old_contents: import numpy as np
  
  from sklearn.datasets import make_regression
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.model_selection import cross_val_score
  from sklearn.multioutput import MultiOutputRegressor
  from sparsereg.efs import EFS
  
  x, y = make_regression(n_samples=100, n_features=10, n_informative=10, n_targets=3)
  
  from benchmark.ode.simple_ode import load_lorenz
  
  data = load_lorenz()
  x, y = data.data, data.target[:, 1]
  
  steps = ("scaler", StandardScaler()), ("estimator", EFS(time=5*60, mu=1, q=4, gen=1000, alpha=0.3))
  model = Pipeline(steps)
  
  model.fit(x, y)
  print(model.score(x, y))
  #print(cross_val_score(model, x, y, cv=10, n_jobs=-1))
  
  
new_contents: import numpy as np
  
  from sklearn.datasets import make_regression
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.model_selection import cross_val_score
  from sklearn.multioutput import MultiOutputRegressor
  from sparsereg.efs import EFS
  
  x, y = make_regression(n_samples=100, n_features=10, n_informative=10, n_targets=3)
  
  steps = ("scaler", StandardScaler()), ("estimator", EFS(time=5*60, mu=1, q=4, gen=1000, alpha=0.3))
  model = MultiOutputRegressor(Pipeline(steps))
  
  model.fit(x, y)
  
  print(cross_val_score(model, x, y, cv=10, n_jobs=-1))
  
  
subject: update example