Various MMM small documentation fixes

.pre-commit-config.yaml

@@ -11,14 +11,14 @@ repos:
           - --exclude=docs/
           - --exclude=scripts/
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.3
+    rev: v0.5.4
     hooks:
       - id: ruff
-        types_or: [ python, pyi, jupyter ]
+        types_or: [python, pyi, jupyter]
         args: ["--fix", "--output-format=full"]
         exclude: ^docs/source/notebooks/clv/dev/
       - id: ruff-format
-        types_or: [ python, pyi, jupyter ]
+        types_or: [python, pyi, jupyter]
         exclude: ^docs/source/notebooks/clv/dev/
   - repo: https://github.com/pre-commit/mirrors-mypy
     rev: v1.10.1

docs/source/index.md

@@ -111,30 +111,36 @@ Start VS Code and ensure that the "Jupyter" extension is installed. Press Ctrl +
 
 ```python
 import pandas as pd
-from pymc_marketing.mmm import DelayedSaturatedMMM
+
+from pymc_marketing.mmm import (
+    GeometricAdstock,
+    LogisticSaturation,
+    MMM,
+)
 
 
 data_url = "https://raw.githubusercontent.com/pymc-labs/pymc-marketing/main/data/mmm_example.csv"
 data = pd.read_csv(data_url, parse_dates=['date_week'])
 
-mmm = DelayedSaturatedMMM(
+mmm = MMM(
+    adstock=GeometricAdstock(l_max=8),
+    saturation=LogisticSaturation(),
     date_column="date_week",
     channel_columns=["x1", "x2"],
     control_columns=[
         "event_1",
         "event_2",
         "t",
     ],
-    adstock_max_lag=8,
     yearly_seasonality=2,
 )
 ```
 
 Initiate fitting and get a visualization of some of the outputs with:
 
 ```python
-X = data.drop('y',axis=1)
-y = data['y']
+X = data.drop("y",axis=1)
+y = data["y"]
 mmm.fit(X,y)
 mmm.plot_components_contributions();
 ```

pymc_marketing/mmm/delayed_saturated_mmm.py

@@ -1683,20 +1683,22 @@ def sample_posterior_predictive(
         Sample from the model's posterior predictive distribution.
 
         Parameters
-        ---------
+        ----------
         X_pred : array, shape (n_pred, n_features)
             The input data used for prediction.
-        extend_idata : Boolean determining whether the predictions should be added to inference data object.
+        extend_idata : bool, optional
+            Boolean determining whether the predictions should be added to inference data object. Defaults to True.
+        combined: bool, optional
+            Combine chain and draw dims into sample. Won't work if a dim named sample already exists. Defaults to True.
+        include_last_observations: bool, optional
+            Boolean determining whether to include the last observations of the training data in order to carry over
+            costs with the adstock transformation. Assumes that X_pred are the next predictions following the
+            training data.Defaults to False.
+        original_scale: bool, optional
+            Boolean determining whether to return the predictions in the original scale of the target variable.
             Defaults to True.
-        combined: Combine chain and draw dims into sample. Won't work if a dim named sample already exists.
-            Defaults to True.
-        include_last_observations: Boolean determining whether to include the last observations of the training
-            data in order to carry over costs with the adstock transformation.
-            Assumes that X_pred are the next predictions following the training data.
-            Defaults to False.
-        original_scale: Boolean determining whether to return the predictions in the original scale
-            of the target variable. Defaults to True.
-        **sample_posterior_predictive_kwargs: Additional arguments to pass to pymc.sample_posterior_predictive
+        **sample_posterior_predictive_kwargs
+            Additional arguments to pass to pymc.sample_posterior_predictive
 
         Returns
         -------

pyproject.toml

@@ -112,6 +112,9 @@ ignore = [
 [tool.ruff.lint.pycodestyle]
 max-line-length = 120
 
+[tool.ruff]
+extend-include = ["*.ipynb"]
+
 [tool.pytest.ini_options]
 addopts = [
     "-v",