🎉 Start removing Diagnostics & Plots in PyMC3 development

🔥 Remove arviz plots
* Remove directly imported arviz plots used in pymc3 plots

🔥 Remove all plots from PyMC3 Plots module

🔥 Remove PyMC3 plots references in Docs

🎨 Mention Plotting & Diagnostics in API page and remove plots reference in __init__.py

⏪ Revert posterior_plot function, test, and docs

🎨 Add deprecation warning to posterior_plot function

🎨 Add context on plot import and import back into __init__.py

✏️ Add warning and details of posterior_plot added

Update docs/source/api/plots.rst

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

Update docs/source/api/plots.rst

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

Update pymc3/plots/__init__.py

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

Update pymc3/plots/__init__.py

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

✏️ Update docs to add stats.rst details

✏️ Minor docs notation for posterioplot function(s)

📝 Add breakline before docstring title

Author: CloudChaoszero

benchmarks/benchmarks/benchmarks.py

@@ -11,10 +11,10 @@
 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
-
 import time
 import timeit
 
+import arviz as az
 import numpy as np
 import pandas as pd
 import theano
@@ -192,7 +192,7 @@ def track_glm_hierarchical_ess(self, init):
                 compute_convergence_checks=False,
             )
             tot = time.time() - t0
-        ess = float(pm.ess(trace, var_names=["mu_a"])["mu_a"].values)
+        ess = float(az.ess(trace, var_names=["mu_a"])["mu_a"].values)
         return ess / tot
 
     def track_marginal_mixture_model_ess(self, init):
@@ -214,7 +214,7 @@ def track_marginal_mixture_model_ess(self, init):
                 compute_convergence_checks=False,
             )
             tot = time.time() - t0
-        ess = pm.ess(trace, var_names=["mu"])["mu"].values.min()  # worst case
+        ess = az.ess(trace, var_names=["mu"])["mu"].values.min()  # worst case
         return ess / tot
 
 
@@ -245,7 +245,7 @@ def track_glm_hierarchical_ess(self, step):
                 compute_convergence_checks=False,
             )
             tot = time.time() - t0
-        ess = float(pm.ess(trace, var_names=["mu_a"])["mu_a"].values)
+        ess = float(az.ess(trace, var_names=["mu_a"])["mu_a"].values)
         return ess / tot
 
 
@@ -304,7 +304,7 @@ def freefall(y, t, p):
             t0 = time.time()
             trace = pm.sample(500, tune=1000, chains=2, cores=2, random_seed=0)
             tot = time.time() - t0
-        ess = pm.ess(trace)
+        ess = az.ess(trace)
         return np.mean([ess.sigma, ess.gamma]) / tot
 
 

docs/source/api.rst

@@ -28,7 +28,6 @@ API Reference
    api/shape_utils
    api/ode
 
-
 Indices and tables
 ===================
 

docs/source/api/plots.rst

@@ -8,16 +8,9 @@ Plots are delegated to the
 `ArviZ <https://arviz-devs.github.io/arviz/index.html>`_.
 library, a general purpose library for
 "exploratory analysis of Bayesian models."
-For plots, ``pymc3.<function>`` are now aliases
-for ArviZ functions. Thus, the links below will redirect you to
-ArviZ docs:
+Refer to its documentation to use the plotting functions directly.
 
-- :func:`pymc3.traceplot <arviz:arviz.plot_trace>`
-- :func:`pymc3.plot_posterior <arviz:arviz.plot_posterior>`
-- :func:`pymc3.forestplot <arviz:arviz.plot_forest>`
-- :func:`pymc3.compareplot <arviz:arviz.plot_compare>`
-- :func:`pymc3.autocorrplot <arviz:arviz.plot_autocorr>`
-- :func:`pymc3.energyplot <arviz:arviz.plot_energy>`
-- :func:`pymc3.kdeplot <arviz:arviz.plot_kde>`
-- :func:`pymc3.densityplot <arviz:arviz.plot_density>`
-- :func:`pymc3.pairplot <arviz:arviz.plot_pair>`
+.. currentmodule:: pymc3.plots.posteriorplot
+
+.. automodule:: pymc3.plots.posteriorplot
+   :members:

docs/source/api/stats.rst

@@ -5,21 +5,4 @@ Statistics and diagnostics are delegated to the
 `ArviZ <https://arviz-devs.github.io/arviz/index.html>`_.
 library, a general purpose library for
 "exploratory analysis of Bayesian models."
-For statistics and diagnostics, ``pymc3.<function>`` are now aliases
-for ArviZ functions. Thus, the links below will redirect you to
-ArviZ docs:
-
-.. currentmodule:: pymc3.stats
-
-
-- :func:`pymc3.bfmi <arviz:arviz.bfmi>`
-- :func:`pymc3.compare <arviz:arviz.compare>`
-- :func:`pymc3.ess <arviz:arviz.ess>`
-- :data:`pymc3.geweke <arviz:arviz.geweke>`
-- :func:`pymc3.hpd <arviz:arviz.hpd>`
-- :func:`pymc3.loo <arviz:arviz.loo>`
-- :func:`pymc3.mcse <arviz:arviz.mcse>`
-- :func:`pymc3.r2_score <arviz:arviz.r2_score>`
-- :func:`pymc3.rhat <arviz:arviz.rhat>`
-- :func:`pymc3.summary <arviz:arviz.summary>`
-- :func:`pymc3.waic <arviz:arviz.waic>`
+Refer to its documentation to use the diagnostics functions directly.

pymc3/__init__.py

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

pymc3/plots/__init__.py

@@ -14,108 +14,17 @@
 
 """PyMC3 Plotting.
 
-Plots are delegated to the ArviZ library, a general purpose library for
-"exploratory analysis of Bayesian models." See https://arviz-devs.github.io/arviz/
-for details on plots.
+Plots are delegated to the `ArviZ <https://arviz-devs.github.io/arviz/>`_ library, a general purpose library for
+exploratory analysis of Bayesian models. For more details, see https://arviz-devs.github.io/arviz/.
+
+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.
 """
 import functools
 import sys
 import warnings
 
 import arviz as az
 
-
-def map_args(func):
-    swaps = [("varnames", "var_names")]
-
-    @functools.wraps(func)
-    def wrapped(*args, **kwargs):
-        for (old, new) in swaps:
-            if old in kwargs and new not in kwargs:
-                warnings.warn(
-                    f"Keyword argument `{old}` renamed to `{new}`, and will be removed in pymc3 3.8"
-                )
-                kwargs[new] = kwargs.pop(old)
-            return func(*args, **kwargs)
-
-    return wrapped
-
-
-# pymc3 custom plots: override these names for custom behavior
-autocorrplot = map_args(az.plot_autocorr)
-forestplot = map_args(az.plot_forest)
-kdeplot = map_args(az.plot_kde)
-plot_posterior = map_args(az.plot_posterior)
-energyplot = map_args(az.plot_energy)
-densityplot = map_args(az.plot_density)
-pairplot = map_args(az.plot_pair)
-
-# Use compact traceplot by default
-@map_args
-@functools.wraps(az.plot_trace)
-def traceplot(*args, **kwargs):
-    try:
-        kwargs.setdefault("compact", True)
-        return az.plot_trace(*args, **kwargs)
-    except TypeError:
-        kwargs.pop("compact")
-        return az.plot_trace(*args, **kwargs)
-
-
-# addition arg mapping for compare plot
-@functools.wraps(az.plot_compare)
-def compareplot(*args, **kwargs):
-    if "comp_df" in kwargs:
-        comp_df = kwargs["comp_df"].copy()
-    else:
-        args = list(args)
-        comp_df = args[0].copy()
-    if "WAIC" in comp_df.columns:
-        comp_df = comp_df.rename(
-            index=str,
-            columns={
-                "WAIC": "waic",
-                "pWAIC": "p_waic",
-                "dWAIC": "d_waic",
-                "SE": "se",
-                "dSE": "dse",
-                "var_warn": "warning",
-            },
-        )
-    elif "LOO" in comp_df.columns:
-        comp_df = comp_df.rename(
-            index=str,
-            columns={
-                "LOO": "loo",
-                "pLOO": "p_loo",
-                "dLOO": "d_loo",
-                "SE": "se",
-                "dSE": "dse",
-                "shape_warn": "warning",
-            },
-        )
-    if "comp_df" in kwargs:
-        kwargs["comp_df"] = comp_df
-    else:
-        args[0] = comp_df
-    return az.plot_compare(*args, **kwargs)
-
-
 from pymc3.plots.posteriorplot import plot_posterior_predictive_glm
 
-# Access to arviz plots: base plots provided by arviz
-for plot in az.plots.__all__:
-    setattr(sys.modules[__name__], plot, map_args(getattr(az.plots, plot)))
-
-__all__ = tuple(az.plots.__all__) + (
-    "autocorrplot",
-    "compareplot",
-    "forestplot",
-    "kdeplot",
-    "plot_posterior",
-    "traceplot",
-    "energyplot",
-    "densityplot",
-    "pairplot",
-    "plot_posterior_predictive_glm",
-)
+__all__ = ["plot_posterior_predictive_glm"]

pymc3/plots/posteriorplot.py

@@ -14,6 +14,8 @@
 
 from __future__ import annotations
 
+import warnings
+
 from typing import TYPE_CHECKING, Any, Callable, Optional, Union
 
 import matplotlib.pyplot as plt
@@ -33,20 +35,33 @@ def plot_posterior_predictive_glm(
     **kwargs: Any
 ) -> None:
     """Plot posterior predictive of a linear model.
-    :Arguments:
-        trace: InferenceData or MultiTrace
-            Output of pm.sample()
-        eval: <array>
-            Array over which to evaluate lm
-        lm: function <default: linear function>
-            Function mapping parameters at different points
-            to their respective outputs.
-            input: point, sample
-            output: estimated value
-        samples: int <default=30>
-            How many posterior samples to draw.
-    Additional keyword arguments are passed to pylab.plot().
+
+    Parameters
+    ----------
+    trace: InferenceData or MultiTrace
+        Output of pm.sample()
+    eval: <array>
+        Array over which to evaluate lm
+    lm: function <default: linear function>
+        Function mapping parameters at different points
+        to their respective outputs.
+        input: point, sample
+        output: estimated value
+    samples: int <default=30>
+        How many posterior samples to draw.
+    kwargs : mapping, optional
+        Additional keyword arguments are passed to ``matplotlib.pyplot.plot()``.
+
+    Warnings
+    --------
+    The `plot_posterior_predictive_glm` function will removed in a future PyMC3 release.
     """
+    warnings.warn(
+        "The `plot_posterior_predictive_glm` function will migrate to Arviz in a future release. "
+        "\nKeep up to date with `ArviZ <https://arviz-devs.github.io/arviz/>`_ for future updates.",
+        DeprecationWarning,
+    )
+
     if lm is None:
         lm = lambda x, sample: sample["Intercept"] + sample["x"] * x
 

pymc3/sampling.py

@@ -416,7 +416,7 @@ def sample(
            ...:     y = pm.Binomial("y", n=n, p=p, observed=h)
            ...:     trace = pm.sample()
 
-        In [3]: pm.summary(trace, kind="stats")
+        In [3]: az.summary(trace, kind="stats")
 
         Out[3]:
             mean     sd  hdi_3%  hdi_97%

pymc3/stats/__init__.py

@@ -333,7 +333,7 @@ class MLDA(ArrayStepShared):
         ...                       tune=100, step=step_method,
         ...                       random_seed=123)
         ...
-        ... pm.summary(trace, kind="stats")
+        ... az.summary(trace, kind="stats")
            mean     sd  hdi_3%  hdi_97%
         x  0.99  0.987  -0.734    2.992
 

pymc3/step_methods/mlda.py

@@ -12,6 +12,7 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
+import arviz as az
 import numpy as np
 import numpy.testing as npt
 import theano.tensor as tt
@@ -146,12 +147,12 @@ def setup_class(cls):
 
     def test_neff(self):
         if hasattr(self, "min_n_eff"):
-            n_eff = pm.ess(self.trace[self.burn :])
+            n_eff = az.ess(self.trace[self.burn :])
             for var in n_eff:
                 npt.assert_array_less(self.min_n_eff, n_eff[var])
 
     def test_Rhat(self):
-        rhat = pm.rhat(self.trace[self.burn :])
+        rhat = az.rhat(self.trace[self.burn :])
         for var in rhat:
             npt.assert_allclose(rhat[var], 1, rtol=0.01)