Adds a new check to the SamplingEDAEngine to analyze prior probabilities.

PiperOrigin-RevId: 871966810

Author: riemanli

meridian/analysis/analyzer.py

@@ -933,7 +933,7 @@ def __init__(
     # Make the meridian object ready for methods in this analyzer that create
     # backend.function computation graphs: it should be frozen for no more
     # internal states mutation before those graphs execute.
-    self.model_context = _get_model_context(meridian, model_context)
+    self._model_context = _get_model_context(meridian, model_context)
 
     if meridian is not None:
       self._inference_data = inference_data or meridian.inference_data
@@ -945,7 +945,11 @@ def __init__(
     else:
       self._inference_data = inference_data
 
-    self.model_context.populate_cached_properties()
+    self._model_context.populate_cached_properties()
+
+  @property
+  def model_context(self) -> context.ModelContext:
+    return self._model_context
 
   @functools.cached_property
   def _model_equations(self) -> equations.ModelEquations:

meridian/analysis/analyzer_test.py

@@ -1324,6 +1324,16 @@ def setUpClass(cls):
         inference_data=cls.inference_data,
     )
 
+  def test_init_populates_cached_properties(self):
+    with mock.patch.object(
+        context.ModelContext, "populate_cached_properties"
+    ) as mock_populate:
+      analyzer.Analyzer(
+          model_context=self.meridian.model_context,
+          inference_data=self.inference_data,
+      )
+      mock_populate.assert_called_once()
+
   def test_use_kpi_direct_calls_non_revenue_with_revenue_per_kpi(self):
     # `use_kpi` is respected
     with warnings.catch_warnings(record=True) as w:

meridian/model/eda/constants.py

@@ -324,6 +324,14 @@
     ' positive correlation with population. If there is low or negative'
     ' correlation, please check your data input.'
 )
+PRIOR_PROBABILITY_INFO = (
+    'Negative baseline is equivalent to the treatment effects getting too much'
+    ' credit. Please review the prior probability of negative baseline together'
+    ' with the channel-level prior mean of contribution. If the prior'
+    ' probability of negative baseline is high, consider custom treatment'
+    ' priors. In particular, a custom `contribution prior` type may be'
+    ' appropriate.'
+)
 PRIOR_PROBABILITY_REPORT_INFO = (
     'Negative baseline is equivalent to the treatment effects getting too much'
     ' credit. Please review the prior probability of negative baseline together'