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Dirichlet doesn't allow basic Theano types #3999

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brandonwillard opened this issue Jul 5, 2020 · 0 comments · Fixed by #4000
Closed

Dirichlet doesn't allow basic Theano types #3999

brandonwillard opened this issue Jul 5, 2020 · 0 comments · Fixed by #4000
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The Dirichlet distribution doesn't allow basic Theano types (e.g. TensorVariable and TensorConstant) for its parameter.

The following raises a TypeError:

import numpy as np
import pymc3 as pm

import theano.tensor as tt


pm.Dirichlet.dist(tt.as_tensor_variable(np.r_[1, 1]))

the relevant output is

TypeError: The vector of concentration parameters (a) must be a python list or numpy array.
brandonwillard added a commit to brandonwillard/pymc that referenced this issue Jul 5, 2020
@brandonwillard
Remove Dirichlet distribution type restrictions
3e21e92 
Closes pymc-devs#3999.
@brandonwillard brandonwillard mentioned this issue Jul 5, 2020
Merged
brandonwillard added a commit to brandonwillard/pymc that referenced this issue Jul 5, 2020
@brandonwillard
Remove Dirichlet distribution type restrictions
9a2da91 
Closes pymc-devs#3999.
@brandonwillard brandonwillard mentioned this issue Jul 5, 2020
Closed
brandonwillard added a commit to brandonwillard/pymc that referenced this issue Jul 17, 2020
@brandonwillard
Remove Dirichlet distribution type restrictions
2ed5620 
Closes pymc-devs#3999.
brandonwillard added a commit to brandonwillard/pymc that referenced this issue Jul 20, 2020
@brandonwillard
Remove Dirichlet distribution type restrictions
c5f01a0 
Closes pymc-devs#3999.
lucianopaz pushed a commit that referenced this issue Jul 21, 2020
@brandonwillard
Remove Dirichlet distribution type restrictions (#4000)
f07c273 
* Remove Dirichlet distribution type restrictions

Closes #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 <brandonwillard@users.noreply.github.com>
gmingas added a commit to alan-turing-institute/pymc3 that referenced this issue Jul 22, 2020
@gmingas @bwengals
@OriolAbril @aloctavodia @aseyboldt @AlexAndorra @ColCarroll @brandonwillard @junpenglao @rpgoldman @tirthasheshpatel
Fix pm.DensityDist bug and incorporate latest upstream changes (#42)
798b89f 
* Update GP NBs to use standard notebook style (pymc-devs#3978)

* update gp-latent nb to use arviz

* rerun, run black

* rerun after fixes from comments

* rerun black

* rewrite radon notebook using ArviZ and xarray (pymc-devs#3963)

* 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

* SMC: refactor, speed-up and run multiple chains in parallel for diagnostics (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 (pymc-devs#3785)

* Honor discard_tuned_samples during KeyboardInterrupt

* Do not compute convergence checks without samples

* Add time values as sampler stats for NUTS (pymc-devs#3986)

* 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 <andorra.alexandre@gmail.com>

Co-authored-by: Alexandre ANDORRA <andorra.alexandre@gmail.com>

* Drop support for py3.6 (pymc-devs#3992)

* Drop support for py3.6

* Update RELEASE-NOTES.md

Co-authored-by: Colin <ColCarroll@users.noreply.github.com>

Co-authored-by: Colin <ColCarroll@users.noreply.github.com>

* Fix Mixture distribution mode computation and logp dimensions

Closes pymc-devs#3994.

* Add more info to divergence warnings (pymc-devs#3990)

* Add more info to divergence warnings

* Add dataclasses as requirement for py3.6

* Fix tests for extra divergence info

* Remove py3.6 requirements

* follow-up of py36 drop (pymc-devs#3998)

* Revert "Drop support for py3.6 (pymc-devs#3992)"

This reverts commit 1bf867e.

* Update README.rst

* Update setup.py

* Update requirements.txt

* Update requirements.txt

Co-authored-by: Adrian Seyboldt <aseyboldt@users.noreply.github.com>

* Show pickling issues in notebook on windows (pymc-devs#3991)

* 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 <junpenglao@gmail.com>

* Use mp_ctx forkserver on MacOS

* Add test for pickle with dill

Co-authored-by: Junpeng Lao <junpenglao@gmail.com>

* Fix keep_size for arviz structures. (pymc-devs#4006)

* 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 <rpgoldman@sift.net>

* SMC-ABC add distance, refactor and update notebook (pymc-devs#3996)

* 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

* add docs for interpretation of length scales in periodic kernel (pymc-devs#3989)

* fix the expression of periodic kernel

* revert change and add doc

* FIXUP: add suggested doc string

* FIXUP: revertchanges in .gitignore

* Fix Matplotlib type error for tests (pymc-devs#4023)

* Fix for issue 4022.

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

* Alternative fix.

* Switch from pm.DensityDist to pm.Potential to describe the likelihood 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 (pymc-devs#4000)

* 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 <brandonwillard@users.noreply.github.com>

Co-authored-by: Bill Engels <w.j.engels@gmail.com>
Co-authored-by: Oriol Abril-Pla <oriol.abril.pla@gmail.com>
Co-authored-by: Osvaldo Martin <aloctavodia@gmail.com>
Co-authored-by: Adrian Seyboldt <aseyboldt@users.noreply.github.com>
Co-authored-by: Alexandre ANDORRA <andorra.alexandre@gmail.com>
Co-authored-by: Colin <ColCarroll@users.noreply.github.com>
Co-authored-by: Brandon T. Willard <brandonwillard@users.noreply.github.com>
Co-authored-by: Junpeng Lao <junpenglao@gmail.com>
Co-authored-by: rpgoldman <rpgoldman@goldman-tribe.org>
Co-authored-by: Robert P. Goldman <rpgoldman@sift.net>
Co-authored-by: Tirth Patel <tirthasheshpatel@gmail.com>
Co-authored-by: Brandon T. Willard <971601+brandonwillard@users.noreply.github.com>
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Remove Dirichlet distribution type restrictions brandonwillard/pymc
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