Set novelty to the threshold when ProximityArchive is empty (#479)

## Description

<!-- Provide a brief description of the PR's purpose here. -->

Previously, we returned inf novelty when the ProximityArchive was empty.
However, this can cause issues in code that expects ranking values to be
finite values, such as in `CMAEvolutionStrategy.check_stop`.
Functionally, this value works just as well as inf because it shows that
the solutions had enough novelty to be added to the archive. Strictly
speaking, since the archive is empty in this case, the novelty is
undefined, so there are many values that would work.

## Status

- [x] I have read the guidelines in

[CONTRIBUTING.md](https://github.com/icaros-usc/pyribs/blob/master/CONTRIBUTING.md)
- [x] I have formatted my code using `yapf`
- [x] I have tested my code by running `pytest`
- [x] I have linted my code with `pylint`
- [x] I have added a one-line description of my change to the changelog
in
      `HISTORY.md`
- [x] This PR is ready to go

HISTORY.md

@@ -8,7 +8,7 @@
 
 - Add NoveltyRanker for novelty search ({pr}`477`)
 - Add proximity_archive_plot for visualizing ProximityArchive ({pr}`476`)
-- Add ProximityArchive for novelty search ({pr}`472`)
+- Add ProximityArchive for novelty search ({pr}`472`, {pr}`479`)
 - Support diversity optimization in Scheduler.tell ({pr}`473`)
 - Allow specifying separate dtypes for solution, objective, and measures
   ({pr}`471`)

ribs/archives/_proximity_archive.py

@@ -244,9 +244,9 @@ def add(self, solution, objective, measures, **fields):
 
             - ``"novelty"`` (:class:`numpy.ndarray` of :attr:`dtypes`
               ["measures"]): The computed novelty of the solutions passed in. If
-              there were no solutions to compute novelty with respect to (e.g.,
-              the archive was empty), the novelty is set to infinity
-              (``numpy.inf``).
+              there were no solutions to compute novelty with respect to (i.e.,
+              the archive was empty), the novelty is set to the
+              :attr:`novelty_threshold`.
 
         Raises:
             ValueError: The array arguments do not match their specified shapes.
@@ -271,9 +271,8 @@ def add(self, solution, objective, measures, **fields):
             # If there are no neighbors for computing nearest neighbors, there
             # is infinite novelty and all solutions are added.
             novelty = np.full(len(data["measures"]),
-                              np.inf,
+                              self.novelty_threshold,
                               dtype=self.dtypes["measures"])
-            eligible = np.ones(len(data["measures"]), dtype=bool)
         else:
             # Compute nearest neighbors.
             k_neighbors = min(len(self), self.k_neighbors)
@@ -283,8 +282,8 @@ def add(self, solution, objective, measures, **fields):
             dists = dists[:, None] if k_neighbors == 1 else dists
 
             novelty = np.mean(dists, axis=1)
-            eligible = novelty >= self.novelty_threshold
 
+        eligible = novelty >= self.novelty_threshold
         n_eligible = np.sum(eligible)
         new_size = len(self) + n_eligible
 

tests/archives/proximity_archive_test.py

@@ -187,7 +187,7 @@ def test_add_single(data, use_list, add_mode):
     # Objective should default to 0.0.
     assert_archive_elite(data.archive, data.solution, 0.0, data.measures)
     assert add_info["status"] == AddStatus.NEW
-    assert add_info["novelty"] == np.inf
+    assert add_info["novelty"] == data.archive.novelty_threshold
 
 
 def test_add_single_after_clear(data):
@@ -199,14 +199,14 @@ def test_add_single_after_clear(data):
     add_info = data.archive.add_single(data.solution, None, data.measures)
 
     assert add_info["status"] == 2
-    assert add_info["novelty"] == np.inf
+    assert add_info["novelty"] == data.archive.novelty_threshold
 
     data.archive.clear()
 
     add_info = data.archive.add_single(data.solution, None, data.measures)
 
     assert add_info["status"] == 2
-    assert add_info["novelty"] == np.inf
+    assert add_info["novelty"] == data.archive.novelty_threshold
 
 
 def test_add_novel_solution():