Pr convert problems.yaml

.github/workflows/check_python_package.yaml

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+name: Check
+
+on:
+  push:
+  pull_request:
+    branches: ["main"]
+
+jobs:
+  test:
+    strategy:
+      fail-fast: false
+      matrix:
+        runs-on: [ubuntu-latest, macos-latest, windows-latest]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
+    runs-on: ${{matrix.runs-on}}
+    steps:
+      - uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install the project
+        run: uv sync --all-extras --dev
+
+      - name: Lint with ruff
+        run: uv run ruff check --output-format=github .
+
+      - name: Run tests
+        run: uv run pytest --cov=opltools --cov-report=term-missing

examples/cobi.py

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+from opltools import Implementation, Generator, Reference, Library, Variable, Constraint
+from pydantic_yaml import to_yaml_str
+
+things = {}
+
+things["cobi_impl"] = Implementation(
+    name="COBI Implementation",
+    description="Python library for COBI (COnstrained BI-objective optimization) problem generator",
+    language="python",
+    links=[
+        {
+            "type": "repository",
+            "url": "https://github.com/numbbo/cobi-problem-generator/",
+        },
+        {
+            "type": "v0.5.0",
+            "url": "https://github.com/numbbo/cobi-problem-generator/releases/tag/v0.5.0",
+        },
+    ],
+    requirements="https://github.com/numbbo/cobi-problem-generator/blob/main/requirements.txt",
+)
+
+things["cobi_problem"] = Generator(
+    name="COBI Problem",
+    description="Generator of COnstrained BI-objective optimization problems",
+    tags={
+        "constrained",
+        "bi-objective",
+        "continuous",
+        "black-box",
+        "location",
+        "multi-peak",
+        "convex-quadratic",
+    },
+    references=[
+        Reference(
+            title="Pareto Set Characterization in Constrained Multiobjective Optimization and the COBI Problem Generator",
+            authors=["Anne Auger", "Dimo Brockhoff", "Luka Opravš", "Tea Tušar"],
+            link={"type": "arxiv", "url": "https://arxiv.org/abs/2604.09131"},
+        )
+    ],
+    objectives={2},
+    variables=[Variable(type="continuous", dim={"min": 1})],
+    implementations={"cobi_impl"},
+    can_evaluate_objectives_independently="no",
+    constraints=[
+        # FIXME: Check
+        Constraint(type="box", hard="yes", number={"min": 1}),
+        Constraint(type="linear", hard="yes", number={"min": 1}),
+        Constraint(type="function", hard="yes", number={"min": 1}),
+    ],
+    noise_type={"none"},
+    dynamic_type=None,
+    allows_partial_evaluation="no",
+    modality={"multi-modal per objective"},
+    fidelity_levels={1},
+    source={"artificial"},
+    code_examples={"./code_153.py"},
+)
+library = Library(things)
+
+print(to_yaml_str(library))

examples/demo.py

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+from opltools import Suite, Problem, Library, Link, Variable, ValueRange
+from pydantic_yaml import to_yaml_str
+from opltools.schema import Implementation
+
+things = {}
+
+things["impl_py_cocoex"] = Implementation(
+    name="coco-experiment Python module",
+    description="Python bindings for the experiment part of the COCO framework",
+    language="python",
+    links=[
+        Link(type="repository", url="https://github.com/numbbo/coco-experiment"),
+        Link(type="package", url="https://pypi.org/project/coco-experiment/"),
+    ],
+    evaluation_time="sub second",
+)
+
+for fnr in range(1, 25):
+    id = f"fn_bbob_f{fnr}"
+    things[f"fn_bbob_f{fnr}"] = Problem(
+        name=f"BBOB F_{fnr}",
+        objectives={1},
+        variables={Variable(type="continuous", dim=ValueRange(min=1, max=80))},
+        implementations=["impl_py_cocoex"],
+    )
+
+things["suite_bbob"] = Suite(
+    name="BBOB",
+    problems={f"fn_bbob_f{fnr}" for fnr in range(1, 25)},
+    implementations=["impl_py_cocoex"],
+)
+
+library = Library(things)
+
+print(to_yaml_str(library))

examples/emdo.py

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+from opltools import Library, Problem, Implementation
+from pydantic_yaml import to_yaml_str
+
+#! - name: Electric Motor Design Optimization
+#!   suite/generator/single: Single Problem
+#!   variable type: Continuous, Integer
+#!   dimensionality: '13'
+#!   objectives: '1'
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'yes'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   source (real-world/artificial): Real-World Application
+#!   implementation: Implementation not freely available
+#!   textual description: The goal is to find a design of a synchronous electric motor
+#!     for power steering systems that minimizes costs and satisfies all constraints.
+#!   reference: https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf (paper in Slovene)
+#!   other info:
+#!     partial evaluations: 'no'
+#!     full name: Electric Motor Design Optimization
+#!     constraint properties: Hard Constraints, Soft Constraints, Box Constraints
+#!     number of constraints: '12'
+#!     description of multimodality: Constraints are multimodal
+#!     key challenges / characteristics: Time-consuming solution evaluation, highly-constrained
+#!       problem
+#!     scientific motivation: Challenging to find good solutions in a limited time
+#!     limitations: 'Unavailability, even if available, it wouldn''t be helpful to use
+#!       for benchmarking due taking a long time to evaluate a single solution '
+#!     implementation languages: Python
+#!     approximate evaluation time: 8 minutes
+#!     general: This is not an available problem, but could be interesting to show to
+#!       researchers which difficulties appear in real-world problems
+
+library = Library({
+    "impl_emdo":  Implementation(
+        name="Electric Motor Design Optimization",
+        description="Not publicly available",
+        language="python",
+        evaluation_time="8 minutes"
+    ),
+    "fn_emdo":  Problem(
+        name="Electric Motor Design Optimization",
+        description="""# Goal
+Find a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.
+
+# Motivation
+Challenging to find good solutions in a limited time
+
+# Key Challenges
+* Time-consuming solution evaluation,
+* highly-constrained problem
+* Constraints are multimodal
+
+This is not an available problem, but could be interesting to show to researchers which difficulties appear in real-world problems""",
+        objectives=[1],
+        variables=[
+            {"type": "continuous", "dim": {"min": None, "max": 13}},
+            {"type": "integer", "dim": {"min": None, "max": 13}},
+        ],
+        modality=["multimodal"],
+        allows_partial_evaluation="no",
+        constraints=[
+            {"type": "box", "hard": "some", "number": 12}
+        ],
+        dynamic_type=["no"],
+        noise_type=["yes"],
+        fidelity_levels=[1],
+        source=["real-world"],
+        references=[
+            {
+                "title": "A Multi-Step Evaluation Process in Electric Motor Design",
+                "lang": "sj",
+                "authors": ["Tea Tušar", "Peter Korošec", "Bogdan Filipič"],
+                "link": {"url": "https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf"}
+            }
+        ],
+        implementations=["impl_emdo"]
+    )
+})
+
+print(to_yaml_str(library))