Class Transformation (1.1.1)

Version 1.1.1

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

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.