glupredkit 0.0.9

GluPredKit aims to make blood glucose model training and prediction more accessible.

Blood Glucose Prediction-Kit

This Blood Glucose (BG) Prediction Framework streamlines the process of data handling, training, and evaluating blood glucose prediction models in Python. Access all features via the integrated Command Line Interface (CLI).

The figure below illustrates an overview over the pipeline including all the stages of this blood glucose prediction framework.

Table of Contents

1. Setup and Installation
   • Regular users: Install using pip
   • Developers: Install using the cloned repository
2. Usage of Command Line Interface
   • Getting Started
   • Parsing Data
   • Preprocessing Data
   • Train a Model
   • Evaluate Models
   • Setting Configurations
3. Contributing with Code
   • Adding Data Source Parsers
   • Adding Data Preprocessors
   • Adding Machine Learning Prediction Models
   • Implementing Custom Evaluation Metrics
   • Adding Evaluation Plots
4. Disclaimers and Limitations
5. License

Setup and Installation

To setup and install this platform, there are two options depending on whether you are a regular user or a developer:

1. Install using pip (regular users): If you want to access the command line interface (CLI) without reading or modifying the code, choose this option.

2. Install using the cloned repository (developers): This is the choice if you want to use the repository and have the code visible, and to potentially edit the code.

Choose which one is relevant for you, and follow the instructions below.

---

Regular users: Install using pip

Open your terminal and go to an empty folder in your command line. Note that all the data storage, trained models and results will be stored in this folder.

Creating a virtual environment is optional, but recommended. Create a virtual environment with the command python3 -m venv glupredkit_venv, and activate it with source glupredkit_venv/bin/activate.

To set up the CLI, simply run the following command:

pip install glupredkit

---

Developers: Install using the cloned repository

First, clone the repository and make sure you are located in the root of the directory in your command line. To set up the repository with all requirements, simply run the following command:

./install.sh

Make sure that the virtual environment bgp-evaluation is activated before you proceed. If not, call source bgp-evaluation/bin/activate.

Usage of Command Line Interface

The command-line tool is designed to streamline the end-to-end process of data handling, preprocessing, model training, evaluation, and configuration for blood glucose prediction. The following is a guide to using this script.

Getting started

1. First, follow the instructions above in "Setup and Installation".
2. Then, navigate to a desired folder in your command line. This is the folder where the datasets, models and results will be stored.
3. Set up the necessary directories for the GluPredKit CLI by running the following command:
   • For regular users: glupredkit setup_directories
   • For developers: python3 -m glupredkit.cli setup_directories

You should now have the following file structure in your desired folder:

data/
│
├── raw/
│
├── processed/
│
├── trained_models/
│
├── figures/
│
└── reports/

Now, you're ready to use the Command Line Interface (CLI) for processing and predicting blood glucose levels.

Note that the prefix for all the commands will be either glupredkit for regular users, or python -m glupredkit.cli for developers. In the examples below we will use glupredkit.

Parsing Data

Description: Parse data from a chosen source and store it as CSV in data/raw using the selected parser. If you have an existing dataset, you can store it in data/raw, skip this step and go directly to preprocessing.

glupredkit parse --parser [tidepool|nightscout|apple_health] USERNAME PASSWORD [--file-name FILE_NAME] [--start-date START_DATE] [--end-date END_DATE]

• --parser: Choose a parser between tidepool, nightscout or apple_health.
• username: Your username for the data source.
• password: Your password for the data source.
• --start-date (Optional): Start date for data retrieval, default is two weeks ago. Format "dd-mm-yyyy".
• --end-date (Optional): End date for data retrieval, default is now. Format "dd-mm-yyyy".

Example

glupredkit parse --parser tidepool [e-mail] mypassword --start-date 01-09-2023 --end-date 30-09-2023

---

Preprocessing Data

Description: Preprocess data from an input CSV file and store the training and test data into separate CSV files.

glupredkit preprocess INPUT_FILE_NAME [--preprocessor [scikit_learn|tf_keras]] [--prediction-horizon PREDICTION_HORIZON] [--num-lagged-features NUM_LAGGED_FEATURES] [--include-hour INCLUDE_HOUR] [--test-size TEST_SIZE] [--num_features NUM_FEATURES] [--cat_features CAT_FEATURES]

• --preprocessor: Choose between scikit_learn and tf_keras for preprocessing.
• input-file-name: Name of the input CSV file containing the data. Note that this file needs to be located in data/raw.
• --prediction-horizon (Optional): Prediction into the future given in time in minutes.
• --num-lagged-features (Optional): The number of samples to use as time-lagged features. CGM values are sampled in 5-minute intervals, so 12 samples equals one hour.
• --test-size (Optional): The fraction in float to how much of the data shall be used as test data.
• --num-features (Optional): List of numerical features, separated by comma. Note that the feature names must be identical to column names in the input file.
• --cat-features (Optional): List of categorical features, separated by comma. Note that the feature names must be identical to column names in the input file.

Example

glupredkit preprocess tidepool_16-09-2023_to_30-09-2023.csv --num-features CGM,insulin --cat-features hour

---

Train a Model

Description: Train a model using the specified training data.

glupredkit train_model --model MODEL_NAME INPUT_FILE_NAME [--prediction-horizon PREDICTION_HORIZON]

• --model: Name of the model file (without .py) to be trained. The file name must exist in src/models.
• input-file-name: Name of the CSV file containing training data. The file name must exist in data/processed.
• --prediction-horizon (Optional): The prediction horizon for the target value in minutes.

Example

glupredkit train_model --model ridge train-data_scikit_learn_ph-60_lag-12.csv

---

Evaluate Models

Description: Evaluate one or more trained models using the specified test data, compute metrics and generate plots. The results will be stored in results/reports for evaluation metrics and in results/figures for plots.

glupredkit evaluate_model --model-files MODEL_FILE_NAMES [--metrics METRICS] [--plots PLOTS] TEST_FILE_NAME [--prediction-horizon PREDICTION_HORIZON]

• --model-files: List of trained model filenames from data/trained_models (without .pkl), separated by comma.
• --metrics (Optional): List of metrics from src/metrics to be computed, separated by comma.
• --plots (Optional): List of plots from src/plots to be generated, separated by comma.
• test-file-name: Name of the CSV file containing test data.

Example

glupredkit evaluate_model test-data_scikit_learn_ph-60_lag-12.csv --model-files ridge_ph-60,arx_ph-60,svr_linear_ph-60 --metrics rmse

---

Setting Configurations

glupredkit set_config --use-mgdl [True|False]

Description: Set whether to use mg/dL or mmol/L for units.

---

That's it! You can now run the desired command with the mentioned arguments. Always refer back to this guide for the correct usage.

Contributing with code

In this section we will explain how you can contribute with enhancing the implementation of parsers, preprocessors, models, evaluation metrics and plots.

Contributing With New Components

Regardless of the component type you're contributing, follow these general steps:

1. Navigate to the corresponding directory in src/.
2. Create a new Python file for your component.
3. Implement your component class, inheriting from the appropriate base class.
4. Add necessary tests and update the documentation.

Here are specifics for various component types:

Parsers

Refers to the fetching of data from data sources (for example Nighscout, Tidepool or Apple Health), and to process the data into the same table.

• Directory: src/parsers
• Base Class: BaseParser

Preprocessors

Refers to the preprocessing of the raw datasets from the parsing-stage. This includes imputation, feature addition, removing NaN values, splitting data etc.

• Directory: src/preprocessors
• Base Class: BasePreprocessor

Machine Learning Prediction Models

Refers to using preprocessed data to train a blood glucose prediction model.

• Directory: src/models
• Base Class: BaseModel

Evaluation Metrics

Refers to different 'scores' to describing the accuracy of the predictions of a blood glucose prediction model. - Directory: src/metrics

• Base Class: BaseMetric

Evaluation Plots

Different types of plots that can illustrate blood glucose predictions together with actual measured values.

• Directory: src/plots
• Base Class: BasePlot

Remember to adhere to our coding and documentation standards when contributing!

Testing

To run the tests, write python tests/test_all.py in the terminal.

Disclaimers and limitations

• Datetimes that are fetched from Tidepool API are received converted to timezone offset +00:00. There is no way to get information about the original timezone offset from this data source.
• Bolus doses that are fetched from Tidepool API does not include the end date of the dose delivery.
• Metrics assumes mg/dL for the input.
• Note that the difference between how basal rates are registered. Bolus doses are however consistent across. Hopefully it is negligable.

License

This project is licensed under the MIT License - see the LICENSE file for details