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michaelosthegeOriolAbril
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Bring back stats and plotting aliases (#4536)
* Restore plots and stats wrappers (see #4528) * Alias stats/plots from ArviZ and add deprecation warnings in old wrappers * Link to ArviZ docs, improve warnings UX * Use intersphinx for docs linking Co-authored-by: Oriol Abril-Pla <[email protected]>
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‎RELEASE-NOTES.md

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# Release Notes
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## PyMC3 vNext (TBD)
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## PyMC3 3.11.2 (TBD)
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### Breaking Changes
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+ ...
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### New Features
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+ `pm.math.cartesian` can now handle inputs that are themselves >1D (see [#4482](https://github.com/pymc-devs/pymc3/pull/4482)).
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+ ...
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+ Statistics and plotting functions that were removed in `3.11.0` were brought back, albeit with deprecation warnings if an old naming scheme is used (see [#4536](https://github.com/pymc-devs/pymc3/pull/4536)). In order to future proof your code, rename these function calls:
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+ `pm.traceplot` → `pm.plot_trace`
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+ `pm.compareplot` → `pm.plot_compare` (here you might need to rename some columns in the input according to the [`arviz.plot_compare` documentation](https://arviz-devs.github.io/arviz/api/generated/arviz.plot_compare.html))
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+ `pm.autocorrplot` → `pm.plot_autocorr`
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+ `pm.forestplot` → `pm.plot_forest`
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+ `pm.kdeplot` → `pm.plot_kde`
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+ `pm.energyplot` → `pm.plot_energy`
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+ `pm.densityplot` → `pm.plot_density`
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+ `pm.pairplot` → `pm.plot_pair`
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### Maintenance
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- ⚠ Our memoization mechanism wasn't robust against hash collisions (#4506), sometimes resulting in incorrect values in, for example, posterior predictives. The `pymc3.memoize` module was removed and replaced with `cachetools`. The `hashable` function and `WithMemoization` class were moved to `pymc3.util` (see #4525).

‎docs/source/api/plots.rst

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Plots are delegated to the
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`ArviZ <https://arviz-devs.github.io/arviz/index.html>`_.
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library, a general purpose library for
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"exploratory analysis of Bayesian models."
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Refer to its documentation to use the plotting functions directly.
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"exploratory analysis of Bayesian models".
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.. automodule:: pymc3.plots.posteriorplot
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:members:
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Functions from the `arviz.plots` module are available through ``pymc3.<function>`` or ``pymc3.plots.<function>``,
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but for their API documentation please refer to the :ref:`ArviZ documentation <arviz:plot_api>`.

‎docs/source/api/stats.rst

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*****
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Stats
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*****
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.. currentmodule:: pymc3.stats
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Statistics and diagnostics are delegated to the
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`ArviZ <https://arviz-devs.github.io/arviz/index.html>`_.
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library, a general purpose library for
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"exploratory analysis of Bayesian models."
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Refer to its documentation to use the diagnostics functions directly.
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"exploratory analysis of Bayesian models".
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Functions from the `arviz.stats` module are available through ``pymc3.<function>`` or ``pymc3.stats.<function>``,
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but for their API documentation please refer to the :ref:`ArviZ documentation <arviz:stats_api>`.

‎pymc3/__init__.py

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@@ -61,6 +61,7 @@ def __set_compiler_flags():
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from pymc3.plots import *
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from pymc3.sampling import *
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from pymc3.smc import *
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from pymc3.stats import *
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from pymc3.step_methods import *
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from pymc3.tests import test
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from pymc3.theanof import *

‎pymc3/plots/__init__.py

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# Copyright 2020 The PyMC Developers
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# Copyright 2021 The PyMC Developers
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyMC3 Plotting.
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"""Alias for the `plots` submodule from ArviZ.
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Plots are delegated to the `ArviZ <https://arviz-devs.github.io/arviz/>`_ library, a general purpose library for
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exploratory analysis of Bayesian models. For more details, see https://arviz-devs.github.io/arviz/.
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Only `plot_posterior_predictive_glm` is kept in the PyMC code base for now, but it will move to ArviZ once the latter adds features for regression plots.
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Plots are delegated to the ArviZ library, a general purpose library for
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"exploratory analysis of Bayesian models."
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See https://arviz-devs.github.io/arviz/ for details on plots.
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"""
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import functools
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import sys
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import warnings
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import arviz as az
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# Makes this module as identical to arviz.plots as possible
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for attr in az.plots.__all__:
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obj = getattr(az.plots, attr)
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if not attr.startswith("__"):
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setattr(sys.modules[__name__], attr, obj)
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def map_args(func, alias: str):
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@functools.wraps(func)
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def wrapped(*args, **kwargs):
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if "varnames" in kwargs:
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raise DeprecationWarning(
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f"The `varnames` kwarg was renamed to `var_names`.", stacklevel=2
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)
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original = func.__name__
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warnings.warn(
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f"The function `{alias}` from PyMC3 is just an alias for `{original}` from ArviZ. "
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f"Please switch to `pymc3.{original}` or `arviz.{original}`.",
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DeprecationWarning,
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stacklevel=2,
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)
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return func(*args, **kwargs)
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return wrapped
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# Always show the DeprecationWarnings
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warnings.filterwarnings("once", category=DeprecationWarning, module="pymc3.plots")
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# Aliases of ArviZ functions
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autocorrplot = map_args(az.plot_autocorr, alias="autocorrplot")
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forestplot = map_args(az.plot_forest, alias="forestplot")
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kdeplot = map_args(az.plot_kde, alias="kdeplot")
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energyplot = map_args(az.plot_energy, alias="energyplot")
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densityplot = map_args(az.plot_density, alias="densityplot")
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pairplot = map_args(az.plot_pair, alias="pairplot")
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traceplot = map_args(az.plot_trace, alias="traceplot")
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# Customized with kwarg reformatting
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@functools.wraps(az.plot_compare)
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def compareplot(*args, **kwargs):
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warnings.warn(
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f"The function `compareplot` from PyMC3 is an alias for `plot_compare` from ArviZ. "
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"It also applies some kwarg replacements. Nevertheless, please switch "
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f"to `pymc3.plot_compare` or `arviz.plot_compare`.",
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DeprecationWarning,
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stacklevel=2,
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)
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if "comp_df" in kwargs:
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comp_df = kwargs["comp_df"].copy()
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else:
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args = list(args)
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comp_df = args[0].copy()
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if "WAIC" in comp_df.columns:
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comp_df = comp_df.rename(
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index=str,
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columns={
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"WAIC": "waic",
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"pWAIC": "p_waic",
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"dWAIC": "d_waic",
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"SE": "se",
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"dSE": "dse",
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"var_warn": "warning",
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},
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)
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elif "LOO" in comp_df.columns:
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comp_df = comp_df.rename(
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index=str,
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columns={
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"LOO": "loo",
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"pLOO": "p_loo",
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"dLOO": "d_loo",
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"SE": "se",
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"dSE": "dse",
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"shape_warn": "warning",
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},
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)
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if "comp_df" in kwargs:
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kwargs["comp_df"] = comp_df
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else:
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args[0] = comp_df
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return az.plot_compare(*args, **kwargs)
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from pymc3.plots.posteriorplot import plot_posterior_predictive_glm
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__all__ = ["plot_posterior_predictive_glm"]
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__all__ = tuple(az.plots.__all__) + (
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"autocorrplot",
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"compareplot",
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"forestplot",
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"kdeplot",
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"plot_posterior",
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"traceplot",
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"energyplot",
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"densityplot",
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"pairplot",
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"plot_posterior_predictive_glm",
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)

‎pymc3/stats/__init__.py

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# Copyright 2021 The PyMC Developers
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Alias for the `stats` submodule from ArviZ.
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Diagnostics and auxiliary statistical functions are delegated to the ArviZ library, a general
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purpose library for "exploratory analysis of Bayesian models."
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See https://arviz-devs.github.io/arviz/ for details.
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"""
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import sys
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import arviz as az
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for attr in az.stats.__all__:
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obj = getattr(az.stats, attr)
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if not attr.startswith("__"):
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setattr(sys.modules[__name__], attr, obj)
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__all__ = tuple(az.stats.__all__)

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