Add possibility to create original scale vars

Author: cetagostini

data/config_files/example_with_original_scale_vars.yml

@@ -0,0 +1,48 @@
+schema_version: 1
+
+# ----------------------------------------------------------------------
+model:
+  target: pymc_marketing.mmm.multidimensional.MMM
+  kwargs:
+    date_column: "date"
+    channel_columns:                                     # explicit for reproducibility
+      - channel_1
+      - channel_2
+      # …
+    target_column: "kpi_registrations"
+
+    # --- media transformations ---------------------------------------
+    adstock:
+      target: pymc_marketing.mmm.GeometricAdstock
+      kwargs: {l_max: 12}        # any other hyper-parameters here
+
+    saturation:
+      target: pymc_marketing.mmm.MichaelisMentenSaturation
+      kwargs: {}                 # default α, λ priors inside the class
+
+# ----------------------------------------------------------------------
+# (optional) sampler options you plan to forward to pm.sample():
+sample_kwargs:
+  tune: 1000
+  draws: 200
+  chains: 8
+  random_seed: 42
+  target_accept: 0.90
+  nuts_sampler: "numpyro"
+
+# ----------------------------------------------------------------------
+# (optional) idata from a previous sample
+# idata_path: "data/idata.nc"
+
+# ----------------------------------------------------------------------
+# (optional) Data paths
+# data:
+#   X_path: "data/X.csv"
+#   y_path: "data/y.csv"
+
+# ----------------------------------------------------------------------
+# (optional) Add original scale contribution variables
+# These will be processed after model building but before attaching idata
+original_scale_vars:
+  - channel_contribution
+  - intercept_contribution

pymc_marketing/mmm/builders/yaml.py

@@ -93,7 +93,12 @@ def build_from_yaml(
     # 4 ───────────────────────────────────────────── build PyMC graph
     model.build_model(X, y)  # this **must** precede any idata loading
 
-    # 5 ──────────────────────────────────────────── attach inference data
+    # 5 ───────────────────────── add original scale contribution variables
+    original_scale_vars = cfg.get("original_scale_vars", [])
+    if original_scale_vars:
+        model.add_original_scale_contribution_variable(var=original_scale_vars)
+
+    # 6 ──────────────────────────────────────────── attach inference data
     if (idata_fp := cfg.get("idata_path")) is not None:
         idata_path = Path(idata_fp)
         if os.path.exists(idata_path):