Rename measure_* columns to measures_* in as_pandas (#396)

## Description

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

In other parts of the library, fields like `solution` and `objective`
are consistently plural or singular. Since the rest of the library uses
`measures` (e.g., EliteBatch has `measures_batch`), this PR changes the
columns in `as_pandas` to start with `measures` instead of `measure`.

## TODO

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

## Questions

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## 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

@@ -6,8 +6,10 @@
 
 #### API
 
-- Add GradientOperatorEmitter to support OMG-MEGA and OG-MAP-Elites ({pr}`348`)
+- **Backwards-incompatible:** Rename `measure_*` columns to `measures_*` in
+  `as_pandas` ({pr}`396`)
 - Add ArrayStore data structure ({pr}`395`)
+- Add GradientOperatorEmitter to support OMG-MEGA and OG-MAP-Elites ({pr}`348`)
 
 #### Improvements
 

ribs/archives/_archive_base.py

@@ -1017,20 +1017,20 @@ def as_pandas(self, include_solutions=True, include_metadata=False):
 
         - 1 column of integers (``np.int32``) for the index, named ``index``.
           See :meth:`index_of` for more info.
-        - :attr:`measure_dim` columns for the measures, named ``measure_0,
-          measure_1, ...``
+        - :attr:`measure_dim` columns for the measures, named ``measures_0,
+          measures_1, ...``
         - 1 column for the objectives, named ``objective``
         - :attr:`solution_dim` columns for the solution parameters, named
           ``solution_0, solution_1, ...``
         - 1 column for the metadata objects, named ``metadata``
 
         In short, the dataframe looks like this:
 
-        +-------+------------+------+------------+-------------+-----+----------+
-        | index | measure_0  | ...  | objective  | solution_0  | ... | metadata |
-        +=======+============+======+============+=============+=====+==========+
-        |       |            | ...  |            |             | ... |          |
-        +-------+------------+------+------------+-------------+-----+----------+
+        +-------+------------+------+-----------+------------+-----+----------+
+        | index | measures_0 | ...  | objective | solution_0 | ... | metadata |
+        +=======+============+======+===========+============+=====+==========+
+        |       |            | ...  |           |            | ... |          |
+        +-------+------------+------+-----------+------------+-----+----------+
 
         Compared to :class:`pandas.DataFrame`, the :class:`ArchiveDataFrame`
         adds methods and attributes which make it easier to manipulate archive
@@ -1054,7 +1054,7 @@ def as_pandas(self, include_solutions=True, include_metadata=False):
 
         measures_batch = self._measures_arr[indices]
         for i in range(self._measure_dim):
-            data[f"measure_{i}"] = measures_batch[:, i]
+            data[f"measures_{i}"] = measures_batch[:, i]
 
         data["objective"] = self._objective_arr[indices]
 

ribs/archives/_archive_data_frame.py

@@ -141,7 +141,7 @@ def measures_batch(self):
         Returns:
             (n, measure_dim) numpy.ndarray: See above.
         """
-        cols = [c for c in self if c.startswith("measure_")]
+        cols = [c for c in self if c.startswith("measures_")]
         return self[cols].to_numpy(copy=True) if cols else None
 
     def index_batch(self):

tests/archives/archive_base_test.py

@@ -392,7 +392,7 @@ def test_as_pandas(name, with_elite, include_solutions, include_metadata,
     data = get_archive_data(name, dtype)
 
     # Set up expected columns and data types.
-    measure_cols = [f"measure_{i}" for i in range(len(data.measures))]
+    measure_cols = [f"measures_{i}" for i in range(len(data.measures))]
     expected_cols = ["index"] + measure_cols + ["objective"]
     expected_dtypes = [np.int32, *[dtype for _ in measure_cols], dtype]
     if include_solutions:
@@ -432,4 +432,4 @@ def test_as_pandas(name, with_elite, include_solutions, include_metadata,
             expected_data += list(data.solution)
         if include_metadata:
             expected_data.append(data.metadata)
-        assert (df.loc[0, "measure_0":] == expected_data).all()
+        assert (df.loc[0, "measures_0":] == expected_data).all()

tests/archives/archive_data_frame_test.py

@@ -27,7 +27,7 @@ def df(data):
     return ArchiveDataFrame({
         "index": index_batch,
         "objective": objective_batch,
-        "measure_0": measures_batch[:, 0],
+        "measures_0": measures_batch[:, 0],
         "solution_0": solution_batch[:, 0],
         "metadata": metadata_batch,
     })
@@ -55,7 +55,7 @@ def test_batch_methods(data, df):
 
 @pytest.mark.parametrize(
     "remove",
-    ["index", "objective", "measure_0", "metadata", "solution_0"],
+    ["index", "objective", "measures_0", "metadata", "solution_0"],
     ids=["index", "objective", "measures", "metadata", "solutions"],
 )
 def test_batch_methods_can_be_none(df, remove):
@@ -65,7 +65,7 @@ def test_batch_methods_can_be_none(df, remove):
     method = {
         "solution_0": df.solution_batch,
         "objective": df.objective_batch,
-        "measure_0": df.measures_batch,
+        "measures_0": df.measures_batch,
         "index": df.index_batch,
         "metadata": df.metadata_batch,
     }[remove]

tests/archives/sliding_boundaries_archive_test.py

@@ -133,7 +133,7 @@ def test_initial_remap():
 
     # Check that all the measures are as expected.
     pandas_measures = archive.as_pandas(include_solutions=False)[[
-        "measure_0", "measure_1"
+        "measures_0", "measures_1"
     ]]
     measures = list(pandas_measures.itertuples(name=None, index=False))
     assert np.isclose(sorted(measures), sorted(expected_measures)).all()

tutorials/tom_cruise_dqd.ipynb

@@ -929,22 +929,22 @@
     "\n",
     "# Compute the min and max measures for which solutions were found.\n",
     "measure_bounds = [\n",
-    "    (round(df['measure_0'].min(), 2), round(df['measure_0'].max(), 2)),\n",
-    "    (round(df['measure_1'].min(), 2), round(df['measure_1'].max(), 2)),\n",
+    "    (round(df['measures_0'].min(), 2), round(df['measures_0'].max(), 2)),\n",
+    "    (round(df['measures_1'].min(), 2), round(df['measures_1'].max(), 2)),\n",
     "]\n",
-    "delta_measure_0 = round((measure_bounds[0][1] - measure_bounds[0][0])/img_freq[0], 2)\n",
-    "delta_measure_1 = round((measure_bounds[1][1] - measure_bounds[1][0])/img_freq[1], 2)\n",
+    "delta_measures_0 = round((measure_bounds[0][1] - measure_bounds[0][0])/img_freq[0], 2)\n",
+    "delta_measures_1 = round((measure_bounds[1][1] - measure_bounds[1][0])/img_freq[1], 2)\n",
     "\n",
     "for col, row in itertools.product(range(img_freq[1]), range(img_freq[0])):\n",
     "    # Compute bounds of a box in measure space.\n",
-    "    measure_0_low = round(measure_bounds[0][0] + delta_measure_0*row, 2)\n",
-    "    measure_0_high = round(measure_bounds[0][0] + delta_measure_0*(row+1), 2)\n",
-    "    measure_1_low = round(measure_bounds[1][0] + delta_measure_1*col, 2)\n",
-    "    measure_1_high = round(measure_bounds[1][0] + delta_measure_1*(col+1), 2)\n",
+    "    measures_0_low = round(measure_bounds[0][0] + delta_measures_0*row, 2)\n",
+    "    measures_0_high = round(measure_bounds[0][0] + delta_measures_0*(row+1), 2)\n",
+    "    measures_1_low = round(measure_bounds[1][0] + delta_measures_1*col, 2)\n",
+    "    measures_1_high = round(measure_bounds[1][0] + delta_measures_1*(col+1), 2)\n",
     "\n",
     "    # Query for a solution with measures within this box.\n",
-    "    query_string = (f\"{measure_0_low} <= measure_0 & measure_0 <= {measure_0_high} & \"\n",
-    "                    f\"{measure_1_low} <= measure_1 & measure_1 <= {measure_1_high}\")\n",
+    "    query_string = (f\"{measures_0_low} <= measures_0 & measures_0 <= {measures_0_high} & \"\n",
+    "                    f\"{measures_1_low} <= measures_1 & measures_1 <= {measures_1_high}\")\n",
     "    df_box = df.query(query_string)\n",
     "\n",
     "    if not df_box.empty:\n",