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// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow_metadata/proto/v0/problem_statement.proto
// Protobuf Java Version: 3.25.4
package org.tensorflow.metadata.v0;
public interface ProblemStatementOrBuilder extends
// @@protoc_insertion_point(interface_extends:tensorflow.metadata.v0.ProblemStatement)
com.google.protobuf.MessageOrBuilder {
/**
*
* Description of the problem statement. For example, should describe how
* the problem statement was arrived at: what experiments were run, what
* side-by-sides were considered.
*
*
* string description = 2;
* @return The description.
*/
java.lang.String getDescription();
/**
*
* Description of the problem statement. For example, should describe how
* the problem statement was arrived at: what experiments were run, what
* side-by-sides were considered.
*
*
* string description = 2;
* @return The bytes for description.
*/
com.google.protobuf.ByteString
getDescriptionBytes();
/**
* repeated string owner = 3;
* @return A list containing the owner.
*/
java.util.List
getOwnerList();
/**
* repeated string owner = 3;
* @return The count of owner.
*/
int getOwnerCount();
/**
* repeated string owner = 3;
* @param index The index of the element to return.
* @return The owner at the given index.
*/
java.lang.String getOwner(int index);
/**
* repeated string owner = 3;
* @param index The index of the value to return.
* @return The bytes of the owner at the given index.
*/
com.google.protobuf.ByteString
getOwnerBytes(int index);
/**
*
* The environment of the ProblemStatement (optional). Specifies an
* environment string in the SchemaProto.
*
*
* string environment = 4;
* @return The environment.
*/
java.lang.String getEnvironment();
/**
*
* The environment of the ProblemStatement (optional). Specifies an
* environment string in the SchemaProto.
*
*
* string environment = 4;
* @return The bytes for environment.
*/
com.google.protobuf.ByteString
getEnvironmentBytes();
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
java.util.List
getMetaOptimizationTargetList();
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
org.tensorflow.metadata.v0.MetaOptimizationTarget getMetaOptimizationTarget(int index);
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
int getMetaOptimizationTargetCount();
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
java.util.List
getMetaOptimizationTargetOrBuilderList();
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
org.tensorflow.metadata.v0.MetaOptimizationTargetOrBuilder getMetaOptimizationTargetOrBuilder(
int index);
/**
* bool multi_objective = 8 [deprecated = true];
* @deprecated tensorflow.metadata.v0.ProblemStatement.multi_objective is deprecated.
* See tensorflow_metadata/proto/v0/problem_statement.proto;l=356
* @return The multiObjective.
*/
@java.lang.Deprecated boolean getMultiObjective();
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
java.util.List
getTasksList();
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
org.tensorflow.metadata.v0.Task getTasks(int index);
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
int getTasksCount();
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
java.util.List
getTasksOrBuilderList();
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
org.tensorflow.metadata.v0.TaskOrBuilder getTasksOrBuilder(
int index);
}
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