Merge branch 'pymc-devs:main' into implement-pmx.fit-option-for-INLA-+-marginalisation-routine

Author: Michal-Novomestsky

CONTRIBUTING.md

@@ -1,3 +1,24 @@
 # Contributing guide
 
 Page in construction, for now go to https://github.com/pymc-devs/pymc-extras#questions.
+
+## Building the documentation
+
+To build the documentation locally, you need to install the necessary
+dependencies and then use `make` to build the HTML files.
+
+First, install the package with the optional documentation dependencies:
+
+```bash
+pip install ".[docs]"
+```
+
+Then, navigate to the `docs` directory and run `make html`:
+
+```bash
+cd docs
+make html
+```
+
+The generated HTML files will be in the `docs/_build/html` directory. You can
+open the `index.html` file in that directory to view the documentation.

docs/api_reference.rst

@@ -56,6 +56,7 @@ Prior
    create_dim_handler
    handle_dims
    Prior
+   register_tensor_transform
    VariableFactory
    sample_prior
    Censored

docs/statespace/models.rst

@@ -6,7 +6,8 @@ Statespace Models
 .. autosummary::
    :toctree: generated
 
-    BayesianSARIMA
+    BayesianETS
+    BayesianSARIMAX
     BayesianVARMAX
 
 *********************

notebooks/deterministic_advi_example.ipynb

@@ -13,10 +13,7 @@
 
     from pymc_extras.deserialize import deserialize
 
-    prior_class_data = {
-        "dist": "Normal",
-        "kwargs": {"mu": 0, "sigma": 1}
-    }
+    prior_class_data = {"dist": "Normal", "kwargs": {"mu": 0, "sigma": 1}}
     prior = deserialize(prior_class_data)
     # Prior("Normal", mu=0, sigma=1)
 
@@ -26,6 +23,7 @@
 
     from pymc_extras.deserialize import register_deserialization
 
+
     class MyClass:
         def __init__(self, value: int):
             self.value = value
@@ -34,6 +32,7 @@ def to_dict(self) -> dict:
             # Example of what the to_dict method might look like.
             return {"value": self.value}
 
+
     register_deserialization(
         is_type=lambda data: data.keys() == {"value"} and isinstance(data["value"], int),
         deserialize=lambda data: MyClass(value=data["value"]),
@@ -80,18 +79,23 @@ class Deserializer:
 
         from typing import Any
 
+
         class MyClass:
             def __init__(self, value: int):
                 self.value = value
 
+
         from pymc_extras.deserialize import Deserializer
 
+
         def is_type(data: Any) -> bool:
             return data.keys() == {"value"} and isinstance(data["value"], int)
 
+
         def deserialize(data: dict) -> MyClass:
             return MyClass(value=data["value"])
 
+
         deserialize_logic = Deserializer(is_type=is_type, deserialize=deserialize)
 
     """
@@ -196,6 +200,7 @@ def register_deserialization(is_type: IsType, deserialize: Deserialize) -> None:
 
         from pymc_extras.deserialize import register_deserialization
 
+
         class MyClass:
             def __init__(self, value: int):
                 self.value = value
@@ -204,6 +209,7 @@ def to_dict(self) -> dict:
                 # Example of what the to_dict method might look like.
                 return {"value": self.value}
 
+
         register_deserialization(
             is_type=lambda data: data.keys() == {"value"} and isinstance(data["value"], int),
             deserialize=lambda data: MyClass(value=data["value"]),

pymc_extras/deserialize.py

@@ -265,7 +265,7 @@ class Chi:
         from pymc_extras.distributions import Chi
 
         with pm.Model():
-            x = Chi('x', nu=1)
+            x = Chi("x", nu=1)
     """
 
     @staticmethod

pymc_extras/distributions/continuous.py

@@ -130,8 +130,7 @@ def histogram_approximation(name, dist, *, observed, **h_kwargs):
     ...     m = pm.Normal("m", dims="tests")
     ...     s = pm.LogNormal("s", dims="tests")
     ...     pot = pmx.distributions.histogram_approximation(
-    ...         "pot", pm.Normal.dist(m, s),
-    ...         observed=measurements, n_quantiles=50
+    ...         "pot", pm.Normal.dist(m, s), observed=measurements, n_quantiles=50
     ...     )
 
     For special cases like Zero Inflation in Continuous variables there is a flag.
@@ -143,8 +142,11 @@ def histogram_approximation(name, dist, *, observed, **h_kwargs):
     ...     m = pm.Normal("m", dims="tests")
     ...     s = pm.LogNormal("s", dims="tests")
     ...     pot = pmx.distributions.histogram_approximation(
-    ...         "pot", pm.Normal.dist(m, s),
-    ...         observed=measurements, n_quantiles=50, zero_inflation=True
+    ...         "pot",
+    ...         pm.Normal.dist(m, s),
+    ...         observed=measurements,
+    ...         n_quantiles=50,
+    ...         zero_inflation=True,
     ...     )
     """
     try:

pymc_extras/distributions/histogram_utils.py

@@ -305,6 +305,7 @@ def R2D2M2CP(
         import pymc_extras as pmx
         import pymc as pm
         import numpy as np
+
         X = np.random.randn(10, 3)
         b = np.random.randn(3)
         y = X @ b + np.random.randn(10) * 0.04 + 5
@@ -339,7 +340,7 @@ def R2D2M2CP(
                 # "c" - a must have in the relation
                 variables_importance=[10, 1, 34],
                 # NOTE: try both
-                centered=True
+                centered=True,
             )
             # intercept prior centering should be around prior predictive mean
             intercept = y.mean()
@@ -365,7 +366,7 @@ def R2D2M2CP(
                 r2_std=0.2,
                 # NOTE: if you know where a variable should go
                 # if you do not know, leave as 0.5
-                centered=False
+                centered=False,
             )
             # intercept prior centering should be around prior predictive mean
             intercept = y.mean()
@@ -394,7 +395,7 @@ def R2D2M2CP(
                 # if you do not know, leave as 0.5
                 positive_probs=[0.8, 0.5, 0.1],
                 # NOTE: try both
-                centered=True
+                centered=True,
             )
             intercept = y.mean()
             obs = pm.Normal("obs", intercept + X @ beta, eps, observed=y)

pymc_extras/distributions/multivariate/r2d2m2cp.py

@@ -113,8 +113,10 @@ class DiscreteMarkovChain(Distribution):
 
         with pm.Model() as markov_chain:
             P = pm.Dirichlet("P", a=[1, 1, 1], size=(3,))
-            init_dist = pm.Categorical.dist(p = np.full(3, 1 / 3))
-            markov_chain = pmx.DiscreteMarkovChain("markov_chain", P=P, init_dist=init_dist, shape=(100,))
+            init_dist = pm.Categorical.dist(p=np.full(3, 1 / 3))
+            markov_chain = pmx.DiscreteMarkovChain(
+                "markov_chain", P=P, init_dist=init_dist, shape=(100,)
+            )
 
     """