ValueError in example_simulated.py

[Naviden]

Hi, running example_simulated.py gives the following error:

/Users/navid/opt/anaconda3/envs/rulefit-venv/bin/python "/Users/navid/Google Drive/PhD/Repositories/XAI-2016/rulefit/example_simulated.py"
/Users/navid/opt/anaconda3/envs/rulefit-venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4675243067167685, tolerance: 0.4523666850279077
  tol, rng, random, positive)
/Users/navid/opt/anaconda3/envs/rulefit-venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.518840165506575, tolerance: 0.6706225710160342
  positive)
Traceback (most recent call last):
  File "/Users/navid/Google Drive/PhD/Repositories/XAI-2016/rulefit/example_simulated.py", line 25, in <module>
    rf.fit(X.values, y)
  File "/Users/navid/Google Drive/PhD/Repositories/XAI-2016/rulefit/rulefit/rulefit.py", line 398, in fit
    self.tree_generator.fit(np.copy(X, order='C'), np.copy(y, order='C'))
  File "/Users/navid/opt/anaconda3/envs/rulefit-venv/lib/python3.7/site-packages/sklearn/ensemble/_gb.py", line 1523, in fit
    self.estimators_.shape[0]))
ValueError: n_estimators=1 must be larger or equal to estimators_.shape[0]=560 when warm_start==True

[CoteDave]

Got the same error, i try to disable warm_start = False but same problem! It happens when I run the model twice. The first time is ok than this error when i run the model again

[davidgibsonp]

I am also seeing this error. I re-ran everything and it works. Fine for my use case but tuning has become tedious.

[Anpr1211]

When you run the RuleFit with any tree generator, after fitting the model, the value of the parameter n_estimators gets changed to 1, and hence we face this issue.

If you initialise the RuleFit again before fitting and specify n_estimators, it seems to work.