Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::TimestampTransformation (v0.11.0)

Version 0.11.0

• 0.58.0 (latest)
• 0.57.0
• 0.56.0
• 0.55.0
• 0.54.0
• 0.53.0
• 0.52.0
• 0.51.0
• 0.50.0
• 0.49.0
• 0.48.0
• 0.47.0
• 0.46.0
• 0.45.0
• 0.44.0
• 0.43.0
• 0.42.0
• 0.41.0
• 0.40.0
• 0.39.0
• 0.38.0
• 0.37.0
• 0.36.0
• 0.35.0
• 0.34.0
• 0.33.0
• 0.32.0
• 0.31.0
• 0.30.0
• 0.29.0
• 0.28.0
• 0.27.0
• 0.26.0
• 0.25.0
• 0.24.0
• 0.23.0
• 0.22.0
• 0.21.0
• 0.20.0
• 0.19.0
• 0.18.0
• 0.17.0
• 0.16.0
• 0.15.0
• 0.14.0
• 0.13.0
• 0.12.0
• 0.11.0
• 0.10.0
• 0.9.1
• 0.8.0
• 0.7.0
• 0.6.0
• 0.5.0
• 0.4.0
• 0.3.0
• 0.2.0
• 0.1.0

Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::TimestampTransformation.

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.

Inherits

• Object

Extended By

• Google::Protobuf::MessageExts::ClassMethods

Includes

• Google::Protobuf::MessageExts

Methods

#column_name

def column_name() -> ::String

Returns

• (::String)

#column_name=

def column_name=(value) -> ::String

Parameter

• value (::String)

Returns

• (::String)

#invalid_values_allowed

def invalid_values_allowed() -> ::Boolean

Returns

• (::Boolean) — 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.

#invalid_values_allowed=

def invalid_values_allowed=(value) -> ::Boolean

Parameter

• value (::Boolean) — 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.

Returns

• (::Boolean) — 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.

#time_format

def time_format() -> ::String

Returns

• (::String) —

  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)

#time_format=

def time_format=(value) -> ::String

Parameter

• value (::String) —

  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)

Returns

• (::String) —

  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)