna('Missing')\n        df[c] = df[c].astype('category')\n    return df\n\ntrain = update(train)\ntest = update(test)\n\ndef create_mapping(column, dataset):\n    unique_values = dataset[column].unique()\n    return {value: idx for idx, value in enumerate(unique_values)}\n\nfor col in cat_c:\n    mapping = create_mapping(col, train)\n    mappingTe = create_mapping(col, test)\n    \n    train[col] = train[col].replace(mapping).astype(int)\n    test[col] = test[col].replace(mappingTe).astype(int)\n\ndef quadratic_weighted_kappa(y_true, y_pred):\n    return cohen_kappa_score(y_true, y_pred, weights='quadratic')\n\ndef threshold_Rounder(oof_non_rounded, thresholds):\n    return np.where(oof_non_rounded < thresholds[0], 0,\n                    np.where(oof_non_rounded < thresholds[1], 1,\n                             np.where(oof_non_rounded < thresholds[2], 2, 3)))\n\ndef evaluate_predictions(thresholds, y_true, oof_non_rounded):\n    rounded_p = threshold_Rounder(oof_non_rounded, thresholds)\n    return -quadratic_weighted_kappa(y_true, rounded_p)\n\ndef TrainML(model_class, test_data):\n    X = train.drop(['sii'], axis=1)\n    y = train['sii']\n\n    SKF = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=SEED)\n    \n    train_S = []\n    test_S = []\n    \n    oof_non_rounded = np.zeros(len(y), dtype=float) \n    oof_rounded = np.zeros(len(y), dtype=int) \n    test_preds = np.zeros((len(test_data), n_splits))\n\n    for fold, (train_idx, test_idx) in enumerate(tqdm(SKF.split(X, y), desc=\"Training Folds\", total=n_splits)):\n        X_train, X_val = X.iloc[train_idx], X.iloc[test_idx]\n        y_train, y_val = y.iloc[train_idx], y.iloc[test_idx]\n\n        model = clone(model_class)\n        model.fit(X_train, y_train)\n\n        y_train_pred = model.predict(X_train)\n        y_val_pred = model.predict(X_val)\n\n        oof_non_rounded[test_idx] = y_val_pred\n        y_val_pred_rounded = y_val_pred.round(0).astype(int)\n        oof_rounded[test_idx] = y_val_pred_rounded\n\n        train_kappa = quadratic_weighted_kappa(y_train, y_train_pred.round(0).astype(int))\n        val_kappa = quadratic_weighted_kappa(y_val, y_val_pred_rounded)\n\n        train_S.append(train_kappa)\n        test_S.append(val_kappa)\n        \n        test_preds[:, fold] = model.predict(test_data)\n        \n        print(f\"Fold {fold+1} - Train QWK: {train_kappa:.4f}, Validation QWK: {val_kappa:.4f}\")\n        clear_output(wait=True)\n\n    print(f\"Mean Train QWK --> {np.mean(train_S):.4f}\")\n    print(f\"Mean Validation QWK ---> {np.mean(test_S):.4f}\")\n\n    KappaOPtimizer = minimize(evaluate_predictions,\n                              x0=[0.5, 1.5, 2.5], args=(y, oof_non_rounded), \n                              method='Nelder-Mead')\n    assert KappaOPtimizer.success, \"Optimization did not converge.\"\n    \n    oof_tuned = threshold_Rounder(oof_non_rounded, KappaOPtimizer.x)\n    tKappa = quadratic_weighted_kappa(y, oof_tuned)\n\n    print(f\"----> || Optimized QWK SCORE :: {Fore.CYAN}{Style.BRIGHT} {tKappa:.3f}{Style.RESET_ALL}\")\n\n    tpm = test_preds.mean(axis=1)\n    tp_rounded = threshold_Rounder(tpm, KappaOPtimizer.x)\n\n    return tp_rounded\n\nimputer = SimpleImputer(strategy='median')\n\nensemble = VotingRegressor(estimators=[\n    ('lgb', Pipeline(steps=[('imputer', imputer), ('regressor', LGBMRegressor(random_state=SEED))])),\n    ('xgb', Pipeline(steps=[('imputer', imputer), ('regressor', XGBRegressor(random_state=SEED))])),\n    ('cat', Pipeline(steps=[('imputer', imputer), ('regressor', CatBoostRegressor(random_state=SEED, silent=True))])),\n    ('rf', Pipeline(steps=[('imputer', imputer), ('regressor', RandomForestRegressor(random_state=SEED))])),\n    ('gb', Pipeline(steps=[('imputer', imputer), ('regressor', GradientBoostingRegressor(random_state=SEED))])),\n    ('tabnet', Pipeline(steps=[('imputer', imputer), ('regressor', TabNetWrapper(**TabNet_Params))]))  # New:TabNet\n])\n\nSubmission2 = TrainML(ensemble, test)\n\nSubmission2 = TrainML(ensemble, test)\nSubmission2 = pd.DataFrame({\n    'id': sample['id'],\n    'sii': Submission2\n})\n\nSubmission2","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T05:03:23.192115Z","iopub.execute_input":"2024-12-07T05:03:23.192403Z","iopub.status.idle":"2024-12-07T05:11:45.696468Z","shell.execute_reply.started":"2024-12-07T05:03:23.192374Z","shell.execute_reply":"2024-12-07T05:11:45.695753Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}