pip install gplearn #python3.7版本
pip install gplearn==0.3.0 #python2.7版本，当前0.4版本的gplearn不再支持python2.7

from sklearn.datasets import load_boston
from gplearn.genetic import SymbolicTransformer›
import pandas as pd
import numpy as np
import gplearn as gp

def data_prepare():boston = load_boston()boston_feature = pd.DataFrame(boston.data, columns=boston.feature_names)boston_label = pd.Series(boston.target).to_frame("TARGET")boston = pd.concat([boston_label, boston_feature], axis=1)return bostondata = data_prepare()

def _logical(x1,x2,x3,x4):return np.where(x1 > x2,x3,x4)
logical = gp.functions.make_function(function = _logical,name = 'logical',arity = 4)
def _boxcox2(x1):with np.errstate(over='ignore', under='ignore'):return (np.power(x1,2)-1)/2
binary = gp.functions.make_function(function = _binary,name = 'binary',arity = 1)
function_set = ['add', 'sub', 'mul', 'div', 'log', 'sqrt', 'abs', 'neg','inv','sin','cos','tan', 'max', 'min',boxcox2,logical]

gp1 = SymbolicTransformer(generations=1, population_size=1000,hall_of_fame=600, n_components=100,function_set=function_set,parsimony_coefficient=0.0005,max_samples=0.9, verbose=1,random_state=0, n_jobs=3)

label = data['TARGET']
train = data.drop(columns=['TARGET'])
gp1.fit(train,label)
new_df2 = gp1.transform(train)

from IPython.display import Image
import pydotplus
graph = gp1._best_programs[0].export_graphviz()
graph = pydotplus.graphviz.graph_from_dot_data(graph)
Image(graph.create_png())