Getting Started

Pflacco offers a variety of different landscape features. These are compartmentalized into three submodules:

• Conventional Exploratory Landscape Features

• Local Optima Networks

• Miscellanous features, which are the product of recent advances in the research community.

The following code example provides a demonstration of the different submodules.

from pflacco.sampling import create_initial_sample
from pflacco.classical_ela_features import calculate_ela_distribution
from pflacco.misc_features import calculate_fitness_distance_correlation
from pflacco.local_optima_network_features import compute_local_optima_network, calculate_lon_features

# Arbitrary objective function
def objective_function(x):
   return x[0]**2 - x[1]**2

dim = 2
# Create inital sample using latin hyper cube sampling
X = create_initial_sample(dim, sample_type = 'lhs')
# Calculate the objective values of the initial sample
# using an arbitrary objective function (here y = x1^2 - x2^2)
y = X.apply(lambda x: objective_function(x), axis = 1)

# Compute an exemplary feature set from the convential ELA features
# of the R-package flacco
ela_distr = calculate_ela_distribution(X, y)
print(ela_distr)

# Compute an exemplary feature set from the novel features
# which are not part of the R-package flacco yet.
fdc = calculate_fitness_distance_correlation(X, y)
print(fdc)

# Compute a Local Optima Network (LON). From this network, LON features can be calculated.
nodes, edges = compute_local_optima_network(f=objective_function, dim=dim, lower_bound=0, upper_bound=1)
lon = calculate_lon_features(nodes, edges)
print(lon)