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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.
Attribute
| Name | Description |
| time_format |
str
The format in which that time field is expressed. The time_format must either be one of: - unix-seconds
- unix-milliseconds
- unix-microseconds
- unix-nanoseconds
(for respectively number of seconds, milliseconds,
microseconds and nanoseconds since start of the Unix epoch);
or be written in strftime syntax.
If time_format is not set, then the default format is RFC
3339 date-time format, where time-offset = "Z"
(e.g. 1985-04-12T23:20:50.52Z)
|
Inheritance
builtins.object > proto.message.Message > TimestampTransformationMethods
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.