finalize basic examples

Author: LennartPurucker

examples/Basics/introduction_tutorial.py

@@ -1,17 +1,16 @@
 # %% [markdown]
-# # Introduction Tutorial & Setup
 # An example how to set up OpenML-Python followed up by a simple example.
 
 # %% [markdown]
-# # Installation
+# ## Installation
 # Installation is done via ``pip``:
 #
 # ```bash
 # pip install openml
 # ```
 
 # %% [markdown]
-# # Authentication
+# ## Authentication
 #
 # For certain functionality, such as uploading tasks or datasets, users have to
 # sing up. Only accessing the data on OpenML does not require an account!
@@ -42,7 +41,7 @@
 openml.config.apikey = "YOURKEY"
 
 # %% [markdown]
-# # Caching
+# ## Caching
 # When downloading datasets, tasks, runs and flows, they will be cached to
 # retrieve them without calling the server later. As with the API key,
 # the cache directory can be either specified through the config file or

examples/Basics/simple_datasets_tutorial.py

@@ -1,5 +1,4 @@
 # %% [markdown]
-# # Datasets
 # A basic tutorial on how to list, load and visualize datasets.
 #
 # In general, we recommend working with tasks, so that the results can
@@ -34,14 +33,11 @@
 
 # %% [markdown]
 # ## Load a dataset
-# X - A dataframe where each row represents one example with
-# the corresponding feature values.
-#
-# y - the classes for each example
-#
-# categorical_indicator - a list that indicates which feature is categorical
-#
-# attribute_names - the names of the features for the examples (X) and
+# * `X` - A dataframe where each row represents one example with
+#   the corresponding feature values.
+# * `y` - the classes for each example
+# * `categorical_indicator` - a list that indicates which feature is categorical
+# * `attribute_names` - the names of the features for the examples (X) and
 # target feature (y)
 
 # %%

examples/Basics/simple_flows_and_runs_tutorial.py

@@ -1,5 +1,4 @@
 # %% [markdown]
-# # Flows and Runs
 # A simple tutorial on how to upload results from a machine learning experiment to OpenML.
 
 # %%
@@ -79,7 +78,7 @@
 print(f"knn_flow was published with the ID {knn_flow.flow_id}")
 
 # %% [markdown]
-# Second, we create a run to store the results of associated with the flow.
+# Second, we create a run to store the results associated with the flow.
 
 # %%
 

examples/Basics/simple_suites_tutorial.py

@@ -1,5 +1,4 @@
 # %% [markdown]
-# # Benchmark suites
 # This is a brief showcase of OpenML benchmark suites, which were introduced by
 # [Bischl et al. (2019)](https://arxiv.org/abs/1708.03731v2). Benchmark suites standardize the
 # datasets and splits to be used in an experiment or paper. They are fully integrated into OpenML
@@ -9,8 +8,7 @@
 import openml
 
 # %% [markdown]
-# OpenML-CC18
-# ===========
+# ## OpenML-CC18
 #
 # As an example we have a look at the OpenML-CC18, which is a suite of 72 classification datasets
 # from OpenML which were carefully selected to be usable by many algorithms. These are all datasets
@@ -30,8 +28,7 @@
 # In this example, we'll focus on how to use benchmark suites in practice.
 
 # %% [markdown]
-# Downloading benchmark suites
-# ============================
+# ## Downloading benchmark suites
 
 # %%
 suite = openml.study.get_suite(99)

examples/Basics/simple_tasks_tutorial.py

@@ -1,22 +1,27 @@
 # %% [markdown]
-# # Tasks
+# A brief example on how to use tasks from OpenML.
 
 # %%
 
 import openml
 
 # %% [markdown]
-# # Get a [task](https://docs.openml.org/concepts/tasks/) for
+# Get a [task](https://docs.openml.org/concepts/tasks/) for
 # [supervised classification on credit-g](https://www.openml.org/search?type=task&id=31&source_data.data_id=31):
 
+# %%
 task = openml.tasks.get_task(31)
 
 # %% [markdown]
 # Get the dataset and its data from the task.
+
+# %%
 dataset = task.get_dataset()
 X, y, categorical_indicator, attribute_names = dataset.get_data(target=task.target_name)
 
 # %% [markdown]
 # Get the first out of the 10 cross-validation splits from the task.
+
+# %%
 train_indices, test_indices = task.get_train_test_split_indices(fold=0)
 print(train_indices[:10])  # print the first 10 indices of the training set

examples/introduction.py

@@ -1,17 +1,15 @@
 # %% [markdown]
 #
-# # OpenML-Python Examples
-#
 # We provide a set of examples here to get started with OpenML-Python. These examples cover various aspects of using the
 # OpenML API, including downloading datasets, uploading results, and working with tasks.
 #
 # ## Basics
 #
-# 1. [Installing and setting up OpenML-Python](../Basics/introduction_tutorial.py)
-# 2. [Downloading datasets](../Basics/simple_dataset_tutorial.py)
-# 3. [Using tasks](../Basics/simple_tasks_tutorial.py)
-# 3. [Uploading experiment results](./.Basics/simple_flows_and_runs_tutorial.py)
-# 4. [Working with collections of tasks](../Basics/simple_suites_tutorial.py)
+# 1. [Installing and setting up OpenML-Python](../Basics/introduction_tutorial/)
+# 2. [Downloading datasets](../Basics/simple_datasets_tutorial/)
+# 3. [Using tasks](../Basics/simple_tasks_tutorial/)
+# 3. [Uploading experiment results](../Basics/simple_flows_and_runs_tutorial/)
+# 4. [Working with collections of tasks](../Basics/simple_suites_tutorial/)
 #
 # ## Advanced
 # 1 .

mkdocs.yml

@@ -50,8 +50,7 @@ nav:
         - Tasks: examples/Basics/simple_tasks_tutorial.py
         - Flows and Runs: examples/Basics/simple_flows_and_runs_tutorial.py
         - Suites: examples/Basics/simple_suites_tutorial.py
-    - Extended: examples/30_extended/
-    - Paper: examples/40_paper/
+    - Advanced: examples/Advanced/
   - Extensions: extensions.md
   - Details: details.md
   - API: reference/