69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
from pydantic import BaseModel
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from typing import List
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import random
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class ModelOutput(BaseModel):
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@classmethod
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def classname(cls) -> str:
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"""Return classname."""
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return cls.__name__
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@classmethod
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def list_fields(cls) -> List[str]:
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"""List options that are stored as attributes."""
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return list(cls.model_fields.keys())
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@classmethod
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def from_random(cls):
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"""Instantiate with random numbers."""
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kwargs = {field: random.random() for field in cls.list_fields()}
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return cls(**kwargs)
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def __repr__(self) -> str:
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model_dict = self.model_dump()
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model_repr_str = f"{self.classname()}("
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model_repr_str += ", ".join([f"{field}={value:.3f}" for field, value in model_dict.items()])
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model_repr_str += ")"
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return model_repr_str
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def highest_score_field(self) -> str:
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"""Return name of field with highest score."""
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model_dict = self.model_dump()
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return max(model_dict, key=lambda k: model_dict[k])
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def highest_score_value(self) -> float:
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"""Return value of field with highest score."""
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model_dict = self.model_dump()
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return max(model_dict.values())
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class ExperientialModelOutput(ModelOutput):
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non_transactional_action: float
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non_transactional_reaction: float
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unidirectional_transactional_action: float
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unidirectional_transactional_reaction: float
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bidirectional_transactional_action: float
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bidirectional_transactional_reaction: float
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conversion: float
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speech_process: float
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classification_overt_taxonomy: float
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analytical_exhaustive: float
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analytical_disarranged: float
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analytical_temporal: float
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analytical_distributed: float
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analytical_topological: float
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analytical_exploded: float
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analytical_inclusive: float
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symbolic_suggestive: float
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symbolic_attributive: float
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if __name__ == '__main__':
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m = ExperientialModelOutput.from_random()
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print(m)
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print(repr(m))
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print(m.highest_score_field())
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print(m.highest_score_value())
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