from ngboost import NGBRegressor
from sklearn.ensemble import RandomForestRegressor
from catboost import CatBoostRegressor
from lightgbm import LGBMRegressor
from xgboost import XGBRegressor
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.model_selection import KFold
import deepforest
import numpy as np
import pandas as pd
from typing import Union
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_moons
from sklearn.utils.validation import check_X_y
import joblib
from sklearn.metrics import r2_score


def average_R2(evals):
    sum = 0
    for item in evals:
        sum += item['R^2']
    return sum/len(evals)