WIP: V4 update test framework for distributions random method #4580
Commits on Apr 3, 2021
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Initial refactoring for RandomVariable use in Model, step methods, an…
…d basic dists These changes can be summarized as follows: - `Model` objects now track fully functional Theano graphs that represent all relationships between random and "deterministic" variables. These graphs are called these "sample-space" graphs. `Model.unobserved_RVs`, `Model.basic_RVs`, `Model.free_RVs`, and `Model.observed_RVs` contain these graphs (i.e. `TensorVariable`s), which are generated by `RandomVariable` `Op`s. - For each random variable, there is now a corresponding "measure-space" variable (i.e. a `TensorVariable` that corresponds to said variable in a log-likelihood graph). These variables are available as `rv_var.tag.value_var`, for each random variable `rv_var`, or via `Model.vars`. - Log-likelihood (i.e. measure-space) graphs are now created for individual random variables by way of the generic functions `logpt`, `logcdf`, `logp_nojac`, and `logpt_sum` in `pymc3.distributions`. - Numerous uses of concrete shape information stemming from `Model` objects (e.g. `Model.size`) have been removed/refactored. - Use of `FreeRV`, `ObservedRV`, `MultiObservedRV`, and `TransformedRV` has been deprecated. The information previously stored in these classes is now tracked using `TensorVariable.tag`, and log-likelihoods are generated using the aforementioned `log*` generic functions.
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Remove shape dependencies from DictToArrayBijection
This commit changes `DictToArrayBijection` so that it returns a `RaveledVars` datatype that contains the original raveled and concatenated vector along with the information needed to revert it back to dictionay/variables form. Simply put, the variables-to-single-vector mapping steps have been pushed away from the model object and its symbolic terms and closer to the (sampling) processes that produce and work with `ndarray` values for said terms. In doing so, we can operate under fewer unnecessarily strong assumptions (e.g. that the shapes of each term are static and equal to the initial test points), and let the sampling processes that require vector-only steps deal with any changes in the mappings.
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Remove newly deprecated classes and functions
Classes and functions removed: - PyMC3Variable - ObservedRV - FreeRV - MultiObservedRV - TransformedRV - ArrayOrdering - VarMap - DataMap - _DrawValuesContext - _DrawValuesContextBlocker - is_fast_drawable - _compile_theano_function - vectorize_theano_function - get_vectorize_signature - _draw_value - draw_values - generate_samples - fast_sample_posterior_predictive Modules removed: - pymc3.distributions.posterior_predictive - pymc3.tests.test_random
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Add non_sequences to uses of Scan Op
This make `aesara.graph.basic.clone_replace` work correctly when `Scan`s are included in a graph.
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Update tests following distributions refactoring
The distributions refactoring moves the random variable sampling to aesara. This relies on numpy and scipy random variables implementation. So, now the only thing we care about testing is that the parametrization on the PyMC side is sendible given the one on the Aesara side (effectively the numpy/scipy one) More details can be found on issue pymc-devs#4554 pymc-devs#4554
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Change tests for more refactored distributions.
More details on commit id 0773620b6f599423315035b97ef082ad32d98fd4
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Change tests for refactored distributions
More details on commit id 0773620b6f599423315035b97ef082ad32d98fd4 comment.
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Remove tests for random variable samples shape and size
Most of the random variable logic has been moved to aesara, as well as most of the relative tests. More details can be found on issue pymc-devs#4554
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