docs: infrastructure to include notebooks as documentation (#38)

Closes #6.

### Summary of Changes

* Configure `mkdocs-jupyter` to be able to include notebooks in the
documentation
* Add an example notebook but don't include it in the navigation of the
documentation yet
  * Will be done later once we have better data in `safe-ds-examples`

.gitignore

@@ -34,6 +34,8 @@ coverage.xml
 
 # mkdocs
 /site/
+/docs/reference/safeds/
+/docs/reference/SUMMARY.md
 
 # MegaLinter
 report/

docs/reference/generate_reference_pages.py

@@ -37,6 +37,10 @@ def list_class_and_function_names_in_module(module_name: str) -> list[str]:
     # Remove the final "__init__" part
     parts = parts[:-1]
 
+    # Skip private modules
+    if any(part.startswith("_") for part in parts):
+        continue
+
     qualified_name = ".".join(parts)
 
     for name in list_class_and_function_names_in_module(qualified_name):

docs/tutorials/machine_learning.md

@@ -2,7 +2,7 @@
 
 ## Create SupervisedDataset
 
-Here is a short introduction to train and predict with a machine learning model in safe-ds.
+Here is a short introduction to train and predict with a machine learning model in Safe-DS.
 
 First we need to create a [SupervisedDataset][safeds.data.SupervisedDataset] from the training data.
 
@@ -24,7 +24,7 @@ to_be_predicted_table = Table({
 sup_dataset = SupervisedDataset(table, target_column="target")
 ```
 
-[SupervisedDatasets][safeds.data.SupervisedDataset] are used in safe-DS to train supervised machine learning models
+[SupervisedDatasets][safeds.data.SupervisedDataset] are used in Safe-DS to train supervised machine learning models
 (e.g. [RandomForest][safeds.ml.classification.RandomForest] for classification and
 [LinearRegression][safeds.ml.regression.LinearRegression] as a regression model), because they keep track of the target
 vector. A [SupervisedDataset][safeds.data.SupervisedDataset] can be created from a [Table][safeds.data.tabular.Table] and
@@ -37,7 +37,7 @@ train with (the sum of the rows). The `to_predicted_table` is the table we want
 does not contain a target vector.
 
 In order to train the [LinearRegression][safeds.ml.regression.LinearRegression]-model we need to make the following calls
-in safe-DS:
+in Safe-DS:
 
 ```python
 linear_reg_model = LinearRegression()
@@ -46,7 +46,7 @@ linear_reg_model.fit(sup_dataset)
 
 As we can see, a [LinearRegression][safeds.ml.regression.LinearRegression]-object is created.
 
-In safe-DS machine learning models are separated in different classes where the different fit and predictions methods
+In Safe-DS machine learning models are separated in different classes where the different fit and predictions methods
 are implemented for the given machine learning model.
 
 ## Predicting new values

mkdocs.yml

@@ -56,6 +56,10 @@ plugins:
   - autorefs
   - glightbox
   - search
+  - mkdocs-jupyter:
+      execute: true
+      allow_errors: false
+      include_source: true
 
 watch:
   - src

pyproject.toml

@@ -35,6 +35,8 @@ mkdocs-glightbox = "^0.3.1"
 mkdocs-literate-nav = "^0.6.0"
 mkdocs-material = "^9.1.2"
 mkdocs-section-index = "^0.3.5"
+mkdocs-jupyter = "^0.23.0"
+safe-ds-examples = "^0.2.0"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]