Class: Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization

Inherits:

Object

• Object
• Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization

Extended by:

Protobuf::MessageExts::ClassMethods

Includes:

Protobuf::MessageExts

Defined in:

proto_docs/google/cloud/aiplatform/v1/explanation_metadata.rb

Overview

Visualization configurations for image explanation.

Defined Under Namespace

Modules: ColorMap, OverlayType, Polarity, Type

Instance Attribute Summary

• #clip_percent_lowerbound ⇒ ::Float

  Excludes attributions below the specified percentile, from the highlighted areas.

• #clip_percent_upperbound ⇒ ::Float

  Excludes attributions above the specified percentile from the highlighted areas.

• #color_map ⇒ ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::ColorMap

  The color scheme used for the highlighted areas.

• #overlay_type ⇒ ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::OverlayType

  How the original image is displayed in the visualization.

• #polarity ⇒ ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::Polarity

  Whether to only highlight pixels with positive contributions, negative or both.

• #type ⇒ ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::Type

  Type of the image visualization.

Instance Attribute Details

#clip_percent_lowerbound ⇒ ::Float

# File 'proto_docs/google/cloud/aiplatform/v1/explanation_metadata.rb', line 235

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

#clip_percent_upperbound ⇒ ::Float

# File 'proto_docs/google/cloud/aiplatform/v1/explanation_metadata.rb', line 235

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

#color_map ⇒ ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::ColorMap

# File 'proto_docs/google/cloud/aiplatform/v1/explanation_metadata.rb', line 235

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

#overlay_type ⇒ ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::OverlayType

# File 'proto_docs/google/cloud/aiplatform/v1/explanation_metadata.rb', line 235

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

#polarity ⇒ ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::Polarity

# File 'proto_docs/google/cloud/aiplatform/v1/explanation_metadata.rb', line 235

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

#type ⇒ ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::Type

# File 'proto_docs/google/cloud/aiplatform/v1/explanation_metadata.rb', line 235

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