Miscellaneous Fixes (#447)

## Description

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

Random fixes across documentation.

## TODO

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

- [x] Fix ArchiveDataframe link in visualize docstrings
- [x] Refer to solution correctly in dqd tutorial visualization
- [x] Remove unused variables in dqd tutorial
- [x] Fix import in mnist training
- [x] Pin Brax to 0.9.3 due to mujoco-mjx installation issues

## 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

.github/workflows/testing.yml

@@ -68,7 +68,8 @@ jobs:
       - name: Test visualize extra
         run: pytest tests/visualize
       - name: Install QDax
-        run: pip install qdax
+        # Brax 0.9.4 fails with Python 3.8 due to mujoco-mjx
+        run: pip install qdax brax==0.9.3
       - name: Test visualize extra for QDax
         run: pytest tests/visualize_qdax
       - name: Install cma dep

ribs/visualize/_cvt_archive_3d_plot.py

@@ -155,10 +155,10 @@ def cvt_archive_3d_plot(
             this argument instead of the data currently in the archive. This
             data can be obtained by, for instance, calling
             :meth:`ribs.archives.ArchiveBase.data` with ``return_type="pandas"``
-            and modifying the resulting :class:`ArchiveDataFrame`. Note that, at
-            a minimum, the data must contain columns for index, objective, and
-            measures. To display a custom metric, replace the "objective"
-            column.
+            and modifying the resulting
+            :class:`~ribs.archives.ArchiveDataFrame`. Note that, at a minimum,
+            the data must contain columns for index, objective, and measures. To
+            display a custom metric, replace the "objective" column.
         measure_order (array-like of int): Specifies the axes order for plotting
             the measures. By default, the first measure (measure 0) in the
             archive appears on the x-axis, the second (measure 1) on y-axis, and

ribs/visualize/_cvt_archive_heatmap.py

@@ -103,10 +103,10 @@ def cvt_archive_heatmap(archive,
             this argument instead of the data currently in the archive. This
             data can be obtained by, for instance, calling
             :meth:`ribs.archives.ArchiveBase.data` with ``return_type="pandas"``
-            and modifying the resulting :class:`ArchiveDataFrame`. Note that, at
-            a minimum, the data must contain columns for index, objective, and
-            measures. To display a custom metric, replace the "objective"
-            column.
+            and modifying the resulting
+            :class:`~ribs.archives.ArchiveDataFrame`. Note that, at a minimum,
+            the data must contain columns for index, objective, and measures. To
+            display a custom metric, replace the "objective" column.
         transpose_measures (bool): By default, the first measure in the archive
             will appear along the x-axis, and the second will be along the
             y-axis. To switch this behavior (i.e. to transpose the axes), set

ribs/visualize/_grid_archive_heatmap.py

@@ -91,10 +91,10 @@ def grid_archive_heatmap(archive,
             this argument instead of the data currently in the archive. This
             data can be obtained by, for instance, calling
             :meth:`ribs.archives.ArchiveBase.data` with ``return_type="pandas"``
-            and modifying the resulting :class:`ArchiveDataFrame`. Note that, at
-            a minimum, the data must contain columns for index, objective, and
-            measures. To display a custom metric, replace the "objective"
-            column.
+            and modifying the resulting
+            :class:`~ribs.archives.ArchiveDataFrame`. Note that, at a minimum,
+            the data must contain columns for index, objective, and measures. To
+            display a custom metric, replace the "objective" column.
         transpose_measures (bool): By default, the first measure in the archive
             will appear along the x-axis, and the second will be along the
             y-axis. To switch this behavior (i.e. to transpose the axes), set

ribs/visualize/_parallel_axes_plot.py

@@ -83,10 +83,10 @@ def parallel_axes_plot(archive,
             this argument instead of the data currently in the archive. This
             data can be obtained by, for instance, calling
             :meth:`ribs.archives.ArchiveBase.data` with ``return_type="pandas"``
-            and modifying the resulting :class:`ArchiveDataFrame`. Note that, at
-            a minimum, the data must contain columns for index, objective, and
-            measures. To display a custom metric, replace the "objective"
-            column.
+            and modifying the resulting
+            :class:`~ribs.archives.ArchiveDataFrame`. Note that, at a minimum,
+            the data must contain columns for index, objective, and measures. To
+            display a custom metric, replace the "objective" column.
         measure_order (list of int or list of (int, str)): If this is a list
             of ints, it specifies the axes order for measures (e.g. ``[2, 0,
             1]``). If this is a list of tuples, each tuple takes the form

ribs/visualize/_sliding_boundaries_archive_heatmap.py

@@ -69,10 +69,10 @@ def sliding_boundaries_archive_heatmap(archive,
             this argument instead of the data currently in the archive. This
             data can be obtained by, for instance, calling
             :meth:`ribs.archives.ArchiveBase.data` with ``return_type="pandas"``
-            and modifying the resulting :class:`ArchiveDataFrame`. Note that, at
-            a minimum, the data must contain columns for index, objective, and
-            measures. To display a custom metric, replace the "objective"
-            column.
+            and modifying the resulting
+            :class:`~ribs.archives.ArchiveDataFrame`. Note that, at a minimum,
+            the data must contain columns for index, objective, and measures. To
+            display a custom metric, replace the "objective" column.
         transpose_measures (bool): By default, the first measure in the archive
             will appear along the x-axis, and the second will be along the
             y-axis. To switch this behavior (i.e. to transpose the axes), set

tutorials/mnist/train_mnist_classifier.py

@@ -18,8 +18,8 @@
 import sys
 
 import torch
-import torch.nn as nn
 import torchvision
+from torch import nn
 
 MEAN_TRANSFORM = 0.1307
 STD_DEV_TRANSFORM = 0.3081

tutorials/tom_cruise_dqd.ipynb

@@ -950,7 +950,10 @@
     "    if not df_box.empty:\n",
     "        # Select the solution with highest objective in the box.\n",
     "        max_obj_idx = df_box['objective'].argmax()\n",
-    "        sol = df_box.loc[:, \"solution_0\":].iloc[max_obj_idx]\n",
+    "        # StyleGAN solutions have 7,168 dimensions, so the final solution col\n",
+    "        # is solution_7167.\n",
+    "        sol = df_box.loc[:, \"solution_0\":\"solution_7167\"].iloc[max_obj_idx]\n",
+    "\n",
     "\n",
     "        # Convert the latent vector solution to an image.\n",
     "        img = classifier.generate_image(np.array(sol))[0].cpu().detach()\n",
@@ -996,11 +999,7 @@
    ],
    "source": [
     "def create_archive_tick_labels(measure_range, num_ticks):\n",
-    "    low_pos = measure_range[0]\n",
-    "    high_pos = measure_range[1]\n",
-    "\n",
     "    delta = (measure_range[1]-measure_range[0])/num_ticks\n",
-    "\n",
     "    ticklabels = [\n",
     "        round( delta*p + measure_range[0], 3)\n",
     "        for p in range(num_ticks+1)\n",