20161026_StatsWithRuocong.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class HypothesisTest():\n",
    "    def __init__(self, data):\n",
    "        self.data = data\n",
    "        self.MakeModel()\n",
    "        self.actual = self.TestStatistic(data)\n",
    "\n",
    "    def PValue(self, iters=1000):\n",
    "        \"Returns p-value of actual data based on simulated data.\"\n",
    "        self.test_stats = [self.TestStatistic(self.RunModel()) \n",
    "                           for _ in range(iters)]\n",
    "\n",
    "        count = sum(1 for x in self.test_stats if x >= self.actual)\n",
    "        return count / iters\n",
    "\n",
    "    def TestStatistic(self, data):\n",
    "        \"Test statistic for the current test.\"\n",
    "        raise UnimplementedMethodException()\n",
    "\n",
    "    def MakeModel(self):\n",
    "        pass\n",
    "\n",
    "    def RunModel(self):\n",
    "        \"Returns a simulated data sample.\"\n",
    "        raise UnimplementedMethodException()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26367687539030094"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.random()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from math import sin\n",
    "\n",
    "class PileFaceTest(HypothesisTest):\n",
    "    \"data = [0, 0, 0, 0]\"\n",
    "\n",
    "    def TestStatistic(self, data):\n",
    "        return sum([sin(x) for x in data]) / len(data) \n",
    "    \n",
    "    def RunModel(self):\n",
    "        data = []\n",
    "        for _ in range(len(self.data)):\n",
    "            x = random.random()\n",
    "            data.append(x)\n",
    "        return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pft = PileFaceTest([0, 0, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.1917714179922031,\n",
       " 0.41156684614997496,\n",
       " 0.9335259573569674,\n",
       " 0.10698235419583546]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pft.RunModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21036774620197413"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pft.TestStatistic([0, 0, 1, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pft.actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pft.PValue(iters=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('bmh')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_test_stat(test, title=\"\"):\n",
    "    \"Plots the test statistic distribution and observed value.\"\n",
    "    plt.hist(test.test_stats, bins=30, cumulative=False, normed=True)\n",
    "    ylim = plt.ylim()\n",
    "    plt.vlines(test.actual, *ylim, label='observed test stat')\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.xlabel('test statistic')\n",
    "    plt.title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.1, 1)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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USvISG6lGiezpsWdSDYuQjwHMRJTkYyy0MT5+v+RFfr314E7nF+LYM1GFto8hzD5HoZq8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\niEiM/X9TJSfCtWSCWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb41da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_test_stat(pft)\n",
    "plt.xlim(-0.1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pft2 = PileFaceTest()"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}