{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from copy import deepcopy\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.ensemble import VotingRegressor as skVotingRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VotingRegressor():\n",
    "    def __init__(self, estimators):\n",
    "        self.estimators = estimators\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.estimators_ = [deepcopy(est).fit(X, y) for _, est in self.estimators]\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        prob = np.array([est.predict(X) for est in self.estimators_]).T\n",
    "        return prob\n",
    "\n",
    "    def predict(self, X):\n",
    "        prob = self.transform(X)\n",
    "        return np.mean(prob, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = load_boston(return_X_y=True)\n",
    "clf1 = LinearRegression()\n",
    "clf2 = RandomForestRegressor(n_estimators=100, random_state=0)\n",
    "eclf1 = VotingRegressor(estimators=[('lr', clf1), ('rf', clf2)]).fit(X, y)\n",
    "eclf2 = skVotingRegressor(estimators=[('lr', clf1), ('rf', clf2)]).fit(X, y)\n",
    "prob1 = eclf1.transform(X)\n",
    "prob2 = eclf2.transform(X)\n",
    "assert np.allclose(prob1, prob2)\n",
    "pred1 = eclf1.predict(X)\n",
    "pred2 = eclf2.predict(X)\n",
    "assert np.allclose(pred1, pred2)"
   ]
  }
 ],
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