Merge pull request #266 from nabenabe0928/code-fix/organize-dir-struc…

…ture

Organize the directory structure

.github/workflows/base.yml

@@ -33,11 +33,6 @@ jobs:
         pip install -r requirements.txt
     - name: Run examples
       run: |
-        python max_trials_callback.py
-        python enqueue_trial.py
-        python quadratic_simple.py
-        python simple_pruning.py
-        python -m py_compile rapids_simple.py  # due to difficulty importing, compile only
         jupyter nbconvert --to notebook --execute quickstart.ipynb
       env:
         OMP_NUM_THREADS: 1

.github/workflows/basic_and_faq_usages.yml

@@ -0,0 +1,42 @@
+name: basic_and_faq_usages
+
+on:
+  schedule:
+    - cron: '0 15 * * *'
+  pull_request:
+    paths:
+      - 'basic_and_faq_usages/**'
+      - '.github/workflows/basic_and_faq_usages.yml'
+
+jobs:
+  examples:
+    if: (github.event_name == 'schedule' && github.repository == 'optuna/optuna-examples') || (github.event_name != 'schedule')
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ['3.7', '3.8', '3.9', '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 -r basic_and_faq_usages/requirements.txt
+    - name: Run examples
+      run: |
+        python basic_and_faq_usages/max_trials_callback.py
+        python basic_and_faq_usages/enqueue_trial.py
+        python basic_and_faq_usages/quadratic_simple.py
+        python basic_and_faq_usages/simple_pruning.py
+        python basic_and_faq_usages/quadratic_simple_constraint.py
+        python basic_and_faq_usages/quadratic_simple_multi_objective.py
+      env:
+        OMP_NUM_THREADS: 1

.github/workflows/rapids.yml

@@ -0,0 +1,36 @@
+name: base
+
+on:
+  schedule:
+    - cron: '0 15 * * *'
+  pull_request:
+    paths:
+      - 'rapids/**'
+      - '.github/workflows/rapids.yml'
+
+jobs:
+  examples:
+    if: (github.event_name == 'schedule' && github.repository == 'optuna/optuna-examples') || (github.event_name != 'schedule')
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ['3.7', '3.8', '3.9', '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'
+
+    - name: Run examples
+      run: |
+        python -m py_compile rapids/rapids_simple.py  # due to difficulty in importing, compile only
+      env:
+        OMP_NUM_THREADS: 1

README.md

@@ -25,9 +25,9 @@ The examples below provide codeblocks similar to the example above for various d
 
 ### Simple Black-box Optimization
 
-* [Quadratic function](./quadratic_simple.py)
-* [Quadratic multi-objective function](./multi_objective/quadratic_simple.py)
-* [Quadratic function with constraints](./quadratic_simple_constraint.py)
+* [Quadratic function](./basic_and_faq_usages/quadratic_simple.py)
+* [Quadratic multi-objective function](./basic_and_faq_usages/quadratic_simple_multi_objective.py)
+* [Quadratic function with constraints](./basic_and_faq_usages/quadratic_simple_constraint.py)
 
 ### Examples with ML Libraries
 
@@ -47,7 +47,7 @@ The examples below provide codeblocks similar to the example above for various d
 * [PyTorch Ignite](./pytorch/pytorch_ignite_simple.py)
 * [PyTorch Lightning](./pytorch/pytorch_lightning_simple.py)
 * [PyTorch Lightning (DDP)](./pytorch/pytorch_lightning_ddp.py)
-* [RAPIDS](./rapids_simple.py)
+* [RAPIDS](./rapids/rapids_simple.py)
 * [Scikit-learn](./sklearn/sklearn_simple.py)
 * [Scikit-learn OptunaSearchCV](./sklearn/sklearn_optuna_search_cv_simple.py)
 * [Scikit-image](./skimage/skimage_lbp_simple.py)
@@ -72,7 +72,7 @@ The following example demonstrates how to implement an objective function that u
 
 The following example demonstrates how to implement pruning logic with Optuna.
 
-* [Simple pruning (scikit-learn)](./simple_pruning.py)
+* [Simple pruning (scikit-learn)](./basic_and_faq_usages/simple_pruning.py)
 
 In addition, integration modules are available for the following libraries, providing simpler interfaces to utilize pruning.
 
@@ -116,7 +116,7 @@ In addition, integration modules are available for the following libraries, prov
 
 ### An example to enqueue trials with given parameter values
 
-* [Enqueuing trials with given parameters](./enqueue_trial.py)
+* [Enqueuing trials with given parameters](./basic_and_faq_usages/enqueue_trial.py)
 
 ### Examples of aim
 

quadratic_simple_constraint.py → basic_and_faq_usages/quadratic_simple_constraint.py

@@ -56,8 +56,8 @@ def constraints(trial):
     else:
         best_trial_number = sorted(feasible_trial_numbers, key=lambda i: study.trials[i].value)[0]
         best_trial = study.trials[best_trial_number]
-        print(f"Best trial was found at Trial#{best_trial_number}")
-        print(f"  Params: {best_trial.params}")
-        print(f"  Value: {best_trial.value}")
+        print("Best trial was found at Trial#{}".format(best_trial_number))
+        print("  Params: {}".format(best_trial.params))
+        print("  Value: {}".format(best_trial.value))
         c1, c2 = best_trial.system_attrs["constraints"]
-        print(f"  Constraints: {c1=}, {c2=}")
+        print("  Constraints: c1={}, c2={}".format(c1, c2))

basic_and_faq_usages/quadratic_simple_multi_objective.py

@@ -0,0 +1,34 @@
+"""
+Optuna example that optimizes simple quadratic functions.
+
+In this example, we optimize two objective values.
+Unlike single-objective optimization, an optimization gives a trade-off between two objectives.
+As a result, we get the best trade-offs between two objectives, a.k.a Pareto solutions.
+
+"""
+
+import optuna
+
+
+# Define two objective functions.
+# We would like to minimize f1 and maximize f2.
+def objective(trial):
+    x = trial.suggest_float("x", -100, 100)
+    y = trial.suggest_categorical("y", [-1, 0, 1])
+    f1 = x**2 + y
+    f2 = -((x - 2) ** 2 + y)
+    return f1, f2
+
+
+if __name__ == "__main__":
+    # We minimize the first objective value and maximize the second objective value.
+    study = optuna.create_study(directions=["minimize", "maximize"])
+    study.optimize(objective, n_trials=500, timeout=1)
+
+    print("Number of finished trials: ", len(study.trials))
+
+    for i, best_trial in enumerate(study.best_trials):
+        print("The {}-th Pareto solution was found at Trial#{}.".format(i, best_trial.number))
+        print("  Params: {}".format(best_trial.params))
+        f1, f2 = best_trial.values
+        print("  Values: f1={}, f2={}".format(f1, f2))

basic_and_faq_usages/requirements.txt

@@ -0,0 +1,3 @@
+optuna
+scikit-learn>=0.19.0
+pandas

rapids/requirements.txt

@@ -0,0 +1,4 @@
+optuna
+scikit-learn>=0.19.0
+cudf
+cuml

requirements.txt

@@ -1,4 +1,4 @@
-jupyter  # used in quickstart.ipynb
-pandas  # used in max_trials_callback.py
-plotly  # used in quickstart.ipynb
-scikit-learn  # used in enqueue_trial.py and quickstart.ipynb
+# Used in quickstart.ipynb
+jupyter
+plotly
+scikit-learn