updated model output definitions

src/model_experiential/classes.py

@@ -0,0 +1,61 @@
+from __future__ import annotations
+from pydantic import BaseModel
+from typing import List
+import random
+
+
+class ExperientialModelOutput(BaseModel):
+    non_transactional_action: float
+    non_transactional_reaction: float
+    unidirectional_transactional_action: float
+    unidirectional_transactional_reaction: float
+    bidirectional_transactional_action: float
+    bidirectional_transactional_reaction: float
+    conversion: float
+    speech_process: float
+    classification_overt_taxonomy: float
+    analytical_exhaustive: float
+    analytical_disarranged: float
+    analytical_temporal: float
+    analytical_distributed: float
+    analytical_topological: float
+    analytical_exploded: float
+    analytical_inclusive: float
+    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()
+    print(m)
+    print(repr(m))
+    print(m.highest_score_field())
+    print(m.highest_score_value())