Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformation

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1/classes.rb,
lib/google/apis/aiplatform_v1/representations.rb,
lib/google/apis/aiplatform_v1/representations.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformation

Returns a new instance of GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformation.



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 25600

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#auto ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationAutoTransformation

Training pipeline will infer the proper transformation based on the statistic of dataset. Corresponds to the JSON property auto

Returns:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 25559

def auto
  @auto
end

#categorical ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationCategoricalTransformation

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. Corresponds to the JSON property categorical

Returns:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 25569

def categorical
  @categorical
end

#numeric ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationNumericTransformation

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. Corresponds to the JSON property numeric

Returns:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 25580

def numeric
  @numeric
end

#text ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTextTransformation

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. Corresponds to the JSON property text

Returns:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 25588

def text
  @text
end

#timestamp ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTimestampTransformation

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. Corresponds to the JSON property timestamp

Returns:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 25598

def timestamp
  @timestamp
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 25605

def update!(**args)
  @auto = args[:auto] if args.key?(:auto)
  @categorical = args[:categorical] if args.key?(:categorical)
  @numeric = args[:numeric] if args.key?(:numeric)
  @text = args[:text] if args.key?(:text)
  @timestamp = args[:timestamp] if args.key?(:timestamp)
end