Merge pull request #277 from kAIto47802/separate-basic-and-faq

Separate basic and faq directories

.github/workflows/basic_and_faq_usages.yml → .github/workflows/basic.yml

@@ -1,12 +1,12 @@
-name: basic_and_faq_usages
+name: basic
 
 on:
   schedule:
     - cron: '0 15 * * *'
   pull_request:
     paths:
-      - 'basic_and_faq_usages/**'
-      - '.github/workflows/basic_and_faq_usages.yml'
+      - 'basic/**'
+      - '.github/workflows/basic.yml'
 
 jobs:
   examples:
@@ -29,14 +29,12 @@ jobs:
         pip install git+https://github.com/optuna/optuna.git
         python -c 'import optuna'
 
-        pip install -r basic_and_faq_usages/requirements.txt
+        pip install -r basic/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
+        python basic/quadratic.py
+        python basic/pruning.py
+        python basic/quadratic_constraint.py
+        python basic/quadratic_multi_objective.py
       env:
         OMP_NUM_THREADS: 1

.github/workflows/faq.yml

@@ -0,0 +1,38 @@
+name: faq
+
+on:
+  schedule:
+    - cron: '0 15 * * *'
+  pull_request:
+    paths:
+      - 'faq/**'
+      - '.github/workflows/faq.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 faq/requirements.txt
+    - name: Run examples
+      run: |
+        python faq/max_trials_callback.py
+        python faq/enqueue_trial.py
+      env:
+        OMP_NUM_THREADS: 1

README.md

@@ -32,9 +32,9 @@ if __name__ == "__main__":
 > In this example repository, you can also find the examples for the following scenarios:
 > 1. [Objective function with additional arguments](./sklearn/sklearn_additional_args.py), which is useful when you would like to pass arguments besides `trial` to your objective function.
 > 
-> 2. [Manually provide trials with sampler](./basic_and_faq_usages/enqueue_trial.py), which is useful when you would like to force certain parameters to be sampled.
+> 2. [Manually provide trials with sampler](./faq/enqueue_trial.py), which is useful when you would like to force certain parameters to be sampled.
 >
-> 3. [Callback to control the termination criterion of study](./basic_and_faq_usages/max_trials_callback.py), which is useful when you would like to define your own termination criterion other than `n_trials` or `timeout`.
+> 3. [Callback to control the termination criterion of study](./faq/max_trials_callback.py), which is useful when you would like to define your own termination criterion other than `n_trials` or `timeout`.
 
 ## Examples for Diverse Problem Setups
 
@@ -43,9 +43,9 @@ Here are the URLs to the example codeblocks to the corresponding setups.
 <details open>
 <summary>Simple Black-box Optimization</summary>
 
-* [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)
+* [Quadratic Function](./basic/quadratic.py)
+* [Quadratic Multi-Objective Function](./basic/quadratic_multi_objective.py)
+* [Quadratic Function with Constraints](./basic/quadratic_constraint.py)
 </details>
 
 <details open>
@@ -93,7 +93,7 @@ If you are looking for an example of reinforcement learning, please take a look
 
 The following example demonstrates how to implement pruning logic with Optuna.
 
-* [Simple pruning (scikit-learn)](./basic_and_faq_usages/simple_pruning.py)
+* [Simple pruning (scikit-learn)](./basic/pruning.py)
 
 In addition, integration modules are available for the following libraries, providing simpler interfaces to utilize pruning.
 

basic/requirements.txt

@@ -0,0 +1,2 @@
+optuna
+scikit-learn>=0.19.0