so4gp 0.0.5

Algorithm implementations of swarm-based optimization algorithms for mining gradual patterns.

SO4GP stands for: "Swarm Optimization for Gradual Patterns". SO4GP applies swarm intelligence to extraction of gradual patterns. It provides Python algorithm implementations of swarm-based optimization algorithms for mining gradual patterns. The algorithm implementations include:

• Ant Colony Optimization
• Genetic Algorithm
• Particle Swarm Optimization
• Random Search
• Local Search

Install Requirements

Before running so4gp, make sure you install the following Python Packages:

pip3 install numpy~=1.21.2 pandas~=1.3.3 python-dateutil~=2.8.2 ypstruct~=0.0.2

Usage

Write the following code:

1. Ant Colony Optimization for GPs (ACO-GRAD)

from so4gp_pkg import so4gp as so
gps = so.run_ant_colony(data_src, min_sup)
print(gps)

where you specify the parameters as follows:

• data_src - [required] data source {either a file in csv format or a Pandas DataFrame}
• min_sup - [optional] minimum support default = 0.5
• max_iterations - [optional] maximum iterations default = 1
• evaporation_factor - [optional] evaporation factor default = 0.5

2. Genetic Algorithm for GPs (GA-GRAD)

from so4gp_pkg import so4gp as so
gps = so.run_genetic_algorithm(data_src, min_sup)
print(gps)

where you specify the parameters as follows:

• data_src - [required] data source {either a file in csv format or a Pandas DataFrame}
• min_sup - [optional] minimum support default = 0.5
• max_iterations - [optional] maximum iterations default = 1
• n_pop - [optional] initial population default = 5
• pc - [optional] offspring population multiple default = 0.5
• gamma - [optional] crossover rate default = 1
• mu - [optional] mutation rate default = 0.9
• sigma - [optional] mutation rate default = 0.9

3. Particle Swarm Optimization for GPs (PSO-GRAD)

from so4gp_pkg import so4gp as so
gps = so.run_particle_swarm(data_src, min_sup)
print(gps)

where you specify the parameters as follows:

• data_src - [required] data source {either a file in csv format or a Pandas DataFrame}
• min_sup - [optional] minimum support default = 0.5
• max_iterations - [optional] maximum iterations default = 1
• n_particles - [optional] initial particle population default = 5
• velocity - [optional] particle velocity default = 0.9
• coeff_p - [optional] personal coefficient rate default = 0.01
• coeff_g - [optional] global coefficient default = 0.9

4. Local Search for GPs (LS-GRAD)

from so4gp_pkg import so4gp as so
gps = so.run_hill_climbing(data_src, min_sup)
print(gps)

where you specify the parameters as follows:

• data_src - [required] data source {either a file in csv format or a Pandas DataFrame}
• min_sup - [optional] minimum support default = 0.5
• max_iterations - [optional] maximum iterations default = 1
• step_size - [optional] step size default = 0.5

5. Random Search for GPs (RS-GRAD)

from so4gp_pkg import so4gp as so
gps = so.run_random_search(data_src, min_sup)
print(gps)

where you specify the parameters as follows:

• data_src - [required] data source {either a file in csv format or a Pandas DataFrame}
• min_sup - [optional] minimum support default = 0.5
• max_iterations - [optional] maximum iterations default = 1

Sample Output

{
	"Algorithm": "RS-GRAD",
	"Best Patterns": [
            [["Age+", "Salary+"], 0.6], 
            [["Expenses-", "Age+", "Salary+"], 0.6]
	],
	"Iterations": 20
}

References

• Owuor, D., Runkler T., Laurent A., Menya E., Orero J (2021), Ant Colony Optimization for Mining Gradual Patterns. International Journal of Machine Learning and Cybernetics.
• Dickson Owuor, Anne Laurent, and Joseph Orero (2019). Mining Fuzzy-temporal Gradual Patterns. In the proceedings of the 2019 IEEE International Conference on Fuzzy Systems (FuzzIEEE). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2019.8858883.