Execute the program according to X. Parameters ---------- X : {array-like}, shape = [n_samples, n_features] Train

Evaluate the raw fitness of the program according to X, y. Parameters ---------- X : {array-like}, shape = [n_samples, n_feat

Turn seed into a np.random.RandomState instance Parameters ---------- seed : None | int | instance of RandomState If seed is None

Return a subtree from a given program often called by donor

Overloads `print` output of the object to resemble a LISP tree.

Overloads `print` output of the object to resemble a LISP tree.

Find the fittest individual from a sub-population.

A report of the progress of the evolution process. Parameters ---------- run_details : dict Information about the

Build a naive random program. ## edited by Yan: every time we append a function node to the program, we append the

Perform the crossover genetic operation on the program. Crossover selects a random subtree from the embedded program to be replac

Fit the Genetic Program according to X, y. Parameters ---------- X : array-like, shape = [n_samples, n_features]

Evaluate the penalized fitness of the program according to X, y. Parameters ---------- parsimony_coefficient : float, optiona

Return a constant node from a given program, which is usually a donor often called by donor

Get number of jobs for the computation. This function reimplements the logic of joblib to determine the actual number of jobs depending on th

Private function used to partition estimators between jobs.

Calculate the weighted Pearson correlation coefficient.

输入fit后的est_gp和fitness的阈值，从最后一代选出超过阈值的公式

Get the indices on which to evaluate the fitness of a program. Parameters ---------- n_samples : int The number o

Get a random subtree from the program. Parameters ---------- random_state : RandomState instance The random n

Return a variable node from a given program, which is usually a donor often called by donor

Perform the hoist mutation operation on the program. Hoist mutation selects a random subtree from the embedded program to be repl

Perform the point mutation operation on the program. Point mutation selects random nodes from the embedded program to be replaced

Predict probabilities on test vectors X. Parameters ---------- X : array-like, shape = [n_samples, n_features] In

Return a copy of the embedded program.

Perform the subtree mutation operation on the program. Subtree mutation selects a random subtree from the embedded program to be repl

Transform X according to the fitted transformer. Parameters ---------- X : array-like, shape = [n_samples, n_features]

Rough check that the embedded program in the object is valid.

Return the ith item of the fitted components.

Overloads `len` output to be the number of fitted components.

Overloads `print` output of the object to resemble a LISP tree.

Overloads `print` output of the object to resemble LISP trees.

Calculates the maximum depth of the program tree.

Get the indices used to measure the program's fitness.

Calculates the number of functions and terminals in the program.

Calculate the log loss.

Calculate the mean absolute error.

Calculate the mean square error.

Private function used to build a batch of programs within a job.

Closure of division (x1/x2) for zero denominator.

Closure of inverse for zero arguments.

Closure of log for zero arguments.

Closure of square root for negative arguments.

Calculate the root mean square error.

Special case of logistic function to transform to probabilities.

Calculate the weighted Spearman correlation coefficient.

Returns a string, Graphviz script for visualizing the program. Parameters ---------- fade_nodes : list, optional

Fit to data, then transform it. Parameters ---------- X : array-like, shape = [n_samples, n_features] Training ve

Make a fitness measure, a metric scoring the quality of a program's fit. This factory function creates a fitness measure object which measures th

Make a function node, a representation of a mathematical relationship. This factory function creates a function node, one of the core nodes in an

Perform regression on test vectors X. Parameters ---------- X : array-like, shape = [n_samples, n_features] Input

Predict classes on test vectors X. Parameters ---------- X : array-like, shape = [n_samples, n_features] Input ve