updated model output definitions

src/model_textual/__init__.py

@@ -1 +1,5 @@
-from .output import model_labels
+from .classes import (
+    InformationValueModelOutput,
+    FramingModelOutput,
+    SalienceModelOutput
+)

src/model_textual/classes.py

@@ -0,0 +1,75 @@
+from pydantic import BaseModel
+from typing import List, Dict
+import random
+
+
+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 InformationValueModelOutput(ModelOutput):
+    given_new: float
+    ideal_real: float
+    central_marginal: float
+
+
+class FramingModelOutput(ModelOutput):
+    frame_lines: float
+    empty_space: float
+    colour_contrast: float
+    form_contrast: float
+
+
+class SalienceModelOutput(ModelOutput):
+    size: float
+    colour: float
+    tone: float
+    form: float
+    positioning: float
+
+
+if __name__ == '__main__':
+    m = InformationValueModelOutput.from_random()
+    print(repr(m))
+    print(m.highest_score_field())
+    print(m.highest_score_value())
+    m = FramingModelOutput.from_random()
+    print(repr(m))
+    print(m.highest_score_field())
+    print(m.highest_score_value())
+    m = SalienceModelOutput.from_random()
+    print(repr(m))
+    print(m.highest_score_field())
+    print(m.highest_score_value())