Class: Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation

Inherits:

Object

Object
Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation

show all

Extended by:: Protobuf::MessageExts::ClassMethods

Includes:: Protobuf::MessageExts

Defined in:: proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_tables.rb

Defined Under Namespace

Classes: AutoTransformation, CategoricalArrayTransformation, CategoricalTransformation, NumericArrayTransformation, NumericTransformation, TextArrayTransformation, TextTransformation, TimestampTransformation

Instance Attribute Summary collapse

Instance Attribute Details

#auto ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::AutoTransformation`

# File 'proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_tables.rb', line 147

class Transformation
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # Training pipeline will infer the proper transformation based on the
  # statistic of dataset.
  # @!attribute [rw] column_name
  #   @return [::String]
  class AutoTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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 [rw] column_name
  #   @return [::String]
  # @!attribute [rw] time_format
  #   @return [::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)
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class TimestampTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Training pipeline will perform following transformation functions.
  # *  The text as is--no change to case, punctuation, spelling, tense, and
  # so
  #    on.
  # *  Tokenize text to words. Convert each words to a dictionary lookup
  # index
  #    and generate an embedding for each index. Combine the embedding of all
  #    elements into a single embedding using the mean.
  # *  Tokenization is based on unicode script boundaries.
  # *  Missing values get their own lookup index and resulting embedding.
  # *  Stop-words receive no special treatment and are not removed.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as numerical array and performs following
  # transformation functions.
  # *  All transformations for Numerical types applied to the average of the
  #    all elements.
  # *  The average of empty arrays is treated as zero.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as categorical array and performs following
  # transformation functions.
  # *  For each element in the array, convert the category name to a
  # dictionary
  #    lookup index and generate an embedding for each index.
  #    Combine the embedding of all elements into a single embedding using
  #    the mean.
  # *  Empty arrays treated as an embedding of zeroes.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as text array and performs following transformation
  # functions.
  # *  Concatenate all text values in the array into a single text value
  # using
  #    a space (" ") as a delimiter, and then treat the result as a single
  #    text value. Apply the transformations for Text columns.
  # *  Empty arrays treated as an empty text.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end

#categorical ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::CategoricalTransformation`

# File 'proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_tables.rb', line 147

class Transformation
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # Training pipeline will infer the proper transformation based on the
  # statistic of dataset.
  # @!attribute [rw] column_name
  #   @return [::String]
  class AutoTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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 [rw] column_name
  #   @return [::String]
  # @!attribute [rw] time_format
  #   @return [::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)
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class TimestampTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Training pipeline will perform following transformation functions.
  # *  The text as is--no change to case, punctuation, spelling, tense, and
  # so
  #    on.
  # *  Tokenize text to words. Convert each words to a dictionary lookup
  # index
  #    and generate an embedding for each index. Combine the embedding of all
  #    elements into a single embedding using the mean.
  # *  Tokenization is based on unicode script boundaries.
  # *  Missing values get their own lookup index and resulting embedding.
  # *  Stop-words receive no special treatment and are not removed.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as numerical array and performs following
  # transformation functions.
  # *  All transformations for Numerical types applied to the average of the
  #    all elements.
  # *  The average of empty arrays is treated as zero.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as categorical array and performs following
  # transformation functions.
  # *  For each element in the array, convert the category name to a
  # dictionary
  #    lookup index and generate an embedding for each index.
  #    Combine the embedding of all elements into a single embedding using
  #    the mean.
  # *  Empty arrays treated as an embedding of zeroes.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as text array and performs following transformation
  # functions.
  # *  Concatenate all text values in the array into a single text value
  # using
  #    a space (" ") as a delimiter, and then treat the result as a single
  #    text value. Apply the transformations for Text columns.
  # *  Empty arrays treated as an empty text.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end

#numeric ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::NumericTransformation`

# File 'proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_tables.rb', line 147

class Transformation
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # Training pipeline will infer the proper transformation based on the
  # statistic of dataset.
  # @!attribute [rw] column_name
  #   @return [::String]
  class AutoTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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 [rw] column_name
  #   @return [::String]
  # @!attribute [rw] time_format
  #   @return [::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)
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class TimestampTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Training pipeline will perform following transformation functions.
  # *  The text as is--no change to case, punctuation, spelling, tense, and
  # so
  #    on.
  # *  Tokenize text to words. Convert each words to a dictionary lookup
  # index
  #    and generate an embedding for each index. Combine the embedding of all
  #    elements into a single embedding using the mean.
  # *  Tokenization is based on unicode script boundaries.
  # *  Missing values get their own lookup index and resulting embedding.
  # *  Stop-words receive no special treatment and are not removed.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as numerical array and performs following
  # transformation functions.
  # *  All transformations for Numerical types applied to the average of the
  #    all elements.
  # *  The average of empty arrays is treated as zero.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as categorical array and performs following
  # transformation functions.
  # *  For each element in the array, convert the category name to a
  # dictionary
  #    lookup index and generate an embedding for each index.
  #    Combine the embedding of all elements into a single embedding using
  #    the mean.
  # *  Empty arrays treated as an embedding of zeroes.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as text array and performs following transformation
  # functions.
  # *  Concatenate all text values in the array into a single text value
  # using
  #    a space (" ") as a delimiter, and then treat the result as a single
  #    text value. Apply the transformations for Text columns.
  # *  Empty arrays treated as an empty text.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end

#repeated_categorical ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::CategoricalArrayTransformation`

# File 'proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_tables.rb', line 147

class Transformation
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # Training pipeline will infer the proper transformation based on the
  # statistic of dataset.
  # @!attribute [rw] column_name
  #   @return [::String]
  class AutoTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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 [rw] column_name
  #   @return [::String]
  # @!attribute [rw] time_format
  #   @return [::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)
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class TimestampTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Training pipeline will perform following transformation functions.
  # *  The text as is--no change to case, punctuation, spelling, tense, and
  # so
  #    on.
  # *  Tokenize text to words. Convert each words to a dictionary lookup
  # index
  #    and generate an embedding for each index. Combine the embedding of all
  #    elements into a single embedding using the mean.
  # *  Tokenization is based on unicode script boundaries.
  # *  Missing values get their own lookup index and resulting embedding.
  # *  Stop-words receive no special treatment and are not removed.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as numerical array and performs following
  # transformation functions.
  # *  All transformations for Numerical types applied to the average of the
  #    all elements.
  # *  The average of empty arrays is treated as zero.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as categorical array and performs following
  # transformation functions.
  # *  For each element in the array, convert the category name to a
  # dictionary
  #    lookup index and generate an embedding for each index.
  #    Combine the embedding of all elements into a single embedding using
  #    the mean.
  # *  Empty arrays treated as an embedding of zeroes.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as text array and performs following transformation
  # functions.
  # *  Concatenate all text values in the array into a single text value
  # using
  #    a space (" ") as a delimiter, and then treat the result as a single
  #    text value. Apply the transformations for Text columns.
  # *  Empty arrays treated as an empty text.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end

#repeated_numeric ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::NumericArrayTransformation`

# File 'proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_tables.rb', line 147

class Transformation
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # Training pipeline will infer the proper transformation based on the
  # statistic of dataset.
  # @!attribute [rw] column_name
  #   @return [::String]
  class AutoTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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 [rw] column_name
  #   @return [::String]
  # @!attribute [rw] time_format
  #   @return [::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)
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class TimestampTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Training pipeline will perform following transformation functions.
  # *  The text as is--no change to case, punctuation, spelling, tense, and
  # so
  #    on.
  # *  Tokenize text to words. Convert each words to a dictionary lookup
  # index
  #    and generate an embedding for each index. Combine the embedding of all
  #    elements into a single embedding using the mean.
  # *  Tokenization is based on unicode script boundaries.
  # *  Missing values get their own lookup index and resulting embedding.
  # *  Stop-words receive no special treatment and are not removed.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as numerical array and performs following
  # transformation functions.
  # *  All transformations for Numerical types applied to the average of the
  #    all elements.
  # *  The average of empty arrays is treated as zero.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as categorical array and performs following
  # transformation functions.
  # *  For each element in the array, convert the category name to a
  # dictionary
  #    lookup index and generate an embedding for each index.
  #    Combine the embedding of all elements into a single embedding using
  #    the mean.
  # *  Empty arrays treated as an embedding of zeroes.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as text array and performs following transformation
  # functions.
  # *  Concatenate all text values in the array into a single text value
  # using
  #    a space (" ") as a delimiter, and then treat the result as a single
  #    text value. Apply the transformations for Text columns.
  # *  Empty arrays treated as an empty text.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end

#repeated_text ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::TextArrayTransformation`

# File 'proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_tables.rb', line 147

class Transformation
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # Training pipeline will infer the proper transformation based on the
  # statistic of dataset.
  # @!attribute [rw] column_name
  #   @return [::String]
  class AutoTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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 [rw] column_name
  #   @return [::String]
  # @!attribute [rw] time_format
  #   @return [::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)
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class TimestampTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Training pipeline will perform following transformation functions.
  # *  The text as is--no change to case, punctuation, spelling, tense, and
  # so
  #    on.
  # *  Tokenize text to words. Convert each words to a dictionary lookup
  # index
  #    and generate an embedding for each index. Combine the embedding of all
  #    elements into a single embedding using the mean.
  # *  Tokenization is based on unicode script boundaries.
  # *  Missing values get their own lookup index and resulting embedding.
  # *  Stop-words receive no special treatment and are not removed.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as numerical array and performs following
  # transformation functions.
  # *  All transformations for Numerical types applied to the average of the
  #    all elements.
  # *  The average of empty arrays is treated as zero.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as categorical array and performs following
  # transformation functions.
  # *  For each element in the array, convert the category name to a
  # dictionary
  #    lookup index and generate an embedding for each index.
  #    Combine the embedding of all elements into a single embedding using
  #    the mean.
  # *  Empty arrays treated as an embedding of zeroes.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as text array and performs following transformation
  # functions.
  # *  Concatenate all text values in the array into a single text value
  # using
  #    a space (" ") as a delimiter, and then treat the result as a single
  #    text value. Apply the transformations for Text columns.
  # *  Empty arrays treated as an empty text.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end

#text ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::TextTransformation`

# File 'proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_tables.rb', line 147

class Transformation
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # Training pipeline will infer the proper transformation based on the
  # statistic of dataset.
  # @!attribute [rw] column_name
  #   @return [::String]
  class AutoTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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 [rw] column_name
  #   @return [::String]
  # @!attribute [rw] time_format
  #   @return [::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)
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class TimestampTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Training pipeline will perform following transformation functions.
  # *  The text as is--no change to case, punctuation, spelling, tense, and
  # so
  #    on.
  # *  Tokenize text to words. Convert each words to a dictionary lookup
  # index
  #    and generate an embedding for each index. Combine the embedding of all
  #    elements into a single embedding using the mean.
  # *  Tokenization is based on unicode script boundaries.
  # *  Missing values get their own lookup index and resulting embedding.
  # *  Stop-words receive no special treatment and are not removed.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as numerical array and performs following
  # transformation functions.
  # *  All transformations for Numerical types applied to the average of the
  #    all elements.
  # *  The average of empty arrays is treated as zero.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as categorical array and performs following
  # transformation functions.
  # *  For each element in the array, convert the category name to a
  # dictionary
  #    lookup index and generate an embedding for each index.
  #    Combine the embedding of all elements into a single embedding using
  #    the mean.
  # *  Empty arrays treated as an embedding of zeroes.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as text array and performs following transformation
  # functions.
  # *  Concatenate all text values in the array into a single text value
  # using
  #    a space (" ") as a delimiter, and then treat the result as a single
  #    text value. Apply the transformations for Text columns.
  # *  Empty arrays treated as an empty text.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end

#timestamp ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::TimestampTransformation`

# File 'proto_docs/google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_tables.rb', line 147

class Transformation
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # Training pipeline will infer the proper transformation based on the
  # statistic of dataset.
  # @!attribute [rw] column_name
  #   @return [::String]
  class AutoTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # 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 [rw] column_name
  #   @return [::String]
  # @!attribute [rw] time_format
  #   @return [::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)
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class TimestampTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Training pipeline will perform following transformation functions.
  # *  The text as is--no change to case, punctuation, spelling, tense, and
  # so
  #    on.
  # *  Tokenize text to words. Convert each words to a dictionary lookup
  # index
  #    and generate an embedding for each index. Combine the embedding of all
  #    elements into a single embedding using the mean.
  # *  Tokenization is based on unicode script boundaries.
  # *  Missing values get their own lookup index and resulting embedding.
  # *  Stop-words receive no special treatment and are not removed.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as numerical array and performs following
  # transformation functions.
  # *  All transformations for Numerical types applied to the average of the
  #    all elements.
  # *  The average of empty arrays is treated as zero.
  # @!attribute [rw] column_name
  #   @return [::String]
  # @!attribute [rw] invalid_values_allowed
  #   @return [::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.
  class NumericArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as categorical array and performs following
  # transformation functions.
  # *  For each element in the array, convert the category name to a
  # dictionary
  #    lookup index and generate an embedding for each index.
  #    Combine the embedding of all elements into a single embedding using
  #    the mean.
  # *  Empty arrays treated as an embedding of zeroes.
  # @!attribute [rw] column_name
  #   @return [::String]
  class CategoricalArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # Treats the column as text array and performs following transformation
  # functions.
  # *  Concatenate all text values in the array into a single text value
  # using
  #    a space (" ") as a delimiter, and then treat the result as a single
  #    text value. Apply the transformations for Text columns.
  # *  Empty arrays treated as an empty text.
  # @!attribute [rw] column_name
  #   @return [::String]
  class TextArrayTransformation
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end

Class: Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation

Defined Under Namespace

Instance Attribute Summary collapse

Instance Attribute Details

#auto ⇒ ::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::AutoTransformation

#categorical ⇒ ::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::CategoricalTransformation

#numeric ⇒ ::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::NumericTransformation

#repeated_categorical ⇒ ::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::CategoricalArrayTransformation

#repeated_numeric ⇒ ::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::NumericArrayTransformation

#repeated_text ⇒ ::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::TextArrayTransformation

#text ⇒ ::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::TextTransformation

#timestamp ⇒ ::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::TimestampTransformation

#auto ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::AutoTransformation`

#categorical ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::CategoricalTransformation`

#numeric ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::NumericTransformation`

#repeated_categorical ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::CategoricalArrayTransformation`

#repeated_numeric ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::NumericArrayTransformation`

#repeated_text ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::TextArrayTransformation`

#text ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::TextTransformation`

#timestamp ⇒ `::Google::Cloud::AIPlatform::V1::Schema::TrainingJob::Definition::AutoMlTablesInputs::Transformation::TimestampTransformation`