so4gp 0.2.3

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:

• (Classical) GRAANK algorithm for extracting GPs
• Ant Colony Optimization algorithm for extracting GPs
• Genetic Algorithm for extracting GPs
• Particle Swarm Optimization algorithm for extracting GPs
• Random Search algorithm for extracting GPs
• Local Search algorithm for extracting GPs

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

In order to run each algorithm for the purpose of extracting GPs, follow the instructions that follow.

First and foremost, import the so4gp python package via:

import so4gp as sgp

1. GRAdual rANKing Algorithm for GPs (GRAANK)

This is the classical approach (initially proposed by Anne Laurent) for mining gradual patterns. All the remaining algorithms are variants of this algorithm.

gp_json = sgp.graank(data_src, min_sup, eq, return_gps=False)
print(gp_json)

# OR

gp_json, gp_list = sgp.graank(data_src, min_sup, eq, return_gps=True)
print(gp_json)

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
• eq - [optional] encode equal values as gradual default = False
• return_gps - [optional] additionally return object GPs default = False

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

gp_json = sgp.acogps(data_src, min_sup)
print(gp_json)

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
• return_gps - [optional] additionally return object GPs default = False

3. Genetic Algorithm for GPs (GA-GRAD)

gp_json = sgp.gagps(data_src, min_sup)
print(gp_json)

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
• return_gps - [optional] additionally return object GPs default = False

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

gp_json = sgp.psogps(data_src, min_sup)
print(gp_json)

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
• return_gps - [optional] additionally return object GPs default = False

5. Local Search for GPs (LS-GRAD)

gp_json = sgp.hcgps(data_src, min_sup)
print(gp_json)

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
• return_gps - [optional] additionally return object GPs default = False

6. Random Search for GPs (RS-GRAD)

import so4gp as sgp

gp_json = sgp.rsgps(data_src, min_sup)
print(gp_json)

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
• return_gps - [optional] additionally return object GPs default = False

Sample Output

The default output is the format of JSON:

{
	"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.
• Laurent A., Lesot MJ., Rifqi M. (2009) GRAANK: Exploiting Rank Correlations for Extracting Gradual Itemsets. In: Andreasen T., Yager R.R., Bulskov H., Christiansen H., Larsen H.L. (eds) Flexible Query Answering Systems. FQAS 2009. Lecture Notes in Computer Science, vol 5822. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04957-6_33