tfdv.infer_schema | TFX | TensorFlow (original) (raw)
tfdv.infer_schema
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Infers schema from the input statistics.
tfdv.infer_schema(
statistics: statistics_pb2.DatasetFeatureStatisticsList,
infer_feature_shape: bool = True,
max_string_domain_size: int = 100,
schema_transformations: Optional[List[Callable[[schema_pb2.Schema, statistics_pb2.
DatasetFeatureStatistics], schema_pb2.Schema]]] = None
) -> schema_pb2.Schema
Used in the notebooks
| Used in the tutorials |
|---|
| TensorFlow Data Validation |
| Args | |
|---|---|
| statistics | A DatasetFeatureStatisticsList protocol buffer. Schema inference is currently supported only for lists with a single DatasetFeatureStatistics proto or lists with multiple DatasetFeatureStatistics protos corresponding to data slices that include the default slice (i.e., the slice with all examples). If a list with multiple DatasetFeatureStatistics protos is used, this function will infer the schema from the statistics corresponding to the default slice. |
| infer_feature_shape | A boolean to indicate if shape of the features need to be inferred from the statistics. |
| max_string_domain_size | Maximum size of the domain of a string feature in order to be interpreted as a categorical feature. |
| schema_transformations | List of transformation functions to apply to the auto-inferred schema. Each transformation function should take the schema and statistics as input and should return the transformed schema. The transformations are applied in the order provided in the list. |
| Returns |
|---|
| A Schema protocol buffer. |
| Raises | |
|---|---|
| TypeError | If the input argument is not of the expected type. |
| ValueError | If the input statistics proto contains multiple datasets, none of which corresponds to the default slice. |
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Last updated 2024-10-18 UTC.