Source code for snapchat.research.gbml.dataset_metadata_pb2

"""
@generated by mypy-protobuf.  Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import google.protobuf.descriptor
import google.protobuf.internal.containers
import google.protobuf.message
import sys

if sys.version_info >= (3, 8):
    import typing as typing_extensions
else:
    import typing_extensions


[docs]
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor



[docs]
class SupervisedNodeClassificationDataset(google.protobuf.message.Message):
    """Stores SupervisedNodeClassificationSample-relevant output"""


[docs]
    DESCRIPTOR: google.protobuf.descriptor.Descriptor



[docs]
    TRAIN_DATA_URI_FIELD_NUMBER: builtins.int


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    TEST_DATA_URI_FIELD_NUMBER: builtins.int


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    VAL_DATA_URI_FIELD_NUMBER: builtins.int


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    train_data_uri: builtins.str


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    test_data_uri: builtins.str


[docs]
    val_data_uri: builtins.str

    def __init__(
        self,
        *,
        train_data_uri: builtins.str = ...,
        test_data_uri: builtins.str = ...,
        val_data_uri: builtins.str = ...,
    ) -> None: ...

[docs]
    def ClearField(self, field_name: typing_extensions.Literal["test_data_uri", b"test_data_uri", "train_data_uri", b"train_data_uri", "val_data_uri", b"val_data_uri"]) -> None: ...




[docs]
global___SupervisedNodeClassificationDataset = SupervisedNodeClassificationDataset



[docs]
class NodeAnchorBasedLinkPredictionDataset(google.protobuf.message.Message):
    """Stores NodeAnchorBasedLinkPredictionSample-relevant output"""


[docs]
    DESCRIPTOR: google.protobuf.descriptor.Descriptor



[docs]
    class TrainNodeTypeToRandomNegativeDataUriEntry(google.protobuf.message.Message):

[docs]
        DESCRIPTOR: google.protobuf.descriptor.Descriptor



[docs]
        KEY_FIELD_NUMBER: builtins.int


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        VALUE_FIELD_NUMBER: builtins.int


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        key: builtins.str


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        value: builtins.str

        def __init__(
            self,
            *,
            key: builtins.str = ...,
            value: builtins.str = ...,
        ) -> None: ...

[docs]
        def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...




[docs]
    class ValNodeTypeToRandomNegativeDataUriEntry(google.protobuf.message.Message):

[docs]
        DESCRIPTOR: google.protobuf.descriptor.Descriptor



[docs]
        KEY_FIELD_NUMBER: builtins.int


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        VALUE_FIELD_NUMBER: builtins.int


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        key: builtins.str


[docs]
        value: builtins.str

        def __init__(
            self,
            *,
            key: builtins.str = ...,
            value: builtins.str = ...,
        ) -> None: ...

[docs]
        def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...




[docs]
    class TestNodeTypeToRandomNegativeDataUriEntry(google.protobuf.message.Message):

[docs]
        DESCRIPTOR: google.protobuf.descriptor.Descriptor



[docs]
        KEY_FIELD_NUMBER: builtins.int


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        VALUE_FIELD_NUMBER: builtins.int


[docs]
        key: builtins.str


[docs]
        value: builtins.str

        def __init__(
            self,
            *,
            key: builtins.str = ...,
            value: builtins.str = ...,
        ) -> None: ...

[docs]
        def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...




[docs]
    TRAIN_MAIN_DATA_URI_FIELD_NUMBER: builtins.int


[docs]
    TEST_MAIN_DATA_URI_FIELD_NUMBER: builtins.int


[docs]
    VAL_MAIN_DATA_URI_FIELD_NUMBER: builtins.int


[docs]
    TRAIN_NODE_TYPE_TO_RANDOM_NEGATIVE_DATA_URI_FIELD_NUMBER: builtins.int


[docs]
    VAL_NODE_TYPE_TO_RANDOM_NEGATIVE_DATA_URI_FIELD_NUMBER: builtins.int


[docs]
    TEST_NODE_TYPE_TO_RANDOM_NEGATIVE_DATA_URI_FIELD_NUMBER: builtins.int


[docs]
    train_main_data_uri: builtins.str


[docs]
    test_main_data_uri: builtins.str


[docs]
    val_main_data_uri: builtins.str

    @property

[docs]
    def train_node_type_to_random_negative_data_uri(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]: ...

    @property

[docs]
    def val_node_type_to_random_negative_data_uri(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]: ...

    @property

[docs]
    def test_node_type_to_random_negative_data_uri(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]: ...

    def __init__(
        self,
        *,
        train_main_data_uri: builtins.str = ...,
        test_main_data_uri: builtins.str = ...,
        val_main_data_uri: builtins.str = ...,
        train_node_type_to_random_negative_data_uri: collections.abc.Mapping[builtins.str, builtins.str] | None = ...,
        val_node_type_to_random_negative_data_uri: collections.abc.Mapping[builtins.str, builtins.str] | None = ...,
        test_node_type_to_random_negative_data_uri: collections.abc.Mapping[builtins.str, builtins.str] | None = ...,
    ) -> None: ...

[docs]
    def ClearField(self, field_name: typing_extensions.Literal["test_main_data_uri", b"test_main_data_uri", "test_node_type_to_random_negative_data_uri", b"test_node_type_to_random_negative_data_uri", "train_main_data_uri", b"train_main_data_uri", "train_node_type_to_random_negative_data_uri", b"train_node_type_to_random_negative_data_uri", "val_main_data_uri", b"val_main_data_uri", "val_node_type_to_random_negative_data_uri", b"val_node_type_to_random_negative_data_uri"]) -> None: ...




[docs]
global___NodeAnchorBasedLinkPredictionDataset = NodeAnchorBasedLinkPredictionDataset



[docs]
class SupervisedLinkBasedTaskSplitDataset(google.protobuf.message.Message):
    """Stores SupervisedLinkBasedTaskSample-relevant output"""


[docs]
    DESCRIPTOR: google.protobuf.descriptor.Descriptor



[docs]
    TRAIN_DATA_URI_FIELD_NUMBER: builtins.int


[docs]
    TEST_DATA_URI_FIELD_NUMBER: builtins.int


[docs]
    VAL_DATA_URI_FIELD_NUMBER: builtins.int


[docs]
    train_data_uri: builtins.str


[docs]
    test_data_uri: builtins.str


[docs]
    val_data_uri: builtins.str

    def __init__(
        self,
        *,
        train_data_uri: builtins.str = ...,
        test_data_uri: builtins.str = ...,
        val_data_uri: builtins.str = ...,
    ) -> None: ...

[docs]
    def ClearField(self, field_name: typing_extensions.Literal["test_data_uri", b"test_data_uri", "train_data_uri", b"train_data_uri", "val_data_uri", b"val_data_uri"]) -> None: ...




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global___SupervisedLinkBasedTaskSplitDataset = SupervisedLinkBasedTaskSplitDataset



[docs]
class DatasetMetadata(google.protobuf.message.Message):
    """Stores final dataset output by SplitGenerator"""


[docs]
    DESCRIPTOR: google.protobuf.descriptor.Descriptor



[docs]
    SUPERVISED_NODE_CLASSIFICATION_DATASET_FIELD_NUMBER: builtins.int


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    NODE_ANCHOR_BASED_LINK_PREDICTION_DATASET_FIELD_NUMBER: builtins.int


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    SUPERVISED_LINK_BASED_TASK_DATASET_FIELD_NUMBER: builtins.int

    @property

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    def supervised_node_classification_dataset(self) -> global___SupervisedNodeClassificationDataset:
        """indicates the output is of SupervisedNodeClassificationSamples"""

    @property

[docs]
    def node_anchor_based_link_prediction_dataset(self) -> global___NodeAnchorBasedLinkPredictionDataset:
        """indicates the output is of NodeAnchorBasedLinkPredictionSamples"""

    @property

[docs]
    def supervised_link_based_task_dataset(self) -> global___SupervisedLinkBasedTaskSplitDataset:
        """indicates the output is of SupervisedLinkBasedTaskSamples"""

    def __init__(
        self,
        *,
        supervised_node_classification_dataset: global___SupervisedNodeClassificationDataset | None = ...,
        node_anchor_based_link_prediction_dataset: global___NodeAnchorBasedLinkPredictionDataset | None = ...,
        supervised_link_based_task_dataset: global___SupervisedLinkBasedTaskSplitDataset | None = ...,
    ) -> None: ...

[docs]
    def HasField(self, field_name: typing_extensions.Literal["node_anchor_based_link_prediction_dataset", b"node_anchor_based_link_prediction_dataset", "output_metadata", b"output_metadata", "supervised_link_based_task_dataset", b"supervised_link_based_task_dataset", "supervised_node_classification_dataset", b"supervised_node_classification_dataset"]) -> builtins.bool: ...


[docs]
    def ClearField(self, field_name: typing_extensions.Literal["node_anchor_based_link_prediction_dataset", b"node_anchor_based_link_prediction_dataset", "output_metadata", b"output_metadata", "supervised_link_based_task_dataset", b"supervised_link_based_task_dataset", "supervised_node_classification_dataset", b"supervised_node_classification_dataset"]) -> None: ...


[docs]
    def WhichOneof(self, oneof_group: typing_extensions.Literal["output_metadata", b"output_metadata"]) -> typing_extensions.Literal["supervised_node_classification_dataset", "node_anchor_based_link_prediction_dataset", "supervised_link_based_task_dataset"] | None: ...




[docs]
global___DatasetMetadata = DatasetMetadata