Let plot_posterior_predictive_glm work with inferencedata too (#4234)

* adapt for both

* add multitrace test

* add inferencedata test, cover all lines

* update release notes

* sort imports (it's not checked yet?)

* fixup test

* fixup PR number

* 🏷️ add type annotations, correct docstring

* sort

* sort

* add copyright note

* use todict

* 🎨

* don't cover import error (matplotlib is a dev requirement)

* don't cover import typechecking

* remove optional mpl import

* remove optional mpl import

* remove optional mpl import

* Update RELEASE-NOTES.md

* use Python3.7+ type hints

Author: MarcoGorelli

Diff for: RELEASE-NOTES.md

@@ -5,6 +5,7 @@ This is the first release to support Python3.9 and to drop Python3.6.
 
 ### New Features
 - `OrderedProbit` distribution added (see [#4232](https://github.com/pymc-devs/pymc3/pull/4232)).
+- `plot_posterior_predictive_glm` now works with `arviz.InferenceData` as well (see [#4234](https://github.com/pymc-devs/pymc3/pull/4234))
 
 ### Maintenance
 - Fixed bug whereby partial traces returns after keyboard interrupt during parallel sampling had fewer draws than would've been available [#4318](https://github.com/pymc-devs/pymc3/pull/4318)

Diff for: pymc3/plots/posteriorplot.py

@@ -12,18 +12,30 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
-try:
-    import matplotlib.pyplot as plt
-except ImportError:  # mpl is optional
-    pass
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Callable, Optional, Union
+
+import matplotlib.pyplot as plt
 import numpy as np
 
+from pymc3.backends.base import MultiTrace
+
+if TYPE_CHECKING:
+    from arviz.data.inference_data import InferenceData
 
-def plot_posterior_predictive_glm(trace, eval=None, lm=None, samples=30, **kwargs):
+
+def plot_posterior_predictive_glm(
+    trace: Union[InferenceData, MultiTrace],
+    eval: Optional[np.ndarray] = None,
+    lm: Optional[Callable] = None,
+    samples: int = 30,
+    **kwargs: Any
+) -> None:
     """Plot posterior predictive of a linear model.
     :Arguments:
-        trace: <array>
-            Array of posterior samples with columns
+        trace: InferenceData or MultiTrace
+            Output of pm.sample()
         eval: <array>
             Array over which to evaluate lm
         lm: function <default: linear function>
@@ -47,6 +59,9 @@ def plot_posterior_predictive_glm(trace, eval=None, lm=None, samples=30, **kwarg
     if "c" not in kwargs and "color" not in kwargs:
         kwargs["c"] = "k"
 
+    if not isinstance(trace, MultiTrace):
+        trace = trace.posterior.to_dataframe().to_dict(orient="records")
+
     for rand_loc in np.random.randint(0, len(trace), samples):
         rand_sample = trace[rand_loc]
         plt.plot(eval, lm(eval, rand_sample), **kwargs)

Diff for: pymc3/tests/test_plots.py

@@ -0,0 +1,68 @@
+#   Copyright 2020 The PyMC Developers
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   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 matplotlib.pyplot as plt
+import numpy as np
+import pytest
+
+from arviz import from_pymc3
+
+import pymc3 as pm
+
+from pymc3.backends.ndarray import point_list_to_multitrace
+from pymc3.plots import plot_posterior_predictive_glm
+
+
+@pytest.mark.parametrize("inferencedata", [True, False])
+def test_plot_posterior_predictive_glm_defaults(inferencedata):
+    with pm.Model() as model:
+        pm.Normal("x")
+        pm.Normal("Intercept")
+    trace = point_list_to_multitrace([{"x": np.array([1]), "Intercept": np.array([1])}], model)
+    if inferencedata:
+        trace = from_pymc3(trace, model=model)
+    _, ax = plt.subplots()
+    plot_posterior_predictive_glm(trace, samples=1)
+    lines = ax.get_lines()
+    expected_xvalues = np.linspace(0, 1, 100)
+    expected_yvalues = np.linspace(1, 2, 100)
+    for line in lines:
+        x_axis, y_axis = line.get_data()
+        np.testing.assert_array_equal(x_axis, expected_xvalues)
+        np.testing.assert_array_equal(y_axis, expected_yvalues)
+        assert line.get_lw() == 0.2
+        assert line.get_c() == "k"
+
+
+@pytest.mark.parametrize("inferencedata", [True, False])
+def test_plot_posterior_predictive_glm_non_defaults(inferencedata):
+    with pm.Model() as model:
+        pm.Normal("x")
+        pm.Normal("Intercept")
+    trace = point_list_to_multitrace([{"x": np.array([1]), "Intercept": np.array([1])}], model)
+    if inferencedata:
+        trace = from_pymc3(trace, model=model)
+    _, ax = plt.subplots()
+    plot_posterior_predictive_glm(
+        trace, samples=1, lm=lambda x, _: x, eval=np.linspace(0, 1, 10), lw=0.3, c="b"
+    )
+    lines = ax.get_lines()
+    expected_xvalues = np.linspace(0, 1, 10)
+    expected_yvalues = np.linspace(0, 1, 10)
+    for line in lines:
+        x_axis, y_axis = line.get_data()
+        np.testing.assert_array_equal(x_axis, expected_xvalues)
+        np.testing.assert_array_equal(y_axis, expected_yvalues)
+        assert line.get_lw() == 0.3
+        assert line.get_c() == "b"