Source code for mlflow.entities.dataset_input
from typing import Optional
from mlflow.entities._mlflow_object import _MlflowObject
from mlflow.entities.dataset import Dataset
from mlflow.entities.input_tag import InputTag
from mlflow.protos.service_pb2 import DatasetInput as ProtoDatasetInput
[docs]class DatasetInput(_MlflowObject):
    """DatasetInput object associated with an experiment."""
    def __init__(self, dataset: Dataset, tags: Optional[list[InputTag]] = None) -> None:
        self._dataset = dataset
        self._tags = tags or []
    def __eq__(self, other: _MlflowObject) -> bool:
        if type(other) is type(self):
            return self.__dict__ == other.__dict__
        return False
    def _add_tag(self, tag: InputTag) -> None:
        self._tags.append(tag)
    @property
    def tags(self) -> list[InputTag]:
        """Array of input tags."""
        return self._tags
    @property
    def dataset(self) -> Dataset:
        """Dataset."""
        return self._dataset
[docs]    def to_proto(self):
        dataset_input = ProtoDatasetInput()
        dataset_input.tags.extend([tag.to_proto() for tag in self.tags])
        dataset_input.dataset.MergeFrom(self.dataset.to_proto())
        return dataset_input
[docs]    @classmethod
    def from_proto(cls, proto):
        dataset_input = cls(Dataset.from_proto(proto.dataset))
        for input_tag in proto.tags:
            dataset_input._add_tag(InputTag.from_proto(input_tag))
        return dataset_input
[docs]    def to_dictionary(self):
        return {
            "dataset": self.dataset.to_dictionary(),
            "tags": {tag.key: tag.value for tag in self.tags},
        }