P2_Matplotlib_kNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#04-Matplotlib-Basic-Kownledge\" data-toc-modified-id=\"04-Matplotlib-Basic-Kownledge-1\">04 Matplotlib Basic Kownledge</a></span><ul class=\"toc-item\"><li><span><a href=\"#折线图\" data-toc-modified-id=\"折线图-1.1\">折线图</a></span></li><li><span><a href=\"#Scatter-Plot散点图\" data-toc-modified-id=\"Scatter-Plot散点图-1.2\">Scatter Plot散点图</a></span></li></ul></li><li><span><a href=\"#05-简单数据探索\" data-toc-modified-id=\"05-简单数据探索-2\">05 简单数据探索</a></span></li><li><span><a href=\"#06-kNN-Handjob\" data-toc-modified-id=\"06-kNN-Handjob-3\">06 kNN Handjob</a></span><ul class=\"toc-item\"><li><span><a href=\"#Train-Test-Split\" data-toc-modified-id=\"Train-Test-Split-3.1\">Train-Test Split</a></span><ul class=\"toc-item\"><li><span><a href=\"#常规思路\" data-toc-modified-id=\"常规思路-3.1.1\">常规思路</a></span></li><li><span><a href=\"#更好做法\" data-toc-modified-id=\"更好做法-3.1.2\">更好做法</a></span></li><li><span><a href=\"#调用sklearn\" data-toc-modified-id=\"调用sklearn-3.1.3\">调用sklearn</a></span></li></ul></li><li><span><a href=\"#kNN分步实现\" data-toc-modified-id=\"kNN分步实现-3.2\">kNN分步实现</a></span></li><li><span><a href=\"#五行封装\" data-toc-modified-id=\"五行封装-3.3\">五行封装</a></span></li><li><span><a href=\"#调用sklearn\" data-toc-modified-id=\"调用sklearn-3.4\">调用sklearn</a></span></li><li><span><a href=\"#自己封装\" data-toc-modified-id=\"自己封装-3.5\">自己封装</a></span></li></ul></li><li><span><a href=\"#07-超参数和模型参数\" data-toc-modified-id=\"07-超参数和模型参数-4\">07 超参数和模型参数</a></span><ul class=\"toc-item\"><li><span><a href=\"#确定超参数的方法\" data-toc-modified-id=\"确定超参数的方法-4.1\">确定超参数的方法</a></span></li><li><span><a href=\"#kNN超参数确定\" data-toc-modified-id=\"kNN超参数确定-4.2\">kNN超参数确定</a></span></li><li><span><a href=\"#kNN网格搜索确定超参\" data-toc-modified-id=\"kNN网格搜索确定超参-4.3\">kNN网格搜索确定超参</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 04 Matplotlib Basic Kownledge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.1010101 ,  0.2020202 ,  0.3030303 ,  0.4040404 ,\n",
       "        0.50505051,  0.60606061,  0.70707071,  0.80808081,  0.90909091,\n",
       "        1.01010101,  1.11111111,  1.21212121,  1.31313131,  1.41414141,\n",
       "        1.51515152,  1.61616162,  1.71717172,  1.81818182,  1.91919192,\n",
       "        2.02020202,  2.12121212,  2.22222222,  2.32323232,  2.42424242,\n",
       "        2.52525253,  2.62626263,  2.72727273,  2.82828283,  2.92929293,\n",
       "        3.03030303,  3.13131313,  3.23232323,  3.33333333,  3.43434343,\n",
       "        3.53535354,  3.63636364,  3.73737374,  3.83838384,  3.93939394,\n",
       "        4.04040404,  4.14141414,  4.24242424,  4.34343434,  4.44444444,\n",
       "        4.54545455,  4.64646465,  4.74747475,  4.84848485,  4.94949495,\n",
       "        5.05050505,  5.15151515,  5.25252525,  5.35353535,  5.45454545,\n",
       "        5.55555556,  5.65656566,  5.75757576,  5.85858586,  5.95959596,\n",
       "        6.06060606,  6.16161616,  6.26262626,  6.36363636,  6.46464646,\n",
       "        6.56565657,  6.66666667,  6.76767677,  6.86868687,  6.96969697,\n",
       "        7.07070707,  7.17171717,  7.27272727,  7.37373737,  7.47474747,\n",
       "        7.57575758,  7.67676768,  7.77777778,  7.87878788,  7.97979798,\n",
       "        8.08080808,  8.18181818,  8.28282828,  8.38383838,  8.48484848,\n",
       "        8.58585859,  8.68686869,  8.78787879,  8.88888889,  8.98989899,\n",
       "        9.09090909,  9.19191919,  9.29292929,  9.39393939,  9.49494949,\n",
       "        9.5959596 ,  9.6969697 ,  9.7979798 ,  9.8989899 , 10.        ])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(0,10,100)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.10083842,  0.20064886,  0.2984138 ,  0.39313661,\n",
       "        0.48385164,  0.56963411,  0.64960951,  0.72296256,  0.78894546,\n",
       "        0.84688556,  0.8961922 ,  0.93636273,  0.96698762,  0.98775469,\n",
       "        0.99845223,  0.99897117,  0.98930624,  0.96955595,  0.93992165,\n",
       "        0.90070545,  0.85230712,  0.79522006,  0.73002623,  0.65739025,\n",
       "        0.57805259,  0.49282204,  0.40256749,  0.30820902,  0.21070855,\n",
       "        0.11106004,  0.01027934, -0.09060615, -0.19056796, -0.28858706,\n",
       "       -0.38366419, -0.47483011, -0.56115544, -0.64176014, -0.7158225 ,\n",
       "       -0.7825875 , -0.84137452, -0.89158426, -0.93270486, -0.96431712,\n",
       "       -0.98609877, -0.99782778, -0.99938456, -0.99075324, -0.97202182,\n",
       "       -0.94338126, -0.90512352, -0.85763861, -0.80141062, -0.73701276,\n",
       "       -0.66510151, -0.58640998, -0.50174037, -0.41195583, -0.31797166,\n",
       "       -0.22074597, -0.12126992, -0.0205576 ,  0.0803643 ,  0.18046693,\n",
       "        0.27872982,  0.37415123,  0.46575841,  0.55261747,  0.63384295,\n",
       "        0.7086068 ,  0.77614685,  0.83577457,  0.8868821 ,  0.92894843,\n",
       "        0.96154471,  0.98433866,  0.99709789,  0.99969234,  0.99209556,\n",
       "        0.97438499,  0.94674118,  0.90944594,  0.86287948,  0.8075165 ,\n",
       "        0.74392141,  0.6727425 ,  0.59470541,  0.51060568,  0.42130064,\n",
       "        0.32770071,  0.23076008,  0.13146699,  0.03083368, -0.07011396,\n",
       "       -0.17034683, -0.26884313, -0.36459873, -0.45663749, -0.54402111])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siny = np.sin(x)\n",
    "siny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 加载图像\n",
    "plt.plot(x,siny)\n",
    "# 进行渲染\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  0.99490282,  0.97966323,  0.95443659,  0.91948007,\n",
       "        0.87515004,  0.8218984 ,  0.76026803,  0.69088721,  0.61446323,\n",
       "        0.53177518,  0.44366602,  0.35103397,  0.25482335,  0.15601496,\n",
       "        0.0556161 , -0.04534973, -0.14585325, -0.24486989, -0.34139023,\n",
       "       -0.43443032, -0.52304166, -0.60632092, -0.68341913, -0.75355031,\n",
       "       -0.81599952, -0.87013012, -0.91539031, -0.95131866, -0.97754893,\n",
       "       -0.9938137 , -0.99994717, -0.9958868 , -0.981674  , -0.95745366,\n",
       "       -0.92347268, -0.88007748, -0.82771044, -0.76690542, -0.69828229,\n",
       "       -0.6225406 , -0.54045251, -0.45285485, -0.36064061, -0.26474988,\n",
       "       -0.16616018, -0.06587659,  0.03507857,  0.13567613,  0.23489055,\n",
       "        0.33171042,  0.4251487 ,  0.51425287,  0.59811455,  0.67587883,\n",
       "        0.74675295,  0.8100144 ,  0.86501827,  0.91120382,  0.94810022,\n",
       "        0.97533134,  0.99261957,  0.99978867,  0.99676556,  0.98358105,\n",
       "        0.96036956,  0.9273677 ,  0.88491192,  0.83343502,  0.77346177,\n",
       "        0.70560358,  0.63055219,  0.54907273,  0.46199582,  0.37020915,\n",
       "        0.27464844,  0.17628785,  0.07613012, -0.0248037 , -0.12548467,\n",
       "       -0.2248864 , -0.32199555, -0.41582217, -0.50540974, -0.58984498,\n",
       "       -0.66826712, -0.7398767 , -0.8039437 , -0.859815  , -0.90692104,\n",
       "       -0.94478159, -0.97301068, -0.99132055, -0.99952453, -0.99753899,\n",
       "       -0.98538417, -0.96318398, -0.93116473, -0.88965286, -0.83907153])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosy = np.cos(x)\n",
    "cosy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 一张图上画两条图线\n",
    "# 先分别加载两条图线，然后再进行渲染\n",
    "\n",
    "# lebel 用于指定图线名称（图示）\n",
    "plt.plot(x, siny, linestyle='--', label='sin(x)')\n",
    "plt.plot(x, cosy, color='red', linestyle='-.', label='cos(x)')\n",
    "# 将x轴的取值范围设为[-2,12]\n",
    "plt.xlim(-2, 12)\n",
    "\n",
    "# 加载图示\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEWCAYAAABliCz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAATiElEQVR4nO3df5BdZ33f8ffHkh1D7dgJWhqwBHIamUF1+eWNg0sSzEAysmYqD4lLrUKJqUHTpCaZhNI4A2MT03YGCE2G4sQR1FUgYGFIh6hUjMokThx+iPG6dhxLjoswP7RxUi1gPDAOGNnf/nGv0e16pXu0995d7T7v14xm7rnne8/56pndz5597p7npqqQJK1+py13A5KkpWHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCX5klyaZLZge0vJ3nFIo/17SQ/Or7upMVbu9wNSKtZVZ213D1IT/AKX5IaYeBrxUhybZIvJvlWkoNJXnmC2rcluTXJB/r1B5JMD+yvJD82sL0ryX9YRE+7ktyU5FP98/x5kmcvdJ4kT0ny7iRfSfJwkk8necrJnlNaLANfK8kXgZ8CzgF+E/jDJM84Qf02YDdwLrAHeO+E+no18HZgHXA38KHj1P0WcBHwT4EfBv498PiEepKexMDXilFVH62qB6vq8ar6CPAF4OITvOTTVbW3qh4DPgg8f0Kt/c+qur2qvgu8BbgkyYbBgiSnAf8a+JWq+puqeqyqPtt/jbQkDHytGElem+TuJN9M8k3gQnpX1cfzdwOPHwHOTDKJP1Q4/MSDqvo28A3gmfNq1gFn0vstRVoWBr5WhP68+PuAa4CnVdW5wL1AFnnIR4CnDmz/yAjtff9qPslZ9KZrHpxX8zXgO8A/GuE80kgMfK0U/wAoYA4gyevoXeEv1t3Av0yyJskW4KUjHGtrkp9Mcga9ufzPV9XhwYKqehy4GfjPSZ7ZP+8lSX5ghPNKJ8XA14pQVQeBdwOfA/4v8E+Az4xwyF8B/hnwTXpvun58hGN9GLie3lTORf3jLeTfAX8F3NGvfQd+D2oJxQ9AkRYvyS5gtqreuty9SMN4dSFJjTDwJakRTulIUiO8wpekRizbapnr1q2rjRs3LtfpJWlFuvPOO79WVVOLee2yBf7GjRuZmZlZrtNL0oqU5CuLfa1TOpLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaMTTwk9yc5EiSe4fU/XiSx5JcMb72JEnj0uUKfxew5UQFSdbQ+7i2fWPoSZI0AUMDv6pup/f5myfyRuCPgCPjaEqSNH4jz+EnOQ94JXBTh9odSWaSzMzNzY16aknSSRjHm7a/A/x6VT02rLCqdlbVdFVNT00tajlnSdIijWM9/GlgdxKAdcDWJEer6uNjOLYkaUxGDvyqOv+Jx0l2AZ8w7CXp1DM08JPcAlwKrEsyC1wPnA5QVUPn7SVJp4ahgV9V27serKquGqkbSdLEeKetJDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaMTTwk9yc5EiSe4+z/9VJ7un/+2yS54+/TUnSqLpc4e8Ctpxg/5eAl1bV84C3AzvH0JckaczWDiuoqtuTbDzB/s8ObO4H1o/eliRp3MY9h3818Mnj7UyyI8lMkpm5ubkxn1qSdCJjC/wkL6MX+L9+vJqq2llV01U1PTU1Na5TS5I6GDql00WS5wHvBy6rqq+P45iSpPEa+Qo/ybOA/w78q6r6P6O3JEmahKFX+EluAS4F1iWZBa4HTgeoqpuA64CnAb+bBOBoVU1PqmFJ0uJ0+Sud7UP2vx54/dg6kiRNhHfaSlIjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEUMDP8nNSY4kufc4+5PkPUkOJbknyYvG36YkaVRdrvB3AVtOsP8yYFP/3w7g90ZvS5I0bkMDv6puB75xgpLLgQ9Uz37g3CTPGFeDkqTxGMcc/nnA4YHt2f5zT5JkR5KZJDNzc3NjOLUkqatxBH4WeK4WKqyqnVU1XVXTU1NTYzi1JKmrcQT+LLBhYHs98OAYjitJGqNxBP4e4LX9v9Z5MfBwVf3tGI4rSRqjtcMKktwCXAqsSzILXA+cDlBVNwF7ga3AIeAR4HWTalaStHhDA7+qtg/ZX8C/HVtHkqSJ8E5bSWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1olPgJ9mS5P4kh5Jcu8D+ZyW5LcldSe5JsnX8rUqSRjE08JOsAW4ELgM2A9uTbJ5X9lbg1qp6IXAl8LvjblSSNJouV/gXA4eq6oGqehTYDVw+r6aAH+w/Pgd4cHwtSpLGoUvgnwccHtie7T836G3Aa5LMAnuBNy50oCQ7kswkmZmbm1tEu5KkxeoS+FnguZq3vR3YVVXrga3AB5M86dhVtbOqpqtqempq6uS7lSQtWpfAnwU2DGyv58lTNlcDtwJU1eeAM4F142hQkjQeXQL/DmBTkvOTnEHvTdk982q+CrwcIMlz6QW+czaSdAoZGvhVdRS4BtgH3Efvr3EOJLkhybZ+2ZuANyT5S+AW4Kqqmj/tI0laRmu7FFXVXnpvxg4+d93A44PAS8bbmiRpnLzTVpIaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjegU+Em2JLk/yaEk1x6n5lVJDiY5kOTD421TkjSqtcMKkqwBbgR+BpgF7kiyp6oODtRsAn4DeElVPZTk6ZNqWJK0OF2u8C8GDlXVA1X1KLAbuHxezRuAG6vqIYCqOjLeNiVJo+oS+OcBhwe2Z/vPDboAuCDJZ5LsT7JloQMl2ZFkJsnM3Nzc4jqWJC1Kl8DPAs/VvO21wCbgUmA78P4k5z7pRVU7q2q6qqanpqZOtldJ0gi6BP4ssGFgez3w4AI1f1xV36uqLwH30/sBIEk6RXQJ/DuATUnOT3IGcCWwZ17Nx4GXASRZR2+K54FxNipJGs3QwK+qo8A1wD7gPuDWqjqQ5IYk2/pl+4CvJzkI3Aa8uaq+PqmmJUknL1Xzp+OXxvT0dM3MzCzLuSVppUpyZ1VNL+a13mkrSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGdAr8JFuS3J/kUJJrT1B3RZJKsqgP2JUkTc7QwE+yBrgRuAzYDGxPsnmBurOBXwY+P+4mJUmj63KFfzFwqKoeqKpHgd3A5QvUvR14J/CdMfYnSRqTLoF/HnB4YHu2/9z3JXkhsKGqPnGiAyXZkWQmyczc3NxJNytJWrwugZ8Fnqvv70xOA34beNOwA1XVzqqarqrpqamp7l1KkkbWJfBngQ0D2+uBBwe2zwYuBP4syZeBFwN7fONWkk4tXQL/DmBTkvOTnAFcCex5YmdVPVxV66pqY1VtBPYD26pqZiIdS5IWZWjgV9VR4BpgH3AfcGtVHUhyQ5Jtk25QkjQea7sUVdVeYO+85647Tu2lo7clSRo377SVpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjOgV+ki1J7k9yKMm1C+z/tSQHk9yT5E+SPHv8rUqSRjE08JOsAW4ELgM2A9uTbJ5XdhcwXVXPAz4GvHPcjUqSRtPlCv9i4FBVPVBVjwK7gcsHC6rqtqp6pL+5H1g/3jYlSaPqEvjnAYcHtmf7zx3P1cAnF9qRZEeSmSQzc3Nz3buUJI2sS+BngedqwcLkNcA08K6F9lfVzqqarqrpqamp7l1Kkka2tkPNLLBhYHs98OD8oiSvAN4CvLSqvjue9iRJ49LlCv8OYFOS85OcAVwJ7BksSPJC4PeBbVV1ZPxtSpJGNTTwq+oocA2wD7gPuLWqDiS5Icm2ftm7gLOAjya5O8me4xxOkrRMukzpUFV7gb3znrtu4PErxtyXJGnMvNNWkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mN6BT4SbYkuT/JoSTXLrD/B5J8pL//80k2jrtRSdJohgZ+kjXAjcBlwGZge5LN88quBh6qqh8Dfht4x7gblSSNpssV/sXAoap6oKoeBXYDl8+ruRz4g/7jjwEvT5LxtSlJGtXaDjXnAYcHtmeBnzheTVUdTfIw8DTga4NFSXYAO/qb301y72KaXoXWMW+sGuZYHONYHONYHPOcxb6wS+AvdKVei6ihqnYCOwGSzFTVdIfzr3qOxTGOxTGOxTGOxTFJZhb72i5TOrPAhoHt9cCDx6tJshY4B/jGYpuSJI1fl8C/A9iU5PwkZwBXAnvm1ewBfqH/+ArgT6vqSVf4kqTlM3RKpz8nfw2wD1gD3FxVB5LcAMxU1R7gvwIfTHKI3pX9lR3OvXOEvlcbx+IYx+IYx+IYx+KYRY9FvBCXpDZ4p60kNcLAl6RGTDzwXZbhmA5j8WtJDia5J8mfJHn2cvS5FIaNxUDdFUkqyar9k7wuY5HkVf2vjQNJPrzUPS6VDt8jz0pyW5K7+t8nW5ejz0lLcnOSI8e7Vyk97+mP0z1JXtTpwFU1sX/03uT9IvCjwBnAXwKb59X8EnBT//GVwEcm2dNy/es4Fi8Dntp//Istj0W/7mzgdmA/ML3cfS/j18Um4C7gh/rbT1/uvpdxLHYCv9h/vBn48nL3PaGx+GngRcC9x9m/FfgkvXugXgx8vstxJ32F77IMxwwdi6q6raoe6W/up3fPw2rU5esC4O3AO4HvLGVzS6zLWLwBuLGqHgKoqiNL3ONS6TIWBfxg//E5PPmeoFWhqm7nxPcyXQ58oHr2A+cmecaw40468BdaluG849VU1VHgiWUZVpsuYzHoano/wVejoWOR5IXAhqr6xFI2tgy6fF1cAFyQ5DNJ9ifZsmTdLa0uY/E24DVJZoG9wBuXprVTzsnmCdBtaYVRjG1ZhlWg8/8zyWuAaeClE+1o+ZxwLJKcRm/V1auWqqFl1OXrYi29aZ1L6f3W9xdJLqyqb064t6XWZSy2A7uq6t1JLqF3/8+FVfX45Ns7pSwqNyd9he+yDMd0GQuSvAJ4C7Ctqr67RL0ttWFjcTZwIfBnSb5Mb45yzyp947br98gfV9X3qupLwP30fgCsNl3G4mrgVoCq+hxwJr2F1VrTKU/mm3TguyzDMUPHoj+N8fv0wn61ztPCkLGoqoeral1VbayqjfTez9hWVYteNOoU1uV75OP03tAnyTp6UzwPLGmXS6PLWHwVeDlAkufSC/y5Je3y1LAHeG3/r3VeDDxcVX877EUTndKpyS3LsOJ0HIt3AWcBH+2/b/3Vqtq2bE1PSMexaELHsdgH/GySg8BjwJur6uvL1/VkdByLNwHvS/Kr9KYwrlqNF4hJbqE3hbeu/37F9cDpAFV1E733L7YCh4BHgNd1Ou4qHCtJ0gK801aSGmHgS1IjDHxJaoSBL0mNMPAlqREGvlatJN+e8PGn+iu83pXkpyZ5LmkcJr20grSavRz466r6haGV0inAK3ytCEnekeSXBrbfluRNSc7qf3bA/07yV0metOpmkkuTfGJg+71Jruo/vijJnye5M8m+hVYcTPLs/jme+JyCZyV5Ab2VPLcmuTvJUwbqz+mv6f6c/vYtSd7QP84XkqxLclqSv0jys2MdKOkEDHytFLuBfzGw/Srgo/SWTn5lVb2I3vID7+66vHaS04H/AlxRVRcBNwP/cYHS99JbivZ5wIeA91TV3cB19D6z4AVV9fdPFFfVw8A1wK4kV9Jbx/59VfUV4B3ATfTuGD1YVf+r+xBIo3FKRytCVd2V5OlJnglMAQ9V1Vf7of2fkvw08Di9JWL/IfB3HQ77HHqLtH2q/zNiDbDQeiSXAD/Xf/xBelf2w/r9VJJ/DtwIPH/g+ff3n/83wAs69CiNjYGvleRj9BbY+xF6V/wAr6b3A+Ciqvpef3XNM+e97ij//2+zT+wPcKCqLjnJPoauR9Jf4vm5wN8DP0xvdUOSPJVjH2xzFvCtkzy3tGhO6Wgl2U1vcb0r6IU/9JbTPtIP+5cBC30O8FeAzel9fvI59FdbpLfM8FR/XXWSnJ7kHy/w+s9ybFG/VwOf7tDrrwL30Vu//eb+byLQm9L5EL3poPd1OI40Nl7ha8Xor5x4NvA3A0vBfgj4H0lmgLuBv17gdYeT3ArcA3yB3ufDUlWPJrkCeE//B8Fa4HeAA/MO8cv0QvvN9JbiPeHKhEkuAF4PXFxV30pyO/DWJH8K/Djwkqp6LMnPJ3ldVf23RQyHdNJcLVOSGuGUjiQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9Jjfh/WWw7qUlhd3MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ylim(0, 1.5)\n",
    "\n",
    "# 加载x轴说明\n",
    "plt.xlabel('value of xx')\n",
    "\n",
    "# 加载标题\n",
    "plt.title('a null pic')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plot散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,siny)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 二维正态分布\n",
    "x = np.random.normal(0,1,1000)\n",
    "y = np.random.normal(0,1,1000)\n",
    "# alpha不透明度，0完全透明，1完全不透明\n",
    "plt.scatter(x,y, alpha='0.5')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 05 简单数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载鸢尾花数据集，字典数据格式\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "                \n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n"
     ]
    }
   ],
   "source": [
    "# 查看数据集说明文件\n",
    "print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 150行4列\n",
    "iris.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 每列的特征名称\n",
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([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, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 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,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 样本类型\n",
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 样本类型代码对应的名称\n",
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可视化\n",
    "# 查看sepal length和sepal width与花分类的关系 \n",
    "\n",
    "# 获得分类集\n",
    "classification = iris.target\n",
    "# 利用条件表达式求出第0类（setosa）的bool数组\n",
    "setosa_bool = classification==0\n",
    "# 同理可得其他两类的bool数组\n",
    "versicolor_bool = classification==1\n",
    "virginica_bool = classification==2\n",
    "\n",
    "# 查看一下setosa_bool\n",
    "setosa_bool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5],\n",
       "       [4.9, 3. ],\n",
       "       [4.7, 3.2],\n",
       "       [4.6, 3.1],\n",
       "       [5. , 3.6],\n",
       "       [5.4, 3.9],\n",
       "       [4.6, 3.4],\n",
       "       [5. , 3.4],\n",
       "       [4.4, 2.9],\n",
       "       [4.9, 3.1],\n",
       "       [5.4, 3.7],\n",
       "       [4.8, 3.4],\n",
       "       [4.8, 3. ],\n",
       "       [4.3, 3. ],\n",
       "       [5.8, 4. ],\n",
       "       [5.7, 4.4],\n",
       "       [5.4, 3.9],\n",
       "       [5.1, 3.5],\n",
       "       [5.7, 3.8],\n",
       "       [5.1, 3.8],\n",
       "       [5.4, 3.4],\n",
       "       [5.1, 3.7],\n",
       "       [4.6, 3.6],\n",
       "       [5.1, 3.3],\n",
       "       [4.8, 3.4],\n",
       "       [5. , 3. ],\n",
       "       [5. , 3.4],\n",
       "       [5.2, 3.5],\n",
       "       [5.2, 3.4],\n",
       "       [4.7, 3.2],\n",
       "       [4.8, 3.1],\n",
       "       [5.4, 3.4],\n",
       "       [5.2, 4.1],\n",
       "       [5.5, 4.2],\n",
       "       [4.9, 3.1],\n",
       "       [5. , 3.2],\n",
       "       [5.5, 3.5],\n",
       "       [4.9, 3.6],\n",
       "       [4.4, 3. ],\n",
       "       [5.1, 3.4],\n",
       "       [5. , 3.5],\n",
       "       [4.5, 2.3],\n",
       "       [4.4, 3.2],\n",
       "       [5. , 3.5],\n",
       "       [5.1, 3.8],\n",
       "       [4.8, 3. ],\n",
       "       [5.1, 3.8],\n",
       "       [4.6, 3.2],\n",
       "       [5.3, 3.7],\n",
       "       [5. , 3.3],\n",
       "       [7. , 3.2],\n",
       "       [6.4, 3.2],\n",
       "       [6.9, 3.1],\n",
       "       [5.5, 2.3],\n",
       "       [6.5, 2.8],\n",
       "       [5.7, 2.8],\n",
       "       [6.3, 3.3],\n",
       "       [4.9, 2.4],\n",
       "       [6.6, 2.9],\n",
       "       [5.2, 2.7],\n",
       "       [5. , 2. ],\n",
       "       [5.9, 3. ],\n",
       "       [6. , 2.2],\n",
       "       [6.1, 2.9],\n",
       "       [5.6, 2.9],\n",
       "       [6.7, 3.1],\n",
       "       [5.6, 3. ],\n",
       "       [5.8, 2.7],\n",
       "       [6.2, 2.2],\n",
       "       [5.6, 2.5],\n",
       "       [5.9, 3.2],\n",
       "       [6.1, 2.8],\n",
       "       [6.3, 2.5],\n",
       "       [6.1, 2.8],\n",
       "       [6.4, 2.9],\n",
       "       [6.6, 3. ],\n",
       "       [6.8, 2.8],\n",
       "       [6.7, 3. ],\n",
       "       [6. , 2.9],\n",
       "       [5.7, 2.6],\n",
       "       [5.5, 2.4],\n",
       "       [5.5, 2.4],\n",
       "       [5.8, 2.7],\n",
       "       [6. , 2.7],\n",
       "       [5.4, 3. ],\n",
       "       [6. , 3.4],\n",
       "       [6.7, 3.1],\n",
       "       [6.3, 2.3],\n",
       "       [5.6, 3. ],\n",
       "       [5.5, 2.5],\n",
       "       [5.5, 2.6],\n",
       "       [6.1, 3. ],\n",
       "       [5.8, 2.6],\n",
       "       [5. , 2.3],\n",
       "       [5.6, 2.7],\n",
       "       [5.7, 3. ],\n",
       "       [5.7, 2.9],\n",
       "       [6.2, 2.9],\n",
       "       [5.1, 2.5],\n",
       "       [5.7, 2.8],\n",
       "       [6.3, 3.3],\n",
       "       [5.8, 2.7],\n",
       "       [7.1, 3. ],\n",
       "       [6.3, 2.9],\n",
       "       [6.5, 3. ],\n",
       "       [7.6, 3. ],\n",
       "       [4.9, 2.5],\n",
       "       [7.3, 2.9],\n",
       "       [6.7, 2.5],\n",
       "       [7.2, 3.6],\n",
       "       [6.5, 3.2],\n",
       "       [6.4, 2.7],\n",
       "       [6.8, 3. ],\n",
       "       [5.7, 2.5],\n",
       "       [5.8, 2.8],\n",
       "       [6.4, 3.2],\n",
       "       [6.5, 3. ],\n",
       "       [7.7, 3.8],\n",
       "       [7.7, 2.6],\n",
       "       [6. , 2.2],\n",
       "       [6.9, 3.2],\n",
       "       [5.6, 2.8],\n",
       "       [7.7, 2.8],\n",
       "       [6.3, 2.7],\n",
       "       [6.7, 3.3],\n",
       "       [7.2, 3.2],\n",
       "       [6.2, 2.8],\n",
       "       [6.1, 3. ],\n",
       "       [6.4, 2.8],\n",
       "       [7.2, 3. ],\n",
       "       [7.4, 2.8],\n",
       "       [7.9, 3.8],\n",
       "       [6.4, 2.8],\n",
       "       [6.3, 2.8],\n",
       "       [6.1, 2.6],\n",
       "       [7.7, 3. ],\n",
       "       [6.3, 3.4],\n",
       "       [6.4, 3.1],\n",
       "       [6. , 3. ],\n",
       "       [6.9, 3.1],\n",
       "       [6.7, 3.1],\n",
       "       [6.9, 3.1],\n",
       "       [5.8, 2.7],\n",
       "       [6.8, 3.2],\n",
       "       [6.7, 3.3],\n",
       "       [6.7, 3. ],\n",
       "       [6.3, 2.5],\n",
       "       [6.5, 3. ],\n",
       "       [6.2, 3.4],\n",
       "       [5.9, 3. ]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出iris.data的前两列，只关注sepal length和sepal width两项特征\n",
    "X = iris.data[:,:2]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 属于setosa类的sepal length和sepal width在空间中的分布情况\n",
    "plt.scatter(X[setosa_bool,0], X[setosa_bool,1], label='setosa',marker='o')\n",
    "\n",
    "# 同理其他两类\n",
    "plt.scatter(X[versicolor_bool,0], X[versicolor_bool,1], label='versicolor', marker='+')\n",
    "plt.scatter(X[virginica_bool,0], X[virginica_bool,1],label='virginica', marker='x')\n",
    "\n",
    "# 加载图示\n",
    "plt.legend()\n",
    "\n",
    "# 坐标轴说明\n",
    "plt.xlabel('length of sepal ')\n",
    "plt.ylabel('width of sepal ')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 发现versicolor和virginica分类效果不好\n",
    "# 利用另外两个特征petal length和petal width再对versicolor和virginica进行分类\n",
    "\n",
    "X = iris.data[:,2:]\n",
    "\n",
    "plt.scatter(X[versicolor_bool,0], X[versicolor_bool,1], label='versicolor', marker='+')\n",
    "plt.scatter(X[virginica_bool,0], X[virginica_bool,1],label='virginica', marker='x')\n",
    "\n",
    "# 加载图示\n",
    "plt.legend()\n",
    "\n",
    "# 坐标轴说明\n",
    "plt.xlabel('length of petal ')\n",
    "plt.ylabel('width of petal ')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 06 kNN Handjob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载数据集\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "target_dict = {0:'setosa',1:'versicolor',2:'virginica'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train-Test Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 常规思路"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先将特征矩阵X与目标向量y合并\n",
    "\n",
    "进行Shuffle\n",
    "\n",
    "最后再进行拆分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 让y与X维度相同，也变成二维\n",
    "y = y.reshape(X.shape[0], -1)\n",
    "# 合并X与y,此时y中元素同X，变成了float型\n",
    "concentrate = np.concatenate([X, y], axis=1)\n",
    "# 对合并后矩阵进行乱序\n",
    "np.random.shuffle(concentrate)\n",
    "# 对数据集进行拆分,注意Y_train和Y_test需要再转化成一维的，并转化为int型\n",
    "X_train = concentrate[:100,:4]\n",
    "Y_train = concentrate[:100,4:][:,0].astype(int)\n",
    "X_test = concentrate[100:,4:]\n",
    "Y_test = concentrate[100:,4:][:,0].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 更好做法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用permutation生成乱序索引，范围从0到样本个数-1\n",
    "\n",
    "利用numpy FancyIndexing的特性使用乱序索引访问原数据集\n",
    "\n",
    "自然得到乱序后得数据集，无需合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuffle_indexs = np.random.permutation(y.size)\n",
    "train_ratio = 0.8\n",
    "train_num = int(train_ratio*y.size)\n",
    "y[shuffle_indexs]\n",
    "X_train = X[shuffle_indexs][:train_num]\n",
    "Y_train = y[shuffle_indexs][:train_num]\n",
    "X_test = X[shuffle_indexs][train_num:]\n",
    "Y_test = y[shuffle_indexs][train_num:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 调用sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.7, 3.3, 5.7, 2.5],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [5.9, 3. , 5.1, 1.8],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.1, 3.5, 1.4, 0.2],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [6.3, 3.4, 5.6, 2.4]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# test_size指定测试数据集占比，默认为0.2\n",
    "# random_state用于指定random过程的seed\n",
    "# 指定random_state后可保证每次随机过程一致，均是在random_state的基础上完成的\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.2)\n",
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## kNN分步实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 返回一个测试点x_test与所有样本点的距离，List\n",
    "def cal_distance(X_train, x_test):\n",
    "    from math import sqrt\n",
    "    distances = []\n",
    "    for x in X_train:\n",
    "        # 计算欧拉距离\n",
    "        dis = sqrt(np.sum((x_test-x)**2))\n",
    "        distances.append(dis)\n",
    "    return distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 指定一个k，求出x_distances中距离最近的k个预测分类\n",
    "# 对这些分类进行投票，返回票数最多的分类,predic_classify是target_dict的key\n",
    "\n",
    "def kNN_classify(k,x_distances,Y_train,target_dict):\n",
    "    '''\n",
    "    target_dict:分类代码与分类的对应关系字典\n",
    "    x_distances:测试点与测试集所有点的距离列表\n",
    "    Y_train:训练集的分类结果\n",
    "    '''\n",
    "    from collections import Counter\n",
    "    \n",
    "    # 对距离列表进行排序，返回元素所在位置\n",
    "    x_index = np.argsort(x_distances)\n",
    "    \n",
    "    # 在训练集中取出前k个距离最近的样本点的索引下标\n",
    "    topk_X = x_index[:k]\n",
    "    \n",
    "    # 得到这k个样本点的分类\n",
    "    classification_k = Y_train[topk_X]\n",
    "    \n",
    "    # 对k个分类结果进行投票，最终结果票多者胜\n",
    "    # 各个分类结果的票数:字典\n",
    "    votes_dict = Counter(classification_k)\n",
    "    \n",
    "    # Counter的most_common(n)函数用于找出票数最高的n的元素,返回为List，List中的每个元素为tuple\n",
    "    # 取出票最高者\n",
    "    predic_classify = votes_dict.most_common(1)[0][0]\n",
    "    return target_dict[predic_classify]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'setosa'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 选择一个测试点\n",
    "x_test = X_test[:1,:]\n",
    "\n",
    "# 计算测试点与训练集的距离列表\n",
    "x_distances = cal_distance(X_train, x_test)\n",
    "\n",
    "# 指定k=6得到预测分类结果\n",
    "k=6\n",
    "kNN_classify(k, x_distances, Y_train, target_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 五行封装"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用生成式 五行核心代码搞定knn\n",
    "def kNN(k,X_train,Y_train,x_test,target_dict):\n",
    "    from math import sqrt\n",
    "    import numpy as np\n",
    "    from collections import Counter\n",
    "    # line1：测试点x_test的距离列表\n",
    "    x_distances = [sqrt( np.sum( (x_test-x_train)**2 ) ) for x_train in X_train]\n",
    "    # line2: 排序后得索引取前k，得其分类结果\n",
    "    classification_k = Y_train[np.argsort(x_distances)[:k]]\n",
    "    # line3：进行分类结果投票\n",
    "    votes_dict = Counter(classification_k)\n",
    "    # line4：票高者胜\n",
    "    predic_classify = votes_dict.most_common(1)[0][0]\n",
    "    # line5：返回具体类型\n",
    "    return target_dict[predic_classify]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'virginica'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试\n",
    "x_test = X_test[:2,:]\n",
    "k = 6\n",
    "kNN(k,X_train,Y_train,x_test,target_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 调用sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# kNN分类器初始化\n",
    "knn_classifier = KNeighborsClassifier(n_neighbors=6)\n",
    "# 模型训练\n",
    "knn_classifier.fit(X_train, Y_train)\n",
    "# 模型预测\n",
    "y_predict = knn_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9777777777777777"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试数据集的准确率\n",
    "accuracy = sum(y_predict==Y_test)/Y_test.size\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sklearn自带两种方法测试准确率：\n",
    "\n",
    "1.使用模型的.score()方法，.score()\n",
    "\n",
    "2.from metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9777777777777777"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .score()\n",
    "knn_classifier.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9777777777777777"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# m\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(Y_test, y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 自己封装"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9777777777777777"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模块handjob必须与当前文件在同一目录下\n",
    "from handjob.kNN import kNNClassifier\n",
    "my_kNN = kNNClassifier(k=6)\n",
    "my_kNN.fit(X_train, Y_train)\n",
    "y_predict = my_kNN.predict(X_test)\n",
    "accuracy = sum(y_predict==Y_test)/Y_test.size\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['versicolor',\n",
       " 'virginica',\n",
       " 'setosa',\n",
       " 'versicolor',\n",
       " 'virginica',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'versicolor',\n",
       " 'virginica',\n",
       " 'setosa',\n",
       " 'virginica',\n",
       " 'setosa',\n",
       " 'virginica',\n",
       " 'versicolor',\n",
       " 'setosa',\n",
       " 'virginica',\n",
       " 'versicolor',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'virginica',\n",
       " 'setosa',\n",
       " 'virginica',\n",
       " 'versicolor',\n",
       " 'virginica',\n",
       " 'versicolor',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'versicolor',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'setosa',\n",
       " 'virginica',\n",
       " 'versicolor',\n",
       " 'virginica',\n",
       " 'versicolor',\n",
       " 'virginica',\n",
       " 'versicolor',\n",
       " 'virginica',\n",
       " 'versicolor',\n",
       " 'setosa',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_target = [target_dict[y] for y in y_predict]\n",
    "y_target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 07 超参数和模型参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "超参数：算法运行之前就需要决定的参数\n",
    "\n",
    "模型参数：算法运行过程中需要学习的参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 确定超参数的方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特有领域知识\n",
    "\n",
    "经验数值\n",
    "\n",
    "实验验证"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## kNN超参数确定\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_k : 7\n",
      "best_score:  1.0\n"
     ]
    }
   ],
   "source": [
    "# 实验验证k\n",
    "best_k = -1\n",
    "best_score = 0.0\n",
    "for k in range(1,11):\n",
    "    knn_classifier = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn_classifier.fit(X_train,Y_train)\n",
    "    score = knn_classifier.score(X_test, Y_test)\n",
    "    if score>best_score:\n",
    "        best_score = score\n",
    "        best_k = k\n",
    "print('best_k :', best_k )\n",
    "print('best_score: ', best_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_k : 7\n",
      "best_score:  1.0\n",
      "best_method:  uniform\n"
     ]
    }
   ],
   "source": [
    "# kNN的另一个超参数 距离权重weights\n",
    "# weights默认为uniform，不设置权重\n",
    "# weights = distance ,将距离倒数设为票数的权重，有效解决平票问题\n",
    "best_method = ''\n",
    "best_k = -1\n",
    "best_score = 0.0\n",
    "for method in ['uniform', 'distance']:\n",
    "    for k in range(1,11):\n",
    "        knn_classifier = KNeighborsClassifier(n_neighbors=k, weights=method)\n",
    "        knn_classifier.fit(X_train,Y_train)\n",
    "        score = knn_classifier.score(X_test, Y_test)\n",
    "        if score>best_score:\n",
    "            best_score = score\n",
    "            best_k = k\n",
    "            best_method = method\n",
    "print('best_k :', best_k )\n",
    "print('best_score: ', best_score)\n",
    "print('best_method: ', best_method)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_k : 7\n",
      "best_p : 2\n",
      "best_score:  1.0\n",
      "Wall time: 39.9 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# kNN的另一个超参数，明科夫斯基距离p\n",
    "# Σ|Xa - Xb|{p * 1/p}\n",
    "# p = 1 曼哈顿距离（一维）\n",
    "# p = 2 欧拉距离 （二维）\n",
    "# p... 高维\n",
    "# 这是一个k*p的网格搜索，此时weights需取distance设置p才有效\n",
    "best_p = -1\n",
    "best_k = -1\n",
    "best_score = 0.0\n",
    "for k in range(1,11):\n",
    "    for p in range(1,5):\n",
    "        knn_classifier = KNeighborsClassifier(n_neighbors=k, weights='distance', p=p)\n",
    "        knn_classifier.fit(X_train,Y_train)\n",
    "        score = knn_classifier.score(X_test, Y_test)\n",
    "        if score>best_score:\n",
    "            best_score = score\n",
    "            best_k = k\n",
    "            best_p = p\n",
    "print('best_k :', best_k )\n",
    "print('best_p :', best_p )\n",
    "print('best_score: ', best_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## kNN网格搜索确定超参"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 为网格搜索确定参数列表，列表中每个元素是一个字典，代表一组搜索\n",
    "grid_parms = [\n",
    "    {\n",
    "        'weights':['uniform'],\n",
    "        'n_neighbors': [i for i in range(1,11)]\n",
    "    },\n",
    "    {\n",
    "        'weights':['distance'],\n",
    "        'n_neighbors': [i for i in range(1,11)],\n",
    "        'p': [i for i in range(1,11)]\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "knn_clf = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 433 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
       "                                            metric='minkowski',\n",
       "                                            metric_params=None, n_jobs=None,\n",
       "                                            n_neighbors=10, p=3,\n",
       "                                            weights='uniform'),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid=[{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                          'weights': ['uniform']},\n",
       "                         {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                          'p': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                          'weights': ['distance']}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "grid_search = GridSearchCV(knn_clf, grid_parms)\n",
    "grid_search.fit(X_train, Y_train)\n",
    "# n_jobs默认为1 单线程\n",
    "# n_jobs = -1 当前计算机cpu所有内核全部进行运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 330 out of 330 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 110 candidates, totalling 330 fits\n",
      "Wall time: 191 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
       "                                            metric='minkowski',\n",
       "                                            metric_params=None, n_jobs=None,\n",
       "                                            n_neighbors=10, p=3,\n",
       "                                            weights='uniform'),\n",
       "             iid='warn', n_jobs=-1,\n",
       "             param_grid=[{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                          'weights': ['uniform']},\n",
       "                         {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                          'p': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                          'weights': ['distance']}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=2)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# verbose训练过程中显示输出提示，输入的整数值越大，返回信息越多\n",
    "grid_search = GridSearchCV(knn_clf, grid_parms,n_jobs=-1, verbose=2 )\n",
    "grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=10, p=3,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 返回搜索到的最佳模型\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9833333333333333"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 返回测试准确率，测试方法为交叉验证CV\n",
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 10, 'weights': 'uniform'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 返回搜索到的最佳超参数\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['virginica',\n",
       " 'virginica',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'setosa',\n",
       " 'virginica',\n",
       " 'versicolor',\n",
       " 'virginica',\n",
       " 'setosa',\n",
       " 'versicolor',\n",
       " 'setosa',\n",
       " 'virginica',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'setosa',\n",
       " 'virginica',\n",
       " 'versicolor',\n",
       " 'virginica',\n",
       " 'versicolor',\n",
       " 'virginica',\n",
       " 'setosa',\n",
       " 'virginica',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'virginica']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用最佳模型进行预测\n",
    "knn_clf = grid_search.best_estimator_\n",
    "y_predict = knn_clf.predict(X_test)\n",
    "y_target = [target_dict[y] for y in y_predict]\n",
    "y_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 最佳模型在实际预测过程中的准确率\n",
    "knn_clf.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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