{"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":"none","dataSources":[{"sourceId":81933,"databundleVersionId":9643020,"sourceType":"competition"},{"sourceId":7453542,"sourceType":"datasetVersion","datasetId":921302}],"dockerImageVersionId":30776,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"
CMI - Problematic Internet Use: Age Group Imputation & Pseudo Labeling
","metadata":{"execution":{"iopub.status.busy":"2024-11-25T06:41:49.857900Z","iopub.execute_input":"2024-11-25T06:41:49.858314Z","iopub.status.idle":"2024-11-25T06:41:49.866729Z","shell.execute_reply.started":"2024-11-25T06:41:49.858281Z","shell.execute_reply":"2024-11-25T06:41:49.865092Z"}}},{"cell_type":"markdown","source":"ℹ️ Info¶¶\n\n* **forked original great work kernels**\n * https://www.kaggle.com/code/ichigoe/lb0-494-with-tabnet\n\n* **My upd(2024/11/25)**\n * This is data preprocessing and imputation by age group\n* **My upd(2024/11/30)**\n * I figured out why the error occurred. When I didn't use the trained imputer on the test data set, all the processes completed without errors.\n * Actually, I don't know what the difference is between hidden test set and open test set, but I know that using imputer only works to the training set.\n\n\n","metadata":{}},{"cell_type":"markdown","source":"# Information\n* 나이대 별로 구분하여 Imputation을 수행하고, 이를 기반으로 Pseudo labeling을 생성한 코드를 담고 있는 notebook 입니다. \n* 이 데이터 세트에는 결측치와 노이즈 값이 있습니다. 저는 결측치를 좀 더 의미있게 채울 수 있을까, 하는 생각을 바탕으로 수행하였습니다.\n* 한 가지 문제가 있으나, 이에 대한 내용은 밑에서 다시 한번 언급하겠습니다.\n* \n* This notebook contains the code to perform imputation by age and generate pseudo labeling based on this.\n* This dataset has missing values ​​and noise values. I did this based on the idea that I could fill in the missing values ​​with something more meaningful.\n* There is one problem, which I will mention again below.low.","metadata":{}},{"cell_type":"markdown","source":"---\n---","metadata":{}},{"cell_type":"markdown","source":"# Based\n- https://www.kaggle.com/code/honganzhu/cmi-piu-competition?scriptVersionId=201912528 Version44 LB0.492","metadata":{}},{"cell_type":"markdown","source":"》# Description of Imported Libraries\n\n- **NumPy (`np`)**: Used for efficient numerical operations, including linear algebra and array manipulation.\n- **Pandas (`pd`)**: Provides data structures like DataFrames for handling structured data, essential for data preprocessing.\n- **Polars (`pl`)**: A faster alternative to pandas for DataFrame operations, particularly useful for large datasets.\n- **Matplotlib & Seaborn (`plt`, `sns`)**: Visualization libraries. Matplotlib is used for basic plots, while Seaborn builds on it to create more advanced statistical visualizations.\n- **LightGBM, XGBoost, CatBoost**: Machine learning libraries used for gradient boosting, which is efficient for both regression and classification tasks.\n- **Colorama**: Enhances console output with colored text, making it easier to highlight important results or warnings.\n- **SciPy (`minimize`)**: Provides optimization routines, such as adjusting thresholds to maximize performance metrics like kappa scores.\n- **OS**: Used for file path manipulations and system-related functions.\n- **Scikit-learn (`sklearn`)**: A powerful machine learning library, providing utilities for cross-validation, metrics, and model cloning.\n- **YDF**: A specialized library for machine learning tasks, likely including decision forests.\n- **ThreadPoolExecutor & TQDM**: Tools for parallelizing tasks and displaying progress bars for long-running loops, improving efficiency and usability.\n- **Warnings**: Filters out unwanted warnings to keep the output clean, useful when dealing with noisy outputs from multiple libraries.\n- **IPython display (`clear_output`)**: A utility for clearing the Jupyter notebook output, often used to avoid clutter in long-running scripts.\n","metadata":{}},{"cell_type":"code","source":"!pip -q install /kaggle/input/pytorchtabnet/pytorch_tabnet-4.1.0-py3-none-any.whl","metadata":{"execution":{"iopub.status.busy":"2024-12-02T02:13:29.287673Z","iopub.execute_input":"2024-12-02T02:13:29.288118Z","iopub.status.idle":"2024-12-02T02:14:13.243686Z","shell.execute_reply.started":"2024-12-02T02:13:29.288079Z","shell.execute_reply":"2024-12-02T02:14:13.242085Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from pytorch_tabnet.tab_model import TabNetRegressor","metadata":{"execution":{"iopub.status.busy":"2024-12-02T02:22:55.721466Z","iopub.execute_input":"2024-12-02T02:22:55.722174Z","iopub.status.idle":"2024-12-02T02:22:55.726828Z","shell.execute_reply.started":"2024-12-02T02:22:55.722136Z","shell.execute_reply":"2024-12-02T02:22:55.725583Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nimport pandas as pd\nimport os\nimport re\nimport seaborn as sns\nimport polars as pl\nimport polars.selectors as cs\nimport matplotlib.pyplot as plt\nimport matplotlib.pyplot as plt\n\nimport warnings\n\n\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 sklearn.model_selection import train_test_split\nfrom tqdm import tqdm\nfrom matplotlib.ticker import MaxNLocator, FormatStrFormatter, PercentFormatter\nfrom sklearn.preprocessing import StandardScaler, RobustScaler, MinMaxScaler\n#from colorama import Fore, Style\nfrom IPython.display import clear_output\nfrom sklearn.impute import SimpleImputer, KNNImputer\nfrom sklearn.pipeline import Pipeline\nfrom torch.utils.data import DataLoader, TensorDataset\nfrom pytorch_tabnet.tab_model import TabNetRegressor\nfrom sklearn.ensemble import VotingRegressor, RandomForestRegressor, GradientBoostingRegressor\nfrom lightgbm import LGBMRegressor\nfrom xgboost import XGBRegressor\nfrom catboost import CatBoostRegressor\n\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import GridSearchCV\nwarnings.filterwarnings('ignore')\npd.options.display.max_columns = None","metadata":{"execution":{"iopub.status.busy":"2024-12-02T02:22:55.968336Z","iopub.execute_input":"2024-12-02T02:22:55.968702Z","iopub.status.idle":"2024-12-02T02:22:55.977931Z","shell.execute_reply.started":"2024-12-02T02:22:55.968670Z","shell.execute_reply":"2024-12-02T02:22:55.976759Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import random\ndef seed_everything(seed):\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = True\nseed_everything(2024)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:22:56.084476Z","iopub.execute_input":"2024-12-02T02:22:56.084848Z","iopub.status.idle":"2024-12-02T02:22:56.092591Z","shell.execute_reply.started":"2024-12-02T02:22:56.084815Z","shell.execute_reply":"2024-12-02T02:22:56.091573Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"target_labels = ['None', 'Mild', 'Moderate', 'Severe']","metadata":{"execution":{"iopub.status.busy":"2024-12-02T02:22:56.283820Z","iopub.execute_input":"2024-12-02T02:22:56.284243Z","iopub.status.idle":"2024-12-02T02:22:56.289520Z","shell.execute_reply.started":"2024-12-02T02:22:56.284206Z","shell.execute_reply":"2024-12-02T02:22:56.288228Z"},"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":{"execution":{"iopub.status.busy":"2024-12-02T02:22:56.466758Z","iopub.execute_input":"2024-12-02T02:22:56.467506Z","iopub.status.idle":"2024-12-02T02:22:56.497869Z","shell.execute_reply.started":"2024-12-02T02:22:56.467466Z","shell.execute_reply":"2024-12-02T02:22:56.496588Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"SEED = 42\nn_splits = 5","metadata":{"execution":{"iopub.status.busy":"2024-12-02T02:22:56.674652Z","iopub.execute_input":"2024-12-02T02:22:56.675108Z","iopub.status.idle":"2024-12-02T02:22:56.680423Z","shell.execute_reply.started":"2024-12-02T02:22:56.675073Z","shell.execute_reply":"2024-12-02T02:22:56.679146Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#
Feature Preprocessing and Enginnering
\n\n- **Feature Selection**: The dataset contains features related to physical characteristics (e.g., BMI, Height, Weight), behavioral aspects (e.g., internet usage), and fitness data (e.g., endurance time). \n\n","metadata":{}},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.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\n\nnumeric_cols = ['Basic_Demos-Age', 'Basic_Demos-Sex', 'CGAS-CGAS_Score', 'Physical-BMI',\n 'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n 'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP', \n 'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND', 'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone',\n 'FGC-FGC_PU', 'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone',\n 'FGC-FGC_SRR', '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','SDS-SDS_Total_Raw', 'SDS-SDS_Total_T',\n 'PreInt_EduHx-computerinternet_hoursday', 'sii', 'id'] ## we use only numerical columns\n\n\ntrain = train[numeric_cols]\ntrain = train.dropna(thresh=16, axis=0) ## First, if there is too little data in each row, we delete it.\n\n\n ## There are values ​​less than 0 in the BIA- and physical- columns, which will cause noise when running KNN Imputer. We will remove these values.\nfor i in numeric_cols:\n if i.startswith('BIA-') or i.startswith('Physical-'):\n train = train.drop(train[(train[i] <= 0)].index, axis = 0)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:23:04.885195Z","iopub.execute_input":"2024-12-02T02:23:04.885603Z","iopub.status.idle":"2024-12-02T02:23:04.969098Z","shell.execute_reply.started":"2024-12-02T02:23:04.885567Z","shell.execute_reply":"2024-12-02T02:23:04.968144Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#
FGC-, FGC-*_Zone mapping
\n - FGC- 와 FGC- Zone 항목을 보면 개수가 일치하지 않는 경향이 있습니다. FGC-의 개수를 바탕으로 FGC-Zone 값이 결정되므로 결측치를 채웠습니다.\n - Looking at the FGC- and FGC-''_Zone, the numbers tend to not match. Since the value of FGC-''_Zone is determined based on FGC- value, i filled in the missing value","metadata":{}},{"cell_type":"code","source":"# train.isna().sum().sort_values() ## FGC-##, FGC-##_Zone have a gap","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:23:05.330667Z","iopub.execute_input":"2024-12-02T02:23:05.331084Z","iopub.status.idle":"2024-12-02T02:23:05.336122Z","shell.execute_reply.started":"2024-12-02T02:23:05.331048Z","shell.execute_reply":"2024-12-02T02:23:05.334776Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"Zone = ['FGC-FGC_SRR', 'FGC-FGC_CU', 'FGC-FGC_PU', 'FGC-FGC_SRL', 'FGC-FGC_TL']\n\nfor i in Zone: \n for j, k in train[(train[i] > 0) & (train[i+'_Zone'].isna())].iterrows():\n if k['FGC-FGC_TL'] > train[train[i+'_Zone'] == 1][i].min():\n train.loc[j, i+'_Zone'] = 1\n else:\n train.loc[j, i+'_Zone'] = 0\n\ntrain.isna().sum().sort_values() ##You can see that FGC-FGC_TL_Zone and FGC-FGC_TL match, and the gap has been reduced in other areas.","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:23:05.563704Z","iopub.execute_input":"2024-12-02T02:23:05.564089Z","iopub.status.idle":"2024-12-02T02:23:05.739819Z","shell.execute_reply.started":"2024-12-02T02:23:05.564054Z","shell.execute_reply":"2024-12-02T02:23:05.738801Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#
Feature Enginnering Fucntion
\n* 기존 baseline 코드의 feature enginnering을 변형 했습니다. 그러나 이 feature가 효과적인지는 확인되지 않았습니다.\n* I've modified the feature engineering in the baseline code, but have not yet verified that this is effective.","metadata":{}},{"cell_type":"code","source":"def feature_engineering(df):\n \n df['Fat_to_Muscle_Ratio'] = df['BIA-BIA_Fat'] / (df['BIA-BIA_SMM'] + 1)\n \n df['BMR_Weight'] = df['BIA-BIA_BMR'] / df['Physical-Weight']\n df['Metabolic_Efficiency'] = df['BIA-BIA_BMR'] / df['BIA-BIA_DEE']\n \n df['DEE_Weight'] = df['BIA-BIA_DEE'] / df['Physical-Weight']\n \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 \n df['Bone_Weight_Ratio'] = df['BIA-BIA_BMC'] / df['BIA-BIA_LDM']\n \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\n # Interaction between age and physical measurements\n df['Age_Height'] = df['Basic_Demos-Age'] * df['Physical-Height']\n df['Age_Weight'] = df['Basic_Demos-Age'] * df['Physical-Weight']\n df['Age_Waist_Circumference'] = df['Basic_Demos-Age'] * df['Physical-Waist_Circumference']\n \n # Interaction between internet usage and health indicators\n df['Internet_Systolic_BP'] = (df['PreInt_EduHx-computerinternet_hoursday'] + 1) * df['Physical-Systolic_BP']\n df['Internet_HeartRate'] = (df['PreInt_EduHx-computerinternet_hoursday'] + 1) * df['Physical-HeartRate']\n \n # Interaction between body composition and cardiovascular indicators\n df['Fat_Systolic_BP'] = df['BIA-BIA_Fat'] * df['Physical-Systolic_BP']\n df['Fat_HeartRate'] = df['BIA-BIA_Fat'] * df['Physical-HeartRate']\n df['Muscle_HeartRate'] = df['BIA-BIA_SMM'] * df['Physical-HeartRate']\n \n # Interaction between body composition and hydration status\n df['TBW_BMR'] = df['BIA-BIA_TBW'] * df['BIA-BIA_BMR']\n df['ICW_BMR'] = df['BIA-BIA_ICW'] * df['BIA-BIA_BMR']\n \n # Interaction between muscle and bone density\n df['SMM_BMC'] = df['BIA-BIA_SMM'] * df['BIA-BIA_BMC']\n df['LST_BMC'] = df['BIA-BIA_LST'] * df['BIA-BIA_BMC']\n\n # df['FGC_ZONE_SUM'] = df['FGC-FGC_SRR'] + df['FGC-FGC_CU'] + df['FGC-FGC_PU'] + df['FGC-FGC_SRL'] + df['FGC-FGC_TL']\n\n return df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:23:05.980670Z","iopub.execute_input":"2024-12-02T02:23:05.981066Z","iopub.status.idle":"2024-12-02T02:23:05.991037Z","shell.execute_reply.started":"2024-12-02T02:23:05.981029Z","shell.execute_reply":"2024-12-02T02:23:05.989841Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#
Age Group Imputation
\n\n* 의약품 관련 분야에서 유아, 청소년, 어른으로 구분하는 기준을 따라 나이대를 분류 했습니다.\n* 그리고 나이, 성별, BMI 로 정렬하고 KNNImputer를 학습시켰습니다.\n\n* I categorized the age groups according to the criteria for dividing children, adolescents, and adults in the pharmaceutical field.\n* then sorted by age, gender, and BMI and learned the knnImputer","metadata":{}},{"cell_type":"code","source":"kids = train[(train['Basic_Demos-Age'] >=0) & (train['Basic_Demos-Age'] <= 13)]\nkids= kids.sort_values(by = ['Basic_Demos-Age','Basic_Demos-Sex','Physical-BMI','sii']).reset_index(drop=True)\nkids_sii = kids['sii']\nkids_sii = kids_sii.replace(np.nan, 'NaN') ## Tranfer np.float type NaN to str type NaN\nkids = kids.drop('sii', axis = 1)\n\nkids_imputer = KNNImputer(n_neighbors=5)\nkids_impute = kids_imputer.fit_transform(kids.drop('id', axis = 1))\nkids_impute = pd.DataFrame(kids_impute, columns = kids.columns.drop('id'))\nkids_impute['id'] = kids['id']\nkids_impute['sii'] = kids_sii\n\nadole = train[(train['Basic_Demos-Age'] >= 14) & (train['Basic_Demos-Age'] <= 19)]\nadole = adole[adole['Physical-Weight'] > 0]\nadole = adole.sort_values(by = ['Basic_Demos-Age','Basic_Demos-Sex','Physical-BMI','sii']).reset_index(drop=True)\nadole_sii = adole['sii']\nadole_sii = adole_sii.replace(np.nan, 'NaN') ## Tranfer np.float type NaN to str type NaN\nadole = adole.drop('sii', axis = 1)\n\nadole_imputer = KNNImputer(n_neighbors=5)\nadole_impute = adole_imputer.fit_transform(adole.drop('id', axis =1))\nadole_impute = pd.DataFrame(adole_impute, columns = adole.columns.drop('id'))\nadole_impute['sii'] = adole_sii\nadole_impute['id'] = adole['id']\n\n\nadult = train[(train['Basic_Demos-Age'] >= 20)]\nadult = adult.sort_values(by = ['Basic_Demos-Age','Basic_Demos-Sex','Physical-BMI','sii']).reset_index(drop= True)\nadult_sii = adult['sii']\nadult_sii = adult_sii.replace(np.nan, 'NaN') ## Tranfer np.float type NaN to str type NaN\nadult = adult.drop('sii', axis = 1)\n\nadult_imputer = KNNImputer(n_neighbors=5)\nadult_impute = adult_imputer.fit_transform(adult.drop('id', axis =1))\nadult_impute = pd.DataFrame(adult_impute, columns = adult.columns.drop('id'))\nadult_impute['sii'] = adult_sii\nadult_impute['id'] = adult['id']\n\nconcat_imputed_data = pd.concat([kids_impute, adole_impute, adult_impute], axis = 0)\n\nconcat_imputed_data = feature_engineering(concat_imputed_data)\n\ntmp = ['CGAS-CGAS_Score',\n 'Physical-Diastolic_BP',\n 'Physical-HeartRate',\n 'Physical-Systolic_BP',\n 'BIA-BIA_FFMI',\n 'SDS-SDS_Total_Raw',\n 'Metabolic_Efficiency',\n 'Muscle_to_Fat',\n 'Bone_Weight_Ratio',\n 'Hydration_Status',\n 'Internet_Systolic_BP',\n 'Internet_HeartRate', 'sii', 'id'] # pycaret's feature_importance \n\nconcat_imputed_data = concat_imputed_data[tmp] \n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:23:06.444342Z","iopub.execute_input":"2024-12-02T02:23:06.444737Z","iopub.status.idle":"2024-12-02T02:23:07.481416Z","shell.execute_reply.started":"2024-12-02T02:23:06.444703Z","shell.execute_reply":"2024-12-02T02:23:07.480309Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"\n#
Pseudo Labeling
\n* 데이터에 결측치를 채운 후 비어있는 sii를 채우기 위해 Pseudo labeling을 수행합니다.\n* 베이스 라인에서도 KNN Imputer를 활용하여 sii를 값을 채웁니다. 목적은 같지만 방식을 살짝 다르게 바꾸었습니다.\n\n* After filling in the missing values in the data, i perform pseudo labeling to fill in empty sii\n* In the baseline, they use knn Imputer to fill in values in sii. The purpose is the same, but the method has benn slightly changed","metadata":{}},{"cell_type":"code","source":"sii_imputed_data = concat_imputed_data[~(concat_imputed_data['sii'] == 'NaN')]\nn_sii_imputed_data = concat_imputed_data[concat_imputed_data['sii'] == 'NaN']\n\n\nknn = KNeighborsClassifier(n_neighbors= 9, weights= 'uniform',n_jobs=-1)\nknn.fit(sii_imputed_data.drop(['sii', 'id'], axis = 1), sii_imputed_data['sii'].astype(int))\n\ny_pred = knn.predict(n_sii_imputed_data.drop(['sii', 'id'], axis = 1))\nn_sii_imputed_data['sii']= y_pred.astype(float)\n\npseudo_data = pd.concat([sii_imputed_data, n_sii_imputed_data], axis = 0).reset_index(drop = True)\n\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:23:07.483170Z","iopub.execute_input":"2024-12-02T02:23:07.483517Z","iopub.status.idle":"2024-12-02T02:23:07.540470Z","shell.execute_reply.started":"2024-12-02T02:23:07.483483Z","shell.execute_reply":"2024-12-02T02:23:07.539085Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"\n#
Test data preprocessing
\n\n* train data에 했던 처리를 유사하게 가져갑니다.\n* test data에서는 imputer의 transform() 함수만 적용하여 추가적인 학습이 이루어지지 않도록 합니다.\n\n* Perform the same preprocessing, did for the train data\n* For the test data, only apply the imputer's transform() function to ensure no additional learning is done","metadata":{}},{"cell_type":"code","source":"test = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')\n\nnumeric_cols = ['Basic_Demos-Age', 'Basic_Demos-Sex', 'CGAS-CGAS_Score', 'Physical-BMI',\n 'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n 'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n 'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND', 'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone',\n 'FGC-FGC_PU', 'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone',\n 'FGC-FGC_SRR', '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',\n 'SDS-SDS_Total_Raw', 'SDS-SDS_Total_T',\n 'PreInt_EduHx-computerinternet_hoursday', 'id']\n\ntest = test[numeric_cols]\n\n\ntest_kids = test[(test['Basic_Demos-Age'] >= 0) & (test['Basic_Demos-Age'] <= 13)]\ntest_kids = test_kids.sort_values(by = ['Basic_Demos-Age','Basic_Demos-Sex','Physical-BMI']).reset_index(drop= True)\n\ntest_adole = test[(test['Basic_Demos-Age'] >= 14) & (test['Basic_Demos-Age'] <= 19)]\ntest_adole = test_adole.sort_values(by = ['Basic_Demos-Age','Basic_Demos-Sex','Physical-BMI']).reset_index(drop= True)\n\ntest_adult = test[(test['Basic_Demos-Age'] >= 20)] #not exist in test data\ntest_adult = test_adult.sort_values(by = ['Basic_Demos-Age','Basic_Demos-Sex','Physical-BMI']).reset_index(drop= True)\n\n\nif ((test['Basic_Demos-Age'] >= 0) & (test['Basic_Demos-Age'] <= 13)).any():\n test_kids_impute = kids_imputer.transform(test_kids.drop('id', axis = 1)) #.reset_index(drop = True)\nelse:\n test_kids_impute = test_kids\n\nif ((test['Basic_Demos-Age'] >= 14) & (test['Basic_Demos-Age'] <= 19)).any():\n test_adole_impute = adole_imputer.transform(test_adole.drop('id', axis =1)) #.reset_index(drop = True)\nelse:\n test_adole_impute = test_adole\n\nif (test['Basic_Demos-Age'] >=20).any(): ## if there is 20y\n test_adult_impute = adult_imputer.transform(test_adult.drop('id', axis =1)) #.reset_index(drop = True)\nelse:\n test_adult_impute = test_adult\n\ntest_kids_impute = pd.DataFrame(test_kids_impute, columns = test_kids.columns.drop('id'))\ntest_kids_impute['id'] = test_kids['id']\n\ntest_adole_impute = pd.DataFrame(test_adole_impute, columns = test_adole.columns.drop('id'))\ntest_adole_impute['id'] = test_adole['id']\n\ntest_adult_impute = pd.DataFrame(test_adult_impute, columns = test_adult.columns.drop('id'))\ntest_adult_impute['id'] = test_adult['id']\n\ntest_imputed_data = pd.concat([test_kids_impute,test_adole_impute, test_adult_impute]).reset_index(drop = True)\n\ntest_imputed_data = feature_engineering(test_imputed_data)\n\ntmp = ['CGAS-CGAS_Score',\n 'Physical-Diastolic_BP',\n 'Physical-HeartRate',\n 'Physical-Systolic_BP',\n 'BIA-BIA_FFMI',\n 'SDS-SDS_Total_Raw',\n 'Metabolic_Efficiency',\n 'Muscle_to_Fat',\n 'Bone_Weight_Ratio',\n 'Hydration_Status',\n 'Internet_Systolic_BP',\n 'Internet_HeartRate', 'id'] # 'id'\n\ntest_imputed_data = test_imputed_data[tmp]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:23:07.852544Z","iopub.execute_input":"2024-12-02T02:23:07.852895Z","iopub.status.idle":"2024-12-02T02:23:07.969527Z","shell.execute_reply.started":"2024-12-02T02:23:07.852865Z","shell.execute_reply":"2024-12-02T02:23:07.968378Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.ensemble import ExtraTreesClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn.linear_model import RidgeClassifier\n\n# # Create model instances\n# Light = LGBMRegressor(**Params, random_state=SEED, verbose=-1, n_estimators=300)\n# XGB_Model = XGBRegressor(**XGB_Params)\n# CatBoost_Model = CatBoostRegressor(**CatBoost_Params)\n# TabNet_Model = TabNetWrapper(**TabNet_Params) # New\n\nrf = RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n criterion='gini', max_depth=None, max_features='sqrt',\n max_leaf_nodes=None, max_samples=None,\n min_impurity_decrease=0.0, min_samples_leaf=1,\n min_samples_split=2, min_weight_fraction_leaf=0.0,\n n_estimators=100, n_jobs=-1,\n oob_score=False, random_state=42, verbose=0,\n warm_start=False)\n\net = ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight={},\n criterion='gini', max_depth=10,\n max_features=0.9615723493923193, max_leaf_nodes=None,\n max_samples=None,\n min_impurity_decrease=0.20689965834214816,\n min_samples_leaf=3, min_samples_split=4,\n min_weight_fraction_leaf=0.0, \n n_estimators=125, n_jobs=-1, oob_score=False,\n random_state=42, verbose=0, warm_start=False)\n\ngbc = GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n learning_rate=0.0533335299201138, loss='log_loss',\n max_depth=7, max_features=0.5052738225713802,\n max_leaf_nodes=None,\n min_impurity_decrease=0.023570448052746877,\n min_samples_leaf=4, min_samples_split=8,\n min_weight_fraction_leaf=0.0, n_estimators=113,\n n_iter_no_change=None, random_state=42,\n subsample=0.6922815882344928, tol=0.0001,\n validation_fraction=0.1, verbose=0,\n warm_start=False)\n\nridge =RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,\n max_iter=None, positive=False, random_state=42, solver='auto',\n tol=0.0001)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:23:08.300390Z","iopub.execute_input":"2024-12-02T02:23:08.300799Z","iopub.status.idle":"2024-12-02T02:23:08.313512Z","shell.execute_reply.started":"2024-12-02T02:23:08.300744Z","shell.execute_reply":"2024-12-02T02:23:08.312010Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n# **》》》Model1.Train**\n---","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import VotingClassifier\n\nvoting_model = VotingClassifier(estimators=[\n ('random', rf),\n ('xgboost', et),\n ('catboost', gbc),\n ('rid',ridge),\n #('tabnet', TabNet_Model)\n],weights=[5.0,4.0,4.0,4.0])\n\nvoting_model = voting_model.fit(pseudo_data.drop(['sii', 'id'],axis =1), pseudo_data['sii'].astype(int))\n\nSubmission1 = voting_model.predict(test_imputed_data.drop('id', axis =1))\n\n# Submission1 = TrainML(voting_model, test)\n\nSubmission1\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:23:09.108231Z","iopub.execute_input":"2024-12-02T02:23:09.108670Z","iopub.status.idle":"2024-12-02T02:23:13.455905Z","shell.execute_reply.started":"2024-12-02T02:23:09.108635Z","shell.execute_reply":"2024-12-02T02:23:13.454702Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"combined = pd.DataFrame({\n 'id': test_imputed_data['id'],\n 'sii': Submission1\n})\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:23:13.458016Z","iopub.execute_input":"2024-12-02T02:23:13.458588Z","iopub.status.idle":"2024-12-02T02:23:13.464743Z","shell.execute_reply.started":"2024-12-02T02:23:13.458538Z","shell.execute_reply":"2024-12-02T02:23:13.463440Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"merge_df = pd.merge(sample.drop('sii', axis = 1), combined, on='id', how ='inner')\n\nmerge_df.to_csv('submission.csv', index=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:23:13.466120Z","iopub.execute_input":"2024-12-02T02:23:13.466554Z","iopub.status.idle":"2024-12-02T02:23:13.482727Z","shell.execute_reply.started":"2024-12-02T02:23:13.466520Z","shell.execute_reply":"2024-12-02T02:23:13.481643Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"merge_df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:23:13.485120Z","iopub.execute_input":"2024-12-02T02:23:13.485567Z","iopub.status.idle":"2024-12-02T02:23:13.501585Z","shell.execute_reply.started":"2024-12-02T02:23:13.485520Z","shell.execute_reply":"2024-12-02T02:23:13.500428Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"\n#
One Issue
\n* 결과를 제출 할 시에 에러가 발생합니다. 코드 상의 문법적 오류는 아닙니다.\n* Outputs과 Logs 상으로는 둘 다 아무런 문제도 없습니다.\n* 아마 hidden dataset으로 인한 문제로 보이긴 합니다.\n* 혹시 이를 해결할 방안이 있으신 분은 저에게 알려주시면 감사하겠습니다.\n\n* An error occurs when sending results.\r\n* There are no problems with Outputs and Logs.\r \n*This is probably a problem with a hidden dataset. \r\n*If anyone has any ideas on how to solve this, I would appreciate it if you could let me know.","metadata":{}},{"cell_type":"markdown","source":"\n#
Error
\n\n\n![image.png](attachment:1df748a2-f88d-4ecc-a822-ddb31ef37b95.png)","metadata":{},"attachments":{"1df748a2-f88d-4ecc-a822-ddb31ef37b95.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"\n#
Output Result
\n\n\n![image.png](attachment:ef373d90-748f-4d48-ab2c-caccb45eda51.png)","metadata":{},"attachments":{"ef373d90-748f-4d48-ab2c-caccb45eda51.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"\n#
Logs Result
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"}}},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}