SubModLib is an easy-to-use, efficient and scalable Python library for submodular optimization with a C++ optimization engine. Submodlib finds its application in summarization, data subset selection, hyper parameter tuning, efficient training etc. Through a rich API, it offers a great deal of flexibility in the way it can be used.

Please check out our latest arxiv preprint: https://arxiv.org/abs/2202.10680

• Rich suite of functions for a wide variety of subset selection tasks:
  • regular set (submodular) functions
  • submodular mutual information functions
  • conditional gain functions
  • conditional mutual information functions
• Supports different types of optimizers
  • naive greedy
  • lazy (accelerated) greedy
  • stochastic (random) greedy
  • lazier than lazy greedy
• Combines the best of Python's ease of use and C++'s efficiency
• Rich API which gives a variety of options to the user. See this notebook for an example of different usage patterns
• De-coupled function and optimizer paradigm makes it suitable for a wide-variety of tasks
• Comprehensive documentation (available here)

• Modelling capabilities of regular submodular functions
• Modelling capabilities of submodular mutual information (SMI) functions
• Modelling capabilities of conditional gain (CG) functions
• Modelling capabilities of conditional mutual information (CMI) functions
• This notebook contains a quantitative analysis of performance of different functions and role of the parameterization in aspects like query-coverage, query-relevance, privacy-irrelevance and diversity for different SMI, CG and CMI functions as observed on synthetically generated dataset.
• This notebook contains similar analysis on ImageNet dataset.
• For a more detailed discussion on all possible usage patterns, please see Different Options of Usage

• $ pip install -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ submodlib

• $ git clone https://github.com/decile-team/submodlib.git
• $ cd submodlib
• $ pip install .
• Latest documentation is available at readthedocs. However, if local documentation is required to be built, follow these steps::
  • $ pip install -U sphinx
  • $ pip install sphinxcontrib-bibtex
  • $ pip install sphinx-rtd-theme
  • $ cd docs
  • $ make clean html
• To run the tests, follow these steps:
  • $ pip install pytest
  • $ pytest # this runs ALL tests
  • $ pytest -m --verbose --disable-warnings -rA # this runs test specified by the . Possible markers are mentioned in pyproject.toml file.

It is very easy to get started with submodlib. Using a submodular function in submodlib essentially boils down to just two steps:

1. instantiate the corresponding function object
2. invoke the desired method on the created object

The most frequently used methods are:

1. f.evaluate() - takes a subset and returns the score of the subset as computed by the function f
2. f.marginalGain() - takes a subset and an element and returns the marginal gain of adding the element to the subset, as computed by f
3. f.maximize() - takes a budget and an optimizer to return an optimal set as a result of maximizing f

For example,