Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix pm.DensityDist bug and incorporate latest upstream changes #42

Merged
merged 23 commits into from
Jul 22, 2020

Conversation

gmingas
Copy link
Collaborator

@gmingas gmingas commented Jul 22, 2020

  • Switches from pm.DensityDist to pm.Potential to describe the likelihood in MLDA notebooks and script examples. This is done because of the bug described here.
  • Changes a few parameters in the MLDA .py example to match the ones in the equivalent notebook.
  • Merges latest upstream master changes.

Closes #40

bwengals and others added 23 commits June 26, 2020 11:02
* update gp-latent nb to use arviz

* rerun, run black

* rerun after fixes from comments

* rerun black
* rewrite radon notebook using ArviZ and xarray

Roughly half notebook has been updated

* add comments on xarray usage

* rewrite 2n half of notebook

* minor fix

* rerun notebook and minor changes

* rerun notebook on pymc3.9.2 and ArviZ 0.9.0

* remove unused import

* add change to release notes
…ostics (pymc-devs#3981)

* first attempt to vectorize smc kernel

* add ess, remove multiprocessing

* run multiple chains

* remove unused imports

* add more info to report

* minor fix

* test log

* fix type_num error

* remove unused imports update BF notebook

* update notebook with diagnostics

* update notebooks

* update notebook

* update notebook
* Honor discard_tuned_samples during KeyboardInterrupt

* Do not compute convergence checks without samples
* Add time values as sampler stats for NUTS

* Use float time counters for nuts stats

* Add timing sampler stats to release notes

* Improve doc of time related sampler stats

Co-authored-by: Alexandre ANDORRA <[email protected]>

Co-authored-by: Alexandre ANDORRA <[email protected]>
* Drop support for py3.6

* Update RELEASE-NOTES.md

Co-authored-by: Colin <[email protected]>

Co-authored-by: Colin <[email protected]>
* Add more info to divergence warnings

* Add dataclasses as requirement for py3.6

* Fix tests for extra divergence info

* Remove py3.6 requirements
Co-authored-by: Adrian Seyboldt <[email protected]>
* Merge close remote connection

* Manually pickle step method in multiprocess sampling

* Fix tests for extra divergence info

* Add test for remote process crash

* Better formatting in test_parallel_sampling

Co-authored-by: Junpeng Lao <[email protected]>

* Use mp_ctx forkserver on MacOS

* Add test for pickle with dill

Co-authored-by: Junpeng Lao <[email protected]>
* Fix posterior pred. sampling keep_size w/ arviz input.

Previously posterior predictive sampling functions did not properly
handle the `keep_size` keyword argument when getting an xarray Dataset
as parameter.

Also extended these functions to accept InferenceData object as input.

* Reformatting.

* Check type errors.

Make errors consistent across sample_posterior_predictive and fast_sample_posterior_predictive, and add 2 tests.

* Add changelog entry.

Co-authored-by: Robert P. Goldman <[email protected]>
* update notebook

* move dist functions out of simulator class

* fix docstring

* add warning and test for automatic selection of sort sum_stat when using wassertein and energy distances

* update release notes

* fix typo

* add sim_data test

* update and add tests

* update and add tests
…-devs#3989)

* fix the expression of periodic kernel

* revert change and add doc

* FIXUP: add suggested doc string

* FIXUP: revertchanges in .gitignore
* Fix for issue 4022.

Check for support for `warn` argument in `matplotlib.use()` call. Drop it if it causes an error.

* Alternative fix.
… in MLDA notebooks and script examples. This is done because of the bug described in arviz-devs/arviz#1279. The commit also changes a few parameters in the MLDA .py example to match the ones in the equivalent notebook.
* Remove Dirichlet distribution type restrictions

Closes pymc-devs#3999.

* Add missing Dirichlet shape parameters to tests

* Remove Dirichlet positive concentration parameter constructor tests

This test can't be performed in the constructor if we're allowing Theano-type
distribution parameters.

* Add a hack to statically infer Dirichlet argument shapes

Co-authored-by: Brandon T. Willard <[email protected]>
to keep mlda_develop updated with recent changes.
@gmingas gmingas merged commit 798b89f into mlda Jul 22, 2020
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

Fix broken arviz step after sampling finishes
9 participants