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anyoptimization · Nov 19, 2023 · cef2a9a · cef2a9a
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -0,0 +1,11 @@
+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
+          - --
diff --git a/docs/source/algorithms/hyperparameters.ipynb b/docs/source/algorithms/hyperparameters.ipynb
@@ -2,35 +2,21 @@
  "cells": [
   {
    "cell_type": "raw",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% raw\n"
-    },
-    "raw_mimetype": "text/restructuredtext"
-   },
+   "metadata": {},
    "source": [
     ".. _nb_algorithms_hyperparameters:"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "# Hyperparameters"
    ]
   },
   {
    "cell_type": "raw",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% raw\n"
-    },
-    "raw_mimetype": "text/restructuredtext"
-   },
+   "metadata": {},
    "source": [
     ".. admonition:: Info\n",
     "    :class: myOwnStyle\n",
@@ -40,29 +26,15 @@
   },
   {
    "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 +47,22 @@
   },
   {
    "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 +95,15 @@
   },
   {
    "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 +138,15 @@
   },
   {
    "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 +181,15 @@
   },
   {
    "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 +225,19 @@
    ]
   }
  ],
- "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
 }
diff --git a/docs/source/algorithms/index.ipynb b/docs/source/algorithms/index.ipynb
@@ -2,9 +2,7 @@
  "cells": [
   {
    "cell_type": "raw",
-   "metadata": {
-    "raw_mimetype": "text/restructuredtext"
-   },
+   "metadata": {},
    "source": [
     ".. _nb_algorithms:"
    ]
@@ -18,9 +16,7 @@
   },
   {
    "cell_type": "raw",
-   "metadata": {
-    "raw_mimetype": "text/restructuredtext"
-   },
+   "metadata": {},
    "source": [
     ".. toctree::\n",
     "   :hidden:\n",
@@ -52,7 +48,8 @@
     "   moo/age2\n",
     "   moo/rvea\n",
     "   moo/sms\n",
-    "   moo/dnsga2\n"
+    "   moo/dnsga2\n",
+    "   moo/kgb\n"
    ]
   },
   {
@@ -64,9 +61,7 @@
   },
   {
    "cell_type": "raw",
-   "metadata": {
-    "raw_mimetype": "text/restructuredtext"
-   },
+   "metadata": {},
    "source": [
     ".. admonition:: Overview\n",
     "    :class: myOwnStyle\n",
@@ -77,7 +72,24 @@
    ]
   }
  ],
- "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
 }