Lesson23-K Nearest Neighbour(KNN)建模.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第23课 K Nearest Neighbour(KNN)建模\n",
    "\n",
    "\n",
    "Python零基础学习建模：https://www.ixigua.com/home/77346806707?utm_source=xiguastudio\n",
    "\n",
    "Python源文件及数据下载链接: https://github.com/rayc2020/LessonPythonCode\n",
    "\n",
    "The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import tree\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='D:\\\\python\\\\Project0-Python-MachineLearning\\\\Lesson23-KNN.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='D:\\\\python\\\\Project0-Python-MachineLearning\\\\Lesson23-distance.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADaCAMAAABqzqVhAAAClFBMVEX///8AAADw/tzz/+Lf/bPv/tnw8PDg/bd/f3/4+Pjx/9/09PTZ2NqatGbFw8iZr223y5OgrYiqx3Tf/rHK7JT5/+qyzYHc/qvs6+7s/tPi/btWVla/v7/n/cj///nr/tCrvI3///OnvX2IiIhwcHCZmZn///b/4cRlZWXl/cIYGBj2///j4+OrkE2QkJC7//+zs7Mybbj/8dVpS3ra///FlnFpns5hIyTs//8AACRLAACNRwDf8sLY8bHS8aGWxeqUTycAADcdAAAyAAAAAD8AABSd4uvbupTr1a7oxq///9tEAADa8v+lpaVGRkawg2EAAErZ/9HCoFQ/doyffUE1XYzt+bjJ1uic1rfrzqu85v9lN1EebZ1SKT7BhVVcksjAmHgsHyNjlLWRXh0AN3r77OBmNACqwtPXw7tJnucuX4DA0tuNwvny28uVSRjj7fyq2u4hTYaEvbp0UQDFsHF3Rx3u5J4jAEK77vVyGQA8grHu5ro8GQApFABhgqWjmGQnAADArYtbZIOAlKxWo88iABsvZZhOLwBaGgA3ZHtnUUuDMgAALFuSdUCoaBFDOmFbVTX+1Znut4hIUl9MOyiV1fuRtN4aACjEgjEAI37cyIS9qpkAIl90eIwTRGKBaF6lsssAAF56xdVBebWaZTqOTVF3f6qbiGefu9zYt6+Ldpm1ezA1NTwpJhj11Jw6ADdCACab0MF9oZ1gZVVrQjjo14RjQxFkpKgAJkZMIFlUKyxykHlHbGm5soJrWC2FbXaudm2067zMu2poLkNfV5O4jJISRVxbnaaIro+IakozRj1rcVl9wbiPbzJ3dkerm1uAOCA7KUJ1Sm+Qj6s1OnNcmqtmTVUaYGthgngQTHmWZWeydlg/WsmBAAATEElEQVR4nO2di18TV9rHZ7ygSF5X12aFXCZMlYQpAemssah4ryYiF8MlJFQxRSOsXRZlISJYUHRxvS2+oHYLVto1W3Tr2g2X0q1lbexaQbGu7ZYX2n/mPWcuYZJMwDATk8b5fTBMZiZxvpwz5zxz5vzmQRBJkiRJkiQpypQQFxeXBn4lPMe+WvvbFWm8W+LixT0q0YX/DkXRojL5/gOr4VuHqmaanXUHJysDOLGqd9Kq1/0mbEcojvDNh96p2nUgy5FIvbVt++00O1ev49mK/W5PFuKY7s8TDcI3N2Yhxq2HXUey9qNo7S4Ufd2xDkUb04y7f49uKQOb0DVIHYpO5iJIDthQW78ny4nuMB39PYpeQUzb0EY7qBDo2h1YOYq+2YAfy6c+jKKnI03mK5azuKTpeKtKb3+3uVSuUrW8udp4ovHkiYqmrUWZrabtp1TrwHHj9m375tQ3Zzn/sMO0q637zI2cP55VnausP1TZsqWhDq09eaYt6/yFvIu7V//pI1VppMl8RXHa1zYUl2QZ8ttX2Y7/FnGMpl7assq4tSHnjYr/3XITSehAO1Mv70Ooeiuvb6Q4t12Ru9qa3r6KxGk3N6Y532v48/sIUj6Zdb4xrWvLqp3n8v8SaTJfgfMzs3vb67irRK7qXvcb2/Hm0j9fA+XDcDrRU5kfwPJcnIXQnJuvV7asZTg1u5pVPWWbr3+44b2G8u21J//YxnBWqdYfiK4WmG5vc+X7jzSdQD+qyTmINlavQ9duX2VEG3LWVaSBU3MfPD9RWD7VoJVyouihbTtMW6/IN7dldaHojbQudPev0B1yF4oeqMGPAc7tq9aja29GmkySJEmSJEmSJEmSJEmSJEmSFAWKE1+RRuKT6vZC0RVpJj5l5kX6CF6MUlSRPoIXo4yXhHOhjFnQ5V/lv2kWG0rVs0uGTZE8jnArn+0F5H9dmBrDJZrP3ki402g7FbuYiUpmQWs4+2HsYiJ6lpP829KPY5hzcQqzYGsmx7/PjeixhFMqltNRg8liFxPJCx4mYIkhfA/dajtk0TqhIXg4hFWFcI+e3lmX0VsSpXUChAlYQnx8AkdMR2MM6ZYu/uAwgtzK1f4UpcGGMhEhD6LoN95rqpXbLlBFYgqx9SU/gFMAkILo5IzPBy+mMyinupnay8ArHuoBY4ar4DXnbwLq7ZJfCtcS/q+Og5yI7gx6ZOr4TlKn2ltlIR7lHfAV+C0BmAnJ8+WC9Sr/RD89fe1vPI62eY+wGnLWHckK8TCbjh7WVpVhspn3DKLE5Oy5QrXoVf5OQraS+oUB0EYvGGyIPmkLNTjKOVZhv63snP2EFMC5SKjmBOPMoH9jxhNoI4cMP0+9w/SlsHz5kbUysNGxmN2Ir2+cNSLLOUeo5gfhVDGcoBlB13Imp8I5RgCi4O8lubZdH63m+yg5+vcDacidNw97OY8Ii48B5/zgmkP9wIU50+wVjHNqFAy3o9sr/DhB4/tpX/CrGOPu1QjWzUYT+PoDoZ7SgZxJlOZTRZPEVVPefPADl0y18303cTQ/KQhnXqZ3ES9Ad3vbopx1dC3G97dNHTz5IINWJf3e9mkDgnPqrXBO5nAto4/682t9CLomzYPXzWBBk14UFBNsDcLJDfvI+qnylB9jztY6TmXE9IyY77L9owzZ6e1+8PUhN13+nISGUpLzmQeUmSYJLsN/YBlwJuXARc3TIrAFbqY3aGg+VkE4V3C6AWMzpzz+tIcuT/vRhqAHpjtzRTfVvMrPC26HNjAH6xy3gleixa0heq0aS4uyD3I6M8yarscTLYCz67HSrQEb+vvAqpHhfjeLqQ5WnlOcpnZuH19IV8IqWf0mbQJivMrL+dlVON+TpC9bc/4hMOQDnGpKmo3jI0uSzRZXrcYyYLWM1qrrrPevmwdB1e30OD8r0nQVmS2jbmLYaike0hR+7rYMuNWM+DnjF3pH+3TjPgGQ6Z8NiPyvH5bi5Ucq07T8l1tk/R64Xi+jIv+mdoGXKl5O9cYv7i7tA5xjasAJKjFYBTkPmYsfqtVPiywXv7x79/KQ2jJ8919F6sIiNVE8FpRTBc/M+JXsanmPbwfv+PEwgt2CveeHNSDSX8PLeQ/+aTADHTN23BA47AI5CSj1xnGINg9wLhiwDnaaCTVBcSpcQ2oCcg5lA1ku9m1Ih5xmoniI+SThxxm3MB8FVTZByYSDWsMav6M0ctDIcd6TtJruUuiP4l8d5tsnNE6CFsVJLHgypHbutVr6reoNHro8AZPlSZElvcisXpK9EWwrLiIKiwhF8RDzSX/OeJkeBV1KXCp9sYnZm6cwtVRXgT+YqsfGtsCjcsj09LmdQ0f8OysC95k158FHj/vHCOel/ruXrYqu/v5+z/0LsF+xXOy/+20RMa+lv3/Eo3j66DF4U3hWoXjCcioC6m08muENbxFTK+fcIulhXDKPXact4BnY/WSSCRDuUNXWkSl4ICExeaNCqBYEcqaCSzIZfbmia+W0QZiB6TodbKQj7+FtbpndjadqYB0QFiMwnAuEal5ge7sCBWEfNdpHuibzvEp5jAqugbMT4JwnVMsCOTPRRCQThn3aYtRH26cpvXAKcC4TquWBnItRFbICdi7kvRU++iBCXpzE5FfCwam/neIT9kVcicn/I1zJAZzxSiWi9J8agsU9j5EyPEqQLRYuWeDxp+YnsJzYTvpSCz+ZH8FxSZ9R5FmKxy6VclvG3izD9EwAKx+NzvFXIZKheSynaRfjd4za8XQBikNXsjOayHsN9tSVK1NvxiIngipXMEsdjWkOGVBNTHKmvsaMgml/uop1p4BYqDQmOVNQhjPn65t0SIvZ343WG3sCpEJfjrlg8bKonEEqSZIkSZIkSZIkSZIkSZIkSZIkSZIkQdoZy9Yyjnzmm8SwTN8vjrYnZ4ZF9psdk5WRPojwS/5jyktxgppaeyM0l+bFquO0qbkqWp1+4gm/lSvvlh6EGrvSz7yLr57r8e0RlIOauchRHtW75If6PZlRfsNYa0DRC+/oZYz09uOvw/5FOeMH/aQShXPJL38lWK/yfzVm3Mq1RCKmf8N5tJHhjAujLxLB7Fu5JlftA+gliwxnYnJS2HyRtFOQU6LVcDFSnNlzw+WLROiq6zeZJmKciwQbI4Nzgqp7wg80YpzTOR4XTeuHnMEXScmv6lKcZPelxiyyAA0S92LVPQOnQbO1bZPonAyPP1bSsDsAKilwzXSc/o0R5HSoXLsbvqpcHCTqxaq7z4AuCC84LSon9ES6vnHDX08umH0dj8UPOe+ywa6a7KRATcsJSnTbBY6diKq35LFJrttDL2NdkfRUXrzg/dUIxvjrRePUJFkGHvWBX12XPrfS9kdoX4X+x+KH1C96Xdc183zLgHu+7y7BfZGsdOPc+YsUJ1b8Ptew3c2GTKfoIsYN13KRaiY/jUic0P9pGRhJNWvUF5d2ejTOXmWfWT080pJfq1EXT7Tkj4Htvf19ZufAduXI6NZHE2bnt+DPom4Bu7ifg5Mc9TkPKU5t8ZbpHqGAGbaU4Q+Yyi4S5wa1GjohR92awVZnp8eSYtYUD6nTP7dC42P6Nx7oNus256QPabrazaDkrUmWPnOOq1ZdfN0MtgX3RTKSu3z9gJATq1bVb0ISEG2wU9T47DCstRh004nHCZ2Q98+qi4cGb3s0lpbHlx9a0h9qCMgypLHUj8F1l4rUgBPuqaZ2GSLAtnkDYzNx4gWNvq4pJWK7WV0qd7XBJwcEuwC3vdtMDZ2JzuleNpDcaQacywbcRDrkVANO6H2cVz9mATiFU5zOcSvYALctq4ec/n5BrrQGf2O1EjG2gqO3tVYijos3gjjHbM9gy4UZQHMkGidBEPDoC7cPqQHn4HseyxOKU+EagziAZTmouk+KCMBJ7TnY7rHUDyloTh5fJFdGnyABOiM4cQJm4LW4wvpKDxDamhGxOYmNz6wE4FQ8vfzlt5CTAJwKmpOoA+smiHkX80cUdY8mFjzNnxjtY7fx+SKnMH38xtrMGh9O8q0gPnTG5IoVXBWPk/JFEtx/4MjZNz7rFAS7lrsLny+Sla3dp1XVfZ/lw2lr5vvgnQuZzLA2OV4qIqdgX2RQTl/7OV51bl8alxMzlPCN0zu8jfCdazdF5BTsi+Txf1Ii69HXuELXwmrIjeNnyvtEbReLMxx+QQpzP+qntTAuisz4UGLyK8sFi5/T+ySPKcHyicx4X9zSXwhXoC8y6iSOLzK68qvxKl4MBf12TM9EQ2RPkJAgNkTmMYGdjUo2GKsyea88MUMsc+L3DjtUUKXamOY0teYynLFdnoY1abqe1NRUZUVMc8r/U8Fertg+m4zdOSeY3htAOGRR+5ReSZIkSZIkSZIkSZIkSZIkSZIkST9rvQSP54J3b+JSo/9mnUBp7c/KENnKn8HNulmKTX5GvlWGpGQiWFxibFZeRxU9xRE+dV2pR4wfjArMciFY1eGxCmK0kwtw6pUI2Zrb4f8g/hcrrDtcjkitHd5wBJyg2nbsk/dE9MGdmDF8KVxh+getcWtFZyJyp31pe6h5wkSVrdkPU8wHVHd8nIbEx1M5B+JpA1CkJP/R/6+M/ySe45W8B2/FqeAz6klXWySd7ncCJp1qHwhNu8H5LuoMpR6N7ZDJItgM4YFP9XVk2D8XzfJqjJIbxznfBVSmpvFT4lUwU7NoXyVItpKAxsG48terRYvSdO28Gd5euHjqleEv5RWi3dXWvbFKrK8SIq3hRsA6R5rDB7M6hMdH7PRvvKOEE09/faZdyO9D6PYCUiSFxhk/d4lwZfOcdDNzyr8qy5xhF6/yYO7aXBBIZnj/NqFxhs0X6ctJDrx2u1PJ6m0qeZDxBpL6vIephImU1mD2ipMHZssZJj+vX3mazqB72D4FOwkDQodrU0iciPG/c1uzjN78e7q3Qi3PsPh5/eut7QzqzQ6J54HmqOnTw6Fx6i73NZMDp9lV5K9D6VfCluc0wIOjO+hN04vBhC2F7+eGxknWN/eeWuhtjULmDO7hncbNOxe+zFkU3M8b6DUCJdrMCYc+OVITGqd8c1sWbIwEcSbNYc26mvQfGJtu9mhtMEzLuJt6tQb18/J4qkycqotg6/ekUZwwFOe/XsPi6H8MJ15eUt3TPvvypCy5n6Bj4FW9/023ZtjtzXxay2PepTfthTtld5uD5jnl845BULY88PMUJ2Y815Zm27WP54JD1/OsDDH9M5flxMq5HuDQOSmr6pNLRWZNklOZ706ivboUJ2RncprSPxrmPcVJpzwN4nPl9cjZzqxlr0BZTv3Jo+483jmOjp1fXEXkKVxO7lWA7uvQ2lsqz6naNTLg1lh6Rwbo9J7/13vpSzPghHlAhy/1K92KN9yarqNmTeEPRG//hHXZXqtz3O1UeoLmOeWfQG3Y421zzzP1FndNVWYH7Z9QvUPVY/l/1rCppfk4Q+w/6fyfrrG6H8zO1A2Qs1ZT+I3HMuq2jI6M9mmc454kZ6cnfUg9DFe5B0vMluxle5f2uzXOVE/QPKe88RDnEXda9vzEDFO5PR2ZtGiHmXzzGqSKnrUrFifhGts47qnrozmZ9J6Wi4+KzOq6s5Tfd/DshqVdD52toJQn3Opl694e06hD5STvcWJUtr11nPxvkItS3LWPZC7LqXbIVSKME6YqfTJGFI/0WC0+nKA8x9T3DwHOc1bLuSUZy+/9Ygj8TZb8a2z5F0vHrZCTL89pME75V9wRhsJroP/EZPrF8q9vyrLki3lgyw/BcVFoBqX7FZ+krqHGQ3T+T8DZ9W6RmeIcY9KYgvNzMN/j/MIKytSsKLjbR6SPW4lhqzp9aPle6/1ODzg/+fKcBuH0myJvOgriIbyguQYpB40wxjfE00Fn/6xh4wSfUbRQ21uKU9FiJeb1eogFvR7FsJW436dQPB1ZAFZ2TRCWb/vvEgrC+RisnyAIS0v/CLH8sQfssOGumT/PKcLX3hqnhjm1iX7xLW+SXuwW7Ec6rpXRnDbfAYRQOWfOc0rZeBnvLsfVO02eUz5OHecxm01rqPZnipM3AS/tXm76dy4Tx/s24CFzhiPPaSAnnT+ZobLDGkiO53o5dQHPGpX/WEk3tR2MCZj7BbPiDEee0wBO3HVIlclqFKUaJNOpTnazrd3f9YF3lzIfbEygOO1+I6Ihc4bHz+vLiRl8jL1b6Fq68wS73RF0pBnTJ1LlGfAU3VDjoVeEa+Z4SLfSRx8ztTTleQ8zI3BViP2naqkI4rmd/BzjYMIU4jiY9/l6AqSf1TiYQEX7uKaDJ/vrbBStnPgDqgHFXZPi3GOJUk4te40Juk1R/gNd4P2bSMjf8IkX7yNXrFix8JRYnLY9onyNYE2NtFLCf7qCwzarVCzOaLn/qfOdNEB+10BmgAIVqzy1dvHujQtSznc+8ajtGX3xiZWjjbz7hyh6fkI0yBDWx4xHeGoUR6bWMDaInAHrSIueERIe4SLOXBGswItKsYR3R1UiBdPH4am5jryoqbS0dHkvycMEHKXR0ipKkvQz0P8DlJvyTM26iOAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='D:\\\\python\\\\Project0-Python-MachineLearning\\\\Lesson23-minkowski.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=sns.load_dataset('iris') #参考第11课，19课，20课\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['species'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(['species'], axis=1)\n",
    "y = data['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation accuracy scores:[1.         0.97368421 0.94736842 0.97368421 0.97368421 1.\n",
      " 0.97368421 0.97368421 0.97368421 0.94736842]\n",
      "Mean Cross-Validation accuracy score: 0.974\n"
     ]
    }
   ],
   "source": [
    "## Importing the model. \n",
    "from sklearn.model_selection import StratifiedShuffleSplit, cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "## calling on the model oject. \n",
    "knn = KNeighborsClassifier(metric='minkowski', p=2)\n",
    "## knn classifier works by doing euclidian distance \n",
    "\n",
    "## doing 10 fold staratified-shuffle-split cross validation \n",
    "cv = StratifiedShuffleSplit(n_splits=10, test_size=.25, random_state=2)\n",
    "\n",
    "accuracies = cross_val_score(knn, X,y, cv = cv, scoring='accuracy')\n",
    "print (\"Cross-Validation accuracy scores:{}\".format(accuracies))\n",
    "print (\"Mean Cross-Validation accuracy score: {}\".format(round(accuracies.mean(),3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy scores are: [0.9552631578947368, 0.9526315789473683, 0.9631578947368421, 0.9578947368421054, 0.9736842105263157, 0.9710526315789474, 0.9763157894736842, 0.968421052631579, 0.9763157894736842, 0.9605263157894737, 0.9736842105263159, 0.968421052631579, 0.976315789473684, 0.9631578947368421, 0.968421052631579, 0.9605263157894737, 0.9657894736842104, 0.9552631578947368, 0.9631578947368421, 0.9552631578947368, 0.9657894736842104, 0.9552631578947368, 0.9605263157894737, 0.9605263157894737, 0.9605263157894737, 0.9552631578947368, 0.9605263157894737, 0.9578947368421054, 0.9605263157894737, 0.9526315789473685]\n",
      "\n",
      "Mean accuracy score: 0.9631578947368421\n"
     ]
    }
   ],
   "source": [
    "## Search for an optimal value of k for KNN.\n",
    "k_range = range(1,31)\n",
    "k_scores = []\n",
    "for k in k_range:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    scores = cross_val_score(knn, X,y, cv = cv, scoring = 'accuracy')\n",
    "    k_scores.append(scores.mean())\n",
    "print(\"Accuracy scores are: {}\\n\".format(k_scores))\n",
    "print (\"Mean accuracy score: {}\".format(np.mean(k_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x159e30b1888>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from matplotlib import pyplot as plt\n",
    "plt.plot(k_range, k_scores, 'bo',linestyle='dashed',linewidth=1,markersize=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid search on KNN classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedShuffleSplit(n_splits=10, random_state=15, test_size=0.3,\n",
       "            train_size=None),\n",
       "             error_score=nan,\n",
       "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
       "                                            metric='minkowski',\n",
       "                                            metric_params=None, n_jobs=None,\n",
       "                                            n_neighbors=5, p=2,\n",
       "                                            weights='uniform'),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'n_neighbors': range(1, 31),\n",
       "                         'weights': ['uniform', 'distance']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "## trying out multiple values for k\n",
    "k_range = range(1,31)\n",
    "## \n",
    "weights_options=['uniform','distance']\n",
    "# \n",
    "param = {'n_neighbors':k_range, 'weights':weights_options}\n",
    "## Using startifiedShufflesplit. \n",
    "cv = StratifiedShuffleSplit(n_splits=10, test_size=.30, random_state=15)\n",
    "# estimator = knn, param_grid = param, n_jobs = -1 to instruct scikit learn to use all available processors. \n",
    "grid = GridSearchCV(KNeighborsClassifier(), param,cv=cv,verbose = False, n_jobs=-1)\n",
    "## Fitting the model. \n",
    "grid.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9666666666666668\n",
      "{'n_neighbors': 13, 'weights': 'distance'}\n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "                     metric_params=None, n_jobs=None, n_neighbors=13, p=2,\n",
      "                     weights='distance')\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_score_)\n",
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Using the best parameters from the grid-search.\n",
    "knn_grid= grid.best_estimator_\n",
    "knn_grid.score(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=StratifiedShuffleSplit(n_splits=10, random_state=None, test_size=0.3,\n",
       "            train_size=None),\n",
       "                   error_score=nan,\n",
       "                   estimator=KNeighborsClassifier(algorithm='auto',\n",
       "                                                  leaf_size=30,\n",
       "                                                  metric='minkowski',\n",
       "                                                  metric_params=None,\n",
       "                                                  n_jobs=None, n_neighbors=5,\n",
       "                                                  p=2, weights='uniform'),\n",
       "                   iid='deprecated', n_iter=40, n_jobs=-1,\n",
       "                   param_distributions={'n_neighbors': range(1, 31),\n",
       "                                        'weights': ['uniform', 'distance']},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "## trying out multiple values for k\n",
    "k_range = range(1,31)\n",
    "## \n",
    "weights_options=['uniform','distance']\n",
    "# \n",
    "param = {'n_neighbors':k_range, 'weights':weights_options}\n",
    "## Using startifiedShufflesplit. \n",
    "cv = StratifiedShuffleSplit(n_splits=10, test_size=.30)\n",
    "# estimator = knn, param_grid = param, n_jobs = -1 to instruct scikit learn to use all available processors. \n",
    "## for RandomizedSearchCV, \n",
    "grid = RandomizedSearchCV(KNeighborsClassifier(), param,cv=cv,verbose = False, n_jobs=-1, n_iter=40)\n",
    "## Fitting the model. \n",
    "grid.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9844444444444445\n",
      "{'weights': 'distance', 'n_neighbors': 13}\n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "                     metric_params=None, n_jobs=None, n_neighbors=13, p=2,\n",
      "                     weights='distance')\n"
     ]
    }
   ],
   "source": [
    "print (grid.best_score_)\n",
    "print (grid.best_params_)\n",
    "print(grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Using the best parameters from the grid-search.\n",
    "knn_ran_grid = grid.best_estimator_\n",
    "knn_ran_grid.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
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