More General Options for Objectives / Constraints in Container Objects #38

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Merged

electronsandstuff merged 29 commits into main from objectives-constraints-options

Jan 26, 2025

dev_notebooks/save_new_legacy_file.ipynb

-Original file line number
+Diff line change
@@ -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

-Original file line number
+Diff line change
@@ -1,3 +1,6 @@
     name: paretobench
     dependencies:
       - pytest
+      - pip
+      - pip:
+        - pre-commit

example_notebooks/container_objects.ipynb

            
                      Original file line number
                      Diff line number
                      Diff line change
                  
    @@ -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

-Original file line number
+Diff line change
@@ -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" },
     ]
@@ Expand Down @@

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