moved definitions of data classes into module where used

shared/mongodb/classes/data_model.py

@@ -0,0 +1,67 @@
+"""Definition of DataModel base class."""
+
+import random
+
+from pydantic import BaseModel, ValidationError
+from torch import Tensor
+
+
+class DataModel(BaseModel):
+    """DataModel base class."""
+
+    @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)
+
+    @classmethod
+    def from_choice(cls, option: str):
+        """Instantiate from choice."""
+        if option is None:
+            raise ValidationError()
+        assert isinstance(option, str), 'option is not a string'
+        allowed_options_list = cls.list_fields()
+        assert (
+            option in allowed_options_list
+        ), f"{option} is not among allowed fields {allowed_options_list}"
+        kwargs = {field: 0 for field in cls.list_fields()}
+        kwargs[option] = 1
+        return cls(**kwargs)
+
+    @classmethod
+    def from_tensor(cls, tensor: Tensor):
+        """Instantiate from list of values."""
+        assert tensor.size(dim=0) == 1, f'tensor batch larger than 1: {tensor}'
+        data_list = [float(t.item()) for t in tensor[0]]
+        kwargs = dict(zip(cls.list_fields(), data_list))
+        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())