Add test for Scheduler with CategoricalArchive (#569)

## Description

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

## TODO

<!-- Notable points that this PR has either accomplished or will
accomplish. -->

- [x] Add test that calls Scheduler with CategoricalArchive
- [x] Fix assert_archive_elites in GridArchive tests

## 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`
- [N/A] I have added a one-line description of my change to the
changelog in
      `HISTORY.md`
- [x] This PR is ready to go

Author: btjanaka

tests/archives/grid_archive_test.py

@@ -69,7 +69,7 @@ def assert_archive_elites(
                 data["objective"][j], objective_batch[i]))
 
             if measures_batch is not None:
-                if data["solution"].dtype.kind == "f":
+                if data["measures"].dtype.kind == "f":
                     measures_match = np.allclose(data["measures"][j],
                                                  measures_batch[i])
                 else:

tests/schedulers/scheduler_test.py

@@ -2,7 +2,7 @@
 import numpy as np
 import pytest
 
-from ribs.archives import GridArchive, ProximityArchive
+from ribs.archives import CategoricalArchive, GridArchive, ProximityArchive
 from ribs.emitters import GaussianEmitter
 from ribs.schedulers import BanditScheduler, Scheduler
 
@@ -431,3 +431,39 @@ def test_constant_active_emitters_bandit_scheduler():
         scheduler.tell(objective, measures)
 
         assert scheduler.active.sum() == expected_active
+
+
+def test_scheduler_with_categorical_archive(add_mode):
+    batch_size = 4
+    archive = CategoricalArchive(
+        solution_dim=2,
+        categories=[
+            ["A", "B", "C"],
+            ["One", "Two", "Three", "Four"],
+        ],
+        dtype={
+            "solution": np.float32,
+            "objective": np.float32,
+            "measures": object,
+        },
+    )
+    emitters = [
+        GaussianEmitter(archive, sigma=1, x0=[0.0, 0.0], batch_size=batch_size)
+    ]
+    scheduler = Scheduler(archive, emitters, add_mode=add_mode)
+
+    measures_batch = [["A", "Four"], ["B", "Three"], ["C", "One"], ["C", "Two"]]
+
+    _ = scheduler.ask()  # Ignore the actual values of the solutions.
+    # We pass in 4 solutions with unique measures, so all should go into
+    # the archive.
+    scheduler.tell(np.ones(batch_size), measures_batch)
+
+    print(archive.data())
+
+    assert_archive_elites(
+        archive=scheduler.archive,
+        batch_size=batch_size,
+        objective_batch=np.ones(batch_size),
+        measures_batch=measures_batch,
+    )