Adding an OpenML example

.github/workflows/openml.yml

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+name: openml
+
+on:
+  schedule:
+    - cron: '0 15 * * *'
+  pull_request:
+    paths:
+      - 'openml/**'
+      - '.github/workflows/openml.yml'
+
+jobs:
+  examples:
+    if: (github.event_name == 'schedule' && github.repository == 'optuna/optuna-examples') || (github.event_name != 'schedule')
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        # TODO(c-bata): Add Python 3.9 here after fixing https://github.com/optuna/optuna-examples/issues/307
+        python-version: ['3.10', '3.11', '3.12']
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: setup-python${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install (Python)
+      run: |
+        python -m pip install --upgrade pip
+        pip install --progress-bar off -U setuptools
+        pip install git+https://github.com/optuna/optuna.git
+        python -c 'import optuna'
+        pip install git+https://github.com/optuna/optuna-integration.git
+        python -c 'import optuna_integration'
+
+        pip install -r openml/requirements.txt
+    - name: Run examples
+      run: |
+        python openml/openml_simple.py
+      env:
+        OMP_NUM_THREADS: 1

openml/openml_simple.py

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+"""
+====================================================
+Hyperparameter Optimization Benchmark with OpenML
+====================================================
+
+In this tutorial, we walk through how to conduct hyperparameter optimization experiments using
+OpenML and OptunaHub.
+"""
+
+############################################################################
+# Please make sure to install the dependencies with:
+# ``pip install -r requirements.txt``
+# Then we import all the necessary modules.
+
+# License: BSD 3-Clause
+
+import logging
+
+import optuna
+
+import openml
+from openml.extensions.sklearn import cat
+from openml.extensions.sklearn import cont
+from sklearn.compose import ColumnTransformer
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.impute import SimpleImputer
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OneHotEncoder
+
+
+logger = logging.Logger(name="Experiment Logger", level=1)
+
+# Set your openml api key if you want to upload your results to OpenML (eg:
+# https://openml.org/search?type=run&sort=date) . To get one, simply make an
+# account (you don't need one for anything else, just to upload your results),
+# go to your profile and select the API-KEY.
+# Or log in, and navigate to https://www.openml.org/auth/api-key
+openml.config.apikey = ""
+
+############################################################################
+# Prepare for preprocessors and an OpenML task
+# ============================================
+
+# OpenML contains several key concepts which it needs to make machine learning research shareable.
+# A machine learning experiment consists of one or several runs, which describe the performance of
+# an algorithm (called a flow in OpenML), its hyperparameter settings (called a setup) on a task.
+# A Task is the combination of a dataset, a split and an evaluation metric We choose a dataset from
+# OpenML, (https://www.openml.org/d/1464) and a subsequent task (https://www.openml.org/t/10101) To
+# make your own dataset and task, please refer to
+# https://openml.github.io/openml-python/main/examples/30_extended/create_upload_tutorial.html
+
+# https://www.openml.org/search?type=study&study_type=task&id=218
+task_id = 10101
+seed = 42
+categorical_preproc = (
+    "categorical",
+    OneHotEncoder(sparse_output=False, handle_unknown="ignore"),
+    cat,
+)
+numerical_preproc = ("numerical", SimpleImputer(strategy="median"), cont)
+preproc = ColumnTransformer([categorical_preproc, numerical_preproc])
+
+############################################################################
+# Define a pipeline for the hyperparameter optimization (this is standark for Optuna)
+# =====================================================
+
+# Optuna explanation
+# we follow the `Optuna <https://github.com/optuna/optuna/>`__ search space design.
+
+# OpenML runs
+# We can simply pass the parametrized classifier to `run_model_on_task` to obtain the performance
+# of the pipeline
+# on the specified OpenML task.
+# Do you want to share your results along with an easily reproducible pipeline, you can set an API
+# key and just upload your results.
+# You can find more examples on https://www.openml.org/
+
+
+def objective(trial: optuna.Trial) -> Pipeline:
+    clf = RandomForestClassifier(
+        max_depth=trial.suggest_int("max_depth", 2, 32, log=True),
+        min_samples_leaf=trial.suggest_float("min_samples_leaf", 0.0, 1.0),
+        random_state=seed,
+    )
+    pipe = Pipeline(steps=[("preproc", preproc), ("model", clf)])
+    logger.log(1, f"Running pipeline - {pipe}")
+    run = openml.runs.run_model_on_task(pipe, task=task_id, avoid_duplicate_runs=False)
+
+    logger.log(1, f"Model has been trained - {run}")
+    if openml.config.apikey != "":
+        try:
+            run.publish()
+
+            logger.log(1, f"Run was uploaded to - {run.openml_url}")
+        except Exception as e:
+            logger.log(1, f"Could not publish run - {e}")
+    else:
+        logger.log(
+            0,
+            "If you want to publish your results to OpenML, please set an apikey",
+        )
+    accuracy = max(run.fold_evaluations["predictive_accuracy"][0].values())
+    logger.log(0, f"Accuracy {accuracy}")
+
+    return accuracy
+
+
+############################################################################
+# Optimize the pipeline
+# =====================
+study = optuna.create_study(direction="maximize")
+logger.log(0, f"Study {study}")
+study.optimize(objective, n_trials=15)
+
+############################################################################
+# Visualize the optimization history
+# ==================================
+fig = optuna.visualization.plot_optimization_history(study)
+fig.show()

openml/requirements.txt

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+openml>=0.15.1
+plotly