docs: improve structure of plugin docs

docs/plugins/ablation_paths.rst

@@ -16,6 +16,8 @@ To learn more about Ablation Paths, please see the paper
 `Efficient Parameter Importance Analysis via Ablation with Surrogates
 <https://doi.org/10.1609/aaai.v31i1.10657>`_.
 
+.. image:: ../images/plugins/ablation_paths.png
+
 Options
 -------
 * **Objective**: Select the objective function you wish to analyze.
@@ -42,5 +44,3 @@ To refine your analysis, you can apply filters after calculation:
     If the performance decreases no matter which hyperparameter is chosen next,
     the hyperparameter with the smallest decrease in performance is chosen.
     If two hyperparameters increase or decrease the performance equally, one is chosen randomly.
-
-.. image:: ../images/plugins/ablation_paths.png

docs/plugins/budget_correlation.rst

@@ -20,13 +20,7 @@ This plugin is capable of answering following questions:
 * Should I consider running further optimization at lower budgets to cover more of the
   configuration space?
 
-
-Options
--------
-
-**Objective**: Here, you can select the objective you wish to analyze the correlation between
-budgets for.
-
+.. image:: ../images/plugins/budget_correlation.png
 
 Correlation Interpretation
 --------------------------
@@ -51,6 +45,3 @@ situation. For reference, here’s a guideline for interpreting correlation scor
 * 0.30-0.39: Moderate relationship
 * 0.20-0.29: Weak relationship
 * 0.01-0.19: No or negligible relationship
-
-
-.. image:: ../images/plugins/budget_correlation.png

docs/plugins/configuration_cube.rst

@@ -6,26 +6,33 @@ Naturally, the space of configurations may be very high dimensional and so this
 to investigate the configurations through 2D or 3D *slices* of the configuration space,
 plotting the values of hyperparameters as points in this *slice* of the space.
 
+The Configuration Cube is helpful to visually inspect and answer the following questions:
+
+* How does the influence of one hyperparameter on the objective score compare to others?
+* Does the optimizer show a preference for specific values of hyperparameters over time?
+
+.. image:: ../images/plugins/configuration_cube.png
+
+Options
+-------
 * **Objective**: Select the objective function you wish to analyze. The objective scores will be
   represented using a color scale.
 
 * **Budget**: Select the multi-fidelity budget to be used. The plugin will only consider trials evaluated
   on the selected budget. The *Combined* budget option displays all configurations but shows scores only
   from the highest budget if a configuration was evaluated with multiple budgets.
 
+To refine your analysis, you can apply filters after calculation:
+
 * **Number of Configurations**: This slider will allow you to select the number of configurations to
   display, where the configurations are ordered by they when they were trialed. Adjusting this slider
   helps to visualize how the optimizer's focus on hyperparameters has evolved over time.
 
 * **Hyperparameters**: Choose the slice of the configuration space to visualize. Selecting:
-        * **One hyperparameter** provides a basic 1D view, where configurations are plotted along a single axis.
-        * **Two hyperparameters** generates a 2D grid view, showing configurations in a plane.
-        * **Three hyperparameters** creates a 3D cube view, plotting configurations in three dimensions. In cases where multiple configurations map to the same point, the one with the highest score for the selected objective is displayed. This is particularly noticeable in the 1D view, where many configurations may share the same hyperparameter value.
 
-The Configuration Cube is helpful to visually inspect and answer the following questions:
-
-* How does the influence of one hyperparameter on the objective score compare to others?
-* Does the optimizer show a preference for specific values of hyperparameters over time?
-
-
-.. image:: ../images/plugins/configuration_cube.png
+    * **One hyperparameter** provides a basic 1D view, where configurations are plotted along a single axis.
+    * **Two hyperparameters** generates a 2D grid view, showing configurations in a plane.
+    * **Three hyperparameters** creates a 3D cube view, plotting configurations in three dimensions.
+      In cases where multiple configurations map to the same point, the one with the highest score for the
+      selected objective is displayed. This is particularly noticeable in the 1D view, where many configurations
+      may share the same hyperparameter value.

docs/plugins/configuration_footprint.rst

@@ -31,6 +31,8 @@ algorithm, here `MDS <https://en.wikipedia.org/wiki/Multidimensional_scaling>`_.
 preserve distances as accurately as possible when mapping from a high-dimensional space to a
 lower-dimensional one. While not perfect, this technique provides valuable insights.
 
+.. image:: ../images/plugins/configuration_footprint.png
+
 There are two plots available. Both share the same axes, with points plotted in consistent
 coordinates. Switching between them will provide a comprehensive understanding of your
 configuration footprint:
@@ -65,6 +67,3 @@ representation of your configuration space.
 There will likely be small clusters of **Evaluated Points**, where the optimizer focused on
 finding optimal configurations, as well as scattered points across valid regions to provide a
 broad understanding of scores for various objectives.
-
-
-.. image:: ../images/plugins/configuration_footprint.png

docs/plugins/configurations.rst

@@ -20,6 +20,7 @@ This plugin is capable of answering following questions:
 * Which values have been used for a certain configuration?
 * How can I access the configuration in Python?
 
+.. image:: ../images/plugins/configurations.png
 
 Overview
 --------
@@ -48,6 +49,3 @@ Code
 When a configuration is selected for deployment, it is essential to access it programmatically.
 The code section provides you with the code necessary to retrieve the configuration, facilitating
 its integration and use in your applications.
-
-
-.. image:: ../images/plugins/configurations.png

docs/plugins/cost_over_time.rst

@@ -24,6 +24,8 @@ This plugin is capable of answering the following questions:
 * How does optimizer A compare to optimizer B? Is optimizer A more effective than optimizer B?
 * How long does it take for the optimizer to reach a specific objective value?
 
+.. image:: ../images/plugins/cost_over_time.png
+
 
 Inputs and Filters
 ------------------
@@ -46,6 +48,3 @@ assessing which optimizer converges more quickly and whether the optimizer conve
 
 In contrast to individual runs, groups show the mean and standard deviation across all runs within
 the group. This feature helps you understand the variability and spread among multiple runs.
-
-
-.. image:: ../images/plugins/cost_over_time.png

docs/plugins/importances.rst

@@ -11,6 +11,8 @@ This plugin is capable of answering following questions:
 * How does the importance of each hyperparameter vary across different budgets?
 * How does the local parameter importance compare to the global importance as determined by fANOVA?
 
+.. image:: ../images/plugins/importances.png
+
 Importance Methods
 ------------------
 
@@ -51,8 +53,6 @@ To refine your analysis, you can apply various filters after calculation:
 
 * **Budgets**: Filter the results to view importance scores specific to certain multi-fidelity budgets, allowing you to analyze how hyperparameter importance varies with budget changes.
 
-.. image:: ../images/plugins/importances.png
-
 .. warning::
     As the fANOVA implementation relies on pyrfr, which cannot be applied to constant hyperparameters.
     As a workaround, we remove constant hyperparameters before calculation.

docs/plugins/overview.rst

@@ -25,6 +25,7 @@ This plugin is capable of answering the following questions:
 * Why did certain trials crash?
 * On which multi-fidelity budgets were the configurations evaluated?
 
+.. image:: ../images/plugins/overview.png
 
 Quick Information
 -----------------
@@ -81,6 +82,3 @@ Configuration Space
 Lastly, you can find details about the configuration space that was optimized. This section
 includes information on the name of each parameter, the possible values that could be explored,
 the default value, and whether the optimization was performed on a logarithmic or a linear scale.
-
-
-.. image:: ../images/plugins/overview.png

docs/plugins/parallel_coordinates.rst

@@ -16,6 +16,8 @@ This can help provide insight and answers to various questions:
 * Which hyperparameter values or combinations are associated with high or low final scores?
 * What preferences does the optimizer exhibit for certain hyperparameters?
 
+.. image:: ../images/plugins/parallel_coordinates.png
+
 Options
 -------
 
@@ -56,5 +58,3 @@ The plot offers some interactive features for in-depth analysis:
 * **Re-arranging:** You can drag and reposition the hyperparameters and the objective to better
   group and compare them. This feature helps in customizing the view to focus on relevant hyperparameters
   for your analysis.
-
-.. image:: ../images/plugins/parallel_coordinates.png

docs/plugins/pareto_front.rst

@@ -16,5 +16,4 @@ This plugin can help answer the following questions:
 * Which configuration should I choose if I slightly favor objective A over objective B?
 * How does optimizer A compare to optimizer B? Is optimizer A more effective than optimizer B?
 
-
 .. image:: ../images/plugins/pareto_front.png

docs/plugins/partial_dependencies.rst

@@ -20,6 +20,8 @@ This plugin is capable of answering the following questions:
   accuracy increase if the learning rate decreases?
 * What are the regions of the hyperparameter space that lead to better or worse performance?
 
+.. image:: ../images/plugins/partial_dependencies.png
+
 Options
 -------
 * **Objective**: Select the objective function you wish to analyze. In the one hyperparameter case,
@@ -40,7 +42,3 @@ To refine your analysis, you can apply filters after calculation:
 
 * **Show ICE Curves**: Selection for whether to to show the ICE curves used during computation
   (only for the one hyperparameter case).
-
-
-
-.. image:: ../images/plugins/partial_dependencies.png

docs/plugins/symbolic_explanations.rst

@@ -20,6 +20,8 @@ To learn more about Symbolic Explanations, please see the paper
 `Symbolic Explanations for Hyperparameter Optimization
 <https://openreview.net/forum?id=JQwAc91sg_x>`_.
 
+.. image:: ../images/plugins/symbolic_explanations.png
+
 Options
 -------
 * **Objective:** Select the objective function you wish to analyze. In the one hyperparameter case, the objective is plotted on the y-axis, in the two hyperparameter case, it is represented by the color.
@@ -43,6 +45,3 @@ Furthermore, you can adjust the following parameters of the symbolic regression:
 * **Random seed:** The random seed to be used in the symbolic regression.
 
 * **Metric:** The metric to evaluate the fitness of the formulas.
-
-
-.. image:: ../images/plugins/symbolic_explanations.png