5.4 交叉验证.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5189436a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fdee034a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
      "\n",
      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
      "    the documentation of this function for further details.\n",
      "\n",
      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
      "    dataset unless the purpose of the code is to study and educate about\n",
      "    ethical issues in data science and machine learning.\n",
      "\n",
      "    In this case special case, you can fetch the dataset from the original\n",
      "    source::\n",
      "\n",
      "        import pandas as pd\n",
      "        import numpy as np\n",
      "\n",
      "\n",
      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
      "        target = raw_df.values[1::2, 2]\n",
      "\n",
      "    Alternative datasets include the California housing dataset (i.e.\n",
      "    func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
      "    dataset. You can load the datasets as follows:\n",
      "\n",
      "        from sklearn.datasets import fetch_california_housing\n",
      "        housing = fetch_california_housing()\n",
      "\n",
      "    for the California housing dataset and:\n",
      "\n",
      "        from sklearn.datasets import fetch_openml\n",
      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
      "\n",
      "    for the Ames housing dataset.\n",
      "    \n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "house = datasets.load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fc17bd34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples total = 506\n"
     ]
    }
   ],
   "source": [
    "print('samples total = ' + str(len(house.target)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9057ed49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.320e-03 1.800e+01 2.310e+00 0.000e+00 5.380e-01 6.575e+00 6.520e+01\n",
      " 4.090e+00 1.000e+00 2.960e+02 1.530e+01 3.969e+02 4.980e+00]\n",
      "24.0\n"
     ]
    }
   ],
   "source": [
    "print(house.data[0])\n",
    "print(house.target[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6d8e30f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
       "        4.9800e+00],\n",
       "       [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
       "        9.1400e+00],\n",
       "       [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
       "        4.0300e+00],\n",
       "       ...,\n",
       "       [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "        5.6400e+00],\n",
       "       [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
       "        6.4800e+00],\n",
       "       [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "        7.8800e+00]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = house.data\n",
    "y = house.target\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "33762754",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.08011358e-01  4.64204584e-02  2.05586264e-02  2.68673382e+00\n",
      " -1.77666112e+01  3.80986521e+00  6.92224640e-04 -1.47556685e+00\n",
      "  3.06049479e-01 -1.23345939e-02 -9.52747232e-01  9.31168327e-03\n",
      " -5.24758378e-01] 36.45948838509001\n"
     ]
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X, y)\n",
    "print(lr.coef_,lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5d0d76dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE = 4.679\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "print('RMSE = %.3f'%np.sqrt(mean_squared_error(y_true = y,\n",
    "                                               y_pred = lr.predict(X))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4acc792f",
   "metadata": {},
   "source": [
    "#### 留出法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f2300f57",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_lo_train = house.data[:-200]\n",
    "y_lo_train = house.target[:-200]\n",
    "X_lo_test = house.data[-200:]\n",
    "y_lo_test = house.target[-200:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9e0f253d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.22531206  0.01236249  0.01996516  0.67898399 -8.10624608  9.14475345\n",
      " -0.04698834 -0.99589822  0.114772   -0.01468724 -0.61916486  0.01680992\n",
      " -0.11589961] -13.026212481552207\n"
     ]
    }
   ],
   "source": [
    "lr_lo = LinearRegression()\n",
    "lr_lo.fit(X_lo_train, y_lo_train)\n",
    "print(lr_lo.coef_,lr_lo.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8d373705",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE = 19.241\n"
     ]
    }
   ],
   "source": [
    "print('RMSE = %.3f'%np.sqrt(mean_squared_error(y_true = y_lo_test, \n",
    "                                               y_pred = lr_lo.predict(X_lo_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1639f4cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4] [0]\n",
      "[0 2 3 4] [1]\n",
      "[0 1 3 4] [2]\n",
      "[0 1 2 4] [3]\n",
      "[0 1 2 3] [4]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import LeaveOneOut\n",
    "\n",
    "sample_x = range(5)\n",
    "loo = LeaveOneOut()\n",
    "for train, test in loo.split(sample_x):\n",
    "    print(train,test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a41e0c1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folds: 506, mean RMSE: 3.383\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "crossvalidation = KFold(n_splits = len(X))\n",
    "scores = cross_val_score(lr, X, y, scoring = 'neg_mean_squared_error',\n",
    "                         cv = crossvalidation)\n",
    "rmse_scores = [np.sqrt(abs(s)) for s in scores]\n",
    "print('Folds: %i, mean RMSE: %.3f'%(len(scores), np.mean(rmse_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5cbdeaab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 3 4] [0 1]\n",
      "[1 3 4] [0 2]\n",
      "[1 2 4] [0 3]\n",
      "[1 2 3] [0 4]\n",
      "[0 3 4] [1 2]\n",
      "[0 2 4] [1 3]\n",
      "[0 2 3] [1 4]\n",
      "[0 1 4] [2 3]\n",
      "[0 1 3] [2 4]\n",
      "[0 1 2] [3 4]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import LeavePOut\n",
    "\n",
    "sample_x = range(5)\n",
    "loo = LeavePOut(p = 2)\n",
    "for train, test in loo.split(sample_x):\n",
    "    print(train,test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2634f12d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folds: 10, mean RMSE: 5.181\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "crossvalidation = KFold(n_splits = 10)\n",
    "scores = cross_val_score(lr, X, y, scoring = 'neg_mean_squared_error',\n",
    "                         cv = crossvalidation)\n",
    "rmse_scores = [np.sqrt(abs(s)) for s in scores]\n",
    "print('Folds: %i, mean RMSE: %.3f'%(len(scores), np.mean(rmse_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99fab561",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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