v0.4.1

Highlights

• Fixing two minor issues when running BP with variable groups defined with different number of states by @antoine-dedieu in #144

Full Changelog: v0.4.0...v0.4.1

v0.4.0

Breaking changes

⚠️ This release changes the high-level API as well as import paths ⚠️

This release makes several major breaking changes to improve usability and efficiency of the package.

1. Interacting with variables through VarGroup objects

We no longer refer to variables by names, but instead directly interact with VarGroup objects. This change has several implications.

• We no longer have a Variable class. Instead, we access individual variables by indexing into VarGroup objects.

• FactorGraph can no longer be initialized with a dictionary of variable groups (as we no longer have names for variables). Instead, we initialize a FactorGraph by

from pgmax import fgraph
fg = fgraph.FactorGraph(variable_groups=variable_groups)

where variable_groups is either a VarGroup or a list of VarGroups.

• We can directly construct Factor/FactorGroup using individual variables, and have a unified add_factors interface for adding Factors and FactorGroups to the FactorGraph.

For example, we can create a PairwiseFactorGroup via:

from pgmax import fgroup
pairwise_factors = fgroup.PairwiseFactorGroup(
    variables_for_factors=variables_for_factors,
    log_potential_matrix=log_potential_matrix,
)

where variables_for_factors is a list of list of individual variables. And we can add factors to a FactorGraph fg by

fg.add_factors(factors=factors)

where factors can be individual Factor, individual FactorGroup, or a list of Factors and FactorGroups.

• We access LBP results by indexing with VarGroup. For example, after running BP, we can get the MAP decoding for the VarGroup visible_variables via

beliefs = bp.get_beliefs(bp_arrays)
map_states_visible = infer.decode_map_states(beliefs)[visible_variables]

2. Efficient construction of FactorGroup

We have implemented efficient construction of FactorGroup. Going forward, we always recommend constructing FactorGroup instead of individual Factor.

3. Improved LBP interface

We first create the functions used to run BP with temperature T via

from pgmax import infer
bp = infer.BP(fg.bp_state, temperature=T)

where bp contains functions that initialize or updates the arrays involved in LBP.

We can initialize bp_arrays by

bp_arrays = bp.init()

apply log potentials, messages and evidence updates by

bp_arrays = bp.update(
    bp_arrays=bp_arrays,
	log_potentials_updates=log_potentials_updates,
	ftov_msgs_updates=ftov_msgs_updates,
	evidence_updates=evidence_updates,
)

and run bp for a certain number of iterations by

bp_arrays = bp.run_bp(bp_arrays, num_iters=num_iters, damping=damping)

Note that we can arbitrarily interleave bp.update with bp.run_bp, which allows flexible control over how we run LBP.

4. Improved high-level module organization

Now we have 5 main high-level modules, fgraph for factor graphs, factor for factors, vgroup for variable groups, fgroup for factor groups, and infer for LBP.

Details of what has changed:

• Speed up the process of adding Factors and compiling wiring for a FactorGraph by moving all the computations to the FactorGroup level, by @antoine-dedieu in #129
• Speed up the process of computing log potentials + wiring for FactorGroup with numba, by @antoine-dedieu in #133
• Make the BP class behavior closer to JAX optimizers by @antoine-dedieu in #135
• Get rid of the Variables and CompositeVariableGroup classes + of the variable names + adopt a simpler representation for variables + rely on numpy arrays to makeNDVarArray efficient, by @antoine-dedieu in #136
• Overall module reorganization, by @antoine-dedieu in #140

Full Changelog: v0.3.0...v0.4.0

v0.3.0

Highlights

• Refactors to support adding different factor types with specialized inference procedures by @antoine-dedieu in #122
• Specialized logical AND/OR factors by @antoine-dedieu in #122 #126
• New example on 2D binary blind deconvolution by @antoine-dedieu in #127

New Contributors

• @antoine-dedieu made his first contribution in #122

Full Changelog: v0.2.3...v0.3.0

v0.2.3

What's Changed

• Links to blog post and companion paper; Documentation updates by @StannisZhou in #111
• Get rid of redundant array shape for log_potentials by @StannisZhou in #116
• Support python 3.9/3.10; Improve documentation for add_factor; Bump up version for new release by @StannisZhou in #120

Full Changelog: v0.2.2...v0.2.3

PGMax release v0.2.2

What's Changed

• Update README by @StannisZhou in #103
• RCN example by @shrinuKushagra in #96
• Add support for sum-product with temperature by @StannisZhou in #104
• Include Grid Markov Random Field example by @StannisZhou in #107
• Changes for blog post by @StannisZhou in #109

New Contributors

• @shrinuKushagra made their first contribution in #96

Full Changelog: v0.2.1...v0.2.2

First public release

What's Changed

• Bump versions for publishing by @StannisZhou in #63
• Example notebook with PMAP sampling of RBMs trained on MNIST digits by @StannisZhou in #80
• Use functools.partial instead of jax.partial by @StannisZhou in #83
• First pass for speeding up graph and evidence operations by @StannisZhou in #84
• Moving to a functional interface by @StannisZhou in #88
• Update README in preparation for making repo public by @StannisZhou in #89
• Pre commit ci test by @NishanthJKumar in #90
• [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #91
• Update dependency requirements to be less aggresive by @StannisZhou in #92
• adds codecov badge to README! by @NishanthJKumar in #94
• Fix GPU memory leak that came up in RCN example by @StannisZhou in #97
• [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #95
• Docs update by @NishanthJKumar in #98
• fixes bug where wrong conf.py path was specified by @NishanthJKumar in #99
• Rtd warning fix by @NishanthJKumar in #100
• includes minor changes to README and documentation by @NishanthJKumar in #101
• Bump up version; Fixes for docs by @StannisZhou in #102

Full Changelog: v0.2.0...v0.2.1

Internal release

Features

• Efficient and scalable max-product belief propagation using a fully flat representation
• A general factor graph interface that supports easy specification of PGMs with pairwise factors and higher-order factors based on explicit enumeration
• Mechanisms for evidence and message manipulation
• 3 example notebooks showcasing the functionalities of PGMax