old_contents: import numpy as np
  import scipy as sp
  import scipy.stats
  from ngboost.distns import Normal
  from ngboost.ngboost import NGBoost
  from ngboost.scores import MLE, CRPS
  from ngboost.learners import default_tree_learner, default_linear_learner
  from ngboost.evaluation import *
  from sklearn.metrics import r2_score
  from matplotlib import pyplot as plt
  from argparse import ArgumentParser
  
  
  if __name__ == "__main__":
  
      argparser = ArgumentParser()
      argparser.add_argument("--lr", type=float, default=0.1)
      argparser.add_argument("--dataset", type=str, default="simulated")
      argparser.add_argument("--noise-lvl", type=float, default=0.25)
      argparser.add_argument("--distn", type=str, default="Normal")
      argparser.add_argument("--natural", action="store_true")
      argparser.add_argument("--score", type=str, default="CRPS")
      args = argparser.parse_args()
  
      np.random.seed(123)
  
      m, n = 1200, 50
      noise = np.random.randn(*(m, 1))
      beta1 = np.random.randn(n, 1)
      X = np.random.randn(m, n) / np.sqrt(n)
      Y = (X @ beta1 + args.noise_lvl * noise).squeeze()
      print(X.shape, Y.shape)
  
      X_train, X_test = X[:1000, :], X[1000:,]
      Y_train, Y_test = Y[:1000], Y[1000:]
  
      ngb = NGBoost(
          n_estimators=400,
          learning_rate=args.lr,
          Dist=Normal,
          Base=default_linear_learner,
          natural_gradient=args.natural,
          minibatch_frac=1.0,
          Score=eval(args.score)(),
          verbose=True,
          verbose_eval=100,
      )
  
      losses = ngb.fit(X_train, Y_train)
      forecast = ngb.pred_dist(X_test)
      print("R2:", r2_score(Y_test, forecast.loc))
  
new_contents: import numpy as np
  import scipy as sp
  import scipy.stats
  from ngboost.distns import Normal
  from ngboost.ngboost import NGBoost
  from ngboost.scores import MLE, CRPS
  from ngboost.learners import default_tree_learner, default_linear_learner
  from ngboost.evaluation import *
  from sklearn.metrics import r2_score
  from matplotlib import pyplot as plt
  from argparse import ArgumentParser
  
  
  if __name__ == "__main__":
  
      argparser = ArgumentParser()
      argparser.add_argument("--lr", type=float, default=0.1)
      argparser.add_argument("--dataset", type=str, default="simulated")
      argparser.add_argument("--noise-lvl", type=float, default=0.25)
      argparser.add_argument("--distn", type=str, default="Normal")
      argparser.add_argument("--natural", action="store_true")
      argparser.add_argument("--score", type=str, default="CRPS")
      args = argparser.parse_args()
  
      np.random.seed(123)
  
      m, n = 1200, 50
      noise = np.random.randn(*(m, 1))
      beta1 = np.random.randn(n, 1)
      X = np.random.randn(m, n) / np.sqrt(n)
      Y = (X @ beta1 + args.noise_lvl * noise).squeeze()
      print(X.shape, Y.shape)
  
      X_train, X_test = X[:1000, :], X[1000:, ]
      Y_train, Y_test = Y[:1000], Y[1000:]
  
      ngb = NGBoost(
          n_estimators=400,
          learning_rate=args.lr,
          Dist=Normal,
          Base=default_linear_learner,
          natural_gradient=args.natural,
          minibatch_frac=1.0,
          Score=eval(args.score)(),
          verbose=True,
          verbose_eval=100,
      )
  
      losses = ngb.fit(X_train, Y_train)
      forecast = ngb.pred_dist(X_test)
      print("R2:", r2_score(Y_test, forecast.loc))
  
subject: Update regression_sim.py