Google Cloud Ai Platform V1 Client - Class InputMetadata (1.9.0)

Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class InputMetadata.

Metadata of the input of a feature.

Fields other than InputMetadata.input_baselines are applicable only for Models that are using Vertex AI-provided images for Tensorflow.

Generated from protobuf message google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata

Namespace

Google \ Cloud \ AIPlatform \ V1 \ ExplanationMetadata

Methods

__construct

Constructor.

Parameters
Name Description
data array

Optional. Data for populating the Message object.
↳ input_baselines 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 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.
↳ input_tensor_name string

Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
↳ encoding int

Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
↳ modality string

Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
↳ feature_value_domain 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.
↳ indices_tensor_name 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.
↳ dense_shape_tensor_name 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.
↳ index_feature_mapping array

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
↳ encoded_tensor_name string

Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table.
↳ encoded_baselines 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.
↳ visualization Google\Cloud\AIPlatform\V1\ExplanationMetadata\InputMetadata\Visualization

Visualization configurations for image explanation.
↳ group_name 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 Attribution.feature_attributions, keyed by the group name.

getInputBaselines

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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

Returns
Type Description
Google\Protobuf\Internal\RepeatedField

setInputBaselines

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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

Parameter
Name Description
var array<Google\Protobuf\Value>
Returns
Type Description
$this

getInputTensorName

Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.

Returns
Type Description
string

setInputTensorName

Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.

Parameter
Name Description
var string
Returns
Type Description
$this

getEncoding

Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.

Returns
Type Description
int

setEncoding

Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.

Parameter
Name Description
var int
Returns
Type Description
$this

getModality

Modality of the feature. Valid values are: numeric, image. Defaults to numeric.

Returns
Type Description
string

setModality

Modality of the feature. Valid values are: numeric, image. Defaults to numeric.

Parameter
Name Description
var string
Returns
Type Description
$this

getFeatureValueDomain

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

Returns
Type Description
Google\Cloud\AIPlatform\V1\ExplanationMetadata\InputMetadata\FeatureValueDomain|null

hasFeatureValueDomain

clearFeatureValueDomain

setFeatureValueDomain

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

Parameter
Name Description
var Google\Cloud\AIPlatform\V1\ExplanationMetadata\InputMetadata\FeatureValueDomain
Returns
Type Description
$this

getIndicesTensorName

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.

Returns
Type Description
string

setIndicesTensorName

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.

Parameter
Name Description
var string
Returns
Type Description
$this

getDenseShapeTensorName

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.

Returns
Type Description
string

setDenseShapeTensorName

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.

Parameter
Name Description
var string
Returns
Type Description
$this

getIndexFeatureMapping

A list of feature names for each index in the input tensor.

Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

Returns
Type Description
Google\Protobuf\Internal\RepeatedField

setIndexFeatureMapping

A list of feature names for each index in the input tensor.

Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

Parameter
Name Description
var string[]
Returns
Type Description
$this

getEncodedTensorName

Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable.

An encoded tensor is generated if the input tensor is encoded by a lookup table.

Returns
Type Description
string

setEncodedTensorName

Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable.

An encoded tensor is generated if the input tensor is encoded by a lookup table.

Parameter
Name Description
var string
Returns
Type Description
$this

getEncodedBaselines

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.

Returns
Type Description
Google\Protobuf\Internal\RepeatedField

setEncodedBaselines

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.

Parameter
Name Description
var array<Google\Protobuf\Value>
Returns
Type Description
$this

getVisualization

Visualization configurations for image explanation.

Returns
Type Description
Google\Cloud\AIPlatform\V1\ExplanationMetadata\InputMetadata\Visualization|null

hasVisualization

clearVisualization

setVisualization

Visualization configurations for image explanation.

Parameter
Name Description
var Google\Cloud\AIPlatform\V1\ExplanationMetadata\InputMetadata\Visualization
Returns
Type Description
$this

getGroupName

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, keyed by the group name.

Returns
Type Description
string

setGroupName

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, keyed by the group name.

Parameter
Name Description
var string
Returns
Type Description
$this