Add Conversion Metrics Properties

dbt_semantic_interfaces/implementations/metric.py

@@ -17,7 +17,11 @@
 )
 from dbt_semantic_interfaces.implementations.metadata import PydanticMetadata
 from dbt_semantic_interfaces.references import MeasureReference, MetricReference
-from dbt_semantic_interfaces.type_enums import MetricType, TimeGranularity
+from dbt_semantic_interfaces.type_enums import (
+    ConversionCalculationType,
+    MetricType,
+    TimeGranularity,
+)
 
 
 class PydanticMetricInputMeasure(PydanticCustomInputParser, HashableBaseModel):
@@ -134,6 +138,26 @@ def post_aggregation_reference(self) -> MetricReference:
         return MetricReference(element_name=self.alias or self.name)
 
 
+class PydanticConversionTypeParams(HashableBaseModel):
+    """Type params to provide context for conversion metrics properties."""
+
+    base_measure: PydanticMetricInputMeasure
+    conversion_measure: PydanticMetricInputMeasure
+    entity: str
+    calculation: ConversionCalculationType = ConversionCalculationType.CONVERSION_RATE
+    window: Optional[PydanticMetricTimeWindow]
+
+    @property
+    def base_measure_reference(self) -> MeasureReference:
+        """Return the measure reference associated with the base measure."""
+        return self.base_measure.measure_reference
+
+    @property
+    def conversion_measure_reference(self) -> MeasureReference:
+        """Return the measure reference associated with the conversion measure."""
+        return self.conversion_measure.measure_reference
+
+
 class PydanticMetricTypeParams(HashableBaseModel):
     """Type params add additional context to certain metric types (the context depends on the metric type)."""
 
@@ -144,9 +168,16 @@ class PydanticMetricTypeParams(HashableBaseModel):
     window: Optional[PydanticMetricTimeWindow]
     grain_to_date: Optional[TimeGranularity]
     metrics: Optional[List[PydanticMetricInput]]
+    conversion_type_params: Optional[PydanticConversionTypeParams]
 
     input_measures: List[PydanticMetricInputMeasure] = Field(default_factory=list)
 
+    @property
+    def conversion_type_params_or_error(self) -> PydanticConversionTypeParams:  # noqa: D
+        if self.conversion_type_params is None:
+            raise ValueError("Expected conversion_type_params to be not None.")
+        return self.conversion_type_params
+
 
 class PydanticMetric(HashableBaseModel, ModelWithMetadataParsing):
     """Describes a metric."""
@@ -172,7 +203,7 @@ def measure_references(self) -> List[MeasureReference]:
     @property
     def input_metrics(self) -> Sequence[PydanticMetricInput]:
         """Return the associated input metrics for this metric."""
-        if self.type is MetricType.SIMPLE or self.type is MetricType.CUMULATIVE:
+        if self.type is MetricType.SIMPLE or self.type is MetricType.CUMULATIVE or self.type is MetricType.CONVERSION:
             return ()
         elif self.type is MetricType.DERIVED:
             assert self.type_params.metrics is not None, f"{MetricType.DERIVED} should have type_params.metrics set"

dbt_semantic_interfaces/protocols/metric.py

@@ -212,6 +212,11 @@ def metrics(self) -> Optional[Sequence[MetricInput]]:  # noqa: D
     def conversion_type_params(self) -> Optional[ConversionTypeParams]:  # noqa: D
         pass
 
+    @property
+    @abstractmethod
+    def conversion_type_params_or_error(self) -> ConversionTypeParams:  # noqa: D
+        pass
+
 
 class Metric(Protocol):
     """Describes a metric."""
@@ -270,9 +275,3 @@ def metadata(self) -> Optional[Metadata]:  # noqa: D
     def label(self) -> Optional[str]:
         """Returns a string representing a human readable label for the metric."""
         pass
-
-    @property
-    @abstractmethod
-    def conversion_params(self) -> ConversionTypeParams:
-        """Accessor for conversion type params, enforces that it's set."""
-        pass