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

Inherits:

Object

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

show all

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 collapse

#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`

Returns Excludes attributions below the specified percentile, from the highlighted areas. Defaults to 62.

Returns:

(::Float) —
Excludes attributions below the specified percentile, from the highlighted areas. Defaults to 62.

# 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`

Returns 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.

Returns:

(::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.

# 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`

Returns 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.

Returns:

(::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.

# 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`

Returns 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.

Returns:

(::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.

# 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`

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

Returns:

(::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization::Polarity) —
Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.

# 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`

Returns 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.

Returns:

(::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.

# 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

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

Overview

Defined Under Namespace

Instance Attribute Summary collapse

Instance Attribute Details

#clip_percent_lowerbound ⇒ ::Float

#clip_percent_upperbound ⇒ ::Float

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

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

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

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

#clip_percent_lowerbound ⇒ `::Float`

#clip_percent_upperbound ⇒ `::Float`

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

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

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

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