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PyMC v3.5 Release Notes

Release Date: 2018-07-21 // almost 6 years ago

  • 🆕 New features

    • ➕ Add documentation section on survival analysis and censored data models
    • ✅ Add check_test_point method to pm.Model
    • ➕ Add Ordered Transformation and OrderedLogistic distribution
    • ➕ Add Chain transformation
    • 👌 Improve error message Mass matrix contains zeros on the diagonal. Some derivatives might always be zero during tuning of pm.sample
    • 👌 Improve error message NaN occurred in optimization. during ADVI
    • Save and load traces without pickle using pm.save_trace and pm.load_trace
    • ➕ Add Kumaraswamy distribution
    • ➕ Add TruncatedNormal distribution
    • Rewrite parallel sampling of multiple chains on py3. This resolves
      long standing issues when transferring large traces to the main process,
      avoids pickling issues on UNIX, and allows us to show a progress bar
      for all chains. If parallel sampling is interrupted, we now return
      partial results.
    • Add sample_prior_predictive which allows for efficient sampling from
      the unconditioned model.
    • 🚚 SMC: remove experimental warning, allow sampling using sample, reduce autocorrelation from
      final trace.
    • Add model_to_graphviz (which uses the optional dependency graphviz) to
      plot a directed graph of a PyMC3 model using plate notation.
    • ➕ Add beta-ELBO variational inference as in beta-VAE model (Christopher P. Burgess et al. NIPS, 2017)
    • Add __dir__ to SingleGroupApproximation to improve autocompletion in interactive environments

    🛠 Fixes

    • 🛠 Fixed grammar in divergence warning, previously There were 1 divergences ... could be raised.
    • 🛠 Fixed KeyError raised when only subset of variables are specified to be recorded in the trace.
    • ✂ Removed unused repeat=None arguments from all random() methods in distributions.
    • 🗄 Deprecated the sigma argument in MarginalSparse.marginal_likelihood in favor of noise
    • 🛠 Fixed unexpected behavior in random. Now the random functionality is more robust and will work better for sample_prior when that is implemented.
    • Fixed scale_cost_to_minibatch behaviour, previously this was not working and always False