experimentscan

experimentscan is a Python library designed to simplify the process of conducting and managing scientific experiments, particularly in fields like machine learning and data science. It provides a framework for defining experimental parameters, running multiple trials with varying configurations, and systematically collecting and analyzing the results. The core functionality of experimentscan revolves around creating "experiments" that encapsulate specific tasks or models. Within an experiment, users can define "runs," each representing a single execution with a particular set of hyperparameters or configurations. The library then handles the execution of these runs, ensuring that each is performed with the specified settings. A key feature is its ability to automatically log metrics and outputs from each run. This logging can include performance scores, training progress, and any other relevant data. experimentscan facilitates the comparison of different runs by providing tools to access and visualize the collected data. This allows researchers to identify the most effective parameters or model architectures. The library aims to reduce the manual effort involved in experimental tracking and reproducibility, making it easier to iterate on research ideas and share findings. It integrates with popular data science libraries and can be extended to support various types of experiments beyond machine learning.