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.
Protobuf type
google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation.TimestampTransformation
If invalid values is allowed, the training pipeline will create a
boolean feature that indicated whether the value is valid.
Otherwise, the training pipeline will discard the input row from
trainining data.
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)
If invalid values is allowed, the training pipeline will create a
boolean feature that indicated whether the value is valid.
Otherwise, the training pipeline will discard the input row from
trainining data.
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)
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)
If invalid values is allowed, the training pipeline will create a
boolean feature that indicated whether the value is valid.
Otherwise, the training pipeline will discard the input row from
trainining data.
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)
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)
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-01-27 UTC."],[],[]]
