{"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":"gpu","dataSources":[{"sourceId":81933,"databundleVersionId":9643020,"sourceType":"competition"},{"sourceId":7453542,"sourceType":"datasetVersion","datasetId":921302},{"sourceId":203489307,"sourceType":"kernelVersion"}],"dockerImageVersionId":30787,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"
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šŸ“Š BrisT1D Blood Glucose Prediction

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Introduction

\r\n \r\nā€‹\r\n
","metadata":{}},{"cell_type":"code","source":"!pip -q install /kaggle/input/pytorchtabnet/pytorch_tabnet-4.1.0-py3-none-any.whl\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from pytorch_tabnet.tab_model import TabNetRegressor\nimport torch\n\nimport 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":"2024-11-18T14:44:05.110453Z","iopub.execute_input":"2024-11-18T14:44:05.111272Z","iopub.status.idle":"2024-11-18T14:44:05.121185Z","shell.execute_reply.started":"2024-11-18T14:44:05.111231Z","shell.execute_reply":"2024-11-18T14:44:05.120265Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"target_labels = ['None', 'Mild', 'Moderate', 'Severe']","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"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},"outputs":[],"execution_count":null},{"cell_type":"code","source":"supervised_usable = (\n train\n .filter(pl.col('sii').is_not_null())\n)\n\nmissing_count = (\n supervised_usable\n .null_count()\n .transpose(include_header=True,\n header_name='feature',\n column_names=['null_count'])\n .sort('null_count', descending=True)\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},"outputs":[],"execution_count":null},{"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},"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},"outputs":[],"execution_count":null},{"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},"outputs":[],"execution_count":null},{"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},"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},"outputs":[],"execution_count":null},{"cell_type":"code","source":"SEED = 42\nn_splits = 5","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"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},"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},"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},"outputs":[],"execution_count":null},{"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},"outputs":[],"execution_count":null},{"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},"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},"outputs":[],"execution_count":null},{"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},"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},"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\ndef 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 \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 = ['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\nfeaturesCols += time_series_cols\n\ntrain = train[featuresCols]\ntrain = train.dropna(subset='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\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 tpTuned = threshold_Rounder(tpm, KappaOPtimizer.x)\n \n submission = pd.DataFrame({\n 'id': sample['id'],\n 'sii': tpTuned\n })\n\n return submission\n\n# 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}\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}\n\n\nCatBoost_Params = {\n 'learning_rate': 0.05,\n 'depth': 6,\n 'iterations': 200,\n 'random_seed': SEED,\n 'cat_features': cat_c,\n 'verbose': 0,\n 'l2_leaf_reg': 10 # Increase this value\n}\n\n# 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:TAbNet\n\n# Combine models using Voting Regressor\nvoting_model = VotingRegressor(estimators=[\n ('lightgbm', Light),\n ('xgboost', XGB_Model),\n ('catboost', CatBoost_Model),\n ('tabnet', TabNet_Model) # New:TabNet\n])\n\n# Train the ensemble model\nSubmission2 = TrainML(voting_model, test)\n\n# Save submission\n#Submission2.to_csv('submission.csv', index=False)\nSubmission2","metadata":{"trusted":true},"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\nSubmission3 = TrainML(ensemble, test)\n\nSubmission3 = TrainML(ensemble, test)\nSubmission3 = pd.DataFrame({\n 'id': sample['id'],\n 'sii': Submission3\n})\n\nSubmission3","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sub1 = Submission1\nsub2 = Submission2\nsub3 = Submission3\n\nsub1 = sub1.sort_values(by='id').reset_index(drop=True)\nsub2 = sub2.sort_values(by='id').reset_index(drop=True)\nsub3 = sub3.sort_values(by='id').reset_index(drop=True)\n\ncombined = pd.DataFrame({\n 'id': sub1['id'],\n 'sii_1': sub1['sii'],\n 'sii_2': sub2['sii'],\n 'sii_3': sub3['sii']\n})\n\ndef majority_vote(row):\n return row.mode()[0]\n\ncombined['final_sii'] = combined[['sii_1', 'sii_2', 'sii_3']].apply(majority_vote, axis=1)\n\nfinal_submission = combined[['id', 'final_sii']].rename(columns={'final_sii': 'sii'})\n\nfinal_submission.to_csv('submission.csv', index=False)\n\nprint(\"Majority voting completed and saved to 'Final_Submission.csv'\")","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"final_submission","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"
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