Bayesian symbolic regression using mcmc sampling.
Paper here: https://arxiv.org/abs/1910.08892
codes/BSR.py: API interface of BSR class
codes/bsr_class.py: definition of BSR class
codes/simulations.py: part of simulation settings in the paper
codes/funcs.py: basic sampling functions
K = 3 # number of trees
MM = 50 # number of iterations
# set hyperparameters alternatively
hyper_params = [{'treeNum': 3, 'itrNum':50, 'alpha1':0.4, 'alpha2':0.4, 'beta':-1}]
# initialize BSR object
my_bsr = BSR(K,MM)
# train (need to fill in parameters)
# train_X is dataframe with each row a datapoint
# train_y is series with default index
my_bsr.fit(train_X,train_y)
# fit new values
# new_X is dataframe of new data
fitted_y = my_bsr.predict(new_X)
# display fitted trees
my_bsr.model()
# complexity, including complexity of each tree & total
complexity = my_bsr.complexity()bsr_paper.pdf: paper for Bayesian Symbolic Regression
Symbolic_Regression_Tree_MCMC.pdf: note for proposed algorithm