/
explanation_metadata.pb.go
1364 lines (1238 loc) · 70.3 KB
/
explanation_metadata.pb.go
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// 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
//
// http://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.
// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// protoc-gen-go v1.26.0
// protoc v3.18.1
// source: google/cloud/aiplatform/v1/explanation_metadata.proto
package aiplatformpb
import (
reflect "reflect"
sync "sync"
_ "google.golang.org/genproto/googleapis/api/annotations"
protoreflect "google.golang.org/protobuf/reflect/protoreflect"
protoimpl "google.golang.org/protobuf/runtime/protoimpl"
structpb "google.golang.org/protobuf/types/known/structpb"
)
const (
// Verify that this generated code is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion)
// Verify that runtime/protoimpl is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20)
)
// Defines how a feature is encoded. Defaults to IDENTITY.
type ExplanationMetadata_InputMetadata_Encoding int32
const (
// Default value. This is the same as IDENTITY.
ExplanationMetadata_InputMetadata_ENCODING_UNSPECIFIED ExplanationMetadata_InputMetadata_Encoding = 0
// The tensor represents one feature.
ExplanationMetadata_InputMetadata_IDENTITY ExplanationMetadata_InputMetadata_Encoding = 1
// The tensor represents a bag of features where each index maps to
// a feature. [InputMetadata.index_feature_mapping][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.index_feature_mapping] must be provided for
// this encoding. For example:
// ```
// input = [27, 6.0, 150]
// index_feature_mapping = ["age", "height", "weight"]
// ```
ExplanationMetadata_InputMetadata_BAG_OF_FEATURES ExplanationMetadata_InputMetadata_Encoding = 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. [InputMetadata.index_feature_mapping][google.cloud.aiplatform.v1.ExplanationMetadata.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"]
// ```
ExplanationMetadata_InputMetadata_BAG_OF_FEATURES_SPARSE ExplanationMetadata_InputMetadata_Encoding = 3
// The tensor is a list of binaries representing whether a feature exists
// or not (1 indicates existence). [InputMetadata.index_feature_mapping][google.cloud.aiplatform.v1.ExplanationMetadata.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"]
// ```
ExplanationMetadata_InputMetadata_INDICATOR ExplanationMetadata_InputMetadata_Encoding = 4
// The tensor is encoded into a 1-dimensional array represented by an
// encoded tensor. [InputMetadata.encoded_tensor_name][google.cloud.aiplatform.v1.ExplanationMetadata.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]
// ```
ExplanationMetadata_InputMetadata_COMBINED_EMBEDDING ExplanationMetadata_InputMetadata_Encoding = 5
// Select this encoding when the input tensor is encoded into a
// 2-dimensional array represented by an encoded tensor.
// [InputMetadata.encoded_tensor_name][google.cloud.aiplatform.v1.ExplanationMetadata.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]]
//
// ```
ExplanationMetadata_InputMetadata_CONCAT_EMBEDDING ExplanationMetadata_InputMetadata_Encoding = 6
)
// Enum value maps for ExplanationMetadata_InputMetadata_Encoding.
var (
ExplanationMetadata_InputMetadata_Encoding_name = map[int32]string{
0: "ENCODING_UNSPECIFIED",
1: "IDENTITY",
2: "BAG_OF_FEATURES",
3: "BAG_OF_FEATURES_SPARSE",
4: "INDICATOR",
5: "COMBINED_EMBEDDING",
6: "CONCAT_EMBEDDING",
}
ExplanationMetadata_InputMetadata_Encoding_value = map[string]int32{
"ENCODING_UNSPECIFIED": 0,
"IDENTITY": 1,
"BAG_OF_FEATURES": 2,
"BAG_OF_FEATURES_SPARSE": 3,
"INDICATOR": 4,
"COMBINED_EMBEDDING": 5,
"CONCAT_EMBEDDING": 6,
}
)
func (x ExplanationMetadata_InputMetadata_Encoding) Enum() *ExplanationMetadata_InputMetadata_Encoding {
p := new(ExplanationMetadata_InputMetadata_Encoding)
*p = x
return p
}
func (x ExplanationMetadata_InputMetadata_Encoding) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ExplanationMetadata_InputMetadata_Encoding) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_aiplatform_v1_explanation_metadata_proto_enumTypes[0].Descriptor()
}
func (ExplanationMetadata_InputMetadata_Encoding) Type() protoreflect.EnumType {
return &file_google_cloud_aiplatform_v1_explanation_metadata_proto_enumTypes[0]
}
func (x ExplanationMetadata_InputMetadata_Encoding) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ExplanationMetadata_InputMetadata_Encoding.Descriptor instead.
func (ExplanationMetadata_InputMetadata_Encoding) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_explanation_metadata_proto_rawDescGZIP(), []int{0, 0, 0}
}
// Type of the image visualization. Only applicable to
// [Integrated Gradients attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution].
type ExplanationMetadata_InputMetadata_Visualization_Type int32
const (
// Should not be used.
ExplanationMetadata_InputMetadata_Visualization_TYPE_UNSPECIFIED ExplanationMetadata_InputMetadata_Visualization_Type = 0
// Shows which pixel contributed to the image prediction.
ExplanationMetadata_InputMetadata_Visualization_PIXELS ExplanationMetadata_InputMetadata_Visualization_Type = 1
// Shows which region contributed to the image prediction by outlining
// the region.
ExplanationMetadata_InputMetadata_Visualization_OUTLINES ExplanationMetadata_InputMetadata_Visualization_Type = 2
)
// Enum value maps for ExplanationMetadata_InputMetadata_Visualization_Type.
var (
ExplanationMetadata_InputMetadata_Visualization_Type_name = map[int32]string{
0: "TYPE_UNSPECIFIED",
1: "PIXELS",
2: "OUTLINES",
}
ExplanationMetadata_InputMetadata_Visualization_Type_value = map[string]int32{
"TYPE_UNSPECIFIED": 0,
"PIXELS": 1,
"OUTLINES": 2,
}
)
func (x ExplanationMetadata_InputMetadata_Visualization_Type) Enum() *ExplanationMetadata_InputMetadata_Visualization_Type {
p := new(ExplanationMetadata_InputMetadata_Visualization_Type)
*p = x
return p
}
func (x ExplanationMetadata_InputMetadata_Visualization_Type) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ExplanationMetadata_InputMetadata_Visualization_Type) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_aiplatform_v1_explanation_metadata_proto_enumTypes[1].Descriptor()
}
func (ExplanationMetadata_InputMetadata_Visualization_Type) Type() protoreflect.EnumType {
return &file_google_cloud_aiplatform_v1_explanation_metadata_proto_enumTypes[1]
}
func (x ExplanationMetadata_InputMetadata_Visualization_Type) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ExplanationMetadata_InputMetadata_Visualization_Type.Descriptor instead.
func (ExplanationMetadata_InputMetadata_Visualization_Type) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_explanation_metadata_proto_rawDescGZIP(), []int{0, 0, 1, 0}
}
// Whether to only highlight pixels with positive contributions, negative
// or both. Defaults to POSITIVE.
type ExplanationMetadata_InputMetadata_Visualization_Polarity int32
const (
// Default value. This is the same as POSITIVE.
ExplanationMetadata_InputMetadata_Visualization_POLARITY_UNSPECIFIED ExplanationMetadata_InputMetadata_Visualization_Polarity = 0
// Highlights the pixels/outlines that were most influential to the
// model's prediction.
ExplanationMetadata_InputMetadata_Visualization_POSITIVE ExplanationMetadata_InputMetadata_Visualization_Polarity = 1
// Setting polarity to negative highlights areas that does not lead to
// the models's current prediction.
ExplanationMetadata_InputMetadata_Visualization_NEGATIVE ExplanationMetadata_InputMetadata_Visualization_Polarity = 2
// Shows both positive and negative attributions.
ExplanationMetadata_InputMetadata_Visualization_BOTH ExplanationMetadata_InputMetadata_Visualization_Polarity = 3
)
// Enum value maps for ExplanationMetadata_InputMetadata_Visualization_Polarity.
var (
ExplanationMetadata_InputMetadata_Visualization_Polarity_name = map[int32]string{
0: "POLARITY_UNSPECIFIED",
1: "POSITIVE",
2: "NEGATIVE",
3: "BOTH",
}
ExplanationMetadata_InputMetadata_Visualization_Polarity_value = map[string]int32{
"POLARITY_UNSPECIFIED": 0,
"POSITIVE": 1,
"NEGATIVE": 2,
"BOTH": 3,
}
)
func (x ExplanationMetadata_InputMetadata_Visualization_Polarity) Enum() *ExplanationMetadata_InputMetadata_Visualization_Polarity {
p := new(ExplanationMetadata_InputMetadata_Visualization_Polarity)
*p = x
return p
}
func (x ExplanationMetadata_InputMetadata_Visualization_Polarity) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ExplanationMetadata_InputMetadata_Visualization_Polarity) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_aiplatform_v1_explanation_metadata_proto_enumTypes[2].Descriptor()
}
func (ExplanationMetadata_InputMetadata_Visualization_Polarity) Type() protoreflect.EnumType {
return &file_google_cloud_aiplatform_v1_explanation_metadata_proto_enumTypes[2]
}
func (x ExplanationMetadata_InputMetadata_Visualization_Polarity) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ExplanationMetadata_InputMetadata_Visualization_Polarity.Descriptor instead.
func (ExplanationMetadata_InputMetadata_Visualization_Polarity) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_explanation_metadata_proto_rawDescGZIP(), []int{0, 0, 1, 1}
}
// The color scheme used for highlighting areas.
type ExplanationMetadata_InputMetadata_Visualization_ColorMap int32
const (
// Should not be used.
ExplanationMetadata_InputMetadata_Visualization_COLOR_MAP_UNSPECIFIED ExplanationMetadata_InputMetadata_Visualization_ColorMap = 0
// Positive: green. Negative: pink.
ExplanationMetadata_InputMetadata_Visualization_PINK_GREEN ExplanationMetadata_InputMetadata_Visualization_ColorMap = 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.
ExplanationMetadata_InputMetadata_Visualization_VIRIDIS ExplanationMetadata_InputMetadata_Visualization_ColorMap = 2
// Positive: red. Negative: red.
ExplanationMetadata_InputMetadata_Visualization_RED ExplanationMetadata_InputMetadata_Visualization_ColorMap = 3
// Positive: green. Negative: green.
ExplanationMetadata_InputMetadata_Visualization_GREEN ExplanationMetadata_InputMetadata_Visualization_ColorMap = 4
// Positive: green. Negative: red.
ExplanationMetadata_InputMetadata_Visualization_RED_GREEN ExplanationMetadata_InputMetadata_Visualization_ColorMap = 6
// PiYG palette.
ExplanationMetadata_InputMetadata_Visualization_PINK_WHITE_GREEN ExplanationMetadata_InputMetadata_Visualization_ColorMap = 5
)
// Enum value maps for ExplanationMetadata_InputMetadata_Visualization_ColorMap.
var (
ExplanationMetadata_InputMetadata_Visualization_ColorMap_name = map[int32]string{
0: "COLOR_MAP_UNSPECIFIED",
1: "PINK_GREEN",
2: "VIRIDIS",
3: "RED",
4: "GREEN",
6: "RED_GREEN",
5: "PINK_WHITE_GREEN",
}
ExplanationMetadata_InputMetadata_Visualization_ColorMap_value = map[string]int32{
"COLOR_MAP_UNSPECIFIED": 0,
"PINK_GREEN": 1,
"VIRIDIS": 2,
"RED": 3,
"GREEN": 4,
"RED_GREEN": 6,
"PINK_WHITE_GREEN": 5,
}
)
func (x ExplanationMetadata_InputMetadata_Visualization_ColorMap) Enum() *ExplanationMetadata_InputMetadata_Visualization_ColorMap {
p := new(ExplanationMetadata_InputMetadata_Visualization_ColorMap)
*p = x
return p
}
func (x ExplanationMetadata_InputMetadata_Visualization_ColorMap) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ExplanationMetadata_InputMetadata_Visualization_ColorMap) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_aiplatform_v1_explanation_metadata_proto_enumTypes[3].Descriptor()
}
func (ExplanationMetadata_InputMetadata_Visualization_ColorMap) Type() protoreflect.EnumType {
return &file_google_cloud_aiplatform_v1_explanation_metadata_proto_enumTypes[3]
}
func (x ExplanationMetadata_InputMetadata_Visualization_ColorMap) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ExplanationMetadata_InputMetadata_Visualization_ColorMap.Descriptor instead.
func (ExplanationMetadata_InputMetadata_Visualization_ColorMap) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_explanation_metadata_proto_rawDescGZIP(), []int{0, 0, 1, 2}
}
// How the original image is displayed in the visualization.
type ExplanationMetadata_InputMetadata_Visualization_OverlayType int32
const (
// Default value. This is the same as NONE.
ExplanationMetadata_InputMetadata_Visualization_OVERLAY_TYPE_UNSPECIFIED ExplanationMetadata_InputMetadata_Visualization_OverlayType = 0
// No overlay.
ExplanationMetadata_InputMetadata_Visualization_NONE ExplanationMetadata_InputMetadata_Visualization_OverlayType = 1
// The attributions are shown on top of the original image.
ExplanationMetadata_InputMetadata_Visualization_ORIGINAL ExplanationMetadata_InputMetadata_Visualization_OverlayType = 2
// The attributions are shown on top of grayscaled version of the
// original image.
ExplanationMetadata_InputMetadata_Visualization_GRAYSCALE ExplanationMetadata_InputMetadata_Visualization_OverlayType = 3
// The attributions are used as a mask to reveal predictive parts of
// the image and hide the un-predictive parts.
ExplanationMetadata_InputMetadata_Visualization_MASK_BLACK ExplanationMetadata_InputMetadata_Visualization_OverlayType = 4
)
// Enum value maps for ExplanationMetadata_InputMetadata_Visualization_OverlayType.
var (
ExplanationMetadata_InputMetadata_Visualization_OverlayType_name = map[int32]string{
0: "OVERLAY_TYPE_UNSPECIFIED",
1: "NONE",
2: "ORIGINAL",
3: "GRAYSCALE",
4: "MASK_BLACK",
}
ExplanationMetadata_InputMetadata_Visualization_OverlayType_value = map[string]int32{
"OVERLAY_TYPE_UNSPECIFIED": 0,
"NONE": 1,
"ORIGINAL": 2,
"GRAYSCALE": 3,
"MASK_BLACK": 4,
}
)
func (x ExplanationMetadata_InputMetadata_Visualization_OverlayType) Enum() *ExplanationMetadata_InputMetadata_Visualization_OverlayType {
p := new(ExplanationMetadata_InputMetadata_Visualization_OverlayType)
*p = x
return p
}
func (x ExplanationMetadata_InputMetadata_Visualization_OverlayType) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ExplanationMetadata_InputMetadata_Visualization_OverlayType) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_aiplatform_v1_explanation_metadata_proto_enumTypes[4].Descriptor()
}
func (ExplanationMetadata_InputMetadata_Visualization_OverlayType) Type() protoreflect.EnumType {
return &file_google_cloud_aiplatform_v1_explanation_metadata_proto_enumTypes[4]
}
func (x ExplanationMetadata_InputMetadata_Visualization_OverlayType) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ExplanationMetadata_InputMetadata_Visualization_OverlayType.Descriptor instead.
func (ExplanationMetadata_InputMetadata_Visualization_OverlayType) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_explanation_metadata_proto_rawDescGZIP(), []int{0, 0, 1, 3}
}
// Metadata describing the Model's input and output for explanation.
type ExplanationMetadata struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// 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 [ExplanationMetadata.inputs][google.cloud.aiplatform.v1.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,
// [featureAttributions][google.cloud.aiplatform.v1.Attribution.feature_attributions] are keyed by
// this key (if not grouped with another feature).
//
// For custom images, the key must match with the key in
// [instance][google.cloud.aiplatform.v1.ExplainRequest.instances].
Inputs map[string]*ExplanationMetadata_InputMetadata `protobuf:"bytes,1,rep,name=inputs,proto3" json:"inputs,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
// 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.
Outputs map[string]*ExplanationMetadata_OutputMetadata `protobuf:"bytes,2,rep,name=outputs,proto3" json:"outputs,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
// Points to a YAML file stored on Google Cloud Storage describing the format
// of the [feature attributions][google.cloud.aiplatform.v1.Attribution.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.
FeatureAttributionsSchemaUri string `protobuf:"bytes,3,opt,name=feature_attributions_schema_uri,json=featureAttributionsSchemaUri,proto3" json:"feature_attributions_schema_uri,omitempty"`
// Name of the source to generate embeddings for example based explanations.
LatentSpaceSource string `protobuf:"bytes,5,opt,name=latent_space_source,json=latentSpaceSource,proto3" json:"latent_space_source,omitempty"`
}
func (x *ExplanationMetadata) Reset() {
*x = ExplanationMetadata{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_explanation_metadata_proto_msgTypes[0]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ExplanationMetadata) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ExplanationMetadata) ProtoMessage() {}
func (x *ExplanationMetadata) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_explanation_metadata_proto_msgTypes[0]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ExplanationMetadata.ProtoReflect.Descriptor instead.
func (*ExplanationMetadata) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_explanation_metadata_proto_rawDescGZIP(), []int{0}
}
func (x *ExplanationMetadata) GetInputs() map[string]*ExplanationMetadata_InputMetadata {
if x != nil {
return x.Inputs
}
return nil
}
func (x *ExplanationMetadata) GetOutputs() map[string]*ExplanationMetadata_OutputMetadata {
if x != nil {
return x.Outputs
}
return nil
}
func (x *ExplanationMetadata) GetFeatureAttributionsSchemaUri() string {
if x != nil {
return x.FeatureAttributionsSchemaUri
}
return ""
}
func (x *ExplanationMetadata) GetLatentSpaceSource() string {
if x != nil {
return x.LatentSpaceSource
}
return ""
}
// Metadata of the input of a feature.
//
// Fields other than [InputMetadata.input_baselines][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.input_baselines] are applicable only
// for Models that are using Vertex AI-provided images for Tensorflow.
type ExplanationMetadata_InputMetadata struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// 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 [Attribution.feature_attributions][google.cloud.aiplatform.v1.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
// [instance][google.cloud.aiplatform.v1.ExplainRequest.instances][]. 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]
// [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].
InputBaselines []*structpb.Value `protobuf:"bytes,1,rep,name=input_baselines,json=inputBaselines,proto3" json:"input_baselines,omitempty"`
// Name of the input tensor for this feature. Required and is only
// applicable to Vertex AI-provided images for Tensorflow.
InputTensorName string `protobuf:"bytes,2,opt,name=input_tensor_name,json=inputTensorName,proto3" json:"input_tensor_name,omitempty"`
// Defines how the feature is encoded into the input tensor. Defaults to
// IDENTITY.
Encoding ExplanationMetadata_InputMetadata_Encoding `protobuf:"varint,3,opt,name=encoding,proto3,enum=google.cloud.aiplatform.v1.ExplanationMetadata_InputMetadata_Encoding" json:"encoding,omitempty"`
// Modality of the feature. Valid values are: numeric, image. Defaults to
// numeric.
Modality string `protobuf:"bytes,4,opt,name=modality,proto3" json:"modality,omitempty"`
// The domain details of the input feature value. Like min/max, original
// mean or standard deviation if normalized.
FeatureValueDomain *ExplanationMetadata_InputMetadata_FeatureValueDomain `protobuf:"bytes,5,opt,name=feature_value_domain,json=featureValueDomain,proto3" json:"feature_value_domain,omitempty"`
// 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.
IndicesTensorName string `protobuf:"bytes,6,opt,name=indices_tensor_name,json=indicesTensorName,proto3" json:"indices_tensor_name,omitempty"`
// 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.
DenseShapeTensorName string `protobuf:"bytes,7,opt,name=dense_shape_tensor_name,json=denseShapeTensorName,proto3" json:"dense_shape_tensor_name,omitempty"`
// A list of feature names for each index in the input tensor.
// Required when the input [InputMetadata.encoding][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding] is BAG_OF_FEATURES,
// BAG_OF_FEATURES_SPARSE, INDICATOR.
IndexFeatureMapping []string `protobuf:"bytes,8,rep,name=index_feature_mapping,json=indexFeatureMapping,proto3" json:"index_feature_mapping,omitempty"`
// Encoded tensor is a transformation of the input tensor. Must be provided
// if choosing
// [Integrated Gradients attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution]
// or [XRAI attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution] and the
// input tensor is not differentiable.
//
// An encoded tensor is generated if the input tensor is encoded by a lookup
// table.
EncodedTensorName string `protobuf:"bytes,9,opt,name=encoded_tensor_name,json=encodedTensorName,proto3" json:"encoded_tensor_name,omitempty"`
// 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.
EncodedBaselines []*structpb.Value `protobuf:"bytes,10,rep,name=encoded_baselines,json=encodedBaselines,proto3" json:"encoded_baselines,omitempty"`
// Visualization configurations for image explanation.
Visualization *ExplanationMetadata_InputMetadata_Visualization `protobuf:"bytes,11,opt,name=visualization,proto3" json:"visualization,omitempty"`
// 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
// [Attribution.feature_attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions], keyed by the group name.
GroupName string `protobuf:"bytes,12,opt,name=group_name,json=groupName,proto3" json:"group_name,omitempty"`
}
func (x *ExplanationMetadata_InputMetadata) Reset() {
*x = ExplanationMetadata_InputMetadata{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_explanation_metadata_proto_msgTypes[1]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ExplanationMetadata_InputMetadata) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ExplanationMetadata_InputMetadata) ProtoMessage() {}
func (x *ExplanationMetadata_InputMetadata) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_explanation_metadata_proto_msgTypes[1]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ExplanationMetadata_InputMetadata.ProtoReflect.Descriptor instead.
func (*ExplanationMetadata_InputMetadata) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_explanation_metadata_proto_rawDescGZIP(), []int{0, 0}
}
func (x *ExplanationMetadata_InputMetadata) GetInputBaselines() []*structpb.Value {
if x != nil {
return x.InputBaselines
}
return nil
}
func (x *ExplanationMetadata_InputMetadata) GetInputTensorName() string {
if x != nil {
return x.InputTensorName
}
return ""
}
func (x *ExplanationMetadata_InputMetadata) GetEncoding() ExplanationMetadata_InputMetadata_Encoding {
if x != nil {
return x.Encoding
}
return ExplanationMetadata_InputMetadata_ENCODING_UNSPECIFIED
}
func (x *ExplanationMetadata_InputMetadata) GetModality() string {
if x != nil {
return x.Modality
}
return ""
}
func (x *ExplanationMetadata_InputMetadata) GetFeatureValueDomain() *ExplanationMetadata_InputMetadata_FeatureValueDomain {
if x != nil {
return x.FeatureValueDomain
}
return nil
}
func (x *ExplanationMetadata_InputMetadata) GetIndicesTensorName() string {
if x != nil {
return x.IndicesTensorName
}
return ""
}
func (x *ExplanationMetadata_InputMetadata) GetDenseShapeTensorName() string {
if x != nil {
return x.DenseShapeTensorName
}
return ""
}
func (x *ExplanationMetadata_InputMetadata) GetIndexFeatureMapping() []string {
if x != nil {
return x.IndexFeatureMapping
}
return nil
}
func (x *ExplanationMetadata_InputMetadata) GetEncodedTensorName() string {
if x != nil {
return x.EncodedTensorName
}
return ""
}
func (x *ExplanationMetadata_InputMetadata) GetEncodedBaselines() []*structpb.Value {
if x != nil {
return x.EncodedBaselines
}
return nil
}
func (x *ExplanationMetadata_InputMetadata) GetVisualization() *ExplanationMetadata_InputMetadata_Visualization {
if x != nil {
return x.Visualization
}
return nil
}
func (x *ExplanationMetadata_InputMetadata) GetGroupName() string {
if x != nil {
return x.GroupName
}
return ""
}
// Metadata of the prediction output to be explained.
type ExplanationMetadata_OutputMetadata struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Defines how to map [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index] to
// [Attribution.output_display_name][google.cloud.aiplatform.v1.Attribution.output_display_name].
//
// If neither of the fields are specified,
// [Attribution.output_display_name][google.cloud.aiplatform.v1.Attribution.output_display_name] will not be populated.
//
// Types that are assignable to DisplayNameMapping:
//
// *ExplanationMetadata_OutputMetadata_IndexDisplayNameMapping
// *ExplanationMetadata_OutputMetadata_DisplayNameMappingKey
DisplayNameMapping isExplanationMetadata_OutputMetadata_DisplayNameMapping `protobuf_oneof:"display_name_mapping"`
// Name of the output tensor. Required and is only applicable to Vertex
// AI provided images for Tensorflow.
OutputTensorName string `protobuf:"bytes,3,opt,name=output_tensor_name,json=outputTensorName,proto3" json:"output_tensor_name,omitempty"`
}
func (x *ExplanationMetadata_OutputMetadata) Reset() {
*x = ExplanationMetadata_OutputMetadata{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_explanation_metadata_proto_msgTypes[2]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ExplanationMetadata_OutputMetadata) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ExplanationMetadata_OutputMetadata) ProtoMessage() {}
func (x *ExplanationMetadata_OutputMetadata) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_explanation_metadata_proto_msgTypes[2]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ExplanationMetadata_OutputMetadata.ProtoReflect.Descriptor instead.
func (*ExplanationMetadata_OutputMetadata) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_explanation_metadata_proto_rawDescGZIP(), []int{0, 1}
}
func (m *ExplanationMetadata_OutputMetadata) GetDisplayNameMapping() isExplanationMetadata_OutputMetadata_DisplayNameMapping {
if m != nil {
return m.DisplayNameMapping
}
return nil
}
func (x *ExplanationMetadata_OutputMetadata) GetIndexDisplayNameMapping() *structpb.Value {
if x, ok := x.GetDisplayNameMapping().(*ExplanationMetadata_OutputMetadata_IndexDisplayNameMapping); ok {
return x.IndexDisplayNameMapping
}
return nil
}
func (x *ExplanationMetadata_OutputMetadata) GetDisplayNameMappingKey() string {
if x, ok := x.GetDisplayNameMapping().(*ExplanationMetadata_OutputMetadata_DisplayNameMappingKey); ok {
return x.DisplayNameMappingKey
}
return ""
}
func (x *ExplanationMetadata_OutputMetadata) GetOutputTensorName() string {
if x != nil {
return x.OutputTensorName
}
return ""
}
type isExplanationMetadata_OutputMetadata_DisplayNameMapping interface {
isExplanationMetadata_OutputMetadata_DisplayNameMapping()
}
type ExplanationMetadata_OutputMetadata_IndexDisplayNameMapping struct {
// 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 [Attribution.output_display_name][google.cloud.aiplatform.v1.Attribution.output_display_name] is populated by locating in the
// mapping with [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index].
IndexDisplayNameMapping *structpb.Value `protobuf:"bytes,1,opt,name=index_display_name_mapping,json=indexDisplayNameMapping,proto3,oneof"`
}
type ExplanationMetadata_OutputMetadata_DisplayNameMappingKey struct {
// 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 [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index] for
// a specific output.
DisplayNameMappingKey string `protobuf:"bytes,2,opt,name=display_name_mapping_key,json=displayNameMappingKey,proto3,oneof"`
}
func (*ExplanationMetadata_OutputMetadata_IndexDisplayNameMapping) isExplanationMetadata_OutputMetadata_DisplayNameMapping() {
}
func (*ExplanationMetadata_OutputMetadata_DisplayNameMappingKey) isExplanationMetadata_OutputMetadata_DisplayNameMapping() {
}
// 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.
type ExplanationMetadata_InputMetadata_FeatureValueDomain struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The minimum permissible value for this feature.
MinValue float32 `protobuf:"fixed32,1,opt,name=min_value,json=minValue,proto3" json:"min_value,omitempty"`
// The maximum permissible value for this feature.
MaxValue float32 `protobuf:"fixed32,2,opt,name=max_value,json=maxValue,proto3" json:"max_value,omitempty"`
// 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.
OriginalMean float32 `protobuf:"fixed32,3,opt,name=original_mean,json=originalMean,proto3" json:"original_mean,omitempty"`
// 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.
OriginalStddev float32 `protobuf:"fixed32,4,opt,name=original_stddev,json=originalStddev,proto3" json:"original_stddev,omitempty"`
}
func (x *ExplanationMetadata_InputMetadata_FeatureValueDomain) Reset() {
*x = ExplanationMetadata_InputMetadata_FeatureValueDomain{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_explanation_metadata_proto_msgTypes[5]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ExplanationMetadata_InputMetadata_FeatureValueDomain) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ExplanationMetadata_InputMetadata_FeatureValueDomain) ProtoMessage() {}
func (x *ExplanationMetadata_InputMetadata_FeatureValueDomain) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_explanation_metadata_proto_msgTypes[5]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ExplanationMetadata_InputMetadata_FeatureValueDomain.ProtoReflect.Descriptor instead.
func (*ExplanationMetadata_InputMetadata_FeatureValueDomain) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_explanation_metadata_proto_rawDescGZIP(), []int{0, 0, 0}
}
func (x *ExplanationMetadata_InputMetadata_FeatureValueDomain) GetMinValue() float32 {
if x != nil {
return x.MinValue
}
return 0
}
func (x *ExplanationMetadata_InputMetadata_FeatureValueDomain) GetMaxValue() float32 {
if x != nil {
return x.MaxValue
}
return 0
}
func (x *ExplanationMetadata_InputMetadata_FeatureValueDomain) GetOriginalMean() float32 {
if x != nil {
return x.OriginalMean
}
return 0
}
func (x *ExplanationMetadata_InputMetadata_FeatureValueDomain) GetOriginalStddev() float32 {
if x != nil {
return x.OriginalStddev
}
return 0
}
// Visualization configurations for image explanation.
type ExplanationMetadata_InputMetadata_Visualization struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// 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.
Type ExplanationMetadata_InputMetadata_Visualization_Type `protobuf:"varint,1,opt,name=type,proto3,enum=google.cloud.aiplatform.v1.ExplanationMetadata_InputMetadata_Visualization_Type" json:"type,omitempty"`
// Whether to only highlight pixels with positive contributions, negative
// or both. Defaults to POSITIVE.
Polarity ExplanationMetadata_InputMetadata_Visualization_Polarity `protobuf:"varint,2,opt,name=polarity,proto3,enum=google.cloud.aiplatform.v1.ExplanationMetadata_InputMetadata_Visualization_Polarity" json:"polarity,omitempty"`
// 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.
ColorMap ExplanationMetadata_InputMetadata_Visualization_ColorMap `protobuf:"varint,3,opt,name=color_map,json=colorMap,proto3,enum=google.cloud.aiplatform.v1.ExplanationMetadata_InputMetadata_Visualization_ColorMap" json:"color_map,omitempty"`
// 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.
ClipPercentUpperbound float32 `protobuf:"fixed32,4,opt,name=clip_percent_upperbound,json=clipPercentUpperbound,proto3" json:"clip_percent_upperbound,omitempty"`
// Excludes attributions below the specified percentile, from the
// highlighted areas. Defaults to 62.
ClipPercentLowerbound float32 `protobuf:"fixed32,5,opt,name=clip_percent_lowerbound,json=clipPercentLowerbound,proto3" json:"clip_percent_lowerbound,omitempty"`
// 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.
OverlayType ExplanationMetadata_InputMetadata_Visualization_OverlayType `protobuf:"varint,6,opt,name=overlay_type,json=overlayType,proto3,enum=google.cloud.aiplatform.v1.ExplanationMetadata_InputMetadata_Visualization_OverlayType" json:"overlay_type,omitempty"`
}
func (x *ExplanationMetadata_InputMetadata_Visualization) Reset() {
*x = ExplanationMetadata_InputMetadata_Visualization{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_explanation_metadata_proto_msgTypes[6]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ExplanationMetadata_InputMetadata_Visualization) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ExplanationMetadata_InputMetadata_Visualization) ProtoMessage() {}
func (x *ExplanationMetadata_InputMetadata_Visualization) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_explanation_metadata_proto_msgTypes[6]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ExplanationMetadata_InputMetadata_Visualization.ProtoReflect.Descriptor instead.
func (*ExplanationMetadata_InputMetadata_Visualization) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_explanation_metadata_proto_rawDescGZIP(), []int{0, 0, 1}
}
func (x *ExplanationMetadata_InputMetadata_Visualization) GetType() ExplanationMetadata_InputMetadata_Visualization_Type {
if x != nil {
return x.Type
}
return ExplanationMetadata_InputMetadata_Visualization_TYPE_UNSPECIFIED
}
func (x *ExplanationMetadata_InputMetadata_Visualization) GetPolarity() ExplanationMetadata_InputMetadata_Visualization_Polarity {
if x != nil {
return x.Polarity
}
return ExplanationMetadata_InputMetadata_Visualization_POLARITY_UNSPECIFIED
}
func (x *ExplanationMetadata_InputMetadata_Visualization) GetColorMap() ExplanationMetadata_InputMetadata_Visualization_ColorMap {
if x != nil {
return x.ColorMap
}
return ExplanationMetadata_InputMetadata_Visualization_COLOR_MAP_UNSPECIFIED
}
func (x *ExplanationMetadata_InputMetadata_Visualization) GetClipPercentUpperbound() float32 {
if x != nil {
return x.ClipPercentUpperbound
}
return 0
}
func (x *ExplanationMetadata_InputMetadata_Visualization) GetClipPercentLowerbound() float32 {