Buynak-Yinyying-Mildige Submission

Problem 1.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2) Plot the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "data=pd.read_table('chickwts.txt',sep=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x115b369d0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.boxplot(by='feed',column='weight',grid=False,figsize=(15,15))\n",
+    "# I am not sure why somehow there is no figure in Jupyter but I did get a barplot in python. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## 3) A hypothesis test\n",
+    "\n",
+    "### Null hypothesis: the weight of chick fed by soybean has no significant difference with the weight of chick fed by sunflower\n",
+    "### Alternative hypothesis: the weight of chick fed by soybean has significant difference with the weight of chick fed by sunflower"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4) Hypothesis test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "le=LabelEncoder()\n",
+    "le.fit(data['feed'])\n",
+    "data['Index']=le.transform(data['feed'])\n",
+    "x=data[(data['feed']=='soybean')|(data['feed']=='sunflower')]['Index']\n",
+    "y=data[(data['feed']=='soybean')|(data['feed']=='sunflower')]['weight']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 145.240592\n",
+      "         Iterations: 85\n",
+      "         Function evaluations: 162\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([ 284.49999051,   64.53691755])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.optimize import minimize\n",
+    "from scipy.stats import norm\n",
+    "\n",
+    "# null hypothesis\n",
+    "def null(p,obs):\n",
+    "    B0=p[0]\n",
+    "    sigma=p[1]\n",
+    "    \n",
+    "    expected=B0\n",
+    "    nll=-1*norm(expected,sigma).logpdf(obs).sum()\n",
+    "    return nll\n",
+    "\n",
+    "initialGuess1=np.array([1,1])\n",
+    "fitNull=minimize(null,initialGuess1,method=\"Nelder-Mead\",options={'disp':True},args=(y))\n",
+    "fitNull.x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 138.469162\n",
+      "         Iterations: 194\n",
+      "         Function evaluations: 360\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([-83.52384926,  82.48810462,  49.73945446])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# alternative hypothesis\n",
+    "def alter(p,obs1,obs2):\n",
+    "    B0=p[0]\n",
+    "    B1=p[1]\n",
+    "    sigma=p[2]\n",
+    "    \n",
+    "    expected=B0+B1*obs1\n",
+    "    nll=-1*norm(expected,sigma).logpdf(obs2).sum()\n",
+    "    return nll\n",
+    "\n",
+    "initialGuess1=np.array([1,1,1])\n",
+    "fitalter=minimize(alter,initialGuess1,method=\"Nelder-Mead\",options={'disp':True},args=(x,y))\n",
+    "fitalter.x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0002331767286918307"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import chi2\n",
+    "D=2*(fitNull.fun-fitalter.fun)\n",
+    "1-chi2.cdf(x=D,df=1) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5) Hypothesis test result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## If we consider alfa=0.05, then p value is smaller than 0.05, we can reject the null hyphothesis and conclude that the weight of chick fed by soybean has significant difference with the weight of chick fed by sunflower. "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Problem 2.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1) Times after noon but before midnight, reported in 24 hours or \"military\" format \n",
+    "\n",
+    "### Times after noon and before midnight, so the numbes before comma should be larger than 12 and smaller than 24.  Meanwhile, the number for minute should be in the range of 0-60."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['15:30']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import re as re\n",
+    "reg1=re.compile('([1][2-9]\\:([0-5][0-9]|[6][0]))|([2][0-3]\\:([0-5][0-9]|[6][0]))')\n",
+    "testset1=['15:30','24:00']\n",
+    "filter(reg1.match,testset1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2) Genus species names \n",
+    "\n",
+    "### First letter should be capitalized, then it comes with the \".\", then six letters in the lower case. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['H.sapien']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg2=re.compile('[A-Z]{1}\\.[a-z]{6}')\n",
+    "testset2=['H.sapien','h.sapien']\n",
+    "filter(reg2.match,testset2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3) Social security numbers\n",
+    "\n",
+    "### Three numbers, then \"-\", then two numbers, then \"-\", then four numbers "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['389-05-4771']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg3=re.compile('[0-9]{3}\\-[0-9]{2}\\-[0-9]{4}')\n",
+    "testset3=['389-05-4771','38-051-41']\n",
+    "filter(reg3.match,testset3)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}