How to use the ngboost.learners.default_tree_learner function in ngboost

if __name__ == "__main__":

    argparser = ArgumentParser()
    argparser.add_argument("--n-estimators", type=int, default=(1 + BLK * 100))
    argparser.add_argument("--lr", type=float, default=0.03)
    argparser.add_argument("--minibatch-frac", type=float, default=0.1)
    argparser.add_argument("--natural", action="store_true")
    args = argparser.parse_args()

    x_tr, y_tr, _ = gen_data(n=100)

    poly_transform = PolynomialFeatures(1)
    x_tr = poly_transform.fit_transform(x_tr)

    ngb = NGBoost(
        Base=default_tree_learner,
        Dist=Normal,
        Score=MLE,
        n_estimators=args.n_estimators,
        learning_rate=args.lr,
        natural_gradient=args.natural,
        minibatch_frac=args.minibatch_frac,
        verbose=True,
    )

    blk = int(args.n_estimators / 100)
    ngb.fit(x_tr, y_tr)

    x_te, y_te, _ = gen_data(n=1000, bound=1.3)
    x_te = poly_transform.transform(x_te)
    preds = ngb.pred_dist(x_te)

if __name__ == "__main__":

    argparser = ArgumentParser()
    argparser.add_argument("--n-estimators", type=int, default=301)
    argparser.add_argument("--lr", type=float, default=0.03)
    argparser.add_argument("--minibatch-frac", type=float, default=0.1)
    argparser.add_argument("--natural", action="store_true")
    args = argparser.parse_args()

    x_tr, y_tr, _ = gen_data(n=50)

    poly_transform = PolynomialFeatures(1)
    x_tr = poly_transform.fit_transform(x_tr)

    ngb = NGBoost(
        Base=default_tree_learner,
        Dist=Normal,
        Score=MLE,
        n_estimators=args.n_estimators,
        learning_rate=args.lr,
        natural_gradient=args.natural,
        minibatch_frac=args.minibatch_frac,
        verbose=True,
    )

    ngb.fit(x_tr, y_tr)

    x_te, y_te, _ = gen_data(n=1000, bound=1.3)
    x_te = poly_transform.transform(x_te)
    preds = ngb.pred_dist(x_te)

    pctles, obs, _, _ = calibration_regression(preds, y_te)

def __init__(
        self,
        Dist=LogNormal,
        Score=LogScore,
        Base=default_tree_learner,
        natural_gradient=True,
        n_estimators=500,
        learning_rate=0.01,
        minibatch_frac=1.0,
        col_sample=1.0,
        verbose=True,
        verbose_eval=100,
        tol=1e-4,
        random_state=None,
    ):

        assert issubclass(
            Dist, RegressionDistn
        ), f"{Dist.__name__} is not useable for regression."
        if not hasattr(Dist, "censored_scores"):
            raise ValueError(

"energy": lambda: pd.read_excel(
        "https://archive.ics.uci.edu/ml/machine-learning-databases/00242/ENB2012_data.xlsx"
    ).iloc[:, :-1],
    "protein": lambda: pd.read_csv("data/uci/protein.csv")[
        ["F1", "F2", "F3", "F4", "F5", "F6", "F7", "F8", "F9", "RMSD"]
    ],
    "yacht": lambda: pd.read_csv(
        "http://archive.ics.uci.edu/ml/machine-learning-databases/00243/yacht_hydrodynamics.data",
        header=None,
        delim_whitespace=True,
    ),
    "msd": lambda: pd.read_csv("data/uci/YearPredictionMSD.txt").iloc[:, ::-1],
}

base_name_to_learner = {
    "tree": default_tree_learner,
    "linear": default_linear_learner,
}


if __name__ == "__main__":

    argparser = ArgumentParser()
    argparser.add_argument("--dataset", type=str, default="concrete")
    argparser.add_argument("--reps", type=int, default=5)
    argparser.add_argument("--n-est", type=int, default=2000)
    argparser.add_argument("--n-splits", type=int, default=20)
    argparser.add_argument("--distn", type=str, default="Normal")
    argparser.add_argument("--lr", type=float, default=0.01)
    argparser.add_argument("--natural", action="store_true")
    argparser.add_argument("--score", type=str, default="MLE")
    argparser.add_argument("--base", type=str, default="tree")

from sklearn.preprocessing import OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import PolynomialFeatures
from dfply import *
from ngboost.distns import LogNormal, Exponential, MultivariateNormal
from ngboost.api import NGBSurvival
from ngboost.scores import MLE, CRPS
from ngboost.learners import default_tree_learner, default_linear_learner
from ngboost.evaluation import *
from sksurv.ensemble import GradientBoostingSurvivalAnalysis as GBSA
from sksurv.metrics import concordance_index_censored

np.random.seed(1)

base_name_to_learner = {
    "tree": default_tree_learner,
    "linear": default_linear_learner,
}


def Y_join(T, E):
    col_event = "Event"
    col_time = "Time"
    y = np.empty(dtype=[(col_event, np.bool), (col_time, np.float64)], shape=T.shape[0])
    y[col_event] = E.values
    y[col_time] = T.values
    return y


if __name__ == "__main__":

    argparser = ArgumentParser()

np.random.seed(123)

dataset_name_to_loader = {
    "housing": lambda: pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data', header=None, delim_whitespace=True),
    "concrete": lambda: pd.read_excel("https://archive.ics.uci.edu/ml/machine-learning-databases/concrete/compressive/Concrete_Data.xls"),
    "wine": lambda: pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv', delimiter=";"),
    "kin8nm": lambda: pd.read_csv("data/uci/kin8nm.csv"),
    "naval": lambda: pd.read_csv("data/uci/naval-propulsion.txt", delim_whitespace=True, header=None).iloc[:,:-1],
    "power": lambda: pd.read_excel("data/uci/power-plant.xlsx"),
    "energy": lambda: pd.read_excel("https://archive.ics.uci.edu/ml/machine-learning-databases/00242/ENB2012_data.xlsx").iloc[:,:-1],
    "protein": lambda: pd.read_csv("data/uci/protein.csv")[['F1', 'F2', 'F3', 'F4', 'F5', 'F6', 'F7', 'F8', 'F9', 'RMSD']],
    "yacht": lambda: pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/00243/yacht_hydrodynamics.data", header=None, delim_whitespace=True),
}

base_name_to_learner = {
    "tree": default_tree_learner,
    "linear": default_linear_learner,
}

score_name_to_score = {
    "MLE": MLE,
    "CRPS": CRPS,
}

class RegressionLogger(object):

    def __init__(self, args):
        self.args = args
        self.verbose = args.verbose
        self.r2s = []
        self.mses = []
        self.nlls = []

def __init__(self, Dist=Normal, Score=LogScore,
                 Base=default_tree_learner, gradient='natural',
                 n_estimators=500, learning_rate=0.01, minibatch_frac=1.0,
                 verbose=True, verbose_eval=100, tol=1e-4,
                 random_state=None):
        self.Dist = Dist
        self.Score = Score
        self.Base = Base
        self.Manifold = manifold(Score, Dist)
        self.gradient = gradient
        self.n_estimators = n_estimators
        self.learning_rate = learning_rate
        self.minibatch_frac = minibatch_frac
        self.verbose = verbose
        self.verbose_eval = verbose_eval
        self.init_params = None
        self.base_models = []
        self.scalings = []