Class Visualization (1.22.1)

Version 1.22.1

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Visualization(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Visualization configurations for image explanation.

Attributes

Name	Description
type_	google.cloud.aiplatform_v1beta1.types.ExplanationMetadata.InputMetadata.Visualization.Type Type of the image visualization. Only applicable to [Integrated Gradients attribution][google.cloud.aiplatform.v1beta1.ExplanationParameters.integrated_gradients_attribution]. OUTLINES shows regions of attribution, while PIXELS shows per-pixel attribution. Defaults to OUTLINES.
polarity	google.cloud.aiplatform_v1beta1.types.ExplanationMetadata.InputMetadata.Visualization.Polarity Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.
color_map	google.cloud.aiplatform_v1beta1.types.ExplanationMetadata.InputMetadata.Visualization.ColorMap The color scheme used for the highlighted areas. Defaults to PINK_GREEN for [Integrated Gradients attribution][google.cloud.aiplatform.v1beta1.ExplanationParameters.integrated_gradients_attribution], which shows positive attributions in green and negative in pink. Defaults to VIRIDIS for [XRAI attribution][google.cloud.aiplatform.v1beta1.ExplanationParameters.xrai_attribution], which highlights the most influential regions in yellow and the least influential in blue.
clip_percent_upperbound	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.
clip_percent_lowerbound	float Excludes attributions below the specified percentile, from the highlighted areas. Defaults to 62.
overlay_type	google.cloud.aiplatform_v1beta1.types.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.

Inheritance

builtins.object > proto.message.Message > Visualization

Classes

ColorMap

ColorMap(value)

The color scheme used for highlighting areas.

Values: COLOR_MAP_UNSPECIFIED (0): Should not be used. PINK_GREEN (1): Positive: green. Negative: pink. VIRIDIS (2): 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. RED (3): Positive: red. Negative: red. GREEN (4): Positive: green. Negative: green. RED_GREEN (6): Positive: green. Negative: red. PINK_WHITE_GREEN (5): PiYG palette.

OverlayType

OverlayType(value)

How the original image is displayed in the visualization.

Values: OVERLAY_TYPE_UNSPECIFIED (0): Default value. This is the same as NONE. NONE (1): No overlay. ORIGINAL (2): The attributions are shown on top of the original image. GRAYSCALE (3): The attributions are shown on top of grayscaled version of the original image. MASK_BLACK (4): The attributions are used as a mask to reveal predictive parts of the image and hide the un-predictive parts.

Polarity

Polarity(value)

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

Values: POLARITY_UNSPECIFIED (0): Default value. This is the same as POSITIVE. POSITIVE (1): Highlights the pixels/outlines that were most influential to the model's prediction. NEGATIVE (2): Setting polarity to negative highlights areas that does not lead to the models's current prediction. BOTH (3): Shows both positive and negative attributions.

Type

Type(value)

Type of the image visualization. Only applicable to [Integrated Gradients attribution][google.cloud.aiplatform.v1beta1.ExplanationParameters.integrated_gradients_attribution].

Values: TYPE_UNSPECIFIED (0): Should not be used. PIXELS (1): Shows which pixel contributed to the image prediction. OUTLINES (2): Shows which region contributed to the image prediction by outlining the region.