Merge branch 'anyoptimization:main' into reconstruct_objs

.github/workflows/deploy.yml

@@ -12,7 +12,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: "Set up Python"
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "${{ matrix.python-version }}"
       - name: "Install dependencies"
@@ -24,7 +24,7 @@ jobs:
           linux: "python setup.py sdist"
           macos: "python setup.py bdist_wheel"
           windows: "python setup.py bdist_wheel"
-      - uses: actions/upload-artifact@v3
+      - uses: actions/upload-artifact@v4
         with:
           name: dist
           path: dist
@@ -39,6 +39,7 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
+          - "3.12"
         include:
           - os: ubuntu-latest
             python-version: "3.11"
@@ -48,7 +49,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.9
       - uses: RalfG/python-wheels-manylinux-build@v0.7.1-manylinux2014_x86_64
@@ -58,7 +59,7 @@ jobs:
           python-versions: "cp37-cp37m cp38-cp38 cp39-cp39 cp310-cp310 cp311-cp311"
       - name: "Remove non-compatible packages"
         run: "sudo rm dist/*linux_x86_64.whl\n"
-      - uses: actions/upload-artifact@v3
+      - uses: actions/upload-artifact@v4
         with:
           name: dist
           path: dist

.github/workflows/testing.yml

@@ -16,7 +16,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Install Python 3
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: '3.10'
       - name: Install Dependencies

.pre-commit-config.yaml

@@ -0,0 +1,12 @@
+repos:
+  - repo: https://github.com/srstevenson/nb-clean
+    rev: 3.1.0
+    hooks:
+      - id: nb-clean
+        args:
+          - --remove-empty-cells
+          - --preserve-cell-metadata
+          - tags
+          - format
+          - raw_mimetype
+          - --

docs/requirements.txt

@@ -16,4 +16,4 @@ pygments
 bs4
 pandoc
 ipykernel
-jinja2==3
+jinja2==3.1.3

docs/source/algorithms/hyperparameters.ipynb

@@ -3,9 +3,6 @@
   {
    "cell_type": "raw",
    "metadata": {
-    "pycharm": {
-     "name": "#%% raw\n"
-    },
     "raw_mimetype": "text/restructuredtext"
    },
    "source": [
@@ -14,21 +11,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "# Hyperparameters"
    ]
   },
   {
    "cell_type": "raw",
    "metadata": {
-    "pycharm": {
-     "name": "#%% raw\n"
-    },
     "raw_mimetype": "text/restructuredtext"
    },
    "source": [
@@ -40,29 +30,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "Most algoriths have **hyperparameters**. For some optimization methods the parameters are already defined and can directly be optimized. For instance, for Differential Evolution (DE) the parameters can be found by:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-08-01T02:36:48.308627Z",
-     "iopub.status.busy": "2022-08-01T02:36:48.308160Z",
-     "iopub.status.idle": "2022-08-01T02:36:48.364512Z",
-     "shell.execute_reply": "2022-08-01T02:36:48.363614Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import json\n",
@@ -75,40 +51,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "If not provided directly, when initializing a `HyperparameterProblem` these variables are directly used for optimization."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "Secondly, one needs to define what exactly should be optimized. For instance, for a single run on a problem (with a fixed random seed) using the well-known parameter optimization toolkit [Optuna](https://optuna.org), the implementation may look as follows:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-08-01T02:36:48.369753Z",
-     "iopub.status.busy": "2022-08-01T02:36:48.369415Z",
-     "iopub.status.idle": "2022-08-01T02:36:59.415863Z",
-     "shell.execute_reply": "2022-08-01T02:36:59.414988Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from pymoo.algorithms.hyperparameters import SingleObjectiveSingleRun, HyperparameterProblem\n",
@@ -141,29 +99,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "Of course, you can also directly use the `MixedVariableGA` available in our framework:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-08-01T02:36:59.419480Z",
-     "iopub.status.busy": "2022-08-01T02:36:59.419084Z",
-     "iopub.status.idle": "2022-08-01T02:37:05.995629Z",
-     "shell.execute_reply": "2022-08-01T02:37:05.994612Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from pymoo.algorithms.hyperparameters import SingleObjectiveSingleRun, HyperparameterProblem\n",
@@ -198,29 +142,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "Now, optimizing the parameters for a **single random seed** is often not desirable. And this is precisely what makes hyper-parameter optimization computationally expensive. So instead of using just a single random seed, we can use the `MultiRun` performance assessment to average over multiple runs as follows:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-08-01T02:37:06.000183Z",
-     "iopub.status.busy": "2022-08-01T02:37:05.999864Z",
-     "iopub.status.idle": "2022-08-01T02:37:21.459474Z",
-     "shell.execute_reply": "2022-08-01T02:37:21.458554Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from pymoo.algorithms.hyperparameters import HyperparameterProblem, MultiRun, stats_single_objective_mean\n",
@@ -255,29 +185,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "Another way of performance measure is the number of evaluations until a specific goal has been reached. For single-objective optimization, such a goal is most likely until a minimum function value has been found. Thus, for the termination, we use `MinimumFunctionValueTermination` with a value of `1e-5`. We run the method for each random seed until this value has been reached or at most `500` function evaluations have taken place. The performance is then measured by the average number of function evaluations (`func_stats=stats_avg_nevals`) to reach the goal."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-08-01T02:37:21.462989Z",
-     "iopub.status.busy": "2022-08-01T02:37:21.462728Z",
-     "iopub.status.idle": "2022-08-01T02:37:38.013305Z",
-     "shell.execute_reply": "2022-08-01T02:37:38.012403Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from pymoo.algorithms.hyperparameters import HyperparameterProblem, MultiRun, stats_avg_nevals\n",
@@ -313,7 +229,19 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 4
 }

docs/source/algorithms/index.ipynb

@@ -52,7 +52,8 @@
     "   moo/age2\n",
     "   moo/rvea\n",
     "   moo/sms\n",
-    "   moo/dnsga2\n"
+    "   moo/dnsga2\n",
+    "   moo/kgb\n"
    ]
   },
   {
@@ -77,7 +78,24 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 4
 }