text: import time
  
  from mgetool.tool import tt
  from numpy import random
  from sklearn.datasets import load_boston
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import r2_score
  
  from fastgplearn.skflow import SymbolicRegressor
  
  if __name__ == "__main__":
      # data
  
      x, y = load_boston(return_X_y=True)
      sr = SymbolicRegressor(population_size=10000, generations=20, stopping_criteria=0.95,
                             store=False, p_mutate=0.2, p_crossover=0.5, select_method="tournament",
                             tournament_size=5, hall_of_fame=3, store_of_fame=50,
                             constant_range=(0, 1.0), constants=None, depth=(2, 5),
                             function_set=('add', 'sub', 'mul', 'div', "pow2", "pow3","exp"),
                             n_jobs=1, verbose=True, random_state=0, method_backend='p_numpy', func_p=None, sci_template="default")
      tt.t
      sr.fit(x,y)
      res = sr.top_n(30)
      tt.t
      tt.p
  
      #
      # sr = SymbolicEstimator(population_size=10000, generations=20,  stopping_criteria=0.95,
      #              hall_of_fame=3, store=False, p_mutate=0.2, p_crossover=0.5,select_method="tournament",
      #              tournament_size=5,
      #              constant_range=(0, 1.0), constants=None, depth=(2, 5),
      #              function_set=('add', 'sub', 'mul', 'div', "pow2", "pow3","exp"),
      #              warm_start=False, low_memory=False,
      #              n_jobs=1, verbose=True, random_state=None, method_backend='p_numpy', func_p=None,sci_template="default")
      # tt.t
      # sr.fit(x,y)
      # tt.t
      # tt.p
  
      # sr = SymbolicEstimator(population_size=10000, generations=20,  stopping_criteria=0.95,
      #              hall_of_fame=3, store=False, p_mutate=0.2, p_crossover=0.5,select_method="tournament",
      #              tournament_size=5,
      #              constant_range=(0, 1.0), constants=None, depth=(2, 5),
      #              function_set=('add', 'sub', 'mul', 'div', "pow2", "pow3","exp"),
      #              warm_start=False, low_memory=False,
      #              n_jobs=1, verbose=True, random_state=None, method_backend='c_torch', func_p=None,sci_template="default")
      # sr.fit(x,y)
  
  
      # sr = SymbolicEstimator(population_size=10000, generations=20,  stopping_criteria=0.95,
      #              hall_of_fame=3, store=False, p_mutate=0.2, p_crossover=0.5,select_method="tournament",
      #              tournament_size=5,
      #              constant_range=(0, 1.0), constants=None, depth=(2, 5),
      #              function_set=('add', 'sub', 'mul', 'div', "pow2", "pow3","exp"),
      #              warm_start=False, low_memory=False,
      #              n_jobs=1, verbose=True, random_state=None, method_backend='p_torch', func_p=None,sci_template="default")
      # tt.t
      # sr.fit(x,y)
      # tt.t
      # tt.p
  
  
  
  
  r2_score