from mlflow.entities._mlflow_object import _MlflowObject
from mlflow.protos.service_pb2 import Dataset as ProtoDataset
[docs]class Dataset(_MlflowObject):
"""Dataset object associated with an experiment."""
def __init__(
self,
name: str,
digest: str,
source_type: str,
source: str,
schema: str | None = None,
profile: str | None = None,
) -> None:
self._name = name
self._digest = digest
self._source_type = source_type
self._source = source
self._schema = schema
self._profile = profile
def __eq__(self, other: _MlflowObject) -> bool:
if type(other) is type(self):
return self.__dict__ == other.__dict__
return False
@property
def name(self) -> str:
"""String name of the dataset."""
return self._name
@property
def digest(self) -> str:
"""String digest of the dataset."""
return self._digest
@property
def source_type(self) -> str:
"""String source_type of the dataset."""
return self._source_type
@property
def source(self) -> str:
"""String source of the dataset."""
return self._source
@property
def schema(self) -> str:
"""String schema of the dataset."""
return self._schema
@property
def profile(self) -> str:
"""String profile of the dataset."""
return self._profile
[docs] def to_proto(self):
dataset = ProtoDataset()
dataset.name = self.name
dataset.digest = self.digest
dataset.source_type = self.source_type
dataset.source = self.source
if self.schema:
dataset.schema = self.schema
if self.profile:
dataset.profile = self.profile
return dataset
[docs] @classmethod
def from_proto(cls, proto):
return cls(
proto.name,
proto.digest,
proto.source_type,
proto.source,
proto.schema if proto.HasField("schema") else None,
proto.profile if proto.HasField("profile") else None,
)
[docs] def to_dictionary(self):
return {
"name": self.name,
"digest": self.digest,
"source_type": self.source_type,
"source": self.source,
"schema": self.schema,
"profile": self.profile,
}
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