CGMissingData 0.1.5

MICE + Random Forest + KNN to handle missing values of CGM device

CGMissingData

CGMissingData is a simple missing-data benchmarking package that runs:

MICE imputation (IterativeImputer)

Random Forest regression

KNN regression

It helps you test model performance under different missing-value rates.

Your CSV must include at least these columns:

LBORRES — glucose value (target)

TimeSeries — time series data

TimeDifferenceMinutes — time difference in minutes

USUBJID — subject ID

How to Run?

1. Envirionment Setup- cd "C:\Path\To\Your\Project"

Create a virtual environment python -m venv .venv

Activate the environment ..venv\Scripts\activate

2. Install python python -m pip install --upgrade pip pip install -e .

3. ..venv\Scripts\python.exe -c "from CGMmissingData import run_missingness_benchmark; r=run_missingness_benchmark('MyData.csv', mask_rates=[0.05, 0.10, 0.20, 0.30, 0.40]); print(r); r.to_csv('results.csv', index=False)" #Ensure your dataset (e.g., MyData.csv) is located in the project file. Execute the benchmark directly from the CLI to generate a results.csv file:

Using Google Colab?

1. !pip -q install CGMissingData==0.1.2 (change the version number depending our new release. You can also try with !pip -q install CGMissingData)

2. from CGMissingData import run_missingness_benchmark

3. df = "/content/drive/MyDrive/CGMExampleData.csv" # your dataset path

4. results = run_missingness_benchmark( "CGMExampleData.csv", # or df mask_rates=[0.05, 0.10, 0.20, 0.30, 0.40] )

5. print(results)

6. results.to_csv("results.csv", index=False)