diff --git a/.flake8 b/.flake8 deleted file mode 100644 index 5b92ae3..0000000 --- a/.flake8 +++ /dev/null @@ -1,18 +0,0 @@ -[flake8] -extend-select = B9 -extend-ignore = E203, E231, E501, E722, W503, B950, B014, W504, E123, E126, E226, E121, B905, SIM113 -per-file-ignores = __init__.py:F401 -max-line-length = 120 -show-source = true -exclude = - .git, - .idea, - .eggs, - __pycache__, - .tox, - docs/source/conf.py, - build, - .nox, - venv, - .venv -application-import-names = mqt.qao diff --git a/.github/codecov.yml b/.github/codecov.yml index 2b41601..897f822 100644 --- a/.github/codecov.yml +++ b/.github/codecov.yml @@ -11,6 +11,3 @@ coverage: patch: default: threshold: 1% -codecov: - notify: - after_n_builds: 8 diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..6b89cc9 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,25 @@ +version: 2 +updates: + - package-ecosystem: "github-actions" + directory: "/" + groups: + github-actions: + patterns: + - "*" + schedule: + interval: "weekly" + day: "wednesday" + time: "06:00" + timezone: "Europe/Vienna" + + - package-ecosystem: "pip" + directory: "/" + groups: + python-dependencies: + patterns: + - "*" + schedule: + interval: "weekly" + day: "friday" + time: "06:00" + timezone: "Europe/Vienna" diff --git a/.github/workflows/cd.yml b/.github/workflows/cd.yml index 36a7013..2f63168 100644 --- a/.github/workflows/cd.yml +++ b/.github/workflows/cd.yml @@ -7,7 +7,7 @@ on: jobs: python-packaging: name: 🐍 Packaging - uses: cda-tum/mqt-workflows/.github/workflows/reusable-python-packaging.yml@v1.0.0 + uses: cda-tum/mqt-workflows/.github/workflows/reusable-python-packaging.yml@v1.0.1 with: pure-python: true diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 7126586..f0e32ce 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -14,21 +14,19 @@ concurrency: jobs: change-detection: name: πŸ” Change - uses: cda-tum/mqt-workflows/.github/workflows/reusable-change-detection.yml@v1.0.0 + uses: cda-tum/mqt-workflows/.github/workflows/reusable-change-detection.yml@v1.0.1 python-tests: name: 🐍 Test needs: change-detection if: fromJSON(needs.change-detection.outputs.run-python-tests) - uses: cda-tum/mqt-workflows/.github/workflows/reusable-python-ci.yml@v1.0.0 - secrets: - token: ${{ secrets.CODECOV_TOKEN }} + uses: cda-tum/mqt-workflows/.github/workflows/reusable-python-ci.yml@v1.0.1 code-ql: name: πŸ“ CodeQL needs: change-detection if: fromJSON(needs.change-detection.outputs.run-code-ql) - uses: cda-tum/mqt-workflows/.github/workflows/reusable-code-ql-python.yml@v1.0.0 + uses: cda-tum/mqt-workflows/.github/workflows/reusable-code-ql-python.yml@v1.0.1 required-checks-pass: # This job does nothing and is only used for branch protection name: 🚦 Check diff --git a/.github/workflows/mypy.yml b/.github/workflows/mypy.yml deleted file mode 100644 index d2984e8..0000000 --- a/.github/workflows/mypy.yml +++ /dev/null @@ -1,25 +0,0 @@ -name: mypy - -on: - pull_request: - push: - branches: - - main - workflow_dispatch: - -concurrency: - group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }} - cancel-in-progress: true - -jobs: - lint: - runs-on: ubuntu-latest - name: Run my[py] linter - steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v4 - with: - python-version: "3.10" - - uses: pre-commit/action@v3.0.0 - with: - extra_args: mypy --all-files diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 5a0b456..33a1cc4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,22 +15,35 @@ ci: repos: # Standard hooks - repo: https://github.com/pre-commit/pre-commit-hooks - rev: "v4.5.0" + rev: v4.6.0 hooks: - id: check-added-large-files - id: check-case-conflict - id: check-docstring-first - id: check-merge-conflict + - id: check-symlinks - id: check-toml - id: check-yaml - id: debug-statements - id: end-of-file-fixer - id: mixed-line-ending + - id: requirements-txt-fixer - id: trailing-whitespace + # Clean jupyter notebooks + - repo: https://github.com/srstevenson/nb-clean + rev: 3.2.0 + hooks: + - id: nb-clean + args: + - --remove-empty-cells + - --preserve-cell-metadata + - raw_mimetype + - -- + # Handling unwanted unicode characters - repo: https://github.com/sirosen/texthooks - rev: "0.6.2" + rev: 0.6.6 hooks: - id: fix-ligatures - id: fix-smartquotes @@ -43,28 +56,9 @@ repos: - id: rst-directive-colons - id: rst-inline-touching-normal - # Clean jupyter notebooks - - repo: https://github.com/srstevenson/nb-clean - rev: "3.1.0" - hooks: - - id: nb-clean - # Check for spelling - - repo: https://github.com/codespell-project/codespell - rev: "v2.2.6" - hooks: - - id: codespell - args: ["-L", "wille,linz,fro"] - exclude: "mqt/benchviewer/templates/legal.html" - - # Format configuration files with prettier - - repo: https://github.com/pre-commit/mirrors-prettier - rev: "v3.0.3" - hooks: - - id: prettier - types_or: [yaml, markdown, html, css, javascript, json] - + # Python linting using ruff - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.4 + rev: v0.4.7 hooks: - id: ruff args: ["--fix", "--show-fixes"] @@ -72,38 +66,37 @@ repos: - id: ruff-format types_or: [python, pyi, jupyter] + # Static type checking using mypy + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.10.0 + hooks: + - id: mypy + files: ^(src/mqt|tests) + args: [] + additional_dependencies: + - pytest + - numpy + # Also run Black on examples in the documentation - repo: https://github.com/adamchainz/blacken-docs rev: 1.16.0 hooks: - id: blacken-docs - additional_dependencies: [black==23.*] + additional_dependencies: [black==24.*] - - repo: https://github.com/pre-commit/mirrors-mypy - rev: v1.6.1 + # Format configuration files with prettier + - repo: https://github.com/pre-commit/mirrors-prettier + rev: v4.0.0-alpha.8 hooks: - - id: mypy - files: ^(src|tests) - args: [] - additional_dependencies: - - importlib_resources - - qubovert - - pytest==7.4.0 - - types-setuptools - - setuptools<=65.6.3 - - sympy==1.12 - - numpy==1.23.5 - - dwave-samplers - - dwave.system - - docplex==2.25.236 - - qiskit==0.45.3 - - qiskit_optimization==0.4.0 - - qiskit-algorithms==0.2.1 - - qiskit_ibm_runtime==0.14.0 - - matplotlib - - pandas - - PyPortfolioOpt - - scikit-learn + - id: prettier + types_or: [yaml, markdown, html, css, scss, javascript, json] + + # Check for spelling + - repo: https://github.com/codespell-project/codespell + rev: v2.3.0 + hooks: + - id: codespell + args: ["-L", "wille,linz", "--skip", "*.ipynb"] # Catch common capitalization mistakes - repo: local @@ -112,7 +105,7 @@ repos: name: Disallow improper capitalization language: pygrep entry: PyBind|Numpy|Cmake|CCache|Github|PyTest|Mqt|Tum - exclude: (.joblib|.pre-commit-config.yaml) + exclude: .pre-commit-config.yaml # Check best practices for scientific Python code - repo: https://github.com/scientific-python/cookie diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..20ae190 --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,30 @@ +version: 2 + +submodules: + include: all + recursive: true + +build: + os: ubuntu-22.04 + tools: + python: "3.11" + jobs: + post_checkout: + # Skip docs build if the commit message contains "skip ci" + - (git --no-pager log --pretty="tformat:%s -- %b" -1 | grep -viq "skip ci") || exit 183 + # Skip docs build if there are no changes related to docs + - | + if [ "$READTHEDOCS_VERSION_TYPE" = "external" ] && git diff --quiet origin/main -- docs/ .readthedocs.yaml src/mqt/ .github/contributing* .github/support*; + then + exit 183; + fi + +sphinx: + configuration: docs/conf.py + +python: + install: + - method: pip + path: . + extra_requirements: + - docs diff --git a/CITATION.bib b/CITATION.bib index 306c981..42f9a00 100644 --- a/CITATION.bib +++ b/CITATION.bib @@ -1,7 +1,6 @@ -@MISC{volpe2024towards, +@INPROCEEDINGS{volpe2024towards, AUTHOR = {D.Volpe and N. Quetschlich and M. Graziano and G. Turvani and R. Wille}, TITLE = {{Towards an Automatic Framework for Solving Optimization Problems with Quantum Computers}}, YEAR = {2024}, - EPRINT = {}, - EPRINTTYPE = {}, + BOOKTITLE = {IEEE International Conference on Quantum Software (QSW)}, } diff --git a/README.md b/README.md index 183fa7d..c2fd567 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ # MQT Quantum Auto Optimizer: Automatic Framework for Solving Optimization Problems with Quantum Computers -MQT Quantum Auto Optimizer is a framework that allows one to automatically translate an optimization problem into a quantum-compliant formulation and to solve it with one of the main quantum solvers (Quantum Annelar, QAOA, VQE and GAS) +MQT Quantum Auto Optimizer is a framework that allows one to automatically translate an optimization problem into a quantum-compliant formulation and to solve it with one of the main quantum solvers (Quantum Annealer, Quantum Approximate Optimization Algorithm, Variational Quantum Eigensolver and Grover Adaptive Search) MQT Quantum Auto Optimizer is part of the [Munich Quantum Toolkit (MQT)](https://mqt.readthedocs.io/) developed by the [Chair for Design Automation](https://www.cda.cit.tum.de/) at the [Technical University of Munich](https://www.tum.de/). This framework has been developed in collaboration with the [VLSI Lab](https://www.vlsilab.polito.it/) of [Politecnico di Torino](https://www.polito.it). diff --git a/docs/Constraints.rst b/docs/Constraints.rst new file mode 100644 index 0000000..f3aac6d --- /dev/null +++ b/docs/Constraints.rst @@ -0,0 +1,45 @@ +Constraints Class +================= + +It manages the constraints of the problem. It is used to store the constraints and to check if a solution satisfies them. + +Constraints types supported +--------------------------- + +Types of constraints supported: + +- *Equality*, writing the equality as a quadratic penalty function, i.e. sum = b imposed with g = (sum-b)^2 +- *Inequality*, moving to equality with the continuous auxiliary variables a to be expanded with the encoding technique of the Variables class +- *Not constraint* among binary variables, i.e. Not(a) = b imposed with g = 2ab - a - b + 1 +- *And constraint* among binary variables, i.e. a and b = c imposed with ab -2(a+b)c + 3c +- *Or constraint* among binary variables, i.e. a or b = c imposed with ab + (a+b)(1-2c) + c +- *Xor constraint* among binary variables, i.e. a xor b = c imposed with 2ab - 2(a+b)c - 4(a+b)\_aux+4_aux c +a+b+c+4+\_aux + +Constraints declarations +------------------------ + +The class provides methods to declare variables: + +- *add_constraint(expression: str, hard: bool = True, variable_precision: bool = True)*: adds a constraint to the list of constraints. + - *expression* is a string that represents the constraint + - *hard* parameter is a boolean that indicates if the constraint is hard or soft. + - *variable_precision* parameter is a boolean that indicates if the constraint is to be considered in the precision of the variables. + +Example: +-------- + +.. code-block:: python + + from mqt.qao.constraints import Constraints + from mqt.qao.variables import Variables + + constraint = Constraints() + variables = Variables() + variables.add_binary_variable("a") + variables.add_binary_variable("b") + variables.add_binary_variable("c") + variables.add_discrete_variable("d", [-1, 1, 3]) + variables.add_continuous_variable("e", -2, 2, 0.25, "", "") + constraint.add_constraint("~a = b", True, True, False) + constraint.add_constraint("a | b = c", True, True, False) + constraint.add_constraint("d + e <= 1", True, True, False) diff --git a/docs/Contributing.rst b/docs/Contributing.rst new file mode 100644 index 0000000..c492978 --- /dev/null +++ b/docs/Contributing.rst @@ -0,0 +1 @@ +.. include:: ../.github/contributing.rst diff --git a/docs/DevelopmentGuide.rst b/docs/DevelopmentGuide.rst new file mode 100644 index 0000000..a8fde66 --- /dev/null +++ b/docs/DevelopmentGuide.rst @@ -0,0 +1,103 @@ +Development Guide +================= + +Ready to contribute to the project? Here is how to set up a local development environment. + +Initial Setup +############# + +1. Fork the `cda-tum/mqt-qao `_ repository on GitHub (see https://docs.github.com/en/get-started/quickstart/fork-a-repo). + +2. Clone your fork locally + + .. code-block:: console + + $ git clone git@github.com:your_name_here/mqt-qao + + +3. Change into the project directory + + .. code-block:: console + + $ cd mqt-qao + +4. Create a branch for local development + + .. code-block:: console + + $ git checkout -b name-of-your-bugfix-or-feature + + Now you can make your changes locally. + +5. (Optional, **highly recommended**) Set up a virtual environment + + .. code-block:: console + + $ python3 -m venv venv + $ source venv/bin/activate + + .. note:: + + If you are using Windows, you can use the following command instead: + + .. code-block:: console + + $ python3 -m venv venv + $ venv\Scripts\activate.bat + + Ensure that pip, setuptools, and wheel are up to date: + + .. code-block:: console + + (venv) $ pip install --upgrade pip setuptools wheel + + +6. (Optional) Install `pre-commit `_ to automatically run a set of checks before each commit. + + .. code-block:: console + + (venv) $ pipx install pre-commit + (venv) $ pre-commit install + + If you use macOS, then pre-commit is in brew, use :code:`brew install pre-commit`. + +Building the Python module +########################## + +The recommended way of building the Python module is to perform an editable install using `pip `_. + + .. code-block:: console + + (venv) $ pip install -e . + +The :code:`--editable` flag ensures that changes in the Python code are instantly available without re-running the command. + +Running Python Tests +-------------------- + +The Python part of the code base is tested by unit tests using the `pytest `_ framework. +The corresponding test files can be found in the :code:`tests/` directory. + + .. code-block:: console + + (venv) $ pip install -e ".[test]" + (venv) $ pytest + +This installs all dependencies necessary to run the tests in an isolated environment, builds the Python package, and then runs the tests. + +Python Code Formatting and Linting +---------------------------------- + +The Python code is formatted and linted using a collection of `pre-commit hooks `_. +This collection includes: + +- `ruff `_ -- an extremely fast Python linter and formatter, written in Rust. +- `mypy `_ -- a static type checker for Python code + + +You can install the hooks manually by running :code:`pre-commit install` in the project root directory. +The hooks will then be executed automatically when committing changes. + + .. code-block:: console + + (venv) $ pre-commit run -a diff --git a/docs/ObjectiveFunction.rst b/docs/ObjectiveFunction.rst new file mode 100644 index 0000000..201b481 --- /dev/null +++ b/docs/ObjectiveFunction.rst @@ -0,0 +1,31 @@ +ObjectiveFunction Class +======================= + +It manages the objective functions of the optimization problem. it permits to specify the weights of the objectives in case of multi-objective optimization and the optimization directions. Since the variables can be declared in array form, matrix expressions for the objective functions are supported. + +Objective functions declarations +-------------------------------- + +The class provides methods to declare variables: + +- *add_objective_function(objective_function: Expr, minimization: bool = True, weight: float = 1)* : add an objective function to the optimization problem. + - *objective_function* is an expression of the variables of the optimization problem. + - *minimization* parameter specifies if the objective function is to be minimized or maximized (optimization direction). + - *weight* parameter is the weight of the objective function in case of multi-objective optimization. + +Example: +-------- + +.. code-block:: python + + from mqt.qao.constraints import Constraints + from mqt.qao.variables import Variables + from mqt.qao.objectivefunction import ObjectiveFunction + + variables = Variables() + constraint = Constraints() + a0 = variables.add_binary_variable("a") + b0 = variables.add_discrete_variable("b", [-1, 1, 3]) + c0 = variables.add_continuous_variable("c", -2, 2, 0.25, "", "") + objective_function = ObjectiveFunction() + objective_function.add_objective_function(a0 + b0 * c0 + c0**2) diff --git a/docs/Problem.rst b/docs/Problem.rst new file mode 100644 index 0000000..4f85945 --- /dev/null +++ b/docs/Problem.rst @@ -0,0 +1,49 @@ +Problem Class +============= + +It may be useful to have a class that represents a problem. It includes the variables, constraints and objective function. This class can be used for constructing the quantum-compliant cost function. + +Problem declarations +-------------------- + +The class provides a method for declaring the problem: + +- *create_problem(var: Variables, constraint: Constraints, objective_functions: ObjectiveFunction)* : This method is used to declare the problem. + - *variables* instances of the Variables class + - *constraints* instances of the Constraints class + - *objective_functions* instances of the ObjectiveFunction class. + +and a method for obtaining the HUBO or PUBO formulation of the problem as qubovert PUBO object: + +- *write_the_final_cost_function( lambda_strategy: str, lambda_value: float = 1)* : This method is used to obtain the HUBO or PUBO formulation of the problem as qubovert PUBO object. The method takes two arguments: + - *lambda_strategy* The strategy to be used for the conversion of the problem to HUBO or PUBO. The possible values are: + - upper_bound_only_positive + - maximum_coefficient + - VLM + - MOC + - MOMC + - upper lower bound naive + - upper lower bound posiform and negaform method + - manual + - *lambda_value* The value of the lambda parameter if the use want to manually select it. The default value is 1.0. + +Example: +-------- + +.. code-block:: python + + from mqt.qao.constraints import Constraints + from mqt.qao.variables import Variables + from mqt.qao.objectivefunction import ObjectiveFunction + + variables = Variables() + constraint = Constraints() + a0 = variables.add_binary_variable("a") + b0 = variables.add_discrete_variable("b", [-1, 1, 3]) + c0 = variables.add_continuous_variable("c", -2, 2, 0.25, "", "") + constraint.add_constraint("c >= 1", True, True, False) + objective_function = ObjectiveFunction() + objective_function.add_objective_function(a0 + b0 * c0 + c0**2) + problem = Problem() + problem.create_problem(variables, constraint, objective_function) + pubo = problem.write_the_final_cost_function(lambda_strategy) diff --git a/docs/Quickstart.ipynb b/docs/Quickstart.ipynb new file mode 100644 index 0000000..cf194f7 --- /dev/null +++ b/docs/Quickstart.ipynb @@ -0,0 +1,138 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ea3d6be5-b9ee-4375-adee-8fa2cb690fca", + "metadata": {}, + "source": [ + "# Quickstart" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "697e4c2b", + "metadata": {}, + "outputs": [], + "source": [ + "from mqt.qao import Constraints, ObjectiveFunction, Problem, Solver, Variables" + ] + }, + { + "cell_type": "markdown", + "id": "02841c4f-a865-452d-a5ca-085a64eccf00", + "metadata": {}, + "source": [ + "## Declare the variables of the problem" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73c0a2f9-adbd-4f85-8843-b6ab2ef53da8", + "metadata": {}, + "outputs": [], + "source": [ + "var = Variables()\n", + "a = var.add_binary_variable(\"a\")\n", + "b = var.add_discrete_variable(\"b\", [-1, 1, 3])\n", + "c = var.add_continuous_variable(\"c\", -2, 2, 0.25)" + ] + }, + { + "cell_type": "markdown", + "id": "dc5b2a05-c062-46d8-b75e-2bfde4795a19", + "metadata": {}, + "source": [ + "## Declare the objective functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3f643db-f833-433c-87f8-65cdc5355304", + "metadata": {}, + "outputs": [], + "source": [ + "obj_func = ObjectiveFunction()\n", + "obj_func.add_objective_function(a + b * c + c**2)" + ] + }, + { + "cell_type": "markdown", + "id": "7b823498-55e1-47df-9b7b-f5f462f38c50", + "metadata": {}, + "source": [ + "## Declare the constraints" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c327466-7905-45b9-980a-f96ca8cffadb", + "metadata": {}, + "outputs": [], + "source": [ + "cst = Constraints()\n", + "cst.add_constraint(\"b + c >= 2\", variable_precision=True)" + ] + }, + { + "cell_type": "markdown", + "id": "53953b24-353e-465e-b4f4-d7c0f2f0f0bf", + "metadata": {}, + "source": [ + "## Declaration of the problem" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe643f31-9d3e-4b7f-875a-af27e3dab5ed", + "metadata": {}, + "outputs": [], + "source": [ + "prb = Problem()\n", + "prb.create_problem(var, cst, obj_func)" + ] + }, + { + "cell_type": "markdown", + "id": "93392cb4", + "metadata": {}, + "source": [ + "## Solve with Simulated Annealing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "010ab172", + "metadata": {}, + "outputs": [], + "source": [ + "solution = Solver().solve_simulated_annealing(prb)" + ] + } + ], + "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": 5 +} diff --git a/docs/References.rst b/docs/References.rst new file mode 100644 index 0000000..2a446d8 --- /dev/null +++ b/docs/References.rst @@ -0,0 +1,6 @@ +References +========== + +If you use *MQT Quantum Auto Optimizer* in your work, we would appreciate if you cited :cite:labelpar:`volpe2024towards`. + +.. bibliography:: diff --git a/docs/Solution.rst b/docs/Solution.rst new file mode 100644 index 0000000..ca62076 --- /dev/null +++ b/docs/Solution.rst @@ -0,0 +1,90 @@ +Solution Class +============== + +It is a class that is used to store all the information about the solution of the problem. + +Solution Attributes +------------------- + +The class has the following attributes: + +- energies: list[float], i.e. the list of energies of the solution obtained in each run +- best_energy, i.e. the lowest energy obtained in all the runs +- best_solution: dict[str, Any], i.e. the best solution obtained in all the runs in binary variables +- best_solution_original_var: dict[str, Any], i.e. the best solution obtained in all the runs in variables originally declared +- solutions_original_var: list[dict[str, Any]], i.e. the list of solutions obtained in each run in variables originally declared +- solutions: list[dict[str, Any]], i.e. the list of solutions obtained in each run in binary variables +- time: float: the time taken to solve the problem +- solver_info: dict[str, Any], i.e. the information about the solver setting to solve the problem + + +Solution Methods +---------------- + +The class has the following methods for helping users in analyzing the solution: + +- *optimal_solution_cost_functions_values()*: This method is used to get the cost functions values of the best solution obtained in all the runs +- *check_constraint_optimal_solution()*: This method is used to check if the best solution obtained in all the runs satisfies the constraints +- *check_constraint_all_solutions()*: This method is used to check if all the solutions obtained in all the runs satisfy the constraints +- *show_cumulative(save: bool = False, show: bool = True, filename: str = "", label: str = "", latex: bool = False)* : This method is used to show the cumulative plot of the energies obtained in all the runs. The parameters are: + - save: bool, i.e. whether to save the plot or not in a file + - show: bool, i.e. whether to show the plot or not + - filename: str, i.e. the name of the file where the plot will be saved + - label: str, i.e. the label of the plot + - latex: bool, i.e. whether to show the plot in latex format or not +- *valid_solutions(weak: bool = True)*: This method is used to get the rate of valid solutions obtained in all the runs. The parameters are: + - weak: bool, i.e. whether to consider in the evaluation the weak constraints +- *p_range(ref_value: float | None = None)*: This method is used to get the p-range of the best solution obtained in all the runs, which is the probability of obtaining a final energy lower than a certain value. The parameters are: + - ref_value: float | None, i.e. the reference value to calculate the p-value +- *tts(ref_value: float | None = None, target_probability: float = 0.99)*: This method is used to get the time-to-solution of the best solution obtained in all the runs, which is the time required to obtain a solution with a certain probability. The parameters are: + - ref_value: float | None, i.e. the reference value to calculate the p-range + - target_probability: float, i.e. the target probability to calculate the time-to-solution +- *wring_json_reports(filename: str = "report", weak: bool = False, ref_value: float | None = None, target_probability: float = 0.99, problem_features: bool = False)* : This method is used to write the reports in json format. The parameters are: + - filename: str, i.e. the name of the file where the report will be saved + - weak: bool, i.e. whether to consider in the evaluation the weak constraints + - ref_value: float | None, i.e. the reference value to calculate the p-range + - target_probability: float, i.e. the target probability to calculate the time-to-solution + - problem_features: bool, i.e. whether to show the problem features in the report or not + + + +Examples: +--------- + +.. code-block:: python + + from mqt.qao.constraints import Constraints + from mqt.qao.variables import Variables + from mqt.qao.objectivefunction import ObjectiveFunction + from mqt.qao.problem import Problem + from mqt.qao.solver import Solver + + variables = Variables() + m1 = variables.add_continuous_variables_array( + "M1", [1, 2], -1, 2, -1, "uniform", "logarithmic 2" + ) + m2 = variables.add_continuous_variables_array( + "M2", [2, 1], -1, 2, -1, "uniform", "logarithmic 2" + ) + objective_function = ObjectiveFunction() + objective_function.add_objective_function(np.matmul(m1, m2).item(0, 0)) + constraint = Constraints() + constraint.add_constraint(constraint_expr, variable_precision=True) + problem = Problem() + problem.create_problem(variables, constraint, objective_function) + solver = Solver() + solution = solver.solve_simulated_annealing( + problem, + max_lambda_update=max_lambda_update, + lambda_update_mechanism=lambda_update, + lambda_strategy=lambda_strategy, + ) + + print(solution.optimal_solution_cost_functions_values()) + print(solution.check_constraint_optimal_solution()) + print(solution.check_constraint_all_solutions()) + solution.show_cumulative() + print(solution.valid_solutions()) + print(solution.p_range()) + print(solution.tts()) + solution.wring_json_reports() diff --git a/docs/Solver.rst b/docs/Solver.rst new file mode 100644 index 0000000..bddd3b8 --- /dev/null +++ b/docs/Solver.rst @@ -0,0 +1,330 @@ +Solver Class +============ + +It interface the problems with the quantum solvers. + +Supported solvers +----------------- + +The framework currently supports the following solvers:ΓΉ + +- D-Wave quantum annealer +- D-Wave simulated annealer +- qiskit Quantum Approximate Optimization Algorithm (QAOA) +- qiskit Variational Quantum Eigensolver (VQE) +- qiskit Grover Adaptive Search (GAS) + +Solver Selection and Configuration +---------------------------------- + +The class provides for exploiting the solver: + +- solve_simulated_annealing( + problem: Problem, + auto_setting: bool = False, + beta_range: list[float] | None = None, + num_reads: int = 100, + annealing_time: int = 100, + num_sweeps_per_beta: int = 1, + beta_schedule_type: str = "geometric", + seed: int | None = None, + initial_states: SampleSet | None = None, + initial_states_generator: str = "random", + max_lambda_update: int = 5, + lambda_update_mechanism: str = "sequential penalty increase", + lambda_strategy: str = "upper lower bound posiform and negaform method", + lambda_value: float = 1.0, + save_time: bool = False, + ) : Solve the problem using the simulated annealer. The parameters are: + - *problem*: the problem to solve + - *auto_setting*: if True, the parameters are automatically set + - *beta_range*: the range of beta values to use + - *num_reads*: the number of reads + - *annealing_time*: the annealing time + - *num_sweeps_per_beta*: the number of sweeps per beta + - *beta_schedule_type*: the beta schedule type + - *seed*: the seed + - *initial_states*: the initial states + - *initial_states_generator*: the initial states generator + - *max_lambda_update*: the maximum lambda update if the constraints are not satisfied + - *lambda_update_mechanism*: the lambda update mechanism among: + - *sequential penalty increase* + - *scaled sequential penalty increase* + - *binary search penalty algorithm* +- solve_dwave_quantum_annealer( + problem: Problem, + token: str, + auto_setting: bool = False, + failover: bool = True, + config_file: str | None = None, + endpoint: str | None = None, + solver: dict[str, str] | str = "Advantage_system4.1", + annealing_time_scheduling: float | list[list[float]] = 20.0, + num_reads: int = 100, + auto_scale: bool = True, + flux_drift_compensation: bool = True, + initial_state: dict[str, int] | None = None, + programming_thermalization: float = 1000.0, + readout_thermalization: float = 0.0, + reduce_intersample_correlation: bool = True, + max_lambda_update: int = 5, + lambda_update_mechanism: str = "sequential penalty increase", + lambda_strategy: str = "upper lower bound posiform and negaform method", + lambda_value: float = 1.0, + save_time: bool = False, + save_compilation_time: bool = False, + ) : Solve the problem using the D-Wave quantum annealer. The parameters are: + - *problem*: the problem to solve + - *token*: the token to access the D-Wave API + - *auto_setting*: if True, the parameters are automatically set + - *failover*: if True, the failover is enabled + - *config_file*: the configuration file + - *endpoint*: the endpoint + - *solver*: the solver to use + - *annealing_time_scheduling*: the annealing time scheduling + - *num_reads*: the number of reads + - *auto_scale*: if True, the problem is automatically scaled + - *flux_drift_compensation*: if True, the flux drift compensation is enabled + - *initial_state*: the initial state + - *programming_thermalization*: the programming thermalization + - *readout_thermalization*: the readout thermalization + - *reduce_intersample_correlation*: if True, the intersample correlation is reduced + - *max_lambda_update*: the maximum lambda update if the constraints are not satisfied + - *lambda_update_mechanism*: the lambda update mechanism among: + - *sequential penalty increase* + - *scaled sequential penalty increase* + - *binary search penalty algorithm* +- solve_grover_adaptive_search_qubo( + problem: Problem, + auto_setting: bool = False, + qubit_values: int = 0, + coeff_precision: float = 1.0, + threshold: int = 10, + num_runs: int = 10, + max_lambda_update: int = 5, + boundaries_estimation_method: str = "", + lambda_update_mechanism: str = "sequential penalty increase", + lambda_strategy: str = "upper lower bound posiform and negaform method", + lambda_value: float = 1.0, + save_time: bool = False, + save_compilation_time: bool = False, + ) : Solve the problem using the Grover Adaptive Search. The parameters are: + - *problem*: the problem to solve + - *auto_setting*: if True, the parameters are automatically set + - *qubit_values*: the number of qubit values, if the user want to specify it manually + - *coeff_precision*: the coefficient precision + - *threshold*: the threshold + - *num_runs*: the number of runs + - *max_lambda_update*: the maximum lambda update if the constraints are not satisfied + - *boundaries_estimation_method*: the boundaries estimation method for estimating the necessary number of qubit value + - *lambda_update_mechanism*: the lambda update mechanism among: + - *sequential penalty increase* + - *scaled sequential penalty increase* + - *binary search penalty algorithm* +- solve_qaoa_qubo( + problem: Problem, + auto_setting: bool = False, + num_runs: int = 10, + optimizer: Optimizer | None = None, + reps: int = 1, + initial_state: QuantumCircuit | None = None, + mixer: QuantumCircuit = None, + initial_point: np.ndarray[Any, Any] | None = None, + aggregation: float | Callable[[list[float]], float] | None = None, + callback: Callable[[int, np.ndarray[Any, Any], float, float], None] | None = None, + max_lambda_update: int = 5, + lambda_update_mechanism: str = "sequential penalty increase", + lambda_strategy: str = "upper lower bound posiform and negaform method", + lambda_value: float = 1.0, + save_time: bool = False, + save_compilation_time: bool = False, + ) : Solve the problem using the Quantum Approximate Optimization Algorithm. The parameters are: + - *problem*: the problem to solve + - *auto_setting*: if True, the parameters are automatically set + - *num_runs*: the number of runs + - *optimizer*: the optimizer + - *reps*: the number of repetitions + - *initial_state*: the initial state + - *mixer*: the mixer + - *initial_point*: the initial point + - *aggregation*: the aggregation function + - *callback*: the callback function + - *max_lambda_update*: the maximum lambda update if the constraints are not satisfied + - *lambda_update_mechanism*: the lambda update mechanism among: + - *sequential penalty increase* + - *scaled sequential penalty increase* + - *binary search penalty algorithm* +- solve_vqe_qubo( + self, + problem: Problem, + auto_setting: bool = False, + num_runs: int = 10, + optimizer: Optimizer | None = None, + ansatz: QuantumCircuit | None = None, + initial_point: np.ndarray[Any, Any] | None = None, + aggregation: float | Callable[[list[float]], float] | None = None, + callback: Callable[[int, np.ndarray[Any, Any], float, float], None] | None = None, + max_lambda_update: int = 5, + lambda_update_mechanism: str = "sequential penalty increase", + lambda_strategy: str = "upper lower bound posiform and negaform method", + lambda_value: float = 1.0, + save_time: bool = False, + save_compilation_time: bool = False, + ) : Solve the problem using the Variational Quantum Eigensolver. The parameters are: + - *problem*: the problem to solve + - *auto_setting*: if True, the parameters are automatically set + - *num_runs*: the number of runs + - *optimizer*: the optimizer + - *ansatz*: the ansatz + - *initial_point*: the initial point + - *aggregation*: the aggregation function + - *callback*: the callback function + - *max_lambda_update*: the maximum lambda update if the constraints are not satisfied + - *lambda_update_mechanism*: the lambda update mechanism among: + - *sequential penalty increase* + - *scaled sequential penalty increase* + - *binary search penalty algorithm* + +For each of them, the outcome is a Solution object. + + +Examples: +--------- + +Simulated Annealing +~~~~~~~~~~~~~~~~~~~ +.. code-block:: python + + from mqt.qao.constraints import Constraints + from mqt.qao.variables import Variables + from mqt.qao.objectivefunction import ObjectiveFunction + from mqt.qao.problem import Problem + from mqt.qao.solver import Solver + + variables = Variables() + m1 = variables.add_continuous_variables_array( + "M1", [1, 2], -1, 2, -1, "uniform", "logarithmic 2" + ) + m2 = variables.add_continuous_variables_array( + "M2", [2, 1], -1, 2, -1, "uniform", "logarithmic 2" + ) + objective_function = ObjectiveFunction() + objective_function.add_objective_function(np.matmul(m1, m2).item(0, 0)) + constraint = Constraints() + constraint.add_constraint(constraint_expr, variable_precision=True) + problem = Problem() + problem.create_problem(variables, constraint, objective_function) + solver = Solver() + solution = solver.solve_simulated_annealing( + problem, + max_lambda_update=max_lambda_update, + lambda_update_mechanism=lambda_update, + lambda_strategy=lambda_strategy, + ) + +Quantum Annealing +~~~~~~~~~~~~~~~~~~~ +.. code-block:: python + + from mqt.qao.constraints import Constraints + from mqt.qao.variables import Variables + from mqt.qao.objectivefunction import ObjectiveFunction + from mqt.qao.problem import Problem + from mqt.qao.solver import Solver + + variables = Variables() + m1 = variables.add_continuous_variables_array( + "M1", [1, 2], -1, 2, -1, "uniform", "logarithmic 2" + ) + m2 = variables.add_continuous_variables_array( + "M2", [2, 1], -1, 2, -1, "uniform", "logarithmic 2" + ) + objective_function = ObjectiveFunction() + objective_function.add_objective_function(np.matmul(m1, m2).item(0, 0)) + constraint = Constraints() + constraint.add_constraint(constraint_expr, variable_precision=True) + problem = Problem() + problem.create_problem(variables, constraint, objective_function) + solver = Solver() + solution = solver.solve_dwave_quantum_annealer( + token, + problem, + max_lambda_update=max_lambda_update, + lambda_update_mechanism=lambda_update, + lambda_strategy=lambda_strategy, + ) + + +Grover Adaptive Search +~~~~~~~~~~~~~~~~~~~~~~ +.. code-block:: python + + from mqt.qao.constraints import Constraints + from mqt.qao.variables import Variables + from mqt.qao.objectivefunction import ObjectiveFunction + from mqt.qao.problem import Problem + from mqt.qao.solver import Solver + + variables = Variables() + constraint = Constraints() + a0 = variables.add_binary_variable("a") + b0 = variables.add_binary_variable("b") + c0 = variables.add_binary_variable("c") + cost_function = cast(Expr, -a0 + 2 * b0 - 3 * c0 - 2 * a0 * c0 - 1 * b0 * c0) + objective_function = ObjectiveFunction() + objective_function.add_objective_function(cost_function) + problem = Problem() + problem.create_problem(variables, constraint, objective_function) + solver = Solver() + solution = solver.solve_grover_adaptive_search_qubo( + problem, qubit_values=6, num_runs=10 + ) + + + +Quantum Approximate Optimization Algorithm +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. code-block:: python + + from mqt.qao.constraints import Constraints + from mqt.qao.variables import Variables + from mqt.qao.objectivefunction import ObjectiveFunction + from mqt.qao.problem import Problem + from mqt.qao.solver import Solver + + variables = Variables() + constraint = Constraints() + a0 = variables.add_binary_variable("a") + b0 = variables.add_binary_variable("b") + c0 = variables.add_binary_variable("c") + cost_function = cast(Expr, -a0 + 2 * b0 - 3 * c0 - 2 * a0 * c0 - 1 * b0 * c0) + objective_function = ObjectiveFunction() + objective_function.add_objective_function(cost_function) + problem = Problem() + problem.create_problem(variables, constraint, objective_function) + solver = Solver() + solution = solver.solve_qaoa_qubo( + problem, + num_runs=10, + ) + + +Variational Quantum Eigensolver +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. code-block:: python + + variables = Variables() + constraint = Constraints() + a0 = variables.add_binary_variable("a") + b0 = variables.add_binary_variable("b") + c0 = variables.add_binary_variable("c") + cost_function = cast(Expr, -a0 + 2 * b0 - 3 * c0 - 2 * a0 * c0 - 1 * b0 * c0) + objective_function = ObjectiveFunction() + objective_function.add_objective_function(cost_function) + problem = Problem() + problem.create_problem(variables, constraint, objective_function) + solver = Solver() + solution = solver.solve_vqe_qubo( + problem, + num_runs=10, + ) diff --git a/docs/Support.rst b/docs/Support.rst new file mode 100644 index 0000000..dbda9df --- /dev/null +++ b/docs/Support.rst @@ -0,0 +1 @@ +.. include:: ../.github/support.rst diff --git a/docs/Usage.rst b/docs/Usage.rst new file mode 100644 index 0000000..c87827b --- /dev/null +++ b/docs/Usage.rst @@ -0,0 +1,31 @@ +Repository Usage +================ +There are two ways how to use MQT Quantum Auto Optimizer: + +#. Via the pip package ``mqt.qao`` +#. Directly via this repository + +Usage via pip package +--------------------- + +MQT Quantum Auto Optimizer is available via `PyPI `_ + +.. code-block:: console + + (venv) $ pip install mqt.qao + + +.. _pip_usage: + +Usage directly via this repository +---------------------------------- + +For that, the repository must be cloned and installed: + +.. code-block:: + + git clone https://github.com/cda-tum/mqt-qao.git + cd mqt-qao + pip install . + +Afterwards, the package can be used as described :ref:`above `. diff --git a/docs/Variable.rst b/docs/Variable.rst new file mode 100644 index 0000000..4c401d4 --- /dev/null +++ b/docs/Variable.rst @@ -0,0 +1,93 @@ +Variables Class +=============== + +It manages the optimization problems variables. + +Variables types supported +------------------------- +Three types of variables are supported: + +- Binary variables: + - unipolar, assuming 0 or 1 values + - bipolar, assuming -1 or 1 values +- Discrete variables, assuming a user-provided list of values +- Continuous variables, assuming all values in a user-provided range with a specified step + +Variables declarations +---------------------- +The class provides methods to declare variables: + +- *add_binary_variable(name: str)*, for adding binary variables + - *name*: variable name +- *add_binary_variable_array(name: str, shape: list[int])*, for adding array of binary variables (supports 1, 2, and 3 dimensional arrays of variables) + - *name*: variable name + - *shape*: list of integers representing the shape of the array +- *add_spin_variable(name: str)*, for adding bipolar binary variables + - *name*: variable name +- *add_spin_variable_array(name: str, shape: list[int])*, for adding array of bipolar binary variables (supports 1, 2, and 3 dimensional arrays of variables). + - *name*: variable name + - *shape*: list of integers representing the shape of the array +- *add_discrete_variable(name: str, values: list[float])*, for adding discrete variables. It needs the set of values. + - *name*: variable name + - *values*: list of values that the variable can assume +- *add_discrete_variable_array(name: str, values: list[float], shape: list[int])*, for adding array of discrete variables. Consider for all the same set of values (supports 1, 2, and 3 dimensional arrays of variables). + - *name*: variable name + - *values*: list of values that the variable can assume + - *shape*: list of integers representing the shape of the array +- *add_continuous_variable(name: str, min_val: float, max_val: float, precision: float, distribution: str = "uniform", encoding_mechanism: str = "")*, for adding continuous variables. It needs the min, max values and the wanted precision. + - *name*: variable name + - *values*: list of values + - *min_val*: minimum value that the variable can assume + - *max_val*: maximum value that the variable can assume + - *precision*: precision of the values that the variable can assume. If logarithmic encoding is considered the values must the wanted power of the base of the logarithm. + - *distribution*: distribution of the values in the range. It can be one among: + - *uniform* + - *geometric* + - *logarithmic* + - *encoding_mechanism*: encoding mechanism for the variable. By default logarithmic encoding is chosen if the precision is a power of two, arithmetic progression otherwise. It can be one among: + - *dictionary* + - *unitary* + - *logarithmic _base_*, with the possibility of specifying the base of the logarithm. Two is considered as default. + - *arithmetic progression* + - *bounded coefficient _bound_*, where _bound_ is the maximum coefficient that can be used in the encoding. +- *add_continuous_variable_array(name: str, min_val: float, max_val: float, precision: float, distribution: str = "uniform", encoding_mechanism: str = "", shape: list[int])*, for adding array of continuous variables. Consider for all the same min, max values and the wanted precision (supports 1, 2, and 3 dimensional arrays of variables). + - *name*: variable name + - *values*: list of values + - *min_val*: minimum value that the variable can assume + - *max_val*: maximum value that the variable can assume + - *precision*: precision of the values that the variable can assume. If logarithmic encoding is considered the values must the wanted power of the base of the logarithm. + - *distribution*: distribution of the values in the range. It can be one among: + - *uniform* + - *geometric* + - *logarithmic* + - *encoding_mechanism*: encoding mechanism for the variable. By default logarithmic encoding is chosen if the precision is a power of two, arithmetic progression otherwise. It can be one among: + - *dictionary* + - *unitary* + - *logarithmic _base_*, with the possibility of specifying the base of the logarithm. Two is considered as default. + - *arithmetic progression* + - *bounded coefficient _bound_*, where _bound_ is the maximum coefficient that can be used in the encoding. + - *shape*: list of integers representing the shape of the array + +Example: +-------- +.. code-block:: python + + from mqt.qao.variables import Variables + + # Variables object declaration + variables = Variables() + + # declaration of a unipolar binary variable + a0 = variables.add_binary_variable("a") + # declaration of a discrete variable, which can assume values -1, 1, 3 + b0 = variables.add_discrete_variable("b", [-1, 1, 3]) + # declaration of a continuous variable, which can assume values in the range [-2, 2] with a precision of 0.25 + c0 = variables.add_continuous_variable("c", -2, 2, 0.25) + + # declaration of a 2D array of continuous variables in the range [-1, 1] with a precision of 0.5 + m1 = variables.add_continuous_variables_array( + "M1", [1, 2], -1, 2, -1, "uniform", "logarithmic 2" + ) + m2 = variables.add_continuous_variables_array( + "M2", [2, 1], -1, 2, -1, "uniform", "logarithmic 2" + ) diff --git a/docs/_static/custom.css b/docs/_static/custom.css new file mode 100644 index 0000000..04260a5 --- /dev/null +++ b/docs/_static/custom.css @@ -0,0 +1,53 @@ +.acknowledgements { + margin-top: 1rem; + padding-bottom: 1rem; + padding-top: 1rem; + border-top: 1px solid var(--color-background-border); + font-size: var(--font-size--small); + color: var(--color-foreground-secondary); +} + +.acknowledgements-logos { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(100px, 1fr)); + grid-gap: 1em; + align-items: center; + margin-top: 0.5rem; +} +.acknowledgement { + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; +} + +/* override the default background color for literal strings */ +body:not([data-theme="light"]) .highlight .sa, +.highlight .sb, +.highlight .sc, +.highlight .dl, +.highlight .sd, +.highlight .s2, +.highlight .se, +.highlight .sh, +.highlight .si, +.highlight .sx, +.highlight .sr, +.highlight .s1, +.highlight .ss, +.highlight .s1, +.highlight .s { + background-color: #00000001; +} + +/* provide dark mode overrides for mystnb variables */ +body:not([data-theme="light"]) { + --mystnb-source-bg-color: #131416; + --mystnb-stdout-bg-color: #1a1c1e; + --mystnb-stderr-bg-color: #442222; + --mystnb-traceback-bg-color: #202020; +} + +body:not([data-theme="light"]) .highlight .gp { + color: #c65d09; +} diff --git a/docs/_static/mqt_dark.png b/docs/_static/mqt_dark.png new file mode 100644 index 0000000..adc910c Binary files /dev/null and b/docs/_static/mqt_dark.png differ diff --git a/docs/_static/mqt_light.png b/docs/_static/mqt_light.png new file mode 100644 index 0000000..be279e6 Binary files /dev/null and b/docs/_static/mqt_light.png differ diff --git a/docs/_static/mqt_qao.png b/docs/_static/mqt_qao.png new file mode 100644 index 0000000..674d1ea Binary files /dev/null and b/docs/_static/mqt_qao.png differ diff --git a/docs/_templates/page.html b/docs/_templates/page.html new file mode 100644 index 0000000..4aad54d --- /dev/null +++ b/docs/_templates/page.html @@ -0,0 +1,76 @@ +{% extends "furo/page.html" %} {% block footer %} +
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+
+{% endblock footer %} diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..5db9e1f --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,141 @@ +"""Sphinx configuration file.""" + +from __future__ import annotations + +import warnings +from importlib import metadata +from pathlib import Path +from typing import TYPE_CHECKING + +import pybtex.plugin +from pybtex.style.formatting.unsrt import Style as UnsrtStyle +from pybtex.style.template import field, href + +ROOT = Path(__file__).parent.parent.resolve() + + +try: + version = metadata.version("mqt.qao") +except ModuleNotFoundError: + msg = ( + "Package should be installed to produce documentation! " + "Assuming a modern git archive was used for version discovery." + ) + warnings.warn(msg, stacklevel=1) + + from setuptools_scm import get_version + + version = get_version(root=str(ROOT), fallback_root=ROOT) + +# Filter git details from version +release = version.split("+")[0] +if TYPE_CHECKING: + from pybtex.database import Entry + from pybtex.richtext import HRef + +project = "MQT Quantum Auto Optimizer" +author = "Chair for Design Automation, Technical University of Munich" +language = "en" +project_copyright = "2024, Chair for Design Automation, Technical University of Munich" +# -- General configuration --------------------------------------------------- + +master_doc = "index" + +templates_path = ["_templates"] +html_css_files = ["custom.css"] + +extensions = [ + "sphinx.ext.napoleon", + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.mathjax", + "sphinx.ext.intersphinx", + "sphinx.ext.autosectionlabel", + "sphinx.ext.viewcode", + "sphinx.ext.githubpages", + "sphinxcontrib.bibtex", + "sphinx_copybutton", + "hoverxref.extension", + "nbsphinx", + "sphinxext.opengraph", + "sphinx_autodoc_typehints", +] + +pygments_style = "colorful" + +add_module_names = False + +modindex_common_prefix = ["mqt.qao."] + +intersphinx_mapping = { + "python": ("https://docs.python.org/3", None), + "typing_extensions": ("https://typing-extensions.readthedocs.io/en/latest/", None), + "qiskit": ("https://qiskit.org/documentation/", None), + "mqt": ("https://mqt.readthedocs.io/en/latest/", None), +} + +nbsphinx_execute = "never" +highlight_language = "python3" +nbsphinx_execute_arguments = [ + "--InlineBackend.figure_formats={'svg', 'pdf'}", + "--InlineBackend.rc=figure.dpi=200", +] +nbsphinx_kernel_name = "python3" + +autosectionlabel_prefix_document = True + +hoverxref_auto_ref = True +hoverxref_domains = ["cite", "py"] +hoverxref_roles = [] +hoverxref_mathjax = True +hoverxref_role_types = { + "ref": "tooltip", + "p": "tooltip", + "labelpar": "tooltip", + "class": "tooltip", + "meth": "tooltip", + "func": "tooltip", + "attr": "tooltip", + "property": "tooltip", +} +exclude_patterns = ["_build", "build", "**.ipynb_checkpoints", "Thumbs.db", ".DS_Store", ".env"] + + +class CDAStyle(UnsrtStyle): + """Custom style for including PDF links.""" + + @staticmethod + def format_url(_e: Entry) -> HRef: + """Format URL field as a link to the PDF.""" + url = field("url", raw=True) + return href()[url, "[PDF]"] + + +pybtex.plugin.register_plugin("pybtex.style.formatting", "cda_style", CDAStyle) + +bibtex_bibfiles = ["refs.bib"] +bibtex_default_style = "cda_style" + +copybutton_prompt_text = r"(?:\(venv\) )?(?:\[.*\] )?\$ " +copybutton_prompt_is_regexp = True +copybutton_line_continuation_character = "\\" + +autosummary_generate = True + + +typehints_use_rtype = False +napoleon_use_rtype = False +napoleon_google_docstring = True +napoleon_numpy_docstring = False + +# -- Options for HTML output ------------------------------------------------- +html_theme = "furo" +html_static_path = ["_static"] +html_theme_options = { + "light_logo": "mqt_dark.png", + "dark_logo": "mqt_light.png", + "source_repository": "https://github.com/cda-tum/mqt-qao/", + "source_branch": "main", + "source_directory": "docs/", + "navigation_with_keys": True, +} diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 0000000..a21d86b --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,48 @@ +Welcome to MQT Quantum Auto Optimizer's documentation! +====================================================== + +MQT Quantum Auto Optimizer is a tool for automatic framework to assist users in utilizing quantum solvers for optimization tasks while preserving interfaces that closely resemble conventional optimization practices and is developed as part of the `Munich Quantum Toolkit `_ (*MQT*) by the `Chair for Design Automation `_ at the `Technical University of Munich `_. + +From a user's perspective, the framework is used as follows: + +.. image:: /_static/mqt_qao.png + :width: 100% + :alt: Illustration of the MQT Quantum Auto Optimizer framework + :align: center + +The framework is designed to be user-friendly and to provide a high-level interface for assisting assist users in utilizing quantum solvers for optimization tasks, not requiring any prior knowledge of quantum computing. +The framework prompts users to specify variables, optimization criteria, as well as validity constraints and, afterwards, allows them to choose the desired solver. Subsequently, it automatically transforms the problem description into a format compatible with the chosen solver and provides the resulting solution. Additionally, the framework offers instruments for analyzing solution validity and quality. + +If you are interested in the theory behind MQT Quantum Auto Optimizer, have a look at the publications in the :doc:`references list `. + + +---- + + .. toctree:: + :hidden: + + self + + .. toctree:: + :maxdepth: 1 + :caption: User Guide + :glob: + + Quickstart + Usage + Variable + Constraints + ObjectiveFunction + Problem + Solver + Solution + References + + .. toctree:: + :maxdepth: 1 + :caption: Developers + :glob: + + Contributing + DevelopmentGuide + Support diff --git a/docs/refs.bib b/docs/refs.bib new file mode 100644 index 0000000..42f9a00 --- /dev/null +++ b/docs/refs.bib @@ -0,0 +1,6 @@ +@INPROCEEDINGS{volpe2024towards, + AUTHOR = {D.Volpe and N. Quetschlich and M. Graziano and G. Turvani and R. Wille}, + TITLE = {{Towards an Automatic Framework for Solving Optimization Problems with Quantum Computers}}, + YEAR = {2024}, + BOOKTITLE = {IEEE International Conference on Quantum Software (QSW)}, +} diff --git a/noxfile.py b/noxfile.py index e0b7845..2581909 100644 --- a/noxfile.py +++ b/noxfile.py @@ -15,25 +15,13 @@ nox.needs_version = ">=2024.3.2" nox.options.default_venv_backend = "uv|virtualenv" -PYTHON_ALL_VERSIONS = ["3.10", "3.11"] +PYTHON_ALL_VERSIONS = ["3.9", "3.10", "3.11", "3.12"] BUILD_REQUIREMENTS = [ "setuptools>=66.1", - "setuptools_scm>=7", + "setuptools_scm>=8.1", "wheel>=0.40", - "matplotlib>=3.8", - "numpy>=1.23", - "qiskit==0.45.3", - "qiskit_optimization==0.4.0", - "qiskit_ibm_runtime==0.14.0", - "pytest==7.4.0", - "scikit-learn>=1.3", - "dimod>=0.12.0", - "dwave-preprocessing>=0.6.3 ", - "dwave-samplers==1.2", - "dwave-system>=1.20", - "pandas>=2.1", - "qubovert>=1.0", + "cython>=3.0", ] if os.environ.get("CI", None): diff --git a/pyproject.toml b/pyproject.toml index ef7e3a2..e13bb21 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [build-system] requires = [ - "setuptools>=61", - "setuptools_scm>=7", + "setuptools>=66.1", + "setuptools_scm>=8.1", ] build-backend = "setuptools.build_meta" @@ -13,28 +13,22 @@ authors = [ { name = "Deborah Volpe", email = "deborah.volpe@polito.it"}, { name = "Nils Quetschlich", email = "nils.quetschlich@tum.de" }, ] -keywords = ["MQT", "quantum computing", "optimization", "QUBO"] +keywords = ["MQT", "quantum-computing", "optimization", "QUBO"] license = { file = "LICENSE" } -requires-python = ">=3.10,<3.12" +requires-python = ">=3.9" dynamic = ["version"] dependencies = [ - "qubovert>=1.0", - "sympy", - "numpy>=1.23", - "dimod>=0.12.0", - "dwave-samplers==1.2", - "dwave-system>=1.20", - "importlib_metadata>=3.6; python_version < '3.10'", - "importlib_resources>=5.9; python_version < '3.10'", - "docplex", - "qiskit=0.45.3", - "qiskit_optimization==0.4.0", - "qiskit_ibm_runtime==0.14.0", - "matplotlib", - "pandas>=2.1", - "PyPortfolioOpt", - "scikit-learn>=1.3", + "qiskit>=1.0", + "qiskit_optimization>=0.6", + "qiskit_aer>=0.14", + "pandas>=2.1.2", + "numpy>=1.26", + "qubovert>=1.2.5", + "matplotlib>=3.8", + "docplex>=2.21.207", + "dwave-system>=1.21.0", + "dwave-samplers>=1.2.0", ] classifiers = [ @@ -45,8 +39,10 @@ classifiers = [ "Operating System :: MacOS", "Operating System :: POSIX :: Linux", "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", "Intended Audience :: Science/Research", "Natural Language :: English", "Topic :: Scientific/Engineering :: Electronic Design Automation (EDA)", @@ -55,7 +51,6 @@ classifiers = [ [project.optional-dependencies] test = ["pytest>=7.2"] coverage = ["mqt.qao[test]", "pytest-cov>=4"] -dev = ["mqt.qao[coverage]"] docs = [ "furo>=2023.08.17", "sphinx~=7.0", @@ -71,6 +66,7 @@ docs = [ "sphinx-autodoc-typehints", "qiskit[visualization]", ] +dev = ["mqt.qao[coverage,docs]"] [project.urls] Homepage = "https://github.com/cda-tum/mqt-qao" @@ -82,37 +78,17 @@ Research = "https://www.cda.cit.tum.de/research/quantum/" [tool.pytest.ini_options] -pythonpath = ["./src"] minversion = "7.2" testpaths = ["tests"] +pythonpath = ["src"] addopts = ["-ra", "--strict-markers", "--strict-config", "--showlocals"] -log_cli_level = "INFO" +log_cli_level = "info" xfail_strict = true filterwarnings = [ "error", - "ignore:.*pkg_resources.*:DeprecationWarning:", - "ignore:.*sre_.*:DeprecationWarning:", - "ignore:.*Rigetti.*:UserWarning:", - "ignore:.*Values in x.*:RuntimeWarning:", - "ignore:.*The Rebase2QuditGatePass has moved to bqskit.passes.retarget.two.*:DeprecationWarning:", - "ignore:.*The least populated class in y has only 3 members, which is less than n_splits=5.*:UserWarning:", - "ignore:.*divide by zero encountered in det.*:RuntimeWarning:", - "ignore:.*invalid value encountered in det.*:RuntimeWarning:", - "ignore:..*env.action_masks to get variables from other wrappers is deprecated and will be removed in v1.0.*:UserWarning:", - "ignore::SyntaxWarning:.*(docplex).*", - "ignore::DeprecationWarning:.*(docplex).*", - "ignore:.*pytorch.*:UserWarning:", - "ignore::DeprecationWarning:.*(importlib).*", - "ignore:.*:DeprecationWarning:qiskit.*", - "ignore:.*:PendingDeprecationWarning:qiskit.*", - "ignore:.*:ImportWarning:.*", - "ignore:.*:DeprecationWarning:qiskit.providers.ibmq.*", - "ignore:.*qiskit.algorithms.*:DeprecationWarning:.*", - "ignore::sklearn.exceptions.InconsistentVersionWarning:sklearn:", + "ignore:.*Qiskit with Python 3.8.*:DeprecationWarning:", ] -[tool.check-wheel-contents] -ignore = "W002" [tool.coverage] run.source = ["mqt.qao"] @@ -129,19 +105,32 @@ precision = 1 [tool.mypy] -mypy_path = "$MYPY_CONFIG_FILE_DIR/src" files = ["src", "tests"] -python_version = "3.11" -strict = true +mypy_path = ["$MYPY_CONFIG_FILE_DIR/src"] +python_version = "3.9" +warn_unused_configs = true enable_error_code = ["ignore-without-code", "redundant-expr", "truthy-bool"] -warn_unreachable = true +strict = true +disallow_untyped_defs = false explicit_package_bases = true -no_implicit_reexport = true -pretty = true +warn_unreachable = true [[tool.mypy.overrides]] -module = ["qubovert.*", "sympy.*", "dwave.samplers.*", "dwave.system.*", "docplex.*", "qiskit.*", "qiskit_optimization.*", "qiskit_ibm_provider.*", "matplotlib.*", "pandas.*", "pypfopt.*", "sklearn.*"] +module = [ + "qiskit.*", + "qiskit_optimization.*", + "qiskit_algorithms.*", + "qiskit_aer.*", + "matplotlib.*", + "qubovert.*", + "sympy.*", + "qiskit_ibm_runtime.*", + "matplotlib.*", + "docplex.*", + "dimod.*", + "dwave.*" +] ignore_missing_imports = true @@ -151,6 +140,7 @@ extend-include = ["*.ipynb"] src = ["src"] preview = true unsafe-fixes = true + [tool.ruff.lint] extend-select = [ "A", # flake8-builtins @@ -186,30 +176,45 @@ extend-select = [ "SLOT", # flake8-slots "SIM", # flake8-simplify "TCH", # flake8-type-checking - "TID", # flake8-tidy-imports + "TID251", # flake8-tidy-imports.banned-api "TRY", # tryceratops "UP", # pyupgrade "YTT", # flake8-2020 ] ignore = [ - "ANN101", # Missing type annotation for self in method - "ANN102", # Missing type annotation for cls in classmethod + "ANN101", # Missing type annotation for `self` in method + "ANN102", # Missing type annotation for `cls` in classmethod "ISC001", # Conflicts with formatter - "E501", # Line too long (Black is enough) - "PLR", # Design related pylint codes + "PLR09", # Too many <...> + "PLR2004", # Magic value used in comparison + "PLC0415", # Import should be at top of file + "PT004", # Incorrect, just usefixtures instead. "S101", # Use of assert detected - "I001", # isort: Missing import + "S404", # `subprocess` module is possibly insecure ] -flake8-unused-arguments.ignore-variadic-names = true isort.required-imports = ["from __future__ import annotations"] +[tool.ruff.lint.flake8-tidy-imports.banned-api] +"typing.Callable".msg = "Use collections.abc.Callable instead." +"typing.Iterator".msg = "Use collections.abc.Iterator instead." +"typing.Mapping".msg = "Use collections.abc.Mapping instead." +"typing.Sequence".msg = "Use collections.abc.Sequence instead." +"typing.Set".msg = "Use collections.abc.Set instead." +"typing.Self".msg = "Use scikit_build_core._compat.typing.Self instead." +"typing_extensions.Self".msg = "Use scikit_build_core._compat.typing.Self instead." +"typing.assert_never".msg = "Use scikit_build_core._compat.typing.assert_never instead." +"importlib.resources".msg = "Use scikit_build_core._compat.importlib.resources instead." +"importlib_resources".msg = "Use scikit_build_core._compat.importlib.resources instead." + [tool.ruff.lint.per-file-ignores] -"*.pyi" = ["D"] # pydocstyle +"tests/**" = ["T20", "ANN"] +"docs/**" = ["T20"] +"noxfile.py" = ["T20", "TID251"] +"*.pyi" = ["D418", "PYI021"] # pydocstyle "*.ipynb" = [ "D", # pydocstyle "E402", # Allow imports to appear anywhere in Jupyter notebooks "I002", # Allow missing `from __future__ import annotations` import - "I001", # Allow missing unsorted import ] [tool.ruff.lint.pydocstyle] diff --git a/scripts/Knapsack/Data/f10_l-d_kp_20_879 b/scripts/Knapsack/Data/f10_l-d_kp_20_879 new file mode 100644 index 0000000..e2cc105 --- /dev/null +++ b/scripts/Knapsack/Data/f10_l-d_kp_20_879 @@ -0,0 +1,21 @@ +20 879 +91 84 +72 83 +90 43 +46 4 +55 44 +8 6 +35 82 +75 92 +61 25 +15 83 +77 56 +40 18 +63 58 +75 14 +29 48 +75 70 +17 96 +78 32 +40 68 +44 92 diff --git a/scripts/Knapsack/Data/f1_l-d_kp_10_269 b/scripts/Knapsack/Data/f1_l-d_kp_10_269 new file mode 100644 index 0000000..4adaa2c --- /dev/null +++ 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0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 diff --git a/scripts/Knapsack/Knapsack.py b/scripts/Knapsack/Knapsack.py new file mode 100644 index 0000000..78b7dd4 --- /dev/null +++ b/scripts/Knapsack/Knapsack.py @@ -0,0 +1,109 @@ +from __future__ import annotations + +import locale +import os +from pathlib import Path +from typing import cast + +import numpy as np +from sympy import Expr + +# for managing symbols +from mqt.qao import Constraints, ObjectiveFunction, Problem, Solver, Variables + +lambdas_method = [ + "upper_bound_only_positive", + "maximum_coefficient", + "VLM", + "MOMC", + "MOC", + "upper lower bound naive", + "upper lower bound posiform and negaform method", +] +lambdas_conf = [ + ("upper_bound_only_positive", "sequential penalty increase"), + ("maximum_coefficient", "sequential penalty increase"), + ("VLM", "sequential penalty increase"), + ("MOMC", "sequential penalty increase"), + ("MOC", "sequential penalty increase"), + ("upper lower bound naive", "sequential penalty increase"), + ("upper lower bound posiform and negaform method", "sequential penalty increase"), + ("upper_bound_only_positive", "scaled sequential penalty increase"), + ("maximum_coefficient", "scaled sequential penalty increase"), + ("VLM", "scaled sequential penalty increase"), + ("MOMC", "scaled sequential penalty increase"), + ("MOC", "scaled sequential penalty increase"), + ("upper lower bound naive", "scaled sequential penalty increase"), + ("upper lower bound posiform and negaform method", "scaled sequential penalty increase"), + ("upper_bound_only_positive", "binary search penalty algorithm"), + ("maximum_coefficient", "binary search penalty algorithm"), + ("VLM", "binary search penalty algorithm"), + ("MOMC", "binary search penalty algorithm"), + ("MOC", "binary search penalty algorithm"), + ("upper lower bound naive", "binary search penalty algorithm"), + ("upper lower bound posiform and negaform method", "binary search penalty algorithm"), +] + +files = os.listdir("Data/") +for file in files: + print(file) + with Path("./Data/" + file).open("r", encoding=locale.getpreferredencoding(False)) as f: + lines = f.readlines() + el = lines[0].split() + objects = int(el[0]) + W_max = float(el[1]) + w = [] + p = [] + for i in range(1, len(lines)): + el = lines[i].split() + p.append(float(el[0])) + w.append(float(el[1])) + p_arr = np.asarray(p) + w_arr = np.asarray(w) + + for lambdas in lambdas_method: + variables = Variables() + obj = variables.add_binary_variables_array("obj", [objects]) + objective_function = ObjectiveFunction() + objective_function.add_objective_function(cast(Expr, np.dot(np.transpose(obj), p_arr)), minimization=False) + constraint = Constraints() + constraint.add_constraint(str(np.dot(np.transpose(obj), w_arr)) + " <= " + format(W_max)) + problem = Problem() + problem.create_problem(variables, constraint, objective_function) + solver = Solver() + solution = solver.solve_simulated_annealing( + problem, lambda_strategy=lambdas, auto_setting=True, save_time=True, max_lambda_update=1 + ) + if not isinstance(solution, bool): + all_satisfy, each_satisfy = solution.check_constraint_optimal_solution() + solution.valid_solutions() + solution.wring_json_reports( + filename="simulated_annealing_knapsack_" + file + "_" + lambdas, problem_features=True + ) + + for lambdas_m, lambdas_update in lambdas_conf: + variables = Variables() + obj = variables.add_binary_variables_array("obj", [objects]) + objective_function = ObjectiveFunction() + objective_function.add_objective_function(cast(Expr, np.dot(np.transpose(obj), p_arr)), minimization=False) + constraint = Constraints() + constraint.add_constraint(str(np.dot(np.transpose(obj), w_arr)) + " <= " + format(W_max)) + problem = Problem() + problem.create_problem(variables, constraint, objective_function) + solver = Solver() + solution = solver.solve_simulated_annealing( + problem, + lambda_strategy=lambdas_m, + lambda_update_mechanism=lambdas_update, + auto_setting=True, + save_time=True, + max_lambda_update=1, + ) + if not isinstance(solution, bool): + all_satisfy, each_satisfy = solution.check_constraint_optimal_solution() + print(all_satisfy, each_satisfy) + solution.valid_solutions() + solution.wring_json_reports( + filename="simulated_annealing_knapsack_" + file + "_" + lambdas_m + "_" + lambdas_update, + problem_features=True, + ) diff --git a/scripts/linear_regression/LinearRegression.py b/scripts/linear_regression/LinearRegression.py new file mode 100644 index 0000000..46231b5 --- /dev/null +++ b/scripts/linear_regression/LinearRegression.py @@ -0,0 +1,160 @@ +from __future__ import annotations + +from typing import cast + +import numpy as np +import pandas as pd +from sklearn.decomposition import PCA +from sklearn.utils import shuffle +from sympy import Expr + +# for managing symbols +from mqt.qao import Constraints, ObjectiveFunction, Problem, Solver, Variables + +df = pd.read_csv("iris_csv.csv") +df = shuffle(df) +d = len(df.columns) - 1 +N = len(df[df.keys()[1]]) +feat = 2 +X = np.zeros([N, d]) +Y = np.zeros(N) +TrainingPercentage = 0.7 +N_Traning = int(N * TrainingPercentage) +N_Test = N - N_Traning +i = 0 +for key in df: + j = 0 + if key != "class": + for el in df[key]: + X[j, i] = el + j += 1 + i += 1 + else: + for el in df[key]: + if el == "Iris-setosa": + Y[j] = 1 + else: + Y[j] = -1 + j += 1 +pca = PCA(n_components=feat) +X = pca.fit_transform(X=X, y=Y) +X_traning = np.hstack((X[:N_Traning, :], np.ones((N_Traning, 1)))) +X_test = np.hstack((X[N_Traning:, :], np.ones((N_Test, 1)))) + +Y_traning = Y[:N_Traning] +Y_test = Y[N_Traning:] +variables = Variables() +w = variables.add_continuous_variables_array("w", [feat + 1, 1], -0.25, 0.25, 0.25) +objective_function = ObjectiveFunction() +objective_function.add_objective_function( + cast( + Expr, + ( + np.dot(np.dot(np.dot(np.transpose(w), np.transpose(X_traning)), X_traning), w) + - 2 * np.dot(np.dot(np.transpose(w), np.transpose(X_traning)), Y_traning) + + np.dot(np.transpose(Y_traning), Y_traning) + )[0, 0], + ) +) +constraint = Constraints() +problem = Problem() +problem.create_problem(variables, constraint, objective_function) +solver = Solver() + +PredictionRes = {} + +solution = solver.solve_simulated_annealing( + problem, auto_setting=True, save_time=True, max_lambda_update=0, save_compilation_time=True +) +if not isinstance(solution, bool): + all_satisfy, each_satisfy = solution.check_constraint_optimal_solution() + print(all_satisfy, each_satisfy) + solution.valid_solutions() + print(solution.best_energy) + solution.wring_json_reports(filename="simulated_annealing_linear_regression_Iris", problem_features=True) + w_conf = solution.best_solution["w"] + Y_obtained_training = np.dot(X_traning, w_conf) + TP_tr = 0 + TN_tr = 0 + FP_tr = 0 + FN_tr = 0 + for i in range(N_Traning): + if Y_obtained_training[i] > 0: + if Y_traning[i] == 1: + TP_tr += 1 + else: + FP_tr += 1 + elif Y_traning[i] == 1: + FN_tr += 1 + else: + TN_tr += 1 + + try: + Accuracy_tr = (TN_tr + TP_tr) / (TN_tr + TP_tr + FP_tr + FN_tr) + except ZeroDivisionError: + Accuracy_tr = 0 + + try: + Precision_tr = TP_tr / (TP_tr + FP_tr) + except ZeroDivisionError: + Precision_tr = 0 + + try: + Recall_tr = TP_tr / (TP_tr + FN_tr) + except ZeroDivisionError: + Recall_tr = 0 + + try: + F1_score_tr = 2 * Precision_tr * Recall_tr / (Precision_tr + Recall_tr) + except ZeroDivisionError: + F1_score_tr = 0 + + PredictionRes["Accuracy training"] = Accuracy_tr + PredictionRes["Precision training"] = Precision_tr + PredictionRes["Recall training"] = Recall_tr + PredictionRes["F1 training"] = F1_score_tr + Y_obtained_test = np.dot(X_test, w_conf) + print(Accuracy_tr, Precision_tr, Recall_tr) + TP_t = 0 + TN_t = 0 + FP_t = 0 + FN_t = 0 + for i in range(N_Test - 1): + if Y_obtained_test[i] > 0: + if Y_test[i] == 1: + TP_t += 1 + else: + FP_t += 1 + elif Y_test[i] == 1: + FN_t += 1 + else: + TN_t += 1 + + try: + Accuracy_t = (TN_t + TP_t) / (TN_t + TP_t + FP_t + FN_t) + except ZeroDivisionError: + Accuracy_t = 0 + + try: + Precision_t = TP_t / (TP_t + FP_t) + except ZeroDivisionError: + Precision_t = 0 + + try: + Recall_t = TP_t / (TP_t + FN_t) + except ZeroDivisionError: + Recall_t = 0 + + try: + F1_score_t = 2 * Precision_t * Recall_t / (Precision_t + Recall_t) + except ZeroDivisionError: + F1_score_t = 0 + print(Accuracy_t, Precision_t, Recall_t) + PredictionRes["Accuracy test"] = Accuracy_t + PredictionRes["Precision test"] = Precision_t + PredictionRes["Recall test"] = Recall_t + PredictionRes["F1 test"] = F1_score_t + + +df1 = pd.DataFrame.from_dict(PredictionRes, orient="index") +df1.to_csv("PredictionResSimulatedAnnealing.csv") diff --git a/scripts/linear_regression/PredictionResSimulatedAnnealing.csv b/scripts/linear_regression/PredictionResSimulatedAnnealing.csv new file mode 100644 index 0000000..53de89d --- /dev/null +++ b/scripts/linear_regression/PredictionResSimulatedAnnealing.csv @@ -0,0 +1,9 @@ +,0 +Accuracy training,1.0 +Precision training,1.0 +Recall training,1.0 +F1 training,1.0 +Accuracy test,1.0 +Precision test,1.0 +Recall test,1.0 +F1 test,1.0 diff --git a/scripts/linear_regression/iris_csv.csv b/scripts/linear_regression/iris_csv.csv new file mode 100644 index 0000000..3c63137 --- /dev/null +++ b/scripts/linear_regression/iris_csv.csv @@ -0,0 +1,101 @@ +sepallength,sepalwidth,petallength,petalwidth,class +5.1,3.5,1.4,0.2,Iris-setosa +4.9,3.0,1.4,0.2,Iris-setosa +4.7,3.2,1.3,0.2,Iris-setosa +4.6,3.1,1.5,0.2,Iris-setosa +5.0,3.6,1.4,0.2,Iris-setosa +5.4,3.9,1.7,0.4,Iris-setosa +4.6,3.4,1.4,0.3,Iris-setosa +5.0,3.4,1.5,0.2,Iris-setosa +4.4,2.9,1.4,0.2,Iris-setosa +4.9,3.1,1.5,0.1,Iris-setosa +5.4,3.7,1.5,0.2,Iris-setosa +4.8,3.4,1.6,0.2,Iris-setosa +4.8,3.0,1.4,0.1,Iris-setosa +4.3,3.0,1.1,0.1,Iris-setosa +5.8,4.0,1.2,0.2,Iris-setosa +5.7,4.4,1.5,0.4,Iris-setosa +5.4,3.9,1.3,0.4,Iris-setosa +5.1,3.5,1.4,0.3,Iris-setosa +5.7,3.8,1.7,0.3,Iris-setosa +5.1,3.8,1.5,0.3,Iris-setosa +5.4,3.4,1.7,0.2,Iris-setosa +5.1,3.7,1.5,0.4,Iris-setosa +4.6,3.6,1.0,0.2,Iris-setosa +5.1,3.3,1.7,0.5,Iris-setosa +4.8,3.4,1.9,0.2,Iris-setosa +5.0,3.0,1.6,0.2,Iris-setosa +5.0,3.4,1.6,0.4,Iris-setosa +5.2,3.5,1.5,0.2,Iris-setosa +5.2,3.4,1.4,0.2,Iris-setosa +4.7,3.2,1.6,0.2,Iris-setosa +4.8,3.1,1.6,0.2,Iris-setosa +5.4,3.4,1.5,0.4,Iris-setosa +5.2,4.1,1.5,0.1,Iris-setosa +5.5,4.2,1.4,0.2,Iris-setosa +4.9,3.1,1.5,0.1,Iris-setosa +5.0,3.2,1.2,0.2,Iris-setosa +5.5,3.5,1.3,0.2,Iris-setosa +4.9,3.1,1.5,0.1,Iris-setosa +4.4,3.0,1.3,0.2,Iris-setosa +5.1,3.4,1.5,0.2,Iris-setosa +5.0,3.5,1.3,0.3,Iris-setosa +4.5,2.3,1.3,0.3,Iris-setosa +4.4,3.2,1.3,0.2,Iris-setosa +5.0,3.5,1.6,0.6,Iris-setosa +5.1,3.8,1.9,0.4,Iris-setosa +4.8,3.0,1.4,0.3,Iris-setosa +5.1,3.8,1.6,0.2,Iris-setosa +4.6,3.2,1.4,0.2,Iris-setosa +5.3,3.7,1.5,0.2,Iris-setosa +5.0,3.3,1.4,0.2,Iris-setosa +7.0,3.2,4.7,1.4,Iris-versicolor +6.4,3.2,4.5,1.5,Iris-versicolor +6.9,3.1,4.9,1.5,Iris-versicolor +5.5,2.3,4.0,1.3,Iris-versicolor +6.5,2.8,4.6,1.5,Iris-versicolor +5.7,2.8,4.5,1.3,Iris-versicolor +6.3,3.3,4.7,1.6,Iris-versicolor +4.9,2.4,3.3,1.0,Iris-versicolor +6.6,2.9,4.6,1.3,Iris-versicolor +5.2,2.7,3.9,1.4,Iris-versicolor +5.0,2.0,3.5,1.0,Iris-versicolor +5.9,3.0,4.2,1.5,Iris-versicolor +6.0,2.2,4.0,1.0,Iris-versicolor +6.1,2.9,4.7,1.4,Iris-versicolor +5.6,2.9,3.6,1.3,Iris-versicolor +6.7,3.1,4.4,1.4,Iris-versicolor +5.6,3.0,4.5,1.5,Iris-versicolor +5.8,2.7,4.1,1.0,Iris-versicolor +6.2,2.2,4.5,1.5,Iris-versicolor +5.6,2.5,3.9,1.1,Iris-versicolor +5.9,3.2,4.8,1.8,Iris-versicolor +6.1,2.8,4.0,1.3,Iris-versicolor +6.3,2.5,4.9,1.5,Iris-versicolor +6.1,2.8,4.7,1.2,Iris-versicolor +6.4,2.9,4.3,1.3,Iris-versicolor +6.6,3.0,4.4,1.4,Iris-versicolor +6.8,2.8,4.8,1.4,Iris-versicolor +6.7,3.0,5.0,1.7,Iris-versicolor +6.0,2.9,4.5,1.5,Iris-versicolor +5.7,2.6,3.5,1.0,Iris-versicolor +5.5,2.4,3.8,1.1,Iris-versicolor +5.5,2.4,3.7,1.0,Iris-versicolor +5.8,2.7,3.9,1.2,Iris-versicolor +6.0,2.7,5.1,1.6,Iris-versicolor +5.4,3.0,4.5,1.5,Iris-versicolor +6.0,3.4,4.5,1.6,Iris-versicolor +6.7,3.1,4.7,1.5,Iris-versicolor +6.3,2.3,4.4,1.3,Iris-versicolor +5.6,3.0,4.1,1.3,Iris-versicolor +5.5,2.5,4.0,1.3,Iris-versicolor +5.5,2.6,4.4,1.2,Iris-versicolor +6.1,3.0,4.6,1.4,Iris-versicolor +5.8,2.6,4.0,1.2,Iris-versicolor +5.0,2.3,3.3,1.0,Iris-versicolor +5.6,2.7,4.2,1.3,Iris-versicolor +5.7,3.0,4.2,1.2,Iris-versicolor +5.7,2.9,4.2,1.3,Iris-versicolor +6.2,2.9,4.3,1.3,Iris-versicolor +5.1,2.5,3.0,1.1,Iris-versicolor +5.7,2.8,4.1,1.3,Iris-versicolor diff --git a/scripts/linear_regression/simulated_annealing_linear_regression_Iris.json 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"valid solution rate": { "0": 1.0 }, + "p range": 1.0, + "TTS": 20970.0, + "qubo": { + "(0, 1)": 24.48071872338975, + "(0,)": 42.24552747666033, + "()": 31.88076750778388, + "(1,)": 42.24552747666033, + "(0, 2)": 0.41675676795773126, + "(0, 3)": 0.41675676795773126, + "(2,)": -3.168051752401938, + "(3,)": -3.168051752401938, + "(1, 2)": 0.41675676795773126, + "(1, 3)": 0.41675676795773126, + "(0, 4)": 1.6633163879133126, + "(0, 5)": 1.6633163879133126, + "(4,)": -3.197456791219274, + "(5,)": -3.197456791219274, + "(1, 4)": 1.6633163879133126, + "(1, 5)": 1.6633163879133126, + "(2, 3)": 1.94836892149705, + "(2, 4)": 0.15914040330596124, + "(2, 5)": 0.15914040330596124, + "(3, 4)": 0.15914040330596124, + "(3, 5)": 0.15914040330596124, + "(4, 5)": 8.75 + }, + "qubo num var": 6, + "qubo contributions": { "2": 15, "1": 6, "0": 1 }, + "qubo contributions average": { "2": 0.0, "1": 0.0, "0": 31.88076750778388 }, + "qubo contributions variance": { "2": 0.0, "1": 0.0, "0": 0.0 }, + "qubo coefficient average": 1.449125795808358, + "qubo coefficient variance": 44.09927701362135, + "pubo": { + "('b0', 'b1')": 24.48071872338975, + "('b0',)": 42.24552747666033, + "()": 31.88076750778388, + "('b1',)": 42.24552747666033, + "('b0', 'b2')": 0.41675676795773126, + "('b0', 'b3')": 0.41675676795773126, + "('b2',)": -3.168051752401938, + "('b3',)": -3.168051752401938, + "('b1', 'b2')": 0.41675676795773126, + "('b1', 'b3')": 0.41675676795773126, + "('b0', 'b4')": 1.6633163879133126, + "('b0', 'b5')": 1.6633163879133126, + "('b4',)": -3.197456791219274, + "('b5',)": -3.197456791219274, + "('b1', 'b4')": 1.6633163879133126, + "('b1', 'b5')": 1.6633163879133126, + "('b2', 'b3')": 1.94836892149705, + "('b2', 'b4')": 0.15914040330596124, + "('b2', 'b5')": 0.15914040330596124, + "('b3', 'b4')": 0.15914040330596124, + "('b3', 'b5')": 0.15914040330596124, + "('b4', 'b5')": 8.75 + }, + "num var": 6, + "pubo contributions": { "2": 15, "1": 6, "0": 1 }, + "pubo contributions average": { + "2": 2.942396125439655, + "1": 11.960006311013037, + "0": 31.88076750778388 + }, + "pubo contributions variance": { + "2": 37.44408929297084, + "1": 458.6065402468196, + "0": 0.0 + }, + "pubo coefficient average": 6.717124875248044, + "pubo coefficient variance": 196.59840831499267, + "lambdas": [] +} diff --git a/src/mqt/qao/__init__.py b/src/mqt/qao/__init__.py index 3077977..e3f5615 100644 --- a/src/mqt/qao/__init__.py +++ b/src/mqt/qao/__init__.py @@ -2,20 +2,20 @@ import logging -from mqt.qao.variables import Variable, Variables from mqt.qao.constraints import Constraints from mqt.qao.objectivefunction import ObjectiveFunction from mqt.qao.problem import Problem from mqt.qao.solvers import Solution, Solver +from mqt.qao.variables import Variable, Variables __all__ = [ - "Variable", - "Variables", "Constraints", "ObjectiveFunction", "Problem", "Solution", "Solver", + "Variable", + "Variables", ] logger = logging.getLogger("mqt-qao") diff --git a/src/mqt/qao/constraints.py b/src/mqt/qao/constraints.py index 85e2ab3..54417e8 100644 --- a/src/mqt/qao/constraints.py +++ b/src/mqt/qao/constraints.py @@ -7,7 +7,7 @@ # for managing symbols from sympy import Expr, expand -from mqt.qao.variables import Variables +from .variables import Variable, Variables class Constraints: @@ -204,11 +204,11 @@ def _not_constraint(self, elem: tuple[str, bool, bool, bool], var: Variables) -> print("Wrong constraint format\n") return False a = self._convert_expression_logic( - expand(cast(Expr, (el[0]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el[0]))).evalf(), var.binary_variables_name_weight, ) b = self._convert_expression_logic( - expand(cast(Expr, (el[1]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el[1]))).evalf(), var.binary_variables_name_weight, ) ret = 2 * a * b - a - b + 1 @@ -218,15 +218,7 @@ def _not_constraint(self, elem: tuple[str, bool, bool, bool], var: Variables) -> print("Wrong constraint format\n") return False - op = [] - for e in el: - for elms in var.binary_variables_name_weight.values(): - if isinstance(elms, list): - for elm in elms: - if not isinstance(elm, str) and e == next(iter(elm[0].variables)): - op.append(elm[0]) # noqa: PERF401 - elif e == next(iter(elms[0].variables)): - op.append(elms[0]) + op = self._logic_constraint(el, var) ret = 2 * op[0] * op[1] - op[0] - op[1] + 1 return ret @@ -243,60 +235,34 @@ def _not_constraint_check(self, constraint: str, variables: Variables, solution: bool -- saying if the constraint is satisfied or not """ el = (constraint).replace("~", "").split("=") - expr1 = expand(cast(Expr, (el[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el[0]))).evalf() + expr2 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = bool(not str(expr1).replace(".", "").isnumeric()) expr2_to_sub = bool(not str(expr2).replace(".", "").isnumeric()) if expr2_to_sub or expr1_to_sub: for var in solution: if var in variables.variables_dict: if isinstance(solution[var], float): - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var].symbol: solution[var]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var].symbol: solution[var]}) + expr1, expr2 = self._constraint_check_sub_single_var( + expr1_to_sub, expr2_to_sub, expr1, expr2, solution[var], variables.variables_dict[var] + ) elif isinstance(solution[var], list): - for i in range(len(solution[var])): - if isinstance(solution[var][i], list): - for j in range(len(solution[var][i])): - if isinstance(solution[var][i][j], list): - for k in range(len(solution[var][i][j])): - if expr1_to_sub: - expr1 = expr1.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - if expr2_to_sub: - expr2 = expr2.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - else: - if expr1_to_sub: - expr1 = expr1.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - if expr2_to_sub: - expr2 = expr2.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - else: - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) + expr1, expr2 = self._constraint_check_sub_list_var( + solution, + variables, + expr1, + expr2, + var, + expr1_to_sub, + expr2_to_sub, + ) try: return bool(expr1) != bool(expr2) except ValueError: return False - def _not_constraint_check_no_sub(self, constraint: str, solution: dict[str, int]) -> bool: + @staticmethod + def _not_constraint_check_no_sub(constraint: str, solution: dict[str, int]) -> bool: """function for checking the not constraint penalty function. Keyword arguments: @@ -307,8 +273,8 @@ def _not_constraint_check_no_sub(self, constraint: str, solution: dict[str, int] bool -- saying if the constraint is satisfied or not """ el = (constraint).replace("~", "").split("=") - expr1 = expand(cast(Expr, (el[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el[0]))).evalf() + expr2 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = True if not str(expr1).replace(".", "").isnumeric(): symbols_in_the_expression1 = expr1.free_symbols @@ -360,15 +326,15 @@ def _and_constraint(self, elem: tuple[str, bool, bool, bool], var: Variables) -> print("Wrong constraint format\n") return False a = self._convert_expression_logic( - expand(cast(Expr, (el2[0]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el2[0]))).evalf(), var.binary_variables_name_weight, ) b = self._convert_expression_logic( - expand(cast(Expr, (el2[1]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el2[1]))).evalf(), var.binary_variables_name_weight, ) c = self._convert_expression_logic( - expand(cast(Expr, (el[1]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el[1]))).evalf(), var.binary_variables_name_weight, ) ret = a * b - 2 * (a + b) * c + 3 * c @@ -383,15 +349,7 @@ def _and_constraint(self, elem: tuple[str, bool, bool, bool], var: Variables) -> return False el = [el2[0], el2[1], el[1]] - op = [] - for e in el: - for elms in var.binary_variables_name_weight.values(): - if isinstance(elms, list): - for elm in elms: - if not isinstance(elm, str) and e == next(iter(elm[0].variables)): - op.append(elm[0]) # noqa: PERF401 - elif e == next(iter(elms[0].variables)): - op.append(elms[0]) + op = self._logic_constraint(el, var) ret = op[0] * op[1] - 2 * (op[0] + op[1]) * op[2] + 3 * op[2] return ret @@ -415,9 +373,9 @@ def _and_constraint_check(self, constraint: str, variables: Variables, solution: if len(el2) != 2: print("Wrong constraint format\n") return False - expr1 = expand(cast(Expr, (el2[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el2[1]))).evalf() # type: ignore[no-untyped-call] - expr3 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el2[0]))).evalf() + expr2 = expand(cast(Expr, (el2[1]))).evalf() + expr3 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = bool(not str(expr1).replace(".", "").isnumeric()) expr2_to_sub = bool(not str(expr2).replace(".", "").isnumeric()) expr3_to_sub = bool(not str(expr3).replace(".", "").isnumeric()) @@ -425,68 +383,27 @@ def _and_constraint_check(self, constraint: str, variables: Variables, solution: for var in solution: if var in variables.variables_dict: if isinstance(solution[var], float): - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var].symbol: solution[var]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var].symbol: solution[var]}) - if expr3_to_sub: - expr3 = expr3.subs({variables.variables_dict[var].symbol: solution[var]}) + expr1, expr2, expr3 = self._constraint_check_three_sub_single_var( + expr1_to_sub, + expr2_to_sub, + expr3_to_sub, + expr1, + expr2, + expr3, + solution[var], + variables.variables_dict[var], + ) elif isinstance(solution[var], list): - for i in range(len(solution[var])): - if isinstance(solution[var][i], list): - for j in range(len(solution[var][i])): - if isinstance(solution[var][i][j], list): - for k in range(len(solution[var][i][j])): - if expr1_to_sub: - expr1 = expr1.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - if expr2_to_sub: - expr2 = expr2.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - if expr3_to_sub: - expr3 = expr3.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - else: - if expr1_to_sub: - expr1 = expr1.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - if expr2_to_sub: - expr2 = expr2.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - if expr3_to_sub: - expr3 = expr3.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - else: - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) - if expr3_to_sub: - expr3 = expr3.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) + expr1, expr2, expr3 = self._constraint_check_three_sub_list_var( + solution, variables, expr1, expr2, expr3, var, expr1_to_sub, expr2_to_sub, expr3_to_sub + ) try: return bool(expr1) and bool(expr2) == bool(expr3) except ValueError: return False - def _and_constraint_check_no_sub(self, constraint: str, solution: dict[str, int]) -> bool: + @staticmethod + def _and_constraint_check_no_sub(constraint: str, solution: dict[str, int]) -> bool: """function for checking the and constraint penalty function. Keyword arguments: @@ -504,9 +421,9 @@ def _and_constraint_check_no_sub(self, constraint: str, solution: dict[str, int] if len(el2) != 2: print("Wrong constraint format\n") return False - expr1 = expand(cast(Expr, (el2[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el2[1]))).evalf() # type: ignore[no-untyped-call] - expr3 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el2[0]))).evalf() + expr2 = expand(cast(Expr, (el2[1]))).evalf() + expr3 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = True if not str(expr1).replace(".", "").isnumeric(): symbols_in_the_expression1 = expr1.free_symbols @@ -565,15 +482,15 @@ def _or_constraint(self, elem: tuple[str, bool, bool, bool], var: Variables) -> print("Wrong constraint format\n") return False a = self._convert_expression_logic( - expand(cast(Expr, (el2[0]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el2[0]))).evalf(), var.binary_variables_name_weight, ) b = self._convert_expression_logic( - expand(cast(Expr, (el2[1]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el2[1]))).evalf(), var.binary_variables_name_weight, ) c = self._convert_expression_logic( - expand(cast(Expr, (el[1]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el[1]))).evalf(), var.binary_variables_name_weight, ) ret = a * b + (a + b) * (1 - 2 * c) + c @@ -588,15 +505,7 @@ def _or_constraint(self, elem: tuple[str, bool, bool, bool], var: Variables) -> return False el = [el2[0], el2[1], el[1]] - op = [] - for e in el: - for elms in var.binary_variables_name_weight.values(): - if isinstance(elms, list): - for elm in elms: - if not isinstance(elm, str) and e == next(iter(elm[0].variables)): - op.append(elm[0]) # noqa: PERF401 - elif e == next(iter(elms[0].variables)): - op.append(elms[0]) + op = self._logic_constraint(el, var) ret = op[0] * op[1] + (op[0] + op[1]) * (1 - 2 * op[2]) + op[2] return ret @@ -619,9 +528,9 @@ def _or_constraint_check(self, constraint: str, variables: Variables, solution: if len(el2) != 2: print("Wrong constraint format\n") return False - expr1 = expand(cast(Expr, (el2[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el2[1]))).evalf() # type: ignore[no-untyped-call] - expr3 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el2[0]))).evalf() + expr2 = expand(cast(Expr, (el2[1]))).evalf() + expr3 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = bool(not str(expr1).replace(".", "").isnumeric()) expr2_to_sub = bool(not str(expr2).replace(".", "").isnumeric()) expr3_to_sub = bool(not str(expr3).replace(".", "").isnumeric()) @@ -629,68 +538,27 @@ def _or_constraint_check(self, constraint: str, variables: Variables, solution: for var in solution: if var in variables.variables_dict: if isinstance(solution[var], float): - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var].symbol: solution[var]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var].symbol: solution[var]}) - if expr3_to_sub: - expr3 = expr3.subs({variables.variables_dict[var].symbol: solution[var]}) + expr1, expr2, expr3 = self._constraint_check_three_sub_single_var( + expr1_to_sub, + expr2_to_sub, + expr3_to_sub, + expr1, + expr2, + expr3, + solution[var], + variables.variables_dict[var], + ) elif isinstance(solution[var], list): - for i in range(len(solution[var])): - if isinstance(solution[var][i], list): - for j in range(len(solution[var][i])): - if isinstance(solution[var][i][j], list): - for k in range(len(solution[var][i][j])): - if expr1_to_sub: - expr1 = expr1.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - if expr2_to_sub: - expr2 = expr2.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - if expr3_to_sub: - expr3 = expr3.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - else: - if expr1_to_sub: - expr1 = expr1.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - if expr2_to_sub: - expr2 = expr2.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - if expr3_to_sub: - expr3 = expr3.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - else: - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) - if expr3_to_sub: - expr3 = expr3.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) + expr1, expr2, expr3 = self._constraint_check_three_sub_list_var( + solution, variables, expr1, expr2, expr3, var, expr1_to_sub, expr2_to_sub, expr3_to_sub + ) try: return bool(expr1) or bool(expr2) == bool(expr3) except ValueError: return False - def _or_constraint_check_no_sub(self, constraint: str, solution: dict[str, int]) -> bool: + @staticmethod + def _or_constraint_check_no_sub(constraint: str, solution: dict[str, int]) -> bool: """function for checking the or constraint penalty function. Keyword arguments: @@ -708,9 +576,9 @@ def _or_constraint_check_no_sub(self, constraint: str, solution: dict[str, int]) if len(el2) != 2: print("Wrong constraint format\n") return False - expr1 = expand(cast(Expr, (el2[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el2[1]))).evalf() # type: ignore[no-untyped-call] - expr3 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el2[0]))).evalf() + expr2 = expand(cast(Expr, (el2[1]))).evalf() + expr3 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = True if not str(expr1).replace(".", "").isnumeric(): symbols_in_the_expression1 = expr1.free_symbols @@ -745,6 +613,19 @@ def _or_constraint_check_no_sub(self, constraint: str, solution: dict[str, int]) except ValueError: return False + @staticmethod + def _logic_constraint(el: list[str], var: Variables) -> list[PUBO]: + op = [] + for e in el: + for elms in var.binary_variables_name_weight.values(): + if isinstance(elms, list): + for elm in elms: + if not isinstance(elm, str) and e == next(iter(elm[0].variables)): + op.append(elm[0]) # noqa: PERF401 + elif e == next(iter(elms[0].variables)): + op.append(elms[0]) + return op + def _xor_constraint(self, elem: tuple[str, bool, bool, bool], var: Variables) -> PUBO | bool: """function for writing the xor constraint penalty function. @@ -767,9 +648,9 @@ def _xor_constraint(self, elem: tuple[str, bool, bool, bool], var: Variables) -> if len(el2) != 2: print("Wrong constraint format\n") return False - a = self._convert_expression_logic(expand(cast(Expr, (el2[0]))).evalf(), var.binary_variables_name_weight) # type: ignore[no-untyped-call] - b = self._convert_expression_logic(expand(cast(Expr, (el2[1]))).evalf(), var.binary_variables_name_weight) # type: ignore[no-untyped-call] - c = self._convert_expression_logic(expand(cast(Expr, (el[1]))).evalf(), var.binary_variables_name_weight) # type: ignore[no-untyped-call] + a = self._convert_expression_logic(expand(cast(Expr, (el2[0]))).evalf(), var.binary_variables_name_weight) + b = self._convert_expression_logic(expand(cast(Expr, (el2[1]))).evalf(), var.binary_variables_name_weight) + c = self._convert_expression_logic(expand(cast(Expr, (el[1]))).evalf(), var.binary_variables_name_weight) ret = a + b + c - 2 * a * c - 2 * b * c - 2 * a * b + 4 * a * b * c else: el = str(elem[0]).replace(" ", "").split("=") @@ -781,21 +662,83 @@ def _xor_constraint(self, elem: tuple[str, bool, bool, bool], var: Variables) -> print("Wrong constraint format\n") return False el = [el2[0], el2[1], el[1]] - op = [] - for e in el: - for elms in var.binary_variables_name_weight.values(): - if isinstance(elms, list): - for elm in elms: - if not isinstance(elm, str) and e == next(iter(elm[0].variables)): - op.append(elm[0]) # noqa: PERF401 - elif e == next(iter(elms[0].variables)): - op.append(elms[0]) + op = self._logic_constraint(el, var) a = op[0] b = op[1] c = op[2] ret = a + b + c - 2 * a * c - 2 * b * c - 2 * a * b + 4 * a * b * c return ret + @staticmethod + def _constraint_check_three_sub_single_var( + expr1_to_sub: bool, + expr2_to_sub: bool, + expr3_to_sub: bool, + expr1: Expr, + expr2: Expr, + expr3: Expr, + sol_val: float, + variable: Variable, + ) -> tuple[Expr, Expr, Expr]: + if expr1_to_sub: + expr1 = expr1.subs({variable.symbol: sol_val}) + if expr2_to_sub: + expr2 = expr2.subs({variable.symbol: sol_val}) + if expr3_to_sub: + expr3 = expr3.subs({variable.symbol: sol_val}) + return expr1, expr2, expr3 + + def _constraint_check_three_sub_list_var( + self, + solution: dict[str, Any], + variables: Variables, + expr1: Expr, + expr2: Expr, + expr3: Expr, + var: str, + expr1_to_sub: bool, + expr2_to_sub: bool, + expr3_to_sub: bool, + ) -> tuple[Expr, Expr, Expr]: + for i in range(len(solution[var])): + if isinstance(solution[var][i], list): + for j in range(len(solution[var][i])): + if isinstance(solution[var][i][j], list): + for k in range(len(solution[var][i][j])): + expr1, expr2, expr3 = self._constraint_check_three_sub_single_var( + expr1_to_sub, + expr2_to_sub, + expr3_to_sub, + expr1, + expr2, + expr3, + solution[var][i][j][k], + variables.variables_dict[var][i][j][k], + ) + else: + expr1, expr2, expr3 = self._constraint_check_three_sub_single_var( + expr1_to_sub, + expr2_to_sub, + expr3_to_sub, + expr1, + expr2, + expr3, + solution[var][i][j], + variables.variables_dict[var][i][j], + ) + else: + expr1, expr2, expr3 = self._constraint_check_three_sub_single_var( + expr1_to_sub, + expr2_to_sub, + expr3_to_sub, + expr1, + expr2, + expr3, + solution[var][i], + variables.variables_dict[var][i], + ) + return expr1, expr2, expr3 + def _xor_constraint_check(self, constraint: str, variables: Variables, solution: dict[str, Any]) -> bool: """function for checking the xor constraint penalty function. @@ -815,9 +758,9 @@ def _xor_constraint_check(self, constraint: str, variables: Variables, solution: if len(el2) != 2: print("Wrong constraint format\n") return False - expr1 = expand(cast(Expr, (el2[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el2[1]))).evalf() # type: ignore[no-untyped-call] - expr3 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el2[0]))).evalf() + expr2 = expand(cast(Expr, (el2[1]))).evalf() + expr3 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = bool(not str(expr1).replace(".", "").isnumeric()) expr2_to_sub = bool(not str(expr2).replace(".", "").isnumeric()) expr3_to_sub = bool(not str(expr3).replace(".", "").isnumeric()) @@ -825,68 +768,27 @@ def _xor_constraint_check(self, constraint: str, variables: Variables, solution: for var in solution: if var in variables.variables_dict: if isinstance(solution[var], float): - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var].symbol: solution[var]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var].symbol: solution[var]}) - if expr3_to_sub: - expr3 = expr3.subs({variables.variables_dict[var].symbol: solution[var]}) + expr1, expr2, expr3 = self._constraint_check_three_sub_single_var( + expr1_to_sub, + expr2_to_sub, + expr3_to_sub, + expr1, + expr2, + expr3, + solution[var], + variables.variables_dict[var], + ) elif isinstance(solution[var], list): - for i in range(len(solution[var])): - if isinstance(solution[var][i], list): - for j in range(len(solution[var][i])): - if isinstance(solution[var][i][j], list): - for k in range(len(solution[var][i][j])): - if expr1_to_sub: - expr1 = expr1.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - if expr2_to_sub: - expr2 = expr2.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - if expr3_to_sub: - expr3 = expr3.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - else: - if expr1_to_sub: - expr1 = expr1.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - if expr2_to_sub: - expr2 = expr2.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - if expr3_to_sub: - expr3 = expr3.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - else: - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) - if expr3_to_sub: - expr3 = expr3.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) + expr1, expr2, expr3 = self._constraint_check_three_sub_list_var( + solution, variables, expr1, expr2, expr3, var, expr1_to_sub, expr2_to_sub, expr3_to_sub + ) try: return bool(expr1) ^ bool(expr2) == bool(expr3) except ValueError: return False - def _xor_constraint_check_no_sub(self, constraint: str, solution: dict[str, int]) -> bool: + @staticmethod + def _xor_constraint_check_no_sub(constraint: str, solution: dict[str, int]) -> bool: """function for checking the xor constraint penalty function. Keyword arguments: @@ -904,9 +806,9 @@ def _xor_constraint_check_no_sub(self, constraint: str, solution: dict[str, int] if len(el2) != 2: print("Wrong constraint format\n") return False - expr1 = expand(cast(Expr, (el2[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el2[1]))).evalf() # type: ignore[no-untyped-call] - expr3 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el2[0]))).evalf() + expr2 = expand(cast(Expr, (el2[1]))).evalf() + expr3 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = True if not str(expr1).replace(".", "").isnumeric(): symbols_in_the_expression1 = expr1.free_symbols @@ -941,6 +843,59 @@ def _xor_constraint_check_no_sub(self, constraint: str, solution: dict[str, int] except ValueError: return False + @staticmethod + def _constraint_check_sub_single_var( + expr1_to_sub: bool, expr2_to_sub: bool, expr1: Expr, expr2: Expr, sol_val: float, variable: Variable + ) -> tuple[Expr, Expr]: + if expr1_to_sub: + expr1 = expr1.subs({variable.symbol: sol_val}) + if expr2_to_sub: + expr2 = expr2.subs({variable.symbol: sol_val}) + return expr1, expr2 + + def _constraint_check_sub_list_var( + self, + solution: dict[str, Any], + variables: Variables, + expr1: Expr, + expr2: Expr, + var: str, + expr1_to_sub: bool, + expr2_to_sub: bool, + ) -> tuple[Expr, Expr]: + for i in range(len(solution[var])): + if isinstance(solution[var][i], list): + for j in range(len(solution[var][i])): + if isinstance(solution[var][i][j], list): + for k in range(len(solution[var][i][j])): + expr1, expr2 = self._constraint_check_sub_single_var( + expr1_to_sub, + expr2_to_sub, + expr1, + expr2, + solution[var][i][j][k], + variables.variables_dict[var][i][j][k], + ) + else: + expr1, expr2 = self._constraint_check_sub_single_var( + expr1_to_sub, + expr2_to_sub, + expr1, + expr2, + solution[var][i][j], + variables.variables_dict[var][i][j], + ) + else: + expr1, expr2 = self._constraint_check_sub_single_var( + expr1_to_sub, + expr2_to_sub, + expr1, + expr2, + solution[var][i], + variables.variables_dict[var][i], + ) + return expr1, expr2 + def _greq_constraint( self, elem: tuple[str, bool, bool, bool], var: Variables, aux: Variables, i: int, j: int ) -> tuple[ @@ -975,7 +930,7 @@ def _greq_constraint( exp = PUBO() if elem[3]: ret_func = self._convert_expression_prec( - expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), var.binary_variables_name_weight, var.variables_dict, ) @@ -984,11 +939,11 @@ def _greq_constraint( var_precision = ret_func[1] else: exp = self._convert_expression( - expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), var.binary_variables_name_weight, ) - min_val, max_val, const = self._min_max_const_estimation(exp) + _min_val, max_val, const = self._min_max_const_estimation(exp) if max_val + const > 0: if len(str(max_val + const).split(".")) == 2: if elem[3] and float("0." + str(max_val + const).split(".")[1]) != 0.0: @@ -1010,8 +965,8 @@ def _greq_constraint( ret = (exp) ** 2, i, j, aux else: el = str(elem[0]).split(">=") - exp = expand(cast(Expr, (el[0] + "-" + el[1]))).evalf() # type: ignore[no-untyped-call] - min_val, max_val, const = self._min_max_const_estimation(exp) + exp = expand(cast(Expr, (el[0] + "-" + el[1]))).evalf() + _min_val, max_val, const = self._min_max_const_estimation(exp) if max_val + const > 0: if len(str(max_val + const).split(".")) == 2: precision = float("0." + str(max_val + const).split(".")[1]) @@ -1048,60 +1003,28 @@ def _greq_constraint_check(self, constraint: str, variables: Variables, solution bool -- saying if the constraint is satisfied or not """ el = constraint.split(">=") - expr1 = expand(cast(Expr, (el[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el[0]))).evalf() + expr2 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = bool(not str(expr1).replace(".", "").isnumeric()) expr2_to_sub = bool(not str(expr2).replace(".", "").isnumeric()) if expr2_to_sub or expr1_to_sub: for var in solution: if var in variables.variables_dict: if isinstance(solution[var], float): - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var].symbol: solution[var]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var].symbol: solution[var]}) + expr1, expr2 = self._constraint_check_sub_single_var( + expr1_to_sub, expr2_to_sub, expr1, expr2, solution[var], variables.variables_dict[var] + ) elif isinstance(solution[var], list): - for i in range(len(solution[var])): - if isinstance(solution[var][i], list): - for j in range(len(solution[var][i])): - if isinstance(solution[var][i][j], list): - for k in range(len(solution[var][i][j])): - if expr1_to_sub: - expr1 = expr1.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - if expr2_to_sub: - expr2 = expr2.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - else: - if expr1_to_sub: - expr1 = expr1.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - if expr2_to_sub: - expr2 = expr2.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - else: - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) + expr1, expr2 = self._constraint_check_sub_list_var( + solution, variables, expr1, expr2, var, expr1_to_sub, expr2_to_sub + ) try: return float(expr1) >= float(expr2) except ValueError: return False - def _greq_constraint_check_no_sub(self, constraint: str, solution: dict[str, int]) -> bool: + @staticmethod + def _greq_constraint_check_no_sub(constraint: str, solution: dict[str, int]) -> bool: """function for checking the grater equal constraint penalty function. Keyword arguments: @@ -1112,8 +1035,8 @@ def _greq_constraint_check_no_sub(self, constraint: str, solution: dict[str, int bool -- saying if the constraint is satisfied or not """ el = (constraint).split(">=") - expr1 = expand(cast(Expr, (el[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el[0]))).evalf() + expr2 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = True if not str(expr1).replace(".", "").isnumeric(): symbols_in_the_expression1 = expr1.free_symbols @@ -1174,7 +1097,7 @@ def _lteq_constraint( exp = PUBO() if elem[3]: ret_func = self._convert_expression_prec( - expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), var.binary_variables_name_weight, var.variables_dict, ) @@ -1183,10 +1106,10 @@ def _lteq_constraint( var_precision = ret_func[1] else: exp = self._convert_expression( - expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), var.binary_variables_name_weight, ) - min_val, max_val, const = self._min_max_const_estimation(exp) + min_val, _max_val, const = self._min_max_const_estimation(exp) const = -const if const - min_val > 0: if len(str(const - min_val).split(".")) == 2: @@ -1209,8 +1132,8 @@ def _lteq_constraint( ret = (exp) ** 2, i, j, aux else: el = str(elem[0]).split("<=") - exp = expand(cast(Expr, (el[0] + "-" + el[1]))).evalf() # type: ignore[no-untyped-call] - min_val, max_val, const = self._min_max_const_estimation(exp) + exp = expand(cast(Expr, (el[0] + "-" + el[1]))).evalf() + min_val, _max_val, const = self._min_max_const_estimation(exp) const = -const if const - min_val > 0: if len(str(const - min_val).split(".")) == 2: @@ -1247,60 +1170,28 @@ def _lteq_constraint_check(self, constraint: str, variables: Variables, solution bool -- saying if the constraint is satisfied or not """ el = constraint.split("<=") - expr1 = expand(cast(Expr, (el[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el[0]))).evalf() + expr2 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = bool(not str(expr1).replace(".", "").isnumeric()) expr2_to_sub = bool(not str(expr2).replace(".", "").isnumeric()) if expr2_to_sub or expr1_to_sub: for var in solution: if var in variables.variables_dict: if isinstance(solution[var], float): - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var].symbol: solution[var]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var].symbol: solution[var]}) + expr1, expr2 = self._constraint_check_sub_single_var( + expr1_to_sub, expr2_to_sub, expr1, expr2, solution[var], variables.variables_dict[var] + ) elif isinstance(solution[var], list): - for i in range(len(solution[var])): - if isinstance(solution[var][i], list): - for j in range(len(solution[var][i])): - if isinstance(solution[var][i][j], list): - for k in range(len(solution[var][i][j])): - if expr1_to_sub: - expr1 = expr1.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - if expr2_to_sub: - expr2 = expr2.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - else: - if expr1_to_sub: - expr1 = expr1.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - if expr2_to_sub: - expr2 = expr2.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - else: - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) + expr1, expr2 = self._constraint_check_sub_list_var( + solution, variables, expr1, expr2, var, expr1_to_sub, expr2_to_sub + ) try: return float(expr1) <= float(expr2) except ValueError: return False - def _lteq_constraint_check_no_sub(self, constraint: str, solution: dict[str, int]) -> bool: + @staticmethod + def _lteq_constraint_check_no_sub(constraint: str, solution: dict[str, int]) -> bool: """function for checking the less equal constraint penalty function. Keyword arguments: @@ -1311,8 +1202,8 @@ def _lteq_constraint_check_no_sub(self, constraint: str, solution: dict[str, int bool -- saying if the constraint is satisfied or not """ el = (constraint).split("<=") - expr1 = expand(cast(Expr, (el[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el[0]))).evalf() + expr2 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = True if not str(expr1).replace(".", "").isnumeric(): symbols_in_the_expression1 = expr1.free_symbols @@ -1373,7 +1264,7 @@ def _grt_constraint( exp = PUBO() if elem[3]: ret_func = self._convert_expression_prec( - expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), var.binary_variables_name_weight, var.variables_dict, ) @@ -1382,10 +1273,10 @@ def _grt_constraint( var_precision = ret_func[1] else: exp = self._convert_expression( - expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), var.binary_variables_name_weight, ) - min_val, max_val, const = self._min_max_const_estimation(exp) + _min_val, max_val, const = self._min_max_const_estimation(exp) if max_val + const > 0: if len(str(max_val + const).split(".")) == 2: if elem[3] and float("0." + str(max_val + const).split(".")[1]) != 0.0: @@ -1410,8 +1301,8 @@ def _grt_constraint( ret = (exp) ** 2, i, j, aux else: el = str(elem[0]).split(">") - exp = expand(cast(Expr, (el[0] + "-" + el[1]))).evalf() # type: ignore[no-untyped-call] - min_val, max_val, const = self._min_max_const_estimation(exp) + exp = expand(cast(Expr, (el[0] + "-" + el[1]))).evalf() + _min_val, max_val, const = self._min_max_const_estimation(exp) if max_val + const > 0: if len(str(max_val + const).split(".")) == 2: precision = float("0." + str(max_val + const).split(".")[1]) @@ -1456,60 +1347,28 @@ def _grt_constraint_check(self, constraint: str, variables: Variables, solution: bool -- saying if the constraint is satisfied or not """ el = constraint.split(">") - expr1 = expand(cast(Expr, (el[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el[0]))).evalf() + expr2 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = bool(not str(expr1).replace(".", "").isnumeric()) expr2_to_sub = bool(not str(expr2).replace(".", "").isnumeric()) if expr2_to_sub or expr1_to_sub: for var in solution: if var in variables.variables_dict: if isinstance(solution[var], float): - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var].symbol: solution[var]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var].symbol: solution[var]}) + expr1, expr2 = self._constraint_check_sub_single_var( + expr1_to_sub, expr2_to_sub, expr1, expr2, solution[var], variables.variables_dict[var] + ) elif isinstance(solution[var], list): - for i in range(len(solution[var])): - if isinstance(solution[var][i], list): - for j in range(len(solution[var][i])): - if isinstance(solution[var][i][j], list): - for k in range(len(solution[var][i][j])): - if expr1_to_sub: - expr1 = expr1.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - if expr2_to_sub: - expr2 = expr2.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - else: - if expr1_to_sub: - expr1 = expr1.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - if expr2_to_sub: - expr2 = expr2.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - else: - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) + expr1, expr2 = self._constraint_check_sub_list_var( + solution, variables, expr1, expr2, var, expr1_to_sub, expr2_to_sub + ) try: return float(expr1) > float(expr2) except ValueError: return False - def _grt_constraint_check_no_sub(self, constraint: str, solution: dict[str, int]) -> bool: + @staticmethod + def _grt_constraint_check_no_sub(constraint: str, solution: dict[str, int]) -> bool: """function for checking the greater equal constraint penalty function. Keyword arguments: @@ -1520,8 +1379,8 @@ def _grt_constraint_check_no_sub(self, constraint: str, solution: dict[str, int] bool -- saying if the constraint is satisfied or not """ el = (constraint).split(">") - expr1 = expand(cast(Expr, (el[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el[0]))).evalf() + expr2 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = True if not str(expr1).replace(".", "").isnumeric(): symbols_in_the_expression1 = expr1.free_symbols @@ -1582,7 +1441,7 @@ def _lt_constraint( exp = PUBO() if elem[3]: ret_func = self._convert_expression_prec( - expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), var.binary_variables_name_weight, var.variables_dict, ) @@ -1591,10 +1450,10 @@ def _lt_constraint( var_precision = ret_func[1] else: exp = self._convert_expression( - expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), var.binary_variables_name_weight, ) - min_val, max_val, const = self._min_max_const_estimation(exp) + min_val, _max_val, const = self._min_max_const_estimation(exp) precision = 1.0 const = -const if const - min_val > 0: @@ -1607,9 +1466,8 @@ def _lt_constraint( precision = float("0." + str(const - min_val).split(".")[1]) if precision == 0.0: precision = 1 - else: - if elem[3]: - precision = min(1, var_precision) + elif elem[3]: + precision = min(1, var_precision) if precision != const - min_val: aux_var = aux.add_continuous_variable("aux" + format(j), precision, const - min_val, precision) j += 1 @@ -1622,8 +1480,8 @@ def _lt_constraint( ret = (exp) ** 2, i, j, aux else: el = str(elem[0]).split("<") - exp = expand(cast(Expr, (el[0] + "-" + el[1]))).evalf() # type: ignore[no-untyped-call] - min_val, max_val, const = self._min_max_const_estimation(exp) + exp = expand(cast(Expr, (el[0] + "-" + el[1]))).evalf() + min_val, _max_val, const = self._min_max_const_estimation(exp) const = -const if const - min_val > 0: if len(str(const - min_val).split(".")) == 2: @@ -1669,60 +1527,28 @@ def _lt_constraint_check(self, constraint: str, variables: Variables, solution: bool -- saying if the constraint is satisfied or not """ el = constraint.split("<") - expr1 = expand(cast(Expr, (el[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el[0]))).evalf() + expr2 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = bool(not str(expr1).replace(".", "").isnumeric()) expr2_to_sub = bool(not str(expr2).replace(".", "").isnumeric()) if expr2_to_sub or expr1_to_sub: for var in solution: if var in variables.variables_dict: if isinstance(solution[var], float): - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var].symbol: solution[var]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var].symbol: solution[var]}) + expr1, expr2 = self._constraint_check_sub_single_var( + expr1_to_sub, expr2_to_sub, expr1, expr2, solution[var], variables.variables_dict[var] + ) elif isinstance(solution[var], list): - for i in range(len(solution[var])): - if isinstance(solution[var][i], list): - for j in range(len(solution[var][i])): - if isinstance(solution[var][i][j], list): - for k in range(len(solution[var][i][j])): - if expr1_to_sub: - expr1 = expr1.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - if expr2_to_sub: - expr2 = expr2.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - else: - if expr1_to_sub: - expr1 = expr1.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - if expr2_to_sub: - expr2 = expr2.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - else: - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) + expr1, expr2 = self._constraint_check_sub_list_var( + solution, variables, expr1, expr2, var, expr1_to_sub, expr2_to_sub + ) try: return float(expr1) < float(expr2) except ValueError: return False - def _lt_constraint_check_no_sub(self, constraint: str, solution: dict[str, int]) -> bool: + @staticmethod + def _lt_constraint_check_no_sub(constraint: str, solution: dict[str, int]) -> bool: """function for checking the less equal constraint penalty function. Keyword arguments: @@ -1733,8 +1559,8 @@ def _lt_constraint_check_no_sub(self, constraint: str, solution: dict[str, int]) bool -- saying if the constraint is satisfied or not """ el = (constraint).split("<") - expr1 = expand(cast(Expr, (el[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el[0]))).evalf() + expr2 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = True if not str(expr1).replace(".", "").isnumeric(): symbols_in_the_expression1 = expr1.free_symbols @@ -1779,13 +1605,13 @@ def _eq_constraint(self, elem: tuple[str, bool, bool, bool], var: Variables) -> if elem[2]: el = str(elem[0]).split("=") exp = self._convert_expression( - expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), # type: ignore[no-untyped-call] + expand(cast(Expr, (el[0] + "-" + el[1]))).evalf(), var.binary_variables_name_weight, ) ret = (exp) ** 2 else: el = str(elem[0]).split("=") - exp = expand(cast(Expr, (el[0] + "-" + el[1]))).evalf() # type: ignore[no-untyped-call] + exp = expand(cast(Expr, (el[0] + "-" + el[1]))).evalf() hamiltonian = self._expression_to_hamiltonian( cast(Expr, exp), list(var.binary_variables_name_weight.values()) ) @@ -1804,60 +1630,28 @@ def _eq_constraint_check(self, constraint: str, variables: Variables, solution: bool -- saying if the constraint is satisfied or not """ el = constraint.split("=") - expr1 = expand(cast(Expr, (el[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el[0]))).evalf() + expr2 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = bool(not str(expr1).replace(".", "").isnumeric()) expr2_to_sub = bool(not str(expr2).replace(".", "").isnumeric()) if expr2_to_sub or expr1_to_sub: for var in solution: if var in variables.variables_dict: if isinstance(solution[var], float): - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var].symbol: solution[var]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var].symbol: solution[var]}) + expr1, expr2 = self._constraint_check_sub_single_var( + expr1_to_sub, expr2_to_sub, expr1, expr2, solution[var], variables.variables_dict[var] + ) elif isinstance(solution[var], list): - for i in range(len(solution[var])): - if isinstance(solution[var][i], list): - for j in range(len(solution[var][i])): - if isinstance(solution[var][i][j], list): - for k in range(len(solution[var][i][j])): - if expr1_to_sub: - expr1 = expr1.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - if expr2_to_sub: - expr2 = expr2.subs( - { - variables.variables_dict[var][i][j][k].symbol: solution[var][i][ - j - ][k] - } - ) - else: - if expr1_to_sub: - expr1 = expr1.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - if expr2_to_sub: - expr2 = expr2.subs( - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - else: - if expr1_to_sub: - expr1 = expr1.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) - if expr2_to_sub: - expr2 = expr2.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) + expr1, expr2 = self._constraint_check_sub_list_var( + solution, variables, expr1, expr2, var, expr1_to_sub, expr2_to_sub + ) try: return float(expr1) == float(expr2) except ValueError: return False - def _eq_constraint_check_no_sub(self, constraint: str, solution: dict[str, int]) -> bool: + @staticmethod + def _eq_constraint_check_no_sub(constraint: str, solution: dict[str, int]) -> bool: """function for checking the equality constraint penalty function. Keyword arguments: @@ -1868,8 +1662,8 @@ def _eq_constraint_check_no_sub(self, constraint: str, solution: dict[str, int]) bool -- saying if the constraint is satisfied or not """ el = (constraint).split("=") - expr1 = expand(cast(Expr, (el[0]))).evalf() # type: ignore[no-untyped-call] - expr2 = expand(cast(Expr, (el[1]))).evalf() # type: ignore[no-untyped-call] + expr1 = expand(cast(Expr, (el[0]))).evalf() + expr2 = expand(cast(Expr, (el[1]))).evalf() expr1_to_sub = True if not str(expr1).replace(".", "").isnumeric(): symbols_in_the_expression1 = expr1.free_symbols @@ -1897,6 +1691,88 @@ def _eq_constraint_check_no_sub(self, constraint: str, solution: dict[str, int]) except ValueError: return False + @staticmethod + def _convert_expression_power( + powers: list[str], binary_variables_name_weight: dict[str, Any], to_add: float + ) -> float | bool: + temp = 0.0 + try: + power = int(powers[1]) + except TypeError: + print("Expression not supported\n") + return False + key = powers[0] + if key not in binary_variables_name_weight: + print("Expression not supported\n") + return False + if isinstance(binary_variables_name_weight[key], list): + encoding = binary_variables_name_weight[key][0] + if encoding == "dictionary": + for elem in binary_variables_name_weight[key]: + if not isinstance(elem, str): + t = 1.0 + t *= elem[0] + if len(elem) == 2: + t *= elem[1] ** power + elif len(elem) == 3: + t = t * elem[1] ** power + elem[2] ** power + temp += t + else: + for elem in binary_variables_name_weight[key]: + if not isinstance(elem, str): + t = 1.0 + t *= elem[0] + if len(elem) == 2: + t *= elem[1] + elif len(elem) == 3: + t = t * elem[1] + elem[2] + temp += t + temp **= power + else: + t = 1.0 + t *= binary_variables_name_weight[key][0] + if len(binary_variables_name_weight[key]) == 2: + t *= (binary_variables_name_weight[key][1]) ** power + elif len(binary_variables_name_weight[key]) == 3: + t = (t * binary_variables_name_weight[key][1] + binary_variables_name_weight[key][2]) ** power + temp += t + to_add *= temp + return to_add + + @staticmethod + def _convert_expression_no_power( + poly_field: str, binary_variables_name_weight: dict[str, Any], to_add: float + ) -> float | bool: + temp = 0.0 + key = poly_field + if key not in binary_variables_name_weight: + try: + to_add *= float(key) + except TypeError: + print("Expression not supported\n") + return False + else: + if isinstance(binary_variables_name_weight[key], list): + for elem in binary_variables_name_weight[key]: + if not isinstance(elem, str): + t = 1.0 + t *= elem[0] + if len(elem) == 2: + t *= elem[1] + elif len(elem) == 3: + t = t * elem[1] + elem[2] + temp += t + else: + t = 1.0 + t *= binary_variables_name_weight[key][0] + if len(binary_variables_name_weight[key]) == 2: + t *= binary_variables_name_weight[key][1] + elif len(binary_variables_name_weight[key]) == 3: + t = t * binary_variables_name_weight[key][1] + binary_variables_name_weight[key][2] + temp += t + to_add *= temp + return to_add + def _convert_expression(self, expr: Expr, binary_variables_name_weight: dict[str, Any]) -> PUBO | bool: """function for translating an expression in the problem variable @@ -1911,84 +1787,19 @@ def _convert_expression(self, expr: Expr, binary_variables_name_weight: dict[str func = PUBO() sign = "+" for field in fields: - if field != "+" and field != "-": + if field not in {"+", "-"}: poly_fields = field.split("*") to_add = 1.0 for poly_field in poly_fields: powers = poly_field.split("^") - temp = 0.0 if len(powers) == 2: - try: - power = int(powers[1]) - except TypeError: - print("Expression not supported\n") - return False - key = powers[0] - if key not in binary_variables_name_weight: - print("Expression not supported\n") - return False - if isinstance(binary_variables_name_weight[key], list): - encoding = binary_variables_name_weight[key][0] - if encoding == "dictionary": - for elem in binary_variables_name_weight[key]: - if not isinstance(elem, str): - t = 1.0 - t *= elem[0] - if len(elem) == 2: - t *= elem[1] ** power - elif len(elem) == 3: - t = t * elem[1] ** power + elem[2] ** power - temp += t - else: - for elem in binary_variables_name_weight[key]: - if not isinstance(elem, str): - t = 1.0 - t *= elem[0] - if len(elem) == 2: - t *= elem[1] - elif len(elem) == 3: - t = t * elem[1] + elem[2] - temp += t - temp = temp**power - else: - t = 1.0 - t *= binary_variables_name_weight[key][0] - if len(binary_variables_name_weight[key]) == 2: - t *= (binary_variables_name_weight[key][1]) ** power - elif len(binary_variables_name_weight[key]) == 3: - t = ( - t * binary_variables_name_weight[key][1] + binary_variables_name_weight[key][2] - ) ** power - temp += t - to_add *= temp + ret = self._convert_expression_power(powers, binary_variables_name_weight, to_add) else: - key = poly_field - if key not in binary_variables_name_weight: - try: - to_add *= float(key) - except TypeError: - print("Expression not supported\n") - return False - else: - if isinstance(binary_variables_name_weight[key], list): - for elem in binary_variables_name_weight[key]: - if not isinstance(elem, str): - t = 1.0 - t *= elem[0] - if len(elem) == 2: - t *= elem[1] - elif len(elem) == 3: - t = t * elem[1] + elem[2] - temp += t - else: - t = 1.0 - t *= binary_variables_name_weight[key][0] - if len(binary_variables_name_weight[key]) == 2: - t *= binary_variables_name_weight[key][1] - elif len(binary_variables_name_weight[key]) == 3: - t = t * binary_variables_name_weight[key][1] + binary_variables_name_weight[key][2] - temp += t - to_add *= temp + ret = self._convert_expression_no_power(poly_field, binary_variables_name_weight, to_add) + + if isinstance(ret, bool): + return False + to_add = ret if sign == "+": func += to_add @@ -1998,6 +1809,167 @@ def _convert_expression(self, expr: Expr, binary_variables_name_weight: dict[str sign = field return func + @staticmethod + def _convert_expression_prec_power( + powers: list[str], + binary_variables_name_weight: dict[str, Any], + variables_dict: dict[str, Any], + to_add: float, + min_precision: float, + ) -> tuple[float, float] | bool: + temp = 0.0 + try: + power = int(powers[1]) + except TypeError: + print("Expression not supported\n") + return False + key = powers[0] + if key not in binary_variables_name_weight: + print("Expression not supported\n") + return False + if isinstance(binary_variables_name_weight[key], list): + encoding = binary_variables_name_weight[key][0] + if encoding == "dictionary": + for elem in binary_variables_name_weight[key]: + if not isinstance(elem, str): + t = 1.0 + t *= elem[0] + if len(elem) == 2: + t *= elem[1] ** power + elif len(elem) == 3: + t = t * elem[1] ** power + elem[2] ** power + temp += t + else: + for elem in binary_variables_name_weight[key]: + if not isinstance(elem, str): + t = 1.0 + t *= elem[0] + if len(elem) == 2: + t *= elem[1] + elif len(elem) == 3: + t = t * elem[1] + elem[2] + temp += t + temp **= power + else: + t = 1.0 + t *= binary_variables_name_weight[key][0] + if len(binary_variables_name_weight[key]) == 2: + t *= (binary_variables_name_weight[key][1]) ** power + elif len(binary_variables_name_weight[key]) == 3: + t = (t * binary_variables_name_weight[key][1] + binary_variables_name_weight[key][2]) ** power + temp += t + to_add *= temp + name = key.split("_") + if len(name) == 1: + if variables_dict[name[0]].type == "c" and min_precision > variables_dict[name[0]].precision: + min_precision = variables_dict[name[0]].precision + elif ( + len(name) == 2 + and name[1].isnumeric() + and name[0] in variables_dict + and variables_dict[name[0]][int(name[1])].type == "c" + ): + min_precision = min(min_precision, variables_dict[name[0]][int(name[1])].precision) + elif ( + len(name) == 3 + and name[1].isnumeric() + and name[2].isnumeric() + and name[0] in variables_dict + and variables_dict[name[0]][int(name[1])][int(name[2])].type == "c" + ): + min_precision = min(min_precision, variables_dict[name[0]][int(name[1])][int(name[2])].precision) + elif ( + len(name) == 4 + and name[1].isnumeric() + and name[2].isnumeric() + and name[3].isnumeric() + and name[0] in variables_dict + and variables_dict[name[0]][int(name[1])][int(name[2])][int(name[3])].type == "c" + and min_precision > variables_dict[name[0]][int(name[1])][int(name[2])][int(name[3])].precision + ): + min_precision = variables_dict[name[0]][int(name[1])][int(name[2])][int(name[3])].precision + elif ( + name is variables_dict.keys() + and variables_dict[name[0]].type == "c" + and min_precision > variables_dict[name[0]].precision + ): + min_precision = variables_dict[name[0]].precision + + return to_add, min_precision + + @staticmethod + def _convert_expression_prec_no_power( + poly_field: str, + binary_variables_name_weight: dict[str, Any], + variables_dict: dict[str, Any], + to_add: float, + min_precision: float, + ) -> tuple[float, float] | bool: + temp = 0.0 + key = poly_field + if key not in binary_variables_name_weight: + try: + to_add *= float(key) + except ValueError: + print("Expression not supported\n") + return False + else: + if isinstance(binary_variables_name_weight[key], list): + for elem in binary_variables_name_weight[key]: + if not isinstance(elem, str): + t = 1.0 + t *= elem[0] + if len(elem) == 2: + t *= elem[1] + elif len(elem) == 3: + t = t * elem[1] + elem[2] + temp += t + else: + t = 1.0 + t *= binary_variables_name_weight[key][0] + if len(binary_variables_name_weight[key]) == 2: + t *= binary_variables_name_weight[key][1] + elif len(binary_variables_name_weight[key]) == 3: + t = t * binary_variables_name_weight[key][1] + binary_variables_name_weight[key][2] + temp += t + to_add *= temp + name = key.split("_") + if len(name) == 1 and variables_dict[name[0]].type == "c": + min_precision = min(min_precision, variables_dict[name[0]].precision) + elif ( + len(name) == 2 + and name[1].isnumeric() + and name[0] in variables_dict + and variables_dict[name[0]][int(name[1])].type == "c" + and min_precision > variables_dict[name[0]][int(name[1])].precision + ): + min_precision = variables_dict[name[0]][int(name[1])].precision + elif len(name) == 3 and name[1].isnumeric() and name[2].isnumeric() and name[0] in variables_dict: + if ( + variables_dict[name[0]][int(name[1])][int(name[2])].type == "c" + and min_precision > variables_dict[name[0]][int(name[1])][int(name[2])].precision + ): + min_precision = variables_dict[name[0]][int(name[1])][int(name[2])].precision + elif ( + len(name) == 4 + and name[1].isnumeric() + and name[2].isnumeric() + and name[3].isnumeric() + and name[0] in variables_dict + and variables_dict[name[0]][int(name[1])][int(name[2])][int(name[3])].type == "c" + ): + min_precision = min( + min_precision, + variables_dict[name[0]][int(name[1])][int(name[2])][int(name[3])].precision, + ) + elif ( + name is variables_dict.keys() + and variables_dict[name[0]].type == "c" + and min_precision > variables_dict[name[0]].precision + ): + min_precision = variables_dict[name[0]].precision + return to_add, min_precision + def _convert_expression_prec( self, expr: Expr, binary_variables_name_weight: dict[str, Any], variables_dict: dict[str, Any] ) -> tuple[PUBO, float] | bool: @@ -2013,172 +1985,26 @@ def _convert_expression_prec( fields = str(expr).replace("**", "^").split(" ") func = PUBO() sign = "+" - min_precision = 1 + min_precision = 1.0 for field in fields: - if field != "+" and field != "-": + if field not in {"+", "-"}: poly_fields = field.split("*") to_add = 1.0 for poly_field in poly_fields: powers = poly_field.split("^") - temp = 0.0 + if len(powers) == 2: - try: - power = int(powers[1]) - except TypeError: - print("Expression not supported\n") - return False - key = powers[0] - if key not in binary_variables_name_weight: - print("Expression not supported\n") - return False - if isinstance(binary_variables_name_weight[key], list): - encoding = binary_variables_name_weight[key][0] - if encoding == "dictionary": - for elem in binary_variables_name_weight[key]: - if not isinstance(elem, str): - t = 1.0 - t *= elem[0] - if len(elem) == 2: - t *= elem[1] ** power - elif len(elem) == 3: - t = t * elem[1] ** power + elem[2] ** power - temp += t - else: - for elem in binary_variables_name_weight[key]: - if not isinstance(elem, str): - t = 1.0 - t *= elem[0] - if len(elem) == 2: - t *= elem[1] - elif len(elem) == 3: - t = t * elem[1] + elem[2] - temp += t - temp = temp**power - else: - t = 1.0 - t *= binary_variables_name_weight[key][0] - if len(binary_variables_name_weight[key]) == 2: - t *= (binary_variables_name_weight[key][1]) ** power - elif len(binary_variables_name_weight[key]) == 3: - t = ( - t * binary_variables_name_weight[key][1] + binary_variables_name_weight[key][2] - ) ** power - temp += t - to_add *= temp - name = key.split("_") - if len(name) == 1: - if ( - variables_dict[name[0]].type == "c" - and min_precision > variables_dict[name[0]].precision - ): - min_precision = variables_dict[name[0]].precision - elif ( - len(name) == 2 - and name[1].isnumeric() - and name[0] in variables_dict - and variables_dict[name[0]][int(name[1])].type == "c" - ): - if min_precision > variables_dict[name[0]][int(name[1])].precision: - min_precision = variables_dict[name[0]][int(name[1])].precision - elif ( - len(name) == 3 - and name[1].isnumeric() - and name[2].isnumeric() - and name[0] in variables_dict - and variables_dict[name[0]][int(name[1])][int(name[2])].type == "c" - ): - if min_precision > variables_dict[name[0]][int(name[1])][int(name[2])].precision: - min_precision = variables_dict[name[0]][int(name[1])][int(name[2])].precision - elif ( - len(name) == 4 - and name[1].isnumeric() - and name[2].isnumeric() - and name[3].isnumeric() - and name[0] in variables_dict - and variables_dict[name[0]][int(name[1])][int(name[2])][int(name[3])].type == "c" - and min_precision - > variables_dict[name[0]][int(name[1])][int(name[2])][int(name[3])].precision - ): - min_precision = variables_dict[name[0]][int(name[1])][int(name[2])][int(name[3])].precision - elif ( - name is variables_dict.keys() - and variables_dict[name[0]].type == "c" - and min_precision > variables_dict[name[0]].precision - ): - min_precision = variables_dict[name[0]].precision + ret = self._convert_expression_prec_power( + powers, binary_variables_name_weight, variables_dict, to_add, min_precision + ) else: - key = poly_field - if key not in binary_variables_name_weight: - try: - to_add *= float(key) - except ValueError: - print("Expression not supported\n") - return False - else: - if isinstance(binary_variables_name_weight[key], list): - for elem in binary_variables_name_weight[key]: - if not isinstance(elem, str): - t = 1.0 - t *= elem[0] - if len(elem) == 2: - t *= elem[1] - elif len(elem) == 3: - t = t * elem[1] + elem[2] - temp += t - else: - t = 1.0 - t *= binary_variables_name_weight[key][0] - if len(binary_variables_name_weight[key]) == 2: - t *= binary_variables_name_weight[key][1] - elif len(binary_variables_name_weight[key]) == 3: - t = t * binary_variables_name_weight[key][1] + binary_variables_name_weight[key][2] - temp += t - to_add *= temp - name = key.split("_") - if len(name) == 1 and variables_dict[name[0]].type == "c": - if min_precision > variables_dict[name[0]].precision: - min_precision = variables_dict[name[0]].precision - elif ( - len(name) == 2 - and name[1].isnumeric() - and name[0] in variables_dict - and variables_dict[name[0]][int(name[1])].type == "c" - and min_precision > variables_dict[name[0]][int(name[1])].precision - ): - min_precision = variables_dict[name[0]][int(name[1])].precision - elif ( - len(name) == 3 - and name[1].isnumeric() - and name[2].isnumeric() - and name[0] in variables_dict - ): - if ( - variables_dict[name[0]][int(name[1])][int(name[2])].type == "c" - and min_precision > variables_dict[name[0]][int(name[1])][int(name[2])].precision - ): - min_precision = variables_dict[name[0]][int(name[1])][int(name[2])].precision - elif ( - len(name) == 4 - and name[1].isnumeric() - and name[2].isnumeric() - and name[3].isnumeric() - and name[0] in variables_dict - and variables_dict[name[0]][int(name[1])][int(name[2])][int(name[3])].type == "c" - ): - if ( - min_precision - > variables_dict[name[0]][int(name[1])][int(name[2])][int(name[3])].precision - ): - min_precision = variables_dict[name[0]][int(name[1])][int(name[2])][ - int(name[3]) - ].precision - else: - if ( - name is variables_dict.keys() - and variables_dict[name[0]].type == "c" - and min_precision > variables_dict[name[0]].precision - ): - min_precision = variables_dict[name[0]].precision + ret = self._convert_expression_prec_no_power( + poly_field, binary_variables_name_weight, variables_dict, to_add, min_precision + ) + if isinstance(ret, bool): + return ret + to_add = ret[0] + min_precision = ret[1] if sign == "+": func += to_add @@ -2188,7 +2014,8 @@ def _convert_expression_prec( sign = field return func, min_precision - def _convert_expression_logic(self, expr: Expr, binary_variables_name_weight: dict[str, Any]) -> boolean_var | bool: + @staticmethod + def _convert_expression_logic(expr: Expr, binary_variables_name_weight: dict[str, Any]) -> boolean_var | bool: """function for translating an expression in the problem variable in case of logic constraints Keyword arguments: @@ -2211,7 +2038,8 @@ def _convert_expression_logic(self, expr: Expr, binary_variables_name_weight: di ret = binary_variables_name_weight[key][0] return ret - def _min_max_const_estimation(self, exp: PUBO) -> tuple[float, float, float]: + @staticmethod + def _min_max_const_estimation(exp: PUBO) -> tuple[float, float, float]: """function for estimating minimum and maximum value and the constraint value Keyword arguments: @@ -2234,6 +2062,52 @@ def _min_max_const_estimation(self, exp: PUBO) -> tuple[float, float, float]: min_val += exp[key] return min_val, max_val, const + @staticmethod + def _expression_to_hamiltonian_power( + powers: list[str], binary_variables_name_weight_val: list[Any], to_add: float + ) -> float | bool: + try: + power = int(powers[1]) + except TypeError: + print("Expression not supported\n") + return False + key = powers[0] + for elm in binary_variables_name_weight_val: + if isinstance(elm, list): + for el in elm: + if not isinstance(el, str) and key == (next(iter(el[0].variables))): + to_add *= el[0] ** power + break + elif not isinstance(elm, str) and key == next(iter(elm[0].variables)): + to_add *= elm[0] ** power + break + return to_add + + @staticmethod + def _expression_to_hamiltonian_no_power( + poly_field: str, binary_variables_name_weight_val: list[Any], to_add: float + ) -> float: + key = poly_field + is_float = False + try: + temp = float(key) + except ValueError: + pass + else: + to_add *= temp + is_float = True + + if not is_float: + for elm in binary_variables_name_weight_val: + if isinstance(elm, list): + for el in elm: + if not isinstance(el, str) and key == next(iter(el[0].variables)): + to_add *= el[0] + elif not isinstance(elm, str) and key == next(iter(elm[0].variables)): + to_add *= elm[0] + + return to_add + def _expression_to_hamiltonian(self, exp: Expr, binary_variables_name_weight_val: list[Any]) -> PUBO: """function for translating an expression in the problem variable into an Hamiltonian when is directly written with the inside binary variables @@ -2248,46 +2122,20 @@ def _expression_to_hamiltonian(self, exp: Expr, binary_variables_name_weight_val hamiltonian = PUBO() sign = "+" for field in fields: - if field != "+" and field != "-": + if field not in {"+", "-"}: poly_fields = field.split("*") to_add = 1.0 for poly_field in poly_fields: powers = poly_field.split("^") if len(powers) == 2: - try: - power = int(powers[1]) - except TypeError: - print("Expression not supported\n") - return False - key = powers[0] - for elm in binary_variables_name_weight_val: - if isinstance(elm, list): - for el in elm: - if not isinstance(el, str) and key == (next(iter(el[0].variables))): - to_add *= el[0] ** power - break - elif not isinstance(elm, str) and key == next(iter(elm[0].variables)): - to_add *= elm[0] ** power - break + t = self._expression_to_hamiltonian_power(powers, binary_variables_name_weight_val, to_add) + if not isinstance(t, bool): + to_add = t else: - key = poly_field - is_float = False - try: - temp = float(key) - except ValueError: - pass - else: - to_add *= temp - is_float = True - - if not is_float: - for elm in binary_variables_name_weight_val: - if isinstance(elm, list): - for el in elm: - if not isinstance(el, str) and key == next(iter(el[0].variables)): - to_add *= el[0] - elif not isinstance(elm, str) and key == next(iter(elm[0].variables)): - to_add *= elm[0] + to_add = self._expression_to_hamiltonian_no_power( + poly_field, binary_variables_name_weight_val, to_add + ) + if sign == "+": hamiltonian += to_add else: diff --git a/src/mqt/qao/objectivefunction.py b/src/mqt/qao/objectivefunction.py index dcac1e9..f064fe0 100644 --- a/src/mqt/qao/objectivefunction.py +++ b/src/mqt/qao/objectivefunction.py @@ -8,7 +8,7 @@ if TYPE_CHECKING: from qubovert import PUBO - from mqt.qao.variables import Variables + from .variables import Variables class ObjectiveFunction: @@ -32,7 +32,113 @@ def add_objective_function(self, objective_function: Expr, minimization: bool = """ if not minimization: objective_function = -objective_function - self.objective_functions.append((cast(Expr, expand(objective_function).evalf()), weight)) # type: ignore[no-untyped-call] + self.objective_functions.append((cast(Expr, expand(objective_function).evalf()), weight)) + + @staticmethod + def rewrite_powers(powers: list[str], var: Variables, to_add: float) -> float | bool: + temp = 0.0 + try: + power = int(powers[1]) + except TypeError: + print("Expression not supported\n") + return False + key = powers[0] + if key not in var.binary_variables_name_weight: + print("Expression not supported\n") + return False + if isinstance(var.binary_variables_name_weight[key], list): + encoding = var.binary_variables_name_weight[key][0] + if encoding == "dictionary": + for elem in var.binary_variables_name_weight[key]: + if not isinstance(elem, str): + t = 1.0 + t *= elem[0] + if len(elem) == 2: + t *= elem[1] ** power + elif len(elem) == 3: + t = t * elem[1] ** power + elem[2] ** power + temp += t + else: + for elem in var.binary_variables_name_weight[key]: + if not isinstance(elem, str): + t = 1.0 + t *= elem[0] + if len(elem) == 2: + t *= elem[1] + elif len(elem) == 3: + t = t * elem[1] + elem[2] + temp += t + temp **= power + elif isinstance(var.binary_variables_name_weight[key], tuple): + t = 1.0 + t *= var.binary_variables_name_weight[key][0] + if len(var.binary_variables_name_weight[key]) == 2: + t *= (var.binary_variables_name_weight[key][1]) ** power + elif len(var.binary_variables_name_weight[key]) == 3: + t = (t * var.binary_variables_name_weight[key][1] + var.binary_variables_name_weight[key][2]) ** power + temp += t + else: + temp = var.binary_variables_name_weight[key] + to_add *= temp + + return to_add + + @staticmethod + def rewrite_no_power(poly_field: str, var: Variables, to_add: float) -> float | bool: + temp = 0.0 + key = poly_field + if key not in var.binary_variables_name_weight: + try: + to_add *= float(key) + except TypeError: + print("Expression not supported\n") + return False + else: + if isinstance(var.binary_variables_name_weight[key], list): + for elem in var.binary_variables_name_weight[key]: + if not isinstance(elem, str): + t = 1.0 + t *= elem[0] + if len(elem) == 2: + t *= elem[1] + elif len(elem) == 3: + t = t * elem[1] + elem[2] + temp += t + elif isinstance(var.binary_variables_name_weight[key], tuple): + t = 1.0 + t *= var.binary_variables_name_weight[key][0] + if len(var.binary_variables_name_weight[key]) == 2: + t *= var.binary_variables_name_weight[key][1] + elif len(var.binary_variables_name_weight[key]) == 3: + t = t * var.binary_variables_name_weight[key][1] + var.binary_variables_name_weight[key][2] + temp += t + else: + temp += var.binary_variables_name_weight[key] + to_add *= temp + return to_add + + def rewrite_elem(self, field: str, var: Variables, func: float, sign: str) -> float | bool: + poly_fields = field.split("*") + to_add = 1.0 + for poly_field in poly_fields: + powers = poly_field.split("^") + + if len(powers) == 2: + t = self.rewrite_powers(powers, var, to_add) + if isinstance(t, bool): + return False + to_add = t + else: + t = self.rewrite_no_power(poly_field, var, to_add) + if isinstance(t, bool): + return False + to_add = t + if sign == "+": + func += to_add + else: + func -= to_add + + return func def rewrite_cost_functions(self, pubo: PUBO, var: Variables) -> PUBO | bool: """function for rewriting the cost functions according with the variable structure @@ -53,102 +159,41 @@ def rewrite_cost_functions(self, pubo: PUBO, var: Variables) -> PUBO | bool: func = 0.0 sign = "+" for field in fields: - if field != "+" and field != "-": - poly_fields = field.split("*") - to_add = 1.0 - for poly_field in poly_fields: - powers = poly_field.split("^") - temp = 0.0 - if len(powers) == 2: - try: - power = int(powers[1]) - except TypeError: - print("Expression not supported\n") - return False - key = powers[0] - if key not in var.binary_variables_name_weight: - print("Expression not supported\n") - return False - if isinstance(var.binary_variables_name_weight[key], list): - encoding = var.binary_variables_name_weight[key][0] - if encoding == "dictionary": - for elem in var.binary_variables_name_weight[key]: - if not isinstance(elem, str): - t = 1.0 - t *= elem[0] - if len(elem) == 2: - t *= elem[1] ** power - elif len(elem) == 3: - t = t * elem[1] ** power + elem[2] ** power - temp += t - else: - for elem in var.binary_variables_name_weight[key]: - if not isinstance(elem, str): - t = 1.0 - t *= elem[0] - if len(elem) == 2: - t *= elem[1] - elif len(elem) == 3: - t = t * elem[1] + elem[2] - temp += t - temp = temp**power - elif isinstance(var.binary_variables_name_weight[key], tuple): - t = 1.0 - t *= var.binary_variables_name_weight[key][0] - if len(var.binary_variables_name_weight[key]) == 2: - t *= (var.binary_variables_name_weight[key][1]) ** power - elif len(var.binary_variables_name_weight[key]) == 3: - t = ( - t * var.binary_variables_name_weight[key][1] - + var.binary_variables_name_weight[key][2] - ) ** power - temp += t - else: - temp = var.binary_variables_name_weight[key] - to_add *= temp - else: - key = poly_field - if key not in var.binary_variables_name_weight: - try: - to_add *= float(key) - except TypeError: - print("Expression not supported\n") - return False - else: - if isinstance(var.binary_variables_name_weight[key], list): - for elem in var.binary_variables_name_weight[key]: - if not isinstance(elem, str): - t = 1.0 - t *= elem[0] - if len(elem) == 2: - t *= elem[1] - elif len(elem) == 3: - t = t * elem[1] + elem[2] - temp += t - elif isinstance(var.binary_variables_name_weight[key], tuple): - t = 1.0 - t *= var.binary_variables_name_weight[key][0] - if len(var.binary_variables_name_weight[key]) == 2: - t *= var.binary_variables_name_weight[key][1] - elif len(var.binary_variables_name_weight[key]) == 3: - t = ( - t * var.binary_variables_name_weight[key][1] - + var.binary_variables_name_weight[key][2] - ) - temp += t - else: - temp += var.binary_variables_name_weight[key] - to_add *= temp - - if sign == "+": - func += to_add - else: - func -= to_add + if field not in {"+", "-"}: + t = self.rewrite_elem(field, var, func, sign) + if isinstance(t, bool): + return False + func = t else: sign = field pubo += func * obj[1] return pubo + @staticmethod + def var_list_substitution(var: str, solution: dict[str, Any], variables: Variables, temp_expression: Expr) -> Expr: + for i in range(len(solution[var])): + if isinstance(solution[var][i], list): + for j in range(len(solution[var][i])): + if isinstance(solution[var][i][j], list): + for k in range(len(solution[var][i][j])): + temp_expression = temp_expression.subs({ + variables.variables_dict[var][i][j][k].symbol: solution[var][i][j][k] + }) + else: + temp_expression = temp_expression.subs({ + variables.variables_dict[var][i][j].symbol: solution[var][i][j] + }) + else: + temp_expression = temp_expression.subs({variables.variables_dict[var][i].symbol: solution[var][i]}) + return temp_expression + + def var_substitution(self, var: str, solution: dict[str, Any], variables: Variables, temp_expression: Expr) -> Expr: + if isinstance(solution[var], float): + temp_expression = temp_expression.subs({variables.variables_dict[var].symbol: solution[var]}) + elif isinstance(solution[var], list): + temp_expression = self.var_list_substitution(var, solution, variables, temp_expression) + return temp_expression + def substitute_values(self, solution: dict[str, Any], variables: Variables) -> dict[str, float] | bool: """function for substituting solutions into the cost functions expressions @@ -166,25 +211,7 @@ def substitute_values(self, solution: dict[str, Any], variables: Variables) -> d temp_expression = obj[0] for var in solution: if var in variables.variables_dict: - if isinstance(solution[var], float): - temp_expression = temp_expression.subs({variables.variables_dict[var].symbol: solution[var]}) # type: ignore[no-untyped-call] - elif isinstance(solution[var], list): - for i in range(len(solution[var])): - if isinstance(solution[var][i], list): - for j in range(len(solution[var][i])): - if isinstance(solution[var][i][j], list): - for k in range(len(solution[var][i][j])): - temp_expression = temp_expression.subs( # type: ignore[no-untyped-call] - {variables.variables_dict[var][i][j][k].symbol: solution[var][i][j][k]} - ) - else: - temp_expression = temp_expression.subs( # type: ignore[no-untyped-call] - {variables.variables_dict[var][i][j].symbol: solution[var][i][j]} - ) - else: - temp_expression = temp_expression.subs( # type: ignore[no-untyped-call] - {variables.variables_dict[var][i].symbol: solution[var][i]} - ) + temp_expression = self.var_substitution(var, solution, variables, temp_expression) try: objective_functions_values[str(obj[0])] = float(temp_expression) except ValueError: diff --git a/src/mqt/qao/problem.py b/src/mqt/qao/problem.py index ca99306..33f77d7 100644 --- a/src/mqt/qao/problem.py +++ b/src/mqt/qao/problem.py @@ -7,7 +7,9 @@ import numpy as np from qubovert import PUBO -from mqt.qao import Constraints, ObjectiveFunction, Variables +from .constraints import Constraints +from .objectivefunction import ObjectiveFunction +from .variables import Variables class Problem: @@ -149,7 +151,8 @@ def update_lambda_cost_function( return self.pubo - def _upper_bound_with_only_positive_coefficient(self, cost_function: PUBO) -> float | bool: + @staticmethod + def _upper_bound_with_only_positive_coefficient(cost_function: PUBO) -> float | bool: """function for estimating the weights for constraints Keyword arguments: @@ -172,7 +175,8 @@ def _upper_bound_with_only_positive_coefficient(self, cost_function: PUBO) -> fl upperbound += cost_function[key] return upperbound - def _maximum_qubo_coefficient(self, cost_function: PUBO) -> float: + @staticmethod + def _maximum_qubo_coefficient(cost_function: PUBO) -> float: """function for estimating the weights for constraints Keyword arguments: @@ -196,7 +200,8 @@ def _maximum_qubo_coefficient(self, cost_function: PUBO) -> float: offset = cost_function[key] return max_coeff + offset - def _vlm(self, cost_function: PUBO) -> float: + @staticmethod + def _vlm(cost_function: PUBO) -> float: """function for estimating the weights for constraints Keyword arguments: @@ -224,7 +229,8 @@ def _vlm(self, cost_function: PUBO) -> float: n_sum[elem] -= cost_function[key] return float(np.max([np.array(list(p_sum.values())), np.array(list(n_sum.values()))])) - def _momc(self, cost_function: PUBO, constraint_function: PUBO) -> float: + @staticmethod + def _momc(cost_function: PUBO, constraint_function: PUBO) -> float: """function for estimating the weights for constraints Keyword arguments: @@ -273,7 +279,8 @@ def _momc(self, cost_function: PUBO, constraint_function: PUBO) -> float: return wc_max return max(1.0, wc_max / wg_min) - def _moc(self, cost_function: PUBO, constraint_function: PUBO) -> float: + @staticmethod + def _moc(cost_function: PUBO, constraint_function: PUBO) -> float: """function for estimating the weights for constraints Keyword arguments: @@ -337,7 +344,8 @@ def _moc(self, cost_function: PUBO, constraint_function: PUBO) -> float: val = v return max(1, val) - def upper_lower_bound_naive_method(self, cost_function: PUBO) -> float: + @staticmethod + def upper_lower_bound_naive_method(cost_function: PUBO) -> float: """function for estimating the weights for constraints Keyword arguments: @@ -357,7 +365,8 @@ def upper_lower_bound_naive_method(self, cost_function: PUBO) -> float: lower_bound += cost_function[key] return upper_bound - lower_bound - def upper_lower_bound_posiform_and_negaform_method(self, cost_function: PUBO) -> float: + @staticmethod + def upper_lower_bound_posiform_and_negaform_method(cost_function: PUBO) -> float: """function for estimating the weights for constraints Keyword arguments: @@ -392,7 +401,8 @@ def upper_lower_bound_posiform_and_negaform_method(self, cost_function: PUBO) -> upperbound += n_sum[key] return upperbound - lowerbound - def _sequential_penalty_increase(self, current_lambda: float) -> float: + @staticmethod + def _sequential_penalty_increase(current_lambda: float) -> float: """function for updating weights for constraints Keyword arguments: @@ -403,7 +413,8 @@ def _sequential_penalty_increase(self, current_lambda: float) -> float: """ return current_lambda * 10 - def _scaled_sequential_penalty_increase(self, current_lambda: float, wu: float, t: int) -> float: + @staticmethod + def _scaled_sequential_penalty_increase(current_lambda: float, wu: float, t: int) -> float: """function for updating weights for constraints Keyword arguments: @@ -417,7 +428,8 @@ def _scaled_sequential_penalty_increase(self, current_lambda: float, wu: float, scale_factor = wu ** (1 / t) return float(round(current_lambda * scale_factor)) - def _binary_search_penalty_algorithm(self, current_lambda: float, wu: float) -> float: + @staticmethod + def _binary_search_penalty_algorithm(current_lambda: float, wu: float) -> float: """function for updating weights for constraints Keyword arguments: diff --git a/src/mqt/qao/solvers.py b/src/mqt/qao/solvers.py index d9f20c9..c9eddca 100644 --- a/src/mqt/qao/solvers.py +++ b/src/mqt/qao/solvers.py @@ -3,7 +3,7 @@ import json import logging -from math import ceil, log, sqrt +from math import ceil, log, log2, sqrt from pathlib import Path from time import time_ns from typing import TYPE_CHECKING, Any @@ -14,34 +14,28 @@ from dwave.samplers import SimulatedAnnealingSampler from dwave.system import DWaveSampler, EmbeddingComposite from matplotlib import rc -from qiskit.algorithms import QAOA, VQE -from qiskit.circuit import QuantumCircuit, Parameter +from qiskit.circuit import Parameter, QuantumCircuit from qiskit.circuit.library import TwoLocal -from qiskit.exceptions import QiskitError +from qiskit.primitives import Sampler +from qiskit_algorithms import QAOA, SamplingVQE +from qiskit_algorithms.optimizers import COBYLA from qiskit_optimization.algorithms import ( GroverOptimizer, - MinimumEigenOptimizationResult, MinimumEigenOptimizer, - OptimizationResult, + SolutionSample, ) -from qiskit_ibm_runtime import QiskitRuntimeService # type: ignore[import-untyped] from qiskit_optimization.translators import from_docplex_mp -from qiskit.providers.basicaer import BasicAer from qubovert import PUBO, QUBO if TYPE_CHECKING: from collections.abc import Callable + from dimod import SampleSet - from qiskit.algorithms.optimizers import Optimizer - from qiskit.opflow import ( - ExpectationBase, - GradientBase, - OperatorBase, - ) + from qiskit_algorithms.optimizers import Optimizer from qiskit_optimization.problems import QuadraticProgram -from mqt.qao.problem import Problem +from .problem import Problem class Solver: @@ -384,11 +378,6 @@ def solve_grover_adaptive_search_qubo( self, problem: Problem, auto_setting: bool = False, - simulator: bool = True, - backend_name: str = "qasm_simulator", - channel: str = "", - token: str = "", - instance: str = "", qubit_values: int = 0, coeff_precision: float = 1.0, threshold: int = 10, @@ -448,43 +437,13 @@ def solve_grover_adaptive_search_qubo( if auto_setting: # To change with the experience # num_runs = 100 threshold = 2 * len(self.qubo.variables) - qubit_values = ceil( - log( - abs(self.problem.upper_lower_bound_posiform_and_negaform_method(scaled_qubo)), - 2, - ) - ) - else: - if boundaries_estimation_method == "upper lower bound posiform and negaform method": - qubit_values = ceil( - log( - abs(self.problem.upper_lower_bound_posiform_and_negaform_method(scaled_qubo)), - 2, - ) - ) - elif boundaries_estimation_method == "naive": - qubit_values = ceil( - log( - abs(self.problem.upper_lower_bound_naive_method(scaled_qubo)), - 2, - ) - ) + qubit_values = ceil(log2(abs(self.problem.upper_lower_bound_posiform_and_negaform_method(scaled_qubo)))) + elif boundaries_estimation_method == "upper lower bound posiform and negaform method": + qubit_values = ceil(log2(abs(self.problem.upper_lower_bound_posiform_and_negaform_method(scaled_qubo)))) + elif boundaries_estimation_method == "naive": + qubit_values = ceil(log2(abs(self.problem.upper_lower_bound_naive_method(scaled_qubo)))) - if simulator: - try: # Load your IBM Quantum account - QiskitRuntimeService.save_account(channel=channel, token=token, instance=instance, overwrite=True) - backend = QiskitRuntimeService().backend(backend_name) - except QiskitError: - print("The chosen simulator doesn't exist or the IBM cannot be used. Qasm simulator will used.") - backend = BasicAer.get_backend("qasm_simulator") - else: - try: - QiskitRuntimeService.save_account(channel=channel, token=token, instance=instance, overwrite=True) - backend = QiskitRuntimeService().backend(backend_name) - except QiskitError: - print("The chosen backend doesn't exist or the IBM cannot be used. Qasm simulator will used.") - backend = BasicAer.get_backend("qasm_simulator") - grover_optimizer = GroverOptimizer(qubit_values, num_iterations=threshold, quantum_instance=backend) + grover_optimizer = GroverOptimizer(qubit_values, num_iterations=threshold, sampler=Sampler()) if save_time: start = time_ns() @@ -523,30 +482,14 @@ def solve_grover_adaptive_search_qubo( scaled_qubo = round(scaled_qubo_c / min_coeff) qp = self._from_qubovert_to_qiskit_model(scaled_qubo) - if auto_setting: + if auto_setting or boundaries_estimation_method == "upper lower bound posiform and negaform method": qubit_values = ceil( - log( - abs(self.problem.upper_lower_bound_posiform_and_negaform_method(scaled_qubo)), - 2, - ) + log2(abs(self.problem.upper_lower_bound_posiform_and_negaform_method(scaled_qubo))) ) - else: - if boundaries_estimation_method == "upper lower bound posiform and negaform method": - qubit_values = ceil( - log( - abs(self.problem.upper_lower_bound_posiform_and_negaform_method(scaled_qubo)), - 2, - ) - ) - elif boundaries_estimation_method == "naive": - qubit_values = ceil( - log( - abs(self.problem.upper_lower_bound_naive_method(scaled_qubo)), - 2, - ) - ) - - grover_optimizer = GroverOptimizer(qubit_values, num_iterations=threshold, quantum_instance=backend) + elif boundaries_estimation_method == "naive": + qubit_values = ceil(log2(abs(self.problem.upper_lower_bound_naive_method(scaled_qubo)))) + + grover_optimizer = GroverOptimizer(qubit_values, num_iterations=threshold, sampler=Sampler()) if save_time: start = time_ns() @@ -580,21 +523,13 @@ def solve_qaoa_qubo( self, problem: Problem, auto_setting: bool = False, - simulator: bool = True, - backend_name: str = "qasm_simulator", - channel: str = "", - token: str = "", - instance: str = "", num_runs: int = 10, - optimizer: Optimizer = None, + optimizer: Optimizer | None = None, reps: int = 1, initial_state: QuantumCircuit | None = None, - mixer: QuantumCircuit | OperatorBase = None, + mixer: QuantumCircuit = None, initial_point: np.ndarray[Any, Any] | None = None, - gradient: GradientBase | Callable[[np.ndarray[Any, Any] | list[Any]], list[Any]] | None = None, - expectation: ExpectationBase | None = None, - include_custom: bool = False, - max_evals_grouped: int = 1, + aggregation: float | Callable[[list[float]], float] | None = None, callback: Callable[[int, np.ndarray[Any, Any], float, float], None] | None = None, max_lambda_update: int = 5, lambda_update_mechanism: str = "sequential penalty increase", @@ -652,31 +587,17 @@ def solve_qaoa_qubo( initial_state = QuantumCircuit(var_num) for _idx in range(var_num): initial_state.h(_idx) - if simulator: - try: - QiskitRuntimeService.save_account(channel=channel, token=token, instance=instance, overwrite=True) - backend = QiskitRuntimeService().backend(backend_name) - except QiskitError: - print("The chosen backend doesn't exist or the IBM cannot be used. Qasm simulator will used.") - backend = BasicAer.get_backend("qasm_simulator") - else: - try: - QiskitRuntimeService.save_account(channel=channel, token=token, instance=instance, overwrite=True) - backend = QiskitRuntimeService().backend(backend_name) - except QiskitError: - print("The chosen backend doesn't exist or the IBM cannot be used. Qasm simulator will used.") - backend = BasicAer.get_backend("qasm_simulator") + + if optimizer is None: + optimizer = COBYLA() qaoa_mes = QAOA( - quantum_instance=backend, + sampler=Sampler(), optimizer=optimizer, reps=reps, initial_state=initial_state, mixer=mixer, initial_point=initial_point, - gradient=gradient, - expectation=expectation, - include_custom=include_custom, - max_evals_grouped=max_evals_grouped, + aggregation=aggregation, callback=callback, ) qaoa = MinimumEigenOptimizer(qaoa_mes) @@ -745,19 +666,11 @@ def solve_vqe_qubo( self, problem: Problem, auto_setting: bool = False, - simulator: bool = True, - backend_name: str = "qasm_simulator", - channel: str = "", - token: str = "", - instance: str = "", num_runs: int = 10, optimizer: Optimizer | None = None, - ansatz: QuantumCircuit | OperatorBase | None = None, + ansatz: QuantumCircuit | None = None, initial_point: np.ndarray[Any, Any] | None = None, - gradient: GradientBase | Callable[[np.ndarray[Any, Any] | list[Any]], list[Any]] | None = None, - expectation: ExpectationBase | None = None, - include_custom: bool = False, - max_evals_grouped: int = 1, + aggregation: float | Callable[[list[float]], float] | None = None, callback: Callable[[int, np.ndarray[Any, Any], float, float], None] | None = None, max_lambda_update: int = 5, lambda_update_mechanism: str = "sequential penalty increase", @@ -801,32 +714,18 @@ def solve_vqe_qubo( compilation_time = -1 logging.getLogger("qiskit").setLevel(logging.ERROR) res = [] - if auto_setting and ansatz is None: + if auto_setting or ansatz is None: ansatz = TwoLocal(num_qubits=len(self.qubo.variables), rotation_blocks="ry", entanglement_blocks="cz") - if simulator: - try: - QiskitRuntimeService.save_account(channel=channel, token=token, instance=instance, overwrite=True) - backend = QiskitRuntimeService().backend(backend_name) - except QiskitError: - print("The chosen backend doesn't exist or the IBM cannot be used. Qasm simulator will used.") - backend = BasicAer.get_backend("qasm_simulator") - else: - try: - QiskitRuntimeService.save_account(channel=channel, token=token, instance=instance, overwrite=True) - backend = QiskitRuntimeService().backend(backend_name) - except QiskitError: - print("The chosen backend doesn't exist or the IBM cannot be used. Qasm simulator will used.") - backend = BasicAer.get_backend("qasm_simulator") - vqe_mes = VQE( - quantum_instance=backend, + if optimizer is None: + optimizer = COBYLA() + + vqe_mes = SamplingVQE( + sampler=Sampler(), optimizer=optimizer, ansatz=ansatz, initial_point=initial_point, - gradient=gradient, - expectation=expectation, - include_custom=include_custom, - max_evals_grouped=max_evals_grouped, + aggregation=aggregation, callback=callback, ) vqe = MinimumEigenOptimizer(vqe_mes) @@ -893,7 +792,8 @@ def solve_vqe_qubo( def get_lambda_updates(self) -> int: return self._number_of_lambda_update - def _from_qubovert_to_qiskit_model(self, qubo: QUBO) -> QuadraticProgram: + @staticmethod + def _from_qubovert_to_qiskit_model(qubo: QUBO) -> QuadraticProgram: """function for converting the qubovert formulation into the qiskit quadratic model one Keyword arguments: @@ -1031,7 +931,7 @@ def create_dwave_annealing_solution( self.energies.append(sample[1] + offset) self.best_energy = samples.first.energy + offset - for sample in list(samples.samples()): # type: ignore[no-untyped-call] + for sample in list(samples.samples()): converted_sol = self.problem.variables.convert_simulated_annealing_solution(pubo.convert_solution(sample)) if isinstance(converted_sol, dict): self.solutions.append(converted_sol) @@ -1053,7 +953,7 @@ def create_dwave_annealing_solution( def create_qiskit_qubo_solution( self, - res: list[MinimumEigenOptimizationResult] | list[OptimizationResult], + res: list[SolutionSample], pubo: PUBO, qubo: QUBO, time: float = -1.0, @@ -1168,7 +1068,7 @@ def show_cumulative( rc("text", usetex=True) plt.rc("text", usetex=True) if label: - n, bins, patches = plt.hist( + plt.hist( self.energies, cumulative=True, histtype="step", @@ -1177,17 +1077,15 @@ def show_cumulative( label=r"\textit{" + label + "}", ) else: - n, bins, patches = plt.hist(self.energies, cumulative=True, histtype="step", linewidth=2, bins=100) + plt.hist(self.energies, cumulative=True, histtype="step", linewidth=2, bins=100) plt.title(r"\textbf{Cumulative distribution}", fontsize=20) plt.xlabel(r"\textit{Energy}", fontsize=20) plt.ylabel(r"\textit{occurrence}", fontsize=20) else: if label: - n, bins, patches = plt.hist( - self.energies, cumulative=True, histtype="step", linewidth=2, bins=100, label=label - ) + plt.hist(self.energies, cumulative=True, histtype="step", linewidth=2, bins=100, label=label) else: - n, bins, patches = plt.hist(self.energies, cumulative=True, histtype="step", linewidth=2, bins=100) + plt.hist(self.energies, cumulative=True, histtype="step", linewidth=2, bins=100) plt.title("Cumulative distribution", fontsize=20) plt.xlabel("Energy", fontsize=20) plt.ylabel("occurrence", fontsize=20) @@ -1206,9 +1104,8 @@ def show_cumulative( plt.savefig(filename + ".png", format="png") plt.savefig(filename + ".pdf", format="pdf") plt.close() - else: - if show: - plt.show() + elif show: + plt.show() def valid_solutions(self, weak: bool = True) -> float: """function for evaluating the rate of valid solution and the amount of violations @@ -1236,7 +1133,7 @@ def valid_solutions(self, weak: bool = True) -> float: else: self.valid_solution_rate[count] = 1 for key in self.valid_solution_rate: - self.valid_solution_rate[key] = self.valid_solution_rate[key] / len(all_satisfied) + self.valid_solution_rate[key] /= len(all_satisfied) return self.valid_solution_rate[0] def p_range(self, ref_value: float | None = None) -> float: diff --git a/src/mqt/qao/variables.py b/src/mqt/qao/variables.py index 99b524e..b21f75d 100644 --- a/src/mqt/qao/variables.py +++ b/src/mqt/qao/variables.py @@ -3,7 +3,7 @@ # numpy for matrix management from __future__ import annotations -from math import ceil, floor, log, sqrt +from math import ceil, floor, log, log2, sqrt from typing import Any import numpy as np @@ -437,6 +437,102 @@ def _variable_binarization( return constraints, i + def conv_var(self, v1: Variable, solution: dict[str, float]) -> float | bool: + temp = 0.0 + if isinstance(self.binary_variables_name_weight[v1.name], list): + for el in self.binary_variables_name_weight[v1.name]: + if not isinstance(el, str): + key = next(iter(el[0].variables)) + if key not in solution: + print("The variable is not found\n") + return False + if solution[key] == 1: + temp += el[1] if len(el) > 1 else 1 + if len(el) == 3: + temp += el[2] + elif next(iter(self.binary_variables_name_weight[v1.name][0].variables)) in solution: + key = next(iter(self.binary_variables_name_weight[v1.name][0].variables)) + if solution[key] == 1: + temp += ( + self.binary_variables_name_weight[v1.name][1] + if len(self.binary_variables_name_weight[v1.name]) > 1 + else 1 + ) + if len(self.binary_variables_name_weight[v1.name]) == 3: + temp += self.binary_variables_name_weight[v1.name][2] + else: + print("The variable is not found\n") + return False + + return temp + + def conv_list(self, v1: list[Variable], solution: dict[str, float]) -> list[float] | bool: + nested_temp_list = [] + for v2 in v1: + if not isinstance(v2, list) and v2.name in self.binary_variables_name_weight: + temp = self.conv_var(v2, solution) + if isinstance(temp, bool): + return False + nested_temp_list.append(temp) + return nested_temp_list + + def convert_solution_var( + self, converted_solution: dict[str, Any], var: str, solution: dict[str, float] + ) -> dict[str, Any] | bool: + """function for converting a solution coming from simulated annealing""" + converted_solution[var] = [] + for j, v in enumerate(self.variables_dict[var]): + if isinstance(v, list): + converted_solution[var].append([]) + for v1 in v: + if not isinstance(v1, list) and v1.name in self.binary_variables_name_weight: + temp = self.conv_var(v1, solution) + if isinstance(temp, bool): + return False + converted_solution[var][j].append(temp) + elif isinstance(v1, list): + nested_temp_list = self.conv_list(v1, solution) + if isinstance(nested_temp_list, bool): + return False + converted_solution[var][j].append(nested_temp_list) + elif v.name in self.binary_variables_name_weight: + temp = self.conv_var(v, solution) + if isinstance(temp, bool): + return False + converted_solution[var].append(temp) + return converted_solution + + def converted_solution_single_var( + self, converted_solution: dict[str, Any], var: str, solution: dict[str, float] + ) -> dict[str, Any] | bool: + converted_solution[var] = 0.0 + if var not in self.binary_variables_name_weight: + print("The variable is not found\n") + return False + if isinstance(self.binary_variables_name_weight[var], list): + for el in self.binary_variables_name_weight[var]: + if not isinstance(el, str): + key = next(iter(el[0].variables)) + if key not in solution: + print("The variable is not found\n") + return False + if solution[key] == 1: + converted_solution[var] += el[1] if len(el) > 1 else 1 + if len(el) == 3: + converted_solution[var] += el[2] + elif next(iter(self.binary_variables_name_weight[var][0].variables)) in solution: + key = next(iter(self.binary_variables_name_weight[var][0].variables)) + if solution[key] == 1: + converted_solution[var] += ( + self.binary_variables_name_weight[var][1] if len(self.binary_variables_name_weight[var]) > 1 else 1 + ) + if len(self.binary_variables_name_weight[var]) == 3: + converted_solution[var] += self.binary_variables_name_weight[var][2] + else: + print("The variable is not found\n") + return False + return converted_solution + def convert_simulated_annealing_solution(self, solution: dict[str, float]) -> dict[str, Any] | bool: """function for converting a solution coming from simulated annealing @@ -449,180 +545,16 @@ def convert_simulated_annealing_solution(self, solution: dict[str, float]) -> di """ converted_solution: dict[str, Any] = {} for var in self.variables_dict: - if isinstance(self.variables_dict[var], Variable): - converted_solution[var] = 0.0 - if var in self.binary_variables_name_weight: - if isinstance(self.binary_variables_name_weight[var], list): - for el in self.binary_variables_name_weight[var]: - if ( - not isinstance(el, str) - and next(iter(el[0].variables)) in solution - and solution[next(iter(el[0].variables))] == 1 - ): - if len(el) > 1: - converted_solution[var] += el[1] - else: - converted_solution[var] += 1 - if not isinstance(el, str) and next(iter(el[0].variables)) in solution and len(el) == 3: - converted_solution[var] += el[2] - if not isinstance(el, str) and next(iter(el[0].variables)) not in solution: - print("The variable is not found\n") - return False - else: - if ( - next(iter(self.binary_variables_name_weight[var][0].variables)) in solution - and solution[next(iter(self.binary_variables_name_weight[var][0].variables))] == 1 - ): - if len(self.binary_variables_name_weight[var]) > 1: - converted_solution[var] += self.binary_variables_name_weight[var][1] - else: - converted_solution[var] += 1 - - if ( - next(iter(self.binary_variables_name_weight[var][0].variables)) in solution - and len(self.binary_variables_name_weight[var]) == 3 - ): - converted_solution[var] += self.binary_variables_name_weight[var][2] - if next(iter(self.binary_variables_name_weight[var][0].variables)) not in solution: - print("The variable is not found\n") - return False - - else: - print("The variable is not found\n") + if not isinstance(self.variables_dict[var], Variable): + temp = self.convert_solution_var(converted_solution, var, solution) + if isinstance(temp, bool): return False + converted_solution = temp else: - converted_solution[var] = [] - j = 0 - for v in self.variables_dict[var]: - if not isinstance(v, list) and v.name in self.binary_variables_name_weight: - temp = 0.0 - if isinstance(self.binary_variables_name_weight[v.name], list): - for el in self.binary_variables_name_weight[v.name]: - if not isinstance(el, str): - if ( - next(iter(el[0].variables)) in solution - and solution[next(iter(el[0].variables))] == 1 - ): - if len(el) > 1: - temp += el[1] - else: - temp += 1 - if next(iter(el[0].variables)) in solution and len(el) == 3: - temp += el[2] - if next(iter(el[0].variables)) not in solution: - print("The variable is not found\n") - return False - else: - if next(iter(self.binary_variables_name_weight[v.name][0].variables)) in solution: - if ( - solution[next(iter(self.binary_variables_name_weight[v.name][0].variables))] == 1 - ) and len(self.binary_variables_name_weight[v.name]) > 1: - temp += self.binary_variables_name_weight[v.name][1] - if ( - solution[next(iter(self.binary_variables_name_weight[v.name][0].variables))] == 1 - ) and len(self.binary_variables_name_weight[v.name]) == 1: - temp += 1 - if len(self.binary_variables_name_weight[v.name]) == 3: - temp += self.binary_variables_name_weight[v.name][2] - else: - print("The variable is not found\n") - return False - converted_solution[var].append(temp) - elif isinstance(v, list): - converted_solution[var].append([]) - k = 0 - for v1 in v: - if not isinstance(v1, list): - if v1.name in self.binary_variables_name_weight: - temp = 0.0 - if isinstance(self.binary_variables_name_weight[v1.name], list): - for el in self.binary_variables_name_weight[v1.name]: - if not isinstance(el, str): - if ( - next(iter(el[0].variables)) in solution - and solution[next(iter(el[0].variables))] == 1 - ): - if len(el) > 1: - temp += el[1] - else: - temp += 1 - if next(iter(el[0].variables)) in solution and len(el) == 3: - temp += el[2] - if next(iter(el[0].variables)) not in solution: - print("The variable is not found\n") - return False - else: - if ( - next(iter(self.binary_variables_name_weight[v1.name][0].variables)) - in solution - ): - if ( - solution[ - next(iter(self.binary_variables_name_weight[v1.name][0].variables)) - ] - == 1 - ) and len(self.binary_variables_name_weight[v1.name]) > 0: - if len(self.binary_variables_name_weight[v1.name]) > 1: - temp += self.binary_variables_name_weight[v1.name][1] - else: - temp += 1 - if len(self.binary_variables_name_weight[v1.name]) == 3: - temp += self.binary_variables_name_weight[v1.name][2] - else: - print("The variable is not found\n") - return False - - converted_solution[var][j].append(temp) - else: - converted_solution[var][j].append([]) - for v2 in v: - if not isinstance(v2, list) and v2.name in self.binary_variables_name_weight: - temp = 0.0 - if isinstance(self.binary_variables_name_weight[v2.name], list): - for el in self.binary_variables_name_weight[v2.name]: - if ( - not isinstance(el, str) - and next(iter(el[0].variables)) in solution - and solution[next(iter(el[0].variables))] == 1 - ): - if len(el) > 1: - temp += el[1] - else: - temp += 1 - if ( - not isinstance(el, str) - and next(iter(el[0].variables)) in solution - and len(el) == 3 - ): - temp += el[2] - if next(iter(el[0].variables)) not in solution: - print("The variable is not found\n") - return False - else: - if ( - next(iter(self.binary_variables_name_weight[v2.name][0].variables)) - in solution - ): - if ( - solution[ - next( - iter( - self.binary_variables_name_weight[v2.name][0].variables - ) - ) - ] - == 1 - ) and len(self.binary_variables_name_weight[v2.name]) > 1: - temp += self.binary_variables_name_weight[v2.name][1] - if len(self.binary_variables_name_weight[v2.name]) == 3: - temp += self.binary_variables_name_weight[v2.name][2] - else: - print("The variable is not found\n") - return False - converted_solution[var][j][k].append(temp) - k += 1 - j += 1 - + temp = self.converted_solution_single_var(converted_solution, var, solution) + if isinstance(temp, bool): + return False + converted_solution = temp return converted_solution @@ -669,7 +601,7 @@ def _dictionary_encoding( i += 1 # Add the needed constraint - constraints.append((format(expand(var_sum).evalf()) + " = 1", True, False, False)) # type: ignore[no-untyped-call] + constraints.append((format(expand(var_sum).evalf()) + " = 1", True, False, False)) return binary_variables_name_weight, constraints, i def _unitary_encoding( @@ -699,9 +631,11 @@ def _unitary_encoding( binary_variables_name_weight[self.name].append("unitary") for w in range(1, samples + 1): if w == 1: - binary_variables_name_weight[self.name].append( - (boolean_var(letter + format(i)), w * unitary_weight, min_val) - ) + binary_variables_name_weight[self.name].append(( + boolean_var(letter + format(i)), + w * unitary_weight, + min_val, + )) else: binary_variables_name_weight[self.name].append((boolean_var(letter + format(i)), w * unitary_weight)) i += 1 @@ -737,9 +671,11 @@ def _domain_well_encoding( binary_variables_name_weight[self.name].append("domain well") for w in range(1, samples + 1): if w == 1: - binary_variables_name_weight[self.name].append( - (boolean_var(letter + format(i)), w * unitary_weight, min_val) - ) + binary_variables_name_weight[self.name].append(( + boolean_var(letter + format(i)), + w * unitary_weight, + min_val, + )) else: binary_variables_name_weight[self.name].append((boolean_var(letter + format(i)), w * unitary_weight)) constraints.append((letter + format(i) + ">=" + letter + format(i - 1), True, False, False)) @@ -820,9 +756,11 @@ def _arithmetic_progression_encoding( binary_variables_name_weight[self.name].append("arithmetic progression") for w in range(1, samples): if w == 1: - binary_variables_name_weight[self.name].append( - (boolean_var(letter + format(i)), w * unitary_weight, min_val) - ) + binary_variables_name_weight[self.name].append(( + boolean_var(letter + format(i)), + w * unitary_weight, + min_val, + )) else: binary_variables_name_weight[self.name].append((boolean_var(letter + format(i)), w * unitary_weight)) i += 1 @@ -875,9 +813,11 @@ def _bounded_coefficient_encoding( eta = floor(v / ux) binary_variables_name_weight[self.name] = [] binary_variables_name_weight[self.name].append("bounded coefficient") - binary_variables_name_weight[self.name].append( - (boolean_var(letter + format(i)), base**lower_power, min_val) - ) + binary_variables_name_weight[self.name].append(( + boolean_var(letter + format(i)), + base**lower_power, + min_val, + )) i += 1 for k in range(lower_power + 1, ro): @@ -920,7 +860,7 @@ def create(self, name: str, unipolar: bool = True) -> Symbol: class Discrete(Variable): - """child class of discrete variables""" + """child class of discrete variables for representing variables that can assume a finite number of values""" def create(self, name: str, values: list[float]) -> Symbol: """function for creating the new discrete variable. @@ -1040,67 +980,65 @@ def move_to_binary( binary_variables_name_weight, i, constraints, values, letter ) self._encoding_mechanism = "dictionary" - else: - if self._encoding_mechanism == "dictionary": + elif self._encoding_mechanism == "dictionary": + values = list(np.arange(self._min, self._max + self.precision, self.precision)) + binary_variables_name_weight, constraints, i = self._dictionary_encoding( + binary_variables_name_weight, i, constraints, values, letter + ) + elif self._encoding_mechanism == "unitary": + binary_variables_name_weight, i = self._unitary_encoding( + binary_variables_name_weight, i, self._max, self._min, self.precision, letter + ) + elif self._encoding_mechanism == "domain well": + binary_variables_name_weight, constraints, i = self._domain_well_encoding( + binary_variables_name_weight, i, self._max, self._min, constraints, self.precision, letter + ) + elif self._encoding_mechanism.startswith("logarithmic"): + try: + base = int(self._encoding_mechanism.split(" ")[1]) + binary_variables_name_weight, i = self._logarithmic_encoding( + binary_variables_name_weight, i, self._max, self._min, base, int(self.precision), letter + ) + self.precision = base**self.precision + except TypeError: values = list(np.arange(self._min, self._max + self.precision, self.precision)) binary_variables_name_weight, constraints, i = self._dictionary_encoding( binary_variables_name_weight, i, constraints, values, letter ) - elif self._encoding_mechanism == "unitary": - binary_variables_name_weight, i = self._unitary_encoding( - binary_variables_name_weight, i, self._max, self._min, self.precision, letter - ) - elif self._encoding_mechanism == "domain well": - binary_variables_name_weight, constraints, i = self._domain_well_encoding( - binary_variables_name_weight, i, self._max, self._min, constraints, self.precision, letter - ) - elif self._encoding_mechanism.startswith("logarithmic"): - try: - base = int(self._encoding_mechanism.split(" ")[1]) - binary_variables_name_weight, i = self._logarithmic_encoding( - binary_variables_name_weight, i, self._max, self._min, base, int(self.precision), letter - ) - self.precision = base**self.precision - except TypeError: - values = list(np.arange(self._min, self._max + self.precision, self.precision)) - binary_variables_name_weight, constraints, i = self._dictionary_encoding( - binary_variables_name_weight, i, constraints, values, letter - ) - self._encoding_mechanism = "dictionary" + self._encoding_mechanism = "dictionary" - elif self._encoding_mechanism == "arithmetic progression": - binary_variables_name_weight, i = self._arithmetic_progression_encoding( - binary_variables_name_weight, i, self._max, self._min, self.precision, letter + elif self._encoding_mechanism == "arithmetic progression": + binary_variables_name_weight, i = self._arithmetic_progression_encoding( + binary_variables_name_weight, i, self._max, self._min, self.precision, letter + ) + elif self._encoding_mechanism.startswith("bounded coefficient"): + try: + ux = int(self._encoding_mechanism.split(" ")[2]) + binary_variables_name_weight, i = self._bounded_coefficient_encoding( + binary_variables_name_weight, + i, + ux, + self._max, + self._min, + 2, + int(log2(self.precision)), + letter, ) - elif self._encoding_mechanism.startswith("bounded coefficient"): - try: - ux = int(self._encoding_mechanism.split(" ")[2]) - binary_variables_name_weight, i = self._bounded_coefficient_encoding( - binary_variables_name_weight, - i, - ux, - self._max, - self._min, - 2, - int(log(self.precision, 2)), - letter, - ) - except TypeError: - values = list(np.arange(self._min, self._max + self.precision, self.precision)) - binary_variables_name_weight, constraints, i = self._dictionary_encoding( - binary_variables_name_weight, i, constraints, values, letter - ) - self._encoding_mechanism = "dictionary" - else: - if (1 / self.precision) % 2 == 0: - binary_variables_name_weight, i = self._logarithmic_encoding( - binary_variables_name_weight, i, self._max, self._min, 2, int(log(self.precision, 2)), letter - ) - self._encoding_mechanism = "logarithmic 2" - else: - binary_variables_name_weight, i = self._arithmetic_progression_encoding( - binary_variables_name_weight, i, self._max, self._min, self.precision, letter - ) - self._encoding_mechanism = "arithmetic progression" + except TypeError: + values = list(np.arange(self._min, self._max + self.precision, self.precision)) + binary_variables_name_weight, constraints, i = self._dictionary_encoding( + binary_variables_name_weight, i, constraints, values, letter + ) + self._encoding_mechanism = "dictionary" + elif (1 / self.precision) % 2 == 0: + binary_variables_name_weight, i = self._logarithmic_encoding( + binary_variables_name_weight, i, self._max, self._min, 2, int(log2(self.precision)), letter + ) + self._encoding_mechanism = "logarithmic 2" + else: + binary_variables_name_weight, i = self._arithmetic_progression_encoding( + binary_variables_name_weight, i, self._max, self._min, self.precision, letter + ) + self._encoding_mechanism = "arithmetic progression" return binary_variables_name_weight, constraints, i diff --git a/tests/test_evaluation.py b/tests/test_evaluation.py index 1fdc018..ca42fe5 100644 --- a/tests/test_evaluation.py +++ b/tests/test_evaluation.py @@ -17,26 +17,6 @@ from mqt.qao.solvers import Solution, Solver from mqt.qao.variables import Variables -# from dwave.cloud import Client - - -def test_example() -> None: - """Expected type of final code: - Problem = Problem - variables = Problem.variables.add_....._variables(.....) - def f(var): - .... - Problem.objective_function.add_objective_function(f(var)) - Problem.constraints.add_constraints(....) - # With an eventual option for saving more information (Problem final size, etc.) - decoded_best_solution, Energy = Problem.solve() - # And eventually for knowing something about resolution statistics - Problem.statistics() - - """ - Variables() - assert 1 == 1 - def test_binary_only() -> None: """Test only the construction of binary variables""" @@ -967,20 +947,20 @@ def test_problem(lambda_strategy: str) -> None: reference_qubo_dict_re = {} for key in reference_qubo_dict: reference_qubo_dict_re[tuple(sorted(key))] = reference_qubo_dict[key] - if lambda_strategy == "upper_bound_only_positive" or lambda_strategy == "upper lower bound naive": - assert [52.25 * 1.1] * 2 == lambdas + if lambda_strategy in {"upper_bound_only_positive", "upper lower bound naive"}: + assert lambdas == [52.25 * 1.1] * 2 assert dict(sorted(qubo_re.items())) == dict(sorted(reference_qubo_dict_re.items())) elif lambda_strategy == "maximum_coefficient": - assert [10.0 * 1.1] * 2 == lambdas + assert lambdas == [10.0 * 1.1] * 2 assert dict(sorted(qubo_re.items())) == dict(sorted(reference_qubo_dict_re.items())) - elif lambda_strategy == "VLM" or lambda_strategy == "MOMC": - assert [12.0 * 1.1] * 2 == lambdas + elif lambda_strategy in {"VLM", "MOMC"}: + assert lambdas == [12.0 * 1.1] * 2 assert dict(sorted(qubo_re.items())) == dict(sorted(reference_qubo_dict_re.items())) elif lambda_strategy == "MOC": - assert [7 * 1.1, 6 * 1.1] == lambdas + assert lambdas == [7 * 1.1, 6 * 1.1] assert dict(sorted(qubo_re.items())) == dict(sorted(reference_qubo_dict_re.items())) elif lambda_strategy == "upper lower bound posiform and negaform method": - assert [31.625 * 1.1] * 2 == lambdas + assert lambdas == [31.625 * 1.1] * 2 assert dict(sorted(qubo_re.items())) == dict(sorted(reference_qubo_dict_re.items())) @@ -1011,7 +991,7 @@ def test_simulated_annealer_solver(lambda_strategy: str) -> None: solver = Solver() sol = solver.solve_simulated_annealing(problem, lambda_strategy=lambda_strategy) if isinstance(sol, Solution): - all_satisfy, each_satisfy = sol.check_constraint_optimal_solution() + all_satisfy, _each_satisfy = sol.check_constraint_optimal_solution() assert sol.best_solution == {"a": 0.0, "b": 3.0, "c": -1.5} assert sol.best_energy < -2.24 # (the range if for having no issues with numerical errors) assert sol.best_energy > -2.26 @@ -1080,7 +1060,7 @@ def test_simulated_annealer_solver_constrained(lambda_strategy: str, constraint_ solver = Solver() solution = solver.solve_simulated_annealing(problem, lambda_strategy=lambda_strategy) if isinstance(solution, Solution): - all_satisfy, each_satisfy = solution.check_constraint_optimal_solution() + all_satisfy, _each_satisfy = solution.check_constraint_optimal_solution() if constraint_expr == "c >= 1": assert solution.best_solution == {"a": 0.0, "b": -1.0, "c": 1.0} or not all_satisfy assert ( @@ -1174,7 +1154,7 @@ def test_simulated_annealer_solver_constrained_lambda_update_mechanism( ) solver.get_lambda_updates() if isinstance(solution, Solution): - all_satisfy, each_satisfy = solution.check_constraint_optimal_solution() + all_satisfy, _each_satisfy = solution.check_constraint_optimal_solution() if constraint_expr == "c >= 1": assert solution.best_solution == {"a": 0.0, "b": -1.0, "c": 1.0} or not all_satisfy assert ( @@ -1376,7 +1356,7 @@ def test_simulated_annealer_solver_constrained_lambda_update_mechanism_and_strat ) solver.get_lambda_updates() if isinstance(solution, Solution): - all_satisfy, each_satisfy = solution.check_constraint_optimal_solution() + all_satisfy, _each_satisfy = solution.check_constraint_optimal_solution() if constraint_expr == "c >= 1": assert solution.best_solution == {"a": 0.0, "b": -1.0, "c": 1.0} or not all_satisfy assert ( @@ -1472,7 +1452,7 @@ def test_simulated_annealing_cost_function_matrix( ) solver.get_lambda_updates() if isinstance(solution, Solution): - all_satisfy, each_satisfy = solution.check_constraint_optimal_solution() + all_satisfy, _each_satisfy = solution.check_constraint_optimal_solution() if constraint_expr == "M1_0_1 >= 1": assert ( solution.best_solution == {"M1": [[-1, 2]], "M2": [[2], [-1]]} @@ -1504,7 +1484,8 @@ def test_gas_solver_basic() -> None: solver = Solver() solution = solver.solve_grover_adaptive_search_qubo(problem, qubit_values=6, num_runs=10) if isinstance(solution, Solution): - all_satisfy, each_satisfy = solution.check_constraint_optimal_solution() + all_satisfy, _each_satisfy = solution.check_constraint_optimal_solution() + print(solution.best_solution) assert solution.best_solution == {"a": 1.0, "b": 0.0, "c": 1.0} print(solution.best_solution) assert solution.best_energy < -5.9 # (the range if for having no issues with numerical errors) @@ -1534,7 +1515,7 @@ def test_qaoa_solver_qubo_basic() -> None: num_runs=10, ) if isinstance(solution, Solution): - all_satisfy, each_satisfy = solution.check_constraint_optimal_solution() + all_satisfy, _each_satisfy = solution.check_constraint_optimal_solution() assert solution.best_solution == {"a": 1.0, "b": 0.0, "c": 1.0} print(solution.best_solution) assert solution.best_energy < -5.9 # (the range if for having no issues with numerical errors) @@ -1564,7 +1545,7 @@ def test_vqe_solver_qubo_basic() -> None: num_runs=10, ) if isinstance(solution, Solution): - all_satisfy, each_satisfy = solution.check_constraint_optimal_solution() + all_satisfy, _each_satisfy = solution.check_constraint_optimal_solution() assert solution.best_solution == {"a": 1.0, "b": 0.0, "c": 1.0} print(solution.best_solution) assert solution.best_energy < -5.9 # (the range if for having no issues with numerical errors)