Alto 5340 intensity score

protos/api.proto

@@ -68,6 +68,8 @@ message Prediction {
   optional string posterior = 2;
   // logit
   optional string logit = 3;
+  // regression score for continuous tasks (e.g. intensity), in (0,1)
+  optional string score = 4;
 }
 
 message InferenceResult {

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "behavioralsignals"
-version = "0.3.0"
+version = "0.4.0"
 description = "Python SDK for Behavioral Signals API"
 readme = "README.md"
 authors = []

src/behavioralsignals/generated/api_pb2.pyi

@@ -46,14 +46,16 @@ class AudioStream(_message.Message):
     def __init__(self, cid: _Optional[int] = ..., x_auth_token: _Optional[str] = ..., config: _Optional[_Union[AudioConfig, _Mapping]] = ..., audio_content: _Optional[bytes] = ...) -> None: ...
 
 class Prediction(_message.Message):
-    __slots__ = ("label", "posterior", "logit")
+    __slots__ = ("label", "posterior", "logit", "score")
     LABEL_FIELD_NUMBER: _ClassVar[int]
     POSTERIOR_FIELD_NUMBER: _ClassVar[int]
     LOGIT_FIELD_NUMBER: _ClassVar[int]
+    SCORE_FIELD_NUMBER: _ClassVar[int]
     label: str
     posterior: str
     logit: str
-    def __init__(self, label: _Optional[str] = ..., posterior: _Optional[str] = ..., logit: _Optional[str] = ...) -> None: ...
+    score: str
+    def __init__(self, label: _Optional[str] = ..., posterior: _Optional[str] = ..., logit: _Optional[str] = ..., score: _Optional[str] = ...) -> None: ...
 
 class InferenceResult(_message.Message):
     __slots__ = ("id", "start_time", "end_time", "task", "prediction", "final_label", "embedding", "level")

src/behavioralsignals/models.py

@@ -100,19 +100,21 @@ def validate_meta_json(cls, v):
                 raise ValueError("meta must be valid JSON string")
         return v
 
+
 class DeepfakeAudioUploadParams(AudioUploadParams):
     enable_generator_detection: bool = Field(
-        False, description="Whether to include prediction for the source of the deepfake (generator model)"
+        False,
+        description="Whether to include prediction for the source of the deepfake (generator model)",
     )
 
 
 class DeepfakeS3UrlUploadParams(S3UrlUploadParams):
     enable_generator_detection: bool = Field(
-        False, description="Whether to include prediction for the source of the deepfake (generator model)"
+        False,
+        description="Whether to include prediction for the source of the deepfake (generator model)",
     )
 
 
-
 class ProcessItem(BaseModel):
     """Individual process in the list"""
 
@@ -188,17 +190,34 @@ def failed_processes(self) -> List[ProcessItem]:
         return [p for p in self.processes if p.is_failed]
 
 
-class ModelPredictions(BaseModel):
+class _SerializableModel(BaseModel):
+    """Base for result models that omits null fields on serialization by default."""
+
+    def model_dump(self, **kwargs):
+        kwargs.setdefault("exclude_none", True)
+        return super().model_dump(**kwargs)
+
+    def model_dump_json(self, **kwargs):
+        kwargs.setdefault("exclude_none", True)
+        return super().model_dump_json(**kwargs)
+
+
+class ModelPredictions(_SerializableModel):
     label: Optional[str] = Field(None, description="The name of the class", example="happy")
     posterior: Optional[str] = Field(
         None, description="The probability of this class being present", example="0.754"
     )
+    score: Optional[str] = Field(
+        None,
+        description="The regression score for continuous tasks (e.g. intensity), bounded in (0,1)",
+        example="0.62",
+    )
     dominantInSegments: Optional[List[int]] = Field(
         None, description="The segments in which this class is dominant"
     )
 
 
-class ResultItem(BaseModel):
+class ResultItem(_SerializableModel):
     id: Optional[str] = Field(None, description="The id of the segment/utterance", example="1")
     startTime: Optional[str] = Field(
         None, description="The start time of the segment/utterance in seconds", example="0.209"
@@ -238,15 +257,15 @@ def et(self) -> float:
         return float(self.endTime)
 
 
-class ResultResponse(BaseModel):
+class ResultResponse(_SerializableModel):
     pid: Optional[int] = Field(None, description="Unique ID for the processing job")
     cid: Optional[int] = Field(None, description="Client ID that requested the processing")
     code: Optional[int] = Field(None, description="Code indicating status")
     message: Optional[str] = Field(None, description="Description of status")
     results: Optional[List[ResultItem]] = None
 
 
-class StreamingResultResponse(BaseModel):
+class StreamingResultResponse(_SerializableModel):
     pid: Optional[int] = Field(None, description="Unique ID for the processing job")
     cid: Optional[int] = Field(None, description="Client ID that requested the processing")
     message_id: Optional[int] = Field(