Move fit_result to model_builder.py and remove redundancies from CLV and MMM

pymc_marketing/clv/models/basic.py

@@ -26,7 +26,6 @@
 from pymc.backends import NDArray
 from pymc.backends.base import MultiTrace
 from pymc.model.core import Model
-from xarray import Dataset
 
 from pymc_marketing.model_builder import ModelBuilder
 from pymc_marketing.model_config import ModelConfig, parse_model_config
@@ -256,23 +255,6 @@ def default_sampler_config(self) -> dict:
     def _serializable_model_config(self) -> dict:
         return self.model_config
 
-    @property
-    def fit_result(self) -> Dataset:
-        """Get the fit result."""
-        if self.idata is None or "posterior" not in self.idata:
-            raise RuntimeError("The model hasn't been fit yet, call .fit() first")
-        return self.idata["posterior"]
-
-    @fit_result.setter
-    def fit_result(self, res: az.InferenceData) -> None:
-        if self.idata is None:
-            self.idata = res
-        elif "posterior" in self.idata:
-            warnings.warn("Overriding pre-existing fit_result", stacklevel=1)
-            self.idata.posterior = res
-        else:
-            self.idata.posterior = res
-
     def fit_summary(self, **kwargs):
         """Compute the summary of the fit result."""
         res = self.fit_result

pymc_marketing/mmm/base.py

@@ -271,13 +271,6 @@ def get_target_transformer(self) -> Pipeline:
             identity_transformer = FunctionTransformer()
             return Pipeline(steps=[("scaler", identity_transformer)])
 
-    @property
-    def fit_result(self) -> Dataset:
-        """Get the posterior data."""
-        if self.idata is None or "posterior" not in self.idata:
-            raise RuntimeError("The model hasn't been fit yet, call .fit() first")
-        return self.idata["posterior"]
-
     def _get_group_predictive_data(
         self,
         group: Literal["prior_predictive", "posterior_predictive"],

pymc_marketing/model_builder.py

@@ -72,7 +72,16 @@
 
         return self.idata[value]
 
-    return property(accessor)
+    def setter(self, res: az.InferenceData) -> None:
+        if self.idata is None:
+            self.idata = res
+        elif "posterior" in self.idata:
+            warnings.warn("Overriding pre-existing fit_result", stacklevel=1)
+            self.idata.posterior = res
+        else:
+            self.idata.posterior = res
+
+    return property(accessor, setter)
 
 
 def create_sample_kwargs(
@@ -959,6 +968,9 @@
     posterior = create_idata_accessor(
         "posterior", "The model hasn't been fit yet, call .fit() first"
     )
+    fit_result = create_idata_accessor(
+        "posterior", "The model hasn't been fit yet, call .fit() first"
+    )
     posterior_predictive = create_idata_accessor(
         "posterior_predictive",
         "The model hasn't been fit yet, call .sample_posterior_predictive() first",