Merge pull request #38 from electronsandstuff/objectives-constraints-…

…options

More General Options for Objectives / Constraints in Container Objects

dev_notebooks/save_new_legacy_file.ipynb

@@ -0,0 +1,59 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prepare a New \"Legacy Test File\"\n",
+    "This notebook loads the data from the first legacy test file and saves it using a new version of the library in the latest save file format. This will ensure a record of the file format exists for the purpose of regression tests."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from paretobench import Experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exp = Experiment.load(\"../tests/legacy_file_formats/paretobench_file_format_v1.0.0.h5\")\n",
+    "exp.save(\"../tests/legacy_file_formats/paretobench_file_format_v1.1.0.h5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "paretobench",
+   "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",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

environment.yml

@@ -1,3 +1,6 @@
 name: paretobench
 dependencies:
   - pytest
+  - pip
+  - pip:
+    - pre-commit

example_notebooks/container_objects.ipynb

@@ -14,6 +14,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import numpy as np\n",
     "import os\n",
     "import paretobench as pb\n",
     "import tempfile"
@@ -36,17 +37,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Population(size=32, vars=10, objs=2, cons=0, fevals=0)\n",
+      "Population(size=32, vars=10, objs=[--], cons=[], fevals=32)\n",
       "Decision variables: (32, 10)\n",
       "Objectives: (32, 2)\n",
       "Constraints: (32, 0)\n",
-      "Function evaluations: 0\n"
+      "Function evaluations: 32\n"
      ]
     }
    ],
    "source": [
     "# Create an example population\n",
-    "pop = pb.Population.from_random(n_objectives=2, n_decision_vars=10, n_constraints=0, pop_size=32)\n",
+    "pop = pb.Population(\n",
+    "    x=np.random.random((32, 10)),  # 32 individuals worth of 10 decision vars\n",
+    "    f=np.random.random((32, 2)),  # 2 objectives\n",
+    "    # Not specifying an array (constraints here) will cause it to populate empty based on other's shape\n",
+    "    fevals=32,\n",
+    ")\n",
     "print(pop)\n",
     "\n",
     "# Examine some of the parameters\n",
@@ -67,12 +73,66 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Population(size=32, vars=10, objs=[--], cons=[>0.0e+00], fevals=32)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a population with some names\n",
+    "pop = pb.Population(\n",
+    "    x=np.random.random((32, 10)),\n",
+    "    f=np.random.random((32, 2)),\n",
+    "    g=np.random.random((32, 1)),  # Add a single constraint this time\n",
+    "    names_x=[f\"Decision var {idx}\" for idx in range(pop.x.shape[1])],\n",
+    "    names_f=[\"Objective_1\", \"Objective_2\"],\n",
+    "    names_g=[\"Important_Constraint\"],\n",
+    ")\n",
+    "pop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Populations default to representing minimization problems with constraints that are satisfied when each g >= 0. This can be modified as in the following code block. Configuring these settings is important when using container methods to find feasible individuals and in calculation of domination."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Population(size=32, vars=10, objs=[+-+], cons=[>3.3e-01,<6.6e-01], fevals=32)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Set naems of the decision variables, objectives, and constraints\n",
-    "pop.names_x = [f\"Decision var {idx}\" for idx in range(pop.x.shape[1])]\n",
-    "pop.names_f = [\"Objective 1\", \"Objective 2\"]\n",
-    "pop.names_g = []"
+    "# Create a population and specify the objective / constraint directions and target\n",
+    "pop = pb.Population(\n",
+    "    x=np.random.random((32, 10)),\n",
+    "    f=np.random.random((32, 3)),\n",
+    "    g=np.random.random((32, 2)),\n",
+    "    obj_directions=\"+-+\",  # \"+\" indicates maximization, \"-\" inidicates minimization\n",
+    "    constraint_directions=\"><\",  # \"<\" means satisifed when g<target and \">\" means satsified when g>target\n",
+    "    constraint_targets=np.array([0.33, 0.66]),  # >0.33 constraint and <0.66 constraint\n",
+    ")\n",
+    "\n",
+    "pop"
    ]
   },
   {
@@ -85,23 +145,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "History(problem='WFG1 (n=10)', reports=15, vars=10, objs=2, cons=0)\n",
-      "Problem: WFG1 (n=10)\n",
-      "Number of reports: 15\n"
+      "History(problem='WFG1', reports=10, vars=30, objs=[--], cons=[])\n",
+      "Problem: WFG1\n",
+      "Number of reports: 10\n"
      ]
     }
    ],
    "source": [
-    "# Create an example history object\n",
-    "hist = pb.History.from_random(n_populations=15, n_objectives=2, n_decision_vars=10, n_constraints=0, pop_size=32)\n",
-    "hist.problem = \"WFG1 (n=10)\"\n",
+    "# Create some population objects which will go into the history. We will use the random generation helper function here.\n",
+    "n_reports = 10\n",
+    "reports = [\n",
+    "    pb.Population.from_random(n_objectives=2, n_decision_vars=30, n_constraints=0, pop_size=50)\n",
+    "    for _ in range(n_reports)\n",
+    "]\n",
+    "\n",
+    "# Construct the history object\n",
+    "hist = pb.History(\n",
+    "    reports=reports,\n",
+    "    problem=\"WFG1\",  # Use ParetoBench single line format for maximum compatibility with plotting, etc\n",
+    ")\n",
     "print(hist)\n",
     "\n",
     "# Print some of the properties\n",
@@ -119,17 +188,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Experiment(name='NSGA-II (default params)', created='2024-12-10', author='The author of ParetoBench', software='ParetoBench example notebook 1.0.0', runs=32)\n",
+      "Experiment(name='NSGA-II (default params)', created='2025-01-26', author='The author of ParetoBench', software='ParetoBench example notebook 1.0.0', runs=32)\n",
       "Number of histories: 32\n",
       "Name: NSGA-II (default params)\n",
-      "Creation time: 2024-12-10 05:21:51.993640+00:00\n"
+      "Creation time: 2025-01-26 06:36:29.299300+00:00\n"
      ]
     }
    ],

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "paretobench"
-version = "0.3.0"
+version = "0.4.0"
 authors = [
   { name="Christopher M. Pierce", email="contact@chris-pierce.com" },
 ]