Package com.google.cloud.aiplatform.v1beta1

Class ModelMonitoringSchema

java.lang.Object
com.google.protobuf.AbstractMessageLite
com.google.protobuf.AbstractMessage
com.google.protobuf.GeneratedMessage
com.google.cloud.aiplatform.v1beta1.ModelMonitoringSchema
All Implemented Interfaces:
ModelMonitoringSchemaOrBuilder, com.google.protobuf.Message, com.google.protobuf.MessageLite, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Serializable
@Generated public final class ModelMonitoringSchema extends com.google.protobuf.GeneratedMessage implements ModelMonitoringSchemaOrBuilder
 The Model Monitoring Schema definition.
Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringSchema
See Also:

  • Field Details

    • FEATURE_FIELDS_FIELD_NUMBER

      public static final int FEATURE_FIELDS_FIELD_NUMBER
      See Also:

    • PREDICTION_FIELDS_FIELD_NUMBER

      public static final int PREDICTION_FIELDS_FIELD_NUMBER
      See Also:

    • GROUND_TRUTH_FIELDS_FIELD_NUMBER

      public static final int GROUND_TRUTH_FIELDS_FIELD_NUMBER
      See Also:

  • Method Details

    • getDescriptor

      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

    • internalGetFieldAccessorTable

      protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessage

    • getFeatureFieldsList

      public List<ModelMonitoringSchema.FieldSchema> getFeatureFieldsList()
       Feature names of the model. Vertex AI will try to match the features from
 your dataset as follows:
 * For 'csv' files, the header names are required, and we will extract the
 corresponding feature values when the header names align with the
 feature names.
 * For 'jsonl' files, we will extract the corresponding feature values if
 the key names match the feature names.
 Note: Nested features are not supported, so please ensure your features
 are flattened. Ensure the feature values are scalar or an array of
 scalars.
 * For 'bigquery' dataset, we will extract the corresponding feature values
 if the column names match the feature names.
 Note: The column type can be a scalar or an array of scalars. STRUCT or
 JSON types are not supported. You may use SQL queries to select or
 aggregate the relevant features from your original table. However,
 ensure that the 'schema' of the query results meets our requirements.
 * For the Vertex AI Endpoint Request Response Logging table or Vertex AI
 Batch Prediction Job results. If the
 [instance_type][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.instance_type]
 is an array, ensure that the sequence in
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields]
 matches the order of features in the prediction instance. We will match
 the feature with the array in the order specified in [feature_fields].
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema feature_fields = 1;
      Specified by:
      getFeatureFieldsList in interface ModelMonitoringSchemaOrBuilder

    • getFeatureFieldsOrBuilderList

      public List<? extends ModelMonitoringSchema.FieldSchemaOrBuilder> getFeatureFieldsOrBuilderList()
       Feature names of the model. Vertex AI will try to match the features from
 your dataset as follows:
 * For 'csv' files, the header names are required, and we will extract the
 corresponding feature values when the header names align with the
 feature names.
 * For 'jsonl' files, we will extract the corresponding feature values if
 the key names match the feature names.
 Note: Nested features are not supported, so please ensure your features
 are flattened. Ensure the feature values are scalar or an array of
 scalars.
 * For 'bigquery' dataset, we will extract the corresponding feature values
 if the column names match the feature names.
 Note: The column type can be a scalar or an array of scalars. STRUCT or
 JSON types are not supported. You may use SQL queries to select or
 aggregate the relevant features from your original table. However,
 ensure that the 'schema' of the query results meets our requirements.
 * For the Vertex AI Endpoint Request Response Logging table or Vertex AI
 Batch Prediction Job results. If the
 [instance_type][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.instance_type]
 is an array, ensure that the sequence in
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields]
 matches the order of features in the prediction instance. We will match
 the feature with the array in the order specified in [feature_fields].
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema feature_fields = 1;
      Specified by:
      getFeatureFieldsOrBuilderList in interface ModelMonitoringSchemaOrBuilder

    • getFeatureFieldsCount

      public int getFeatureFieldsCount()
       Feature names of the model. Vertex AI will try to match the features from
 your dataset as follows:
 * For 'csv' files, the header names are required, and we will extract the
 corresponding feature values when the header names align with the
 feature names.
 * For 'jsonl' files, we will extract the corresponding feature values if
 the key names match the feature names.
 Note: Nested features are not supported, so please ensure your features
 are flattened. Ensure the feature values are scalar or an array of
 scalars.
 * For 'bigquery' dataset, we will extract the corresponding feature values
 if the column names match the feature names.
 Note: The column type can be a scalar or an array of scalars. STRUCT or
 JSON types are not supported. You may use SQL queries to select or
 aggregate the relevant features from your original table. However,
 ensure that the 'schema' of the query results meets our requirements.
 * For the Vertex AI Endpoint Request Response Logging table or Vertex AI
 Batch Prediction Job results. If the
 [instance_type][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.instance_type]
 is an array, ensure that the sequence in
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields]
 matches the order of features in the prediction instance. We will match
 the feature with the array in the order specified in [feature_fields].
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema feature_fields = 1;
      Specified by:
      getFeatureFieldsCount in interface ModelMonitoringSchemaOrBuilder

    • getFeatureFields

      public ModelMonitoringSchema.FieldSchema getFeatureFields(int index)
       Feature names of the model. Vertex AI will try to match the features from
 your dataset as follows:
 * For 'csv' files, the header names are required, and we will extract the
 corresponding feature values when the header names align with the
 feature names.
 * For 'jsonl' files, we will extract the corresponding feature values if
 the key names match the feature names.
 Note: Nested features are not supported, so please ensure your features
 are flattened. Ensure the feature values are scalar or an array of
 scalars.
 * For 'bigquery' dataset, we will extract the corresponding feature values
 if the column names match the feature names.
 Note: The column type can be a scalar or an array of scalars. STRUCT or
 JSON types are not supported. You may use SQL queries to select or
 aggregate the relevant features from your original table. However,
 ensure that the 'schema' of the query results meets our requirements.
 * For the Vertex AI Endpoint Request Response Logging table or Vertex AI
 Batch Prediction Job results. If the
 [instance_type][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.instance_type]
 is an array, ensure that the sequence in
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields]
 matches the order of features in the prediction instance. We will match
 the feature with the array in the order specified in [feature_fields].
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema feature_fields = 1;
      Specified by:
      getFeatureFields in interface ModelMonitoringSchemaOrBuilder

    • getFeatureFieldsOrBuilder

      public ModelMonitoringSchema.FieldSchemaOrBuilder getFeatureFieldsOrBuilder(int index)
       Feature names of the model. Vertex AI will try to match the features from
 your dataset as follows:
 * For 'csv' files, the header names are required, and we will extract the
 corresponding feature values when the header names align with the
 feature names.
 * For 'jsonl' files, we will extract the corresponding feature values if
 the key names match the feature names.
 Note: Nested features are not supported, so please ensure your features
 are flattened. Ensure the feature values are scalar or an array of
 scalars.
 * For 'bigquery' dataset, we will extract the corresponding feature values
 if the column names match the feature names.
 Note: The column type can be a scalar or an array of scalars. STRUCT or
 JSON types are not supported. You may use SQL queries to select or
 aggregate the relevant features from your original table. However,
 ensure that the 'schema' of the query results meets our requirements.
 * For the Vertex AI Endpoint Request Response Logging table or Vertex AI
 Batch Prediction Job results. If the
 [instance_type][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.instance_type]
 is an array, ensure that the sequence in
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields]
 matches the order of features in the prediction instance. We will match
 the feature with the array in the order specified in [feature_fields].
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema feature_fields = 1;
      Specified by:
      getFeatureFieldsOrBuilder in interface ModelMonitoringSchemaOrBuilder

    • getPredictionFieldsList

      public List<ModelMonitoringSchema.FieldSchema> getPredictionFieldsList()
       Prediction output names of the model. The requirements are the same as the
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields].
 For AutoML Tables, the prediction output name presented in schema will be:
 `predicted_{target_column}`, the `target_column` is the one you specified
 when you train the model.
 For Prediction output drift analysis:
 * AutoML Classification, the distribution of the argmax label will be
 analyzed.
 * AutoML Regression, the distribution of the value will be analyzed.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema prediction_fields = 2;
      Specified by:
      getPredictionFieldsList in interface ModelMonitoringSchemaOrBuilder

    • getPredictionFieldsOrBuilderList

      public List<? extends ModelMonitoringSchema.FieldSchemaOrBuilder> getPredictionFieldsOrBuilderList()
       Prediction output names of the model. The requirements are the same as the
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields].
 For AutoML Tables, the prediction output name presented in schema will be:
 `predicted_{target_column}`, the `target_column` is the one you specified
 when you train the model.
 For Prediction output drift analysis:
 * AutoML Classification, the distribution of the argmax label will be
 analyzed.
 * AutoML Regression, the distribution of the value will be analyzed.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema prediction_fields = 2;
      Specified by:
      getPredictionFieldsOrBuilderList in interface ModelMonitoringSchemaOrBuilder

    • getPredictionFieldsCount

      public int getPredictionFieldsCount()
       Prediction output names of the model. The requirements are the same as the
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields].
 For AutoML Tables, the prediction output name presented in schema will be:
 `predicted_{target_column}`, the `target_column` is the one you specified
 when you train the model.
 For Prediction output drift analysis:
 * AutoML Classification, the distribution of the argmax label will be
 analyzed.
 * AutoML Regression, the distribution of the value will be analyzed.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema prediction_fields = 2;
      Specified by:
      getPredictionFieldsCount in interface ModelMonitoringSchemaOrBuilder

    • getPredictionFields

      public ModelMonitoringSchema.FieldSchema getPredictionFields(int index)
       Prediction output names of the model. The requirements are the same as the
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields].
 For AutoML Tables, the prediction output name presented in schema will be:
 `predicted_{target_column}`, the `target_column` is the one you specified
 when you train the model.
 For Prediction output drift analysis:
 * AutoML Classification, the distribution of the argmax label will be
 analyzed.
 * AutoML Regression, the distribution of the value will be analyzed.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema prediction_fields = 2;
      Specified by:
      getPredictionFields in interface ModelMonitoringSchemaOrBuilder

    • getPredictionFieldsOrBuilder

      public ModelMonitoringSchema.FieldSchemaOrBuilder getPredictionFieldsOrBuilder(int index)
       Prediction output names of the model. The requirements are the same as the
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields].
 For AutoML Tables, the prediction output name presented in schema will be:
 `predicted_{target_column}`, the `target_column` is the one you specified
 when you train the model.
 For Prediction output drift analysis:
 * AutoML Classification, the distribution of the argmax label will be
 analyzed.
 * AutoML Regression, the distribution of the value will be analyzed.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema prediction_fields = 2;
      Specified by:
      getPredictionFieldsOrBuilder in interface ModelMonitoringSchemaOrBuilder

    • getGroundTruthFieldsList

      public List<ModelMonitoringSchema.FieldSchema> getGroundTruthFieldsList()
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;
      Specified by:
      getGroundTruthFieldsList in interface ModelMonitoringSchemaOrBuilder

    • getGroundTruthFieldsOrBuilderList

      public List<? extends ModelMonitoringSchema.FieldSchemaOrBuilder> getGroundTruthFieldsOrBuilderList()
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;
      Specified by:
      getGroundTruthFieldsOrBuilderList in interface ModelMonitoringSchemaOrBuilder

    • getGroundTruthFieldsCount

      public int getGroundTruthFieldsCount()
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;
      Specified by:
      getGroundTruthFieldsCount in interface ModelMonitoringSchemaOrBuilder

    • getGroundTruthFields

      public ModelMonitoringSchema.FieldSchema getGroundTruthFields(int index)
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;
      Specified by:
      getGroundTruthFields in interface ModelMonitoringSchemaOrBuilder

    • getGroundTruthFieldsOrBuilder

      public ModelMonitoringSchema.FieldSchemaOrBuilder getGroundTruthFieldsOrBuilder(int index)
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;
      Specified by:
      getGroundTruthFieldsOrBuilder in interface ModelMonitoringSchemaOrBuilder

    • isInitialized

      public final boolean isInitialized()
      Specified by:
      isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
      Overrides:
      isInitialized in class com.google.protobuf.GeneratedMessage

    • writeTo

      public void writeTo(com.google.protobuf.CodedOutputStream output) throws IOException
      Specified by:
      writeTo in interface com.google.protobuf.MessageLite
      Overrides:
      writeTo in class com.google.protobuf.GeneratedMessage
      Throws:
      IOException

    • getSerializedSize

      public int getSerializedSize()
      Specified by:
      getSerializedSize in interface com.google.protobuf.MessageLite
      Overrides:
      getSerializedSize in class com.google.protobuf.GeneratedMessage

    • equals

      public boolean equals(Object obj)
      Specified by:
      equals in interface com.google.protobuf.Message
      Overrides:
      equals in class com.google.protobuf.AbstractMessage

    • hashCode

      public int hashCode()
      Specified by:
      hashCode in interface com.google.protobuf.Message
      Overrides:
      hashCode in class com.google.protobuf.AbstractMessage

    • parseFrom

      public static ModelMonitoringSchema parseFrom(ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException

    • parseFrom

      public static ModelMonitoringSchema parseFrom(ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException

    • parseFrom

      public static ModelMonitoringSchema parseFrom(com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException

    • parseFrom

      public static ModelMonitoringSchema parseFrom(com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException

    • parseFrom

      public static ModelMonitoringSchema parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException

    • parseFrom

      public static ModelMonitoringSchema parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException

    • parseFrom

      public static ModelMonitoringSchema parseFrom(InputStream input) throws IOException
      Throws:
      IOException

    • parseFrom

      public static ModelMonitoringSchema parseFrom(InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Throws:
      IOException

    • parseDelimitedFrom

      public static ModelMonitoringSchema parseDelimitedFrom(InputStream input) throws IOException
      Throws:
      IOException

    • parseDelimitedFrom

      public static ModelMonitoringSchema parseDelimitedFrom(InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Throws:
      IOException

    • parseFrom

      public static ModelMonitoringSchema parseFrom(com.google.protobuf.CodedInputStream input) throws IOException
      Throws:
      IOException

    • parseFrom

      public static ModelMonitoringSchema parseFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Throws:
      IOException

    • newBuilderForType

      public ModelMonitoringSchema.Builder newBuilderForType()
      Specified by:
      newBuilderForType in interface com.google.protobuf.Message
      Specified by:
      newBuilderForType in interface com.google.protobuf.MessageLite

    • newBuilder

      public static ModelMonitoringSchema.Builder newBuilder()

    • newBuilder

      public static ModelMonitoringSchema.Builder newBuilder(ModelMonitoringSchema prototype)

    • toBuilder

      public ModelMonitoringSchema.Builder toBuilder()
      Specified by:
      toBuilder in interface com.google.protobuf.Message
      Specified by:
      toBuilder in interface com.google.protobuf.MessageLite

    • newBuilderForType

      protected ModelMonitoringSchema.Builder newBuilderForType(com.google.protobuf.AbstractMessage.BuilderParent parent)
      Overrides:
      newBuilderForType in class com.google.protobuf.AbstractMessage

    • getDefaultInstance

      public static ModelMonitoringSchema getDefaultInstance()

    • parser

      public static com.google.protobuf.Parser<ModelMonitoringSchema> parser()

    • getParserForType

      public com.google.protobuf.Parser<ModelMonitoringSchema> getParserForType()
      Specified by:
      getParserForType in interface com.google.protobuf.Message
      Specified by:
      getParserForType in interface com.google.protobuf.MessageLite
      Overrides:
      getParserForType in class com.google.protobuf.GeneratedMessage

    • getDefaultInstanceForType

      public ModelMonitoringSchema getDefaultInstanceForType()
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder