Getting started

Installation

To install NiaARMTS with pip, use:

pip install niaarmts

Usage

Fixed Interval Time Series Numerical Association Rule Mining

from niapy.algorithms.basic import ParticleSwarmAlgorithm
from niapy.task import Task
from niaarmts import Dataset
from niaarmts.NiaARMTS import NiaARMTS

# Load dataset
dataset = Dataset()
dataset.load_data_from_csv('intervals.csv', timestamp_col='timestamp')

# Create an instance of NiaARMTS
niaarmts_problem = NiaARMTS(
    dimension=dataset.calculate_problem_dimension(),  # Adjust dimension dynamically
    lower=0.0,  # Lower bound of solution space
    upper=1.0,  # Upper bound of solution space
    features=dataset.get_all_features_with_metadata(),  # Pass feature metadata
    transactions=dataset.get_all_transactions(),  # Dataframe containing all transactions
    interval='true',  # Whether we're dealing with interval data
    alpha=1.0,  # Weight for support in fitness calculation
    beta=1.0,  # Weight for confidence in fitness calculation
    gamma=1.0,  # Weight for inclusion in fitness calculation # if 0.0 then inclusion metric is omitted
    delta=1.0  # Weight for amplitude in fitness calculation # if 0.0 then amplitude metric is omitted
)

# Define the optimization task
task = Task(problem=niaarmts_problem, max_iters=100)  # Run for 100 iterations

# Initialize the Particle Swarm Optimization algorithm
pso = ParticleSwarmAlgorithm(population_size=40, min_velocity=-1.0, max_velocity=1.0, c1=2.0, c2=2.0)

# Run the algorithm
best_solution = pso.run(task)

# Output the best solution and its fitness value
print(f"Best solution: {best_solution[0]}")
print(f"Fitness value: {best_solution[1]}")

Segmented Interval Time Series Numerical Association Rule Mining

from niapy.algorithms.basic import ParticleSwarmAlgorithm
from niapy.task import Task
from niaarmts import Dataset
from niaarmts.NiaARMTS import NiaARMTS

# Load dataset
dataset = Dataset()
dataset.load_data_from_csv('ts.csv', timestamp_col='timestamp')

# Create an instance of NiaARMTS
niaarmts_problem = NiaARMTS(
    dimension=dataset.calculate_problem_dimension(),  # Adjust dimension dynamically
    lower=0.0,  # Lower bound of solution space
    upper=1.0,  # Upper bound of solution space
    features=dataset.get_all_features_with_metadata(),  # Pass feature metadata
    transactions=dataset.get_all_transactions(),  # Dataframe containing all transactions
    interval='false',  # Whether we're dealing with interval data
    alpha=1.0,  # Weight for support in fitness calculation
    beta=1.0,  # Weight for confidence in fitness calculation
    gamma=1.0,  # Weight for inclusion in fitness calculation # if 0.0 then inclusion metric is omitted
    delta=1.0  # Weight for amplitude in fitness calculation # if 0.0 then amplitude metric is omitted
)

# Define the optimization task
task = Task(problem=niaarmts_problem, max_iters=100)  # Run for 100 iterations

# Initialize the Particle Swarm Optimization algorithm
pso = ParticleSwarmAlgorithm(population_size=40, min_velocity=-1.0, max_velocity=1.0, c1=2.0, c2=2.0)

# Run the algorithm
best_solution = pso.run(task)

# Output the best solution and its fitness value
print(f"Best solution: {best_solution[0]}")
print(f"Fitness value: {best_solution[1]}")