Day 27 - Confidence Interval using bootstrap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "from pandas import read_csv\n",
    "from sklearn.utils import resample\n",
    "from sklearn.metrics import accuracy_score\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "ValueError",
     "evalue": "Percentiles must be in the range [0, 100]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-c844a776d70b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m95\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m \u001b[0mlower\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpercentile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmedians\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lenovo\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36mpercentile\u001b[1;34m(a, q, axis, out, overwrite_input, interpolation, keepdims)\u001b[0m\n\u001b[0;32m   3536\u001b[0m     \u001b[0mq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrue_divide\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100.0\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# handles the asarray for us too\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3537\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0m_quantile_is_valid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3538\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Percentiles must be in the range [0, 100]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3539\u001b[0m     return _quantile_unchecked(\n\u001b[0;32m   3540\u001b[0m         a, q, axis, out, overwrite_input, interpolation, keepdims)\n",
      "\u001b[1;31mValueError\u001b[0m: Percentiles must be in the range [0, 100]"
     ]
    }
   ],
   "source": [
    "#load dataset\n",
    "x=numpy.array([180,162,158,172,168,150,171,183,165,176])\n",
    "\n",
    "#config bootstrap\n",
    "n_iterations=1000\n",
    "n_size=int(len(x))\n",
    "\n",
    "#run bootstrap\n",
    "medians=list()\n",
    "for i in range(n_iterations):\n",
    "    #prepare train and test sets\n",
    "    s=resample(x,n_samples=n_size);\n",
    "    m=numpy.median(s);\n",
    "    #print(m)\n",
    "    medians.append(m)\n",
    "    \n",
    "#plot scores\n",
    "pyplot.hist(medians)\n",
    "pyplot.show()\n",
    "\n",
    "#confidence interval\n",
    "alpha=95\n",
    "p=((1.0-alpha)/2.0)*100\n",
    "lower=numpy.percentile(medians,p)\n",
    "\n",
    "p=(alpha+((1.0-alpha)/2.0))*100\n",
    "upper=numpy.percentile(medians,p)\n",
    "print(\"%.If confidence interval %. If and @.If\" % (alpha*100,lower,upper))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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