Some optimization algorithms for mining gradual patterns.
Project description
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 aPandas 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 aPandas 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 aPandas 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 aPandas 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 aPandas 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 aPandas 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
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