Python implementation of Gradual Pattern (GP) mining algorithms
SO4GP is a high-performance Python library designed to optimize the extraction of gradual patterns from large-scale datasets. By integrating advanced computation techniques and data management strategies, the library significantly reduces processing time and memory overhead during knowledge discovery.
Implemented Extraction Algorithms
The library provides native Python implementations for the core and meta-heuristic gradual pattern mining algorithms. Here are some examples:
- GRAANK: The foundational classical approach for mining gradual patterns.
- Ant Colony Optimization (AntGRAANK): Meta-heuristic ACO algorithm for search-space pruning GP candidates.
- Genetic Algorithm (GeneticGRAANK): Meta-heuristic GA for search-space pruning GP candidates.
- Particle Swarm Optimization (ParticleGRAANK): Meta-heuristic PSO algorithm for search-space pruning GP candidates.
- Random Search (HillClimbingGRAANK): Baseline stochastic search variant for pruning GP candidates.
- Clustering-based Mining (ClusterGP): Applies K-Means clustering approximation to mine GPs.
- TGRAANK: extends GRAANK to mine GPs with temporal lags.
What are Gradual Patterns?
A Gradual Pattern (GP) is a co-occurring set of gradual items (GI) that captures covariations between attributes. A pattern's quality is measured quantitatively by its computed support value.
Example
Consider a dataset containing 10 objects with 3 attributes: age, salary, and cars. An extracted GP might look like:
$${\text{age}^+, \text{salary}^-} \quad [\text{Support} = 0.8]$$
This output explicitly reveals that in 80% of the dataset (8 out of 10 objects), an increase in age ($^+$) strongly
correlates with a simultaneous decrease in salary ($^-$).
Installation
pip install so4gp
Usage
To use any algorithm to mine GPs, follow the instructions that follow.
First and foremost, import the so4gp python package via:
import so4gp as sgp
# OR
from so4gp.algorithms import GRAANK, TGRAANK, ClusterGP
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.
import pandas as pd
from so4gp.algorithms import GRAANK
df = pd.DataFrame(
[
[30, 3, 1, 10],
[35, 2, 2, 8],
[40, 4, 2, 7],
[50, 1, 1, 6],
[52, 7, 1, 2],
],
columns=["Age", "Salary", "Cars", "Expenses"],
)
miner_gp = GRAANK(
data_source=df,
min_sup=0.5,
)
results = miner_gp.discover()
print(results)
where you specify the parameters as follows:
- data_source - [required] data source {either a
file in csv formator aPandas DataFrame} - min_sup - [optional] minimum support
default = 0.5 - eq - [optional] encode equal values as gradual
default = False
Sample Output
The default output is the format of JSON:
{
"Algorithm": "GRAANK",
"Patterns": [
[["Age+", "Salary+"], 0.6],
[["Expenses-", "Age+", "Salary+"], 0.6]
]
}
Contributors ✨
Thanks go to these incredible people:
Made with contrib.rocks.
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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