kpt-grpc-demo/google/cloud/aiplatform/v1/explanation_metadata.proto

// 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.

syntax = "proto3";

package google.cloud.aiplatform.v1;

import "google/api/field_behavior.proto";
import "google/protobuf/struct.proto";

option csharp_namespace = "Google.Cloud.AIPlatform.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/aiplatform/v1;aiplatform";
option java_multiple_files = true;
option java_outer_classname = "ExplanationMetadataProto";
option java_package = "com.google.cloud.aiplatform.v1";
option php_namespace = "Google\\Cloud\\AIPlatform\\V1";
option ruby_package = "Google::Cloud::AIPlatform::V1";

// Metadata describing the Model's input and output for explanation.
message ExplanationMetadata {
  // 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.
  message InputMetadata {
    // 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.
    message FeatureValueDomain {
      // The minimum permissible value for this feature.
      float min_value = 1;

      // The maximum permissible value for this feature.
      float max_value = 2;

      // 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.
      float original_mean = 3;

      // 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.
      float original_stddev = 4;
    }

    // Visualization configurations for image explanation.
    message Visualization {
      // Type of the image visualization. Only applicable to
      // [Integrated Gradients attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution].
      enum 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;
      }

      // Whether to only highlight pixels with positive contributions, negative
      // or both. Defaults to POSITIVE.
      enum 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;
      }

      // The color scheme used for highlighting areas.
      enum 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;
      }

      // How the original image is displayed in the visualization.
      enum 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;
      }

      // 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 type = 1;

      // Whether to only highlight pixels with positive contributions, negative
      // or both. Defaults to POSITIVE.
      Polarity polarity = 2;

      // 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 color_map = 3;

      // 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.
      float clip_percent_upperbound = 4;

      // Excludes attributions below the specified percentile, from the
      // highlighted areas. Defaults to 62.
      float clip_percent_lowerbound = 5;

      // 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 overlay_type = 6;
    }

    // Defines how a feature is encoded. Defaults to IDENTITY.
    enum 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. [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"]
      // ```
      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. [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"]
      // ```
      BAG_OF_FEATURES_SPARSE = 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"]
      // ```
      INDICATOR = 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]
      // ```
      COMBINED_EMBEDDING = 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]]
      // ```
      CONCAT_EMBEDDING = 6;
    }

    // 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].
    repeated google.protobuf.Value input_baselines = 1;

    // Name of the input tensor for this feature. Required and is only
    // applicable to Vertex AI-provided images for Tensorflow.
    string input_tensor_name = 2;

    // Defines how the feature is encoded into the input tensor. Defaults to
    // IDENTITY.
    Encoding encoding = 3;

    // Modality of the feature. Valid values are: numeric, image. Defaults to
    // numeric.
    string modality = 4;

    // The domain details of the input feature value. Like min/max, original
    // mean or standard deviation if normalized.
    FeatureValueDomain feature_value_domain = 5;

    // 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.
    string indices_tensor_name = 6;

    // 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.
    string dense_shape_tensor_name = 7;

    // 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.
    repeated string index_feature_mapping = 8;

    // 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.
    string encoded_tensor_name = 9;

    // 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.
    repeated google.protobuf.Value encoded_baselines = 10;

    // Visualization configurations for image explanation.
    Visualization visualization = 11;

    // 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.
    string group_name = 12;
  }

  // Metadata of the prediction output to be explained.
  message OutputMetadata {
    // 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.
    oneof display_name_mapping {
      // 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].
      google.protobuf.Value index_display_name_mapping = 1;

      // 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.
      string display_name_mapping_key = 2;
    }

    // Name of the output tensor. Required and is only applicable to Vertex
    // AI provided images for Tensorflow.
    string output_tensor_name = 3;
  }

  // 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].
  map<string, InputMetadata> inputs = 1 [(google.api.field_behavior) = REQUIRED];

  // 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.
  map<string, OutputMetadata> outputs = 2 [(google.api.field_behavior) = REQUIRED];

  // 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.
  string feature_attributions_schema_uri = 3;

  // Name of the source to generate embeddings for example based explanations.
  string latent_space_source = 5;
}