Class: Google::Cloud::AIPlatform::V1::ExplanationMetadata

Inherits:
Object
  • Object
show all
Extended by:
Protobuf::MessageExts::ClassMethods
Includes:
Protobuf::MessageExts
Defined in:
proto_docs/google/cloud/aiplatform/v1/explanation_metadata.rb

Overview

Metadata describing the Model's input and output for explanation.

Defined Under Namespace

Classes: InputMetadata, InputsEntry, OutputMetadata, OutputsEntry

Instance Attribute Summary collapse

Instance Attribute Details

#feature_attributions_schema_uri ⇒ ::String

Returns Points to a YAML file stored on Google Cloud Storage describing the format of the [feature attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions]. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

Returns:

  • (::String) —

    Points to a YAML file stored on Google Cloud Storage describing the format of the [feature attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions]. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.



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# File 'proto_docs/google/cloud/aiplatform/v1/explanation_metadata.rb', line 67

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

  # Metadata of the input of a feature.
  #
  # Fields other than
  # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#input_baselines InputMetadata.input_baselines}
  # are applicable only for Models that are using Vertex AI-provided images for
  # Tensorflow.
  # @!attribute [rw] input_baselines
  #   @return [::Array<::Google::Protobuf::Value>]
  #     Baseline inputs for this feature.
  #
  #     If no baseline is specified, Vertex AI chooses the baseline for this
  #     feature. If multiple baselines are specified, Vertex AI returns the
  #     average attributions across them in
  #     {::Google::Cloud::AIPlatform::V1::Attribution#feature_attributions Attribution.feature_attributions}.
  #
  #     For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape
  #     of each baseline must match the shape of the input tensor. If a scalar is
  #     provided, we broadcast to the same shape as the input tensor.
  #
  #     For custom images, the element of the baselines must be in the same
  #     format as the feature's input in the
  #     {::Google::Cloud::AIPlatform::V1::ExplainRequest#instances instance}[]. The
  #     schema of any single instance may be specified via Endpoint's
  #     DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
  #     [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
  #     {::Google::Cloud::AIPlatform::V1::PredictSchemata#instance_schema_uri instance_schema_uri}.
  # @!attribute [rw] input_tensor_name
  #   @return [::String]
  #     Name of the input tensor for this feature. Required and is only
  #     applicable to Vertex AI-provided images for Tensorflow.
  # @!attribute [rw] encoding
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Encoding]
  #     Defines how the feature is encoded into the input tensor. Defaults to
  #     IDENTITY.
  # @!attribute [rw] modality
  #   @return [::String]
  #     Modality of the feature. Valid values are: numeric, image. Defaults to
  #     numeric.
  # @!attribute [rw] feature_value_domain
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::FeatureValueDomain]
  #     The domain details of the input feature value. Like min/max, original
  #     mean or standard deviation if normalized.
  # @!attribute [rw] indices_tensor_name
  #   @return [::String]
  #     Specifies the index of the values of the input tensor.
  #     Required when the input tensor is a sparse representation. Refer to
  #     Tensorflow documentation for more details:
  #     https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
  # @!attribute [rw] dense_shape_tensor_name
  #   @return [::String]
  #     Specifies the shape of the values of the input if the input is a sparse
  #     representation. Refer to Tensorflow documentation for more details:
  #     https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
  # @!attribute [rw] index_feature_mapping
  #   @return [::Array<::String>]
  #     A list of feature names for each index in the input tensor.
  #     Required when the input
  #     {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#encoding InputMetadata.encoding}
  #     is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
  # @!attribute [rw] encoded_tensor_name
  #   @return [::String]
  #     Encoded tensor is a transformation of the input tensor. Must be provided
  #     if choosing
  #     [Integrated Gradients
  #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution]
  #     or [XRAI
  #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution]
  #     and the input tensor is not differentiable.
  #
  #     An encoded tensor is generated if the input tensor is encoded by a lookup
  #     table.
  # @!attribute [rw] encoded_baselines
  #   @return [::Array<::Google::Protobuf::Value>]
  #     A list of baselines for the encoded tensor.
  #
  #     The shape of each baseline should match the shape of the encoded tensor.
  #     If a scalar is provided, Vertex AI broadcasts to the same shape as the
  #     encoded tensor.
  # @!attribute [rw] visualization
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization]
  #     Visualization configurations for image explanation.
  # @!attribute [rw] group_name
  #   @return [::String]
  #     Name of the group that the input belongs to. Features with the same group
  #     name will be treated as one feature when computing attributions. Features
  #     grouped together can have different shapes in value. If provided, there
  #     will be one single attribution generated in
  #     {::Google::Cloud::AIPlatform::V1::Attribution#feature_attributions Attribution.feature_attributions},
  #     keyed by the group name.
  class InputMetadata
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods

    # Domain details of the input feature value. Provides numeric information
    # about the feature, such as its range (min, max). If the feature has been
    # pre-processed, for example with z-scoring, then it provides information
    # about how to recover the original feature. For example, if the input
    # feature is an image and it has been pre-processed to obtain 0-mean and
    # stddev = 1 values, then original_mean, and original_stddev refer to the
    # mean and stddev of the original feature (e.g. image tensor) from which
    # input feature (with mean = 0 and stddev = 1) was obtained.
    # @!attribute [rw] min_value
    #   @return [::Float]
    #     The minimum permissible value for this feature.
    # @!attribute [rw] max_value
    #   @return [::Float]
    #     The maximum permissible value for this feature.
    # @!attribute [rw] original_mean
    #   @return [::Float]
    #     If this input feature has been normalized to a mean value of 0,
    #     the original_mean specifies the mean value of the domain prior to
    #     normalization.
    # @!attribute [rw] original_stddev
    #   @return [::Float]
    #     If this input feature has been normalized to a standard deviation of
    #     1.0, the original_stddev specifies the standard deviation of the domain
    #     prior to normalization.
    class FeatureValueDomain
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods
    end

    # Visualization configurations for image explanation.
    # @!attribute [rw] type
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::Type]
    #     Type of the image visualization. Only applicable to
    #     [Integrated Gradients
    #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution].
    #     OUTLINES shows regions of attribution, while PIXELS shows per-pixel
    #     attribution. Defaults to OUTLINES.
    # @!attribute [rw] polarity
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::Polarity]
    #     Whether to only highlight pixels with positive contributions, negative
    #     or both. Defaults to POSITIVE.
    # @!attribute [rw] color_map
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::ColorMap]
    #     The color scheme used for the highlighted areas.
    #
    #     Defaults to PINK_GREEN for
    #     [Integrated Gradients
    #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution],
    #     which shows positive attributions in green and negative in pink.
    #
    #     Defaults to VIRIDIS for
    #     [XRAI
    #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution],
    #     which highlights the most influential regions in yellow and the least
    #     influential in blue.
    # @!attribute [rw] clip_percent_upperbound
    #   @return [::Float]
    #     Excludes attributions above the specified percentile from the
    #     highlighted areas. Using the clip_percent_upperbound and
    #     clip_percent_lowerbound together can be useful for filtering out noise
    #     and making it easier to see areas of strong attribution. Defaults to
    #     99.9.
    # @!attribute [rw] clip_percent_lowerbound
    #   @return [::Float]
    #     Excludes attributions below the specified percentile, from the
    #     highlighted areas. Defaults to 62.
    # @!attribute [rw] overlay_type
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::OverlayType]
    #     How the original image is displayed in the visualization.
    #     Adjusting the overlay can help increase visual clarity if the original
    #     image makes it difficult to view the visualization. Defaults to NONE.
    class Visualization
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods

      # Type of the image visualization. Only applicable to
      # [Integrated Gradients
      # attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution].
      module Type
        # Should not be used.
        TYPE_UNSPECIFIED = 0

        # Shows which pixel contributed to the image prediction.
        PIXELS = 1

        # Shows which region contributed to the image prediction by outlining
        # the region.
        OUTLINES = 2
      end

      # Whether to only highlight pixels with positive contributions, negative
      # or both. Defaults to POSITIVE.
      module Polarity
        # Default value. This is the same as POSITIVE.
        POLARITY_UNSPECIFIED = 0

        # Highlights the pixels/outlines that were most influential to the
        # model's prediction.
        POSITIVE = 1

        # Setting polarity to negative highlights areas that does not lead to
        # the models's current prediction.
        NEGATIVE = 2

        # Shows both positive and negative attributions.
        BOTH = 3
      end

      # The color scheme used for highlighting areas.
      module ColorMap
        # Should not be used.
        COLOR_MAP_UNSPECIFIED = 0

        # Positive: green. Negative: pink.
        PINK_GREEN = 1

        # Viridis color map: A perceptually uniform color mapping which is
        # easier to see by those with colorblindness and progresses from yellow
        # to green to blue. Positive: yellow. Negative: blue.
        VIRIDIS = 2

        # Positive: red. Negative: red.
        RED = 3

        # Positive: green. Negative: green.
        GREEN = 4

        # Positive: green. Negative: red.
        RED_GREEN = 6

        # PiYG palette.
        PINK_WHITE_GREEN = 5
      end

      # How the original image is displayed in the visualization.
      module OverlayType
        # Default value. This is the same as NONE.
        OVERLAY_TYPE_UNSPECIFIED = 0

        # No overlay.
        NONE = 1

        # The attributions are shown on top of the original image.
        ORIGINAL = 2

        # The attributions are shown on top of grayscaled version of the
        # original image.
        GRAYSCALE = 3

        # The attributions are used as a mask to reveal predictive parts of
        # the image and hide the un-predictive parts.
        MASK_BLACK = 4
      end
    end

    # Defines how a feature is encoded. Defaults to IDENTITY.
    module Encoding
      # Default value. This is the same as IDENTITY.
      ENCODING_UNSPECIFIED = 0

      # The tensor represents one feature.
      IDENTITY = 1

      # The tensor represents a bag of features where each index maps to
      # a feature.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#index_feature_mapping InputMetadata.index_feature_mapping}
      # must be provided for this encoding. For example:
      # ```
      # input = [27, 6.0, 150]
      # index_feature_mapping = ["age", "height", "weight"]
      # ```
      BAG_OF_FEATURES = 2

      # The tensor represents a bag of features where each index maps to a
      # feature. Zero values in the tensor indicates feature being
      # non-existent.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#index_feature_mapping InputMetadata.index_feature_mapping}
      # must be provided for this encoding. For example:
      # ```
      # input = [2, 0, 5, 0, 1]
      # index_feature_mapping = ["a", "b", "c", "d", "e"]
      # ```
      BAG_OF_FEATURES_SPARSE = 3

      # The tensor is a list of binaries representing whether a feature exists
      # or not (1 indicates existence).
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#index_feature_mapping InputMetadata.index_feature_mapping}
      # must be provided for this encoding. For example:
      # ```
      # input = [1, 0, 1, 0, 1]
      # index_feature_mapping = ["a", "b", "c", "d", "e"]
      # ```
      INDICATOR = 4

      # The tensor is encoded into a 1-dimensional array represented by an
      # encoded tensor.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#encoded_tensor_name InputMetadata.encoded_tensor_name}
      # must be provided for this encoding. For example:
      # ```
      # input = ["This", "is", "a", "test", "."]
      # encoded = [0.1, 0.2, 0.3, 0.4, 0.5]
      # ```
      COMBINED_EMBEDDING = 5

      # Select this encoding when the input tensor is encoded into a
      # 2-dimensional array represented by an encoded tensor.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#encoded_tensor_name InputMetadata.encoded_tensor_name}
      # must be provided for this encoding. The first dimension of the encoded
      # tensor's shape is the same as the input tensor's shape. For example:
      # ```
      # input = ["This", "is", "a", "test", "."]
      # encoded = [[0.1, 0.2, 0.3, 0.4, 0.5],
      #            [0.2, 0.1, 0.4, 0.3, 0.5],
      #            [0.5, 0.1, 0.3, 0.5, 0.4],
      #            [0.5, 0.3, 0.1, 0.2, 0.4],
      #            [0.4, 0.3, 0.2, 0.5, 0.1]]
      # ```
      CONCAT_EMBEDDING = 6
    end
  end

  # Metadata of the prediction output to be explained.
  # @!attribute [rw] index_display_name_mapping
  #   @return [::Google::Protobuf::Value]
  #     Static mapping between the index and display name.
  #
  #     Use this if the outputs are a deterministic n-dimensional array, e.g. a
  #     list of scores of all the classes in a pre-defined order for a
  #     multi-classification Model. It's not feasible if the outputs are
  #     non-deterministic, e.g. the Model produces top-k classes or sort the
  #     outputs by their values.
  #
  #     The shape of the value must be an n-dimensional array of strings. The
  #     number of dimensions must match that of the outputs to be explained.
  #     The
  #     {::Google::Cloud::AIPlatform::V1::Attribution#output_display_name Attribution.output_display_name}
  #     is populated by locating in the mapping with
  #     {::Google::Cloud::AIPlatform::V1::Attribution#output_index Attribution.output_index}.
  # @!attribute [rw] display_name_mapping_key
  #   @return [::String]
  #     Specify a field name in the prediction to look for the display name.
  #
  #     Use this if the prediction contains the display names for the outputs.
  #
  #     The display names in the prediction must have the same shape of the
  #     outputs, so that it can be located by
  #     {::Google::Cloud::AIPlatform::V1::Attribution#output_index Attribution.output_index}
  #     for a specific output.
  # @!attribute [rw] output_tensor_name
  #   @return [::String]
  #     Name of the output tensor. Required and is only applicable to Vertex
  #     AI provided images for Tensorflow.
  class OutputMetadata
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # @!attribute [rw] key
  #   @return [::String]
  # @!attribute [rw] value
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata]
  class InputsEntry
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # @!attribute [rw] key
  #   @return [::String]
  # @!attribute [rw] value
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::OutputMetadata]
  class OutputsEntry
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end

#inputs ⇒ ::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata}

Returns Required. Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature.

An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI.

For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature).

For custom images, the key must match with the key in instance.

Returns:

  • (::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata}) —

    Required. Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature.

    An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI.

    For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature).

    For custom images, the key must match with the key in instance.



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# File 'proto_docs/google/cloud/aiplatform/v1/explanation_metadata.rb', line 67

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

  # Metadata of the input of a feature.
  #
  # Fields other than
  # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#input_baselines InputMetadata.input_baselines}
  # are applicable only for Models that are using Vertex AI-provided images for
  # Tensorflow.
  # @!attribute [rw] input_baselines
  #   @return [::Array<::Google::Protobuf::Value>]
  #     Baseline inputs for this feature.
  #
  #     If no baseline is specified, Vertex AI chooses the baseline for this
  #     feature. If multiple baselines are specified, Vertex AI returns the
  #     average attributions across them in
  #     {::Google::Cloud::AIPlatform::V1::Attribution#feature_attributions Attribution.feature_attributions}.
  #
  #     For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape
  #     of each baseline must match the shape of the input tensor. If a scalar is
  #     provided, we broadcast to the same shape as the input tensor.
  #
  #     For custom images, the element of the baselines must be in the same
  #     format as the feature's input in the
  #     {::Google::Cloud::AIPlatform::V1::ExplainRequest#instances instance}[]. The
  #     schema of any single instance may be specified via Endpoint's
  #     DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
  #     [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
  #     {::Google::Cloud::AIPlatform::V1::PredictSchemata#instance_schema_uri instance_schema_uri}.
  # @!attribute [rw] input_tensor_name
  #   @return [::String]
  #     Name of the input tensor for this feature. Required and is only
  #     applicable to Vertex AI-provided images for Tensorflow.
  # @!attribute [rw] encoding
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Encoding]
  #     Defines how the feature is encoded into the input tensor. Defaults to
  #     IDENTITY.
  # @!attribute [rw] modality
  #   @return [::String]
  #     Modality of the feature. Valid values are: numeric, image. Defaults to
  #     numeric.
  # @!attribute [rw] feature_value_domain
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::FeatureValueDomain]
  #     The domain details of the input feature value. Like min/max, original
  #     mean or standard deviation if normalized.
  # @!attribute [rw] indices_tensor_name
  #   @return [::String]
  #     Specifies the index of the values of the input tensor.
  #     Required when the input tensor is a sparse representation. Refer to
  #     Tensorflow documentation for more details:
  #     https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
  # @!attribute [rw] dense_shape_tensor_name
  #   @return [::String]
  #     Specifies the shape of the values of the input if the input is a sparse
  #     representation. Refer to Tensorflow documentation for more details:
  #     https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
  # @!attribute [rw] index_feature_mapping
  #   @return [::Array<::String>]
  #     A list of feature names for each index in the input tensor.
  #     Required when the input
  #     {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#encoding InputMetadata.encoding}
  #     is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
  # @!attribute [rw] encoded_tensor_name
  #   @return [::String]
  #     Encoded tensor is a transformation of the input tensor. Must be provided
  #     if choosing
  #     [Integrated Gradients
  #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution]
  #     or [XRAI
  #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution]
  #     and the input tensor is not differentiable.
  #
  #     An encoded tensor is generated if the input tensor is encoded by a lookup
  #     table.
  # @!attribute [rw] encoded_baselines
  #   @return [::Array<::Google::Protobuf::Value>]
  #     A list of baselines for the encoded tensor.
  #
  #     The shape of each baseline should match the shape of the encoded tensor.
  #     If a scalar is provided, Vertex AI broadcasts to the same shape as the
  #     encoded tensor.
  # @!attribute [rw] visualization
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization]
  #     Visualization configurations for image explanation.
  # @!attribute [rw] group_name
  #   @return [::String]
  #     Name of the group that the input belongs to. Features with the same group
  #     name will be treated as one feature when computing attributions. Features
  #     grouped together can have different shapes in value. If provided, there
  #     will be one single attribution generated in
  #     {::Google::Cloud::AIPlatform::V1::Attribution#feature_attributions Attribution.feature_attributions},
  #     keyed by the group name.
  class InputMetadata
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods

    # Domain details of the input feature value. Provides numeric information
    # about the feature, such as its range (min, max). If the feature has been
    # pre-processed, for example with z-scoring, then it provides information
    # about how to recover the original feature. For example, if the input
    # feature is an image and it has been pre-processed to obtain 0-mean and
    # stddev = 1 values, then original_mean, and original_stddev refer to the
    # mean and stddev of the original feature (e.g. image tensor) from which
    # input feature (with mean = 0 and stddev = 1) was obtained.
    # @!attribute [rw] min_value
    #   @return [::Float]
    #     The minimum permissible value for this feature.
    # @!attribute [rw] max_value
    #   @return [::Float]
    #     The maximum permissible value for this feature.
    # @!attribute [rw] original_mean
    #   @return [::Float]
    #     If this input feature has been normalized to a mean value of 0,
    #     the original_mean specifies the mean value of the domain prior to
    #     normalization.
    # @!attribute [rw] original_stddev
    #   @return [::Float]
    #     If this input feature has been normalized to a standard deviation of
    #     1.0, the original_stddev specifies the standard deviation of the domain
    #     prior to normalization.
    class FeatureValueDomain
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods
    end

    # Visualization configurations for image explanation.
    # @!attribute [rw] type
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::Type]
    #     Type of the image visualization. Only applicable to
    #     [Integrated Gradients
    #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution].
    #     OUTLINES shows regions of attribution, while PIXELS shows per-pixel
    #     attribution. Defaults to OUTLINES.
    # @!attribute [rw] polarity
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::Polarity]
    #     Whether to only highlight pixels with positive contributions, negative
    #     or both. Defaults to POSITIVE.
    # @!attribute [rw] color_map
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::ColorMap]
    #     The color scheme used for the highlighted areas.
    #
    #     Defaults to PINK_GREEN for
    #     [Integrated Gradients
    #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution],
    #     which shows positive attributions in green and negative in pink.
    #
    #     Defaults to VIRIDIS for
    #     [XRAI
    #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution],
    #     which highlights the most influential regions in yellow and the least
    #     influential in blue.
    # @!attribute [rw] clip_percent_upperbound
    #   @return [::Float]
    #     Excludes attributions above the specified percentile from the
    #     highlighted areas. Using the clip_percent_upperbound and
    #     clip_percent_lowerbound together can be useful for filtering out noise
    #     and making it easier to see areas of strong attribution. Defaults to
    #     99.9.
    # @!attribute [rw] clip_percent_lowerbound
    #   @return [::Float]
    #     Excludes attributions below the specified percentile, from the
    #     highlighted areas. Defaults to 62.
    # @!attribute [rw] overlay_type
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::OverlayType]
    #     How the original image is displayed in the visualization.
    #     Adjusting the overlay can help increase visual clarity if the original
    #     image makes it difficult to view the visualization. Defaults to NONE.
    class Visualization
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods

      # Type of the image visualization. Only applicable to
      # [Integrated Gradients
      # attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution].
      module Type
        # Should not be used.
        TYPE_UNSPECIFIED = 0

        # Shows which pixel contributed to the image prediction.
        PIXELS = 1

        # Shows which region contributed to the image prediction by outlining
        # the region.
        OUTLINES = 2
      end

      # Whether to only highlight pixels with positive contributions, negative
      # or both. Defaults to POSITIVE.
      module Polarity
        # Default value. This is the same as POSITIVE.
        POLARITY_UNSPECIFIED = 0

        # Highlights the pixels/outlines that were most influential to the
        # model's prediction.
        POSITIVE = 1

        # Setting polarity to negative highlights areas that does not lead to
        # the models's current prediction.
        NEGATIVE = 2

        # Shows both positive and negative attributions.
        BOTH = 3
      end

      # The color scheme used for highlighting areas.
      module ColorMap
        # Should not be used.
        COLOR_MAP_UNSPECIFIED = 0

        # Positive: green. Negative: pink.
        PINK_GREEN = 1

        # Viridis color map: A perceptually uniform color mapping which is
        # easier to see by those with colorblindness and progresses from yellow
        # to green to blue. Positive: yellow. Negative: blue.
        VIRIDIS = 2

        # Positive: red. Negative: red.
        RED = 3

        # Positive: green. Negative: green.
        GREEN = 4

        # Positive: green. Negative: red.
        RED_GREEN = 6

        # PiYG palette.
        PINK_WHITE_GREEN = 5
      end

      # How the original image is displayed in the visualization.
      module OverlayType
        # Default value. This is the same as NONE.
        OVERLAY_TYPE_UNSPECIFIED = 0

        # No overlay.
        NONE = 1

        # The attributions are shown on top of the original image.
        ORIGINAL = 2

        # The attributions are shown on top of grayscaled version of the
        # original image.
        GRAYSCALE = 3

        # The attributions are used as a mask to reveal predictive parts of
        # the image and hide the un-predictive parts.
        MASK_BLACK = 4
      end
    end

    # Defines how a feature is encoded. Defaults to IDENTITY.
    module Encoding
      # Default value. This is the same as IDENTITY.
      ENCODING_UNSPECIFIED = 0

      # The tensor represents one feature.
      IDENTITY = 1

      # The tensor represents a bag of features where each index maps to
      # a feature.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#index_feature_mapping InputMetadata.index_feature_mapping}
      # must be provided for this encoding. For example:
      # ```
      # input = [27, 6.0, 150]
      # index_feature_mapping = ["age", "height", "weight"]
      # ```
      BAG_OF_FEATURES = 2

      # The tensor represents a bag of features where each index maps to a
      # feature. Zero values in the tensor indicates feature being
      # non-existent.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#index_feature_mapping InputMetadata.index_feature_mapping}
      # must be provided for this encoding. For example:
      # ```
      # input = [2, 0, 5, 0, 1]
      # index_feature_mapping = ["a", "b", "c", "d", "e"]
      # ```
      BAG_OF_FEATURES_SPARSE = 3

      # The tensor is a list of binaries representing whether a feature exists
      # or not (1 indicates existence).
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#index_feature_mapping InputMetadata.index_feature_mapping}
      # must be provided for this encoding. For example:
      # ```
      # input = [1, 0, 1, 0, 1]
      # index_feature_mapping = ["a", "b", "c", "d", "e"]
      # ```
      INDICATOR = 4

      # The tensor is encoded into a 1-dimensional array represented by an
      # encoded tensor.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#encoded_tensor_name InputMetadata.encoded_tensor_name}
      # must be provided for this encoding. For example:
      # ```
      # input = ["This", "is", "a", "test", "."]
      # encoded = [0.1, 0.2, 0.3, 0.4, 0.5]
      # ```
      COMBINED_EMBEDDING = 5

      # Select this encoding when the input tensor is encoded into a
      # 2-dimensional array represented by an encoded tensor.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#encoded_tensor_name InputMetadata.encoded_tensor_name}
      # must be provided for this encoding. The first dimension of the encoded
      # tensor's shape is the same as the input tensor's shape. For example:
      # ```
      # input = ["This", "is", "a", "test", "."]
      # encoded = [[0.1, 0.2, 0.3, 0.4, 0.5],
      #            [0.2, 0.1, 0.4, 0.3, 0.5],
      #            [0.5, 0.1, 0.3, 0.5, 0.4],
      #            [0.5, 0.3, 0.1, 0.2, 0.4],
      #            [0.4, 0.3, 0.2, 0.5, 0.1]]
      # ```
      CONCAT_EMBEDDING = 6
    end
  end

  # Metadata of the prediction output to be explained.
  # @!attribute [rw] index_display_name_mapping
  #   @return [::Google::Protobuf::Value]
  #     Static mapping between the index and display name.
  #
  #     Use this if the outputs are a deterministic n-dimensional array, e.g. a
  #     list of scores of all the classes in a pre-defined order for a
  #     multi-classification Model. It's not feasible if the outputs are
  #     non-deterministic, e.g. the Model produces top-k classes or sort the
  #     outputs by their values.
  #
  #     The shape of the value must be an n-dimensional array of strings. The
  #     number of dimensions must match that of the outputs to be explained.
  #     The
  #     {::Google::Cloud::AIPlatform::V1::Attribution#output_display_name Attribution.output_display_name}
  #     is populated by locating in the mapping with
  #     {::Google::Cloud::AIPlatform::V1::Attribution#output_index Attribution.output_index}.
  # @!attribute [rw] display_name_mapping_key
  #   @return [::String]
  #     Specify a field name in the prediction to look for the display name.
  #
  #     Use this if the prediction contains the display names for the outputs.
  #
  #     The display names in the prediction must have the same shape of the
  #     outputs, so that it can be located by
  #     {::Google::Cloud::AIPlatform::V1::Attribution#output_index Attribution.output_index}
  #     for a specific output.
  # @!attribute [rw] output_tensor_name
  #   @return [::String]
  #     Name of the output tensor. Required and is only applicable to Vertex
  #     AI provided images for Tensorflow.
  class OutputMetadata
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # @!attribute [rw] key
  #   @return [::String]
  # @!attribute [rw] value
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata]
  class InputsEntry
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # @!attribute [rw] key
  #   @return [::String]
  # @!attribute [rw] value
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::OutputMetadata]
  class OutputsEntry
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end

#latent_space_source ⇒ ::String

Returns Name of the source to generate embeddings for example based explanations.

Returns:

  • (::String) —

    Name of the source to generate embeddings for example based explanations.



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# File 'proto_docs/google/cloud/aiplatform/v1/explanation_metadata.rb', line 67

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

  # Metadata of the input of a feature.
  #
  # Fields other than
  # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#input_baselines InputMetadata.input_baselines}
  # are applicable only for Models that are using Vertex AI-provided images for
  # Tensorflow.
  # @!attribute [rw] input_baselines
  #   @return [::Array<::Google::Protobuf::Value>]
  #     Baseline inputs for this feature.
  #
  #     If no baseline is specified, Vertex AI chooses the baseline for this
  #     feature. If multiple baselines are specified, Vertex AI returns the
  #     average attributions across them in
  #     {::Google::Cloud::AIPlatform::V1::Attribution#feature_attributions Attribution.feature_attributions}.
  #
  #     For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape
  #     of each baseline must match the shape of the input tensor. If a scalar is
  #     provided, we broadcast to the same shape as the input tensor.
  #
  #     For custom images, the element of the baselines must be in the same
  #     format as the feature's input in the
  #     {::Google::Cloud::AIPlatform::V1::ExplainRequest#instances instance}[]. The
  #     schema of any single instance may be specified via Endpoint's
  #     DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
  #     [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
  #     {::Google::Cloud::AIPlatform::V1::PredictSchemata#instance_schema_uri instance_schema_uri}.
  # @!attribute [rw] input_tensor_name
  #   @return [::String]
  #     Name of the input tensor for this feature. Required and is only
  #     applicable to Vertex AI-provided images for Tensorflow.
  # @!attribute [rw] encoding
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Encoding]
  #     Defines how the feature is encoded into the input tensor. Defaults to
  #     IDENTITY.
  # @!attribute [rw] modality
  #   @return [::String]
  #     Modality of the feature. Valid values are: numeric, image. Defaults to
  #     numeric.
  # @!attribute [rw] feature_value_domain
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::FeatureValueDomain]
  #     The domain details of the input feature value. Like min/max, original
  #     mean or standard deviation if normalized.
  # @!attribute [rw] indices_tensor_name
  #   @return [::String]
  #     Specifies the index of the values of the input tensor.
  #     Required when the input tensor is a sparse representation. Refer to
  #     Tensorflow documentation for more details:
  #     https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
  # @!attribute [rw] dense_shape_tensor_name
  #   @return [::String]
  #     Specifies the shape of the values of the input if the input is a sparse
  #     representation. Refer to Tensorflow documentation for more details:
  #     https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
  # @!attribute [rw] index_feature_mapping
  #   @return [::Array<::String>]
  #     A list of feature names for each index in the input tensor.
  #     Required when the input
  #     {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#encoding InputMetadata.encoding}
  #     is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
  # @!attribute [rw] encoded_tensor_name
  #   @return [::String]
  #     Encoded tensor is a transformation of the input tensor. Must be provided
  #     if choosing
  #     [Integrated Gradients
  #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution]
  #     or [XRAI
  #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution]
  #     and the input tensor is not differentiable.
  #
  #     An encoded tensor is generated if the input tensor is encoded by a lookup
  #     table.
  # @!attribute [rw] encoded_baselines
  #   @return [::Array<::Google::Protobuf::Value>]
  #     A list of baselines for the encoded tensor.
  #
  #     The shape of each baseline should match the shape of the encoded tensor.
  #     If a scalar is provided, Vertex AI broadcasts to the same shape as the
  #     encoded tensor.
  # @!attribute [rw] visualization
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization]
  #     Visualization configurations for image explanation.
  # @!attribute [rw] group_name
  #   @return [::String]
  #     Name of the group that the input belongs to. Features with the same group
  #     name will be treated as one feature when computing attributions. Features
  #     grouped together can have different shapes in value. If provided, there
  #     will be one single attribution generated in
  #     {::Google::Cloud::AIPlatform::V1::Attribution#feature_attributions Attribution.feature_attributions},
  #     keyed by the group name.
  class InputMetadata
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods

    # Domain details of the input feature value. Provides numeric information
    # about the feature, such as its range (min, max). If the feature has been
    # pre-processed, for example with z-scoring, then it provides information
    # about how to recover the original feature. For example, if the input
    # feature is an image and it has been pre-processed to obtain 0-mean and
    # stddev = 1 values, then original_mean, and original_stddev refer to the
    # mean and stddev of the original feature (e.g. image tensor) from which
    # input feature (with mean = 0 and stddev = 1) was obtained.
    # @!attribute [rw] min_value
    #   @return [::Float]
    #     The minimum permissible value for this feature.
    # @!attribute [rw] max_value
    #   @return [::Float]
    #     The maximum permissible value for this feature.
    # @!attribute [rw] original_mean
    #   @return [::Float]
    #     If this input feature has been normalized to a mean value of 0,
    #     the original_mean specifies the mean value of the domain prior to
    #     normalization.
    # @!attribute [rw] original_stddev
    #   @return [::Float]
    #     If this input feature has been normalized to a standard deviation of
    #     1.0, the original_stddev specifies the standard deviation of the domain
    #     prior to normalization.
    class FeatureValueDomain
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods
    end

    # Visualization configurations for image explanation.
    # @!attribute [rw] type
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::Type]
    #     Type of the image visualization. Only applicable to
    #     [Integrated Gradients
    #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution].
    #     OUTLINES shows regions of attribution, while PIXELS shows per-pixel
    #     attribution. Defaults to OUTLINES.
    # @!attribute [rw] polarity
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::Polarity]
    #     Whether to only highlight pixels with positive contributions, negative
    #     or both. Defaults to POSITIVE.
    # @!attribute [rw] color_map
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::ColorMap]
    #     The color scheme used for the highlighted areas.
    #
    #     Defaults to PINK_GREEN for
    #     [Integrated Gradients
    #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution],
    #     which shows positive attributions in green and negative in pink.
    #
    #     Defaults to VIRIDIS for
    #     [XRAI
    #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution],
    #     which highlights the most influential regions in yellow and the least
    #     influential in blue.
    # @!attribute [rw] clip_percent_upperbound
    #   @return [::Float]
    #     Excludes attributions above the specified percentile from the
    #     highlighted areas. Using the clip_percent_upperbound and
    #     clip_percent_lowerbound together can be useful for filtering out noise
    #     and making it easier to see areas of strong attribution. Defaults to
    #     99.9.
    # @!attribute [rw] clip_percent_lowerbound
    #   @return [::Float]
    #     Excludes attributions below the specified percentile, from the
    #     highlighted areas. Defaults to 62.
    # @!attribute [rw] overlay_type
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::OverlayType]
    #     How the original image is displayed in the visualization.
    #     Adjusting the overlay can help increase visual clarity if the original
    #     image makes it difficult to view the visualization. Defaults to NONE.
    class Visualization
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods

      # Type of the image visualization. Only applicable to
      # [Integrated Gradients
      # attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution].
      module Type
        # Should not be used.
        TYPE_UNSPECIFIED = 0

        # Shows which pixel contributed to the image prediction.
        PIXELS = 1

        # Shows which region contributed to the image prediction by outlining
        # the region.
        OUTLINES = 2
      end

      # Whether to only highlight pixels with positive contributions, negative
      # or both. Defaults to POSITIVE.
      module Polarity
        # Default value. This is the same as POSITIVE.
        POLARITY_UNSPECIFIED = 0

        # Highlights the pixels/outlines that were most influential to the
        # model's prediction.
        POSITIVE = 1

        # Setting polarity to negative highlights areas that does not lead to
        # the models's current prediction.
        NEGATIVE = 2

        # Shows both positive and negative attributions.
        BOTH = 3
      end

      # The color scheme used for highlighting areas.
      module ColorMap
        # Should not be used.
        COLOR_MAP_UNSPECIFIED = 0

        # Positive: green. Negative: pink.
        PINK_GREEN = 1

        # Viridis color map: A perceptually uniform color mapping which is
        # easier to see by those with colorblindness and progresses from yellow
        # to green to blue. Positive: yellow. Negative: blue.
        VIRIDIS = 2

        # Positive: red. Negative: red.
        RED = 3

        # Positive: green. Negative: green.
        GREEN = 4

        # Positive: green. Negative: red.
        RED_GREEN = 6

        # PiYG palette.
        PINK_WHITE_GREEN = 5
      end

      # How the original image is displayed in the visualization.
      module OverlayType
        # Default value. This is the same as NONE.
        OVERLAY_TYPE_UNSPECIFIED = 0

        # No overlay.
        NONE = 1

        # The attributions are shown on top of the original image.
        ORIGINAL = 2

        # The attributions are shown on top of grayscaled version of the
        # original image.
        GRAYSCALE = 3

        # The attributions are used as a mask to reveal predictive parts of
        # the image and hide the un-predictive parts.
        MASK_BLACK = 4
      end
    end

    # Defines how a feature is encoded. Defaults to IDENTITY.
    module Encoding
      # Default value. This is the same as IDENTITY.
      ENCODING_UNSPECIFIED = 0

      # The tensor represents one feature.
      IDENTITY = 1

      # The tensor represents a bag of features where each index maps to
      # a feature.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#index_feature_mapping InputMetadata.index_feature_mapping}
      # must be provided for this encoding. For example:
      # ```
      # input = [27, 6.0, 150]
      # index_feature_mapping = ["age", "height", "weight"]
      # ```
      BAG_OF_FEATURES = 2

      # The tensor represents a bag of features where each index maps to a
      # feature. Zero values in the tensor indicates feature being
      # non-existent.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#index_feature_mapping InputMetadata.index_feature_mapping}
      # must be provided for this encoding. For example:
      # ```
      # input = [2, 0, 5, 0, 1]
      # index_feature_mapping = ["a", "b", "c", "d", "e"]
      # ```
      BAG_OF_FEATURES_SPARSE = 3

      # The tensor is a list of binaries representing whether a feature exists
      # or not (1 indicates existence).
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#index_feature_mapping InputMetadata.index_feature_mapping}
      # must be provided for this encoding. For example:
      # ```
      # input = [1, 0, 1, 0, 1]
      # index_feature_mapping = ["a", "b", "c", "d", "e"]
      # ```
      INDICATOR = 4

      # The tensor is encoded into a 1-dimensional array represented by an
      # encoded tensor.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#encoded_tensor_name InputMetadata.encoded_tensor_name}
      # must be provided for this encoding. For example:
      # ```
      # input = ["This", "is", "a", "test", "."]
      # encoded = [0.1, 0.2, 0.3, 0.4, 0.5]
      # ```
      COMBINED_EMBEDDING = 5

      # Select this encoding when the input tensor is encoded into a
      # 2-dimensional array represented by an encoded tensor.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#encoded_tensor_name InputMetadata.encoded_tensor_name}
      # must be provided for this encoding. The first dimension of the encoded
      # tensor's shape is the same as the input tensor's shape. For example:
      # ```
      # input = ["This", "is", "a", "test", "."]
      # encoded = [[0.1, 0.2, 0.3, 0.4, 0.5],
      #            [0.2, 0.1, 0.4, 0.3, 0.5],
      #            [0.5, 0.1, 0.3, 0.5, 0.4],
      #            [0.5, 0.3, 0.1, 0.2, 0.4],
      #            [0.4, 0.3, 0.2, 0.5, 0.1]]
      # ```
      CONCAT_EMBEDDING = 6
    end
  end

  # Metadata of the prediction output to be explained.
  # @!attribute [rw] index_display_name_mapping
  #   @return [::Google::Protobuf::Value]
  #     Static mapping between the index and display name.
  #
  #     Use this if the outputs are a deterministic n-dimensional array, e.g. a
  #     list of scores of all the classes in a pre-defined order for a
  #     multi-classification Model. It's not feasible if the outputs are
  #     non-deterministic, e.g. the Model produces top-k classes or sort the
  #     outputs by their values.
  #
  #     The shape of the value must be an n-dimensional array of strings. The
  #     number of dimensions must match that of the outputs to be explained.
  #     The
  #     {::Google::Cloud::AIPlatform::V1::Attribution#output_display_name Attribution.output_display_name}
  #     is populated by locating in the mapping with
  #     {::Google::Cloud::AIPlatform::V1::Attribution#output_index Attribution.output_index}.
  # @!attribute [rw] display_name_mapping_key
  #   @return [::String]
  #     Specify a field name in the prediction to look for the display name.
  #
  #     Use this if the prediction contains the display names for the outputs.
  #
  #     The display names in the prediction must have the same shape of the
  #     outputs, so that it can be located by
  #     {::Google::Cloud::AIPlatform::V1::Attribution#output_index Attribution.output_index}
  #     for a specific output.
  # @!attribute [rw] output_tensor_name
  #   @return [::String]
  #     Name of the output tensor. Required and is only applicable to Vertex
  #     AI provided images for Tensorflow.
  class OutputMetadata
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # @!attribute [rw] key
  #   @return [::String]
  # @!attribute [rw] value
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata]
  class InputsEntry
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # @!attribute [rw] key
  #   @return [::String]
  # @!attribute [rw] value
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::OutputMetadata]
  class OutputsEntry
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end

#outputs ⇒ ::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::OutputMetadata}

Returns Required. Map from output names to output metadata.

For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters.

For custom images, keys are the name of the output field in the prediction to be explained.

Currently only one key is allowed.

Returns:

  • (::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::OutputMetadata}) —

    Required. Map from output names to output metadata.

    For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters.

    For custom images, keys are the name of the output field in the prediction to be explained.

    Currently only one key is allowed.



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# File 'proto_docs/google/cloud/aiplatform/v1/explanation_metadata.rb', line 67

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

  # Metadata of the input of a feature.
  #
  # Fields other than
  # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#input_baselines InputMetadata.input_baselines}
  # are applicable only for Models that are using Vertex AI-provided images for
  # Tensorflow.
  # @!attribute [rw] input_baselines
  #   @return [::Array<::Google::Protobuf::Value>]
  #     Baseline inputs for this feature.
  #
  #     If no baseline is specified, Vertex AI chooses the baseline for this
  #     feature. If multiple baselines are specified, Vertex AI returns the
  #     average attributions across them in
  #     {::Google::Cloud::AIPlatform::V1::Attribution#feature_attributions Attribution.feature_attributions}.
  #
  #     For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape
  #     of each baseline must match the shape of the input tensor. If a scalar is
  #     provided, we broadcast to the same shape as the input tensor.
  #
  #     For custom images, the element of the baselines must be in the same
  #     format as the feature's input in the
  #     {::Google::Cloud::AIPlatform::V1::ExplainRequest#instances instance}[]. The
  #     schema of any single instance may be specified via Endpoint's
  #     DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
  #     [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
  #     {::Google::Cloud::AIPlatform::V1::PredictSchemata#instance_schema_uri instance_schema_uri}.
  # @!attribute [rw] input_tensor_name
  #   @return [::String]
  #     Name of the input tensor for this feature. Required and is only
  #     applicable to Vertex AI-provided images for Tensorflow.
  # @!attribute [rw] encoding
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Encoding]
  #     Defines how the feature is encoded into the input tensor. Defaults to
  #     IDENTITY.
  # @!attribute [rw] modality
  #   @return [::String]
  #     Modality of the feature. Valid values are: numeric, image. Defaults to
  #     numeric.
  # @!attribute [rw] feature_value_domain
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::FeatureValueDomain]
  #     The domain details of the input feature value. Like min/max, original
  #     mean or standard deviation if normalized.
  # @!attribute [rw] indices_tensor_name
  #   @return [::String]
  #     Specifies the index of the values of the input tensor.
  #     Required when the input tensor is a sparse representation. Refer to
  #     Tensorflow documentation for more details:
  #     https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
  # @!attribute [rw] dense_shape_tensor_name
  #   @return [::String]
  #     Specifies the shape of the values of the input if the input is a sparse
  #     representation. Refer to Tensorflow documentation for more details:
  #     https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
  # @!attribute [rw] index_feature_mapping
  #   @return [::Array<::String>]
  #     A list of feature names for each index in the input tensor.
  #     Required when the input
  #     {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#encoding InputMetadata.encoding}
  #     is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
  # @!attribute [rw] encoded_tensor_name
  #   @return [::String]
  #     Encoded tensor is a transformation of the input tensor. Must be provided
  #     if choosing
  #     [Integrated Gradients
  #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution]
  #     or [XRAI
  #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution]
  #     and the input tensor is not differentiable.
  #
  #     An encoded tensor is generated if the input tensor is encoded by a lookup
  #     table.
  # @!attribute [rw] encoded_baselines
  #   @return [::Array<::Google::Protobuf::Value>]
  #     A list of baselines for the encoded tensor.
  #
  #     The shape of each baseline should match the shape of the encoded tensor.
  #     If a scalar is provided, Vertex AI broadcasts to the same shape as the
  #     encoded tensor.
  # @!attribute [rw] visualization
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization]
  #     Visualization configurations for image explanation.
  # @!attribute [rw] group_name
  #   @return [::String]
  #     Name of the group that the input belongs to. Features with the same group
  #     name will be treated as one feature when computing attributions. Features
  #     grouped together can have different shapes in value. If provided, there
  #     will be one single attribution generated in
  #     {::Google::Cloud::AIPlatform::V1::Attribution#feature_attributions Attribution.feature_attributions},
  #     keyed by the group name.
  class InputMetadata
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods

    # Domain details of the input feature value. Provides numeric information
    # about the feature, such as its range (min, max). If the feature has been
    # pre-processed, for example with z-scoring, then it provides information
    # about how to recover the original feature. For example, if the input
    # feature is an image and it has been pre-processed to obtain 0-mean and
    # stddev = 1 values, then original_mean, and original_stddev refer to the
    # mean and stddev of the original feature (e.g. image tensor) from which
    # input feature (with mean = 0 and stddev = 1) was obtained.
    # @!attribute [rw] min_value
    #   @return [::Float]
    #     The minimum permissible value for this feature.
    # @!attribute [rw] max_value
    #   @return [::Float]
    #     The maximum permissible value for this feature.
    # @!attribute [rw] original_mean
    #   @return [::Float]
    #     If this input feature has been normalized to a mean value of 0,
    #     the original_mean specifies the mean value of the domain prior to
    #     normalization.
    # @!attribute [rw] original_stddev
    #   @return [::Float]
    #     If this input feature has been normalized to a standard deviation of
    #     1.0, the original_stddev specifies the standard deviation of the domain
    #     prior to normalization.
    class FeatureValueDomain
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods
    end

    # Visualization configurations for image explanation.
    # @!attribute [rw] type
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::Type]
    #     Type of the image visualization. Only applicable to
    #     [Integrated Gradients
    #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution].
    #     OUTLINES shows regions of attribution, while PIXELS shows per-pixel
    #     attribution. Defaults to OUTLINES.
    # @!attribute [rw] polarity
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::Polarity]
    #     Whether to only highlight pixels with positive contributions, negative
    #     or both. Defaults to POSITIVE.
    # @!attribute [rw] color_map
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::ColorMap]
    #     The color scheme used for the highlighted areas.
    #
    #     Defaults to PINK_GREEN for
    #     [Integrated Gradients
    #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution],
    #     which shows positive attributions in green and negative in pink.
    #
    #     Defaults to VIRIDIS for
    #     [XRAI
    #     attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution],
    #     which highlights the most influential regions in yellow and the least
    #     influential in blue.
    # @!attribute [rw] clip_percent_upperbound
    #   @return [::Float]
    #     Excludes attributions above the specified percentile from the
    #     highlighted areas. Using the clip_percent_upperbound and
    #     clip_percent_lowerbound together can be useful for filtering out noise
    #     and making it easier to see areas of strong attribution. Defaults to
    #     99.9.
    # @!attribute [rw] clip_percent_lowerbound
    #   @return [::Float]
    #     Excludes attributions below the specified percentile, from the
    #     highlighted areas. Defaults to 62.
    # @!attribute [rw] overlay_type
    #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::OverlayType]
    #     How the original image is displayed in the visualization.
    #     Adjusting the overlay can help increase visual clarity if the original
    #     image makes it difficult to view the visualization. Defaults to NONE.
    class Visualization
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods

      # Type of the image visualization. Only applicable to
      # [Integrated Gradients
      # attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution].
      module Type
        # Should not be used.
        TYPE_UNSPECIFIED = 0

        # Shows which pixel contributed to the image prediction.
        PIXELS = 1

        # Shows which region contributed to the image prediction by outlining
        # the region.
        OUTLINES = 2
      end

      # Whether to only highlight pixels with positive contributions, negative
      # or both. Defaults to POSITIVE.
      module Polarity
        # Default value. This is the same as POSITIVE.
        POLARITY_UNSPECIFIED = 0

        # Highlights the pixels/outlines that were most influential to the
        # model's prediction.
        POSITIVE = 1

        # Setting polarity to negative highlights areas that does not lead to
        # the models's current prediction.
        NEGATIVE = 2

        # Shows both positive and negative attributions.
        BOTH = 3
      end

      # The color scheme used for highlighting areas.
      module ColorMap
        # Should not be used.
        COLOR_MAP_UNSPECIFIED = 0

        # Positive: green. Negative: pink.
        PINK_GREEN = 1

        # Viridis color map: A perceptually uniform color mapping which is
        # easier to see by those with colorblindness and progresses from yellow
        # to green to blue. Positive: yellow. Negative: blue.
        VIRIDIS = 2

        # Positive: red. Negative: red.
        RED = 3

        # Positive: green. Negative: green.
        GREEN = 4

        # Positive: green. Negative: red.
        RED_GREEN = 6

        # PiYG palette.
        PINK_WHITE_GREEN = 5
      end

      # How the original image is displayed in the visualization.
      module OverlayType
        # Default value. This is the same as NONE.
        OVERLAY_TYPE_UNSPECIFIED = 0

        # No overlay.
        NONE = 1

        # The attributions are shown on top of the original image.
        ORIGINAL = 2

        # The attributions are shown on top of grayscaled version of the
        # original image.
        GRAYSCALE = 3

        # The attributions are used as a mask to reveal predictive parts of
        # the image and hide the un-predictive parts.
        MASK_BLACK = 4
      end
    end

    # Defines how a feature is encoded. Defaults to IDENTITY.
    module Encoding
      # Default value. This is the same as IDENTITY.
      ENCODING_UNSPECIFIED = 0

      # The tensor represents one feature.
      IDENTITY = 1

      # The tensor represents a bag of features where each index maps to
      # a feature.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#index_feature_mapping InputMetadata.index_feature_mapping}
      # must be provided for this encoding. For example:
      # ```
      # input = [27, 6.0, 150]
      # index_feature_mapping = ["age", "height", "weight"]
      # ```
      BAG_OF_FEATURES = 2

      # The tensor represents a bag of features where each index maps to a
      # feature. Zero values in the tensor indicates feature being
      # non-existent.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#index_feature_mapping InputMetadata.index_feature_mapping}
      # must be provided for this encoding. For example:
      # ```
      # input = [2, 0, 5, 0, 1]
      # index_feature_mapping = ["a", "b", "c", "d", "e"]
      # ```
      BAG_OF_FEATURES_SPARSE = 3

      # The tensor is a list of binaries representing whether a feature exists
      # or not (1 indicates existence).
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#index_feature_mapping InputMetadata.index_feature_mapping}
      # must be provided for this encoding. For example:
      # ```
      # input = [1, 0, 1, 0, 1]
      # index_feature_mapping = ["a", "b", "c", "d", "e"]
      # ```
      INDICATOR = 4

      # The tensor is encoded into a 1-dimensional array represented by an
      # encoded tensor.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#encoded_tensor_name InputMetadata.encoded_tensor_name}
      # must be provided for this encoding. For example:
      # ```
      # input = ["This", "is", "a", "test", "."]
      # encoded = [0.1, 0.2, 0.3, 0.4, 0.5]
      # ```
      COMBINED_EMBEDDING = 5

      # Select this encoding when the input tensor is encoded into a
      # 2-dimensional array represented by an encoded tensor.
      # {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata#encoded_tensor_name InputMetadata.encoded_tensor_name}
      # must be provided for this encoding. The first dimension of the encoded
      # tensor's shape is the same as the input tensor's shape. For example:
      # ```
      # input = ["This", "is", "a", "test", "."]
      # encoded = [[0.1, 0.2, 0.3, 0.4, 0.5],
      #            [0.2, 0.1, 0.4, 0.3, 0.5],
      #            [0.5, 0.1, 0.3, 0.5, 0.4],
      #            [0.5, 0.3, 0.1, 0.2, 0.4],
      #            [0.4, 0.3, 0.2, 0.5, 0.1]]
      # ```
      CONCAT_EMBEDDING = 6
    end
  end

  # Metadata of the prediction output to be explained.
  # @!attribute [rw] index_display_name_mapping
  #   @return [::Google::Protobuf::Value]
  #     Static mapping between the index and display name.
  #
  #     Use this if the outputs are a deterministic n-dimensional array, e.g. a
  #     list of scores of all the classes in a pre-defined order for a
  #     multi-classification Model. It's not feasible if the outputs are
  #     non-deterministic, e.g. the Model produces top-k classes or sort the
  #     outputs by their values.
  #
  #     The shape of the value must be an n-dimensional array of strings. The
  #     number of dimensions must match that of the outputs to be explained.
  #     The
  #     {::Google::Cloud::AIPlatform::V1::Attribution#output_display_name Attribution.output_display_name}
  #     is populated by locating in the mapping with
  #     {::Google::Cloud::AIPlatform::V1::Attribution#output_index Attribution.output_index}.
  # @!attribute [rw] display_name_mapping_key
  #   @return [::String]
  #     Specify a field name in the prediction to look for the display name.
  #
  #     Use this if the prediction contains the display names for the outputs.
  #
  #     The display names in the prediction must have the same shape of the
  #     outputs, so that it can be located by
  #     {::Google::Cloud::AIPlatform::V1::Attribution#output_index Attribution.output_index}
  #     for a specific output.
  # @!attribute [rw] output_tensor_name
  #   @return [::String]
  #     Name of the output tensor. Required and is only applicable to Vertex
  #     AI provided images for Tensorflow.
  class OutputMetadata
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # @!attribute [rw] key
  #   @return [::String]
  # @!attribute [rw] value
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata]
  class InputsEntry
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # @!attribute [rw] key
  #   @return [::String]
  # @!attribute [rw] value
  #   @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata::OutputMetadata]
  class OutputsEntry
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end