{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Moderne Methoden der Datenanalyse SS2024\n",
    "# Practical Exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2.1 (Voluntary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "“Should I carry an umbrella or should I risk to get wet?” A possible answer to this question\n",
    "is to look at the weather forecast. But as we all know it is not always reliable.\n",
    "Let’s assume that if it will rain, the forecast predicts this correctly in 80 % of the cases. If\n",
    "it will not rain, the forecast is assumed to be accurate in 90 % of the cases. In Sun-City\n",
    "the a-priori rain probability is only 5 %, in Equal-City it’s 50 % and in Rain-City it’s 95 %.\n",
    "Calculate (on a sheet of paper or with a short program) the four probabilities that it will\n",
    "(not) rain if (no) rain is predicted for the three cities.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two different risks of a wrong decision:\n",
    "- Carry an umbrella, but it does not rain.\n",
    "- Don’t carry an umbrella in case it rains."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which are the three possible strategies and which of them is the optimal one to minimize\n",
    "the risk of a wrong decision in each of the three cities? Calculate and compare the risk for\n",
    "each of the three possible strategies. Determine the optimal strategy when the second risk\n",
    "is considered 10 or 100 times more serious."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The history saving thread hit an unexpected error (OperationalError('attempt to write a readonly database')).History will not be written to the database.\n"
     ]
    }
   ],
   "source": [
    "def weather(iCity):\n",
    "    # A: it rains     B: forecasts predict rain\n",
    "    \n",
    "    pRainPredicted_if_Rain = 0.8          # P(B|A)\n",
    "    pRainNotPredicted_if_NotRain = 0.9    # P(not B| not A)\n",
    "    \n",
    "    pRain = [0.05, 0.5, 0.95]             # prior P(A)\n",
    "    cityName = [\"Sun-City\", \"Equal-City\", \"Rain-City\"]\n",
    "    \n",
    "    # P(not B|A) = 1 - P(B|A)\n",
    "    pRainNotPredicted_if_Rain = 1. - pRainPredicted_if_Rain\n",
    "    # P(B|not A) = 1 - P(not B|not A)\n",
    "    pRainPredicted_if_NotRain = 1. - pRainNotPredicted_if_NotRain\n",
    "    # P(not A) = 1 - P(A)\n",
    "    pNotRain = 1. - pRain[iCity]\n",
    "    \n",
    "    \n",
    "    # P(A and B) = P(A) * P(B|A)\n",
    "    pRain_and_RainPredicted = pRain[iCity] * pRainPredicted_if_Rain\n",
    "    # P(not A and B) = P(not A) * P(B|not A)\n",
    "    pNotRain_and_RainPredicted = pNotRain * pRainPredicted_if_NotRain\n",
    "    # P(A and not B) = P(A) * P(not B|A)\n",
    "    pRain_and_RainNotPredicted = pRain[iCity] * pRainNotPredicted_if_Rain\n",
    "    \n",
    "    \n",
    "    # P(B) = P(A and B) + P(not A and B)\n",
    "    pRainPredicted = pRain_and_RainPredicted + pNotRain_and_RainPredicted\n",
    "    # P(not B) = 1 - P(B)\n",
    "    pRainNotPredicted = 1. - pRainPredicted\n",
    "    \n",
    "    # TODO : Find all the four probability combinations using Bayes Theorem and print the results for each city\n",
    "    \n",
    "    # TODO : Evaluate the risk assesment\n",
    "    # Risk1: carry an umbrella but it does not rain\n",
    "    # Risk2: not carry an umbrella and it rains\n",
    "    # Strategies: A. always carry an umbrella , B. look at forecasts, C. never carry an umbrella\n",
    "    \n",
    "    print('\\n')\n",
    "    print('Strategy A: always carry an umbrella with you')\n",
    "    risk1_always = pNotRain;\n",
    "    risk2_always = 0.\n",
    "    print('   Risk 1: you carry an umbrella but it does not rain  p = {0:.1f} %'.format(risk1_always * 100))\n",
    "    print('   Risk 2: you don\\'t carry an umbrella and it rains   p = {0:.1f} %'.format(risk2_always * 100))\n",
    "    \n",
    "    # TODO: Determine the optimal strategy when Risk2 is considered 10 or 100 times more serious.\n",
    "    \n",
    "    # TODO: Evaluate the results for each city\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2.2 (Obligatory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test your calculations for the previous exercise ‘experimentally’ by writing a Monte Carlo\n",
    "simulation: Simulate N weather events, generate uniformly distributed random numbers\n",
    "between 0 and 1 for the rain forecast and compare them to the corresponding probability\n",
    "given in the above exercise. Make sure that you only use the values given in the text in\n",
    "your code. Then count the number of events in each category (rain and no rain predicted).\n",
    "Finally, use these numbers to determine the fraction of wrong weather forecasts and compare\n",
    "this number to the probability calculated before. Repeat the simulation for different values\n",
    "of N."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the any of the random number generators introduced in the first exercise sheet to setup this Monte Carlo experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "#from ROOT import gRandom\n",
    "\n",
    "def weatherMC(iCity, nDays):\n",
    "    \n",
    "    # A: it rains     B: forecasts predict rain\n",
    "    \n",
    "    pRainPredicted_if_Rain = 0.8          # P(B|A)\n",
    "    pRainNotPredicted_if_NotRain = 0.9    # P(not B| not A)\n",
    "    \n",
    "    pRain = [0.05, 0.5, 0.95]             # prior P(A)\n",
    "    cityName = [\"Sun-City\", \"Equal-City\", \"Rain-City\"]\n",
    "    \n",
    "    # Counter\n",
    "    countRain = 0\n",
    "    countNotRain = 0\n",
    "    countRainPredicted = 0\n",
    "    countRainNotPredicted = 0\n",
    "    countRain_RainPredicted = 0\n",
    "    countRain_RainNotPredicted = 0\n",
    "    countNotRain_RainPredicted = 0\n",
    "    countNotRain_RainNotPredicted = 0\n",
    "    \n",
    "    #gRandom.SetSeed()\n",
    "    \n",
    "    rainy = np.random.rand(nDays) < pRain[iCity]\n",
    "    r2 = np.random.rand(nDays)\n",
    "    pred = (r2 < pRainPredicted_if_Rain)*rainy + (r2 > pRainNotPredicted_if_NotRain)*(1-rainy)\n",
    "    \n",
    "    countRain = np.sum(rainy)\n",
    "    countNotRain = np.sum(1-rainy)\n",
    "    countRainPredicted = np.sum(pred)\n",
    "    countRainNotPredicted = np.sum(1-pred)\n",
    "    countRain_RainPredicted = np.sum(rainy*pred)\n",
    "    countRain_RainNotPredicted = np.sum(rainy*(1-pred))\n",
    "    countNotRain_RainPredicted = np.sum((1-rainy)*pred)\n",
    "    countNotRain_RainNotPredicted = np.sum((1-rainy)*(1-pred))\n",
    "    \n",
    "    #Print results\n",
    "    print(f\"P(rain)={countRain/nDays}, P(¬ rain)={countNotRain/nDays}\") #∩\n",
    "    print(f\"P(rainPred)={countRainPredicted/nDays}, P(¬rainPred)={countRainNotPredicted/nDays}\")\n",
    "    print(f\"P(rain ∩ rainPred)={countRain_RainPredicted/nDays}, P(rain ∩ ¬rainPred)={countRain_RainNotPredicted/nDays}, P(¬rain ∩ rainPred)={countNotRain_RainPredicted/nDays}, P(¬rain ∩ ¬rainPred)={countNotRain_RainNotPredicted/nDays}\")\n",
    "\n",
    "    print(f\"P( (rain ∩ ¬rainPred) + (¬rain ∩ rainPred) )= {(countRain_RainNotPredicted+countNotRain_RainPredicted)/nDays}\")\n",
    "    print(\"\")\n",
    "    print(f\"P_rainpred(¬rain) = {countNotRain_RainPredicted/countRainPredicted}\")\n",
    "    print(f\"P_¬rainpred(rain) = {countRain_RainNotPredicted/countRainNotPredicted}\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "P(rain)=0.049969, P(¬ rain)=0.950031\n",
      "P(rainPred)=0.135605, P(¬rainPred)=0.864395\n",
      "P(rain ∩ rainPred)=0.039911, P(rain ∩ ¬rainPred)=0.010058, P(¬rain ∩ rainPred)=0.095694, P(¬rain ∩ ¬rainPred)=0.854337\n",
      "P( (rain ∩ ¬rainPred) + (¬rain ∩ rainPred) )= 0.105752\n",
      "\n",
      "P_rainpred(¬rain) = 0.7056819438811254\n",
      "P_¬rainpred(rain) = 0.011635884057635687\n"
     ]
    }
   ],
   "source": [
    "#weatherMC(0,100)\n",
    "#weatherMC(1,100)\n",
    "#weatherMC(2,100)\n",
    "\n",
    "print(\"\")\n",
    "\n",
    "weatherMC(0,1000000)\n",
    "#weatherMC(1,1000000)\n",
    "#weatherMC(2,1000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Exercise 2.3 (Obligatory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A famous logical decision problem is the so-called ”Monty Hall Dilemma”, named after the\n",
    "host of an American television game show (”Let’s make a deal”). In German it is referred\n",
    "to as the ”Ziegenproblem”. Imagine you are contestant in a game show. There are three\n",
    "doors with an automobile behind one of them and goats behind the other two doors. The\n",
    "automobile and the two goats are assigned randomly to the three doors and you don’t have\n",
    "any prior knowledge about where the goats or the automobile are. ”First you point toward\n",
    "a door,” says the host of the show. ”Then I’ll open one of the other doors to reveal a goat.\n",
    "After I’ve shown you the goat, you make your final choice whether to stick with your initial\n",
    "choice of doors, or to switch to the remaining door. You win whatever is behind the door.”\n",
    "You begin by pointing to door number 1. The host shows you that door number 3 has a\n",
    "goat. What do you think - shall you stay with your initial choice (door 1) or switch to\n",
    "door number 2 to win the car? What are the probabilities to win the car? Calculate the\n",
    "probabilities by hand and write a programme that simulates a large number of such games\n",
    "to validate your calculated results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Theoretical Calculation"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Baum.png](attachment:771331ad-a826-4bd7-aa6c-87b7cd03af7c.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python Approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def MontyHall_calculation():  \n",
    "    #When you first walk into the gameshow, the probability of the car being behind one of the 3 doors is:\n",
    "    p_1 = 1./3\n",
    "    p_2 = 1./3\n",
    "    p_3 = 1./3\n",
    "    \n",
    "    #You choose door 1. \n",
    "    #If the prize is behind door 1 too, find the probability that the host reveals door 3.\n",
    "    1/2\n",
    "    \n",
    "    #If the prize is behind door 2, find the probability that the host reveals door 3. And the same if prize is behind door 3.\n",
    "    1, 0\n",
    "    \n",
    "    #The host reveals door 3 has a cute snow goat.\n",
    "    # Find The probability of the car being behind door 2, given that door 3 has been revealed.\n",
    "    2/3 == 1/3 + 1/3\n",
    "    \n",
    "    # TODO: Calculate the probalitiy of winning the car after switching the door 2 and also the probability to win a car after sticking to door 1\n",
    "    2/3, 1/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MontyHall(Switch_door, Ngames):\n",
    "    doors = [\"A\", \"B\", \"C\"]\n",
    "\n",
    "    goat_pos = np.random.randint(3, size=Ngames)\n",
    "    p_stay = np.sum(goat_pos == 0)/Ngames\n",
    "\n",
    "    p_switch = np.sum((1-(goat_pos == 0)))/Ngames\n",
    "    print(p_switch if Switch_door else p_stay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.33364\n",
      "0.66465\n"
     ]
    }
   ],
   "source": [
    "MontyHall(False, 100000)\n",
    "MontyHall(True, 100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.2"
  }
 },
 "nbformat": 4,
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}