Fix README links (#827)

* Fixes the navigation links
* Turn mentioned code components into links (clickable is always better
than non-clickable)]
* Enables linkcheck for components pages
* Make all existing links linkcheck-compatible

Author: AdrianSosic

README.md

@@ -15,13 +15,11 @@
 [![Logo](https://raw.githubusercontent.com/emdgroup/baybe/main/docs/_static/banner2.svg)](https://github.com/emdgroup/baybe/)
 
  
-<a href="https://emdgroup.github.io/baybe/">Homepage<a/>
+[Documentation](https://emdgroup.github.io/baybe/)
 &nbsp;•&nbsp;
-<a href="https://emdgroup.github.io/baybe/stable/userguide/userguide.html">User Guide<a/>
+[API Reference](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.html)
 &nbsp;•&nbsp;
-<a href="https://emdgroup.github.io/baybe/stable/_autosummary/baybe.html">Documentation<a/>
-&nbsp;•&nbsp;
-<a href="https://emdgroup.github.io/baybe/stable/misc/contributing_link.html">Contribute<a/>
+[Contribute](https://emdgroup.github.io/baybe/stable/misc/contributing_link.html)
 &nbsp;
 </div>
 
@@ -63,53 +61,50 @@ BayBE offers a range of ✨**built&#8209;in&nbsp;features**✨, including:
     🛠️ Flexible modeling options
   </summary>
   <div style="padding: 10px;">
-    <ul>
-      <li>Use both continuous and discrete parameters within a single <a href="https://emdgroup.github.io/baybe/stable/examples/Searchspaces/hybrid_space.html">hybrid search space</a>.</li>
-      <li>Exclude undesired or impossible parameter configurations (e.g., to define a maximal number of mixture components) using <a href="https://emdgroup.github.io/baybe/stable/components/constraints.html">constraints</a>.</li>
-      <li>Choose between different optimization strategies to balance exploration and exploitation of the search space:
-        <ul>
-          <li>Smartly acquire training data for model building via <a href="https://emdgroup.github.io/baybe/stable/concepts/active_learning.html">active learning</a>.</li>
-          <li>Conduct AB testing via <a href="https://emdgroup.github.io/baybe/stable/examples/Multi_Armed_Bandit/Multi_Armed_Bandit.html">bandit models</a>.</li>
-        </ul>
-      </li>
-      <li>Specify the desired target value via <a href="https://emdgroup.github.io/baybe/stable/components/transformations.html">target transformations</a>.</li>
-      <li>Optimize multiple targets at the same time via <a href="https://emdgroup.github.io/baybe/stable/components/objectives.html#paretoobjective">Pareto optimization</a> or <a href="https://emdgroup.github.io/baybe/stable/components/objectives.html#desirabilityobjective">desirability scalarization</a>.</li>
-    </ul>
+
+  - Use both continuous and discrete parameters within a single [hybrid search space](https://emdgroup.github.io/baybe/stable/examples/Searchspaces/hybrid_space.html).
+  - Exclude undesired or impossible parameter configurations (e.g., to define a maximal number of mixture components) using [constraints](https://emdgroup.github.io/baybe/stable/components/constraints.html).
+  - Choose between different optimization strategies to balance exploration and exploitation of the search space:
+    - Smartly acquire training data for model building via [active learning](https://emdgroup.github.io/baybe/stable/concepts/active_learning.html).
+    - Conduct AB testing via [bandit models](https://emdgroup.github.io/baybe/stable/examples/Multi_Armed_Bandit/Multi_Armed_Bandit.html).
+  - Specify the desired target value via [target transformations](https://emdgroup.github.io/baybe/stable/components/transformations.html).
+  - Optimize multiple targets at the same time via [Pareto optimization](https://emdgroup.github.io/baybe/stable/components/objectives.html#paretoobjective) or [desirability scalarization](https://emdgroup.github.io/baybe/stable/components/objectives.html#desirabilityobjective).
+
   </div>
 </details>
 <details style="border:2px solid #535353; border-radius: 7px; margin: 5px;">
   <summary style="background-color: #535353; color: white; padding: 10px; border-radius: 5px; cursor: pointer;">
     📚 Mechanisms for leveraging additional information
   </summary>
   <div style="padding: 10px;">
-    <ul>
-      <li>Capture relationships between categories via <a href="https://emdgroup.github.io/baybe/stable/components/parameters.html#customdiscreteparameter">custom encodings for categorical</a> data.</li>
-      <li>Use built-in <a href="https://emdgroup.github.io/baybe/stable/components/parameters.html#substanceparameter">chemical encodings</a> for chemistry-related parameters.</li>
-      <li>Add mechanistic process understanding via <a href="https://emdgroup.github.io/baybe/stable/components/surrogates.html#using-custom-models">custom surrogate</a> models.</li>
-      <li>Leverage additional data from similar campaigns to accelerate optimization via <a href="https://emdgroup.github.io/baybe/stable/concepts/transfer_learning.html">transfer learning</a>.</li>
-    </ul>
+
+  - Capture relationships between categories via [custom encodings for categorical](https://emdgroup.github.io/baybe/stable/components/parameters.html#customdiscreteparameter) data.
+  - Use built-in [chemical encodings](https://emdgroup.github.io/baybe/stable/components/parameters.html#substanceparameter) for chemistry-related parameters.
+  - Add mechanistic process understanding via [custom surrogate](https://emdgroup.github.io/baybe/stable/components/surrogates.html#custom-surrogates) models.
+  - Leverage additional data from similar campaigns to accelerate optimization via [transfer learning](https://emdgroup.github.io/baybe/stable/concepts/transfer_learning.html).
+
   </div>
 </details>
 <details style="border:2px solid #535353; border-radius: 7px; margin: 5px;">
   <summary style="background-color: #535353; color: white; padding: 10px; border-radius: 5px; cursor: pointer;">
     🔗 Advanced optimization workflows
   </summary>
   <div style="padding: 10px;">
-    <ul>
-      <li>Run campaigns <a href="https://emdgroup.github.io/baybe/stable/concepts/async.html">asynchronously</a> with partial measurements and pending experiments.</li>
-      <li>Store BayBE objects and use API wrappers with the <a href="https://emdgroup.github.io/baybe/stable/concepts/serialization.html">serialization</a> functionality.</li>
-    </ul>
+
+  - Run campaigns [asynchronously](https://emdgroup.github.io/baybe/stable/concepts/async.html) with partial measurements and pending experiments.
+  - Store BayBE objects and use API wrappers with the [serialization](https://emdgroup.github.io/baybe/stable/concepts/serialization.html) functionality.
+
   </div>
 </details>
 <details style="border:2px solid #535353; border-radius: 7px; margin: 5px;">
   <summary style="background-color: #535353; color: white; padding: 10px; border-radius: 5px; cursor: pointer;">
     🔍 Performance evaluation tools
   </summary>
   <div style="padding: 10px;">
-    <ul>
-      <li>Gain <a href="https://emdgroup.github.io/baybe/stable/components/insights.html">insights</a> about the optimization campaigns by analyzing model behavior and feature importance.</li>
-      <li>Conduct benchmarks to select between different Bayesian optimization settings via <a href="https://emdgroup.github.io/baybe/stable/concepts/simulation.html">backtesting</a>.</li>
-    </ul>
+
+  - Gain [insights](https://emdgroup.github.io/baybe/stable/components/insights.html) about the optimization campaigns by analyzing model behavior and feature importance.
+  - Conduct benchmarks to select between different Bayesian optimization settings via [backtesting](https://emdgroup.github.io/baybe/stable/concepts/simulation.html).
+
   </div>
 </details>
 
@@ -143,8 +138,10 @@ For more information on this step, see our
 
 ### Defining the Optimization Objective
 
-In BayBE's language, the reaction yield can be represented as a `NumericalTarget`,
-which we wrap into a `SingleTargetObjective`:
+In BayBE's language, the reaction yield can be represented as a
+[`NumericalTarget`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.targets.numerical.NumericalTarget.html),
+which we wrap into a
+[`SingleTargetObjective`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.objectives.single.SingleTargetObjective.html):
 
 ```python
 from baybe.targets import NumericalTarget
@@ -153,18 +150,21 @@ from baybe.objectives import SingleTargetObjective
 target = NumericalTarget(name="Yield")
 objective = SingleTargetObjective(target=target)
 ```
-In cases where we are confronted with multiple (potentially conflicting) targets 
-(e.g., yield vs selectivity),
-the `ParetoObjective` or `DesirabilityObjective` can be used to define how the targets should be balanced.
-For more details, see the
+In cases where we are confronted with multiple (potentially conflicting) targets (e.g.,
+yield vs selectivity), the
+[`ParetoObjective`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.objectives.pareto.ParetoObjective.html)
+or
+[`DesirabilityObjective`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.objectives.desirability.DesirabilityObjective.html)
+can be used to define how the targets should be balanced. For more details, see the
 [objectives section](https://emdgroup.github.io/baybe/stable/components/objectives.html)
 of the user guide.
 
 ### Defining the Search Space
 
 Next, we inform BayBE about the available "control knobs", that is, the underlying
-reaction parameters we can tune to optimize the yield. 
-In this case we tune granularity, pressure and solvent, each being encoded as a `Parameter`.
+reaction parameters we can tune to optimize the yield. In this case we tune granularity,
+pressure and solvent, each being encoded as a
+[`Parameter`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.parameters.base.Parameter.html).
 We also need to specify which values individual parameters can take.
 
 ```python
@@ -208,7 +208,8 @@ relationships between our parameters. Details can be found in the
 In this example, we assume no further constraints.
 
 With the parameter definitions at hand, we can now create our
-`SearchSpace` based on the Cartesian product of all possible parameter values:
+[`SearchSpace`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.searchspace.core.SearchSpace.html)
+based on the Cartesian product of all possible parameter values:
 
 ```python
 from baybe.searchspace import SearchSpace
@@ -226,7 +227,7 @@ As an optional step, we can specify details on how the optimization of the exper
 performed. If omitted, BayBE will choose a default Bayesian optimization setting.
 
 For our example, we combine two recommenders via a so-called meta recommender named
-`TwoPhaseMetaRecommender`:
+[`TwoPhaseMetaRecommender`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.recommenders.meta.sequential.TwoPhaseMetaRecommender.html):
 
 1. In cases where no measurements have been made prior to the interaction with BayBE,
    the parameters will be recommended with the `initial_recommender`.
@@ -252,7 +253,9 @@ of the user guide.
 
 ### The Optimization Loop
 
-We can now construct a `Campaign` that performs the Bayesian optimization of the experimental configurations:
+We can now construct a
+[`Campaign`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.campaign.Campaign.html)
+that performs the Bayesian optimization of the experimental configurations:
 
 ```python
 from baybe import Campaign
@@ -288,7 +291,9 @@ measure the reaction yield.
 29        fine            5.0  Solvent B
 ```
 
-Next, we need to conduct the recommended experiments and record the corresponding `Target` values.
+Next, we need to conduct the recommended experiments and record the corresponding
+[`Target`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.targets.base.Target.html)
+values.
 
 ```python
 df["Yield"] = [
@@ -305,14 +310,17 @@ print(df)
 29        fine            5.0  Solvent B   59.4
 ```
 
-Now, we can add the newly measured `Target` values to the `Campaign`:
+Now, we can add the newly measured
+[`Target`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.targets.base.Target.html)
+values to the
+[`Campaign`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.campaign.Campaign.html):
 
 ```python
 campaign.add_measurements(df)
 ```
 
 With the newly provided data, BayBE can produce a refined recommendation for the next iteration.
-This loop typically continues until a desired `Target` value is achieved in the experiment.
+This loop typically continues until a desired [`Target`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.targets.base.Target.html) value is achieved in the experiment.
 
 ### Inspect the Progress of the Experimental Configuration Optimization
 
@@ -322,14 +330,19 @@ by tuning the solvent, base and ligand
 Each line shows the best target value that was cumulatively achieved after a given number of experimental iterations.
 
 
-Different lines show outcomes of `Campaigns` with different settings.
+Different lines show outcomes of
+[`Campaign`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.campaign.Campaign.html)s
+with different settings.
 
 ![Substance Encoding Example](./examples/Backtesting/full_lookup_light.svg)
 
-In particular, the five `Campaigns` differ in how molecules are encoded within 
-each chemical `Parameter`. Instead of simply one-hot encoding each SMILES string,
-`SubstanceParameter` can be used to directly compute chemical fingerprints from 
-the input SMILES.
+In particular, the five
+[`Campaign`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.campaign.Campaign.html)s
+differ in how molecules are encoded within each chemical
+[`Parameter`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.parameters.base.Parameter.html).
+Instead of simply one-hot encoding each SMILES string,
+[`SubstanceParameter`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.parameters.substance.SubstanceParameter.html)
+can be used to directly compute chemical fingerprints from the input SMILES.
 We can see that optimization is more efficient when 
 using chemical encodings (e.g., *MORDRED*) rather than encoding categories with *one-hot* encoding. The latter is, in fact, no better than *randomly* suggesting parameter configurations at each experimental iteration.
 
@@ -396,7 +409,7 @@ pip install 'baybe[chem,simulation]'
 The available groups are:
 - `extras`: Installs all dependencies required for optional features.
 - `benchmarking`: Required for running the benchmarking module.
-- `chem`: Cheminformatics utilities (e.g. for the `SubstanceParameter`).
+- `chem`: Cheminformatics utilities (e.g. for the [`SubstanceParameter`](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.parameters.substance.SubstanceParameter.html)).
 - `docs`: Required for creating the documentation.
 - `examples`: Required for running the examples/streamlit.
 - `lint`: Required for linting and formatting.

docs/conf.py

@@ -204,8 +204,6 @@
 linkcheck_ignore = [
     r"https://github.com/b-shields/edbo/blob*",
     r"https://doi.org/10.26434/chemrxiv.10001986/v2",
-    # New components/ pages don't exist on stable yet; remove once released
-    r"https://emdgroup\.github\.io/baybe/stable/components/.*",
 ]