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API Reference

Genetic Programming (GP)

New (V 0.1.1)

Genetic programming (GP) is a type of evolutionary algorithm that uses a process similar to natural selection to evolve computer programs. In GP, a population of candidate solutions is evolved over generations using operations such as selection, mutation, and crossover, similar to how biological evolution works.

Parameters:

  • pop_size (int): The size of trees population. Default is 100.
  • initial_min_depth (int): The minimum depth of the trees during initializing them. Default is 0.
  • initial_max_depth (int): The maximum depth of the trees during initializing them. Default is 6.
  • min_depth (int): The minimum depth of the trees. Default is 1.
  • max_depth (int): The maximum depth of the trees. Default is 15.
  • error_function (function): The error function used to calculate the fitness of the trees.
  • expressions (list): A list of functions that can be used as functional nodes in the trees. Default is [Add, Sub, Mul].
  • terminals (list): A list of functions that can be used as leaf nodes in the trees. Default is [Variable, Constant].
  • target_error (float): The target error for the model. Default is 0.0.
  • max_evaluations (int): The maximum number of times the error function can be evaluated. Default is 10000.
  • max_generations (int): The maximum number of generations that the model can run for. Default is -1, which means no maximum number of generations.
  • max_time (int): The maximum amount of time the model can run for, in seconds. Default is None, which means no maximum time.
  • elite_number (int): The number of elite individuals Default is 2.
  • crossover_probability (float): The crossover probability. Default is 0.9.
  • mutation_probability (float): The mutation probability. Default is 0.1.
  • tournament_size (int): The tournament size. Default is 4.
  • selection_method (str): The selection method (wheel or tournament). Default is tournament.
  • verbose (bool): A flag indicating whether to print progress messages during model fitting. Default is True.

Example:

from symlearn.models import GP
model = GP(
        pop_size=population_size,
        max_evaluations=max_evaluations,
        initial_min_depth=initial_min_depth,
        initial_max_depth=initial_max_depth,
        min_depth=min_depth,
        max_depth=max_depth,
        error_function=error_function,
        expressions=expressions,
        terminals=terminals,
        target_error=target_error,
        verbose=verbose
        )
# fit data into model
model.fit(X_train, y_train)
y_fit = model.predict(X_train)
y_pred = model.predict(X_test)

Firefly Programming (FFP)

Firefly programming (FFP) is an automatic programming method that uses the principles of symbolic regression to solve problems 1.

Parameters:

  • pop_size (int): The size of trees population. Default is 100.
  • initial_min_depth (int): The minimum depth of the trees during initializing them. Default is 0.
  • initial_max_depth (int): The maximum depth of the trees during initializing them. Default is 6.
  • min_depth (int): The minimum depth of the trees. Default is 1.
  • max_depth (int): The maximum depth of the trees. Default is 15.
  • error_function (function): The error function used to calculate the fitness of the trees.
  • expressions (list): A list of functions that can be used as functional nodes in the trees. Default is [Add, Sub, Mul].
  • terminals (list): A list of functions that can be used as leaf nodes in the trees. Default is [Variable, Constant].
  • target_error (float): The target error for the model. Default is 0.0.
  • max_evaluations (int): The maximum number of times the error function can be evaluated. Default is 10000.
  • max_generations (int): The maximum number of generations that the model can run for. Default is -1, which means no maximum number of generations.
  • max_time (int): The maximum amount of time the model can run for, in seconds. Default is None, which means no maximum time.
  • verbose (bool): A flag indicating whether to print progress messages during model fitting. Default is True.

Example:

from symlearn.models import FFP
model = FFP(pop_size=50,
        max_evaluations=2500,
        initial_min_depth=0,
        initial_max_depth=6,
        min_depth=1,
        max_depth=15,
        error_function=sum_of_difference,
        expressions=expressions,
        terminals=terminals,
        target_error=0,
        verbose=False
        )
# fit data into model
model.fit(X_train, y_train)
y_fit = model.predict(X_train)
y_pred = model.predict(X_test)

Difference-based Firefly Programming (DFFP)

Difference-based Firefly programming (DFFP) is an extended version of the standard firefly programming (FFP) 2.

Parameters:

  • pop_size (int): The size of trees population. Default is 100.
  • alpha (float): Alpha controlling argument value. Default is 0.1.
  • beta (float): Beta controlling argument value. Default is 0.5.
  • gamma (float): Gamma controlling argument value. Default is 1.5.
  • initial_min_depth (int): The minimum depth of the trees during initializing them. Default is 0.
  • initial_max_depth (int): The maximum depth of the trees during initializing them. Default is 6.
  • min_depth (int): The minimum depth of the trees. Default is 1.
  • max_depth (int): The maximum depth of the trees. Default is 15.
  • error_function (function): The error function used to calculate the fitness of the trees.
  • expressions (list): A list of functions that can be used as functional nodes in the trees. Default is [Add, Sub, Mul].
  • terminals (list): A list of functions that can be used as leaf nodes in the trees. Default is [Variable, Constant].
  • target_error (float): The target error for the model. Default is 0.0.
  • max_evaluations (int): The maximum number of times the error function can be evaluated. Default is 10000.
  • max_generations (int): The maximum number of generations that the model can run for. Default is -1, which means no maximum number of generations.
  • max_time (int): The maximum amount of time the model can run for, in seconds. Default is None, which means no maximum time.
  • verbose (bool): A flag indicating whether to print progress messages during model fitting. Default is True.

Example:

from symlearn.models import DFFP
model = DFFP(pop_size=50,
        alpha=0.01,
        beta=0.05,
        gamma=0.1,
        max_evaluations=5000,
        initial_min_depth=0,
        initial_max_depth=6,
        min_depth=1,
        max_depth=15,
        error_function=sum_of_difference,
        expressions=expressions,
        terminals=terminals,
        target_error=0,
        verbose=False
        )
# fit data into model
model.fit(X_train, y_train)
y_fit = model.predict(X_train)
y_pred = model.predict(X_test)

Immune Plasma Programming (IPP)

Immune Plasma Programming (IPP) is an automatic programming method that uses the principles of symbolic regression to solve regression problems.

Parameters:

  • pop_size (int): The size of trees population. Default is 100.
  • donors_number (int): The number of donors. Default is 1.
  • receivers_number (int): The number of receivers. Default is 1.
  • initial_min_depth (int): The minimum depth of the trees during initializing them. Default is 0.
  • initial_max_depth (int): The maximum depth of the trees during initializing them. Default is 6.
  • min_depth (int): The minimum depth of the trees. Default is 1.
  • max_depth (int): The maximum depth of the trees. Default is 15.
  • error_function (function): The error function used to calculate the fitness of the trees.
  • expressions (list): A list of functions that can be used as functional nodes in the trees. Default is [Add, Sub, Mul].
  • terminals (list): A list of functions that can be used as leaf nodes in the trees. Default is [Variable, Constant].
  • target_error (float): The target error for the model. Default is 0.0.
  • max_evaluations (int): The maximum number of times the error function can be evaluated. Default is 10000.
  • max_generations (int): The maximum number of generations that the model can run for. Default is -1, which means no maximum number of generations.
  • max_time (int): The maximum amount of time the model can run for, in seconds. Default is None, which means no maximum time.
  • verbose (bool): A flag indicating whether to print progress messages during model fitting. Default is True.

Example:

from symlearn.models import IPP
model = IPP(pop_size=100,
            donors_number=3,
            receivers_number=3,
            max_evaluations=2500,
            initial_min_depth=0,
            initial_max_depth=6,
            min_depth=1,
            max_depth=15,
            error_function=sum_of_difference,
            expressions=expressions,
            terminals=terminals,
            target_error=0,
            verbose=False
            )
# fit data into model
model.fit(X_train, y_train)
y_fit = model.predict(X_train)
y_pred = model.predict(X_test)

References

  1. https://ieeexplore.ieee.org/abstract/document/9302201/ ↩

  2. https://www.sciencedirect.com/science/article/abs/pii/S092054892300003X ↩