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"""
Population class implementation for evaluating genetic programming individuals.
"""
from joblib import Parallel, delayed
from slim_gsgp.algorithms.GP.representations.tree_utils import _execute_tree
[docs]
class Population:
def __init__(self, pop):
"""
Initializes a population of Trees.
This constructor sets up the population with a list of Tree objects,
calculating the size of the population and the total node count.
Parameters
----------
pop : List
The list of individual Tree objects that make up the population.
Returns
-------
None
"""
self.population = pop
self.size = len(pop)
self.nodes_count = sum(ind.node_count for ind in pop)
self.fit = None
[docs]
def evaluate(self, ffunction, X, y, n_jobs=1):
"""
Evaluates the population given a certain fitness function, input data (X), and target data (y).
Attributes a fitness tensor to the population.
Parameters
----------
ffunction : function
Fitness function to evaluate the individuals.
X : torch.Tensor
The input data (which can be training or testing).
y : torch.Tensor
The expected output (target) values.
n_jobs : int
The maximum number of concurrently running jobs for joblib parallelization.
Returns
-------
None
"""
# Evaluates individuals' semantics
y_pred = Parallel(n_jobs=n_jobs)(
delayed(_execute_tree)(
individual.repr_, X,
individual.FUNCTIONS, individual.TERMINALS, individual.CONSTANTS
) for individual in self.population
)
# Evaluate fitnesses
self.fit = [ffunction(y, y_pred_ind) for y_pred_ind in y_pred]
# Assign individuals' fitness
[self.population[i].__setattr__('fitness', f) for i, f in enumerate(self.fit)]