Merge branch 'main' into menten-as-tensorvariable

pymc-labs · Oct 17, 2024 · 5eee159 · 5eee159
2 parents 6fdcc1d + aef9791
commit 5eee159
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diff --git a/.github/workflows/label-blank-issue.yml b/.github/workflows/label-blank-issue.yml
@@ -0,0 +1,16 @@
+# Taken from scikit-learn repo
+# https://github.com/scikit-learn/scikit-learn/blob/main/.github/workflows/label-blank-issue.yml
+name: Label Blank Issue
+
+on:
+  issues:
+    types: [opened]
+
+jobs:
+  label-blank-issues:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: andymckay/[email protected]
+        with:
+          add-labels: "Needs Triage"
+          ignore-if-labeled: true
diff --git a/docs/source/_static/causal_ladder.png b/docs/source/_static/causal_ladder.png
diff --git a/docs/source/notebooks/index.md b/docs/source/notebooks/index.md
@@ -10,6 +10,7 @@ Here you will find a collection of examples and how-to guides for using PyMC-Mar
 mmm/mmm_example
 mmm/mmm_budget_allocation_example
 mmm/mmm_lift_test
+mmm/mmm_counterfactuals
 mmm/mmm_tvp_example
 mmm/mmm_time_varying_media_example
 mmm/mmm_components

diff --git a/docs/source/notebooks/mmm/mmm_counterfactuals.ipynb b/docs/source/notebooks/mmm/mmm_counterfactuals.ipynb
diff --git a/docs/source/notebooks/mmm/mmm_roas.ipynb b/docs/source/notebooks/mmm/mmm_roas.ipynb
diff --git a/pymc_marketing/mmm/mmm.py b/pymc_marketing/mmm/mmm.py
@@ -808,9 +808,9 @@ class MMM(
     we consider a Bayesian linear model of the form:
 
     .. math::
-        y_{t} = \\alpha + \\sum_{m=1}^{M}\\beta_{m}f(x_{m, t}) +  \\sum_{c=1}^{C}\\gamma_{c}z_{c, t} + \\varepsilon_{t},
+        y_{t} = \alpha + \sum_{m=1}^{M}\beta_{m}f(x_{m, t}) +  \sum_{c=1}^{C}\gamma_{c}z_{c, t} + \varepsilon_{t},
 
-    where :math:`\\alpha` is the intercept, :math:`f` is a media transformation function and :math:`\\varepsilon_{t}` is the error therm
+    where :math:`\alpha` is the intercept, :math:`f` is a media transformation function and :math:`\varepsilon_{t}` is the error therm
     which we assume is normally distributed. The function :math:`f` encodes the contribution of media on the target variable.
     Typically we consider two types of transformation: adstock (carry-over) and saturation effects.