sweep_python

Simple sweep optimizer written in Python.

The approaches in this repository are exhaustive searches through a combination of hyperparameters (the inputs for the feasible decision space of the objective function).

Table of Contents

• Sweep Optimization
  • Grid-based Sweep
  • Random Search
• Requirements
• Implementation
  • Initialization
  • State Machine-based Structure
  • Constraint Handling
  • Search Types
    • basic_grid
    • random_search
  • Multi-Object Optimization
  • Objective Function Handling
    • Creating a Custom Objective Function
    • Internal Objective Function Example
• Error Handling
• Example Implementations
  • Basic Sweep Example
  • Detailed Messages
  • Realtime Graph
• References
• Related Publications and Repositories
• Licensing
• How to Cite

Sweep Optimization

Grid-based Sweep

A grid-based sweep optimizer, often referred to as grid search, is a simple yet effective optimization technique commonly used for hyperparameter tuning in machine learning models. This method systematically explores a specified subset of the hyperparameter space by evaluating the performance of a model with all possible combinations of the provided hyperparameter values.

Random Search

Random search is an optimization method where solutions are randomly sampled from a defined space, evaluated, and iteratively improved based on the evaluations, aiming to find an optimal or near-optimal solution. Random search is generally not as efficient as more advanced optimization algorithms like gradient-based methods or evolutionary algorithms, especially in problems where the search space is structured or the objective function has a particular shape that can be exploited.

Requirements

This project requires numpy, pandas, and matplotlib for the full demos. To run the optimizer without visualization, only numpy and pandas are requirements

Use 'pip install -r requirements.txt' to install the following dependencies:

contourpy==1.2.1
cycler==0.12.1
fonttools==4.51.0
importlib_resources==6.4.0
kiwisolver==1.4.5
matplotlib==3.8.4
numpy==1.26.4
packaging==24.0
pandas==2.2.3
pillow==10.3.0
pyparsing==3.1.2
python-dateutil==2.9.0.post0
pytz==2025.1
six==1.16.0
tzdata==2025.1
zipp==3.18.1

Optionally, requirements can be installed manually with:

pip install  matplotlib, numpy, pandas

This is an example for if you've had a difficult time with the requirements.txt file. Sometimes libraries are packaged together.

Implementation

Initialization

    # Constant variables
    NO_OF_PARTICLES = 11         # Number of particles in swarm
    TOL = 10 ** -18              # Convergence Tolerance
    MAXIT = 10000                # Maximum allowed iterations
    BOUNDARY = 1                 # int boundary 1 = random,      2 = reflecting
                                 #              3 = absorbing,   4 = invisible

    # Objective function dependent variables
    func_F = func_configs.OBJECTIVE_FUNC  # objective function
    constr_F = func_configs.CONSTR_FUNC   # constraint function

    LB = func_configs.LB              # Lower boundaries, [[0.21, 0, 0.1]]
    UB = func_configs.UB              # Upper boundaries, [[1, 1, 0.5]]   
    OUT_VARS = func_configs.OUT_VARS  # Number of output variables (y-values)
    TARGETS = func_configs.TARGETS    # Target values for output

    # optimizer constants
    WEIGHTS = [[0.5, 0.7, 0.78]]       # Update vector weights
    BETA = 0.5                         # Float constant controlling influence 
                                       # between the personal and global best positions

    best_eval = 1
    parent = None            # for the optimizer test ONLY
    suppress_output = True   # Suppress the console output of particle swarm
    allow_update = True      # Allow objective call to update state 

    # Constant variables
    opt_params = {'NO_OF_PARTICLES': [NO_OF_PARTICLES], # Number of particles in swarm
                'BOUNDARY': [BOUNDARY],                 # int boundary 1 = random,      2 = reflecting
                                                        #   3 = absorbing,   4 = invisible
                'WEIGHTS': [WEIGHTS],                   # Update vector weights
                'BETA':  [BETA] }                       # Float constant controlling influence 
                                                        #       between the personal and global best positions

    opt_df = pd.DataFrame(opt_params)
    myOptimizer = swarm(LB, UB, TARGETS, TOL, MAXIT,
                            func_F, constr_F,
                            opt_df,
                            parent=parent)  

    # arguments should take the form: 
    # swarm([[float, float, ...]], [[float, float, ...]], [[float, ...]], float, int,
    # func, func,
    # dataFrame,
    # class obj) 
    #  
    # opt_df contains class-specific tuning parameters
    # NO_OF_PARTICLES: int
    # weights: [[float, float, float]]
    # boundary: int. 1 = random, 2 = reflecting, 3 = absorbing,   4 = invisible
    # beta: float

State Machine-based Structure

This optimizer uses a state machine structure to control the movement of the particles, call to the objective function, and the evaluation of current positions. The state machine implementation preserves the initial algorithm while making it possible to integrate other programs, classes, or functions as the objective function.

A controller with a while loop to check the completion status of the optimizer drives the process. Completion status is determined by at least 1) a set MAX number of iterations, and 2) the convergence to a given target using the L2 norm. Iterations are counted by calls to the objective function.

Within this while loop are three function calls to control the optimizer class:

• complete: the complete function checks the status of the optimizer and if it has met the convergence or stop conditions.
• step: the step function takes a boolean variable (suppress_output) as an input to control detailed printout on current particle (or agent) status. This function moves the optimizer one step forward.
• call_objective: the call_objective function takes a boolean variable (allow_update) to control if the objective function is able to be called. In most implementations, this value will always be true. However, there may be cases where the controller or a program running the state machine needs to assert control over this function without stopping the loop.

Additionally, get_convergence_data can be used to preview the current status of the optimizer, including the current best evaluation and the iterations.

The code below is an example of this process:

    while not myOptimizer.complete():
        # step through optimizer processing
        # this will update particle or agent locations
        myOptimizer.step(suppress_output)
        # call the objective function, control 
        # when it is allowed to update and return 
        # control to optimizer
        myOptimizer.call_objective(allow_update)
        # check the current progress of the optimizer
        # iter: the number of objective function calls
        # eval: current 'best' evaluation of the optimizer
        iter, eval = myOptimizer.get_convergence_data()
        if (eval < best_eval) and (eval != 0):
            best_eval = eval
        
        # optional. if the optimizer is not printing out detailed 
        # reports, preview by checking the iteration and best evaluation

        if suppress_output:
            if iter%100 ==0: #print out every 100th iteration update
                print("Iteration")
                print(iter)
                print("Best Eval")
                print(best_eval)

Constraint Handling

Users must create their own constraint function for their problems, if there are constraints beyond the problem bounds. This is then passed into the constructor. If the default constraint function is used, it always returns true (which means there are no constraints).

Search Types

More search types will be added, but for initial deployment, a standard grid search is used.

basic_grid

The basic grid search uses the current position of a particle (or agent), and increments it one step towards the upper bounds based on the defined problem space. It can use 1 or more particles (or agents) to search a space. If one particle is used, it will start at the lower bound of the decision space, and increment based on the minimum resolution until the particle reaches the maximum boundary limit.

Resolution is a multi-dimensional vector to allow for tuning in all dimensions of the input space.

This method does not tend to converge with a small error tolerance.

random_search

The random search generates NO_OF_PARTICLES agents in order to search the defined problem space. Each agent is independent and does not move from its initial generated position.

Multi-Object Optimization

The no preference method of multi-objective optimization, but a Pareto Front is not calculated. Instead the best choice (smallest norm of output vectors) is listed as the output.

Objective Function Handling

The optimizer minimizes the absolute value of the difference of the target outputs and the evaluated outputs. Future versions may include options for function minimization when target values are absent.

Creating a Custom Objective Function

Custom objective functions can be used by creating a directory with the following files:

• configs_F.py
• constr_F.py
• func_F.py

configs_F.py contains lower bounds, upper bounds, the number of input variables, the number of output variables, the target values, and a global minimum if known. This file is used primarily for unit testing and evaluation of accuracy. If these values are not known, or are dynamic, then they can be included experimentally in the controller that runs the optimizer's state machine.

constr_F.py contains a function called constr_F that takes in an array, X, of particle positions to determine if the particle or agent is in a valid or invalid location.

func_F.py contains the objective function, func_F, which takes two inputs. The first input, X, is the array of particle or agent positions. The second input, NO_OF_OUTS, is the integer number of output variables, which is used to set the array size. In included objective functions, the default value is hardcoded to work with the specific objective function.

Below are examples of the format for these files.

configs_F.py:

OBJECTIVE_FUNC = func_F
CONSTR_FUNC = constr_F
OBJECTIVE_FUNC_NAME = "one_dim_x_test.func_F" #format: FUNCTION NAME.FUNCTION
CONSTR_FUNC_NAME = "one_dim_x_test.constr_F" #format: FUNCTION NAME.FUNCTION

# problem dependent variables
LB = [[0]]             # Lower boundaries
UB = [[1]]             # Upper boundaries
IN_VARS = 1            # Number of input variables (x-values)
OUT_VARS = 1           # Number of output variables (y-values) 
TARGETS = [0]          # Target values for output
GLOBAL_MIN = []        # Global minima sample, if they exist. 

constr_F.py, with no constraints:

def constr_F(x):
    F = True
    return F

constr_F.py, with constraints:

def constr_F(X):
    F = True
    # objective function/problem constraints
    if (X[2] > X[0]/2) or (X[2] < 0.1):
        F = False
    return F

func_F.py:

import numpy as np
import time

def func_F(X, NO_OF_OUTS=1):
    F = np.zeros((NO_OF_OUTS))
    noErrors = True
    try:
        x = X[0]
        F = np.sin(5 * x**3) + np.cos(5 * x) * (1 - np.tanh(x ** 2))
    except Exception as e:
        print(e)
        noErrors = False

    return [F], noErrors

Internal Objective Function Example

There are three functions included in the repository:

1. Himmelblau's function, which takes 2 inputs and has 1 output
2. A multi-objective function with 3 inputs and 2 outputs (see lundquist_3_var)
3. A single-objective function with 1 input and 1 output (see one_dim_x_test)

Each function has four files in a directory:

1. configs_F.py - contains imports for the objective function and constraints, CONSTANT assignments for functions and labeling, boundary ranges, the number of input variables, the number of output values, and the target values for the output
2. constr_F.py - contains a function with the problem constraints, both for the function and for error handling in the case of under/overflow.
3. func_F.py - contains a function with the objective function.
4. graph.py - contains a script to graph the function for visualization.

Other multi-objective functions can be applied to this project by following the same format (and several have been collected into a compatible library, and will be released in a separate repo)

Plotted Himmelblau’s Function with 3D Plot on the Left, and a 2D Contour on the Right

$$f(x, y) = (x^2 + y - 11)^2 + (x + y^2 - 7)^2$$

Global Minima	Boundary	Constraints
f(3, 2) = 0	$-5 \leq x,y \leq 5$
f(-2.805118, 3.121212) = 0	$-5 \leq x,y \leq 5$
f(-3.779310, -3.283186) = 0	$-5 \leq x,y \leq 5$
f(3.584428, -1.848126) = 0	$-5 \leq x,y \leq 5$

Plotted Multi-Objective Function Feasible Decision Space and Objective Space with Pareto Front

$$\text{minimize}: \begin{cases} f_{1}(\mathbf{x}) = (x_1-0.5)^2 + (x_2-0.1)^2 \\\ f_{2}(\mathbf{x}) = (x_3-0.2)^4 \end{cases}$$

Num. Input Variables	Boundary	Constraints
3	$0.21\leq x_1\leq 1$ $0\leq x_2\leq 1$ $0.1 \leq x_3\leq 0.5$	$x_3\gt \frac{x_1}{2}$ or $x_3\lt 0.1$

Plotted Single Input, Single-objective Function Feasible Decision Space and Objective Space with Pareto Front

$$f(\mathbf{x}) = sin(5 * x^3) + cos(5 * x) * (1 - tanh(x^2))$$

Num. Input Variables	Boundary	Constraints
1	$0\leq x\leq 1$	$0\leq x\leq 1$

Local minima at $(0.444453, -0.0630916)$

Global minima at $(0.974857, -0.954872)$

Example Implementations

Basic Sweep Example

main_test.py provides a sample use case of the optimizer.

Detailed Messages

main_test_details.py provides an example using a parent class, and the self.suppress_output flag to control error messages that are passed back to the parent class to be printed with a timestamp. This implementation sets up the hooks for integration with AntennaCAT in order to provide the user feedback of warnings and errors.

Realtime Graph

Grid Search. Left: particle search locations, Right: fitness function results (open circles), and target (red star)

Random Search. Left: particle search locations, Right: fitness function results (open circles), and target (red star)

main_test_graph.py provides an example using a parent class, and the self.suppress_output and flag to control error messages that are passed back to the parent class to be printed with a timestamp. Additionally, a realtime graph shows particle locations at every step.

The figures above are a snapshots of the search. The left shows all of the search locations of a single particle (NOTE: toggle a the 'clear' boolean to turn this feature off), and the right side shows the target (marked by a star) and the fitness function locations (the open circles). While the fitness of the particle is very close to the target, it does not come closer than the 10E-6 tolerance, so the search does not converge.

NOTE: if you close the graph as the code is running, the code will continue to run, but the graph will not re-open.

References

This repo does not currently reference any code of papers for the sweep algorithm.

Related Publications and Repositories

This software works as a stand-alone implementation, and as one of the optimizers integrated into AntennaCAT.

Licensing

The code in this repository has been released under GPL-2.0

How to Cite

This code can be referenced using the following DOI:

10.5281/zenodo.15048577

In IEEE format:

L. Linkous, "sweep_python". GitHub, 2024. [Software]. https://github.com/LC-Linkous/sweep_python. DOI: 10.5281/zenodo.15048577