matt-graham/mici: v0.1.6

New features and efficiency improvements Added framework for adaptation of transition parameters via classes in new mici.adapters module and associated sample_chains_with_adaptive_warm_up methods added to sampler classes. Initial adapters implemented are a dual averaging step size adapter, a diagonal metric adapter based on estimated variances and dense metric adapter based on estimated covariance matrices. New labelled sequence progress bar implementation to use with adaptive sampling chains and generalisation of progress bars to allow wrapping arbitrary sequences. Improved matrix operation efficiency. Repeated calls to matrix constructors avoided by caching matrices constructed by transpose / sqrt / inv methods and explicit matrix-vector multiplies used for inverted factored definite matrices to avoid bottleneck of scipy.linalg.solve_triangular which is much slower than a direct matrix-vector multiply. Remove redundant recaluclation of product of momentum and inverse metric when calculating value and gradient of h2 Hamiltonian component. Bug fixes Non-display of progress bars when running on Google Colab fixed. Backwards incompatible changes Previously ExplicitLeapfrogIntegrator was aliased to LeapfrogIntegrator. ExplicitLeapfrogIntegrator has now been renamed to LeapfrogIntegrator with no aliases. The dictionaries of chain statistics returned by sample_chain and sample_chains call no longer contain an hamiltonian key. Instead by default the traces dictionary returned by these methods includes a hamiltonian entry (as generated by the default trace function used when a custom list of trace functions are not specified by the trace_funcs keyword argument). The rationale for this change is to standardize the chain statistics dictionary as only including values which don't correspond directly to a function of the chain states at the end of each iteration but instead generally depend on the whole transition, with the trace_funcs keyword argument already allowing recording of quantities outside this category. The dictionaries of chain statistics returned by sample_chain and sample_chains call no longer contain an accept_prob key due to the ambiguity of its meaning and differing usage in the static Metropolis and dynamic HMC integration transitions. Both classes of transitions now return a accept_stat statistic in the statistics dictionary, this corresponding to an overall measure of the probability of accepting a move both due to non-zero changes in the Hamiltonian over a trajectory and any early termination of trajectories due to convergence errors or non-reversible steps when using an implicit integrator. This single summary statistic is intended to both provide a quick summary of sampling performance e.g. in the monitored statistics in chain progress bars, and as the default target for adaptation of the integrator step size. Individual transitions also provide additional acceptance probability related statistics with differing meanings. The dynamic integration transitions (subclasses of DynamicIntegrationTransition and corresponding Dynamic*HMC sampler classes) additionally return an av_metrop_accept_prob statistic which is the average Metropolis acceptance probability for a move from the initial state to each of the states on the whole trajectory tree built (including subtrees which are invalid under the no-U-turn criteria); this does not include any adjustment for early termination of trajectories due to convergence errors / non-reversible steps in implicit integrators so should be used with caution in such cases. The Metropolis integration transitions (subclasses of MetropolisIntegrationTransition and corresponding *MetropolisHMC sampler classes) additionally return a metrop_accept_prob statistic which is the Metropolis acceptance probability of the move proposed by the transition without any adjustment for rejection due to convergence errors / non-reversible steps in implicit integrators. Documentation, internal naming and API has been standardized to use -ize rather than -ise endings. The only public facing change outside of the documentation is the renaming of mici.systems.SoftAbsRegularisedPositiveDefiniteMatrix to mici.systems.SoftAbsRegularizedPositiveDefiniteMatrix