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https://github.com/wassname/optuna-dashboard.git
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61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING
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from optuna.storages import BaseStorage
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from .preferential._study import PreferentialStudy
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if TYPE_CHECKING:
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from typing import Literal
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OUTPUT_COMPONENT_TYPE = Literal["Note", "Artifact"]
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_SYSTEM_ATTR_FEEDBACK_COMPONENT_TYPE = "preference:component_type"
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_SYSTEM_ATTR_FEEDBACK_ARTIFACT_KEY = "preference:component_artifact_key"
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def _register_output_component(
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study_id: int,
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storage: BaseStorage,
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component_type: OUTPUT_COMPONENT_TYPE,
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artifact_key: str | None = None,
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) -> None:
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storage.set_study_system_attr(
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study_id=study_id,
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key=_SYSTEM_ATTR_FEEDBACK_COMPONENT_TYPE,
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value=component_type,
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)
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if artifact_key is not None:
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storage.set_study_system_attr(
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study_id=study_id,
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key=_SYSTEM_ATTR_FEEDBACK_ARTIFACT_KEY,
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value=artifact_key,
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)
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def register_output_component(
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study: PreferentialStudy,
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component_type: OUTPUT_COMPONENT_TYPE,
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artifact_key: str = "",
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) -> None:
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"""Register output component to the study.
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Args:
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study:
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The study to register the output component.
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component_type:
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The type of the output component.
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artifact_key:
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When the component_type is "Artifact",
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this argument is used as the attribute key of the artifact.
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Each trial displays the artifact whose id is the value of the attribute.
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"""
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_register_output_component(
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study_id=study._study._study_id,
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storage=study._study._storage,
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component_type=component_type,
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artifact_key=artifact_key,
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)
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