Class Transformation (1.8.1)

Transformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

This message has oneof_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Attributes

NameDescription
auto google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs.Transformation.AutoTransformation
This field is a member of `oneof`_ ``transformation_detail``.
numeric google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs.Transformation.NumericTransformation
This field is a member of `oneof`_ ``transformation_detail``.
categorical google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs.Transformation.CategoricalTransformation
This field is a member of `oneof`_ ``transformation_detail``.
timestamp google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs.Transformation.TimestampTransformation
This field is a member of `oneof`_ ``transformation_detail``.
text google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs.Transformation.TextTransformation
This field is a member of `oneof`_ ``transformation_detail``.

Inheritance

builtins.object > proto.message.Message > Transformation

Classes

AutoTransformation

AutoTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Training pipeline will infer the proper transformation based on the statistic of dataset.

CategoricalTransformation

CategoricalTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Training pipeline will perform following transformation functions.

  • The categorical string as is--no change to case, punctuation, spelling, tense, and so on.

  • Convert the category name to a dictionary lookup index and generate an embedding for each index.

  • Categories that appear less than 5 times in the training dataset are treated as the "unknown" category. The "unknown" category gets its own special lookup index and resulting embedding.

NumericTransformation

NumericTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Training pipeline will perform following transformation functions.

  • The value converted to float32.

  • The z_score of the value.

  • log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.

  • z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.

  • A boolean value that indicates whether the value is valid.

TextTransformation

TextTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Training pipeline will perform following transformation functions.

  • The text as is--no change to case, punctuation, spelling, tense, and so on.

  • Convert the category name to a dictionary lookup index and generate an embedding for each index.

TimestampTransformation

TimestampTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Training pipeline will perform following transformation functions.

  • Apply the transformation functions for Numerical columns.

  • Determine the year, month, day,and weekday. Treat each value from the timestamp as a Categorical column.

  • Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed.