mlmon.py

from ml_performance_monitoring.monitor import wrap_model
import numpy as np
from sklearn.metrics import mean_squared_error
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import xgboost as xgb

boston_dataset = load_boston()

X, y = (
    boston_dataset["data"],
    boston_dataset["target"],)

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=123
)

print(X_train[:5], y_train[:5])

xg_reg = xgb.XGBRegressor(
    objective="reg:linear",
    colsample_bytree=0.3,
    learning_rate=0.1,
    max_depth=5,
    alpha=10,
    n_estimators=10,
)

xg_reg.fit(X_train, y_train)