Proposes API for thresholded metrics

A more complete proposal of the behavior/contract can be found here: https://docs.google.com/document/d/1xT8Fq2fc2j3ZkrNamFEK9I2OQg5988X4xV8fQFXBUH0/edit?resourcekey=0-9uxb8KFe_JCOesZo4Z317Q#heading=h.da395nbulm5q

(Ideally contractual information belongs in the proto, but AutoTFX-specific details don't seem appropriate here so I think the above will have to be translated into some kind of AutoTFX g3doc instead?)

PiperOrigin-RevId: 407688935

RELEASE.md

@@ -6,6 +6,9 @@
 
 ## Bug Fixes and Other Changes
 
+*   A `threshold_config` is added to MetaOptimizationTarget to allow for
+    expressing thresholded optimization goals.
+
 ## Breaking Changes
 
 ## Deprecations

tensorflow_metadata/proto/v0/problem_statement.proto

@@ -277,23 +277,53 @@ message Task {
 // proxy function to optimize model weights. Target definitions include tasks,
 // metrics, and any weighted combination of them.
 message MetaOptimizationTarget {
+  reserved 2;
   // The name of a task in this problem statement producing the
   // prediction or classification for the metric.
   string task_name = 1;
 
-  // The type of the meta-objective.
-  // If this is not specified, use the type of the task.
-  Type type = 2;
-
   // The performance metric to be evaluated.
   // The prediction or classification is based upon the task.
   // The label is from the type of the task, or from the override_task.
   PerformanceMetric performance_metric = 3;
 
-  // If a model spec has multiple meta optimization targets, the weight
-  // of each can be specified. If not specified, value is 1.
-  // If multi_objective is specified, then the weights are ignored.
-  double weight = 4;
+  // Configuration for thresholded meta-optimization targets.
+  message ThresholdConfig {
+    // If specified, indicates a threshold that the user wishes the metric to
+    // stay under (for MINIMIZE type), or above (for MAXIMIZE type). The
+    // optimization process need not prefer models that are higher (or lower)
+    // on the thresholded metric so long as the threshold is respected.
+    // E.g., if `threshold` for a MAXIMIZE type metric X is .9, the optimization
+    // process will prefer a solution with X = .92 over a solution with X = .88,
+    // but may not prefer a solution with X = .95 over a solution with X = .92.
+    // Unless otherwise specified by the PerformanceMetric, threshold is
+    // best effort. It does not provide a hard guarantee about the properties of
+    // the final model, but rather serves as a "target" to guide the
+    // optimization process. The user is responsible for validating that final
+    // model metrics are in an acceptable range for the application.
+    // A problem statement may, however, be rejected if the specified target
+    // is impossible to achieve. Keep this in mind if running the optimization
+    // on a recurring basis, as shifts in the data could push a previously
+    // achievable target to being unachievable (and thus yield no solution).
+    // The units and range for the threshold will be the same as the
+    // valid output range of the associated performance_metric.
+    double threshold = 1;
+  }
+
+  // Describes how to combine with other objectives.
+  oneof objective_combination {
+    // If a model spec has multiple meta optimization targets, the weight
+    // of each can be specified. The final objective is then a weighted
+    // combination of the multiple objectives. If not specified, value is 1.
+    // If multi_objective is specified, then the weights are ignored.
+    double weight = 4;
+
+    // Secondary meta optimization targets can be thresholded, meaning that the
+    // optimization process prefers solutions above (or below) the threshold,
+    // but need not prefer solutions higher (or lower) on the metric if the
+    // threshold is met.
+    ThresholdConfig threshold_config = 5;
+  }
 }
 
 message ProblemStatement {