v1.3.0

[1.3.0] - 2022-03-09

• [FIX] Fix critical regression on AlgorithmSettings.save
• [FIX] Fix some presentation issues with convergence plots
• [FIX/FEAT] Raise a LeaspyConvergenceError if variances of params collapse to zero
• [FIX] Improve and robustify AlgorithmSettings.set_logs
• [FIX] AlgorithmSettings dict parameters are now recursively merged with defaults parameters
• [FIX] Fix 'mode_real' and 'mean_real' personalization algorithms (bug with initial temperature / annealing)
• [PERF] Slightly improve performance of Gibbs samplers and fit algorithm
• [PERF] Initial adaptative std-dev used in Gibbs samplers is now parameter dependent (i.e. scaled) to speed-up convergence
• [FEAT] In ScipyMinimize algorithm, user can now tune parameters sent to scipy.optimize.minimize and customize how convergence issues are logged
• [FEAT] Hyper-parameters of the samplers can now be tuned in the 'mcmc_saem', 'mode_real' and 'mean_real' algorithms
• [FEAT] The n_burn_in_iter_frac and annealing.n_iter_frac parameters were introduced to dynamically adjust the fraction of iterations independently of n_iter parameter (for 'mcmc_saem', 'mode_real' and 'mean_real')
• [FEAT] The computed RMSE at end of fit with Bernoulli noise model is now per feature
• [FEAT] More models / individual parameters in leaspy.Loader
• [DOC] Improve documentation & add a docstrings validator
• [CHORE] Clean-up code & tests
• [TESTS] Much more functional tests for all the models & personalization algorithms supported
• [COMPAT] PyTorch >=1.7.1 is now required