Comparation of GPR and KRR

KRR_GPR_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "input_x = pd.DataFrame(boston['data'], columns=boston['feature_names'])\n",
    "input_y = boston['target']\n",
    "mms = MinMaxScaler().fit(input_x)\n",
    "train_x, test_x, train_y, test_y = train_test_split(mms.transform(input_x), input_y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.gaussian_process.kernels import WhiteKernel\n",
    "\n",
    "krr = KernelRidge(kernel='rbf', alpha=1.0, gamma=1.0)\n",
    "gpr = GaussianProcessRegressor(alpha=1.0, kernel=RBF(length_scale=1.0), optimizer=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 44 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KernelRidge(alpha=1.0, coef0=1, degree=3, gamma=1.0, kernel='rbf',\n",
       "      kernel_params=None)"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "krr.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 51 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GaussianProcessRegressor(alpha=1.0, copy_X_train=True,\n",
       "             kernel=RBF(length_scale=1), n_restarts_optimizer=0,\n",
       "             normalize_y=False, optimizer=None, random_state=None)"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "gpr.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8363809363920776, 0.649983463876857)"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "krr.score(train_x, train_y), krr.score(test_x, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8146886102829334, 0.6398643616279389)"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpr.score(train_x, train_y), gpr.score(test_x, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "238.8410776079586"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((krr.predict(train_x) - gpr.predict(train_x))**2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "krr = KernelRidge(kernel='rbf', alpha=1.0, gamma=1.0)\n",
    "gpr = GaussianProcessRegressor(alpha=1.0, kernel=RBF(length_scale=1.0/np.sqrt(2.0)), optimizer=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 11 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KernelRidge(alpha=1.0, coef0=1, degree=3, gamma=1.0, kernel='rbf',\n",
       "      kernel_params=None)"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "krr.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 20 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GaussianProcessRegressor(alpha=1.0, copy_X_train=True,\n",
       "             kernel=RBF(length_scale=0.707), n_restarts_optimizer=0,\n",
       "             normalize_y=False, optimizer=None, random_state=None)"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "gpr.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8363809363920776, 0.649983463876857)"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "krr.score(train_x, train_y), krr.score(test_x, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8363809363920777, 0.6499834638768573)"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpr.score(train_x, train_y), gpr.score(test_x, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5646977168968247e-25"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((krr.predict(train_x) - gpr.predict(train_x))**2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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