Fix docs

optuna/samplers/_nsgaiii.py

@@ -65,14 +65,14 @@ class NSGAIIISampler(BaseSampler):
             `target` points since the algorithm prioritizes individuals around reference points.
 
         dividing_parameter:
-            An parameter to determine the density of default reference points. This parameter
+            A parameter to determine the density of default reference points. This parameter
             determines how many divisions are made between reference points on each axis. The
             smaller this value is, the less reference points you have. The default value is 3.
             Note that this parameter is not used when ``reference_points`` is not :obj:`None`.
 
     .. note::
         Other parameters than ``reference_points`` and ``dividing_parameter`` are the same as
-        :class:`~optuna.samplers.nsgaii.NSGAIISampler`.
+        :class:`~optuna.samplers.NSGAIISampler`.
 
     """
 

optuna/terminator/callback.py

@@ -23,7 +23,7 @@ class TerminatorCallback:
             A terminator object which determines whether to terminate the optimization by
             assessing the room for optimization and statistical error. Defaults to a
             :class:`~optuna.terminator.Terminator` object with default
-            improvement_evaluator and error_evaluator.
+            ``improvement_evaluator`` and ``error_evaluator``.
 
     Example:
 

optuna/terminator/erroreval.py

@@ -47,7 +47,7 @@ def evaluate(
 
         Args:
             trials:
-                A list of trials to consider. The best trial in `trials` is used to compute the
+                A list of trials to consider. The best trial in ``trials`` is used to compute the
                 statistical error.
 
             study_direction:

optuna/terminator/terminator.py

@@ -90,7 +90,7 @@ class Terminator(BaseTerminator):
                     break
 
     .. seealso::
-        Please refer to :class:`~optuna.terminator.TerminationCallback` for how to use
+        Please refer to :class:`~optuna.terminator.TerminatorCallback` for how to use
         the terminator mechanism with the :func:`~optuna.study.Study.optimize` method.
 
     """

optuna/visualization/_terminator_improvement.py

@@ -43,10 +43,10 @@ def plot_terminator_improvement(
     """Plot the potentials for future objective improvement.
 
     This function visualizes the objective improvement potentials, evaluated
-    with `improvement_evaluator`.
+    with ``improvement_evaluator``.
     It helps to determine whether we should continue the optimization or not.
     You can also plot the error evaluated with
-    `error_evaluator` if the `plot_error` argument is set to :obj:`True`.
+    ``error_evaluator`` if the ``plot_error`` argument is set to :obj:`True`.
     Note that this function may take some time to compute
     the improvement potentials.
 
@@ -94,7 +94,7 @@ def objective(trial):
             for their improvement.
         plot_error:
             A flag to show the error. If it is set to :obj:`True`, errors
-            evaluated by `error_evaluator` are also plotted as line graph.
+            evaluated by ``error_evaluator`` are also plotted as line graph.
             Defaults to :obj:`False`.
         improvement_evaluator:
             An object that evaluates the improvement of the objective function.

optuna/visualization/matplotlib/_terminator_improvement.py

@@ -34,10 +34,10 @@ def plot_terminator_improvement(
     """Plot the potentials for future objective improvement.
 
     This function visualizes the objective improvement potentials, evaluated
-    with `improvement_evaluator`.
+    with ``improvement_evaluator``.
     It helps to determine whether we should continue the optimization or not.
     You can also plot the error evaluated with
-    `error_evaluator` if the `plot_error` argument is set to :obj:`True`.
+    ``error_evaluator`` if the ``plot_error`` argument is set to :obj:`True`.
     Note that this function may take some time to compute
     the improvement potentials.
 
@@ -83,7 +83,7 @@ def objective(trial):
             A :class:`~optuna.study.Study` object whose trials are plotted for their improvement.
         plot_error:
             A flag to show the error. If it is set to :obj:`True`, errors
-            evaluated by `error_evaluator` are also plotted as line graph.
+            evaluated by ``error_evaluator`` are also plotted as line graph.
             Defaults to :obj:`False`.
         improvement_evaluator:
             An object that evaluates the improvement of the objective function.