Geochemistry : Automated Machine Learning Python Framework for Tabular Data

Although machine learning (ML) has brought new insights into geochemistry research, its implementation is laborious and time-consuming. Here, we announce Geochemistry pi, an open-source automated ML Python framework. Geochemists only need to provide tabulated data and select the desired options to clean data and run ML algorithms. The process operates in a question-and-answer format, and thus does not require that users have coding experience. After either automatic or manual parameter tuning, the automated Python framework provides users with performance and prediction results for the trained ML model. Based on the scikit-learn library, Geochemistry pi has established a customized automated process for implementing classification, regression, dimensionality reduction, and clustering algorithms. The Python framework enables extensibility and portability by constructing a hierarchical pipeline architecture that separates data transmission from the algorithm application. The AutoML module is constructed using the Cost-Frugal Optimization and Blended Search Strategy hyperparameter search methods from the A Fast and Lightweight AutoML Library, and the model parameter optimization process is accelerated by the Ray distributed computing framework. The MLflow library is integrated into ML lifecycle management, which allows users to compare multiple trained models at different scales and manage the data and diagrams generated. In addition, the front-end and back-end frameworks are separated to build the web portal, which demonstrates the ML model and data science workflow through a user-friendly web interface. In summary, Geochemistry pi provides a Python framework for users and developers to accelerate their data mining efficiency with both online and offline operation options. Geochemistry pi is a helpful tool for scientists who work with geochemical data. One of its standout features is its simplicity. Scientists can use the tool to perform machine learning (ML) on the tabular data by answering a series of questions about what they want to discover. The tool does the rest by using advanced ML techniques to uncover insights from the data. Even scientists without coding skills can use Geochemistry pi effectively. This tool is built on a reliable library called scikit-learn, ensuring that it works well with different ML methods. It is also flexible, allowing researchers to customize it to fit their specific needs. Geochemistry pi separates data processing from ML tasks, making it adaptable and expandable. It includes features for continuous training and managing the entire ML process. To prove its effectiveness, Geochemistry pi was tested against previous geochemical studies in areas such as regression, classification, clustering, and dimensional reduction. The results showed that it could replicate the findings of these studies accurately. Accessible through a web portal or command line, Geochemistry pi is a valuable asset for geochemists and researchers looking to analyze large geochemical data sets. Open-source Python framework for machine learning applications in geochemistryAutomated pipeline for tabular dataQuestion-and-answer format obviates the need for coding experience