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Add support for discrete variables in plot_bpv #1379

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merged 16 commits into from
Sep 23, 2020
Merged

Add support for discrete variables in plot_bpv #1379

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e594039
fix plot_bpv for discrete variables
aloctavodia Sep 11, 2020
54adcc8
update changelog
aloctavodia Sep 11, 2020
186b4cd
add tests
aloctavodia Sep 11, 2020
1fecc13
minor change
aloctavodia Sep 11, 2020
46a24eb
add samples for discrete variables
aloctavodia Sep 12, 2020
0bba9a5
use interpolation instead of randomization
aloctavodia Sep 14, 2020
4a6bb6c
Merge branch 'master' into bpv_discrete
aloctavodia Sep 23, 2020
40ad344
add interpolation to bokeh, fix bug in p_value reference distribution…
aloctavodia Sep 23, 2020
11c8d81
add interpolation to bokeh, fix bug in p_value reference distribution…
aloctavodia Sep 23, 2020
13c9dcc
fix axis interpolation
aloctavodia Sep 23, 2020
3c8837b
fix shape reference distribution bokeh
aloctavodia Sep 23, 2020
e35e73b
Merge branch 'master' into bpv_discrete
aloctavodia Sep 23, 2020
bffb8a3
remove print
aloctavodia Sep 23, 2020
976250c
fix test
aloctavodia Sep 23, 2020
6f28af8
fix tests
OriolAbril Sep 23, 2020
11615e0
remove blank line
aloctavodia Sep 23, 2020
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3 changes: 2 additions & 1 deletion arviz/plots/backends/bokeh/bpvplot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -87,7 +87,8 @@ def plot_bpv(
pp_var_name, _, pp_vals = pp_plotters[i]

obs_vals = obs_vals.flatten()
pp_vals = pp_vals.reshape(total_pp_samples, -1)
if pp_vals.ndim > 2:
pp_vals = pp_vals.reshape(total_pp_samples, -1)

if obs_vals.dtype.kind == "i" or pp_vals.dtype.kind == "i":
x = np.linspace(0, 1, len(obs_vals))
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3 changes: 2 additions & 1 deletion arviz/plots/backends/matplotlib/bpvplot.py
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Expand Up @@ -86,7 +86,8 @@ def plot_bpv(
pp_var_name, _, pp_vals = pp_plotters[i]

obs_vals = obs_vals.flatten()
pp_vals = pp_vals.reshape(total_pp_samples, -1)
if pp_vals.ndim > 2:
pp_vals = pp_vals.reshape(total_pp_samples, -1)
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I think this will break the ArviZ shape convention and interpret the draw dimension as the observations.


if obs_vals.dtype.kind == "i" or pp_vals.dtype.kind == "i":
x = np.linspace(0, 1, len(obs_vals))
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5 changes: 3 additions & 2 deletions arviz/tests/base_tests/test_plots_bokeh.py
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Expand Up @@ -1037,8 +1037,9 @@ def test_plot_bpv(models, kwargs):


def test_plot_bpv_discrete():
fake = {"a": np.random.poisson(2.5, 1000)}
fake_model = from_dict(posterior_predictive=fake, observed_data=fake)
fake_obs = {"a": np.random.poisson(2.5, 100)}
fake_pp = {"a": np.random.poisson(2.5, (10, 100))}
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As a 2 dim array this will be understood as a (chain, draw) array, you should add a dimension at the beggining.

fake_model = from_dict(posterior_predictive=fake_pp, observed_data=fake_obs)
axes = plot_bpv(
fake_model,
backend="bokeh",
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5 changes: 3 additions & 2 deletions arviz/tests/base_tests/test_plots_matplotlib.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1406,7 +1406,8 @@ def test_plot_bpv(models, kwargs):


def test_plot_bpv_discrete():
fake = {"a": np.random.poisson(2.5, 1000)}
fake_model = from_dict(posterior_predictive=fake, observed_data=fake)
fake_obs = {"a": np.random.poisson(2.5, 100)}
fake_pp = {"a": np.random.poisson(2.5, (10, 100))}
fake_model = from_dict(posterior_predictive=fake_pp, observed_data=fake_obs)
axes = plot_bpv(fake_model)
assert not isinstance(axes, np.ndarray)