fix: check that prediction_proba is not None before checking its type (…

…#61)

* fix: check that prediction_proba is not None before checking its type

* fix: using matched value to populate ValueError text

* fix: removed ModelType in ValueError text

---------

Co-authored-by: lorenzodagostinoradicalbit <lorenzo.dagostino@radicalbit.ai>

api/app/models/model_dto.py

@@ -72,19 +72,19 @@ def validate_target(self) -> Self:
             case ModelType.BINARY:
                 if not is_number(self.target.type):
                     raise ValueError(
-                        f'target must be a number for a ModelType.BINARY, has been provided [{self.target}]'
+                        f'target must be a number for a {checked_model_type}, has been provided [{self.target}]'
                     )
                 return self
             case ModelType.MULTI_CLASS:
                 if not is_number_or_string(self.target.type):
                     raise ValueError(
-                        f'target must be a number or string for a ModelType.MULTI_CLASS, has been provided [{self.target}]'
+                        f'target must be a number or string for a {checked_model_type}, has been provided [{self.target}]'
                     )
                 return self
             case ModelType.REGRESSION:
                 if not is_number(self.target.type):
                     raise ValueError(
-                        f'target must be a number for a ModelType.REGRESSION, has been provided [{self.target}]'
+                        f'target must be a number for a {checked_model_type}, has been provided [{self.target}]'
                     )
                 return self
             case _:
@@ -97,31 +97,37 @@ def validate_outputs(self) -> Self:
             case ModelType.BINARY:
                 if not is_number(self.outputs.prediction.type):
                     raise ValueError(
-                        f'prediction must be a number for a ModelType.BINARY, has been provided [{self.outputs.prediction}]'
+                        f'prediction must be a number for a {checked_model_type}, has been provided [{self.outputs.prediction}]'
                     )
-                if not is_optional_float(self.outputs.prediction_proba.type):
+                if not is_none(self.outputs.prediction_proba) and not is_optional_float(
+                    self.outputs.prediction_proba.type
+                ):
                     raise ValueError(
-                        f'prediction_proba must be an optional float for a ModelType.BINARY, has been provided [{self.outputs.prediction_proba}]'
+                        f'prediction_proba must be an optional float for a {checked_model_type}, has been provided [{self.outputs.prediction_proba}]'
                     )
                 return self
             case ModelType.MULTI_CLASS:
                 if not is_number_or_string(self.outputs.prediction.type):
                     raise ValueError(
-                        f'prediction must be a number or string for a ModelType.MULTI_CLASS, has been provided [{self.outputs.prediction}]'
+                        f'prediction must be a number or string for a {checked_model_type}, has been provided [{self.outputs.prediction}]'
                     )
-                if not is_optional_float(self.outputs.prediction_proba.type):
+                if not is_none(self.outputs.prediction_proba) and not is_optional_float(
+                    self.outputs.prediction_proba.type
+                ):
                     raise ValueError(
-                        f'prediction_proba must be an optional float for a ModelType.MULTI_CLASS, has been provided [{self.outputs.prediction_proba}]'
+                        f'prediction_proba must be an optional float for a {checked_model_type}, has been provided [{self.outputs.prediction_proba}]'
                     )
                 return self
             case ModelType.REGRESSION:
                 if not is_number(self.outputs.prediction.type):
                     raise ValueError(
-                        f'prediction must be a number for a ModelType.REGRESSION, has been provided [{self.outputs.prediction}]'
+                        f'prediction must be a number for a {checked_model_type}, has been provided [{self.outputs.prediction}]'
                     )
-                if not is_none(self.outputs.prediction_proba.type):
+                if not is_none(self.outputs.prediction_proba) and not is_none(
+                    self.outputs.prediction_proba.type
+                ):
                     raise ValueError(
-                        f'prediction_proba must be None for a ModelType.REGRESSION, has been provided [{self.outputs.prediction_proba}]'
+                        f'prediction_proba must be None for a {checked_model_type}, has been provided [{self.outputs.prediction_proba}]'
                     )
                 return self
             case _: