TypeError: _quantile_dispatcher() got an unexpected keyword argument 'method' about puncc HOT 1 OPEN

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from catboost import CatBoostRegressor
import numpy as np
from deel.puncc.api.prediction import DualPredictor
from deel.puncc.regression import CQR
from deel.puncc.metrics import regression_mean_coverage, regression_sharpness

# Generate a random regression problem
X, y = make_regression(
    n_samples=1000, n_features=4, n_informative=2, random_state=0, shuffle=False
)

# Split data into train and test
X, X_test, y, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# Split train data into fit and calibration
X_fit, X_calib, y_fit, y_calib = train_test_split(
    X, y, test_size=0.2, random_state=0
)

# reg_low catboost model
param = {
    "loss_function": "Quantile:alpha=0.05",
    "learning_rate": 0.4607417710785185,
    "l2_leaf_reg": 0.03572230525884548,
    "depth": 4,
    "boosting_type": "Plain",
    "bootstrap_type": "MVS",
    "min_data_in_leaf": 8,
}
reg_low = CatBoostRegressor(**param)

# reg_high catboost parameters
param = {
    "loss_function": "Quantile:alpha=0.95",
    "learning_rate": 0.002097382718709981,
    "l2_leaf_reg": 0.07411180923916862,
    "depth": 1,
    "boosting_type": "Plain",
    "bootstrap_type": "Bayesian",
    "min_data_in_leaf": 5,
    "bagging_temperature": 9.119533192831474,
}
reg_high = CatBoostRegressor(**param)

# Dual predictor definition
predictor = DualPredictor(models=[reg_low, reg_high])

# Initialization of CQR conformalizer
crq = CQR(predictor)

# Fitting/calibration
crq.fit(X_fit=X_fit, y_fit=y_fit, X_calib=X_calib, y_calib=y_calib)

# Conformal prediction for alpha = 5%
y_pred, y_pred_lower, y_pred_upper = crq.predict(X_test, alpha=0.05)

# Results
coverage = regression_mean_coverage(y_test, y_pred_lower, y_pred_upper)
width = regression_sharpness(y_pred_lower=y_pred_lower,
y_pred_upper=y_pred_upper)
print(f"Marginal coverage: {np.round(coverage, 2)}")
print(f"Average width: {np.round(width, 2)}")