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Fix do/conditioning model docs #6936

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Merged
merged 5 commits into from
Oct 2, 2023
Merged

Fix do/conditioning model docs #6936

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afa2d82
fix docs distribution
Sep 30, 2023
99cac55
fix code blocks
Sep 30, 2023
ec9a874
fix model drop down
Oct 1, 2023
91d29ce
fix subsection redundant name
Oct 1, 2023
0103e17
fxt others
Oct 1, 2023
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4 changes: 1 addition & 3 deletions docs/source/api.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -9,9 +9,7 @@ API

api/distributions
api/gp
api/model/core
api/model/fgraph
api/model/transform/conditioning
api/model
api/samplers
api/vi
api/smc
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11 changes: 11 additions & 0 deletions docs/source/api/model.rst
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@@ -0,0 +1,11 @@
Model
------

.. automodule:: pymc.model

.. toctree::
:maxdepth: 2

model/core
model/conditioning
model/fgraph
9 changes: 1 addition & 8 deletions ...urce/api/model/transform/conditioning.rst → docs/source/api/model/conditioning.rst
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Expand Up @@ -5,14 +5,7 @@ Model Conditioning
.. autosummary::
:toctree: generated/

change_value_transforms
do
observe

Others
------

.. autosummary::
:toctree: generated/

change_value_transforms
remove_value_transforms
4 changes: 1 addition & 3 deletions docs/source/api/model/core.rst
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@@ -1,6 +1,3 @@
Model
-----

Model creation and inspection
-----------------------------

Expand All @@ -16,6 +13,7 @@ Model creation and inspection
Others
------

.. currentmodule:: pymc.model.core
.. autosummary::
:toctree: generated/

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4 changes: 2 additions & 2 deletions pymc/model/transform/conditioning.py
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Expand Up @@ -299,7 +299,7 @@ def change_value_transforms(
new_p = untransformed_p['p']
std_q = ((1 / pm.find_hessian(mean_q, vars=[new_p])) ** 0.5)[0]

print(f" Mean, Standard deviation\np {mean_q['p']:.2}, {std_q[0]:.2}")
print(f" Mean, Standard deviation\\np {mean_q['p']:.2}, {std_q[0]:.2}")
# Mean, Standard deviation
# p 0.67, 0.16

Expand Down Expand Up @@ -379,7 +379,7 @@ def remove_value_transforms(
with remove_value_transforms(transformed_m) as untransformed_m:
new_p = untransformed_m["p"]
std_q = ((1 / pm.find_hessian(mean_q, vars=[new_p])) ** 0.5)[0]
print(f" Mean, Standard deviation\np {mean_q['p']:.2}, {std_q[0]:.2}")
print(f" Mean, Standard deviation\\np {mean_q['p']:.2}, {std_q[0]:.2}")

# Mean, Standard deviation
# p 0.67, 0.16
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