code: def get_best_alpha_under_max_rules(
      X,
      y,
      rules: List[str],
      penalty="l1",
      prediction_task="regression",
      max_rules=30,
      cv=True,
      random_state=None,
  ) -> float:
      coef_zero_threshold = 1e-6 / np.mean(np.abs(y))
      alpha_scores = []
  
      if prediction_task == "regression":
          alphas = _alpha_grid(X, y)
      elif prediction_task == "classification":
          # LogisticRegression accepts inverse of regularization
          alphas = np.flip(np.logspace(-4, 4, num=100, base=10))
  
      # alphas are sorted from highest to lowest regularization
      for i, alpha in enumerate(alphas):
          if prediction_task == "regression":
              m = Lasso(
                  alpha=alpha, random_state=random_state, max_iter=2000
              )
              cv_scoring = "neg_mean_squared_error"
          else:
              m = LogisticRegression(
                  penalty=penalty,
                  C=(1 / alpha),
                  solver="liblinear",
                  random_state=random_state,
              )
              cv_scoring = "accuracy"
  
          m.fit(X, y)
          rule_coefs = m.coef_.flatten()
          rule_count = np.sum(np.abs(rule_coefs) > coef_zero_threshold)
  
          if rule_count > max_rules:
              break
  
          if cv:
              fold_scores = cross_val_score(
                  m, X, y, cv=5, scoring=cv_scoring
              )
              alpha_scores.append(np.mean(fold_scores))
  
      if cv and np.all(alpha_scores != alpha_scores[0]):
          # check for rare case in which diff alphas lead to identical scores
          final_alpha = alphas[np.argmax(alpha_scores)]
      else:
          final_alpha = alphas[i - 1]
  
      return final_alpha
  
file_path: imodels/imodels/util/score.py
imports: from typing import List, Tuple
  from warnings import warn
  import pandas
  import numpy
  from sklearn.utils import indices_to_mask
  from sklearn.linear_model import Lasso, LogisticRegression
  from sklearn.linear_model._coordinate_descent import _alpha_grid
  from sklearn.model_selection import cross_val_score
  from imodels.util.rule import Rule
label_desc: Use image node_id 4 for calling a global function with example usage: get_best_alpha_under_max_rules(X, y, rules, penalty, prediction_task, max_rules, cv, random_state) and returns: final_alpha