updated models

src/model_experiential/classes.py

@@ -1,10 +1,45 @@
-from __future__ import annotations
 from pydantic import BaseModel
 from typing import List
 import random
 
 
-class ExperientialModelOutput(BaseModel):
+class ModelOutput(BaseModel):
+
+    @classmethod
+    def classname(cls) -> str:
+        """Return classname."""
+        return cls.__name__
+
+    @classmethod
+    def list_fields(cls) -> List[str]:
+        """List options that are stored as attributes."""
+        return list(cls.model_fields.keys())
+    
+    @classmethod
+    def from_random(cls):
+        """Instantiate with random numbers."""
+        kwargs = {field: random.random() for field in cls.list_fields()}
+        return cls(**kwargs)
+
+    def __repr__(self) -> str:
+        model_dict = self.model_dump()
+        model_repr_str = f"{self.classname()}("
+        model_repr_str += ", ".join([f"{field}={value:.3f}" for field, value in model_dict.items()])
+        model_repr_str += ")"
+        return model_repr_str
+    
+    def highest_score_field(self) -> str:
+        """Return name of field with highest score."""
+        model_dict = self.model_dump()
+        return max(model_dict, key=lambda k: model_dict[k])
+    
+    def highest_score_value(self) -> float:
+        """Return value of field with highest score."""
+        model_dict = self.model_dump()
+        return max(model_dict.values())
+
+
+class ExperientialModelOutput(ModelOutput):
     non_transactional_action: float
     non_transactional_reaction: float
     unidirectional_transactional_action: float
@@ -24,34 +59,6 @@ class ExperientialModelOutput(BaseModel):
     symbolic_suggestive: float
     symbolic_attributive: float
 
-    @classmethod
-    def list_fields(cls) -> List[str]:
-        """List options that are stored as attributes."""
-        return list(cls.model_fields.keys())
-    
-    @classmethod
-    def from_random(cls) -> ExperientialModelOutput:
-        """Instantiate with random numbers."""
-        kwargs = {field: random.random() for field in cls.list_fields()}
-        return ExperientialModelOutput(**kwargs)
-    
-    def __repr__(self) -> str:
-        model_dict = self.model_dump()
-        model_repr_str = "ExperientialModelOutput("
-        model_repr_str += ", ".join([f"{field}={value:.3f}" for field, value in model_dict.items()])
-        model_repr_str += ")"
-        return model_repr_str
-    
-    def highest_score_field(self) -> str:
-        """Return name of field with highest score."""
-        model_dict = self.model_dump()
-        return max(model_dict, key=lambda k: model_dict[k])
-    
-    def highest_score_value(self) -> float:
-        """Return value of field with highest score."""
-        model_dict = self.model_dump()
-        return max(model_dict.values())
-
 
 if __name__ == '__main__':
     m = ExperientialModelOutput.from_random()

src/model_interpersonal/classes.py

@@ -1,5 +1,5 @@
 from pydantic import BaseModel
-from typing import List, Dict
+from typing import List
 import random
 
 
@@ -88,35 +88,6 @@ class ModalityDepthModelOutput(ModelOutput):
 #     modality_color: ModalityColorModelOutput
 #     modality_depth: ModalityDepthModelOutput
 
-#     @classmethod
-#     def list_fields(cls) -> List[str]:
-#         """List options that are stored as attributes."""
-#         return list(cls.model_fields.keys())
-    
-#     @classmethod
-#     def from_random(cls) -> InterpersonalModelOutput:
-#         """Instantiate with random numbers."""
-#         print(cls.model_fields)
-#         kwargs = {field: info.annotation.from_random() for field, info in cls.model_fields.items()}
-#         return InterpersonalModelOutput(**kwargs)
-    
-#     def __repr__(self) -> str:
-#         model_dict = self.model_dump()
-#         model_repr_str = "ExperientialModelOutput("
-#         model_repr_str += ", ".join([f"{field}={value:.3f}" for field, value in model_dict.items()])
-#         model_repr_str += ")"
-#         return model_repr_str
-    
-#     def highest_score_field(self) -> str:
-#         """Return name of field with highest score."""
-#         model_dict = self.model_dump()
-#         return max(model_dict, key=lambda k: model_dict[k])
-    
-#     def highest_score_value(self) -> float:
-#         """Return value of field with highest score."""
-#         model_dict = self.model_dump()
-#         return max(model_dict.values())
-
 
 if __name__ == '__main__':
     m = ContactModelOutput.from_random()

src/model_textual/classes.py

@@ -1,5 +1,5 @@
 from pydantic import BaseModel
-from typing import List, Dict
+from typing import List
 import random