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]}")