so4gp 0.0.6

Some optimization algorithms for mining gradual patterns.

SO4GP stands for: "Some Optimizations for Gradual Patterns". SO4GP applies optimizations such as swarm intelligence, HDF5 chunks, SVD and many others in order to improve the efficiency of extracting gradual patterns. It provides Python algorithm implementations for these optimization techniques. 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. https://doi.org/10.1007/s13042-021-01390-w
• 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.