# 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
# Explanation of a prediction (provided in
# {::Google::Cloud::AIPlatform::V1::PredictResponse#predictions PredictResponse.predictions})
# produced by the Model on a given
# {::Google::Cloud::AIPlatform::V1::ExplainRequest#instances instance}.
# @!attribute [r] attributions
# @return [::Array<::Google::Cloud::AIPlatform::V1::Attribution>]
# Output only. Feature attributions grouped by predicted outputs.
#
# For Models that predict only one output, such as regression Models that
# predict only one score, there is only one attibution that explains the
# predicted output. For Models that predict multiple outputs, such as
# multiclass Models that predict multiple classes, each element explains one
# specific item.
# {::Google::Cloud::AIPlatform::V1::Attribution#output_index Attribution.output_index}
# can be used to identify which output this attribution is explaining.
#
# By default, we provide Shapley values for the predicted class. However,
# you can configure the explanation request to generate Shapley values for
# any other classes too. For example, if a model predicts a probability of
# `0.4` for approving a loan application, the model's decision is to reject
# the application since `p(reject) = 0.6 > p(approve) = 0.4`, and the default
# Shapley values would be computed for rejection decision and not approval,
# even though the latter might be the positive class.
#
# If users set
# {::Google::Cloud::AIPlatform::V1::ExplanationParameters#top_k ExplanationParameters.top_k},
# the attributions are sorted by
# {::Google::Cloud::AIPlatform::V1::Attribution#instance_output_value instance_output_value}
# in descending order. If
# {::Google::Cloud::AIPlatform::V1::ExplanationParameters#output_indices ExplanationParameters.output_indices}
# is specified, the attributions are stored by
# {::Google::Cloud::AIPlatform::V1::Attribution#output_index Attribution.output_index}
# in the same order as they appear in the output_indices.
# @!attribute [r] neighbors
# @return [::Array<::Google::Cloud::AIPlatform::V1::Neighbor>]
# Output only. List of the nearest neighbors for example-based explanations.
#
# For models deployed with the examples explanations feature enabled, the
# attributions field is empty and instead the neighbors field is populated.
class Explanation
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Aggregated explanation metrics for a Model over a set of instances.
# @!attribute [r] mean_attributions
# @return [::Array<::Google::Cloud::AIPlatform::V1::Attribution>]
# Output only. Aggregated attributions explaining the Model's prediction
# outputs over the set of instances. The attributions are grouped by outputs.
#
# For Models that predict only one output, such as regression Models that
# predict only one score, there is only one attibution that explains the
# predicted output. For Models that predict multiple outputs, such as
# multiclass Models that predict multiple classes, each element explains one
# specific item.
# {::Google::Cloud::AIPlatform::V1::Attribution#output_index Attribution.output_index}
# can be used to identify which output this attribution is explaining.
#
# The
# {::Google::Cloud::AIPlatform::V1::Attribution#baseline_output_value baselineOutputValue},
# {::Google::Cloud::AIPlatform::V1::Attribution#instance_output_value instanceOutputValue}
# and
# {::Google::Cloud::AIPlatform::V1::Attribution#feature_attributions featureAttributions}
# fields are averaged over the test data.
#
# NOTE: Currently AutoML tabular classification Models produce only one
# attribution, which averages attributions over all the classes it predicts.
# {::Google::Cloud::AIPlatform::V1::Attribution#approximation_error Attribution.approximation_error}
# is not populated.
class ModelExplanation
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Attribution that explains a particular prediction output.
# @!attribute [r] baseline_output_value
# @return [::Float]
# Output only. Model predicted output if the input instance is constructed
# from the baselines of all the features defined in
# {::Google::Cloud::AIPlatform::V1::ExplanationMetadata#inputs ExplanationMetadata.inputs}.
# The field name of the output is determined by the key in
# {::Google::Cloud::AIPlatform::V1::ExplanationMetadata#outputs ExplanationMetadata.outputs}.
#
# If the Model's predicted output has multiple dimensions (rank > 1), this is
# the value in the output located by
# {::Google::Cloud::AIPlatform::V1::Attribution#output_index output_index}.
#
# If there are multiple baselines, their output values are averaged.
# @!attribute [r] instance_output_value
# @return [::Float]
# Output only. Model predicted output on the corresponding [explanation
# instance][ExplainRequest.instances]. The field name of the output is
# determined by the key in
# {::Google::Cloud::AIPlatform::V1::ExplanationMetadata#outputs ExplanationMetadata.outputs}.
#
# If the Model predicted output has multiple dimensions, this is the value in
# the output located by
# {::Google::Cloud::AIPlatform::V1::Attribution#output_index output_index}.
# @!attribute [r] feature_attributions
# @return [::Google::Protobuf::Value]
# Output only. Attributions of each explained feature. Features are extracted
# from the [prediction
# instances][google.cloud.aiplatform.v1.ExplainRequest.instances] according
# to [explanation metadata for
# inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs].
#
# The value is a struct, whose keys are the name of the feature. The values
# are how much the feature in the
# {::Google::Cloud::AIPlatform::V1::ExplainRequest#instances instance} contributed
# to the predicted result.
#
# The format of the value is determined by the feature's input format:
#
# * If the feature is a scalar value, the attribution value is a
# {::Google::Protobuf::Value#number_value floating number}.
#
# * If the feature is an array of scalar values, the attribution value is
# an {::Google::Protobuf::Value#list_value array}.
#
# * If the feature is a struct, the attribution value is a
# {::Google::Protobuf::Value#struct_value struct}. The keys in the
# attribution value struct are the same as the keys in the feature
# struct. The formats of the values in the attribution struct are
# determined by the formats of the values in the feature struct.
#
# The
# {::Google::Cloud::AIPlatform::V1::ExplanationMetadata#feature_attributions_schema_uri ExplanationMetadata.feature_attributions_schema_uri}
# field, pointed to by the
# {::Google::Cloud::AIPlatform::V1::ExplanationSpec ExplanationSpec} field of the
# {::Google::Cloud::AIPlatform::V1::Endpoint#deployed_models Endpoint.deployed_models}
# object, points to the schema file that describes the features and their
# attribution values (if it is populated).
# @!attribute [r] output_index
# @return [::Array<::Integer>]
# Output only. The index that locates the explained prediction output.
#
# If the prediction output is a scalar value, output_index is not populated.
# If the prediction output has multiple dimensions, the length of the
# output_index list is the same as the number of dimensions of the output.
# The i-th element in output_index is the element index of the i-th dimension
# of the output vector. Indices start from 0.
# @!attribute [r] output_display_name
# @return [::String]
# Output only. The display name of the output identified by
# {::Google::Cloud::AIPlatform::V1::Attribution#output_index output_index}. For
# example, the predicted class name by a multi-classification Model.
#
# This field is only populated iff the Model predicts display names as a
# separate field along with the explained output. The predicted display name
# must has the same shape of the explained output, and can be located using
# output_index.
# @!attribute [r] approximation_error
# @return [::Float]
# Output only. Error of
# {::Google::Cloud::AIPlatform::V1::Attribution#feature_attributions feature_attributions}
# caused by approximation used in the explanation method. Lower value means
# more precise attributions.
#
# * For Sampled Shapley
# {::Google::Cloud::AIPlatform::V1::ExplanationParameters#sampled_shapley_attribution attribution},
# increasing
# {::Google::Cloud::AIPlatform::V1::SampledShapleyAttribution#path_count path_count}
# might reduce the error.
# * For Integrated Gradients
# {::Google::Cloud::AIPlatform::V1::ExplanationParameters#integrated_gradients_attribution attribution},
# increasing
# {::Google::Cloud::AIPlatform::V1::IntegratedGradientsAttribution#step_count step_count}
# might reduce the error.
# * For [XRAI
# attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution],
# increasing
# {::Google::Cloud::AIPlatform::V1::XraiAttribution#step_count step_count} might
# reduce the error.
#
# See [this introduction](/vertex-ai/docs/explainable-ai/overview)
# for more information.
# @!attribute [r] output_name
# @return [::String]
# Output only. Name of the explain output. Specified as the key in
# {::Google::Cloud::AIPlatform::V1::ExplanationMetadata#outputs ExplanationMetadata.outputs}.
class Attribution
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Neighbors for example-based explanations.
# @!attribute [r] neighbor_id
# @return [::String]
# Output only. The neighbor id.
# @!attribute [r] neighbor_distance
# @return [::Float]
# Output only. The neighbor distance.
class Neighbor
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Specification of Model explanation.
# @!attribute [rw] parameters
# @return [::Google::Cloud::AIPlatform::V1::ExplanationParameters]
# Required. Parameters that configure explaining of the Model's predictions.
# @!attribute [rw] metadata
# @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadata]
# Optional. Metadata describing the Model's input and output for explanation.
class ExplanationSpec
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Parameters to configure explaining for Model's predictions.
# @!attribute [rw] sampled_shapley_attribution
# @return [::Google::Cloud::AIPlatform::V1::SampledShapleyAttribution]
# An attribution method that approximates Shapley values for features that
# contribute to the label being predicted. A sampling strategy is used to
# approximate the value rather than considering all subsets of features.
# Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
# @!attribute [rw] integrated_gradients_attribution
# @return [::Google::Cloud::AIPlatform::V1::IntegratedGradientsAttribution]
# An attribution method that computes Aumann-Shapley values taking
# advantage of the model's fully differentiable structure. Refer to this
# paper for more details: https://arxiv.org/abs/1703.01365
# @!attribute [rw] xrai_attribution
# @return [::Google::Cloud::AIPlatform::V1::XraiAttribution]
# An attribution method that redistributes Integrated Gradients
# attribution to segmented regions, taking advantage of the model's fully
# differentiable structure. Refer to this paper for
# more details: https://arxiv.org/abs/1906.02825
#
# XRAI currently performs better on natural images, like a picture of a
# house or an animal. If the images are taken in artificial environments,
# like a lab or manufacturing line, or from diagnostic equipment, like
# x-rays or quality-control cameras, use Integrated Gradients instead.
# @!attribute [rw] examples
# @return [::Google::Cloud::AIPlatform::V1::Examples]
# Example-based explanations that returns the nearest neighbors from the
# provided dataset.
# @!attribute [rw] top_k
# @return [::Integer]
# If populated, returns attributions for top K indices of outputs
# (defaults to 1). Only applies to Models that predicts more than one outputs
# (e,g, multi-class Models). When set to -1, returns explanations for all
# outputs.
# @!attribute [rw] output_indices
# @return [::Google::Protobuf::ListValue]
# If populated, only returns attributions that have
# {::Google::Cloud::AIPlatform::V1::Attribution#output_index output_index}
# contained in output_indices. It must be an ndarray of integers, with the
# same shape of the output it's explaining.
#
# If not populated, returns attributions for
# {::Google::Cloud::AIPlatform::V1::ExplanationParameters#top_k top_k} indices of
# outputs. If neither top_k nor output_indices is populated, returns the
# argmax index of the outputs.
#
# Only applicable to Models that predict multiple outputs (e,g, multi-class
# Models that predict multiple classes).
class ExplanationParameters
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# An attribution method that approximates Shapley values for features that
# contribute to the label being predicted. A sampling strategy is used to
# approximate the value rather than considering all subsets of features.
# @!attribute [rw] path_count
# @return [::Integer]
# Required. The number of feature permutations to consider when approximating
# the Shapley values.
#
# Valid range of its value is [1, 50], inclusively.
class SampledShapleyAttribution
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# An attribution method that computes the Aumann-Shapley value taking advantage
# of the model's fully differentiable structure. Refer to this paper for
# more details: https://arxiv.org/abs/1703.01365
# @!attribute [rw] step_count
# @return [::Integer]
# Required. The number of steps for approximating the path integral.
# A good value to start is 50 and gradually increase until the
# sum to diff property is within the desired error range.
#
# Valid range of its value is [1, 100], inclusively.
# @!attribute [rw] smooth_grad_config
# @return [::Google::Cloud::AIPlatform::V1::SmoothGradConfig]
# Config for SmoothGrad approximation of gradients.
#
# When enabled, the gradients are approximated by averaging the gradients
# from noisy samples in the vicinity of the inputs. Adding
# noise can help improve the computed gradients. Refer to this paper for more
# details: https://arxiv.org/pdf/1706.03825.pdf
# @!attribute [rw] blur_baseline_config
# @return [::Google::Cloud::AIPlatform::V1::BlurBaselineConfig]
# Config for IG with blur baseline.
#
# When enabled, a linear path from the maximally blurred image to the input
# image is created. Using a blurred baseline instead of zero (black image) is
# motivated by the BlurIG approach explained here:
# https://arxiv.org/abs/2004.03383
class IntegratedGradientsAttribution
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# An explanation method that redistributes Integrated Gradients
# attributions to segmented regions, taking advantage of the model's fully
# differentiable structure. Refer to this paper for more details:
# https://arxiv.org/abs/1906.02825
#
# Supported only by image Models.
# @!attribute [rw] step_count
# @return [::Integer]
# Required. The number of steps for approximating the path integral.
# A good value to start is 50 and gradually increase until the
# sum to diff property is met within the desired error range.
#
# Valid range of its value is [1, 100], inclusively.
# @!attribute [rw] smooth_grad_config
# @return [::Google::Cloud::AIPlatform::V1::SmoothGradConfig]
# Config for SmoothGrad approximation of gradients.
#
# When enabled, the gradients are approximated by averaging the gradients
# from noisy samples in the vicinity of the inputs. Adding
# noise can help improve the computed gradients. Refer to this paper for more
# details: https://arxiv.org/pdf/1706.03825.pdf
# @!attribute [rw] blur_baseline_config
# @return [::Google::Cloud::AIPlatform::V1::BlurBaselineConfig]
# Config for XRAI with blur baseline.
#
# When enabled, a linear path from the maximally blurred image to the input
# image is created. Using a blurred baseline instead of zero (black image) is
# motivated by the BlurIG approach explained here:
# https://arxiv.org/abs/2004.03383
class XraiAttribution
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Config for SmoothGrad approximation of gradients.
#
# When enabled, the gradients are approximated by averaging the gradients from
# noisy samples in the vicinity of the inputs. Adding noise can help improve
# the computed gradients. Refer to this paper for more details:
# https://arxiv.org/pdf/1706.03825.pdf
# @!attribute [rw] noise_sigma
# @return [::Float]
# This is a single float value and will be used to add noise to all the
# features. Use this field when all features are normalized to have the
# same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where
# features are normalized to have 0-mean and 1-variance. Learn more about
# [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization).
#
# For best results the recommended value is about 10% - 20% of the standard
# deviation of the input feature. Refer to section 3.2 of the SmoothGrad
# paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1.
#
# If the distribution is different per feature, set
# {::Google::Cloud::AIPlatform::V1::SmoothGradConfig#feature_noise_sigma feature_noise_sigma}
# instead for each feature.
# @!attribute [rw] feature_noise_sigma
# @return [::Google::Cloud::AIPlatform::V1::FeatureNoiseSigma]
# This is similar to
# {::Google::Cloud::AIPlatform::V1::SmoothGradConfig#noise_sigma noise_sigma},
# but provides additional flexibility. A separate noise sigma can be
# provided for each feature, which is useful if their distributions are
# different. No noise is added to features that are not set. If this field
# is unset,
# {::Google::Cloud::AIPlatform::V1::SmoothGradConfig#noise_sigma noise_sigma}
# will be used for all features.
# @!attribute [rw] noisy_sample_count
# @return [::Integer]
# The number of gradient samples to use for
# approximation. The higher this number, the more accurate the gradient
# is, but the runtime complexity increases by this factor as well.
# Valid range of its value is [1, 50]. Defaults to 3.
class SmoothGradConfig
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Noise sigma by features. Noise sigma represents the standard deviation of the
# gaussian kernel that will be used to add noise to interpolated inputs prior
# to computing gradients.
# @!attribute [rw] noise_sigma
# @return [::Array<::Google::Cloud::AIPlatform::V1::FeatureNoiseSigma::NoiseSigmaForFeature>]
# Noise sigma per feature. No noise is added to features that are not set.
class FeatureNoiseSigma
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# Noise sigma for a single feature.
# @!attribute [rw] name
# @return [::String]
# The name of the input feature for which noise sigma is provided. The
# features are defined in
# [explanation metadata
# inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs].
# @!attribute [rw] sigma
# @return [::Float]
# This represents the standard deviation of the Gaussian kernel that will
# be used to add noise to the feature prior to computing gradients. Similar
# to {::Google::Cloud::AIPlatform::V1::SmoothGradConfig#noise_sigma noise_sigma}
# but represents the noise added to the current feature. Defaults to 0.1.
class NoiseSigmaForFeature
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
end
# Config for blur baseline.
#
# When enabled, a linear path from the maximally blurred image to the input
# image is created. Using a blurred baseline instead of zero (black image) is
# motivated by the BlurIG approach explained here:
# https://arxiv.org/abs/2004.03383
# @!attribute [rw] max_blur_sigma
# @return [::Float]
# The standard deviation of the blur kernel for the blurred baseline. The
# same blurring parameter is used for both the height and the width
# dimension. If not set, the method defaults to the zero (i.e. black for
# images) baseline.
class BlurBaselineConfig
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Example-based explainability that returns the nearest neighbors from the
# provided dataset.
# @!attribute [rw] example_gcs_source
# @return [::Google::Cloud::AIPlatform::V1::Examples::ExampleGcsSource]
# The Cloud Storage input instances.
# @!attribute [rw] nearest_neighbor_search_config
# @return [::Google::Protobuf::Value]
# The full configuration for the generated index, the semantics are the
# same as {::Google::Cloud::AIPlatform::V1::Index#metadata metadata} and should
# match
# [NearestNeighborSearchConfig](https://cloud.google.com/vertex-ai/docs/explainable-ai/configuring-explanations-example-based#nearest-neighbor-search-config).
# @!attribute [rw] presets
# @return [::Google::Cloud::AIPlatform::V1::Presets]
# Simplified preset configuration, which automatically sets configuration
# values based on the desired query speed-precision trade-off and modality.
# @!attribute [rw] neighbor_count
# @return [::Integer]
# The number of neighbors to return when querying for examples.
class Examples
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# The Cloud Storage input instances.
# @!attribute [rw] data_format
# @return [::Google::Cloud::AIPlatform::V1::Examples::ExampleGcsSource::DataFormat]
# The format in which instances are given, if not specified, assume it's
# JSONL format. Currently only JSONL format is supported.
# @!attribute [rw] gcs_source
# @return [::Google::Cloud::AIPlatform::V1::GcsSource]
# The Cloud Storage location for the input instances.
class ExampleGcsSource
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# The format of the input example instances.
module DataFormat
# Format unspecified, used when unset.
DATA_FORMAT_UNSPECIFIED = 0
# Examples are stored in JSONL files.
JSONL = 1
end
end
end
# Preset configuration for example-based explanations
# @!attribute [rw] query
# @return [::Google::Cloud::AIPlatform::V1::Presets::Query]
# Preset option controlling parameters for speed-precision trade-off when
# querying for examples. If omitted, defaults to `PRECISE`.
# @!attribute [rw] modality
# @return [::Google::Cloud::AIPlatform::V1::Presets::Modality]
# The modality of the uploaded model, which automatically configures the
# distance measurement and feature normalization for the underlying example
# index and queries. If your model does not precisely fit one of these types,
# it is okay to choose the closest type.
class Presets
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# Preset option controlling parameters for query speed-precision trade-off
module Query
# More precise neighbors as a trade-off against slower response.
PRECISE = 0
# Faster response as a trade-off against less precise neighbors.
FAST = 1
end
# Preset option controlling parameters for different modalities
module Modality
# Should not be set. Added as a recommended best practice for enums
MODALITY_UNSPECIFIED = 0
# IMAGE modality
IMAGE = 1
# TEXT modality
TEXT = 2
# TABULAR modality
TABULAR = 3
end
end
# The {::Google::Cloud::AIPlatform::V1::ExplanationSpec ExplanationSpec} entries
# that can be overridden at [online
# explanation][google.cloud.aiplatform.v1.PredictionService.Explain] time.
# @!attribute [rw] parameters
# @return [::Google::Cloud::AIPlatform::V1::ExplanationParameters]
# The parameters to be overridden. Note that the
# attribution method cannot be changed. If not specified,
# no parameter is overridden.
# @!attribute [rw] metadata
# @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadataOverride]
# The metadata to be overridden. If not specified, no metadata is overridden.
# @!attribute [rw] examples_override
# @return [::Google::Cloud::AIPlatform::V1::ExamplesOverride]
# The example-based explanations parameter overrides.
class ExplanationSpecOverride
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# The {::Google::Cloud::AIPlatform::V1::ExplanationMetadata ExplanationMetadata}
# entries that can be overridden at [online
# explanation][google.cloud.aiplatform.v1.PredictionService.Explain] time.
# @!attribute [rw] inputs
# @return [::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ExplanationMetadataOverride::InputMetadataOverride}]
# Required. Overrides the [input
# metadata][google.cloud.aiplatform.v1.ExplanationMetadata.inputs] of the
# features. The key is the name of the feature to be overridden. The keys
# specified here must exist in the input metadata to be overridden. If a
# feature is not specified here, the corresponding feature's input metadata
# is not overridden.
class ExplanationMetadataOverride
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# The [input
# metadata][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata]
# entries to be overridden.
# @!attribute [rw] input_baselines
# @return [::Array<::Google::Protobuf::Value>]
# Baseline inputs for this feature.
#
# This overrides the `input_baseline` field of the
# {::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata ExplanationMetadata.InputMetadata}
# object of the corresponding feature's input metadata. If it's not
# specified, the original baselines are not overridden.
class InputMetadataOverride
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# @!attribute [rw] key
# @return [::String]
# @!attribute [rw] value
# @return [::Google::Cloud::AIPlatform::V1::ExplanationMetadataOverride::InputMetadataOverride]
class InputsEntry
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
end
# Overrides for example-based explanations.
# @!attribute [rw] neighbor_count
# @return [::Integer]
# The number of neighbors to return.
# @!attribute [rw] crowding_count
# @return [::Integer]
# The number of neighbors to return that have the same crowding tag.
# @!attribute [rw] restrictions
# @return [::Array<::Google::Cloud::AIPlatform::V1::ExamplesRestrictionsNamespace>]
# Restrict the resulting nearest neighbors to respect these constraints.
# @!attribute [rw] return_embeddings
# @return [::Boolean]
# If true, return the embeddings instead of neighbors.
# @!attribute [rw] data_format
# @return [::Google::Cloud::AIPlatform::V1::ExamplesOverride::DataFormat]
# The format of the data being provided with each call.
class ExamplesOverride
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# Data format enum.
module DataFormat
# Unspecified format. Must not be used.
DATA_FORMAT_UNSPECIFIED = 0
# Provided data is a set of model inputs.
INSTANCES = 1
# Provided data is a set of embeddings.
EMBEDDINGS = 2
end
end
# Restrictions namespace for example-based explanations overrides.
# @!attribute [rw] namespace_name
# @return [::String]
# The namespace name.
# @!attribute [rw] allow
# @return [::Array<::String>]
# The list of allowed tags.
# @!attribute [rw] deny
# @return [::Array<::String>]
# The list of deny tags.
class ExamplesRestrictionsNamespace
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
end
end
end
end