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)","metadata":{"id":"T5KGWDNbKA2G"}},{"cell_type":"markdown","source":"**Hello I am a student from Korea**\n\n*The goal of this competition is to predict sii, the dependent variable provided by this competition. To predict sii, the missing values ​​in train_df are filled using parquet data containing the participants' activity information, and then sii is finally predicted.*","metadata":{"id":"g98KjSkQJxnU"}},{"cell_type":"markdown","source":"# Index","metadata":{"id":"R_iAFDU6qCPQ"}},{"cell_type":"markdown","source":"> 1. Load data\n\n> 2. Train data columns analysis\n\n> 3. Check Null data\n\n> 4. Demographics\n\n> 5. Correlation analysis train_df and Actigrapy\n\n> 6. Autoencoder for data compression and feature extraction\n\n> 7. Feature_engineering\n\n> 8. Null value handling and data preparation\n\n> 9. Evaluation method\n\n> 10. Train plan\n\n> 11. Parameter settings\n\n> 12. Ensemble model using StackingRegressor -> VotingRegressor\n\n> 13. Submission Separation\n\n> 14. Submission1\n\n> 15. Submission2\n\n> 16. Submission3\n\n> 17. Final\n\n\n\n\n\n\n\n\n\n\n","metadata":{"id":"8XCc6GQuqKi5"}},{"cell_type":"code","source":"!pip -q install /kaggle/input/pytorchtabnet/pytorch_tabnet-4.1.0-py3-none-any.whl","metadata":{"id":"i2zAoDDs67E9","outputId":"f213bb7d-d730-4042-b4d8-d52bdf45890c","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:24:33.361006Z","iopub.execute_input":"2024-12-09T03:24:33.361715Z","iopub.status.idle":"2024-12-09T03:25:14.965858Z","shell.execute_reply.started":"2024-12-09T03:24:33.361680Z","shell.execute_reply":"2024-12-09T03:25:14.964807Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip install colorama","metadata":{"id":"bDQxNY8L7LDX","outputId":"7bb3aacc-e5ab-4758-8bc9-a46c296f24a2","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:25:14.968105Z","iopub.execute_input":"2024-12-09T03:25:14.968847Z","iopub.status.idle":"2024-12-09T03:25:55.235030Z","shell.execute_reply.started":"2024-12-09T03:25:14.968804Z","shell.execute_reply":"2024-12-09T03:25:55.233960Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip install catboost","metadata":{"id":"Ffz8e6MX7w6c","outputId":"d361a827-fd50-4c35-a4c1-76b43f2efd8e","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:25:55.236536Z","iopub.execute_input":"2024-12-09T03:25:55.236911Z","iopub.status.idle":"2024-12-09T03:26:35.446885Z","shell.execute_reply.started":"2024-12-09T03:25:55.236870Z","shell.execute_reply":"2024-12-09T03:26:35.445974Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport os\nimport re\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\nimport plotly.subplots as sp\nimport plotly.express as px\nfrom concurrent.futures import ThreadPoolExecutor\nfrom colorama import Fore, Style\nfrom IPython.display import clear_output\nfrom IPython.display import display\nimport warnings\nwarnings.filterwarnings('ignore')\npd.options.display.max_columns = None","metadata":{"id":"opWVPlcx8lnj","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:26:35.448969Z","iopub.execute_input":"2024-12-09T03:26:35.449515Z","iopub.status.idle":"2024-12-09T03:26:37.227118Z","shell.execute_reply.started":"2024-12-09T03:26:35.449456Z","shell.execute_reply":"2024-12-09T03:26:37.226477Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.base import clone, BaseEstimator, RegressorMixin\nfrom sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\nfrom sklearn.ensemble import StackingRegressor\nfrom sklearn.linear_model import Ridge\nfrom sklearn.experimental import enable_iterative_imputer\nfrom sklearn.impute import IterativeImputer\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, roc_curve, auc\nfrom sklearn.metrics import cohen_kappa_score\nfrom sklearn.model_selection import StratifiedKFold\nfrom scipy.optimize import minimize\nfrom sklearn.ensemble import VotingRegressor, RandomForestRegressor, GradientBoostingRegressor, HistGradientBoostingRegressor, ExtraTreesRegressor\nfrom sklearn.impute import SimpleImputer, KNNImputer\nfrom sklearn.pipeline import Pipeline\nfrom lightgbm import LGBMRegressor\nfrom xgboost import XGBRegressor\nfrom catboost import CatBoostRegressor\nfrom sklearn.model_selection import GridSearchCV","metadata":{"id":"UsLDId4N8v8J","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:26:37.228428Z","iopub.execute_input":"2024-12-09T03:26:37.229034Z","iopub.status.idle":"2024-12-09T03:26:40.535116Z","shell.execute_reply.started":"2024-12-09T03:26:37.228985Z","shell.execute_reply":"2024-12-09T03:26:40.534450Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from pytorch_tabnet.tab_model import TabNetRegressor\nfrom pytorch_tabnet.callbacks import Callback\n\nfrom keras.models import Model\nfrom keras.layers import Input, Dense\nfrom keras.optimizers import Adam\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport pytorch_tabnet","metadata":{"id":"Wb1U6G0J9Bmv","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:26:40.536103Z","iopub.execute_input":"2024-12-09T03:26:40.536696Z","iopub.status.idle":"2024-12-09T03:26:56.117544Z","shell.execute_reply.started":"2024-12-09T03:26:40.536667Z","shell.execute_reply":"2024-12-09T03:26:56.116510Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":" # 1. Load data","metadata":{"id":"QOlIkgOl_0ZL"}},{"cell_type":"code","source":"target_labels = ['None', 'Mild', 'Moderate', 'Severe']","metadata":{"id":"LZe49LA06fUt","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:26:56.118828Z","iopub.execute_input":"2024-12-09T03:26:56.119844Z","iopub.status.idle":"2024-12-09T03:26:56.124258Z","shell.execute_reply.started":"2024-12-09T03:26:56.119798Z","shell.execute_reply":"2024-12-09T03:26:56.123388Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\ntest_df = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')\ndata_dict = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/data_dictionary.csv')","metadata":{"id":"Vc2BWyG8_Poo","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:26:56.126495Z","iopub.execute_input":"2024-12-09T03:26:56.126779Z","iopub.status.idle":"2024-12-09T03:26:56.211948Z","shell.execute_reply.started":"2024-12-09T03:26:56.126752Z","shell.execute_reply":"2024-12-09T03:26:56.211153Z"}},"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)","metadata":{"id":"7mN1Sh356fUt","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:26:56.213186Z","iopub.execute_input":"2024-12-09T03:26:56.213919Z","iopub.status.idle":"2024-12-09T03:26:56.397492Z","shell.execute_reply.started":"2024-12-09T03:26:56.213873Z","shell.execute_reply":"2024-12-09T03:26:56.396563Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df","metadata":{"id":"ka6Pxp65_DNE","outputId":"9868835b-861a-445e-ba4b-2679efe58be8","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:26:56.398681Z","iopub.execute_input":"2024-12-09T03:26:56.399036Z","iopub.status.idle":"2024-12-09T03:26:56.496417Z","shell.execute_reply.started":"2024-12-09T03:26:56.398995Z","shell.execute_reply":"2024-12-09T03:26:56.495454Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data_dict","metadata":{"id":"ayajjqDgAW5Y","outputId":"eca4037c-49a8-40ae-98c2-8665dd8d69a4","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:26:56.497630Z","iopub.execute_input":"2024-12-09T03:26:56.497927Z","iopub.status.idle":"2024-12-09T03:26:56.509904Z","shell.execute_reply.started":"2024-12-09T03:26:56.497898Z","shell.execute_reply":"2024-12-09T03:26:56.509025Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":" # 2. Train data columns analysis","metadata":{"id":"XDIZMsiQrA9H"}},{"cell_type":"code","source":"data_dict['Instrument'].unique()","metadata":{"id":"euO8EBZHB5ft","outputId":"fbdc7fc4-2c77-4f61-9248-c2ab504bfcc4","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"Basic_data = data_dict[data_dict['Instrument'] == 'Demographics']\nBasic_data","metadata":{"id":"OvRORd4BAgm0","outputId":"e87f2d06-ab9e-4c8a-a887-104814a90d2f","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#Children's Global Assessment Scale\nCGAS_data = data_dict[data_dict['Instrument'] == \"Children's Global Assessment Scale\"]\nCGAS_data","metadata":{"id":"OBSq4hK2BdqR","outputId":"15249568-5bf7-4761-9313-ccd2742a5050","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#Physical Measures\nPhysical_data = data_dict[data_dict['Instrument'] == \"Physical Measures\"]\nPhysical_data","metadata":{"id":"AsH-n_ImCDPn","outputId":"03010c69-8cf4-4404-92d4-9de66dd124e0","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#FitnessGram Vitals and Treadmill\nFitness_data = data_dict[data_dict['Instrument'] == \"FitnessGram Vitals and Treadmill\"]\nFitness_data","metadata":{"id":"5KK1zWUSCFZN","outputId":"a9ea970e-6fd5-43d4-de0e-cc940879e7b5","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#FitnessGram Child\nFGC_data = data_dict[data_dict['Instrument'] == \"FitnessGram Child\"]\nFGC_data","metadata":{"id":"5lDDYGV5CQrm","outputId":"88047638-c938-41e3-f9d9-1ab7e98ae2d4","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#Bio-electric Impedance Analysis\nBIA_data = data_dict[data_dict['Instrument'] == \"Bio-electric Impedance Analysis\"]\nBIA_data","metadata":{"id":"w4ETREGWHhRj","outputId":"0cd64bb6-077b-44bf-8a6a-1c6996576067","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#Physical Activity Questionnaire (Adolescents)\nPAQA_data = data_dict[data_dict['Instrument'] == \"Physical Activity Questionnaire (Adolescents)\"]\nPAQA_data","metadata":{"id":"nuYvGhVIH4tT","outputId":"54b6e36b-ffe5-4974-b5a3-22201bbeb7ee","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#Physical Activity Questionnaire (Children)\nPAQC = data_dict[data_dict['Instrument'] == \"Physical Activity Questionnaire (Children)\"]\nPAQC","metadata":{"id":"RSCUb5ezIu7D","outputId":"5241c47c-c34c-49b6-91d5-80e420795a09","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#Parent-Child Internet Addiction Test\nPCIAT_data = data_dict[data_dict['Instrument'] == \"Parent-Child Internet Addiction Test\"]\nPCIAT_data","metadata":{"id":"UyX9_8IjHMOI","outputId":"ba0161ab-129c-409e-e0aa-2760b41de403","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#Sleep Disturbance Scale\nSDS_data = data_dict[data_dict['Instrument'] == \"Sleep Disturbance Scale\"]\nSDS_data","metadata":{"id":"9iJ0wGQGI-Oz","outputId":"3a258388-59de-4776-c6a2-3f395bbd75a3","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#Internet Use\nPreInt_data = data_dict[data_dict['Instrument'] == \"Internet Use\"]\nPreInt_data","metadata":{"id":"TGRJRdaNJQGE","outputId":"c9927c50-d12c-4f15-aba8-e40fca205885","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data_dict[data_dict['Field'].str.contains('PreInt_EduHx-computerinternet_hoursday')]['Value Labels'].iloc[0]","metadata":{"id":"KTN4vkuzJfka","outputId":"326f6615-d862-4af2-e652-fe61802a31f3","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"\n# 3. Check Null data\n\n\n","metadata":{"id":"jYRsUVKZKnoN"}},{"cell_type":"code","source":"for col in train_df.columns:\n    msg = 'column: {:>10}\\t Percent of NaN value: {:.2f}%'.format(col, 100 * (train_df[col].isnull().sum() / train_df[col].shape[0]))\n    print(msg)","metadata":{"id":"o9YjWQHbKrRM","outputId":"1167fe6c-c42a-4573-bdea-d9257065772d","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for col in test_df.columns:\n    msg = 'column: {:>10}\\t Percent of NaN value: {:.2f}%'.format(col, 100 * (test_df[col].isnull().sum() / test_df[col].shape[0]))\n    print(msg)","metadata":{"id":"k7BqI_eIK1eo","outputId":"ae5fee52-e51d-4ff0-824a-897c110506c6","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"supervised_usable = train_df  \n\nmissing_count = supervised_usable.isnull().sum().to_frame('null_count')\nmissing_count['feature'] = missing_count.index\nmissing_count['null_ratio'] = missing_count['null_count'] / len(supervised_usable)\nmissing_count = missing_count.sort_values('null_count', ascending=False)\n\nplt.figure(figsize=(12, 6))\nsns.barplot(x='feature', y='null_count', data=missing_count, palette='viridis')\nplt.xticks(rotation=90)\nplt.title('Total Null Values in train_df')\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"What about null values ​​in data with no missing values ​​in Sii?","metadata":{"id":"PUALpcPysl5Y"}},{"cell_type":"code","source":"supervised_usable = train_df[train_df['sii'].notnull()]\n\nmissing_count = supervised_usable.isnull().sum().to_frame('null_count')\nmissing_count['feature'] = missing_count.index\nmissing_count['null_ratio'] = missing_count['null_count'] / len(supervised_usable)\nmissing_count = missing_count.sort_values('null_count', ascending=False)\n\nplt.figure(figsize=(12, 6))\nsns.barplot(x='feature', y='null_count', data=missing_count, palette='viridis')\nplt.xticks(rotation=90)\nplt.title('Train_df without null values ​​in sii')\nplt.tight_layout()\nplt.show()","metadata":{"id":"FQpieEwC6fUu","outputId":"c92e18cd-0aad-4ec3-8966-a3969e819aa1","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Cleaner than the entire data","metadata":{"id":"ZfqCE3yPs1FF"}},{"cell_type":"code","source":"pciat_min_max = train_df.groupby('sii')['PCIAT-PCIAT_Total'].describe()[['min', 'max']]\n\nif train_df['PCIAT-PCIAT_Total'].dtype != 'int64' and train_df['PCIAT-PCIAT_Total'].dtype != 'float64':\n    train_df['PCIAT-PCIAT_Total'] = pd.to_numeric(train_df['PCIAT-PCIAT_Total'], errors='coerce')\n    pciat_min_max = train_df.groupby('sii')['PCIAT-PCIAT_Total'].describe()[['min', 'max']]\n\npciat_min_max = pciat_min_max.rename(\n    columns={'min': 'Minimum PCIAT_total Score', 'max': 'Maximum PCIAT_total Score'}\n)\n\npciat_min_max","metadata":{"id":"7YbxJK9O6fUu","outputId":"57df3971-bbfc-4892-8f08-f6d44228727d","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_cols = set(train_df.columns)\ntest_cols = set(test_df.columns)\ncolumns_not_in_test = sorted(list(train_cols - test_cols))\ndata_dict[data_dict['Field'].isin(columns_not_in_test)]","metadata":{"id":"tEp-__bFOMws","outputId":"1e4904ab-f5eb-4f10-a777-e2d04e4c891c","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 4. Demographics\n\n","metadata":{"id":"bjOwdFja6fUu"}},{"cell_type":"markdown","source":"I will divide the age into groups and visualize the relationship with Sii.","metadata":{"id":"WC7EPYxd6fUu"}},{"cell_type":"code","source":"season_probabilities = train_df['Basic_Demos-Enroll_Season'].value_counts(normalize=True)\nseason_probabilities","metadata":{"id":"GWKeoX3h6fUu","outputId":"554db6ae-2f31-4029-d34e-2276e2d186b0","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df['Age_Group'] = pd.cut(\n    train_df['Basic_Demos-Age'],\n    bins=[4, 12, 18, 22],\n    labels=['Children (5-12)', 'Adolescents (13-18)', 'Adults (19-22)']\n)","metadata":{"id":"5ANMK4-fPX5j","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sns.barplot(x='Age_Group', y='sii', data=train_df, palette='rainbow', hue='Basic_Demos-Sex')","metadata":{"id":"Yi2C0VYYPiBd","outputId":"f5222225-424f-4ffa-ee5c-b215be8dc23a","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"Age_Group_counts = train_df['Age_Group'].unique()\nAge_Group_counts","metadata":{"id":"x3lbgaCNP1Vg","outputId":"bf0a2cff-930d-424e-a367-c1287de395d7","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"f, ax = plt.subplots(1, len(Age_Group_counts), figsize=(14, 6))\n\nfor i, age_group in enumerate(Age_Group_counts):\n  age_group_data = train_df[train_df['Age_Group'] == age_group]\n  sii_counts = age_group_data['sii'].value_counts()\n  ax[i].pie(sii_counts, labels=sii_counts.index, autopct='%1.1f%%', startangle=90, colors=plt.cm.Set3.colors)\n  ax[i].set_title(f'SII distribution for {age_group}')","metadata":{"id":"UuAvloPbPyRr","outputId":"6ad5ecee-08d3-474f-9963-36634dfff63a","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"The Adolescents group appears to have the highest sii","metadata":{"id":"rSbcGFRRQNB0"}},{"cell_type":"markdown","source":"# 5. Correlation analysis train_df and Actigrapy\n\n","metadata":{"id":"O7iq62X96fUv"}},{"cell_type":"code","source":"selected_columns = [\n    'PCIAT-PCIAT_Total', 'Basic_Demos-Age', 'Basic_Demos-Sex', 'CGAS-CGAS_Score', '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',\n    'Fitness_Endurance-Time_Sec', 'FGC-FGC_CU', 'FGC-FGC_GSND', 'FGC-FGC_GSD',\n    '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',\n    'BIA-BIA_FFM', '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', 'BIA-BIA_TBW'\n]\n\nselected_df = train_df[train_df['sii'].notnull()][selected_columns]\n\ncorr_matrix = selected_df.corr()\n\npciat_corr = corr_matrix[['PCIAT-PCIAT_Total']]\ndisplay(pciat_corr.style.background_gradient(cmap='coolwarm'))","metadata":{"id":"5AEiq8Tj6fUv","outputId":"0352bbc6-4ceb-44fa-82be-c9a6a618ceca","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"filtered_corr = pciat_corr[abs(pciat_corr['PCIAT-PCIAT_Total']) > 0.1]\nfiltered_corr = filtered_corr.drop('PCIAT-PCIAT_Total')\nfiltered_corr = filtered_corr.sort_values(by=['PCIAT-PCIAT_Total'], key=abs, ascending=False)\n\nplt.figure(figsize=(10, 6))\nsns.barplot(x=filtered_corr.index, y='PCIAT-PCIAT_Total', data=filtered_corr, palette='rainbow')\nplt.xticks(rotation=90)\nplt.title('Correlation with PCIAT-PCIAT_Total (Absolute Value > 0.1)')\nplt.ylabel('Correlation')\nplt.tight_layout()\nplt.show()","metadata":{"id":"-35UTMpSVvr1","outputId":"967b2a16-5906-4fd3-c01d-da4da419ddbb","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> Actigraphy (parquet data)\n\n","metadata":{"id":"JxrBuo9v6fUv"}},{"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":{"id":"WQFKjTTM6fUv","outputId":"d417fe56-4b31-44b3-e8ab-eb1bb41046e3","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"actigraphy2 = pl.read_parquet('/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=02cebf33/part-0.parquet')\nactigraphy2","metadata":{"id":"1G1MKeyfGFGR","outputId":"2a07dfcb-e2fa-4d76-e335-4fa0bbc3c997","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"actigraphy['non-wear_flag'].value_counts()","metadata":{"id":"dJue8yQCWO9_","outputId":"b9fe1ac3-b33d-4e24-80ad-2ae5540d4e68","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"actigraphy2['non-wear_flag'].value_counts()","metadata":{"id":"W9ZvWFOcGQLn","outputId":"ccf48014-433d-4914-aca9-e279a2c96bed","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"The value of non-wear_flag displayed as a decimal means the probability of wearing the machine at that time.","metadata":{"id":"M40jHEyeG1Gg"}},{"cell_type":"code","source":"actigraphy = actigraphy.with_columns((pl.col('time_of_day') / 3.6e12).alias('time_of_day_hours'))\n\nax = sns.histplot(x='time_of_day_hours', data=actigraphy, bins=24)\nax.set_xlabel('Time of Day (hours)')\nplt.xticks(range(25))\n\nplt.show()","metadata":{"id":"iLjkbqma_GNm","outputId":"ad0c535f-921b-4aa5-e76f-714a6d110f2e","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"\nIt can be seen that the more it is located in the middle, the more activity it has. This is because the left and right sides appear to be in a sleeping state.","metadata":{"id":"lg09rJa0t0at"}},{"cell_type":"code","source":"actigraphy['relative_date_PCIAT'].value_counts()","metadata":{"id":"xma7k2GKUdtY","outputId":"09b70cc3-6f5e-44b7-a964-70d97d86526f","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"relative_date_PCIAT indicates how many days have passed since the PCIAT survey was conducted, and Time_of_day indicates the time when the data was recorded.\nAdd these two and divide by 86400e9, which converts one day to nanoseconds, and we will be able to find the exact time.","metadata":{"id":"sGpGLvGDPFCQ"}},{"cell_type":"code","source":"plt.scatter(actigraphy['relative_date_PCIAT'], actigraphy['battery_voltage'],color='red', label='Battery Voltage (mV)', s=1)","metadata":{"id":"KzO-E4xBQboQ","outputId":"55e64346-5f5d-4be8-8c35-bcbab4ff5f4b","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Looking at the record of the battery voltage, the data was recorded for about 50 days after the PCIAT test was performed.","metadata":{"id":"Xt6C_zKQSTKk"}},{"cell_type":"code","source":"def plot_parquet_data_hour(train_df, id, col='non-wear_flag',\n                     label='Worn (0 = Worn, 1 = Not Worn)',\n                     title='Non-Wear Flag',\n                     x_col='time_of_day_hours',\n                     x_label='Time of Day (hours)'):\n\n    parquet_path = f'/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id={id}/part-0.parquet'\n\n    df = pl.read_parquet(parquet_path).to_pandas()\n    df['day_time'] = df['relative_date_PCIAT'] + df['time_of_day'] / 86400e9\n    df['time_of_day_hours'] = df['time_of_day'] / 3.6e12\n\n    plt.figure(figsize=(18, 12))\n\n    plt.subplot(7, 1, 1)\n    plt.scatter(df[x_col], df['X'], label='X')\n    plt.title('X')\n    plt.ylabel('Movement X')\n\n    plt.subplot(7, 1, 2)\n    plt.scatter(df[x_col], df['Y'], label='Y')\n    plt.title('Y')\n    plt.ylabel('Movement Y')\n\n    plt.subplot(7, 1, 3)\n    plt.scatter(df[x_col], df['Z'], label='Z')\n    plt.title('Z')\n    plt.ylabel('Movement Z')\n\n    plt.subplot(7, 1, 4)\n    plt.scatter(df[x_col], df['enmo'], label='ENMO')\n    plt.title('ENMO (Euclidean Norm Minus One)')\n    plt.ylabel('Movement Intensity')\n\n    plt.subplot(7, 1, 5)\n    plt.scatter(df[x_col], df['anglez'], label='Angle Z')\n    plt.title('Angle Z')\n    plt.ylabel('Angle (degrees)')\n\n    plt.subplot(7, 1, 6)\n    plt.scatter(df[x_col], df['light'], label='Light')\n    plt.title('Ambient Light')\n    plt.ylabel('Light (lux)')\n\n    plt.subplot(7, 1, 7)\n    plt.scatter(df[x_col], df[col], label=col)\n    plt.title(f'{title}')\n    plt.ylabel(f'{label}')\n    plt.xlabel(f'{x_label}')\n\n    plt.tight_layout()\n    plt.show()\n\nplot_parquet_data_hour(train_df, '0417c91e')","metadata":{"id":"JYOK6E6bL_yy","outputId":"01fbb9a6-840e-4c23-c8da-ebc03b2d684e","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plot_parquet_data_hour(train_df, '02cebf33')","metadata":{"id":"HFtOYojdHqXY","outputId":"842354e0-929d-4b65-a099-83613df6ae02","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"After about 30 days, the number of dots begins to decrease.","metadata":{"id":"XkZKc1O6PgDH"}},{"cell_type":"code","source":"target_id = '0417c91e'\nmatching_row = train_df[train_df['id'] == target_id]\nmatching_row","metadata":{"id":"WbWAj0iL9qkP","outputId":"284eee33-1f62-4020-ef84-b86ffde53ba3","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"target_id = '02cebf33'\nmatching_row = train_df[train_df['id'] == target_id]\nmatching_row","metadata":{"id":"JS5mo_UJHBm7","outputId":"514f5baf-022a-4e41-f5a1-b928daf374ef","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"\nLet’s also check the data for men.","metadata":{"id":"dPCcmYDxHOyL"}},{"cell_type":"code","source":"train_df[train_df['Basic_Demos-Sex'] == 0]","metadata":{"id":"NSLk0g4dHyNY","outputId":"1f18f753-f8ea-43e0-8701-3b339fef6c06","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"target_ids = train_df[train_df['Basic_Demos-Sex'] == 0]['id'].tolist()\n\nparquet_dir = '/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet'\n\nparquet_ids = []\nfor filename in os.listdir(parquet_dir):\n  if filename.startswith('id='):\n    parquet_ids.append(filename.split('=')[1])","metadata":{"id":"RF98pXfnHPdi","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"common_ids = list(set(target_ids) & set(parquet_ids))\ncommon_ids","metadata":{"id":"xJVWlNO9IkAF","outputId":"c83fef32-13b0-4fc9-b0f9-fda1011411d6","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"filtered_train_df = train_df[train_df['id'].isin(common_ids)]\nfiltered_train_df","metadata":{"id":"xAfuOrgYIlmV","outputId":"1921cfa5-56d3-450a-dbff-05527233f4c1","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plot_parquet_data_hour(train_df, '01085eb3')","metadata":{"id":"U5OXwo2ZI_UE","outputId":"8db86224-70fe-459b-e4eb-a165da41c707","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"target_id = '01085eb3'\nmatching_row = train_df[train_df['id'] == target_id]\nmatching_row","metadata":{"id":"YrAQXlC0JHTV","outputId":"1178e3a7-241d-47f0-e252-3b9529e98c77","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"path_01085eb3 = '/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=01085eb3/part-0.parquet'\ndf = pl.read_parquet(path_01085eb3).to_pandas()\naverage_enmo = df['enmo'].mean()\nprint(average_enmo)","metadata":{"id":"tMQwWvt0Ksda","outputId":"af6e5f52-be46-4970-d0c3-95bc817c187c","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"path_02cebf33 = '/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=02cebf33/part-0.parquet'\ndf = pl.read_parquet(path_02cebf33).to_pandas()\naverage_enmo = df['enmo'].mean()\nprint(average_enmo)","metadata":{"id":"RtvXUlnxKuKM","outputId":"abade677-c0a9-4f95-89c9-98058fe46423","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"path_0417c91e = '/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=0417c91e/part-0.parquet'\ndf = pl.read_parquet(path_0417c91e).to_pandas()\naverage_enmo = df['enmo'].mean()\nprint(average_enmo)","metadata":{"id":"OoxHr-drLGFf","outputId":"f19609b3-b19c-4da1-d88b-bcb1fdc4560c","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def plot_parquet_data_hour_multi_enmo(train_df, participant_ids):\n    plt.figure(figsize=(18, 6))  # Adjust figure size as needed\n\n    for participant_id in participant_ids:\n        parquet_path = f'/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id={participant_id}/part-0.parquet'\n        df = pl.read_parquet(parquet_path).to_pandas()\n        df['time_of_day_hours'] = df['time_of_day'] / 3.6e12\n\n        plt.plot(df['time_of_day_hours'], df['enmo'], label=participant_id)\n\n    plt.title('ENMO Comparison')\n    plt.xlabel('Time of Day (hours)')\n    plt.ylabel('ENMO')\n    plt.legend()\n    plt.tight_layout()\n    plt.show()","metadata":{"id":"1s5AqJSxMVm-","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"participant_ids = ['01085eb3', '02cebf33', '0417c91e']\nplot_parquet_data_hour_multi_enmo(train_df, participant_ids)","metadata":{"id":"cTaRhjSlMW_B","outputId":"abb11696-3bed-4b29-a607-fc572f2d4ce7","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"1. id=0417c91e enmo : 0.058441196\n2. id=02cebf33 enmo : 0.014279276\n3. id=01085eb3 enmo : 0.03294586\n\n\n\n","metadata":{"id":"7DpqJtuxMeB8"}},{"cell_type":"code","source":"train_df.columns","metadata":{"id":"wtk6z0RIONYU","outputId":"5df912c4-deca-4e1b-a95d-7ef7948dbf85","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"enmo_values = {'0417c91e': 0.058441196, '02cebf33': 0.014279276, '01085eb3': 0.03294586}\n\nsubset_train_df = train_df[train_df['id'].isin(enmo_values.keys())]\nsubset_train_df['enmo'] = subset_train_df['id'].map(enmo_values)\n\ncorrelations = subset_train_df[['Basic_Demos-Age', 'Basic_Demos-Sex',\n       'CGAS-CGAS_Score', 'Physical-BMI', 'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n       'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n       'Fitness_Endurance-Max_Stage','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', 'PCIAT-PCIAT_01', 'PCIAT-PCIAT_02',\n       'PCIAT-PCIAT_03', 'PCIAT-PCIAT_04', 'PCIAT-PCIAT_05', 'PCIAT-PCIAT_06',\n       'PCIAT-PCIAT_07', 'PCIAT-PCIAT_08', 'PCIAT-PCIAT_09', 'PCIAT-PCIAT_10',\n       'PCIAT-PCIAT_11', 'PCIAT-PCIAT_12', 'PCIAT-PCIAT_13', 'PCIAT-PCIAT_14',\n       'PCIAT-PCIAT_15', 'PCIAT-PCIAT_16', 'PCIAT-PCIAT_17', 'PCIAT-PCIAT_18',\n       'PCIAT-PCIAT_19', 'PCIAT-PCIAT_20', 'PCIAT-PCIAT_Total',\n       'SDS-SDS_Total_Raw', 'SDS-SDS_Total_T',\n       'PreInt_EduHx-computerinternet_hoursday', 'enmo']].corr()\nenmo_correlations = correlations['enmo']\ndisplay(enmo_correlations.to_frame().style.background_gradient(cmap='coolwarm'))\n","metadata":{"id":"2-_W6yJGN_fJ","outputId":"953963b5-5c61-445e-d7fc-17e552b1ed49","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"It is difficult to perform correlation analysis with three pieces of data.\nTo find the correct correlation, let's import the entire parquet data, then calculate the average of the column values ​​and perform correlation analysis.","metadata":{"id":"tqnYooJCuw2n"}},{"cell_type":"code","source":"parquet_dir = '/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet'\nenmo_means = {}\n\nfor filename in tqdm(os.listdir(parquet_dir), desc=\"Calculating enmo means\"):\n    file_id = filename.split('=')[1]\n    file_path = os.path.join(parquet_dir, filename, 'part-0.parquet')\n    df = pl.read_parquet(file_path).to_pandas()\n    enmo_means[file_id] = df['enmo'].mean()\n\ntrain_df = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\ntrain_df['enmo_mean'] = train_df['id'].map(enmo_means)\n\ncolumns_for_correlation = ['Basic_Demos-Age', 'Basic_Demos-Sex',\n       'CGAS-CGAS_Score', 'Physical-BMI', 'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n       'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n       'Fitness_Endurance-Max_Stage','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', 'PCIAT-PCIAT_01', 'PCIAT-PCIAT_02',\n       'PCIAT-PCIAT_03', 'PCIAT-PCIAT_04', 'PCIAT-PCIAT_05', 'PCIAT-PCIAT_06',\n       'PCIAT-PCIAT_07', 'PCIAT-PCIAT_08', 'PCIAT-PCIAT_09', 'PCIAT-PCIAT_10',\n       'PCIAT-PCIAT_11', 'PCIAT-PCIAT_12', 'PCIAT-PCIAT_13', 'PCIAT-PCIAT_14',\n       'PCIAT-PCIAT_15', 'PCIAT-PCIAT_16', 'PCIAT-PCIAT_17', 'PCIAT-PCIAT_18',\n       'PCIAT-PCIAT_19', 'PCIAT-PCIAT_20', 'PCIAT-PCIAT_Total',\n       'SDS-SDS_Total_Raw', 'SDS-SDS_Total_T',\n       'PreInt_EduHx-computerinternet_hoursday']\ncorrelations = train_df[['enmo_mean'] + columns_for_correlation].corr()\n\ndisplay(correlations[['enmo_mean']].style.background_gradient(cmap='coolwarm'))\n","metadata":{"id":"S1xEgAKXUvTE","outputId":"8f71e7d8-7588-4299-8b88-8bf9b5483f86","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"display(correlations[['Basic_Demos-Age']].style.background_gradient(cmap='coolwarm'))\n","metadata":{"id":"S1cWMlSvJ9iu","outputId":"584065aa-6ddc-4e23-e063-1cc8e7cb63f5","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"display(correlations[['Basic_Demos-Age']].style.background_gradient(cmap='coolwarm'))\n","metadata":{"id":"68ePJ9QlJ-qZ","outputId":"44a431ba-8d1e-4297-b546-64490243f33b","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"anglez_means = {}  # Initialize the dictionary\n\nfor filename in tqdm(os.listdir(parquet_dir), desc=\"Calculating anglez means\"):\n    file_id = filename.split('=')[1]\n    file_path = os.path.join(parquet_dir, filename, 'part-0.parquet')\n    df = pl.read_parquet(file_path).to_pandas()\n    anglez_means[file_id] = df['anglez'].mean()\n\ntrain_df['anglez_mean'] = train_df['id'].map(anglez_means)\n\ncolumns_for_correlation = ['Basic_Demos-Age', 'Basic_Demos-Sex',\n       'CGAS-CGAS_Score', 'Physical-BMI', 'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n       'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n       'Fitness_Endurance-Max_Stage','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', 'PCIAT-PCIAT_01', 'PCIAT-PCIAT_02',\n       'PCIAT-PCIAT_03', 'PCIAT-PCIAT_04', 'PCIAT-PCIAT_05', 'PCIAT-PCIAT_06',\n       'PCIAT-PCIAT_07', 'PCIAT-PCIAT_08', 'PCIAT-PCIAT_09', 'PCIAT-PCIAT_10',\n       'PCIAT-PCIAT_11', 'PCIAT-PCIAT_12', 'PCIAT-PCIAT_13', 'PCIAT-PCIAT_14',\n       'PCIAT-PCIAT_15', 'PCIAT-PCIAT_16', 'PCIAT-PCIAT_17', 'PCIAT-PCIAT_18',\n       'PCIAT-PCIAT_19', 'PCIAT-PCIAT_20', 'PCIAT-PCIAT_Total',\n       'SDS-SDS_Total_Raw', 'SDS-SDS_Total_T',\n       'PreInt_EduHx-computerinternet_hoursday']\n\ncorrelations = train_df[['anglez_mean'] + columns_for_correlation].corr()\n\ndisplay(correlations[['anglez_mean']].style.background_gradient(cmap='coolwarm'))","metadata":{"id":"DoEDFSmzX7jb","outputId":"ae7d2d8c-b24d-44a9-d0f7-881794778262","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#light\nlight_means = {}  # Initialize the dictionary\n\nfor filename in tqdm(os.listdir(parquet_dir), desc=\"Calculating anglez means\"):\n    file_id = filename.split('=')[1]\n    file_path = os.path.join(parquet_dir, filename, 'part-0.parquet')\n    df = pl.read_parquet(file_path).to_pandas()\n    light_means[file_id] = df['light'].mean()\n\ntrain_df['light_mean'] = train_df['id'].map(light_means)\n\ncolumns_for_correlation = ['Basic_Demos-Age', 'Basic_Demos-Sex',\n       'CGAS-CGAS_Score', 'Physical-BMI', 'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n       'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n       'Fitness_Endurance-Max_Stage','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', 'PCIAT-PCIAT_01', 'PCIAT-PCIAT_02',\n       'PCIAT-PCIAT_03', 'PCIAT-PCIAT_04', 'PCIAT-PCIAT_05', 'PCIAT-PCIAT_06',\n       'PCIAT-PCIAT_07', 'PCIAT-PCIAT_08', 'PCIAT-PCIAT_09', 'PCIAT-PCIAT_10',\n       'PCIAT-PCIAT_11', 'PCIAT-PCIAT_12', 'PCIAT-PCIAT_13', 'PCIAT-PCIAT_14',\n       'PCIAT-PCIAT_15', 'PCIAT-PCIAT_16', 'PCIAT-PCIAT_17', 'PCIAT-PCIAT_18',\n       'PCIAT-PCIAT_19', 'PCIAT-PCIAT_20', 'PCIAT-PCIAT_Total',\n       'SDS-SDS_Total_Raw', 'SDS-SDS_Total_T',\n       'PreInt_EduHx-computerinternet_hoursday']\n\ncorrelations = train_df[['light_mean'] + columns_for_correlation].corr()\n\ndisplay(correlations[['light_mean']].style.background_gradient(cmap='coolwarm'))","metadata":{"id":"F2vfrMmUP4TZ","outputId":"a37358af-6264-410f-8abb-afb813e371bb","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 6. Autoencoder for data compression and feature extraction","metadata":{"id":"9S_R3sS202BD"}},{"cell_type":"markdown","source":"\n\n* process_file function : Reads the Parquet file and converts it into DataFrame format.and Remove the unnecessary 'step' column and calculate statistical information for the remaining columns.\n\n* load_time_series function : Processes all files in the specified directory and extracts statistical information for each file.\nImprove processing speed by performing parallel processing using ThreadPoolExecutor.\n\n* AutoEncoder: Trains the model to minimize the difference between the reconstructed data and the original data through the encoder and decoder.\n\n* perform_autoencoder : Trains a model using Huber Loss as the loss function and uses the trained encoder to transform the input data into a low-dimensional latent representation.\n","metadata":{"id":"cv9Ep5VD55xz"}},{"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","metadata":{"id":"RWzV8yXE6fUw","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:26:56.511522Z","iopub.execute_input":"2024-12-09T03:26:56.511891Z","iopub.status.idle":"2024-12-09T03:26:56.521543Z","shell.execute_reply.started":"2024-12-09T03:26:56.511849Z","shell.execute_reply":"2024-12-09T03:26:56.520866Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class 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.LeakyReLU(0.2),\n            nn.Linear(encoding_dim*3, encoding_dim*2),\n            nn.LeakyReLU(0.2),\n            nn.Linear(encoding_dim*2, encoding_dim),\n            nn.LeakyReLU(0.2)\n        )\n        self.decoder = nn.Sequential(\n            nn.Linear(encoding_dim, input_dim*2),\n            nn.LeakyReLU(0.2),\n            nn.Linear(input_dim*2, input_dim*3),\n            nn.LeakyReLU(0.2),\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","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:26:56.522675Z","iopub.execute_input":"2024-12-09T03:26:56.523006Z","iopub.status.idle":"2024-12-09T03:26:56.532641Z","shell.execute_reply.started":"2024-12-09T03:26:56.522972Z","shell.execute_reply":"2024-12-09T03:26:56.531907Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def 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 = F.smooth_l1_loss\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","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:26:56.533619Z","iopub.execute_input":"2024-12-09T03:26:56.533933Z","iopub.status.idle":"2024-12-09T03:26:56.543084Z","shell.execute_reply.started":"2024-12-09T03:26:56.533894Z","shell.execute_reply":"2024-12-09T03:26:56.542210Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 7. feature_engineering\n\n\n","metadata":{"id":"KwoKJEmpFhD4"}},{"cell_type":"markdown","source":"* The reason for setting up feature engineering is that it plays an important role in improving machine learning model performance, data understanding, overfitting prevention, and model interpretability. It is expected that applying feature engineering in this competition will improve the performance of the Internet usage problem prediction model.","metadata":{"id":"1eEIuPsSNqqb"}},{"cell_type":"code","source":"def 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    #Age\n    df['Internet_Hours_Age'] = df['PreInt_EduHx-computerinternet_hoursday'] * df['Basic_Demos-Age']\n    df['Physical-Waist_Age'] = df['Basic_Demos-Age'] * df['Physical-Waist_Circumference']\n    df['BMI_Age'] = df['Physical-BMI'] * df['Basic_Demos-Age']\n    df['Physical-Height_Age'] = df['Basic_Demos-Age'] * df['Physical-Height']\n    df['SDS_InternetHours'] = df['SDS-SDS_Total_T'] * df['PreInt_EduHx-computerinternet_hoursday']\n\n    #SDS\n    df['SDS_BMI'] = df['BIA-BIA_BMI'] * df['SDS-SDS_Total_T']\n    df['CGAS_SDS'] = df['CGAS-CGAS_Score'] * df['SDS-SDS_Total_T']\n    df['CGAS_Endurance_Mins'] = df['CGAS-CGAS_Score'] * df['Fitness_Endurance-Time_Mins']\n    df['SDS_Activity'] = df['BIA-BIA_Activity_Level_num'] * df['SDS-SDS_Total_T']\n\n    df['BMI_Systolic_BP'] = df['BIA-BIA_BMI'] * df['Physical-Systolic_BP']\n    df['Age_Systolic_BP'] = df['Basic_Demos-Age'] * df['Physical-Systolic_BP']\n    df['PreInt_Systolic_BP'] = df['Physical-Systolic_BP'] * df['PreInt_EduHx-computerinternet_hoursday']\n    df['PAQ_A_Activity'] = df['BIA-BIA_Activity_Level_num'] * df['PAQ_A-PAQ_A_Total']\n    df['Activity_CU_PU'] = df['BIA-BIA_Activity_Level_num'] * df['FGC-FGC_CU'] * df['FGC-FGC_PU']\n\n    #FGC\n    df['FGC_CU_PU'] = df['FGC-FGC_CU'] * df['FGC-FGC_PU']\n    df['FGC_CU_PU_Age'] = df['FGC-FGC_CU'] * df['FGC-FGC_PU'] * df['Basic_Demos-Age']\n    df['FGC_GSND_GSD'] = df['FGC-FGC_GSND'] * df['FGC-FGC_GSD']\n    df['FGC_GSND_GSD_Age'] = df['FGC-FGC_GSND'] * df['FGC-FGC_GSD'] * df['Basic_Demos-Age']\n    df['CGAS_CU_PU'] = df['CGAS-CGAS_Score'] * df['FGC-FGC_CU'] * df['FGC-FGC_PU']\n    df['PreInt_FGC_CU_PU'] = df['PreInt_EduHx-computerinternet_hoursday'] * df['FGC-FGC_CU'] * df['FGC-FGC_PU']\n    df['Endurance_CU_PU'] = df['Fitness_Endurance-Time_Mins'] * df['FGC-FGC_CU'] * df['FGC-FGC_PU']\n\n    return df","metadata":{"id":"RZIz_5JVFW1a","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:26:56.544376Z","iopub.execute_input":"2024-12-09T03:26:56.544598Z","iopub.status.idle":"2024-12-09T03:26:56.556326Z","shell.execute_reply.started":"2024-12-09T03:26:56.544575Z","shell.execute_reply":"2024-12-09T03:26:56.555565Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"\n\n# 8. Null value handling and data preparation\n\n* After loading the series_train.parquet and series_test.parquet files,\n I will apply an autoencoder to retrieve important information from the data and use it to fill in missing values.\n\n* Originally, I was going to use random forests to fill in missing values. Random Forest has the advantage of high accuracy and the ability to process various types of data. However, it was very slow to run, so I decided to use knn instead of random forest.knn has the advantage of very fast execution speed compared to random forest.","metadata":{"id":"pOnz3JZbht_a"}},{"cell_type":"code","source":"submission = 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()\n\ntrain_ts_encoded[\"id\"] = train_ts[\"id\"]\ntest_ts_encoded[\"id\"] = test_ts[\"id\"]\n\ntrain = pd.merge(train_df, train_ts_encoded, how=\"left\", on='id')\ntest = pd.merge(test_df, test_ts_encoded, how=\"left\", on='id')\n\n#imputer = IterativeImputer(estimator=RandomForestRegressor(), random_state=0)\n\nimputer = KNNImputer(n_neighbors=10)\n\nnumeric_cols = train.select_dtypes(include=['float64', 'int64']).columns\nimputed_data = imputer.fit_transform(train[numeric_cols])\n\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\nif 'sii' in train.columns:\n    train = train.dropna(subset='sii')\n\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',\n                'Internet_Hours_Age', 'Physical-Waist_Age', 'BMI_Age', 'SDS_InternetHours',\n                'SDS_BMI', 'CGAS_SDS', 'CGAS_Endurance_Mins', 'SDS_Activity',\n                'BMI_Systolic_BP', 'Age_Systolic_BP', 'PreInt_Systolic_BP', 'PAQ_A_Activity',\n                'Activity_CU_PU', 'FGC_CU_PU', 'FGC_CU_PU_Age', 'FGC_GSND_GSD', 'FGC_GSND_GSD_Age',\n                'CGAS_CU_PU', 'PreInt_FGC_CU_PU', 'Endurance_CU_PU',\n]\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',\n                'Internet_Hours_Age', 'Physical-Waist_Age', 'BMI_Age', 'SDS_InternetHours',\n                'SDS_BMI', 'CGAS_SDS', 'CGAS_Endurance_Mins', 'SDS_Activity',\n                'BMI_Systolic_BP', 'Age_Systolic_BP', 'PreInt_Systolic_BP', 'PAQ_A_Activity',\n                'Activity_CU_PU', 'FGC_CU_PU', 'FGC_CU_PU_Age', 'FGC_GSND_GSD', 'FGC_GSND_GSD_Age',\n                'CGAS_CU_PU', 'PreInt_FGC_CU_PU', 'Endurance_CU_PU',\n]\nfeaturesCols += time_series_cols\ntest = test[featuresCols]","metadata":{"id":"oLJ8VVbU6fUw","outputId":"6a340817-bcd6-45b2-a649-b818852d3d1b","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T04:05:36.002152Z","iopub.execute_input":"2024-12-09T04:05:36.002553Z","iopub.status.idle":"2024-12-09T04:07:05.195973Z","shell.execute_reply.started":"2024-12-09T04:05:36.002523Z","shell.execute_reply":"2024-12-09T04:07:05.194751Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"\nLet's check if there is an infinite value:","metadata":{"id":"hXLIkevkwxD2"}},{"cell_type":"code","source":"if np.any(np.isinf(train)):\n    print(\"exist inf value\")\n\n    inf_rows, inf_cols = np.where(np.isinf(train))\n\n    for row, col in zip(inf_rows, inf_cols):\n        print(f\"columns {row}, row {train.columns[col]}: {train.iloc[row, col]}\")\n\n    train = train.replace([np.inf, -np.inf], np.nan)","metadata":{"id":"g_YQ4qZJwcTW","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T04:07:05.198127Z","iopub.execute_input":"2024-12-09T04:07:05.198608Z","iopub.status.idle":"2024-12-09T04:07:05.206559Z","shell.execute_reply.started":"2024-12-09T04:07:05.198556Z","shell.execute_reply":"2024-12-09T04:07:05.205763Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 9. Evaluation method","metadata":{"id":"tTxsgueONi07"}},{"cell_type":"markdown","source":"1. Quadratic Weighted Kappa : This metric is used to evaluate the agreement between two ratings. It's particularly useful for ordinal ratings, like the SII ratings in the CMI-PIU competition.\n\n2. Threshold Rounder : This function takes continuous predictions and rounds them to discrete SII ratings (0, 1, 2, or 3) based on predefined thresholds.\n\n3. Evaluate Predictions : Returns the negative value of the kappa score. In optimization, it is common to maximize positive values ​​while minimizing negative values.","metadata":{"id":"C7X4kP9OOCHR"}},{"cell_type":"code","source":"def 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\noptimal_thresholds = [0.5, 1.5, 2.5]\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":{"id":"FiuXKkrw6fUw","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T04:07:05.350091Z","iopub.execute_input":"2024-12-09T04:07:05.350468Z","iopub.status.idle":"2024-12-09T04:07:05.356593Z","shell.execute_reply.started":"2024-12-09T04:07:05.350436Z","shell.execute_reply":"2024-12-09T04:07:05.355643Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 10. Train plan\n\n","metadata":{"id":"gbChSJl16fUw"}},{"cell_type":"markdown","source":"I will use four models: LightGBM, Random Forest, CatBoost, and TabNet. And later I added xgboost\r\n\r\nWhy were these five models selected?","metadata":{"id":"8wloVbEGtgCy"}},{"cell_type":"markdown","source":"\n\n1. LightGBM : serves as a foundation for ensemble models with fast learning speed and high prediction performance. It helps improve the overall performance of the ensemble by quickly learning patterns that other models might miss.\n\n2. Random Forest : predicts by combining multiple decision trees, errors in individual trees are offset, and various patterns are learned to achieve high prediction accuracy and can be applied to a variety of problems. -> ExtraTreesRegressor -> x \n\n3. CatBoost : improves the performance of ensemble models by effectively processing these features when there are many categorical features in the dataset. It reduces errors that may occur when other models do not properly handle categorical characteristics and enables more accurate predictions.\n\n4. TabNet is a deep learning model that learns nonlinear relationships in data that other GBDT-based models may miss and increases the diversity of ensemble models. This prevents overfitting of the ensemble model and achieves stronger prediction performance.\n\n5. XGBoost not only has excellent performance, but also greatly improves learning speed by parallelizing the learning process. It can efficiently utilize multi-core CPUs to quickly learn even large datasets.","metadata":{"id":"8_VBFEB2twhx"}},{"cell_type":"markdown","source":"* Submission Separation : After dividing the submission process into three using the principles of ensemble learning and majority voting,\nWe will sort each submission based on id and combine them with sii_1, sii_2, and sii_3 columns to create a combined data frame.","metadata":{"id":"cuWoLqI4hq6c"}},{"cell_type":"code","source":"n_splits = 5\nSEED = 42","metadata":{"id":"C99iOwyHf4l_","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T04:07:07.033980Z","iopub.execute_input":"2024-12-09T04:07:07.034327Z","iopub.status.idle":"2024-12-09T04:07:07.038462Z","shell.execute_reply.started":"2024-12-09T04:07:07.034297Z","shell.execute_reply":"2024-12-09T04:07:07.037559Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.model_selection import GridSearchCV\nfrom sklearn.ensemble import RandomForestClassifier\nfrom imblearn.over_sampling import SMOTE","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T04:07:07.313187Z","iopub.execute_input":"2024-12-09T04:07:07.313710Z","iopub.status.idle":"2024-12-09T04:07:07.318129Z","shell.execute_reply.started":"2024-12-09T04:07:07.313676Z","shell.execute_reply":"2024-12-09T04:07:07.317152Z"}},"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\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_df = pd.DataFrame({\n        'id': submission['id'],\n        'sii': tpTuned\n    })\n\n    return submission_df","metadata":{"id":"euwvMpTS6fUw","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T04:07:07.664670Z","iopub.execute_input":"2024-12-09T04:07:07.665262Z","iopub.status.idle":"2024-12-09T04:07:07.675154Z","shell.execute_reply.started":"2024-12-09T04:07:07.665205Z","shell.execute_reply":"2024-12-09T04:07:07.674302Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"\nAnd we will use Voting Regressor's Soft Voting method, which can combine these four models into one.","metadata":{"id":"JfGPONX_uuCZ"}},{"cell_type":"markdown","source":"# 11. Parameter settings\n","metadata":{"id":"Fdhx43jxR3b9"}},{"cell_type":"code","source":"from sklearn.linear_model import Ridge\nfrom sklearn.svm import SVR","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T04:07:08.581205Z","iopub.execute_input":"2024-12-09T04:07:08.581589Z","iopub.status.idle":"2024-12-09T04:07:08.585642Z","shell.execute_reply.started":"2024-12-09T04:07:08.581560Z","shell.execute_reply":"2024-12-09T04:07:08.584769Z"}},"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,\n    'lambda_l2': 0.01\n}\n\nRF_Params = {\n    'n_estimators': 200,\n    'max_depth': 6,\n    'max_features': 0.8,\n    'min_samples_split': 2,\n    'min_samples_leaf': 1,\n    'bootstrap': True,\n    'random_state': SEED\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,\n        'task_type': 'CPU'\n}\n\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,  \n    'reg_lambda': 5,  \n    'random_state': SEED,\n    'tree_method': 'gpu_hist',\n}\n\nExtraTrees_Params = {\n    'n_estimators': 100,\n    'criterion': 'squared_error',\n    'max_depth': None,\n    'min_samples_split': 2,\n    'min_samples_leaf': 1,\n    'max_features': 'auto',\n    'bootstrap': True,\n    'random_state': SEED,\n    'n_jobs': -1\n}\n\nHistGB_Params = {\n    'max_iter': 200, \n    'max_depth': 6,  \n    'learning_rate': 0.5, \n    'loss': 'squared_error', \n    'max_leaf_nodes': None,  \n    'random_state': SEED\n}\n\nRidge_Params = {\n    'alpha': 1.0, \n    'solver': 'auto', \n    'random_state': SEED \n}\n\nSVR_Params = {\n    'C': 1.0,  # 규제 강도 (값이 클수록 과적합 위험)\n    'kernel': 'rbf',  # 커널 (radial basis function)\n    'epsilon': 0.1,  # 허용 오차 (작을수록 예측이 더 민감)\n    'degree': 3,  # 다항 커널 사용 시 차수\n}","metadata":{"id":"eM5p4c_A6fUx","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T04:07:08.861728Z","iopub.execute_input":"2024-12-09T04:07:08.862039Z","iopub.status.idle":"2024-12-09T04:07:08.869530Z","shell.execute_reply.started":"2024-12-09T04:07:08.862011Z","shell.execute_reply":"2024-12-09T04:07:08.868631Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"![tabnet.png](data:image/png;base64,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)","metadata":{"id":"ZrrHnv7bSh6s"}},{"cell_type":"markdown","source":"*TabNet is an architecture specialized for analyzing tabular data using deep learning.*\n\nKey features:\n1. Sequential Attention: TabNet uses a sequential attention mechanism to decide which features to focus on at each step. This allows the model to focus on the most important features and reduce the influence of noise or irrelevant features.\n\n2. Interpretability: TabNet provides a feature selection mask to help you understand which features were important to the model in making its predictions. This interpretability increases the reliability of the model and is useful for better understanding the decision-making process.\n\n3. High Performance: TabNet outperforms existing models (neural networks, decision trees, etc.) on a variety of tabular datasets. This is because sequential attention and feature masking allow the model to effectively learn complex patterns in the data.","metadata":{"id":"Y-a4tdNqS2bT"}},{"cell_type":"markdown","source":"*Model training process*\n\n* step1 :\n1. Feature transformation: Input features are transformed through a feature transformer. The feature transformer uses batch normalization and GLU activation functions to nonlinearly transform features.\n\n2. Attention calculation: The transformed features are passed to the attention transformer to calculate attention weights. Attention weights indicate the importance of each feature. At this stage, information from previous decision stages is reflected through selectors (prior scales), reducing attention to features that were important in previous stages. This allows the model to take a variety of characteristics into account.\n\n3. Mask Generation: A feature mask is created based on the attention weights. The feature mask consists of values ​​between 0 and 1, indicating the importance of each feature.\n\n4. Feature selection: Using feature masks, only important features are selected from the input features. The selected characteristics are passed on to the next step.\n\n5. Generate output: The selected features are converted back through a feature transformer, and the values ​​are passed to the decoder and used to generate the final prediction.\n","metadata":{"id":"b7Go1rtRTQ6t"}},{"cell_type":"code","source":"class TabNetWrapper(BaseEstimator, RegressorMixin):\n    def __init__(self, **kwargs):\n        self.model = TabNetRegressor(**kwargs)\n        self.kwargs = kwargs\n        self.imputer = KNNImputer(n_neighbors=5)\n        #self.imputer = SimpleImputer(strategy='median')\n        self.best_model_path = 'best_tabnet_model.pt'\n\n    def fit(self, X, y):\n        X_imputed = self.imputer.fit_transform(X)\n\n        if hasattr(y, 'values'):\n            y = y.values\n\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', 'mae', 'rmse'],\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        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\nTabNet_Params = {\n    'n_d': 64,\n    'n_a': 64,\n    'n_steps': 5,\n    'gamma': 1.5,\n    'n_independent': 2,\n    'n_shared': 2,\n    'lambda_sparse': 1e-4,\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__()\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\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        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)","metadata":{"id":"Ijht_ggc6fUx","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T04:07:10.095782Z","iopub.execute_input":"2024-12-09T04:07:10.096561Z","iopub.status.idle":"2024-12-09T04:07:10.110213Z","shell.execute_reply.started":"2024-12-09T04:07:10.096525Z","shell.execute_reply":"2024-12-09T04:07:10.109428Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 12. Ensemble model using StackingRegressor -> VotingRegressor\n\n\n*StackingRegressor is a method that trains a meta-model using predictions from multiple base models as input, and uses this meta-model to generate the final prediction. Advantage: High performance can be expected by non-linearly combining predictions from basic models*\n\n->\n\n* \nBut the execution time was too long, so I decided to use votingregressor instead of Stackingregressor. Votingregressor may have slightly lower performance than Stackingregressor, but it has the advantage of being very fast*.","metadata":{"id":"S6w1pDk56fUx"}},{"cell_type":"code","source":"Light = LGBMRegressor(**Params, random_state=SEED, verbose=-1, n_estimators=300)\nRF_Model = RandomForestRegressor(**RF_Params)\nCatBoost_Model = CatBoostRegressor(**CatBoost_Params)\nXGB_Model = XGBRegressor(**XGB_Params)\nTabNet_Model = TabNetWrapper(**TabNet_Params) \nExtraTrees_Model = ExtraTreesRegressor(**ExtraTrees_Params)\nHistGB_Model = HistGradientBoostingRegressor(**HistGB_Params)\nRidge_Model = Ridge(**Ridge_Params)\nSVR_Model = SVR(**SVR_Params)\n","metadata":{"id":"X69q_TVU6fUx","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T04:07:10.905297Z","iopub.execute_input":"2024-12-09T04:07:10.905648Z","iopub.status.idle":"2024-12-09T04:07:10.912288Z","shell.execute_reply.started":"2024-12-09T04:07:10.905617Z","shell.execute_reply":"2024-12-09T04:07:10.911365Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 13. Submission Separation\n\n\n* Reasons for separating submissions into 3 types :\n\n1. Leverage different models : Different models learn and predict data features differently. By combining the prediction results of multiple models, you can compensate for the shortcomings of individual models and take advantage of their strengths.\n\n2. Avoid overfitting : A single model can overfit the training data, resulting in poor prediction performance on new data. Using multiple models can reduce the risk of overfitting and improve generalization performance.\n\n3. Improved prediction stability : The prediction results of a single model can be sensitive to small changes in the data. Averaging prediction results from multiple models can increase the stability of prediction results.\n\n4. Majority Voting : Majority voting is a method of selecting the result predicted by the most models among the prediction results of multiple models as the final prediction. This can reduce errors in individual models and improve overall prediction performance.","metadata":{"id":"4AYbHOJMikMA"}},{"cell_type":"markdown","source":"# 14. Submission1\n","metadata":{}},{"cell_type":"code","source":"ridge_pipeline = Pipeline([\n    ('imputer', SimpleImputer(strategy='mean')),  \n    ('ridge', Ridge(alpha=1.0))\n])\n\nestimators = [\n    ('lightgbm', Light),\n    ('xgboost', XGB_Model),\n    ('catboost', CatBoost_Model),\n    ('tabnet', TabNet_Model),\n    #('ridge', ridge_pipeline),\n    #('svr', SVR(C=1.0, kernel='rbf'))\n]\n\n# VotingRegressor\nvoting_model = VotingRegressor(\n    estimators=estimators,\n    weights=[4.0, 4.0, 5.0, 4.0] \n)\n\n# StackingRegressor\nstacking_model = StackingRegressor(\n    estimators=estimators,\n    #final_estimator=Ridge(alpha=1.0),\n    #cv=5  \n)\n\nensemble_model = VotingRegressor(\n    estimators=[\n        ('stacking', stacking_model)\n    ]\n)\n\nSubmission1 = TrainML(ensemble_model, test)","metadata":{"id":"nuRLOTY96fUx","outputId":"98543fb3-9535-4be4-b0ca-e7487bf9be32","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T04:07:32.812384Z","iopub.execute_input":"2024-12-09T04:07:32.812761Z","iopub.status.idle":"2024-12-09T04:24:07.357497Z","shell.execute_reply.started":"2024-12-09T04:07:32.812730Z","shell.execute_reply":"2024-12-09T04:24:07.356599Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 15. Submission2\n","metadata":{}},{"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\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,\n    'lambda_l2': 0.01\n}\n\nRF_Params = {\n    'n_estimators': 200,\n    'max_depth': 6,\n    'max_features': 0.8,\n    'min_samples_split': 2,\n    'min_samples_leaf': 1,\n    'bootstrap': True,\n    'random_state': SEED\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\n}\n\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,  \n    'reg_lambda': 5,\n    'random_state': SEED\n}\n\nLight = LGBMRegressor(**Params, random_state=SEED, verbose=-1, n_estimators=300)\nRF_Model = XGBRegressor(**RF_Params)\nCatBoost_Model = CatBoostRegressor(**CatBoost_Params)\nXGB_Model = XGBRegressor(**XGB_Params)\nTabNet_Model = TabNetWrapper(**TabNet_Params)\n\nestimators = [\n    ('lightgbm', Light),\n    #('RandomForest', RF_Model),\n    ('xgboost', XGB_Model),\n    ('catboost', CatBoost_Model),\n    ('tabnet', TabNet_Model),\n    #('hist_gradient_boosting', HistGradientBoostingRegressor()),\n    #('ExtraTrees', ExtraTrees_Model)\n]\n\nvoting_model = VotingRegressor(\n    estimators=estimators,\n    weights=[0.25, 0.25, 0.3, 0.2] \n)\n\n# StackingRegressor\nstacking_model = StackingRegressor(\n    estimators=estimators,\n    #final_estimator=Ridge(alpha=1.0),\n    #cv=5  \n)\n\nensemble_model = VotingRegressor(\n    estimators=[\n        ('stacking', stacking_model)\n    ]\n)\n\nSubmission2 = TrainML(ensemble_model, test)","metadata":{"id":"G2buWCkZ6fUx","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T04:26:08.279956Z","iopub.execute_input":"2024-12-09T04:26:08.280316Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 16. Submission3\n","metadata":{}},{"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 = StackingRegressor(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])\n\nSubmission3 = TrainML(ensemble, test)\nSubmission3 = pd.DataFrame({\n    'id': sample['id'],\n    'sii': Submission3\n})\n\nSubmission3","metadata":{"id":"XXLLTdbG6fUy","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T02:08:19.464932Z","iopub.execute_input":"2024-12-09T02:08:19.465395Z","iopub.status.idle":"2024-12-09T02:20:29.099072Z","shell.execute_reply.started":"2024-12-09T02:08:19.465348Z","shell.execute_reply":"2024-12-09T02:20:29.098265Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"\n\n# 17. Final\n\n","metadata":{"id":"77SD2ILkj1hB"}},{"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\nsum_submission = combined[['id', 'final_sii']].rename(columns={'final_sii': 'sii'})\nsum_submission.to_csv('submission.csv', index=False)\n","metadata":{"id":"Aw-qxe5B6fUz","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sum_submission","metadata":{"id":"63NNQC_k6fUz","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}