External library examples for RESSPECT

This collection of example classes demonstrates the minimum code needed to define the various plugin classes that RESSPECT supports. For more information about RESSPECT please see the following:

• Repository: https://github.com/lsstdesc/resspect
• ReadTheDocs: https://lsst-resspect.readthedocs.io/en/latest/

Feel free to copy or fork this repository as a way to get started using an externally defined feature extractor, classifier, or query strategy for RESSPECT.

Note that it is not a requirement to implement all three example classes, they are simply grouped together here to be concise. Implementing only one or two of the different classes shown here is completely acceptable.

Example usage of externally library

RESSPECT has two primary function to drive active learning:

• learn_loop (link)
• time_domain_loop (link)

To define the use of an external class and organize the large number of input parameters necessary for each of these a configuration class is used, either: LoopConfiguration (link) for learn_loop or, TimeDomainConfiguration (link) for time_domain_loop.

To make use of an externally defined feature extractor, classifier, or query strategy, simply define the class to be used in the configuration object. The following example shows how to use the libpath to specify which of the external classes RESSPECT should use when running learn_loop.

from resspect.loop_configuration import LoopConfiguration
from resspect.learn_loop import learn_loop

# Define the loop configuration
loop_config = LoopConfiguration(
    # ... preceding parameters
    strategy='resspect_external.example_query_strategy.ExampleQueryStrategy',
    classifier='resspect_external.example_classifier.ExampleClassifier',
    features_method='resspect_external.example_feature_extractor.ExampleFeatureExtractor',
    # ... additional parameters
)

# Use loop configuration to call active learning loop
learn_loop(loop_config)

Note in this example code, we instruct RESSPECT to make use of the ExampleFeatureExtractor as well as the ExampleClassifier and ExampleQueryStrategy classes by providing the libpath string as the parameters.

Dev Guide - Getting Started

Before installing any dependencies or writing code, it's a great idea to create a virtual environment. LINCC-Frameworks engineers primarily use conda to manage virtual environments. If you have conda installed locally, you can run the following to create and activate a new environment.

>> conda create -n <env_name> python=3.11
>> conda activate <env_name>

Once you have created a new environment, you can install this project for local development using the following commands:

>> ./.setup_dev.sh
>> conda install pandoc

Notes:

1. ./.setup_dev.sh will initialize pre-commit for this local repository, so that a set of tests will be run prior to completing a local commit. For more information, see the Python Project Template documentation on pre-commit
2. Install pandoc allows you to verify that automatic rendering of Jupyter notebooks into documentation for ReadTheDocs works as expected. For more information, see the Python Project Template documentation on Sphinx and Python Notebooks

Acknoledgements

This project was automatically generated using the LINCC-Frameworks python-project-template.

For more information about the project template see the documentation.