2023/machine_learning/sklearn_cross_validation_and_grid_search.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型的评估与参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from preamble import *\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi'] = 100\n",
    "mpl.rcParams['legend.numpoints'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# create a synthetic dataset\n",
    "X, y = make_blobs(random_state=0)\n",
    "# split data and labels into a training and a test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "# Instantiate a model and fit it to the training set\n",
    "logreg = LogisticRegression().fit(X_train, y_train)\n",
    "# evaluate the model on the test set\n",
    "logreg.score(X_test, y_test)\n",
    "# we predicted the correct class on 88% of the samples in X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 交叉验证/Cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "hide_input": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_cross_validation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sklearn中的交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross-validation scores:  [0.967 1.    0.933 0.967 1.   ]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "iris = load_iris()\n",
    "logreg = LogisticRegression(max_iter=1000)\n",
    "\n",
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=5)\n",
    "print(\"cross-validation scores: \", scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9733333333333334"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K折交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "print(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "hide_input": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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",
      "image/png": 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cgPjMLQBZ3uKYqly5cukepH3//n1UqVIl3UPUU28vS23f6NGjUbVqVbRr1w7ly5fHsGHDcOjQIbVpZs2ahfj4eFStWhXOzs746quvcOXKFdX7b9++xaNHj9T+AKBUqVJo3bo1tm/frhp327ZtKFq0qOqWKkD7dZ2V+/fvo0iRIqpbPVNltM613b40Xb4m6z1VdttsZlILTCdPnsTr169x6dIltGjRAi1btlQVo06ePAkLCwvUqVMnR1kAwNjYWPVcprRtzK59bm5u6NGjBwICAlCqVCl06dIFGzZsQGJiomqc+/fvo2zZsmrFDCDzdfVxH8vK7du3IQgCfH19YW1trfaXetto6g8ppH5mH9Okn6YW5TTppx+7fv06unXrBktLS1hYWMDa2hoDBw4EANX25+DggMmTJ2P9+vUoVaoUPD09sXr1arXts0+fPmjWrBl8fHxQpkwZ9O3bF9u3b89RYUrbvvjxelMoFKhcubLquWupBewhQ4ak+xzWr1+PxMTETPuaJtsQERFRQcBiFBFRPjE0NETDhg0xb948rF27Fu/fv8eOHTuynW7cuHGYO3cuevfuje3bt+PIkSMICQlByZIlMzzRMjAwyHA+wkcPQ9bEvHnzMHnyZLRs2RIbN27E4cOHERISgpo1a2a4bE2fL5MbPmXZpUuXRlRUFPbv3696fk+7du0wZMgQ1TgtW7bEnTt38PPPP6NWrVpYv3496tWrh/Xr1wMQC0y2trZqf6n69u2LW7duISoqCgCwfft2tG7dGqVKlVKNo+261hVtt6/ckNNttmzZsnBwcMCJEydw5swZCIKApk2bokWLFvj7779x//59nDx5Eq6urp/0q46ZtS87CoUCO3fuxJkzZzB27Fg8fPgQw4YNQ/369ZGQkJCjeWqznad+flOmTEFISEiGf5UrV85RO3QhPj4ebm5uuHz5MmbNmoVff/0VISEhque2pd3+lixZgitXrmDatGl4+/Ytxo8fj5o1a6oeym9iYoITJ07g6NGjGDRoEK5cuYI+ffrAw8NDowfNp6Xrvpg6zaJFizL9HJRKZYbT5sY2RERElB/4AHMiIj3QoEEDAOJtbKkyu51l586dGDJkCJYsWaIa9u7dO8THx+do2XZ2dgDE/1vv6OioGv706dN0V3rs3LkT7u7u+Omnn9SGx8fHqxVSNFnWx1JvrcpK6pU9165dy9FJs52dHa5cuYKUlBS1YkTq7T+p7QPEYmGnTp3QqVMnpKSkYPTo0fjf//4HX19f1bJLlCiBoUOHYujQoUhISEDLli3h7+8PHx8feHp6ZnqbX9euXfHFF1+obtW7desWpk6dqjbOp67rj3OnpKTgzp07ale2ZLTONd2+srvd6uPla7reP1WLFi1w4sQJODg4wMXFBebm5qhTpw4sLS1x6NAhXLx4McNfM0xLm2w50aRJEzRp0gRz587F5s2bMWDAAGzduhU+Pj6ws7PD0aNH8erVK7Wro7RZV5m1P7V/FytWDG3atMlyHnZ2djnup6nLuXbtWrbjphUeHo64uDjs3r0bLVu2VA2/d+9ehuM7OzvD2dkZM2bMwOnTp9GsWTP88MMPmDNnDgCgSJEiaN26NVq3bo2lS5di3rx5mD59OsLCwrLNn5a2ffHj9SYIAm7fvq16iHzqfszCwkKrdqSV1TZERERUEPDKKCKiPBQWFpbh1R2pzxJJWygwMzPLsMBkYGCQbh6rVq3S+v/2p2rTpg2KFSuGVatWqc13+fLlGi17x44dqufMZMfW1hYuLi4ICgpK9xPsmtyi2LZtW5ibm2P+/Pl49+6d2nuaXOnVvn17PHr0SO15TR8+fMCqVaugVCrh5uYGQPwlvrSKFCmiOpFMvR3m43GUSiUqV66set/W1hZt2rRR+0tlZWUFT09PbN++HVu3boWhoSG6du2qNr9PXddptWvXDgCwcuVKteGafsYZbV9mZmYAoFERVNP1rgstWrRATEwMtm3bprptr0iRInB1dcXSpUvx/v37bJ8XlfqLlDkt8Gbm+fPn6dati4sLAGm7at++PZKTk/H999+rjbds2TIoFArVZ5mVzNpfunRptGrVCv/73//UCt+pnj59qvp3+/btcfbsWURGRqq9v2nTpmyXb21tjZYtW+Lnn3/GgwcP1N7Lqp+mXnGWdpykpCSsWbNGbbyXL1/iw4cPasOcnZ1RpEgR1Xp89uxZuvl/vK41pW1f/OWXX/Dq1SvV6507dyI2Nlb12dWvXx+VKlXC4sWLM7yaKe3n8DFNtiEiIqKCgFdGERHloXHjxuHNmzfo1q0bqlevjqSkJJw+fRrbtm2Dvb292gO369evj6NHj2Lp0qWq248aN26Mjh07Ijg4GJaWlnBycsKZM2dw9OhRlCxZMkdtsra2xpQpUzB//nx07NgR7du3x6VLl3Dw4MF0/9e/Y8eOmDVrFoYOHQpXV1dcvXoVmzZtUruiKjvz589Hhw4d0Lx5cwwbNgzPnj3DqlWrULNmzWxvM7GwsMCyZcvg4+ODhg0bon///ihevDguX76MN2/eICgoKMvpR4wYgf/973/w9vbGhQsXYG9vj507dyIiIgLLly9XXYni4+ODZ8+e4bPPPkP58uVx//59rFq1Ci4uLqpn9zg5OaFVq1aoX78+SpQogT/++AM7d+7E2LFjNVoPffr0wcCBA7FmzRp4enqqHkycShfrOpWLiwv69euHNWvW4MWLF3B1dUVoaChu376dblxNty8XFxcYGBhgwYIFePHiBYyMjPDZZ5+hdOnS6eap6XrXhdRC082bNzFv3jzV8JYtW+LgwYMwMjJCw4YNs5yHiYkJnJycsG3bNlStWhUlSpRArVq1cvQMpLSCgoKwZs0adOvWDZUqVcKrV6+wbt06WFhYoH379gCATp06wd3dHdOnT0dMTAzq1KmDI0eOYN++fZg4cWK6535p2/7Vq1ejefPmcHZ2xvDhw+Ho6IjHjx/jzJkz+Oeff3D58mUAwNdff43g4GB4eXlhwoQJMDMzw48//qi6yi07K1euRPPmzVGvXj2MGDECDg4OiImJwYEDB1S3p37M1dUVxYsXx5AhQzB+/HgoFAoEBwenK74cO3YMY8eORa9evVC1alV8+PABwcHBMDAwQI8ePQCIz3Q7ceIEOnToADs7Ozx58gRr1qxB+fLl0bx582zbn5a2fbFEiRJo3rw5hg4disePH2P58uWoXLkyhg8fDkAsjq5fvx7t2rVDzZo1MXToUJQrVw4PHz5EWFgYLCws8Ouvv2Y4b022ISIiogIhb3+8j4iocDt48KAwbNgwoXr16oJSqRQMDQ2FypUrC+PGjRMeP36sNu6ff/4ptGzZUjAxMREAqH5q/Pnz58LQoUOFUqVKCUqlUvD09BT+/PPPdD9HnvqT4B//BHjqz4+HhYWphiUnJwsBAQGCra2tYGJiIrRq1Uq4du1ahj9x/uWXX6rGa9asmXDmzBnBzc1NcHNzS7eMzH56fdeuXUKNGjUEIyMjwcnJSdi9e7cwZMgQwc7OTqP1uH//fsHV1VUwMTERLCwshEaNGglbtmxRve/m5ibUrFkzw2kfP36sWn+GhoaCs7OzsGHDBrVxdu7cKbRt21YoXbq0YGhoKFSsWFH44osvhNjYWNU4c+bMERo1aiRYWVkJJiYmQvXq1YW5c+cKSUlJGmV4+fKl6rPduHFjuvc1Xdf37t0TAKhl8PPzEz7+in/79q0wfvx4oWTJkoKZmZnQqVMn4e+//xYACH5+fqrxNN2+BEEQ1q1bJzg6OgoGBgZq29THbRQEzdZ7apZFixalWx8ftzMrpUuXFgCo9alTp04JAIQWLVqkGz+jbe/06dNC/fr1BUNDQ7VlDxkyRDAzM0s3j4zW+ccuXrwo9OvXT6hYsaJgZGQklC5dWujYsaPwxx9/qI336tUrYdKkSULZsmWFYsWKCVWqVBEWLVokpKSkqI0HQBgzZkyGy8qs/YIgCHfu3BEGDx4s2NjYCMWKFRPKlSsndOzYUdi5c6faPK5cuSK4ubkJxsbGQrly5YTZs2cLP/30kwBAuHfvXpZZBUEQrl27JnTr1k2wsrISjI2NhWrVqgm+vr6q91P3UWnnFRERITRp0kQwMTERypYtK3z99dfC4cOH1bavu3fvCsOGDRMqVaokGBsbCyVKlBDc3d2Fo0ePquYTGhoqdOnSRShbtqxgaGgolC1bVujXr59w69atbNv9qfu9LVu2CFOnThVKly4tmJiYCB06dBDu37+fbjmXLl0SunfvLpQsWVIwMjIS7OzshN69ewuhoaGZriNNtyEiIiJ9pxCEHDzBloiIiIiIiIiIKAf4zCgiIiIiIiIiIsozLEYREREREREREVGeYTGKiIiIiIiIiIjyDItRRERERERERESUZ1iMIiIiIiIiIiKiPMNiFBERERERERER5RkWo4iIiIiIiIiIKM+wGEVERERERERERHmGxSgiIiIiIiIiIsozLEYREREREREREVGeYTGKiIiIiIiIiIjyDItRRERERERERESUZ1iMIiIiIiIiIiKiPMNiFBERERERERER5Zmi+d0A0o2AgACNx01MTMTGjRvx5MkTDBo0COXLl9d6ef/88w+Cg4NRunRpDBw4EEZGRlrP4/jx4wgLC4O7uzvc3Ny0np45JMwhYQ5Rbufw8/PTaB5z5syBr68vZs+ejRkzZmjdhlevXsHLywvXrl1DSEgIGjVqpPU8IiMj4eHhgVq1auHQoUMwNzfXeh7MIWIOCXNI9CGHLuhDDrl8HswhYQ4RcxDRx3hlVCHDE20Jc0iYQ8QcEl3k0IcDPrkcuDKHhDlEzKFb+pBDLp8Hc0iYQ8QcRJQRFqMKEX05QZXLiTZziJhDwhwSfTjgk8uBK3NImEPEHLqlDznk8nkwh4Q5RMxBRJlhMaqQ0JcTVLmcaDOHiDkkzCHRhwM+uRy4MoeEOUTMIYmMjNR6mo/pSw65fB7MIWIOEXMQUVZYjCoE9OUEVS4n2swhYg4Jc0j04YBPLgeuzCFhDhFzSFJzfAp9yiGXz4M5mCMVcxBRdliMkjl9OUGVy4k2c4iYQ8Ic6vL7gE8uB67MIWEOEXNI0ubIKX3LIZfPgzmYA2AOItIMi1Eypi8nqHI50WYOEXNImCM9HrgyRyrmkDCHKDdy5IQ+5pDL58EczMEcRKQpFqNkSl9OUOVyos0cIuaQMEfGeODKHABzpMUcIuaQMIeEOUTMIZFLDiLKHotRMqQvJ6hyOdFmDhFzSJhDd3jgKmEOCXOImEPCHBLmEDGHhDkk+pCDiDTDYpTM6MsJqlxOtJlDxBwS5tAdHrhKmEPCHCLmkDCHhDlEzCFhDok+5CAizbEYJSP6coIqlxNt5hAxh4Q5dIcHrhLmkDCHiDkkzCFhDhFzSJhDog85iEg7LEbJhL6coMrlRJs5RMwhYQ7J8ePHtZ4mLR64SphDwhwi5pAwh4Q5RMwhYQ6JPuQgIu0pBEEQ8rsR9OkqVqyoFyeocjnRZg7mSIs5JKk5cvrVwQNXCXNImEPEHBLmkDCHiDkkzCHRlxwsYBFpj1dGyYS+nKDK5USbOZgjFXNI0ubICR64SphDwhwi5pAwh4Q5RMwhYQ6JPuUgIu2xGCUT+nKCKpcTbeZgDoA50vrUHDxwlTCHhDlEzCHRRQ4Assghl8+DOUTMIZFjDiLSHotRMlFQT1DlcqLNHBLmkDCHiAeuEuaQMIeIOSS6KkQBKPA55PJ5MIeIOSRyzUFE2mMxqpDShxNUuZxoM4eEOSTMIeKBq4Q5JMwhYg6JLgtRAAp0Drl8HswhYg4JcxBRWixG5VBMTAwUCgWioqIAAOHh4VAoFIiPj8/XdmlCH05Q5XKizRwS5pAwh0hfDvjkcuDKHCLmkDCH7uhLDrl8HswhYg4JcxDRxwplMerp06cYNWoUKlasCCMjI9jY2MDT0xMRERE5nqerqytiY2NhaWkJAAgMDISVlVW208XGxqJ///6oWrUqihQpgokTJ+a4DZrQhxNUuZxoM4eEOSTMIdKXAz65HLgyh4g5JMyhO/qSQy6fB3OImEPCHESUkaL53YD80KNHDyQlJSEoKAiOjo54/PgxQkNDERcXl+N5GhoawsbGRuvpEhMTYW1tjRkzZmDZsmU5Xr4m9OEEVS4n2swhYQ4Jc4j05YBPLgeuzCFiDglz6I6+5JDL58EcIuaQMAcRZabQXRkVHx+PkydPYsGCBXB3d4ednR0aNWqEqVOnonPnzqrxFAoF1q5di3bt2sHExASOjo7YuXNnpvNNe5teeHg4hg4dihcvXkChUEChUMDf3z/D6ezt7bFixQoMHjxYdVVVbtCHE1S5nGgzh4Q5JMwh0pcDPrkcuDKHiDkkzCGZM2eO1tOkpU855PJ5MAdzpMUcRJSVQleMUiqVUCqV2Lt3LxITE7Mc19fXFz169MDly5cxYMAA9O3bF9HR0dkuw9XVFcuXL4eFhQViY2MRGxuLKVOm6CqC1vThBFUuJ9rMIWEOCXNI9OGATy4HrswhYg4Jc0hSc+SUvuWQy+fBHMyRijmIKDuFrhhVtGhRBAYGIigoCFZWVmjWrBmmTZuGK1eupBu3V69e8PHxQdWqVTF79mw0aNAAq1atynYZhoaGsLS0hEKhgI2NDWxsbKBUKnWWITExES9fvlT7+/DhQ4bj6sMJqlxOtJlDwhwS5lCX3wd8cjlwZQ4Rc0iYQ5I2R07oYw65fB7MwRwAcxCRZhSCIAj53Yj88O7dO5w8eRJnz57FwYMHERkZifXr18Pb2xuAeJteUFAQBg8erJpm0qRJiIqKQlhYGGJiYuDg4IBLly7BxcUF4eHhcHd3x/Pnz2FlZYXAwEBMnDhRq1/Xa9WqFVxcXLB8+fIsx/P390dAQIDaMD8/v0xvBSQiIiIiIiIi0heF7sqoVMbGxvDw8ICvry9Onz4Nb29v+Pn55XezNDJ16lS8ePFC7W/q1Kn53SwiIiIiIiIiomxpXYwKCgrCgQMHVK+//vprWFlZwdXVFffv39dp4/KSk5MTXr9+rTbs7Nmz6V7XqFFDo/kZGhoiOTlZZ+1Ly8jICBYWFmp/Obkth4iIiIiIiIgor2ldjJo3bx5MTEwAAGfOnMHq1auxcOFClCpVCpMmTdJ5A3UtLi4On332GTZu3IgrV67g3r172LFjBxYuXIguXbqojbtjxw78/PPPuHXrFvz8/BAZGYmxY8dqtBx7e3skJCQgNDQU//33H968eZPpuFFRUYiKikJCQgKePn2KqKgo3Lhx45NyEhERERERERHpo6LaTvD333+jcuXKAIC9e/eiR48eGDFiBJo1a4ZWrVrpun06p1Qq0bhxYyxbtgx37tzB+/fvUaFCBQwfPhzTpk1TGzcgIABbt27F6NGjYWtriy1btsDJyUmj5bi6umLkyJHo06cP4uLisnymU926dVX/vnDhAjZv3gw7OzvExMTkNCYRERERERERkV7S+gHmpUuXxuHDh1G3bl3UrVsXkydPxqBBg3Dnzh3UqVMHCQkJudXWPKVQKLBnzx507do1v5tCRERERERERCQbWl8Z5eHhAR8fH9StWxe3bt1C+/btAQDXr1+Hvb29rttHREREREREREQyovUzo1avXo2mTZvi6dOn2LVrF0qWLAlAvL2sX79+Om8gERERERERERHJh9a36REREREREREREeWU1rfpAUB8fDwiIyPx5MkTpKSkqIYrFAoMGjRIZ40jIiIiIiIiIiJ50frKqF9//RUDBgxAQkICLCwsoFAopJkpFHj27JnOG0lERERERERERPKgdTGqatWqaN++PebNmwdTU9PcahcREREREREREcmQ1sUoMzMzXL16FY6OjrnVJiIiIiIiIiIikimtf03P09MTf/zxR260hYiIiIiIiIiIZE7rB5h36NABX331FW7cuAFnZ2cUK1ZM7f3OnTvrrHFERERERERERCQvWt+mV6RI5hdTKRQKJCcnf3KjSLfmzJkDX19fzJ49GzNmzNB6+levXsHLywvXrl1DSEgIGjVqpPU8IiMj4eHhgVq1auHQoUMwNzfXeh7MIWIOCXNI9CEHABgbG6N06dIYOHAgjIyMtJ7++PHjCAsLg7u7O9zc3LSePjExERs3bsSTJ08waNAglC9fXut5/PPPPwgODmYOMEcq5pDoW453795pPT0gn/0uc0iYQ8QcEn3IQUSZ0/rKqJSUlNxoB+USfhGImEPCHBLmEOmqEAVAb05Q5XKizRzMkYo5RB/nyAm57HeZQ8IcIuaQ6EMOIsqa1s+MooKDXwQi5pAwh4Q5RLosRAHQmxNUuZxoMwdzAMyRShc55LLfZQ4Jc4iYQ6IPOYgoezkqRh0/fhydOnVC5cqVUblyZXTu3BknT57UddvoE/CLQMQcEuaQMIdI14UoAAX2BFUuJ9rMIWEOEXNI5LLfZQ4Jc4iYQ6IPOYhIM1oXozZu3Ig2bdrA1NQU48ePx/jx42FiYoLWrVtj8+bNudFG0hK/CETMIWEOCXOIcqMQlRP6cIIqlxNt5pAwh4g5JHLZ7zKHhDlEzCHRhxxEpDmtH2Beo0YNjBgxApMmTVIbvnTpUqxbtw7R0dE6bSBph18EIuaQMIeEOUS5WYgKCAjQeFx9OEGVy4k2c0iYQ1RYcvj5+WU7D7nsd5lDwhwi5pDoQw4i0o7WV0bdvXsXnTp1Sje8c+fOuHfvnk4aRTnDLwIRc0iYQ8IcIl3l+FQ80RYxh4Q5JMwh4hVREuaQMIeIOST6kIOItKd1MapChQoIDQ1NN/zo0aOoUKGCThpF2uMXgYg5JMwhYQ6RLnN8Cn04QZXLiTZzSJhDxBwSue13mYM5UjGHRF9yEJH2imo7wZdffonx48cjKioKrq6uAICIiAgEBgZixYoVOm8gaYZfBMyRFnNImEOk6xw5pQ8nqHI50WYOCXOImEMix/0uczAHwBxp6VOOiIgIraclKuy0LkaNGjUKNjY2WLJkCbZv3w5AfI7Utm3b0KVLF503kDTDLwLmSMUcEuYQ5UaOnNCHE1S5nGgzh4Q5RMyhTo77XeZgDuaQ6FsOItKe1sUoAOjWrRu6deum67bQJ+AXAXMAzJEWc4j0JYc+nKDK5USbOSTMIWKO9LjfZY5UzCFhDomucxCR9rR+ZhTJg1y/CJiDOQDmSKUvOfThBFUuJ9rMIWEOEXNkrLDvd5lDxBwS5pDoQw4i0vDKqBIlSuDWrVsoVaoUihcvDoVCkem4z54901njKHfwi0DCHBLmEDGHhIUoCXOImEPCHBJ9yPGxwrzfZQ4Rc0iYQ6IPOYhIpFExatmyZarOvmzZsiyLUaTf+EUgYQ4Jc4iYQ8JClIQ5RMwhYQ6JPuTQBbnsd5lDxBwS5pDoQw4ikmhUjBoyZIjq397e3rnVFspl/CKQMIeEOUTMIWEhSsIcIuaQMIdEH3Loglz2u8whYg4Jc0j0IQcRqdP6mVEGBgZ48uRJuuFxcXEwMDDQSaNI9/hFIGEOCXOImEPCQpSEOUTMIWEOib7k+FRy2e8yh4g5JMwh0YccRJSe1sUoQRAyHJ6YmAhDQ8NPbhDpHr8IJMwhYQ4Rc0hYiJIwh4g5JMwh0accn0Iu+13mEDGHhDkk+pCDiDKm0W16ALBy5UoAgEKhwPr166FUKlXvJScn48SJE6hevbruW6inYmJi4ODggEuXLsHFxQXh4eFwd3fH8+fPYWVlld/NU+EXgYQ5JMwhYg6JLnIA0JsTVLmcaDMHc6RiDknaHDkll/0uc4iYQ8IcEn3IQUSZ07gYtWzZMgDilVE//PCD2i15hoaGsLe3xw8//KD7FuaCp0+fYubMmThw4AAeP36M4sWLo06dOpg5cyaaNWuWo3m6uroiNjYWlpaWAIDAwEBMnDgR8fHxWU63e/durF27FlFRUUhMTETNmjXh7+8PT0/PHLUjLX4RSJhDwhwi5pDoqhAFQG9OUOVyos0czAEwR1of58gJuex3mUPEHBLmkOhDDiLKmsbFqHv37gEQTzR2796N4sWL51qjcluPHj2QlJSEoKAgODo64vHjxwgNDUVcXFyO52loaAgbGxutpztx4gQ8PDwwb948WFlZYcOGDejUqRPOnTuHunXr5rg9/CKQMIeEOUTMIdFlIQqA3pygyuVEmzmYgzkkusghl/0uc4iYQ8IcEn3IQUTZ07gYlSosLCw32pFn4uPjcfLkSYSHh6sOpuzs7NLtZBQKBdasWYP9+/cjPDwctra2WLhwIXr27JnhfNPephcVFYWhQ4eq5gMAfn5+8Pf3Tzfd8uXL1V7PmzcP+/btw6+//prjYhS/CCTMIWEOEXNIdF2Iygl9OUGVy4k2c4iYQ8IcErnsd5lDxBwS5pDoQw4i0ozWxShAPCDYv38/Hjx4gKSkJLX3li5dqpOG5RalUgmlUom9e/eiSZMmWR7M+Pr64rvvvsOKFSsQHByMvn374urVq6hRo0aWy3B1dcXy5csxc+ZM3Lx5U7VcTaSkpODVq1coUaKE5qHS4BeBhDkkzCFiDgkLURK5nGgzh4g5JMwhkct+lzlEzCFhDok+5CAizWldjAoNDUXnzp3h6OiIP//8E7Vq1UJMTAwEQUC9evVyo406VbRoUQQGBmL48OH44YcfUK9ePbi5uaFv376oXbu22ri9evWCj48PAGD27NkICQnBqlWrsGbNmiyXYWhoCEtLSygUCq1v3Vu8eDESEhLQu3fvTMdJTExEYmKi2jAjIyMkJSXxi+D/MYeEOUTMIWEhSiKXE23mEDGHhDkkctnvMoeIOSTMIdGHHESkHYUgCII2EzRq1Ajt2rVDQEAAzM3NcfnyZZQuXRoDBgyAl5cXRo0alVtt1al3797h5MmTOHv2LA4ePIjIyEisX78e3t7eAMTb64KCgjB48GDVNJMmTUJUVBTCwsKy/TU9TR9gntbmzZsxfPhw7Nu3D23atMl0PH9/fwQEBKgNy+w2QCIiIiIiIiIifVJE2wmio6NVBZqiRYvi7du3UCqVmDVrFhYsWKDzBuYWY2NjeHh4wNfXF6dPn4a3tzf8/PzyrT1bt26Fj48Ptm/fnmUhCgCmTp2KFy9eqP1NnTo1j1pKRERERERERJRzWhejzMzMVM+JsrW1xZ07d1Tv/ffff7prWR5zcnLC69ev1YadPXs23evsnheVytDQEMnJyRqNu2XLFgwdOhRbtmxBhw4dsh3fyMgIFhYWan85uWydiIiIiIiIiCivaf3MqCZNmuDUqVOoUaMG2rdvjy+//BJXr17F7t270aRJk9xoo07FxcWhV69eGDZsGGrXrg1zc3P88ccfWLhwIbp06aI27o4dO9CgQQM0b94cmzZtQmRkJH766SeNlmNvb4+EhASEhoaiTp06MDU1hampabrxNm/ejCFDhmDFihVo3LgxHj16BAAwMTGBpaXlpwcmIiIiIiIiItIjWl8ZtXTpUjRu3BgAEBAQgNatW2Pbtm2wt7fXuFCTn5RKJRo3boxly5ahZcuWqFWrFnx9fTF8+HB8//33auMGBARg69atqF27Nn755Rds2bIFTk5OGi3H1dUVI0eORJ8+fWBtbY2FCxdmON6PP/6IDx8+YMyYMbC1tVX9TZgw4ZOzEhERERERERHpG60eYJ6cnIyIiAjUrl0bVlZWudis/KdQKLBnzx507do1v5tCRERERERERCQbWl0ZZWBggLZt2+L58+e51R4iIiIiIiIiIpIxrW/Tq1WrFu7evZsbbSEiIiIiIiIiIpnT6jY9ADh06BCmTp2K2bNno379+jAzM1N738LCQqcNJCIiIiIiIiIi+dC6GFWkiHQxlUKhUP1bEAQoFAokJyfrrnVERERERERERCQrRbWdICwsLDfaQUREREREREREhYDWV0YRERERERERERHllNYPMAeAkydPYuDAgXB1dcXDhw8BAMHBwTh16pROG0dERERERERERPKidTFq165d8PT0hImJCS5evIjExEQAwIsXLzBv3jydN5CIiIiIiIiIiORD62LUnDlz8MMPP2DdunUoVqyYanizZs1w8eJFnTaOiIiIiIiIiIjkResHmN+8eRMtW7ZMN9zS0hLx8fG6aBN9gsjISHh4eKBWrVo4dOgQzM3NtZ7HnDlz4Ovri9mzZ2PGjBlaT//q1St4eXnh2rVrCAkJQaNGjbSeB3NImEPEHBJ9yKEL+pBDLp8Hc0iYQ1KYcwQEBKj+nZiYiI0bN+LJkycYNGgQypcvr3Ub/vnnHwQHB6N06dIYOHAgjIyMtJ7H8ePHERYWBnd3d7i5uWk9PXNImEPCHKL8zuHn56f18ogKO62vjLKxscHt27fTDT916hQcHR110ijKGR64SphDxBwS5pDoIsen0pcccvk8mEPEHBLmEOX3CWqqgn6inYo5RMwhYQ4iyimti1HDhw/HhAkTcO7cOSgUCvz777/YtGkTpkyZglGjRuVGG0kD+nDAJ5cDV+aQMIeEOUSpOT6FPuWQy+fBHMyRFnOI9OUEVS4n2swhYg4JcxDRp9D6Nr1vv/0WKSkpaN26Nd68eYOWLVvCyMgIU6ZMwbhx43KjjaSB/D7gk8uBK3NImEPCHKK0OXJK33LI5fNgDuZIxRwifTlBlcuJNnOImEPCHJLjx49rPQ0RAQpBEIScTJiUlITbt28jISEBTk5OUCqVum4baaFZs2Y8cGUOAMyRFnNImEPEHBLmkDCHSC45AKBixYp6cYIqlxNt5mCOtJhDkpojh6fURIWa1rfpDRs2DK9evYKhoSGcnJzQqFEjKJVKvH79GsOGDcuNNpIGeODKHABzpMUcEuYQMYeEOSTMIZJLjlT6coIqlxNt5mCOVMwhSZuDiLSndTEqKCgIb9++TTf87du3+OWXX3TSKNIeD1yZgzkkzCFhDhFzSJhDwhwiueRIS19OUOVyos0czAEwR1qfmoOItHhm1MuXLyEIAgRBwKtXr2BsbKx6Lzk5Gb///jtKly6dK40k3eOBq4g5JMwhYQ4Rc0iYQ8IcIuaQ6EOOjxXUE1S5nGgzh4Q5JMxBRGlpXIyysrKCQqGAQqFA1apV072vUCgQEBCg08ZR7uCBq4g5JMwhYQ4Rc0iYQ8IcIuaQ6EMOXdCHE1S5nGgzh4Q5JMxBRB/TuBiV+mC2zz77DLt27UKJEiVU7xkaGsLOzg5ly5bNlUaS7vDAVcQcEuaQMIeIOSTMIWEOEXNI9CGHLujDCapcTrSZQ8IcEuYgooxoXIxK7XD37t1DxYoVoVAocq1RlDt44CpiDglzSJhDxBwS5pAwh4g5JPqSIyfLTUsfTlDlcqLNHBLmkDAHEWVGo2LUlStXUKtWLRQpUgQvXrzA1atXMx23du3aOmsc6Q4PXEXMIWEOCXOImEPCHBLmEDGHRJ9yREREaD1tKn04QZXLiTZzSJhDwhxElBWNilEuLi549OgRSpcuDRcXFygUCgiCkG48hUKB5ORknTeSPg0PXEXMIWEOCXOIdJEDgCxyyOXzYA4Jc4jkmCOn9OEEVS4n2swhYQ4JcxBRdjQqRt27dw/W1taqf1PBwQNXEXNImEPCHCJdFaIAFPgccvk8mEPCHCK55sgJfThBlcuJNnNImEPCHESkCY2KUXZ2dhn+m/QbD1xFzCFhDglziHRZiAJQoHPI5fNgDglziJhDog8nqHI50WYOCXNImIOINFUkvxtAuUMfDvjkcuDKHBLmEDFH5gpqDrl8HswhYQ4Rc0j04QRVLifazCFhDglzEJE2WIySIX044JPLgStzSJhDxBy6pQ855PJ5MIeEOUTMIdGHE1S5nGgzh4Q5JMxBRNpiMSqHYmJioFAoEBUVBQAIDw+HQqFAfHx8vrZLHw745HLgyhwS5hAxh27pQw65fB7MIWEOEXNI9OEEVS4n2swhYQ4JcxBRTmhcjJLTr+Q9ffoUo0aNQsWKFWFkZAQbGxt4enp+0s8Du7q6IjY2FpaWlgCAwMBAWFlZZTvdqVOn0KxZM5QsWRImJiaoXr06li1blqM26MMBn1wOXJlDwhwi5pBERkZqPc3H9CWHXD4P5hAxh4g5JPpwgiqXE23mkDCHhDmIKKc0eoA5AJQrVw7e3t4YNmwYqlatmpttynU9evRAUlISgoKC4OjoiMePHyM0NBRxcXE5nqehoSFsbGy0ns7MzAxjx45F7dq1YWZmhlOnTuGLL76AmZkZRowYofF89OGATy4HrswhYQ4Rc0hSc7x48ULraVPpUw65fB7MwRypmEOiDyeocjnRZg4Jc0iYQ5SYmKj1NEQEKARBEDQZcfbs2QgKCsK9e/fg6uqKzz//HL1794apqWlut1Gn4uPjUbx4cYSHh2e5s1EoFFizZg3279+P8PBw2NraYuHChejZsycA8TY9BwcHXLp0CS4uLggPD4e7uzueP3+OqKgouLu7q83Pz88P/v7+GrWxe/fuMDMzQ3BwsMa5FAoFD1yZQ4U5JMwh0nWOnF5Jqm855PJ5MAdzAMyR1vDhw/XiBFUuJ9rMIWIOCXOIUnM8ePBA62mJCjuNb9Pz9fXF7du3ERoaCkdHR4wdOxa2trYYPnw4zp07l5tt1CmlUgmlUom9e/dmW8X29fVFjx49cPnyZQwYMAB9+/ZFdHR0tstwdXXF8uXLYWFhgdjYWMTGxmLKlCkate/SpUs4ffq01jtDHrgyRyrmkDCHKDdy5IQ+5pDL58EczMEc6vTlBFUuJ9rMwRxpMYcobQ4i0p7WDzBv1aoVgoKC8OjRIyxZsgTR0dFo2rQpatasiaVLl+ZGG3WqaNGiCAwMRFBQEKysrNCsWTNMmzYNV65cSTdur1694OPjg6pVq2L27Nlo0KABVq1ale0yDA0NYWlpCYVCARsbG9jY2ECpVGY5Tfny5WFkZIQGDRpgzJgx8PHxyXTcxMREvHz5Uu3vq6++yj78R3jgKmEOCXOImEPCHBLmEDGHhDkk+pAjlb6coMrlRJs5mCMVc4g+zkFE2tP4Nr2sHDhwAIMHD0Z8fHyBedD5u3fvcPLkSZw9exYHDx5EZGQk1q9fD29vbwDibW9BQUEYPHiwappJkyYhKioKYWFhWd6mZ2VlhcDAQEycOFHjX9e7d+8eEhIScPbsWXz77bf4/vvv0a9fvwzH9ff3R0BAgNowbW4DJCIiIiIiIiLKLxo/wPxjb968wfbt27FhwwacOnUKlSpVytHVOfnF2NgYHh4e8PDwgK+vL3x8fODn56cqRuU1BwcHAICzszMeP34Mf3//TItRU6dOxeTJk9WG5eT/CBARERERERER5TWtb9M7ffo0fHx8YGtrizFjxsDe3h5hYWG4desWvv3229xoY55wcnLC69ev1YadPXs23esaNWpoND9DQ8McXyWWkpKS5fOsjIyMYGFhofbHYhQRERERERERFQQaXxm1cOFCbNiwAbdu3UKDBg2waNEi9OvXL0fPFMhPcXFx6NWrF4YNG4batWvD3Nwcf/zxBxYuXIguXbqojbtjxw40aNAAzZs3x6ZNmxAZGYmffvpJo+XY29sjISEBoaGhqFOnDkxNTTP85cHVq1ejYsWKqF69OgDgxIkTWLx4McaPH//pYYmIiIiIiIiI9IzGxahFixZh4MCB2LFjB2rVqpWbbcpVSqUSjRs3xrJly3Dnzh28f/8eFSpUwPDhwzFt2jS1cQMCArB161aMHj0atra22LJlC5ycnDRajqurK0aOHIk+ffogLi4u02c6paSkYOrUqbh37x6KFi2KSpUqYcGCBfjiiy90EZeIiIiIiIiISK9o/ADz9+/fo1ixYrndHr2hUCiwZ88edO3aNb+bQkREREREREQkGxo/M+rkyZNwcnLCy5cv07334sUL1KxZEydPntRp44iIiIiIiIiISF40LkYtX74cw4cPh4WFRbr3LC0t8cUXX2Dp0qU6bRwREREREREREcmLxrfp2dnZ4dChQ5n+mtyff/6Jtm3b4sGDBzptIBERERERERERyYfGV0Y9fvw4y2dGFS1aFE+fPtVJo4iIiIiIiIiISJ40LkaVK1cO165dy/T9K1euwNbWVieNIiIiIiIiIiIiedK4GNW+fXv4+vri3bt36d57+/Yt/Pz80LFjR502joiIiIiIiIiI5EXjZ0Y9fvwY9erVg4GBAcaOHYtq1aoBEJ8VtXr1aiQnJ+PixYsoU6ZMrjaYiIiIiIiIiIgKLo2LUQBw//59jBo1CocPH0bqZAqFAp6enli9ejUcHBxyraFERERERERERFTwaVWMSvX8+XPcvn0bgiCgSpUqKF68eG60jYiIiIiIiIiIZCZHxSgiIiIiIiIiIqKc0PgB5kRERERERERERJ+KxSgiIiIiIiIiIsozLEYREREREREREVGeYTGKiIiIiIiIiIjyDItRRERERERERESUZ1iMIiIiIiIiIiKiPMNiFBERERERERER5RkWo4iIiIiIiIiIKM+wGEVERERERERERHmGxSgiIiIiIiIiIsozLEYREREREREREVGeYTGKiIiIiIiIiIjyDItRRERERERERESUZ1iMIiIiIiIiIiKiPMNiFBERERERERER5RkWo4iIiIiIiIiIKM8UiGKUQqHA3r1782XZ9vb2WL58ucbjBwYGwsrK6pOXm5+ZiYiIiIiIiIhyS74Xox49eoRx48bB0dERRkZGqFChAjp16oTQ0ND8bhoREREREREREelYvhajYmJiUL9+fRw7dgyLFi3C1atXcejQIbi7u2PMmDH52TSiAo1X1hFRfuN+iIj0AfdFRET6KV+LUaNHj4ZCoUBkZCR69OiBqlWrombNmpg8eTLOnj2b6XTffPMNqlatClNTUzg6OsLX1xfv379XvX/58mW4u7vD3NwcFhYWqF+/Pv744w8AwP3799GpUycUL14cZmZmqFmzJn7//XeN27x06VI4OzvDzMwMFSpUwOjRo5GQkJBuvL1796JKlSowNjaGp6cn/v77b7X39+3bh3r16sHY2BiOjo4ICAjAhw8fNG4HFV76fjWhIAiYOXMmbG1tYWJigjZt2uCvv/7K72YRkQ7p+35o9+7daNu2LUqWLAmFQoGoqKj8bhIR5QJ93he9f/8e33zzjeq8oWzZshg8eDD+/fff/G4aEZFeKJpfC3727BkOHTqEuXPnwszMLN37WT13ydzcHIGBgShbtiyuXr2K4cOHw9zcHF9//TUAYMCAAahbty7Wrl0LAwMDREVFoVixYgCAMWPGICkpCSdOnICZmRlu3LgBpVKpcbuLFCmClStXwsHBAXfv3sXo0aPx9ddfY82aNapx3rx5g7lz5+KXX36BoaEhRo8ejb59+yIiIgIAcPLkSQwePBgrV65EixYtcOfOHYwYMQIA4Ofnp3FbqPCJiYlBs2bNYGVlhUWLFsHZ2Rnv37/H4cOHMWbMGPz555/53UQsXLgQK1euRFBQEBwcHODr6wtPT0/cuHEDxsbG+d08IvpEBWE/9Pr1azRv3hy9e/fG8OHD87s5RJQL9H1f9ObNG1y8eBG+vr6oU6cOnj9/jgkTJqBz586q/0lORFSoCfnk3LlzAgBh9+7d2Y4LQNizZ0+m7y9atEioX7++6rW5ubkQGBiY4bjOzs6Cv7+/xu20s7MTli1blun7O3bsEEqWLKl6vWHDBgGAcPbsWdWw6OhoAYBw7tw5QRAEoXXr1sK8efPU5hMcHCzY2tqqXmeV+d27d8KLFy/U/t69e6dxJiq42rVrJ5QrV05ISEhI997z589V//54+/n666+FKlWqCCYmJoKDg4MwY8YMISkpSfV+VFSU0KpVK0GpVArm5uZCvXr1hPPnzwuCIAgxMTFCx44dBSsrK8HU1FRwcnISDhw4kGH7UlJSBBsbG2HRokWqYfHx8YKRkZGwZcuWT0xPRPpA3/dDad27d08AIFy6dCnHeYlIPxWkfVGqyMhIAYBw//597QMTEclMvl0ZJQhCjqfdtm0bVq5ciTt37iAhIQEfPnyAhYWF6v3JkyfDx8cHwcHBaNOmDXr16oVKlSoBAMaPH49Ro0bhyJEjaNOmDXr06IHatWtrvOyjR49i/vz5+PPPP/Hy5Ut8+PAB7969w5s3b2BqagoAKFq0KBo2bKiapnr16rCyskJ0dDQaNWqEy5cvIyIiAnPnzlWNk5ycnG4+mZk/fz4CAgLUhvn5+cHf31/jHFTwFISrCe/du4dHjx6hTZs2qmGWlpZo3Lgxzpw5g759+37CGiCi/FYQ9kNEJH8FdV/04sULKBQKnfzyNhFRQZdvxagqVapAoVBofQntmTNnMGDAAAQEBMDT0xOWlpbYunUrlixZohrH398f/fv3x4EDB3Dw4EH4+flh69at6NatG3x8fODp6YkDBw7gyJEjmD9/PpYsWYJx48Zlu+yYmBh07NgRo0aNwty5c1GiRAmcOnUKn3/+OZKSkrItIqVKSEhAQEAAunfvnu49TW5jmjp1KiZPnqw2zMjISKNlU8F1+/ZtCIKA6tWraz3tjBkzVP+2t7fHlClTsHXrVtWB14MHD/DVV1+p5l2lShXV+A8ePECPHj3g7OwMAHB0dMx0OY8ePQIAlClTRm14mTJlVO8RUcFVEPZDRCR/BXFf9O7dO3zzzTfo16+f2v9EJyIqrPLtAeYlSpSAp6cnVq9ejdevX6d7Pz4+PsPpTp8+DTs7O0yfPh0NGjRAlSpVcP/+/XTjVa1aFZMmTcKRI0fQvXt3bNiwQfVehQoVMHLkSOzevRtffvkl1q1bp1GbL1y4gJSUFCxZsgRNmjRB1apVM3wI4YcPH9TuBb958ybi4+NRo0YNAEC9evVw8+ZNVK5cOd1fkSLZfyRGRkawsLBQ+2MxSv4+9WrCZs2awcbGBkqlEjNmzMCDBw9U76deTdimTRt89913uHPnjuq98ePHY86cOWjWrBn8/Pxw5cqVT8pBRAUX90NEpA8K2r7o/fv36N27NwRBwNq1a3PcdiIiOcnXX9NbvXo1kpOT0ahRI+zatQt//fUXoqOjsXLlSjRt2jTDaapUqYIHDx5g69atuHPnDlauXIk9e/ao3n/79i3Gjh2L8PBw3L9/HxERETh//ryqEDRx4kQcPnwY9+7dw8WLFxEWFqZ6LzuVK1fG+/fvsWrVKty9exfBwcH44Ycf0o1XrFgxjBs3DufOncOFCxfg7e2NJk2aoFGjRgCAmTNn4pdffkFAQACuX7+O6OhobN26Ve3/1BB97FOvJmzfvj1+++03XLp0CdOnT0dSUpJqHH9/f1y/fh0dOnTAsWPH4OTkpOpXPj4+uHv3LgYNGoSrV6+iQYMGWLVqVYbLsrGxAQA8fvxYbfjjx49V7xFRwVUQ9kNEJH8FaV+UWoi6f/8+QkJCeFUUEVGq/Htclejff/8VxowZI9jZ2QmGhoZCuXLlhM6dOwthYWGqcfDRgwe/+uoroWTJkoJSqRT69OkjLFu2TLC0tBQEQRASExOFvn37ChUqVBAMDQ2FsmXLCmPHjhXevn0rCIIgjB07VqhUqZJgZGQkWFtbC4MGDRL++++/TNv38QPMly5dKtja2gomJiaCp6en8MsvvwgAVA9K3LBhg2BpaSns2rVLcHR0FIyMjIQ2bdqke1DhoUOHBFdXV8HExESwsLAQGjVqJPz444+ZZiYSBEHw8vLS+mGdixcvFhwdHdXG/fzzz1V9JiN9+/YVOnXqlOF73377reDs7Jzhe6kPMF+8eLFq2IsXL/gAcyIZ0ff9UFp8gDmRfBWEfVFSUpLQtWtXoWbNmsKTJ08yD0NEVAjlezGKiDR3584dwcbGRnBychJ27twp3Lp1S7hx44awYsUKoXr16qrx0h547du3TyhatKiwZcsW4fbt28KKFSuEEiVKqA683rx5I4wZM0YICwsTYmJihFOnTgmVKlUSvv76a0EQBGHChAnCoUOHhLt37woXLlwQGjduLPTu3TvTNn733XeClZWVsG/fPuHKlStCly5dBAcHB1VBmIgKtoKwH4qLixMuXbokHDhwQAAgbN26Vbh06ZIQGxuba+uFiPKWvu+LkpKShM6dOwvly5cXoqKihNjYWNVfYmJirq4bIqKCgMUoogImN64m7NWrl2BhYaGTqwlTUlIEX19foUyZMoKRkZHQunVr4ebNm7mxKogon+TWVc3ly5cXDAwMPnk/tGHDBgFAuj8/P79cWBtElF/0eV+UemVmRn9p20dEVFgpBOETngBIRLLw8uVLWFpa4sWLF3yWARHlG+6LiEgfcF9ERJT78vUB5kREREREREREVLiwGEVERERERERERHmGxSgiIiIiIiIiIsozLEYREYyMjODn5wcjI6P8bgoRFWLcFxGRPuC+iIgo9/EB5kRERERERERElGd4ZRQREREREREREeUZFqOIiIiIiIiIiCjPsBhFRERERERERER5hsUookKqVatWmDhxYpbj2NvbY/ny5XnSHiIqnLgvIiJ9wH0REVHeYjGKqIDy9vaGQqFI93f79u08a8P169fRo0cP2NvbQ6FQ8ACNqBDSh33RunXr0KJFCxQvXhzFixdHmzZtEBkZmWfLJ6L8pw/7ot27d6NBgwawsrKCmZkZXFxcEBwcnGfLJyIqSFiMIirAvLy8EBsbq/bn4OCQZ8t/8+YNHB0d8d1338HGxibPlktE+iW/90Xh4eHo168fwsLCcObMGVSoUAFt27bFw4cP86wNRJT/8ntfVKJECUyfPh1nzpzBlStXMHToUAwdOhSHDx/OszYQERUULEYRFWBGRkawsbFR+zMwMAAAHD9+HI0aNYKRkRFsbW3x7bff4sOHD5nO68mTJ+jUqRNMTEzg4OCATZs2Zbv8hg0bYtGiRejbty+MjIx0louICpb83hdt2rQJo0ePhouLC6pXr47169cjJSUFoaGhOstIRPovv/dFrVq1Qrdu3VCjRg1UqlQJEyZMQO3atXHq1CmdZSQikoui+d0AItK9hw8fon379vD29sYvv/yCP//8E8OHD4exsTH8/f0znMbb2xv//vsvwsLCUKxYMYwfPx5PnjzJ24YTkazk177ozZs3eP/+PUqUKKGDFERU0OXHvkgQBBw7dgw3b97EggULdJSEiEg+WIwiKsB+++03KJVK1et27dphx44dWLNmDSpUqIDvv/8eCoUC1atXx7///otvvvkGM2fORJEi6hdF3rp1CwcPHkRkZCQaNmwIAPjpp59Qo0aNPM1DRAWTvu2LvvnmG5QtWxZt2rT59HBEVGDow77oxYsXKFeuHBITE2FgYIA1a9bAw8NDt0GJiGSAxSiiAszd3R1r165VvTYzMwMAREdHo2nTplAoFKr3mjVrhoSEBPzzzz+oWLGi2nyio6NRtGhR1K9fXzWsevXqsLKyyt0ARCQL+rQv+u6777B161aEh4fD2Ng4h4mIqCDSh32Rubk5oqKikJCQgNDQUEyePBmOjo5o1arVp4UjIpIZFqOICjAzMzNUrlw5v5tBRIWcvuyLFi9ejO+++w5Hjx5F7dq187s5RJTH9GFfVKRIEVUbXFxcEB0djfnz57MYRUT0ET7AnEiGatSogTNnzkAQBNWwiIgImJubo3z58unGr169Oj58+IALFy6oht28eRPx8fF50Vwikqm83BctXLgQs2fPxqFDh9CgQQOdtJ+I5CE/j4tSUlKQmJiYo3YTEckZi1FEMjR69Gj8/fffGDduHP7880/s27cPfn5+mDx5crrnIgBAtWrV4OXlhS+++ALnzp3DhQsX4OPjAxMTkyyXk5SUhKioKERFRSEpKQkPHz5EVFQUbt++nVvRiKgAyat90YIFC+Dr64uff/4Z9vb2ePToER49eoSEhITcikZEBUhe7Yvmz5+PkJAQ3L17F9HR0ViyZAmCg4MxcODA3IpGRFRgsRhFJEPlypXD77//jsjISNSpUwcjR47E559/jhkzZmQ6zYYNG1C2bFm4ubmhe/fuGDFiBEqXLp3lcv7991/UrVsXdevWRWxsLBYvXoy6devCx8dH15GIqADKq33R2rVrkZSUhJ49e8LW1lb1t3jxYl1HIqICKK/2Ra9fv8bo0aNRs2ZNNGvWDLt27cLGjRt5XERElAGFkPZ6VSIiIiIiIiIiolzEK6OIiIiIiIiIiCjPsBhFRERERERERER5hsUoIiIiIiIiIiLKMyxGERERERERERFRnmExioiIiIiIiIiI8gyLUURERERERERElGdYjCIiIiIiIiIiojzDYhQREREREREREeUZFqOIiIhIlvz9/eHi4pLfzSAiIiKij7AYRUREVIh5e3tDoVBAoVCgWLFiKFOmDDw8PPDzzz8jJSVFq3kFBgbCysoqdxqaA1OmTEFoaKhW09jb22P58uW50yAiIiIiAsBiFBERUaHn5eWF2NhYxMTE4ODBg3B3d8eECRPQsWNHfPjwIb+bl2NKpRIlS5bM72YQERER0UdYjCIiIirkjIyMYGNjg3LlyqFevXqYNm0a9u3bh4MHDyIwMFA13tKlS+Hs7AwzMzNUqFABo0ePRkJCAgAgPDwcQ4cOxYsXL1RXWvn7+wMAgoOD0aBBA5ibm8PGxgb9+/fHkydPsmyTvb09Zs+ejX79+sHMzAzlypXD6tWr1cZ58OABunTpAqVSCQsLC/Tu3RuPHz9Wvf/xbXre3t7o2rUrFi9eDFtbW5QsWRJjxozB+/fvAQCtWrXC/fv3MWnSJFUGALh//z46deqE4sWLw8zMDDVr1sTvv/+e09VNREREVOixGEVERETpfPbZZ6hTpw52796tGlakSBGsXLkS169fR1BQEI4dO4avv/4aAODq6orly5fDwsICsbGxiI2NxZQpUwAA79+/x+zZs3H58mXs3bsXMTEx8Pb2zrYNixYtQp06dXDp0iV8++23mDBhAkJCQgAAKSkp6NKlC549e4bjx48jJCQEd+/eRZ8+fbKcZ1hYGO7cuYOwsDAEBQUhMDBQVXDbvXs3ypcvj1mzZqkyAMCYMWOQmJiIEydO4OrVq1iwYAGUSqW2q5SIiIiI/l/R/G4AERER6afq1avjypUrqtcTJ05U/dve3h5z5szByJEjsWbNGhgaGsLS0hIKhQI2NjZq8xk2bJjq346Ojli5ciUaNmyIhISELIs6zZo1w7fffgsAqFq1KiIiIrBs2TJ4eHggNDQUV69exb1791ChQgUAwC+//IKaNWvi/PnzaNiwYYbzLF68OL7//nsYGBigevXq6NChA0JDQzF8+HCUKFECBgYGqiu4Uj148AA9evSAs7OzKgMRERER5RyvjCIiIqIMCYKgulUNAI4ePYrWrVujXLlyMDc3x6BBgxAXF4c3b95kOZ8LFy6gU6dOqFixIszNzeHm5gZALPJkpWnTpuleR0dHAwCio6NRoUIFVSEKAJycnGBlZaUaJyM1a9aEgYGB6rWtrW22twyOHz8ec+bMQbNmzeDn56dWoCMiIiIi7bEYRURERBmKjo6Gg4MDACAmJgYdO3ZE7dq1sWvXLly4cEH1DKekpKRM5/H69Wt4enrCwsICmzZtwvnz57Fnz55sp8stxYoVU3utUCiy/dVAHx8f3L17F4MGDcLVq1fRoEEDrFq1KjebSURERCRrLEYRERFROseOHcPVq1fRo0cPAOLVTSkpKViyZAmaNGmCqlWr4t9//1WbxtDQEMnJyWrD/vzzT8TFxeG7775DixYtUL169WyvREp19uzZdK9r1KgBAKhRowb+/vtv/P3336r3b9y4gfj4eDg5OWmdN6sMAFChQgWMHDkSu3fvxpdffol169bleBlEREREhR2LUURERIVcYmIiHj16hIcPH+LixYuYN28eunTpgo4dO2Lw4MEAgMqVK+P9+/dYtWoV7t69i+DgYPzwww9q87G3t0dCQgJCQ0Px33//4c2bN6hYsSIMDQ1V0+3fvx+zZ8/WqF0RERFYuHAhbt26hdWrV2PHjh2YMGECAKBNmzZwdnbGgAEDcPHiRURGRmLw4MFwc3NDgwYNcrwu7O3tceLECTx8+BD//fcfAPFZWYcPH8a9e/dw8eJFhIWFqYpiRERERKQ9FqOIiIgKuUOHDsHW1hb29vbw8vJCWFgYVq5ciX379qmer1SnTh0sXboUCxYsQK1atbBp0ybMnz9fbT6urq4YOXIk+vTpA2trayxcuBDW1tYIDAzEjh074OTkhO+++w6LFy/WqF1ffvkl/vjjD9StWxdz5szB0qVL4enpCUC8vW7fvn0oXrw4WrZsiTZt2sDR0RHbtm37pHUxa9YsxMTEoFKlSrC2tgYAJCcnY8yYMahRowa8vLxQtWpVrFmz5pOWQ0RERFSYKQRBEPK7EURERERp2dvbY+LEiWq/4EdERERE8sAro4iIiIiIiIiIKM+wGEVERERERERERHmGt+kREREREREREVGe4ZVRRERERERERESUZ1iMIiIiIiIiIiKiPMNiFBERERERERER5RkWo4iIiIiIiIiIKM+wGEVERERERERERHmGxSgiIiIiIiIiIsozLEYREREREREREVGeYTGKiIiIiIiIiIjyDItRRERERERERESUZ1iMIiIiIiIiIiKiPMNiFBERERERERER5RkWo4iIiIiIiIiIKM+wGEVERERERERERHmGxSgiIiIiIiIiIsozLEYREREREREREVGeYTGKiIiIiIiIiIjyDItRRERERERERESUZ1iMIiIiIiIiIiKiPMNiFBERERERERER5RkWo4iIiIiIiIiIsmBvb4/ly5drPH54eDgUCgXi4+NzrU2ZCQwMhJWVVZ4vVxssRhERERERERGRTs2ZMwcKhQJz5szJ0fSvXr1Cs2bNYGlpqdV0CoUiyz9/f/8ctef8+fMYMWKExuO7uroiNjZW6/bnF22LbZ+qaJ4tiYiIiIiIiIhkb86cOfD19cXs2bMxY8YMrad/9eoVvLy8cO3aNYSEhGg1bWxsrOrf27Ztw8yZM3Hz5k3VMKVSqfq3IAhITk5G0aLZl0asra21aoehoSFsbGy0mqYw4ZVRRERERERERKQTui5ENWrUSKvpbWxsVH+WlpZQKBSq13/++SfMzc1x8OBB1K9fH0ZGRjh16hTu3LmDLl26oEyZMlAqlWjYsCGOHj2qNt+PrxxSKBRYv349unXrBlNTU1SpUgX79+9Xvf/xbXqpt84dPnwYNWrUgFKphJeXl1rx7MOHDxg/fjysrKxQsmRJfPPNNxgyZAi6du2aZebAwEBUrFgRpqam6NatG+Li4tTezy5fq1atcP/+fUyaNEl1BRkAxMXFoV+/fihXrhxMTU3h7OyMLVu2aPNxZIrFKCIiIiIiIiL6ZPldiNLUt99+i++++w7R0dGoXbs2EhIS0L59e4SGhuLSpUvw8vJCp06d8ODBgyznExAQgN69e+PKlSto3749BgwYgGfPnmU6/ps3b7B48WIEBwfjxIkTePDgAaZMmaJ6f8GCBdi0aRM2bNiAiIgIvHz5Env37s2yDefOncPnn3+OsWPHIioqCu7u7ulujcwu3+7du1G+fHnMmjULsbGxqgLZu3fvUL9+fRw4cADXrl3DiBEjMGjQIERGRmbZJo0IRERERERERESfYPbs2QIAYfbs2Tma/uXLl4Krq6tgYWEhnDt3Tidt2rBhg2Bpaal6HRYWJgAQ9u7dm+20NWvWFFatWqV6bWdnJyxbtkz1GoAwY8YM1euEhAQBgHDw4EG1ZT1//lzVFgDC7du3VdOsXr1aKFOmjOp1mTJlhEWLFqlef/jwQahYsaLQpUuXTNvZr18/oX379mrD+vTpo5Y7J/ky06FDB+HLL7/Mdrzs8MooIiIiIiIiIsqxgnJFVKoGDRqovU5ISMCUKVNQo0YNWFlZQalUIjo6Otsro2rXrq36t5mZGSwsLPDkyZNMxzc1NUWlSpVUr21tbVXjv3jxAo8fP1bLbmBggPr162fZhujoaDRu3FhtWNOmTXWSLzk5GbNnz4azszNKlCgBpVKJw4cPZzudJvgAcyIiIiIiIiLKkYJWiALEwlFaU6ZMQUhICBYvXozKlSvDxMQEPXv2RFJSUpbzKVasmNprhUKBlJQUrcYXBEHL1msvp/kWLVqEFStWYPny5XB2doaZmRkmTpyY7XSaYDGKiIiIiIiIiLRWEAtRGYmIiIC3tze6desGQLySKCYmJk/bYGlpiTJlyuD8+fNo2bIlAPHKpIsXL8LFxSXT6WrUqIFz586pDTt79qzaa03yGRoaIjk5Od10Xbp0wcCBAwEAKSkpuHXrFpycnHISUQ1v0yMiIiIiIiIircilEAUAVapUwe7duxEVFYXLly+jf//+WV7hlFvGjRuH+fPnY9++fbh58yYmTJiA58+fq37dLiPjx4/HoUOHsHjxYvz111/4/vvvcejQIbVxNMlnb2+PEydO4OHDh/jvv/9U04WEhOD06dOIjo7GF198gcePH+skK4tRRERERERERKQxORWiAGDp0qUoXrw4XF1d0alTJ3h6eqJevXp53o5vvvkG/fr1w+DBg9G0aVMolUp4enrC2Ng402maNGmCdevWYcWKFahTpw6OHDmS7jPRJN+sWbMQExODSpUqwdraGgAwY8YM1KtXD56enmjVqhVsbGzQtWtXnWRVCHlxgyIREREREREREWksJSUFNWrUQO/evTF79uz8bo5O8ZlRRERERERERET57P79+zhy5Ajc3NyQmJiI77//Hvfu3UP//v3zu2k6x9v0iIiIiIiIiIjyWZEiRRAYGIiGDRuiWbNmuHr1Ko4ePYoaNWrkd9N0jrfpERERERERERFRnuGVUURERERERERElGdYjCIiIiIiIiIiojzDYhQREREREREREeUZFqOIiIj0kL29Pby9vdWG/fXXX2jbti0sLS2hUCiwd+9eBAYGQqFQICYmRmfL9vb2hr29vc7mVxi1atUKrVq1Ur2OiYmBQqFAYGBgttPmxvrPje2EiIiIKKdYjCIiokLp6tWr6NmzJ+zs7GBsbIxy5crBw8MDq1atUhtv3rx52Lt3b6604fTp0/D390d8fLxG4w8ZMgRXr17F3LlzERwcjAYNGuRKu7S1Z88etGvXDqVKlYKhoSHKli2L3r1749ixY/ndtEInN7dXIiIiIl3hr+kREVGhc/r0abi7u6NixYoYMmQIbGxs8Pfff+Ps2bO4c+cObt++rRpXqVSiZ8+eGl3Roq3Fixfjq6++wr1799JdCZOYmIgiRYqgWLFiAIC3b9/C1NQU06dPx5w5c1TjJScn4/379zAyMoJCodBJu7y9vREeHp7tVTSCIGDYsGEIDAxE3bp10bNnT9jY2CA2NhZ79uzBhQsXEBERAVdXV520qyBJvSoqPDwcgLiuEhMTUaxYMRgYGGQ5rabrPyOZba+5sZ0QERER5VTR/G4AERFRXps7dy4sLS1x/vx5WFlZqb335MmTHM/39evXMDMz+8TWiYyMjNReP336FADStdfAwCDb4kZuWbJkCQIDAzFx4kQsXbpUrcgxffp0BAcHo2jRzA81dLm+9J1CoYCxsXG+LT8/txMiIqLCLiYmBg4ODrh06RJcXFzyuzl6gbfpERFRoXPnzh3UrFkzXWEHAEqXLq36t0KhwOvXrxEUFASFQgGFQqF6jpO/vz8UCgVu3LiB/v37o3jx4mjevDkA4MqVK/D29oajoyOMjY1hY2ODYcOGIS4uTjVvf39/fPXVVwAABwcH1fxTr4ZJ+8wof39/2NnZAQC++uorKBQK1ZVUmT0L6ODBg2jRogXMzMxgbm6ODh064Pr16+ny7t27F7Vq1YKxsTFq1aqFPXv2aLQO3759i/nz56N69epYvHhxhlfbDBo0CI0aNVJr5/HjxzF69GiULl0a5cuXV427Zs0a1KxZE0ZGRihbtizGjBmT7vbFv/76Cz169ICNjQ2MjY1Rvnx59O3bFy9evFCNExISgubNm8PKygpKpRLVqlXDtGnTsszSsWNHODo6Zvhe06ZN1W6H3LBhAz777DOULl0aRkZGcHJywtq1a7NdX5k9M0rT9b948WK4urqiZMmSMDExQf369bFz5061cbLaXjPbTjRZ761atUKtWrVw48YNuLu7w9TUFOXKlcPChQuzzU1ERPIUEBCg9jdt2jRUrFgRxsbGGD58eLr3NfkbPnw4jI2NUbFiRUybNk3tPW2kfgdm9ufv75/j3KnP7MwL3t7e6Nq1a54sKz/wyigiIip07OzscObMGVy7dg21atXKdLzg4GD4+PigUaNGGDFiBACgUqVKauP06tULVapUwbx585B653tISAju3r2LoUOHwsbGBtevX8ePP/6I69ev4+zZs1AoFOjevTtu3bqFLVu2YNmyZShVqhQAwNraOl07unfvDisrK0yaNAn9+vVD+/btoVQqs2z3kCFD4OnpiQULFuDNmzdYu3YtmjdvjkuXLqkKWUeOHEGPHj3g5OSE+fPnIy4uDkOHDlUrEmXm1KlTePbsGSZOnKjVFTejR4+GtbU1Zs6cidevXwMQi20BAQFo06YNRo0ahZs3b2Lt2rU4f/48IiIiUKxYMSQlJcHT0xOJiYkYN24cbGxs8PDhQ/z222+Ij4+HpaUlrl+/jo4dO6J27dqYNWsWjIyMcPv2bURERGTZpj59+mDw4ME4f/48GjZsqBp+//59nD17FosWLVINW7t2LWrWrInOnTujaNGi+PXXXzF69GikpKRgzJgxGq8HQLv1v2LFCnTu3BkDBgxAUlIStm7dil69euG3335Dhw4dAGi2vaalyXpP9fz5c3h5eaF79+7o3bs3du7ciW+++QbOzs5o166dVrmJiEheEhMTsXHjRjx58gSDBg3S6DjiY//88w+Cg4NRunRpDBw4MN0V4tqIjY1V/Xvbtm2YOXMmbt68qRqW1TEU5R0Wo4iIqNCZMmUK2rVrBxcXFzRq1AgtWrRA69at4e7urnYCPnDgQIwcORKOjo4YOHBghvOqU6cONm/erDZs9OjR+PLLL9WGNWnSBP369cOpU6fQokUL1K5dG/Xq1cOWLVvQtWvXLH89rXbt2rCwsMCkSZNQr169TNsCAAkJCRg/fjx8fHzw448/qoYPGTIE1apVw7x581TDv/nmG5QpUwanTp2CpaUlAMDNzQ1t27ZVXYmVmejoaACAs7NzluN9rESJEggNDVUVsJ4+fYr58+ejbdu2OHjwIIoUES/arl69OsaOHYuNGzdi6NChuHHjBu7du4cdO3agZ8+eqvnNnDlT9e+QkBAkJSXh4MGDquKeJrp06QIjIyNs27ZNrRi1fft2KBQK9O7dWzXs+PHjMDExUb0eO3YsvLy8sHTpUq2LUdqs/1u3bqVbbr169bB06VJVMUqT7TWVpus91b///otffvkFgwYNAgB8/vnnsLOzw08//cRiFBFRIaZvhSgAsLGxUf079ReI0w5bv349lixZonpm5/jx4zF69GgAQFJSEiZPnoxdu3bh+fPnKFOmDEaOHImpU6eqjtW6desGQPyfm5k93zEyMhJffPEFoqOjUatWLUyfPl3t/eTkZIwYMQLHjh3Do0ePULFiRYwePRoTJkwAIP4Po6CgIABQXX0eFhaGVq1a4ZtvvsGePXvwzz//wMbGBgMGDMDMmTPVjmELAt6mR0REhY6HhwfOnDmDzp074/Lly1i4cCE8PT1Rrlw57N+/X6t5jRw5Mt2wtEWDd+/e4b///kOTJk0AABcvXvy0xmcjJCQE8fHx6NevH/777z/Vn4GBARo3boywsDAA4v81jIqKwpAhQ1SFEEBcN05OTtku5+XLlwAAc3Nzrdo3fPhwtSupjh49iqSkJEycOFFVEEkdz8LCAgcOHAAAVRsPHz6MN2/eZDjv1Nsu9+3bh5SUFI3bZGFhgXbt2mH79u1I+7su27ZtQ5MmTVCxYkXVsLSf7YsXL/Dff//Bzc0Nd+/eVbtdMDvarv+0y33+/DlevHiBFi1a5Hh70nS9p1IqlWoFLkNDQzRq1Ah3797N0fKJiKjg08dCVHY2bdqEmTNnYu7cuYiOjsa8efPg6+urKvysXLkS+/fvx/bt23Hz5k1s2rRJVYQ6f/48APGW/djYWNXrjyUkJKBjx45wcnLChQsX4O/vjylTpqiNk5KSgvLly2PHjh24ceMGZs6ciWnTpmH79u0AxP9x2rt3b3h5eSE2NhaxsbGqH4QxNzdHYGAgbty4gRUrVmDdunVYtmxZbqyuXMViFBERFUoNGzbE7t278fz5c0RGRmLq1Kl49eoVevbsiRs3bmg8HwcHh3TDnj17hgkTJqBMmTIwMTGBtbW1ajxtChY58ddffwEAPvvsM1hbW6v9HTlyRPWA9vv37wMAqlSpkm4e1apVy3Y5FhYWAIBXr15p1b6P11dqOz5epqGhIRwdHVXvOzg4YPLkyVi/fj1KlSoFT09PrF69Wm199unTB82aNYOPjw/KlCmDvn37Yvv27WqFqUePHqn9vX37VjXt33//jTNnzgAQnyt24cIF9OnTR61dERERaNOmDczMzGBlZQVra2vVM6m0+Wy1Xf+//fYbmjRpAmNjY5QoUQLW1tZYu3ZtjrcnTdd7qvLly6d7Lljx4sXx/PnzHC2fiIgKtoJYiAIAPz8/LFmyBN27d4eDgwO6d++OSZMm4X//+x8A4MGDB6hSpQqaN28OOzs7NG/eHP369QMgPUrBysoKNjY2GT5aAQA2b96MlJQU/PTTT6hZsyY6duyoek5oqmLFiiEgIAANGjSAg4MDBgwYgKFDh6qKUUqlEiYmJjAyMoKNjQ1sbGxgaGgIAJgxYwZcXV1hb2+PTp06YcqUKarpChLepkdERIWaoaEhGjZsiIYNG6Jq1aoYOnQoduzYAT8/P42mT3vFSqrevXvj9OnT+Oqrr+Di4gKlUomUlBR4eXlpdcVOTqTOPzg4WO2S9FRZ/bqdNqpXrw4AuHr1qlYP18xofWlqyZIl8Pb2xr59+3DkyBGMHz8e8+fPx9mzZ1G+fHmYmJjgxIkTCAsLw4EDB3Do0CFs27YNn332GY4cOQIDAwPY2tqqzXPDhg3w9vZGp06dYGpqiu3bt8PV1RXbt29HkSJF0KtXL9W4d+7cQevWrVG9enUsXboUFSpUgKGhIX7//XcsW7Ys1z7bkydPonPnzmjZsiXWrFkDW1tbFCtWDBs2bEh3i2huyey5YGmvJCMiosKjIBaiXr9+jTt37uDzzz/H8OHDVcM/fPigukrZ29sbHh4eqFatGry8vNCxY0e0bdtWq+VER0ejdu3aar+i27Rp03TjrV69Gj///DMePHiAt2/fIikpSaNf2tu2bRtWrlyJO3fuICEhAR8+fFD9T8KChMUoIiKi/5f6q2lpH3yZ0a/EZeX58+cIDQ1FQECA2vOMUq9YSkvbeWsi9YHVpUuXRps2bTIdL/WZRBm1K+1DPjPTvHlzFC9eHFu2bMG0adO0eoh5Ru24efOm2i/aJSUl4d69e+kyODs7w9nZGTNmzMDp06fRrFkz/PDDD5gzZw4AoEiRImjdujVat26NpUuXYt68eZg+fTrCwsLQpk0bhISEqM2vZs2aAAAzMzN07NgRO3bswNKlS7Ft2za0aNECZcuWVY3766+/IjExEfv371e7dS/11sec5NZk/e/atQvGxsY4fPiw2sH6hg0b0k2r6Tal7XonIiJKq6AVogDx9jkAWLduHRo3bqz2XupxTL169XDv3j0cPHgQR48eRe/evdGmTZt0v2D7qbZu3YopU6ZgyZIlaNq0KczNzbFo0SKcO3cuy+nOnDmDAQMGICAgAJ6enrC0tMTWrVuxZMkSnbYvL/A2PSIiKnTCwsIyvKLj999/B6B+65KZmVm6n7rPSurBzMfzX758ebpxzczMAECr+WfH09MTFhYWmDdvHt6/f5/u/adPnwIAbG1t4eLigqCgILVbvUJCQjS6TdHU1BTffPMNoqOj8c0332S4Pjdu3IjIyMgs59OmTRsYGhpi5cqVavP46aef8OLFC9XDuV++fIkPHz6oTevs7IwiRYogMTERgHh75MdS/w9j6jht2rRR+0t7pVSfPn3w77//Yv369bh8+XK6W/Qy+mxfvHiRYVEoO9qsfwMDAygUCiQnJ6uGxcTEZPjT0ppur5qudyIioowUtEIUAJQpUwZly5bF3bt3UblyZbW/tI8RsLCwQJ8+fbBu3Tps27YNu3btUh1jFCtWTO37OCM1atTAlStX8O7dO9Wws2fPqo0TEREBV1dXjB49GnXr1kXlypVx584dtXEMDQ3TLev06dOws7PD9OnT0aBBA1SpUiXdrfUFBa+MIiKiQmfcuHF48+YNunXrhurVqyMpKQmnT5/Gtm3bYG9vr/YrYvXr18fRo0exdOlSlC1bFg4ODun+b1paFhYWaNmyJRYuXIj379+jXLlyOHLkCO7du5du3Pr16wMApk+fjr59+6JYsWLo1KmTqkiVExYWFli7di0GDRqEevXqoW/fvrC2tsaDBw9w4MABNGvWDN9//z0AYP78+ejQoQOaN2+OYcOG4dmzZ1i1ahVq1qyp+r+HWfnqq69w/fp1LFmyBGFhYejZsydsbGzw6NEj7N27F5GRkTh9+nSW87C2tsbUqVMREBAALy8vdO7cGTdv3sSaNWvQsGFD1UOzjx07hrFjx6JXr16oWrUqPnz4gODgYBgYGKBHjx4AgFmzZuHEiRPo0KED7Ozs8OTJE6xZswbly5dH8+bNs83Tvn17mJubY8qUKWrzTdW2bVsYGhqiU6dO+OKLL5CQkIB169ahdOnSalfTaUrT9d+hQwcsXboUXl5e6N+/P548eYLVq1ejcuXKuHLlito8Nd1eNV3vREREGSlohahUAQEBGD9+PCwtLeHl5YXExET88ccfeP78OSZPnoylS5fC1tYWdevWRZEiRbBjxw7Y2NiofiTF3t4eoaGhaNasGYyMjFC8ePF0y+jfvz+mT5+O4cOHY+rUqYiJicHixYvVxqlSpQp++eUXHD58GA4ODggODsb58+fVimL29vY4fPgwbt68iZIlS8LS0hJVqlTBgwcPsHXrVjRs2BAHDhzAnj17cnWd5RZeGUVERIXO4sWL4e7ujt9//x2TJ0/G5MmTERkZidGjR+PcuXOqAw4AWLp0KerXr48ZM2agX79+WLt2bbbz37x5s+oB21OnTkWxYsVw8ODBdOM1bNgQs2fPxuXLl+Ht7Y1+/fqprlz6FP3790doaCjKlSuHRYsWYcKECdi6dStcXFzUCm1eXl7YsWMHkpOTMXXqVOzevRsbNmxQ3a6YnSJFiuCXX37Bzp07UapUKSxevBgjRozAqlWr4ODggPDw8AyfkfAxf39/fP/993jw4AEmTZqE7du3Y8SIEThy5IjqZ4rr1KkDT09P/Prrr5g8eTL8/f2hVCpx8OBB1S8Vdu7cGRUrVsTPP/+MMWPGYPXq1WjZsiWOHTum9ot1mTE2Nkbnzp3x6tUruLu7o3Tp0mrvV6tWDTt37oRCocCUKVPwww8/YMSIEaqfYdaWpuv/s88+w08//YRHjx5h4sSJ2LJlCxYsWKD6aem0tNleNVnvREREuqAPhSgA8PHxwfr167FhwwY4OzvDzc0NgYGBqiKQubk5Fi5ciAYNGqBhw4aIiYnB77//rvrl2SVLliAkJAQVKlRA3bp1M1yGUqnEr7/+iqtXr6Ju3bqYPn06FixYoDbOF198ge7du6NPnz5o3Lgx4uLiMHr0aLVxhg8fjmrVqqFBgwawtrZGREQEOnfujEmTJmHs2LFwcXHB6dOn4evrmwtrKvcpBD55koiIiIiIiIiI8givjCIiIiIiIiIiojzDYhQREREREREREeUZFqOIiIiIiIiIiCjPsBhFRERERERERER5hsUoIiIiIiIiIiLKMyxGERERERERERFRnmExioiIiIiIiIiI8gyLUURERERERERElGeK5ncDSDcCAgK0niYxMREbN27EkydPMGjQIJQvX17refzzzz8IDg5G6dKlMXDgQBgZGWk9j+PHjyMsLAzu7u5wc3PTevq8zuHn55fh8Dlz5sDX1xezZ8/GjBkztG7Dq1ev4OXlhWvXriEkJASNGjXSeh6RkZHw8PBArVq1cOjQIZibm2s9D+YQMYdEmxyZ7YsKWj/PjD7lePfundbTpipo21VmmEPEHOlVrFhRFv28IOyvMjsuAuSzXTGHhDlEutxfERVWvDKqkCpsB0pZ0UUOAPn+hSaXL2bmkMglh1z6ub7lyCm5bFfMIWKOjMmlnxfkHHLZrphDwhwiFqKIdIPFqEKIB0oSXRWiAPCLmTlUmEMil36ujzlyQi7bFXOImCNzcunnBTUHAFlsV3LpH8wh0YccRCRiMaqQ4YGSRJeFKAD8YmYOAMyRllz6uVxyyGW7Yg4Rc+iWXPq5PuRIVdC3K7n0D+aQ6EMOIpKwGFWI8EBJoutCVE7wi1nCHBK55JBLP5dLDrlsV8whYg7dkks/14ccaRXk7Uou/YM5JPqQg4jUsRhVSPBAScJClEQfvpiZQyKXHHLp53LJIZftijlEzCGJjIzUepqPyaWf60OOjxXk7Uou/YM5RPqQg4jSYzGqEOCBkoSFKIk+fDEzh0QuOeTSz+WSQy7bFXOImEOSmuNTyKWf60MOXdCn7Uou/YM59CMHEWWMxSiZ44GSRFc5PgW/mCXMIZFLDgCy6edyyCGX7Yo5RMwhSZsjp+TSz/Uhhy7o23Yll/7BHPmfg4gyx2KUjPFASaLLHDnFL2YJc0jkkiOVXPp5Qc8hl+2KOUTMIfk4R07IpZ/rS45PpY/blVz6B3OwEEWkz1iMyqGYmBgoFApERUUBAMLDw6FQKBAfH5+v7UrFAyWJrnPkBL+YJcwhkUuOtOTSzwtyDrlsV8whYg4JbyWW6FOOTyGX7Yo5RMxBRNoolMWop0+fYtSoUahYsSKMjIxgY2MDT09PRERE5Hierq6uiI2NhaWlJQAgMDAQVlZW2U4XGxuL/v37o2rVqihSpAgmTpyY4zak4oGSRB9y8ItZwhwSueT4VHLp5/qSA4Astiu59A/mEOlLDrn0c33LkVNy2a6YQ8QcRKStovndgPzQo0cPJCUlISgoCI6Ojnj8+DFCQ0MRFxeX43kaGhrCxsZG6+kSExNhbW2NGTNmYNmyZTleftr58UBJpA85+MUsYQ6JXHJ8Krn0c33Jkaqgb1dy6R/MIdKXHHLp5/qYIyfksl0xh4g5iCgnCt2VUfHx8Th58iQWLFgAd3d32NnZoVGjRpg6dSo6d+6sGk+hUGDt2rVo164dTExM4OjoiJ07d2Y637S36YWHh2Po0KF48eIFFAoFFAoF/P39M5zO3t4eK1aswODBg1VXVeUUD5Qk+pCDX8wS5pDIJcenkks/15ccaRXk7Uou/YM5RPqSQy79XC455LJdMYeIOYgopwpdMUqpVEKpVGLv3r1ITEzMclxfX1/06NEDly9fxoABA9C3b19ER0dnuwxXV1csX74cFhYWiI2NRWxsLKZMmaKrCEhMTMTLly/V/l6/fq0XBxhyOVBiIUrEHBLmkMyZM0fradKSSz/XlxyfSp+2K7n0D+bQnxxy6edyySGX7Yo5RMxBRJ9CIQiCkN+NyGu7du3C8OHD8fbtW9SrVw9ubm7o27cvateurRpHoVBg5MiRWLt2rWpYkyZNUK9ePaxZswYxMTFwcHDApUuX4OLigvDwcLi7u+P58+ewsrJCYGAgJk6cqNUDzVu1agUXFxcsX748y/H8/f0REBCgNszPzy/Tq6+IiIiIiIiIiPRFobsyChCfGfXvv/9i//798PLyQnh4OOrVq4fAwEC18Zo2bZrutSZXRuW2qVOn4sWLF2p/U6dOze9mERERERERERFlq1AWowDA2NgYHh4e8PX1xenTp+Ht7Q0/P7/8bpZGjIyMYGFhofaXH7d2EBERERERERFpq9AWoz7m5OSE169fqw07e/Zsutc1atTQaH6GhoZITk7WWfuIiIiIiIiIiOSgaH43IK/FxcWhV69eGDZsGGrXrg1zc3P88ccfWLhwIbp06aI27o4dO9CgQQM0b94cmzZtQmRkJH766SeNlmNvb4+EhASEhoaiTp06MDU1hampaYbjRkVFAQASEhLw9OlTREVFwdDQEE5OTp+UlYiIiIiIiIhI3xS6YpRSqUTjxo2xbNky3LlzB+/fv0eFChUwfPhwTJs2TW3cgIAAbN26FaNHj4atrS22bNmicYHI1dUVI0eORJ8+fRAXF5flA8br1q2r+veFCxewefNm2NnZISYmJqcxiYiIiIiIiIj0UqH8NT1NKBQK7NmzB127ds3vphARERERERERyQafGUVERERERERERHmGxSgiIiIiIiIiIsozhe6ZUZri3YtERERERERERLrHK6OIiIiIiIiIiCjPsBhFRERERERERER5hsUoIiIiIiIiIiLKMyxGERERERERERFRnmExioiIiIiIiIiI8gx/TU9GIiMj4eHhgVq1auHQoUMwNzfXeh5z5syBr68vZs+ejRkzZmg9/atXr+Dl5YVr164hJCQEjRo10noeBTVHQECA2ut//vkHwcHBKF26NAYOHAgjIyOt23H8+HGEhYXB3d0dbm5uWk+fmJiIjRs34smTJxg0aBDKly+v9Tz0Mce6deu0nkdB3a4+xhwS5sjYx/siTehjPy8I+ys/P78Mhzdr1qzAb1dy6R/MIcnrHBntiwpiP8+IvuXI6a+AF8TtKiP6loO/yk6kGV4ZJRP8IpDoQw4eKEn0IYdctivmkDCH7siln+tDjlQFfbuSS/9gDok+5JBLP9fHHDkhl+1KH3MQkWZYjJIJfhGI9CEHD5Qk+pBDLtsVc0iYQ3fk0s/1IUdaBXm7kkv/YA6JPuSQSz+XSw65bFdyyUFUWGldjAoKCsKBAwdUr7/++mtYWVnB1dUV9+/f12njSHP8ItCPHPpwgCGXAyUWoiTMIWEO0atXr7Se5mNy6ef6kONjBXm7kkv/YA6RPuSQSz+XSw65bFdyyUFUmGldjJo3bx5MTEwAAGfOnMHq1auxcOFClCpVCpMmTdJ5A0kz/CLI/xz6cIAhlwMlFqIkzCFhDlFqjk8hl36uDzl0QZ+2K7n0D+bQjxxy6edyySGX7UouOYgKO60fYP7333+jcuXKAIC9e/eiR48eGDFiBJo1a4ZWrVrpun2kIX4R5G8OAPl+gCGXAyUWoiTMIWEOUdocOSWXfq4POXRB37YrufQP5sj/HHLp53LJIZftSi45iCgHV0YplUrExcUBAI4cOQIPDw8AgLGxMd6+favb1lGu4ReBSFeFKAA8UIJ+5JDLdsUcEuYQfZwjJ+TSz/Ulx6fSx+1KLv2DOfL/RFsu/VwOOeSyXcklBxGJtC5GeXh4wMfHBz4+Prh16xbat28PALh+/Trs7e113T7KBfwiEOmyEAWAB0p6kEMu2xVzSJhDxFuJJfqU41PIZbtiDglzqJNLPy/oOeSyXcklBxFJtC5GrV69Gk2bNsXTp0+xa9culCxZEgBw4cIF9OvXT+cNJN3iF4FIVznS4oFS/uYA+KuSqZhDxBwSufRzfcuRU3LZrphDwhzpyaWfF+Qcctmu5JKDiNRp/cwoKysrfP/99+mGBwQE6KRBlHv4RSDSZY4XL15oPW0qHiiJdFWIAvirkgBzpGIOiVz6uT7myAm5bFfMIWGOjMmlnxfUHABksV3JtX8QUQ6KUQAQHx+PyMhIPHnyBCkpKarhCoUixwdnlLv4RSDSdY6c4oGSSJeFKIC/KskcIuaQyKWfyyWHXLYr5pAwh+7IpZ/rQ45UBX27Yv8gkjeti1G//vorBgwYgISEBFhYWEChUKjeYzFKP/GLQJQbOXKCB0oiXReiAP6qJHMwR1py6edyySGX7Yo5JMyhO3Lp5/qQI62CvF2xfxDJn9bPjPryyy8xbNgwJCQkID4+Hs+fP1f9PXv2LDfaSJ+AXwQifcnBAyVRbhSickIu2xVziJhDIpd+LpccctmumEPCHKJXr15pPc3H5NLP9SHHxwrydiWH/kFEWdO6GPXw4UOMHz8epqamudEe0iF+EYj0JQcPlEQsREnYPyTMIWIhSiKXHHLZrphDwhyi1ByfQi79XB9y6II+bVcFvX8QUfa0LkZ5enrijz/+yI22kA7xi0CkLzl4oCTSVY5PJZftijlEzKFOLv1cDjnksl0xh4Q5RGlz5JRc+rk+5NAFfduuCnL/ICLNaP3MqA4dOuCrr77CjRs34OzsjGLFiqm937lzZ501jnKGXwQifcnBAyWRLnOsW7dO62lTyWW7Yg4Rc6Qnl35e0HPIZbtiDglziD7OkRNy6ef6kuNT6eN2VZD7R06WS1QYaV2MGj58OABg1qxZ6d5TKBRITk7+9FYVADExMXBwcMClS5fg4uKC8PBwuLu74/nz57Cyssq3dvGLQKQvOXigJNJ1jpySy3bFHCLmyJhc+nlBziGX7Yo5JMwh4q3EEn3KMW/ePK2nTSWX7UqfckRERGg9LVFhpPVteikpKZn+FZRC1NOnTzFq1ChUrFgRRkZGsLGxgaen5yftOFxdXREbGwtLS0sAQGBgoEZFqd27d8PDwwPW1tawsLBA06ZNcfjw4Ry1gV8EIn3JwQMlUW7kyAm5bFfMIWKOzMmlnxfUHABksV3JpX8wh0Qfcsiln+tbjpySy3albzmISDNaF6PkoEePHrh06RKCgoJw69Yt7N+/H61atUJcXFyO52loaAgbGxsoFAqtpjtx4gQ8PDzw+++/48KFC3B3d0enTp1w6dIlrebDLwKRvuTggZJIX3LIZbtiDhFz6JZc+rk+5EhV0LcrufQP5pDoQw659HN9zJETctmu9DEHEWkmR8Wo48ePo1OnTqhcuTIqV66Mzp074+TJk7puW66Ij4/HyZMnsWDBAri7u8POzg6NGjXC1KlT1Z53pVAosHbtWrRr1w4mJiZwdHTEzp07M51veHg4FAoF4uPjER4ejqFDh+LFixdQKBRQKBTw9/fPcLrly5fj66+/RsOGDVGlShXMmzcPVapUwa+//qpVLn4R6E8OHiiJ9CWHXLYr5hAxh27JpZ/rQ460CvJ2JZf+wRwSfcghl34ulxxy2a7kkoOosNK6GLVx40a0adMGpqamGD9+PMaPHw8TExO0bt0amzdvzo026pRSqYRSqcTevXuzfeCfr68vevTogcuXL2PAgAHo27cvoqOjs12Gq6srli9fDgsLC8TGxiI2NhZTpkzRqH0pKSl49eoVSpQoodH4qfhFoB859OEAQy4HSixESZhDxBySyMhIraf5mFz6uT7k+FhB3q7k0j+YQ6QPOeTSz+WSQy7blVxyEBVmWhej5s6di4ULF2Lbtm2qYtS2bdvw3XffYfbs2bnRRp0qWrQoAgMDERQUBCsrKzRr1gzTpk3DlStX0o3bq1cv+Pj4oGrVqpg9ezYaNGiAVatWZbsMQ0NDWFpaQqFQwMbGBjY2NlAqlRq1b/HixUhISEDv3r0zHScxMREvX75U+/vqq680mn9a/CKQsBAlkUsOuWxXzCFiDklqjk8hl36uDzl0QZ+2K7n0D+bQjxxy6edyySGX7UouOYgKO4UgCII2ExgZGeH69euoXLmy2vDbt2+jVq1aePfunU4bmFvevXuHkydP4uzZszh48CAiIyOxfv16eHt7AxBv0wsKCsLgwYNV00yaNAlRUVEICwvL9tf0AgMDMXHiRMTHx2vcps2bN2P48OHYt28f2rRpk+l4/v7+CAgIUBvm5+eX6a2ARERERERERET6QusroypUqIDQ0NB0w48ePYoKFSropFF5wdjYGB4eHvD19cXp06fh7e0NPz+/fGvP1q1b4ePjg+3bt2dZiAKAqVOn4sWLF2p/U6dOzaOWEhERERERERHlXFFtJ/jyyy8xfvx4REVFwdXVFQAQERGBwMBArFixQucNzCtOTk7Yu3ev2rCzZ8+qXRl19uxZ1K1bV6P5GRoaIjk5WaNxt2zZgmHDhmHr1q3o0KFDtuMbGRnl6PJgIiIiIiIiIqL8pnUxatSoUbCxscGSJUuwfft2AECNGjWwbds2dOnSRecN1LW4uDj06tULw4YNQ+3atWFubo4//vgDCxcuTNf+HTt2oEGDBmj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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_stratified_cross_validation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 交叉验证相关设定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kfold = KFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.   , 1.   , 0.867, 0.933, 0.833])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(logreg, iris.data, iris.target, cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kfold = KFold(n_splits=3)\n",
    "cross_val_score(logreg, iris.data, iris.target, cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.98, 0.96, 0.96])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kfold = KFold(n_splits=3, shuffle=True, random_state=0)\n",
    "cross_val_score(logreg, iris.data, iris.target, cv=kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 随机打乱数据：在上面的代码中，我们设置了 `shuffle=True`，这意味着在进行交叉验证之前，我们会随机打乱数据的顺序。\n",
    "- 这可以帮助我们得到更稳定的性能评分，特别是当数据的顺序有某种特定的模式时。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 留一交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of cv iterations:  150\n",
      "mean accuracy:  0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import LeaveOneOut\n",
    "loo = LeaveOneOut()\n",
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=loo)\n",
    "print(\"number of cv iterations: \", len(scores))\n",
    "print(\"mean accuracy: \", scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: ``LeaveOneOut()`` is equivalent to ``KFold(n_splits=n)`` and\n",
    "``LeavePOut(p=1)`` where ``n`` is the number of samples.\n",
    "\n",
    "- 留一法交叉验证的优点是利用了最大量的数据进行训练，因此通常可以得到较低偏差的性能估计。\n",
    "- 然而，它的缺点是计算成本较高，特别是当样本数量较大时。因此，留一法通常在样本数量较少，且计算资源充足的情况下使用。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 乱序分割交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.947, 0.947, 0.987, 0.947, 0.96 , 0.987, 0.973, 0.933, 0.96 ,\n",
       "       0.96 ])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "shuffle_split = ShuffleSplit(test_size=.5, train_size=.5, n_splits=10)\n",
    "cross_val_score(logreg, iris.data, iris.target, cv=shuffle_split)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 随机排列交叉验证的优点是它可以处理大数据集，并且可以灵活地设置训练集和测试集的大小。\n",
    "- 此外，由于每次迭代都会随机打乱样本的顺序，因此可以得到更稳定的性能估计。\n",
    "- 然而，它的缺点是每次迭代的训练集和测试集可能会有重叠，这可能会导致性能估计的高方差。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分组与交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "hide_input": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Group_KFold\n"
     ]
    },
    {
     "data": {
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",
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Group_KFold\")\n",
    "mglearn.plots.plot_label_kfold()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GroupKFold\n",
    "from mglearn.datasets import make_blobs\n",
    "# create synthetic dataset\n",
    "X, y = make_blobs(n_samples=12, random_state=0)\n",
    "# assume the first three samples belong to the same group, then the next four etc.\n",
    "labels = [0, 0, 0, 1, 1, 1, 1, 2, 2, 3, 3, 3]\n",
    "cross_val_score(logreg, X, y, groups=labels, cv=GroupKFold(n_splits=3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分组K折交叉验证（GroupKFold）特别适合处理数据中存在自然分组的情况。以下是一些具体的例子：\n",
    "\n",
    "1. 医疗数据：在医疗数据中，我们可能有来自同一患者的多个样本，例如在不同时间点收集的数据，或者来自不同身体部位的数据。在这种情况下，我们希望模型能够泛化到新的患者，而不仅仅是已经见过的患者。因此，我们可以将每个患者的所有样本划分为同一组，然后使用分组K折交叉验证来评估模型的性能。\n",
    "\n",
    "2. 时间序列数据：在时间序列数据中，我们可能有来自同一时间段的多个样本，例如在同一天或同一小时内收集的数据。在这种情况下，我们希望模型能够泛化到新的时间段，因此我们可以将同一时间段的所有样本划分为同一组，然后使用分组K折交叉验证来评估模型的性能。\n",
    "\n",
    "3. 实验数据：在实验数据中，我们可能有来自同一实验条件的多个样本。在这种情况下，我们希望模型能够泛化到新的实验条件，因此我们可以将同一实验条件的所有样本划分为同一组，然后使用分组K折交叉验证来评估模型的性能。\n",
    "\n",
    "在这些例子中，分组K折交叉验证可以帮助我们更准确地评估模型在新的组（例如新的患者、新的时间段、新的实验条件）上的性能，而不是在已经见过的组上的性能。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 分组K折交叉验证的优点是它可以处理数据中的组结构，例如，当数据中的样本存在自然的分组时（例如，同一组的样本可能来自同一人、同一日、同一实验等），或者当我们希望模型能够泛化到新的组时。\n",
    "- 然而，它的缺点是每个折的样本数量可能会不均衡，这可能会影响性能评分的稳定性。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 网格搜索/Grid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 最简单的网格搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training set: 112   size of test set: 38\n",
      "best score:  0.9736842105263158\n",
      "best parameters:  {'C': 100, 'gamma': 0.001}\n"
     ]
    }
   ],
   "source": [
    "# naive grid search implementation\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=0)\n",
    "print(\"Size of training set: %d   size of test set: %d\" % (X_train.shape[0], X_test.shape[0]))\n",
    "\n",
    "best_score = 0\n",
    "\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        # for each combination of parameters\n",
    "        # train an SVC\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        svm.fit(X_train, y_train)\n",
    "        # evaluate the SVC on the test set \n",
    "        score = svm.score(X_test, y_test)\n",
    "        # if we got a better score, store the score and parameters\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "            \n",
    "print(\"best score: \", best_score)\n",
    "print(\"best parameters: \", best_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736842105263158"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 最简单的网格搜索的使用场景是当我们需要手动搜索模型的超参数空间时，例如当我们不确定哪一组超参数能够给出最优性能时。\n",
    "- 它的优点是可以系统地搜索超参数空间，而且实现简单。\n",
    "- 然而，它的缺点是计算成本较高，特别是当超参数空间较大或者模型训练时间较长时。\n",
    "- 此外，这种方法可能会导致过拟合，因为它在测试集上选择了最优的超参数，而测试集本应被保留用于最终的性能评估。\n",
    "- 为了避免这个问题，我们通常会使用更复杂的网格搜索方法，例如带交叉验证的网格搜索。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 验证集可以帮助我们了解情况，防止在训练集上过拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "hide_input": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "threefold_split\n"
     ]
    },
    {
     "data": {
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",
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"threefold_split\")\n",
    "mglearn.plots.plot_threefold_split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training set: 84   size of validation set: 28   size of test set: 38\n",
      "best score on validation set:  0.9642857142857143\n",
      "best parameters:  {'C': 10, 'gamma': 0.001}\n",
      "test set score with best parameters:  0.9210526315789473\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# split data into train+validation set and test set\n",
    "X_trainval, X_test, y_trainval, y_test = train_test_split(iris.data, iris.target, random_state=0)\n",
    "# split train+validation set into training and validation set\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X_trainval, y_trainval, random_state=1)\n",
    "\n",
    "print(\"Size of training set: %d   size of validation set: %d   size of test set: %d\" % (X_train.shape[0], X_valid.shape[0], X_test.shape[0]))\n",
    "best_score = 0\n",
    "\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        # for each combination of parameters\n",
    "        # train an SVC\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        svm.fit(X_train, y_train)\n",
    "        # evaluate the SVC on the test set \n",
    "        score = svm.score(X_valid, y_valid)\n",
    "        # if we got a better score, store the score and parameters\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "\n",
    "# rebuild a model on the combined training and validation set, and evaluate it on the test set\n",
    "svm = SVC(**best_parameters)\n",
    "svm.fit(X_trainval, y_trainval)\n",
    "test_score = svm.score(X_test, y_test)\n",
    "print(\"best score on validation set: \", best_score)\n",
    "print(\"best parameters: \", best_parameters)\n",
    "print(\"test set score with best parameters: \", test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 使用验证集的优点是可以帮助我们了解模型在未见过的数据上的性能，从而防止在训练集上过拟合。\n",
    "- 此外，它还可以帮助我们选择超参数，从而优化模型的性能。\n",
    "- 然而，它的缺点是需要更多的数据，因为我们需要将数据划分为训练集、验证集和测试集。\n",
    "- 在数据量较小的情况下，我们可能会使用交叉验证来代替单独的验证集，以更有效地利用数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 网格搜索与交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=10, gamma=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=10, gamma=0.1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=10, gamma=0.1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reference: manual_grid_search_cv\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        # for each combination of parameters\n",
    "        # train an SVC\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        # perform cross-validation\n",
    "        scores = cross_val_score(svm, X_trainval, y_trainval, cv=5)\n",
    "        # compute mean cross-validation accuracy\n",
    "        score = np.mean(scores)\n",
    "        # if we got a better score, store the score and parameters\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "# rebuild a model on the combined training and validation set\n",
    "svm = SVC(**best_parameters)\n",
    "svm.fit(X_trainval, y_trainval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "hide_input": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x180 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_grid_search_overview()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "              'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "param_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "grid_search = GridSearchCV(SVC(), param_grid, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: [0.001, 0.01, 0.1, 1, 10, 100],\n",
       "                         &#x27;gamma&#x27;: [0.001, 0.01, 0.1, 1, 10, 100]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: [0.001, 0.01, 0.1, 1, 10, 100],\n",
       "                         &#x27;gamma&#x27;: [0.001, 0.01, 0.1, 1, 10, 100]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
       "                         'gamma': [0.001, 0.01, 0.1, 1, 10, 100]})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736842105263158"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 10, 'gamma': 0.1}\n",
      "0.9731225296442687\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=10, gamma=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=10, gamma=0.1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=10, gamma=0.1)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# grid_search.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3e16503fd0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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",
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mglearn.plots.plot_cross_val_selection()\n",
    "scores = grid_search.cv_results_\n",
    "cv = 5\n",
    "length = len(scores['rank_test_score'] )\n",
    "\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.xlim(-1, length)\n",
    "plt.ylim(0, 1.1)\n",
    "for i in range(cv):\n",
    "    marker_cv = plt.plot(scores[f'split{i}_test_score'], '^', c='gray', markersize=5, alpha=.5)\n",
    "slic = scores['rank_test_score'] == 1\n",
    "marker_mean = plt.plot(scores['mean_test_score'], '-', c='k', alpha=1, markersize=10)\n",
    "marker_best = plt.plot(np.arange(length)[slic], scores['mean_test_score'][slic], 'o', c='red', fillstyle=\"none\", alpha=1, markersize=20, markeredgewidth=3)\n",
    "\n",
    "plt.xticks(range(length), [str(param).strip(\"{}\").replace(\"'\", \"\") for param in scores['params']], rotation=90);\n",
    "plt.ylabel(\"validation accuracy\")\n",
    "plt.xlabel(\"parameter settings\")\n",
    "plt.legend([marker_cv[0], marker_mean[0], marker_best[0]], [\"cv accuracy\", \"mean accuracy\", \"best parameter setting\"], loc=(1.05, .4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 检视交叉验证的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.002, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001,\n",
       "        0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001,\n",
       "        0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001,\n",
       "        0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001]),\n",
       " 'std_fit_time': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0.]),\n",
       " 'mean_score_time': array([0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001,\n",
       "        0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001,\n",
       "        0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.   ,\n",
       "        0.001, 0.001, 0.001, 0.001, 0.   , 0.   , 0.001, 0.001, 0.001]),\n",
       " 'std_score_time': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0.]),\n",
       " 'param_C': masked_array(data=[0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.01, 0.01,\n",
       "                    0.01, 0.01, 0.01, 0.01, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,\n",
       "                    1, 1, 1, 1, 1, 1, 10, 10, 10, 10, 10, 10, 100, 100,\n",
       "                    100, 100, 100, 100],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_gamma': masked_array(data=[0.001, 0.01, 0.1, 1, 10, 100, 0.001, 0.01, 0.1, 1, 10,\n",
       "                    100, 0.001, 0.01, 0.1, 1, 10, 100, 0.001, 0.01, 0.1, 1,\n",
       "                    10, 100, 0.001, 0.01, 0.1, 1, 10, 100, 0.001, 0.01,\n",
       "                    0.1, 1, 10, 100],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 0.001, 'gamma': 0.001},\n",
       "  {'C': 0.001, 'gamma': 0.01},\n",
       "  {'C': 0.001, 'gamma': 0.1},\n",
       "  {'C': 0.001, 'gamma': 1},\n",
       "  {'C': 0.001, 'gamma': 10},\n",
       "  {'C': 0.001, 'gamma': 100},\n",
       "  {'C': 0.01, 'gamma': 0.001},\n",
       "  {'C': 0.01, 'gamma': 0.01},\n",
       "  {'C': 0.01, 'gamma': 0.1},\n",
       "  {'C': 0.01, 'gamma': 1},\n",
       "  {'C': 0.01, 'gamma': 10},\n",
       "  {'C': 0.01, 'gamma': 100},\n",
       "  {'C': 0.1, 'gamma': 0.001},\n",
       "  {'C': 0.1, 'gamma': 0.01},\n",
       "  {'C': 0.1, 'gamma': 0.1},\n",
       "  {'C': 0.1, 'gamma': 1},\n",
       "  {'C': 0.1, 'gamma': 10},\n",
       "  {'C': 0.1, 'gamma': 100},\n",
       "  {'C': 1, 'gamma': 0.001},\n",
       "  {'C': 1, 'gamma': 0.01},\n",
       "  {'C': 1, 'gamma': 0.1},\n",
       "  {'C': 1, 'gamma': 1},\n",
       "  {'C': 1, 'gamma': 10},\n",
       "  {'C': 1, 'gamma': 100},\n",
       "  {'C': 10, 'gamma': 0.001},\n",
       "  {'C': 10, 'gamma': 0.01},\n",
       "  {'C': 10, 'gamma': 0.1},\n",
       "  {'C': 10, 'gamma': 1},\n",
       "  {'C': 10, 'gamma': 10},\n",
       "  {'C': 10, 'gamma': 100},\n",
       "  {'C': 100, 'gamma': 0.001},\n",
       "  {'C': 100, 'gamma': 0.01},\n",
       "  {'C': 100, 'gamma': 0.1},\n",
       "  {'C': 100, 'gamma': 1},\n",
       "  {'C': 100, 'gamma': 10},\n",
       "  {'C': 100, 'gamma': 100}],\n",
       " 'split0_test_score': array([0.348, 0.348, 0.348, 0.348, 0.348, 0.348, 0.348, 0.348, 0.348,\n",
       "        0.348, 0.348, 0.348, 0.348, 0.696, 0.913, 1.   , 0.348, 0.348,\n",
       "        0.696, 0.913, 1.   , 0.957, 0.913, 0.391, 0.913, 1.   , 1.   ,\n",
       "        0.957, 0.87 , 0.522, 1.   , 1.   , 1.   , 0.957, 0.87 , 0.522]),\n",
       " 'split1_test_score': array([0.348, 0.348, 0.348, 0.348, 0.348, 0.348, 0.348, 0.348, 0.348,\n",
       "        0.348, 0.348, 0.348, 0.348, 0.696, 0.913, 0.913, 0.348, 0.348,\n",
       "        0.696, 0.913, 0.957, 0.913, 0.957, 0.435, 0.913, 0.957, 0.957,\n",
       "        0.957, 0.913, 0.522, 0.957, 0.913, 0.957, 0.957, 0.913, 0.522]),\n",
       " 'split2_test_score': array([0.364, 0.364, 0.364, 0.364, 0.364, 0.364, 0.364, 0.364, 0.364,\n",
       "        0.364, 0.364, 0.364, 0.364, 0.682, 0.909, 1.   , 0.364, 0.364,\n",
       "        0.682, 1.   , 1.   , 1.   , 1.   , 0.545, 1.   , 1.   , 1.   ,\n",
       "        1.   , 1.   , 0.591, 1.   , 1.   , 1.   , 1.   , 1.   , 0.591]),\n",
       " 'split3_test_score': array([0.364, 0.364, 0.364, 0.364, 0.364, 0.364, 0.364, 0.364, 0.364,\n",
       "        0.364, 0.364, 0.364, 0.364, 0.682, 0.864, 0.909, 0.364, 0.364,\n",
       "        0.682, 0.909, 0.909, 0.909, 0.818, 0.5  , 0.909, 0.909, 0.955,\n",
       "        0.864, 0.818, 0.591, 0.909, 0.955, 0.864, 0.864, 0.818, 0.591]),\n",
       " 'split4_test_score': array([0.409, 0.409, 0.409, 0.409, 0.409, 0.409, 0.409, 0.409, 0.409,\n",
       "        0.409, 0.409, 0.409, 0.409, 0.727, 0.909, 0.955, 0.409, 0.409,\n",
       "        0.727, 0.955, 0.955, 0.955, 0.955, 0.636, 0.955, 0.955, 0.955,\n",
       "        0.955, 0.955, 0.682, 0.955, 0.955, 0.955, 0.955, 0.955, 0.682]),\n",
       " 'mean_test_score': array([0.366, 0.366, 0.366, 0.366, 0.366, 0.366, 0.366, 0.366, 0.366,\n",
       "        0.366, 0.366, 0.366, 0.366, 0.696, 0.902, 0.955, 0.366, 0.366,\n",
       "        0.696, 0.938, 0.964, 0.947, 0.928, 0.502, 0.938, 0.964, 0.973,\n",
       "        0.946, 0.911, 0.581, 0.964, 0.964, 0.955, 0.946, 0.911, 0.581]),\n",
       " 'std_test_score': array([0.022, 0.022, 0.022, 0.022, 0.022, 0.022, 0.022, 0.022, 0.022,\n",
       "        0.022, 0.022, 0.022, 0.022, 0.017, 0.019, 0.04 , 0.022, 0.022,\n",
       "        0.017, 0.035, 0.034, 0.033, 0.062, 0.086, 0.035, 0.034, 0.022,\n",
       "        0.045, 0.063, 0.059, 0.034, 0.033, 0.05 , 0.045, 0.063, 0.059]),\n",
       " 'rank_test_score': array([22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 17, 16,  6, 22,\n",
       "        22, 17, 11,  3,  8, 13, 21, 11,  3,  1,  9, 14, 19,  3,  2,  7,  9,\n",
       "        14, 19], dtype=int32)}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = grid_search.cv_results_\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def heatmap(values, xlabel, ylabel, xticklabels, yticklabels, cmap=None,\n",
    "            vmin=None, vmax=None, ax=None, fmt=\"%0.2f\"):\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "    # plot the mean cross-validation scores\n",
    "    img = ax.pcolor(values, cmap=cmap, vmin=None, vmax=None)\n",
    "    img.update_scalarmappable()\n",
    "    ax.set_xlabel(xlabel)\n",
    "    ax.set_ylabel(ylabel)\n",
    "    ax.set_xticks(np.arange(len(xticklabels)) + .5)\n",
    "    ax.set_yticks(np.arange(len(yticklabels)) + .5)\n",
    "    ax.set_xticklabels(xticklabels)\n",
    "    ax.set_yticklabels(yticklabels)\n",
    "    ax.set_aspect(1)\n",
    "\n",
    "    for y, value in zip(range(6), img.get_array()):\n",
    "        for x in range(6):\n",
    "            # print(x+0.5, y+0.5)\n",
    "            ax.text(x+0.5, y+0.5, fmt % value[x], color='k' if value[x] > 0.9 else 'w', ha=\"center\", va=\"center\")\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyQuadMesh at 0x7f3e1643b700>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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",
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_scores = scores['mean_test_score']\n",
    "mean_scores = np.array(mean_scores).reshape(6, 6)\n",
    "\n",
    "# plot the mean cross-validation scores\n",
    "heatmap(mean_scores, xlabel='gamma', ylabel='C', xticklabels=param_grid['gamma'],\n",
    "                      yticklabels=param_grid['C'], cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 微调"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gridsearch_failures\n"
     ]
    },
    {
     "data": {
      "application/pdf": 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",
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(20, 3))\n",
    "\n",
    "param_grid_linear = {'C': np.linspace(5, 15, 6),\n",
    "                     'gamma':  np.linspace(0.01, 0.5, 6)}\n",
    "\n",
    "param_grid_one_log = {'C': np.linspace(1, 2, 6),\n",
    "                     'gamma':  np.logspace(-3, 2, 6)} # log\n",
    "\n",
    "param_grid_range = {'C': np.logspace(-3, 2, 6),        # log\n",
    "                     'gamma':  np.logspace(-7, -2, 6)} # log\n",
    "\n",
    "\n",
    "for param_grid, ax in zip([param_grid_linear, param_grid_one_log,\n",
    "                           param_grid_range], axes):\n",
    "    grid_search = GridSearchCV(SVC(), param_grid, cv=5)\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    mean_scores = grid_search.cv_results_['mean_test_score']\n",
    "    mean_scores = np.array(mean_scores).reshape(6, 6)\n",
    "\n",
    "    # plot the mean cross-validation scores\n",
    "    scores_image = heatmap(mean_scores, xlabel='gamma', ylabel='C', xticklabels=[f'{i:.2f}' for i in param_grid['gamma']],\n",
    "                                         yticklabels=[f'{i:.2f}' for i in param_grid['C']], cmap=\"viridis\", ax=ax)\n",
    "    \n",
    "plt.colorbar(scores_image, ax=axes.tolist())\n",
    "print(\"gridsearch_failures\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 嵌套交叉验证\n",
    "\n",
    "嵌套交叉验证是一种更为严谨的模型评估和选择方法，它包含了两层交叉验证：内层交叉验证用于超参数选择，外层交叉验证用于评估选择好超参数的模型的性能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:  [0.967 1.    0.967 0.967 1.   ]\n",
      "Mean cross-validation score:  0.9800000000000001\n"
     ]
    }
   ],
   "source": [
    "# 法一：\n",
    "scores = cross_val_score(\n",
    "    GridSearchCV(\n",
    "        SVC(), param_grid, cv=5\n",
    "    ), \n",
    "    iris.data, iris.target, cv=5\n",
    ")\n",
    "print(\"Cross-validation scores: \", scores)\n",
    "print(\"Mean cross-validation score: \", scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "自定义嵌套交叉验证函数：\n",
    "\n",
    "- 最外层：这一层是外层交叉验证的循环，它将数据集分为训练+验证集和测试集。\n",
    "- 中间层：这一层是超参数网格的循环，它遍历所有的超参数组合。\n",
    "- 最内层：这一层是内层交叉验证的循环，它将训练+验证集进一步分为训练集和验证集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 法二：\n",
    "# 更灵活，可以接受任何分类器和参数网格。\n",
    "def nested_cv(X, y, inner_cv, outer_cv, Classifier, parameter_grid):\n",
    "    outer_scores = []\n",
    "    outer_model = []\n",
    "    # for each split of the data in the outer cross-validation\n",
    "    # (split method returns indices)\n",
    "    for training_samples, test_samples in tqdm(outer_cv.split(X, y)):\n",
    "        # find best parameter using inner cross-validation:\n",
    "        best_score = -np.inf\n",
    "        # iterate over parameters\n",
    "        for parameters in parameter_grid:\n",
    "            # accumulate score over inner splits\n",
    "            cv_scores = []\n",
    "            # iterate over inner cross-validation\n",
    "            for inner_train, inner_test in inner_cv.split(X[training_samples], y[training_samples]):\n",
    "                # build classifier given parameters and training data\n",
    "                clf = Classifier(**parameters)\n",
    "                clf.fit(X[inner_train], y[inner_train])\n",
    "                # evaluate on inner test set\n",
    "                score = clf.score(X[inner_test], y[inner_test])\n",
    "                cv_scores.append(score)\n",
    "            # compute mean score over inner folds\n",
    "            mean_score = np.mean(cv_scores)\n",
    "            if mean_score > best_score:\n",
    "                # if better than so far, remember parameters\n",
    "                best_score = mean_score\n",
    "                best_params = parameters\n",
    "        # build classifier on best parameters using outer training set\n",
    "        clf = Classifier(**best_params)\n",
    "        clf.fit(X[training_samples], y[training_samples])\n",
    "        # evaluate \n",
    "        outer_scores.append(clf.score(X[test_samples], y[test_samples]))\n",
    "        outer_model.append(clf)\n",
    "    return outer_scores, best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "hide_input": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5it [00:01,  3.58it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.9666666666666667, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import ParameterGrid, StratifiedKFold\n",
    "outer_scores, best_params = nested_cv(iris.data, iris.target, StratifiedKFold(5), StratifiedKFold(5), SVC, ParameterGrid(param_grid))\n",
    "outer_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 嵌套交叉验证的优点是可以更准确地评估模型的性能，因为它同时考虑了模型的训练误差和验证误差，而且还考虑了超参数选择的不确定性。\n",
    "- 此外，它还可以防止过拟合，因为它在选择超参数时使用了交叉验证，而不是单独的验证集。\n",
    "- 然而，它的缺点是计算成本较高，因为它需要进行两层交叉验证。\n",
    "\n",
    "嵌套交叉验证通常在我们需要同时进行模型选择和性能评估时使用，例如在比较不同的模型或者不同的超参数设置时。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary>点击折叠/展开 for StratifiedKFold</summary>\n",
    "    \n",
    "StratifiedKFold是sklearn库中的一个类，用于实现分层k折交叉验证。分层k折交叉验证是k折交叉验证的一个变种，它的特点是在每一折中，每个类别的样本比例大致和完整数据集中相同。\n",
    "\n",
    "在标准的k折交叉验证中，数据被随机分为k个子集，每次使用k-1个子集进行训练，1个子集进行测试。这种方法的问题是，如果数据集的类别分布不均匀，那么在一些折中，某个类别的样本可能会很少，这可能会影响模型的训练和验证。\n",
    "\n",
    "分层k折交叉验证解决了这个问题。在分层k折交叉验证中，每一折都尽可能保持每个类别的样本比例，这样可以确保在每次训练和验证时，每个类别的样本都足够多。\n",
    "\n",
    "StratifiedKFold类的主要参数包括：\n",
    "\n",
    "- n_splits：折数，即将数据集划分为多少份。\n",
    "- shuffle：是否打乱数据顺序。\n",
    "- random_state：随机种子，用于控制随机打乱的结果。\n",
    "\n",
    "StratifiedKFold的主要方法是split，它接受特征和标签，返回每一折的训练集和测试集的索引。\n",
    "\n",
    "分层k折交叉验证通常在类别不均衡的数据集上使用，例如在某个类别的样本远多于其他类别的样本时。通过使用分层k折交叉验证，我们可以确保在每次训练和验证时，每个类别的样本都足够多，从而提高模型的性能。\n",
    "  \n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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