# frozen_string_literal: true
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Auto-generated by gapic-generator-ruby. DO NOT EDIT!
module Google
module Cloud
module AIPlatform
module V1
# Metadata describing the Model's input and output for explanation.
# @!attribute [rw] inputs
# @return [::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 {::Google::Cloud::AIPlatform::V1::ExplanationMetadata#inputs 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,
# {::Google::Cloud::AIPlatform::V1::Attribution#feature_attributions featureAttributions} are keyed by
# this key (if not grouped with another feature).
#
# For custom images, the key must match with the key in
# {::Google::Cloud::AIPlatform::V1::ExplainRequest#instances instance}.
# @!attribute [rw] outputs
# @return [::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.
# @!attribute [rw] feature_attributions_schema_uri
# @return [::String]
# Points to a YAML file stored on Google Cloud Storage describing the format
# of the {::Google::Cloud::AIPlatform::V1::Attribution#feature_attributions feature attributions}.
# The schema is defined as an OpenAPI 3.0.2 [Schema
# Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
# 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.
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
# {::Google::Cloud::AIPlatform::V1::ExplanationParameters#integrated_gradients_attribution Integrated Gradients attribution}
# or {::Google::Cloud::AIPlatform::V1::ExplanationParameters#xrai_attribution 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
# {::Google::Cloud::AIPlatform::V1::ExplanationParameters#integrated_gradients_attribution 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
# {::Google::Cloud::AIPlatform::V1::ExplanationParameters#integrated_gradients_attribution Integrated Gradients attribution},
# which shows positive attributions in green and negative in pink.
#
# Defaults to VIRIDIS for
# {::Google::Cloud::AIPlatform::V1::ExplanationParameters#xrai_attribution 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
# {::Google::Cloud::AIPlatform::V1::ExplanationParameters#integrated_gradients_attribution 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
end
end
end
end