From cc054298396a88937de4303d72c393ab0d0468c7 Mon Sep 17 00:00:00 2001 From: Chris Beaumont Date: Wed, 8 Jan 2014 09:16:53 -0500 Subject: [PATCH] Revert "update README" This reverts commit 5670dd6c0107d2ec7364bb7dbe4fccea58589dc8. --- HW0_solutions.ipynb | 795 +++++++++ HW1_solutions.ipynb | 1319 +++++++++++++++ HW2_solutions.ipynb | 3545 +++++++++++++++++++++++++++++++++++++++ HW3_solutions.ipynb | 1467 ++++++++++++++++ HW4_solutions.ipynb | 3857 +++++++++++++++++++++++++++++++++++++++++++ HW5_solutions.ipynb | 977 +++++++++++ README.md | 12 +- 7 files changed, 11966 insertions(+), 6 deletions(-) create mode 100644 HW0_solutions.ipynb create mode 100644 HW1_solutions.ipynb create mode 100644 HW2_solutions.ipynb create mode 100644 HW3_solutions.ipynb create mode 100644 HW4_solutions.ipynb create mode 100644 HW5_solutions.ipynb diff --git a/HW0_solutions.ipynb b/HW0_solutions.ipynb new file mode 100644 index 0000000..a8c3857 --- /dev/null +++ b/HW0_solutions.ipynb @@ -0,0 +1,795 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Homework 0\n", + "\n", + "### Due Tuesday, September 10 (but no submission is required)\n", + "\n", + "---\n", + "\n", + "Welcome to CS109 / STAT121 / AC209 / E-109 (http://cs109.org/). In this class, we will be using a variety of tools that will require some initial configuration. To ensure everything goes smoothly moving forward, we will setup the majority of those tools in this homework. While some of this will likely be dull, doing it now will enable us to do more exciting work in the weeks that follow without getting bogged down in further software configuration. This homework will not be graded, however it is essential that you complete it timely since it will enable us to set up your accounts. You do not have to hand anything in, with the exception of filling out the online survey. \n", + "\n", + "## Class Survey, Piazza, and Introduction\n", + "\n", + "**Class Survey**\n", + "\n", + "Please complete the mandatory course survey located [here](https://docs.google.com/spreadsheet/viewform?formkey=dFg1ZFJwLWJ6ZWhWR1JJb0tES3lGMEE6MA#gid=0). It should only take a few moments of your time. Once you fill in the survey we will sign you up to the course forum on Piazza and the dropbox system that you will use to hand in the homework. It is imperative that you fill out the survey on time as we use the provided information to sign you up for these services. \n", + "\n", + "**Piazza**\n", + "\n", + "Go to [Piazza](https://piazza.com/harvard/fall2013/cs109/home) and sign up for the class using your Harvard e-mail address. \n", + "\n", + "You will use Piazza as a forum for discussion, to find team members, to arrange appointments, and to ask questions. Piazza should be your primary form of communication with the staff. Use the staff e-mail (staff@cs109.org) only for individual requests, e.g., to excuse yourself from a mandatory guest lecture. All readings, homeworks, and project descriptions will be announced on Piazza first. \n", + "\n", + "**Introduction**\n", + "\n", + "Once you are signed up to the Piazza course forum, introduce yourself to your classmates and course staff with a follow-up post in the introduction thread. Include your name/nickname, your affiliation, why you are taking this course, and tell us something interesting about yourself (e.g., an industry job, an unusual hobby, past travels, or a cool project you did, etc.). Also tell us whether you have experience with data science. \n", + "\n", + "## Programming expectations\n", + "\n", + "All the assignments and labs for this class will use Python and, for the most part, the browser-based IPython notebook format you are currently viewing. Knowledge of Python is not a prerequisite for this course, **provided you are comfortable learning on your own as needed**. While we have strived to make the programming component of this course straightforward, we will not devote much time to teaching prorgramming or Python syntax. Basically, you should feel comfortable with:\n", + "\n", + "* How to look up Python syntax on Google and StackOverflow.\n", + "* Basic programming concepts like functions, loops, arrays, dictionaries, strings, and if statements.\n", + "* How to learn new libraries by reading documentation.\n", + "* Asking questions on StackOverflow or Piazza.\n", + "\n", + "There are many online tutorials to introduce you to scientific python programming. [Here is one](https://github.com/jrjohansson/scientific-python-lectures) that is very nice. Lectures 1-4 are most relevant to this class.\n", + "\n", + "## Getting Python\n", + "\n", + "You will be using Python throughout the course, including many popular 3rd party Python libraries for scientific computing. [Anaconda](http://continuum.io/downloads) is an easy-to-install bundle of Python and most of these libraries. We recommend that you use Anaconda for this course.\n", + "\n", + "Please visit [this page](https://github.com/cs109/content/wiki/Installing-Python) and follow the instructions to set up Python\n", + "\n", + "\n", + "\n", + "## Hello, Python\n", + "\n", + "The IPython notebook is an application to build interactive computational notebooks. You'll be using them to complete labs and homework. Once you've set up Python, please download this page, and open it with IPython by typing\n", + "\n", + "```\n", + "ipython notebook \n", + "```\n", + "\n", + "For the rest of the assignment, use your local copy of this page, running on IPython.\n", + "\n", + "Notebooks are composed of many \"cells\", which can contain text (like this one), or code (like the one below). Double click on the cell below, and evaluate it by clicking the \"play\" button above, for by hitting shift + enter" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "x = [10, 20, 30, 40, 50]\n", + "for item in x:\n", + " print \"Item is \", item" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Item is 10\n", + "Item is 20\n", + "Item is 30\n", + "Item is 40\n", + "Item is 50\n" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Python Libraries\n", + "\n", + "We will be using a several different libraries throughout this course. If you've successfully completed the [installation instructions](https://github.com/cs109/content/wiki/Installing-Python), all of the following statements should run." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#IPython is what you are using now to run the notebook\n", + "import IPython\n", + "print \"IPython version: %6.6s (need at least 1.0)\" % IPython.__version__\n", + "\n", + "# Numpy is a library for working with Arrays\n", + "import numpy as np\n", + "print \"Numpy version: %6.6s (need at least 1.7.1)\" % np.__version__\n", + "\n", + "# SciPy implements many different numerical algorithms\n", + "import scipy as sp\n", + "print \"SciPy version: %6.6s (need at least 0.12.0)\" % sp.__version__\n", + "\n", + "# Pandas makes working with data tables easier\n", + "import pandas as pd\n", + "print \"Pandas version: %6.6s (need at least 0.11.0)\" % pd.__version__\n", + "\n", + "# Module for plotting\n", + "import matplotlib\n", + "print \"Mapltolib version: %6.6s (need at least 1.2.1)\" % matplotlib.__version__\n", + "\n", + "# SciKit Learn implements several Machine Learning algorithms\n", + "import sklearn\n", + "print \"Scikit-Learn version: %6.6s (need at least 0.13.1)\" % sklearn.__version__\n", + "\n", + "# Requests is a library for getting data from the Web\n", + "import requests\n", + "print \"requests version: %6.6s (need at least 1.2.3)\" % requests.__version__\n", + "\n", + "# Networkx is a library for working with networks\n", + "import networkx as nx\n", + "print \"NetworkX version: %6.6s (need at least 1.7)\" % nx.__version__\n", + "\n", + "#BeautifulSoup is a library to parse HTML and XML documents\n", + "import BeautifulSoup\n", + "print \"BeautifulSoup version:%6.6s (need at least 3.2)\" % BeautifulSoup.__version__\n", + "\n", + "#MrJob is a library to run map reduce jobs on Amazon's computers\n", + "import mrjob\n", + "print \"Mr Job version: %6.6s (need at least 0.4)\" % mrjob.__version__\n", + "\n", + "#Pattern has lots of tools for working with data from the internet\n", + "import pattern\n", + "print \"Pattern version: %6.6s (need at least 2.6)\" % pattern.__version__" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "IPython version: 1.0.0 (need at least 1.0)\n", + "Numpy version: 1.7.1 (need at least 1.7.1)\n", + "SciPy version: 0.12.0 (need at least 0.12.0)\n", + "Pandas version: 0.11.0 (need at least 0.11.0)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "Mapltolib version: 1.4.x (need at least 1.2.1)\n", + "Scikit-Learn version: 0.14.1 (need at least 0.13.1)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "requests version: 1.2.3 (need at least 1.2.3)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "NetworkX version: 1.7 (need at least 1.7)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "BeautifulSoup version: 3.2.1 (need at least 3.2)\n", + "Mr Job version: 0.4 (need at least 0.4)\n", + "Pattern version: 2.6 (need at least 2.6)\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If any of these libraries are missing or out of date, you will need to [install them](https://github.com/cs109/content/wiki/Installing-Python#installing-additional-libraries) and restart IPython" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hello matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The notebook integrates nicely with Matplotlib, the primary plotting package for python. This should embed a figure of a sine wave:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#this line prepares IPython for working with matplotlib\n", + "%matplotlib inline \n", + "\n", + "# this actually imports matplotlib\n", + "import matplotlib.pyplot as plt \n", + "\n", + "x = np.linspace(0, 10, 30) #array of 30 points from 0 to 10\n", + "y = np.sin(x)\n", + "z = y + np.random.normal(size=30) * .2\n", + "plt.plot(x, y, 'ro-', label='A sine wave')\n", + "plt.plot(x, z, 'b-', label='Noisy sine')\n", + "plt.legend(loc = 'lower right')\n", + "plt.xlabel(\"X axis\")\n", + "plt.ylabel(\"Y axis\") " + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 3, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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Rm2vNmhwb83TaMT3z559YOekejrWYiP37tbiUqj6YORNITMTdST+gY0cgNlaz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\nA61atVJ6gIW+ev78Ofr06QM7Ozs0btwYZ86ckTo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+ "text": [ + "" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If that last cell complained about the `%matplotlib` line, you need to update IPython to v1.0, and restart the notebook. See the [installation page](https://github.com/cs109/content/wiki/Installing-Python)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hello Numpy\n", + "\n", + "The Numpy array processing library is the basis of nearly all numerical computing in Python. Here's a 30 second crash course. For more details, consult Chapter 4 of Python for Data Analysis, or the [Numpy User's Guide](http://docs.scipy.org/doc/numpy-dev/user/index.html)" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print \"Make a 3 row x 4 column array of random numbers\"\n", + "x = np.random.random((3, 4))\n", + "print x\n", + "print\n", + "\n", + "print \"Add 1 to every element\"\n", + "x = x + 1\n", + "print x\n", + "print\n", + "\n", + "print \"Get the element at row 1, column 2\"\n", + "print x[1, 2]\n", + "print\n", + "\n", + "# The colon syntax is called \"slicing\" the array. \n", + "print \"Get the first row\"\n", + "print x[0, :]\n", + "print\n", + "\n", + "print \"Get every 2nd column of the first row\"\n", + "print x[0, ::2]\n", + "print" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Make a 3 row x 4 column array of random numbers\n", + "[[ 0.57900652 0.03366009 0.16879928 0.75102823]\n", + " [ 0.1953485 0.84906771 0.23505389 0.23498041]\n", + " [ 0.54731531 0.79778484 0.55777833 0.0765986 ]]\n", + "\n", + "Add 1 to every element\n", + "[[ 1.57900652 1.03366009 1.16879928 1.75102823]\n", + " [ 1.1953485 1.84906771 1.23505389 1.23498041]\n", + " [ 1.54731531 1.79778484 1.55777833 1.0765986 ]]\n", + "\n", + "Get the element at row 1, column 2\n", + "1.23505388985\n", + "\n", + "Get the first row\n", + "[ 1.57900652 1.03366009 1.16879928 1.75102823]\n", + "\n", + "Get every 2nd column of the first row\n", + "[ 1.57900652 1.16879928]\n", + "\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print the maximum, minimum, and mean of the array. This does **not** require writing a loop. In the code cell below, type `x.m`, to find built-in operations for common array statistics like this" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "print \"Max is \", x.max()\n", + "print \"Min is \", x.min()\n", + "print \"Mean is \", x.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Max is 1.84906771031\n", + "Min is 1.03366009099\n", + "Mean is 1.41886847666\n" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the `x.max` function again, but use the `axis` keyword to print the maximum of each row in x." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "print x.max(axis=1)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[ 1.75102823 1.84906771 1.79778484]\n" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a way to quickly simulate 500 coin \"fair\" coin tosses (where the probabily of getting Heads is 50%, or 0.5)" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "x = np.random.binomial(500, .5)\n", + "print \"number of heads:\", x" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "number of heads: 258\n" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Repeat this simulation 500 times, and use the [plt.hist() function](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist) to plot a histogram of the number of Heads (1s) in each simulation" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "\n", + "# 3 ways to run the simulations\n", + "\n", + "# loop\n", + "heads = []\n", + "for i in range(500):\n", + " heads.append(np.random.binomial(500, .5))\n", + "\n", + "# \"list comprehension\"\n", + "heads = [np.random.binomial(500, .5) for i in range(500)]\n", + "\n", + "# pure numpy\n", + "heads = np.random.binomial(500, .5, size=500)\n", + "\n", + "histogram = plt.hist(heads, bins=10)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "heads.shape" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 15, + "text": [ + "(500,)" + ] + } + ], + "prompt_number": 15 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Monty Hall Problem\n", + "\n", + "\n", + "Here's a fun and perhaps surprising statistical riddle, and a good way to get some practice writing python functions\n", + "\n", + "In a gameshow, contestants try to guess which of 3 closed doors contain a cash prize (goats are behind the other two doors). Of course, the odds of choosing the correct door are 1 in 3. As a twist, the host of the show occasionally opens a door after a contestant makes his or her choice. This door is always one of the two the contestant did not pick, and is also always one of the goat doors (note that it is always possible to do this, since there are two goat doors). At this point, the contestant has the option of keeping his or her original choice, or swtiching to the other unopened door. The question is: is there any benefit to switching doors? The answer surprises many people who haven't heard the question before.\n", + "\n", + "We can answer the problem by running simulations in Python. We'll do it in several parts.\n", + "\n", + "First, write a function called `simulate_prizedoor`. This function will simulate the location of the prize in many games -- see the detailed specification below:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "simulate_prizedoor\n", + "\n", + "Generate a random array of 0s, 1s, and 2s, representing\n", + "hiding a prize between door 0, door 1, and door 2\n", + "\n", + "Parameters\n", + "----------\n", + "nsim : int\n", + " The number of simulations to run\n", + "\n", + "Returns\n", + "-------\n", + "sims : array\n", + " Random array of 0s, 1s, and 2s\n", + "\n", + "Example\n", + "-------\n", + ">>> print simulate_prizedoor(3)\n", + "array([0, 0, 2])\n", + "\"\"\"\n", + "def simulate_prizedoor(nsim):\n", + " #compute here\n", + " return answer\n", + "#your code here\n", + "\n", + "def simulate_prizedoor(nsim):\n", + " return np.random.randint(0, 3, (nsim))" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, write a function that simulates the contestant's guesses for `nsim` simulations. Call this function `simulate_guess`. The specs:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "simulate_guess\n", + "\n", + "Return any strategy for guessing which door a prize is behind. This\n", + "could be a random strategy, one that always guesses 2, whatever.\n", + "\n", + "Parameters\n", + "----------\n", + "nsim : int\n", + " The number of simulations to generate guesses for\n", + "\n", + "Returns\n", + "-------\n", + "guesses : array\n", + " An array of guesses. Each guess is a 0, 1, or 2\n", + "\n", + "Example\n", + "-------\n", + ">>> print simulate_guess(5)\n", + "array([0, 0, 0, 0, 0])\n", + "\"\"\"\n", + "#your code here\n", + "\n", + "def simulate_guess(nsim):\n", + " return np.zeros(nsim, dtype=np.int)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 10 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, write a function, `goat_door`, to simulate randomly revealing one of the goat doors that a contestant didn't pick." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "goat_door\n", + "\n", + "Simulate the opening of a \"goat door\" that doesn't contain the prize,\n", + "and is different from the contestants guess\n", + "\n", + "Parameters\n", + "----------\n", + "prizedoors : array\n", + " The door that the prize is behind in each simulation\n", + "guesses : array\n", + " THe door that the contestant guessed in each simulation\n", + "\n", + "Returns\n", + "-------\n", + "goats : array\n", + " The goat door that is opened for each simulation. Each item is 0, 1, or 2, and is different\n", + " from both prizedoors and guesses\n", + "\n", + "Examples\n", + "--------\n", + ">>> print goat_door(np.array([0, 1, 2]), np.array([1, 1, 1]))\n", + ">>> array([2, 2, 0])\n", + "\"\"\"\n", + "#your code here\n", + "\n", + "def goat_door(prizedoors, guesses):\n", + " \n", + " #strategy: generate random answers, and\n", + " #keep updating until they satisfy the rule\n", + " #that they aren't a prizedoor or a guess\n", + " result = np.random.randint(0, 3, prizedoors.size)\n", + " while True:\n", + " bad = (result == prizedoors) | (result == guesses)\n", + " if not bad.any():\n", + " return result\n", + " result[bad] = np.random.randint(0, 3, bad.sum())" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Write a function, `switch_guess`, that represents the strategy of always switching a guess after the goat door is opened." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "switch_guess\n", + "\n", + "The strategy that always switches a guess after the goat door is opened\n", + "\n", + "Parameters\n", + "----------\n", + "guesses : array\n", + " Array of original guesses, for each simulation\n", + "goatdoors : array\n", + " Array of revealed goat doors for each simulation\n", + "\n", + "Returns\n", + "-------\n", + "The new door after switching. Should be different from both guesses and goatdoors\n", + "\n", + "Examples\n", + "--------\n", + ">>> print switch_guess(np.array([0, 1, 2]), np.array([1, 2, 1]))\n", + ">>> array([2, 0, 0])\n", + "\"\"\"\n", + "#your code here\n", + "\n", + "def switch_guess(guesses, goatdoors):\n", + " result = np.zeros(guesses.size)\n", + " switch = {(0, 1): 2, (0, 2): 1, (1, 0): 2, (1, 2): 1, (2, 0): 1, (2, 1): 0}\n", + " for i in [0, 1, 2]:\n", + " for j in [0, 1, 2]:\n", + " mask = (guesses == i) & (goatdoors == j)\n", + " if not mask.any():\n", + " continue\n", + " result = np.where(mask, np.ones_like(result) * switch[(i, j)], result)\n", + " return result" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Last function: write a `win_percentage` function that takes an array of `guesses` and `prizedoors`, and returns the percent of correct guesses" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "win_percentage\n", + "\n", + "Calculate the percent of times that a simulation of guesses is correct\n", + "\n", + "Parameters\n", + "-----------\n", + "guesses : array\n", + " Guesses for each simulation\n", + "prizedoors : array\n", + " Location of prize for each simulation\n", + "\n", + "Returns\n", + "--------\n", + "percentage : number between 0 and 100\n", + " The win percentage\n", + "\n", + "Examples\n", + "---------\n", + ">>> print win_percentage(np.array([0, 1, 2]), np.array([0, 0, 0]))\n", + "33.333\n", + "\"\"\"\n", + "#your code here\n", + "\n", + "def win_percentage(guesses, prizedoors):\n", + " return 100 * (guesses == prizedoors).mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 13 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, put it together. Simulate 10000 games where contestant keeps his original guess, and 10000 games where the contestant switches his door after a goat door is revealed. Compute the percentage of time the contestant wins under either strategy. Is one strategy better than the other?" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "\n", + "nsim = 10000\n", + "\n", + "#keep guesses\n", + "print \"Win percentage when keeping original door\"\n", + "print win_percentage(simulate_prizedoor(nsim), simulate_guess(nsim))\n", + "\n", + "#switch\n", + "pd = simulate_prizedoor(nsim)\n", + "guess = simulate_guess(nsim)\n", + "goats = goat_door(pd, guess)\n", + "guess = switch_guess(guess, goats)\n", + "print \"Win percentage when switching doors\"\n", + "print win_percentage(pd, guess).mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Win percentage when keeping original door\n", + "32.35\n", + "Win percentage when switching doors\n", + "67.14\n" + ] + } + ], + "prompt_number": 14 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many people find this answer counter-intuitive (famously, PhD mathematicians have incorrectly claimed the result must be wrong. Clearly, none of them knew Python). \n", + "\n", + "One of the best ways to build intuition about why opening a Goat door affects the odds is to re-run the experiment with 100 doors and one prize. If the game show host opens 98 goat doors after you make your initial selection, would you want to keep your first pick or switch? Can you generalize your simulation code to handle the case of `n` doors?" + ] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/HW1_solutions.ipynb b/HW1_solutions.ipynb new file mode 100644 index 0000000..eebdd1d --- /dev/null +++ b/HW1_solutions.ipynb @@ -0,0 +1,1319 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Homework 1. Which of two things is larger? (SOLUTIONS) \n", + "\n", + "Due: Thursday, September 19, 11:59 PM\n", + "\n", + " Download this assignment\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Useful libraries for this assignment\n", + "\n", + "* [numpy](http://docs.scipy.org/doc/numpy-dev/user/index.html), for arrays\n", + "* [pandas](http://pandas.pydata.org/), for data frames\n", + "* [matplotlib](http://matplotlib.org/), for plotting\n", + "* [requests](http://docs.python-requests.org/en/latest/), for downloading web content\n", + "* [pattern](http://www.clips.ua.ac.be/pages/pattern), for parsing html and xml pages\n", + "* [fnmatch](http://docs.python.org/2/library/fnmatch.html) (optional), for Unix-style string matching" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# special IPython command to prepare the notebook for matplotlib\n", + "%matplotlib inline \n", + "\n", + "from fnmatch import fnmatch\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import requests\n", + "from pattern import web\n", + "\n", + "\n", + "# set some nicer defaults for matplotlib\n", + "from matplotlib import rcParams\n", + "\n", + "#these colors come from colorbrewer2.org. Each is an RGB triplet\n", + "dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),\n", + " (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),\n", + " (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),\n", + " (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),\n", + " (0.4, 0.6509803921568628, 0.11764705882352941),\n", + " (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),\n", + " (0.6509803921568628, 0.4627450980392157, 0.11372549019607843),\n", + " (0.4, 0.4, 0.4)]\n", + "\n", + "rcParams['figure.figsize'] = (10, 6)\n", + "rcParams['figure.dpi'] = 150\n", + "rcParams['axes.color_cycle'] = dark2_colors\n", + "rcParams['lines.linewidth'] = 2\n", + "rcParams['axes.grid'] = True\n", + "rcParams['axes.facecolor'] = '#eeeeee'\n", + "rcParams['font.size'] = 14\n", + "rcParams['patch.edgecolor'] = 'none'" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This was the [XKCD comic](http://xkcd.com/1131/) after the 2012 Presidential election:\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The comic refers to the fact that Nate Silver's statistical model (which is based mostly on combining information from pre-election polls) correctly predicted the outcome of the 2012 presidential race in all 50 states. \n", + "\n", + "Polling data isn't a perfect predictor for the future, and some polls are more accurate than others. This means that election forecastors must consider prediction uncertainty when building models.\n", + "\n", + "In this first assignment, you will perform a simple analysis of polling data about the upcoming Governor races. The assignment has three main parts:\n", + "\n", + "**First** you will build some tools to download historical polling data from the web, and parse it into a more convenient format. \n", + "\n", + "**Next** you will use these tools to aggregate and visualize several past Governor races\n", + "\n", + "**Finally** you will run a bootstrap analysis to estimate the probable outcome of current Governor races, given the level of precision of historical polls.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Part 1: Collect and Clean\n", + "\n", + "The [Real Clear Politics](http://www.realclearpolitics.com) website archives many political polls. In addition, they combine related polls to form an \"RCP average\" estimate of public opinion over time. For example, the chart on [this page](http://www.realclearpolitics.com/epolls/2012/president/us/general_election_romney_vs_obama-1171.html) shows historical polling data for the Obama-Romney presidential race. The chart is an average of the polling data table below the chart.\n", + "\n", + "The data used to generate plots like this are stored as XML pages, with URLs like:\n", + "\n", + "http://charts.realclearpolitics.com/charts/[id].xml\n", + "\n", + "Here, [id] is a unique integer, found at the end of the URL of the page that displays the graph. The id for the Obama-Romney race is 1171:\n", + "\n", + "http://charts.realclearpolitics.com/charts/1171.xml\n", + "\n", + "Opening this page in Google Chrome or Firefox will show you the XML content in an easy-to-read format. Notice that XML tags are nested inside each other, hierarchically (the jargony term for this is the \"Document Object Model\", or \"DOM\"). The first step of webscraping is almost always exploring the HTML/XML source in a browser, and getting a sense of this hierarchy.\n", + "\n", + "---\n", + "\n", + "#### Problem 0\n", + "\n", + "The above XML page includes 5 distinct tags (one, for example, is `chart`). List these tags, and depict how they nest inside each other using an indented list. For example:\n", + "\n", + "* Page\n", + " * Section\n", + " * Paragraph\n", + " * Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* chart\n", + " * series\n", + " * value\n", + " * graphs\n", + " * graph\n", + " * value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "#### Problem 1\n", + "\n", + "We want to download and work with poll data like this. Like most programming tasks, we will break this into many smaller, easier pieces\n", + "\n", + "Fill in the code for the `get_poll_xml` function, that finds and downloads an XML page discussed above\n", + "\n", + "**Hint** \n", + "\n", + "`requests.get(\"http://www.google.com\").text` downloads the text from Google's homepage" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "get_poll_xml\n", + "\n", + "Given a poll_id, return the XML data as a text string\n", + "\n", + "Inputs\n", + "------\n", + "poll_id : int\n", + " The ID of the poll to fetch\n", + "\n", + "Returns\n", + "-------\n", + "xml : str\n", + " The text of the XML page for that poll_id\n", + "\n", + "Example\n", + "-------\n", + ">>> get_poll_xml(1044)\n", + "u'1/27/2009\n", + "...etc...\n", + "\"\"\" \n", + "#your code here \n", + "\n", + "def get_poll_xml(poll_id):\n", + " url = \"http://charts.realclearpolitics.com/charts/%i.xml\" % int(poll_id)\n", + " return requests.get(url).text" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are some other functions we'll use later. `plot_colors` contains hints about parsing XML data." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# \"r\"egular \"e\"xpressions is kind of a mini-language to\n", + "# do pattern matching on text\n", + "import re\n", + "\n", + "def _strip(s):\n", + " \"\"\"This function removes non-letter characters from a word\n", + " \n", + " for example _strip('Hi there!') == 'Hi there'\n", + " \"\"\"\n", + " return re.sub(r'[\\W_]+', '', s)\n", + "\n", + "def plot_colors(xml):\n", + " \"\"\"\n", + " Given an XML document like the link above, returns a python dictionary\n", + " that maps a graph title to a graph color.\n", + " \n", + " Both the title and color are parsed from attributes of the tag:\n", + " -> {'the title': '#ff0000'}\n", + " \n", + " These colors are in \"hex string\" format. This page explains them:\n", + " http://coding.smashingmagazine.com/2012/10/04/the-code-side-of-color/\n", + " \n", + " Example\n", + " -------\n", + " >>> plot_colors(get_poll_xml(1044))\n", + " {u'Approve': u'#000000', u'Disapprove': u'#FF0000'}\n", + " \"\"\"\n", + " dom = web.Element(xml)\n", + " result = {}\n", + " for graph in dom.by_tag('graph'):\n", + " title = _strip(graph.attributes['title'])\n", + " result[title] = graph.attributes['color']\n", + " return result" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 10 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "#### Problem 2\n", + "\n", + "Even though `get_poll_xml` pulls data from the web into Python, it does so as a block of text. This still isn't very useful. Use the `web` module in `pattern` to parse this text, and extract data into a pandas DataFrame.\n", + "\n", + "**Hints**\n", + "\n", + "* You might want create python lists for each column in the XML. Then, to turn these lists into a DataFrame, run\n", + "\n", + "`pd.DataFrame({'column_label_1': list_1, 'column_label_2':list_2, ...})`\n", + "\n", + "* use the pandas function `pd.to_datetime` to convert strings into dates" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + " Function\n", + " ---------\n", + " rcp_poll_data\n", + "\n", + " Extract poll information from an XML string, and convert to a DataFrame\n", + "\n", + " Parameters\n", + " ----------\n", + " xml : str\n", + " A string, containing the XML data from a page like \n", + " get_poll_xml(1044)\n", + " \n", + " Returns\n", + " -------\n", + " A pandas DataFrame with the following columns:\n", + " date: The date for each entry\n", + " title_n: The data value for the gid=n graph (take the column name from the `title` tag)\n", + " \n", + " This DataFrame should be sorted by date\n", + " \n", + " Example\n", + " -------\n", + " Consider the following simple xml page:\n", + " \n", + " \n", + " \n", + " 1/27/2009\n", + " 1/28/2009\n", + " \n", + " \n", + " \n", + " 63.3\n", + " 63.3\n", + " \n", + " \n", + " 20.0\n", + " 20.0\n", + " \n", + " \n", + " \n", + " \n", + " Given this string, rcp_poll_data should return\n", + " result = pd.DataFrame({'date': pd.to_datetime(['1/27/2009', '1/28/2009']), \n", + " 'Approve': [63.3, 63.3], 'Disapprove': [20.0, 20.0]})\n", + "\"\"\"\n", + "#your code here\n", + " \n", + "def rcp_poll_data(xml): \n", + " dom = web.Element(xml)\n", + " result = {}\n", + " \n", + " dates = dom.by_tag('series')[0] \n", + " dates = {n.attributes['xid']: str(n.content) for n in dates.by_tag('value')}\n", + " \n", + " keys = dates.keys()\n", + " \n", + " result['date'] = pd.to_datetime([dates[k] for k in keys])\n", + " \n", + " for graph in dom.by_tag('graph'):\n", + " name = graph.attributes['title']\n", + " data = {n.attributes['xid']: float(n.content) \n", + " if n.content else np.nan for n in graph.by_tag('value')}\n", + " result[name] = [data[k] for k in keys]\n", + " \n", + " result = pd.DataFrame(result) \n", + " result = result.sort(columns=['date'])\n", + " \n", + " return result" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output from `rcp_poll_data` is much more useful for analysis. For example, we can plot with it:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def poll_plot(poll_id):\n", + " \"\"\"\n", + " Make a plot of an RCP Poll over time\n", + " \n", + " Parameters\n", + " ----------\n", + " poll_id : int\n", + " An RCP poll identifier\n", + " \"\"\"\n", + "\n", + " # hey, you wrote two of these functions. Thanks for that!\n", + " xml = get_poll_xml(poll_id)\n", + " data = rcp_poll_data(xml)\n", + " colors = plot_colors(xml)\n", + "\n", + " #remove characters like apostrophes\n", + " data = data.rename(columns = {c: _strip(c) for c in data.columns})\n", + "\n", + " #normalize poll numbers so they add to 100% \n", + " norm = data[colors.keys()].sum(axis=1) / 100 \n", + " for c in colors.keys():\n", + " data[c] /= norm\n", + " \n", + " for label, color in colors.items():\n", + " plt.plot(data.date, data[label], color=color, label=label) \n", + " \n", + " plt.xticks(rotation=70)\n", + " plt.legend(loc='best')\n", + " plt.xlabel(\"Date\")\n", + " plt.ylabel(\"Normalized Poll Percentage\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you've done everything right so far, the following code should reproduce the graph on [this page](http://www.realclearpolitics.com/epolls/other/president_obama_job_approval-1044.html)" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "poll_plot(1044)\n", + "plt.title(\"Obama Job Approval\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 13, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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xoU+fPqxbt04vvhkzZrB582b69etHnz59OHToEE2bNiU2NlanXlJSEi1btsTV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FjhVCW8UKFCBAYGOg3E0tu++fLl48SJEyxduvSxOKbn7b/OUcqUDxCMAYrtnU7KLltDQ0mOiiJ+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\nXi64vtLCbbMx2XfGSuOTXdq4vowxVv1w28ydMcYYY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+ "text": [ + "" + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Part 2: Aggregate and Visualize\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Problem 3\n", + "\n", + "Unfortunately, these data don't have any error bars. If a candidate leads by 10% in the RCP average, is she a shoo-in to win? Or is this number too close to call? Does a 10% poll lead mean more 1 day before a race than it does 1 week before? Without error estimates, these questions are impossible to answer.\n", + "\n", + "To get a sense of how accurate the RCP polls are, you will gather data from many previous Governor races, where the outcome is known.\n", + "\n", + "This url has links to many governer races. \n", + "\n", + "http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html\n", + "\n", + "Notice that each link to a governor race has the following URL pattern:\n", + "\n", + "http://www.realclearpolitics.com/epolls/[YEAR]/governor/[STATE]/[TITLE]-[ID].html\n", + "\n", + "\n", + "Write a function that scans html for links to URLs like this\n", + "\n", + "**Hint** The [fnmatch](http://docs.python.org/2/library/fnmatch.html) function is useful for simple string matching tasks." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + " Function\n", + " --------\n", + " find_governor_races\n", + "\n", + " Find and return links to RCP races on a page like\n", + " http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html\n", + " \n", + " Parameters\n", + " ----------\n", + " html : str\n", + " The HTML content of a page to scan\n", + " \n", + " Returns\n", + " -------\n", + " A list of urls for Governer race pages\n", + " \n", + " Example\n", + " -------\n", + " For a page like\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " find_governor_races would return\n", + " ['http://www.realclearpolitics.com/epolls/2010/governor/ma/massachusetts_governor_baker_vs_patrick_vs_cahill-1154.html',\n", + " 'http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html']\n", + "\"\"\"\n", + "#your code here\n", + "\n", + "def is_gov_race(l):\n", + " \"\"\"return True if a URL refers to a Governor race\"\"\" \n", + " pattern = 'http://www.realclearpolitics.com/epolls/????/governor/??/*-*.html'\n", + " return fnmatch(l, pattern)\n", + " \n", + "def find_governor_races(html):\n", + " dom = web.Element(html)\n", + " links = [a.attributes.get('href', '') for a in dom.by_tag('a')] \n", + " links = [l for l in links if is_gov_race(l)]\n", + " #eliminate duplicates!\n", + " links = list(set(links))\n", + " return links" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 14 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Problem 4\n", + "\n", + "At this point, you have functions to find a collection of governor races, download historical polling data from each one,\n", + "parse them into a numerical DataFrame, and plot this data.\n", + "\n", + "The main question we have about these data are how accurately they predict election outcomes. To answer this question, we\n", + "need to grab the election outcome data.\n", + "\n", + "Write a function that looks up and returns the election result on a page like [this one](http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html). \n", + "\n", + "**Remember to look at the HTML source!**\n", + "\n", + "You can do this by selection `view->developer->view source` in Chrome, or `Tools -> web developer -> page source` in Firefox. Altenatively, you can right-click on a part of the page, and select \"inspect element\"" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + " Function\n", + " --------\n", + " race_result\n", + "\n", + " Return the actual voting results on a race page\n", + " \n", + " Parameters\n", + " ----------\n", + " url : string\n", + " The website to search through\n", + " \n", + " Returns\n", + " -------\n", + " A dictionary whose keys are candidate names,\n", + " and whose values is the percentage of votes they received.\n", + " \n", + " If necessary, normalize these numbers so that they add up to 100%.\n", + " \n", + " Example\n", + " --------\n", + " >>> url = 'http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html'\n", + " >>> race_result(url)\n", + " {'Brown': 56.0126582278481, 'Whitman': 43.9873417721519}\n", + "\"\"\"\n", + "#your code here\n", + " \n", + "def race_result(url):\n", + " \n", + " dom = web.Element(requests.get(url).text)\n", + " \n", + " table = dom.by_tag('div#polling-data-rcp')[0]\n", + " result_data = table.by_tag('tr.final')[0]\n", + " td = result_data.by_tag('td')\n", + "\n", + " results = [float(t.content) for t in td[3:-1]]\n", + " tot = sum(results) / 100\n", + " \n", + " #get table headers\n", + " headers = table.by_tag('th')\n", + " labels = [str(t.content).split('(')[0].strip() for t in headers[3:-1]]\n", + " \n", + " return {l:r / tot for l, r in zip(labels, results)}" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 15 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are some more utility functions that take advantage of what you've done so far." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def id_from_url(url):\n", + " \"\"\"Given a URL, look up the RCP identifier number\"\"\"\n", + " return url.split('-')[-1].split('.html')[0]\n", + "\n", + "\n", + "def plot_race(url):\n", + " \"\"\"Make a plot summarizing a senate race\n", + " \n", + " Overplots the actual race results as dashed horizontal lines\n", + " \"\"\"\n", + " #hey, thanks again for these functions!\n", + " id = id_from_url(url)\n", + " xml = get_poll_xml(id) \n", + " colors = plot_colors(xml)\n", + "\n", + " if len(colors) == 0:\n", + " return\n", + " \n", + " #really, you shouldn't have\n", + " result = race_result(url)\n", + " \n", + " poll_plot(id)\n", + " plt.xlabel(\"Date\")\n", + " plt.ylabel(\"Polling Percentage\")\n", + " for r in result:\n", + " plt.axhline(result[r], color=colors[_strip(r)], alpha=0.6, ls='--')" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 16 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that this is done, we can easily visualize many historical Governer races. The solid line plots the poll history, the dotted line reports the actual result.\n", + "\n", + "If this code block fails, you probably have a bug in one of your functions." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "page = requests.get('http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html').text.encode('ascii', 'ignore')\n", + "\n", + "for race in find_governor_races(page):\n", + " plot_race(race)\n", + " plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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fyxYJKbm4nJSDFwKdhQ6JVECpIs/JyQnZ2dnw8PCAh4cHjh8/jubNmyMpKQkS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\nBf7+/hBCwMXFBZMmTUJxcTGWL18OALhz5w6++eYbNGzYsMLrYZNnZ3J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LUH3gsNphuJxcUYHyL75CxbovIZdXqB1Og4Kefgwhc55ROwyn5Y14CFWmnfDp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NnpyscSRCL1wq3BISEti1axeJiYmV7l+zZg0pKSkeCczotOwJkcKter7Up2ME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Nm7hw4QIcHR21CzejkTEiIiKq22q9ztiNGzfg6+ur2R44cCAGDhwItVqN69ev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AjbUSrZt5wtpKJfzclkbU1KpBAzkPDw8cPXoUgYGBetujo6PRuXNnpKWl4cKF\nCwgNDa3UAsHVwYEcERHVdPdy8hG+4Gfcy87HpBFt0fexitduPXL2BpasP4x2LbyEPk2gpLax/7cL\nWq2Ez5cO1D2RgsomaiBn0NSqJEm4cOFCqe2XLl3SFWVtbQ2lGa/1IsqDd6eQ/Jiv/JipWMxXrNqW\nbx17G6yc0xMfL+j30EEcAHjVK34CUlLqPYPPUdVMT19MRmGRFs0DPDiIK4cxvq8G3ewwfvx4PPfc\nc7h69So6dOgAADh+/DjeffddTJgwAQBw4MABBAcHCyuUiIioNqrn5mDwvl4edQAAyan3UKTVCl1M\n9/gfNwEAHR7xEXYOejiDplYLCwsRGRmJVatWITm5eNE7Ly8vTJ8+HbNmzYJKpUJ8fDyUSiX8/PyE\nFw1wapWIiKgs4+bsQvrdXGxY1B9q9zpCzlFUpMXYObuQlZ2Pj+b3ha9n5daVrY1MuvyIlZUV5syZ\ngzlz5iAjIwNA8QK8DypZmJeIiIhMx7teHaTfzUVSyj1hA7mLMSnIys6Hr6cTB3EmVulrri4uLqUG\ncbVZbevXMDbmKz9mKhbzFYv5Ppynxz99cilZBu1flUyPny9+bGbHYE6rVsRseuQkScLGjRvx9ddf\n4/r168jLy4NCoYAkSVAoFIiJiRFdJxERERnAu17xVbik24YN5Az17c8XER2XCq+6jjh89gYAoAMH\nciZn0BW5yMhIvPrqq2jXrh3i4uIwZMgQtGrVCnfu3MGzzz4rukazVhtWGDcl5is/ZioW8xWL+T6c\nV93iK3KJKYbduWpIpom3s/Dljxdw4kIifth/FanpOXB2tEWzAI9q1VrTGeP7atAVufXr1+OTTz7B\n8OHDsXbtWkydOhUBAQFYtGgR4uPjRddIREREBvKuV7mpVUP8fqr47/qQpmq0beGF22nZaN/SW+hd\nsWQYg/4P3LhxAx07dgQA2Nvb6xb9HTVqFLZt2yauOgvAfg2xmK/8mKlYzFcs5vtwJUuQJBl4Rc6Q\nTH8/dR0AMLBbEIb2aIZJI9qiXUvvqhdZSxjj+2rQQM7Lywu3b98GUHx36uHDhwEAf/31V615KC4R\nEZElcHW2g52NFbKy85GVnV/t48UnZiAuIQN17K3RprmnDBWSnAwayHXr1g27du0CADz//PN49dVX\n8cQTT2DEiBEYOnSo0ALNHfs1xGK+8mOmYjFfsZjvwykUCnjVLb4ql2jADQ8Py7TkalxoGz8+Q7WS\nzKpHTqsnxQS+AAAgAElEQVTVAgBefPFFuLm5QaPR4D//+Q8mTZoktEAiIiKqHK96johLyEBSShYC\nG7pX+TiSJOkGcmHtuF6sOTK4R+7B56iOHDkSa9aswZQpU5CYmCisOEvAfg2xmK/8mKlYzFcs5muY\nyvTJVZRpzI103Lx1F65OtggOrCdbfbWF2awj16hRIyQlJUGtVuttT01Nhb+/P4qKioQUR0RERJXn\nVa9kCRL9qVVJkqA5fR1/J2bqtl29FIu4dNcyjxMdmwoA6NKmPlQq3qFqjgwayJXn3r17sLOzk6sW\ni8R+DbGYr/yYqVjMVyzmaxjvf9aS+/eiwNv2XsbnO//41962OB13scLjhbXntGpVmLxH7uWXX9b9\n+vXXX4eDg4PudWFhIY4fP46QkBBx1REREVGl6W52eGBq9dDp6/h85x9QKIBB3YLgYG9t4LEc0aJx\nXSF1UvVVOJD744/7o/ZLly7BxsZG99rGxgbt2rXDrFmzxFVnATQaDf+FKBDzlR8zFYv5isV8DVPP\n3QFKhQKp6dmIuX4HaRm5WP75cQDAhEGPYGjPZrp9mak4xsi2woHc/v37AQATJkzA6tWr4ezsLLQY\nIiIiqj5rKxXqujvgVuo9TF+6V7e9V6g/hvRoasLKSG4KSZIkUxdRFQqFAmlpaaYug4iIyCz9/Ps1\n/HTwL5T8Nd+ycT2Ej2gDK960YBLu7u4QMeQyaCCXk5ODVatW4ZdffsGtW7d0a8oBxQOq8+fPy17Y\nw3AgR0RERJZC1EDOoGH5lClTsGzZMvj7+2Pw4MEYNmyY3k9txjWNxGK+8mOmYjFfsZiv/JipOGaz\njtyOHTvw3//+Fz179hRdDxERkcUJCAhAenq6qcsgE3J1dUVMTIzRz2vQ1Kqfnx9++eUXNG1qPg2S\nnFolIiJz4e7uzr+TarmHfQdMOrU6e/ZsLF++XEgBRERERFQ1Bg3k9u3bh2+//RaNGjVC3759MWDA\nAAwcOFD339qMvQViMV/5MVOxmK9YzJcsidn0yHl4eGDw4MFlvqdQKGQtiIiIiIgMw3XkiIiIqok9\ncmTWPXIAIEkSTp48iW+//RZZWcUP4c3KykJBQYHsRRERERHRwxk0kEtOTkbnzp3RoUMHjBkzBrdu\n3QIAvPrqq3zWKvs1hGK+8mOmYjFfsZiv5RLRVx8SEoIpU6boXms0Gnh4eODw4cOynqeqjPF9NWgg\nN3PmTKjVaqSmpsLBwUG3ffjw4dizZ4+w4oiIiMh0tmzZAg8PD92PWq1Gq1atMHXqVCQmJlbqWAqF\nQva++rKOWdt69w262eGXX37BL7/8Ajc3N73tAQEBiI+PF1KYpejataupS6jRmK/8mKlYzFcs5msa\nc+bMgb+/P3Jzc3H06FF8++23OHz4MA4dOgR7e3uDjiFJkvBBVpcuXZCQkABra2uh5zGUMb6vBg3k\ncnJyygwlJSUFdnZ2shdFRERE5qN79+5o164dAGDs2LFwc3PDunXrsHv3brN6VKdCoYCNjY2pyzAq\ng6ZWH3vsMWzatElvW2FhIZYtW4Ynn3xSRF0Wg/0aYjFf+TFTsZivWMzXPDz22GMAgPj4eHzwwQfo\n27cvAgMD4ePjg65du+KLL74w6DiV+WxkZCRatmwJPz8/DBo0CJcuXSq1T1k9clOmTIGPjw8SExMx\nduxYNGjQAEFBQZg/fz60Wq3e57OzszF//nwEBwfD29sbjz76KFatWlXlu03NZh259957D2FhYThx\n4gTy8vIwa9YsXLhwARkZGTh06JDoGomIiMiMxMbGAgDc3NwQGRmJPn36YOjQoVAoFPjpp58wY8YM\nFBUVYcKECRUe56OPPjLos4sXL8by5cvRq1cv9OzZE+fPn8fw4cORn59vUL1arRbDhw9Hu3btEBER\ngf3792Pt2rXw9/fHs88+C6B46nfs2LE4cOAAxo4di9atW2P//v2IiIhAfHw83n///SplJZrB68gl\nJibiww8/xKlTpyBJEtq2bYspU6bA29tbdI1l4jpyRERkLmrqOnJbtmzByy+/jK1btyIkJAS5ubk4\nduwYXnvtNeTl5eHEiRNwdXUt1WY1bNgw/P333zh58qRu24ABA6BUKrFz507dttzc3Id+NiUlBa1a\ntUL37t2xZcsW3X7vvPMOIiMjMXr0aHzwwQcAiq+ADRo0CD/88ANCQ0MBFF+R++abbzB37ly9lTa6\ndesGpVKJX375BQDw888/Y+zYsZgzZw5mz56t22/q1Kn4+uuvodFo0Lx583KzMtU6cgZdkQMAb29v\nREREyF4AERFRbXLTvalRzuObFi3bsYYPH673ulmzZli6dCm8vLx02woKCpCVlQWtVouuXbti//79\nuHv3LpycnMo9bskgrqLPHjhwAAUFBZg4caLeZydNmoTIyEiDfw/PPPOM3uuOHTti69atutdRUVFQ\nqVSYNGmS3n5TpkzB119/jb1791Y4kDMVgwZya9asgZubG8aOHau3/csvv0RmZiYmT54spDhLoNFo\neBeVQMxXfsxULOYrFvM1jWXLliEoKAi2trbw8/ODr6+v7r3du3cjMjISFy5cQFFRkW67QqFAZmZm\nhQM5Qz57/fp1AEDjxo31Puvu7g5XV1eD6rexsYFardbb5urqivT0dN3r69evo169enB2dtbbr0mT\nJlAqlbo6KsMY31eDBnIrV67E5s2bS21v2LAhnn322Vo9kCMiIqoMOa+UGUubNm10d60+6OjRoxg3\nbhxCQ0OxfPlyeHl5wcbGBlFRUfjwww8rnEqszmdLGDpVWZPXljNoIHfz5k34+fmV2u7n54cbN27I\nXpQl4b8MxWK+8mOmYjFfsZivedm5cyccHBzw3Xff6S37cfDgQdk+W79+fQDAtWvX4O/vr9uempqK\njIyM6v4W9M5z4MABZGZm6l2Vu3btGrRara6OyjDG99Wg5Ue8vLxw5syZUtvPnDmDunXryl4UERER\nmT+VSgUAetOi6enp+Oqrrx56FczQzz7xxBOwtrbGZ599pvf5jz/+2OA6Dbki16dPH2i1WnzyySd6\n29etWweFQoFevXoZfD5jMmggN2bMGEybNg1RUVEoKChAQUEB9uzZg+nTp+Ppp58WXaNZ45pGYjFf\n+TFTsZivWMzXvPTt2xfZ2dkYOnQoNm7ciOXLl6Nbt27w9PQsc9rzwW2GftbDwwNTp05FVFQURo0a\nhU8//RQzZszQPT5MrinY3r1744knnsDSpUsxffp0bNiwAePHj8eWLVswYcIENGvWzMBU7jObdeQW\nLFiA2NhY9OnTB0pl8dhPq9VixIgRWLRokdACiYiIyHQquprVpUsXrFu3DitWrMC8efPg6+uL8PBw\nuLi4YNq0aaWO8+CxKvPZefPmwdbWFps2bYJGo0H79u2xbds2jBo1yqBnrZa37d/bv/jiCyxduhTb\nt2/Ht99+i/r162P+/Pml6jEnD11HTqvV4vLly2jQoAESExN1U6ytW7dGUFCQUYosC9eRIyIic1FT\n15Ejw5n1OnIhISG4dOkSAgMDERgYKHsRRERERFR5D+2RUyqVaNq0KW7fvm2MeiwO+zXEYr7yY6Zi\nMV+xmC9ZEmN8Xw262eG9997DrFmzcObMmWpdFlywYAGUSqXej4+PT6l9fH194eDggG7duuHixYtV\nPh8RERFRTWbQs1adnJyQm5uLoqIiWFlZwdbW9v4B/ll92RALFizAf//7X+zfv1+3TaVSwcPDA0Dx\nytGLFy/G5s2bERQUhIiICGg0GkRHR8PR0VG/cPbIERGRmWCPHJl1j9yaNWtkO6FKpSr1mAyg+Nbg\nlStXYu7cuRgyZAgAYPPmzVCr1diyZQvCw8Nlq4GIiIioJjBoIDdhwgTZThgTEwNfX1/Y2tqiY8eO\nWLJkCfz9/REbG4vk5GS9Bffs7OwQFhaGw4cPm+1Ajs/9E4v5yo+ZisV8xWK+ZEmM8X01qEcOAJKS\nkvDee+/hpZdeQkpKCoDiAmNjYw0+WadOnbB582bs2bMH69evR1JSEkJDQ5GWloakpCQAgKenp95n\n1Gq17j0iIiIius+gK3KnTp1C9+7dERAQgAsXLmD27NmoW7cu9u7di6tXr2LLli0GnaxPnz66X7dq\n1QqdO3eGv78/Nm/ejI4dO5b7ufIWI5w8eTIaNGgAAHBxcUFwcLBu5Ftypwhf8zVf67/u2rWrWdVT\n014z39qbLxGg/33QaDQGj5GqyqCbHZ544gmEhYUhIiICTk5OOHfuHAICAnDkyBGMHDkS8fHxVS6g\ne/fuaN68OWbNmoXGjRvjxIkTaNeune79/v37Q61WY+PGjfqF82YHIiIyE7zZgUx1s4NBU6unT58u\ns0/Oy8sLycnJVT55bm4uLl26BG9vb/j7+8PLywtRUVF672s0GoSGhlb5HKLxX2JiMV/5MVOxmK9Y\nzJcsidmsI2dvb1/mKDM6OrrMO1DLM2vWLBw8eBCxsbE4duwY/vOf/yAnJwfjx48HAMyYMQPLli3D\n999/jwsXLmDChAlwcnLCmDFjDD4HERERUW1h0NRqeHg4EhMTsXXrVtSrVw/nzp2DQqHAoEGD0L17\nd6xcudKgk40ePRoHDx5ESkoK6tWrh86dO2PRokVo1qyZbp+FCxfi448/xp07d9CpUyesXbsWLVq0\nKF04p1aJiMhMcGqVTDW1atBALiMjA/3798e5c+eQnZ0NT09PJCcno0uXLti9e3epxXqNgQM5IiIy\nFxzIkVn3yLm4uECj0WDnzp1YunQppk+fjj179uDgwYMmGcSZE/ZriMV85cdMxWK+YjFf00hJScHC\nhQvRuXNn1K9fH35+fnjssccQERHBJcIqYIzv60OXH9m6dSt27NiB/Px89OjRA7NmzSp3ORAiIiKq\nWc6dO4cRI0YgKysLQ4cOxaRJk6BQKPDnn3/iiy++wI8//ojjx4+busxaq8Kp1fXr12PSpEkIDAyE\nra0tLly4gNdeew1Lly41Zo1l4tQqERGZi5o6tZqZmYkuXbqgsLAQO3bsQNOmTUu9v2bNGsybN89E\nFZoPs5xaXb16NebNm4fo6GicP38en332GT744APZiyAiIiLzs2nTJiQkJGDRokWlBnEA4OzsrDeI\nCwkJwZQpU0rtN2DAAAwcOFBvW2pqKmbMmIFmzZrBx8cHXbp0weeff663T3x8PDw8PLBq1Sps2LAB\nbdq0gZ+fH4YMGYIbN25Aq9XivffeQ8uWLeHr64unn366zMHUxo0bERoaCh8fHzRv3hyvvPIK0tPT\nqxqLWalwIBcTE6O3ftzYsWORn5/P+fAHsF9DLOYrP2YqFvMVi/ka188//wx7e3sMHjzYoP0VCkWZ\n7Vf/3p6bm4uBAwfi66+/xrBhwxAREQG1Wo2ZM2di1apVpT6/fft2rF+/HuHh4Zg8eTKOHj2KCRMm\nICIiAvv27cPMmTMxfvx47NmzB2+88YbeZyMjIzFr1ix4enoiIiICQ4YMwVdffYVBgwYhPz+/kolU\njsl75HJycuDk5HR/Zysr2NraIjs7W3hhRERENdGAKf81ynl+WDui2seIjo5GkyZNYGVl0BM9yyVJ\nkt5AbvPmzbh8+TLWrVuHkSNHAgAmTpyIYcOGYdmyZXjmmWfg5uam2z8xMREnT56Es7MzAECr1WLF\nihXIzc3FgQMHoFKpABTflLF9+3asWLECtra2SElJQWRkJB5//HF89913uhqCg4MxdepUfP7553j+\n+eer9XsztYf+n/nwww91gzlJklBQUIANGzbAw8NDt88rr7wirkIzV/KsPRKD+cqPmYrFfMVivsZ1\n9+5dIatTREVFoV69ehgx4v5gU6lU4qWXXsLBgwexf/9+DBkyRPfegAEDdIM4AGjbti0AYMSIEbpB\nXMn27777Djdv3kRAQAAOHDiAgoICvPjii3oDyZEjRyIiIgJRUVFCB3LG+L5WOJBr0KABNm3apLfN\ny8ur1ANga/NAjoiIqDLkuFJmLE5OTsjKypL9uDdu3IC/v3+padjAwEAAwPXr1/W2+/n56b0uGdT5\n+vqWub2k/63kOE2aNNHbT6lUwt/fv9R5LFGFPXJxcXGIjY3V+ylrW23Gfg2xmK/8mKlYzFcs5mtc\nQUFBuHr1KgoKCgzav7zlyYqKiqpVx4NX3R6kVJY9jBFxd2hVmM2zVomIiKj26devH3Jzc7Fz506D\n9nd1dUVGRkap7WVdYYuJiYFWq9XbfvXqVQDFM4JyqF+/vt5xS2i1WsTExMh2HlPiQK6a2K8hFvOV\nHzMVi/mKxXyNa8KECfD29sabb76JK1eulHr/7t27ePvtt3WvGzVqhJMnT+pdwduzZw8SEhL0Pten\nTx+kpKRg27Ztum1arRYfffQR7Ozs8MQTT8hSf7du3WBjY4OPP/5Y7yrd1q1bcfv2bfTu3VuW85TH\n5D1yREREVHs5Ozvjyy+/xMiRI9GtWzcMGzYMbdq0gUKhwOXLl/Hdd9/B3d1dt+THuHHjsGvXLgwf\nPhyDBg1CbGwstm3bBn9/f72B1DPPPIPNmzdj2rRpOH/+PBo2bIjdu3fj4MGDeOutt+Dq6ipL/e7u\n7pg1axaWLFmCoUOHol+/foiLi8OGDRsQHByMcePGyXIeU+IVuWpiv4ZYzFd+zFQs5isW8zW+1q1b\n49ChQwgPD8eJEyfw5ptvYt68edBoNHjmmWfw448/6vbt3r07Fi1ahGvXrmHevHk4deoUvvnmG/j4\n+Oj1z9na2mLXrl0YNWoUtm3bhvnz5+PWrVtYuXIlpk2bZlBd5fXj/Xv7q6++ivfffx/JycmYP38+\ntm/fjjFjxmDHjh2wtrauQiKGM8b3tcJHdJkzc3lEl0aj4aV+gZiv/JipWMxXLHPNt6Y+oosMV9Z3\n4MHvq6hHdHEgR0REVE0cyJFZPmtVt5NSCZVKBaVSqfejUqng4OCAkJCQMh+pQURERETiGDSQW7t2\nLTw8PPDCCy9g/fr1WL9+PV544QXUrVsXixYtQvfu3TF37lysXr1adL1mh/0aYjFf+TFTsZivWMyX\nLInJn7VaIioqCkuWLNF7jMVzzz2HDh06YOfOndi1axeaNm2KNWvWGNykSERERETVY1CPXJ06dXDu\n3LlSj7i4evUqQkJCkJ2djWvXriE4OBg5OTnCin0Qe+SIiMhcsEeOzLpHzsPDA99//32p7Tt37kTd\nunUBAFlZWXBxcZG3OiIiIiIql0EDuQULFmDOnDno168fFixYgAULFqBfv36YM2cOFi5cCADYu3ev\nbCsxWxL2a4jFfOXHTMVivmIxX7IkZtMjN3HiRDRv3hyrV6/Grl27AADNmjWDRqNBp06dAACzZ88W\nVyURERERlcJ15IiIiKqJPXJkqh65Sj1rNSEhAbdu3YJWq9Xb3rZtW1mLIiIisiSurq5wd3c3dRlk\nQnI9H7ayDBrInTlzBk8//TQuX75c6j2FQoGioiLZC7MU5vq4mJqC+cqPmYrFfMUy13xjYmJMXUIp\nhfE3kPfrIQDFV4Fs2reGdatmpfYz10xrAmNka9BALjw8HA0aNMCnn34Kb2/vch9US0RERObhTvgs\n5B8/c3+DSgWP/66HXbcupiuKZGfwOnKnT59G06ZNjVGTQdgjR0REVDZJkpDYsC2krGw4PD0MRbdS\nkLf3ABTOTqi351tYN21s6hJrHZP2yLVq1QpJSUlmNZAjIiKismlT0iBlZUPh5AjX1YsBSULaszOQ\n+8MepI4KR90dm6B0cTboWIo6DlBYWwuumKrKoHXk3nnnHfzf//0f9u7di+TkZKSlpen91GZc00gs\n5is/ZioW8xWL+RqmKO46AMDKvwEUCgUUSiXcPlwG6zatUPT3DSS36YHEgA5IDOiAXQGtdb8u6yep\nZRiKbqWY+HdkmcxmHbkePXoAAHr37l3qvdp+swMREZG5KYyLBwCoGtXXbVM62MPjqw+RNnEGCi5d\nfWDnHCis7Ms8jpSbC21KGnK2/wTHF8cLrZmqxqAeuf3791f4vime6MAeOSIiorJlvvsB7i5dA8fp\nL8DlrVlVPk729t248/xMWLdvDXXUtzJWWPuYtEeuNj56i4iIyFIVlkytNqz/kD0rZtf7CSgc7FFw\n8iwK/75e7eOR/MrtkTt9+rRuyvT06dMV/tRm7NcQi/nKj5mKxXzFYr6GKemRU/k3eOi+FWWqrOMA\nuz7dAQA5O36Wp7haxKQ9cu3bt0dSUhLUajXat29f7gHYI0dERGReSnrkrPyrfwXNfmg/5Gz/CTnb\nd8Npeni1j0fyKrdHLi4uDg0aNIBSqURcXFyFB2nUqJGA0irGHjkiIqLStNk5SPRrDVhZwSfxPBQq\nVbWOJ+XlI7FpKKTMu1Af+xnWgQEyVVq7GL1H7sHBmSkGakRERFR5umnVBr7VHsQBgMLWBvb9eyL7\n6+3I+X43rF5+HoU3EmDl5wOFvV21j0/VU2GPnKE/tRn7NcRivvJjpmIxX7GY78MV/v3PjQ6NDJtW\nNSRT+6H9AAB3Iz9Egm8IbnXsi5SRL1S9yFrC5D1yhmCPHBERkfkoir2/GLBcbMM6QdWofvHVPisr\nQKtFvuY4im4mQeXrJdt5qPIq7JEzFHvkiIiIzEP6axG49+lXcF40B05TnpXtuNqse9DeSYfK2xNp\nE2cg98e9cIl8C44Tx8h2jprMpD1yREREZBkKY/+5Y9XAqVVDKR3rQOlYBwBg16c7cn/ci9yff+VA\nzsTKHchVpvetbdu2shRjiTQaDbp27WrqMmos5is/ZioW8xWL+T5cUUmPnIFLj1QlU7uejwMKBfJ+\nPwrt3SwonRwrXWdtYIzvK3vkiIiIagipqAiFf98EAKgEPoVBVc8DNo+2Rv7xM8j77RDsB5Z+FjsZ\nB3vkiIiIaojC6zeRHNIdSs968L4k9o7Ju6s+QebC92E/ajDc1y0Teq6agD1yREREtYwkSUif8Sa0\naXfgGrkAKs96Fe5fVNIf19BPeG12fZ5E5sL3kRe1H1JhIRRWBj2+nWRW7jpy/5aUlIQ333wTw4YN\nw/Dhw/HWW28hOTm5yid+5513oFQq8fLLL+ttX7BgAXx9feHg4IBu3brh4sWLVT6HMXBNI7GYr/yY\nqVjMV6zalm/ewaPI/mIrcn/ah9s9hiP/j0sV7l8YdwOAYc9YLVHVTK2CAqAKaAhtWjryT5yt0jFq\nOpOuI/egQ4cOoU+fPvD09ETnzp0hSRK+/PJLrFixAv/73/8QGhpaqZMePXoU69evxyOPPAKFQqHb\nvmzZMixfvhybN29GUFAQIiIi0LNnT0RHR8PRkY2URERUu2St/hQAoHB1QdHNRKT0HQ2HccOhsLEu\nc//842cAAFaN5FtDrjwKhQL2fboja91GpI6aBEUdeyhsbeH6/kLYdecNKcZSbo/cgzp37ozg4GB8\n9NFHUCqLL+IVFRXhpZdewoULF3D48GGDT5iRkYF27dphw4YNWLBgAYKDg7F69WpIkgQfHx9MmzYN\nc+fOBQDk5uZCrVYjMjIS4eH6D+pljxwREdVk+X9cwu3HB0NRxwGeJ/YgY2Ekcr7dadBn3Teugv2g\nPoIr/KfGHsOBggLdNlUDX3ge2c3Hd/2L0XvkHnT27Fls2rRJN4gDAJVKhZkzZ6JNmzaVOmF4eDiG\nDx+Oxx9/XO83FBsbi+TkZPTq1Uu3zc7ODmFhYTh8+HCpgRwREVFNlrWm+Gqcw7jhUHmp4bZuGez7\n9UBh7N8Vfk7p5gq7/j2MUSJsgpvD+9pRSFn3AAlIGfE8Ci9ewd21n8F51mSj1GBqkiTh3qdfwbpF\nEGy7dDD6+Q0ayLm4uCAmJgZNmzbV2x4XFwdXV1eDT7Z+/XrExMRgy5YtAKA3rZqUlAQA8PT01PuM\nWq1GQkKCwecwNq5pJBbzlR8zFYv5ilVb8i28fhM53/8MqFRwnDwBwD9TmQN6VfzBKqhupkonR+Cf\ndeRc35mHlEHjkbXyE9QZPbRWPL4r//ejyPi/RVB6uMHrz4NQ2Njo3jPG99Wgmx1GjRqF5557Dl9+\n+SViY2MRGxuLL774As899xxGjx5t0Imio6Mxb948fPXVV1CpVACKR7GGXGZ8cMBHRERU02Wt2wQU\nFcF+aD9Y+fmYuhyD2T7WCXYDekPKzkHGgvcg5edX/qew0NS/jUrJ2bUHAKBNvYPcn381+vkN6pHL\ny8vDa6+9hg8//BCF/wRsY2ODl156CcuWLYPNA6PP8mzatAkTJ07UDeKA4j47hUIBlUqFCxcuoFmz\nZjhx4gTatWun26d///5Qq9XYuHGjfuEKBUaNGoUGDYobOl1cXBAcHKwb+ZbcKcLXfM3XfM3XfG1p\nr+9MmYO212+j7s9f40RBtsnrqczrA999jzuT/w+PFhRfhDmBfADAo7Ax7LWyCLY9wtBrbSRUHu4m\n//1U9FoqKsKuwHaQ0jPwKGxg++RjuDx9AkpoNBrExxcvCfPNN98I6ZEzaCBXIjs7G9euXQMANG7c\nGHXq1DH4RBkZGbh586butSRJePbZZxEUFITXX38dzZs3h6+vL15++WW9mx08PT0RGRmJF154Qb9w\n3uxAREQ1VGJQZ2hT0uB1SfPQtePMUda6TchcvAJSYRWe/PTPjRMKN1c4vfIirPy8AQCqhn6wad1K\nzjKrLe/wCaQ8NRZKb09oU9OAgkJ4nvtNV/ODTHKzQ3Z2NmbPno0dO3YgPz8fPXr0wJo1a1C3bt1K\nn8jFxQUuLi562xwcHODm5oYWLVoAAGbMmIElS5agWbNmCAwMxNtvvw0nJyeMGWO+D+TVaGpHv4ap\nMF/5MVOxmK9YtSFfSauFNi0dAKB0N7wPvapEZOo4eYKut6+yCqL/Qsb/RSDv4FFkvrn0/htKJdRH\nfoJ1YIA8RcqgZFrVYfgAFMXfRM6On5H99XY4z54CwDjf1wp75N566y1s2rQJTz31FEaPHo2oqCi8\n+OKLsp1coVDo9b+99tprmDlzJqZMmYJHH30UycnJiIqKqtSVPyIiIkumTc8AtFooXJyhsC57vbia\nzIAezEUAACAASURBVLppY3h8vwnun62E/ZB+sBvQG1aBAYBWi5yd/zN1eTqSVoucH4oHcvYDe8Nh\n7H8AANlffQdJqzVaHRVOrTZu3Bhvv/227oaG48ePIzQ0FHl5eXq9bqbAqVUiIqqJCq78hVud+kEV\n0BBeJ6NMXY5ZyPnfr0gb8xKsQ1pC/dt2U5cDAMg7dhopfUdD5ecDz3O/ApKE5NZPouhGAlwWz4VV\nE3+9/X1GDjH+1Or169cRFhame92hQwdYW1sjISEB9evXl70YIiKi2k6begcAoPJwN3El5sPuiS5Q\n1HFAwbk/UXj9Jqzq+5q6JN20qv3A3sWziwoFHMYMxd13P0DGvHeMVkeFU6uFhYWw/tdlXSsrKxQ8\nsIJzbVdy9wqJwXzlx0zFYr5i1YZ8SwZyyrpuRjmfJWSqsLOF7ZOPAQByf9pn4mqKb9jM/aH4aqnd\nwN667Y6TxsF++ADY9giDbY8wnG3bXPdrUSq8IgcA48aNg42NDRQKRXHhubkIDw+Hvb09gOIpzl27\ndgkrkIiIqDbRphS3DSndjTOQsxT2T/VE7q49yPlpHxxfHG/SWgrO/IGiGwlQenvCpn2IbrvSzRXu\nH0fqXrtoNKhbcrOD+3dCaqlwIPfMM8/oBnAlnn76ab19avtivTX97ilTY77yY6ZiMV+xakO+2rSS\nK3LGmVq1lEztej0BWFsj/8hJFKWkQWWkfMqim1Yd0AsKZfmTm8bItsKB3KZNm4QXQERERPcVlVyR\nY4+cHqWzE2wf64i8XzXI/fkX1Bk33CR1SJJ0fyA3qI9JaniQQY/oovJZQm+BJWO+8mOmYjFfsWpD\nvtrUkoEce+T+zf6pngCAe59vLf2zZTuKUsWvZlHwxyUUxV2H0rMebDq0qXBfY2T70B45IiIiMh7d\nXasmnDo0V3Z9nwReXYCCU+eQfupcqfdtez6Out9+IrQG3dW4/j2gMPFSbEAlH9FlTriOHBER1US3\nug1Fwbk/UW/vVti0e8TU5Zid7G92IO/wiVLbc777EVJOLtRHdsO6aWMh55YkCckd+qDorzjU3bEJ\ntmGdDf6sSR7RRURERMZl7OVHLI3DqMFwGDW41HaFtRXubfwGWR9thtuKCCHnLrx0BUV/xUHp4Qab\n0EeFnKOy2CNXTZbUW2CJmK/8mKlYzFes2pDv/R4540yt1pRM6/yzJEn2tzuE9crl7CyeVrXr3xMK\nq4dfCzNGthzIERERmQntvWxIObmArQ0UdRxMXY5FsQ4MgG2vJ4DcPNzb+E2p9wuv30TOj3t1P4Wx\n8ZU6viRJume92j+wCLCpsUeOiIjITBRev4nkkO5Q+XjB68IBU5djcXIPHEHqkAlQetaD19lfobC1\nQcHFaNxd+Qlyvv8ZKCrS7atwc4X3xd+hsLUx6NgFf17GrccGQenuCq9LGij+9eSrh2GPHBERUQ2n\ne6oD71itEtuwTrBq2RSFf0YjwecRQKEAtNriN62sYNu9KxT2dsg/egra1DvIP3kWtl06GHTs7O9+\nAlC8dlxlB3EicWq1mmpKb4G5Yr7yY6ZiMV+xanq+uhsdjPh4rpqUqUKhgPPcaYCtDSBJgFYLhaMD\n6oSPg+epKNTdtgEeX6yF/X8GAADyDh416LiSJCFn+z8DuaH9Da6H68gRERHVIrobHXjHapXZ9+sB\nn5vnigdyAKBQlHqMlm1YJ9z7+HPkHTwCzJ320GMWnDyHovibxc9W7dxeRNlVxity1WQpz6izVMxX\nfsxULOYrVk3PtyjF+FfkamKmCqUSCpWq+KeMZ6Hahj4KKJXIP3Ue2qx7Dz1e9nc/AgAchvSr8Nmq\n/2aMbDmQIyIiMhPatJI15NgjJ5LSxRnWrVsBhYXIP3qqwn2loqL7d6sO7WeM8irFoqdW78ycX2pb\neYsAlrWvHPtrNBp07dpV2PFr+/5Hk26gk5ef2dRTE/Z/MFNzqKem7V/yZ4K51FPT9tdoNGj5XZTZ\n1CP3/iU3Ozz4eC7R9fw8eqLen7NyH99c95fy8wEAGQsjkfPTvnL3T3t6MrTJt6FwcsS9L7bi3hfb\nDK7n33+HicArckRERGZCd0XOiFOrtZXK2xMAoE28Ve4+kiSh4PI1AICVf30ACmOUVilcR46IiMhM\n3O47GvnHTqPuj18W93GRMNrsHCQGPAoUFML72lEo3VxL7XNv49dIf3UBFHUcoNbsglXD+lU+n6h1\n5HhFjoiIyEwY+/FctZnSwR42j7YBJAl5muOl3s8/ewHpcxcDAFxXLqrWIE4kDuSqqSatv2OOmK/8\nmKlYzFesmp5vUWo6AEDpwXXkjME2rBMA4N7mb3Fv0zd6P2nPTgfyC1Bn4mg4DHuqSsfnOnJERES1\nhFRYCOlOOqBQQOnmYupyagXbsM64+85q5P2qQd6vpQdd1iEt4fL2XBNUZjj2yBEREZmBolspSGrW\nBUp3V3hfO2bqcmoFSZKQ9f/s3XdUVNf2B/DvzNCLIEiXJiBYUFFQMIq9xAr2+Gyx/TQajSXxJfHZ\ngiYaNfrUREwBNGBXorGjiCIQRaWJCiIICiK9l2Hm/P7wzcQRjZQZLoP7sxZrZe4MMzvby5l9793n\n3N2/oebxk1rP8fV0obNgJgSmxnL5LLrXKiGEENKCSW/PRf1xTYbH40H30zlch9Eo1CPXSO9zb0FT\noPzKH+VUsSi/itWS8/v37bmatpBryTnlWlPklgo5QgghpBn4+4wcrSFH6o565AghhJBmoPS3IBSt\nXA+tmZPfetcAorxoHTlCCCGkBfv79lx0Ro7UHRVyjUS9BYpF+ZU/yqliUX4Vq6XlV5RfgCKfH1D4\nbx9Unr0MoOlvz9XSctqc0DpyhBBCSAtW9msQSrfvldkmsFbsTdZJy0I9coQQQghH8mYsRuWfl6D1\nr/FQ7eQIvmFraHqPAE+FzrO0NLSOHCGEENLCCO89BADoLJwF1Y7tOY6GKCPqkWsk6i1QLMqv/FFO\nFYvyq1gtKb/i0jKIUtMBVVWoONhyFkdLymlzQ+vIEUIIIS1Uzf1kAICqox14qqocR0OUFfXIEUII\nIRwo8z+MwuVroDlpDAz2fs91OETBaB05QgghpAUR3k8CAKh2dOQ4EqLMqJBrJOotUCzKr/xRThWL\n8qtYLSm/kokOqp24LeRaUk6bG1pHjhBCSJNhFZWoefKU6zBqUbGxBE9Dnesw5Iox1mwKOaLcqEeO\nEEIImFiMF33GoOZBMteh1KLavQuMLh0Bj8fjOhS5qXmahewu/cE3bA3TpMgW9f9G3ozWkSOEEKIw\n1dGxqHmQDJ6mBgSWFlyHIyXKeAbhnThUXYuCRj8PrsORm5r/9cepdGxPRRxpFCrkGik8PBx9+vTh\nOowWi/Irf5RTxVLW/FaeugAA0Jo5GfqbvuI4mr8Vb/0RJZt2ovQnf2j081Da/L5Oelm1GUx0aCk5\nbY6aIrc02YEQQt5zjDFU/K+Q0xw7nONoZGnPmgJoqKPq4lUIkx9zHY7cUH8ckRfqkSOEkPdc9e04\n5AyZCL6ZCUzjr4LHb17H+AVLV6P8wFFoz5kK/e/Xch2OXGT3HoWaB8kwunwMai7OXIdDmgCtI0cI\nIUQhKv44BwDQHD202RVxAKCzYCYAoPzgSYgLCjmOpvFYVTVqHqUCPB5UHO25DocouSbtkduzZw/2\n7duHtLQ0AECnTp2wevVqjBgxQvqadevW4eeff0ZBQQF69eqFPXv2oGPHjk0ZZr1Qb4FiUX7lj3Kq\nWMqWX8YYKv7432VVr+Z1WVVCtYMD1Af0QVVoOM4MGgsP63Zch9QorLISqKmBir0N+FqaXIejdPus\nMmmK3DZpIWdpaYktW7bAwcEBYrEY/v7+8PLywq1bt9C1a1ds3rwZ27dvR0BAANq3b48NGzZgyJAh\nePjwIXR0dJoyVEIIeS8IYxIgyngGvqkR1Hp25zqct9L5dA6qQsMhSktHVdpzrsORCzV3V65DIC0A\n5z1yhoaG+O677zB37lyYm5tjyZIl+PLLLwEAlZWVMDY2xtatWzF//nyZ36MeOUKIoonyC1C63Rfi\nsnKuQ1GYmsQkVN+6C+1506C/+T9ch/OPquPvQ5zbMsZ9nooAaq7dwNPU4DoU0kRa3DpyIpEIR48e\nRWVlJTw9PZGamors7GwMHTpU+hoNDQ14enoiIiKiViFHCCGKVrJxB8r8DnEdRpPQHDeS6xDeSc25\nA9chENLsNHkhFx8fDw8PD1RVVUFTUxNHjhyBo6MjIiIiAAAmJiYyrzc2NkZmZmZTh1ln1FugWJRf\n+aOc1k3N0yyU/X4c4PGg982/wdOq25mTyEdJ8LBvr+Do5EtgYQb1Xs33suqraP+VP8qp4rS4HjkA\ncHJyQlxcHIqKinD06FFMmTIFoaGh//g7b1v1+pNPPoGVlRUAQE9PD87OztKESW5Uq+jHEk31ee/b\nY4nmEg89fn8el/ruh7NQCE3vEYjpYl/n39cID8ddgPP46/34lS+cZhEPPW6yx/Hx8c0qnpbyGACC\ngoIQFBQEReK8R27IkCFo27Yt1qxZAzs7O9y6dQs9evSQPj9y5EgYGxvDz89P5veoR44Qoiii5y/w\n3GUQUFUN4/DTUO2oXGfYCCHNT4tdR04kEkEsFsPW1hampqa4ePGi9LnKykqEh4ejd+/eHEZICHnf\nlOz6FaiqhsaooVTEEUKaNZWm/LB///vfGDVqFNq2bYuSkhIEBQUhLCwM58+fBwB89tln2LRpE5yc\nnODg4AAfHx/o6upi6tSpTRlmvYSHU2+BIlF+5e99yakw8SEKlqwGK6+o9+/WpKQBAHQ//6Tev/u+\n5JcrlF/5o5wqTlPktkkLuezsbEybNg3Pnz+Hnp4eunbtivPnz2PIkCEAgC+++AIVFRVYtGgRCgoK\n4O7ujosXL0JbW7spwyRE6THGIEp/ClZVXeu5moxMCJNSOIjq7VRsLMFTU5Pb+7GqauTPW4ma+0kN\nfg/NscNpliQhpNnjvEeuoXg8Hh7/34qm+zwVFWjNmAhVB+VeUZy8H0p+8EXxN9u5DqPOBFYWMDz8\nM1Qd7eTyfkXfbEfpD74Q2NnA0G8nUN/bTvH5ULG3AU+lSY91CSEtmKJ65JS6kIuHUZN+prqnO9oE\nBzTpZxJSX+LyCmR36Q9xfiEEdjbg8d8867u5EBeVQPwiF7zW+jAM+qnRy2BU345DzrDJAIA2ZwKV\nZlkNQkjL1uIWBJYHvY1fNs0HMaDIZzuqrkWhJuMZVCwtpE9Rb4FiUX7rr+LIHxDnF0K1uzOMLh2t\ntXxPc8upuLwCBXOWofJCKHK9Z0Fz7HDw+IIGv1/Vjb8AsRg6n87hpIhrbvltaSi/8kc5VZwW1yMn\nbzoLZzXZZ1XHxKPi2J8oPxSMVp8varLPJaQ+mFiM0h9fLtWj88nst67B2JzwtTRhcGA3CleuR/n+\nI6g4/Eej31OlvR1afblUDtERQkjzptSXVptyHbnK0BvIGz8bAhtLmNy+pBRfkOT9U3EhFPkfLYCg\nrTlM7lxSqh4vxhiqrkZAlNnIG6Lz+dDo3xsCM5N3v5YQQpoIXVrlmLqnOwTmphClZaA66jbUPVy5\nDolwoDouEcK7CXJ9T4GVBdRcOoOvr9fo95KcjdOeP12pijjg5cGZxoAPuA6DEEKUinKN9BziCQTQ\nnOKF0u17UR50QlrIUW+BYjWn/FZcCEX+vz4BxGKFvL/A1gp8XZ2GvwFjEMbfB09HG9ozJr71Zc0p\npy0R5VexKL/yRzlVHOqRa2a0/lfIVfxxDnqbvgJPWwtMLAZT0Bf7q3j1XT6ByJUw4QEK5i4HxGKo\nD+0PgYmcZkyLRBAmPYYwPhGi1HSI5PCW2vOmgd9KVw7vRAghpLmjHrl6yhk+BdU37777hXKmOcUL\nrfd81yx788Rl5YBQyHUYCiPOL0TumBkQZT6H5oRRaO27Ve7/DkwoRM2jNLDq2gv41gdPVRUqjnbg\nCRo+65MQQoj80Tpyr+GqkKs4G4KC+SvBKiqb7kP/90/U+uft0Bo/suk+tw5Kdv36cuHZmhquQ1E4\ntV7d0eakP3ga6lyHQgghRMlQIfcargq51zXF9e+y/UdR+Nlq8A30YRxxBgLjNgr9vLoqP/4nCua9\nvLsGT6+VQj7jZk0FeqpoKuS960O1sxMM/HZC0MaA61AajfphFIvyq1iUX/mjnCrOq7mlWavvMa3p\nE1Dxx3lUhYajcOU6GATs4vwSa9Vfd1Cw+OWCzHobv1TYmn5twsNhTgMMIYQQ8kZ0Rk5J1DzNxIve\no8BKy6Dm5gKoqXIajzDhAVhRMbTnTIXeljWcF5aEEEJIc0aXVl/zvhVyAFB24CgKl67mOgwp9cGe\nMAz6SenWKyOEEEKaGhVyr2kuhVxT9xZUxyWCFZc02ee9DU9dHao9uih8WRTq3ZA/yqliUX4Vi/Ir\nf5RTxaEeOVKLWpeOXIdACCGEkGaCzsgRQgghhCiYos7I0e0CCCGEEEKUFBVyjRQeHs51CC0a5Vf+\nKKeKRflVLMqv/FFOFacpckuFHCGEEEKIkqIeOUIIIYQQBaMeOUIIIYQQIoMKuUai3gLFovzKH+VU\nsSi/ikX5lT/KqeJQjxwhhBBCCHkr6pEjhBBCCFEw6pEjhBBCCCEyqJBrJOotUCzKr/xRThWL8qtY\nlF/5o5wqDvXIEUIIIYSQt6IeOUIIIYQQBaMeOUIIIYQQIoMKuUai3gLFovzKH+VUsSi/ikX5lT/K\nqeJQjxwhhBBCCHkr6pEjhBBCCFEw6pEjhBBCCCEyqJBrJOotUCzKr/xRThWL8qtYlF/5o5wqDvXI\nEUIIIYSQt6IeOUIIIYQQBaMeOUIIIYQQIoMKuUai3gLFovzKH+VUsSi/ikX5lT/KqeJQjxwhhBBC\nCHkr6pEjhBBCCFEw6pEjhBBCCCEyqJBrJOotUCzKr/xRThWL8qtYlF/5o5wqDvXIEUIIIYSQt6Ie\nOUIIIYQQBaMeOUIIIYQQIqNJC7lvv/0Wbm5u0NPTg7GxMcaMGYN79+7Vet26detgYWEBLS0tDBgw\nAImJiU0ZZr1Qb4FiUX7lj3KqWJRfxaL8yh/lVHFaXI9cWFgYFi9ejMjISFy5cgUqKioYPHgwCgoK\npK/ZvHkztm/fjt27d+PWrVswNjbGkCFDUFpa2pSh1ll8fDzXIbRolF/5o5wqFuVXsSi/8kc5VZym\nyK2Kwj/hFefPn5d5fODAAejp6SEiIgIjR44EYww7duzAl19+CW9vbwBAQEAAjI2NERQUhPnz5zdl\nuHVSVFTEdQgtGuVX/iinikX5VSzKr/xRThWnKXLLaY9ccXExxGIxWrduDQBITU1FdnY2hg4dKn2N\nhoYGPD09ERERwVWYhBBCCCHNEqeF3NKlS+Hi4gIPDw8AwPPnzwEAJiYmMq8zNjaWPtfcpKencx1C\ni0b5lT/KqWJRfhWL8it/lFPFaZLcMo4sW7aMWVhYsNTUVOm2GzduMB6PxzIyMmRe+/HHH7Phw4fL\nbOvatSsDQD/0Qz/0Qz/0Qz/00+x/unbtqpB6qkl75CSWLVuGI0eOIDQ0FDY2NtLtpqamAIDs7Gy0\nbdtWuj07O1v6nERMTEyTxEoIIYQQ0lw1+aXVpUuX4vDhw7hy5Qrat28v85ytrS1MTU1x8eJF6bbK\nykqEh4ejd+/eTR0qIYQQQkiz1qRn5BYtWoTff/8dwcHB0NPTk/a96erqQltbGzweD5999hk2bdoE\nJycnODg4wMfHB7q6upg6dWpThkoIIYQQ0uw16S26+Hw+eDxerVtUrFu3DmvWrJE+Xr9+PXx9fVFQ\nUAB3d3fs2bMHHTt2bKowCSGEEEKUgtLea1VZMMbA4/G4DoOQf0T7qeJQbhVLLBaDz6e7TSqKpESg\nfVh+Xh0T5LH/UiHXRMRiMXg8Hv0xyBFjDIwxGsRJs5eWlgaBQADg5ZUJc3NzGgvkKDk5GWZmZhCL\nxVBRUYGWlhbXISm9kpISVFdXw9DQULqNijr5KSkpga6urlzei5NZqy2ZUCjEX3/9hfj4eCQmJsLR\n0RGTJk2CsbEx16G1GJmZmdDS0oK+vr5cj2reN2KxGE+ePMGdO3eQmZmJwYMHo0OHDjLPU04bp7Ky\nEjt37sRvv/2GlJQUGBkZwc3NDb1798bAgQPh5uZGX4qNEBMTA19fX1y8eBFpaWmwt7fHwIEDMWrU\nKHh6esrti/J9kpWVBX9/f1y4cAHPnj2Dmpoaxo0bhxkzZsDBwYHr8JReQUEBTp48iRMnTiAhIQF2\ndnYYNWoUhg8fLjP+1gedkZOz1atX48iRIygrK0Pnzp2RkpKC1NRU9O3bFytWrMCoUaNo4G6gkJAQ\nfPPNNxAKhcjPz4epqSlmzpyJ6dOnQ0WFjknqSlKg7dy5Ezt37oRIJIKmpiaSkpJgZWWFWbNmYdmy\nZdDT0+M6VKW3fft27Nu3D1OnTsXEiRNx8+ZNBAcHIzo6Gpqamli1ahXmzJnDdZhKy8PDA61atcLo\n0aPRtWtXXL58GYGBgUhNTcXgwYOxY8cOODk50UFJPUycOBGZmZno0KEDevTogQcPHuDs2bNISUnB\nhx9+CB8fH7i4uFDLQAMtXboUoaGhaN++Pfr06YNbt27hwoULKC8vx+TJk+Hj4wMLC4v65Vchq9O9\np/Ly8piGhgYLDg5mQqGQZWVlsdjYWBYQEMC8vLyYk5MT+/XXX7kOUymFhYUxW1tbNnnyZPbdd9+x\n77//no0fP54ZGBgwS0tLtnnzZlZRUcF1mEojJyeH6ejoMD8/P5aYmMgePXrEIiIi2JdffsmsrKyY\nhYUFO378ONdhKr2OHTuyn3/+udb258+fs5UrVzItLS22bds2DiJTfg8fPmTa2tosPz+/1nM3btxg\nnp6ezNnZWWbRefLPCgsLmYaGBouLi5NuEwqF7MWLF+zo0aOsf//+bMSIESw7O5vDKJWbtrY2u3r1\nqsy28vJyFhgYyLp168bc3d1ZWlpavd6TCjk58vf3Z506dWJCoVBmu0gkYo8fP2YrV65kampqLCoq\niqMIlZe3tzebOXOm9LFQKGR5eXksMjKSLV++nHXs2JEFBARwF6CSEIvFjDHGdu/ezZydnZlIJJJ5\nXiQSscTERDZnzhzm6OhIX4KNUFRUxD744AO2evVqxtjLfbaiooLV1NRIX7N06VLm6enJcnJyuApT\naZ09e5bZ29uzmJgYxhhjVVVVrKKiQrpPJyUlMVtbW/b9999zGaZSCQ0NZfb29iwpKanWcyKRiEVF\nRTFDQ0O2detWDqJTftHR0czS0pLduXOHMfYyp6+OB7GxsczCwoJt2LChXu9L55rlyN7eHqWlpbhw\n4YLMdj6fD1tbW2zZsgVDhgxBSEgIRxEqL6FQCFtbW+ljFRUVGBgYwN3dHVu2bEGfPn2wdetW5OTk\ncBhl8yc5VW9ubg7GGDIzM2We5/P56NChA/7zn/9AW1sbly5d4iLMFqFVq1bw8vJCQEAAYmJioKKi\nAg0NDQgEAlRXVwMA5s6diwcPHkAkEnEcrfIZMGAAtLS0sG3bNlRXV0NNTQ0aGhrg8/kQiURwcHDA\nhAkTEBkZCQC1lr0itbm4uEBVVRWrV69GSUmJzHN8Ph+9evXCkiVLcOXKFY4iVG6dOnVC27ZtsWPH\nDgAvcyqZBMUYQ5cuXbBy5Upcvny5Xu9LhZwcubi4wNXVFWvXrkVgYCAyMzNRU1MjfZ7H46GkpATl\n5eUAQIN3PQwaNAibNm3C2bNnUVFRIfOcQCDA119/jeLiYjx58gQADdrv4uHhgYqKCowbNw7nzp1D\nUVGRzPPW1tbQ0dFBdnY2gJd9daT+pk6dii5dusDV1RVeXl44ceIExGIx1NTUkJGRgUOHDsHQ0BAm\nJiaU43pgjEFDQwMbN27ElStX4OrqinXr1iE6OhrAyzHh4cOHOHfuHD744AMANN7WhZ6eHr7//nvE\nxcVhzpw5+P333/HgwQPpd1Zpaam0v4vUn4aGBpYvX47z589j+PDh8Pf3x+PHjwG8rA+qqqpw69Yt\ntGnTpl7vS5Md5CwlJQXLli1DZGQknJ2dMWbMGNja2kJNTQ23bt3Cjh07cOfOHdjY2FADbj2UlJRg\n0aJFSExMxMSJEzF48GBYWlpKZwMfP34cs2bNqnUUSd4uLi4OK1asQElJCVxdXdGrVy/Y2dnBwcEB\nx48fx8qVK5GQkED7aiMJhULs378fx44dw4MHD1BWVoZ27dqhqKgIqqqqWL9+Pby9vVFTU0OTdhog\nIiIC+/fvR0xMjPQgr02bNkhPT4e5uTnOnz8PTU1Nas6vI7FYjEOHDsHX11c6E9jKygqVlZVISUlB\neXk5zpw5A2tra65DVVonTpyAn58fnj59CmNjYxgbG8PIyAiJiYlISkrC4cOH4ebmVuf3o0JOQS5d\nuoRdu3YhPDwchoaGqK6uho6ODlavXo2PPvqIvhjrQTIAP378GNu2bcP+/fuhqqqKfv36wcTEBHfv\n3kVlZSVGjhyJTZs20RdiHUhy+ujRI/j7++OPP/5AVVUVNDU18fDhQ1hZWWHhwoVYtmwZ7auNIMmd\nWCzG48ePkZiYiPT0dKSkpEBLSwsLFy6EhYUFFRj19Po+WVZWhps3byI2NhYvXrxAZmYmunXrhlmz\nZkFfX5/24Tp4U47Onz+P4OBgZGZmQlVVFSYmJlixYgXs7Ow4ilJ5vX4gkZubi3PnzuH69evIzc3F\n8+fPYWJigrVr16Jbt271em8q5ORIJBJBLBZDVVVVuk0oFOLGjRswNDSEpaUl9PX1AdBq7/Xx+gBT\nU1ODwMBABAcHo6amBsbGxhg7diyGDBkCTU1NGrTfQXKJSdKbIXH9+nUkJyejffv2MDExka4ZRftq\nw7E6LKBK+W0YkUgEkUgEgUAgsy+/fiBH+a0foVAIADLfY9XV1bXyTOpPUiMIBAKZ76j8/HwYGBg0\n+H2pkJODFy9eyCz4yxhDdXU1+Hy+zB8DaZzq6mrweDyZnFZWVkJDQ4PDqJTD277MJE33ampqlkUH\n2gAAIABJREFUdXo9ebfY2Fg8e/YMAwcOlO6bjDHpAQaPx4NQKJRpdCZ1d/LkSbi7u8PMzEy6rbq6\nGowxqKurSx+/vk+Tt7ty5QpMTEzQqVMn6TaxWAyhUAiBQEBXOBopPj5e5kQOUHufbcyYK1i3bt06\neQT6Phs7dixu3bqF8vJytG7dGrq6ulBRUYFAIIBYLIZYLEZRURH1adRTbm4u/vzzT2lOJUeEIpEI\nQqEQPB6PBus6kuxz3t7eSE1NhYGBAYyNjWVyWlNTI72NHO2jDTdmzBhs3boV/v7+SEtLg7GxMczN\nzaVFHADcuXMHFy5cQPfu3TmOVrnk5+fD1dUV27dvx6lTp8Dn8+Hs7Aw1NTVpsSEUCnH8+HGoqanV\nu2n8fdWzZ0+cOXMG165dQ0lJCUxNTdGqVSuoqKiAz+eDMYaQkBAYGhpCXV2dxod6cnFxwQ8//IC7\nd+9CTU0Njo6OMgWyWCxGXFwcBAIBtLW16/3+VMg10rFjx7BlyxaoqakhLCwMoaGh0uUE2rRpAw0N\nDYhEInTr1g1ubm6wtLTkOmSlsXHjRqxduxaJiYm4d+8eRCIRjIyMoKmpKR1g0tLScO7cOXTu3JkG\nl7eQHDwcOXIEGzduRFlZGY4ePYqQkBAUFRXB1NQUenp6EAgEKCkpQf/+/eHp6Slzj0VSN8XFxdi+\nfTvWrVsHFxcX/Pnnn/Dx8cHhw4dRVFQkPSqfM2cOsrKyMGHCBOl9mMm7HT58GElJSfDx8UF5eTn2\n7t2LNWvWICoqCq1bt4aDgwMYY3BxccG0adPQtm1bOnh+h7NnzyI4OBjjxo1DXl4eQkJCcOTIEdy6\ndQsikQhWVlZQU1ODg4MDOnfujC5dunAdslKJjo6Gn58fZsyYgWfPniEgIAA//fQTHj58CAMDA7Rt\n2xY8Hg/dunWDgYEBevXqVe/PoEurjbRo0SIUFxdj+fLluHPnDkJCQpCamgoejwdra2u4u7ujqqoK\n69atq7VsBvlnXbt2hY2NDXR1dfHo0SMAL5fFcHV1Rf/+/eHm5gYfHx8EBAQgOTmZBuy3kORl3rx5\nKC4uxtSpU5GQkIBbt24hIyMDAoEAXbt2xejRo1FSUoLp06fTUhgNdPPmTWzYsAELFy7EyJEjUVpa\nivj4eBw5cgTHjh1DVlYWevbsiaioKNy4cQMeHh7SPi/ybuvXr0dycjK2bNkCQ0NDJCcnIyIiAseP\nH0dYWBi0tLRgZ2eH58+fIyMjg8aEOli3bh1u3bqFffv2QSAQIDw8HFFRUYiLi8OLFy/QunVrtGrV\nClevXq21TBF5t127duH06dPYvn079PX1cfv2bURGRiI8PBypqakwMzODi4sL/P39kZeXh1atWtX/\nQ+q1fDCRIRKJ2I4dO9inn34qs/3u3bvsu+++Y6NHj2bu7u6Mx+OxOXPmMMZYrbs+kDd79OgRc3Nz\nY4cPH2aMMRYTE8M2b97MxowZw1xdXVnfvn3Zxx9/zHR0dNh///tfxhjl9p9UV1ezTz75hM2bN0+6\nLT09nR07doytWLGCDR06lLm6ujIejyd9DeWz/rKzs9nvv//OHj16VOu5vLw8dvbsWebs7MwcHBwY\nY3/faYPUTXR0NPP19ZXZJhKJWG5uLvvrr7/Yxo0bGY/HY5s2bWKM0T5cFzExMWzr1q2svLxcZvu9\ne/fYb7/9xj755BPG4/HY3LlzOYpQuUVERLBVq1axvLw86baysjIWFxfHDhw4wBYtWsQEAgEbPXp0\ngz+Dzsg1UnV1NQoLC2FsbAyhUFhrxurJkycxZcoUREdHo3v37nT0XUclJSU4d+4cTE1N4enpKd0u\nFAoRHh6OS5cu4fz584iNjUVpaSn1H9aBUChEWloaHBwcas3svX//Ps6ePYvPP/8ct2/fhouLC+2r\njSQSicDj8WTyLBaL0b17dwwePBhbt26lpXIaQSgUQkVFReZvPiYmBt27d0dqaiqsra1pBns9Sfpk\nX/27T0lJgZOTE65fvw53d3cOo1N+NTU1EAgEMvtsamoqOnXqhAMHDmD8+PENel8aQRpBskK7sbGx\nzLIjNTU10hmrubm50NLSQvfu3cEYoy/GOtLV1ZXZqSUDjKqqKgYMGIABAwbg2bNnMDU1haamJn0h\nvoNIJIKqqirs7e0BQHobI+DlMiQdOnTAjRs3YGxsDBcXF9pXG+D1AwlJ/l7Nc1ZWFoRCIRYvXgwA\nVGTUw+tFmWS8fbVgjo6Ohru7O6ytrelApA5e32clYyj73yxrgUCA69evQ1NTk4q4Bnh9H5Tk99Ux\n4fHjxxAIBA0u4gAq5BqFz+ejqKgIenp6MgPMq38MfD4fq1atAvCyGKHlSOruTX8AjDEwxlBYWIgD\nBw4gICAAwD+v00X+zuWbCg3g5cASGxuL2bNnSx9TYVw/lZWVOHXqFEpLS1FZWQkHBwf07dsXmpqa\n0tfo6elh3759sLGxkY4PpG6ePXuG69evQ01NDQKBQNp8/+p+7OnpiZ49e3IYpXIRiUQIDQ1F69at\nYWBgAF1dXRgYGMisczZw4EAcO3aM40iVk0AgwO3bt6Gvrw+hUAh9fX2YmprK7LMmJib46aefGvU5\ndGm1gZKTk3Hw4EGEhobiyZMn8PDwwOjRozFgwACYmJi88Xfo0l/d3b9/H/Hx8ejQoQMsLS2ho6MD\nFRUVmSPFW7du1es2Ju8byf6WnZ2Nixcv4tixY1BVVYWHhwdcXV3RsWNHGBkZyZzpkJzZpH21fuLi\n4vDVV18hLCwMmpqa0jNChoaGGDVqFCZNmiSz7hmpnx9//BF+fn7SSU1WVlYwMjJCt27dMG7cOPTp\n04frEJXOmTNn8MMPPyAxMRHPnz+HtrY2evbsiQkTJmDcuHFv/R4jdRMREYE9e/bgwoULyM/Ph42N\nDdzc3ODp6YmhQ4dKF1yXByrkGqhv374oKytD3759YWJigsuXLyM8PBxt2rTBkiVLsHLlSggEAlqY\nsp7Kysrw1VdfISgoCK1atUJaWhqMjIwwatQozJ8/v9bRNvXAvNvIkSORkJCA3r17o6ysDOHh4aio\nqEC/fv3w9ddfo2/fvgDoQKMxxo0bB6FQiK1bt8LR0RE3b97EzZs3ERkZifj4ePTt2xd79uzhOkyl\n1bp1a3zxxRdYsGAB1NTUEBISgosXLyIiIgJCoRAbN27E2LFjqcWiHmxsbDBq1CiMGTMGXbt2xV9/\n/YVff/0V58+fh6WlJXbs2IFRo0bV6v0mddOjRw/Y2NhgxowZcHZ2xrlz5/DHH38gJiYGNjY22Lp1\nKzw9PeWT3wZPk3iPhYSEMCMjI5afny+z/dmzZ2zt2rXM3NycLVy4kNXU1HAUofLatGkTc3FxYX5+\nfuz+/fssMTGR7dixg3Xr1o3xeDw2ZcoUlpmZyRijGX//RJKbCxcuMCMjI/b48WOZGXznz59ngwYN\nYjwej61bt46JRCKuQm0RLCws2NWrV2ttLyoqYoGBgUxDQ4N98cUXHESm/IKDg5m9vf0bn0tPT2cL\nFixgurq6LC4urokjU14RERGsTZs2rLKystZzL168YHPmzGEODg4sKSmJg+iUX3JyMtPR0WGFhYW1\nnnvw4AEbP348MzY2ZtHR0XL5PDqV0QC3b99Gu3btpLffqampgUgkgrm5OdatW4dNmzYhMDAQ165d\n4zhS5XP48GHMnDkTs2bNgpOTEzp06IClS5fizp07OH78OGJjY7Fv3z4A1Bf3TyS5CQ0Nla7HJxAI\nUFVVBQAYNmwYQkJCsG3bNvj7++Px48dchqvU8vPz4ejoCH9/f9TU1AB4OSaIxWK0atUKU6dOxbff\nfosbN24gJyeH42iVj5qaGqqrq3H27FkAL1cKqKqqgkgkgqWlJbZv3w5nZ2ecPHmS40iVR2lpKVq3\nbo27d+8CeHllo6qqCtXV1TAyMsKaNWugoaGBwMBAjiNVTllZWTAxMUFUVBQAoKqqClVVVRCLxXB0\ndISfnx9sbW1x/PhxuazZSYVcA4wcORKPHj3CiRMnAEDmdlwAMHPmTPTr1w9hYWEA/r5xNvlnlZWV\nsLOzQ3JysnQbYww1NTVgjMHb2xtTp07FiRMnqPCoo4EDB+Lhw4dISEgAj8eDuro6GGOorKwEAEyf\nPh2mpqY4c+YMx5EqLwMDA0yfPh2hoaH4+eefUV5eLr3ziISjoyOSkpJgZGTEYaTKafjw4XBycsKW\nLVuQmJgINTU1qKurSxvGNTU1YWZmhuzsbAB/zwgkb9e/f3/o6upi1apVuH//Pvh8PtTV1aGmpibt\nQezXrx8ePHjAdahKqW/fvrC1tcX27dtRUFAAdXV1qKurS1cL0NXVxdChQxEdHS2X1iAq5BrA0dER\nM2bMwKeffor58+fj7NmzyMvLk/6DZGVl4c6dO3B2dgYAWiW/jjQ0NDB8+HD8+OOP2Lp1K7KyssDj\n8WS+FGfMmIH09HRoaWkBoCL5Xdzc3GBtbY2+ffti48aNSElJAY/Hk55N1tHRQUZGBmxsbADQl2BD\neXt7Y8KECVi6dCk6deqE//znP4iOjkZSUhICAwPxww8/4MMPPwQA6Vk78m7sf32b3333HSoqKuDs\n7IwBAwbg4MGDyMvLw+PHj7F3716EhYVh+vTpXIerFBhjUFVVRUBAAKqrqzF27FjMmjULhw8fRk5O\nDng8Hs6fP4+TJ0/C29ub63CVjuQ7af369dKxdfbs2bhy5QqAlzNZo6KicPLkSQwbNkwun0mTHRqo\ntLQUP/74I06fPo3Kykq0bdsWBgYG0NPTQ1RUFCoqKqSnrUn9bNy4EYcOHYKdnR08PDzg5uaGfv36\n4cWLF1izZg2io6Nx9+5dmuhQR8XFxdi0aRNCQkIgEAhgZ2eHnj17wtTUFAEBAXj8+DEePnzIdZgt\nwqNHj7Bv3z7pWWNzc3MIhUKMGDEC69evh5WVFe23DVRdXY1jx47h4MGDCA8PR1FREczNzaGhoYFp\n06aBbhteN+yVSU1xcXE4duwYIiMj8eLFC+Tm5oIxBhUVFQwcOBD+/v7cBqvknj59ioCAAFy6dAnJ\nycmorKyEtbU1Xrx4ARcXFxw9elR6UN0YVMg1UmJiIs6ePYuYmBjk5+cjKysLQ4cOxYIFC2Bra0uL\nUtaDZIDJy8vDqVOnEBwcjPT0dKiqqiI9PR1FRUX44IMP8Pnnn2PYsGE0Q60e8vLyEB4ejuvXr+PR\no0e4f/8+MjMzMXnyZOlsYNpXG0YoFKKkpARaWlrQ0NCAUChEZWUlcnNzERcXB0tLS3Tv3p3rMJWS\nZJ+UFL8ikQgFBQXIyclBUVERUlNT4ebmJl3omorkunl97ExKSkJcXBxKSkpQVlYGe3t7DB8+nMMI\nW46KigqkpKTg0aNHyM7OxpMnT9ClSxd4e3tDXV1dLp9BhVw9MMZw//59hIWFwcLCAqNHj5ZpuM/J\nyaEemEaorKyEmpqazEAcFRWF+Ph4CAQC6OjoYPDgwTAwMOAwSuWRkZGBxMRE9O7dG7q6utLtmZmZ\nACDdV2lpgYYpKSnBsWPHsHr1aujr62P69On497///dbXM1repV6SkpLg6+uLQ4cOoVOnTli7di0+\n+OADrsNSatnZ2Th16hSCgoKgra2Nzz//HP369eM6rBajuLgYly9fxt69e2FtbY3PP/9cruvFvQ0V\ncvXw7bffYvfu3TAwMIBIJMLEiROxdu3aWkeANGDXX1hYGH755RdkZGSgV69eWLFiBYyNjWu9jo64\n68bX1xd79uxBbm4uKioqsHbtWnz66ae1zrhRPhtuw4YNOHHiBIYPHw4tLS1s3boVs2fPxo4dO6Sv\nEQqFEIlEcrl88r4ZOHAgqqurMXr0aNy4cQPR0dE4e/YsunXrJh1jS0tLoa2tTeNtHc2YMQO3b9+G\nm5sbCgsLkZWVhQMHDqB9+/a0GLgcrFixAmfPnkX79u2RmZmJ/Px8HD16VHqLTh6Pp5grSXJZxOQ9\nkJCQwMzMzFhgYCCLi4tju3fvZpqamiwoKIgxxqRrdKWnpzPGGK3LVQ+nTp1iPXr0YD179mTLly9n\nbm5uzMfHhzH2Mq+0Xlz93Lt3j9na2rJ169ax8PBw5uPjw2xsbNjNmzcZY4xVV1czxhgrLi7mMkyl\nZ2pqyoKDg6WPg4KCmJmZGbt9+7Z027Fjx9iWLVu4CE+pXbx4kbVt25ZlZWUxxhgrKytjw4YNYyNH\njmSM/b1O4n/+8x+WkJDAWZzKJDExkenr67PExERWXV3NHj16xNzd3dmECRMYY3/n9KeffmKPHz/m\nMlSllJeXx1q1asXCwsJYRUUFe/HiBRswYAAbM2YMq6mpka4re/LkSZaYmCjXz6ZCro4+/fRT5uXl\nJbNt48aNzMPDg1VXVzOxWMyys7MZj8djz5494yhK5eTu7s6+/vprJhKJWE1NDdu1axczNTWVFh6M\nMXb79m22c+dODqNs/iQHDwsWLJDZVysqKthHH33Exo8fzxhj0n3Vysqq1qLWpG4iIiKYra0te/78\nOROJRNIvwTFjxrDly5dLX2dnZ8e2bdvGGGO0QHg9zJ07l82ZM4cx9vd+HRsby2xsbFhUVBRjjLH7\n9+8zHo/HysrKOItTmXz11VdszJgxMtvi4uKYsbExi4yMZIwxlpuby3g8Hi0E3AA7d+5k7u7uMtuS\nkpKYhYWFNL+VlZWMx+Ox8PBwuX42XVOpo3v37klvZSQSicAYw8yZM1FQUIDg4GDweDwEBgbC0dER\n5ubmtIxDHRUUFODx48eYNm0a+Hw+BAIBFi9eDBcXF+zevVv6Oh8fH5w+fRoALZHxNpJLpLGxsRg9\nejSAl5dONTQ0sGTJEkRFReHGjRvSfRV4eesjymf9paenw8rKCiUlJeDz+dIlRf7v//4Phw4dQnFx\nMZKSkvDkyRMsWLAAAOgSdj1UVFRAS0sLNTU14PP5qKqqQpcuXdCzZ0/puPDzzz/D09NT+jryz54/\nfw4zMzPpGpJCoRDOzs4YPHiwNKcBAQFwdHRskr6uliYlJQVOTk7S/FZXV8PBwQGDBw/G1q1bAQDB\nwcFo06aN3Hs9aWSpg9LSUri5uaGkpATAy3VgeDweLCwsMHjwYPj6+gIA9u/fj3nz5gGg9c3qKiYm\nBu3atUNBQQGAv9fc27x5M86dO4f4+HjU1NQgJCQE33zzDZehKoX8/HzY29vjyZMnAP4uHtzd3dG1\na1f8+OOPAIBffvkFy5cvB0D7akNI8qmtrQ3g5YQRxhiGDRsGKysr7Nq1C4cPH0avXr2khQb1HdUN\nYwz/+te/oK+vL+3ZkszuW7x4Mc6ePYuUlBScOHECn3zyCQC6y8u7iMVijB07FmZmZtJ+Tckkp0WL\nFuHq1atIT0/HsWPHMGvWLA4jVU6MMQwaNAhqamrS/ErusT5//nzpagGHDx/G5MmT5f75NNmhjmJj\nYyEUCuHq6irTIJ6amopevXrh66+/xooVK1BcXAwtLS1qGK2jjIwM+Pr6YsqUKejcubO0kOPz+fDy\n8kL79u0xaNAgfPTRR8jPz6e81sFff/0FAOjVqxfEYjF4PB54PB5u3ryJcePGYdeuXRg/fjzKysqg\nqalJOZWzoKAgrFu3DmlpaTh06BDGjRtHS+U0wuv7p5eXF1JSUvD06VPpASB5t/LycpSWlsLY2Fgm\np4wxfPjhh+DxeAgJCUFBQQF0dHQ4jlb5MMZQUFAAAwODWpPIRowYATU1NZw5cwb379+XLpcjzw8n\nDSTp3VixYgXj8XjSRtxXb05O3i0jI+ON248fP8569OjB2rZty1atWsUYo9zW1esTRCR5mzJlCuPx\neNJeGcpnw/xTv1tlZSVzcnJiPB6vCSNqWd40wUky3v7xxx+Mx+NJe+hoH26806dPMx6Px4YNG8Z1\nKC2KZJ8NDQ1lPB6PdenSRSGfI1hHy2HXCXvDWQvJYxMTE4SGhsLHxwe2tra0pEM9tWrV6o3b27dv\nD19fXyQnJ+Pw4cPStdDo7NG7vZ6jV/fHkydP4ocffoC9vT3tqw30tpyJxWKoqqrC3d0d7u7ucHFx\ngVAopIWW6+lNf+M8Hg9isRhOTk4wMTHB9OnTYWhoCMYY7cONwBiDo6MjGGOYO3cu2rZty3VILQaP\nx4NIJIK1tTWEQiGmTp2KDh06yP9zGKNLq/IQFRUFd3d3rsNoca5fv45Lly5hw4YNVHTIycWLFzF0\n6FCuwyCENKE3nYx4VVlZmbTnk8hfZWWlwtaTpEKONHuSAeZdA9H7TCwWgzFGZ36aCbrdmXxIvp7o\n756Qt6PTG/9AMoiUlZWBMQaRSCRtxn/T64hiSI4SaTB/s7KyMunSLcDLIuJtS4rQviof78ojFXEN\n92puJRN12Ms1TzmMSjlJxoG4uDjcvHmT42haHkk9kJubi6dPnwLgZnksKuT+geQf6fvvv0dISAgE\nAsEbL+1RgdFwrxbGbyuUyT8bNWoUvL29cfz4cVRVVUEgEMgUda/mlPbVhpOsVRYcHIyNGzciPj4e\nZWVlHEfV8vB4POTk5CA5ORl37txBSUmJtKAj9SPJ2WeffYZLly4BePNBCBXJjfPbb79h4cKFKC8v\n5+Qgjgq5fyAQCCAWi3Hnzh2MGjUKO3fuREVFhfTsHGmYVwcNPp+PFy9eAIC0UJbklwaXdysuLoa7\nuztEIhG++uoruLm5YfHixbh27RoAyBx80KKpjSNZPiQpKQlr1qzBkCFDMGnSJAQEBCA1NVW6ECgA\nOiCpJ0m+8vPz8dVXX6Fdu3Zwd3fH0qVLsXz5cpw7d47jCJVPRkYGtmzZgpiYGFy9ehWTJk0CAJll\nRwAgLy+PiuQGkoytdnZ2iI6ORs+ePXH58mUwxiAWi5tsHKBZq+/A4/Hw0UcfQU1NDUFBQVBRUYGr\nqys13TeCZNLChQsXsGHDBvz22284cuQIMjMzYWFhgdatW4PP59PgUgfq6uoYOHAg3N3d0aFDB2hp\naeHu3bs4cOAADh48iGfPnsHExARGRka0zzaCZD2+nJwcJCYmoqSkBMOHD0dWVhZ2796NoKAgPH/+\nHHw+H3Z2drTv1pNIJAKfz8f69etx9OhRbNy4EUuWLAGPx0NkZCQCAwPRvn17tG/fnutQlcaVK1fw\nf//3fzhw4AB0dHTQvXt36OvrQ1dXV3qGs7KyEv369cOECROgpaXFdchKq2PHjpgzZw6io6Nx9uxZ\n2NrawtbWtsnGAZrs8A5CoRAqKiooKSnBtm3bsHXrVkyaNAmbNm2CmZkZzaRsBFtbW9jb28PBwQHl\n5eWIi4tDSUkJunTpgiFDhmDWrFlQV1enL8V/8PoEkLKyMjx48AAxMTG4efMm7t69i6KiIhgaGuKL\nL76Al5cXh9EqL8mCvsuXL8eDBw+wf/9+tGnTBgDw+PFjfP755zh58iSAl3d92LVrF3r06MFlyErJ\n3t4e3377LSZOnCiz/aOPPkJ6ejouXrxIMyvrSV1dHRYWFsjOzoa6ujpGjhyJmTNnwsnJCb6+vjh8\n+DCSkpK4DlNpSa50qKio4N69e1izZg1OnTqFf//731i2bBkMDAwUH4RCVqdrwU6dOsX69OnDvvzy\nS1ZSUsJ1OEpHstDnmTNnmJ2dnXT7ixcvWGhoKNuyZQsbP348Mzc3Zw8ePOAqTKUhWXCysLCQPXny\nROa5nJwcFhYWxv773/+yYcOGsVOnTsn8Dqm/Ll26MB8fH8bYy0WBq6urGWOMXbt2jc2ZM4eFhYUx\nNzc35uXlxWWYSkWyP1ZVVbHNmzezAwcOMMZe5ley2G9UVBQzNDRkd+7c4SxOZZWQkMAYYyw3N5ft\n27eP9e7dm6moqDBNTU3WqVMntn//fo4jVH6vL2C9f/9+NmLECLZ169YmWbCazsi9gWTpgIiICDx+\n/BhWVlZISEiApqYmDA0NsWPHDly9ehWDBg3CDz/8gM6dO3MdstKQnMG8cuUKgoOD8e2339Y6wk5L\nS0NqaioGDBjAUZTKg/3vjNzevXuxatUqfPjhhxgzZgzGjh0rk9f09HRYWlrS2c1GEIvFWLlyJW7d\nuoXr16/Xeq5Tp074/fffkZqaitWrVyMoKAjdu3fnKFrlIRkTPvvsM/z4449wcnLC6dOnYW1tLX3N\n5cuX4e3tjeLiYg4jVR6SM8iXL19Gbm4uPD09YWZmJn3+2bNnuHLlCqytrdG3b18aF+pJUiOcOnUK\nBw8ehJ2dHZ4+fQo1NTWYmZkhOTkZx48fh1AoRGZmJkxNTRUaDxVy/2DixIm4ceMGxGIxOnTogKdP\nn0JVVRUeHh5IS0tDcnIyzM3N4efnp5DVmluqyspKTJgwAbGxsdi1axdd7pOD8PBwXL58GTExMbh/\n/z5UVFTg6emJqVOnok+fPgBAbQByEB4ejrFjx8LJyQkff/wxRo0aBV1dXWzfvh3btm1DYWEhnjx5\nAnd3d9y+fRvm5uZch6w0AgICEBwcjNDQUKioqGDixIkYNmwYwsPDUVJSgnbt2mHVqlWoqqqCuro6\n1+EqBRcXF4wbNw4LFiyAkZERrW8oZ9u2bUNwcDBUVVVhZWWFzMxMVFRUoHPnzsjOzoa+vj5+++03\nhcdBhdw/iI6ORqdOncAYQ3Z2NmxtbVFSUoKqqiq0adMGhYWFmDx5MgwNDfHrr79CU1OT65CVQmxs\nLD7//HNkZGQgLy8PAwcOxKBBgzBkyBDY2NhwHZ7SYowhLS0NMTExuHHjBo4fP468vDwYGRnh/Pnz\ncHBw4DrEFiEiIgI7d+5EWloaMjMzkZOTg/bt22PhwoVYuHAhNm7ciKCgINy7d4/rUJWKSCRCeXk5\nUlNTERwcjOPHj+PevXsQi8WYMWMGvvnmG1haWnIdZrMnOWCLjIzEiBEjkJaWBj09PQB/n8E/deoU\nNDQ0MGjQICrsGqGkpER668jy8nLphJFXtzcFKuQagP1veQwVFRWEhYVh2LBhyMjIgJGREdehNXuS\nQaagoEA6Lf7u3bvIysqCtrY2LC0tMXfuXPTr14/rUJWaWCxGQEAAvvvuO0yePBkbNmxc4BL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oNBo8//zzuqN2AODs7IxmzZrhxx9/1K2LiYlBfHw8srKy0LhxYwDAxo0b4e7u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\nGUlkZ2dj6tSpeo1YIRaL5Ybriwpri9Vqva5NpbUF+PXfhpu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dL8sSiGVJRGrofBKG4iSMqOJU5/llSqD5LZFEFSc7uHVmrPGyY69evZCamoqE\nhASsX78et956K9avX49jx45ZPcdW543Zs2cjPj4eABAYGIjevXvzU5SW/yF0m24LvX38+HFRjccT\ntwdEdwIAHGTrEdKoeaIn4lVdWYJbYNqQWCzxofOJbnvqtqvOp/r9v+HHhUsBgxH9g8MBAAfKrwNA\ni7f3X70II3QYZJ6N8lR8upqTsb1790IWGiyq/18W2dnZuHz5Mlrj8T5jw4cPR3JyMiIjI7FkyRLI\nZA2TdUajkb/i8sZvhvqMEeJ8+lN/onjwWCi634TI/ds8OpaKZ15G7dovEbj8JfjNmOrRsRDSXpU+\n8iTqtv7g0HN90u9D8KrXnDwi4Qr7DIfxSj4ij+2CIr6Tx8YhlGj3pqyrq8Pp06cxfPhwzJw5E5Mm\nTeIf4zgO99xzD6ZMmYIZM2Z4cJSEdBz8MqWnumo3YtlmhWrGCHEdrlYDAAh4cR68+vYS/kS5HKo7\nbnXRqARqZplSqtyajGVmZmLcuHGIi4tDcXExsrKyoNVqMW3aNISHhyM8PNzqeC8vL0RFRSEpKcmd\nw2x3srOluVeXu1GcGhXmCqwZc2W8GPMGxFKuGevo55MQFCdhXBUnS88wZf/boBp0h9Nf35UsjV9x\nQ82Ys+NUv/83VL/+PoLefBmKxM5OfW0LtyZj+fn5SE9PR0lJCcLDw5GamoqcnBzExcW5cxiEkJZY\nZsbUnp8ZA1/AL83WFoRIQUMDV+ldjcgX8Lu4C3/JmIcAABXPLEHYf9a65D3cmoxt3LjRruPz8vJc\nNJKOhT51CkNxatTawgl9xtpK6suUdD4JQ3ESxlVx4rQi6RnmiGZmxlx5PrHl5S57bdqbkhDCs9Re\niKNmzPzHQaLLlIRIAafVApDozJiXaT7JXftTMj4+LnttSsY6gMaX2ZKWUZzsmxlzdbwsyZjlk7vU\n0PkkDMVJGFfFie+mL8VkzM19xlzZWJaSMUJIA13D3pSexidjNDNGiMtYygAkvUzpwpkxtqa24YYL\n98qmZKwDoJoMYShOIq0Zq6OasfaM4iSMy2rG2lkBv7PjZCwo4r/mKqud+tqNUTJGCOGJqs+YeQx8\nuw1CiFNxRmND8btawjNjLuwzZrzWkIyxFZUuex9KxkSG4zhcL9egsKRG8L+KatszB1STIQzFqVEb\nCTHUjHlbZsakmYzR+SQMxUkYV8SJ/9lSq8C4cAnOVZqbGXN2nNhGM2NsletmxjzagZ809fm3J7Dp\n+9N2P+/yokZbAAAgAElEQVT5GQMxsK/4t4MgIldv2QBYDDNj5poxiSZjhIidlJcoAYDxMs+eu3Jm\nrMA9M2OUjInMnxdLAQBB/moolfJWj6/V6FCr1eP8pbIWkzGqyRCG4iSymjHLzJhEC/jpfBKG4iSM\nS7rvS7nHGMBvhwRX1oxdK2y4Ua8Dp61zyVWVlIyJTK3WdFK9OHMQuieEtnr8t7vP4aNNR6GpM7h6\naKQD4GvGxFA/QjVjhLiU5GfGXLw3pe7ocdSu/dLqPrayCnIXJGNUMyYymjrTSeXj7SXoeB+1l9Xz\nmkM1GcJQnETWZ8zyC68NyRjHstCfOQ9D3mUnjUo4Op+EoTgJ45I4SbjHGABAYW766oI+Y5xej5Lx\n05rcz1ZUOeX1b0TJmMhotKYZLl/ByZjpZLSVjBEiGN9nTNj550oNNWOOt7aofOFVFA8cg6KUkaj9\n8htnDY2QdkHSPcbQMDPmij5jxoIicOYeYwGLM+F1e18Arqsbo2RMZBydGdPaWKakmgxhKE4iqxlz\nQtNX/R+nGr4+cabNY7IHnU/CUJyEcUnNmNSXKb2aLlM6K07GqwUAAGW/W+H/1AzIggJM7+WiKyop\nGRMRvcEInd4IuYyByqv14n0A8PZufZmSEKFE1WfMCdshsaUNG/u6smEjIVLEb4Xkwm1+XMoyM2Zw\nwcxYvikZk3eKBgDIAk3JGM2MdQAabcOsmNCeL0KWKakmQxiKE8DVmzcKF0HNGBQKQCYDjEZwBscu\nUGHLKxq+rnZvMkbnkzAUJ2Fc0meMX6aUZjLmyr0pLTNjfDIWZEnGqGas3bNcSWlZehSCL+DX0tWU\nxAnEtDclw7Sp8SvHcWDLGiVjla75JUqIVDUsU0q0ZqyZZUpn4WfGYm9Ixlz0e0RwMvbdd99hzJgx\n6NGjB65cuQIAWLNmDXbt2uWSgXVEltktocX7gLCrKakmQxiKU6NlCxHUjAFt2xKJq6oGjMZGt2uc\nNi4h6HwShuIkjEv7jEm0ZsyVfcYMN8yMMYGBAADjxavQ/3kB+j8vwFha5pT3AgQmY1988QXS0tKQ\nlJSEvLw86M3fuNFoxOuvv+60wXR0ll5hQov3AUCllEPGMNDpjTAYWVcNjXQQYqoZAxpvFm5/MtZ4\nVgygmTFCbsTPjEl1mdKFHfiNV68BaDozptn4DYoHjEbxgNEoTB4M/dkLTnk/QcnY8uXLsWbNGqxc\nuRJeXg2JwoABA3D06FGnDIQ0LFP62rFMyTAMvM11Y9oWZseoJkMYilOjqylVrS9buCVeasfbW1iK\n9+VxsabXcOG+cs2h80kYipMwrtmbUuLLlC7cm9KYb+q8b5kZUw8fAuWAFCiSEqBISgDj7wcYjdD/\ncdIp7ycoGTt//jwGDhzY5H4/Pz9UVdGnTWexFPB725GMAY2XKqlujLSRiGrGgEZ/JBxob2Ep3pd3\nMW0TxlZWg+M4p42NEKmT/DKlZXLIwZkxrl6HypffgK5RCxwAYKtqwFVWgfFWQxYSDACQx0Qi/Lsv\nEXlgByIP7IBP+n2mY0ucs1QpKBmLiYnB2bNnm9z/66+/omvXrk4ZCAFqtaY/hPbUjAENy5ot1Y1R\nTYYwFCdx9RkD2tbegp8Ziww3LcMYjeA0WqeOzxY6n4ShOAnj0j5jkl2mNHfgd7BmTLPxG9S8swbX\n/3If/0FNf/IMCrqnAgDksVEtdjaQhYUAAIzuTMYee+wxPP3009i7dy84jsPly5exbt06PPPMM5g1\na5ZTBkLsb/hqwbe30FKvMdI2oq0Zc2RmzFwzJgsJBhPob3od6jVGCI+/YEeiy5Ro496UxoJi/mv9\nkeMAAM1/tgHmD6XKQf1bfK48zLR3NHu91KH3vpGgZOzZZ5/FxIkTMXLkSGg0GgwfPhyzZs3CrFmz\n8MQTTzhlIMT+rZAsvFvpwk81GcJQnBolY2LoM4ZGWyJpHagZKzfNjMlCgiDzNyVj7izip/NJGIqT\nMC7tMybRZUpLawvoG/722RMnY9F1/uvaz74CABj+NBXkByx+BkFvvdzicy0zY6yTrqgU3NrilVde\nwfXr13HgwAHs378fxcXFyMrKcsogiAk/M+ZwzRjNjJE2qhdZzZi3ZUsknd3PNZZakrFgMJbu2W4u\n4idEzBqWKaU5M9ZQwG//7wcAMF7O57/WfvsDOJblkzH18ME2m6/zy5TXnZOMKew52NfXF/369XPK\nG5OmarVtW6bU1lPNWFtQnOybGXNPnzHL1ZRtW6aUBfiZXseNyRidT8JQnIShPmPNaKaA3544Ga82\nJGNcRSX0J87AkHcFYBgounax+Vx5uHmZ0kkzY4KSsWHDhjWbITIMA5VKhaSkJEybNg233XabUwbV\nUWkcaG0BUBd+4jxcnUhrxhxpbVHWaJnSCTNjHMcBrV2NyTCCtzIjxNMkX8DfTGsLoTiWheGKqZeY\nauSdqN+5B5qNmwGjEfIuca3GhF+mdGfNWI8ePXDkyBFcu3YNnTp1QmxsLK5du4bDhw8jMjISv/zy\nC/r3748ff/zRKYPqqGodLuC3vUxJNRnCdPQ4cSwLWPaAVLZ+DrqlZkxtSgrZ66UwFl236x9rXj6Q\nhQTxBfyO1ozV5xxGQefbcC2sh81/Rf3uQX3OYQB0PglFcRKG+ow1ZZnBd2RvSra4BKjXQRYSBPVw\n02ya5ouvAQCKbq13iWAC/AEvL3A1tQ7N3N9I0MyYr68vMjIysHLlSv4+juMwf/58MAyDo0eP4umn\nn8ZLL72EESNGtHlQQllaQbQXNRrHWlt4e7e+WTghrWpULyaa2R3zzFjVkjdRteRNh15CFhIEWYD5\nakoHt0TSfv0tuBqN6UZLseE4GHMvoWTcI4jM+c6h9yHEnTiNecZZosuUjK8PAIAtug6OZcHIhG+3\nbTTPisnjYqHsb1rVs/yMe3VLbP29GQaysGCwBcUwlpRBYW4O6yhBydgnn3yCnJycJgOZOXMmUlNT\n8eabb2LGjBn49NNP2zQYez2Y+V+3vp+7ODoz1tLVlFSTIUxHjJOxrBz1e/YDLMv34BJSLwa4J17e\no+9C3fZdDvcHU6bcAnlMVMMypaMzY9kHAQBh2zdC1b/5cgxOr0fJfdOh23cIuqMnMPj+MQ69V0fT\nEX/uHEF9xpqSd4mDPC4Wxiv50B06BlX/2wTHyWAu3pfHxcKrVzIYXx9wtaZkTMjMGGBqb8EWFIMt\nKQXckYxxHIcTJ04gKSnJ6v7Tp0/zjdK8vLwgsyMrdQZ7rzqUgqTOwQgJtO8HwxKHnD/ycfVN8eyI\nEBrkg7kP94NKadd1IsSNKv6xCHVbvre6j/Hz8dBomlINugNRR9pe/sC0oYDfWFwCw58XwPh4Q3lr\nr5bfw8sLii5x0O07BE6jcXishLiDsbQMhlN/ApDwMiXDQP3Xkaj9YB3qtu5s9oMSZ56xBsNAnhDP\nz/ob8i4BABTxsWAUCgRmLYBm8zbIQ0PgPfZuQe8vs/Qac0LjV0F/JadNm4ZHH30U586dwx133AEA\nOHjwIF5//XVkZGQAAPbs2YPevXu3eUD2+Peb97n1/cSqU6RpCaa6VofTuU2LCSuLziIwsru7hwWg\nFMPu6Iw7esd44L3tl52d3eE+pRuvmD4dqoYNhiw4EADg/deRgp4rpXhZZsaMhcUwXC2w67n1u34F\nACj73drqrCHj4w0A4DRaScXHkyhOwjgzTmxNLYpSGhIOxtvbKa/rCd5jTclY7eeboPv9BA7W1eDu\nVW/CK8m01Fj9+ipUL38PAKC6cyD8ZmeAq9ehZpVpJc+rb08AgG/GZPhmTLbrvWVhpq2S3JaMrVix\nApGRkXj77bdRVFQEAIiKisIzzzyDzMxMAMCoUaMwevToNg+I2K9LbBA+Wjwa5VXNX3F25LAvbktp\nuZOwK2zedRY5v+fjejnNEIiZpUYi8JXn4ZV8k4dH4zqWZKxu+0+o2/6TQ6+hGnxHq8fwyZgDTWoJ\ncRfjtUJ+lthn2mTIY6M8PCLHKfvdCnnnTjBeugpd9kHooYPm800IXPIcAKB+7wH+2Po9+1C/Zx9/\nWz36LnhPdLycwDIzVj77OcjCQ6G+a4jDryUoGVMoFFiwYAEWLFiAyspKAEBgYKDVMfHx8a2+zuLF\ni7FkyRKr+6KionDt2jUYDAa88MIL2LFjBy5cuICAgAAMGzYMr732GuLi4oR+Px1WdLgfosP9mn3s\n5q7ur105fq7YlIyV1br9vR3VET+dW2okHFmalFK8lHfcCq+UPmALihx6PhMcCO9JY1s/zjzDwNVq\nJBUfT6I4CePMOFkuZPG6tReC317SytHixsjlCN+5CYbTf0J38Cj6vbIS+j9z+ccN5/MAAGE7/gXN\nhq9hzC8EACgS4hCwMNOuov8bNf4AW/3OR65Pxhq7MQmzV3JyMnbv3s3flsvlAIDa2locPXoUL774\nIvr27YuKigrMnz8fo0aNwh9//MEfR6QhPNj0x/16Gc2MiRlrTsZkvuKpE3MFWWAAInZ+5fL3oZkx\nIgWWfnuWq4ylTh4WAvmQAZCFh6HqlZV8F322ugZs4XVApYQy5Rao7rjVqe/r89D9gNGIin8sAlta\n0abXEpQSchyHTz75BCNHjkRycjISEhKQmJjI/9cecrkcERER/L/QUNM0X2BgIH744QdMmjQJSUlJ\n6NevHz788EOcPn0aZ86csf87IzxP9PGJCPEFABRLKBnraP2OOI5rmBlzIBnraPESgvExXXzDajQU\nH4EoTsI4M05ctWlmjPFvH8mYhSIxHodkBhgvXQVXVw/DhYvm+7uAccGEDiOTQX33MAANe+E6SlAy\n9sYbb2D+/PlISUnBxYsXcd9996FXr14oLy/H9OnT7XrD3NxcxMbGIjExEenp6cjLy2vxWMuSaHBw\nsF3vQTwvPMQ8M0Y1Y+JVrzM1eVV6CW5nQWxjfEznPc2METFrmBlrvrRFqhilEvKoCIDjYLiQ15CM\ntbK1UVtYLnxiyyr47hKOELRMuWbNGnz00UeYNGkSVq1ahSeeeAKJiYnIysrC5cuXBb/ZgAEDsH79\neiQnJ6OoqAhLly7FwIEDcfLkSYSEhFgdq9PpMH/+fIwbNw4xMdK4Gk+sPFGTERrkDRnDoKxSC73B\nCC+F+JeZO1rtCltrqudjfH0den5Hi5cQMssyZa2W4nODypp6XClo2nonKLI7Tpy73uT+LrGB8POh\nDwkWzjyf2CrLzFj7SsYAYGDfW1F3bRf0Z3NhOGea7FEkJbjs/RhvNRhvNThtHbhaDRg/x36fCkrG\nrl69iv79TVfjeXt7o6rK9AP14IMP4o477sCaNWsEvdmoUaP4r3v16oXU1FQkJCRg/fr1mDdvHv+Y\nwWDA1KlTUVVVha1bt7b4erNnz+YvHAgMDETv3r35E9YypUu3PXM7Z/8+sDV5gG8XlFZocf7MMVGN\nj25nw1h0HTfBVC8mhvG0h9u3m5OxnGuXEdioFYFYxuep27/88iuWr90PmV8XAKZ2OwD4ljvN3Q4P\n8cV/PvqHKMbf3m7vO3kcWugw3Fwz5unxOPO2oltXHPpuO7y3b8ftOlNPsUNsPdQu/Hn8zUcBVqvD\nX8srIPPztVpSzs7OFjRpxXAC5tUSExOxadMmpKSk4Pbbb8ff/vY3zJ49Gzt27MCUKVNQVuZ4j43h\nw4ejR48eWLVqFQBTIpaeno6TJ09i9+7diIiIaH7gDNOm9+1IsrM908fn2Td/wuncEix7+i/o3a35\n/49i4qk4eYr+1J8oHjwWiu43IXL/Nruf39HiJUT93oMoGfswlKm34+zzsyg+ZhqtHpMzN0MmY9Aj\nIdTqsWsXTyCmi3Uz3ZMXSsAwwOZ3H4Dczc3ExcqZP28V/1yG2v9bj4CsBfCfY1+pkdj9+PKr6PHO\nOqv7wnb8y+nF+40VDx0P/YkzCN+9Gcpbbm7xuJCQkBaXMgXNjA0bNgxbtmxBSkoK/v73v2PevHn4\n6quvcOTIEaSlpTk2egB1dXU4ffo0hg8fDgDQ6/V48MEHcerUKZuJGJGG8BAfnM419Rw7dMK+RpuN\nqVVyjB56E4L8pbllh1i1pXifNM+yrQyndWz7pvZKW2/aNzfQT4XX/jHc6rHsbGWTJOPBzM2o1eqh\n0erh7yvN7vBiZukxJmuHy5TK23pD0f0mGItLAJj2mVT26enS95SFBAEw1Y05SlAytmbNGrAsCwB4\n/PHHERwcjOzsbDzwwAOYOXOm4DfLzMzEuHHjEBcXh+LiYmRlZUGr1WLatGkwGAyYNGkSfvvtN3z7\n7bfgOA6FhaZ+IEFBQVBLdCNTMfDUp3PLzgCHThS0KRkDAAYM0se49geqo81isDWmmjGZP9WMOQtf\nwE81Y1a09QYAgLe66Z+c5uLk56NErVaP6lodJWNmzjyfLDVj7a2AHwCGjv0rMPavbn1PWbCbkrGr\nV6+iU6dO/O3Jkydj8uTJ4DgOV65cEdTwFQDy8/ORnp6OkpIShIeHIzU1FTk5OYiLi8PFixexZcsW\nMAyDlJQUq+etW7cOjzzyiB3fFhGDccOSEOCrRL3O6PBrnLpQggPHr6FGq3PiyAhAM2OuYGltQVdT\nWtPWmZIxtUrQnxz4+ypRVFqLGq3elcPqsPjWFu2kz5inMZYrKtvQ3kLQT0aXLl1QWFjYZNmwtLQU\nCQkJMBqF/bHduHGjzfewzL4R5/JUbY+vtxJj7kxq/UAbvNUXcOD4Nej0jid0QnW0GiiOv5rSsWSs\no8VLCH5mzNxnjOJjUmeZGVN5NXmsuThZrqKsqaUPYRbOPJ/Y6vbV9LUxT/zcyULMe1SWVUB/+hxq\n3l8LTqcHGMB7wr3wHj2i1dcQ9jGlBbW1tbR8SFxK6WVqiaHTU6LubCzNjDmdpQM/q6Gascb4ZUqB\nM2OWZKxaQ8mYK1hqxtpjawtPaLxMWbX8PdRt+Z5/rG7Hz1D9/hN/TEts/mQ8+eST/Nf//Oc/4ePT\n8EvbYDDg4MGD6NOnj0ODJ+4j5U/nSi/TlVTumBmTcpwcYdkkXEZ9xpyGUZvrm+rqMWjgQM8ORkQs\nyVhzy5TNnUf+/MxYvWsHJiFOrRmrNteLtsOaMU/8XuIL+CsqoTtkauMU+OoL0G7+DrqDR1Hz4ecI\nWPCkrZewnYwdP36c//r06dNQNurSrVQqkZKSgszMTIe/AUJaY2kWq3dDMiaEsaAIhktXPD0M4WRy\nKPv0BKNq2jyTMxfwO9qkkDTFyGRgfLzBabTgNFqKrVmd+WrK5gr4m+Pna07GNFQz5gr8zFg7XKb0\nBEsypj9xBmxBEZjAAPjOmAqv3j1Q8tepqPnwM/jNtt1CxOZPhmVD74yMDLz77rsICAhwzsiJW0m5\ndoVfpjR4vmaMrapG0e13S6442yd9IoJXvdrk/rYuU0r5vHIlxlsNTqNF9u49GPrX0Z4ejijYWqZs\nvmbMVFtGy5QNnPXzxun1pt9hcjm/rN6eeKRmzLwEaThpal6svL0PGJkMqoH9oBzYD7p9h1C79gub\nryHoY8q6devaNlJCHOTFL1N6vmbMeK0InLYOjLcaXjYa+4kFV18P/bGT0P12tPnHzcmYzI9qxpyJ\n8fEBSsvB1UsraXclu6+mtNSMUQG/07GN6sUYhvHwaNoHy/6UFsp+ffmv/TNno3TidNSs/tTmawj6\nydBqtXjnnXewa9cuFBcXW131yDAM/vjjD3vGTdxMyrMXSjcuU7YWJ0sjT0W3rgjf3vKVwWLB1mpQ\nEHcrDBevgjMYwCisf9zb2tpCyueVK1kavw68ubeHRyIedTZmxprtM2buLVZDM2M8Z/28ce24Xgzw\nUM1YaLDVbeXtDcmY6s5UeN12C/RHbOdJgpKxOXPmYPPmzZg0aRIGDhxolU17MrMun7ewyX3Bby8R\nfCwdL/7jvWvrMeXoVfifVKK84BePjoctMm1mbCwo4p/v6fjYOl7m6wNZdATYgmKUP/5skyundAeO\nAGg+GRPD+KV6PONr3iy80RWVUhq/K47vduE6phRUwTut+S1pbjw+slKLKcev4Wjioy4ZT0c+3jIz\n1rithZTGL8bjZcFB8Hl4EnQ5v0GR2AXa/26H9tsf+OMYdesb3gtKxv773//iq6++wsiRI4UcTkQm\np/AqBkR1av1AEZLJTMk+y7a6hWqb5RRexb02HucMpk/3ULSpI4xbKRK7QFdQDLaqGvIbkjHOYCqO\nljlYZE41Y82z9Brbe/AAhqXc4uHRiIPBYPr5VbfQZ+zGvTUUClN5As2MNbDr93gLPTs5vR5seSWA\n9lu874m/dwzDIPidpfztG5M3Rec46HKOADaqbQRtFN6pUyfs2rUL3bt3d3y0TkYbhQsn5T+axaW1\neHThNoQH++CTpa7d4qK1OGm37kTZI09APfouhG5Y7dKxOEv50y9C8/kmBC5/CX4zplo9VnzX/dAf\nPYHwnZugdCBpkPJ55UolaTNQ/+MvOPvSkxg+7wlPD0cUXvloL3J+z8fzMwZiYF/rP5TNnUelFRpk\nvLAVwQFqfPbqOHcOVbSE/ryVz1sIzfp/t3qc+p5hCN34f84YmqiI9fdSacZTSNqyocWNwmVCXuSZ\nZ57BW2+91eKLEHET44kplFJprhlzw9WUrdaM1ZkKshlv6VyBpOjaGQBguHCxyWOWPmNUM+ZclivU\nUhO6engk4lFnZ5+xxk1f6e+OidCft/qde0xfyOWmWfxm/jHeaqhHDbf9QhIl1t9LPmm2P1QIWm/5\n8ccf8euvv2LHjh24+eaboVAowDAMOI4DwzDYsmWLUwZLyI0sBfxiuJqS01iSMensOqFI7AIAMORe\navIYX8BPV1M6FXXhb0pbZ+4zJvBqSpVSAS+FDHoDi3qdUfBVmKShZU3U2b2QhwS3cjRxF/W9d9l8\nXNAZHhoaigkTJjT7GF0aK35inbYVgm9tIYI+Y5arKaWVjJlmxuqzD6Bo8Firx4yFxQCoZszZZOZk\nbN+JPzASEz08GnGwt88YYNosvKyyDm+sy+H7DboCwzAYNTgRvZMiWj/Yg4T+vFkuHHF0Zw2pE+vv\npdZyJeozRkRNIZeBYQCDgYWRZSGXCVpZd4mGZUoJJWNdu0AWGQ626DoMp/5s8rg8Npr2p3MyyzI2\np6WtfCzs7TMGANFhfiirrMOBP665ali80nINXvuH9JftOJ0O0OtNS5TKphdLEPES/JPBcRwOHz6M\nCxcuYMyYMfDz80NNTQ1UKhW8vOh/upiJ8VOCUAzDwEshh05vhN7AQq50XTLWas2YRno1Y4xKicgD\nO2C8crXZx+Wd48DIHZt1kPJ55UqWZcq+x8+j4rmsFo9TptwCn7Tx7hqWR9XpzDNj6qZ/K1o6j557\nNBUnzl+HK0vGKqrrsObrYyirEn+DXntmxRhfnw67aiXV30uCkrGioiKMHz8eBw8eBMMwOHfuHPz8\n/DB//nyo1Wq88847rh4n6cCUXuZkTG+EWum52pGGZUqVx8bgCFmAH2Q9kz09jA5DHhUOANAf+cNm\no8fatTKoRw236vfUXtlapmxJcKA3hqTEu2pIAEytM9Z8fQyV1e1jFpPlL8qRzgdGYiLoJ2PevHmI\niIhAaWkp4uMbfjgmTZqEJ56gS7fFTqxr6EIp3bQlUqs1Y3WmX9hSmhlzJamfV67i8+B9YLy9sffo\nEaR2TWr2mOo3VoMtKQN7vbTdJ2N6gxEGAwuZjIGXounMtifPI19vL8hlDDR1euj0RpfWprWVkDhx\nGvMWZw5eId0eSPX3kqBkbNeuXdi1axeCg62vzEhMTMTly5ddMjBCLLwU7mtvYQu/BCChmjHifoy3\nGj4PToB3pzD4tfBHQfPV/0zJmLkBZ3vWeCsksS2dMQyDQH81yiq1qKypR3iwtJMY/gppH2l/Hx2R\n4L0pm6sLKykpgVpNf5jEToqfEhqzfFrVuXh/ytb3ppReAb8rSf28cjVb8ZEFBwEA2PIKdw3HY1pb\novT0eRTkrzIlY9V1LSZjnF6P+uyDfLLjKEatgmrIADCq1rfHuZG9NWMdlafPJ0cJSsaGDBmCdevW\n4dVXX+XvMxgMWL58Oe66y3bvDELaystNy5StkeLVlEScZMGBAAC2rNyl78OyHDh4tmmqRmvqMSbW\nXmGB/qYa0AobdWO1n32Fymea37fQXgEvzIX//FlOea0bNcyMUSmF1Aj66VixYgWGDh2KQ4cOob6+\nHpmZmThx4gQqKyuxd+9eV4+RtJFU19AtlG5apmy1ZkyCV1O6ktTPK1ezFR+ZuRmnK5cpL+ZXYMHb\nP6PWnAx5WnNXUgKeP48C/UwfrmwV8RtyTeU4ipu7QZHg2EUFxqIS6H87Bt3vpxx6vpA4WRq+duSZ\nMU+fT44SlIzdfPPNOH78OD744AOoVCrU1dUhLS0Nc+bMQXR0tKvHSDo4dy1TtkaqV1MS8ZEFWWbG\nXLdM+f3eXD4Rk3m4VothgP63xHh0DC0JMs+MVVa33N6Cq6oGAPjNeBi+09Iceh/dkT9wfcQkGPKa\n7obhLJaZsY5cwC9VgueNo6OjsWSJc6ZpiXtJ8VNCY5YrsPQuXqZsfW9KupqyMamfV65ms2YsxFwz\nVuGamTGW5bDvmKm33JvPjkC3ziEueR9n8PR5JGSZkq2uAQAwAY43SLbMqBkvXuG3ErQH1YwJ4+nz\nyVGCkrH33nsPwcHBmDp1qtX9GzZsQFVVFWbPnu2SwRECNJoZ8/TVlJaZMbpohbRRQ81Y05mxunoD\njp0pgsHo+IeP4rJalFXWISLUF0nxtD+hLUH+5mXKGlszY6ZkrC1tSGTBQWACA8BVVoG9Xgp5RJjD\nr9USqhmTLkHJ2MqVK7F+/fom93fu3BnTp0+nZEzkpLqGbuFlScZ0IqkZ86FkDJD+eeVqNmvG+Ksp\nm86MfbHtBP67q+nWVY4Y1LeT6NpJ3MjT55GgmTHzMmVbe8IpEuKgP3YShrzLdidj9vQZ68gzY54+\nnxwlKBnLz89Hp06dmtzfqVMnXL3a/DYrhDiLUuG+zcJtabiakj51krZpSMaaXk1ZcN00C9MjMRQh\nga3s644AACAASURBVI6fa94qBe67q5vDz+8oAv1bL+Dnqk3JWFv3cVUkdIb+2EkYL14G+t/Wptdq\nDt+Bn/qMSY6gZCwqKgpHjx5Fly5drO4/evQowsKcP9VKnEuKnxIaUyrNV1OKpc+Ymgr4AemfV64m\nqGasmZmxmlodAODhsb3Ru1uEawYnIp4+j4L8TD/PhSU1+GzL8SaPJ8UHo4sTlikBQG6uGzPkXbH7\nufbUjHXkAn5Pn0+OEpSMTZkyBU899RR8fX0xbNgwAMBPP/2Ep59+Gg899JBLB0iIpbWFJ/uMcRxH\nTV+J09iqGavWmJIxf1/7G4MS+wUFqOGlkKFWq8em7083eVzGMFhdaZ4Za0MBPwAousQBAOp27m7b\nVdkMA/Xdw+CVfJPV3XzNGO1NKTmCkrHFixcjLy8Po0aNgkxmWjJiWRZpaWnIyspy6QBJ20l1Dd3C\ny00F/DbjVK8DOA5QKcHIxbt/nTtJ/bxyNVvxYQL8AZkMXHUNOL0eTKMdTmo6WDLm6fNI6SXHollD\ncOZiaZPHfj5wCQWFFUBdHSCXt7kWS9GtKwBAf/QE9EdP2PXcQ9ChHxrOCe033yFi92arY6hmzPPn\nk6NaTcZYlsX58+exZs0aLFmyBEePHgUA9O3bF926UT0CcT3LRuGuXqa0ha6kJM7EyGSQBQWALasA\nW1EFeXgoANMMbLV5mdLPp2MkY2LQJzkSfZIjm9xfU6vDzitFAEz1Ym29GELZry8CVyyE8co1u5/r\nffUS/Dp1BlgWNe9/Av2Zc+CMRqsPhyztTSlZgmbG+vTpg9OnTyMpKQlJSUmuHhNxMil+SmjMXcuU\nNq+k1Jp7jNGVlDypn1eu1lp8ZCFBpmSsvIJPxur1RugNLJRecqiU4tw+yNnEfB7d1DkE2XpTeYKs\njcX7gGljcr9HHSvtuafR15qvtoAtLoGxoAiKTg3NdLlaqhkT8/lkS6s/7TKZDN27d8f169dx0003\ntXY4IU5nWaZ09XZItjR036daDOIcTJCpiL98zvN8YbjOYMSkImDXsImeHBoxS4oPhrfOXCvaxnox\nZ1IkdoauuASGC5duSMaoZkyqZEIOWrFiBTIzM3H06FFwnOObzi5evBgymczqX0xMTJNjYmNj4ePj\ng2HDhuHUKcf28SINsrOzPT2ENlHyG4W7vmasJXQlZVNSP69crbX4eHVLBADoD/+O+p+zUf9zNrhf\n9+OuP/cjoa7MHUMUBTGfR1FhfgiWGQAARh9fj46lcZwUCZ0BAMYbtlaiDvziPp9sETQPnpaWhrq6\nOqSkpEChUEClaviDxDAMqqqqBL9hcnIydu/ezd+WN1rvXr58Od566y2sX78e3bp1w5IlSzBy5Eic\nPXsWfn7i+VRC3MtLDFdTaqnHGHGuoBWL4D1pHGAw8PcVLVgGxYVchHAt97wi7iOTMYgPMP2ZrFOK\n54OYoqspGTPk3pCM1dYCoJoxKRK8HZKzyOVyREQ07Z3DcRxWrlyJ559/Hvfddx8AYP369YiIiMCX\nX36Jxx57zGlj6GikuoZuoXTTMqXtmjHzJ06qGeNJ/bxytdbiw3irob4z1eo+XVQUFBdyEcx2nGRM\n7OdREEzJsk7t2QSncZz4fmU3JGMszYyJ/nxqiaBkLCMjw2lvmJubi9jYWKhUKvTv3x/Lli1DQkIC\n8vLyUFRUhLvvvps/Vq1WY+jQodi3bx8lYx2YJRmr9+DVlCy/TEnJGHGdem9f+AAINHacZEzs/Mz/\nL3RK8fzsKxItM2OX+fs4oxGoqwcYhnohSpDgy3UKCwvx+eefIzc3F1lZWQgLC0N2djZiY2ORkJAg\n6DUGDBiA9evXIzk5GUVFRVi6dCkGDhyIkydPorCwEAAQGWl9eXFERASuXbP/MmDSQKp9VyyU9XXI\n3LkGIVuqcPJV1+2zd9Sgwa2K5j9Rehl08AVQruNAe06YSP28cjVH4qNV+yIYgJ++5U2r2xuxn0c+\nBlMyVq/ybILTOE6WmjHdmfM4GT8AAMBwLAIBGFQq0e9H6kpiP59aIigZO3z4MIYPH47ExEScOHEC\nzzzzDMLCwrBz506cO3cOX375paA3GzVqFP91r169kJqaioSEBKxfvx79+/dv8XktnVizZ89GfLxp\nujYwMBC9e/fm/ydYivjotvRvxxRewsWS8ygH+KaHh2DqxeTM21dhwDDU2zxe7huJJJHFx1O3jx8/\nLqrxiO22I/ExKE01iReunYey0R8VMXw/HfW2t74eh6BDfvl19AM8Np7G59OeI0dwJiAQ46oqEVRT\nbvX7Ki8oBldEFL+OfNvy9eXLDTOYLWE4AZdH/uUvf8HQoUOxZMkS+Pv74/fff0diYiL279+PyZMn\nC3qjlgwfPhw9evRAZmYmunbtikOHDiElJYV/fMyYMYiIiMCnn35qPXCGQVlZx7niqCOr+2E3Sh+c\nCab/7ZAvX+yRMZzKLcHHW04h+Y7ueOlx6X3qItKwddZruPXfn6L0nntxy8aVnh4OAXB08tOI2LkD\nx9P/hlGrnvP0cAAAVwqr8MTL29BVqcdzj5rqDo0s8PzKn1HpG4jN76d16NkxsQoJCWmxI4WgmbEj\nR47gk08+aXJ/VFQUioqKHB5YXV0dTp8+jeHDhyMhIQFRUVH44Ycf+GSsrq4O2dnZeOONNxx+DyJ9\nXJ1ptkoVHoLQWzzTdLhAGYCqnVdQrzN45P1Jx1AlNy2FqbW1Hh4JsVDVm4riaxXi2RGh4HoNWJkc\nfkkxiGz0O7Eq4HcYjSzfOJhIh6A+Y97e3s3OQp09e7bZKyNbkpmZiV9++QV5eXk4cOAAHnjgAWi1\nWkybNg0AMHfuXCxfvhybN2/GiRMnkJGRAX9/f0yZMkXwe5CmGk+ZShFXb+5+r3btL0NbceIvItB5\n7iICsZH6eeVqQuKj1xtRrzPw/8plpnPcq7bG1cMTDbGfR0qtqZFqNePZZKxxnK4VmzYujwn3tzpG\nrTLNr9TVd9wPjWI/n1oiaGZs/PjxePnll7Fp0yb+vry8PDz77LO4//77Bb9Zfn4+0tPTUVJSgvDw\ncKSmpiInJwdxcaad7J999llotVrMmTMH5eXlGDBgAH744Qf4+nq22R7xLMvMGKPyXJ8fldLS64yS\nMeIcm74/jc+2HLe6L65MhwkAFDUdJxkTO2VeHgCgOEA8l+5cu246P6LDrftvqpVy1GhMyViAn3j6\nopHWCUrGVqxYgTFjxiA8PBwajQaDBw9GUVERBg0ahKVLlwp+s40bN7Z6zKJFi7Bo0SLBr0laJ8Ur\nSxrj6k3Fqa7ufm8rTpZkjGbGGkj9vHK11uJz+GQBAEChkEFmru/R+5r+uCpqq107OBER83lkLCyG\nvKQEWi8VrnkHeXQsjeNUYE7GYiJuSMYsM2MduJxCzOeTLYKSscDAQGRnZ+Onn37C4cOHwbIsUlJS\nMGLECFePjxBRzIw1LFN23F9yxLmqak0fMt5+dgS6xJr+0LNVNSj4ZgW4ikpPDo2Y6X4/CQC4FBwD\nTZ14Poi1ODNGy5SS1WoytmnTJvz3v/+FTqfDiBEjkJmZSVdpSEx2tjT7rvDMNWNw8cyYrTiplKYf\nFU82nhUbyZ9XLtZafGo0pmTM37fR9nL+voBcDq5GA06nA6MUT9G4q4j5PNIfPQEAuBwSg9o6vVNe\nMy+/AiXlGruf9/vRg+hz6x3gOKCkTAMZwyAy1LqEp2FmrOP+nhLz+WSLzWRszZo1mDlzJpKSkqBS\nqfCf//wHeXl5eO2119w1PkIazYx57g8TLVMSZ+I4DtW1lmSs4bxmGAay4ECwJWVgK6ogjxBPnVJ7\nZiwpg+F8bpP767MPAAAuhcRCo9WD47g2TUZcLarCU8t+cOi5lUVnEZij429Hhvry+/ZaqJU0MyZV\nNpOxd999Fy+88AKysrIAAOvWrcMTTzxByZjESPFTQmP81ZQuXqa0FSeloqGAv62/kNsLqZ9XrmYr\nPnX1BhiMLFRKeZMWBHwyVl7ZIZIxT59HnMGA4sFjwRaXtHhMfngnGJzQMuL85XIAQGiQNxJi7axB\n6xnd8DUDjOjfpckhtEzp+fPJUTaTsdzcXKt9KadOnYrHHnsMhYWFiIqKcvXYCAHgvgJ+W2QyBkov\nOXR6I3R6I79sSYgj+Fkxn6azvbIgc/1YeYVbx9RRsddLTYmY0gvKW3s3eVyZ0gdaXSRQq0OtVt+m\nZKywxFTr9Zd+8ciY0Mfh12mJ2jyDr+3AyZhU2fyLotVq4e/f0MdEoVBApVJBo7F/vZt4jlTX0C04\nyybdLp4Zay1OlmSsXkfJGCD988rVbMWnupl6MQsmOBAAoN20Bfrjpx1+f8bPFz4Tx3h0eV8IT59H\nxhJTD03FTQkI3978Ff8+i79DZa0OGq0ewQGO71FZcN3UzDcqzK+VI5sSEifLzFhHvtDI0+eTo1r9\ni/LBBx/wCRnHcdDr9Vi7di1CQ0P5Y/7xj3+4boSkw+OXKb092zdHZe7hQ73GSFs1Vy9mYVmarP30\nX21+H7a0HP5P/K3Nr9OesddLAQDy8NAWj/FRewFAm4v4LTNj0Q4kY0JYkjGaGZMem8lYfHw81q1b\nZ3VfVFRUk43BKRkTNyl+SmiMqzMvU3qwZgygIv4bSf28cjVb8bGVjPnPmwlZgD84na7JY0IZi0pQ\n9+330G7eJvpkzNPnEVtiSsZkYS0nY77epmRMo3VSMhZufzImJE78zFgHTsY8fT45ymYydvHiRTcN\ng5CWcW5qbdEa6jVGnKW61nRO+zVTM6ZIiEfg0gVten3u/9m77/im6v1/4K+T0d2mE2ih7DIKlCEF\noTJElKXIEET2EES9DL8oCqjglSHDwRV/V0EFZAgqw4JcBC9b9iyXTQsUOujeSbM+vz/SpA1N25M2\nyTlp3s/How/J/vTtafLO5/M+749ShZRDx6G59D9oHzyErFF4jZ6vNtOlG5YpJcGBFd7Hy5iM1WBm\nTFWsRVauCjKpBEEBntV+nsoYz6akmTHnQ4UvLsBZ19BNHHQ2ZVVxMtaJ0TKlgdMfV3bGp2bMz8LM\nmC1wnh7weKE3lLv2IfO1NyEJqTjRcAROIoH3tHHwHPBcuduEPo6MM2N8lilrMjNmnBWrG+QNqYTX\nttBmrKsZc933KKGPp+qiZIyInqnPmJ03Cq+KO20WTmzEuEzpY6dkDAC8Rg2Bctc+aG/eAW7a7WV4\nY0qVxWRMaMaaMT7LlGevJqNAWb3l40ephi2u6gXbb69lqhlzXpSMuQBn/JZQlhj6jAGAm7FmjGbG\nADj/cWVvfGrG/CycTWkrHs/3QsjR3WACt8jQPniEnFkfQp9nefNzoY8j49mUlc0eKnwN/59OXUnC\nqStJNXq9BvX8qvU4PnHypL0pBT+eqouSMSJ6jirgrwrNjBFbqaxmzJbc2rW26/PzIX3wEADACsXZ\nEsm0TFnJzFj/mKbQaHQ1nnFyl0sxqFdEjZ6j0uc3fmGkmTGnQ8mYC3DWNXQj08yYgHtTAmX2p3Th\nb51lOftxZW+Vxad0X0px9wCzBc7bsCzHCgst3i70caQ3FvBXUjOm8PXA2JfKN4R1JF41Y1TAL/jx\nVF2UjBHRK92bUiQzY7RMSarwOLMQKRkFuJdkeYkwK9fQyNgVkjGJtxcAQF9gORkTEmOsTGsLYU9y\nsAUq4HdevJIxiUQCjuPAGDO7nuM4uLu7IyIiApMnT8asWbPsMkhSM874LcGMqbWFfT+4+PYZU9Mb\nHYBacFzZycnLj7Bs3UkAwE8HK98U2lIH/lrHwx2QSgG1BkytBudm/ncs5HHECovAlCpwnh7gSpJG\nsbKmz5grz4w56/sSr2Tsm2++wcKFCzF06FB06dIFAHD27Fns3r0bc+fOxaNHjzBv3jxwHIeZM2fa\ndcDE9Zj2phR6ZsxUwO+6b3Skag+ScwEA/r7u8PeteOucVk2D4O9b+5MxjuPAeXuB5eWDFSnLJWMV\nYYwZ9ox8YhLAlnSPUgAYzqTkOM5ur+MotB2S8+KVjB04cABLly7F66+/brpuypQp6NKlC37//XfE\nxsaiZcuW+PrrrykZEyFnXUM3Km1tIfzelIBzLQHo9Qz//Pdx3LyXafH2thEhWDAtplofRM5+XNmL\n8YOwZd0CfPjOywKPRhyMyZi+oBASf4XZbRUdR9nT34Py1z0OGZ8kOMAhr1MT/GrGaKNwZ31f4p2M\nrVy5stz1PXv2xIwZMwAAffv2xTvvvGPb0RGXx7RaQKcDZDJwMmFLHJ2x6WtaViEuXE+t8PYzccko\nUmng7Vn7a5ccRVVsOD6MyTsBJD7e0ANgBfzPqFSfumB4bHCgYZnTXiQSeI0Zbr/ndyA3uRQSjoNW\nq4dWp4dMan1zWSIMXp9uQUFB2LVrF9577z2z63///XcEBxs2tS0oKIBCobD0cCIwZ/yWYOTIhq+1\ncW/K5DRDb6c2zYLx4XTz3+/Nf+5HTr4KqmJttZIxZz6u7Mk4M9ahUxeBRyIenI+hHotZKOKv6DjS\n5xhOfqh7/gAkfr72G5yT4PP3ZqjjlkKp0kJVrLV76xQxctb3JV7J2KJFizB16lQcPnzYrGbswIED\nWLduHQDg4MGD6N27t90GSlyTWM6kBJxzb8rkNEPX7wb1/Mq9MXt5ypCT79pLGvagKknWjW0GCMD5\nGNpb6Hn2GmNqtWEWTSoF52v9ptquzNNdBqVKi0+/PQG5zPLMmFQqwYgXWqNtRIiDR+e67iZmVXo7\nrznMyZMn48SJE1AoFIiNjUVsbCz8/f1x4sQJTJo0CQDw3nvvYdu2bTUfMbG5EydOCD2EanNkMlZV\nnEx9xpxomTKpJBkLCyn/gWYs9lVVMxlz5uPKnozJ+q3rFwUeiXgY21tY6jVm6TjS5+QZHufvVysK\n622B799baIhhFvF6fAau3Eqz+HPxeipij9y253AFI9b3pYOn7lV6O++vbt26dUO3bt1qPCBCrGJq\nayH8zJhTLlOmG5YpQ+uUX+bxpAaRdmE8PtxkVDNmZGr8yrPXmD7HcEYq50+lL9b6ePozuPMgCxWd\ng5rwKBvrd8WhsKj6m54T62i0Ohy/8LDS+1g1j56cnIy0tDTo9Xqz6zt16mT96Gxgzdbz5a77x+jO\nvO/rOvf3wOWSy+IYD//767NzoIoeDM5fAc+t5+08Hg9UVG2wZut5ZOUqAQD3HuWYHi90fKq6f0pJ\nzdjRcw9w4VqK2W2PswxLRkpV+WSMz/OXrc0Qy+8rhvsbZ8a6dusuivGI4f5qjwhoowfjbQvLlM88\n80y5++vTMqCKHoyJ+gd2GY9z3r/0fbyy+3t5ynH8ouUP/n+M7gyvkk3PC5WlyZg4f9/q3l+Yz7u/\nTt3DzXuZCAvxMX0JNkrPKjLtR1sRXsnYpUuXMGbMGNy8ebPcbRzHQadznpkC4mR0hsSfs+fZVDxJ\nJYblEp3efn2PbEmr0+NxViE4DvDykJe7XSY1/D7VXaYklhlrxowzqQRAyZnQep5nUzK14YNLEkAz\nY7bmXfJeUKSimTFbKVJp8PWW89CX9MR7OioMijI9Bh+l5VX5HBx7sq2+BdHR0QgKCsLHH3+M0NDQ\ncmv4jRs3tnLoNcdxHLKyKi+IIwbO2ncFAIpPnUfGoDFwe/ophOzbatfXqipO95NyMGPpASh83PFC\nTFO7jsUWlCoN9h69i5AAL/y4+MVyt/9r8zkcPHUP/xjdGf2q8fs483FlT69//AceZxbi9QEBePnF\n54UejijkLfsX8ld+A9/33obfPPNelJaOo6Ltu5H95vvwHPESAr9b5cihipat/t6y81QYPy8WCh93\nbF5e+/rgCfG+lJpRgKkL95kuTxneHkP6tAQAPEzNw9uL90MqleDopmnldjIy4jUzdv36dVy8eBEt\nW7a0wbAJ4U9MZ1P6+RjGkFtQjF//vCHwaPhrFGZ5dqGmBfzEMmPNmFxOPZ6MjGdTMp5nU+qzDTVj\nTzaIJTXn5WH4uy+kmTGbyXtiCfLWvdKJol/23wBjQN+nG+Popoqfg1cy1rZtW6SmplIy5qScefaC\nFTum+z5QdZwCFZ6YPy0GiSm5dh+LrUgkHHp0Crd4W02TMWc+ruzJWDPWu2dPgUciHpKSPmOWWltY\nOo4oGSvPVn9vbnIpZFIJtFo91BpdrWtOLMT7Un6B4XMqyN8TmTlK044nKekFOHY+EVIJh1deaI2P\nKnkOXsnYsmXL8P777+PTTz9FVFQU5HLz+pPAQOff7Z6IlIO2QuKrW/v66Na+vtDDsAlPD+PZlPQN\n2VYYY6aZMaoZK2XchNvasympZsz2OI6Dl6cceQXFKFJpal0yJgRjcX7rpsG4eD0VGdlFuJuYjT1H\nbkPPGPp2bYy6Qd6VPgevefS+ffvi7Nmz6NevH0JDQxEcHGz6CQmhpnFiJ9a+K3wwB7a2cOY4VUdN\nW1u4Wrz40Gj10DMGmUyCU6dOCj0c0ShdpuTbZ4ySsSfZ8u/NVMSvrH1fxIR4X8ovMiRjCh93tGhs\nmJx6Z/lBHDrzABKOw4h+rat8Dl4zY4cOHarBMC1btmwZFixYgLfffhtff/01ACAvLw8ffPAB9uzZ\ng8zMTDRs2BDTp0/H7Nmzbf76xDkwleEgF0PNWG1DNWO2Z1yidKfZBjPGPmN8O/CblikD/O02Jlfm\n6VlSN1YLkzEh5BcaJg18vd0wqGdzpKQXQKvTgwPwXLcmCLPQ5/FJvJIxW29zdPr0aaxbtw5RUVFm\nZ2bOnj0bR48exebNm9GkSRMcPXoUU6dORXBwMMaOHWvTMbgSoWp7GGPQ3rgNVqSq9nNo7xq6Fouh\nZqy28XSv2cyYq8WLD9NWSO4yik8ZkkqWKSurGeMUfvYdmBOx5fFUm9tbCFIzVrJM6evthqfb18fT\n1ShlqTAZu3jxItq3bw+pVIqLFyvf1sOapq+5ubkYO3Ys1q9fj0WLFpnddu7cOYwfPx69evUCAIwb\nNw4//PADzp49S8mYEyrasA05cxbZ5LnEUjNWm9DMmO0ZY0n1YuZMy5R8+4zl0jKlPRkbv9bGZUoh\n5JmSsep/TlWYjHXu3BmpqamoU6cOOne23JUWsL7p67Rp0zBixAj06tWrXL+NAQMGIDY2FlOmTEGD\nBg1w8uRJXL58GXPnzuX9/KQ8ofpBqS/EAQCkjcMhCQyo9vNw3p7weuUlWw2rQq7WN8uz5NuxpQ78\nfLhavPgoLrNJOMWnFGc8m9LCzJilONEyZXm2PJ68LXThry0OHzmGG6neyMkzrMg0qOeHcS+1tese\np2WXKaurwmQsISEBwcHBpn/bwrp165CQkICtWw3NO58MzvLlyzF+/Hg0bNgQspKOzWvWrMHAgQNt\n8vrEsXSPkgEA/isXwuO5HgKPhjzJk2bGbM5UM+Zm1U5ztZ6kZGZMn52DvCVfmt1W+PA+8o6eM7uu\ntLUFLVPag1ctXqa8dS8T/zmfWnrFlST06BSOJg3sl9gblyn97JGMle2qb4sO+7du3cKCBQtw4sQJ\nSEu2tmGMmc2Ovfvuuzhz5gz27NmDRo0a4ejRo5gzZw4aNWqEfv361XgMrkqob+faR4a9EKXhYYK8\nvrVcbRbDg2rGbK5sWwuKTynO2wucpweYUoX8z781u60tgHwLj5GEBIGTl9/Gy1XZ8ngyLVPWwmTM\nv14LANfQvUMDZOcpcSMhE48e5zskGbPLMmVVdWJl8akZO3XqFDIyMtCmTRvTdTqdDsePH8d3332H\njIwMfPXVV9i9ezcGDRoEwNBs9vLly1i1apXFZOytt95Cw4YNAQAKhQLt2rUzHbDG01vpsjCXjx87\nhszEe4iGBNL6oYKPhy6Xv2x8A1EVa0UxntpwWeLTGACQ/ugGTpyQCj4esVz++8wZqOe/hc5KQ7J6\n6oFhtaVbo6YVXnaLikQoIIrx17bLD+OvIvdxPAqVLUQxHltefpiaj9zHt+Cl5xDarAVuJGTi8JGj\n4JSN7fb6D+7GoVitha/3ELPbjf9OTExEVSrcm1Ii4beVB9+asdzcXCQlJZkuM8YwadIktGjRAvPn\nz0d4eDj8/f0RGxtrSsYA4I033kB8fDz++uuvcq9Le1Pyc+KE42tXdI/Tkdr6GUiCAhB657RDX7u6\nhIiTkFTFWoz4v51wk0ux46vhVj/e1eLFx+GzD/DFxjPo1bkhno7QUnx4oOOIH1vG6T/H4/H/tl3A\nC92bYMaYaJs8p1i8+uaXKJLUx8p3n0NiSi6+3nIevaMbYc7ErnZ5Pa1Oj6Ezf4OE47DrX69AIqm4\nNi0wMND6vSltVSdmpFAooFCYnxnj5eWFgIAAREZGAgCee+45fPDBB/Dx8UHDhg1x9OhRbNq0CStX\nrrTpWIj96R4a6sWkDZxjidIVubtJwXGAWqODTqeHVEp7KdaUsf7OsASsFXYwhFSgthbw6/UM6dlK\neAcBDer6QqfTAwCS0iwthNuGcYXBx9ut0kSsKhUmY7aoE6sKx3FmRfxbtmzBvHnzMHbsWGRmZqJx\n48ZYvHgx3n77bbuPpTYT4lunsXjfWerFANergeI4Dh7uMihVWqjUWnh7Wld86mrx4qO0gJ9qxvii\nOPFjyzgZa8aqeya1WKVnF8E7qDkC/Dzg4+WG+nUNzVaT0/LBGLPLGZU5+YazNmtyJiXgwJoxSw4f\nPmx2OSQkBN9//321nouIi9Y4M1Y/tIp7EiF5GpOxYuuTMWei1emxec9VZGQrq/V4X283jBvcznQW\nWkVKC/grfGslRHCmmbFaVMCv0+uRmGI4Cze8nuEsXIWPO7w95ShUapCVq8KVW4+Rnm3odRca7INn\nOoXXaDbr0o1UfLzmGICanUkJVNFnjA9r+4wRxxOkZizJcCalLNx5NtV2xdqVmpxR6UzxunY3HTsO\n3qrRczSo64tBvSIqvY+qzMyYM8VHSBQnfmwZJ+OXilv3MnHozH306drYJs8rlE/+33Gcv2b4zMl9\nfAsNejYDYMhP6tfxxe0HWZi4YE+5xz16nIfRg9pW+3Wv3Hps+nf3Dg2q/TyAA2vGiP3okh8jy558\nNwAAIABJREFU7fkR0D9Ot3h7BlMjiXPwrIfesFYvbUAzY2Lm6W54Uz50+j6CAwyNOdu1qGP6Zllb\n5BUY6jpaNgnCiz2bW/XYG/cysO9YPK7eSa8yGSvb9JUQsfL384CE46BnDF/+dBZPRdaDwtdD6GFV\ny8PUPFMiJuE4uLvJ0K19aWLUNNwftx8YTvYLVHigT9fGUGt02HvkLn7edx1RLeqibURItV47J9/Q\n7HXGmM54oXvTGv0egtaM1VTma9OFHoLNyTu2hd/cf1j1GPWFK9CnPK7w9mjIAKav6dCsJqkbAreu\n1VvCFoIrfjs31jn8euCm6br6dX3x7ccDqnysM8WrUGlIxhqF+qF3l0ZWPbZF40DsOxaP/91Jr7Lu\nREU1Y1ajOPFjyzgpfNyx8O0eWFiyxFao1DhtMnbq8iMAQJ+ujfHO+C7lbh/3UjtENAoEB6BLVH0o\nfAy9wBgD9hy5g4vXU9A2IgS37mXiwvUUKHzc0f+ZZrxOaMotMCRjxuesCd5f31JTU/HNN9/g+vXr\nkEgkiIyMxFtvvYW6devWeBDVpfrzcNV3cjKqPw/De+IoSOsE836MPr8AAOA5cjACvvnMXkOznkRi\n1y0oSM1NeLkd6tfxhV7PoNHq8Nfp+6ZtRMQgJb0A2/5z3VQYb60WjQMxrG8r01lj1amLCw3xQaDC\nA1m5KjxMzUPD0Ir3SywsMrwOzYwRsevUuh4ahvohMSUPxRrnLTU6dcXQMqt7B8slMX4+7hZnrZqW\nNIFNyyoCYwxL1/2NrFzDe1+AwpPXsmNuycyYLRJZXu8Yf//9N/r374+6deuiW7duYIxh8+bN+PLL\nL7F//3507969xgOpjsCt/xbkde0l991PoEtOhT4n16pkjJUkYxKFHzhp+Q2KqSaDH1eMU/OGgWje\nMBCAYVbnr9P3odHym0V1RLwOnEzAoTP3q/34vy89Qp+ujcskY9Z3dOc4Dm0j6uDY+UQs/+FUhd+C\ntTo9biRkAgDqBXu75PFUHRQnfuwRJze54fNCrXbOZCwrV4m7idnwcJOhQyvDxBDfONUNMmzRlZZV\niJT0AlMiBgD3HuXwS8YcPTP27rvv4rXXXsO3335ragar0+nw5ptv4t1338XJkydrPJDq8OzfR5DX\ntZf8Vf8PuuRUsLwCqx5nnBnjfH3sMSziIuQyw9+2Rquz22ng1jJu1/J8tybo2LqeVY/9cedlZOQo\nkZtfbFqmrE4yBgBd2oXh2PlEJKbkVXo/qYTDhJej0KppME4kV+ulCHEYd2My5qQzY8ZkqF6wt9Vn\nMNcpScYeZxbhenyG2W0PUyv/Oze9fklbC4Wvg5Kxy5cvY8OGDWZd+aVSKd555x107NixxoMgBhJf\nQ08UfZ51DepMM2O+3hZvp2+d/Lh6nKQSCSQSDno9g07PIJNWnow5Il4ajWGWrlXTIPR4Ktyqx+49\negcZOUrkFRajqGRmzKuayVjPp8JRv44Piqroy1QvyNv0Ju/qxxNfFCd+7BEnNzdDMuasy5Takln8\nsvVdfOMU5O8JCcchO0+Jq3fSAAA9ngrH8QsP8ehx1Z/BqmItitU6uMml8HSveVkCr2dQKBRISEhA\ny5Ytza6/f/8+/P3tt/mmqzHObDErkzGaGSO2IpdJUKzWQaPRQSaCjvzGDwnjcoo1jCcn5BWoUVCS\njPl4Ve+sYo7jTMu5hNQWbk4+M6Yt6bAvk1n/XiWTShDk74n07CIcu/AQgGEG/viFh0hKy69yVxLj\nrJyfj7tNVhF4/QajRo3ClClTsHnzZty7dw/37t3Dpk2bMGXKFLz22ms1HgQxkPgZkim9lcuULL/Q\n8PgKkrGym5aSilGcALnM8ObMp27MEfHS1CAZ8yup48gvOzNWRdNWW6LjiR+KEz/2iJO7k9eMmZKx\nMkmTNXEyzmJrtXrIZRK0bR6CYH9PaLV6PM4srPSxxiVKfxssUQI8Z8aWL18OxhgmT54MrdYwTe/m\n5oY333wTy5cvt8lACMD5VW+ZkmbGiK2U1o05vhWKJaaZMVk1krGSmbH8QnVpAb+X45IxQsTO+CXH\n2Zcp5dWYGQOAOoFeuFby7/at6kIul6JBPT9k5CjxMDUPYXV8K3ysLYv3AZ4zY+7u7li9ejVycnJw\n+fJlXL58GZmZmfjyyy/h5lZ7t1BxNElJMmasAeOrtGbMcjJGNRn8UJzMi/ir4piasZJkzK06y5SG\nN8m8guLSZMyBM2N0PPFDceLHHnEyFr07/TJlNWrGAEMyZtQ/xtD+wtjwevF3f+OPY3crfGxpWwsH\nJGNFRUV4++23Ub9+fYSEhGDKlCkICwtDVFQUvL0tF4uT6pNUc2aM0cwYsRFrlikdQW2aGbP+m69x\nZiyvUF16NiXNjBFi4vQ1Y9rq14wBgHuZwvvObQy7xXRsXdo7df/x+Aofa9wgXOFjm2a5lf4GCxcu\nxIYNG/Diiy/itddew4EDBzB9eu3rei8WXMnZkNVtbUE1YzVDcSozM8bjzdkR8VKXnE1ZvQJ+wzfW\nnHwVlCotOK50+ydHoOOJH4oTP/aIk3HG2VVrxvo+3QQtmwRhzsSupmL96LZh+PfH/QHAtKm4JaZl\nSkfUjO3cuRPff/+9qUh/7Nix6N69O3Q6HaQWmouSmjHNjOXTzBgRhkxkNWPGb+zyGpxNaSzE9fKQ\nQyIRvncaIWLh7uQ1Y8b3qeqe+R3g54FV7z5X7vr6dXzhJpeiUKlBkUoDLw85VMVa/LL/OvIKDbPs\n/7tr2AvaVjVjlSZjDx8+RM+ePU2Xu3TpArlcjuTkZISHW9fzh1TNWMBvTWsLxhj0VZxNSTUZ/FCc\nSmeg+CRjjoiXMRlzr8HZlKnphi8rjjyTEqDjiS+KEz926TPm7MuUuvIF/LaIE8dxCAnwQlJaPjKy\ni9AwVIEzV5PN9vA1Cg2xzSRIpcmYVquFXG7+BiaTyaDRaGzy4sRc6cyYFcuUxWpAowHc5ODc6WQK\nUjPWFPA7Qk1mxow1Yzo9A0D1YoQ8yemTMVPTV9vPeIcEGpKx9CxDMmZsZdG+ZR0808kwGRXg54HI\nZvy3LqxMla0txo0bBzc3N3AcB8YYVCoVpk2bBk9PTwCGDDI2NtYmg3F1XDX6jFVVLwbQ3m98UZys\n7zNm73iptcY+Y9YvQ/h4uYHjAGbIxRx6JiVAxxNfFCd+7BEnd2MH/lpWM2aLOAX7G3KcjBxD3VhB\nkWF5snXTYPR/plmNn/9JlSZj48ePNyVhRmPGjDG7jxj2r6stJNVZpqR6MWJDYuszpqlBAb9UKoG3\np5vpTdS7mt33CamtasvMWHXPpqxMSEnbi/QsYzJm316FlSZjGzZssMuLEsuMCZU1rS34zIzRt05+\nKE7i6jOm0+uh1enBcdUv0PX1Lk3GqGZMnChO/FDNWHmmmrFq9hmrTHBASTJWckalsT2Oj6d9vtQJ\nv/kcMeE8PQCZDChWgxWreT2GFRiK92lmjNiCrGSZUiuCmbGys2LVnYH3K3Omkw/VjBFixr2WJGN2\nmRkrScYyspUASmfGqru/bVUoGRMRjuNK96fkWcTPt2aMVI3iZN3MmL3jVZNNwo3qlumwbfym6yh0\nPPFDceLHnn3GnLW1hdZCawtbxcm4TBl3Ow03EzLKlDsIsExJHI/z8wWycgx1Y8GBVd6/tGaMdkQg\nNWdMxozNVoVUk03CjaYM74A2zUPgJpcipmMDWw2NkFrBzck3CtfYcWYsOMALEo6DnjG89/kh03sj\nzYy5CGu3RKKaMduhOJU9m1L4mjF1DTYJNwpUeGJgz+bo260JPKlmTJQoTvxQzVh5lmbGbBUnDzcZ\npr/ayXTZeFKTvZIxmhkTGWPtV97iLyGpU3X/Eu3NO2aPI6QmjC0kxHA2pboGm4QTQqrm7B34LTV9\ntaUBPZrhbmIWDpy8Z7rO25OWKV2CrGF9qP8Gig9Zt+4tbRBW4W3Ux4cfipO4+oyZGr7a6Y3W3uh4\n4ofixI894mTam9JZkzFT01fb9xkzCirpNwYAEgkHT3f7pE2UjImMYvEHcO/dHcyKDugSH294vNDb\nfoMiLkNMHfhrskk4IaRqzl4zZqnpq62VTcYMjaTt01uVkjGRkQT4w2vEYJs+J33r5IfiVFoIy6e1\nhcNqxpw0GaPjiR+KEz/2iFPpMqUWjDGna+JunMG39d6UZQX5l56Fba8lSoAK+AkhZVizTGlvzp6M\nESJ2UqkEUgkHxsTRW9Baxn1nHTkzZi+UjLkA6uPDD8VJXH3GnD0Zo+OJH4oTP/aKkzP3GrO0HZKt\n4xSkKJuM0cwYIcQB5HIxzozR2xQh9uLMXfgdUTPm6106GyaV2O916F3OBVBNBj8Up7JNX4XvM2ac\nnXPWmTE6nvihOPFjrzg5c68xjYWZMVvHqWwdXbFaa9PnLkuwZGzZsmWQSCSYMWOG2fW3b9/GsGHD\nEBAQAG9vbzz11FO4efOmQKMkxLWULlMKPzNmi+2QCCGVM/59FTvhGZWWNgq3J5UdYyRIMnb69Gms\nW7cOUVFRZlnnvXv3EBMTg2bNmuHw4cO4du0alixZAh8famhaE1STwQ/FqbTbPZ9iXnvHy7hRuLwG\nHfiFRMcTPxQnfuxdM+aMM2OOqBkrS2PHGDk8GcvNzcXYsWOxfv16BAQEmN22YMEC9O/fHytXrkSH\nDh3QuHFj9O/fHw0a0J5yhDiCTER9xowzY+40M0aI3dSGmjGpnWfGZo7pDDe5FG+M7FT1navJ4cnY\ntGnTMGLECPTq1QuMMdP1er0ee/fuRevWrdG/f3/UqVMHXbp0wS+//OLoIdY6VJPBD8XJugJ+u9eM\nGTvwO2kBPx1P/FCc+LF3zVihUgO1Rsf7x56zRHzZc2/Ksp7v3hS/fD4UbSNCbP7cRg5t+rpu3Tok\nJCRg69atAMwL49LS0lBQUIClS5di8eLFWLFiBf773/9izJgx8PHxwcCBAx05VEJckphqxpy9tQUh\nzsD49/Xpt9Yv7417qS1G9o+09ZB409h5b8qy7D375rCvnLdu3cKCBQuwZcsWSKWG//mMMdPsmF5v\nCOqQIUMwe/ZsREVF4Z133sHIkSOxZs0aRw2zVqKaDH4oTtRnzJboeOKH4sSPveLUNao+PN1lkMsk\nvH+MM1FXbqXZZUx86Sy0tnDW48lhM2OnTp1CRkYG2rRpY7pOp9Ph+PHj+O6771BQUACZTIbISPMs\nu1WrVti+fbvF53zrrbfQsGFDAIBCoUC7du1MU5TG/yF0mS7zvXz16lVRjUeIyxGtOwIAku9fw4kT\nfoLG686N6wD84SaXiiY+dDzRZaEu2+t46hfTFN4s2arHb/nlD3z3y0UUNwkSND7GZcrz507Dx8tN\nVP+/jE6cOIHExERUhWNlC7fsKDc3F0lJSabLjDFMmjQJLVq0wPz58xEZGWk6k/Knn34y3W/cuHHI\nzs7G3r17zQfOccjKynLE0AlxGVm5SkyYvwf+vh7Y9Jlt90i11rJ1J3Hy8iO8P6UbnukULuhYCCGl\n7iZm453lB9G0gT9Wz3tBsHGMencXCpUa/LxyiF23KrKVwMBAVJRyyRw1CIVCAYVCYXadl5cXAgIC\nTLNhc+fOxciRI9GjRw88++yzOHz4MLZv347ff//dUcMkxKVZs0xpb86+TElIbWXcFUPoMzAtNX11\nVg5LxizhOM6siP/ll1/G2rVrsXTpUsyaNQstWrTApk2bMGDAAAFH6fxOnDhhmj4lFaM4WbdReFXx\nunY3HZtir1b7ZIBHj/MBOG8yRscTPxQnfsQUJ7F07bfU9FVMcbKGoMnY4cOHy103YcIETJgwQYDR\nEELKzowxxsy+LFlr11+3cC0+o0bjkXAc6gV71+g5CCG2VZqMCXfWtU6vh17PwHGARFL99ymxEDQZ\nI47hjN8ShEBxMpy+LZFw0OsZdHoGmbTiN7nK4sUYw/UEQyL20fRnoPBxr9Z4gvw9ERzgVa3HCo2O\nJ34oTvyIKU5upn6Ews2M6XSG2iuZVGL2pVFMcbIGJWOEEDNymQTFakNTR1k1e+s8epyP/EI1Avw8\nEN02tEYzbIQQcTHtZyngMqWlhq/OrHb8FqRSZU+zJRWjOBnwrRurLF43SmbFIpsFu2wiRscTPxQn\nfsQUJ2M5g1ZrWCoUgrHh65PF+2KKkzVoZowQYsb4RvvzvmvwcK/4LeLuzXjEZ/pZvO3yzccAgNZN\ng20/QEKIoDiOg5tcatgWSauDu5vjU4naNjNGyZgLcNY1dEejOBkofNyRnafC3qN3q7inJy4n3qz0\nHvbcy03s6Hjih+LEj9jiJJdJTPtUCpGMWeq+D4gvTnxRMkYIMfN/E7ri/LWUGj9PvWAfNAsPsMGI\nCCFi4yaXlmwuLswZldoKlimdFSVjLsBZ+644GsXJoEkDfzRp4F/l/ShelaP48ENx4kdscRL6jEpN\nBcuUYosTX7UjpSSEEEKIw5jOqFQLk4yZGr7Wkpmx2vFbkEo547cEIVCcrEPxqhzFhx+KEz9ii5PQ\nM2MVFfCLLU58UTJGCCGEEKuU7k9JNWO2UDt+C1IpZ+274mgUJ+tQvCpH8eGH4sSP2OJk7Eco1P6U\nFc2MiS1OfFEyRgghhBCruLkJm4xpKmht4axqx29BKuWsa+iORnGyDsWrchQffihO/IgtTm5imRmT\nUc0YIYQQQlxQac2YMMlYRU1fnVXt+C1IpZx1Dd3RKE7WoXhVjuLDD8WJH7HFScizKXU6Pf53Nx1A\n7dmbkpIxQgghhFhFyD5jv/x5w7RdmxudTUmchbOuoTsaxck6FK/KUXz4oTjxI7Y4lc6MVa+1BWMM\ne47cwe0HWVY/NjElz/Tv/s80M7tNbHHii7ZDIoQQQohV5PKaFfDfSMjE2l8vAQBi14wAx3G8H5tX\nUAwA+HRGL7RsElSt1xcbmhlzAc66hu5oFCfrULwqR/Hhh+LEj9jiZFwerG4yVqhUm/59+751s2PG\nZMzP263cbWKLE1+UjBFCCCHEKjXtM6ZUaU3/PnYh0arH5hUakjGFr3u1XluMKBlzAc66hu5oFCfr\nULwqR/Hhh+LEj9jiZOwzpqnmdkiFSo3p3ycuPgJjjNfjGGPIKzDMqvl6l0/GxBYnvigZI4QQQohV\nTGdTVnNmrEhVmoxl5SqhLNZWcu9SSpUWWp0enu4y0xhqA0rGXICzrqE7GsXJOhSvylF8+KE48SO2\nONW0z1jZmTEAyM0v5vU44xKlr4/lJUqxxYkvSsYIIYQQYpWank1ZVC4ZU/F6nDFpU1SQjDkrSsZc\ngLOuoTsaxck6FK/KUXz4oTjxI7Y41fRsyrLLlACQY+XMmKUzKQHxxYkvSsYIIYQQYpXSsymrV8Bv\nnBnzLUmqcgv4zYwZi/f9aGaMOBtnXUN3NIqTdShelaP48ENx4kdscSo9m7KaNWMlM2NhdXwBADl5\nPGfGjD3GqGaMEEIIIa6spn3GjDNjocE+AIAcnjVjVS1TOitKxlyAs66hOxrFyToUr8pRfPihOPEj\ntjgZZ8bUNTybMqyOIRnLLeA3M2a8X0UNX8UWJ74oGSOEEEKIVeRyYwF/NWvGnlim5N3awrQVEtWM\nESfjrGvojkZxsg7Fq3IUH34oTvyILU7uJa0tcvJUWLD6CBasPoJF3xzD7QdV7zPJGCudGQupepmy\nUKnGwm+OYcbSP3HlZhoAqhmzmWXLlkEikWDGjBkWb3/jjTcgkUjw+eefO3hkhBBCCKmMl6ccvt5u\n0Or0iLudhrjbabhwPRW7/3uryscWq3XQ6xnc5FIEB3gBqHxm7Nj5h7h4PRX3k3KhUmvhJpeifl1f\nm/0uYiAT4kVPnz6NdevWISoqChzHlbv9t99+w7lz5xAWFmbxdmIdZ11DdzSKk3UoXpWj+PBDceJH\nbHGSy6T4en4/PErNAwCkZhZgzdYLSEzJq/KxxlkxLw85/LzdwHGGwnydTg+ptPwc0Zm4JADAuJfa\nIrptGAL9PSts+iq2OPHl8Jmx3NxcjB07FuvXr0dAQEC52x88eIDZs2fj559/hlwud/TwCCGEEMJD\nkL8n2reqi/at6qJX50bgOOBRal6VWyQZ68W8PeWQSiXw9XYHY0Beodrifa/cTgPHAS/ENEWTBv61\nrvs+IEAyNm3aNIwYMQK9evUqt0u7VqvFa6+9ho8++ggtW7Z09NBqLWddQ3c0ipN1KF6Vo/jwQ3Hi\nR+xx8nCXoW6QN3R6huS0gkrva2xr4eVpmHAxJleTP9qLV97ZgZH/txOj3t2F0XN3Y/KHe6HV6tG6\naTD8fT2qHIfY41QRhy5Trlu3DgkJCdi6dSsAlFuCXLhwIerUqYM33njDkcMihBBCSA01ClUgNaMQ\niSm5aBSmqPB+hWVmxgCga1QYHqbmQavVQ1vBY17o3tTWwxUVhyVjt27dwoIFC3DixAlIpYazMBhj\nptmxI0eOYOPGjbh8+bLZ456cPSPWc9Y1dEejOFmH4lU5ig8/FCd+nCFODcMUOHM1GQ+Sc9HjqYrv\nZ5wZ8/QwpCATXo7CmBfbQq9npT+s9L8yqQQ+XvyavDpDnCzhmIOynQ0bNmDy5MmmRAwAdDodOI6D\nRCLBe++9h+XLl0MikZjdLpFIEBYWhsTERPOBcxxGjRqFhg0bAgAUCgXatWtn+h9hnKqky3SZLtNl\nukyX6bL9L+s8GmLV+tOQqhLRMEyBxs2jAAD378YBgOnyxQtnkJici+Ev98OscV1EM35bXzb+25i/\nbNu2rcIJJoclY7m5uUhKSjJdZoxh0qRJaNGiBebPn4/g4GBkZGSY3d6vXz+MHj0aU6dORUREhPnA\nOQ5ZWVX3MyGGg8F4kJCKUZysQ/GqHMWHH4oTP84Qp+S0fEz/53/AN6sYPagNXhvYxqZjEHOcAgMD\nK0zGZI4ahEKhgEJhvobs5eWFgIAAREZGAgDq1KljdrtcLke9evXKJWKEEEIIEZewOr5YNvtZPM4s\nrPK+bnIpOrcNdcConIPDkjFLOI6jPmIOINZvCWJDcbIOxatyFB9+KE78OEuc2jQPQZvmIYK9vrPE\n6UkOW6a0NVqmJIQQQoizqGyZkvamdAFliwlJxShO1qF4VY7iww/FiR+KEz/OGidKxgghhBBCBETL\nlIQQQgghdkbLlIQQQgghIkXJmAtw1jV0R6M4WYfiVTmKDz8UJ34oTvw4a5woGSOEEEIIERDVjBFC\nCCGE2BnVjBFCCCGEiBQlYy7AWdfQHY3iZB2KV+UoPvxQnPihOPHjrHGiZIwQQgghREBUM0YIIYQQ\nYmdUM0YIIYQQIlKUjLkAZ11DdzSKk3UoXpWj+PBDceKH4sSPs8aJkjFCCCGEEAFRzRghhBBCiJ1R\nzRghhBBCiEhRMuYCnHUN3dEoTtaheFWO4sMPxYkfihM/zhonSsYIIYQQQgRENWOEEEIIIXZGNWOE\nEEIIISJFyZgLcNY1dEejOFmH4lU5ig8/FCd+KE78OGucKBkjhBBCCBEQ1YwRQgghhNgZ1YwRQggh\nhIgUJWMuwFnX0B2N4mQdilflKD78UJz4oTjx46xxomSMEEIIIURAVDNGCCGEEGJnVDNGCCGEECJS\nlIy5AGddQ3c0ipN1KF6Vo/jwQ3Hih+LEj7PGiZIxQgghhBABUc0YIYQQQoidibZmbNmyZZBIJJgx\nYwYAQKvV4v3330f79u3h4+ODsLAwjBkzBg8fPhRymIQQQgghdiNYMnb69GmsW7cOUVFR4DgOAFBY\nWIhLly7hww8/xKVLl/D777/j4cOH6N+/P3Q6nVBDdXrOuobuaBQn61C8Kkfx4YfixA/FiR9njZMg\nyVhubi7Gjh2L9evXIyAgwHS9QqHAgQMHMGLECERERCA6Ohrfffcdbty4gZs3bwox1Frh6tWrQg/B\nKVCcrEPxqhzFhx+KEz8UJ36cNU6CJGPTpk3DiBEj0KtXrwrXT41yc3MBwCxpI9YxxpBUjuJkHYpX\n5Sg+/FCc+KE48eOscZI5+gXXrVuHhIQEbN26FQBMS5SWqNVqzJkzB4MHD0ZYWJijhkgIIYQQ4jAO\nTcZu3bqFBQsW4MSJE5BKpQAAxpjF2TGtVouxY8ciLy8Pe/fudeQwa53ExEShh+AUKE7WoXhVjuLD\nD8WJH4oTP04bJ+ZA69evZxzHMZlMZvrhOI5JJBIml8uZWq1mjDGm0WjYK6+8wlq3bs0eP35s8bma\nNWvGANAP/dAP/dAP/dAP/Yj+p3379hXmRw7tM5abm4ukpCTTZcYYJk2ahBYtWmD+/PmIjIyERqPB\nqFGjcP36dRw5cgR169Z11PAIIYQQQhzOocuUCoUCCoXC7DovLy8EBAQgMjISWq0WI0aMwPnz57Fn\nzx4wxpCamgoA8Pf3h4eHhyOHSwghhBBid4Jvh8RxnKmI/9GjR4iNjUVKSgqeeuophIWFmX5++eUX\ngUdKCCGEEGJ7TrsdkiWMsUrPziTlGf/3U9yqptfrIZEI/v2FEELo864axPx5V6uSMcBwJoVEIjHN\nuIWGhooy8EIrKiqCUqlEUFCQ6ToxH6hCspSE0RshsVZKSgoKCwvRrFkzs2OHjqWKsZKz7elLkGX3\n7983dSaQSCQICwujY8mC/Px8qNVqUX/eObzPmL2oVCqsXbsWGzduRFxcHPz9/dG9e3d0794d/fr1\nQ4cOHYQeoiikpaVhy5Yt+PPPP3H//n14e3tj9OjRGDJkCJo1ayb08ERFqVTi0KFDOHDgAC5fvozG\njRtj1KhR6N27Nzw9PYUenuhkZWXh559/hlKphE6nQ4sWLfDss8/C399f6KEJKjs7G9988w22b9+O\n1NRUaLVa9OjRA6+++ipefvll+Pj4CD1E0UlOToaXlxf8/f1NH5Y0M11KpVJh9erV+PHHHxEfH4+Q\nkBBER0eje/fu6NOnD6Kjo0WTZAgpJSUFGzZswJ9//omkpCS4ublh2LBhGD9+PCIiIoQenplaMzP2\n5ZdfYsOGDZg0aRKGDx+OFStW4N///jeCg4Ph5+eHr776CgMHDhR6mIJ79dVXkZOTg8jLEHJTAAAg\nAElEQVTISEREROCjjz5CdnY2AGDUqFFYunQpGjduLOwgRWL+/Pk4dOgQFAoFoqKisHr1ami1WgQG\nBmLWrFmYOXNmuRNSXI1xVufIkSP4+OOPcf36dfj5+aFevXpQKpXw9/fHgAEDMGbMGNSvX1/o4Qpi\n7ty5OHz4MPr06YPnn38ejx49wq+//oq//voLoaGh+PTTTzFmzBiz+llX9ddff+HTTz+FRqNBVlYW\n6tWrhwkTJmDcuHGQyWrN3EGNffHFF1i7di1Gjx6NESNG4OzZs9i9ezfOnz8PT09PvP/++5gyZYrQ\nwxTciBEjkJycjNatW+Opp57CzZs3sW/fPsTHx2PAgAFYvHgxOnbsKI7Z6Rq2DhONyMhItnHjRtPl\nlJQU9sorr7CtW7eyN954gzVo0ICdPXtWwBEKLzs7m3l6erJr166Zrtu0aRN79dVX2Zo1a1hUVBSb\nM2cOY4wxvV4v1DBFw9fXlx08eJAxxpharWbLly9nL7/8Mps7dy5r2rQp++yzzwQeofB0Oh1jjLFn\nnnmGTZw4kaWmpjLGGIuLi2M//PADmzhxIouMjGRTpkwx3dfV1KtXj+3atavc9ffu3WMzZ85kTZs2\nZfv37xdgZOJy9OhR1qRJE/bqq6+yzz77jK1cuZINHz6cBQYGsvDwcLZ8+XKmVCqFHqYoREZGsnXr\n1pW7PjU1lb377rvMy8uLff755wKMTDxycnKYh4cHi4uLM12n0WhYWloa+/XXX1nv3r3ZwIEDK+xl\n6mi1IhlLT09nUVFRbM+ePYwxZmoeGxAQwE6ePMkYY6xTp07szTffZIy5bqKxa9cu1qVLF1N8GGMs\nISGB+fv7M7VazWJjY5lMJmOnT58WcJTicPDgQda6dWumUqlM12VkZLA6deqwpKQktnbtWiaTydiZ\nM2cEHKU45Ofns3r16rELFy6Uu02pVLJdu3Yxb29v9tFHHwkwOmElJyezdu3asQ0bNpiu02q1TKvV\nMsYMHxjPP/88Gzx4MMvPzxdqmKIwdOhQNmHCBNNljUbDMjMz2alTp9j//d//lfvC7apyc3NZTEwM\n+/DDDxljhjgplUrTMcUYY7NmzWI9e/Zk6enpQg1TcIcPH2bNmzdnt2/fLnebTqdjp0+fZkFBQWzV\nqlUCjK68WrEAHxwcjPbt22PVqlVgjEEul+Onn36CVqtFx44dAQCzZ8/GrVu3UFBQIPx0pECaNm2K\nlJQUfPvtt9BqtQCA1atXo02bNpDL5Rg4cCD69euHv/76S+CRCk+hUECv12Pnzp2m6zZv3ozAwECE\nhYVh6tSpePbZZ/H3338LOEpxUCqViIiIwJo1a6DRaMAYg06ng06ng4eHB4YMGYJFixbhyJEjpiVx\nVxEaGoouXbrg448/xv/+9z8AgFQqNRVdKxQKzJs3D1evXoVcLhdyqILTaDRo0qSJ6bJMJkNgYCCe\nfvpprFixAs888wxWrVqF9PR0AUcpPD8/PwwZMgQbN27E5cuXIZPJ4OHhAalUCrVaDQB4/fXXcfPm\nTeh0OoFHK5yOHTtCLpfjww8/RH5+vtltEokEXbt2xcyZM3Ho0CGBRmiuViRjADB16lSkpaUhPDwc\nYWFhWLx4MebOnWtqFJucnIzs7Gz4+PhAr9cLPFphREVFYejQodi8eTNmzpyJmJgY/Pzzz/jnP/8J\nwPAhUVRUZErUXFl0dDQ6duyI7777DitXrsTrr7+OZcuWYc6cOab7+Pj44P79+8INUiRCQkIwceJE\nHDp0CGvWrEFBQYFZwgEAYWFhePDgAQICAgQcqTCWLFmCli1bYvTo0ZgzZw727t2LlJQUAIZdSbZu\n3YqGDRvC3d3dpT88n3vuOSxduhT79u2DUqk0u00qlWLBggXIy8vDgwcPAMDinsauYvTo0YiKikLn\nzp0xZMgQ7Ny5E3q9Hm5ubnj48CG2bduGoKAg1K1b12U/7xQKBVauXIm4uDhMmTIFmzdvxs2bN1FU\nVAQAKCgowOHDh9GiRQuBR2pQawr4AeD8+fM4deoUMjIy0KtXL8TExMDd3R23b9/GqFGjMH78eMye\nPRtardblikF1Oh2kUikePXqEr7/+GleuXEGDBg0wdOhQDBo0CABw7tw59OnTB5cuXULz5s0FHrFw\nWEkx55UrV7BkyRLcuXMHvr6+GDZsGGbPng0AePjwITp16oSdO3eiR48eAo9YWIwxFBYWYvny5fji\niy/g6+uLkSNHYujQofD398exY8ewdetW9OrVCytWrBB6uA5lPJauXbuGH3/8EcePH4der4efnx+U\nSiUyMjLg6+uLzz//HM8++6zp79QV5efn4+2338b169cxYsQI9O3bF+Hh4ahTpw4AYMeOHZg4cWK5\nWQ5XpdFo8NNPP+G3337DzZs3UVhYiKZNmyI3NxdyuRyffPIJhg4d6pKfd0Z6vR7btm3Dd999h/v3\n76N58+Zo2LAhVCoV4uPjUVRUhD/++AONGjUSeqi1Kxl78o2MMQatVotNmzbh999/x7Zt2+Dp6SmO\nMycE8OTvXTZeWVlZWLNmDS5evIjdu3cLNURRysrKgkQiMbVoyM3NxerVq7F7925cvHhR4NGJS2Ji\nIjZu3IjffvsN165dQ0hICORyOUaMGIEFCxYgMDBQ6CE6jKVWDDdv3sR///tf3Lt3D2q1Gp6enpgx\nYwYaNGgg0CjFwfjelJCQgM8//xw//fQT5HI5evXqhbp16+LSpUtQqVQYNGgQli5d6tIJBlB6bOn1\neiQkJOD69etITExEfHw8vLy88Oabb6J+/fou+TkHWP7b279/P3bv3o3k5GTI5XLUrVsXc+bMEU1L\nJ6dPxi5evIglS5YgNTUVLVq0QKNGjfDUU08hJibG7I0/Pz8fvr6+Ltmrpri4GHFxcfjjjz9w69Yt\ndO/eHb1790bTpk3h7e0NxhiUSiXy8/MhkUgQEhIi9JAFwxhDRkYGDh48iKSkJDz99NNo27Yt/Pz8\nIJVKodPpUFRUhJSUFOj1erRq1UroIYuCVqsFx3GQSqVgjKG4uBj5+fm4fv06QkNDRbMUIARjHZ2b\nm5vQQxGtJ9+XtVottmzZgt27d0Or1aJOnTp4+eWX8fzzz8PT09Ml38fLYjwalrrqpENZGo0GAMzq\nMdVqdbkyCjFw6mTs999/x5w5c9C0aVO0aNECd+7cQUZGBiQSCZ566im89dZbiIqKEnqYgvvss8/w\n/fffw9PTEw0bNsT58+eRlZWFmJgYzJ07l/qvlbF9+3Z89tlnSEtLg4+PD+Lj4xEUFIThw4dj9uzZ\nLp1UPKmoqAjFxcUW68Bc/YPgyJEjKCgowIsvvmh2fXFxMSQSicsX61dErVaD4ziz+KhUKlPtryu7\ncuUKkpKS0KdPH1M8GGOmxJTjOGg0GkgkEtElGo506NAh1K1bF23atDFdp9frodFoIJVKxTuj6qjT\nNu0hOjqazZs3jxUVFZmuu3HjBlu+fDlr0aIFCwsLY8eOHRNwhOLg7e3NfvvtN5aRkcHUajVTKpXs\n4MGDbOjQoczNzY1Nnz7d5U+rNwoNDWVLly5lly5dYpmZmSwhIYF98cUXLDIyksnlcrZo0SKz480V\nGfuFLV68mE2ZMoXt3r2bxcfHl+sBpdfrWXp6OsvIyBBimIJq2bIlk0qlLDIyks2YMcNiC5SDBw+y\nQ4cOCTA68UhPT2fbt29nSUlJZtdrtVqmUqnM2jW4uujoaMZxHGvQoAGbMWMGO3/+fLn7nD17ln3/\n/fcCjE48goODWevWrdnIkSPZDz/8wJKTk81u1+v17MCBAyw7O1tUba6cNhkrLCxkkZGRbMeOHYwx\nQ2+xJwPbt29fNnbsWMaY6/YWO3jwIKtfvz5LS0tjjDGzxps6nY5t2bKFBQUFsb179wo1RNE4e/Ys\nCwoKstibp6CggH355ZesQYMG7OjRowKMTnzc3d1ZQEAA8/b2Zp06dWIfffQR++9//8sePXpk+hBd\ntGgRmzt3rsAjdax79+6x+vXrs08//ZTNnTuXde/enYWFhbHOnTuzxYsXs/v37zPGGIuKijL1PnTV\nhrjz589n/v7+bMiQIezDDz9kBw4cYLm5uWb3uXfvHtu6davLvoczZugt1qRJE7Z69Wq2bNkyFhUV\nxTiOY61bt2ZLlixh9+7dY4wx1q9fPzZq1CjGmGseU3/88QcLDg5ms2fPZkOGDGGdOnViXbp0YVOn\nTmW7du1ihYWFjDHGOI5jmzdvFni05pw2GdNqtWzy5Mmsa9euZn+8KpXK1NR09+7drGnTpuzhw4dC\nDVNwd+7cYa1bt2Y///yz2fUajYYxZviDHTduHJs0aZIQwxOVuLg41qZNG7Zv3z7TdXq93hSrvLw8\nNmjQIDZ9+nSXfKMr6+jRo6xDhw7s2rVr7OrVq+ytt95ioaGhzM/Pjz333HPsq6++YgcPHmTe3t5s\n69atQg/XoXbu3MliYmLYqVOnmFarZVeuXGE//fQTmzZtGouKimJhYWGse/fujOM404eoqx5PUVFR\nbPDgwWzMmDGsa9eurGvXrmzkyJFsxYoVph1TPv30U9a8eXPGmOt+qT5z5gwbNGiQ6Utzfn4+O3ny\nJJs9ezZr0KABk0qlrFu3bozjOFOjc1ecVVy4cCEbOHAge/ToEUtJSWG//vormzNnDnv++edZ+/bt\nWe/evdngwYOZn5+f0EMtx2mTMcYMHXbr1KnDunXrxg4cOFDu9p9//pmFhoYKMDLx0Gq1bOLEiczD\nw4N99NFH7OrVq+XuM3bsWDZmzBgBRicuWq2WDRgwgAUGBrL169dbnCGbM2cOGzhwoACjE5fjx4+z\nN998s9xSyf79+9mwYcOYv78/8/LyEuWbnr2lp6ez9evXswcPHphdn5mZyU6fPs2+/fZb1rhxY9at\nWzfGmOsmYnfv3mXR0dFs+/btjDHGLl++zJYvX84GDx7MOnfuzHr06MEmTZrEfHx82L/+9S/GWOmX\nSFfz+PFjtnnzZnb37t1yt2VmZrJ9+/axdu3asYiICMaY6yatly9fZqtWrSpXSnLt2jX2448/srfe\neotxHMdef/11gUZYMact4GclBcI3btzAu+++iwMHDiA4OBiDBw9G9+7dceDAAVy8eBHjx4/HvHnz\nXPpUaJ1Oh4ULF+LAgQPw8fFBmzZt0KpVK3Tq1Al79+7F2rVrERsbi27dugk9VMEVFRXhH//4B65c\nuYImTZogOjoaHTp0wDPPPINdu3Zh7ty5WL16NUaMGCH0UAWlVCqRkJCAli1bQiaTQaPRmBVdazQa\ndOzYEd27d8fatWsFHKmwjE1cyxZUFxUVoUWLFnj//fcxY8YMl31vys/Px3/+8x/Uq1cPPXv2NF2v\n0Whw4sQJHDx4EPv378eVK1dQUFDg0m2JytLpdOA4zuxsUr1ej06dOqFv375YtWqVyx5TZZU9w9so\nPj4erVq1wvHjx/H0008LOLrynDIZs/QHefToUezbtw9HjhxBfHw8oqKiMHnyZAwbNgxeXl4u+Udc\n9ndWKpU4duwYdu7ciRs3biAvLw83b95Es2bNMG/ePIwdO1bg0Qqr7KnyycnJiI2NxZ49e5Camgql\nUonbt28jODgYU6dOxaeffirwaMVFq9VCIpFAIpGYko/09HSEhYXh0KFD6N27t7ADFAmdTgeJRIK4\nuDg899xzePDggam1jKu9NxmVTVYtfXhOmDAB6enp2Ldvn8smGBUdH2Vjl5SUhBdeeAF//PEHGjdu\n7JKtPyqKEys541QqlWLDhg2YOXMm8vLyBBhh5ZwyGVMqlYiNjUVBQQFUKhUiIyMRExNj1scnJycH\n/v7+Lv1Gp9VqcfjwYfj7+6NevXqoX78+JBIJ0tPTER8fj0aNGsHDw8Mlt6ix5M6dO6hXrx68vLxM\nHwiXLl1CfHw8wsLC4Ovri3bt2gk8SvHIysoy6+Wn1+vBGINUKsXt27fx+eef47vvvhNwhI6nVqux\nY8cOMMYQHByMwMBANGvWzOxvrKioCOfOnUOvXr1cuuN+RZihfAY5OTkIDg7Gxo0bMW7cOJeN1ZOf\ndxEREejRowc8PT1N9ykoKMCVK1cQExPjsp95xs+7gIAABAYGwtfXF4GBgabehxzHITExETdv3sQL\nL7wg9HDLcbpkLC4uDvPnz8fRo0fh6emJRo0aQaPRICQkBIMHD8awYcNQv359AK7d6+iPP/7Al19+\nievXryM1NRWenp7o3LkzXn31VYwZMwYKhULoIYrG5cuXsXbtWvz555+4f/8+mjRpgj59+mD48OHo\n16+f0MMTFZVKhf/85z9Yv349ioqKUFRUhGeffRZTp05F48aNTffT6/VQq9Uu1R/q77//xsKFC/G/\n//0PxcXF0Gg0aNGiBbp06YKhQ4fSsfSEGzdu4OrVq2jdujXCw8Ph4+MDmUxmNpNx7tw5REdHCz1U\nwVj6vNPpdAgKCsKLL76IkSNHIjQ0VOhhCu7Jzztvb2906dIFr7zyCoYNG4a6desKPcQqOd085qJF\ni8BxHM6fP4+0tDR88803eP311xESEoJ169ZhyZIlpvu6aiIGAG+//TZatWqFDRs2ICUlBT///DP8\n/f3xzjvvoGPHjoiNjQUAl91Etqy33noL8fHxmD17Ng4fPoyxY8fi8OHDGDhwIJ5//nlcu3YNgGvH\nyvi7r1ixAp988gmKi4vRqVMndOjQAb/++itatmyJl156CadOnQIASCQSl0rEAOC9995DeHg4du/e\njezsbFy4cAGDBw/GiRMnMHz4cMyfPx9qtdqlNwMHgMLCQsyaNQs9e/bEvHnz0KFDB7Rq1QrTp0/H\n2bNnzZYqo6OjXfrvztLn3eTJk1GnTh388MMPWLx4sdBDFIUnP++2bNkCHx8fzJ49GzExMdi7dy+A\n0o78ouSoMwVspX79+uzIkSPlrs/NzWVbtmxhHh4eLtfX6EknT55kwcHBTKVSlbstLS2NTZkyhUVE\nRLDbt28LMDpxuX37NvPy8mKZmZnlbjt58iTr2bMna9u2LUtISBBgdOITEhLCNm7caLqcm5vLbt++\nzTZu3Miee+459sILL7hkK5mcnBwWGBjIbt26xRgrfzbbxo0bWXBwMFu/fr3F213J0qVLWceOHdn6\n9evZjRs32PXr19lXX33FOnTowDiOY6NGjTI16nTlODFGn3d81JbPO6eaGcvKykLLli2xYcMGaLVa\nAIZ1Yr1eDz8/P4wePRrLli3D33//jfT0dIFHK5yCggIEBATg0qVLAAyzGsXFxVCr1QgJCcHHH38M\nDw8PbNmyReCRCu/u3bsICwtDYmIiAEPNT3FxMfR6Pbp164bvv/8eRUVF2LFjh8AjFV58fDz8/PzQ\nunVr03V+fn6IiIjA2LFj8cUXX+DatWtYvXq1ae88V5GXl4fGjRvjl19+AWCYlTceSwAwfvx4DB06\nFL/88gsKCgpcetZ++/btmDBhAiZOnIhWrVqhdevWmDVrFi5evIgdO3bgypUrpjNwXTlO9HnHT235\nvHOqZCwwMBDjxo3D4cOHsW7dOhQVFUEmk5mdNdKyZUvcvn3bpTe77t27N3x9ffH+++/jxo0bkEgk\ncHd3h5ubGxhjaNiwIXr16oWbN28KPVTB9enTB97e3vj888+hVqvh5uYGd3d305mBERERGD58OP7+\n+2+hhyq4evXqoUGDBvjggw/KvflLJBJERUXhk08+wdGjR11uKS48PBx9+/bFmjVrTAmZ8Vgy6tmz\nJ+7duwcfHx+hhik4lUqFZs2a4c6dO6brGGPQarVgjGHo0KEYPXo0du7ciYSEBAFHKjz6vOOntnze\nSRctWrRI6EFYo0mTJsjIyMAnn3yCTZs2ITMzE35+figoKMD+/fvx7bffomPHjhg6dKjplHtXwkrO\nZouJicHu3bvx448/4sKFCyguLkbdunXh7e2N/fv3Y8WKFZg1axbatm0r9JAFwxiDTCZDeHg41qxZ\ng59++gmPHz+Gr68vwsLCIJFIcOvWLSxduhQvvfQSunfvLvSQBeXm5oYmTZpg165duHDhAjw8PODt\n7Q2ZTGbqMbZx40ZkZWVh8uTJAo/W8WJiYpCcnIwlS5Zg69atSEpKQkhICAICArBjxw588cUXGDBg\nAPr27euS700AIJPJkJeXh4ULF8Lb2xtNmjSBr6+vaaNrAGjcuDGWL1+Od955Bz4+Pi59IhZ93lWu\nNn3eOd3ZlEZ3797F2rVrTd+gwsLCoNFoMHDgQHzyySdo2LChy/daiYuLw2+//YZTp04hLS0NGRkZ\npgSkT58+2LBhg7CDFZETJ05g8+bNuHz5MpRKJTiOQ3BwMB48eIDQ0FD8+eefZqeSuyLGGHQ6HbZt\n24aVK1fixo0b6Ny5M3r06AGJRIKLFy/iwYMHWLVqFV588UWhh+sw7Il+fgcOHMD+/ftx5swZ3Lhx\nA1KpFL6+vhg0aBBWrFiBwMBAl3xvKmvJkiXYtm0bmjVrhm7duiE6Ohq9evVCWloaPv74Y5w/fx6X\nLl1y+TgZ0eedZbXp886pkjGNRoP8/Hx4eXnBw8MDGo0GKpUKGRkZiIuLQ3h4ODp16iT0MAX3ZHPE\n27dvIy4uDvn5+SgsLETz5s3Rv39/AUcoTvn5+Th37hzi4uLw+PFjpKamon379pg4cSL8/f2FHp7o\nHD9+HN9//z3i4uIQFBQEDw8PzJkzB88++6zQQ3O4/Px8+Pr6mi5nZ2cjMTERRUVFyM7Ohre3N3r1\n6iXgCMXB+OGZmZmJ2NhY7N69G4mJiZDL5UhMTERubi5iYmLw3nvvoV+/fi7b6BWgzzu+asvnnVMk\nY/n5+fjtt9/w4Ycfwt/fH+PGjcMHH3xQ4f1ddVr78ePHiI2NxdatW+Ht7Y333nuPPgAqoFQqcfbs\nWWzZsgUhISGYPn06wsPDTbe78oeAJWlpaYiLi0Nqaio6deqEyMhIs9szMjIQHBws0OiEk5GRgR07\nduCrr76CRqPBzJkz8eabb5ptDUVKqVQquLm5mc3gnD59GlevXoVUKoWPjw/69u1r1kzY1dDnHT+1\n7fPOKZKxf/7zn9i5cyf69+8PLy8vrFq1CpMnT8ZXX31luo9Go4FOp3O53kb/v717j6uizP8A/pmD\nBxRIBOUiuQe5KQIqiCDiPVC0LMAVLZY1I7Q03by2GroKZG2aGbmZa7ouKCatgCKWCgqCghe0EAU1\nUwQU5F6i3M7h+/vDnRG8FP22eI7M8/6POSOvD+PMfL/nmZlnWpsxYwbOnj0Ld3d31NbWorS0FDt2\n7EC/fv2k5kKuB+7DVq1ahdjYWJibm0tP48TExEClUknbSI7D/q2Jf39aWhrWrVuHgwcPwsnJCc7O\nzti0aROMjY0feSel3CxatAjHjh3DqFGjYGBggJiYGEREROC1116Tjrnm5mYIgiD75v7YsWPYunUr\niouLMWzYMCxevBhmZmaPrCf3447Xu/bpdPWuQybQ+B9ZWFjQ3r17pZ937dpFvXv3prNnz0rL9uzZ\nQ2vXrmURTyvk5+dTjx49KD8/n5qamujq1avk6elJU6dOJaIH8/V8/vnnsp8zq6qqinr06EHffPMN\nVVRU0LfffksODg4UGhpKRERqtZqIiNLT06myspJlVKbEfWbIkCE0f/58ysvLo/T0dLKzs6MZM2a0\nWffatWtUUlLCIiZThoaGlJmZSRqNhtRqNS1fvpz69u3bZlts27aNEhISGKZkLykpidzc3MjDw4MW\nLVpE7u7u9N577xERUXNzs+znE2uN17tf1hnrndY3Y1lZWWRtbU1lZWWk0WikjfzSSy/RokWLpPVs\nbW1p/fr1RPSgmMrJu+++Sy+99FKbZefPnyczMzPKzs4mIqLKykoSBEHrJ7/7vW3YsIGGDx/eZtnJ\nkyfJ0tKSCgoKiIiosbGRBEGgEydOsIioNS5dukQ9evSgiooKadnp06fJyMiIjh8/Li3z8/OjNWvW\nsIjITHx8PA0cOPCRySYHDx5MH3zwgfSzvr4+xcbGEhGRRqPp0IzawtPTk8LCwqSmdePGjWRhYUGn\nT5+W1jl79ixFRUUxTMker3ft0xnrndaPBRcVFUGlUuHOnTtQKBTS5HdvvPEGdu/ejZ9++glXrlzB\njRs38OabbwKALIe4y8rK0Lt3bzQ0NAC4P4w9cOBAae4j4P60A/3794e9vT3LqMxdvHgRgwcPRnNz\nM4gIzc3NGDZsGNzd3REVFQUASExMhKmpqWyns6D/3r1w+PBhDB06VBryJyK4u7vjlVdewaZNmwBA\nuhl7+vTpLCN3uOLiYpiamkpzromvWvnLX/6C6OhoAEB6ejoEQUBQUBAAeZ6bampqcO3aNQQHB0Oh\nUEBHRwfz5s2Dq6urdG4CgPfeew/79+8HANnNUyfi9a59OmO90/r/RU9PTwwePBgGBgYAAKVSCSKC\nr68vVCoVNm7ciLi4OAwbNgz6+vpQq9VPzzXi30hLSwv8/PzQu3dv6R4C8T6et956C+np6SgqKsKe\nPXswc+ZMhknZ02g0GD16tDQ3liAI0rYKCQnBgQMHUFVVhS+//BLTpk1jnJYd8RgyNTVFr1690NDQ\nIN1HBwBBQUHIzs5GSUkJ9uzZA1tbW9ja2rKM3OEmTZqE0aNHo2fPngDuH3MajQbTp08HESEuLg7x\n8fHSk1xiYZWb7777DjY2NqipqQHw4D2nH374Ib755hvk5eVBrVYjNTUVkZGRLKMyx+vdL+u09Y7h\nqNz/LDY2luzt7UmpVFJ8fDwR3b//QI7u3r1Lt2/fJqK273NraWkhX19fmjhxInXp0oXu3LnDKqLW\nqKmpkYauH75s5ObmRrNnzyalUknff/89i3hapbKykr766qvHfjZmzBgKCwsjFxcX+vDDDzs4mXao\nr69/7PKIiAhydnYmhUJBJ0+eJCJ5Xk4iIioqKqKwsDDKy8sjovvHnHjc+fn50dKlS+ngwYNkbGxM\nRPx9lE/C690DnbHeaX0z9nMnsIaGBnJwcCBBEDow0dNn//79JAgC+fr6so6itUbXb9kAABj4SURB\nVMTikJiYSIIg0ODBgxkn0n7p6ekkCAIJgkB1dXWs42iV0tJS0tfXJzMzMyLiDcaTXh4fHx9Pbm5u\n1KdPH/rrX/9KRPJtMIh4vfstPK317qmY2uJxxMefc3JykJeXh9dee032j9k/Dv330d7Vq1dj4sSJ\n8PT0ZB1Ja7W0tKC+vh5z587F5MmTERgYyDoSc/Qzj4bX19dj2rRpMDQ0xJdfftnBybSXeG46dOgQ\nmpubMXnyZH5uegKNRoOhQ4ciNzcXJSUlsLS0lP3UFo/D6137PM317qltxri2fq5oAsDdu3el+xC4\nn3f79m2YmZnJ7l6MX0Pc30pLS9HU1AQrKyvWkbRK69Mq349+XmZmJlJSUhAREcEbMa5dOmO9e+qb\nMY1GAx0dHdYxOI6TmZqaGhgZGfHm4TcgFs9fKrJyx+td56X1Z5Ff6hXlvGOKj3+fP38ep0+fZpzm\n6fGUf//oUOK2amlpeex0A3LbluKTgNevX8eCBQtQXV3NOFHnII5iyL0R4/XuyTp7vdPKZkx8BHzv\n3r1Ys2YN8vLycPfuXcaptI944lqwYAFSUlIAPP5gllvBfBzxQD506BAOHz6MyspKxomeDuI+Js4P\nBbSdA0quxXPr1q34/vvv0atXL358/QKxgQXu7zutf+Z4vWuvzl7vdFavXr2adYiHicP+SUlJCAsL\nQ0JCAk6cOAG1Wg0jIyN069ZNes9bS0uLLAtCcXExNm/eDENDQyxfvhybN29Gz549pW0hDvdXVVVB\nX1+fcVr2xH1q/PjxsLGxwfDhw6U5fARBQGNjo+zfHSgSj6mSkhIkJSUhJiYGiYmJ0NfXh7W1tawv\ny4nHl46ODgRBwMiRI6Gjo8Mvrz2k9fYQBAHl5eUwMDCAQqGAIAggImk/k/t24/Xul8mh3mndWVX8\n1lRRUQFdXV14eXkhKCgITU1NmD17Nry8vLBs2TIcPnwYgDxnHwaAnJwcvPvuuxg5ciSMjY1x7tw5\nlJSUSN8KBEFAQ0MDfHx8ZD8KJI7kJCQkAADmzp0rHbDiwXzmzBnk5eU9td+qfisajQYKhQL19fX4\n85//jPnz5+PixYsoKCjA1KlT4enp2SkvEbSHuG+cO3cOM2fOxCeffILs7GwAD/YjPupzn7gdDh06\nhBkzZuDll1/GCy+8gHXr1qGwsBCCIEgNrZzxetc+sqh3v/fcGb+WOMfMwoULadKkSW3eiffDDz/Q\nlClTpLmNhg8fTjk5OayiagVdXV2ytrYmfX19MjY2puDgYEpJSaHi4mJasWIF2dvbs47InDjH01tv\nvUWBgYFEdH8/E+cWa2lpofDwcHrllVeYZdQ2GzZsIHt7e7p27Ro1NjZSUVERHThwgHx9fcnX15d+\n+ukn1hGZycnJoRdeeIGsrKxIX1+fgoKCKCMjg3UsrdS3b1/y8fGhOXPm0Kuvvkqurq5kZ2dHU6ZM\noc8//5zq6+tlPQcbr3e/Tmeud1rXjIkGDRpE7733HhHdnwivqamJiIgyMjLo9ddfp2PHjpG7uzv5\n+/uzjMnchQsXiOj+TOlbtmwhLy8v6tKlC3Xr1o2cnJwoJiaGcULtsWPHDhowYECbFzuLhcDX15eW\nLVvGKppW2LNnj7Q/vf3227R69epH1klLSyOVSkVxcXEdHU9rtLS0UG1tLV25coWioqLI09OTFAoF\nWVtb05tvvinNDC5X4jF14MABsrW1lZaXl5dTWloarV27lv74xz+SpaUlXbp0iVVMrcLrXft05nqn\nlc2YRqOhhQsX0siRIx/7mYODA+Xk5NB//vMf6t+/P509e5ZBSnbEb1Opqam0e/duunXrVpvPS0pK\nKCYmho4dOybrb50PKygooF69etHEiRPp66+/ppqaGiIi2rZtG3Xv3p2uXbvGOCE7GRkZpFQqady4\ncbRq1SoKDw8nf39/unfv3iPr2tjY0NatWxmk1C5iwSS6P1q2cuVKsrCwoNzcXCKS76z74ojzkSNH\naP78+Y99O8P169fp6NGjHR1NK/F69/PkUu+0shkjIsrMzCQTExPy8vKiL774gkpLS6muro4iIiLI\nyMiIiIgKCwvJwsKCbt68yTgtGy4uLhQREUHl5eVEJN933/0aKSkpNGzYMPLw8KDhw4dTnz59yNbW\nlv72t7+xjsaMeAJLTU2lOXPmkI2NDZmampIgCBQcHEyHDh2imzdvUmNjI8XFxZFKpaK7d+8yTt2x\nxAbj2rVrtGTJEgoKCqKgoCCKjIyUmq+mpiaqqqpiGVNr1NfX0wsvvEB9+vShxMRE1nG0Hq93v6yz\n1zutnvQ1KysLUVFRKCwsxK1bt1BRUYF+/fphzpw5mDNnDtasWYNdu3bh4sWLrKN2GHGG6uzsbDz/\n/PMoLCyEkZERgAdPlCQlJaFr167w9vaW9bw0j0NEuH37Nr788kvcuHED5ubm8PLywpgxY1hH0xrN\nzc34+uuvsXv3bmRkZMDQ0BCmpqaoqamBpaUlgoOD8eqrr7KO2WHE46qurg7u7u5QKpWwsbGBjo4O\nKioqoFAosH79eri5ubGOqjVyc3OxdOlSFBcXo6qqCs899xy8vb0xfvx49O3bl3U8rcTr3aPkVO+0\n5ll+tVqNLl264MaNG6ioqICdnR28vLxgb2+PnJwcVFRUwNDQEI6OjnBwcMCJEyeQlpaGxYsXs47O\nxJEjR+Dl5SXtmK1VVVVh7969mDBhAoNk2kOcrbq2thbbt2/H5cuXcevWLQQHB2PhwoWs42kdtVoN\nHR0dKJVK+Pn5wc/PD+Xl5fjqq6+wd+9elJSUoLq6GqNHj2YdtUO1tLRAR0cHW7duhVKpRGpqKszM\nzFBXV4czZ87g73//OyZNmoRTp07B2tqadVzmWlpaMHjwYMTFxeG7775Deno6vv32W2zZsgWxsbH4\nwx/+gNDQUFl/AeL17teRQ73TmmZMnEdl8eLFSEhIwNSpUxEQEIDRo0dj0qRJj6xvYWGBBQsWPPaz\nzkx8tHnAgAHYvHkzzpw5A3d3d+ngBoDU1NTH7rRyI35LmjlzJsrLy+Hq6or09HS4uroCAH744Qc0\nNTXBwcFB9o/YE5G0/+Tm5uLUqVMYMmQIhg4dinnz5mHevHm4dOkSdu/eLbuGQ9yPqqur4e/vDzMz\nMwCAoaEhxo0bB2dnZ3h7eyMtLU1226Y1cRTj7t27qKmpgUqlwrhx4zBu3DgUFxcjKysLp06dQlpa\nmjSpqVzfRcnrXfvIqt4xvET6WC0tLRQdHU3Dhw8nQRDI0tKS5syZQ9988w1dvXpVundD7iorK8nN\nzY38/Pzo4sWLRERUW1tL8fHx1KtXL8rOzmackC1xPzlx4gQZGxtTWVkZEREZGBhIj4fHxMRQWFiY\ndCM/R7Rs2TKys7MjT09PEgSBdu3aRUREjY2NjJOxIe5HVVVVFB4eTu7u7pSfn99mnZaWFrK1taUt\nW7YQUee7l6W9xHsPN2/eTN27d6fAwEDauXPnIzfwX7hwgZ/H/4vXu/aRQ73Tqmbs4SchKisrKTw8\nnExNTemZZ56hAQMG0J07d4iIZLuTtt5GR44cIScnJ1IqleTo6Eienp5kbm5OS5cuZZhQO4j7xzvv\nvCPNLRYVFUUDBw6UPtu+fTu5u7szy6gtxOYhPj6eHBwc6PDhw5Sbm0vdu3enH374gYiIVq1aRfHx\n8dKTTXKzc+dOab6n8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EBnry6WsDrB2OMIOKSi1jXv6FnPxS3p0SR0ykf73PcSmniMT/HOL3badwcXbgX6/2x6+R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6Hl1SCgDb1cPZi62uO0mthrp9G5TsOQjtoeNQ3tPL6ue0Jmf7fdUeu/5HjQVu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\nr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rVo1cy5YtsWnTJnTq1MkWYzILa+SIiEgtcnkFzkf0ANzdEHHhkFXnCAgI4P+P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/lpiZOTFFfnrdmQsJCUFWVla1bs8bN24gNDQU5eXlep1s9erVdZ6j8u4fEVk3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2/YDi5orX6/8kOyeft5bt4sT5yo2alMXuijdbnT9hryTfxTk66HmgcwN6dazP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RVWVUT1yTJk3w/vvvY+DAgXB1dcXRo0cRGhqKmTNnIiMjA4sXL7ZErAbYE0dE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ly6F/YM9cDVIbtqvT5+ShwRzIEVmI4GWa6UmI6kqbkQVtdg4ED3fIgwIsXU6d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\n8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EFJdWALjzdzQ4sB0sa1n0tzaHopNx6mQUQkP74c3nQ4X2rp67pMSXe2Nx+cad2bGWjtaY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MCVT0+ETWyKzJ2GOPPYaJEyfi1KlT6NatGwBg165dmDt3LsaNGwcA+OOPP9C2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\npYfsq3mfiGrGnjEiojooSzqDgq9XQi5v2NNwJX/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MhFqthomJCczNzaFSqSAIApRKJTZv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\nOpRCQkKovLy8ymNarZb0ej0VFhZKVJ38WFtb0969e4mI6LfffqOhQ4e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Gr4DImjGTyQSTyQSl0py7ffbZZ8jIyEDHjh3x5JNPQqUSd0FciWrGCCGEkIYJ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fMeWGXCmhs5PKPdvspz10XHxlMgaP/yXLsVzOtzvPoLxaB6PJApVSekNgPQkf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\n6NGjadmyp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xR44cwcKFCzFixAh8+OGHUKvVXvk9uoJ07/IeBuWNEYT3sUfFRg5O8Fmh1/aolXIkxATD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\nYmKIiKioqIg4jmOLv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Dh9YYXt2xYwdycnKQkJBQa4j0/fffR69evfDkk09i4cKFaNOmDR599FGsWrWqydfOycnB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aHLmiEgAgBTTN/1lqSdCYodWbfFdWeTqUBukqiqE3Kl/OwZx9DpazOTCdzoBf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\noIKPP/6YZ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xb11dHaZNm4aBAwea++qEfkPJLWA4chT6P4/Br20b4e+5kq/f/EBERETOY9c6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h8/jtTUVDg7OyM7Oxuurq7YuHEjnJ2dOZUg99KlS1i0aBG8vb0xcuRIAHUnGxUUFODixYtYsWIF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\n/wGOJtPcqvfcTQAAAABJRU5ErkJggg==\n", 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jp+LWMRLQ62FMz4RcVq52OQDYI0dERNrmkgM5Xq0jzvVZS54ecOvQDpBlGE6eUbGqa7R8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\ny5dffsmMGTN4+PAhlSpVkos+SyE5OZmNGzcybNgwmjVrpkvkjIyMGDB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hIWWej4+PR6tWrXD79m2cOXMGERERuHPnjtGC/SdeBiUiUQ6duIYlXx2GVitj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CgJEjRyIoqHQzTDc3N7Rv3948jVn2l1fTcdk5S6/nsfTHd+ZK7nh4XPPxRx99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\nK8vHQCQnJ+Ps7Exubi5GRkb6KUXee+89Nm/ezP3794mPj+fGjRu8//7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UKpUKBoMBKpUKWVlZVh9Qo9EgODi40nYfHx9s3brVYttnn32GDh064MSJE+jQ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\nF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wdXVFly5dAACzZ8/GJ598gk6dOiE4OBjLly+Hk5MTJk6caNCx6J69bpSLBmXB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\nkZmZiTFjxsDR0ZG19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L6WMxRkSkJ6vBL8Nh/aeAygwFX2xE7sRZEEpLxQ5LL4Ig4O6KL5A95C0Id/Nh0a8Xmu7b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\nyovozmEaBnkbxqU0NjZi6NChGD58OLZv3w6geb/K0aNHG+/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+ "text": [ + "" + ] + } + ], + "prompt_number": 17 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Briefly summarize these graphs -- how accurate is the typical poll a day before the election? How often does a prediction one month before the election mispredict the actual winner?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Based on visual inspection, these polls generally seem to be within about 5% of the election outcome. The accuracy is higher shortly before the election (usually within 2% or so). In about 20% of cases, the eventual winner trails at some point during the last month of polling -- examples include the Brady/Quinn Illinois race, the Foley/Malloy Connecticut race, the Scott/Sink Florida Race, the Kitzhaber/Dudley Oregon Race, the Corzine/Christie New Jersey race, and the Caprio/Robitaille/Chafee Rhode Island Race.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Part 3: Analysis\n", + "\n", + "#### Problem 5\n", + "\n", + "You are (finally!) in a position to do some quantitative analysis.\n", + "\n", + "We have provided an `error_data` function that builds upon the functions you have written. It computes a new DataFrame with information about polling errors.\n", + "\n", + "Use `error_data`, `find_governer_races`, and `pd.concat` to construct a Data Frame summarizing the forecast errors\n", + "from all the Governor races\n", + "\n", + "**Hint** \n", + "\n", + "It's best to set `ignore_index=True` in `pd.concat`" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def party_from_color(color):\n", + " if color in ['#0000CC', '#3B5998']:\n", + " return 'democrat'\n", + " if color in ['#FF0000', '#D30015']:\n", + " return 'republican'\n", + " return 'other'\n", + "\n", + "\n", + "def error_data(url):\n", + " \"\"\"\n", + " Given a Governor race URL, download the poll data and race result,\n", + " and construct a DataFrame with the following columns:\n", + " \n", + " candidate: Name of the candidate\n", + " forecast_length: Number of days before the election\n", + " percentage: The percent of poll votes a candidate has.\n", + " Normalized to that the canddidate percentages add to 100%\n", + " error: Difference between percentage and actual race reulst\n", + " party: Political party of the candidate\n", + " \n", + " The data are resampled as necessary, to provide one data point per day\n", + " \"\"\"\n", + " \n", + " id = id_from_url(url)\n", + " xml = get_poll_xml(id)\n", + " \n", + " colors = plot_colors(xml)\n", + " if len(colors) == 0:\n", + " return pd.DataFrame()\n", + " \n", + " df = rcp_poll_data(xml)\n", + " result = race_result(url)\n", + " \n", + " #remove non-letter characters from columns\n", + " df = df.rename(columns={c: _strip(c) for c in df.columns})\n", + " for k, v in result.items():\n", + " result[_strip(k)] = v \n", + " \n", + " candidates = [c for c in df.columns if c is not 'date']\n", + " \n", + " #turn into a timeseries...\n", + " df.index = df.date\n", + " \n", + " #...so that we can resample at regular, daily intervals\n", + " df = df.resample('D')\n", + " df = df.dropna()\n", + " \n", + " #compute forecast length in days\n", + " #(assuming that last forecast happens on the day of the election, for simplicity)\n", + " forecast_length = (df.date.max() - df.date).values\n", + " forecast_length = forecast_length / np.timedelta64(1, 'D') # convert to number of days\n", + " \n", + " #compute forecast error\n", + " errors = {}\n", + " normalized = {}\n", + " poll_lead = {}\n", + " \n", + " for c in candidates:\n", + " #turn raw percentage into percentage of poll votes\n", + " corr = df[c].values / df[candidates].sum(axis=1).values * 100.\n", + " err = corr - result[_strip(c)]\n", + " \n", + " normalized[c] = corr\n", + " errors[c] = err\n", + " \n", + " n = forecast_length.size\n", + " \n", + " result = {}\n", + " result['percentage'] = np.hstack(normalized[c] for c in candidates)\n", + " result['error'] = np.hstack(errors[c] for c in candidates)\n", + " result['candidate'] = np.hstack(np.repeat(c, n) for c in candidates)\n", + " result['party'] = np.hstack(np.repeat(party_from_color(colors[_strip(c)]), n) for c in candidates)\n", + " result['forecast_length'] = np.hstack(forecast_length for _ in candidates)\n", + " \n", + " result = pd.DataFrame(result)\n", + " return result" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 18 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "function\n", + "---------\n", + "all_error_data\n", + "\n", + "Calls error_data on all races from find_governer_races(page),\n", + "and concatenates into a single DataFrame\n", + "\n", + "Parameters\n", + "-----------\n", + "None\n", + "\n", + "Examples\n", + "--------\n", + "df = all_error_data()\n", + "\"\"\"\n", + "#your code here\n", + "def all_error_data():\n", + " data = [error_data(race_page) for race_page in find_governor_races(page)]\n", + " return pd.concat(data, ignore_index=True)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 19 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "errors = all_error_data()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 20 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a histogram of the error of every polling measurement in the data" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "errors.error.hist(bins=50)\n", + "plt.xlabel(\"Polling Error\")\n", + "plt.ylabel('N')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 21, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 21 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Problem 6\n", + "\n", + "Compute the standard deviation of the polling errors. How much uncertainty is there in the typical RCP poll?" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "\n", + "errors.error.std()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 22, + "text": [ + "5.2626670137896898" + ] + } + ], + "prompt_number": 22 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Problem 7\n", + "\n", + "Repeat this calculation for the data where `errors.forecast_length < 7` (i.e. the polls within a week of an election). How much more/less accurate are they? How about the data where `errors.forecast_length > 30`? \n", + "\n", + "**Comment on this in 1 or 2 sentences**. Does this make sense?" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "print \"< 7 days: %0.2f\" % errors[errors.forecast_length < 7].error.std()\n", + "print \">30 days: %0.2f\" % errors[errors.forecast_length > 30].error.std()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "< 7 days: 3.29\n", + ">30 days: 5.33\n" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*This is basically what we would expect, and what the plots suggest as well -- the accuracy of polls shortly before the election are about 40% more accurate than polls more than a month before the election.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Problem 8\n", + "\n", + "**Bootstrap resampling** is a general purpose way to use empirical data like the `errors` DataFrame to estimate uncertainties. For example, consider the [Viriginia Governor Race](http://www.realclearpolitics.com/epolls/2013/governor/va/virginia_governor_cuccinelli_vs_mcauliffe-3033.html). If we wanted to estimate how likey it is that McAuliffe will win given the current RCP data, the approch would be:\n", + "\n", + "1. Pick a large number N of experiments to run (say N=1000).\n", + "2. For each experiment, randomly select a value from `errors.error`. We are assuming that these numbers represent a reasonable error distribution for the current poll data.\n", + "3. Assume that the error on McAullife's current polling score is given by this number (and, by extension, the error on Cuccinelli's poll score is the opposite). Calculate who actually wins the election in this simulation.\n", + "4. Repeat N times, and calculate the percentage of simulations where either candidate wins.\n", + "\n", + "Bootstrapping isn't foolproof: it makes the assumption that the previous Governor race errors are representative of the Virginia race, and it does a bad job at estimating very rare events (with only ~30 races in the errors DataFrame, it would be hard to accurately predict probabilities for 1-in-a-million scenarios). Nevertheless, it's a versatile technique.\n", + "\n", + "Use bootstrap resampling to estimate how likely it is that each candidate could win the following races.\n", + "\n", + " * [Virginia Governor](http://www.realclearpolitics.com/epolls/2013/governor/va/virginia_governor_cuccinelli_vs_mcauliffe-3033.html)\n", + " * [New Jersey Governor](http://www.realclearpolitics.com/epolls/2013/governor/nj/new_jersey_governor_christie_vs_buono-3411.html)\n", + " \n", + "**Summarize your results in a paragraph. What conclusions do you draw from the bootstrap analysis, and what assumptions did you make in reaching this conclusion. What are some limitations of this analysis?**\n", + " " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "\n", + "def bootstrap_result(c1, c2, errors, nsample=1000):\n", + " \"\"\"\n", + " Given the current polling data for 2 candidates, return the\n", + " bootstrap-estimate for the win probability of each candidate\n", + " \n", + " Parameters\n", + " ----------\n", + " c1 : float\n", + " The current proportion of poll votes for candidate 1\n", + " c2 : float\n", + " The current proportio of poll votes for candidate 2\n", + " errors : DataFrame\n", + " The errors DataFrame\n", + " nsample : int\n", + " The number of bootstrap iteraionts. Default=1000\n", + " \n", + " Returns\n", + " -------\n", + " p1, p2\n", + " The probability that each candidate will win, based on the bootstrap simulations\n", + " \"\"\"\n", + " #first, normalize votes to 100\n", + " tot = (c1 + c2)\n", + " c1 = 100. * c1 / tot\n", + " c2 = 100. * c2 / tot\n", + " \n", + " indices = np.random.randint(0, errors.shape[0], nsample)\n", + " errors = errors.error.irow(indices).values\n", + " \n", + " #errors are symmetrical -- an overestimate for candidate 1 \n", + " #is an underestimate for candidate 2\n", + " c1_actual = c1 - errors\n", + " c2_actual = c2 + errors\n", + " \n", + " p1 = (c1_actual > c2_actual).mean()\n", + " p2 = 1 - p1\n", + " return p1, p2\n", + "\n", + "\n", + "#Look up the data as of 9/24/2013\n", + "#virginia\n", + "nsample = 10000\n", + "mcauliffe, cuccinelli = 43.0, 39.0\n", + "\n", + "pm, pc = bootstrap_result(mcauliffe, cuccinelli, errors, nsample=nsample)\n", + "print \"Virginia Race\"\n", + "print \"-------------------------\"\n", + "print \"P(McAuliffe wins) = %0.2f\" % pm\n", + "print \"P(Cuccinelli wins) = %0.2f\" % pc\n", + "\n", + "#new jersey\n", + "print \"\\n\\n\"\n", + "print \"New Jersey Race\"\n", + "print \"-----------------------\"\n", + "christie, buono = 55.4, 31.8\n", + "pc, pb = bootstrap_result(christie, buono, errors, nsample=nsample)\n", + "print \"P(Christie wins) = %0.2f\" % pc\n", + "print \"P(Buono wins) = %0.2f\" % pb" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Virginia Race\n", + "-------------------------\n", + "P(McAuliffe wins) = 0.71\n", + "P(Cuccinelli wins) = 0.29\n", + "\n", + "\n", + "\n", + "New Jersey Race\n", + "-----------------------\n", + "P(Christie wins) = 0.98\n", + "P(Buono wins) = 0.02\n" + ] + } + ], + "prompt_number": 33 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*The Virginia race is currently fairly close. Our bootstrap simulations assume that historical RCP poll accuracies are representative of the uncertainty in the current polls. In 10,000 of these simulations, McCauliffe won 70% of the time.*\n", + "\n", + "*The New Jersey race, on the other hand, is less close. In 10,000 simulations, a candidate enjoying a lead as big as Chris Christie wins about 98% of the time*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parting Thoughts\n", + "\n", + "For comparison, most of the predictions in Nate Silver's [presidental forecast](http://fivethirtyeight.blogs.nytimes.com/fivethirtyeights-2012-forecast/) had confidences of >95%. This is more precise than what we can estimate from the RCP poll alone. His approach, however, is the same basic idea (albeit he used many more polls, and carefully calibrated each based on demographic and other information). Homework 2 will dive into some of his techniques further.\n", + "\n", + "\n", + "## How to submit\n", + "\n", + "To submit your homework, create a folder named lastname_firstinitial_hw0 and place this notebook file in the folder. If your notebook requires any additional data files to run (it shouldn't), add them to this directory as well. Compress the folder (please use .zip compression) and submit to the CS109 dropbox in the appropriate folder. If we cannot access your work because these directions are not followed correctly, we will not grade your work." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "*css tweaks in this cell*\n", + "" + ] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/HW2_solutions.ipynb b/HW2_solutions.ipynb new file mode 100644 index 0000000..d8e2813 --- /dev/null +++ b/HW2_solutions.ipynb @@ -0,0 +1,3545 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Homework 2: Desperately Seeking Silver (Solutions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Due Thursday, Oct 3, 11:59 PM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "
\n", + "
\n", + "\n", + "In HW1, we explored how to make predictions (with uncertainties) about upcoming elections based on the Real Clear Politics poll. This assignment also focuses on election prediction, but we are going to implement and evaluate a number of more sophisticated forecasting techniques. \n", + "\n", + "We are going to focus on the 2012 Presidential election. Analysts like Nate Silver, Drew Linzer, and Sam Wang developed highly accurate models that correctly forecasted most or all of the election outcomes in each of the 50 states. We will explore how hard it is to recreate similarly successful models. The goals of this assignment are:\n", + "\n", + "1. To practice data manipulation with Pandas\n", + "1. To develop intuition about the interplay of **precision**, **accuracy**, and **bias** when making predictions\n", + "1. To better understand how election forecasts are constructed\n", + "\n", + "The data for our analysis will come from demographic and polling data. We will simulate building our model on October 2, 2012 -- approximately one month before the election. \n", + "\n", + "### Instructions\n", + "\n", + "The questions in this assignment are numbered. The questions are also usually italicised, to help you find them in the flow of this notebook. At some points you will be asked to write functions to carry out certain tasks. Its worth reading a little ahead to see how the function whose body you will fill in will be used.\n", + "\n", + "**This is a long homework. Please do not wait until the last minute to start it!**\n", + "\n", + "The data for this homework can be found at [this link](https://www.dropbox.com/s/vng5x10b837ahnc/hw2_data.zip). Download it to the same folder where you are running this notebook, and uncompress it. You should find the following files there:\n", + "\n", + "1. us-states.json\n", + "2. electoral_votes.csv\n", + "3. predictwise.csv\n", + "4. g12.csv\n", + "5. g08.csv\n", + "6. 2008results.csv\n", + "7. nat.csv\n", + "8. p04.csv\n", + "9. 2012results.csv\n", + "10. cleaned-state_data2012.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Setup and Plotting code" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline\n", + "from collections import defaultdict\n", + "import json\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "from matplotlib import rcParams\n", + "import matplotlib.cm as cm\n", + "import matplotlib as mpl\n", + "\n", + "#colorbrewer2 Dark2 qualitative color table\n", + "dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),\n", + " (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),\n", + " (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),\n", + " (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),\n", + " (0.4, 0.6509803921568628, 0.11764705882352941),\n", + " (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),\n", + " (0.6509803921568628, 0.4627450980392157, 0.11372549019607843)]\n", + "\n", + "rcParams['figure.figsize'] = (10, 6)\n", + "rcParams['figure.dpi'] = 150\n", + "rcParams['axes.color_cycle'] = dark2_colors\n", + "rcParams['lines.linewidth'] = 2\n", + "rcParams['axes.facecolor'] = 'white'\n", + "rcParams['font.size'] = 14\n", + "rcParams['patch.edgecolor'] = 'white'\n", + "rcParams['patch.facecolor'] = dark2_colors[0]\n", + "rcParams['font.family'] = 'StixGeneral'\n", + "\n", + "\n", + "def remove_border(axes=None, top=False, right=False, left=True, bottom=True):\n", + " \"\"\"\n", + " Minimize chartjunk by stripping out unnecesasry plot borders and axis ticks\n", + " \n", + " The top/right/left/bottom keywords toggle whether the corresponding plot border is drawn\n", + " \"\"\"\n", + " ax = axes or plt.gca()\n", + " ax.spines['top'].set_visible(top)\n", + " ax.spines['right'].set_visible(right)\n", + " ax.spines['left'].set_visible(left)\n", + " ax.spines['bottom'].set_visible(bottom)\n", + " \n", + " #turn off all ticks\n", + " ax.yaxis.set_ticks_position('none')\n", + " ax.xaxis.set_ticks_position('none')\n", + " \n", + " #now re-enable visibles\n", + " if top:\n", + " ax.xaxis.tick_top()\n", + " if bottom:\n", + " ax.xaxis.tick_bottom()\n", + " if left:\n", + " ax.yaxis.tick_left()\n", + " if right:\n", + " ax.yaxis.tick_right()\n", + " \n", + "pd.set_option('display.width', 500)\n", + "pd.set_option('display.max_columns', 100)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#this mapping between states and abbreviations will come in handy later\n", + "states_abbrev = {\n", + " 'AK': 'Alaska',\n", + " 'AL': 'Alabama',\n", + " 'AR': 'Arkansas',\n", + " 'AS': 'American Samoa',\n", + " 'AZ': 'Arizona',\n", + " 'CA': 'California',\n", + " 'CO': 'Colorado',\n", + " 'CT': 'Connecticut',\n", + " 'DC': 'District of Columbia',\n", + " 'DE': 'Delaware',\n", + " 'FL': 'Florida',\n", + " 'GA': 'Georgia',\n", + " 'GU': 'Guam',\n", + " 'HI': 'Hawaii',\n", + " 'IA': 'Iowa',\n", + " 'ID': 'Idaho',\n", + " 'IL': 'Illinois',\n", + " 'IN': 'Indiana',\n", + " 'KS': 'Kansas',\n", + " 'KY': 'Kentucky',\n", + " 'LA': 'Louisiana',\n", + " 'MA': 'Massachusetts',\n", + " 'MD': 'Maryland',\n", + " 'ME': 'Maine',\n", + " 'MI': 'Michigan',\n", + " 'MN': 'Minnesota',\n", + " 'MO': 'Missouri',\n", + " 'MP': 'Northern Mariana Islands',\n", + " 'MS': 'Mississippi',\n", + " 'MT': 'Montana',\n", + " 'NA': 'National',\n", + " 'NC': 'North Carolina',\n", + " 'ND': 'North Dakota',\n", + " 'NE': 'Nebraska',\n", + " 'NH': 'New Hampshire',\n", + " 'NJ': 'New Jersey',\n", + " 'NM': 'New Mexico',\n", + " 'NV': 'Nevada',\n", + " 'NY': 'New York',\n", + " 'OH': 'Ohio',\n", + " 'OK': 'Oklahoma',\n", + " 'OR': 'Oregon',\n", + " 'PA': 'Pennsylvania',\n", + " 'PR': 'Puerto Rico',\n", + " 'RI': 'Rhode Island',\n", + " 'SC': 'South Carolina',\n", + " 'SD': 'South Dakota',\n", + " 'TN': 'Tennessee',\n", + " 'TX': 'Texas',\n", + " 'UT': 'Utah',\n", + " 'VA': 'Virginia',\n", + " 'VI': 'Virgin Islands',\n", + " 'VT': 'Vermont',\n", + " 'WA': 'Washington',\n", + " 'WI': 'Wisconsin',\n", + " 'WV': 'West Virginia',\n", + " 'WY': 'Wyoming'\n", + "}" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is some code to plot [State Chloropleth](http://en.wikipedia.org/wiki/Choropleth_map) maps in matplotlib. `make_map` is the function you will use." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#adapted from https://github.com/dataiap/dataiap/blob/master/resources/util/map_util.py\n", + "\n", + "#load in state geometry\n", + "state2poly = defaultdict(list)\n", + "\n", + "data = json.load(file(\"data/us-states.json\"))\n", + "for f in data['features']:\n", + " state = states_abbrev[f['id']]\n", + " geo = f['geometry']\n", + " if geo['type'] == 'Polygon':\n", + " for coords in geo['coordinates']:\n", + " state2poly[state].append(coords)\n", + " elif geo['type'] == 'MultiPolygon':\n", + " for polygon in geo['coordinates']:\n", + " state2poly[state].extend(polygon)\n", + "\n", + " \n", + "def draw_state(plot, stateid, **kwargs):\n", + " \"\"\"\n", + " draw_state(plot, stateid, color=..., **kwargs)\n", + " \n", + " Automatically draws a filled shape representing the state in\n", + " subplot.\n", + " The color keyword argument specifies the fill color. It accepts keyword\n", + " arguments that plot() accepts\n", + " \"\"\"\n", + " for polygon in state2poly[stateid]:\n", + " xs, ys = zip(*polygon)\n", + " plot.fill(xs, ys, **kwargs)\n", + "\n", + " \n", + "def make_map(states, label):\n", + " \"\"\"\n", + " Draw a cloropleth map, that maps data onto the United States\n", + " \n", + " Inputs\n", + " -------\n", + " states : Column of a DataFrame\n", + " The value for each state, to display on a map\n", + " label : str\n", + " Label of the color bar\n", + "\n", + " Returns\n", + " --------\n", + " The map\n", + " \"\"\"\n", + " fig = plt.figure(figsize=(12, 9))\n", + " ax = plt.gca()\n", + "\n", + " if states.max() < 2: # colormap for election probabilities \n", + " cmap = cm.RdBu\n", + " vmin, vmax = 0, 1\n", + " else: # colormap for electoral votes\n", + " cmap = cm.binary\n", + " vmin, vmax = 0, states.max()\n", + " norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)\n", + " \n", + " skip = set(['National', 'District of Columbia', 'Guam', 'Puerto Rico',\n", + " 'Virgin Islands', 'American Samoa', 'Northern Mariana Islands'])\n", + " for state in states_abbrev.values():\n", + " if state in skip:\n", + " continue\n", + " color = cmap(norm(states.ix[state]))\n", + " draw_state(ax, state, color = color, ec='k')\n", + "\n", + " #add an inset colorbar\n", + " ax1 = fig.add_axes([0.45, 0.70, 0.4, 0.02]) \n", + " cb1=mpl.colorbar.ColorbarBase(ax1, cmap=cmap,\n", + " norm=norm,\n", + " orientation='horizontal')\n", + " ax1.set_title(label)\n", + " remove_border(ax, left=False, bottom=False)\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " ax.set_xlim(-180, -60)\n", + " ax.set_ylim(15, 75)\n", + " return ax" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Today: the day we make the prediction" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# We are pretending to build our model 1 month before the election\n", + "import datetime\n", + "today = datetime.datetime(2012, 10, 2)\n", + "today" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 4, + "text": [ + "datetime.datetime(2012, 10, 2, 0, 0)" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Background: The Electoral College\n", + "\n", + "US Presidential elections revolve around the Electoral College . In this system, each state receives a number of Electoral College votes depending on it's population -- there are 538 votes in total. In most states, all of the electoral college votes are awarded to the presidential candidate who recieves the most votes in that state. A candidate needs 269 votes to be elected President. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thus, to calculate the total number of votes a candidate gets in the election, we add the electoral college votes in the states that he or she wins. (This is not entirely true, with Nebraska and Maine splitting their electoral college votes, but, for the purposes of this homework, we shall assume that the winner of the most votes in Maine and Nebraska gets ALL the electoral college votes there.) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is the electoral vote breakdown by state:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*As a matter of convention, we will index all our dataframes by the state name*" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "electoral_votes = pd.read_csv(\"data/electoral_votes.csv\").set_index('State')\n", + "electoral_votes.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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Votes
State
California 55
Texas 38
New York 29
Florida 29
Illinois 20
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 5, + "text": [ + " Votes\n", + "State \n", + "California 55\n", + "Texas 38\n", + "New York 29\n", + "Florida 29\n", + "Illinois 20" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To illustrate the use of `make_map` we plot the Electoral College" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "make_map(electoral_votes.Votes, \"Electoral Vlotes\");" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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M4OLFiwA0a9aMBg0a4O/vT8eOHenfvz/ly5fn66+/JiEhgVGjRgGwfv365/Yf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GXLlli/fj22bNmC/fv3Iy4ursbGNsSa1YyMDAQFBen8DkBmZmaVttDy8PBAQUGBTi/e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\ncAUgICAA0dHRyM7OR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+ "text": [ + "" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Question 1: Simulating elections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The PredictWise Baseline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will start by examining a successful forecast that [PredictWise](http://www.predictwise.com/results/2012/president) made on October 2, 2012. This will give us a point of comparison for our own forecast models.\n", + "\n", + "PredictWise aggregated polling data and, for each state, estimated the probability that the Obama or Romney would win. Here are those estimated probabilities:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "predictwise = pd.read_csv('data/predictwise.csv').set_index('States')\n", + "predictwise.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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ObamaRomneyVotes
States
Alabama 0.000 1.000 9
Alaska 0.000 1.000 3
Arizona 0.062 0.938 11
Arkansas 0.000 1.000 6
California 1.000 0.000 55
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 7, + "text": [ + " Obama Romney Votes\n", + "States \n", + "Alabama 0.000 1.000 9\n", + "Alaska 0.000 1.000 3\n", + "Arizona 0.062 0.938 11\n", + "Arkansas 0.000 1.000 6\n", + "California 1.000 0.000 55" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.1** Each row is the probability predicted by Predictwise that Romney or Obama would win a state. The votes column lists the number of electoral college votes in that state. *Use `make_map` to plot a map of the probability that Obama wins each state, according to this prediction*." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "make_map(predictwise.Obama, \"P(Obama): PredictWise\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 8, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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m1q1bR61atdi2bRsjR45k7NixN71XIW6XJKs57L13J+FWVcLQy6AqIUSBZDab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9lzvG7YzqijxpCbG/8d8MSBJFgVQohWu994dCSArt2AtXkBhOrj0rbKH45ZOIq1j91IoGp7XNpM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\nmcpJto6+VmoAG0ecS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+ "text": [ + "" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Later on in this homework we will explore some approaches to estimating probabilities like these and quatifying our uncertainty about them. But for the time being, we will focus on how to make a prediction assuming these probabilities are known.\n", + "\n", + "Even when we assume the win probabilities in each state are known, there is still uncertainty left in the election. We will use simulations from a simple probabilistic model to characterize this uncertainty. From these simulations, we will be able to make a prediction about the expected outcome of the election, and make a statement about how sure we are about it.\n", + "\n", + "**1.2** We will assume that the outcome in each state is the result of an independent coin flip whose probability of coming up Obama is given by a Dataframe of state-wise win probabilities. *Write a function that uses this **predictive model** to simulate the outcome of the election given a Dataframe of probabilities*." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "simulate_election\n", + "\n", + "Inputs\n", + "------\n", + "model : DataFrame\n", + " A DataFrame summarizing an election forecast. The dataframe has 51 rows -- one for each state and DC\n", + " It has the following columns:\n", + " Obama : Forecasted probability that Obama wins the state\n", + " Votes : Electoral votes for the state\n", + " The DataFrame is indexed by state (i.e., model.index is an array of state names)\n", + " \n", + "n_sim : int\n", + " Number of simulations to run\n", + " \n", + "Returns\n", + "-------\n", + "results : Numpy array with n_sim elements\n", + " Each element stores the number of electoral college votes Obama wins in each simulation. \n", + "\"\"\"\n", + "\n", + "#Your code here\n", + "def simulate_election(model, n_sim):\n", + " #each column simulates a single outcome from the 50 states + DC\n", + " #Obama wins the simulation if the random number is < the win probability\n", + " simulations = np.random.uniform(size=(51, n_sim))\n", + " obama_votes = (simulations < model.Obama.values.reshape(-1, 1)) * model.Votes.values.reshape(-1, 1)\n", + " #summing over rows gives the total electoral votes for each simulation\n", + " return obama_votes.sum(axis=0)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cells takes the necessary DataFrame for the Predictwise data, and runs 10000 simulations. We use the results to compute the probability, according to this predictive model, that Obama wins the election (i.e., the probability that he receives 269 or more electoral college votes)" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "result = simulate_election(predictwise, 10000)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 10 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#compute the probability of an Obama win, given this simulation\n", + "#Your code here\n", + "print (result >= 269).mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "0.9956\n" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.3** **Now, write a function called `plot_simulation` to visualize the simulation**. This function should:\n", + "\n", + "* Build a histogram from the result of simulate_election\n", + "* Overplot the \"victory threshold\" of 269 votes as a vertical black line (hint: use axvline)\n", + "* Overplot the result (Obama winning 332 votes) as a vertical red line\n", + "* Compute the number of votes at the 5th and 95th quantiles, and display the difference (this is an estimate of the outcome's uncertainty)\n", + "* Display the probability of an Obama victory \n", + " " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "plot_simulation\n", + "\n", + "Inputs\n", + "------\n", + "simulation: Numpy array with n_sim (see simulate_election) elements\n", + " Each element stores the number of electoral college votes Obama wins in each simulation.\n", + " \n", + "Returns\n", + "-------\n", + "Nothing \n", + "\"\"\"\n", + "\n", + "#your code here\n", + "\n", + "def plot_simulation(simulation): \n", + " plt.hist(simulation, bins=np.arange(200, 538, 1), \n", + " label='simulations', align='left', normed=True)\n", + " plt.axvline(332, 0, .5, color='r', label='Actual Outcome')\n", + " plt.axvline(269, 0, .5, color='k', label='Victory Threshold')\n", + " p05 = np.percentile(simulation, 5.)\n", + " p95 = np.percentile(simulation, 95.)\n", + " iq = int(p95 - p05)\n", + " pwin = ((simulation >= 269).mean() * 100)\n", + " plt.title(\"Chance of Obama Victory: %0.2f%%, Spread: %d votes\" % (pwin, iq))\n", + " plt.legend(frameon=False, loc='upper left')\n", + " plt.xlabel(\"Obama Electoral College Votes\")\n", + " plt.ylabel(\"Probability\")\n", + " remove_border()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets plot the result of the Predictwise simulation. Your plot should look something like this:\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "plot_simulation(result)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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jY+MSy7EzMzNhZmamUv3Q0FB899134g8TKxQKKBQK6Ojo4PLly/jss8/EsmPG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\nAIDt27fjnXfewe7du/HKK69g48aNSElJwSeffIKtW7di0KBBuH79eqn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+ "text": [ + "" + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluating and Validating our Forecast\n", + "\n", + "The point of creating a probabilistic predictive model is to simultaneously make a forecast and give an estimate of how certain we are about it. \n", + "\n", + "However, in order to trust our prediction or our reported level of uncertainty, the model needs to be *correct*. We say a model is *correct* if it honestly accounts for all of the mechanisms of variation in the system we're forecasting.\n", + "\n", + "In this section, we **evaluate** our prediction to get a sense of how useful it is, and we **validate** the predictive model by comparing it to real data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.4** Suppose that we believe the model is correct. Under this assumption, we can **evaluate** our prediction by characterizing its **accuracy** and **precision** (see [here](http://celebrating200years.noaa.gov/magazine/tct/accuracy_vs_precision_556.jpg) for an illustration of these ideas). *What does the above plot reveal about the **accuracy** and **precision** of the PredictWise model?*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "***Background***: To make a prediction, we take information that we have now, and try to identify likely outcomes in the future based on this information. The model we've created expresses our uncertainty as a probability distribution over the likely outcomes of the election that are consistent with the information we have now. We call this distribution over outcomes the **predictive distribution**. Simulating from this model and plotting a histogram allows us to visualize the predictive distribution. When we say a model is correct, we mean that the predictive distribution matches the true distribution of election outcomes when the information leading up to the election matches the information that we have now.\n", + "\n", + "Usually, people are interested in having a single value as the prediction (\"Obama will get 332 votes\"). To obtain a **prediction**, we summarize the predictive distribution with a single point, usually by taking its expectation. We can evaluate a prediction by its accuracy and precision.\n", + "\n", + "***Answer***: To evaluate the **accuracy** of our prediction, we can check to see whether the expectation of our predictive distribution seems to match the expectation of the true outcome. In this case, much of the predictive distribution's mass lies on or around the real outcome (that is, the histogram is approximately centered on the actual outcome of Obama=332 votes). So, based on the outcome we observed, the model seems accurate. To make a more rigorous statement about accuracy, we would want to have more replications (that is, more elections) to see whether the expectation of the predictive distribution consistently matches the true outcomes.\n", + "\n", + "To evaluate the **precision** of our prediction, we look at the spread of the histogram. Because we are assuming the model is correct, we can interpret the spread of the histogram as a measure of the variability among the election outcomes that are consistent with the information we have. If our current information does not constrain the likely election outcomes very much, then the difference between our prediction and the true outcome can vary widely. The spread of the histogram is 60 votes, which is relatively large. Whether the prediction is precise *enough* is a question of what you want to do with your prediction. For example, if you want to be able to call the winner of a close election (say the candidates are separated by less than 30 votes), this prediction would not be precise enough to identify a winner with 95% confidence. To handle this, we might wish to incorporate more information into the model to reduce the spread of likely election outcomes.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.5** Unfortunately, we can never be *absolutely sure* that a model is correct, just as we can never be absolutely sure that the sun will rise tomorrow. But we can test a model by making predictions assuming that it is true and comparing it to real events -- this constitutes a hypothesis test. After testing a large number of predictions, if we find no evidence that says the model is wrong, we can have some degree of confidence that the model is right (the same reason we're still quite confident about the sun being here tomorrow). We call this process **model checking**, and use it to **validate** our model.\n", + "\n", + "*Describe how the graph provides one way of checking whether the prediction model is correct. How many predictions have we checked in this case? How could we increase our confidence in the model's correctness?*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "***Answer***: The graph shows a hypothesis test. The histogram approximates the predictive distribution of election outcomes (in terms of electoral votes) *assuming our model for the election is true*. By comparing this to the true outcome of the election, we can see whether the observed electoral vote count would be highly atypical if the model were true. In this case, it appears the true outcome is quite typical among the model's predicted outcomes, so we do not reject our model.\n", + "\n", + "In this case, we have checked only one prediction, since there is only one true outcome that we've compared to the predictive distribution.\n", + "\n", + "To increase our confidence, we would want to test more outcomes against predictions that were made in the same way. For example, we could apply the same procedure (including PredictWise's computation of the statewise probability estimates) to different elections and see whether each of the hypothesis tests fails to reject the model in those cases as well. We could also break the election down into state-by-state outcomes, and test the prediction for each state against that state's outcome.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gallup Party Affiliation Poll" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will try to **estimate** our own win probabilities to plug into our predictive model.\n", + "\n", + "We will start with a simple forecast model. We will try to predict the outcome of the election based the estimated proportion of people in each state who identify with one one political party or the other.\n", + "\n", + "Gallup measures the political leaning of each state, based on asking random people which party they identify or affiliate with. [Here's the data](http://www.gallup.com/poll/156437/heavily-democratic-states-concentrated-east.aspx#2) they collected from January-June of 2012:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "gallup_2012=pd.read_csv(\"data/g12.csv\").set_index('State')\n", + "gallup_2012[\"Unknown\"] = 100 - gallup_2012.Democrat - gallup_2012.Republican\n", + "gallup_2012.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DemocratRepublicanDem_AdvNUnknown
State
Alabama 36.0 49.6-13.6 3197 14.4
Alaska 35.9 44.3 -8.4 402 19.8
Arizona 39.8 47.3 -7.5 4325 12.9
Arkansas 41.5 40.8 0.7 2071 17.7
California 48.3 34.6 13.7 16197 17.1
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 14, + "text": [ + " Democrat Republican Dem_Adv N Unknown\n", + "State \n", + "Alabama 36.0 49.6 -13.6 3197 14.4\n", + "Alaska 35.9 44.3 -8.4 402 19.8\n", + "Arizona 39.8 47.3 -7.5 4325 12.9\n", + "Arkansas 41.5 40.8 0.7 2071 17.7\n", + "California 48.3 34.6 13.7 16197 17.1" + ] + } + ], + "prompt_number": 14 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each row lists a state, the percent of surveyed individuals who identify as Democrat/Republican, the percent whose identification is unknown or who haven't made an affiliation yet, the margin between Democrats and Republicans (`Dem_Adv`: the percentage identifying as Democrats minus the percentage identifying as Republicans), and the number `N` of people surveyed.\n", + "\n", + "**1.6** This survey can be used to predict the outcome of each State's election. The simplest forecast model assigns 100% probability that the state will vote for the majority party. *Implement this simple forecast*." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "simple_gallup_model\n", + "\n", + "A simple forecast that predicts an Obama (Democratic) victory with\n", + "0 or 100% probability, depending on whether a state\n", + "leans Republican or Democrat.\n", + "\n", + "Inputs\n", + "------\n", + "gallup : DataFrame\n", + " The Gallup dataframe above\n", + "\n", + "Returns\n", + "-------\n", + "model : DataFrame\n", + " A dataframe with the following column\n", + " * Obama: probability that the state votes for Obama. All values should be 0 or 1\n", + " model.index should be set to gallup.index (that is, it should be indexed by state name)\n", + " \n", + "Examples\n", + "---------\n", + ">>> simple_gallup_model(gallup_2012).ix['Florida']\n", + "Obama 1\n", + "Name: Florida, dtype: float64\n", + ">>> simple_gallup_model(gallup_2012).ix['Arizona']\n", + "Obama 0\n", + "Name: Arizona, dtype: float64\n", + "\"\"\"\n", + "\n", + "#your code here\n", + "def simple_gallup_model(gallup):\n", + " return pd.DataFrame(dict(Obama=(gallup.Dem_Adv > 0).astype(float)))" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 15 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we run the simulation with this model, and plot it." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "model = simple_gallup_model(gallup_2012)\n", + "model = model.join(electoral_votes)\n", + "prediction = simulate_election(model, 10000)\n", + "\n", + "plot_simulation(prediction)\n", + "plt.show()\n", + "make_map(model.Obama, \"P(Obama): Simple Model\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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pIyIiItIDTNqIiIiI9ACTNiIiIiI9wKSNiIiISA/k6btHKfd4eHjA2toan376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\nJZ/bnvz4449WvXp1mzx5sjPm9XqdWyGdTQMGDLCaNWsWGO/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PdSqViqCgILy8vNxDAZKTkzPd32w2oygKp06dumnb3U7FJR5eUqzmsOXLl9On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\ncLmuy/RTZrI5XIouHJPUOTYex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+ "text": [ + "" + ] + } + ], + "prompt_number": 16 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.7** Attempt to **validate** the predictive model using the above simulation histogram. *Does the evidence contradict the predictive model?*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "***Answer***: We do not predict the exactly correct result (red line). According to the predictive model, the true outcome has probability 0. Thus, the evidence contradicts the predictive model, and we should reject it.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Adding Polling Uncertainty to the Predictive Model\n", + "\n", + "The model above is brittle -- it includes no accounting for uncertainty, and thus makes predictions with 100% confidence. This is clearly wrong -- there are numerous sources of uncertainty in estimating election outcomes from a poll of affiliations. \n", + "\n", + "The most obvious source of error in the Gallup data is the finite sample size -- Gallup did not poll *everybody* in America, and thus the party affilitions are subject to sampling errors. How much uncertainty does this introduce?\n", + "\n", + "On their [webpage](http://www.gallup.com/poll/156437/heavily-democratic-states-concentrated-east.aspx#2) discussing these data, Gallup notes that the sampling error for the states is between 3 and 6%, with it being 3% for most states. (The calculation of the sampling error itself is an exercise in statistics. Its fun to think of how you could arrive at the sampling error if it was not given to you. One way to do it would be to assume this was a two-choice situation and use binomial sampling error for the non-unknown answers, and further model the error for those who answered 'Unknown'.)\n", + "\n", + "**1.8** Use Gallup's estimate of 3% to build a Gallup model with some uncertainty. Assume that the `Dem_Adv` column represents the mean of a Gaussian, whose standard deviation is 3%. Build the model in the function `uncertain_gallup_model`. *Return a forecast where the probability of an Obama victory is given by the probability that a sample from the `Dem_Adv` Gaussian is positive.*\n", + "\n", + "\n", + "**Hint**\n", + "The probability that a sample from a Gaussian with mean $\\mu$ and standard deviation $\\sigma$ exceeds a threhold $z$ can be found using the the Cumulative Distribution Function of a Gaussian:\n", + "\n", + "$$\n", + "CDF(z) = \\frac1{2}\\left(1 + {\\rm erf}\\left(\\frac{z - \\mu}{\\sqrt{2 \\sigma^2}}\\right)\\right) \n", + "$$\n" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "uncertain_gallup_model\n", + "\n", + "A forecast that predicts an Obama (Democratic) victory if the random variable drawn\n", + "from a Gaussian with mean Dem_Adv and standard deviation 3% is >0\n", + "\n", + "Inputs\n", + "------\n", + "gallup : DataFrame\n", + " The Gallup dataframe above\n", + "\n", + "Returns\n", + "-------\n", + "model : DataFrame\n", + " A dataframe with the following column\n", + " * Obama: probability that the state votes for Obama.\n", + " model.index should be set to gallup.index (that is, it should be indexed by state name)\n", + "\"\"\"\n", + "# your code here\n", + "from scipy.special import erf\n", + "def uncertain_gallup_model(gallup):\n", + " sigma = 3\n", + " prob = .5 * (1 + erf(gallup.Dem_Adv / np.sqrt(2 * sigma**2)))\n", + " return pd.DataFrame(dict(Obama=prob), index=gallup.index)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 17 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We construct the model by estimating the probabilities:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "model = uncertain_gallup_model(gallup_2012)\n", + "model = model.join(electoral_votes)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 18 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once again, we plot a map of these probabilities, run the simulation, and display the results" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "make_map(model.Obama, \"P(Obama): Gallup + Uncertainty\")\n", + "plt.show()\n", + "prediction = simulate_election(model, 10000)\n", + "plot_simulation(prediction)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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eGcITqh9PWX1uey4rLk5pzQRWrcjqfw7zqqUg2kfkTVg6FIq7DChA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\nXgbrpCbj19qG4UehBDsd23fA5XLdc2z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oZDniGDL2Ivr27cuiRYv4asKbpGh2Om4s4ffN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\nBlNSUoJpmvz4w0q++PxzRlca9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s6qCgIPz9998ICwvDli1bOOhj7AOqsJsq169fv9Sy8adPn8LQ0FDc1tbWhpqa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\naGtfReJf0x1ybndsbKw0vQEARUVFiIuLw/jx47U+sk8O7Y6OjkZxcTH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+ "text": [ + "" + ] + } + ], + "prompt_number": 19 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.9** *Attempt to **validate** the above model using the histogram. Does the predictive distribution appear to be consistent with the real data? Comment on the accuracy and precision of the prediction.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "***Answer***: The predictive distribution is consistent with the real data -- the real outcome seems like a typical outcome according to the model. The accuracy is not very good as the center of the distribution falls fairly far from the observed outcome, but the precision is only marginally worse than in the predictwise case.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Biases\n", + "\n", + "While accounting for uncertainty is one important part of making predictions, we also want to avoid systematic errors. We call systematic over- or under-estimation of an unknown quantity **bias**. In the case of this forecast, our predictions would be biased if the estimates from this poll *systematically* over- or under-estimate vote proportions on election day. There are several reasons this might happen:\n", + "\n", + "1. **Gallup is wrong**. The poll may systematically over- or under-estimate party affiliation. This could happen if the people who answer Gallup phone interviews might not be a representative sample of people who actually vote, Gallup's methodology is flawed, or if people lie during a Gallup poll.\n", + "1. **Our assumption about party affiliation is wrong**. Party affiliation may systematically over- or under-estimate vote proportions. This could happen if people identify with one party, but strongly prefer the candidate from the other party, or if undecided voters do not end up splitting evenly between Democrats and Republicans on election day.\n", + "1. **Our assumption about equilibrium is wrong**. This poll was released in August, with more than two months left for the elections. If there is a trend in the way people change their affiliations during this time period (for example, because one candidate is much worse at televised debates), an estimate in August could systematically miss the true value in November.\n", + "\n", + "One way to account for bias is to calibrate our model by estimating the bias and adjusting for it. Before we do this, let's explore how sensitive our prediction is to bias." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.10** *Implement a `biased_gallup` forecast, which assumes the vote share for the Democrat on election day will be equal to `Dem_Adv` shifted by a fixed negative amount.* We will call this shift the \"bias\", so a bias of 1% means that the expected vote share on election day is `Dem_Adv`-1.\n", + "\n", + "**Hint** You can do this by wrapping the `uncertain_gallup_model` in a function that modifies its inputs." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "biased_gallup_poll\n", + "\n", + "Subtracts a fixed amount from Dem_Adv, beofore computing the uncertain_gallup_model.\n", + "This simulates correcting a hypothetical bias towards Democrats\n", + "in the original Gallup data.\n", + "\n", + "Inputs\n", + "-------\n", + "gallup : DataFrame\n", + " The Gallup party affiliation data frame above\n", + "bias : float\n", + " The amount by which to shift each prediction\n", + " \n", + "Examples\n", + "--------\n", + ">>> model = biased_gallup(gallup, 1.)\n", + ">>> model.ix['Flordia']\n", + ">>> .460172\n", + "\"\"\"\n", + "#your code here\n", + "def biased_gallup(gallup, bias):\n", + " g2 = gallup.copy()\n", + " g2.Dem_Adv -= bias\n", + " return uncertain_gallup_model(g2)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 20 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.11** *Simulate elections assuming a bias of 1% and 5%, and plot histograms for each one.*" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "model = biased_gallup(gallup_2012, 1)\n", + "model = model.join(electoral_votes)\n", + "prediction = simulate_election(model, 10000)\n", + "plot_simulation(prediction)\n", + "plt.show()\n", + "\n", + "model = biased_gallup(gallup_2012, 5)\n", + "model = model.join(electoral_votes)\n", + "prediction = simulate_election(model, 10000)\n", + "plot_simulation(prediction)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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qSsnJyQV+XzMyMkhTU5PmzJkjXqsBAwaQhYUF5eTk0MmTJ0lTU1P84lba4JZ9nZRL7vtj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\njywSkYmsXLlSVCqVXLhwoUbd77//LiqVSl599VUREfHw8JC5c+dKRUW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jRoxA3bp1YW9vj8mTJ6tPiv/222/x9OlTmJqaIj4+Xu3Za9++Pc6dOwcPDw8U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\n33Nycny+X3x5vGRmZprRo0ebS5cumV9++cU0bdrUjBkzxhj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+ "text": [ + "" + ] + } + ], + "prompt_number": 21 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that even a small bias can have a dramatic effect on the predictions. Pundits made a big fuss about bias during the last election, and for good reason -- it's an important effect, and the models are clearly sensitive to it. Forecastors like Nate Silver would have had an easier time convincing a wide audience about their methodology if bias wasn't an issue.\n", + "\n", + "Furthermore, because of the nature of the electoral college, biases get blown up large. For example, suppose you mis-predict the party Florida elects. We've possibly done this as a nation in the past :-). Thats 29 votes right there. So, the penalty for even one misprediction is high." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Estimating the size of the bias from the 2008 election\n", + "\n", + "While bias can lead to serious inaccuracy in our predictions, it is fairly easy to correct *if* we are able to estimate the size of the bias and adjust for it. This is one form of **calibration**.\n", + "\n", + "One approach to calibrating a model is to use historical data to estimate the bias of a prediction model. We can use our same prediction model on historical data and compare our historical predictions to what actually occurred and see if, on average, the predictions missed the truth by a certain amount. Under some assumptions (discussed in a question below), we can use the estimate of the bias to adjust our current forecast.\n", + "\n", + "In this case, we can use data from the 2008 election. (The Gallup data from 2008 are from the whole of 2008, including after the election):" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "gallup_08 = pd.read_csv(\"data/g08.csv\").set_index('State')\n", + "results_08 = pd.read_csv('data/2008results.csv').set_index('State')\n", + "\n", + "prediction_08 = gallup_08[['Dem_Adv']]\n", + "prediction_08['Dem_Win']=results_08[\"Obama Pct\"] - results_08[\"McCain Pct\"]\n", + "prediction_08.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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Dem_AdvDem_Win
State
Alabama -0.8-21.58
Alaska-10.6-21.53
Arizona -0.4 -8.52
Arkansas 12.5-19.86
California 19.4 24.06
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 22, + "text": [ + " Dem_Adv Dem_Win\n", + "State \n", + "Alabama -0.8 -21.58\n", + "Alaska -10.6 -21.53\n", + "Arizona -0.4 -8.52\n", + "Arkansas 12.5 -19.86\n", + "California 19.4 24.06" + ] + } + ], + "prompt_number": 22 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.12** *Make a scatter plot using the `prediction_08` dataframe of the democratic advantage in the 2008 Gallup poll (X axis) compared to the democratic win percentage -- the difference between Obama and McCain's vote percentage -- in the election (Y Axis). Overplot a linear fit to these data.*\n", + "\n", + "**Hint**\n", + "The `np.polyfit` function can compute linear fits, as can `sklearn.linear_model.LinearModel`" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "\n", + "plt.plot(prediction_08.Dem_Adv, prediction_08.Dem_Win, 'o')\n", + "plt.xlabel(\"2008 Gallup Democrat Advantage\")\n", + "plt.ylabel(\"2008 Election Democrat Win\")\n", + "fit = np.polyfit(prediction_08.Dem_Adv, prediction_08.Dem_Win, 1)\n", + "x = np.linspace(-40, 80, 10)\n", + "y = np.polyval(fit, x)\n", + "plt.plot(x, y)\n", + "print fit" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[ 1.26390486 -11.32855786]\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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l0CbigpLkFpfD20MPPcTDDz/MPffcU6LiSovCm4iIZzntF0hdP4+klTNwpJwA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\nYGpqqjM4QaPRAADvbyMiIqIni9SRa9euiZOT031f48aNk+3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+ "text": [ + "" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that a lot of states in which Gallup reported a Democratic affiliation, the results were strongly in the opposite direction. Why might that be? You can read more about the reasons for this [here](http://www.gallup.com/poll/114016/state-states-political-party-affiliation.aspx#1)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A quick look at the graph will show you a number of states where Gallup showed a Democratic advantage, but where the elections were lost by the democrats. Use Pandas to list these states." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "prediction_08[(prediction_08.Dem_Win < 0) & (prediction_08.Dem_Adv > 0)]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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Dem_AdvDem_Win
State
Arkansas 12.5-19.86
Georgia 3.6 -5.21
Kentucky 13.5-16.23
Louisiana 9.4-18.63
Mississippi 1.1-13.18
Missouri 10.9 -0.14
Montana 3.9 -2.26
North Dakota 0.6 -8.63
Oklahoma 5.6-31.30
South Carolina 0.1 -8.97
South Dakota 1.3 -8.41
Tennessee 5.0-15.07
Texas 2.4-11.77
West Virginia 18.8-13.12
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 24, + "text": [ + " Dem_Adv Dem_Win\n", + "State \n", + "Arkansas 12.5 -19.86\n", + "Georgia 3.6 -5.21\n", + "Kentucky 13.5 -16.23\n", + "Louisiana 9.4 -18.63\n", + "Mississippi 1.1 -13.18\n", + "Missouri 10.9 -0.14\n", + "Montana 3.9 -2.26\n", + "North Dakota 0.6 -8.63\n", + "Oklahoma 5.6 -31.30\n", + "South Carolina 0.1 -8.97\n", + "South Dakota 1.3 -8.41\n", + "Tennessee 5.0 -15.07\n", + "Texas 2.4 -11.77\n", + "West Virginia 18.8 -13.12" + ] + } + ], + "prompt_number": 24 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We compute the average difference between the Democrat advantages in the election and Gallup poll" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print (prediction_08.Dem_Adv - prediction_08.Dem_Win).mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "8.06803921569\n" + ] + } + ], + "prompt_number": 25 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "***Answer*** The bias was roughly 8% in favor of the Democrats in the Gallup Poll, meaning that you would want to adjust predictions based on this poll down by that amount. This was the result of people in a number of Southern and Western states claiming to be affiliated as Democrats, then voting the other way. Or, since Gallup kept polling even after the elections, it could also represent people swept away by the 2008 election euphoria in those states. This is an illustration of why one needs to be carefull with polls.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.13** * **Calibrate** your forecast of the 2012 election using the estimated bias from 2008. Validate the resulting model against the real 2012 outcome. Did the calibration help or hurt your prediction?*" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "model = biased_gallup(gallup_2012, 8.06)\n", + "model = model.join(electoral_votes)\n", + "prediction = simulate_election(model, 10000)\n", + "plot_simulation(prediction)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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LCwOQPifv6tWr6NSpU16dAhEREdEHKc9G6BRFwZ49ezBr1iyEhobi/Pnz2Ldv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\nybNHjlU23333nQYNGqT09HSnR6VJVx6XlpaW5nKbL/TLwIE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+ "text": [ + "" + ] + } + ], + "prompt_number": 26 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "***Answer***: This was a disaster. The 8% calibration completey destroys the accuracy of our prediction in 2012. Our calibration made the assumptions that a) the bias in 2008 was the same as 2012, and b) the bias in each state was the same.\n", + "\n", + "There are several ways in which these assumptions may have been violated. Gallup may have changed their methodology to account for this bias already, leading to a different bias in 2012 from what there was in 2008. The state-by-state biases may have also been different -- voters in highly conservative states may have responded to polls differently from voters in libreral states, for instance. It might have been better to callibrate the bias on a state-wide or clustered basis.\n", + "\n", + "\n", + "*Note: The \"your answer here\" box was missing for this question.*\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.14** *Finally, given that we know the actual outcome of the 2012 race, and what you saw from the 2008 race would you trust the results of the an election forecast based on the 2012 Gallup party affiliation poll?*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "***Answer***: No. You should answer this question as though you had not yet seen the results of the 2012 election. The results from 2008 would suggest that the party affiliation poll is a highly biased predictor of the acutal election outcome. Given that calibrating the model to counteract this bias would rely on unrealistic assumptions, it would seem unwise to use the 2012 party affiliation poll to predict the election.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Question 2: Logistic Considerations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous forecast, we used the strategy of taking some side-information about an election (the partisan affiliation poll) and relating that to the predicted outcome of the election. We tied these two quantities together using a very simplistic assumption, namely that the vote outcome is deterministically related to estimated partisan affiliation.\n", + "\n", + "In this section, we use a more sophisticated approach to link side information -- usually called **features** or **predictors** -- to our prediction. This approach has several advantages, including the fact that we may use multiple features to perform our predictions. Such data may include demographic data, exit poll data, and data from previous elections.\n", + "\n", + "First, we'll construct a new feature called PVI, and use it and the Gallup poll to build predictions. Then, we'll use **logistic regression** to estimate win probabilities, and use these probabilities to build a prediction." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The Partisan Voting Index\n", + "\n", + "The Partisan Voting Index (PVI) is defined as the excessive swing towards a party in the previous election in a given state. In other words:\n", + "\n", + "$$\n", + "PVI_{2008} (state) = \n", + "Democratic.Percent_{2004} ( state ) - Republican.Percent_{2004} ( state) - \\\\ \n", + " \\Big ( Democratic.Percent_{2004} (national) - Republican.Percent_{2004} (national) \\Big )\n", + "$$\n", + "\n", + "To calculate it, let us first load the national percent results for republicans and democrats in the last 3 elections and convert it to the usual `democratic - republican` format." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "national_results=pd.read_csv(\"data/nat.csv\")\n", + "national_results.set_index('Year',inplace=True)\n", + "national_results.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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DemRep
Year
2004 48 51
2008 53 46
2012 51 47
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 27, + "text": [ + " Dem Rep\n", + "Year \n", + "2004 48 51\n", + "2008 53 46\n", + "2012 51 47" + ] + } + ], + "prompt_number": 27 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us also load in data about the 2004 elections from `p04.csv` which gets the results in the above form for the 2004 election for each state." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "polls04=pd.read_csv(\"data/p04.csv\")\n", + "polls04.State=polls04.State.replace(states_abbrev)\n", + "polls04.set_index(\"State\", inplace=True);\n", + "polls04.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DemRep
State
Alabama 37 63
Alaska 34 62
Arizona 44 55
Arkansas 45 54
California 54 45
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 28, + "text": [ + " Dem Rep\n", + "State \n", + "Alabama 37 63\n", + "Alaska 34 62\n", + "Arizona 44 55\n", + "Arkansas 45 54\n", + "California 54 45" + ] + } + ], + "prompt_number": 28 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pvi08=polls04.Dem - polls04.Rep - (national_results.xs(2004)['Dem'] - national_results.xs(2004)['Rep'])\n", + "pvi08.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 29, + "text": [ + "State\n", + "Alabama -23\n", + "Alaska -25\n", + "Arizona -8\n", + "Arkansas -6\n", + "California 12\n", + "dtype: int64" + ] + } + ], + "prompt_number": 29 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.1** *Build a new DataFrame called `e2008`.* The dataframe `e2008` must have the following columns:\n", + "\n", + "* a column named pvi with the contents of the partisan vote index `pvi08`\n", + "* a column named `Dem_Adv` which has the Democratic advantage from the frame `prediction_08` of the last question **with the mean subtracted out**\n", + "* a column named `obama_win` which has a 1 for each state Obama won in 2008, and 0 otherwise\n", + "* a column named `Dem_Win` which has the 2008 election Obama percentage minus McCain percentage, also from the frame `prediction_08`\n", + "* **The DataFrame should be indexed and sorted by State**" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "e2008=pd.DataFrame(dict(pvi=pvi08, Dem_Win = prediction_08.Dem_Win, Dem_Adv=prediction_08.Dem_Adv-prediction_08.Dem_Adv.mean()))\n", + "e2008['obama_win']=1*(prediction_08.Dem_Win > 0)\n", + "e2008 = e2008.sort_index()\n", + "e2008.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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Dem_AdvDem_Winpviobama_win
State
Alabama-13.154902-21.58-23 0
Alaska-22.954902-21.53-25 0
Arizona-12.754902 -8.52 -8 0
Arkansas 0.145098-19.86 -6 0
California 7.045098 24.06 12 1
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 30, + "text": [ + " Dem_Adv Dem_Win pvi obama_win\n", + "State \n", + "Alabama -13.154902 -21.58 -23 0\n", + "Alaska -22.954902 -21.53 -25 0\n", + "Arizona -12.754902 -8.52 -8 0\n", + "Arkansas 0.145098 -19.86 -6 0\n", + "California 7.045098 24.06 12 1" + ] + } + ], + "prompt_number": 30 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We construct a similar frame for 2012, obtaining `pvi` using the 2008 Obama win data which we already have. There is no `obama_win` column since, well, our job is to predict it!" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pvi12 = e2008.Dem_Win - (national_results.xs(2008)['Dem'] - national_results.xs(2008)['Rep'])\n", + "e2012 = pd.DataFrame(dict(pvi=pvi12, Dem_Adv=gallup_2012.Dem_Adv - gallup_2012.Dem_Adv.mean()))\n", + "e2012 = e2012.sort_index()\n", + "e2012.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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Dem_Advpvi
State
Alabama-14.684314-28.58
Alaska -9.484314-28.53
Arizona -8.584314-15.52
Arkansas -0.384314-26.86
California 12.615686 17.06
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 31, + "text": [ + " Dem_Adv pvi\n", + "State \n", + "Alabama -14.684314 -28.58\n", + "Alaska -9.484314 -28.53\n", + "Arizona -8.584314 -15.52\n", + "Arkansas -0.384314 -26.86\n", + "California 12.615686 17.06" + ] + } + ], + "prompt_number": 31 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We load in the actual 2012 results so that we can compare our results to the predictions." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "results2012 = pd.read_csv(\"data/2012results.csv\")\n", + "results2012.set_index(\"State\", inplace=True)\n", + "results2012 = results2012.sort_index()\n", + "results2012.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Winner
State
Alabama 0
Alaska 0
Arizona 0
Arkansas 0
California 1
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 32, + "text": [ + " Winner\n", + "State \n", + "Alabama 0\n", + "Alaska 0\n", + "Arizona 0\n", + "Arkansas 0\n", + "California 1" + ] + } + ], + "prompt_number": 32 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exploratory Data Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.2** Lets do a little exploratory data analysis. *Plot a scatter plot of the two PVi's against each other. What are your findings? Is the partisan vote index relatively stable from election to election?*" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "plt.plot(e2008.pvi, e2012.pvi, 'o', label='Data')\n", + "fit = np.polyfit(e2008.pvi, e2012.pvi, 1)\n", + "x = np.linspace(-40, 80, 10)\n", + "y = np.polyval(fit, x)\n", + "plt.plot(x, x, '--k', alpha=.3, label='x=y')\n", + "plt.plot(x, y, label='Linear fit')\n", + "plt.xlabel(\"2004 PVI\")\n", + "plt.ylabel(\"2008 PVI\")\n", + "plt.legend(loc='upper left')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 33, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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H1WWJuD0FORERcRnDkc+pFc+TtnYqABVbPEKVgXMx+wS4uDIRz6AgJyIipS49\nPZ3EQ3sJ2jyJnH1fgdlC2CPTCO74pLbaErkOCnIiIlKqEhISOLhlJRHbXiEn5ySWimFEjlyCf4O2\nri5NxOMoyImISKm4uD7c6U3ziP5pDmZHLj4xzYkasxyv0KquLk/EIynIiYiI09ntdnZ+vx3LpmlE\nHvkCgKC2Qwnr+xZmLx8XVyfiuRTkRETE+bJOEPjv0Xif3A9WH6r0m0lQm0GurkrE4ynIiYiIU+XE\nf03yO73xzjyBJaQq0WOW41uruavLEikTFORERMQpDMMgff1MTi75Bzjy8WtwH5EjFmENDHN1aSJl\nhoKciIiUKJvNhinPxulPRpL13WIAQjr/g8o9JmOy6MeOSEly283rHA4H7dq1Y8uWLQAkJiYycuRI\n5syZQ//+/dm3b5+LKxQRkUulp6ez/T9LOTyxOVnfLcbkE0DkyCWEPTJVIU7ECdz2/6rZs2ezZ88e\nTCYThmHQvXt3pk6dSvv27WnTpg1dunTh0KFDWCwWV5cqIiJcWB/u8LoPqfLddCy52XhVqUvU2BX4\nRDd0dWkiZZZbtsh9++23xMTEEBgYCMCGDRuIi4ujbdu2ADRo0AAvLy9WrVrlwipFRAQujIXb+8se\nEhb/k8hvJmHJzSagaXeqv/C9QpyIk7ldkDt9+jTbtm3jgQceAC58g9i6dSsxMTFYrf9rQKxXrx4b\nN250VZkiIvJ/jsT9jH3RUCrvX4zJBJX++jJRY1Zg8Q9ydWkiZZ7bda2+9dZbPP/884WOnThxgqCg\nwt8QgoKCOH78eGmWJiIil7Ad34t5wSNUSP0Vk38wUcMXEtD4z64uS6TccKsgN3fuXPr27Yu3t3eh\n4xaLBS8vr0LHHA5HaZYmIiKXyPp+KSkfDMaw5+BT/XYiR8fiHV7L1WWJlCtuF+TGjh1b8G+bzUbH\njh0xDIOGDQuPs0hPT6dmzZpF3ufFF18s+Hvbtm0LxtaJiMjNM/LzOBX7T9K+eAOAiq36UmXAHMw+\n/i6uTMT9bd68mc2bN5fY/UyGYRgldrcSFhMTw0cffYSXlxedOnUiMzOz4LXatWvz6quv0qtXr0LX\nXJzlKiIiJctmsxG3ayuhW17mfPzXYLES1vt1gtuPwmQyubo8EY90s7nFrVrkrqRly5bUqFGDTZs2\n0a5dOw4cOEBOTg7dunVzdWki4kbWbljH7NiF2HHgjZkRPfvSpX1HV5dVJqSnp7Pni0+ptGUS58+d\nxhIUQdTK4iGXAAAgAElEQVSopfjVu9fVpYmUax4R5EwmE6tXr2bSpEnExcWxY8cO1qxZg5+fn6tL\nExE3sXbDOsbPn0Fah0YFx8bPnwGgMHeTEhIS+G3VdMJ/moPZkYdP7ZZEj47FGhLl6tJEyj237lq9\nEepaFSmfug7rz+6WkZcdb/p9Cp/PWVD6BZURe3fvImfVswQfWw9A0H0jCO/zBiar91WuFJFrUS66\nVkVErsZO0TPZbUZ+KVdSduSeTsC6ZCDBKXvB6kPEE3MIvOdxV5clIn+gICciZYL3FdY39zFpG78b\nkRO3ieR3e2POOoUltDrRT36Gb42mri5LRC7hdjs7iIjciBE9+xKyfm+hYyHr9jK8Rx8XVeSZDMPg\nzH9f5/i0juRnncK/UQdqTtqpECfipjRGTkTKjLUb1jFn+SJsRj4+JgvDe/TRRIdrZBgG2WknyVw8\nhrM/LAcgtOs/qfTXlzCZ1aop4iw3m1sU5EREyjmbzcZPX62k4pcT8Mr4DbNvRSKGLKBCs7+4ujSR\nMk+THURE5Ialp6ezb+VMQrdOw5KXg6VKfar9bSXekfVdXZqIXAMFORGRcur3346RtGQCYXFLAfC/\n4y9EDVmA2a+iawsTkWumICciUg6lJR3j1Ls9CT3xI4bJTOUekwl94ClttSXiYRTkRETKGdvvP5M+\n82EqnDwK/iFUG70U/1vvd3VZInIDFORERMqRzG2fcmLBcAz7OXxqNiNqdCxelWu4uiwRuUEKciIi\n5YCRl8vJJf8gfcMsAAL/NIDwfu9g9vZ1cWUicjMU5EREyjDDMNi/Ywt+XzxL3tHvwOJF+GNvE9R2\nqMbDiZQBCnIiImWUzWbj57ULqLDuWfLOp2EJjiJqdCx+dVq6ujQRKSEKciIiZVBaWhrxi14kZOcc\nTEYeXrVbUW3sCqxBVVxdmoiUIAU5EZEy5vixw6R8OJzQ3zYCUPH+MUQ8+homq5eLKxORkqYgJyJS\nhuSePMb59/9KYNJeDKsvEQPnEnR3H1eXJSJOoiAnIlJGZO9dR/Lsvjiyz2CpHEPVJz/Dp1pjV5cl\nIk6kICci4uEMwyBt7VROrXgODIOAxp2JGPYJloAQV5cmIk6mICci4sEyTyaRtXg02T+uBqDSX14g\ntPtzmMxmF1cmIqVBQU5ExAMZhsG+rz+H5aPxzkrE7BdExLCPqXB7V1eXJiKlSEFORKSErN2wjtmx\nC7HjwBszI3r2pUv7jiX+HJvNxi+xr1Nh82TMeecxValP9f/3b7yr1CnxZ4mIe1OQExEpAWs3rGP8\n/BmkdWhUcGz8/BkAJRrm0k6f4tf5YwjatwwAn6YPUW34R5h9AkrsGSLiOUyGYRiuLqIkmUwmythb\nEhEP0HVYf3a3jLzseNPvU/h8zoISeUZuRir7X30Av5SfMEwWQnu8SuUH/p+22hLxYDebW9QiJyJS\nAuw4ijxuM/JL5P7nj+0iaWYP/E7/juEfStUxsQQ0aFsi9xYRz6UgJyJSArwpepaoj8ly0/fO+OZD\nUj8ahZFnw7dWCyJHL8MrtOpN31dEPF+x89M3bdp01Rt8/fXXJVaMiIinGtGzLyHr9xY6FrJuL8N7\n3PiuCo5cGyc+GsmJDwZj5NkIajuUqv/cpBAnIgWKHSPXrl07+vXrh9VadMOd3W7n008/ZfPmzc6q\n77ppjJyIuMraDeuYs3wRNiMfH5OF4T363PBEh9/3/YBt8XCM47sxWX0If3wWQa0HlnDFIuJqN5tb\nig1y5mtcUNLhKHpsiCsoyImIJzMMg/1ffIxp1d+x2jKwhFQleuwKfGPudHVpIuIEN5tbik1qEydO\nJDc3F4fDUeQfm83GCy+8cMMPFxGR/zl//jw/vf93rMsGY7VlYK51DzVe2qkQJyJXVGyLXEJCAtWq\nVSv2BomJiURHR5d4YTdKLXIi4onSTyZz9J1+BBy7MDbZ774xVO07HZNFc9JEyjKntsh9/PHHV72B\nO4U4ERFPZE89TNpbnQg4tgnD6kfloQup9vhbCnEiclXFtsgFBATQpk0b+vbty1//+lf8/PxKs7Yb\nohY5EfEkZ3/+Dynv9cORk44lvA7RY1bgW63R1S8UkTLBqS1yCxYsYOXKlXh5eTFgwACGDBmi5UZE\nREqA4XBwetUkkt7qjiMnnYCm3an54g6FOBG5Lte1RVdSUhIff/wxX3/9NS1atODxxx8nJibGmfVd\nN7XIiYi7y0w9TtbCkWT/vBZMJio9NInQruMxXeNKASJSdjh1+ZGi5OTksHDhQp599llOnTrF4MGD\nef/992+4gJKmICci7uy3H9aT9dETeJ9NxhwQQuTwhQTc1snVZYmIizh1r9Vz584VjIs7ePAg7777\nLh999BFZWVl06dKF0aNH06FDhxt+uIhIeWEYBnErpmP+7wt459twhNcn5qm1eIW5V6+GiHiWYoPc\njBkzqFWrFu+//z4bN24kJCSEoUOHMnLkSGrUqFFaNYqIeLTzOdnEzx6C3y9LATA3eYg6Iz/G7OPv\n4spExNNd084OTZs2ZfTo0Tz66KP4+vqWWnE3Ql2rIuJO8jJTOTi1K9bEXRgmCxUfmkxkt39gMplc\nXZqIuAGndq02aNCA2bNn07p16xt+gIhIeXXu8Pckz+qJNS0Rh38lIkctI6hhW1eXJSJlSLEtcrt2\n7aJZs2alWc9NU4uciLiaYRhkbJnLyU+fxMiz41v3HqJGLcUaHOnq0kTEzTh1HbmGDRvy2GOPERwc\nTK1atZg1a9YNP0hEpDxw2M9z4sOhpC4YgZFnJ/j+UVR7ZoNCnIg4RbFdq9OnT2fHjh088cQTZGdn\nM2HCBIKDg3nsscdKqz4REY9x+uheTs55FPOJ/Zi8fKkyYA6B9/RzdVkiUoYV27XaoUMHVq1aRUBA\nAHChq3XSpEmsXr261Aq8XupaFRFXOLZ5CecWj8Biy4TgqlT/+yp8azR1dVki4uac2rUaExNTEOIA\nmjVrRlBQUKFz9uzZc8MPFxHxdA6Hg/3z/4FtQT8stkwcNe8mZtIuhTgRKRXFdq0eO3as0N6qhmGQ\nmZlZcCw3N5d58+axePFi51YpIuKGbFlnOPhmL3yObALA0noUdQe8iclscXFlIlJeXNM6csXewGQi\nPz+/xArasmULY8eO5ejRo7Rq1Yp58+ZRrVo1EhMTmTx5Mo0bN2b79u08/fTTNGzYsMh61LUqIs5m\nT44naWYP7En7cVj9CX58DhGt+xa8vnbDOmbHLsSOA2/MjOjZly7tO7qwYhFxR05dR27UqFH87W9/\nw2Ip+rfLvLw8Zs+efcMPv1Rqairz589n4cKFJCYmMmzYMAYOHMj69evp3r07U6dOpX379rRp04Yu\nXbpw6NChK9YmIuIsZ3etImXuABzns/CKbEDY8MVUqHFbwetrN6xj/PwZpHVoVHBs/PwZAApzIlKi\nim2RS01NJTw8vNgbXMs512rJkiV06dKFihUrArBgwQJGjBjB559/Tvfu3cnMzMRqvZA969evzyuv\nvMLDDz9c6B5qkRMRZzEc+Zxe+SJnPn8FgAp3PkzEoA8w+1UsdF7XYf3Z3fLy5Uaafp/C53MWlEap\nIuIhnDrZ4VoCWkmFOIDevXsXhDiAKlWqUL16dbZu3UpMTExBiAOoV68eGzduLLFni4gUJ+dMMgmv\nd7kQ4kxmKveaSuSopZeFOAA7jiLvYTNKbhiKiAhcJci52o8//siIESNISUm5bLZsUFAQx48fd1Fl\nIlKenNz7Dccm3sn5fevJ8wlktrUdvVf/TLfhA1i7Yd1l53tf4Vurj0lDQUSkZLltkMvOzuaXX35h\nzJgxWCwWvLy8Cr3ucBT9G6+ISEk6+vkMTr/ZEevZFNL8qjIy43Zi776D/S2j2d0ykvHzZ1wW5kb0\n7EvI+r2FjoWs28vwHn1Ks3QRKQeKnexwJZmZmRw6dIh69eoV6gotSdOnT2fmzJlYLBaioqL49ttv\nC72enp5OzZo1i7z2xRdfLPh727Ztadu2rVNqFJGyy5FrI/7dQVh+WowZyG/0F16J9+XXTtUKnZfW\noRFzli8qNInh4t/nLF+EzcjHx2Rh+KCx1zTRQbNdRcq2zZs3s3nz5hK7X7GTHeLj4xk9ejRpaWm8\n/PLLdO7cmdjYWAYNGoTZbMbf359PP/2U++67r8QKApg7dy733XcftWvXBuDrr7+ma9euZGZmFpxT\nu3ZtXn31VXr16lX4DWmyg4gU4XoCUl56MkffeBDj910YJite3V4i5qFn6DT8cfa3jL7s/Fu/S2Td\ne5+USI2XznYNWb+XKQOvLQSKiOdx6vIjL7/8Ms899xzBwcHMnDmT9PR0BgwYwFNPPcVLL72E3W5n\nwoQJJRrkFixYgJ+fH7m5uRw4cIATJ05w9OhRatasyaZNm2jXrh0HDhwgJyeHbt26ldhzRcR93Gir\n1JWuu57lQM4d2krSrF4YGSk4KoQTOvgTwm9vDzh/7Nvs2IWFaoSiW/xERC4qNsjdeeedtGnTBoA3\n3niDqKgoHnzwQf71r38B4OfnR7Vq1Yq7xXX54osvGDJkSKEFhk0mE/Hx8bRu3ZpJkyYRFxfHjh07\nWLNmDX5+fiX2bBFxDze6Bltx111LQDIMg4yvZpO6+O+Qn4df/TZEjlyMNahKwTUjeva9vMVs3V6G\nDxp7E+/4fzTbVUSuV7FB7syZMzgcDnJychgwYADVq1cnIiKCU6dOUblyZZKTk9m5c2eJFfPnP/+Z\n3NzcK76+YMECAEaOHFlizxQR93KjrVLFXXe1gOSwnyP1oxFkbr3QPRrS6e9U7vkqJmvhSVY3M/bt\nWmi2q4hcr2KDXMeOHalXrx5JSUk0a9aMjRs3kpKSQqNGjTAMA7vdzvLly0urVhEpB64Wuq7UfVrc\ndcUFpLOJ8SS8+Rcspw5i8vanysC5BLbsfcX6urTv6LRuTme3+IlI2VNskLv33nuJj4/nzJkzhIWF\nARAREcGRI0fYu3cvdevWJSQkpFQKFZHyobjQVVz3aXHXDe/Rp8iANKLtHSS8dBcW+1kcQVWJ+cca\nfKrdVuR9SoOzW/xEpOwpdtYqXFivbeXKlWzcuLFgAd7o6Gjatm1Lt27d3G6cmmatini2ImdurtvL\nlEFjmR278IpbX10prE35vyC0dsO6goDki5kx1c5T/dcVmDCwVWtFnf+3Ar+QKpfdW0TEmW42txQb\n5Pbt20e3bt3w8vKiXr16BAUF4XA4SE9PJz4+npycHNasWUOzZs1uuICSpiAn4vn+GLoutqh1ad+R\njsP6Fbv8x5Wu+6P8c5kcev1hzL9uxMBE/t3DqT/wLSzWG1pWU0Tkpjh1+ZFp06axdu1aGjRoUOTr\nBw4cYMqUKQWTEERESsKVxqFdbTLA1cav2RL3kzTzYcwpB8n3CsC31wxiOgwokZpFRFyh2C26WrVq\ndcUQB3DLLbdw1113lXhRIiJFKWrrK+vibzh5+hQdh/Wj67D+Re59CpD1wwp+f7kVuSkH8a56G5ET\nvlGIExGPV2yL3J49e9iwYQNt27bFWkS3w6ZNm9ixY4eWAxGRUnHpZICM1FOc8vfl+ANXXnPOyM/j\n1IrnSPvPawBUbNmbKk+8j9knoJSrFxEpecWOkUtMTKR79+7ExcURExNDUFAQXl5eZGRkcOTIEWrU\nqMHq1aupVatWadZcLI2REyk/ug7rf8XJD5/PWUBuRiqJ7/bGHr8FzBbCHnmN4I5jMZlMLqhWRORy\nTh0jFx0dzc6dO9mwYQPbtm0jJSUFq9VKZGQk7dq1o2XLlvqGKCIuU9zacZnx20ic2QPL2ROYK4YR\nNWop/re0KeUKRUSc66rTtAzDIDMzk9TUVJKSkjAMg7y8PBISErj99tvdbvkRESk/rjT54W7bIZKn\n3ofFkYut8i1Ej4nFv8atpVydiIjzXdfyI8HBwQXLjxw4cIDs7GzWrl2r5UdExCUuXXPOy5HH39Yv\nobNvMgDnGjxI/VEL8K0Q6MoyRUSuSMuPiEi59cfJD36OLAbbv6Gm72kcZi/yOzzLbY88i9lc7OR8\nERGPVmyQ0/IjIuLuurTvSLuqPiS/25t8+2kIisL30TnUbNnF1aWJiDhdsb+qXlx+JC8vr8jXLy4/\nIiLiCoZhkPblWxyf1oH8zFT8b72f2v/6SSFORMqNa15+pFatWgQGBmr5ERFxCw5bNifmDyHr+6UA\nhDzwNJUffhmTRVttiYjncOpeq3DhN94NGzawdevWguVHoqKiaNeuHa1atbrhBzuLgpxI2WdLOcTR\n6V0xn/oVk28FIgbNp2Lzh11dlojIdXN6kLuS3NxcFi1axD333EOdOnVuuICSpiAnUral7VzFiff7\nY7afJbdiNLWf/g++1Rpd/UIRETd0s7ml2DFyCQkJdOjQAT8/P2655RbeeustHI4LC3B6eXlRs2ZN\n6tevf8MPFxG5VobDQeKSCZyc9TBm+1lyqt5NxIRvFeJEpFwrNsgNHjyYQ4cOMXv2bN58802ys7Pp\n06cPJ0+eBKBKlSpq/RIRp8vPTufI1E5kfzEVAxNnmw/llmfXUSmyuqtLExFxqWJHBW/fvp3Y2Fg6\ndeoEQOfOncnOzmb69Ok89thjpVKgiJQ/azesY3bsQuw4qOHIYLhpOxXtZ8hyWJlrv4MudR/kDu0q\nIyJSfJCLiYkhOjq60LGAgABeeOEF5s6dq+25RKTE/XG3hvtS4xgdvw4/Ry4HrcFMbPggqcHhbF0w\nC7PZXLAgsIhIeVVs1+qMGTN47bXXyMrKuuy1IUOGkJeXh8VicVpxIuLZ1m5YR9dh/ek4rB9dh/Vn\n7YZ1Vz138Evjyby/ASN+3chzcWvwc+TyZZWGPHGiLqnB4QCkdWjEnOWLSuttiIi4rWJb5Nq0aUOD\nBg1Yu3YtvXv3vuz1AQMGEBER4bTiRMRzXboPKsD4+TMALmtJ++O5PifimLZ7KbdnJZJnMjOr9n38\nO+p2bAe24vOHa2xGfmm8DRERt3bVTQjDw8OLDHEX/fnPfy7RgkSkbJgdu7BQiIMrt6RdPPeWjEQ+\nqfAzt2clcsrqz9+bPMK/o5uCyQT/N2P+Ih+TegNERLSbtIg4hR1HkceLakmzG/l0TfiRt3cvoYo1\njx+z/RhS56/sC6oKQOaCL/FpGFNwfsi6vQzv0cc5hYuIeBDtZSMiV/XHWaTemBnRs+9VJxp4X+H3\nxEtb0hz28/S1f0erI0cBWOZbhxlBjTi/9RB5Z3ZhDQ0kwGSlevwZgtLM+JgsDB80VhMdRERQkBOR\nq7iesW5/NKJn38uuC1m3l+GDxhb8O/f07yTN7EkrjnLeYWJqaAs239oKby8r5w4nEfCnJvjUrwZA\n+PcpfD5nQQm/OxERz6YgJyLFKm6sW3FB7uJrc5YvwmbkX9aSlrP/K5Jn9yE/6xTm0Br8VK0vGQd+\nJ3/xFnIqeOHbqFZBiANNbhARKYqCnIgU62pj3Yrrdu3SvuNlYc8wDNL+O51TsRPAcOB/Wycih31K\nnQqh9Ae6DuvP7paRlz1PkxtERC6nyQ4iUqzixrpd7Hbd3TKS/S2j2d0ykvHzZ1xxvTjHuSyS3+nF\nqWXjwXAQ2u1Zov/+OZYKoQXnjOjZl5D1ewtdp8kNIiJFMxllbLNUk8mk/V9FSlBRY+RC1u1lyqCx\nzI5dWGTrWdMixrPZk+P57Y3uGCd/xewXSMSQBVS448ErPrNQl2yPPprcICJl0s3mFnWtikixihvr\n9nbsJ0Vec+l4tqxdq0h673FM9mxsFasRNXYFFeo2K/aZCm4iIlenICciwPWPdYOrLzFiOPI5Efss\nmf99DROQVfUeKvd/j0p1GzjtfYiIlCcKciLilCVG8s+eJmHWo9gPfIWBmfSmA6nb71VCQ0OveD8R\nEbk+GiMnIlecKVrUWLdLFTWe7f66YSTN7EHeqWPk+wSRdf+LNHlwGD4+PsXeS0SkvNEYORG5adez\nndalLu12zdz6MQn/eggj9zw+Mc0JGfQRFaLqYjZrkryISElTkBORa95OqzhGnp3UxePI+OpdAAJb\nDyT8sZmYvX1LpEYREbmcfkUWkZteuy0vLYljk9uS8dW7mKzehA+YQ8TAuQpxIiJOpjFyIgLc+Npt\n5w5+S8KMHnD2JEaFcBLu/SdvbdpV5OzXS593pVmyIiLlxc3mFgU5EbkhhmGQvuEdUhePw+TIIyes\nETtq9mPm1s2FZ7Gu38uUgWMLhbQiFxku4jwRkbJOQe4SCnIiJe/S1rORDz1Mk2OxZH+36MLr2VVZ\n5d2UX39PwDSww2XXXzr79WZmyYqIlCWatSoiTnVp61nkuXQsix4n25xFnsmLaanVWNe5C+YAX7JW\nnSKwiHtcOvv1ZmbJiojI/yjIiZQxJT32bHbswoIQ1/zMUZ6NW0Og+TwnqcjMs0345sHmmL3+71uJ\no+iAduns15KYJSsiIpq1KlKmXGw9290ykv0to9ndMpLx82ewdsO6G76nHQcmw6Dvb9t59ZflBOad\nZ3tobaZ5d+JMaHVMXv/7fdCnYQwZK74udH1Rs19vdpasiIhcoDFyImWIM8ae9Rj6KA9U/JF7T/+K\nA/ioxj18WqMVt+84gWEYlz3PFp+A97Z4GjZoUOzs1xudJSsiUpaUqzFyiYmJTJ48mcaNG7N9+3ae\nfvppGjZs6OqyRNxGSY89syXuZ6L3N/ieTibL6sMrt3Tl+0q1CvZTBS6bfRrxWwZTJr5y1VB26Y4Q\nIiJy/TwmyBmGQffu3Zk6dSrt27enTZs2dOnShUOHDmGxaFyNCJTs2LOsHbEkzxuIrz2HNO9wXj/b\nhKxDPjT9NYXhgwovE1KoZW2QlhARESktHtO1un79eh588EEyMzOxWi/kz/r16/PKK6/w8MMPF5yn\nrlUpz4pcn23dXqZcR7gy8vM4tXwCaf99HYDMaq3xeWgqDW+/U/ulioiUsHLTtbp161Zq1apVEOIA\n6tWrx8aNGwsFOZHy7GJYu9EWsrzMkyS925vzBzZjmMycajKQqg9NoEaNGs4sW0REbpDHBLmUlBQC\nAwuvUBUUFMTx48ddVJGI+yiJJUfOH/mBpFk9yTuTgMMvlNR7/sltXZ4gJCTESVWLiMjN8pggZ7Va\n8fLyKnTMcYU1q1588cWCv7dt25a2bds6sTIR1yqqO3X8/BkA1xzmMrZ8QOonozHy7PjWbkn48EXE\nBEbg4+PjlJpFRMqrzZs3s3nz5hK7n8eMkXvllVdYtmwZu3fvLjj2wAMPULNmTd59992CYxojJ+XN\nzSw54si1cXLhk2RsngtA0H3DCXv0DcxeCnAiIqXhZnOLx4xcbteuHUeOHCl0LD4+Xq1tUu7d6JIj\nuWeO8/vk1mRsnovJ6kOVQR9Q5fF3FOJERDyIxwS5li1bUqNGDTZt2gTAgQMHyMnJoVu3bi6uTMS1\nbmTJkZy4zRyb2Az7sZ3kV6hC9IQtBP1pgJMqFBERZ/GYMXImk4nVq1czadIk4uLi2LFjB2vWrMHP\nz8/VpYm41IiefYtccuTigr1/ZBgG6eve5uTSp8GRT3Z4E851epnaVZtcdm5J79kqIiIlz2PGyF0r\njZGT8uhatrty2LJJ+WAwZ3csA+B0/Yep8MAEGt7W+LL14Ypcj279XqYM1GK/IiIl6WZzi4KcSDlg\nTzlE0qwe2I/vxWH1JaX536jVeRjVq1cv8nxn7NkqIiKXKzcLAovIjTm7ew0p7z2O41wGXhH1ye48\nhSaN/1Ts+nAlvWeriIg4h4KcSBllOBycXv0yZ1ZPAqBCs79QZfCHWPwCr3Jlye7ZKiIizuMxs1ZF\n5NrlZ6eR9Fb3CyHOZKZyj1eIHL2cL7Z+R9dh/ek4rB9dh/Vn7YZ1RV4/omdfQtbvLXQsZN1ehvfo\nUxrli4jINVKLnEgZY0vYQ+LbfyXv1FHMAaFEjlhIQKOO17UDxM3u2SoiIqVDkx1EypDM7YtImT8U\ncs9xPrgW9q7TubP9g4AmMIiIuCNNdhARjLxcTi57hvR1bwOQUaMd9vsmcGfLewrO0QQGEZGyR0FO\nxMPlpaeQPPtRzsV/jWGyktpkEMH3DadZo0aF1ofTBAYRkbJHQU7kEp60o8G5X7eTNKsX+elJUCGc\n483HUbddryLXh7ueHSBERMQzKMiJ/MH1TAhwJcMwyNj0HqkL/wb5ufjVu5fIkUuJ9qpIQEBAkddo\nAoOISNmjyQ4if1AaEwJutsXPYT9H6iejyfzmQj3BHcYQ9shrmKxeJVKfiIiUHk12EClBzp4QcLMt\nfrmnfiNpVk9sx3Zh8vajyoA5BN79mEd1B4uISMlRkBP5A2dPCJgdu7BQiANI69CIOcsXXTV4Ze/b\nQPK7fXBknyY3oArRYz8jsH5Lj+kOFhGRkqedHUT+wNk7GtxIi59hGJxZO43E6Z1xZJ/mbJU7SOk8\nC1NEA6D4cCgiImWbWuRE/sDZEwKut8XPcS6LlA8GcnbnZwCcuqUXxj0juPeuFvj4+ABaH05EpDxT\nkBO5RJf2HZ3WJXk9S4DYkw6QNKsH9qQ48q3+JDf/G2F396Jhw4ZaH05ERAAFOZFSda0tflm7VnJi\n7hM4zmfhHd0QHp5Bncq1tD6ciIgUouVHRIpR2rNBDUc+pz+byJk1UwCocFdPIgbOw+xb4ap1FgqH\nPfpoooOIiAe42dyiICdyBUXNBg1Zv5cpA52ziG7+2dMkz+5Dzr4NYDJTuddUQv78d0wmU4k/S0RE\n3IOC3CUU5KSklMbiwBedP/YjSbN6knfqGKaAUKJHLcX/1vtK9BkiIuJ+tCCwiJOU1mzQjG8/IvWj\nkRi55zkXUpeTf3qOWvXblOgzRESkbFKQE7kCZ88GNfLspC4eR8ZX7wKQXrMDGS3HcGeLu7FYNONU\nRESuTgsCi1yBMxcHzktLImHK/WR89S6G2UrKHSPJ7fA8rdu1JyQk5KbvLyIi5YPGyIkUwxmzQXPi\nvyH53d7kZ6RgDo7it2bjCL/9/svWhxMRkbJPkx0uoSAn7sowDNI3vMPJJeMgPw+/Bu2IHLEImyWA\ngHiEt+QAAB6GSURBVIAAV5cnIiIuoMkOIh7AYcvhxILhZG1fCEDIn8dRuecrmCxW/U8oIiI3TD9D\nRG7QtS4WbE89QvLMHtgSfsbkE0DEwLlUbPGICyoWEZGyRkFO5AYUtVjw+PkzAAqFuew9/yX5vX44\nstOwV4giaOBHVLxD68OJiEjJ0MhqkRswO3ZhoRAHkNahEXOWLwLAcDg4/e/JJL7Zjf/f3n1HRXXt\nfQP/zlBHlCaIIApoMBoLAYntJiqJJWqMKWpUiBoLdlP0Rp5lvNcSfdIu8YmNF/BqYolXzYpGzeWR\nRMEAiSYYX4lS5DVGihRR6TBtv38Q5jo0YWQa8/2sNWvlnH04Z88vM5Nfzj77t9WV91DuORQ5Y/8B\nuPsbo7tERNRB8Y4ckQ5aKhasqipFQcxcVP56EgIS3HliNqqeDMPwp4aytAgREbUrJnJEOmiuWHAv\nUY5bG4dBUXgdatvOyH/qbdg9MQ6jgoNhZ2dn4F4SEVFHx6FVIh00VSx48nfxWKOOh6LwOmx7Dka3\ntefhMXI6RowYwSSOiIj0gnXkiHRUXyxYoVbgZdUVhKiuAQC6DJ8FjzeiIbXrZOQeEhGRqWNB4AaY\nyJEhKcuKcXv3LFSnnwOsrOE+8xM4j10BiURi7K4REZEZYEFgIh20tgZcS2pu/Iz8HdOhvJsD4dAV\nPVd9hU6PP6OnHhMRETXGRI7Mmi4JWWtrwLWkNHEPivavgFDKUe36OPKGr0W3bgPRnoOp7ZFsEhFR\nx8ZEjsyWrglZSzXgHpYoqRW1KD74JkoTYur+rvdE3AlciEEBQUhJ/bndEq/2SDaJiKjj46xVMlsP\nK8rbnJZqwLVEUZKD3P8eg9KEGAipDW4PWYmyEW9i5NOjkZaVgYh/fobLwz1xbXgPXB7uiYh/fobT\n351p25v6k67vjYiILAsTOTJbuiZkzdWAs5NYNfs3VenncGvDU6i5cRHWXXuheNJnsA6ajlGjRsHF\nxaXdEy9d3xsREVkWJnJktnRJyICma8C5nPkNS6bNbnSsEAL34j5F7scToCovRqcBY+Gz8RcMmfy6\nVn249k68dH1vRERkWZjIkdlqS0L2oMljx+OD+asQeKEAT/yUh8ALBfhgwapGz56paypQsHs2ig+v\nAdQquL4QgR6rv4VV566QyWSQSv/z9WnvxEvX90ZERJaFdeTIrNUX5a0VKthJrLBk2ux2mQwgL7iO\n/O2vQp53FRK7zui+aC+6BL/SYj8aTk5wOfNbkwlia+nrvRERkelgQeAGmMjRo6r49SQKoudAXV0G\neRdvqF/5HwwOeemhf8fEi4iI2oqJXANM5EhXQq1GyYlNuHtiMwCg3Gs4bge/CR///hg4cCBXayAi\nonbHlR2IdNCw2O7yF1/E4My9qLzybwiJFHcGhOF+v1cxePBg9OrVy9jdJSIiapLJJXLr169HbGws\nhBBYtGgRNm/erGk7fvw4fvrpJ7i6uiInJweRkZGwsbExYm/JHDV8nq13RREcjsxFpaQawt4ZucFv\nQe0zHCODg+Hi4mLk3hIRETXPpIZWY2NjoVQqMXr0aJw8eRIRERHYv38/QkNDkZqaitdeew1ZWVmQ\nSqVYu3YtbG1ttRI9gEOr9HAvLJ6Ly8M9AQDPFl7Dmqz/hb1aiRyJK/7ywU/ILKhAv379NKVFiIiI\n9OVR8xaTKj+iUqmwZMkS9O/fH++++y5GjRqF5ORkAEBkZCTGjBmjKfnw0ksvISoqCnK53JhdJjMk\nhxpWahWWZ5/FexmnYa9WIs5jICJtxsLeow8CAgKYxBERkVkwqaHVxYsXa217eHhonk9KTk7GihUr\nNG3+/v4oKSnBlStXEBwcbNB+knnrKmrxjytHMLg0FwqJFDseew4nPQMQeLGw0bH6Xrhe3+cnIqKO\nzaQSuYaysrLw6aefAgAKCwvh5OSkaXN2dgYA5ObmMpGjVqvO/hHrrRJgXVqCYisZNgx8CenO3nXF\ndhes0jpW3wvX6/v8RETU8ZlsIvfNN98gPDwcXl5eAABra2utiQ1qdd2SSE2NK2/YsEHzz2PGjMGY\nMWP02lcyfUIIlJ6LQtHBt2GtUqDI3gcbb3mhqrIcgc4FWNJE4d6W1k9tj0RL3+cnIiLTk5CQgISE\nhHY7n8ESuZycHAQFBTXbPnXqVMTGxgIA8vLykJaWhnXr1mnaPT09UVpaqtm+f/8+AKBHjx6NzvVg\nIkekllej6IvlKEv6HABw97EXcG/QPET2fgwDBgzQWmrrQfpeuF7f5yciItPT8AbTxo0bH+l8Bkvk\nevbsieLi4oceV15ejs8//1wriVMoFAgJCcH169c1+zIyMuDk5ITAwEC99Jc6BkXxTeTvmI7aPy5B\nWNnhdtAyVPqGIGDQoIfWh9P3wvX6Pj8REXV8JjW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+ "text": [ + "" + ] + } + ], + "prompt_number": 33 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "***Answer***: There is a reasonably well fit line which hugs the `x=y` line. This tells us that the PVI seems relatively stable from election to election and may thus make a good predictor.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.3** Lets do a bit more exploratory data analysis. *Using a scatter plot, plot `Dem_Adv` against `pvi` in both 2008 and 2012. Use colors red and blue depending upon `obama_win` for the 2008 data points. Plot the 2012 data using gray color. Is there the possibility of making a linear separation (line of separation) between the red and the blue points on the graph?*" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "\n", + "plt.xlabel(\"Gallup Democrat Advantage (from mean)\")\n", + "plt.ylabel(\"pvi\")\n", + "colors=[\"red\",\"blue\"]\n", + "ax=plt.gca()\n", + "for label in [0, 1]:\n", + " color = colors[label]\n", + " mask = e2008.obama_win == label\n", + " l = '2008 McCain States' if label == 0 else '2008 Obama States'\n", + " ax.scatter(e2008[mask]['Dem_Adv'], e2008[mask]['pvi'], c=color, s=60, label=l)\n", + "\n", + "ax.scatter(e2012['Dem_Adv'], e2012['pvi'], c='gray', s=60, \n", + " marker=\"s\", label='2012 States', alpha=.3)\n", + "plt.legend(frameon=False, scatterpoints=1, loc='upper left')\n", + "remove_border()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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4OJh8cY83QwxvT6lCBWDePCAoCOjZE+jVC9i1C5g7V64rSStWrEBUVBSio6Ox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\nq7S2S5cuSceOHeXxxx+XrKysCv1+le2TTz4p0esGDx4sLVu2LNFrdTq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+ "text": [ + "" + ] + } + ], + "prompt_number": 34 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "***Answer***: Yes, it seems that with 3-4 misclassifications, we could draw a line to divide the data into two parts.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The Logistic Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Logistic regression is a probabilistic model that links observed binary data to a set of features.\n", + "\n", + "Suppose that we have a set of binary (that is, taking the values 0 or 1) observations $Y_1,\\cdots,Y_n$, and for each observation $Y_i$ we have a vector of features $X_i$. The logistic regression model assumes that there is some set of **weights**, **coefficients**, or **parameters** $\\beta$, one for each feature, so that the data were generated by flipping a weighted coin whose probability of giving a 1 is given by the following equation:\n", + "\n", + "$$\n", + "P(Y_i = 1) = \\mathrm{logistic}(\\sum \\beta_i X_i),\n", + "$$\n", + "\n", + "where\n", + "\n", + "$$\n", + "\\mathrm{logistic}(x) = \\frac{e^x}{1+e^x}.\n", + "$$\n", + "\n", + "When we *fit* a logistic regression model, we determine values for each $\\beta$ that allows the model to best fit the *training data* we have observed (the 2008 election). Once we do this, we can use these coefficients to make predictions about data we have not yet observed (the 2012 election).\n", + "\n", + "Sometimes this estimation procedure will overfit the training data yielding predictions that are difficult to generalize to unobserved data. Usually, this occurs when the magnitudes of the components of $\\beta$ become too large. To prevent this, we can use a technique called *regularization* to make the procedure prefer parameter vectors that have smaller magnitude. We can adjust the strength of this regularization to reduce the error in our predictions.\n", + "\n", + "We now write some code as technology for doing logistic regression. By the time you start doing this homework, you will have learnt the basics of logistic regression, but not all the mechanisms of cross-validation of data sets. Thus we provide here the code for you to do the logistic regression, and the accompanying cross-validation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first build the features from the 2008 data frame, returning `y`, the vector of labels, and `X` the feature-sample matrix where the columns are the features in order from the list `featurelist`, and each row is a data \"point\"." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "def prepare_features(frame2008, featureslist):\n", + " y= frame2008.obama_win.values\n", + " X = frame2008[featureslist].values\n", + " if len(X.shape) == 1:\n", + " X = X.reshape(-1, 1)\n", + " return y, X" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 35 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use the above function to get the label vector and feature-sample matrix for feeding to scikit-learn. We then use the usual scikit-learn incantation `fit` to fit a logistic regression model with regularization parameter `C`. The parameter `C` is a hyperparameter of the model, and is used to penalize too high values of the parameter co-efficients in the loss function that is minimized to perform the logistic regression. We build a new dataframe with the usual `Obama` column, that holds the probabilities used to make the prediction. Finally we return a tuple of the dataframe and the classifier instance, in that order." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def fit_logistic(frame2008, frame2012, featureslist, reg=0.0001):\n", + " y, X = prepare_features(frame2008, featureslist)\n", + " clf2 = LogisticRegression(C=reg)\n", + " clf2.fit(X, y)\n", + " X_new = frame2012[featureslist]\n", + " obama_probs = clf2.predict_proba(X_new)[:, 1]\n", + " \n", + " df = pd.DataFrame(index=frame2012.index)\n", + " df['Obama'] = obama_probs\n", + " return df, clf2" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 36 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are not done yet. In order to estimate `C`, we perform a grid search over many `C` to find the best `C` that minimizes the loss function. For each point on that grid, we carry out a `n_folds`-fold cross-validation. What does this mean?\n", + "\n", + "Suppose `n_folds=10`. Then we will repeat the fit 10 times, each time randomly choosing 50/10 ~ 5 states out as a test set, and using the remaining 45/46 as the training set. We use the average score on the test set to score each particular choice of `C`, and choose the one with the best performance." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from sklearn.grid_search import GridSearchCV\n", + "\n", + "def cv_optimize(frame2008, featureslist, n_folds=10, num_p=100):\n", + " y, X = prepare_features(frame2008, featureslist)\n", + " clf = LogisticRegression()\n", + " parameters = {\"C\": np.logspace(-4, 3, num=num_p)}\n", + " gs = GridSearchCV(clf, param_grid=parameters, cv=n_folds)\n", + " gs.fit(X, y)\n", + " return gs.best_params_, gs.best_score_\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 37 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we write the function that we use to make our fits. It takes both the 2008 and 2012 frame as arguments, as well as the featurelist, and the number of cross-validation folds to do. It uses the above defined `logistic_score` to find the best-fit `C`, and then uses this value to return the tuple of result dataframe and classifier described above. This is the function you will be using." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def cv_and_fit(frame2008, frame2012, featureslist, n_folds=5):\n", + " bp, bs = cv_optimize(frame2008, featureslist, n_folds=n_folds)\n", + " predict, clf = fit_logistic(frame2008, frame2012, featureslist, reg=bp['C'])\n", + " return predict, clf" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 38 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.4** *Carry out a logistic fit using the `cv_and_fit` function developed above. As your featurelist use the features we have: `Dem_Adv` and `pvi`." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "res, clf = cv_and_fit(e2008, e2012, ['Dem_Adv', 'pvi'])\n", + "predict2012_logistic = res.join(electoral_votes)\n", + "predict2012_logistic.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ObamaVotes
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Alaska 0.008093 3
Arizona 0.069288 11
Arkansas 0.031901 6
California 0.994956 55
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 39, + "text": [ + " Obama Votes\n", + "State \n", + "Alabama 0.004234 9\n", + "Alaska 0.008093 3\n", + "Arizona 0.069288 11\n", + "Arkansas 0.031901 6\n", + "California 0.994956 55" + ] + } + ], + "prompt_number": 39 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.5** *As before, plot a histogram and map of the simulation results, and interpret the results in terms of accuracy and precision.*" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#code to make the histogram\n", + "#your code here\n", + "\n", + "prediction = simulate_election(predict2012_logistic, 10000)\n", + "plot_simulation(prediction)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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4cYoZVkWLFtWZXfTixQvExcWps8+AxA8zIHEWUvKb2aOjoxEWFoZvvvkmQ8cR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\n/ZL0u9/9LtftExISchy+DAgI0JkzZ+xeV6Y777xTU6dOVdu2bdWsWTO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+ "text": [ + "" + ] + } + ], + "prompt_number": 40 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#code to make the map\n", + "#your code here\n", + "\n", + "make_map(predict2012_logistic.Obama, \"P(Obama): Logistic\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 41, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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YPEpRSvOVQVVCiNvGsmXLmD59Opqm8fXXXxMcHMyTTz55RblevXrx8ccfk56e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\nxowZg9lsxmw206dPH9avX4+fnx979+4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Kd3Xon6OotllglHH0lefzzPPPNcOdCCGEaO2k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\n2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+ "text": [ + "" + ] + } + ], + "prompt_number": 41 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "***Answer***: The precision has worsened with respect to the predictwise (and even the gallup) model. The accuracy has improved with respect to the gallup model, but is not as good as in the predictwise model.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Classifier Decision boundary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One nice way to visualize a 2-dimensional logistic regression is to plot the probability as a function of each dimension. This shows the **decision boundary** -- the set of parameter values where the logistic fit yields P=0.5, and shifts between a preference for Obama or McCain/Romney.\n", + "\n", + "The function below draws such a figure (it is adapted from the scikit-learn website), and overplots the data." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from matplotlib.colors import ListedColormap\n", + "def points_plot(e2008, e2012, clf):\n", + " \"\"\"\n", + " e2008: The e2008 data\n", + " e2012: The e2012 data\n", + " clf: classifier\n", + " \"\"\"\n", + " Xtrain = e2008[['Dem_Adv', 'pvi']].values\n", + " Xtest = e2012[['Dem_Adv', 'pvi']].values\n", + " ytrain = e2008['obama_win'].values == 1\n", + " \n", + " X=np.concatenate((Xtrain, Xtest))\n", + " \n", + " # evenly sampled points\n", + " x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n", + " y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n", + " xx, yy = np.meshgrid(np.linspace(x_min, x_max, 50),\n", + " np.linspace(y_min, y_max, 50))\n", + " plt.xlim(xx.min(), xx.max())\n", + " plt.ylim(yy.min(), yy.max())\n", + "\n", + " #plot background colors\n", + " ax = plt.gca()\n", + " Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n", + " Z = Z.reshape(xx.shape)\n", + " cs = ax.contourf(xx, yy, Z, cmap='RdBu', alpha=.5)\n", + " cs2 = ax.contour(xx, yy, Z, cmap='RdBu', alpha=.5)\n", + " plt.clabel(cs2, fmt = '%2.1f', colors = 'k', fontsize=14)\n", + " \n", + " # Plot the 2008 points\n", + " ax.plot(Xtrain[ytrain == 0, 0], Xtrain[ytrain == 0, 1], 'ro', label='2008 McCain')\n", + " ax.plot(Xtrain[ytrain == 1, 0], Xtrain[ytrain == 1, 1], 'bo', label='2008 Obama')\n", + " \n", + " # and the 2012 points\n", + " ax.scatter(Xtest[:, 0], Xtest[:, 1], c='k', marker=\"s\", s=50, facecolors=\"k\", alpha=.5, label='2012')\n", + " plt.legend(loc='upper left', scatterpoints=1, numpoints=1)\n", + "\n", + " return ax" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 42 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.6** *Plot your results on the classification space boundary plot. How sharp is the classification boundary, and how does this translate into accuracy and precision of the results?*" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "points_plot(e2008, e2012, clf)\n", + "plt.xlabel(\"Dem_Adv (from mean)\")\n", + "plt.ylabel(\"PVI\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 43, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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gJs85X92z9957jz59+vCrX/3KNXby5Enq6uo89AmEEEIIAXIm7bIFBAS0WFQ3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oqMWcUG2e5aXDWIlUxFKIZzxQYGw8ILWEmSKNBJFOg0mA1q0HsilWEwmlQLRVTPuYWOQw0LyOyU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\nO1CGrFXLp0Q8RvDMSXzlVXhKy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+ "text": [ + "" + ] + } + ], + "prompt_number": 43 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "***Answer***: The sharpness of the classifier boundary, as defined by the closeness of the contours near a probability of 0.5 gives us a sense of precision. Imagine that the boundary is very tight, tighter than what you can see in the graph. Then most states will be away from the 0.5 line, and the spread in the results will be less, or the precision higher. This is not the only consideration: indeed one must ask, how many states fall smack into the middle, say between 0.3 and 0.7. The more that do, the less precise the results will be, as there will be a greater number of simulations in which they will cross-over to another party. \n", + "\n", + "To assess accuracy, we would have to see the actual outcome of the states in 2012. Accuracy would be a function of the number of states that end up on the \"wrong\" side of the 0.5 line, and how deep they end up on the wrong side. In terms of characterizing the 2008 outcomes, it seems that the classifier is quit eaccurate, with most misclassifications appearing in grey area of the classification boundary.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Question 3: Trying to catch Silver: Poll Aggregation\n", + "\n", + "In the previous section, we tried to use heterogeneous side-information to build predictions of the election outcome. In this section, we switch gears to bringing together homogeneous information about the election, by aggregating different polling result together.\n", + "\n", + "This approach -- used by the professional poll analysists -- involves combining many polls about the election itself. One advantage of this approach is that it addresses the problem of bias in individual polls, a problem we found difficult to deal with in problem 1. If we assume that the polls are all attempting to estimate the same quantity, any individual biases should cancel out when averaging many polls (pollsters also try to correct for known biases). This is often a better assumption than assuming constant bias between election cycles, as we did above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following table aggregates many of the pre-election polls available as of October 2, 2012. We are most interested in the column \"obama_spread\". We will clean the data for you:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "multipoll = pd.read_csv('data/cleaned-state_data2012.csv', index_col=0)\n", + "\n", + "#convert state abbreviation to full name\n", + "multipoll.State.replace(states_abbrev, inplace=True)\n", + "\n", + "#convert dates from strings to date objects, and compute midpoint\n", + "multipoll.start_date = multipoll.start_date.apply(pd.datetools.parse)\n", + "multipoll.end_date = multipoll.end_date.apply(pd.datetools.parse)\n", + "multipoll['poll_date'] = multipoll.start_date + (multipoll.end_date - multipoll.start_date).values / 2\n", + "\n", + "#compute the poll age relative to Oct 2, in days\n", + "multipoll['age_days'] = (today - multipoll['poll_date']).values / np.timedelta64(1, 'D')\n", + "\n", + "#drop any rows with data from after oct 2\n", + "multipoll = multipoll[multipoll.age_days > 0]\n", + "\n", + "#drop unneeded columns\n", + "multipoll = multipoll.drop(['Date', 'start_date', 'end_date', 'Spread'], axis=1)\n", + "\n", + "#add electoral vote counts\n", + "multipoll = multipoll.join(electoral_votes, on='State')\n", + "\n", + "#drop rows with missing data\n", + "multipoll.dropna()\n", + "\n", + "multipoll.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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PollsterStateMoEObama (D)Romney (R)Sampleobama_spreadpoll_dateage_daysVotes
0 Rasmussen Reports Washington 4.5 52 41 500 112012-09-26 00:00:00 6.0 12
1 Gravis Marketing Washington 4.6 56 39 625 172012-09-21 12:00:00 10.5 12
2 Elway Poll Washington 5.0 53 36 405 172012-09-10 12:00:00 21.5 12
3 SurveyUSA Washington 4.4 54 38 524 162012-09-08 00:00:00 24.0 12
4 SurveyUSA Washington 4.4 54 37 524 172012-08-01 12:00:00 61.5 12
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 44, + "text": [ + " Pollster State MoE Obama (D) Romney (R) Sample obama_spread poll_date age_days Votes\n", + "0 Rasmussen Reports Washington 4.5 52 41 500 11 2012-09-26 00:00:00 6.0 12\n", + "1 Gravis Marketing Washington 4.6 56 39 625 17 2012-09-21 12:00:00 10.5 12\n", + "2 Elway Poll Washington 5.0 53 36 405 17 2012-09-10 12:00:00 21.5 12\n", + "3 SurveyUSA Washington 4.4 54 38 524 16 2012-09-08 00:00:00 24.0 12\n", + "4 SurveyUSA Washington 4.4 54 37 524 17 2012-08-01 12:00:00 61.5 12" + ] + } + ], + "prompt_number": 44 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1** Using this data, compute a new data frame that averages the obama_spread for each state. Also compute the standard deviation of the obama_spread in each state, and the number of polls for each state.\n", + "\n", + "*Define a function `state_average` which returns this dataframe*\n", + "\n", + "**Hint**\n", + "\n", + "[pd.GroupBy](http://pandas.pydata.org/pandas-docs/dev/groupby.html) could come in handy" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "state_average\n", + "\n", + "Inputs\n", + "------\n", + "multipoll : DataFrame\n", + " The multipoll data above\n", + " \n", + "Returns\n", + "-------\n", + "averages : DataFrame\n", + " A dataframe, indexed by State, with the following columns:\n", + " N: Number of polls averaged together\n", + " poll_mean: The average value for obama_spread for all polls in this state\n", + " poll_std: The standard deviation of obama_spread\n", + " \n", + "Notes\n", + "-----\n", + "For states where poll_std isn't finite (because N is too small), estimate the\n", + "poll_std value as .05 * poll_mean\n", + "\"\"\"\n", + "#your code here\n", + "\n", + "def state_average(multipoll):\n", + " groups = multipoll.groupby('State')\n", + " n = groups.size()\n", + " mean = groups.obama_spread.mean()\n", + " std = groups.obama_spread.std()\n", + " std[std.isnull()] = .05 * mean[std.isnull()]\n", + " return pd.DataFrame(dict(N=n, poll_mean=mean, poll_std=std))" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 45 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets call the function on the `multipoll` data frame, and join it with the `electoral_votes` frame." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "avg = state_average(multipoll).join(electoral_votes, how='outer')\n", + "avg.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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Npoll_meanpoll_stdVotes
State
AlabamaNaN NaN NaN 9
AlaskaNaN NaN NaN 3
Arizona 20 -5.500000 4.559548 11
Arkansas 3-20.333333 4.041452 6
California 20 18.950000 5.548589 55
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 46, + "text": [ + " N poll_mean poll_std Votes\n", + "State \n", + "Alabama NaN NaN NaN 9\n", + "Alaska NaN NaN NaN 3\n", + "Arizona 20 -5.500000 4.559548 11\n", + "Arkansas 3 -20.333333 4.041452 6\n", + "California 20 18.950000 5.548589 55" + ] + } + ], + "prompt_number": 46 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of the reddest and bluest states are not present in this data (people don't bother polling there as much). The `default_missing` function gives them strong Democratic/Republican advantages" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def default_missing(results):\n", + " red_states = [\"Alabama\", \"Alaska\", \"Arkansas\", \"Idaho\", \"Wyoming\"]\n", + " blue_states = [\"Delaware\", \"District of Columbia\", \"Hawaii\"]\n", + " results.ix[red_states, [\"poll_mean\"]] = -100.0\n", + " results.ix[red_states, [\"poll_std\"]] = 0.1\n", + " results.ix[blue_states, [\"poll_mean\"]] = 100.0\n", + " results.ix[blue_states, [\"poll_std\"]] = 0.1\n", + "default_missing(avg)\n", + "avg.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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Npoll_meanpoll_stdVotes
State
AlabamaNaN-100.00 0.100000 9
AlaskaNaN-100.00 0.100000 3
Arizona 20 -5.50 4.559548 11
Arkansas 3-100.00 0.100000 6
California 20 18.95 5.548589 55
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 47, + "text": [ + " N poll_mean poll_std Votes\n", + "State \n", + "Alabama NaN -100.00 0.100000 9\n", + "Alaska NaN -100.00 0.100000 3\n", + "Arizona 20 -5.50 4.559548 11\n", + "Arkansas 3 -100.00 0.100000 6\n", + "California 20 18.95 5.548589 55" + ] + } + ], + "prompt_number": 47 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Unweighted aggregation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.2** *Build an `aggregated_poll_model` function that takes the `avg` DataFrame as input, and returns a forecast DataFrame*\n", + "in the format you've been using to simulate elections. Assume that the probability that Obama wins a state\n", + "is given by the probability that a draw from a Gaussian with $\\mu=$poll_mean and $\\sigma=$poll_std is positive." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "aggregated_poll_model\n", + "\n", + "Inputs\n", + "------\n", + "polls : DataFrame\n", + " DataFrame indexed by State, with the following columns:\n", + " poll_mean\n", + " poll_std\n", + " Votes\n", + "\n", + "Returns\n", + "-------\n", + "A DataFrame indexed by State, with the following columns:\n", + " Votes: Electoral votes for that state\n", + " Obama: Estimated probability that Obama wins the state\n", + "\"\"\"\n", + "#your code here\n", + "def aggregated_poll_model(polls):\n", + " sigma = polls.poll_std\n", + " prob = .5 * (1 + erf(polls.poll_mean / np.sqrt(2 * sigma ** 2)))\n", + " return pd.DataFrame(dict(Obama=prob, Votes=polls.Votes))" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 48 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.3** *Run 10,000 simulations with this model, and plot the results. Describe the results in a paragraph -- compare the methodology and the simulation outcome to the Gallup poll. Also plot the usual map of the probabilities*" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "model = aggregated_poll_model(avg)\n", + "sims = simulate_election(model, 10000)\n", + "plot_simulation(sims)\n", + "plt.xlim(250, 400)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 49, + "text": [ + "(250, 400)" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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2bdu2L90ElePo6AhHR8cv3QzGSoUSC/YsLS0ld00Hsh46PmrUKHFbEARYWFgg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\nv3BwcBAJCQlS2fXr10Xbtm1FWFiYVCaXy0VMTExdNFFLaGio6NSpU4X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+ "text": [ + "" + ] + } + ], + "prompt_number": 49 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "***Answer***: The accuracy of this poll is somewhat greater than just taking the gallup poll. This is probably because\n", + " 1. We're using as inputs polls that are trying to predict the election, so there is nothing \"lost in translation\", and\n", + " 1. We are averaging many polls together, so some of their biases are likely to offset each other.\n", + "\n", + "One problem is that we treated all polls as equal. Thus a bad poll with a small sample size is given equal footing as a good poll with a large one. Thus we are introducing bias both due to individual poll biases and individual poll sampling errors. \n", + "\n", + "Furthermore, we estimate the standard deviation by simply taking the spread of the percentages in the polls, without considering their individual margins of error. In the limit of very limit sampling error per poll, this might be ok. However in states with some polls with large margins of error (Kansas, for eg), this can lead to an overestimate of the standard deviation, pushing the predicted probability toward 0.5. This, in turn, can hurt precision since it increases the chance that a state will flip sides in a simulation. \n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "make_map(model.Obama, \"P(Obama): Poll Aggregation\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 50, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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jWex2OwC9evVi9uzZpKSk4HK5OH/+vNcwjGnTptGvXz/eeOMNTw/x7dBqtZw5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OHxSwjWlGH4Gw6XWUE/W9/9B0Xfvt08Tsk3b5ApnYwmgZmB9pWXs7CY76xhPmVMnnUN2QeP3ud1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\nti7eXTWl5Em2c/bZZ/PRe+/TR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+ "text": [ + "" + ] + } + ], + "prompt_number": 50 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Weighted Aggregation\n", + "\n", + "Not all polls are equally valuable. A poll with a larger margin of error should not influence a forecast as heavily. Likewise, a poll further in the past is a less valuable indicator of current (or future) public opinion. For this reason, polls are often weighted when building forecasts. \n", + "\n", + "A weighted estimate of Obama's advantage in a given state is given by\n", + "\n", + "$$\n", + "\\mu = \\frac{\\sum w_i \\times \\mu_i}{\\sum w_i}\n", + "$$\n", + "\n", + "where $\\mu_i$ are individual polling measurements or a state, and $w_i$ are the weights assigned to each poll. The uncertainty on the weighted mean, assuming each measurement is independent, is given by\n", + "\n", + "The estimate of the variance of $\\mu$, when $\\mu_i$ are unbiased estimators of $\\mu$, is\n", + "\n", + "$$\\textrm{Var}(\\mu) = \\frac{1}{(\\sum_i w_i)^2} \\sum_{i=1}^n w_i^2 \\textrm{Var}(\\mu_i).$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Whats the matter with Kansas?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to find an estimator of the variance of $\\mu_i$, $Var(\\mu_i)$. In the case of states that have a lot of polls, we expect the bias in $\\mu$ to be negligible, and then the above formula for the variance of $\\mu$ holds. However, lets take a look at the case of Kansas." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "multipoll[multipoll.State==\"Kansas\"]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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PollsterStateMoEObama (D)Romney (R)Sampleobama_spreadpoll_dateage_daysVotes
427 SurveyUSA Kansas 4.4 39 48 510 -92011-11-19 12:00:00 317.5 6
428 SurveyUSA Kansas 3.5 31 56 800-252011-11-10 00:00:00 327.0 6
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 51, + "text": [ + " Pollster State MoE Obama (D) Romney (R) Sample obama_spread poll_date age_days Votes\n", + "427 SurveyUSA Kansas 4.4 39 48 510 -9 2011-11-19 12:00:00 317.5 6\n", + "428 SurveyUSA Kansas 3.5 31 56 800 -25 2011-11-10 00:00:00 327.0 6" + ] + } + ], + "prompt_number": 51 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are only two polls in the last year! And, the results in the two polls are far, very far from the mean.\n", + "\n", + "Now, Kansas is a safely Republican state, so this dosent really matter, but if it were a swing state, we'd be in a pickle. We'd have no unbiased estimator of the variance in Kansas. So, to be conservative, and play it safe, we follow the same tack we did with the unweighted averaging of polls, and simply assume that the variance in a state is the square of the standard deviation of `obama_spread`.\n", + "\n", + "This will overestimate the errors for a lot of states, but unless we do a detailed state-by-state analysis, its better to be conservative. Thus, we use:\n", + "\n", + "$\\textrm{Var}(\\mu)$ = `obama_spread.std()`$^2$ .\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The weights $w_i$ should combine the uncertainties from the margin of error and the age of the forecast. One such combination is:\n", + "\n", + "$$\n", + "w_i = \\frac1{MoE^2} \\times \\lambda_{\\rm age}\n", + "$$\n", + "\n", + "where\n", + "\n", + "$$\n", + "\\lambda_{\\rm age} = 0.5^{\\frac{{\\rm age}}{30 ~{\\rm days}}}\n", + "$$\n", + "\n", + "This model makes a few ad-hoc assumptions:\n", + "\n", + "1. The equation for $\\sigma$ assumes that every measurement is independent. This is not true in the case that a given pollster in a state makes multiple polls, perhaps with some of the same respondents (a longitudinal survey). But its a good assumption to start with.\n", + "1. The equation for $\\lambda_{\\rm age}$ assumes that a 30-day old poll is half as valuable as a current one\n", + "\n", + "**3.4** Nevertheless, it's worth exploring how these assumptions affect the forecast model. *Implement the model in the function `weighted_state_average`*" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "weighted_state_average\n", + "\n", + "Inputs\n", + "------\n", + "multipoll : DataFrame\n", + " The multipoll data above\n", + " \n", + "Returns\n", + "-------\n", + "averages : DataFrame\n", + " A dataframe, indexed by State, with the following columns:\n", + " N: Number of polls averaged together\n", + " poll_mean: The average value for obama_spread for all polls in this state\n", + " poll_std: The standard deviation of obama_spread\n", + " \n", + "Notes\n", + "-----\n", + "For states where poll_std isn't finite (because N is too small), estimate the\n", + "poll_std value as .05 * poll_mean\n", + "\"\"\"\n", + "\n", + "#your code here\n", + "\n", + "def weights(df):\n", + " lam_age = .5 ** (df.age_days / 30.)\n", + " w = lam_age / df.MoE ** 2\n", + " return w\n", + "\n", + "def wmean(df):\n", + " w = weights(df)\n", + " result = (df.obama_spread * w).sum() / w.sum()\n", + " return result\n", + "\n", + "def wsig(df):\n", + " return df.obama_spread.std()\n", + "\n", + "def weighted_state_average(multipoll):\n", + " \n", + " groups = multipoll.groupby('State')\n", + " poll_mean = groups.apply(wmean)\n", + " poll_std = groups.apply(wsig)\n", + " poll_std[poll_std.isnull()] = poll_mean[poll_std.isnull()] * .05\n", + " \n", + " return pd.DataFrame(dict(poll_mean = poll_mean, poll_std = poll_std))" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 52 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.5** *Put this all together -- compute a new estimate of `poll_mean` and `poll_std` for each state, apply the `default_missing` function to handle missing rows, build a forecast with `aggregated_poll_model`, run 10,000 simulations, and plot the results, both as a histogram and as a map.*" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "average = weighted_state_average(multipoll)\n", + "average = average.join(electoral_votes, how='outer')\n", + "default_missing(average)\n", + "model = aggregated_poll_model(average)\n", + "sims = simulate_election(model, 10000)\n", + "plot_simulation(sims)\n", + "plt.xlim(250, 400)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 53, + "text": [ + "(250, 400)" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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rd1cn5fXe5c+Xl+fdkeGDBw+iV69eX6OqjLH/oMTERBw/fhyNGzfGiBEjvnV1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\n6N69Oz777LNqfQ0V2bRpEywtLSGXy3H27FmpPDc3Fx9++CFat26NCxc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+ "text": [ + "" + ] + } + ], + "prompt_number": 53 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your map code here\n", + "make_map(model.Obama, \"P(Obama): Weighted Polls\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 54, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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v3h3ISNA6duwIZJxTy5YtWbduHfv376dq1aoe+5hMJo/E8xqbzZbpMQIDA+nS\npQszZ87EZrNhMpnQarW0a9fujuO9PobMWpntdjsmkwlFUUhOTr7r+jOj0WRMsXjhwgWP9QULFgQ8\nb2RuZ8aMGUybNo21a9dSrVo1Tpw4kX2BigeCJKvZbOXKlbzcqzcxcXFoJVEVQuQzFStWvCFxKVSo\nEP369WPjxo3uR/XX27t3L4cPH+bdd991JymZiYmJcb+munfv3jRp0oSSJUsC3PMsAosWLWLu3LkM\nHz78lvUlJSXh6+tLoUKF7vgYFStWpHr16ly5coWuXbu617/wwgvYbDaefvrpG/apUKEC8+fP92j1\nTEtLY/r06Tc9zsyZMylSpAjvvvsuP/zwAytXrryhtftWVCqVR6tyWFgYu3fv5p9//vEoN2HCBEJD\nQ/H19fV4sUF2eOSRR/D19b1hkFtMTAzly5d3J623c+jQIS5dusSAAQM4evQo/v7+7inShLhGktVs\nFhMdzey5P/GoWU+gjPwXQuSia5OvX/s7My1atHD3A73e559/Tv369Xn++ec9ugNERUXRq1cvXnjh\nBd588033erVa7ZGgHTx4kHPnzvHOO+8AEBsby4EDB7BYLPz2228kJiYSExPD5cuXgYwp/a5PuK4l\nn3a73b3u+q4D10bz//XXXyQnJ7Nu3TogY3DY9S3FZ86ccSfMALNmzeKRRx7J8nRLXbt25YknnvDo\nQtCuXTsMBkOmr0UePHgw58+fp1WrVmzcuJG1a9fywgsv0LlzZ4/zuTYTgsPhoH379rRr1466desS\nEhLCn3/+6XEDYbfbPVpmr9Vx7e/g4GAuXrzIlStX2LdvH//73//w9fWlXbt2LFq0iK1bt9KnTx9q\n1aoFQP/+/dm4cSOzZs3C5XKxf/9+EhISOHToENHR0Zleh9t9lnx8fBg5ciRLlixx3+DYbDaWLVvm\nMQ+r3W73ePnA9dcBMuYgvzY4LTQ0lFatWlGsWLFMjykeXpqPPvroo7wO4kHybMdOXElNpTYB+EmX\nYHEbUd4OmnZs6x40IsTdWrt2LV9//TWxsbHExsbi7+9/wyNryHiL0KhRoxgyZIhHP1EvLy969OiB\nxWJh3LhxrFmzhp9//plly5YxePBgj3lZAUqVKsXSpUvZsGED27Zt46+//mLOnDnuWQO8vLxYuHAh\n8+fPp1GjRgQHB7NkyRLKlSvHmTNn+O6770hNTaVmzZqoVCqmT5/Ovn37UKvV1KxZk82bN/Pdd9+R\nnJxM9epbMGIVAAAgAElEQVTVady4MZs3b2batGmcPXuW4cOHs3z5cvbs2eMxeGjEiBGMHDmS8uXL\nAxndEdavX0+/fv3w8/O77XUsXLgwer3ePSANMuYXvXTpkvtNV9crU6YMoaGhrF69mlmzZnH+/Hkm\nTJhA+fLlCQ8P5+uvv+bs2bNotVoeffRRdDodq1atYt26dSxYsIBFixaxcOFC5s6dy0svvcSGDRv4\n7rvviI2NpUaNGnh5efHJJ5+4p9Fq3LgxZcqUYdmyZXz//fc0atSIqlWrUr9+fbZu3cr333/P7t27\nGTBggLtrQePGjUlJSWHKlClMmTKFwoULk5qaSp06dahevTqBgYF39Vl66qmn8PPz4+OPP+bkyZMs\nX76cl19+mc6dO6MoCrNmzWLWrFkkJiZSsWJFdDod48eP58CBAyiKQtWqVUlNTaVv376YTCYiIiK4\ndOkSH3744S1b8MXDR6XIHBHZqmbVahw8cphWhFASn7wOR+Rz2/3NfDx7eqYtNkLklNGjR2M0Gnn7\n7bfzOpRstXnzZqZOncqKFSvyOpSbOn78ODNmzODLL790rzOZTMybN49ChQrJd4EQmZBuANls+84/\n6dyxExfVdmzk3ze93G8cuOR6CpFNPvjgA3bu3MnRo0fzOpRsk5CQwPTp0z0m8M+PXnvttRumvzIY\nDJQtW5YKFSrkUVRC5G+SrGYzf39/Pp/4Jc5KxVilT8SFggNpvL5b6TjYr05lif4yc1TRrPFPZa3x\nCuk4br+zECJTarWaxYsXs2bNGs6fP5/X4dyz1NRUZsyYwezZs7P0qD8vORwOJk2axKFDh7BarcTH\nx7Nw4UIOHTqU6aN2IYR0A8gxiqJg0PsQYlNzDjNtCaGYdAu4I3ZcrDYk06pTB/q9MoCKFSsSExPD\n8qXL+GbiVzxu8rnnrhYuFNR5OGuDdAMQ4uESHR3NkCFD2Lx5M3a7nRo1avD666/f8ApXIcS/ZARQ\nDlGpVKxeu4bFCxay9Y8dnI+6TLHMp90T/3EWEwd9bVw2p9Pj2e7MmvsTFy5cYPCAV9i2fTsLFy8i\nqEAwb775Jk9TgDB8b1rXHl0aVpXCU9Z/W1vOY0YBUnDwJ4l0pShBMnNDntCrNFile4d4iO3cuZOd\nO3fSrVu3vA5FPKCCgoJITEzM6zDuiSSrOahZs2Y0a9aMTm3b8dup9VTEmyCyPpfew2ivdzqxARrm\nL1xO/fr1cblcfPThKCZ+8QUV7T5Uc6jo3uFZ0u1W6moKUMZpuGldsVjYZ7tMcb0/J0lnly6VIL2R\n2NQkKpUPo3qN6jRLSuTQzgM0NEuymhesuBikKoXm6us+NSrQqFRorjZ2X/v3te1qbr39xv1vte0/\ndatUqDQq1FcLqDRqz2W1GrUmo8y17WqNCpX66v5Xy2dsU3ksq9Uqd/lr2z2W1ar/7K++ejz1dbFk\nrMtY1qC6uk2tVru3X4vz+mX11f1U19elVqO+Otr6xrr/s6zWgPrqyGy1GpXm+mVNRrlbLWs0cLWu\njO3/Lrvrvu68blqXSg0qNYpKfd2yyr2vcnU7121XPJZVnvurPctmWrfKs27F/VpacCmKu4OXS8l4\nmua6ukK5bh2A6+o+HmWv7pt5XRlPff7dft3+KO59AJyujH87rx1LUXC6+Pff18XldClX1123/eo6\nAOfVel0uz2V33S7FvS5je8b+1+q+9icry47/blcyK+/yWHbcpm7F9W+civKfZdf1L5jI2Obervxn\n+er+AIrr3/IZy4q7vHvZo/zVZZfz6rIz44/zP8v/2Z5x3P9sc2ZW1uWx7LpN3QBJB2Zxv5NkNRe8\n/tZQVq5dI31Xb8GBi9OYOEY60ZGxHD58mJd69mL9r78S6vSirTkQ/6utn+XTblPZVVEaGzjhgiWF\neC8d48aNo0nTpgQEBLjf152amkqp4iXY5UqjglVHQbmZEEIIIfIVSVZzwdEjR/Dz9iHQKq13NxPu\nbUZbuSSLxo4hMDCQn2bNZtvS1bR0BRJwl4/o6zr98AVsNUvzV/henE4nJpOJgIAAdxk/Pz9+376N\nRYsWMe2ryXhrtNRI01GGm7fYCiGEECL3yGwAOcxqtfLuO+9S22rASy53pg7pzER521myYjlt27YF\n4MPRH5Ggtt/TixVUqCiNgcjjx/n99995tFp1XunX36OMoiicOXOGmjVrkmxO52LaFTYQT7g2DeW6\nlnAFBae0jAshhBC5TlpWs4HNZmPsmDH4+vkRceQor70xhDJlyhAUFIS3tzfLV66gS4dOFDLrCJbH\nzB6cKOznCpGHT3i83jA6OhqtWnPPI/V1qCll8+J/3V4gPSGRTp2f89j+/cyZjHzjLRLMGX0LGtZ/\nireGD+O9d95lzYVYqqV5EWVUOG66jEGro7s9NNdmD0hPT2ffvn2kp6eTnp6Or68vLVu2zJVjCyGE\nEPmFJKt34fDhw5w7d47SpUvz2fhPmL9wASV0fgS4NFjUCr+u+AWTy84PP/5Al+efp3nz5nw5+Sve\nHPIGRpWWBukGSVrJGFyz3yudSmFhHokqwOj3P6CKTX9P9ZtwMpcL4AD/VB+8DT40b9GCgwcPsmnj\nRt4cOpTAoCDS7FYqla/Alu3bKFKkCJDxLvCVK1cy4u3hmMwmTJfNhJUtx/kYM6Wys4uA3cmo997n\nyJEjbFr/GzqdjsFDXqdixYq0bdkKa1IKPmoNNrsdi4+WuIT47Du2EEIIcR+QZPUOmUwmqlWrRhm/\nAqTjINippaerGDqL5yP+CFJZvHARXa7Onfdy37681KcPs2fP5u1XX6elOSBLfTGjsXBCb6OWxcc9\nwOhBYMfFMl08TVu2YPrM7zy27dixgz/+/JN2BN3TMVRAWX0gpy3J2FxOSpQqScmSJSlfrhxOl4vq\nNWrw7LPPcmDY2wx96y0KFCjw774qFZ06daJDhw6kpaXh7e3NTwvm075NWw6o7RSya6lo8aJAJjcd\nThQsOFGjwgsV2lt0/3jSbCDmWDzLxn6FRaMQalYxZG8fLqRfoZ4SRBUlY8qto6RStFXje7oeQggh\nxP1IktU7ZDAYaNKgIef3Hqa22ZvCeKPLJBkxoiUmOtpjnVqtpk+fPlgtFt55axiPKL7UsN58Unsr\nLk5qzARWr8S6fw7TxVIQTR5OYJ+dtKgo7vJGBYSGhrrX7969m7bPtKKh2TfL/VWTyXi17SZNIo84\njRTFm0C8SMTGUxZfLuosrFq3hkOHDlGmTBkAPnjvfZo1a4ZKpeLj8eNvWrdarcbf3x+ARo0akXgl\nmUOHDrFxwwbGjx1HaasXGpdCmlFLOi5SbGZMdhv+RiMulwuTxUKg3kABtTe+aQ7Ku/QY0aKgoEWN\nF2pKYaDUdXPwVkoFJ75oUHFUa8LPocKigZiYGOLi4ihcuPCdX3AhhBDiPiUjfu7C6vW/8uI7Q0io\nWYwVPolEkEoqDvcAnGTsnNFaKVaieKb7Dxw0iH2HDrLXcfmGQTsKChcws9WYxiLvePyrleenuXOp\n/VgdjpPFOZvyuWjM7FWncknnonfflz22vTP0bWqavCmexTdTJWNnjXciK4jDolaILWpkd6iKedo4\nTpT1Z6E2jooVw5j/00988t4oAKZPm8aYcWNRqe488ddoNNSsWZNhw4cTeeok9V9+nlbvDmL8rOms\n2LyeyDOnsNqsXL6STFJqCmmmdLbu3sWYH77h8f7Ps9aYwo/qC2z2SfUYwHXDcVBxBhM7HPGc0dmp\n4fQlfd9xypcuQ6tmzUlJSbnj2MWtHbLmj9+v3bEJeR2C2/ajZ/I6BAC2/r0vr0Nw2759e16HAMDe\nnX/kdQhuJ/b9ldchAJB0Yn9eh+BmiT2S1yE8UCRZvQsGg4EPRn3I7v372LDtd2x1yrMxyMwyQyI7\nVEmsM1zhqZ6dmf79zJvWUb58eZo0bMRSn8ts9Tex3c/MGv9UZmtjOV7al9c/H8OlhHj+3h9OWFgY\nn3wxgQM+FpKw5+KZ5oz9fnbqDerBDwvm0r59e/f6EydOsG9fOBWuvpEqFQdnMGVah4KCDRdbSKBk\n6dKsXLkSk8nE2ejznLlwjpi4WCJOncBqs7H34AFWrfqFNIeVJ2o/xoBXXsmW8wgJCWHqtG8ZO24c\nzz77LHXq1KFw4cKo1f/+Wnl5eVG5cmU6d+7M1GnfEp94mbi4OIwlCrPUkMhebSqXsGLG6U5e47Gy\nyzuNXYaMc69i06NBxWNWI89bC3FiZzhz5sy567gVRWHHjh0M6NuPf/75594uwgPkkC09r0MAYHfc\n5bwOwW1HPklWt0myeoO9u/JPsnpy/995HQIASSfzU7J6NK9DeKBIN4B79Nhjj7FzT8Yv6oEDB5g0\n4QtGd+pI586db7vvb1s2c+zYMY4fP47VaqVcuXKEhYXh5+d3Q9nHH3+czyZ+ybi3R9Am3R/VfdYd\nIAU727VXKO3QkWg1MXLkSPdgpuu5gD3e6Xg5IdyR8Z/2AErdUO4Pg4lT9ivUrlmLiVO+pm7duu5t\nWq3W3f/0Wuvp1GnfUq1aNR555JEcOLus0+l0FCpUiMORxzh48CCzvv+BX9esJeZiHHaHA6OXN94G\nH1559TWeadWK90aO5NyWfRRUMvrGeqGmutmb9995F6vFwltvv52lFmJFUdi2bRt79+5lzvc/culC\nNEVMKn5euJAp076lZ8+eOX3qQgghxF2RZDUb1axZkznz593RPpUrV6Zy5cpZKjtgwACmTZ7CyWMX\n3a2P94NjqjRO6O2YdFpCGjRg5WuDM01UK1SoQMSJ48yYPp2x48YB0IxC7u1XsHNcbcbsrcJSwJek\nyHP4+GStu0D37t2z52SyUY0aNfhqymS+mjIZgJSUFOLi4ihXrhwajYZuXbqwafNmnsPzWoXgTVtz\nEF999DE7tm5j3s8LM73Bud62bdvo2LotZZzeFLdpqEcgKlRUMNl4e+CrxF+8xFvD3s6xcxVCCCHu\nlkq59kJbke+ZzWae79yFiC07aWLxz+twbsuKi1gs/KlLZfCbQxg3bhxa7c3vj44ePcqrA15h+84/\nMXp509YajB9a7Lj422DmgmKmywvdKRwayrsjR2A0GnPxbHLfqPc/4JtJX1PLpKcMPje0pjtQ2O2d\nTlohI6vXr7tpq/GKFSvo9WJPytu9edx2401OuDqFp4e+zOcTJuTIedzK3fQbFkIIkXW+vr6kpqbm\ndRj3RFpW7yMfjxvHP5t20MQWcPvC+cAanyQqVKnE8PbtGPLGG7dMVAE+GDGSxD8O0otiJFvt6FGj\noPCHTzo1nmnMT++9R61atXIp+rw3etxYGjRuxOsDB3Ew9iJlTVrCFAM+aICMGRXqWX2JvJBGvcfr\n8u13M+jRo8cN9Vy8eJHiTh21bZkn974uNVs3byEyMpIKFSp49LnNaXKvLIQQ4nYkWb2PPFK1Knpv\nb3S2/D8uzoITk8vBn3t2Z7n17PTp0yT4qglPS+GoNp0AnQ8qlYqylcKYt3AhOt3D9yKFZs2aceR4\nJH/99RfffD2Z5atWUcVhoJrDiPZqS2tFfClg0vF6/4GEhITQvHlzIGOqqz/++IN5s+cQZFNlOu3Z\nCdIoiQ/7Is9Rr1Yd6tStyy+/rsXb2ztXz1MIIYS4GekGcB9JSEigZLHi9LCFZpp4uFA4STq+aCnK\nvb396V7FYmFHgJXh775DYGAgTZs2pUKFCrfcx+l0snnzZpYvWUr/ga9gsVjw9/cnLCzsoUxUMxMV\nFcXgAa+wdetWSml8KWaCUhjQoOIfUij/QiumfPst48eNY9o331JMa8THplDbarjhM3PtDV8NCaYy\nfjhR2OGTRonHa7B2w3q55kIIIfIFSVbvM6WKFueJWBdB1705SUEhCjMHjTb0wQGQnEaTVCNqVJwi\nnSLoPSbYV1C4goPAHHwjlgUnx0jDoVFh12k4h4nSZcqwdsNvFCtWLMeO+7CIjY1l5cqVzPx2GqZT\n0TQy+5KKgzWGK+i9vQk1q9DYnPi71FS8+oKB/7qAmbVcoqY2mLqOjAFaThSWeCcwb+ki2rZtm9un\nle9cvHjR46UVQtxKdHR0nn2/KYrCkiVLOHfuHHXq1KFx48Z5EofIOxaLBZvN5n6RzYMk/z9PFh7K\nlS3LFRxAxgCbSNJY55vKqVK+zPx5HkcjIwgsWYyfdZfYQgIninizwZCCCae7ji3GNBYTQyK2G+q3\n4uIoqaz3TeUc5ruOU4+GRwngMac/9cxGupoLYou8wKfjP7nrOsW/ihQpwsCBA9m1dw/6skU5Tjr+\neFHG4c1jSVrqW3w5oUpnB4l8zzkOkkIydmKwuOsoePWGJ07372dDg4paVgMv9+rN3LlzcTgc9xxr\ndHQ0gwYNYvr06fTq1YsjRzKfLPu7775jzJgxjB49mg8++OCej3svsZw9e5YePXrQtWvXPIvDYrEw\ncOBAChYsSIkSJfj222/zLBZFURg+fDglS5akaNGizJo1K0/iuN6mTZto1qxZtsdxJ7Fs2rQJtVrt\n/pPdc7BmNY6UlBSaN2/OuXPnePvtt3MkUc1KLH379vW4Hmq1mm7duuV6HA6Hg1GjRjF16lSGDx/O\n2LFjszWG/EZRFGbPnk1YWBh79uy5abnc+I7NMYq4byQlJSnlSpVWmlNQeYFiir/eoDSu30BZu3at\n4nQ6Pcpu2rRJeaJ2HSU8PFwZ+e4IpZQhSOlPSaUrRZRCQcHKl59PUHz1PkorQpT+lFTaEao84lNA\nMep9lLYtn1Hat2+vPKoNUgZQKlv+dKOoEmTwVf744488unoPrnnz5imhBn+lG0Uzv+4abwVQAMVX\n76P0oJh7e3nvQAVQ+lPSY782hCilfYOV4qFFlP379991bC6XS6lVq5ayceNGRVEU5ejRo0qZMmUU\nh8PhUW7lypVKvXr13Mtdu3ZVvv/++7s+7r3EoiiKEhUVpbz66qtKgwYNsjWGO4ljzJgxyuLFi5Uj\nR44ob775pqJSqbL99yerscyfP1/ZsWOHoiiKsnTpUsXLy0sxmUy5Hsc1Fy9eVJ566inl6aefzrYY\n7iaWV155RQkPD1fCw8OVgwcP5kkcTqdTadasmTJ8+PBsPf6dxmIymZTXX39dOXnypBIVFaWcPXtW\nefPNN5W5c+fmahyKoiiTJk1SvvjiC/dy48aNc+T/ngsXLigDBw5Upk2bpvTs2VM5fPjwDWUsFosy\nfPhw5bPPPlO6deumLF++PNvjuHTpknL+/HlFpVIpmzdvzrRMbnzH5iRJVu8jM2fOVIxe3kodTbDi\nrzcoH773Xpb2czgcSq3qNZSnVAWUzhRRCgYGK2azWVm5cqUS5OevaNUapXypMsqXX3yhXLp0SZk/\nf77ibzAqbQi55yT1aW2IUiGgkOJvMCqTJk70iCsyMlKx2Ww5cakeKi6XS/lq0iQlwMeotKDQDT+D\n/pRU6uoKKYDySv8BSjV9Qfe2ThRWAKVrJonuAEopT1NAKRZaWElMTLyr2DZs2KD4+PgodrvdvS4s\nLExZunSpR7l69eopY8eOdS8vWLBAqVq16t1dkHuM5ZpRo0YpTz31VLbGcCdxzJgxw2O5dOnSymef\nfZYnsURFRbn/bTKZFL1er6Snp+d6HIqS8Xn/8MMPlZkzZyqNGzfOthjuNJbjx48r9evXV1avXq1Y\nrdY8i2PBggWK0WhULBZLtsdwJ7FcuXJFMZvNHvvVq1fvrr877jYORVGUwYMHK+9d9/9jp06dlDVr\n1mRbHIqS9cT53Xffdf8up6SkKCEhIcrx48ezNZZrbpWs5sZ3bE6SbgD3kZdffpn3Rn1IzZ4d2H1g\nH6OvTpx/OxqNhrkLF/CP3owONd4WB3PmzKFDhw4kplwhOeUKx8+cYuhbb7H6l194ve8AWpj8KU7W\nJty/mdOY2K1NZdiEjzkSGcEbb77p3mYymXisdh0mfP45ycnJ93Sch51KpWLIG2/w6+aNRBbVsc0n\n1aPbhwoVNW0GyvoW4HJSInadmlQc2HC5p8E6oc78tbZh+BKSZKfrs51xuVx3HNuff/5J2bJlPaYt\nCwsLY8uWLe5lm83G3r17qVSpkntdhQoVOHLkCAkJCXd8zHuJJTdkNY7+/ft7LIeGhlKyZMk8ieX6\n465evZqpU6diMBhyPQ7IeJTZu3fv206Fl9OxhIeHYzab6dSpEyVKlGDTpk15EsesWbMoWrQo77zz\nDo899hgtW7YkOjo612Px9/dHr/93YG90dDQ6nY6goKBcjQOgY8eOTJ48mU2bNrFv3z5cLhfPPPNM\ntsUBGV1Ajh075u5yUblyZby8vFi5cqVHuWnTprmnXPTz86NBgwZMnjw5W2O5ndz6js1JkqzeR1Qq\nFSPeG8nMH3+kYsWKd7RvlSpVeH/UKJZ5xeNdKJBGjRq5txmNRvf0UgvnzuNRs54C3PtIcBtOHq9T\nh379+lG8eHH3+vPnz/Pxxx9jtMOY0WMoEBzM/7p3l6T1Hj355JMcO3mC1v17slQXzy/GZNJwcIgU\nLmJFm25lyZIlRKRc4le/VBZ4XeSc2kqwzsB+VzK7NSko3DjesrbNyPHd+xg7evQdxxQXF3dDZ/+A\ngAAuXLjgXk5MTMRutxMQ8O/8wYGBgQAe5e5VVmLJDXcTh8ViITk5mQ4dOuRZLAkJCQwdOpSePXvy\n559/4nQ6byiT03Hs3r2bggULUqZMmWw79t3G0q1bN8LDwzlz5gx16tTh2WefJS4uLtfjCA8Pp0uX\nLnz11Vfs2bMHo9FI3759sy2OO4nleqtWraJdu3Z5EkezZs0YO3YszzzzDIMGDWLRokVoNJpsjSUr\nifOlS5dISUnxuLErUaIEBw4cyNZYbie3vmNzkiSrD5Fh7wwnIfEyx06e8LjDukZRFPbt308I9z7H\nphOFs0aFTl06e6w/ffo0VStXZumUmdS3+tLMHkh3pSh/Ll3LtGnT7vm4DzsfHx++/GoS0XGxDBz2\nJit0CezRpLJRl0zBx6sybtw4wsqUpXy5cqxYtZKD3ia8XRk3KoecyWz3ukIcFtJxuBNXDSoamox8\nNeFLNmzYcEfxaLVavLw8Z534bwvttS/768tdK6Nk42QlWYklN9xNHDNnzmTixIlZfr1wTsRSsGBB\nxo8fz6JFi1i1ahVz5szJ1TiuXLnC+vXree6557LtuHcby/WKFy/O0qVLKVy4MKtWrcr1ONLT03nq\nqafcy/3792fjxo3ZMjjyTmO53i+//EL79u2zLYY7iUNRFOLi4vj44485deoUTZs2xWTK/OnR3cpK\n4hwYGIhareb48ePudf7+/sTHx2drLLeTW9+xOUmS1YeMr6/vTefPPH/+PE67A1/u/g7UhosEbKw3\npFD5idoMGjzYvU1RFHr/70UeMet5OtWAHjUX9C52Bli5rHHk6puTHnRBQUF8MGoUO3f/Tb/+/Um3\nWUg6cop5n31NsTNXsB08TevWrXm8bl1K16rKC92640IhTrGyXpXAQqLZqUtz12dESwOzL927dCUt\nLe0WR/ZUtGhRrly54rEuOTnZY3qfAgUK4OXl5VHuWit7dk4DlJVYcsOdxnHo0CG0Wi2tW7fO81j0\nej0dOnTg9ddfZ9++fbkax7Zt2xg/fjw+Pj74+PjQv39/tm/fjsFg4PDhw7kay3/5+PjQokWLbH06\nlNU4QkNDSU9Pdy8XL14cl8uVJ7Fck5KSQlxcHOXLl8+2GO4kjokTJ5Kamso777zD3r17OXv2LJ99\n9lm2xpKVxFmn09GxY0e+/vprHA4HNpuNv//+m0KFCmVrLLeTW9+xOUmyA+G2e/duimgNN7yD/nas\nONnlk84Wv3QWecfzR7CdoWM/YN3GDe5HLy6Xi5UrV/LHrp0cVafxveocizQXqdujI7N/WcbuA/sY\nOnRoTpzWQ61GjRosXriQFhSiYZqBRqkGwvAlSmMFwLL1ABcPH+fo4cMYfAy0cxTkeaUI5TT+nHCm\nEI/VXVdR9BRyaJkxfXqWj//0009z+vRpj3WRkZEeU+uoVCoaN27MiRMn3OsiIiKoXLkyISEhd3nm\ndxdLbriTOGJiYti8eTMDBw50r8vOFrO7vSYFChTw6NqTG3G0b98ei8WC2WzGbDYzc+ZMGjVqhMlk\nomrVqrkaS2acTmemT6xyOo569ep5tNxZLBaMRiMFCxbM9ViuWbt2bbb3Eb2TOLZs2eL+TJQqVYoh\nQ4YQHh6erbFkNXH+4YcfCAsLo1OnTnzyySekpKTw5JNPZmsst5Nb37E5SZJV4bbzjz/xS7uz/wit\nONnCZZQyoXw6ewZRF84TdzmeN4cORaVScfHiRd564w3eeOMNnn32WbQaDXaVgrfWi1Jeviy7+vis\nUqVKN9yliuyhKApmnB79UZs4AulMEariTxOTH5GRkVQoV54EbPigob4zAKvTwWZdssdgrRomb8Z8\n+BHr16/P0rGfeOIJSpUqxe+//w5kfEGaTCbatm3L+++/z6FDh4CM+RlXr17t3m/dunX06dMnO07/\njmO5Jqe6CGQ1jitXrrj73UVERHDkyBE++eQTLBbLrarPkVg2bdrE+fPngYzP0/bt27P153OnP5tr\nceTEI8ysxjJx4kQiIiKAjEfCkZGRtGnTJtfjGDBgAEuWLHHvt337dvr165dtcdxJLNesXLky27sA\n3EkcNWvW5J9//nHvZzabqVOnTrbGktXEOSAggBkzZrB69Wr69u1LeHh4tn+3QeaP9XP7OzYn5cxw\nSnFf+nPbNkKUrCWMCgo79KkkuGxUrlOLd94bmeljyqYNG8OZixyxJ6H6P3v3HV9FlTZw/Dczt6cn\nJCQklBBCRzoiiohYaCp2FAv2grqWdXftymvZXXcVV1FUFLFjRVRAxQIqIEWQKr2FAElIz+0z8/6R\nGCkhJOQmuSHP9/NJuzNz5plLuHnumXOeA4zRk4jXK4YhBGC+zcOaNWvo2LFj6C5EHOS7BfM5b+Qo\nbLvddCACgMQDxiXvw4cn4KdDZgf2rikfb5VVsXhAWcDPx7ZchvpjSMNJHFaGeCK5YuxlbN6+rXKQ\n/s0l4G0AACAASURBVJEoisJnn33GxIkTWb9+PUuWLOGLL77A5XIxd+5c+vTpQ48ePbj44ovZsWMH\nDz74IE6nk7Zt24a8p72msUD5H/xZs2aRlZXFp59+yujRo0P2ZqomcXTr1o3zzjuPBQsW8PLLL1ce\ne/nllxMZGRmSOGoaS48ePXj77bcr/9impqby+OOPh7RHpjb/Ngce88fE0FCqSSzdu3fn66+/5v/+\n7/+4+eabiYmJ4aOPPgpphYKaPiennXYa1113HTfeeCMZGRlkZWXx9NNPhyyO2sQC5TPPf/31VwYN\nGhTSGGoTx0MPPcRdd93F/fffT2JiIsXFxTz55JMhjeXAxHno0KGHJc6XXnrpYb+zN954I/fee29I\ne+ABcnNzefXVV1EUhXfffZfU1FQ6d+7c4K+x9UmWWxWV0lomMzhHI6YGy7BuoozdGbG0bdeO9z6Y\nQXx8fOW2goIC/u/RR/ll4WIWLluC3WIlSlc50Yw5rBzWgmgPw68bR69evbjiiitk3Go9+eGHH7ho\n1LkMd0cTiYUsPGxwBBjgdeFCY66ziPQ+PQgsXEtPM5oiAsx1FvHia68y6V9PE/HbTjryZ5K0yF7K\nSVdewJRXX2nEqxJCiMazdetWJk6cyIABA1iyZAm33347ffv2pV+/ftx///1ccMEFAJSUlHDzzTeT\nkZHBxIkTGznqpkmSVQHAlJde4r577uU8TxyOI0yw8mGw3FLCVs2Lbhh8OXcOp59++kH7vP3mm9w+\n4TbaBG208qrMJZdOznhO9Lgqa3oCBDFYTyllGmxXPcQlJ7F1x/Z66SkR5Z6Y+H/866mnONsbSz5+\nFtpKMXWd/kYMhaafFif35Pff1nBeaXl5k2y8bGgXwV/uuYt77r6HSwJJlf+GXnRmOgv47qcFlTUE\nhRBCHOybb75h1apVjBo1KuQ9qs2JJKvNnNvt5vGJE3n5+cmc5Y4+Yq9qEQG+dBQQNE0eeuRhxo8f\nT0pKykH76LpOUnwCpxY7aVlxm7mE8uoCh07aCmDwOuVj4OxWGz8vWkjfvn3r4QrFgZ6bNIl/3/8I\nNp9OgRIkwepkt7+ECKeL8y48n/fen8Fl/iRsqAQweMuyhzK3m5TEJEYURRBxwMihjZSyq100q9ev\nO6gYuBBCCBFKzfKea15eHn6/v7HDaHSzZ88mo207Pv3fVEa6Y6q9/V9EkN69elFcWsI//vGPwxJV\ngEWLFkEgSOIBCwpEYTksUfWi86l9P3EOF9OnT2dvzj5JVBvIrRMmUKwY7DY8dNRdnOmN4UIjmdZl\nCvtz87BaLAQw8KIzx1VMZvsMAoEAQV1HPeTfMZMItH1F/PWupjPuSQghRNPT7JLVVatWkZiYyN//\n/vfGDqVRLV26lMsuuoR+eSpDPJEH9ZhVJceik9m5MxaL5Yi36tPT0+nQuTMLXdUXXy4miOa088Jr\nr3LllVcedZKOCB2r1Up8TDROi5V1dg/ZeInCQmsc/L5hAy7VgobCAmcZIy4+n9Xr1+FyuVCALbgJ\n8OcMeQWFXh4HU155GZ/Pd+STCiGEEHXQ7JLVDh068Jc77uDxxx9v7FAaTX5+PueMGMlAj4tUjn77\ntpQgG6weHplY/XKbqampvPz6VAoth5f82UwZCx2lfBVRzLeuEsZfcw2XX365jFFtBKVlbiJNjcGn\nDWGHo7xUWUvs7Ni1k32lRXzhKuS866+gbbt2RDiddM3syLCzzsLdPY33rTkssZdiVJTB2osPRVE4\nc8jQkK9HLoQQQkAzLF3lcrmY9NxzjR1Go/riiy+I9UI6riq3GxUVOVdZytho9eIJ+vnbXffSunXr\no7bdpk0bvKrJHFsBhqbQ1WOjJXaWOt1ceMnFOB0O/jd5csjXaRY1d/LJg4iNjWX89ddx+c/lS1da\nUElzxnDmdRfRr18/li5dyscvv0Ki6qDj5mJ2bv2OskgLUZqN3U4T1SijXyCSzkTSWncwY9lSqZMr\nhBCiXjS7ZLU5mz17NrfccCMpqal4Fb3KfbLw8K2lAG8wwLCTT+OXV16mQ4cONS4pFR8fz7adO4iO\njqZXz14sWbMer2py7133MvGJ5tubHU5mzf4SgOnTp6Ppf/aCtyjRwTB47KGH2bUnGw0FVVH4zurn\nokAitmKVAA4+JZ/NNgW7Dt2NCFQUIiw2zhk+gllzZtOyZcvGujQhhBDHIakG0Azs2rWLfz35FO9O\nf5P+HhcFSpBE00rripqnJiabKKOIIFucQf75zH9Ys2oVz0+eXKfb9EuWLOHB++5n+ttvVTkhSzSu\nzZs3M2zIaSTn+entd7EXHxszonB73PTKDpKMg224+Z48riINS8WooQL8fOcowbCqxAQU2visZJou\nvo8o46lpL3HxxRc38pUJIYQ4njS7MavNhWmazJw5k1NPGkSXzI78OO0DzvHEkY6LPmZ0ZaIKUEiQ\nlVEBNrgC3Hvf37n55pt54cUXjylRvemmmyqXtRswYABffztPEtUw1aFDB5b/tpLNzgC5+LCh4PP7\nuf+Rh/k1wkcAgyK7go5JoGKMql7x9TRvFEVlpST37sp6uxcNhRg/XH7ZZcRGRfPKK6/Uy1KYQggh\nmh/pWT0O5ebmcu1VV7N0wc90d9toh7OyV+xAv6tuNkQE8OlBrhh/Nc+98HydelKffeYZ7r7nHp5+\n+mn++te/1uUSRAN66aWX+Pe9D9CvzMGvrW1s2r6VUwYNIn/57+xUvaSlppK7dx8AVkXDh47H6+XM\nM88kPi6Ore/PpQfRGJgEMSkmyC8RHtI6Z/Lf/02iT58+UodVCCHEMZOe1ePMtm3b6NuzF7u//YXR\n7lg6EFFlolpCkJ/I5+SzhjHnu3n8b/ILdZ6ZHx8fz03X3yCJahNzww03oLWIYQceALZv386aVatp\nG7ShqCqjzzuXlikp9BvQnxMG9sfv8zPAiOabefOYv2ABZVr5+10VBRsqLbAxoiwa7detXDJ8NDFR\n0dz/j39gGIdXiRBCCCGORnpWjyPBYJAO7dJpvcdLNyOi2n11TNZQwhKliI2bNpKRkdFAUYpwNGfO\nHM4/91w6dcjkky8+58SefTi/LJb3Hbmcc845fP/xLLoaEaxx+olt1ZLN27ZiR+NkI4Yk7ERVM1fT\ng878iDI8Do0169eRmJjYgFcmhBCiqZOe1ePIJ598AkXuoyaqABoKRU6Vu/7yF0lUBSNGjODV11/n\njrvvom3btuwvK2YqO+mYmUmrtFRUu41MIunssaIqCs899xxBDByo1SaqAE40hpdF08qjkNqqFR3a\npbN169YGujIhhBBNnfSsNjGGYbB69Wq+mjuX+d9+R7+BJ/Lj9z9w9fXXMfXFKbBkI92IqvJYPwb5\nBMjHT4EdCuPsbNiyGZer6nqr1fF6vfz+++907txZxiMeh3r37ElRcTFfff0155w9gm07tjPKSCQR\nG6vVMtY5fcTFx5Ofl8fZnhiKCdLuCHV7DxTEYJlWwsBrLmbKq680wJUIIYRo6qTOahOyePFixl54\nMZ7iElICFuJ98Pn3y9iplzL+px8r9zPsFtr5rPxu84JukKrbWBzhodjvpUO7dHr16cMFAwdwzjnn\nHFOimp+fzxmnDWXdunU88MjDPPTQQ6G8TBEGfl25Er/fj91u54677+KjTz5m28+rSPLbOcGIpGWZ\nlflmPoNOOYVPvp2HTbPSJuBEpfpxzxZUSp0aJ51ycgNdiRBCiKZOktUmQNd1Jj76KJP++wwneiJo\nT+yfG4OQZw+y3+dnGC34ljxWBQtZa7dwzU3X896777Eibx+TnpjEhNtuC8nKUXPmzGHD+vXEWhwy\nhOA4pSgKdrsdgFtvm0BKaivuX34z+Mu3t8TOKW6TpStWcN111zHrnQ8gAHvxkogd7QhJqxudfbqH\nsWPHNtSlCCGEaOJkGECYMwyDq8aN48dZcznVHUEEFjZpHuw6tKmolRrEpIQANlTW2b2sM0v4xz/u\n45HHHkVRFCKcLopKikO2xKnX6+WNN95g/dp13P/gA7Ji0XHMNE3cbjeLFy9mzKhz6Odz0YlIoHyS\n3ieO/bTLzCC4ejs5LtjnLuZK0nBS9e+aF52PnfmUuMsa8jKEEEI0YdKzGsZM0+S2W27lx1lzOcMd\nhRWVMoIsthRjtWm08jjwobM0wkvQNNnizqdFRAI7f8+qnHG9YMEC4uPjQ5aoAjgcDm6++eaQtSfC\nl9vtJjIyEofdjtfnY4ndpJXPQRQWNBR6eB147Xa2RkFhSTEdIhNwlh75d82OimKWl1hLT09vwCsR\nQgjRVEk1gDD24uTJfPr2e5xekahC+WpT0dHRlHjdLFWK+NxZyJlXX0psZhu6derM5VeMo6CgoLKN\nwYMH061bt8a6BNHERUREMP7Kq/D6fKQkJuFyOikiULk9HRfb1m1g4uP/x9lnncVudxF78FZuLyNI\nCcHKnxUU2ipOPvvsswa9DiGEEE2XDAMIQz6fj19//ZVBgwZhUzUuNpKJxIIfg89dhfxv6svk5+dT\nUFDAWWedxa+//sq9E+4g03CyQ/Px2KT/MHToUIYPH86uXbsa+3LEceC6a67h9TfeoAtRnEr8Qdu2\nUMY88rjkkkv44IMPsCoqKbZIIrCw3pdPjGZnrJ5cuf9qioke1o85875u6MsQQgjRBMkwgDBSVFTE\n8DPOZPGypaiKgl3R8Bk6WXjYa9FxqwajLhjDZZdddtBxwWCQUiPAJruFqOgYWrZsSffu3RvpKsTx\naMTIkaxcsZKyLVlQevC2VBy0xM6mdb8DYNE0sgNlOCNc4INReovKfT3orHb4mP3oww0ZvhBCiCZM\nhgE0ok2bNjHu0rG0Tm5FckIiV44bx+JlSwFo7YylvxkDwFKbmw3BItr168GLL085rJ2BAweyaNEi\n5sz7ml+WL+OdN6YDcNutExruYsRx7aKLL+bxp57EoxisU8qYad/PV47CioUBNHoSTYv4eO688048\nwQAokJubS3x0DDp/3rxZafdwxVVXcsoppzTi1QghhGhKZBhAA/N4PDzy0MOsWbWKn378iS4BB+10\nBwrwlaOIQm/5LGlVVUltmcxjTzzOtddeS5tWqezYnXXU9svKyoiPi+eaq6/mhZdexGKRznMRGsFg\nkIy27XBn55JXUcMqyubgVH80JlDUP52vf/iOhQsXEgwGGT58OJ3SM+iyvYxkHGzHzZJoP5u3bSU+\nPr76kwkhhBAVJJNpYK9Nncp7k1+hg9fKRSRgO6Bzu51XYyVwww038Pzzz6NpGoqi8OmHHzH2inE1\nat9utzN7zmyGDRtWT1cgmiuLxcInsz6jf//+DDbj+VktZPh557Dm829p49Xw+Xy4XC7OOOOMymPO\nv/hC/vOf/9JWcVEUbWXuV19LoiqEEKJWpGe1Ac2dO5erLh/HyQU2WmKnmAD7CaBSXiv1F3spJT4v\nnTIz+X3jxsYOV4gqvfD889x91910yszk8zmzGXrKYPbk5PDMs89w64SDh56UlJSwaNEinnr8CV6f\n/oaUqxJCCFFrkqw2kLy8PBITEzmdFmQSwQq7m981N/379sMwdHTd4M6//ZXzzz8f0zQJBAJkZ2fT\ntm1bFKX6JSyFaGhFRUX4/f7Ker5CCCFEfZFktQGlJaeQuM+D12UhCy9btm0lKSmpyn2/+OILzjnn\nHM4cejpff/dtA0cqhBBCCBEepBpAPSkpKeGJJ57g999/r3zs489mMuyu67jt34+xe0/2ERNVgB49\nejCwb3969e7dEOEKIYQQQoQl6VkNsfnz53Pl2Mvp1KUz877/jisvH8eb77zd2GEJIYQQQjRJ0rNa\nR8FgkOXLl+P1esnKyuLfTz7Frr3Z7Nmdzcizh3PTrbc0dohCCCGEEE2W9Kweo2AwyLhLx/Ll7Nno\nwSApqa3I2r2bJ594ksxOHTn33HNlYpQQQgghRB1JsnqMXn75ZR6586+c5Y2lkACfs69yW3Z2Nikp\nKY0YnRBCCCHE8UGGARyjfv36sc9byltkVa7m8wdN0xopKiGEEEKI44skq0eRn5/PW2+9xYwZMziw\nE/rt6W/S3hpNNy2mchWqSy+8iK1bj1yOSgghhBBC1I4st1qNTZs2ccpJg4jzQb7hw+FwkJaWxqJF\ni5j62msQ9NFFiWaFw8Pz/3qO2+64o7FDFkIIIYQ4rsiY1SMIBoNcOOZ8dn35Iy2xsczhISopgZy9\n+yjzewEYccaZtG7Thjv/eg9dunRp5IiFEEIIIY4/0rNahezsbC449zz2rNuE36FTlhLFC089zxtT\nX2NXVhZnnnY6jz35OCeddFJjhyqEEEIIcVyTntUqXHLBhaydNQ+rZqH16Sfy2ZdfoKoqpmlSWlpK\nVFRUY4cohBBCCNEsyASrKgw7+yx2qD4KWjh58913UNXyp0lRFElUhRBCCCEaULMdBrB69WrunHA7\ngUCA+Qt/OqiA/7hx4wgGg4wfP56IiIhGjFIIIYQQonlrNsMAAoEAeXl5lcX6v/rqK4YPH050ZCTb\nd+4kLi6ukSMUQgghhBCHahbJqq7rdMnsSEFhIbv37sFmszV2SEIIIYQQogaaxZhVVVXZtG0rvXr2\nwjCMxg5HCCGEEELUUNgmq7NmzaJH5y5MnTqV6jp/582bxwP3319tW4qi4PV6+eb7b3E4HKEOVQgh\nhBBC1JOwHAbg9/s5ddDJ7Fq+mr1KgBUrV3DCCScctp9hGGiaVvn9gZOkhBBCCCFE0xdW1QB+/vln\n9u3bx+OPPsaaNWsIYtCjew969OjBW2++yb6cHDp16kRkZCSnnXYaiqLw4osv0r59e0lUhRBCCCGO\nQ/Xes2qaJnv37iUhIaHaiU3Z2dmkpqZW/tw5I5NTTxvC3+77BxkZGVx68SV88NGHldtzcnJITEys\nz9CFEEIIIUQjC0my+uB997F+7Xr+99LkyoRz0rPP8torr/L4P59izJgxTJgwgYsuuohevXoRGxt7\nWBu6rvPpp5+yZvVqRo0eTb9+/Q7qLS0qKuL777+nc+fOdOjQAYslrDqFhRBCCCFEPQhJsnrDtdfy\n5rTpdOvRnV9X/YbH4yEhLh6Pz0tUZCR6mRe3GQRgypQp3HTTTXUOXAghhBBCHP9CUg3g1ttvx49B\n7969gfJeUI/Py8gzz8JhsxOp2Wjfpi2nn3IqXbt2DcUphRBCCCFEMxCyMavTpk3jsssuqywNtWLF\nCnr27Mnbb7/N/G+/4/mXXsTlcoXiVEIIIYQQopkIy9JVQgghhBBCQBgvCiCEEEIIIYQkq0IIIYQQ\nImxJsiqEEEIIIcKWJKtCCCGEECJsSbIqhBBCCCHCliSrQgghhBAibEmyKoQQQgghwpYkq0IIIYQQ\nImxJsiqEEEIIIcKWJKtCCCGEECJsSbIqhBBCCCHCliSrQgghhBAibEmyKoQQQgghwpYkq0IIIYQQ\nImxJsiqEEEIIIcKWJKtCCCGEECJsSbIqhBBCNIIZM2YwefJkCgoKGjsUIcKaYpqm2dhBCCGEEM1N\nSmobCn0aqfF2Nm1Yj6IojR2SEGFJelaFEEKIRmBiYqQOYs+evWRlZTV2OEKELUtjByCEEOFmxYoV\n7Nu3r9HOn5WVRVpa2kGPlZSUEAgEiI+PB6jshTvWr9VtM02z2g/DMGjMm3JFRUWYpklsbGyjxVCV\nsrIyvF4vCQkJNdrf5/UCYItKZO3atbRu3bo+wxOiyZJkVQghDhAMBhl08ik4YltBI92VLc7eTHpU\nCzT1z5tfu8sKUU1IiYwFTEDBrPjKQV//+HywI6WW5kHflbdZkboedPlK5dfy8ygHPdqw9pUV4Td0\nWkfFN8r5jyTXU0KRrhOT3K5G+wdt8SgWJz4tijVr1jB8+PD6DVCIJkqSVSGEOERsXDy5ZjRKfCcU\ni6PhA8jezJASJ9YDRmrNpYRY1cbJxc6GjyfMLDN97MTL0KLwei5yUPlU3Y+71ekoSs1G2SlAwBLN\nol+W1W9wQjRhMmZVCCEqmKbJmPMv5Ol/PcVJmdE48xongZBZr0ejhOVzlIQdTVUxy/JqdZwW04Y5\nc7+iVVpb+p14Eu+99x5LlizB7/cDUFpaynfffcf69esbdfiFEI1FelaFEKLC7Nmz+ebb71j5228s\n/GkBHTt3heTGiUXmhR+ZwqGDFMJDEAPd0NGsteuNV+xRGJ0uZr+/lIKcndx672P4S/IYPfwM4uJi\neeWVV4lOakPQW0JyUiJTXnyeM844o56uQojwI8mqEKLZ8Pv92Gy2I27ftGkTisVB/v48tm/fjj0i\nFk8DxnegcEzGwokZhn2rfgwwDVR7dK2PVTQrijMOnHF4AcOTz5wFyzEtLqydzsEXnYppmuws2Mr5\nF1zEddddx7PP/EfKXYlmQYYBCCGahV9++YWYmFgmTZpEIBCocp+rr76ayc/+k6uvvopLLr0UX2kB\nZtDXwJHKMICmyoEKpoFpGnVuS3XGE2hzJsFWJ6NGpwLl1Rq0+Ax8yYN49fXpnHnWWbTv0ImpU6fW\n+XxChDNJVoUQzcKLU14mENmWh556jpTU1vz7308ze/Zs5s2bR2lpKdnZ2ezYsYPrrruOWZ9/yb69\ne+nStRumu3bjD0NF+suOLFyfGxUVFBX0qt8MhYoWl04gog3fzpvHLqMVE27/CwMHDWbXrl1A+RjX\nhx9+hIzMzixdurTG7fr9fhYtWlRfYQtxzGQYgBCiWdi1azdEJuOLTcfrzmPipDewEsA0ArgL9qJq\nGoqiMnLkCE4bciperxd/UGftnu1Q0bPVkMI1IRPVU1ULpr8MxWKv3/OknYg9pTeKxYEZn8nqLd/z\n0EMP07t3L/73wovsLVXxmzaGnXEWDqeT9PYZ5O/fz2OPPMjll19+UFumaTJz5kwuuOCCyp+FCCeS\nrAohmoWTTuzPj+u+BEBxtcDvaoG/YpvZMoiuqOAvY+biXThLNvLFzI9wOp18fdrpGEk9w2JsoI+6\n314+Hih/fArDnCpKsVBWshvVVb81YBVFhYqyaopmRW91Mh/PW85H367Ab2+H2joDzQjiK2uPz+pk\nxf4SIJYbJ9zFuHHjADAMg61bt3L5VeNZsvAnAF57/fV6jVuIYyHDAIQQzcKgQSfh8O7BrOIWraJa\nUBQVxR6F1qIz3pguDB06lFGjRqHGZzZoomoY5QnpoROs+hDDZqOU3aa3wWIJb43/5qEq7YMWzLzf\nG/y8ii2CYOpg9FYnoyV0QFEUFM2KGp2K6oxHjWmDYo8k6GoFwIMPPYyiKDz3/PMsWfgTHTp1ZeXK\nlVx7zTUNHrsQRyPJqhCiWRg5ciRjRp+Nowa1U5W4Dihx7dm/fz/B6IwGiO7okrDThxjmkovb1Bs7\nHHEEvYnB8BRgeAoaOxQATD2AuW8lts2fELnnO8afN4idO3fyfxMfA2D8VVfx+uuvs3rlcnr27NnI\n0QpRNUlWhRDNgqIoTHr2Gfz5O446Jk9RFBRnxfrutogGiK5mehODQ7Wwq9EKaoWTMBwDAFhRaaHY\nMPc3fO/qofSinagbPuCsXi359qvPycvZy5QXJ9O6devKffr06cM111yDw9EIK7UJUUOSrAohmo2E\nhAQio6LAX3LUfRWrq/ybRihdVR2LooZpmib+0FLXULyFjRqDnrcBx96fmfvlLGbN/IT+/fvXeDhL\nIBDg22+/paysrJ6jFHXh9Xr57LPPyMnJaexQ6p0kq0KIZqXHCT1rVI5KccQBYJbsru+QasVmKmxW\nPejNeMa2WfkpPFlRMI1go53fzF1DbNk6fln0M6eeemqtjv3tt99on9mJS66+kdTWbdm0aVM9RSnq\n6t6//Z1Lx41nyNBhlY+Zpsl7773HqlWrGjGy0JNkVQjRrNx+6004SzYcfUdHDI74NmCPqf+gDqCq\n5S/LR1qhabjRgmKCTCOLTYoMBwhHFhRopGRVL9qJs2gdy5f+QpcuXWp17Lp16zhrxCgiBo0ltvtg\n2rVvT1paWj1FKuri559/5vVp0wmmDiZ7925mz55Nz979cEVEcv0tdzDolFP5/PPPGzvMkJFkVQjR\nrJx33nl4S/KP2vOlKCp6m2GokS0bKLKasaFyqZFCT6L40cijxGy8HjxRNRsqpt7w/y56cRbW3Qv4\n7NOPDxqXWhNTXn6ZEwedQtyp48A08az9ga9nf4HT6aynaMWxcrvdXHLpZfha9EVxJhB0JTP2qutZ\nWxBJMGMM/naj8CYPZuzlV/D99983drghIXVWhRDNisViIbV1G3Z7C8HVorHDOWZ9iCVXCTCTfVxk\nJuNUtMYOSVSwoWIa9buK1aH0/Zux5fzCl1/Mqrz1n5OTQ4sWLSp766uyZ88enn/+eSa/Oo3ON01C\n0aysf3ECvyz8iaSkpIYKX9TC3X+9l0I9AjWxLQCBlgMJcHDvoxKRiK9FP268eQKbNqxrlDhDSXpW\nhRDNTvdu3TEbeQLM0dRkSObZZiIRWPha3V/v8YSTMB6uCkACNnRvMaZZ/4s4mKaBsncpsaWr+XH+\n9wwZMoSNGzdyzfU3kpLSikcem1jlcW63mzlz5tCjVx/e+mENHa6ciCupDQUblmLVVE46ZTDtOnTi\niquvqVzGVTS++fPn89Zb7+Jv0eeo+yqOGDze42OokCSrQohmZ0C/PqiBosYOIyTONhPYa3hZoRQ3\nq0lXhy6aEE7isIBpUN8LF5imSWDVO7Qglx/nf8/ChQvp3rMvfQYMYtbyvThiW9IiPuGgY2bNmkVq\n23Ri4xO4+ta7SDn3LtpfeDeRqZkAqFYbfjQyr/sPLcb8nYV7dLr16MmaNWtqHJff7+edd97h/gce\nYO3atSG95uastLSUsZddgS+xb42X8zWN4+M1QYYBCCGanVGjRvLPp/9LIKEbiqVp15d0YGE4icwz\n97OeUtopLvob0ViV47sv4kgT0MJB+bK4Sr2vfKZvno3hKyEry033E3oSk94fJW0ocT27g6JAWS5T\np73BmDHnEggEmPzSFKa+Po32Yx8iI70Hinb40JGk3qeT0O1krK4oACJS2qNFxnHDLRNYuOCHL18I\nQwAAIABJREFUw64pNzeXu+/9Owt+/In8/bnYbA78fi/RaR1xe/047Ha6detWr89Dc/HCC5MpMlyo\nMW1qeIRSuSJeUyfJqhCi2enbty9jzh3NzO9/I9iyf2OHU6XapGJpODnDTGAFRaw0iuhDFNZ6iyw8\nhHPPqh0VRdUwfMWo9uh6O0+weA8tR0/EltAO0zQPSyRdJ91C9vo5ZGR2RA/4iW1/Al1ueR5nQqsj\ntqlabKgW20GPpQw8l3UvzOaRRx/FXpF8nnfuubz9zjvccefdxJ4wlMTz76NVZCyGHkBBwR6byO6f\nPmHbzl14PB5yc3P55ZdfWP7rCnr37sWll1xSL8/J8eyXpcvw25JqfktcCe83dbUhyaoQotnx+/3M\nnPkZ/uSTwm4s1LH2hKThpJAAHtXEYR7fk61MwvuPsIpKhGbDm78FNaV3PZ+tPEGtqhdXURQiuo7E\n2elMCha+hid/M4645NqfQdNIv/xh3pg9HUOzUfDv/6IYOo64JNLHTSSqTecqj4tu140PXr+Pd995\nB7/XQ1yrdmgJrem3Zp0kq7Xk9Xr59dcVYO9Ui6MUTOlZFUKIpik3N5dgMIDpLUIr3YXfkYgW36Gx\nwypnBFGAAAYatUs6t+OhLc2j1FD49quWGxKI5MusJWhJ3VA029EPqEeqZiXu5BvJ+fgvLH96PIk9\nTyO+6yAi0zqiVFMp4ECuxNakj30QKB8r6y/OwxoZh6odOY2ISutE3wc/RPd5CJQWYo2Kp3DTMvSc\npSG5rubkqvHXkudWUVISa3GUctSlpZsKSVaFEM1Oamoq015/jQceeoTs/dkYeZsxc9eiomMmntCo\niatqsWFxxLLaV0p/s3YLErg1kxa6JfwzuToL/z/AaThRVQ0z4Gn0ZBXKF5tIOv9pSjfOZ9+qRez+\neSaYBi26DSK+x6kkdD0JRa3ZmyNFUbDH1CxpUlQNizMSizPyjwdwu93HehnNkmEYzJk9G3/rs1Bq\nORZdklUhhGjCfl2xkpziAGb7kViMIGbhNkzNjpG1GAIetJY9Qno+w70fAmWUZ5JKxZeK7+HPx4GA\nI479gRzQa3eOoGniqGVvrKg/qqpC0FcvbZtBL5g6qDX/M65aHER3PRu6ng2Ad+96itZ/w/4P/8tW\nu4P00Tej2V1EpmZii4w9prhKszfz+9S/oVksxPcYQuKJ56CoGnmr5tNq8IXEtD+B32ZO4m9/+xsv\nTnmZvv0H8N3Xc9GqmOwlym3YsAFTtaDYImt3oCITrIQQoklb8ONC/DGdKifAKM748q9WF+xZBnVM\nVo2yHIy8DZj+EjTTj+4t/rPywEG9Hebhj5k6+3UTE7NWE4nsqOxV/bQxj/+hAPvx876257DHFRRM\njty5rEC12zlgm/nHeNAqenKVAx49UluGYaLooU1WTdPEzP4F/55VOOLbYotLPea2HMldcCR3wTAM\nSlZ9xuZPJoEJuq+MmLZdaXfOLUSlVT9GsnT3JrK/nIzVEQGahf1bVvHSC//jpIEDeX3aG7zw0l1o\nEfEkOBXWLp+DzRVJaeF+nn76aQAW/fwT69ato0eP0L45PJ5ER0ejB/1VTqI7GulZFUKIJsxut1XU\nwjyYEplMMOir1cQrwzBQVRXDMDB2L0Yp3oGhB7HEpGFEpmBa7Fhi2tS4Z8QwDAJr32OhUUgfMxpn\nDXpLDQwCGBQpelO4S15nEVg4UT98mEQVbwM49Ak59Omp6uk69Njq9zn8ZwODn7RSFC20dRmMvN/R\nc9bRcuTD2BLahaRNVVWJ6XU+Mb3OByDoKaZw8Rusm/Ygfe+djsXhOmh/974drJ58O63PuIKSTcu4\naex59OvXD5/PR69evWjVqhUul4unnnyC9PR23HTjjfzzpZfo0qULVquV9u3bs379en777TesNpsk\nqkfx9jvvoqjH8Htk6NjtTbs03x8U83hJu4UQohauv+Emps1ZiZZ4cA1I0zQI/vYmWucxqI7yW6GG\npwCyfsb0l2KaJqrNhRmbAYEylOKd6L4yNHskRtCHYnOhtDoRJTK51uPLDmSU5aDt+gm7z81lZspR\ne1iLCDCDbK4mDTsqi5VCepiRRCvHXxGrZWYRO/FwHrWf2d5QPtFyyXdFY+l0Xo3Hgh6NGfThWzmd\nhFNvxdW2X0jarE7enEdQzCA9b5+M1RVF0FvGzk/+Q9H2tZTk5wIw7KwRzHj3LRISyhcfmDx5Mrfd\ndhumabJr1y669+yFLSoeNehl8nPP0rt3bzIyMuo99uPFihUr6Nu3L2rbIaix6bU61nDnkVi6gmVL\nFpOaeuw98OFAelaFEM1SSnISBP2HPa4oKtbETgQ2zAKbs3wcq6cQS2InaDUAVdEwSnZjZC9Fi0yC\n5D5YXEmYnnw0exQ4YuuUpP5BjUhC7zgG96o3KSJI7FEqp0agoaIQxGCFWsI6o4TfKeVaM63ei9M3\nNOWAz+FoPSXkEsCeOTJkiSqA6S8FI4hpBEPWZnXiz36EnE/upHT3RuIy+7JnwQf0b5/IU+/+SHR0\nNGVlZXTocPBkxO9+mA+Ul4e78ZbbaDFwDGnDriR//WL+8si/KNy1memvv8pFF13UINfQlOXm5jLs\njLNQW5+MEtOu1scrtihKzCgyMjtx6y0388x//xP6IBuIJKtCiGapsLAYjnSLNvUkLCn9MYt3o3jy\nsLQdimKPqkyPNFcCaovOB83yVuxRIY/RzF1DhGIhxjz6S7UKRKlWZhh7sJoaw0nkC3IIYmIN48Tu\nWIVzndW2OFHMEgx3Hlp0WsjaVRyxWNJOpGDhazhTe6Ha6v8Wr6kHsLqiMQJ+cpd8yVOLf6ZTpyOP\nY530zH959r//wWazUeZ2Y29dvtpSfJeBxHcZSOHmFVxx1dU8+vhTTH7uGYYMGVLv19BUPf3003gM\nK2p85jEdr1js+FsOxDDtBIMN8wanvoRbPWwhhGgQBYWF1c6kVlQLamxb1JS+VSaiDVGOyJq7ln5m\ndI0mWamoXGy0pDcxXGqm0AonkaqV7XjqPc6GFu6ptwsLfQwXgY2z0fM3h6xdRdVQY9thGjq1LhVx\nDDzbFhH0lpH940dkz3+fwYNPqTZRBWjdujVt2rRh+vTp7NuTTdnOdQdtj+3Qm773z8DoeS7nXjSW\nnn0H8Ntvv9XnZTQpgUCAxYsXM2LkOTw76X+Yx9CjehhXIl98Oafu7TQi6VkVQjRLBUVFIZ/8EkqG\nrwQ96CWDpBofo6LSmz8nHXUwnCxRisgwXajH2VCAcNePWByGysLt80Nat9co3IEtNhXVFhGyNo/E\nmX4S9q0LyV39E06Hnfe/+YqHHn6EUSNHMHDgwCMe98MPP3Dn3x8g+YxrSes84LDtFmckiScMIa5j\nf3Yv+JAJd9zFT/O/q89LaRI2bNhA127dMHQdJaYtWueLMELxpjjgISElnoKCAqKjo5tkmTDpWRVC\nNEv78/ZDGBRrPxJj7wpaqk60OvQj9iOGgGKyTC0OYWSNT6n8FN5aYj+kTFkIaDYwG+aWrqqquNr2\nR/e5ufTii7jsiqv557//y/Lly6vc3zRN7rjzLt566y1i2p9AUu/T/1wMoAoWh4vkgaNZ9PMCcnNz\n6+symoypr0+j7ZCLiE7vjmqPDtndGyWuPes2biU+IYEpU6aEpM2GJj2rQohmadPGDShJ4Ttezlq4\nnf5mizq1oaIy3GjBp+ylL9FotehdXaeUssJsvCT3j6EPB4b8x8SqfNNHomlvjLAaX9CLYmm4Orqq\nzYUzOp4WCfFs2bieqJh4Ro4cedh+hYWFvPLqqzz/3CSGDh2KotSs5zd3+TcMO+MsEhNrs4xoeFiz\nZg233vYXli9biisikqioKKKjozFME4/Hjcftwe0upbS0lIiISDRNQ7NYsGiWyt7NYDBIIBBADwYp\nKi7ihBv/Ren+fXhK94YsTkXV8LcZjlacxStTpzFhwoSQtd1QJFkVQjQ7BQUFlJSWQFotV4RpIIbf\nDaZBdAheohOxYwJ+jBrVa/3DfiWIzVDoSXSdY6iNP/ohjcrFEsofMyumVJnASnRSaJ7JquovRouq\n+dCQuvDn7yC46n0G9DmBKa++BsDT/3qK9PSDSyhlZWXRsXMX4tK7Ede6Axs2bMTe7fQanSOp71ks\nfO56srOzadWqVcivoT74/X5uvmUC78+YQSCuK7Qbhd8IUqj7MIsD5e+wVA0lwoqh5mMWLaY0+XTK\nf5mNio+K32ZFBUUDU8comIXmcGFxRYARCGnMiqpBdBobNvxCVlYWaWmhm/jXECRZFUI0Oxs2bMAV\n3QJ3GI7jNIwg2sbPaK1FEqGH5iU6RXUy3ciirRrBCKP63totuPEq5Ws3RaCRQf2PjaytjYobp9k8\nR7EZvmKU+IapmRncOJdxl13KG2++jf2Ei4lZP5Orr77qoH1M02TGjBnEtc6kw/h/svmth8n+7UdO\nvO7cGp3DFh1PfHp3Fi1axIUXXlgflxFSwWCQMedfyPyl6wmkn4NiOfBNU9Rho1MUXymmoqIcZYyx\nkbMaR2wiMW27kLPye9APL6tXF2bAjekvRdWskqwKIURT4HA4KmZUhx8zZw0RusHpZlzI2jzXSKKE\nIDP03XiIw6mU97AapokXAwcqfgzcGHxLHpiQqhz/S7Y2RUrLXpRt+hpLdEtc7QdhcdRPz7epByja\nuoxfooPYM4ehFG7ljtsn4HAcXC5r69at3PfAg3S9/t8AtL3gHpKHXY09puZDWFRXNAUFBSGNvz7o\nus4ll17GgqVr8acMrlENXbN0N5or4eiNW1wYFXWfba5oTN0fsmHZav461KJteItzufO++6udHBeu\nJFkVQjQ7xcXFqJbwvI1sK9hENzMSNcQziKKwEKnZ2KSX4a9YZnaT4qHI9KOhEMBEQ6EDERjAJrOU\nVmF7qz18a6zWNy2uHbQfRsmqWRQt/5Dkcx/HGpMS8vMY/jI0m52t+4O4Tjyb3E/v4vbb3j9sv4SE\nBBRFISa9fMlUa0QM1ojDl8Gtjl6yv0mssHTrhNv5esESfK2G1HyxB3ssetFONNOsfnEOU8eoqIWq\nOSIwjWCNXwFMIwi+YhRnPABGWS74ilCc8Vh3z8fvKeG+++7nzjv/Qnx8fA1bDS+SrAohmp2cnBxM\nLfwSMSPoRfeV0oH6uUXXRrfzEwVEYiECjdamnZEk4kanmCBxWEmgfAayG51gA9TyFLWnxWegxWdg\n7F7Mvs8fwtl+EHEDx6OqoRsaoTljSbzgfyiqii9nE23TM6qcBLVt2zZiWtbt99VbmBv2t6U//vhj\n3n73ffxtR6BUU5/5UEp8JsbuxaD7wHL4Ig6mvxT2r0fPXU+3qx4C/khWa/5/z549n7K8nVi6jcUo\n2Iyi+9D3rcbuimLKSy8wYsSIJjmB7UCSrAohmp2cnBwCR1m+tFGoNlRUVlJMd6KICPFL9CDiScNJ\nK+xYDqhcGImFpEN6URMUGwWmL6TnD5nm27F6EDV1INbotng2zcGemEFkZmirWygVyW+gcDe9e/Ws\ncp/pb72Nq023Y2rfNE12fvoMLgtkZGQcc5z1KS8vj5tumcDcr+fhSx50DHdkKiZUHaEMlbH1G5wx\nsXS/+WniO/UDQLM7oRZL6qoVpcyUTZ9i+H1Y7U6sTifPPfsfrrrqqqMc3TRIsiqEaHb27t2Hz7CE\nXaFpVVXR0wayKmcV+XohI/S6la6qShtkLOrxRI1KwZrcneLlM3CkdMUSGfoeNKM0B6v18N/FzZs3\nM/W1afS4c+oxtevN30vRhl/YtX0rLperrmHWixGjz2X1jmL0tiNRj2ERETN3PaojBkU5wquNEaT9\nuTdVJqoAVmdkjXtWjbIcnFYoCAbRNI3ly5fj8/kYNGhQrWMNZ+H2Wi2EEPVuV9busBwGAKC16ITp\nSsQmL8/Vks7VPykp/VHj0tn72f0ULHknpG3r3hK8m7/n0YceOGzbtGlvEN/7DGzRtR8HWbZnG1lf\nvki//gPQdZ0nn/onKWltGDx0GPv27QtF6CGxfds2dEtktUszV0cp2oYSn1nlNjPoxQh6sR0yxrcm\nwwBM08DU/ZiF2xl2+umVdVv79u173CWqIMmqEKIZ2p29p0ELq9eW3Z1Laz18V9cS4UVRFNTWp2BJ\nP53SjT9QuvnHkLXt27uOAQMH0aHD4UvGtm3bBjXgrnWbpmmy48OnuOH8YXz43ttcNPZyXpgxh+QL\n72O7P5J7/v6PUIQeEj//OB9l3wrwFh7T8aZiOeItfaNgC874lsSkdz/ocYsrsnzowKFt6X5M08Q0\nTay5v2Kun0G0vo/77/v7McXWlEiyKoRodvbs2VvlZIdwEfS7SQ7bmfgiHCmKghaXjqXNyRQvey9k\n7eqeIjIz2le5rX379gTys2vdZunujVgNH488/DDLly9n2YpVZFz2ANFtutD67Gv58P33CQYbZknZ\noykrK8PmigZH7LE1EJGE4q66p1gt3U1C18PLSFkckXBIz6rpLyO4+h2MPcuwbvuceLWIjRs2sHXz\nRrp3735YG8cbSVaFEM2KaZps27YZxVG78joNy8QqL8/iGGgxrdED3pC1p9oi2J29p8ptS5YuxZqU\nXuW26uheN5qm8c0333Dl+GtpNeImVEv5nQRrRAwRcYls2bKlTnGHSk5ODlZXTPVlp6phBtyYVfxf\nNt15BEv20fq0Sw7bZnFGAuU9qH9w5i3hjDPPpHWkl/v/djdZu3bQvn174uJCV485nMkEKyFEs5Kd\nnY2uGxDGwwBE9RSUJjFm1YuOaRroxVkNd1JfKQC6HkA7hglBh1L2LOfCq26qctuiJcuxtay617U6\nMRm9cPcaydirriPxpDG06H7KQdsjk9uxdu1aOnXqdEwxh8qePXv46KOPoA41j9UWndG3fIXiLTr4\nDfLe5bTsPRRnwuE1cstLkClg6qBYML2F+EryuPmm/zSJVb7qgySrQohmZebMmWgxaehhuNSqqBmz\nSaSqMM9SDAawa0GDn9u/93ecqT3q1Iap+ynJWssll1xc5fbRw8/kt6kfwomjatWuoii0GnwRrQZf\nVOV2NT6NNWvXcsEFF9Q65uqYpsmGDRvweDxYLBYcDgfp6elYLIenQjt27OCkkweTH3ARjGh3zPc5\n1IgkcERhluUclKzq7v2knjLmyAcqKhg6pqKh7l/DVePGMmpU7Z7n44kkq0KIZuWVqdPwOlLlJnsT\npoR4da/6EqXaiB11NamDG7Y3bMWkG3Fv+q7OyaovZzMZHTsTE1P1kJkLL7yQO+/5K218nvLaoCGi\nRcazZ29OSNoyTZO5c+fy/gcf8sUXX+LzB7HYnJimgaEH8HtKefCBB+nfv2/5LX+rlVWr1zD5xZfw\nRneCVl3q/FqhYGIesOKVaeiYQT+RaR2PfIymYbrzsASLSItVeeKJJw5b6rY5kWRVCNFsuN1u1q1d\njdJlbGOHIuqgqfSsBjQF3R+68aM1lXraWDZ98DSGYdRpVatAYRYnDuh3xO0tWrSg34ATyVu/iKRe\npx/zeQ5ldUWxe8/mkLS1f/9+Ro8ejZLcF6XFyWCPwX/AXRXTX8q/X5iKYkwBezQKJn7TSjB5CIoz\nRONBTROUA5ZnDbhRNCsW25EnUaafPZ6tc6Zhc7n4YulikpKSQhNLEyWdC0KIZmPt2rW4YlrUfF1v\nEbYCGHjQq/zwouNDx4eBDwM/BoEQftQ0WW7nMdm94MN6fiYOV7D2Z2zJ3eu8/KpqdVJUVFztPuPH\nXUbZutCVygKIad+TBT98j2EcXr6pthISErDa7CjxmSiO2MMmSim2SHythuJNOxNv4ol4EgeiJ/UN\nXaJKeU1UDnzN0f2oWvV9ha0GjQZTp2VSEp07dw5ZLE2V9KwKIZqNBQt+RLceYwkaETa86KymhDWU\nHPT4oSnkoUllKPpjTSBTieRkMw77Ufp78giSPKCacYn1xBoRg5mbX+d2tIh4NmxcXu0+o0eP5o67\n7qH2NQGOzBGfjGp3sW7dujqXZVIUhU6du7Jh/yaMhEYq8WSaBy0qoLj3oViPMvlNUUlo35WrxoV2\n3G5TJcmqEKLZeHnqa3idaXJLqYlzotGRCPrQ8G88CvHzjZLPO2YWvZUYtikeggeUGotXbPTRo4jC\ngttpJTGhVYPH6GjZDtYtqXM79pad2L7oFbZs2UJGRkaV+yQkJOB1l2Ka5jGXdzqUaRh4SgpITk4O\nSXtvTX+dk04Zgh7fLWQx1oZpGgcvt1q2h5Z9z6hyXyMYZM0bj5C3diGYJvfeu6iBogxvkqwKIZqN\nAf37sfPbVRjRqY0dimiiYrFxsZHMDtwsVoqwo9LdiCBY0W+7Q/HyAdl0UCIp8nvo1OXEBo8xodvJ\nbJ31InlfPUH0ieOxxR7b77uiWohI6cTSpUuPmKxaLBY0TcMI+tGsdV/IomzPNkqyNhAVFU2LFi3q\n3J5pmjz3/AuYikZ5v3gjJKuGjrHlK3RFQUHFNA2yfv6cfcvnoWoWVM2KarGiWO0E3MUEfAEsmeeg\n7fgar9dLZGRkg8ccbiRZFUI0G3ff+RdmfTmKhp/yIo43bXHR1nAd9vgJZjT78fOVmYuiWSjZ+Tv2\nHokNGps9OoF+975B1nfvsHf2oyRf8Cya4xgTnqCHxMQjx19SUoKiapVF/evCCAZY/t9rARg++rw6\ntwfw5Zdf8sHHswi0HX5w72YDMYwgph7A0nlM+SQrQwdTxzSCGBUfVHyYRhBcsaipHcBixzB0bDZZ\ndhkkWRVCNCO6rqNZ6l4oXYjqJGDjTFrwiX8vm2f+j4TupzT47WdHfDIdLroHd85Osj+8vXzcJAoo\nCqCUf6nq50OYehCX6/Ck/A/btm0jJik1JNdXvH0tSSmtuOn667nttgl1asswDCY99xwPPfwovqSB\nqFrjJH1m/hZUeySK/eDyX0d7ttTc3+jdbwDR0dH1F1wTIsmqEKLZsNlsGHqgscMQzUAidq6gFe95\nCvDk7MTVsm2jxNFx7H0sf+Z6zJiOqImdK5JWE0yj4vuKr388fghzy+xqV5L68ccfcaZUPUSgNkzT\nZP/KeVw7fjwTJz5Wp7aysrK4ZOzlrPp9G/7WZ6LaGy/hM4t2oNVy2JFpmvj3rGTm8ux6iqrpkXkG\nQohmIzMzE3dxfnkpGSHqWQRWohULBRuXNVoMjvhk0kfdiFqyFVO1oFidKFYXii0SxR6FYo8pL+nk\njENxxh/0gSMOi9VGQUFBlW2bpsmr094ksvPJdY4z+5tpRBbv4C933H7MbRiGwZQpU2jdujXLtpXh\nSxuG0oiJKoAaKMZ01X6imKpqDB12Fhs3bqyHqJoeSVaFEM2Gy+UiLj4BPFX/8RUi1FJ9ULi+cWd0\nJ/YcSlRKO5RNM2t1nKIoEJfJU//8d5XbZ8yYwe68IhK6DqpzjPt/+47PPvnomCsArF69mt59+3Pv\nw08BYMR3aZQxqgcyjCC6txTFEYtpBDHNmhVPUxQFpcslbMrXuGXCHfUcZdOgmDV99oQQ4jjw/PPP\nc9e9/0Cxx1J+O7T89qdZeSvUqLgbapQvPKMo5et0K2r5Hz9FBVWr+L6KMX4VL6nKHyvWVO6j/Pl9\n5a3YA27DVvxsmiZmURZJmgvtkIVFlYrPhz1W8YBilv8QNA2KCaDW4I+1ckg10j9uBvv1IFFonE/K\nUdtoaJ+xl9Y4GqV0VW0V4OcjSx6dLn+AxBOGNFocpmny833DUTLPRbVH1fw4fxnW7bMpLirAYjl4\n5GCP3v0w+1xIQteT6hSbN38vvz1zLWWlJWhazRfsME2TefPm8eDDj7F69SqCLfuhJXbDu+RFNEdU\nvc77NyNaoaYNrHYfwwiir3634gCDP4dZVLwW/PEaYhigWVA1G1pUCqSV91SbvhKcu+eRvz/3sOe+\nuWneVy+EaHbGjBnDX+/9O0FnUuXkkoO+KmrF9+V/SEzTKP9DY+iYZvlMXgyj/PuqxviV5aKi42h1\nwgGJcMUfqj+SUkWtSGaVisRXKZ8prJT/bMS3pdhxQEJR2adgHHBKs+J784CP8i/+/B0483fR26jZ\nDHDlgI8/kuENlBJEhkvUVRw2BgQjWPn5i42arGIaGEYQrZYTjRRbBFZXDIsWLWLw4MGVj3/xxRfs\n3J1Nz8sH1CmsgLuETdPv54knnqxxourxeHh20nNMfull8gsK8QeC2HpcjsVSXjrL1uV8CNZfzQ/T\nCBLc8g2moqClVlOazDDA1LH3vwVFUSpW5DIqZ/+jl3/Vdy9G9xRDi44E9qzAWpGsYnGgqzamTZvG\nDTfcAFRMEq1FQn+8kGRVCNGs5OTkoFntGIldUSx1rwt5KH3Pr1hML7H9x4W87ZoqXf8VzsI9ZOoR\nx9zGXsVPgekLYVTNVyei/p+9+w6MozgbMP7M7l5V77LlItu4YNwbuILBdAMm9IQQahJqIKGFjxoH\nQnoIhIROKCaEXgw2mOCCCwZs3HGvsixZvZyu7O58f0huuEh3utOdrPkljuy73ZlZ6ZR7b/add1hU\ntQPbDEalxFMktn/6EobTE9Fr3ufI5pVXX2X8+PFYlsVvfvMbpv72YdwZuax5/tdHPFfaNo60bDqP\nvwg76McK+rGCDdihAFbQT8WahYwceCy/+uWtLRrLrFmz+MnV1+H3dMY17KckVxdT+eUrB1yXnhr7\njRiEZhDcMBORUoCW2uXQB5k+0Iy9lRIat7/VDtjNCkB6MrEtC5F1LOz8Gru+FC0pF6E7CKT24c67\nf82OHUX84Q9/wOEwqKk58ha4RyMVrCqK0qEMHTqUkyaMY+ayjeg5/WPUi8quUvbZTD3erM5xC1Sl\nlGyf8wZ0PSmiW+O2LXj6qadYu2wF24t2ECyvYYDtRZTXQ/naI55bh8kmGihfvRChGQhNb/pjNAZt\nQmPetjWcduZkHv/bnzEM45AbEEgpuf3Ou3j6+ZfwjriK1O7DAQjV7IrgilpPz+yJ4c3EDlQDhwlW\nQz5EODPZ9SUgbeS2edjdJuCo24JM60Ot4dlbIeGOO+5v/eDbIRWsKorSoWiaxvlTzmUF6Y5QAAAg\nAElEQVTet38jNvOGbb9DjpLYMnDgry6jbudGkju3vsxTS0nLorZoHZvefRyhG8ik3MgaEoJkYeBd\ntIEBCPJJ/V7m9OHVYrJdBCj44XOHPcY2gyxZPYPBQ4YRDPp56803Of/88ykrK+OZ556npKSEoqIi\nPv1iKRlnP4LuTozao5o3E1m1CXKOO/QBIR8ijA8oroYd9B4yjGXLl6FvnsnxY8ewYOF0dE3jnB9c\nyE03/JyJEydGafTtiwpWFUXpcHr37o1u1sWuAzWxquwnHzc9QgZrXryPkfdMi3l/UkpWPXMnlRu/\nbUx58eQiek9pug0dUYukak66WZ6ojnMPzXCSMuhcnJ2Oww7UcfmV1zD6X8+wYP58kgpHYnrz0M0A\naaf+H5rz8BsUtDWt2wRCy6dhLX8FvfdZaJ7MA56Xpr/Z2XRpm8hQPTLUgPBL+vYZT15eLtdcfRUX\nXXQRFRUVJCUl4Xa7Y3kpCU8Fq4qidDirV6/GNFq+IjpcMs7RqpRqfjfR9CGJHWawTfpqKNtB9ZaV\naH0vQDi9xH85Tst+H1w5jbPOjrMf5tsdy8j+wYUJM4t6KMJw4Sg8keCGGVgbZkCPk9GS9yu9ZQXZ\nUyFUShu9dCkOqx5TGJi2htuqxldZhBnwo2kaZ0+5lKf++Q/S0/dVucjKymrjq0pMKlhVFKXDmfvF\nAhq01AR4E4+Vpq01lYSh0Xi7W0oZ861XBaKxn0AlJNBMZEsZyTmk9JsU72E0S0obq2gxIrMPQndh\nbfoUkdMf8oY0fmCtL8H07UZUbsFRvZahfbtww89vobKyklWrVqFpOo888rDaUrUFVLCqKEqH4/V6\nELYZm8aPohixGpPFNL+BgonEROJAY8+88r4/TXVn9xbG4hC1Y/cdIdi/ENe+Oer926wmhI2kHqtF\n19END92Jb9DWGRe2WUPD7u14c7vFtC9PThe6nHgRxV9+AinhbfV5SK24UXAU/TocxN72BdL0oXUe\ngaYZiPRC5NbPsCs3QXohsm4XusNFb1cRl958Jb+45RZCoRAvv/wK//znPwG47rprGTx4cJyvJPGp\nYFVRlA7H6XC0eDeZyLT/pNVO0kWlblLWgoCw1g7iMzTSew9urCGriX0bKAhxwN7zcu8GCE2hqGTv\n83JPLVohGkNXIUATTTOR+76mAghBRQtmKH3lu9hZtovuDfENVjU0kgwXdUXrYx6sAiDBRkRtm8rW\nBJ1H49ZD0goSLF6G3vtstKZSVJonA7vPD5AbpqOHfAhPKsLyk5mVycO/fZiHHvoNuq7jSi9AKzge\nu+hL/vXU0/zzyX/E+WoSnwpWFUXpUKSUvPf+h4iUWM1mHB1zSYV4KGzhgpo11LI83cuQG/8S41GF\nb+fimex48/F4DwOAIBJXeoQr8sPQUFbEjrlvIvYUl2+1yKPNo+O34RCEBroTgvWwXzljTdNAmpDc\nCdH9RKTpZ+GWnYjC0xHudKzqrQjfZnLtIp5+/33OOeecuF1CexLfjXMVRVHa2Lx586j3BxGe7Nh1\nEveZpP12tGqj3pTmmVYIoUd3jsg2TYK+GvwV++qNVq5djMOTjJbWBjO48bZnRr6NWaWr0B1utIwe\nBzxuV2/DCtQh9pQJ012IpFxE7Q6cm95nUJaPZ5/4I9u2blKBahjUzKqiKB3KE0/+C5+nG1qMF7nE\nXxtf31H//Wy9/IBg1eM3YTRtsdv4R2v8qmlNmRJ20y66TSkTTcGYbPq3LW0kEluC3RSm7Zl1yhky\nEWE42L1iHjJ70FG8gHCfikUvxzil59CkGcQ2g2D60Qx302MBjOKFDB91PMtWzsaha5hmEIdhcO45\n53DH7c+p/NQIqWBVUZQOw+fz8cH77yN6xnpGI85zjXHpXs2vNudkMnlNFnGJI5ss4cBC7vvTVG5M\n0xqXmjVuzHngV0MIDMS+r2jogBCCEjvIX5fNISRt9GMvRHdFszRb4v5sdU8Kpq+6zfs18gdjl61B\nlq+HvIEASH8lUtoMOO5YfnXbLYwYMQKn00lBQUHMK0Ac7VSwqihKh6HrOqYZAt0R03465ttSx7zq\ncHjQyRUu1sgGLjaSo9p2nubkVGcmHwXKwJHU/AlhaumOVW1NdyXHJVgVhhNH3gCCRd9gpxeiuVLQ\nkvOxe5zFf958l6uvupKePXu2+biOVipnVVGUDsPlcjFw0BD0suUxvXWYuPNQSryNlhksCVSz247+\nBgGaBKcnvRU7VR1GK17QkthliEjbxF+2KTaNt4DIH4ojpw9s/mTfY+40HC5vXFITjmYqWFUUpUP5\ndOZH9M4WGOXL4j2Uo0xizrwlmiycFAgP/w3tjnpAYyOxtcS6Ybqnym4sNGxf1pQ6EadQxg5h+6sP\nulMTqC2nb9++8RnTUSqxXtWKoigxlpWVxf9mfUKv3n0xvV0Q3lhUBYh/4Bb/ESSG2qIN1NRX8QxV\n8R7KXlKCIyT4UqvhBEda1NpNFjpOq4FQ1FrcI/KgWiJj9loMlKwhybRoiG1WzyFJK0RwxX8QmgE9\nz9j3uOlHCEF2dgyrjXRAKlhVFKXDycnJ4W9/+RO/uOM+/F1Pi/7ihw4WKUpI2Gu2/D6Oc6XxY0dO\nvIdygFVmHa/5S5ljVnOuM4tj9dbnmfbWvYR8u7FtO36zjd8Ty5vhZtlG0tBpiGEf+5NSYpWsbPx7\n1UawTbR+PzjwmEA13boXqgVVUaaCVUVROqSrrrqKX91xFwTr4KhbOR2PMSTum7MGOEViBG97DHWk\nMlBP5v6GzZTbIaJRZypTc+DVDGrL10LOsa1vcD+t+unGIHCT0sZfvpWeONgZqMPa9MkBz9tmAOmv\nOXTQKCWmvwbDE96stmUGkWYApI1IykPrc+7BB/nKGDRuYFjtKs1TwaqiKB2SEIJhw4YzZ/1uRDSD\n1USeZuyIhJawP46vzBo0CScYqVFr8xxHFv/d+RVWSgGaO0rttnKBVSzUfPM6hm0xjEwsuwqrdMcB\nz+/ET31SFmT2aRxHoAbZUA5mA7KhAgCzvhzhyULLbi6wl0h/FVRvAQS6Mwmt12kHH2WbuKrXcdcd\nT0bhCpX9qWBVUZQOq7B7N2avPvoWWsVlbjdBb3sKkRhz3YfyrVVHZ92NHsVoeqgjhW0EWbxxOqG+\nF6AZzqi1HQlJ9D4r7FmQ5tuymOoV07nAzMSNzniyDjp2FrvZ6smA1ALsqi3IirU4gYAZwIvGZPL4\nlhrW+asQGT0R2hGmtnevJLR7FZorFWnVo6V1OvRxFWuZMGEcw4cPj8LVKvtLrPsiiqIobWjRl4sR\nnsyot9vR8tX27LiUsBJ0aFe48tlu+Vln+aLa7mQjkx4YONa+jW36o9p2uGQUw9XaZe+w6+3bKZv9\nOBPMZDI5fCBuN205bG/+DGPXUnrbLq408zmZLHzYfOauYzyZGLqBrN562MoMdtUWQkVfAaC5khGO\nJOzaXVgVGw+8zmAdjsrv+PMffx+Va1UOpIJVRVE6rPxO+WBGN1BI2Mgo1hI1QE/UcQFJmsEgPYlP\nzCoC0o5au7oQXOXMp49w4Fz/Qevbq99Jth3Zkvto/TZUzX+aym/eIL9iN6eYKfTlyJsq2IBZvgF3\nsI6fWHlMtDMAqMWkU69+eHv04m13FbquYW1fALu+OnjsDZVYWz4HQC88CeFMguQ8HE4nydUrkCXf\nIk0/MlCLY9unPPjAffTv3z9KV6zsTwWriqJ0WBdMORd3qCwGLcc7QGrrgDmxA/REHt35rhxqsXio\nfjO1thm1dnUhuMiRTTBQj223LhAWDVV0l+6IzrWRSNuiZObvKZv3dERjCVUXU712DufZWZxBLr1o\nvnJCNzx0x8tlZg7afqHONqdFQb/BXP7oC6T3PRYbuOyhJ7DK1h80u2rXNObBuvOPQ0vvgdQMsEzc\nKVk8+Y/HueDEY9E3vIe2cz633HQ9d95xe9jXprSMClYVRemwRo8ejfDtjnq70or+7kSJL94B+uEk\n6rgauTWd+zzdMTSNemlFtW3PnjCtZltj8foISXca20Rk6QQbRQMacMyO9ci1n1O58MWwzq9Z8jpV\nS94kyeEmj5YHzP1J4QyZhbFfmLOYSmrdDk788S04XG4um/oM1/79v3TqfRyarmF99ybWrm+Roaa7\nLXaITp06oTc1YQsH2CGCWhKrVq/hP6+9yr3/dw8iUMUdt/8qrOtSwqOCVUVROqxQKLSvHE2UaGnd\nCVXuoHb1jKi1megSuQCCSLCSVW1JCEGG7sTa8jniu3fQv3srohxWK7M3m0Vk1UxTpE6S7mSMnc6p\nMpuG1TMx/bUtOjdYVUTl0nep3zAXpx35/Ph2GthIPStdQS558B8kpTfmqRtOJzndjyE5PYtfv/sN\nxx4/HnvXUly7vgDAoVmMHTsWI9QU6GsOhDQJpvXjscf+zq5du7j55pv4+KPpZGUdvMhLiZ6O+1us\nKEqHN2rUKK748Y9wlX4Zta0vhTcLveckald+QN362VFps11I4NzQRE4D2EMiYxLwX+XK51p3Z+5L\nKiTor4H60rDbELqbYBizvrWYlBFkJ35Wizp8duOeWnm4yNCclL76U0rf+AVmfQWWvw4A2/Sz+/Mn\nqFnx4d526la8RydcDCWVs4KRleGysZnlqmUW5Uz8yS/o2n/oIY/TNI0L7v4TutNNyJ2HtE1E9RYu\nvPBCzGBToK45EEiEKwU7/Rh69ToGIQQTJ06MaGxKy6nSVYqidGh//9tfmT//eNZUrEdk9YlKm1py\nPhSeTM3SN9AcbryFJ0Sl3UQVy/3fldbJ1Zzkak52WH6cmoGV1i3sNoySpfQVyS2O+pdqtay1axAI\n+sgkeu+XY3qenUslIVbU1LLpPzdi2SYOzQDDhRZsILBzOQycDIBEB2kzivC2LrWxacAmCYNvqSE1\nJ4/Tr7+XnsPGHPG8NV98gmVZiKwBgCQU9FNcXIyd1FiqSugOaArazezBuOq2sX37do477riwxqeE\nTwWriqJ0aE6nkxeff5YJJ51MwJ2OlpQblXa11ALoPoGqxS8jdBeeroee0TlqJHCsKtvF3Gps7bSD\njcFWmGzbxg7UMoCCFh0vkfikSTc8nEr2AYubAHQE2Tg5yc6gACedceOzLXYFAxSQynu+Eso+/i3p\nE26gbv1cBsvwt6GdSwWbHCHOC2Wx3isZe/6V9Bo+9ojnNNRW8/Yf78aRNxCaaq46PSm8/vrryD3X\noBlgm9jBetAcGO4Udu+Ofs67cjCVBqAoSoc3fPhwXn/tVZxFc5GBluXTtYSW1h2961gqFz6Lf9fq\nqLXbrPjsChCPTpuniXYTqooYfQ+llHxmVhLI6hf2uXbRl6QKB54W7gf7jVZLKUFGkH5QoLo/gaAP\nySRjkIuLQaSShZNL6IR/x3J2vvVLkg03xxH+7f9yr0FGQXfedVYQcjoYMHFys+dsWjIf2wyh1Rdh\nV20FwErpzqJFiwiGLKSvDOFMxgr5sda8ibVyGtUlW1i+fHnY41PCp2ZWFUVRgMmTJ/OrX93GX595\nnWD+kW8XhkPL6AnSpHLek2SedCuunGOi1vaRtGXomMgLrBJ4YG3Gj02lFULkDgrrPKt4KXr5OsaR\n06LjV2l1LLOrOIksso5QsP9IkjDwCIPeAZ0hpIV9/i78VFsBfnrf35G2xJ2UjMvb/OxsQb/BeJJT\nsQI16LuXYAmBTO4KfIvTtx3/7lXohRNxDLwcaNxaVV/3JpdffnnYY1TCp2ZWFUVRmtz6i1sIVTbO\nqkjbwq7cFJWFV1pmH/ROw6iY/RjBim2tbq95bTuX6EBAIL47JSmHp+2Zs63aFNZ5RvVmuuGm8xFK\nRm2jgZ34sZAssMs5iawW1UE9kpC06IoHRwQhyv+SGpjwwxvIKigku2sPkjNbFmin5xVwxxsLmXLn\nH8D0k1S1DLn5UwA+++RjbrzxRlw16/YeL2uLGDhoCJmZ0d8BTzmYClYVRVGa7HnjkaYfWbUJa+sc\nCNZEpW2R3R89byDl//sTZk1JVNpMFA4E0gzFexiH1G5KV8Xw84VLaPzYnY+2fT525eYWnWPbNpa/\nhkGkHpTzK5EEsVkr6plBKYtEFdtpwCWMVgeqAIbQqCD811M5QRpCAU644MqI++43ZhI3PDeD0Zdc\nh8ftAqBTp06YtkS60pBSYteXolWs5eILz4+4HyU87eS3WFEUJfaEEFxxxU9w7ZwNNdsBoprDKnIH\noWf1pmzWo5j+6ATBSjNE+yhdBbFNWBhgJHOBMwexbS5WyYrmT6gvxUJSQ4hXKGKm2M1aUU8lQd7V\nSnmdnXxLNT3xYguYyW66S09UxnocKazQ6sI6x8ZmtruO/mMnoRuRbQ27R2p2HsdPuYJBp55Hp4Iu\n1NTUMOnkiZiV27BWvYa96VNCNcWcf74KVtuKyllVFEXZz9NP/ZN+ffswZ+4cli6x2RmIclCZPwLN\nDFA+cyo5Z01Fc0S2jaXSUoL2E67G1ggjhQ0EWL1rKcGGckThSYc9VgaqgMYFUx5bJyRtFogKLCRd\nbDe65kSzJcNII9N2EsTGGaX5r/4ymRWyhi+p5HgyWnTOAqqwM9I58+aHojIGgEk//TWfoPG3vz/O\nmlUrcTsEXYaPp75sF5ecdzbHHNM2+eeKmllVFEU5gBCCX/3qlxzT6xhKa4IIb3g1HlvSPl3GgCuN\nsplTsaO4H7xysPayvEoS+7EKIbjMkcOtnq64q7djb19w+GND9bg1J5fanbiQTkwmj/NlHoV4OI1s\nzrVzmUwemU0LqaIVqO5pazyZrBM+bOwWnbM5CU6/4d4WLaYKR+2urZx+6iQ+/PBD0HRyCvsy5Nje\n/P7R30W1H+XIVLCqKIpyCBIwk7pGre7q/oTQEN1OQqJR8envsO2WvSG3nJpJ3J/6bhwoS3NwmjMD\nZ8Phd7MSvt30sl0HPJaOk0nkHLEkVbR0xYMXnf9QzCqav7vh9zfQfcDwqI9jy4pvGDduHE/840n6\njT2V2l1bOXfy2Y0fOpU2o4JVRVGUQ8jPy8Ut62PWvtAMRI9TMRvqqJz915j10+FpmgpWDyFF6Agz\ncNDj0jaxqzZjmwF2aME4jKyRjuBUmUUtJgu06kMeY2LzsV7GU2zF6fFiOF2HPK41CgcMY86cOXz4\n8Qx6HT+R9V/N48wzz4x6P8qRqWBVURTlEK677jrMys1IK3Zv2EJ3ovU6g2BVEZXzn4pZP4ryfV01\nN/6g7+BZ/aKFaDsW0qshwFl2dFNgwuHH5kNvLd2OG4ZmOKggiA+Tt/VSSgmwkhqmeSqwex/DoFPO\n5WdPvoOmR3cZjpQS3eXh22XLWb1yOVUlOxk56ng6d+4c1X6U5qlgVVEU5RD21k+U0b5FfyDh8KD3\nOpOG4lVUff1aTPvqiGK1K1R7V2IHMUTTrlSV65BbP0c2VECwnl6mg5PJJjVOa7BXUcN/vRV0HTaa\nK/7wEqMvuJL3PdW85amCgs68K0pZke3k1Ovv4aq/vsaUO35PWm70A8jK4u0UrfqG63/+MzRNZ8P8\nmfz0mqui3o/SPFUNQFEU5TBOPe10Pv1mDTJ3aEz7Ea4UjF5n4Fv/EXpSFinHnhbT/jqUdhSrtuVQ\ne+tekoRG1Y6FWJUb6SqdbK+ejnB4KdNtsNpwMPuZTwUbUzTOunkq/cefgRCCiT+5lYJ+Q/BVVzDo\nlPPw1VSSlJ4V07zRbz95i+/mzSQYDFFSUkJGRga9u+YzZcqUmPWpHJ6Q0dieRVEU5ShUXFzMyFGj\nKQs4ISkXO71PTPuza4uxNs8iY/TVeLpGvlik6uvX8G+YjUccOB+xt7j7gV++d8yRHtk3Tyma/kcg\nMKWNhcRIb+a2sRAITUdoGkLTiCw8kyBl485i0kZKmr5KbF8dIhRsCmIECLAsC6RNuu5sOntfiXuJ\npOm/cc9rbbBNfuntRrYW2TalkVhu1jLNX4ImNK6UXSgnyJdUMpEsPHGYy9pBA5+4aph861QGTpzc\n5v3vb+4rjzP7lScBuO+++zjvvPMYNmyYWlgVJ2pmVVEU5TA6derEooVfMG3aNKY+/Dv8rhyEp2V1\nHyOhpXSCrmOpXPQCmjsDV07PiNoRriTyHW7Od2Q1/rspKNz/bXZPDtie915xiOO+fwt9T6BnI5ES\nbBqDvDVmHQtkkFB2MwF2U4DZGFxGnl4hAPYGu6LpIgRG3TwG28l0w7M3ALWwqcYEq3Hb0QOv7/t/\nj18gMluUt3mfg4wUSpwhFlt1YEEWTs4ir83HAbCOOhZ5Ahw37oy4B6oAEy6/GaEbfPX+q7wzax5/\n/utfWffddxQUFMR7aB2SClYVRVGOoEuXLtx5552sWbuOlz5ehh7DYBVAy+iJMBuomPM3ck6/FyMl\n/NJZQgiSNIPjHMkxGOHBaqWJhkRP794m/R2OKFpIUsjYW/tzj/iEX+GqiEuvJhLXIW7529g0YJMU\n4zDBxmYFdXzrDjDm0p9zwvlXxLS/cIy/7HrGX3Y9AO/89mYWLFjARRddFOdRdUwqWFUURWnG6tWr\nefH559CPaZuSNSLnOPRQPeWzHiXn7N+iOb1t0q/Ssaww61lq1pLDwakH31DNEmrwCKNpDzDZOJMu\nZePMetNjOgJNaOhCoCPQRWMVVovG2WoDgW5LDCka/07jDLeGoExY1Lk1gg0+UpPzsS2TL997GU3T\nEUI0posIrfHvuo7D5cadlILTm4zbm4zD7cXhcmE43ThcLjTdaDqvKdVEiKZ0kaa0ERq/Snvf35FN\n9wuaHpcc+vi0Lr34fM4cFazGiQpWFUVRmlFWVgaAiMEGAYfVaSQiVE/ZzKlknz0VTVP/d90c01/P\nl8LHUq028kYknGvn4m7jYjkSyX9DZbjEvn4b84IPTuHY/98HP37445PROcfIRBeC9aaPV/zFSCQ+\nbF7Xd+09TgB+2yJfuBkvM9BoDDAbg9HGgFNHIBCEsAliE5KSIDZBaWMicaJhIglgE8DGL2wsrTF1\nxEJiC/DYFq4GCyE8yMoa1k57DgRI0ZjaIfdcvxBU2UFS0tNxOBwEgkFCwSCmZWKbFpZlYlvWfoHp\nYVJM9nwvxX6JLnuSr5u+iKZc573fyf1yVDfl5vLkE08cum0lptT/+ymKojQjJyeH1Mw8GkTbBTBC\nCGTXCchNM6j835/ImnR3m/XdXlnYDJBppFqRv7V9Tjm1hHAT/QLzRyaQIYnB9xd77b8gbP9H93/m\n4OcP1cIaUUd/4aGP4WWd3QDA6eSgCYFt78lH3pebnC4d5Isjfx9c4QT1378A2Jc8fYRVbg3S4lV2\n8uwTf+GM01pWKWNP/VhNi87vbE1NDT36DcCyLHRdj0qbSsupYFVRFKUZXbp0wQo2YNcUoaW23QIL\noelQOInguvepXPAMGWOua7O+2yNdaPSUXlJxRNzGnDjljno0g56Wl15Ed2/7PWxsisQuiu0AffCi\nSUkeLnppsekvWvzS4iVZRPfCbpw0YUKLz4tWkLqH1+slPy+PdevWceyxx0a1baV5alMARVGUZqSk\npDDj4+k4SxYig3Vt2rcwXOi9zqChaDk1K94/5DG2bVK95D9UzPoj1Z/9AbOmBC9tN/sT77JPRwOn\nFNTHsLjpbCpIEhqjHWlYUvK1VUc33DHrr7UC0uIzrZL3KaWgSwErln2D292245VSMn/hIu68516S\ns/LYum0bq1atatMxKI1UsKooitIC48aN4847bsdVvoS2Lk8tXCnoPU+lbs1MfFsXH/R8qHwb/nWf\nc3JtJRmVRfi3fcUYvW0qASjR4bahTovNbmk+TLbgo4twMj9UzduBUkwkw0iNSX+tsZo6Hre38DI7\noWc+o846haeferLNx/HSq9Po3vtYTjnjbOYvWszzzz/P119/zZlnts0iS+VAKg1AURSlhe759d28\n8OK/KareikgvbNO+taRc6DaeqsUvYaTk48zstvc5PSkDgWCyO5sT7XS2WH4GGIl9e1c5UCoOakXs\nZlazhYvtVojtVohqGaQr7qjfKm+tUhlgLhV43W6uufpK/vi7h+M2liefeobS3bsBMHSNO+64g7S0\nNN544w2GDRsWt3F1VIn1SlUURUlgTqeTl//9As6ypUjbbPP+tfRCjLxBVHz+Fyz/vnQEzZ2GhcSU\nkhTNYKAjuYPutNN+ExLSMaiVoZi07cXgXJnHFJnHaJmODXTHE5O+ImFJyVbpYwZlXPGTy6koKYpr\noAqwYPZnNFSV4a8uZ86nM5j+zpsM6H8s8+fPj+u4OioVrCqKooRhwoQJ9O3TG7F7RZunAwCQMxAt\npRPlsx7BbgqYNU3DEAKfjNOG7gkknrtQtUYWTurs2ASr+1uq19IZF8dqKTHvqyWC0uYdUcrnzlom\nnXcmj//1z/EeEtD4O7X/B74hgwcxYthQNmzYEMdRdVwqWFUURQnTW2/8h06uGmTVljbvWwgBXcZi\n21A5+7G9jxtCp14Fq+1WNk6C2NgxnB2uw2Sn5WNwAuSqrpF1vCqKeZEdZPTqSvHOLbz67xcSLjVh\njxdeeoXf//mvKmc1ThLzVaEoipLAevbsyWuvvoy1dTZ2PAJWzUDrcSrBiu1Uff0aALrTw04r0OZj\n2W9Ucey7/TPQMNDwxbAiwBytks7CQzctvikAy0Ud80Qldzx4D6//91W+Xjwfw0jcJTTrN2zg3gd/\nw6JFizjjjDPiPZwOKXFfHYqiKAlszJgx3HTTzTz5Vnxy2ITDg97rNHzrp+PI7IaZfQyryrYwPAFm\nzZTI6EIQPNzuS1FQj8lwmRLXzxVBabPQruCNN1/j9FMnxW8gLfCPfz3FO+99yIaNG7n//vsZMGBA\nvIfUYalgVVEUJUIjR47A/fJrBAOFCFdam/cvPJno3U+k+uvX8HQfQVkb5DwqsSNp3NY0VjQEwTgu\nQiuRAd6UxXTOyz9ioPrM8y/yzNPPsnNHEbpu4PG48aYkk5KaSlp6GhnpaYwcMYKbrv9ZTMf79PMv\n8n//dy9DhgxRgWqcqWBVURQlQldccQXV1dXc9X8PYPaaEpcxaGndEJ2G0LB1MTR/OsIAACAASURB\nVLrhjcsYEkb7LQYANBahj1VuXjUhKuwAeWRQ04IPNVbTN9MGbCR75ns1wECgIdARTf/WMDj8rlFS\nNm7fWo1JfnYOa9cs3/ucaZqUlZezdds23vtgOm+/+Q67dhYzUqRyDB5sIFATIlBSToDdVAjJVmHx\n+ptv88rLr7JowdxWfFeal5eXx8CBA2Pah9I8FawqiqK0wmmnncY99/+Gti9ktZ/s4zB2f0ffZvZx\nP+oJ2nXAKpExm1l9i2Ik8A4lLR6LBTgQTf/Z962VTcGrbHpE7nmuBRkMWhkkZeYC+35cAtAR5Opu\n+kgPZ2sFuMXhd2CzpSRXM1i4ajUzPvmEM047rUXXFK6H7r2Hiy++mE8//ZSRI0fGpA+lZVSwqiiK\n0gperxdfTSWaFULoke9J3xpCCKQzidKQPy79K9ERqzSA1dQigQu0PAq1ls2+z5LllFlBJpPX4n4k\nkm+pYbNo4HyZu3eWWBMHz7hK2RjwCmC6thtNCC4gF6E1f/2aEIwS6WzVglz70xsp6JSPw+nEcDhw\nuJw4HQ6uufIn/GDKuS0e+6GcefppVFdXx6dEnXIAFawqiqK0QkFBQeNftMPPBLUF2XUcS757m7FG\nKr2MxCn43qbaeUwhgVi8itZo9Ywlo8WBKoAtBJ4wkxIEgjQMQhoY9pHPFULsvdYSO8CleqewN7IY\nLVPZXR3Ert6FhcRGEpJQJ2x+MnsuXT+bwcjhw8Nqc+26dSxYtJjtO3ZQXLyLs846i1GjRoXVhhJ9\nKlhVFEVphYqKCnTDEfc4SXOlYKZ0YV6wpuMGq7R+oXsQmw34yKHtUypikQawnBpq7BB99PC2363D\nIiWCECEVB/4wd3dL0R18TiXnyxwch5iFPZwuwk0X4T7kc0kYnH32FDasW0VqavMVMiorq7juhpv4\n6pslnH7aaXTt1g2fP8Cjjz7a4vEosaOCVUVRlFbIyMhg8JAhrNg4F6fLiy0lfk8XhMMDQke4265K\ngMgfwrfrPsDvzMEdxpt+a8U7UI+mUaSzhGrSMXCikYJBbhsFrrFIA6jHpMDwkhrm232dDNGZ8Bfs\npWIQlBY29iFv/x/KBVYub2olPC+LGKNnMJDksPv9vlEyleJgkPEnnsKypV8d8dgNGzdy/iU/5Mwz\nz+Ktd97F6XS2un8lutSmAIqiKK2g6zqfzvyYh+74GX+b+kum3nEdxybvpiC4CvfO/6Ht/hYZw9qZ\n+9O8WWiedP7ZsBNL5dlFZChpjBYZfEEFi0U177GLFdTEvN89e1dF8015Bw2spJbOESy889kWaYSf\ng+1EQ0dQEcaSQ0NoXGznkS+dzDbLMaPw+yKE4Cyy2LVlO1dd+/PDHldVVc3k8y/i1ltv47HHHlOB\naoJSwaqiKEorpaenc9ddd3H11Vdz2223smLZErZsWs+mDesY2NmJXraizcYS6jKGjaF6amVc6xO0\na/1lClfTjctkZwaLNL6hmo9EKb6mAMxuybL3MO3Zt0pEaWbVxmahVk1fI5UTZPgbRfhlZMEqQIrm\nZBfhLfbThMZpIge3prNe+iLq9/ucQuMHIpe33niLF19+5aDnpZTcfNuvOPOss7jhhhui0qcSGyoN\nQFEUJUZycnJ44flnGXXCaCzNCRl9oGkRiYjBbXq7agv61jmc6c0lXYtPZYKjhd4UNB4rk3EiKBcW\nr8oitKZCTroQjJcZ9CS8XNDDMbGjOnu0hBpC0mKSzAg7kTckG+d5kyMcUYZwspvINqjItA02G36O\njUIqAECmcHKmls0tt/ySEcOHMaB//73PLVj0JUuXL2f58lej0pcSO2pmVVEUJYYGDBjA/HlzOaGH\nG8em9xHfvY6z9Mg5dJFy7VzMZFcWpzsyYtJ+ootF4kMKBkNI4xQ7kx/RhRPJ4gSRyWCZwhxRwUIq\n+ZASttOw95y11O2dhf2+IDZ1mPgwWUr13llak+jNqsK+xVrOCD4UNWDhQEOLMERIszSqI6w8PJYM\n1pt1VMro7cZ2jJbEMJHKpEln4vM1ztpKKZn+0Qwuu+yHeDwdd0Fie6FmVhVFUWJs6NChzJvzOcuX\nLyc5OZnhI0YR8JWhebOj1odt2zQE6xmb2ilqbbY/sSuqD+BF5xiSQDYGgy6pMZcK8oWbT+VuUjDI\nw8V31JGEQQEuTiCdakwWaNWNgaodwkTiFjohabFS1DFZ5gDRmz3yY7JS1HG6iOz15cPG0LQWFfk/\nlBQMtuqBiM5PEw4KRRKv28VcoOWTI6KTQzqGNLb6Sxgy7HhSMzPYtn07DofBxx/PiEr7SmypYFVR\nFKWNDBo0CIBzzpnMK29/iiMtFzN7cFRSAjRNQxcaDdLGc4Tdf45me3ZDagsCQT+S0RAcI5OoIsRH\nlLKGOo4nHZ8mqSLES3YRAAPtVDyaQSo6bjQC0qYzbhaJKj5iN0NlStRmVpdQQ47moo+ILEXBJy2M\nVrwmUzAISKv5Aw/jNJnF57KMt+0SrtO6oIVZf/VQVlNPg8dg3OjR/PKO28nJyeGdd95heJh1WJX4\nUMGqoihKG/vZT6+jsrKKVatWUrpzNoG8sQij9eWRdKHhb6PKAwdpqyixGW2Z2yYQ9G3KrczCyQ8p\nYBsN9MC7d1bxK62GEDaj7fRDzjSOttP5RtNYICvREFQQRAAZRD6juF0LMEqmRvwz8TWlAUQqFYOA\nbbXqNTFRZPNvUcQKWctgEf4Csf3tkA0sTgqy8MvF9OvXb+/jt99+e6vaVdqOyllVFEVpY2PHjuWD\n999l/bq1/OTic3DumIU0A61q067cTMi2yNA67hyEREY17zNcOqIxUN3PSDuVMXb6Yc9xojHaTucK\nuuAUOu+wi7fZRdF+ObDh8kkTdyve3huw0e3IM4CT0DGxCbbyg9M4O505dgUrZV3EbdhS8oXHz7+e\nfeaAQFVpX1SwqiiKEie6rvOPJx7n6h9fimvXF0g7slunthlE2zaXKzz58UsBSICyrtGuU9qWnOhc\nKjvxE7qQorvwtaI8Vm+ZxCd2WcR72jcIG6eMPOjXEHjQ2UXrPoD1EkmcSBafW+URt/GdrCO/ZyEX\nXnhhq8aixFd7/b1WFEU5ajz2t78yftRAnGVLI2tA0zClzRBHSnQHFoYEiFWbclYTJB8hAgYaRhTe\nln2Y9NCTEBHmetYLG08rx5GqOdndymAVoAsubCRlMhjR+cVewfW33BTx90JJDCpYVRRFiTNN03j1\n5ZcQtduQ/qoIzjdwCp2qKJb7aa/Umxpk46QqwtJR0JizmtTKJS3pwkF5hLVW9+cVBj3wMl2W4Q9z\n0ZaUkjJh0rNnz1aPQ4mvjpvcpCiKkkAyMzO59pqr+cfrs8E9JOzzdd3g3UA5V7nz0TvgLJJtN942\nb88zq3tY0mYj9VQ2BXvigD9ib0C+p0yX1nSU1nTMdvwki8g3haiXJl1ascALINXS2CYirwiwv1PI\n5G1284pdzCVaPimi+dDlO7uOIhEkt7A748ePj8o4lPhRH0IVRVESxPhxY0kissUkgc4nsDJUR7Hd\n+luv4UqEFICIi4ImIBuowaQUPyX4KcZPEX524GcbDWyhgS3Cz0bhY6PWwDrNxzrNxxrdx2rdR6UI\nYUeYrwrQYFukRLjV6h4pGASiFGFoQuNCmYeOYGULfj+qZYjZzjpGX3Mpb73/Hoah5uXaO/UTVBRF\nSRB9+/ZFBmojOzk5D10zqLRNusRjjVUHnM2NFYfQGEoyvY+0lav83tf9fEgJlpQRl47yS4u01qYB\n4MBnR56KcCg9pJttBBh9hGPKZZDprhruuuNO7nvwwaj2r8SPClYVRVEShNPpxLIOzvOTtgnBemSo\nHkL1yJAPtwjhIABmA8H6akL1tfRwJtPX8B6i5TbQipk8JbpMJIVEVrfXkhILSQqt+8STiQNb2uzC\nT75wt6qtPbzoBDn8QitLSj5x1/HAo49w4003RaVPJTGoYFVRFCVBFBYWkp6aTGnRfDwODWHWNwai\ngQaycvMo6FxAt25d6dWzkO7dulFQUEBBQQFfffUVL9w3lWtlRtzGruZVE0cWDlbJOobLVBxh7kTV\ngIWOQGtllqCGoKuexHKrlnyiE6zm42KhVcUavY5jRfIBz222fXzhaWDUuLHccOONUelPSRwqWFUU\nRUkQTqeTGR99yIcffkhhYSHdu3ensLCQvLw8NO3wwcPCBQvYEqznMwljHWm4o7B9q9J+jSWDaaKY\n7dJPTxHeTHsDdmOAG4WJ8v5WEp+K3a1vqEmOcDFGpjPXrqSftq801xbbx/+8Pt54521OOeUUVabq\nKKSCVUVRlAQyYMAABgwYENY5t952G+PGj+eRBx/iof/9jzFaMhO0FNI68G5WHZmGhlPoEe0g5ZMW\nhqZBFBbyp2IQlDY+TLwtWMHfEv1JZjE1bJd+ugkPFTLIXLePl1+bxqRJk6LSh5J41MdvRVGUo8CI\nESN4+8MPWLJyBd0uPZffmcX8x66kxIqsmHo4ZILUA1D2EUAogp9LA1ZUNiaAxu1nBbAmwgoXh6IJ\njVQMSghSJoO86ajkzgfv4+yzz45aH0riUcGqoijKUaRnz57869ln2LB1CxNuvpbHRTnPU8FmM/K9\n5ltG3XpNFCY2NVaQgggWNjVgY7Riq9X9raaWNOFguEiPSnt7uKXGKlnHDEc1f3n8MW6/4w516/8o\np+4RKYqiHIVycnKY+vDD3H3PPTz/3HP84eFHSAnUc1LIzXFGEloCvLnLpS8QjOLMr/MomX/RkNQJ\nM+K80c9EOenCSWYEGwM0SAsjSpUd/Jokxdaj/jnmLLJ5V5aSXpDPNddcE93GlYSkglVFUZSjWFJS\nEjffcgvX33ADb7zxBo888CAf7irlpJCbkY5UjCgFrd+fjJMhHzJYf+RzLJOzyKUgSqvF4x9+R8dI\nK5VZlNELL6lhFuf/XJRTTpCLRX5EfQc0cFjRCfo32nVMJDMqbe1PIgh4Hbz8n9fUjGoHoYJVRVGU\nDsAwDC677DIuvfRSPvvsMx6+/wE+WracE0linJEa9QoCzpJFpOh+vEnJhz3G17kzn+8uZayZRi8Z\np/qwCag7XrJwslKrZ4wd3i30XVqQU2QW6RFut6oDdpRykLUYfXxYSx1Dhg1lxIgRMWlfSTwqWFUU\nRelAhBBMmjSJSZMmsXTpUn77wIM8NGsW47VkJmipJGvR2f7KoQueffpfnHXWWUc87osvvuD8M86m\nR70nZsFNe5SMjinCCxo/Yzc1VpAc3Rlxv7rduKlANHTWvWyyGuhxpJ24IrArVWfqL26JaptKYjs6\nEnwURVGUsA0dOpS33n+Pr5Z9S/aU05gaKuJdq5KqKG+TeSRjx46loHs3voviivGjQRoGpXYgrHPq\nsBgsUkhtRZkoQ4iDUjoilWZpVBPd15ItJbtCPoYMGRLVdpXEpmZWFUVROrjevXvzwisvM/XR3/GH\nR37Hoy++yFAjmQKzZVHLRrMBW5hYu9fsfSxQX9Wic4UQPPfSi1xw3hTKKusY70tqKnjUsQ0ljZWy\nqHG70pbm9ApIaWU9UwOBFCIqmwJU6haZVmTpCIezGR+9+/bhmGOOiWq7SmJTwaqiKIoCQJcuXfj7\nk//gvoce5MnHn2Dzxo0tOq9zdTWu2joKunbd+5hh9GTgwIEtOn/48OGs3biBUUOHsW5NCX05fJ5r\nR2GgUYCbZVod+XbzwWoxfnbJAANE6753jcFqq5rYq1IGGRjlFIAqTCaeqor/dzQqWFUURVEOkJOT\nwwO/eahN+3S5XDz/8kucPOFEHD6NnqgFV+PIYJq9kwYsPBw5lzi56e38OJHSqj4NRNQWWOUJNzvw\nM4DUqLQXlDZrPSHuO/HEqLSntB8qWFUURVESwvDhw/lszmxOOXEiWT4HaWGWbTraeDFwCA2fbD5Y\nNWncWnU6Zc0eK4DjZeohc1sbg9UDrRc+dmrh5c/aAiqsAAER/pavh1OHSUZmptqtqgNSwaqiKIqS\nMEaMGMG9D9zHPx96lFN8BnoHz19t6RynB41j8IJl03BQuHmgzTQwSE8i9RAhgIF20NnfiGryBw0h\nO7+ghaMBh8NBnmUyf/rbhLBxRFgaTUpJAzZeoePHJiM9urthKe2DClYVRVGUhHLrbbfx+az/8eH8\nhYz2ecnFFe8hxcVuAljSJqMFM8xuDE4hp9njGjDZhI8sDl3eSkcgvxci5ws31bt28uDzb6Fp4QWd\nK2bPYm19Pd2lBzcaBiKsQv4rtXrm2eX00VLwWoK01OikFCjtiwpWFUVRlITicDiYPvNjpk2bxo0/\nu57x9TJqu1y1J0uooYvmRbOjN7tcRACPMA67c5khBLaUBLB5ke1MJpexVhr/3VXMB//+J+dddWNY\n/Z38wyuZ8cJTLLKqMaWFBJxSw6XpuIWBR+gkSQ2vJfCg40Vr+qrjQadUDzFs3CTskMnm5d/Qw6dK\nnHVEKlhVFEVREo4Qgh/96Ec4HA7uuvYGCmrjPaK2Vyz8nGE3P1sajkI8fEEF22UDXYXnoOcNGoPV\nrfgAKCFAAR6cQsftCX9l/yU33cUlN9219991VZWUFm1jd/EOyop3UrG7mIqSYipLSyktL6OhtpqA\nr5ZAMEjQNskwHei+eh547k1WfvkFn730ROQXr7RbKlhVFEVREtZJJ53E7mA9ko5XfzUW12ug0VW6\nWSLq6IoHS0o2SB873Tbd/DrJQsdGUuK06d6pO7XF1RCEFAyWzf+coeNOZv2KJZTs2Eoo4CevayH9\nh48mt0u3FvWfnJ5BcnoGPY8b3Oyx9/34HNYtX8LATl0ASM/OpaSkpFXXr7RPagcrRVEUJWHl5OTQ\no7CQTU0zfR1FDSYBaZETg3zdnngpthr4zq5jmruCsiFd+cE9t7Ikz2CavYucnBzWWTVMnTqVUH46\n8/QqimwfyxfO4TdXT2HTgk/oniQY0Cmd8lWLuf+KyTz90O3s3rkjquMcc+YUUrwpjDlzCgDp2TmU\nqmC1QxJSyugUVFMURVGUGJg1axaXT7mQKfUdayX4C2znXPIOuxgqUiY2r4qduJO8vP3+e0ycOHHv\nc7Zto2kawWAQh8PB7t27ueeuu+nctQtXXnklPXr0OGiBVFVVFX/805/411NPc9OjT3LcyDFH7N9X\nW4MnOSWshVbQWBng2gn92bRhAzk50U2PUBKbClYVRVGUhFZfX09megZXmZ3jPZQ248fmVXZwAZ1I\nj2K9WQvJTG8NrrxMLv3RD5k6dWrU2v7ss8+45LLLuOimXzNxyiUHPT/nvdd595nHKC0uYuwZ53Ld\nA3/C4Qxv5njq1efz+J9+z0knnRSlUSvtgUoDUBRFURKay+XCtMywd1YKNFNvNJEtpII8zR3VQBWg\nhhClIR9du3XlkYcf5pmnn45a26eccgpfzJ3LjBefYOZ/XjjguY2rlvHk/b/kqisup6qqinQHPHr9\nD6mrqQqrj4KefVm5cmXUxqy0D2pmVVEURUl4A/r2o9u6Srpx8Ar275NIqjB5Ry+hiyeNtGDj7ebk\noCQLB3m4EmqxVhlBXGik7Lfm+d9iBxNlFhk4qCZEEEkX3Di/N8dUh8knRgUnmKnk40Jr5rokks34\nWO8M0jvoZH2Bm807tkf1etavX8+o40/gsY8WAYL3n3+C2e9O46fXXsvFF1/MkCFDsG2bq666mgrp\n4se3P9Ditme89gJa2VaefSZ6QbaS+FQ1AEVRFCXh3XTbrfzt9nvpVt/8sfM9PkrdNrdedSsDhwym\nrKwMy7JYvmQpM2fOZECFRW/CL8MUC1876/kmWEZ/kcJ4mUkAmw3UE5I2H1NKbkYWhd264fJ4eH3p\nN/SVyQwOenGhYSN5z1lOXdDPdOEnzenhokD2EQNxgaAzbj4NllHp9nL68SdG/Zp69+6N0+nkl+eO\np7amitPPOJMVy5bRqVOnvcdomsZDDz3I4KFDGTTmJAaPadk4+gwezrP3vxT1MSuJTc2sKoqiKAmv\ntraWY/v0Ja88yPBQ0mFnEIvx82WWxaZtW/F6vQc9//HHH3PNxT/krLpUjDjPrlpInmUbl156KTPe\n/4BOtout0sekU05h9PhxnHzyyYwaNWrv8SUlJdx1+x28+9bbdLfd6CGL9e4gm7ZsQUpJ/7796FFl\nM4y0IwaslYT4JKWO2fPmMmDAAHRdj/q1rVy5Eq/XS/fu3Y/Y/nPPPcez097gtr8+36J2bdvm5tNH\nsuCLufTu3Ttaw1USnMpZVRRFURJeSkoKy1auwDGwB8v1w0+vbkiyeGDqbw4ZqAKcccYZjD75RN71\nVFCPGavhtogGFLrTGDZsGB9+MpOf//4BNm/bynvTP+Tuu+8+IFAFyMvL48WXX2LB14u56tF7GX3d\npSxfuZKcnBxyc3P53aO/o7pHFv/z1mEdIr83hM18Tx2f6RU4nU5SUlJiEqgCDBgwgJ49ezbb/rhx\n41i77Gu+nPVRi9rVNI1hJ07i/fffj8YwlXZCzawqiqIo7ca2bdsYMnAQPRoMBoe8B+VwvpVUybyv\nv6Rfv35HbOfaK69i3uvvMcGfgh7HGdZvqWbUjT/i709EZ2emUCjEyCHDyF9dQiFeJJLSppzYekw+\npJTOSWkUNOis1OtITU3l36++w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+ "text": [ + "" + ] + } + ], + "prompt_number": 54 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.6** *Discuss your results in terms of bias, accuracy and precision, as before*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "***Answer***: The accuracy of this poll is higher than before, primarily as a result of removing bias in the calculation of the weighted means. As per the discussion earlier, the precision is really not much better, as we use the same method to calculate the standrd deviations.\n", + "\n", + "This points to the importance of getting a better grip on these standard deviations to improve the precisions of one's forecasts. Pollsters engage in trend analysis, a state by state weighting of the standard deviation, weighting pollsters, and other methods to estimate the standard deviations more accurately.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For fun, but not to hand in, play around with turning off the time decay weight and the sample error weight individually." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Parting Thoughts: What do the pros do?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The models we have explored in this homework have been fairly ad-hoc. Still, we have seen predicting by simulation, prediction using heterogeneous side-features, and finally by weighting polls that are made in the election season. The pros pretty much start from poll-averaging, adding in demographics and economic information, and moving onto trend-estimation as the election gets closer. They also employ models of likely voters vs registered voters, and how independents might break. At this point, you are prepared to go and read more about these techniques, so let us leave you with some links to read:\n", + "\n", + "1. Skipper Seabold's reconstruction of parts of Nate Silver's model: https://github.com/jseabold/538model . We've drawn direct inspiration from his work , and indeed have used some of the data he provides in his repository\n", + "\n", + "2. The simulation techniques are partially drawn from Sam Wang's work at http://election.princeton.edu . Be sure to check out the FAQ, Methods section, and matlab code on his site.\n", + "\n", + "3. Nate Silver, who we are still desperately seeking, has written a lot about his techniques: http://www.fivethirtyeight.com/2008/03/frequently-asked-questions-last-revised.html . Start there and look around\n", + "\n", + "4. Drew Linzer uses bayesian techniques, check out his work at: http://votamatic.org/evaluating-the-forecasting-model/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How to submit\n", + "\n", + "To submit your homework, create a folder named lastname_firstinitial_hw2 and place this notebook file in the folder. Also put the data folder in this folder. **Make sure everything still works!** Select Kernel->Restart Kernel to restart Python, Cell->Run All to run all cells. You shouldn't hit any errors. Compress the folder (please use .zip compression) and submit to the CS109 dropbox in the appropriate folder. If we cannot access your work because these directions are not followed correctly, we will not grade your work." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "*css tweaks in this cell*\n", + "" + ] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/HW3_solutions.ipynb b/HW3_solutions.ipynb new file mode 100644 index 0000000..2c82182 --- /dev/null +++ b/HW3_solutions.ipynb @@ -0,0 +1,1467 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Homework 3. Bayesian Tomatoes\n", + "\n", + "
\n", + "\n", + "
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\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this assignment, you'll be analyzing movie reviews from [Rotten Tomatoes](http://www.rottentomatoes.com). This assignment will cover:\n", + "\n", + " * Working with web APIs\n", + " * Making and interpreting predictions from a Bayesian perspective\n", + " * Using the Naive Bayes algorithm to predict whether a movie review is positive or negative\n", + " * Using cross validation to optimize models\n", + "\n", + "Useful libraries for this assignment\n", + "\n", + "* [numpy](http://docs.scipy.org/doc/numpy-dev/user/index.html), for arrays\n", + "* [scikit-learn](http://scikit-learn.org/stable/), for machine learning\n", + "* [json](http://docs.python.org/2/library/json.html) for parsing JSON data from the web.\n", + "* [pandas](http://pandas.pydata.org/), for data frames\n", + "* [matplotlib](http://matplotlib.org/), for plotting\n", + "* [requests](http://docs.python-requests.org/en/latest/), for downloading web content" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline\n", + "\n", + "import json\n", + "\n", + "import requests\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "pd.set_option('display.width', 500)\n", + "pd.set_option('display.max_columns', 30)\n", + "\n", + "# set some nicer defaults for matplotlib\n", + "from matplotlib import rcParams\n", + "\n", + "#these colors come from colorbrewer2.org. Each is an RGB triplet\n", + "dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),\n", + " (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),\n", + " (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),\n", + " (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),\n", + " (0.4, 0.6509803921568628, 0.11764705882352941),\n", + " (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),\n", + " (0.6509803921568628, 0.4627450980392157, 0.11372549019607843),\n", + " (0.4, 0.4, 0.4)]\n", + "\n", + "rcParams['figure.figsize'] = (10, 6)\n", + "rcParams['figure.dpi'] = 150\n", + "rcParams['axes.color_cycle'] = dark2_colors\n", + "rcParams['lines.linewidth'] = 2\n", + "rcParams['axes.grid'] = False\n", + "rcParams['axes.facecolor'] = 'white'\n", + "rcParams['font.size'] = 14\n", + "rcParams['patch.edgecolor'] = 'none'\n", + "\n", + "\n", + "def remove_border(axes=None, top=False, right=False, left=True, bottom=True):\n", + " \"\"\"\n", + " Minimize chartjunk by stripping out unnecesary plot borders and axis ticks\n", + " \n", + " The top/right/left/bottom keywords toggle whether the corresponding plot border is drawn\n", + " \"\"\"\n", + " ax = axes or plt.gca()\n", + " ax.spines['top'].set_visible(top)\n", + " ax.spines['right'].set_visible(right)\n", + " ax.spines['left'].set_visible(left)\n", + " ax.spines['bottom'].set_visible(bottom)\n", + " \n", + " #turn off all ticks\n", + " ax.yaxis.set_ticks_position('none')\n", + " ax.xaxis.set_ticks_position('none')\n", + " \n", + " #now re-enable visibles\n", + " if top:\n", + " ax.xaxis.tick_top()\n", + " if bottom:\n", + " ax.xaxis.tick_bottom()\n", + " if left:\n", + " ax.yaxis.tick_left()\n", + " if right:\n", + " ax.yaxis.tick_right()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 30 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Rotten Tomatoes gathers movie reviews from critics. An [entry on the website](http://www.rottentomatoes.com/m/primer/reviews/?type=top_critics) typically consists of a short quote, a link to the full review, and a Fresh/Rotten classification which summarizes whether the critic liked/disliked the movie.\n", + "\n", + "\n", + "When critics give quantitative ratings (say 3/4 stars, Thumbs up, etc.), determining the Fresh/Rotten classification is easy. However, publications like the New York Times don't assign numerical ratings to movies, and thus the Fresh/Rotten classification must be inferred from the text of the review itself.\n", + "\n", + "This basic task of categorizing text has many applications. All of the following questions boil down to text classification:\n", + "\n", + " * Is a movie review positive or negative?\n", + " * Is an email spam, or not?\n", + " * Is a comment on a blog discussion board appropriate, or not?\n", + " * Is a tweet about your company positive, or not?\n", + " \n", + "\n", + "Language is incredibly nuanced, and there is an entire field of computer science dedicated to the topic (Natural Language Processing). Nevertheless, we can construct basic language models using fairly straightforward techniques. \n", + "\n", + "## The Data\n", + "\n", + "You will be starting with a database of Movies, derived from the MovieLens dataset. This dataset includes information for about 10,000 movies, including the IMDB id for each movie. \n", + "\n", + "Your first task is to download Rotten Tomatoes reviews from 3000 of these movies, using the Rotten Tomatoes API (Application Programming Interface)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Working with Web APIs\n", + "Web APIs are a more convenient way for programs to interact with websites. Rotten Tomatoes has a nice API that gives access to it's data in JSON format.\n", + "\n", + "To use this, you will first need to [register for an API key](http://developer.rottentomatoes.com/member/register). For \"application URL\", you can use anything -- it doesn't matter.\n", + "\n", + "After you have a key, the [documentation page](http://developer.rottentomatoes.com/iodocs) shows the various data you can fetch from Rotten Tomatoes -- each type of data lives at a different web address. The basic pattern for fetching this data with Python is as follows (compare this to the `Movie Reviews` tab on the documentation page):" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "api_key = 'YOUR KEY HERE'\n", + "movie_id = '770672122' # toy story 3\n", + "url = 'http://api.rottentomatoes.com/api/public/v1.0/movies/%s/reviews.json' % movie_id\n", + "\n", + "#these are \"get parameters\"\n", + "options = {'review_type': 'top_critic', 'page_limit': 20, 'page': 1, 'apikey': api_key}\n", + "data = requests.get(url, params=options).text\n", + "data = json.loads(data) # load a json string into a collection of lists and dicts\n", + "\n", + "print json.dumps(data['reviews'][0], indent=2) # dump an object into a json string" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "{\n", + " \"publication\": \"Village Voice\", \n", + " \"links\": {\n", + " \"review\": \"http://www.villagevoice.com/2010-06-15/film/toys-are-us-in-toy-story-3/full/\"\n", + " }, \n", + " \"quote\": \"When teenaged Andy plops down on the grass to share his old toys with a shy little girl, the film spikes with sadness and layered pleasure -- a concise, deeply wise expression of the ephemeral that feels real and yet utterly transporting.\", \n", + " \"freshness\": \"fresh\", \n", + " \"critic\": \"Eric Hynes\", \n", + " \"date\": \"2013-08-04\"\n", + "}\n" + ] + } + ], + "prompt_number": 31 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem 1: Get the data\n", + "Here's a chunk of the MovieLens Dataset:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from io import StringIO \n", + "movie_txt = requests.get('https://raw.github.com/cs109/cs109_data/master/movies.dat').text\n", + "movie_file = StringIO(movie_txt) # treat a string like a file\n", + "movies = pd.read_csv(movie_file, delimiter='\\t')\n", + "\n", + "#print the first row\n", + "movies[['id', 'title', 'imdbID', 'year']].irow(0)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 32, + "text": [ + "id 1\n", + "title Toy story\n", + "imdbID 114709\n", + "year 1995\n", + "Name: 0, dtype: object" + ] + } + ], + "prompt_number": 32 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### P1.1\n", + "\n", + "We'd like you to write a function that looks up the first 20 Top Critic Rotten Tomatoes reviews for a movie in the `movies` dataframe. This involves two steps:\n", + "\n", + "1. Use the `Movie Alias` API to look up the Rotten Tomatoes movie id from the IMDB id\n", + "1. Use the `Movie Reviews` API to fetch the first 20 top-critic reviews for this movie\n", + "\n", + "Not all movies have Rotten Tomatoes IDs. In these cases, your function should return `None`. The detailed spec is below. We are giving you some freedom with how you implement this, but you'll probably want to break this task up into several small functions.\n", + "\n", + "**Hint**\n", + "In some situations, the leading 0s in front of IMDB ids are important. IMDB ids have 7 digits" + ] + }, + { + "cell_type": "code", + "collapsed": true, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "fetch_reviews(movies, row)\n", + "\n", + "Use the Rotten Tomatoes web API to fetch reviews for a particular movie\n", + "\n", + "Parameters\n", + "----------\n", + "movies : DataFrame \n", + " The movies data above\n", + "row : int\n", + " The row of the movies DataFrame to use\n", + " \n", + "Returns\n", + "-------\n", + "If you can match the IMDB id to a Rotten Tomatoes ID:\n", + " A DataFrame, containing the first 20 Top Critic reviews \n", + " for the movie. If a movie has less than 20 total reviews, return them all.\n", + " This should have the following columns:\n", + " critic : Name of the critic\n", + " fresh : 'fresh' or 'rotten'\n", + " imdb : IMDB id for the movie\n", + " publication: Publication that the critic writes for\n", + " quote : string containing the movie review quote\n", + " review_data: Date of review\n", + " rtid : Rotten Tomatoes ID for the movie\n", + " title : Name of the movie\n", + " \n", + "If you cannot match the IMDB id to a Rotten Tomatoes ID, return None\n", + "\n", + "Examples\n", + "--------\n", + ">>> reviews = fetch_reviews(movies, 0)\n", + ">>> print len(reviews)\n", + "20\n", + ">>> print reviews.irow(1)\n", + "critic Derek Adams\n", + "fresh fresh\n", + "imdb 114709\n", + "publication Time Out\n", + "quote So ingenious in concept, design and execution ...\n", + "review_date 2009-10-04\n", + "rtid 9559\n", + "title Toy story\n", + "Name: 1, dtype: object\n", + "\"\"\"\n", + "#your code here\n", + "def base_url():\n", + " return 'http://api.rottentomatoes.com/api/public/v1.0/'\n", + "\n", + "def rt_id_by_imdb(imdb):\n", + " \"\"\"\n", + " Queries the RT movie_alias API. Returns the RT id associated with an IMDB ID,\n", + " or raises a KeyError if no match was found\n", + " \"\"\"\n", + " url = base_url() + 'movie_alias.json'\n", + " \n", + " imdb = \"%7.7i\" % imdb\n", + " params = dict(id=imdb, type='imdb', apikey=api_key)\n", + " \n", + " r = requests.get(url, params=params).text\n", + " r = json.loads(r)\n", + " \n", + " return r['id']\n", + "\n", + "\n", + "def _imdb_review(imdb):\n", + " \"\"\"\n", + " Query the RT reviews API, to return the first page of reviews \n", + " for a movie specified by its IMDB ID\n", + " \n", + " Returns a list of dicts\n", + " \"\"\" \n", + " rtid = rt_id_by_imdb(imdb)\n", + " url = base_url() + 'movies/{0}/reviews.json'.format(rtid)\n", + "\n", + " params = dict(review_type='top_critic',\n", + " page_limit=20,\n", + " page=1,\n", + " country='us',\n", + " apikey=api_key)\n", + " data = json.loads(requests.get(url, params=params).text)\n", + " data = data['reviews']\n", + " data = [dict(fresh=r['freshness'], \n", + " quote=r['quote'], \n", + " critic=r['critic'], \n", + " publication=r['publication'], \n", + " review_date=r['date'],\n", + " imdb=imdb, rtid=rtid\n", + " ) for r in data]\n", + " return data\n", + "\n", + "def fetch_reviews(movies, row):\n", + " m = movies.irow(row)\n", + " try:\n", + " result = pd.DataFrame(_imdb_review(m['imdbID']))\n", + " result['title'] = m['title']\n", + " except KeyError:\n", + " return None\n", + " return result" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 33 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### P1.2\n", + "\n", + "Use the function you wrote to retrieve reviews for the first 3,000 movies in the movies dataframe.\n", + "\n", + "##### Hints\n", + "* Rotten Tomatoes limits you to **10,000 API requests a day**. Be careful about this limit! Test your code on smaller inputs before scaling. You are responsible if you hit the limit the day the assignment is due :)\n", + "* This will take a while to download. If you don't want to re-run this function every time you restart the notebook, you can save and re-load this data as a CSV file. However, please don't submit this file" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "build_table\n", + "\n", + "Parameters\n", + "----------\n", + "movies : DataFrame\n", + " The movies data above\n", + "rows : int\n", + " The number of rows to extract reviews for\n", + " \n", + "Returns\n", + "--------\n", + "A dataframe\n", + " The data obtained by repeatedly calling `fetch_reviews` on the first `rows`\n", + " of `movies`, discarding the `None`s,\n", + " and concatenating the results into a single DataFrame\n", + "\"\"\"\n", + "#your code here\n", + "def build_table(movies, rows):\n", + " dfs = [fetch_reviews(movies, r) for r in range(rows)]\n", + " dfs = [d for d in dfs if d is not None]\n", + " return pd.concat(dfs, ignore_index=True)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 34 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#you can toggle which lines are commented, if you\n", + "#want to re-load your results to avoid repeatedly calling this function\n", + "\n", + "#critics = build_table(movies, 3000)\n", + "#critics.to_csv('critics.csv', index=False)\n", + "critics = pd.read_csv('critics.csv')\n", + "\n", + "\n", + "#for this assignment, let's drop rows with missing quotes\n", + "critics = critics[~critics.quote.isnull()]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 35 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem 2: Explore\n", + "\n", + "Before delving into analysis, get a sense of what these data look like. Answer the following questions. Include your code!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.1** How many reviews, critics, and movies are in this dataset?\n" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "n_reviews = len(critics)\n", + "n_movies = critics.rtid.unique().size\n", + "n_critics = critics.critic.unique().size\n", + "\n", + "\n", + "print \"Number of reviews: %i\" % n_reviews\n", + "print \"Number of critics: %i\" % n_critics\n", + "print \"Number of movies: %i\" % n_movies" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Number of reviews: 15557\n", + "Number of critics: 622\n", + "Number of movies: 1923\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.2** What does the distribution of number of reviews per reviewer look like? Make a histogram" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Your code here\n", + "def histogram_style():\n", + " remove_border(left=False)\n", + " plt.grid(False)\n", + " plt.grid(axis='y', color='w', linestyle='-', lw=1)\n", + "\n", + "critics.groupby('critic').rtid.count().hist(log=True, bins=range(20), edgecolor='white')\n", + "plt.xlabel(\"Number of reviews per critic\")\n", + "plt.ylabel(\"N\")\n", + "histogram_style()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.3** List the 5 critics with the most reviews, along with the publication they write for" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Your code here\n", + "#note: there are a few valid ways to deal with critics that write for several publications.\n", + "\n", + "counts = critics.groupby(['critic', 'publication']).critic.count()\n", + "counts.sort()\n", + "counts[-1:-6:-1]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 6, + "text": [ + "critic publication \n", + "Roger Ebert Chicago Sun-Times 1074\n", + "James Berardinelli ReelViews 803\n", + "Janet Maslin New York Times 516\n", + "Variety Staff Variety 431\n", + "Jonathan Rosenbaum Chicago Reader 412\n", + "dtype: int64" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.4** Of the critics with > 100 reviews, plot the distribution of average \"freshness\" rating per critic" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Your code here\n", + "\n", + "df = critics.copy()\n", + "df['fresh'] = df.fresh == 'fresh'\n", + "grp = df.groupby('critic')\n", + "counts = grp.critic.count() # number of reviews by each critic\n", + "means = grp.fresh.mean() # average freshness for each critic\n", + "\n", + "means[counts > 100].hist(bins=10, edgecolor='w', lw=1)\n", + "plt.xlabel(\"Average rating per critic\")\n", + "plt.ylabel(\"N\")\n", + "plt.yticks([0, 2, 4, 6, 8, 10])\n", + "histogram_style()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.5**\n", + "Using the original `movies` dataframe, plot the rotten tomatoes Top Critics Rating as a function of year. Overplot the average for each year, ignoring the score=0 examples (some of these are missing data). Comment on the result -- is there a trend? What do you think it means?" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Your code here\n", + "\n", + "data = movies[['year', 'rtTopCriticsRating']]\n", + "data = data.convert_objects(convert_numeric=True)\n", + "data = data[(data['rtTopCriticsRating'] > 0)]\n", + "means = data.groupby('year').mean().dropna()\n", + "\n", + "plt.plot(data['year'], data['rtTopCriticsRating'], 'o', mec='none', alpha=.2, label='Data')\n", + "plt.plot(means.index, means['rtTopCriticsRating'], '-', label='Yearly Average')\n", + "plt.legend(loc='lower left', frameon=False)\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"Average Score\")\n", + "remove_border()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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J6c3xlUAjLZiR1q3Z56XKk7xqmMgr1Y/PdP1hVN8mn5cKd0FSepPPi8NqZUZh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xD0EVpwHrIgoHvllwjDrCeHQ+hpxPDHzyH0txAuXoD/xqco/PyDeM2JNdUi8Bss7FpwIQ9cdg/P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RaM\nFnOEEEIIIRaMFnOEEEIIIRaMFnOEEEIIIRaMFnOEEEIIIRaMFnOEEEIIIRbs/wHbNLwE8jkrYAAA\nAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 64 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Answer** *This graph shows a trend towards a lower average score, as well as a greater abundance of low scores, with time. This is probably at least partially a selection effect -- Rotten Tomatoes probably doesn't archive reviews for all movies, especially ones that came out before the website existed. Thus, reviews of old movies are more often \"the classics\". Mediocre old movies have been partially forgotten, and are underrepresented in the data.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 3: Sentiment Analysis\n", + "\n", + "You will now use a [Naive Bayes classifier](https://en.wikipedia.org/wiki/Naive_Bayes_classifier) to build a prediction model for whether a review is fresh or rotten, depending on the text of the review. See Lecture 9 for a discussion of Naive Bayes.\n", + "\n", + "Most models work with numerical data, so we need to convert the textual collection of reviews to something numerical. A common strategy for text classification is to represent each review as a \"bag of words\" vector -- a long vector\n", + "of numbers encoding how many times a particular word appears in a blurb.\n", + "\n", + "Scikit-learn has an object called a `CountVectorizer` that turns text into a bag of words. Here's a quick tutorial:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "text = ['Hop on pop', 'Hop off pop', 'Hop Hop hop']\n", + "print \"Original text is\\n\", '\\n'.join(text)\n", + "\n", + "vectorizer = CountVectorizer(min_df=0)\n", + "\n", + "# call `fit` to build the vocabulary\n", + "vectorizer.fit(text)\n", + "\n", + "# call `transform` to convert text to a bag of words\n", + "x = vectorizer.transform(text)\n", + "\n", + "# CountVectorizer uses a sparse array to save memory, but it's easier in this assignment to \n", + "# convert back to a \"normal\" numpy array\n", + "x = x.toarray()\n", + "\n", + "print\n", + "print \"Transformed text vector is \\n\", x\n", + "\n", + "# `get_feature_names` tracks which word is associated with each column of the transformed x\n", + "print\n", + "print \"Words for each feature:\"\n", + "print vectorizer.get_feature_names()\n", + "\n", + "# Notice that the bag of words treatment doesn't preserve information about the *order* of words, \n", + "# just their frequency" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Original text is\n", + "Hop on pop\n", + "Hop off pop\n", + "Hop Hop hop\n", + "\n", + "Transformed text vector is \n", + "[[1 0 1 1]\n", + " [1 1 0 1]\n", + " [3 0 0 0]]\n", + "\n", + "Words for each feature:\n", + "[u'hop', u'off', u'on', u'pop']\n" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1**\n", + "\n", + "Using the `critics` dataframe, compute a pair of numerical X, Y arrays where:\n", + " \n", + " * X is a `(nreview, nwords)` array. Each row corresponds to a bag-of-words representation for a single review. This will be the *input* to your model.\n", + " * Y is a `nreview`-element 1/0 array, encoding whether a review is Fresh (1) or Rotten (0). This is the desired *output* from your model.\n", + " \n", + "Make sure to remove items with no review text" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#hint: Consult the scikit-learn documentation to\n", + "# learn about what these classes do do\n", + "from sklearn.cross_validation import train_test_split\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "\n", + "\"\"\"\n", + "Function\n", + "--------\n", + "make_xy\n", + "\n", + "Build a bag-of-words training set for the review data\n", + "\n", + "Parameters\n", + "-----------\n", + "critics : Pandas DataFrame\n", + " The review data from above\n", + " \n", + "vectorizer : CountVectorizer object (optional)\n", + " A CountVectorizer object to use. If None,\n", + " then create and fit a new CountVectorizer.\n", + " Otherwise, re-fit the provided CountVectorizer\n", + " using the critics data\n", + " \n", + "Returns\n", + "-------\n", + "X : numpy array (dims: nreview, nwords)\n", + " Bag-of-words representation for each review.\n", + "Y : numpy array (dims: nreview)\n", + " 1/0 array. 1 = fresh review, 0 = rotten review\n", + "\n", + "Examples\n", + "--------\n", + "X, Y = make_xy(critics)\n", + "\"\"\"\n", + "def make_xy(critics, vectorizer=None):\n", + " #Your code here \n", + " if vectorizer is None:\n", + " vectorizer = CountVectorizer()\n", + " X = vectorizer.fit_transform(critics.quote)\n", + " X = X.tocsc() # some versions of sklearn return COO format\n", + " Y = (critics.fresh == 'fresh').values.astype(np.int)\n", + " return X, Y" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "X, Y = make_xy(critics)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 13 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**3.2** Next, randomly split the data into two groups: a\n", + "training set and a validation set. \n", + "\n", + "Use the training set to train a `MultinomialNB` classifier,\n", + "and print the accuracy of this model on the validation set\n", + "\n", + "**Hint**\n", + "You can use `train_test_split` to split up the training data" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Your code here\n", + "xtrain, xtest, ytrain, ytest = train_test_split(X, Y)\n", + "clf = MultinomialNB().fit(xtrain, ytrain)\n", + "\n", + "print \"Accuracy: %0.2f%%\" % (100 * clf.score(xtest, ytest))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Accuracy: 77.15%\n" + ] + } + ], + "prompt_number": 14 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.3:**\n", + "\n", + "We say a model is **overfit** if it performs better on the training data than on the test data. Is this model overfit? If so, how much more accurate is the model on the training data compared to the test data?" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code here. Print the accuracy on the test and training dataset\n", + "training_accuracy = clf.score(xtrain, ytrain)\n", + "test_accuracy = clf.score(xtest, ytest)\n", + "\n", + "print \"Accuracy on training data: %0.2f\" % (training_accuracy)\n", + "print \"Accuracy on test data: %0.2f\" % (test_accuracy)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Accuracy on training data: 0.90\n", + "Accuracy on test data: 0.77\n" + ] + } + ], + "prompt_number": 16 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Interpret these numbers in a few sentences here*\n", + "\n", + "**Answer** *Some overfitting seems to be happening here, since the error rate on the test data (23%) is more than twice as large as the error rate on the training data (10%). It's possible (though unlikely) that the accuracy difference is a product of chance, and not a symptom of overfitting. This could be tested with cross-validation, by repeatedly fitting and scoring the classifier on different train/test splits. If the performance on the training data is consistently better than the test data, then overfitting has occured. This is the case here.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.4: Model Calibration**\n", + "\n", + "Bayesian models like the Naive Bayes classifier have the nice property that they compute probabilities of a particular classification -- the `predict_proba` and `predict_log_proba` methods of `MultinomialNB` compute these probabilities. \n", + "\n", + "Being the respectable Bayesian that you are, you should always assess whether these probabilities are **calibrated** -- that is, whether a prediction made with a confidence of `x%` is correct approximately `x%` of the time.\n", + "\n", + "Let's make a plot to assess model calibration. Schematically, we want something like this:\n", + "\n", + "\n", + "\n", + "In words, we want to:\n", + "\n", + "* Take a collection of examples, and compute the freshness probability for each using `clf.predict_proba`\n", + "* Gather examples into bins of similar freshness probability (the diagram shows 5 groups -- you should use something closer to 20)\n", + "* For each bin, count the number of examples in that bin, and compute the fraction of examples in the bin which are fresh\n", + "* In the upper plot, graph the expected P(Fresh) (x axis) and observed freshness fraction (Y axis). Estimate the uncertainty in observed freshness fraction $F$ via the [equation](http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval) $\\sigma = \\sqrt{F (1-F) / N}$\n", + "* Overplot the line y=x. This is the trend we would expect if the model is calibrated\n", + "* In the lower plot, show the number of examples in each bin\n", + "\n", + "**Hints**\n", + "\n", + "The output of `clf.predict_proba(X)` is a `(N example, 2)` array. The first column gives the probability $P(Y=0)$ or $P(Rotten)$, and the second gives $P(Y=1)$ or $P(Fresh)$.\n", + "\n", + "The above image is just a guideline -- feel free to explore other options!" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "calibration_plot\n", + "\n", + "Builds a plot like the one above, from a classifier and review data\n", + "\n", + "Inputs\n", + "-------\n", + "clf : Classifier object\n", + " A MultinomialNB classifier\n", + "X : (Nexample, Nfeature) array\n", + " The bag-of-words data\n", + "Y : (Nexample) integer array\n", + " 1 if a review is Fresh\n", + "\"\"\" \n", + "#your code here\n", + "\n", + "def calibration_plot(clf, xtest, ytest):\n", + " prob = clf.predict_proba(xtest)[:, 1]\n", + " outcome = ytest\n", + " data = pd.DataFrame(dict(prob=prob, outcome=outcome))\n", + "\n", + " #group outcomes into bins of similar probability\n", + " bins = np.linspace(0, 1, 20)\n", + " cuts = pd.cut(prob, bins)\n", + " binwidth = bins[1] - bins[0]\n", + " \n", + " #freshness ratio and number of examples in each bin\n", + " cal = data.groupby(cuts).outcome.agg(['mean', 'count'])\n", + " cal['pmid'] = (bins[:-1] + bins[1:]) / 2\n", + " cal['sig'] = np.sqrt(cal.pmid * (1 - cal.pmid) / cal['count'])\n", + " \n", + " #the calibration plot\n", + " ax = plt.subplot2grid((3, 1), (0, 0), rowspan=2)\n", + " p = plt.errorbar(cal.pmid, cal['mean'], cal['sig'])\n", + " plt.plot(cal.pmid, cal.pmid, linestyle='--', lw=1, color='k')\n", + " plt.ylabel(\"Empirical P(Fresh)\")\n", + " remove_border(ax)\n", + " \n", + " #the distribution of P(fresh)\n", + " ax = plt.subplot2grid((3, 1), (2, 0), sharex=ax)\n", + " \n", + " plt.bar(left=cal.pmid - binwidth / 2, height=cal['count'],\n", + " width=.95 * (bins[1] - bins[0]),\n", + " fc=p[0].get_color())\n", + " \n", + " plt.xlabel(\"Predicted P(Fresh)\")\n", + " remove_border()\n", + " plt.ylabel(\"Number\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 17 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "calibration_plot(clf, xtest, ytest)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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VSlJSUvDy8gKuv/5VqVS0aNGi0vk3Bz8hhKgr0q7m8uyOH1BrNLzYqT/z47YD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\nwoULJCQkyL8vQggh9EJvwe/kyZOsXr0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QyxdCcCwfERFRC8dHvc2EEAKZmZmYN28eampq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+ "text": [ + "" + ] + } + ], + "prompt_number": 18 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.5** We might say a model is *over-confident* if the freshness fraction is usually closer to 0.5 than expected (that is, there is more uncertainty than the model predicted). Likewise, a model is *under-confident* if the probabilities are usually further away from 0.5. Is this model generally over- or under-confident? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Answer** *This model is over-confident. For a properly calibrated model, we would expect ~10% of the P(Fresh)~0.1 reviews to actually be fresh. However, the actual freshness rate is closer to 30%. Likewise, for reviews where P(Fresh) ~0.9, the actuall freshness fraction is closer to 0.7. In other words, there is more uncertainty in the outcome than implied by the model.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Cross Validation\n", + "\n", + "Our classifier has a few free parameters. The two most important are:\n", + "\n", + " 1. The `min_df` keyword in `CountVectorizer`, which will ignore words which appear in fewer than `min_df` fraction of reviews. Words that appear only once or twice can lead to overfitting, since words which occur only a few times might correlate very well with Fresh/Rotten reviews by chance in the training dataset.\n", + " \n", + " 2. The [`alpha` keyword](http://scikit-learn.org/stable/modules/naive_bayes.html#multinomial-naive-bayes) in the Bayesian classifier is a \"smoothing parameter\" -- increasing the value decreases the sensitivity to any single feature, and tends to pull prediction probabilities closer to 50%. \n", + "\n", + "As discussed in lecture and HW2, a common technique for choosing appropriate values for these parameters is **cross-validation**. Let's choose good parameters by maximizing the cross-validated log-likelihood.\n", + "\n", + "**3.6** Using `clf.predict_logproba`, write a function that computes the log-likelihood of a dataset" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "log_likelihood\n", + "\n", + "Compute the log likelihood of a dataset according to \n", + "a bayesian classifier. \n", + "The Log Likelihood is defined by\n", + "\n", + "L = Sum_fresh(logP(fresh)) + Sum_rotten(logP(rotten))\n", + "\n", + "Where Sum_fresh indicates a sum over all fresh reviews, \n", + "and Sum_rotten indicates a sum over rotten reviews\n", + " \n", + "Parameters\n", + "----------\n", + "clf : Bayesian classifier\n", + "x : (nexample, nfeature) array\n", + " The input data\n", + "y : (nexample) integer array\n", + " Whether each review is Fresh\n", + "\"\"\"\n", + "#your code here\n", + "\n", + "def log_likelihood(clf, x, y):\n", + " prob = clf.predict_log_proba(x)\n", + " rotten = y == 0\n", + " fresh = ~rotten\n", + " return prob[rotten, 0].sum() + prob[fresh, 1].sum()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 19 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a function to estimate the cross-validated value of a scoring function, given a classifier and data" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from sklearn.cross_validation import KFold\n", + "\n", + "def cv_score(clf, x, y, score_func):\n", + " \"\"\"\n", + " Uses 5-fold cross validation to estimate a score of a classifier\n", + " \n", + " Inputs\n", + " ------\n", + " clf : Classifier object\n", + " x : Input feature vector\n", + " y : Input class labels\n", + " score_func : Function like log_likelihood, that takes (clf, x, y) as input,\n", + " and returns a score\n", + " \n", + " Returns\n", + " -------\n", + " The average score obtained by randomly splitting (x, y) into training and \n", + " test sets, fitting on the training set, and evaluating score_func on the test set\n", + " \n", + " Examples\n", + " cv_score(clf, x, y, log_likelihood)\n", + " \"\"\"\n", + " result = 0\n", + " nfold = 5\n", + " for train, test in KFold(y.size, nfold): # split data into train/test groups, 5 times\n", + " clf.fit(x[train], y[train]) # fit\n", + " result += score_func(clf, x[test], y[test]) # evaluate score function on held-out data\n", + " return result / nfold # average" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 20 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.7**\n", + "\n", + "Fill in the remaining code in this block, to loop over many values of `alpha` and `min_df` to determine\n", + "which settings are \"best\" in the sense of maximizing the cross-validated log-likelihood" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#the grid of parameters to search over\n", + "alphas = [0, .1, 1, 5, 10, 50]\n", + "min_dfs = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1]\n", + "\n", + "#Find the best value for alpha and min_df, and the best classifier\n", + "best_alpha = None\n", + "best_min_df = None\n", + "max_loglike = -np.inf\n", + "\n", + "for alpha in alphas:\n", + " for min_df in min_dfs: \n", + " vectorizer = CountVectorizer(min_df = min_df) \n", + " X, Y = make_xy(critics, vectorizer)\n", + " \n", + " #your code here\n", + " clf = MultinomialNB(alpha=alpha)\n", + " loglike = cv_score(clf, X, Y, log_likelihood)\n", + "\n", + " if loglike > max_loglike:\n", + " max_loglike = loglike\n", + " best_alpha, best_min_df = alpha, min_df" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 21 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print \"alpha: %f\" % best_alpha\n", + "print \"min_df: %f\" % best_min_df" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "alpha: 5.000000\n", + "min_df: 0.001000\n" + ] + } + ], + "prompt_number": 22 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.8** Now that you've determined values for alpha and min_df that optimize the cross-validated log-likelihood, repeat the steps in 3.1, 3.2, and 3.4 to train a final classifier with these parameters, re-evaluate the accuracy, and draw a new calibration plot." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Your code here\n", + "\n", + "vectorizer = CountVectorizer(min_df=best_min_df)\n", + "X, Y = make_xy(critics, vectorizer)\n", + "xtrain, xtest, ytrain, ytest = train_test_split(X, Y)\n", + "\n", + "clf = MultinomialNB(alpha=best_alpha).fit(xtrain, ytrain)\n", + "\n", + "calibration_plot(clf, xtest, ytest)\n", + "\n", + "# Your code here. Print the accuracy on the test and training dataset\n", + "training_accuracy = clf.score(xtrain, ytrain)\n", + "test_accuracy = clf.score(xtest, ytest)\n", + "\n", + "print \"Accuracy on training data: %0.2f\" % (training_accuracy)\n", + "print \"Accuracy on test data: %0.2f\" % (test_accuracy)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Accuracy on training data: 0.78\n", + "Accuracy on test data: 0.74\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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duyASiRAXFweRSISXXnpJuT/z/l3898xuHMpIBAD42jjhS/9R6G5Xu6E/tUz1GrETCAS4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\nBljad9u2bcO2bduwYsUKCL7WazOW9N/f/vY3dOnSBdnZ2ejSpQt8fX0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+ "text": [ + "" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.9** Discuss the various ways in which Cross-Validation has affected the model. Is the new model more or less accurate? Is overfitting better or worse? Is the model more or less calibrated?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Answer** *The new model is slightly less accurate on the test data (74% vs 77% on the original model). However, it is both better calibrated and less over-fit than before. In other words, while the classification accuracy is slightly worse, the probabilities themselves are more accurate. The model is still slightly over-confident when making low P(Fresh) predictions. However, the calibration plot shows the model is usually within 1 error bar of the expected performance where P(Fresh) >= 0.2. Finally, the new model makes less-conclusive predictions on average -- the histogram in the calibration plot is more uniformly distributed, with fewer predictions clustered around P(Fresh) = 0 or 1.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*To think about/play with, but not to hand in: What would happen if you tried this again using a function besides the log-likelihood -- for example, the classification accuracy?*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Part 4: Interpretation. What words best predict a fresh or rotten review?\n", + "\n", + "**4.1**\n", + "Using your classifier and the `vectorizer.get_feature_names` method, determine which words best predict a positive or negative review. Print the 10 words\n", + "that best predict a \"fresh\" review, and the 10 words that best predict a \"rotten\" review. For each word, what is the model's probability of freshness if the word appears one time?\n", + "\n", + "#### Hints\n", + "\n", + "* Try computing the classification probability for a feature vector which consists of all 0s, except for a single 1. What does this probability refer to?\n", + "\n", + "* `np.eye` generates a matrix where the ith row is all 0s, except for the ith column which is 1." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "## Your code here\n", + "\n", + "words = np.array(vectorizer.get_feature_names())\n", + "\n", + "x = np.eye(xtest.shape[1])\n", + "probs = clf.predict_log_proba(x)[:, 0]\n", + "ind = np.argsort(probs)\n", + "\n", + "good_words = words[ind[:10]]\n", + "bad_words = words[ind[-10:]]\n", + "\n", + "good_prob = probs[ind[:10]]\n", + "bad_prob = probs[ind[-10:]]\n", + "\n", + "print \"Good words\\t P(fresh | word)\"\n", + "for w, p in zip(good_words, good_prob):\n", + " print \"%20s\" % w, \"%0.2f\" % (1 - np.exp(p))\n", + " \n", + "print \"Bad words\\t P(fresh | word)\"\n", + "for w, p in zip(bad_words, bad_prob):\n", + " print \"%20s\" % w, \"%0.2f\" % (1 - np.exp(p))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Good words\t P(fresh | word)\n", + " masterpiece 0.90\n", + " delight 0.89\n", + " remarkable 0.89\n", + " touching 0.87\n", + " perfect 0.87\n", + " rare 0.86\n", + " witty 0.86\n", + " superb 0.86\n", + " captures 0.86\n", + " delightful 0.85\n", + "Bad words\t P(fresh | word)\n", + " worst 0.21\n", + " dull 0.21\n", + " disappointment 0.20\n", + " disappointing 0.20\n", + " unfunny 0.20\n", + " bland 0.18\n", + " uninspired 0.17\n", + " pointless 0.17\n", + " lame 0.13\n", + " unfortunately 0.12\n" + ] + } + ], + "prompt_number": 22 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**4.2**\n", + "\n", + "One of the best sources for inspiration when trying to improve a model is to look at examples where the model performs poorly. \n", + "\n", + "Find 5 fresh and rotten reviews where your model performs particularly poorly. Print each review." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Your code here\n", + "x, y = make_xy(critics, vectorizer)\n", + "\n", + "prob = clf.predict_proba(x)[:, 0]\n", + "predict = clf.predict(x)\n", + "\n", + "bad_rotten = np.argsort(prob[y == 0])[:5]\n", + "bad_fresh = np.argsort(prob[y == 1])[-5:]\n", + "\n", + "print \"Mis-predicted Rotten quotes\"\n", + "print '---------------------------'\n", + "for row in bad_rotten:\n", + " print critics[y == 0].quote.irow(row)\n", + " print\n", + "\n", + "print \"Mis-predicted Fresh quotes\"\n", + "print '--------------------------'\n", + "for row in bad_fresh:\n", + " print critics[y == 1].quote.irow(row)\n", + " print" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Mis-predicted Rotten quotes\n", + "---------------------------\n", + "It survives today only as an unusually pure example of a typical 50s art-film strategy: the attempt to make the most modern and most popular of art forms acceptable to the intelligentsia by forcing it into an arcane, antique mold.\n", + "\n", + "The Waterboy is arguably Sandler's most enjoyable motion picture to date, but it's still far from a masterpiece.\n", + "\n", + "Pale Rider does nothing to disprove the wisdom that this genre is best left to the revival houses. A double feature of Shane and Eastwood's High Plains Drifter will do just fine, thanks.\n", + "\n", + "It's a sad day when an actor who's totally, beautifully in touch with his dark side finds himself stuck in a movie that's scared of its own shadow.\n", + "\n", + "Walken is one of the few undeniably charismatic male villains of recent years; he can generate a snakelike charm that makes his worst characters the most memorable, and here he operates on pure style.\n", + "\n", + "Mis-predicted Fresh quotes\n", + "--------------------------\n", + "The gangland plot is flimsy (bad guy Peter Greene wears too much eyeliner), and the jokes are erratic, but it's a far better showcase for Carrey's comic-from-Uranus talent than Ace Ventura.\n", + "\n", + "It's a one-joke movie, a funhouse ride, the cinematic equivalent of having a rubber spider thrown in your lap. But it doesn't matter if you reject the wispy script or the plot, which has as much substance as a spider's web; you'll jump every time.\n", + "\n", + "There's too much talent and too strong a story to mess it up. There was potential for more here, but this incarnation is nothing to be ashamed of, and some of the actors answer the bell.\n", + "\n", + "This tough-to-peg whodunit keeps you going for two hours, despite a few James Bond-ish (or Jane Bond-ish) turns that play less preposterously than you might assume were they to be divulged.\n", + "\n", + "Some of the gags don't work, but fewer than in any previous Brooks film that I've seen, and when the jokes are meant to be bad, they are riotously poor. What more can one ask of Mel Brooks?\n", + "\n" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**4.3** What do you notice about these mis-predictions? Naive Bayes classifiers assume that every word affects the probability independently of other words. In what way is this a bad assumption? In your answer, report your classifier's Freshness probability for the review \"This movie is not remarkable, touching, or superb in any way\"." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "clf.predict_proba(vectorizer.transform(['This movie is not remarkable, touching, or superb in any way']))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 25, + "text": [ + "array([[ 0.01755082, 0.98244918]])" + ] + } + ], + "prompt_number": 25 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Answer** *Many mis-predictions seem due to the fact that the quotes use more ambivalent language -- quotes along the lines of \"this should have been a good movie, but it wasn't\". Words like \"but\", \"not\", etc. act to negate the sentiment of words. However, because Naive Bayes treats each word separately, it isn't able to capture these kind of word interactions. Because the quote \"this movie is not remarkable, touching, or superb in any way\" contains typically positive words like remarkabke/touching/superb, the classifier gives it P(Fresh)=0.98.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**4.4**\n", + "If this was your final project, what are 3 things you would try in order to build a more effective review classifier? What other exploratory or explanatory visualizations do you think might be helpful?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are many things worth trying. Some examples:\n", + "\n", + "1. You could try to build a NB model where the features are word pairs instead of words. This would be smart enough to realize that \"not good\" and \"so good\" mean very different things. This technique doesn't scale very well, since these features are much more sparse (and hence harder to detect repeatable patterns within).\n", + "2. You could try a model besides NB, that would allow for interactions between words -- for example, a Random Forest classifier.\n", + "3. You could consider adding supplemental features -- information about genre, director, cast, etc.\n", + "4. You could build a visualization that prints word reviews, and visually encodes each word with size or color to indicate how that word contributes to P(Fresh). For example, really bad words could show up as big and red, good words as big and green, common words as small and grey, etc." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to Submit\n", + "\n", + "To submit your homework, create a folder named lastname_firstinitial_hw3 and place this notebook file in the folder. Double check that this file is still called HW3.ipynb, and that it contains your solutions. Please do **not** include the critics.csv data file, if you created one. Compress the folder (please use .zip compression) and submit to the CS109 dropbox in the appropriate folder. If we cannot access your work because these directions are not followed correctly, we will not grade your work.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "*css tweaks in this cell*\n", + "" + ] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/HW4_solutions.ipynb b/HW4_solutions.ipynb new file mode 100644 index 0000000..9cf570b --- /dev/null +++ b/HW4_solutions.ipynb @@ -0,0 +1,3857 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "HW4: Do we really need Chocolate Recommendations?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Before You Start\n", + "\n", + "This is a **long** homework. Please start early. It uses a lot of different (and sometimes complex) concepts, so you might find yourself reading a lot. So, please, give yourself a lot of time.\n", + "\n", + "Also, please see this [link](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/InstructionsForAmazonEMR.ipynb) on getting an Amazon Web Services account soon, so that you dont delay its creation. This class gives you $100 in credits which you will use for this homework, possibly your project, and any other projects you might like.\n", + "\n", + "Finally, please go to the labs. The one on 18th October (Today) will cover Gibbs Sampling and Bayesian Normal distributions. The one on the 25th will cover Map-Reduce. Both will help on the homework." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Collaborative Filtering systems\n", + "\n", + "In this homework, you will create a recommendation system for **restaurants** using [collaborative filtering](http://en.wikipedia.org/wiki/Collaborative_filtering) (CF). The general structure of a recommendation system is that there are users and there are items. Users express explicit or implicit preferences towards certain items. CF thus relies on users' past behavior.\n", + "\n", + "There are two primary approaches to CF: neighboorhood and latent factor model. The former is concerned with computing the relationships between items or between users. In the latter approach you have a model of hidden factors through which users and items are transformed to the same space. For example, if you are rating movies we may transform items into genre factors, and users into their preference for a particular genre.\n", + "\n", + "Factor models generally lead to more accurate recommenders. One of the reasons for this is the sparsity of the item-user matrix. Most users tend to rate barely one or two items. Latent factor models are more expressive, and fit fewer parameters. However, neighborhood models are more prevalent, as they have an intuitive aspect that appeals to users(if you liked this you will like that) and online(a new preference can be incorporated very quickly).\n", + "\n", + "Most recommenders today combine neighboorhood CF with model based CF, and SVD based matrix factorization approaches.\n", + "\n", + "To see the example of a simple beer recommender, go [here](http://nbviewer.ipython.org/20a18d52c539b87de2af). This homework is inspired by the one there but we go after food instead, and go deeper into the problem of making recommendations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### User and Item based approaches\n", + "\n", + "Original approaches to neighborhood based CF used user-user models. By this we mean that rating estimates are made from recorded ratings of like minded users. However, since most users tend to rate very few items, this is usually a losing proposition for explicit-rating based recommenders. Thus, most neighborhood based systems such as Amazon these days rely on item-item approaches. In these methods, a rating is estimated by other ratings made by the user on \"similar\" or \"nearby\" items: we have a K-Nearest-Neighbors algorithm, in effect." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Outline of this Homework\n", + "\n", + "The outline of this homework is as follows:\n", + "\n", + "1. Create a database of item-item similarities. Use this to implement a neighborhood-based CF recommender that can answer simple questions like \"give me more restaurants like this one\". This part of the homework assumes that the similaties calculated make good \"global recommendations\".\n", + "\n", + "2. In the second part, we go one step further and attempt to predict the rating that a user will give an item they have not seen before. This requires that we find the restaurants that *this* user would rate as similar (not just those which are globally similar). \n", + "\n", + "3. In the third part, we implement a factor-based CF recommender using a Bayesian model. While quite a bit more complex, this allows us to pool information both about similar users and about similar restaurants.\n", + "\n", + "5. We will scale up our system by creating a recommender on the lines of Q1 and Q2 that works on the entire data set. We will use the map-reduce paradigm to split the computation over multiple machines." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will start simply, by working on a subset of the restaurant data before generalizing to the entire data set in Problem 4. The complete data set has 150,000 reviews, but we shall start with just about 7000. You will create this smaller set by taking all the users who had rated more than 60 restaurants, and all the businesses which had greater than 150 reviews from the larger data set. This is not a random set: indeed we use it as it a computationally tractable set that is a bit less sparse than the entire data set." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline\n", + "from collections import defaultdict\n", + "import json\n", + "\n", + "import numpy as np\n", + "import scipy as sp\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "from matplotlib import rcParams\n", + "import matplotlib.cm as cm\n", + "import matplotlib as mpl\n", + "\n", + "#colorbrewer2 Dark2 qualitative color table\n", + "dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),\n", + " (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),\n", + " (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),\n", + " (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),\n", + " (0.4, 0.6509803921568628, 0.11764705882352941),\n", + " (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),\n", + " (0.6509803921568628, 0.4627450980392157, 0.11372549019607843)]\n", + "\n", + "rcParams['figure.figsize'] = (10, 6)\n", + "rcParams['figure.dpi'] = 150\n", + "rcParams['axes.color_cycle'] = dark2_colors\n", + "rcParams['lines.linewidth'] = 2\n", + "rcParams['axes.facecolor'] = 'white'\n", + "rcParams['font.size'] = 14\n", + "rcParams['patch.edgecolor'] = 'white'\n", + "rcParams['patch.facecolor'] = dark2_colors[0]\n", + "rcParams['font.family'] = 'StixGeneral'\n", + "\n", + "\n", + "def remove_border(axes=None, top=False, right=False, left=True, bottom=True):\n", + " \"\"\"\n", + " Minimize chartjunk by stripping out unnecesasry plot borders and axis ticks\n", + " \n", + " The top/right/left/bottom keywords toggle whether the corresponding plot border is drawn\n", + " \"\"\"\n", + " ax = axes or plt.gca()\n", + " ax.spines['top'].set_visible(top)\n", + " ax.spines['right'].set_visible(right)\n", + " ax.spines['left'].set_visible(left)\n", + " ax.spines['bottom'].set_visible(bottom)\n", + " \n", + " #turn off all ticks\n", + " ax.yaxis.set_ticks_position('none')\n", + " ax.xaxis.set_ticks_position('none')\n", + " \n", + " #now re-enable visibles\n", + " if top:\n", + " ax.xaxis.tick_top()\n", + " if bottom:\n", + " ax.xaxis.tick_bottom()\n", + " if left:\n", + " ax.yaxis.tick_left()\n", + " if right:\n", + " ax.yaxis.tick_right()\n", + " \n", + "pd.set_option('display.width', 500)\n", + "pd.set_option('display.max_columns', 100)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Description of the data set\n", + "\n", + "The data set has been extracted from the Yelp Phoenix restaurants dataset. It is available [here](https://dl.dropboxusercontent.com/u/75194/bigdf.csv)." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "fulldf=pd.read_csv(\"bigdf.csv\")\n", + "fulldf.head(2)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_idbusiness_iddatereview_idstarsusefulvotes_reviewuser_namecategoriesbiz_namelatitudelongitudebusiness_avgbusiness_review_countuser_avguser_review_count
0 rLtl8ZkDX5vH5nAx9C3q5Q 9yKzy9PApeiPPOUJEtnvkg 2011-01-26 00:00:00 fWKvX83p0-ka4JS3dc6E5A 5 5 Jason [Breakfast & Brunch, Restaurants] Morning Glory Cafe 33.390792-112.012504 3.87156 109 3.796954 197
1 SBbftLzfYYKItOMFwOTIJg 9yKzy9PApeiPPOUJEtnvkg 2008-05-04 00:00:00 DASdFe-g0BgfN9J2tanStg 5 1 Jennifer [Breakfast & Brunch, Restaurants] Morning Glory Cafe 33.390792-112.012504 3.87156 109 3.473684 57
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 2, + "text": [ + " user_id business_id date review_id stars usefulvotes_review user_name categories biz_name latitude longitude business_avg business_review_count user_avg user_review_count\n", + "0 rLtl8ZkDX5vH5nAx9C3q5Q 9yKzy9PApeiPPOUJEtnvkg 2011-01-26 00:00:00 fWKvX83p0-ka4JS3dc6E5A 5 5 Jason [Breakfast & Brunch, Restaurants] Morning Glory Cafe 33.390792 -112.012504 3.87156 109 3.796954 197\n", + "1 SBbftLzfYYKItOMFwOTIJg 9yKzy9PApeiPPOUJEtnvkg 2008-05-04 00:00:00 DASdFe-g0BgfN9J2tanStg 5 1 Jennifer [Breakfast & Brunch, Restaurants] Morning Glory Cafe 33.390792 -112.012504 3.87156 109 3.473684 57" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data frame is a frame of reviews. We have joined in information about users and businesses into this frame so that you have only one frame to work with." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This information is for the reviews themselves:" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + " 'stars': (star rating, integer 1-5),\n", + " 'date': (date, formatted like '2011-04-19'),\n", + " 'review_id': (unique id for the review)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a description of the data fields in this dataframe, on the business side" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + " 'business_id': (a unique identifier for this business),\n", + " 'biz_name': (the full business name),\n", + " 'latitude': (latitude),\n", + " 'longitude': (longitude),\n", + " 'business_review_count': (review count for the restaurant[this is a repeated field for all reviews of the restaurant]),\n", + " 'categories': [(localized category names)],\n", + " 'business_avg': (average stars over all users reviews for business[this is a repeated field for all reviews of the restaurant])." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And Finally, a set of fields for users" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + " 'user_id': (unique user identifier),\n", + " 'user_name': (first name, last initial, like 'Matt J.'),\n", + " 'user_review_count': (count of restaurants reviewed by user[this is a repeated field for all reviews by the user]),\n", + " 'user_avg': (floating point average of users reviews over all businesses, like 4.31[this is a repeated field for all reviews by the user])." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this data set, every user has only one review for each restaurant. Convince yourself of this. (This answer does not need to be submitted)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Our Recommender\n", + "\n", + "To motivate our recommendation system, consider the follwing example. Let's pretend we are in Boston for a second. Lets say the average rating of restaurants here by all the users is 3.5. Sandrine's at Harvard square is better than an average restaurant, so it tends to be rated 0.5 stars above the average (over all the users). However, you are a curmudgeon, who tends to rate 0.2 stars below the average. Then a baseline estimate for the recommendation for Sandrine's, for you, is 3.5+0.5-0.2=3.8.\n", + "\n", + "These baseline estimates thus adjust the data by accounting for the systematic tendencies for some users who give higher ratings than others, and for some restaurants to recieve higher ratings than others. We can write the baseline estimate $\\hat Y_{um}^{baseline}$ for an unknown rating $Y_{um}$ for user $u$ and restaurant or business $m$ as:\n", + "\n", + "$$ \\hat Y_{um}^{baseline} = \\hat \\mu + \\hat \\theta_{u0} + \\hat \\gamma_{m0} $$\n", + "\n", + "where the unknown parameters $\\theta_{u0}$ and $\\gamma_{m0}$ indicate the deviations, or biases, of user $u$ and item $m$, respectively, from some intercept parameter $\\mu$. (The reason for the strange notation with 0s will become clear in Problem 3)\n", + "\n", + "Notice that the $\\theta_{u0}$ and $\\gamma_{m0}$ are parameters which need to be fit. The simplest thing to start with, and something we will do for Problems 1 and 2 (but not 3), is to replace them by their \"mean\" estimates from the data. Thus:\n", + "\n", + "$$ \\hat Y^{baseline}_{um} = \\bar Y + (\\bar Y_u - \\bar Y) + (\\bar Y_m - \\bar Y)$$\n", + "\n", + "where $\\bar Y_u$ = `user_avg`, the average of all a user $u$'s ratings and $\\bar Y_m$ = `business_avg`, the average of all ratings for a restaurant $m$. $\\bar Y$ is the average rating over all reviews.\n", + "\n", + "The final two terms correspond to the user-specific and item-specific bias in ratings, that is, how their ratings tend to systematically diverge from the global average. This is the simplest possible way to predict a rating, based only on information about *this* user and *this* restaurant.\n", + "\n", + "Can we do a better job of predicting the rating $Y_{um}$ user $u$ would give to restaurant $r$? According to the central dogma of CF, we ought to be able to use the responses of *similar* users regarding *similar* restaurants to get a better prediction. \n", + "\n", + "We can make an estimate of $Y_{um}$ as:\n", + "\n", + "$$ \\hat{Y_{um}} = \\hat Y_{um}^{baseline}\\, + \\,\\frac{\\sum\\limits_{j \\in S^{k}(m)} s_{mj} ( Y_{uj} - \\hat Y_{uj}^{baseline} )}{\\sum\\limits_{j \\in S^{k}(m)} s_{mj} } $$\n", + "\n", + "where $s^{k}(m)$ is the $k$ neighbor items of item $m$ based on some pooling criterion, for example, those items which have been rated by user $u$.\n", + "\n", + "In the next two problems, we will focus on using similar restaurants, or the item neighborhood.\n", + "To do this, we compute a *similarity measure* $s_{mj}$ between the $m$th and $j$th items. This similarity might be measured via [cosine similarity](http://en.wikipedia.org/wiki/Cosine_similarity), [pearson co-efficient](http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient) or using other distance based measures. Here we shall use the Pearson coefficient. This measures the tendency of users to rate items similarly. Since most ratings are unknown, it is computed on the \"common user support\" (`n_common`), which is the set of common raters of both items. \n", + "\n", + "In the first problem we shall set $S$ to the global neighborhood of the item, and in the second we shall set it to those items which have been rated by user $u$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Q1. Writing a simple \"global\" recommender" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have a way to pool information between similar restaurants to try to predict a user's recommendation. But how do we choose the neighborhood to pool over? We begin with the simplest choice. We calculate the similarity between items using their entire common user support, and rank the nearest neighbors of an item by this similarity. We call this a \"global\" recommender because it assumes that every user perceives the similarity between restaurants in the same way. Later on, we will implement a more specific recommender that pools information based on which items seem the most similar *to this user*.\n", + "\n", + "The global recommender does have the advantage of dealing with the possible sparsity of the user's rated items, but also the disadvantage of giving one answer for all users, without taking the user's preferences into account. This is a classic case of bias-variance tradeoff.\n", + "\n", + "Lets implement this simpler global recommender first.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exploratory Data Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.1** Visualize the sparsity of the full data set by plotting two histograms of the review count grouped by the `user_id` and `business_id` respectively. Are there more users or more businesses? " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "urc=fulldf.groupby('user_id').review_id.count()\n", + "ax=urc.hist(bins=50, log=True)\n", + "remove_border(ax)\n", + "plt.xlabel(\"Reviews per user\")\n", + "plt.grid(False)\n", + "plt.grid(axis = 'y', color ='white', linestyle='-')\n", + "plt.title(\"Review Count per User\");" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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lyipv5wYAAGhIXF7ny9vb+4LVpbkLCAAAoGIuh6+2bdvq5MmT5bbl5eU5Fvhzxpw5c7Rt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\n/6xrrrlGe/fuVWFhoaZMmaI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+ "text": [ + "" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "brc=fulldf.groupby('business_id').review_id.count()\n", + "ax=brc.hist(bins=50, log=True)\n", + "remove_border(ax)\n", + "plt.xlabel(\"Reviews per restaurant\")\n", + "plt.grid(False)\n", + "plt.grid(axis = 'y', color ='white', linestyle='-')\n", + "plt.title(\"Review Count per Restaurant\");" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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tiHNjVBAAgNKDka/zgLcjWoxmAQDg+xj5AgAAcBDhCwAAwEE+Eb5YagIAAPgL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\n5XduiYQvFmfNKyoq6oz+SE5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+ "text": [ + "" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "print \"Number of Reviews\",fulldf.shape[0]\n", + "print \"Number of Users\", fulldf.user_id.unique().shape[0], \"Number of Businesses\", fulldf.business_id.unique().shape[0]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Number of Reviews 149319\n", + "Number of Users 34789 Number of Businesses 4503\n" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*your answer here*: There are more users than businesses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.2** Compute the average rating of reviews in the data set and a histogram of all the ratings in the dataset." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "print \"Mean stars over all reviews:\",fulldf.stars.mean()\n", + "stars=fulldf.stars\n", + "ax=stars.hist(bins=5)\n", + "remove_border(ax)\n", + "plt.xlabel(\"Star rating\")\n", + "plt.grid(False)\n", + "plt.grid(axis = 'y', color ='white', linestyle='-')\n", + "plt.title(\"Star ratings over all reviews\");" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Mean stars over all reviews: 3.74141268023\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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AAFiMsAYAAGAxwhoAAIDFCGsAAAAWI6wBAABYjLAGAABgMcIaAACAxQhrAAAA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\n1uvXX39dkyZNkp+f+3+SFaH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+ "text": [ + "" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following function is used to re-compute review counts and averages whenever you subset a reviews data frame. We'll use it soon to construct a smaller, more computationally tractable data frame." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def recompute_frame(ldf):\n", + " \"\"\"\n", + " takes a dataframe ldf, makes a copy of it, and returns the copy\n", + " with all averages and review counts recomputed\n", + " this is used when a frame is subsetted.\n", + " \"\"\"\n", + " ldfu=ldf.groupby('user_id')\n", + " ldfb=ldf.groupby('business_id')\n", + " user_avg=ldfu.stars.mean()\n", + " user_review_count=ldfu.review_id.count()\n", + " business_avg=ldfb.stars.mean()\n", + " business_review_count=ldfb.review_id.count()\n", + " nldf=ldf.copy()\n", + " nldf.set_index(['business_id'], inplace=True)\n", + " nldf['business_avg']=business_avg\n", + " nldf['business_review_count']=business_review_count\n", + " nldf.reset_index(inplace=True)\n", + " nldf.set_index(['user_id'], inplace=True)\n", + " nldf['user_avg']=user_avg\n", + " nldf['user_review_count']=user_review_count\n", + " nldf.reset_index(inplace=True)\n", + " return nldf" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.3** Create a smaller data set in dataframe `smalldf` by looking for those businesses with more than 150 reviews and those users with more than 60 reviews. Include all the columns that were there in the parent dataframe. Since you have created a subset of the data set, use the method provided above to recalculate the averages. Print the number of unique users and items in this data set. \n", + "\n", + "Note that while this cut makes sure we have prolific users, the cut on businesses restores sparsity by reducing the number of reviews per user." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "smallidf=fulldf[(fulldf.user_review_count > 60) & (fulldf.business_review_count > 150)]\n", + "smalldf=recompute_frame(smallidf)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How does this compare to the parent data set, in terms of size and sparsity? Once again, plot histograms of the review count grouped by user, and by the review count grouped by business, respectively, and describe the results" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "print \"Total Number of Reviews\", smalldf.shape[0]\n", + "print \"Users in this set\", smalldf.user_id.unique().shape[0], \"Restaurants\",smalldf.business_id.unique().shape[0]\n", + "plt.figure()\n", + "ax=smalldf.groupby('user_id').review_id.count().hist()\n", + "remove_border(ax)\n", + "plt.xlabel(\"Reviews per user\")\n", + "plt.grid(False)\n", + "plt.grid(axis = 'y', color ='white', linestyle='-')\n", + "plt.figure()\n", + "ax=smalldf.groupby('business_id').review_id.count().hist()\n", + "remove_border(ax)\n", + "plt.xlabel(\"Reviews per restaurant\")\n", + "plt.grid(False)\n", + "plt.grid(axis = 'y', color ='white', linestyle='-')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Total Number of Reviews 6165\n", + "Users in this set 240 Restaurants 172\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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GG27QxIkTlZeXp61bt6qoqEhut1sul0uSlJKSIqfTqQ0bNlyKmQEAALqt88bVtGnT1Ldv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\n+++rublZjz/+uF5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+ "text": [ + "" + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*your answer here*: As we made a very specific cut on the data set, making sure we had those businesses with greater than 150 reviews, and those users with more than 60 reviews, this data set is not as sparse as the full one, with a range in both graphs where the histogram is somewhat flat rather than steeply declining. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.4** Compute histograms of the average user rating in the smaller data set, and the average business rating in the smaller data set. Print the overall mean." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "plt.figure()\n", + "avg_ratings_by_user=smalldf.groupby('user_id').stars.mean()\n", + "ax=avg_ratings_by_user.hist()\n", + "remove_border(ax)\n", + "plt.xlabel(\"Average review score\")\n", + "plt.grid(False)\n", + "plt.grid(axis = 'y', color ='white', linestyle='-')\n", + "plt.title(\"Average User Rating\")\n", + "plt.figure()\n", + "\n", + "avg_ratings_by_biz=smalldf.groupby('business_id').stars.mean()\n", + "ax=avg_ratings_by_biz.hist()\n", + "remove_border(ax)\n", + "plt.xlabel(\"Average review score\")\n", + "plt.grid(False)\n", + "plt.grid(axis = 'y', color ='white', linestyle='-')\n", + "plt.title(\"Average Restaurant Rating\")\n", + "plt.figure()\n", + "\n", + "print smalldf.stars.mean()\n", + "plt.figure()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "3.86763990268\n" + ] + }, + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 10, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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WqR8Q+q9//UsdO3aUl5eXvL29z/nx04v93tewYcMUFBTkeh8eHu66jAjg6tC0aVOtWrXK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\nrmPk5eVpwoQJeuWVV1S3bl0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zXp8F4OqMaBvu7ghlRtbK42l5gRuFzRhjSptp9uzZ6tKli1q0aCFJWrt2rR57\n7DGdOnXKOU+LFi30hz/8Qb/+9a8vXonNpjKsBkAZNJ430t0RyiSj/wSyVhJPypvRf4K7IwDXVKmn\nAhMTE+Xj46Pi4mKlp6drzZo12rdvn4KCgrR69WpJUnp6uvLy8hQZGVnpgQEAAKqqK54KXLlypQYO\nHOjyfCqbzabdu3erU6dOGjdunNLS0rRx40atWLFCPj4+lR4YAACgqrpisXrkkUdUXFx82emJiYmS\npCFDhlgaCgAAwBPxW4EAAAAWoVgBAABYhGIFAABgEYoVAACARShWAAAAFqFYAQAAWIRiBQAAYBGK\nFQAAgEUoVgAAABahWAEAAFiEYgUAAGARihUAAIBFKFYAAAAWoVgBAABYhGIFAABgEYoVAACARShW\nAAAAFqFYAQAAWIRiBQAAYBGKFQAAgEUoVgAAABahWAEAAFiEYgUAAGARihUAAIBFKFYAAAAWoVgB\nAABYhGIFAABgEYoVAACARShWAIBKU1zicHeEMvOkrKi6qrk7AADg+uVt91LjeSPdHaNMMvpPcHcE\nXAeu6ohVQUGBTp06ddnpOTk5ysvLq3AoAAAAT1SmYmWMUWJiooKDg7Vp0yaXaaGhobLb7bLb7erQ\noYNq1qxZKUEBAACqujIVq+PHjys8PFwZGRmy2WzO8dTUVEVERGjz5s3avHmz1q5dW2lBAQAAqroy\nXWPVsGHDS44nJCSoTZs2qlOnjlq2bGlpMAAAAE9T7rsCHQ6HcnJyNGnSJIWEhCg6OlrFxcVWZgMA\nAPAo5S5WXl5eSk5OVmZmphYsWKDk5GTFxsZamQ0AAMCjVPg5VjabTX369NGUKVOUlJRkRSYAAACP\nZNlzrB5//HENHTr0stPj4+Od/w4LC1NYWJhVqwZuKCPahrs7QpmRtfJ4Ul5PygpUlGXFyuFwKCQk\n5LLTf1qsAJTf5G0p7o5QJiPuDidrJfGkvJ6WFaioMp8KLCkpkXT+mVaStGnTJs2ZM8c5Pm3aNI0a\nNaoSIgIAAHiGMh2xOnbsmGbPni2bzaZFixYpICBAWVlZiouLU1JSkiIiItS+fXtFRUVVdl4AAIAq\nq8zPsYqNjXW56+/2229XZmZmpQUDAADwNBW+KxAAAADnUawAAAAsQrECAACwCMUKAADAIhQrAAAA\ni1CsAAAALEKxAgAAsAjFCgAAwCIUKwAAAItQrAAAACxCsQIAALAIxQoAAMAiFCsAAACLUKwAAAAs\nQrECAAADlYyiAAAWhElEQVSwCMUKAADAIhQrAAAAi1CsAAAALEKxAgAAsAjFCgAAwCIUKwAAAItQ\nrAAAACxCsQIAALAIxQoAAMAiFCsAAACLUKwAAAAsQrECAACwCMUKAADAIhQr3PCKSxzujgAAuE5U\nc3cAwN287V5qPG+ku2OUSUb/Ce6OAAC4gqs6YlVQUKBTp05VVhYAAACPVqZiZYxRYmKigoODtWnT\nJuf44cOHNWTIEM2cOVMxMTHatWtXpQUFAACo6spUrI4fP67w8HBlZGTIZrNJOl+2oqKi1LNnTw0a\nNEgjR45UZGSkHA6uVwEAADemMhWrhg0bqnHjxi5jKSkpSktLU1hYmCSpVatW8vb21tKlSy0PCQAA\n4AnKfVfg+vXr1bx5c1Wr9r/r34ODg7Vq1SpLggEAAHiacherrKws1a1b12WsXr16ysjIqHAoAAAA\nT1Tuxy1Uq1ZN3t7eLmMlJSWXnT8+Pt7577CwMOcpRKAqGNE23N0RyoyslcOTskqeldeTsgIVVe5i\nddttt2ndunUuYydPnlRQUNAl5/9psQKqmsnbUtwdoUxG3B1O1krgSVklz8rraVmBiir3qcCwsDDt\n27fPZWz37t0ciQIAADesMherC6f5jDGSpAceeECBgYFavXq1JCk9PV15eXmKjIyshJgAAABVX5lO\nBR47dkyzZ8+WzWbTokWLFBAQoNtvv13Lli3TuHHjlJaWpo0bN2rFihXy8fGp7MwAAABVUpmKVcOG\nDRUbG6vY2FiX8ebNmysxMVGSNGTIEMvDAQAAeJJyX2MFAAAAVxQrAAAAi1CsAAAALEKxAgAAsAjF\nCgAAwCIUKwAAAItQrAAAACxCsQIAALAIxQoAAMAiFCsAAACLUKwAAAAsQrECAACwCMUKAADAIhQr\nAAAAi1CsAAAALEKxAgAAsAjFCgAAwCIUKwAAAItQrAAAACxCsUKlKC5xuDsCAADXXDV3B8D1ydvu\npcbzRro7Rplk9J/g7ggAgOsER6wAAAAsQrECAACwCMUKAADAIhQrAAAAi1CsAAAALEKxAgAAsAjF\nCgAAwCIUKwAAAItQrAAAACxiebHKyclRXl6e1YsFAACo8iwpVqGhobLb7bLb7erQoYNq1qxpxWIB\nAAA8SoV/KzA1NVURERGaOnWqJKlx48YVDgUAAOCJKnzEKiEhQTVq1FCdOnXUrl07NWrUyIpcAAAA\nHqdCxcrhcCgnJ0eTJk1SSEiIoqOjVVxcbFU2AAAAj1KhYuXl5aXk5GRlZmZqwYIFSk5OVmxsrFXZ\nAAAAPEqFr7GSJJvNpj59+qigoEBxcXF67733LponPj7e+e+wsDCFhYVZsWpUYSPahrs7QpmRtXKQ\ntfJ4Ul5PygpUlM0YY6xa2LFjx9S0aVPl5+e7rsRmk4WrgYdoPG+kuyOUSUb/CWStBGStPJ6U19Oy\nAhVl6XOsHA6HQkJCrFwkAACAx6hQsdq0aZPmzJmjkpISSdK0adM0atQoS4IBAAB4mgpdY5WVlaW4\nuDglJSUpIiJC7du3V1RUlFXZAAAAPEqFilVkZKQyMzOtygIAAODR+BFmAAAAi1CsAAAALGLJc6xQ\n+fLOFcnLZnN3jDKy6SYv/qcFALjx8NfPg3T7cpqO5p12d4xSLe72G7VqcKu7YwAAcM1RrDxIbmG+\nThbllz6jmzlMibsjAADgFlxjBQAAYBGKFQAAgEUoVgAAABahWAEAAFiEYgUAAGARihUAAIBFKFYA\nAAAWoVgBAABYhGIFAABgEYoVAACARShWAAAAFqFYAQAAWIRiBQAAYBGKFQAAgEUoVgAASCoucbg7\nQpmRteqq5u4AAABUBd52LzWeN9LdMcoko/8Ej8p6I+GIFQAAgEUoVgAAABahWAEAAFiEYgUAAGAR\nihUAAIBFKFYAAAAWoVgBAABYhGIFAABgEYoVAACARShWAAAAFqlQsTp8+LCGDBmimTNnKiYmRrt2\n7bIqFwAAgMcpd7EyxigqKko9e/bUoEGDNHLkSEVGRsrhuLF+bPF6V5h+0N0RUAFsP8/G9vNcbDvP\n9s0335T7veUuVikpKUpLS1NYWJgkqVWrVvL29tbSpUvLHQZVD//n4NnYfp6N7ee52HaezS3Fav36\n9WrevLmqVavmHAsODtaqVavKHQYAAMCTVSt9lkvLyspS3bp1Xcbq1aunjIyMCofCpT13+/06VVRw\nTde5+ruD6nzHg1f1nptr1K6kNAAAVG02Y4wpzxtffvll7dixQ2vWrHGO9erVS2fPntWyZctc5v3F\nL36hvXv3ViwpAADANRATE6PExMRyvbfcR6xuu+02rVu3zmXs5MmTCgoKumje//73v+VdDQAAgMco\n9zVWnTt31r59+1zGdu/e7byYHQAA4EZT7mJ1//33KzAwUKtXr5YkpaenKy8vT5GRkZaFAwAA8CTl\nPhVos9m0bNkyjRs3Tmlpadq4caNWrFghHx+fq1rO4cOHFRAQUN4YAMqJfQ9wD/a9qicnJ0c1atRQ\nzZo1K7ysCj15vXnz5kpMTNSQIUPUv39/DRgwQHXr1lVERIQOHTp0yfekpKTIbrc7/1u7dm1FIsAC\nW7duVceOHeXr66uHH35YJ06cuOR8H374ocaNG6e33npLcXFx1zglLqes2499r+oqKSlR586dXW4G\n+in2vaqttO3Hvlc1hYaGOrdJhw4dLlmqyrPvlfuI1U8dPXpUH330kRYuXKjDhw/rxRdf1IABA/T1\n119fNO9nn32mzZs3n195tWpq06aNFRFQTkVFRVq8eLFSUlJUUlKi8PBwTZ48We+8847LfMuWLdP8\n+fO1fv16SdIzzzyjuXPn6vnnn3dHbPx/Zd1+EvteVTZjxgxt375dNpvtomnse1XflbafxL5XFaWm\npioiIkJTp06VJDVu3Piiecq771nyI8yrVq3S9OnT1bp1a0VERCg+Pv6iOwYl6fvvv9eOHTt05MgR\ntW7dmv9xVQE//vij4uPj5ePjo1q1aumhhx6Sl5fXRfP98Y9/VLdu3Zyve/TooYSEhGsZFZdQ1u3H\nvld1rVu3Ts2aNbvouYAXsO9VbaVtP/a9qikhIUE1atRQnTp11K5dOzVq1Oiiecq771lSrKKjo1Wn\nTh3na39/fwUGBl40X2pqqvLz8/XEE0+oSZMmSklJsWL1qAB/f39Vr15dklRYWKjs7Gy9+uqrLvMU\nFRVp8+bNuv32251jLVu21K5du3T8+PFrmheuyrL9JPa9qurEiRP67rvv1L1790tOZ9+r2krbfhL7\nXlXkcDiUk5OjSZMmKSQkRNHR0SouLnaZpyL7niXF6ue2bNmiQYMGXTQeHR2t1NRU7d+/X/fee696\n9uyprKysyoiAq7R8+XK1b99eKSkp2rlzp8u0nJwcFRcXq169es6x+vXrSxJP2q8irrT9JPa9qioh\nIUHDhw+/7HT2vaqttO0nse9VRV5eXkpOTlZmZqYWLFig5ORkxcbGusxTkX3P8mJ19uxZ7dixQ6+8\n8spl52ncuLGWLFmiW2655aKntMM9IiMjtXTpUnXq1El9+vRxmXbh9yC9vb2dYyUlJZKkcj64Hxa7\n0vb7Kfa9qmP27Nnq3bu384i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+ "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Common Support\n", + "\n", + "Lets now make a histogram of the common user support (the number of common reviewers) of each pair of restaurants on the smaller set, and print the mean. Pay attention to the code, as you will use parts of it later. (This code takes a bit of time to run, so be patient).\n", + "\n", + "The common support is an important concept, as for each pair of restaurants, its the number of people who reviewed both. It will be used to modify similarity between restaurants. If the common support is low, the similarity is less believable." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "restaurants=smalldf.business_id.unique()\n", + "supports=[]\n", + "for i,rest1 in enumerate(restaurants):\n", + " for j,rest2 in enumerate(restaurants):\n", + " if i < j:\n", + " rest1_reviewers = smalldf[smalldf.business_id==rest1].user_id.unique()\n", + " rest2_reviewers = smalldf[smalldf.business_id==rest2].user_id.unique()\n", + " common_reviewers = set(rest1_reviewers).intersection(rest2_reviewers)\n", + " supports.append(len(common_reviewers))\n", + "print \"Mean support is:\",np.mean(supports)\n", + "plt.hist(supports)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Mean support is: 6.84679722562\n" + ] + }, + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 11, + "text": [ + "(array([ 7.02000000e+03, 4.98700000e+03, 1.79400000e+03,\n", + " 5.90000000e+02, 1.95000000e+02, 7.60000000e+01,\n", + " 2.20000000e+01, 1.00000000e+01, 1.00000000e+01,\n", + " 2.00000000e+00]),\n", + " array([ 0. , 5.1, 10.2, 15.3, 20.4, 25.5, 30.6, 35.7, 40.8,\n", + " 45.9, 51. ]),\n", + " )" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, even though we chose a subset of the dataframe in which every restaurant had 150 reviews and every user had atleast made 60, the common support of most pairs of restaurants is really low, indeed less than 10!." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculating Similarity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Users rate restaurants on a scale of 1-5. Even though this rating is integer valued, for the purposes of this assignment we shall treat it as a real number.\n", + "\n", + "Even though each reviewer uses the same 5-star scale when rating restaurants, comparing two users by comparing their raw user ratings can be problematic. Consider a user whose average rating is 2. This is a curmudgeonly user. Consider another whose average rating is 4. This is a rather enthusiastic one. How should we compare a 3 rating by the curmudgeonly one to a 5 rating of the enthusiastic one?\n", + "\n", + "It is for this purpose that we must subtract the average rating of the user from the actual rating of the restaurants in computing the similarity of two restaurants. This makes the above ratings by the two users comparable. We do this in the function `pearson_sim` defined below.\n", + "\n", + "If there is no common support (`n_common=0`), we have no basis for making a similarity estimate, and so we set the similarity to 0. In the case that the individual restaurant rating variance is 0, such as in the case where there is only one common reviewer (`n_common=1`), we return the `NaN` that the scipy `pearsonr` returns. We will deal with it soon," + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from scipy.stats.stats import pearsonr\n", + "def pearson_sim(rest1_reviews, rest2_reviews, n_common):\n", + " \"\"\"\n", + " Given a subframe of restaurant 1 reviews and a subframe of restaurant 2 reviews,\n", + " where the reviewers are those who have reviewed both restaurants, return \n", + " the pearson correlation coefficient between the user average subtracted ratings.\n", + " The case for zero common reviewers is handled separately. Its\n", + " ok to return a NaN if any of the individual variances are 0.\n", + " \"\"\"\n", + " if n_common==0:\n", + " rho=0.\n", + " else:\n", + " diff1=rest1_reviews['stars']-rest1_reviews['user_avg']\n", + " diff2=rest2_reviews['stars']-rest2_reviews['user_avg']\n", + " rho=pearsonr(diff1, diff2)[0]\n", + " return rho" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function `get_restaurant_reviews` defined below takes a restaurant `business_id` and a set of users, and returns the reviews of that restaurant by those users. You will use this function in calculating a similarity function, in **1.5**." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def get_restaurant_reviews(restaurant_id, df, set_of_users):\n", + " \"\"\"\n", + " given a resturant id and a set of reviewers, return the sub-dataframe of their\n", + " reviews.\n", + " \"\"\"\n", + " mask = (df.user_id.isin(set_of_users)) & (df.business_id==restaurant_id)\n", + " reviews = df[mask]\n", + " reviews = reviews[reviews.user_id.duplicated()==False]\n", + " return reviews" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 13 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.5** Write a function `calculate_similarity` that operates between two restaurants and calculates a similarity for them, taking a dataframe and a similarity function `similarity_func`. An example of the `similarity_func` is the `pearson_sim` we defined above. `calculate_similarity` operates as follows: \n", + "\n", + "1. For each of the two restaurants, get the set of reviewers who have reviewed the restaurant and compute the intersection of these two sets. Also compute the number of common reviewers `n_common`.\n", + "\n", + "2. Use the function `get_restaurant_reviews` defined below to get the reviews for each restaurant as made by these common reviewers. Notice that `get_restaurant_reviews` returns a sub data frame of reviews.\n", + "\n", + "3. Calculate the similarity using `similarity_func` which takes the two reviews dataframes from part 2 and the number of common reviewers `n_common` as arguments\n", + "\n", + "4. Return the similarity and `n_common` in a tuple `(sim, n_common)`. If the similarity is a `NaN`, set the similarity to 0.\n" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "calculate_similarity\n", + "\n", + "Parameters\n", + "----------\n", + "rest1 : string\n", + " The id of restaurant 1\n", + "rest2 : string\n", + " The id of restaurant 2\n", + "df : DataFrame\n", + " A dataframe of reviews, such as the smalldf above\n", + "similarity_func : func\n", + " A function like pearson_sim above which takes two dataframes of individual\n", + " restaurant reviews made by a common set of reviewers, and the number of\n", + " common reviews. This function returns the similarity of the two restaurants\n", + " based on the common reviews.\n", + " \n", + "Returns\n", + "--------\n", + "A tuple\n", + " The first element of the tuple is the similarity and the second the\n", + " common support n_common. If the similarity is a NaN, set it to 0\n", + "\"\"\"\n", + "#your code here\n", + "def calculate_similarity(rest1, rest2, df, similarity_func):\n", + " # find common reviewers\n", + " rest1_reviewers = df[df.business_id==rest1].user_id.unique()\n", + " rest2_reviewers = df[df.business_id==rest2].user_id.unique()\n", + " common_reviewers = set(rest1_reviewers).intersection(rest2_reviewers)\n", + " n_common=len(common_reviewers)\n", + " #get reviews\n", + " rest1_reviews = get_restaurant_reviews(rest1, df, common_reviewers)\n", + " rest2_reviews = get_restaurant_reviews(rest2, df, common_reviewers)\n", + " sim=similarity_func(rest1_reviews, rest2_reviews, n_common)\n", + " if np.isnan(sim):\n", + " return 0, n_common\n", + " return sim, n_common" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 14 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Making a database of similarities\n", + "\n", + "We now move to calculating a global database of pairwise restaurant similarities.\n", + "We provide you here with a function to make a database of the similarities for each pair of restaurants in the database. The class `Database` is initialized in its constructor by taking as arguments a dataframe of reviews. The method `populate_by calculating` iterates over every possible pair of business_id's in the dataframe and populates the database with similarities and common supports. It takes as arguments a function the similarity function `similarity_func` like `pearson_sim` (`calculate_similarity` then uses this to calculate the similarity). The `get` method on the database can be used to retrieve the similarity for two business ids." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(See Thu Oct 17th's class video for information about classes)" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "class Database:\n", + " \"A class representing a database of similaries and common supports\"\n", + " \n", + " def __init__(self, df):\n", + " \"the constructor, takes a reviews dataframe like smalldf as its argument\"\n", + " database={}\n", + " self.df=df\n", + " self.uniquebizids={v:k for (k,v) in enumerate(df.business_id.unique())}\n", + " keys=self.uniquebizids.keys()\n", + " l_keys=len(keys)\n", + " self.database_sim=np.zeros([l_keys,l_keys])\n", + " self.database_sup=np.zeros([l_keys, l_keys], dtype=np.int)\n", + " \n", + " def populate_by_calculating(self, similarity_func):\n", + " \"\"\"\n", + " a populator for every pair of businesses in df. takes similarity_func like\n", + " pearson_sim as argument\n", + " \"\"\"\n", + " items=self.uniquebizids.items()\n", + " for b1, i1 in items:\n", + " for b2, i2 in items:\n", + " if i1 < i2:\n", + " sim, nsup=calculate_similarity(b1, b2, self.df, similarity_func)\n", + " self.database_sim[i1][i2]=sim\n", + " self.database_sim[i2][i1]=sim\n", + " self.database_sup[i1][i2]=nsup\n", + " self.database_sup[i2][i1]=nsup\n", + " elif i1==i2:\n", + " nsup=self.df[self.df.business_id==b1].user_id.count()\n", + " self.database_sim[i1][i1]=1.\n", + " self.database_sup[i1][i1]=nsup\n", + " \n", + "\n", + " def get(self, b1, b2):\n", + " \"returns a tuple of similarity,common_support given two business ids\"\n", + " sim=self.database_sim[self.uniquebizids[b1]][self.uniquebizids[b2]]\n", + " nsup=self.database_sup[self.uniquebizids[b1]][self.uniquebizids[b2]]\n", + " return (sim, nsup)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 15 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets run `make_database` and store the result in the global variable `db`. Lets print out an example entry. Running this function will take a bit of time." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "db=Database(smalldf)\n", + "db.populate_by_calculating(pearson_sim)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 16 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "db.get(\"z3yFuLVrmH-3RJruPEMYKw\", \"zruUQvFySeXyEd7_rQixBg\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 17, + "text": [ + "(0.39904554525734559, 7)" + ] + } + ], + "prompt_number": 17 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### K-Nearest restaurants (in similarity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now going to find the k-nearest restaurants to a given restaurant based on the database of similarities that we calculated. But we have a problem.\n", + "\n", + "Consider the two cases where there is just one common reviewer, and where there are 40. In the former case, we might get a artificially high similarity based on the tastes of just this user, and thus we must reduce its importance in the nearest-neighbor calculation. In the latter case, we would get a much more unbiased estimator of the similarity of the two restaurants.\n", + "\n", + "To control the effect of small common supports, we can **shrink** our pearson co-efficients. We shall do this by using the \"regularization\" parameter `reg`:\n", + "\n", + "$$s_{mj} = \\frac{N_{common}\\, \\rho_{mj}}{N_{common}+reg} $$\n", + "\n", + "where $N_{common}$ (`n_common`) is the common reviewer support and $\\rho_{ij}$ is the pearson co-relation coefficient.\n", + "\n", + "Recall the notions of regularization introduced in class. We want to reduce the variance in our estimates, so we pull our estimates in toward a conservative point in a way that strongly corrals in estimates when there is very little data, but allows the data to speak when there is a lot. This can be shown as equivalent to adding in a `reg` amount of bayesian prior, as Joe has alluded to in class. \n", + "\n", + "A good value of the regularizer is intuitively one that dosent affect the similarity when the common support is high ~ 10, but has a large effect when the support is small. In this case, values of 2-4 are good. Usually, the value of `reg` is determined using cross-validation, but for the sake of simplicity we will generally set it to 3.\n", + "\n", + "We define a function `shrunk_sim` which takes the `sim` and `n_common` obtained from the database, and shrinks the similarity down using the regularizer `reg`." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def shrunk_sim(sim, n_common, reg=3.):\n", + " \"takes a similarity and shrinks it down by using the regularizer\"\n", + " ssim=(n_common*sim)/(n_common+reg)\n", + " return ssim" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 18 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.6** Now we can move to writing a `knearest` function, which finds the `k` nearest neighbors of a given restaurant based on the shrunk similarities we calculate. Note that as defined here, the nearest neighbors are global over the entire set of restaurants, as opposed to being restricted to the restaurants a user has reviewed(we shall do that in the next problem). Thus, this is an expensive function!\n", + "\n", + "Write a `knearest` that returns a *k-length sorted list* of 3-tuples each corresponding to a restaurant. The tuple structure is `(business_id, shrunken similarity score, common support)` where the similarity score and common support are with respect to the restaurant whose neighbors we are finding, and the `business_id` is the id of the \"nearby\" restaurant found. The nearby restaurants are found from a supplied numpy array of restaurants `set_of_restaurants`. The spec for the function is given below. HINT: use `itemgetter` from the `operator` module to do the sorting." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "knearest\n", + "\n", + "Parameters\n", + "----------\n", + "restaurant_id : string\n", + " The id of the restaurant whose nearest neighbors we want\n", + "set_of_restaurants : array\n", + " The set of restaurants from which we want to find the nearest neighbors\n", + "dbase : instance of Database class.\n", + " A database of similarities, on which the get method can be used to get the similarity\n", + " of two businessed. e.g. dbase.get(rid1,rid2)\n", + "k : int\n", + " the number of nearest neighbors desired, default 7\n", + "reg: float\n", + " the regularization.\n", + " \n", + " \n", + "Returns\n", + "--------\n", + "A sorted list\n", + " of the top k similar restaurants. The list is a list of tuples\n", + " (business_id, shrunken similarity, common support).\n", + "\"\"\"\n", + "#your code here\n", + "from operator import itemgetter\n", + "def knearest(restaurant_id, set_of_restaurants, dbase, k=7, reg=3.):\n", + " \"\"\"\n", + " Given a restaurant_id, dataframe, and database, get a sorted list of the\n", + " k most similar restaurants from the entire database.\n", + " \"\"\"\n", + " similars=[]\n", + " for other_rest_id in set_of_restaurants:\n", + " if other_rest_id!=restaurant_id:\n", + " sim, nc=dbase.get(restaurant_id, other_rest_id)\n", + " ssim=shrunk_sim(sim, nc, reg=reg)\n", + " similars.append((other_rest_id, ssim, nc ))\n", + " similars=sorted(similars, key=itemgetter(1), reverse=True)\n", + " return similars[0:k]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 19 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ok it's time to recommend!\n", + "\n", + "Lets choose the two very different businesses in the dataframe" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "testbizid=\"eIxSLxzIlfExI6vgAbn2JA\"\n", + "testbizid2=\"L-uPZxooP_ziXCtRrWi8Pw\"" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 20 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We provide functions to look up a business name given a business id, and a username given a user id." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def biznamefromid(df, theid):\n", + " return df['biz_name'][df['business_id']==theid].values[0]\n", + "def usernamefromid(df, theid):\n", + " return df['user_name'][df['user_id']==theid].values[0]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 21 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print testbizid, biznamefromid(smalldf,testbizid)\n", + "print testbizid2, biznamefromid(smalldf, testbizid2)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "eIxSLxzIlfExI6vgAbn2JA Lobbys Beef Burgers Dogs\n", + "L-uPZxooP_ziXCtRrWi8Pw Caf\u00e9 Monarch\n" + ] + } + ], + "prompt_number": 22 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "####Get top matches\n", + "\n", + "Its now time to answer the question: \"if you liked this, you might also like these\". We use our `testbizid` and `testbizid2` to compute the `k=7` nearest neighbors with a regularization of `3.` . We print these top 7 matches names, along with their similarity coefficient and common support." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "tops=knearest(testbizid, smalldf.business_id.unique(), db, k=7, reg=3.)\n", + "print \"For \",biznamefromid(smalldf, testbizid), \", top matches are:\"\n", + "for i, (biz_id, sim, nc) in enumerate(tops):\n", + " print i,biznamefromid(smalldf,biz_id), \"| Sim\", sim, \"| Support\",nc" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "For Lobbys Beef Burgers Dogs , top matches are:\n", + "0 La Condesa Gourmet Taco Shop | Sim 0.598714448434 | Support 6\n", + "1 Citizen Public House | Sim 0.571428571429 | Support 4\n", + "2 FnB | Sim 0.527129890943 | Support 5\n", + "3 Defalco's Italian Grocery | Sim 0.519456555658 | Support 6\n", + "4 Republic Ramen + Noodles | Sim 0.519140146937 | Support 5\n", + "5 unPhogettable | Sim 0.5 | Support 3\n", + "6 Haus Murphy's | Sim 0.467637235308 | Support 3\n" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "tops2=knearest(testbizid2, smalldf.business_id.unique(), db, k=7, reg=3.)\n", + "print \"For \",biznamefromid(smalldf, testbizid2), \", top matches are:\"\n", + "for i, (biz_id, sim, nc) in enumerate(tops2):\n", + " print i,biznamefromid(smalldf,biz_id), \"| Sim\", sim, \"| Support\",nc" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "For Caf\u00e9 Monarch , top matches are:\n", + "0 Postino Arcadia | Sim 0.625 | Support 5\n", + "1 The Main Ingredient Ale House And Caf\u00e9 | Sim 0.571428571429 | Support 4\n", + "2 Brio Tuscan Grille | Sim 0.571428571429 | Support 4\n", + "3 Kazimierz World Wine Bar | Sim 0.5 | Support 3\n", + "4 Harlow's Cafe | Sim 0.5 | Support 3\n", + "5 The Fry Bread House | Sim 0.5 | Support 3\n", + "6 Cien Agaves Tacos & Tequila | Sim 0.5 | Support 3\n" + ] + } + ], + "prompt_number": 24 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that these two restaurants are in somewhat different orbits :-)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets now turn our attention to another question: what are the top recommendations for a user? To answer this we must find the user's top rated restaurants, find the nearest neighbors of these restaurants, merge these lists while removing the duplicates and the ones that the user has already rated, and sort by the restaurant's average rating. We provide the code to get the user's top choices in a subset data frame." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def get_user_top_choices(user_id, df, numchoices=5):\n", + " \"get the sorted top 5 restaurants for a user by the star rating the user gave them\"\n", + " udf=df[df.user_id==user_id][['business_id','stars']].sort(['stars'], ascending=False).head(numchoices)\n", + " return udf\n", + "testuserid=\"7cR92zkDv4W3kqzii6axvg\"\n", + "print \"For user\", usernamefromid(smalldf,testuserid), \"top choices are:\" \n", + "bizs=get_user_top_choices(testuserid, smalldf)['business_id'].values\n", + "[biznamefromid(smalldf, biz_id) for biz_id in bizs]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "For user Vern top choices are:\n" + ] + }, + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 25, + "text": [ + "['Local Breeze',\n", + " \"Carly's Bistro\",\n", + " 'Tee Pee Mexican Food',\n", + " 'District American Kitchen and Wine Bar',\n", + " 'Los Reyes de la Torta']" + ] + } + ], + "prompt_number": 25 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get top recommendations for user." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.7** Its your job now to write a function `get_top_recos_for_user` which takes as arguments a `userid`, the `n` top choices for the user, the dataframe, `k`, and a regularizer, and returns the top recommendations obtained from combining the restaurants that are neighbors of each of the `n` choices, in the way described in the previous paragraph. This returned list is a list of tuples `(restaurant_id, business_avg)` sorted by `business_avg` where `business_avg` is the average rating of the restaurant over the dataframe." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "get_top_recos_for_user\n", + "\n", + "Parameters\n", + "----------\n", + "userid : string\n", + " The id of the user for whom we want the top recommendations\n", + "df : Dataframe\n", + " The dataframe of restaurant reviews such as smalldf\n", + "dbase : instance of Database class.\n", + " A database of similarities, on which the get method can be used to get the similarity\n", + " of two businesses. e.g. dbase.get(rid1,rid2)\n", + "n: int\n", + " the n top choices of the user by star rating\n", + "k : int\n", + " the number of nearest neighbors desired, default 8\n", + "reg: float\n", + " the regularization.\n", + " \n", + " \n", + "Returns\n", + "--------\n", + "A sorted list\n", + " of the top recommendations. The list is a list of tuples\n", + " (business_id, business_avg). You are combining the k-nearest recommendations \n", + " for each of the user's n top choices, removing duplicates and the ones the user\n", + " has already rated.\n", + "\"\"\"\n", + "#your code here\n", + "def get_top_recos_for_user(userid, df, dbase, n=5, k=7, reg=3.):\n", + " bizs=get_user_top_choices(userid, df, numchoices=n)['business_id'].values\n", + " rated_by_user=df[df.user_id==userid].business_id.values\n", + " tops=[]\n", + " for ele in bizs:\n", + " t=knearest(ele, df.business_id.unique(), dbase, k=k, reg=reg)\n", + " for e in t:\n", + " if e[0] not in rated_by_user:\n", + " tops.append(e)\n", + "\n", + " #there might be repeats. unique it\n", + " ids=[e[0] for e in tops]\n", + " uids={k:0 for k in list(set(ids))}\n", + "\n", + " topsu=[]\n", + " for e in tops:\n", + " if uids[e[0]] == 0:\n", + " topsu.append(e)\n", + " uids[e[0]] =1\n", + " topsr=[] \n", + " for r, s,nc in topsu:\n", + " avg_rate=df[df.business_id==r].stars.mean()\n", + " topsr.append((r,avg_rate))\n", + " \n", + " topsr=sorted(topsr, key=itemgetter(1), reverse=True)\n", + "\n", + " if n < len(topsr):\n", + " return topsr[0:n]\n", + " else:\n", + " return topsr" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 26 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets print the top recommendations for `testuserid`, with a regularization of 3." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print \"For user\", usernamefromid(smalldf,testuserid), \"the top recommendations are:\"\n", + "toprecos=get_top_recos_for_user(testuserid, smalldf, db, n=5, k=7, reg=3.)\n", + "for biz_id, biz_avg in toprecos:\n", + " print biznamefromid(smalldf,biz_id), \"| Average Rating |\", biz_avg" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "For user Vern the top recommendations are:\n", + "Rokerij | Average Rating | 4.37931034483\n", + "Wildfish Seafood Grille | Average Rating | 4.29411764706\n", + "Cornish Pasty Company | Average Rating | 4.20689655172\n", + "Pappadeaux Seafood Kitchen | Average Rating | 4.18518518519\n", + "Four Peaks Brewing Co | Average Rating | 4.16666666667\n" + ] + } + ], + "prompt_number": 27 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem 2: A user based recommender with predicted ratings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is all very nice. We can provide ratings based on global similarities to a restaurant. However, in many cases this is not enough.\n", + "\n", + "For example, it is hard to judge if the above recommendations are any good. In the usual testing paradigm, say that we break the dataframe into train and test. Based on the training set, I am recommended restaurant B. Now, I have rated B, but that information is in the testing set. I have no way of comparing the rating I give B in the testing set, to the similarity computed from the training set that was used to make the recomendation. The best I could do is to compare the average rating of restaurant B in the training set to my rating of restaurant B in the test set. \n", + "\n", + "In this section, we shift our focus to more fine-grained predictions about each user, and try to predict what _rating_ a user would give to a restaurant they have never tried before. To do this, we will try to personalize the information we use even further, and only pool information from restaurants that the user has rated.\n", + "\n", + "This allows us to return to the original problem of prediction $Y_{um}$ for a restaurant $m$ that user $u$ has never rated before. Using our newly computed similarity metrics, we can modify our original baseline estimate by pulling in information from the user's neighborhood of the restaurant $m$, and predict $Y_{um}$ as:\n", + "\n", + "$$ \\hat{Y_{um}} = \\hat Y^{baseline}_{um}\\, + \\,\\frac{\\sum\\limits_{j \\in S^{k}(m;u)} s_{mj} ( Y_{uj} - \\hat Y^{baseline}_{uj} )}{\\sum\\limits_{j \\in S^{k}(m;u)} s_{mj} } $$\n", + "\n", + "where $s^{k}(m;u)$ is the $k$ neighbor items of item $m$ which have been rated by user $u$.\n", + "\n", + "Now, this is not a particularly good assumption, especially in the situation where a restaurant is new (new item problem) or a user is new (cold start problem), or in the case when there are very few reviewers of a restaurant, or very few reviews by a user respectively. However, one must start somewhere!\n", + "\n", + "Notice that in adding in the similarity term, we subtract the baseline estimate from the observed rating of the user's neighbor items." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Defining the predicted rating" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.1** Write a function `knearest_amongst_userrated`, analogous to the `knearest` function we defined above, to find the nearest `k` neighbors to a given restaurant from the **restaurants that the user has already rated**. This function will take as arguments the `restaurant_id`, the `user_id`, the dataframe of reviews, the database, the `k`, and the regularizer `reg`. Just like before, return a *k-length sorted list* of 3-tuples each corresponding to a restaurant. HINT: use the `knearest` function you defined earlier" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "knearest_amongst_userrated\n", + "\n", + "Parameters\n", + "----------\n", + "restaurant_id : string\n", + " The id of the restaurant whose nearest neighbors we want\n", + "user_id : string\n", + " The id of the user, in whose reviewed restaurants we want to find the neighbors\n", + "df: Dataframe\n", + " The dataframe of reviews such as smalldf\n", + "dbase : instance of Database class.\n", + " A database of similarities, on which the get method can be used to get the similarity\n", + " of two businessed. e.g. dbase.get(rid1,rid2)\n", + "k : int\n", + " the number of nearest neighbors desired, default 7\n", + "reg: float\n", + " the regularization.\n", + " \n", + " \n", + "Returns\n", + "--------\n", + "A sorted list\n", + " of the top k similar restaurants. The list is a list of tuples\n", + " (business_id, shrunken similarity, common support).\n", + "\"\"\"\n", + "#your code here\n", + "def knearest_amongst_userrated(restaurant_id, user_id, df, dbase, k=7, reg=3.):\n", + " dfuser=df[df.user_id==user_id]\n", + " bizsuserhasrated=dfuser.business_id.unique()\n", + " return knearest(restaurant_id, bizsuserhasrated, dbase, k=k, reg=reg)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 28 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.2** Now write a function that returns the predicted rating for a user and an item using the formula at the beginning of this problem. Include code to deal with the possibility that the sum of scores that goes in the denominator is 0: return an predicted rating of the baseline portion of the formula in that case. This function `rating` takes as arguments the dataframe, the database, the wanted `restaurant_id` and `user_id`, and `k` as well as the regularizer." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "rating\n", + "\n", + "Parameters\n", + "----------\n", + "df: Dataframe\n", + " The dataframe of reviews such as smalldf\n", + "dbase : instance of Database class.\n", + " A database of similarities, on which the get method can be used to get the similarity\n", + " of two businessed. e.g. dbase.get(rid1,rid2)\n", + "restaurant_id : string\n", + " The id of the restaurant whose nearest neighbors we want\n", + "user_id : string\n", + " The id of the user, in whose reviewed restaurants we want to find the neighbors\n", + "k : int\n", + " the number of nearest neighbors desired, default 7\n", + "reg: float\n", + " the regularization.\n", + " \n", + " \n", + "Returns\n", + "--------\n", + "A float\n", + " which is the impued rating that we predict that user_id will make for restaurant_id\n", + "\"\"\"\n", + "#your code here\n", + "def rating(df, dbase, restaurant_id, user_id, k=7, reg=3.):\n", + " mu=df.stars.mean()\n", + " users_reviews=df[df.user_id==user_id]\n", + " nsum=0.\n", + " scoresum=0.\n", + " nears=knearest_amongst_userrated(restaurant_id, user_id, df, dbase, k=k, reg=reg)\n", + " restaurant_mean=df[df.business_id==restaurant_id].business_avg.values[0]\n", + " user_mean=users_reviews.user_avg.values[0]\n", + " scores=[]\n", + " for r,s,nc in nears:\n", + " scoresum=scoresum+s\n", + " scores.append(s)\n", + " r_reviews_row=users_reviews[users_reviews['business_id']==r]\n", + " r_stars=r_reviews_row.stars.values[0]\n", + " r_avg=r_reviews_row.business_avg.values[0]\n", + " rminusb=(r_stars - (r_avg + user_mean - mu))\n", + " nsum=nsum+s*rminusb\n", + " baseline=(user_mean +restaurant_mean - mu)\n", + " #we might have nears, but there might be no commons, giving us a pearson of 0\n", + " if scoresum > 0.:\n", + " val = nsum/scoresum + baseline\n", + " else:\n", + " val=baseline\n", + " return val\n", + " " + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 29 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the top-recommendations in the variable `toprecos` from the previous section, we compute the predicted rating and compare it with the average rating over all users available inside the tuples that make up `toprecos`. We use a `k` of 7 and regularization 3. For comparision we also print this users' average rating. Do you notice anything interesting about how the order has changed from when we did this with the global similarities? (for you to think, not to answer)" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print \"User Average\", smalldf[smalldf.user_id==testuserid].stars.mean(),\"for\",usernamefromid(smalldf,testuserid)\n", + "print \"Predicted ratings for top choices calculated earlier:\"\n", + "for biz_id,biz_avg in toprecos:\n", + " print biznamefromid(smalldf, biz_id),\"|\",rating(smalldf, db, biz_id, testuserid, k=7, reg=3.),\"|\",\"Average\",biz_avg " + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "User Average 3.5652173913 for Vern\n", + "Predicted ratings for top choices calculated earlier:\n", + "Rokerij | 4.71714023074 | Average 4.37931034483\n", + "Wildfish Seafood Grille | 4.27594504172 | Average 4.29411764706\n", + "Cornish Pasty Company | 4.62810510121 | Average 4.20689655172\n", + "Pappadeaux Seafood Kitchen | 4.08845573953 | Average 4.18518518519\n", + "Four Peaks Brewing Co | 4.26174734161 | Average 4.16666666667\n" + ] + } + ], + "prompt_number": 30 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Testing the ratings\n", + "\n", + "Let us compare the predicted ratings with a user's ratings. Note that we are doing this on the same set that we constructed the predictions with, so this is not a validation of the procedure, but simply a check of the procedure's fit. We first write a helper function to return the user score for a restaurant, and the restaurant's average score over all users." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def get_other_ratings(restaurant_id, user_id, df):\n", + " \"get a user's rating for a restaurant and the restaurant's average rating\"\n", + " choice=df[(df.business_id==restaurant_id) & (df.user_id==user_id)]\n", + " users_score=choice.stars.values[0]\n", + " average_score=choice.business_avg.values[0]\n", + " return users_score, average_score" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 31 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the user `testuserid`, we loop over the variable `bizs` (which is a set of restaurants the user has rated) and print the predicted rating, and the actual rating and restaurant average rating obtained using the function above. We again use `k=7` and a regularization of 3." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print \"for user\",usernamefromid(smalldf,testuserid), 'avg', smalldf[smalldf.user_id==testuserid].stars.mean() \n", + "for biz_id in bizs:\n", + " print \"----------------------------------\"\n", + " print biznamefromid(smalldf, biz_id)\n", + " print \"Predicted Rating:\",rating(smalldf, db, biz_id, testuserid, k=7, reg=3.) \n", + " u,a=get_other_ratings(biz_id, testuserid, smalldf)\n", + " print \"Actual User Rating:\",u,\"Avg Rating\",a" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "for user Vern avg 3.5652173913\n", + "----------------------------------\n", + "Local Breeze\n", + "Predicted Rating: 4.2280987611\n", + "Actual User Rating: 5 Avg Rating 4.0\n", + "----------------------------------\n", + "Carly's Bistro\n", + "Predicted Rating: 3.99008654065\n", + "Actual User Rating: 5 Avg Rating 3.5\n", + "----------------------------------\n", + "Tee Pee Mexican Food\n", + "Predicted Rating: 3.52640184162\n", + "Actual User Rating: 5 Avg Rating 3.04347826087\n", + "----------------------------------\n", + "District American Kitchen and Wine Bar\n", + "Predicted Rating: 3.80281696528\n", + "Actual User Rating: 4 Avg Rating 3.55263157895\n", + "----------------------------------\n", + "Los Reyes de la Torta\n", + "Predicted Rating: 3.41514298977\n", + "Actual User Rating: 4 Avg Rating 4.13157894737\n" + ] + } + ], + "prompt_number": 32 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.3** Explain in words why the predicted ratings are lower than the actual ratings. How do the user average rating and restaurant average rating affect this? How does sparsity affect the predicted ratings?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*your answer here*\n", + "\n", + "Recall that bizs (defined just above question 1.7) has restaurants sorted by Vern's actual star ratings. This means that in this sample, we are looking at Vern's top rated restaurants.\n", + "\n", + "The predicted ratings are lower because these are Vern's top 5 choices, which represent the largest positive deviations away from Vern's mean rating of 3.57. Because we are looking at the upper tail of Vern's rating distribution, but pooling information together from the K nearest neighbors among Vern's rated restaurants to construct the predicted rating, the predicted ratings should fall closer to Vern's user mean than the true ones do. Taking into account the average restaurant rating helps a little bit here because we can adjust the predicted rating to reflect an overall very good restaurant, but it does not counteract the effect of looking at the upper tail of Vern's ratings.\n", + "\n", + "Note that if we were to take Vern's bottom 5 restaurants, we would see the opposite effect.\n", + "\n", + "In general, the larger K is (assuming that the similarities within this neighborhood are positive), the closer the predicted rating will be to Vern's user average (this is the bias limit in the bias-variance tradeoff). Similarly, the smaller K is, the more likely we are to have user ratings that are close to the observed rating (the variance limit). The sparsity of the data affects how quickly we move from the variance limit to the bias limit as we increase K. If there were a lot of very similar restaurants in the dataset that Vern had ranked very highly, even with K relatively large, it would be possible to see a predicted rating much closer to the extremely positive ratings we see here in Vern's top 5 (see the results in question 4.4). As these data are now, however, even the most similar 7 restaurants to these that Vern rated so highly lie closer to Vern's mean." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Error Analysis\n", + "\n", + "This next function takes a set of actual ratings, and a set of predicted ratings, and plots the latter against the former. We can use a graph of this kind to see how well or badly we do in our predictions. Since the nearest neighbor models can have alternating positive and negative similarities (the sum of similarity weights in the denominator can get large), the ratings can get very large. Thus we restrict ourselves to be between -10 and 15 in our ratings and calculate the fraction within these bounds. We also plot the line with unit slope, line sehments joining the means, and a filled in area representing one standard deviation from the mean.\n", + "\n", + "The first argument to `compare_results` is a numpy array of the actual star ratings obtained from the dataframe, while the second argument is the numpy array of the predicted ones. (*Feel free to improve this function for your display*)" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def compare_results(stars_actual, stars_predicted, ylow=-10, yhigh=15, title=\"\"):\n", + " \"\"\"\n", + " plot predicted results against actual results. Takes 2 arguments: a\n", + " numpy array of actual ratings and a numpy array of predicted ratings\n", + " scatterplots the predictions, a unit slope line, line segments joining the mean,\n", + " and a filled in area of the standard deviations.\"\n", + " \"\"\"\n", + " fig=plt.figure()\n", + " df=pd.DataFrame(dict(actual=stars_actual, predicted=stars_predicted))\n", + " ax=plt.scatter(df.actual, df.predicted, alpha=0.2, s=30, label=\"predicted\")\n", + " plt.ylim([ylow,yhigh])\n", + " plt.plot([1,5],[1,5], label=\"slope 1\")\n", + " xp=[1,2,3,4,5]\n", + " yp=df.groupby('actual').predicted.mean().values\n", + " plt.plot(xp,yp,'k', label=\"means\")\n", + " sig=df.groupby('actual').predicted.std().values\n", + " plt.fill_between(xp, yp - sig, yp + sig, \n", + " color='k', alpha=0.2)\n", + " plt.xlabel(\"actual\")\n", + " plt.ylabel(\"predicted\")\n", + " plt.legend(frameon=False)\n", + " remove_border()\n", + " plt.grid(False)\n", + " plt.title(title)\n", + " print \"fraction between -15 and 15 rating\", np.mean(np.abs(df.predicted) < 15)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 33 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.4** For each review in the data set, obtain a prediction from the entire dataframe `smalldf`. Use the function `compare_results` above to plot the predicted ratings against the observed ones. Make 4 such graphs, at k=3 and k=10, and for reg=3. and reg=15.\n", + "\n", + "Note that this analysis is not strictly a model check because we are testing on the training set. However, since the user averages would change each time a cross-validation split was done on the set, we would incur the prohibitive expense of redoing the database each time. This would be better done on a cluster, using map-reduce or other techniques. While we explore map-reduce later in this homework, we shall not do any cross-validation.\n", + "\n", + "Explain the results you get in the graphs in words." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "def make_results_plot(df,k,reg):\n", + " uid=smalldf.user_id.values\n", + " bid=smalldf.business_id.values\n", + " actual=smalldf.stars.values\n", + " predicted=np.zeros(len(actual))\n", + " counter=0\n", + " for user_id, biz_id in zip(uid,bid):\n", + " predicted[counter]=rating(smalldf, db, biz_id, user_id, k=k, reg=reg) \n", + " counter=counter+1\n", + " compare_results(actual, predicted)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 34 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "print \"k=3, reg=3.\"\n", + "make_results_plot(smalldf,3,3.)\n", + "plt.title(\"k=3, reg=3.\")\n", + "\n", + "print \"k=3, reg=15.\"\n", + "make_results_plot(smalldf,3,15.,)\n", + "plt.title(\"k=3, reg=15.\")\n", + "\n", + "print \"k=10, reg=3.\"\n", + "make_results_plot(smalldf,10,3.)\n", + "plt.title(\"k=10, reg=3.\")\n", + "\n", + "print \"k=10, reg=15.\"\n", + "make_results_plot(smalldf,10,15.,)\n", + "plt.title(\"k=10, reg=15.\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "k=3, reg=3.\n", + "fraction between -15 and 15 rating" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " 1.0\n", + "k=3, reg=15.\n", + "fraction between -15 and 15 rating" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " 0.999837793998\n", + "k=10, reg=3.\n", + "fraction between -15 and 15 rating" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " 0.996431467964\n", + "k=10, reg=15.\n", + "fraction between -15 and 15 rating" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " 0.997080291971\n" + ] + }, + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 35, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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tQgBGozxH6sLARkREqlFVBeza1RwYqqpkhW3UqNS2qy+z24GiIjl2LRwGBg4E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\naq6cDQPXrJaRzZzNS5EpptM1Sy1dDNH5GyFDC5j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1dfKy3Q44HMltE7XFwEZERJoRbyPyeMeo+/j9wI8/yl61cBhITQWys5PdKmqN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\nUGVlpQYMGKCWlpY7NzgAiGAD4CeKioo0c+ZMZWZmKjo6WpL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S8aCN7OzsmI8lEgn+8Y9/jPjf8zyPG2+88ZRn6WQymSBHziVFYJswYQK+/PLL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CgPfGBY6uurjwwgsxbdo0XHPNNXjsscfw0Ucf4a677orumBSZ73bw4EHs3bsX\nixcvRmFhIa677jps3LgR27Ztw9133w29Xo+kpCTcfPPNqKiowMMPP4xQKIS6ujpUVVWhqqoKtbW1\nsXnTBoECG0kIg8EAURQptBFCSAIJAl+s4PEAFRVAeTkvIzJcXq8Xv/rVr3Drrbdi7dq12Lp1Kxob\nG/H6669DEASsWbMmGuomT56Md955Bz6fD1dffTVWrVqFO++8E6eddhoAYOPGjfjZz36Ga6+9FlOn\nTkVbWxumT5+OvLw8BINBvPjii7DZbNi8eTO+++47yGQyfPDBB5g7dy4efPBB3HvvvVi4cGG0h++8\n887DrFmz8OMf/xj79u2DSqXCBx98gOnTp+P222/HsmXLMGnSJNx0000AgNtuuw1//OMf8fLLL2PC\nhAl4/vnnMX/+fMydOxdut3v4b9YgCSwRM+fiSBCEhEwGHK9aWlqwe/fuIVd9drlcYIxhzpw50PSe\nUEEIISTuOjv5dlSRHR8FAcjPP/aChWNZtGgRCgsL8corr8SukYR62Ehi6XQ6CIKAHTt2jOg3FUKI\ndIVCIVgsFtTU1MDj8SS6OWNeZ2dXWAP4CtOOjsS1h/SPynqQhNPpdHC5XCgpKcGcOXOgpRLnhIxL\nwWAQVqsVVVVVCIVCkMlkqKioQFZWFiZOnAij0ZiQ1XljXX+lQoazMU0oFIrOJyOxQ4GNSEIktG3f\nvh1z586FTqdLdJMIISMkEAigsbERVVVVYIwhJSUFim4pwul0YseOHdBoNCguLkZGRgYV4I4ho5Fv\naxUZ5FCrhz4c+tprr6GsrAw1NTXYtGkTli5dSv9XMUJz2MiwDHcOW29utxuBQABz586FPhbVGwkh\nkuX3+2GxWFBdXQ0APYKa18uH5pKTu2qJBQIBdHZ2AgAmTJiAvLw8+pyIEa8XaGzk73lGBnB0QSWR\nEApsZFhiHdgAXsTQ5/Nh7ty50WXYhJCxw+v1or6+PrpKMDU1FXK5HACfS9XY2FWFPymJF4DtPkQn\niiKcTicCgQCMRiOKiopgNpujj0FOjN8PVFfz0Abw97q4OPZlRMjw0JAokRyNRhNdiDBnzpwBq2MT\nQkaXSEX5+vp6yOVyGI1GyHpVeHU6+ST4CK+X75+ZkdF1TCaTITU1NfqYu3fvhlKpRGFhIbKzs5Gc\nnDwSL2fMcDh4aJPJeGAWRaClhQKb1FBgI5IU+cCNLESg0EbI6OVyuVBbWwuLxQKVSoW0tLQBFw8E\nAjw4hMN4eAD1AAAgAElEQVR8eE6h6LsBencajQYajQahUAiHDh1CZWUlMjMzUVBQgNTUVFqkMAiM\n8dBmt/PLen1s9hMlsUWBjUhWcnJyj562yDdqQsjo4HQ6UVNTA6vVCrVajfT09OMGqKQk3qPmdPIg\nodHwKvzHo1AoYDabwRhDR0cHtm/fDq1WG12koBzOsscxLhwGLBYe2ESRz18rKEh0q0hvFNiIpCUl\nJQFANLQZjcYEt4gQcjwOhwPV1dVobm5GUlISMrqPZx5HpB4YY/wkCDxQDJYgCNDr9dDr9fD7/fjh\nhx8gCALy8vIwYcIEWqTQD5eL97BZrfw9D4epDpsUUWAjktc7tJkGs/sxIWTE2e12HDp0CG1tbUhO\nTj6hoBYRCPANyPV6Hh7kciAYHFp71Go11Go1RFFEY2Mj6urqYDKZoosUes+fG698Ph7aInPWvF6+\nVRWRFgpsZFRISkqCIAgoKSnBmWeeCbPZnOgmEULAN/q22+2oqKhAR0cHNBrNkIJaRKSMR6Ssh0bD\nT8PRe5HCrl27oFaro4sUIl8KxyuVCsjO5gsNGON12WjdhvRQYCOjhlqtRkpKSjS0xbKUCCHkxDDG\n0NraioqKCnR2dkKn0w0rqEWoVLyAa1sbDw+hEJCXF4MGH9V9kUJVVRUqKiqQnZ2N/Pz8cbtIIS2N\nnyL1ypXKnqtyiTRQYCOjikqlQmpqKnbu3InZs2fH5A8EIWTwRFFEc3MzKisr4Xa7YTAYYvp72NnJ\nw1pra1eJCYcj9oVcuy9SsNvtaGxshF6vR1FREdLT08fVIoW0NODkk4Gamq6AnJub6FaR3iiwkVFH\npVLBaDSitLQUs2bNQmZmZqKbRMiYFw6H0dTUhIqKCvh8vpgHtQiHg69YjCw+aGwEsrKA/PyYPxUA\nvkjBYDDAYDDA5/Nh3759kMvlyM/PR25u7rjYJi8c5iejkfdqymQ8uI2jzDoqUGAjo5JSqewR2rKy\nshLdJELGpFAoFN2QPRAIICUlJa47kITDfC/L7lX3I+Et3pKSkpCUlIRwOAyLxYKamhqYzWYUFRXB\nZDKN2UUKdnvPVaFuN5/PFq+QTIaGAhsZtZRKJcxmM3bv3o3TTz8d2dnZiW4SIWNGMBhEQ0MDDh06\nhHA4jJSUlBEpYK3V8h41r5cHNY0GGOkSjHK5PLpIwe1291ikkJWVNeYWKYRCfY8FAiPfDnJsFNjI\nqBaZh7J3716IoohcmnhByLD4/X40NDSguroajLEeG7KPhKysrjlsjPG5azk5I/b0fWi1Wmi1WoRC\nIVRWVuLgwYPIzc1FXl7emCnmrdN1bUsF8FW6tLmM9NDm72RY4rH5+1CEQiG0tbVhxowZmDBhQkLb\nQsho5PP5cOTIEdTU1EAQhB4bso80xvgQXSgEmEw8TEgFYwxOpxN+vx96vR7FxcVIT08f0VAbD21t\nQHMzD21mM5CZyYMbkY7R/RNGyFGRnrZ9+/aBMYa8WNYBIGQM83g8qK+vx+HDhyGTyWA0GhMW1CIE\nYeSHQQdLEITo0LDP50NZWRnkcjkmTpyI3NxcaEfpjulmMz8R6aLARsYMhUKBtLQ0fP/99xBFERMn\nTkx0kwiRLLfbjbq6Ohw5cgQKhWJMT6qPl+6LFOrr61FdXY309HQUFBTQ+0lijgIbGVPkcjnS0tKw\nf/9+iKKIwsLCRDeJEEnp7OxEbW0tGhoaoFKpkJaWNi6LxcaSXC6P7nPscrmwc+dOJCUloaioCFlZ\nWVCr1Qlu4eC43Xw4Wqul4VApojlsZFikMoetN1EU0dLSgilTpqC4uDjRzSEk4To6OlBdXY2mpqbo\nriEkfoLBIJxOJxhjyMnJQX5+vmTfc1EE6uq6SntoNEBhId91gkgH9bCRMUkmkyE9PR0VFRUAQKGN\njFvt7e2orq5GS0vLkDdkJycuUnaIMYaWlhZYLBYYDIboTgpSWqTQ3s5PES4Xr8NGi+6lRTo/MYTE\nWCS0VVZWgjGGk046KdFNImRERLZbqqqqQnt7+7A3ZCdDN9AihYKCAuTk5EhikYLP1/dYpHAxkQ4K\nbGRMk8lkSEtLQ2VlJURRxKRJk2i+DhmzGGNoa2tDRUUFnE4ntFotBTUJ6b5Ioa6uDocOHUJGRkZ0\nkUKiPpsic9a6zybS6xPSFHIMFNjImCeTyZCRkYFDhw5BFEVMmTKFQhsZU0RRRGtrKyoqKuByuaDX\n60d1UBNFwOPp2ulAQqOHMSGXy2EymQDwRQolJSVISkrCSSedhMzMTKhGePJYaiqQnc2HQRnje4pK\nbFoyAS06IMMk1UUH/YnMJSkoKMDUqVMptJFB8/v5HzKp7UgUDofR3NyMyspKeDweGAyGUb9tUigE\nNDR0DcmpVHwu1ShZaDlkgUAAnZ2dYIxhwoQJyMvLi+uerb2FQnzRQTjMdzkY6+/3aDTGvrcQMjBB\nEJCeno66ujowxjBt2jQKbeSYGAMsFl4FHuDDRBMnJr7HJxQKwWazoaqqCn6/HwaDYVT3qHXX2cl7\n1yL8fj4hPisrcW0aCSqVKrpIwWazob6+HikpKSguLkZaWlpcixkHg8ChQ13ve1MTUFzMezeJdFBg\nI+NKJLQdPnw4GtqouCUZSHt71zARADgc/I9YdnZi2hMMBmG1WlFVVYVQKISUlJQR7YUZCcEgn08V\nCPD3Xa3mx8aLyLZgAOD1erFnzx7I5XIUFRUhOzsbmjikqPb2niE5EOA/91R7XFoosJFxRxAEZGRk\noL6+HqIo4uSTT6bQRvrl9faciA3w4qIjLRAIoKGhAYcOHUrIhuwjSa3me1pGiriq1cAppyS6VYmR\nnJyM5ORkhEIhVFdXo7KyEhkZGSgsLITRaIzZCEEoxENaW1vXXqKhUEwemsTQ2PyNJ2QQMjIy0NDQ\nAMYYTjnlFAptpI/k5L6r50ayCoPf749uyA5gTAe1iHCYzxUMBLoWHYhioluVWJG9kgG+SGHHjh3Q\naDQoKiqKySIFtRooL+dDoQBgMAAXXTTcVpNYG9u/+YQcR3p6OhobG6OhLdGbXhNpMRp5EVG7nV/W\n64H09Pg/r9frRX19Perq6qJDZOPlZzMc5u9z97IS4XDi2iM1Op0OOp0OgUAABw4cwIEDB5CXl4cJ\nEyYMeXjc4eChjTH+Xicnd+16QKSDAhsZ99LT02Gz2SCKImbMmDFu/jCS4xMEID8fyMwcmVWibrcb\nhw8fRn19fbT0w3jr+dVqeUCO9KoJAqDTJbZNUhTZB1YURVitVhw+fBipqakoLi6G2Ww+oc8xh4MP\nQefm8vfb7+dz2Ii0UGAjBEBaWhqam5uxd+9ezJw5k0Ib6SHeJQ5cLhdqa2thsVjG/YbsGg3v2Tx8\nmM+jmjCB1wkj/ZPJZD0WKZSWlkKlUkV3UkhOTj7uY2i1fP7awYP8PS8sBE47Ld4tJyeKAhshR6Wl\npaGtrQ179+7FaaedNubnCpHEczqdqKmpgdVqhVqtRnp6+rgNahEeD1BWBhw5wnvZWlp4oDhaZ5Yc\nQ/dFCjU1NaiqqkJmZiYmTpx4zEUKgQAvp6JWA0ol36qq+6pRIg30F4mQbsxmczS0zZw5k0IbiQuH\nw4FDhw6hpaUFSUlJY6aGWizU1gLbt/NSE6LIh0PNZuDssxPdstFDoVBEd1Lo6OjAjh07oNVqUVxc\njIyMDCiVyh63j6wMVan4HDadjhZ6SBH9NSKkl0ho2717N04//fQ+H26EDJXdbsehQ4fQ1tZGG7IP\nwOHgqxUjZSUiNcHI0Oj1euj1evj9fvzwww8QBCG6k4L+6MqOtDQ+FO31AnI5D25jvVDxaESBjZB+\nmM1m2O127N69G7NmzaLQRoYssiF7ZWUlOjo6KKgdh0bD56xFFh5otTSHLRbUajXUanWPRQpGoxFF\nRUVISjJj8mQ5fviB97BNmkRD0FJEgY2QAZhMJrS3t2PXrl2YPXv2iG/ITEa3yIbslZWV6OzshE6n\no6A2COnpwBln8EUHjPGenkTtLDEWdV+k4PF4UFpaivZ2NZzOAuTkZEOtTkYwyBchUC+btFBgI+QY\njEYjHA4HSktLKbSRQRFFEc3NzaioqIhuyE5BbfBMJuCkk/gwnSjyIq6ZmYlu1dik0Wig0WjgcoVQ\nV3cIhw5VYOLEWTAaM+H1Jrp1pDcKbIQcR2pqKhwOB3bu3IkzzjgD6njXeCCjUjgcRlNTEyoqKuDz\n+ZCSkkJBbQhEEWht7VolmpHBS3uQ+DEYFNBozKisbIfP58app/KgTKSFAhshgxAJbbt27aLQNs6I\nIuB0dvX29F44HAqFohuyBwKBMbkh+0iy2fgig1CIz6fq6AAsFt7jRuIjFOKLPVwuvugg8v4TaaHA\nRsggpaamoqOjI9rTlhTvsvck4UIhoK6Ol5gIh4GUFKCggG/dEwwGoxuyh8NhpKSkICUlJdFNHvW8\nXh7QIlsjaTTUwxZvkVW4WVl8SFqn43MIp09PbLtITxTYCDkBKSkpcDqdKCkpwZlnnjmoKuJk9Gpr\nAyoreXhgjK9YFAQ/lEoLampqwBgbFxuyjySFgm+TFCncyhgwznbnGnGM8VN7O//XYOA9bURa6FOG\nkBNkMBjQ2dmJkpISzJkzh0LbGNbe3vVHLBDwoaGhHnV1tZg2bXxtyD6SkpN5L2ZrKx+GNploPlW8\nyWS89l1TE9/xAABOPz2xbSJ9UWAjZAj0ej1cLhd27NiBOXPmQKPRJLpJJA5CoQA6Ohyw2xvhdNog\nlyswbZoJZjN1+cRLUhIwcSIffo7MG6TN3+PL5+MFihnjm78Hg7yXk0gLBTZChkin0/UIbVqtNtFN\nIjHg9XrR3t6OhoYG1Nfb4XIxhMPJMBrTYDAIVJsqzrRavp+lUskDhFLJj5H4CYUAv58HNZ+PzyMM\nBhPdKtIbBTZChiES2iLDoxTaRh/GGFwuF9ra2mCxWOByuSCTyaDRaFBQkIaUFF51nzHe60MV4OMr\nFOLzpxQK3sOmUPAFHyR+FAoejDs7eXDLyeGXibRQYCNkmHr3tOlo/EbyRFGE0+lES0sLLBYL/H4/\nZDIZdDod0tPTe9xWEPgftMhwEYmvQIAv9nA4ui5T4dz4CoV4r5rJBOj1XUOkRFpGdWCz2+1ISkqi\n+UMk4XQ6HdxudzS0RTZVJtIRCoXgcDhgs9lgtVoRCoWgVCqh1+sHrJvmcvG6YIzxyz4f7/0xGkew\n4eOM388DW1MT72EzGoHi4kS3auzTaPjPeyDAixVHfuaJdIy6wDZ//nx89913AIDJkyfj4MGDCW4R\nIZxWq4XH44mGNiqemnh+vx8OhwMNDQ1oaWkBYwxqtRopKSmDWuHp9fb8w8UY/6NGgS1+PB7+Poti\nV0kPlyvRrRrbFAr+PkeGRuVyvlqXSMuoCmylpaVYvHgxNmzYAACYQNUUicRoNBoIghANbVRIdeR5\nPB60t7fjyJEj6OjoAGMMycnJMJvNEE5wTLO/8mo0tye+/H6goYEP0wkC0NgIFBYmulVjn1bLV4bq\n9VRGRapGVWBbv349ZsyYAb1ej0mTJiW6OYT0Kzk5GYIgRIvrpqamJrpJYxpjDJ2dnWhtbYXFYoHH\n44FMJoNWq0XaMPcz0uu76lOFw4DZDNB/Z3yJIg9t5eX8PIW1+BNFIC+P96qZTPw9p/ma0jNqAls4\nHIbdbseTTz6J5cuX46qrrsLrr78OJX3dJRIU2bYqsnqUQltshcNhOJ1ONDc3o7GxEX6/H3K5vN9F\nA8MRDPJetsjKUJWKhwnalSx+3G4eis8+m18Oh/ncQRI/KhVQXQ1UVfGf7aYm4Gc/S3SrSG+jJrDJ\n5XJ8+OGHYIzhzTffxC233IKVK1fi8ccfT3TTCOlXJLRFhkeNNPFpWILBIBwOB6xWK2w2G8LhMFQq\nFXQ6XdzmC7pcfFuqzk4+n0qn40NHNNIdP8nJfD5Vayu/nJJCATne3G7Aau0qn9LYCDQ3J7ZNpK9R\nE9giBEHAL37xC/h8PqxevZoCG5G0pKSkHnPaTFTE64T4fL7oooHW1lYwxpCUlASj0QjZCGwwGQrx\njbG7rxKl3B1f4TBQUwMcOcLfd7MZOPXURLdq7PGJIVj9LlgDLnwrd8H7k04EVA50av4PF7StRCBA\n265JzagLbBH/+Z//iTvuuKPf6x588MHo+YULF2LhwoUj0yhC+qFWq2EwGKJz2sxmc6KbJGlutxt2\nux1HjhyB0+mEIAhDXjQwXJHVcu3tPEgYDLToIN78fmDSJGDq1K5Nyf3+RLdqdPKL4Wgos/o70Rhw\nwep3odHfCXuo2ziz5ujpqB2Vdvz2zNhNLSCxMWoDWzgcxpQpU/q9rntgI0QKIqUkIqFtuJPhxxLG\nGJxOZ3TRgNfrjS4aiOV8tKEIBnkB1+ZmHthEkbbsibfI1lSRIbqMDFrocSwBMQxbJIh1C2SNARfa\ngt4B76cQZMhSaZGt0kFs0aOuVIdQE5DsnQK/zAyLZQRfBBmUURPYdu7cibKyMtx4442QyWR4+umn\ncf/99ye6WYQMmkqlQmpqKnbu3InZs2cjIyMj0U1KmHA4jI6ODjQ1NaGxsRHBYBAKhQI6nU5SO0W4\nXIDFwnvaZDIeImjVYnyFQsC+fUB9Pb+clkaFc4NiGE0Bd59AZvW70Br0YKAat3IIyFRpkaPWI1ut\nQ45KFz2fptRAfrTHemst0LwfaGpqR0iphz5HRr2aEjRqApvNZsPq1avxxhtvYPHixZg7dy4uueSS\nRDeLkBOiUqlgNBpRWlqKWbNmIXMc7bkTCATgcDjQ2NiI5uZmiKIIpVIJnU4HRX8FzyQgHOb7Kra1\ndZ2nfS3jy+nkJSVOPZWH5I6Orm2qxrKgKKI56O4TyBr9nWgNeiAOcD8ZBGSptMhR65DdLZBlq3TI\nUGkgF44/1zOyBbIg8Pfc7+dzB4m0SPNTsh9LliyB1WpNdDMIGTalUgmTyYTdu3fj9NNPR1ZWVqKb\nFDderxft7e1oaGiA3W6PFrEdqUUDw5WUxCvv+3w8qLlctGIx3iIlPfx+PgStVgPZ2YluVWyEmYjm\ngKdPILMGXGgOeCAO0FcmA6LDl90DWY5ahwyVFopBhLL++Hw+2O12VFe3QqNpA3AEXq+In/xkPWQy\nWnQgNaMmsBEyligUih6hLXus/EUC0NnZiba2NjQ0NMDpdEImk0Gj0YzKeXt+P1+teOAAH6o76SRg\n2rREt2ps02p5j2ZVFQ9sEybw9320CDMRLQFPn0DW6HehOeBGeIBQJgDIUGp6DF/yYKZHpkoL5SC/\n4IiiiI6ODrS1taG1tRWtra1oa2uLXu5+3jXAnl8HDqzCWWeNn97/0YICGyEJolAoYDabsWfPHoii\niNzc3OPex+vtqrqfkcEr8SeaKIpwOp1oaWmBxWKB3++HTCaDTqcb9fP0WluBsrKuVYo//ACcfnpi\n2zTWRTZ+V6n4ZaeTX5aSMGNoDXr6Hb5sDroRGmDndAFAulLTJ5DlqHXIUmmhPEavls/nO2b4ipxv\na2tDeJDj9pHPILXajHDYDMYM0GqLMHGiguawSRAFNkISKPKBWVZWBsbYMffH9XqBnTv5JHhR5JOx\n58zpqsI/kkKhUI9FA6FQKDofbSxteh8O88Ktzc38PTcae24GT2KPMV5Gpa2NX9ZoEjNvUGQMbUFv\nv8OXtoAbITbQrDLArEyOTvDPOjp0yUOZDqpuoUwURTgcDrQdacKuo6GrdYB/3W73oNtuMBhgNpuR\nlpYGs9k84HmDwQCZTIbt24HNm4G6unaoVEWw2cxUvkaCKLCRYWGM/yEjQxcJbfv27YMoisjPz+/3\ndkeOAHv38knYAO91SEsbucDm9/ujRWxbWlrAGINKpYLBYIBcPjbnu+j1wGmn8Z9xQeD/JrjSyJgn\nl3eVUxFFvjVYvH68RMZgD3r7Hb60BVwIHiOUmRRJfQJZtkqPbLUWLBDqNhxpweHWVuzupzfMbrcP\nujdMqVRGA1d/Aaz7ZVWke3KQQiHekxkKdS30oB426aHARoasqQmoqADq6nhwS0/nv+zkxCkUCqSl\npeGHH36AKIooKCjocxu7nU96F0X+fgcCgM0W33Z5PB60t7fDYrGgvb0dgiAgKSkpIUVsEyElBZg4\nkW9EHgrx8hKjcCreqBL5LFGp+PnICsahPx6DPeTrE8isR0NagA0cmIyKpOgE/yyFBnofQ5LLD8Hp\nQafdgba2OrS2tmJXrzB2Ir1hKSkpPQLYQL1iBoMhbr9zjPHP7sjDK5X0RVyKhhXYmpqa8Pnnn+Oa\na66JVXvIKNHRwfebCwT4cIXdzr8J01LwoZPL5UhLS8P+/fvBGENhr4JfOh1/nxsbu4ZEzzortm1g\njEUXDVgsFrhcLskUsU2E9vauBQeM8YnwaWm08CCeBIGvxO3o4OeVSr5S9FgYY3CE/GgMdB4NZi40\nBjphOxrMfOLAoUwfFmByi9C7Q0hyBSF3esGcbvgdnXC02VHf1oY9bW1ob28fdG+YSqU65lBk5LzJ\nZDrh3rB4kMu7Vj8LAn+/aUhUegYMbN9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OcqxePXHbMH1/tluGWqIoCvzJ2CQJmfhzaJqk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\nNyQqItwSpTnxesdXHQCRQPh8TNh8Ph/q6uqwMAfHCFMp4MiR8S1Rmw1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+ "text": [ + "" + ] + } + ], + "prompt_number": 35 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*your answer here*\n", + "\n", + "If you are a bit confused by the look of these graphs, you should be!\n", + "\n", + "For k=3, the predicted values are quite well-behaved, with the exception of several predictions that have extremely large magnitudes. It appears that the predicted values are pulled into the mean star rating, which sits somewhere around 3.8, so ratings on the low end are overestimated, and similarly ratings on the high end underestimated. The regularization does not appear to have a strong effect when k = 3.\n", + "\n", + "For k=10, the predicted values are much less stable, with many more extreme predictions. The means appear to track better with the true means. The regularization has a much more extreme, although indirect, effect on the appearance of the plot. Since regularization has stronger effects on similarity scores between restaurants that have small common support, we can see that increasing k makes the predictions more sensitive to the regularization because in a small dataset, the common support between a restaurant and it's 10-nearest one will be quite small.\n", + "\n", + "Note that this example does not seem to follow the standard bias-variance tradeoff, where we would expect small k to give a unbiased estimates that capture the extremes, while we would expect large k to give biased estimates that pull extreme values toward the mean. A large reason for this failure for this example to capture this behavior is that we have defined similarity scores that can be positive or negative, and the bias-variance logic is based on the more standard setting where we average together values that have strictly positive weights. When you have negative weights, it's possible that the sum of $s_{ij}$'s in a neighborhood can get close to zero, making our estimator $\\hat Y_{um}$ explode in the positive or negative direction since this would entail dividing by (nearly) zero. Thus for those restaurants where the denominator goes to 0 (more likely to happen at larger k as you have more chances of it there being more weights to add), the ratings are unstable, even numerically!\n", + "\n", + "This problem is less pronounced in large datasets or with small k because the k-nearest restaurants are likely to have positive similarity with the current one. However, with small datasets, we can find that even with k relatively small (in this case, around 10), there are negative similarities in the k-neighborhood that make the estimator unstable. This sort of instability would be much less pronounced in the large dataset.\n", + "\n", + "If we were to rescale the similarities to be positive, say between 0 and 1, the behavior of the estimator with respect to $k$ and $reg$ would be quite different. (SEE BELOW!)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### IMPORTANT SOLUTION ADDENDUM\n", + "\n", + "The wide wings we see above are in the error graph are due to small values of the denominator in the similarity sum. If you were writing a production recommender, using the ratings as is from Q2 would be a very bad idea. \n", + "\n", + "Indeed the very idea of nearest neighbor should engender in you the idea of distances; and distances should not be negative.\n", + "Furthermore, you would not be able to use distances other than those implied by the correlation coefficient in the calculation of nearest neighbors (such as a Manhattan distance or Jacard similarity).\n", + "\n", + "We can fix this for the case of perarson coefficient by just a simple rescaling to the 0-1 range!\n", + "\n", + "$$ \\rho \\rightarrow \\frac{\\rho+1}{2} $$.\n", + "\n", + "This translates into changing the shrunk score:\n", + "\n", + "$$ s \\rightarrow \\frac{s}{2} + \\frac{f}{2} $$\n", + "\n", + "where \n", + "\n", + "$$ f = \\frac{N_{common}}{N_{common}+reg} $$\n", + "\n", + "Note that the new quantity is really an inverse distance, and thus we'll sort by its smallness as before." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def knearest_pos(restaurant_id, set_of_restaurants, dbase, k=7, reg=3.):\n", + " \"\"\"\n", + " Given a restaurant_id, dataframe, and database, get a sorted list of the\n", + " k most similar restaurants from the entire database.\n", + " \"\"\"\n", + " similars=[]\n", + " for other_rest_id in set_of_restaurants:\n", + " if other_rest_id!=restaurant_id:\n", + " sim, nc=dbase.get(restaurant_id, other_rest_id)\n", + " ssim=shrunk_sim(sim, nc, reg=reg)\n", + " similars.append((other_rest_id, ssim/2.0 + float(nc)/(float(nc)+reg), nc ))\n", + " similars=sorted(similars, key=itemgetter(1), reverse=True)\n", + " return similars[0:k]\n", + "\n", + "def knearest_amongst_userrated_pos(restaurant_id, user_id, df, dbase, k=7, reg=3.):\n", + " dfuser=df[df.user_id==user_id]\n", + " bizsuserhasrated=dfuser.business_id.unique()\n", + " return knearest_pos(restaurant_id, bizsuserhasrated, dbase, k=k, reg=reg)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 36 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def rating_pos(df, dbase, restaurant_id, user_id, k=7, reg=3.):\n", + " mu=df.stars.mean()\n", + " users_reviews=df[df.user_id==user_id]\n", + " nsum=0.\n", + " scoresum=0.\n", + " nears=knearest_amongst_userrated_pos(restaurant_id, user_id, df, dbase, k=k, reg=reg)\n", + " restaurant_mean=df[df.business_id==restaurant_id].business_avg.values[0]\n", + " user_mean=users_reviews.user_avg.values[0]\n", + " scores=[]\n", + " for r,sold,nc in nears:\n", + " s=sold/2.0\n", + " shrink_factor=float(nc)/(float(nc)+reg)\n", + " s=s+shrink_factor/2.0\n", + " scoresum=scoresum+s\n", + " scores.append(s)\n", + " r_reviews_row=users_reviews[users_reviews['business_id']==r]\n", + " r_stars=r_reviews_row.stars.values[0]\n", + " r_avg=r_reviews_row.business_avg.values[0]\n", + " rminusb=(r_stars - (r_avg + user_mean - mu))\n", + " nsum=nsum+s*rminusb\n", + " baseline=(user_mean +restaurant_mean - mu)\n", + " #we might have nears, but there might be no commons, giving us a pearson of 0\n", + " if scoresum > 0.:\n", + " val = nsum/scoresum + baseline\n", + " else:\n", + " val=baseline\n", + " return val" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 37 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def make_results_plot_pos(df,k,reg):\n", + " uid=smalldf.user_id.values\n", + " bid=smalldf.business_id.values\n", + " actual=smalldf.stars.values\n", + " predicted=np.zeros(len(actual))\n", + " counter=0\n", + " for user_id, biz_id in zip(uid,bid):\n", + " predicted[counter]=rating_pos(smalldf, db, biz_id, user_id, k=k, reg=reg) \n", + " counter=counter+1\n", + " compare_results(actual, predicted, ylow=1, yhigh=5)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 38 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NOTICE: when comparing graphs note that the limits have changed to make the bias-variance comparison more visible. The regularizer is set to 1 to reduce the regularization to something small, rather than something optimal, as well. Also to make things more extreme, I choose k=15 rather than k=10." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print \"k=2, reg=1.\"\n", + "make_results_plot_pos(smalldf,2,1.)\n", + "plt.title(\"k=2, reg=1.\")\n", + "\n", + "print \"k=2, reg=15.\"\n", + "make_results_plot_pos(smalldf,2,15.,)\n", + "plt.title(\"k=2, reg=15.\")\n", + "\n", + "print \"k=15, reg=1.\"\n", + "make_results_plot_pos(smalldf,15,1.)\n", + "plt.title(\"k=15, reg=1.\")\n", + "\n", + "print \"k=15, reg=15.\"\n", + "make_results_plot_pos(smalldf,15,15.,)\n", + "plt.title(\"k=15, reg=15.\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "k=2, reg=1.\n", + "fraction between -15 and 15 rating" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " 1.0\n", + "k=2, reg=15.\n", + "fraction between -15 and 15 rating" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " 1.0\n", + "k=15, reg=1.\n", + "fraction between -15 and 15 rating" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " 1.0\n", + "k=15, reg=15.\n", + "fraction between -15 and 15 rating" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " 1.0\n" + ] + }, + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 39, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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lOBzHOHSogUsuCfdrQQb+ZRzr4ujRo6xcuZLdu3cDEBsby/z587nuuuswGAzU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\nZFarXOszZ2Q2h0uZ4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0JSBEGUACzGAgt//CQuDnn+mGpIsY4vcY/BIxW7NmDQoKCi7ZJbNx40YcPnwY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\nLh0tmpu9H0Li4qzZe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8+/btw3PPPYd3330X48aNwz//+c+LO2BewicCmOvXr8fYsWMxYsQIbNiwAS+/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InEmMFAY4Lq6xO9svRFEEgo+P2RaQs5ObEZYj4RjWOjpaSEosAuF/2Zn89NLgzDxB8WZsyohEJA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\nDI4FAODPo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xS/F4DkGgLli9nlLHMhlQXAz4exN+Z41jz64XM5vNkMlkCAgIQHR0tA9WTvu4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\nm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+ "text": [ + "" + ] + } + ], + "prompt_number": 39 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice firstly, that at low regularization, at low k (variance limit), the predictions follow the green line more than at high k ( bias limit). This is, as mentioned earlier, due to the bias limit pulling the extreme groups of ratings in towards the mean of all the reviews. Note that increasing bias decreases the variation **between** the rating groups, resulting in a flatter black curve.\n", + "\n", + "The second thing to notice that as k goes up, the precision as measured by the width of the grey region (which measures the variance **WITHIN** rating groups, not between them) gets smaller. \n", + "\n", + "The third thing to notice is that adding in regularization in the variance (low k) limit actually increases the bias, which is not surprising as it tends to push things towards the center. In many machine learning situations this is a desirable thing, as it reduces overfitting. In the high bias limit, as expected, it does not have much of an impact. Note that regularization had more of an impact in the previous set of graphs, as it pushed similarity towards 0.\n", + "\n", + "One might ask: why not plot these graphs in the homework in the first place: using distances is the correct way to do it, and dosent lead to the subtraction problem. Its a good question, and you should use positive similarities in a production recommender. For the puposes of this homework, i think the k=10 graphs in the previous set illustrate the problem with sparsity in a much more dramatic way, leading to the \"bellows\" in the graph. However, in real life, there's no real justification for using similarities that can be negative as weights. If we think of KNN as a way of constructing local averages, you need those averages to be constructed using positive weights. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.5** Outline a process, in words, for choosing the nearest neighbor parameter `k`. For this question fix the regularization parameter `reg` at `3`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*your answer here*\n", + "\n", + "We could use $F$-fold cross-validation (usually called $k$-fold cross-validation, but we've already used $k$ here to mean something else!). Specifically, we could randomly partition the data into $F$ equally sized folds (pieces), making sure that each fold includes at least one rating for each restaurant and one rating by each user (it would probably best to make sure that if $K$ were the maximum $k$ you would be considering, that each user and each restaurant appear at least $K$ times in each fold). For each value of $k$, and for each fold, we could repeat the procedure above for predicting user ratings by computing similarities using $F-1$ of the folds to compute similarities and computing the prediction error in the held out fold. We could then choose the $k$ with the smallest average prediction error across the folds, and recompute the recommender on the whole dataset using the chosen value of $k$.\n", + "\n", + "If we wanted to both choose a good value for $k$ and check how well we could expect the result to generalize, we could divide the dataset into $F+1$ folds, and keep the last fold out as a verification set. We could perform the cross-validation above on $F$ folds to select $k$, then upon selecting $k$ use all $F$ folds to create a recommender, and see how well this recommender predicted ratings in the final validation set." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Q3 Bayesian Chocolates: Model based recommendations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this part of the homework, you will use your newly minted Bayesian and Gibbs sampler skills to write a recommender that uses Bayesian techniques to impute ratings." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Model-Based Recommendations\n", + "\n", + "\n", + "####A Note on Frequentist and Bayesian Procedures\n", + "\n", + "In the previous section we implemented a **procedure** (a set of instructions for processing data) for giving recommendations and predicting user ratings for restaurants. This procedure involved a number of arbitrary choices -- for example, the particular measure of similarity between restaurants, or the weighting scheme for constructing a predicted rating. It also gave no sense of uncertainty -- in the case of giving recommendations, there was no statement about how we would expect the ranking from the procedure to compare to the user's true opinions of restaurants, and in the case of predicting ratings, there was no confidence interval for the prediction.\n", + "\n", + "It is possible in repeated applications of the above procedure to see how it performs in the long run. Based on this long-run performance we could potentially justify certain functional choices and compute measurements of uncertainty. This framework of proposing a procedure first, then evaluating its performance in real or hypothetical replications of the experiment is an example of a *frequentist* approach to a problem. One aspect of the frequentist approach is that the proposed procedure does not necessarily have to be derived from a model (although it often is). While this means that a proposed procedure may be more flexible or robust than a model-based procedure, it also means that there is no natural way to justify certain functional choices or construct uncertainty estimates.\n", + "\n", + "In contrast, the *Bayesian* approach to a problem always begins with a **probablistic model** for how the data were generated. Assuming this model is true, the posterior distribution over unknown quantities (either parameters to be estimated or unobserved data to be predicted) gives a single coherent expression of what the observed data tell us about the unknowns. By summarizing the posterior distribution, we can derive the exact functional form of a procedure for constructing estimates or predictions. We call a procedure derived from this Bayesian approach a **Bayes rule** (not to be confused with Bayes' Theorem). Using the posterior distribution, we can also give a sense of how uncertain we are about the estimate or prediction we have constructed.\n", + "\n", + "####Outline for this Problem\n", + "\n", + "In this section, we construct a **model** of how ratings are generated, and use this model to build a recommendation and ratings prediction system. We will take a Bayesian approach here, and construct our estimates and predictions from summaries of the *posterior distribution* of the model's parameters, which we will compute using a *Gibbs sampler*. We will also give measures of uncertainty based on the posterior distribution. We will evaluate predictions from this approach in the same way we evalutated predictions from the KNN procedure above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###The Latent Factor Model###\n", + "\n", + "####Model Overview####\n", + "\n", + "The central dogma in constructing a recommendation system using collaborative filtering is that *similar users will rate similar restaurants similarly*. In the previous section, we explicitly encoded this idea by using a similarity function to identify similar restaurants. We also assumed that either all users were the same (the global approach) or that only the current user was similar enough to make a recommendation (the user-specific approach). In this section, we will use a model that allows us to identify both similar users and similar restaurants as a function of **latent factors**.\n", + "\n", + "We can think of latent factors as properties of restaurants (e.g., spiciness of food or price) that users have a positive or negative preference for. We do not observe these factors or the users' preferences directly, but we assume that they affect how users tend to rate restaurants. For example, if a restaurant serves a lot of spicy food and a user dislikes spicy food, then the restaurant would have a high \"spiciness\" factor, and the user would have a strongly negative preference, resulting in a prediction of a low rating. Note that if users have similar preferences, then according to the model, they will behave similarly, and likewise, if restaurants have similar latent factors, they will be rated similarly by similar users. Latent factors thus give us an intuitive way to specify a generative model the obeys the central dogma.\n", + "\n", + "One issue that comes up with latent factor models is determining how many latent factors to include. There may be a number of different unmeasured properties that affect ratings in different ways -- for example, in addition to the spiciness factor above, there may also be a price factor that affects how users rate a restaurant. We deal with the problem of choosing the number of latent factors to include in the same way we deal with choosing $K$ in a $K$-nearest neighbors problem.\n", + "\n", + "####Rating Model Specification####\n", + "\n", + "To make this model concrete, we can write down our probability model as a generative process. First, we define the following quantities:\n", + "\n", + "Counts:\n", + "\n", + "* $L$: The number of latent factors.\n", + "\n", + "* $U$: The number of users.\n", + "\n", + "* $M$: The number of items (restaurants).\n", + "\n", + "* $N$: The number of observed ratings.\n", + "\n", + "Data:\n", + "\n", + "* $Y_{um}$: The star rating given to restaurant $m$ by user $u$.\n", + "* $Y$: The full collection of observed star ratings.\n", + "\n", + "Item-specific quantities:\n", + "\n", + "* $\\gamma_m$: An item-specific parameter vector of length $L+1$. The first element of $\\gamma_m$, denoted $\\gamma_m[0]$ is the item-specific bias. The remaining $L$ elements of $\\gamma_m$, denoted $\\gamma_m[1:]$, are the latent factors associated with item $m$.\n", + "\n", + "* $\\Gamma$: An $M$ by $L+1$ matrix where the $m$th row is $\\gamma_m$.\n", + "\n", + "User-specific quantities:\n", + "\n", + "* $\\theta_u$: A user-specific parameter vector of length $L+1$. The first element of $\\theta_u$, denoted $\\theta_u[0]$ is the user-specific bias. The remaining $L$ elements of $\\theta_u$, denoted $\\theta_u[1:]$, are user $u$'s preferences for the latent factors.\n", + "\n", + "* $\\Theta$: A $U$ by $L+1$ matrix where the $u$th row is $\\theta_u$.\n", + "\n", + "Global quantities:\n", + "\n", + "* $\\mu$: The overall ratings mean.\n", + "\n", + "* $\\sigma$: The residual variance of ratings after the mean, bias terms, and latent factors have been taken into account.\n", + "\n", + "Using these quantities, we can specify our model for each rating $Y_{um}$ similarly to a linear regression:\n", + "\n", + "$$Y_{um} = \\mu + \\theta_{u}[0] + \\gamma_{m}[0] + \\theta_{u}[1:]^{\\top}\\gamma_{m}[1:] + \\epsilon_{um}$$\n", + "\n", + "where\n", + "\n", + "$$\\epsilon_{um} \\sim N(0, \\sigma).$$\n", + "\n", + "Note that while this looks like a linear regression, it is of a slightly different form because the latent factor term involves the product of two unknowns. This is like a linear regression where we forgot to measure some covariates.\n", + "\n", + "We also assume the following priors on the user-specific and item-specific parameters:\n", + "\n", + "$$\n", + "\\begin{align*}\n", + "\\gamma_m &\\sim MVN(\\mathbf 0, \\Lambda_\\gamma^{-1})\\\\\n", + "\\theta_u &\\sim MVN(\\mathbf 0, \\Lambda_\\theta^{-1}),\n", + "\\end{align*}\n", + "$$\n", + "\n", + "where $MVN$ means multivariate normal, $\\mathbf 0$ is vector of length $L+1$ filled with zeros, and $\\Lambda_\\theta^{-1}$ and $\\Lambda_\\gamma^{-1}$ are $L+1 \\times L+1$ covariance matrices. $\\mu$ and $\\sigma$ also have priors, but they are not relevant to your task so we won't write them here.\n", + "\n", + "#### Goal for this Model####\n", + "Using this model, we want to make inference about all of the quantities that, if we knew them, would allow us to sample $Y_{um}$ for any user and any item. These quantities are $\\mu$, $\\sigma$, and the elements of $\\Theta$ and $\\Gamma$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1**: Given the goal specified above, how many quantities (counting a vector of $L$ items as $L$ quantities) are we trying to make inference about? Express your answer in terms of the variables in the \"Counts\" section above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*your answer here*\n", + "\n", + "There are $$U \\times (L+1) + M \\times (L+1) + 2$$ quantities to estimate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Gibbs Sampling from the Posterior###\n", + "\n", + "Our goal is to compute the **posterior distribution** over the unknowns $\\mu$, $\\sigma$, $\\Gamma$, and $\\Theta$ given $Y$, which reflects how much we know about these quantities given the data we have observed. We write this distribution as $P(\\mu, \\sigma, \\Gamma, \\Theta \\mid Y)$.\n", + "\n", + "The most general way to learn about the posterior distribution is to sample from it. This can be challenging, particularly in problems that are very high dimensional (see your answer to the question above). One strategy for for sampling from high-dimensional distributions is **Gibbs sampling**, which we discussed in class and lab.\n", + "\n", + "Gibbs sampling breaks down the posterior probability distribution into blocks of unknowns, and samples iteratively from each block assuming that the values of the other blocks (and the data) are known and fixed. In this case, we will break down the posterior distribution into blocks of $\\mu$, $\\sigma$, each vector $\\gamma_m$, and each vector $\\theta_u$. We have already implemented the draws for $\\mu$ and $\\sigma$. You will need to implement the draws for each $\\gamma_m$ and each $\\theta_u$. Luckily, the structures of these draws are similar, so you will only need to implement two functions.\n", + "\n", + "First, we'll derive the form of the draws below. Note that you don't need to be able to follow these derivations fully -- you'll just need to be able to use the result at the end.\n", + "\n", + "####Distribution of $\\gamma_{m'}$ given $Y, \\mu, \\sigma, \\Gamma_{-m'}, \\Theta$####\n", + "\n", + "Intuitively, this is the distribution of the item-specific parameters for item $m'$, imagining that all of the other unknowns are fixed.\n", + "\n", + "More precisely, we want to draw from the distribution of $\\gamma_{m'}$ conditional on the data $Y$ and all other unknowns -- that is, $\\mu$, $\\sigma$, all of $\\Theta$, and all of $\\Gamma$ except for $\\gamma_{m'}$, which we denote $\\Gamma_{-m}$.\n", + "\n", + "Note that in the model specification above, the only places that $\\gamma_{m'}$ appears are in the regression equations for each $Y_{um}$ that involves item $m'$. If we write out just these equations, we get a system of the following form,\n", + "\n", + "$$Y_{um'} = \\mu + \\theta_{u}[0] + \\gamma_{m'}[0] + \\theta_{u}[1:]^{\\top}\\gamma_{m'}[1:] + \\epsilon_{um'},$$\n", + "\n", + "with one equation for each $u$ that rated item $m'$. Now, because \n", + "\n", + "If we move all of the fully known terms to the left-hand side, we obtain the system:\n", + "\n", + "$$Y_{um'} - \\mu - \\theta_{u}[0] = \\gamma_{m'}[0] + \\theta_{u}[1:]^{\\top}\\gamma_{m'}[1:] + \\epsilon_{um'}.$$\n", + "\n", + "Notice that, because we assume that $\\theta_{u}$ is known, this equation now fits cleanly into the form of a linear regression, where $\\gamma_{m'}$ is the vector of unknown coefficients. This means that the posterior distribution for $\\gamma_{m'}$ conditional on everything else is the same as the posterior for the coefficients of a Bayesian linear regression of $(Y_{um'} - \\mu - \\theta_{u}[0])$ on $\\theta_{u}[1:]$ and an intercept.\n", + "\n", + "Let's denote the set of users who rated item $m'$ as $(u_1, \\cdots, u_g)$. Then, we can define the following vector and matrix:\n", + "\n", + "\\begin{align*}\n", + "Y_{m'} = \\left(\\begin{array}{c} Y_{u_1m'}-\\mu-\\theta_{u_1}[0]\\\\ \\vdots \\\\ Y_{u_gm'}-\\mu-\\theta_{u_g}[0]\\end{array}\\right), \\qquad\n", + "X_{m'} &= \\left(\\begin{array}{cc} 1 & \\theta_{u_1}[1:]^\\top \\\\ \\vdots & \\vdots \\\\ 1 & \\theta_{u_g}[1:]^\\top\\end{array}\\right),\n", + "\\end{align*}\n", + "\n", + "where $Y_{m'}$ is a vector of length $g$ and $X_{m'}$ is a $g \\times L+1$ matrix.\n", + "\n", + "The draw from $\\gamma_{m'}$ given everything else then has the form:\n", + "$$ \\gamma_{m'} \\mid Y, \\mu, \\sigma, \\Gamma_{-m'}, \\Theta \\sim MVN\\left(Q_{m'}^{-1} \\frac{1}{\\sigma^2}X_{m'}^\\top Y_{m'}, Q_{m'}^{-1}\\right)$$\n", + "where\n", + "$$ Q_{m'} = \\left(\\frac{1}{\\sigma^2}X_{m'}^\\top X_{m'} + \\Lambda_\\gamma\\right).$$\n", + "\n", + "#### Distribution of $\\theta_{u'}$ given $Y, \\mu, \\sigma, \\Gamma, \\Theta_{-u'}$####\n", + "\n", + "Intuitively, this is the distribution of the user-specific parameters for user $u'$, imagining that all of the other unknowns are fixed.\n", + "\n", + "We can use a very similar argument to the one above. We can denote the set of items rated by user $u'$ as $(m_1, \\cdots, m_g)$ and define the vector and matrix:\n", + "\\begin{align*}\n", + "Y_{u'} = \\left(\\begin{array}{c} Y_{u'm_1}-\\mu-\\gamma_{m_1}[0] \\\\ \\vdots \\\\ Y_{u'm_g}-\\mu-\\gamma_{m_g}[0]\\end{array}\\right), \\qquad\n", + "X_{u'} &= \\left(\\begin{array}{cc} 1 & \\gamma_{m_1}[1:]^\\top \\\\ \\vdots & \\vdots \\\\ 1 & \\gamma_{m_g}[1:]^\\top\\end{array}\\right),\n", + "\\end{align*}\n", + "\n", + "where $Y_{u'}$ is a vector of length $g$ and $X_{u'}$ is a $g \\times L+1$ matrix.\n", + "\n", + "the draw from $\\theta_{u'}$ given everything else has the form:\n", + "$$ \\theta_{u'} \\mid Y, \\mu, \\sigma, \\Gamma, \\Theta_{-u'} \\sim MVN\\left(Q_{u'}^{-1} \\frac{1}{\\sigma^2}X_{u'}^\\top Y_{u'}, Q_{u'}^{-1}\\right)$$\n", + "where\n", + "$$ Q_{u'}= \\left(\\frac{1}{\\sigma^2}X_{u'}^\\top X_{u'} + \\Lambda_\\theta\\right).$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.2** We will only ask you to implement a tiny portion of the Gibbs sampler. Complete the following functions that implement the conditional posterior draws for $\\gamma_m$ and $\\theta_u$ derived above.\n", + "\n", + "**Hint**: `np.random.multivariate_normal` is a good function to know." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "gamma_m_draw\n", + "\n", + "Draw a single sample from the conditional posterior distribution\n", + "of gamma_m.\n", + "\n", + "Inputs\n", + "-------\n", + "X_m: A g-by-L+1 matrix, defined above. \n", + "Y_m: A 1D vector of length g, defined above.\n", + "sig2: Residual _variance_, as defined above.\n", + "Lambda_gamma: Prior precision matrix.\n", + "\n", + "Outputs\n", + "--------\n", + "Single draw from conditional posterior, defined above.\n", + "\"\"\"\n", + "#Item-specific parameters given all else\n", + "#your code here\n", + "def gamma_m_draw(X_m, Y_m, sig2, Lambda_gamma):\n", + "\n", + " #Compute matrices that define conditional posterior.\n", + " Q_m_inv = np.linalg.inv(np.dot(X_m.T, X_m)/sig2+Lambda_gamma)\n", + " XtY = np.dot(X_m.T, Y_m)\n", + "\n", + " #Draw item-specific parameters.\n", + " return np.random.multivariate_normal(np.dot(Q_m_inv, XtY)/sig2, Q_m_inv)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 40 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "theta_u_draw\n", + "\n", + "Draw a single sample from the conditional posterior distribution\n", + "of gamma_m.\n", + "\n", + "Inputs\n", + "-------\n", + "X_u: A g-by-L+1 matrix, defined above. \n", + "Y_u: A 1D vector of length g, defined above.\n", + "sig2: Residual _variance_, as defined above.\n", + "Lambda_theta: Prior precision matrix.\n", + "\n", + "Outputs\n", + "--------\n", + "Single draw from conditional posterior, defined above.\n", + "\"\"\"\n", + "#User-specific parameters given all else\n", + "#your code here\n", + "def theta_u_draw(X_u, Y_u, sig2, Lambda_theta):\n", + " #Compute matrices that define conditional posterior.\n", + " Q_u_inv = np.linalg.inv(np.dot(X_u.T, X_u)/sig2+Lambda_theta)\n", + " XtY = np.dot(X_u.T, Y_u)\n", + " \n", + " #Draw the user-specific parameters\n", + " return np.random.multivariate_normal(np.dot(Q_u_inv, XtY)/sig2, Q_u_inv)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 41 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is the Gibbs sampler skeleton that your functions fit into. Look over the structure to see how for each draw from the posterior, the sampler iterates through $\\mu$, $\\sigma$, $\\gamma_m$ for each item, and $\\theta_u$ for each user." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "factor_gibbs\n", + "\n", + "Runs a gibbs sampler to infer mean, variance, user-specific, and item-specific\n", + "parameters.\n", + "\n", + "Inputs\n", + "-------\n", + "data: A dataframe containing ratings data.\n", + "L: Dimension of latent factors.\n", + "maxit: Number of samples to draw from posterior.\n", + "Lambda_theta_diag: Hyperparameter controlling regularization of Theta.\n", + "Lambda_gamma_diag: Hyperparameter controlling regularization of Gamma.\n", + "progress: if true, print iteration number every 100 iterations.\n", + "\n", + "Outputs\n", + "--------\n", + "Dictionary with elements\n", + "mu: Draws of mu. 1D array of length maxiter.\n", + "sig2: Draws of sig2, residual _variance_. 1D array of length maxiter.\n", + "theta: Draws of Theta. U-by-L-by-maxiter array.\n", + "gamma: Draws of Gamma. M-by-L-by-maxiter array.\n", + "EY: Draws of fitted values of Y. N-by-maxiter array.\n", + "\"\"\"\n", + "def factor_gibbs(data, L, maxit, Lambda_theta_diag, Lambda_gamma_diag, progress=True):\n", + " data = data.copy()\n", + " N = data.shape[0]\n", + "\n", + " #Create indices that allow us to map users and restaurants to rows\n", + " #in parameter vectors.\n", + " uusers, uidx = np.unique(data.user_id, return_inverse=True)\n", + " uitems, midx = np.unique(data.business_id, return_inverse=True)\n", + "\n", + " nusers = uusers.size\n", + " nitems = uitems.size\n", + "\n", + " #Add numerical indices to dataframe.\n", + " data[\"uidx\"] = uidx\n", + " data[\"midx\"] = midx\n", + "\n", + " #Group observations by user and by business.\n", + " ugroups = data.groupby(\"uidx\")\n", + " mgroups = data.groupby(\"midx\")\n", + "\n", + " all_avg = data.stars.mean()\n", + " u_avg = ugroups.stars.mean()\n", + " m_avg = mgroups.stars.mean()\n", + "\n", + " #Initialize parameters and set up data structures for\n", + " #holding draws.\n", + " #Overall mean\n", + " mu = all_avg\n", + " mu_draws = np.zeros(maxit)\n", + " #Residual variance\n", + " sig2 = 0.5\n", + " sig2_draws = np.zeros(maxit)\n", + "\n", + " #Matrix of user-specific bias and L latent factors.\n", + " theta = np.zeros([nusers, L+1])\n", + " theta[:,0] = u_avg-all_avg\n", + " theta_draws = np.zeros([nusers, L+1, maxit])\n", + "\n", + " #Matrix of item-specific bias and L latent factors.\n", + " gamma = np.zeros([nitems, L+1])\n", + " gamma[:,0] = m_avg-all_avg\n", + " gamma_draws = np.zeros([nitems, L+1, maxit])\n", + "\n", + " #Matrix for holding the expected number of stars\n", + " #for each observation at each draw from the posterior.\n", + " EY_draws = np.zeros([data.shape[0], maxit])\n", + "\n", + " #Inverse covariance matrices from the prior on each theta_u\n", + " #and gamma_b. These are diagonal, like Ridge regression.\n", + " Lambda_theta = np.eye(L+1)*Lambda_theta_diag\n", + " Lambda_gamma = np.eye(L+1)*Lambda_gamma_diag\n", + "\n", + " #Main sampler code\n", + " for i in range(maxit):\n", + " if i%100==0 and progress:\n", + " print i\n", + "\n", + " #The entire regression equation except for the overall mean.\n", + " nomu = np.sum(theta[data.uidx,1:]*gamma[data.midx,1:], axis=1) +\\\n", + " theta[data.uidx,0] + gamma[data.midx,0]\n", + "\n", + " #Compute the expectation of each observation given the current\n", + " #parameter values.\n", + " EY_draws[:,i]=mu+nomu\n", + "\n", + " #Draw overall mean from a normal distribution\n", + " mu = np.random.normal(np.mean(data.stars-nomu), np.sqrt(sig2/N))\n", + " #Draw overall residual variance from a scaled inverse-Chi squared distribution.\n", + " sig2 = np.sum(np.power(data.stars-nomu-mu,2))/np.random.chisquare(N-2)\n", + " \n", + " #For each item\n", + " for mi,itemdf in mgroups:\n", + " #Gather relevant observations, and subtract out overall mean and\n", + " #user-specific biases, which we are holding fixed.\n", + " Y_m = itemdf.stars-mu-theta[itemdf.uidx,0]\n", + " #Build the regression design matrix implied by holding user factors\n", + " #fixed.\n", + " X_m = np.hstack((np.ones([itemdf.shape[0],1]),\n", + " theta[itemdf.uidx,1:]))\n", + " gamma[mi,:] = gamma_m_draw(X_m, Y_m, sig2, Lambda_gamma)\n", + " \n", + " #For each user\n", + " for ui,userdf in ugroups:\n", + " #Gather relevant observations, and subtract out overall mean and\n", + " #business-specific biases, which we are holding fixed.\n", + " Y_u = userdf.stars-mu-gamma[userdf.midx,0]\n", + " #Build the regression design matrix implied by holding business factors\n", + " #fixed.\n", + " X_u = np.hstack((np.ones([userdf.shape[0],1]),\n", + " gamma[userdf.midx,1:]))\n", + " \n", + " theta[ui,:] = theta_u_draw(X_u, Y_u, sig2, Lambda_theta)\n", + "\n", + " #Record draws\n", + " mu_draws[i] = mu\n", + " sig2_draws[i] = sig2\n", + " theta_draws[:,:,i] = theta\n", + " gamma_draws[:,:,i] = gamma\n", + "\n", + " return {\"mu\": mu_draws, \"sig2\": sig2_draws,\n", + " \"theta\": theta_draws, \"gamma\": gamma_draws,\n", + " \"EY\": EY_draws}" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 42 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Posterior Summaries###\n", + "\n", + "Once you have posterior draws from the sampler, the most natural thing to do is to compute the **posterior mean** of each quantity you are intersted in. To do this, we simply need to take the average value of each quantity across the samples drawn from the sampler. Before taking the average, however, we will want to ignore the first 20-30% of samples because these correspond the **burnin period**, the time during which the sampler is still looking for the main meat of the distribution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "####Ok it's time to recommend!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.3** Now that you have the Gibbs sampler, draw 1000 samples from the posterior distribution using a two-dimensional latent factor and prior precisions `Lambda_theta_diag` and `Lambda_gamma_diag` both equal to 0.1.\n", + "\n", + "Compute the posterior mean of the fitted values for each $Y_{um}$, eliminating the first 200 samples. Call these the `prediction`. These constitute our recommendations. True to the bayesian paradigm, we dont just have mean predictions, but entire distributions. But currently we are only interested in the means." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "gibbs_out = factor_gibbs(smalldf, 2, 1000, 0.1, 0.1)\n", + "burnin = 200\n", + "predicted=np.mean(gibbs_out['EY'][:,burnin:], axis=1)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "0\n", + "100" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "200" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "300" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "400" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "500" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "600" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "700" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "800" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "900" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + } + ], + "prompt_number": 43 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the predictions against the observed data.You can use the `compare_results` function defined in the previous section. How do the fitted values compare to those from the KNN procedure?" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "compare_results(smalldf.stars.values, predicted, ylow=1, yhigh=5, title=\"From Gibbs Sampler\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "fraction between -15 and 15 rating 1.0\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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g4NGzlvBm5ckIAjfw1et5xEat5vt/ZUwf0QhYrbxDsLSU15h5ewM9ezZ/u4yG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kAAAg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\nfoiysjLe0nczi8WCD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+ "text": [ + "" + ] + } + ], + "prompt_number": 44 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*your answer here*\n", + "\n", + "The results from the latent factor model appear to be better-behaved than those from the KNN procedure (with negative similarities) since there are no extreme values. In terms of the non-extreme values from the KNN procedure, the latent factor model appears to make similar predictions to the KNN procedure when k = 3., Specifically, the average prediction line for each class is too compressed toward the total data mean of around 3.8, meaning that we are in the bias limit where we are pooling too much information between ratings. Thus, we are overpredicting low ratings and underpredicting high ratings.\n", + "\n", + "(If we compare to the KNN procedure with strictly positive weights, as in the homework addendum, we see again that the recommenders make comparable predictions, although the latent factor model again appears to fit slightly better than at both the bias or variance limits of the KNN procedure).\n", + "\n", + "There is also a bias-variance tradeoff here. In this case, it appears that we have proposed a model that is too simple (thus close to the bias limit) because of how the ratings are pulled in toward the data mean. In this case, proposing more latent factors increases the flexibility of the model, and thus moves us toward the variance limit. Thus, the plot suggests that we may want to reduce the bias at the cost of increasing variance, for example by considering a latent factor model with more factors (say, $L = 15$) to obtain a better fit (see ADDENDUM below)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### IMPORTANT SOLUTION ADDENDUM" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "gibbs_out = factor_gibbs(smalldf, 15, 1000, 0.1, 0.1)\n", + "burnin = 200\n", + "predicted=np.mean(gibbs_out['EY'][:,burnin:], axis=1)\n", + "compare_results(smalldf.stars.values, predicted, ylow=1, yhigh=5, title=\"From Gibbs Sampler\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "0\n", + "100" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "200" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "300" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "400" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "500" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "600" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "700" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "800" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "900" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "fraction between -15 and 15 rating" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " 1.0\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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IW4ZSiv9sWkFs/FqsKMJONcT6bVt8vPQ4O0N2Njg5VXYvxcUqbMQsNTWV119/naVLl9K7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0cMoaSkJLi6ukIulyM4OJgfrnvCRFFEdHQ0YmJiMHZs/zvlDKtgduXKFUycOBFOTg9vjZs0aRLO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\n", + "text": [ + "" + ] + } + ], + "prompt_number": 45 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fit here looks much better, both in terms of tracking the green line and in terms of the within-group precision (as measured by the grey band). A model at the variance limit (that is, one that is extremely overfit) with low bias would have a mean line that tracks exactly with the green line and an almost non-existent gray area. To tell whether this plot represents simply a good prediction model or a one that is woefully overfit, we would need to look at out-of-sample data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Q4 Scaling Up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All our recommenders suffer from problems having to do with the fact that we subsetted an already sparse user-item matrix. The more items we have, the more items we may find in the vicinity of a given item, and thus we are likely to give a more robust average rating to the given item.\n", + "\n", + "In this problem we shall use Amazon Elastic Map-Reduce to tackle the entire user-restaurant matrix. We shall do this in two parts: we'll use MRJob locally on your machine to on the smaller data set to calclate the pearson database, and then we'll tackle the entire data set on Amazon.\n", + "\n", + "The larger set has 35000 users and 4500 items. Computing the 4500X4500 similarity matrix on one machine will be prohibitively expensive. Thus we'll adopt a strategy where we'll split the calculation over multiple machines using the map-reduce paradigm, with mappers and reducers working on multiple machines \n", + "\n", + "Then we calculate the k-nearest neighbors in the 'space' of the user: this involves a database lookup and an iteration over the items a user has rated. Since the latter is usually not a very large number, this computation can be managed on a front end machine (even if storing the database will take a lot of memory).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll first create subset data frames, which have just those columns which we will send to the map-reduce. We'll also strip out the header and index of the frame. The reason for doing this is: unless we pre-populate the machines on Amazon with software, we can *rely only on the regular python library, numpy, and scipy being there (and at python 2.6)*, and thus we will need to parse the csv file, line by line (`mrjob` uses hadoop's stream protocol and thus needs to be fed line by line)." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "subsetoffull=fulldf[['user_id','business_id', 'stars','business_avg','user_avg']]\n", + "subsetoffull.to_csv(\"subset-full.csv\", index=False, header=False)\n", + "subsetofsmall=smalldf[['user_id','business_id', 'stars','business_avg','user_avg']]\n", + "subsetofsmall.to_csv(\"subset-small.csv\", index=False, header=False)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 46 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Running mrjob locally\n", + "\n", + "`mrjob` scripts cannot be run from the ipython notebook, as they fork themselves on execution. Thus you must write the code for mrjob in a separate file which you must submit along with this homework, in the same folder as the python notebook file.\n", + "\n", + "If you have not done so already (you were supposed to do this as part of HW 0), you will first need to install `mrjob`. The appropriate equivalent of the following incantation should do the job:\n", + "\n", + " ~/anaconda/bin/pip install mrjob\n", + " \n", + "\n", + " \n", + "To familiarize yourself with the structure of an `mrjob` script, please read [this](http://mrjob.readthedocs.org/en/latest/guides/quickstart.html#writing-your-first-job) . Run the examples in that document to familiarize yourself with `mrjob`.\n", + "\n", + "The kind of script you will be writing is in the section \"Writing your second job\" in that document. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All mrjob tasks use the map-reduce strategy to divide up computation across computers. You should work through the mrjob tutorial to gain familiarity with this, but we\u2019ll also outline the basic process here:\n", + "\n", + "1. During the first map step, mrjob calls a mapper function with a key (which for the first step is None), and a value (which for the first step is a line of data from an input file). This function does whatever it wants with this data, and yields a key and value. The key is used in step 2 to gather up the values from all the different mappers into groups\n", + "\n", + "2. mrjob collects the outputs from all the mappers, and gathers them into subsets with the same key value (this is similar to what pandas.groupby does). It passes each of these subsets to a reducer (or \u201ccollector\u201d) function, whose job is to synthesize this list of grouped data into something useful (e.g., computing the mean of all the inputs). It then yields the key and reduced value. \n", + "\n", + "3. If there are any additional steps, mrjob feeds each output from a reducer function in step 2 to the next mapper. Otherwise, it prints the output.\n", + "\n", + "The point behind map-reduce is to agree upon a common framework to split up a large computational job into smaller tasks. mrjob then has a lot of freedom to organize how these tasks run in parallel, on many machines" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Writing your script" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**4.1** Write a MRJOB script, called `computesim.py`. The object of this script is to take a csv file and return a tuple `(rho, n_common)` as `calculate_similarity` for pairs of restaurants. See `skeleton.py` below for the SPEC of this file. Your job is to fill in those methods. You MUST use this skeleton.\n", + "\n", + "This script is to be run like so (substitute your own operating system's call):\n", + "\n", + " ~/anaconda/bin/python computesim.py subset-small.csv > output.small.local.txt\n", + "\n", + "Thus, when the script below is run in this fashion, mrjob will read the data line-by-line from subset-small.csv, and pass it to the first \"step\".\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "####Algorithm to calculate pearson similarities\n", + "\n", + "Here is the description of the algorithm for RestaurantSimilarities.\n", + "\n", + "Your code will have two steps. Each step will have a mapper and a reducer. These are described in turn here:\n", + "\n", + "1. `line_mapper` will split the line, yielding the `user_id` as key, and the rest as value. This method's implementation is provided for you.\n", + "\n", + "2. `users_items_collector` is a reducer. It is passed ALL mapper outputs corresponding to a particular `user_id`. Put these emissions into a list, and re-emit the `user_id` with this list.\n", + "\n", + "3. `pair_items_mapper` takes the `user_id` and the list. It dosent do anything with the `user_id`, however, it takes every combination (thus len(list) choose 2) of 2 `business_id`s from the passed on list (see combinations in itertools in the python documentation) and sends on the remaining information keyed on the tuple `(restaurant1, restaurant2)`. Be sure to handle the case where the restaurant id's are flipped: include them somehow under the same key.\n", + "\n", + "4. `calc_sim_collector` is passed ALL sent on list information for the pair of restaurants that was emitted in the previous step. Note that thse will come from different `user_id`s. This sort of collection is key to this style of programming. This list information should now correspond to all the common support of the two restaurants. Use this information to calculate this common support and the pearson similarity. Return the aforementioned tuple by yielding it keyed by the tuple of restaurants. This information will be sent to the output file. The output keys and values will both be in JSON format, separated by a tab.\n", + "\n", + "The output should be saved in a file via redirection as `output.small.local.txt`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "####Skeleton File for this problem\n", + "\n", + "You can access it [here](https://raw.github.com/cs109/content/master/skeleton.py) or just run the next cell to see it." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from pygments import highlight\n", + "from pygments.lexers import PythonLexer\n", + "from pygments.formatters import HtmlFormatter\n", + "from IPython.display import HTML\n", + "import urllib\n", + "skelcode = urllib.urlopen(\"https://raw.github.com/cs109/content/master/skeleton.py\").read()\n", + "skelhtml=highlight(skelcode, PythonLexer(), HtmlFormatter())\n", + "HTML(skelhtml)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
import numpy as np\n",
+        "\n",
+        "from mrjob.job import MRJob\n",
+        "from itertools import combinations, permutations\n",
+        "\n",
+        "from scipy.stats.stats import pearsonr\n",
+        "\n",
+        "\n",
+        "class RestaurantSimilarities(MRJob):\n",
+        "\n",
+        "    def steps(self):\n",
+        "        "the steps in the map-reduce process"\n",
+        "        thesteps = [\n",
+        "            self.mr(mapper=self.line_mapper, reducer=self.users_items_collector),\n",
+        "            self.mr(mapper=self.pair_items_mapper, reducer=self.calc_sim_collector)\n",
+        "        ]\n",
+        "        return thesteps\n",
+        "\n",
+        "    def line_mapper(self,_,line):\n",
+        "        "this is the complete implementation"\n",
+        "        user_id,business_id,stars,business_avg,user_avg=line.split(',')\n",
+        "        yield user_id, (business_id,stars,business_avg,user_avg)\n",
+        "\n",
+        "\n",
+        "    def users_items_collector(self, user_id, values):\n",
+        "        """\n",
+        "        #iterate over the list of tuples yielded in the previous mapper\n",
+        "        #and append them to an array of rating information\n",
+        "        """\n",
+        "        pass\n",
+        "\n",
+        "\n",
+        "    def pair_items_mapper(self, user_id, values):\n",
+        "        """\n",
+        "        ignoring the user_id key, take all combinations of business pairs\n",
+        "        and yield as key the pair id, and as value the pair rating information\n",
+        "        """\n",
+        "\t   pass #your code here\n",
+        "\n",
+        "    def calc_sim_collector(self, key, values):\n",
+        "        """\n",
+        "        Pick up the information from the previous yield as shown. Compute\n",
+        "        the pearson correlation and yield the final information as in the\n",
+        "        last line here.\n",
+        "        """\n",
+        "        (rest1, rest2), common_ratings = key, values\n",
+        "\t    #your code here\n",
+        "        yield (rest1, rest2), (rho, n_common)\n",
+        "\n",
+        "\n",
+        "#Below MUST be there for things to work\n",
+        "if __name__ == '__main__':\n",
+        "    RestaurantSimilarities.run()\n",
+        "
\n" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 47, + "text": [ + "" + ] + } + ], + "prompt_number": 47 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Explanation for those funny `yield` keywords\n", + "\n", + "The functions above \u201cyield\u201d values, and do not \u201creturn\u201d them. They are **generators**. Here is an example:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def upper_generator(words):\n", + " for word in words:\n", + " yield word.upper()\n", + "\n", + "words = ['a', 'couple', 'of', 'words', 'to', 'process']\n", + "\n", + "print upper_generator(words)\n", + "print list(upper_generator(words))\n", + "for u in upper_generator(words):\n", + " print u\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "['A', 'COUPLE', 'OF', 'WORDS', 'TO', 'PROCESS']\n", + "A\n", + "COUPLE\n", + "OF\n", + "WORDS\n", + "TO\n", + "PROCESS\n" + ] + } + ], + "prompt_number": 48 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can read more [here](http://nedbatchelder.com/text/iter.html). Also see Thu Oct 17th's class video for information about classes and generators." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Include `computesim.py` in your submission in the same folder as the notebook. Uncommenting and running the following cell should **output your code in here**." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "thecode = open(\"computesim.py\").read()\n", + "thehtml=highlight(thecode, PythonLexer(), HtmlFormatter())\n", + "HTML(thehtml)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
import numpy as np\n",
+        "\n",
+        "from mrjob.job import MRJob\n",
+        "from itertools import combinations, permutations\n",
+        "from math import sqrt\n",
+        "\n",
+        "from scipy.stats.stats import pearsonr\n",
+        "\n",
+        "class RestaurantSimilarities(MRJob):\n",
+        "\n",
+        "    def steps(self):\n",
+        "        thesteps = [\n",
+        "            self.mr(mapper=self.line_mapper, reducer=self.users_items_collector),\n",
+        "            self.mr(mapper=self.pair_items_mapper, reducer=self.calc_sim_collector)\n",
+        "        ]\n",
+        "        return thesteps\n",
+        "\n",
+        "    def line_mapper(self,_,line):\n",
+        "        user_id,business_id,stars,business_avg,user_avg=line.split(',')\n",
+        "        yield user_id, (business_id,stars,business_avg,user_avg)\n",
+        "\n",
+        "    def users_items_collector(self, user_id, values):\n",
+        "        ratings=[]\n",
+        "        for business_id,stars,business_avg,user_avg in values:\n",
+        "            ratings.append((business_id,(stars, user_avg)))\n",
+        "        yield user_id, ratings\n",
+        "\n",
+        "    def pair_items_mapper(self, user_id, values):\n",
+        "        ratings = values\n",
+        "        for biz1tuple, biz2tuple in combinations(ratings, 2):\n",
+        "            biz1, biz1r=biz1tuple\n",
+        "            biz2, biz2r=biz2tuple\n",
+        "            if biz1 <= biz2 :\n",
+        "                yield (biz1, biz2), (biz1r, biz2r)\n",
+        "            else:\n",
+        "                yield (biz2, biz1), (biz2r, biz1r)\n",
+        "\n",
+        "    def calc_sim_collector(self, key, values):\n",
+        "        (rest1, rest2), common_ratings = key, values\n",
+        "        diff1=[]\n",
+        "        diff2=[]\n",
+        "        n_common=0\n",
+        "\n",
+        "\n",
+        "        for rt1, rt2 in common_ratings:\n",
+        "            diff1.append(float(rt1[0])-float(rt1[1]))\n",
+        "            diff2.append(float(rt2[0])-float(rt2[1]))\n",
+        "            n_common=n_common+1\n",
+        "        if n_common==0:\n",
+        "            rho=0.\n",
+        "        else:\n",
+        "            rho=pearsonr(diff1, diff2)[0]\n",
+        "            if np.isnan(rho):\n",
+        "                rho=0.\n",
+        "        yield (rest1, rest2), (rho, n_common)\n",
+        "\n",
+        "\n",
+        "#Below MUST be there for things to work!\n",
+        "if __name__ == '__main__':\n",
+        "    RestaurantSimilarities.run()\n",
+        "
\n" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 49, + "text": [ + "" + ] + } + ], + "prompt_number": 49 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Checking the results\n", + "\n", + "Let us load the data from the file" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "output_small_local=[[json.loads(j) for j in line.strip().split(\"\\t\")] for line in open(\"./output.small.local.txt\")]\n", + "output_small_local[0]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 50, + "text": [ + "[[u'-4A5xmN21zi_TXnUESauUQ', u'-AAig9FG0s8gYE4f8GfowQ'],\n", + " [0.384365693729571, 5]]" + ] + } + ], + "prompt_number": 50 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will Implement a function `make_database_from_pairs` which takes a dataframe of restaurants `smalldf` and the output parsed in the previous command to create the database like before. By the nature of the map-reduce algorithms these only contain those restaurant pairs with common support. The `Database` constructor initializes the remaining similarities to 0.\n", + "\n", + "The function will take the dataframe and `bizpairs` obtained by parsing the EMR output file which have the key of business pairs and value the pair of pearson correlation and `n_common`. It will return an instance of the `Database` class.\n", + "\n", + "This function will take a long time to run on large data sets.\n" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def make_database_from_pairs(df, bizpairs):\n", + " \"\"\"\n", + " make the database from the pairs returned from mrjob.\n", + " df is the dataframe, smalldf or fulldf.\n", + " bizpairs are a list of elements, each of which is a list of two\n", + " lists. The first of these lists has the two business id's, while\n", + " the second has the similarity and the common support\n", + " Returns an instance of the Database class.\n", + " \"\"\"\n", + " dbase=Database(df)\n", + " cache={}\n", + " for bp,corrs in bizpairs:\n", + " b1,b2=bp\n", + " i1=dbase.uniquebizids[b1]\n", + " i2=dbase.uniquebizids[b2]\n", + " sim,nsup=corrs\n", + " dbase.database_sim[i1][i2]=sim\n", + " dbase.database_sim[i2][i1]=sim\n", + " dbase.database_sup[i1][i2]=nsup\n", + " dbase.database_sup[i2][i1]=nsup\n", + " if cache.has_key(b1):\n", + " nsup1=cache[b1]\n", + " else:\n", + " nsup1=dbase.df[dbase.df.business_id==b1].user_id.count()\n", + " cache[b1]=nsup1\n", + " if cache.has_key(b2):\n", + " nsup2=cache[b2]\n", + " else:\n", + " nsup2=dbase.df[dbase.df.business_id==b2].user_id.count()\n", + " cache[b2]=nsup2\n", + " dbase.database_sim[i1][i1]=1.0\n", + " dbase.database_sim[i2][i2]=1.0\n", + " dbase.database_sup[i1][i1]=nsup1\n", + " dbase.database_sup[i2][i2]=nsup2\n", + " return dbase" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 51 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will store the output in variable `db_mrjob_local`." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "db_mrjob_local=make_database_from_pairs(smalldf, output_small_local)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 52 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We print a pair to see that our answers are identical." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print db.get(\"zruUQvFySeXyEd7_rQixBg\", \"z3yFuLVrmH-3RJruPEMYKw\")\n", + "print db_mrjob_local.get(\"zruUQvFySeXyEd7_rQixBg\", \"z3yFuLVrmH-3RJruPEMYKw\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(0.39904554525734559, 7)\n", + "(0.39904554525734542, 7)\n" + ] + } + ], + "prompt_number": 53 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**4.2** Lets test that our results are overall the same as before" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "sums=0.\n", + "count=0\n", + "for k in db.uniquebizids.keys():\n", + " for k2 in db.uniquebizids.keys():\n", + " count=count+1\n", + " sums=sums+db.get(k,k2)[0]-db_mrjob_local.get(k,k2)[0]\n", + "print sums, count" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "-8.65973959208e-15 29584\n" + ] + } + ], + "prompt_number": 54 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Running on Amazon Elastic Map Reduce(EMR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, we shall shift to running on Amazon EMR. \n", + "\n", + "------------\n", + "\n", + "*Read [this document](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/InstructionsForAmazonEMR.ipynb) for instructions on how to set yourself up on Amazon.*\n", + "\n", + "------------\n", + "\n", + "#### Reproduce the results with the smaller file on EMR\n", + "\n", + "Test the smaller file and make sure it has the same results. For example, you could use the incantation:\n", + "\n", + " ~/anaconda/bin/python computesim.py -r emr --num-ec2-instances 2 subset-small.csv > output.small.emr.txt\n", + "\n", + "You do **NOT** need to submit any results from that exploration to us.\n", + "\n", + "**Important**: Please always make sure that your code is bug free, before actually submitting it to amazon. Try to run the job locally first and see if it produces the desired result. Then, if this worked, you are ready to proceed to the cloud. The homework problems are small and your free credit should provide you with a lot of room for running and testing on Amazon. However, it is your responsibility to make sure the jobs terminate properly and do not cause excessive costs.\n", + "\n", + "You can always monitor your currently running jobs (in the US-East sector) using [this overview at region US-EAST-1](https://console.aws.amazon.com/elasticmapreduce/home?region=us-east-1) of your MapReduce job flows." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Running the larger job" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**4.3** Run the script on the larger file `subset-full.csv`. Use between 4-8 instances on EMR on Amazon. Save the output in `output.full.emr.txt`. Your incantation will be something like:\n", + "\n", + " ~/anaconda/bin/python computesim.py -r emr --num-ec2-instances 5 subset-full.csv > output.full.emr.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might elect to [save the file on S3](http://mrjob.readthedocs.org/en/latest/guides/emr-quickstart.html#sending-output-to-a-specific-place) and bring it over manually.\n", + "\n", + "Try and think about what size job would be best to run on Amazon, given that there is a setup time. There is a way to persistently set up machines (the mrjob documentation provides the details), but then remember you will be billed for that setup and need to monitor it. However, a persistent setup might come useful for your projects." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Loading the full output from EMR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets load the output in. **CAUTION** The next two cells will also take a lot of time to run and load. " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "output_full_emr=[[json.loads(j) for j in l.strip().split(\"\\t\")] for l in open(\"./output.full.emr.txt\")]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 55 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function will take a very long time to run, on the order of 5 minutes or more, depending on your computer" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "dbfull=make_database_from_pairs(fulldf, output_full_emr)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 56 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**4.4** For `testuserid`, once again, print out the ratings using the `bizs` list as before. How have they changed with respect to Question 2? Why might this be?" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "print \"for user\",usernamefromid(fulldf,testuserid), 'avg', fulldf[fulldf.user_id==testuserid].stars.mean() \n", + "for i in bizs:\n", + " print \"=========\"\n", + " print biznamefromid(fulldf, i), i\n", + " print rating(fulldf, dbfull, i, testuserid, k=7, reg=3.) \n", + " u,a=get_other_ratings(i, testuserid, fulldf)\n", + " print \"User Score:\",u,\"Avg score\",a" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "for user Vern avg 3.58227848101\n", + "=========\n", + "Local Breeze" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " GAPqG0WNBBidKeZTMpEZ-w\n", + "4.81062901451" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "User Score: 5 Avg score 3.86363636364\n", + "=========\n", + "Carly's Bistro zmFc8M-hS4uuyY0hklIpoQ\n", + "4.79797424984" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "User Score: 5 Avg score 3.65079365079\n", + "=========\n", + "Tee Pee Mexican Food" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " soiGohHtWOltGeomkSxzEw\n", + "4.23927633965" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "User Score: 5 Avg score 3.13636363636\n", + "=========\n", + "District American Kitchen and Wine Bar 9ziO3NpoNTKHvIKCBFB_fQ\n", + "4.08622622369" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "User Score: 4 Avg score 3.575\n", + "=========\n", + "Los Reyes de la Torta bzDs0u8I-z231QVdIQWkrA\n", + "3.94718985123" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "User Score: 4 Avg score 4.24796747967\n" + ] + } + ], + "prompt_number": 57 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*your answer here*\n", + "\n", + "I copy here the results from the small set:\n", + "\n", + " for user Vern avg 3.5652173913\n", + " ----------------------------------\n", + " Local Breeze\n", + " Predicted Rating: 4.2280987611\n", + " Actual User Rating: 5 Avg Rating 4.0\n", + " ----------------------------------\n", + " Carly's Bistro\n", + " Predicted Rating: 3.99008654065\n", + " Actual User Rating: 5 Avg Rating 3.5\n", + " ----------------------------------\n", + " Tee Pee Mexican Food\n", + " Predicted Rating: 3.52640184162\n", + " Actual User Rating: 5 Avg Rating 3.04347826087\n", + " ----------------------------------\n", + " District American Kitchen and Wine Bar\n", + " Predicted Rating: 3.80281696528\n", + " Actual User Rating: 4 Avg Rating 3.55263157895\n", + " ----------------------------------\n", + " Los Reyes de la Torta\n", + " Predicted Rating: 3.41514298977\n", + " Actual User Rating: 4 Avg Rating 4.13157894737\n", + " \n", + "One can see that the predicted rating is much closer to the actual one in the larger set. The reason for this:\n", + "We are still working with user Vern from the previous set, so we are choosing a \"prolific\" user who is likely to have a dense neighborhood of restaurants in the larger set (by the choice of our cut to create the small set).\n", + "\n", + "Note that if we evalusted our predictions on the entire data set, rather than cherry-picking users like Vern, our average prediction error may become worse. There is because the larger data set is more likely to be of higher sparsity, as we will include more users who rated very few restaurants." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**4.5** Outline another `step` (in words) in the mrjob map-reduce class to implement a simple but scalable recommender of the global type that we did in Question 1.5 to 1.7." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*your answer here*\n", + "\n", + "We can add one additional map and one additional reduce step to achieve 1.5 and 1.6.\n", + "\n", + "MAP STEP:\n", + " \n", + " def ranking_mapper(self, restaurants, values):\n", + " sim, n_common = values\n", + " rest1, rest2 = restaurants\n", + " if int(n_common) > 0:\n", + " yield (rest1, sim), (rest2, n_common)\n", + "\n", + "\n", + "REDUCE STEP:\n", + " \n", + " def top_similar_collector(self, key, values):\n", + " rest1, sim = key\n", + " for rest2, n_common in values:\n", + " yield None, (rest1, rest2, sim, n_common)\n", + " \n", + "Note that by default mrjob does an alphanumeric sort on sim, which is not what we want. Indeed it is complicated, but possible to have this addition run locally (on mac/linux) you would need to specify the sort binary as `sort -nr`. On Hadoop, the parameters outlined [here](http://pythonhosted.org/mrjob/job.html#mrjob.job.MRJob.jobconf) can be used. Of-course, you could do the final sorting on a front end machine anyway.\n", + "\n", + "UPDATE: Brandon suggests an even simpler solution that will work both locally and on EMR, without having to use any hadoop sorting specifics. \n", + "We change to:\n", + "\n", + "MAP STEP:\n", + " \n", + " def ranking_mapper(self, restaurants, values):\n", + " sim, n_common = values\n", + " rest1, rest2 = restaurants\n", + " if int(n_common) > 0:\n", + " yield (rest1), (sim, rest2, n_common)\n", + "\n", + "\n", + "REDUCE STEP:\n", + " \n", + " def top_similar_collector(self, key, values):\n", + " rest1 = key\n", + " for sim, rest2, n_common in sorted(values, reverse=True):\n", + " yield None, (rest1, rest2, sim, n_common)\n", + " \n", + "Full code:\n" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "thecode = open(\"computesim2.py\").read()\n", + "thehtml=highlight(thecode, PythonLexer(), HtmlFormatter())\n", + "HTML(thehtml)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
import numpy as np\n",
+        "\n",
+        "from mrjob.job import MRJob\n",
+        "from itertools import combinations, permutations\n",
+        "from math import sqrt\n",
+        "import mrjob\n",
+        "\n",
+        "from scipy.stats.stats import pearsonr\n",
+        "\n",
+        "class RestaurantSimilarities(MRJob):\n",
+        "\n",
+        "    def steps(self):\n",
+        "        thesteps = [\n",
+        "            self.mr(mapper=self.line_mapper, reducer=self.users_items_collector),\n",
+        "            self.mr(mapper=self.pair_items_mapper, reducer=self.calc_sim_collector),\n",
+        "            self.mr(mapper=self.ranking_mapper, reducer=self.top_similar_collector)\n",
+        "        ]\n",
+        "        return thesteps\n",
+        "\n",
+        "    def line_mapper(self,_,line):\n",
+        "        user_id,business_id,stars,business_avg,user_avg=line.split(',')\n",
+        "        yield user_id, (business_id,stars,business_avg,user_avg)\n",
+        "\n",
+        "    def users_items_collector(self, user_id, values):\n",
+        "        ratings=[]\n",
+        "        for business_id,stars,business_avg,user_avg in values:\n",
+        "            ratings.append((business_id,(stars, user_avg)))\n",
+        "        yield user_id, ratings\n",
+        "\n",
+        "    def pair_items_mapper(self, user_id, values):\n",
+        "        ratings = values\n",
+        "        for biz1tuple, biz2tuple in combinations(ratings, 2):\n",
+        "            biz1, biz1r=biz1tuple\n",
+        "            biz2, biz2r=biz2tuple\n",
+        "            if biz1 <= biz2 :\n",
+        "                yield (biz1, biz2), (biz1r, biz2r)\n",
+        "            else:\n",
+        "                yield (biz2, biz1), (biz2r, biz1r)\n",
+        "\n",
+        "    def calc_sim_collector(self, key, values):\n",
+        "        (rest1, rest2), common_ratings = key, values\n",
+        "        diff1=[]\n",
+        "        diff2=[]\n",
+        "        n_common=0\n",
+        "\n",
+        "\n",
+        "        for rt1, rt2 in common_ratings:\n",
+        "            diff1.append(float(rt1[0])-float(rt1[1]))\n",
+        "            diff2.append(float(rt2[0])-float(rt2[1]))\n",
+        "            n_common=n_common+1\n",
+        "        if n_common==0:\n",
+        "            rho=0.\n",
+        "        else:\n",
+        "            rho=pearsonr(diff1, diff2)[0]\n",
+        "            if np.isnan(rho):\n",
+        "                rho=0.\n",
+        "        yield (rest1, rest2), (rho, n_common)\n",
+        "\n",
+        "    def ranking_mapper(self, restaurants, values):\n",
+        "        sim, n_common = values\n",
+        "        rest1, rest2 = restaurants\n",
+        "        if int(n_common) > 0:\n",
+        "            yield (rest1), (sim, rest2, n_common)\n",
+        "\n",
+        "    def top_similar_collector(self, key, values):\n",
+        "        rest1 = key\n",
+        "        for sim, rest2, n_common in sorted(values, reverse=True):\n",
+        "            yield None, (rest1, rest2, sim, n_common)\n",
+        "\n",
+        "#Below MUST be there for things to work!\n",
+        "if __name__ == '__main__':\n",
+        "    RestaurantSimilarities.run()\n",
+        "
\n" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 4, + "text": [ + "" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To complete the recommender to the level of 1.7, we can now take the user's top restaurants, and repeat the process with the output of `top_similar_collector`, which could be stored in a hash table with restaurant keys and arrays of nearest neighbors." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Submission Instructions:\n", + "\n", + "Restart and run your notebook one last time (you do not have to rerun the Amazon EMR script computesim.py), to make sure the output from each cell is up to date. To submit your homework, create a folder named lastname_firstinitial_hw4 and place your solutions in the folder. Double check that the file is still called HW4.ipynb, and that it contains your code. Also include the `computesim.py` script and the `output.small.local.txt` data file. Do **NOT** include the data file `output.full.emr.txt` from the larger run (its huge, so we will check your answers to 4.4 instead). Compress the folder (please use .zip compression) and submit to the CS109 dropbox in the appropriate folder. If we cannot access your work because these directions are not followed correctly, we will not grade your work!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###FINI\n", + "\n", + "You have developed all kinds of recommenders. We hope it was fun. Time constraints prevented us from going into model checking, but perhaps you would like to try that on your own. Or use S3 or a hosted database as a place to store sharded similarities. You might want to take a gander at Yelp's entire Phoenix dataset, or use the other attributes present in the data set. So many possibilities!\n", + "\n", + "If you'd like to learn more, please read Chris Volinksy's papers on the Netflix prize. There are also comprehensive reviews [here](http://arxiv.org/abs/1202.1112) and [here](http://www.grouplens.org/system/files/FnT%20CF%20Recsys%20Survey.pdf)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*css tweaks in this cell*\n", + "" + ] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/HW5_solutions.ipynb b/HW5_solutions.ipynb new file mode 100644 index 0000000..5e2b212 --- /dev/null +++ b/HW5_solutions.ipynb @@ -0,0 +1,977 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Homework 5: Networks and Congress\n", + "\n", + "*Due Friday, November 15, 11:59pm*\n", + "\n", + "\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline\n", + "\n", + "import json\n", + "\n", + "import numpy as np\n", + "import networkx as nx\n", + "import requests\n", + "from pattern import web\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# set some nicer defaults for matplotlib\n", + "from matplotlib import rcParams\n", + "\n", + "#these colors come from colorbrewer2.org. Each is an RGB triplet\n", + "dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),\n", + " (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),\n", + " (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),\n", + " (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),\n", + " (0.4, 0.6509803921568628, 0.11764705882352941),\n", + " (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),\n", + " (0.6509803921568628, 0.4627450980392157, 0.11372549019607843),\n", + " (0.4, 0.4, 0.4)]\n", + "\n", + "rcParams['figure.figsize'] = (10, 6)\n", + "rcParams['figure.dpi'] = 150\n", + "rcParams['axes.color_cycle'] = dark2_colors\n", + "rcParams['lines.linewidth'] = 2\n", + "rcParams['axes.grid'] = False\n", + "rcParams['axes.facecolor'] = 'white'\n", + "rcParams['font.size'] = 14\n", + "rcParams['patch.edgecolor'] = 'none'\n", + "\n", + "def remove_border(axes=None, top=False, right=False, left=True, bottom=True):\n", + " \"\"\"\n", + " Minimize chartjunk by stripping out unnecessary plot borders and axis ticks\n", + " \n", + " The top/right/left/bottom keywords toggle whether the corresponding plot border is drawn\n", + " \"\"\"\n", + " ax = axes or plt.gca()\n", + " ax.spines['top'].set_visible(top)\n", + " ax.spines['right'].set_visible(right)\n", + " ax.spines['left'].set_visible(left)\n", + " ax.spines['bottom'].set_visible(bottom)\n", + " \n", + " #turn off all ticks\n", + " ax.yaxis.set_ticks_position('none')\n", + " ax.xaxis.set_ticks_position('none')\n", + " \n", + " #now re-enable visibles\n", + " if top:\n", + " ax.xaxis.tick_top()\n", + " if bottom:\n", + " ax.xaxis.tick_bottom()\n", + " if left:\n", + " ax.yaxis.tick_left()\n", + " if right:\n", + " ax.yaxis.tick_right()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The website [govtrack.us](http://www.govetrack.us) collects data on activities in the Senate and House of Representatives. It's a great source of information for making data-driven assessments about Congress." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Problem 1.\n", + "\n", + "The directories at http://www.govtrack.us/data/congress/113/votes/2013 contain JSON information about every vote cast for the current (113th) Congress. Subdirectories beginning with \"S\" correspond to Senate votes, while subdirectories beginning with \"H\" correspond to House votes.\n", + "\n", + "Write two functions: one that downloads and parses a single Senate vote page given the vote number, and another that repeatedly calls this function to build a full collection of Senate votes from the 113th Congress." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "get_senate_vote\n", + "\n", + "Scrapes a single JSON page for a particular Senate vote, given by the vote number\n", + "\n", + "Parameters\n", + "----------\n", + "vote : int\n", + " The vote number to fetch\n", + " \n", + "Returns\n", + "-------\n", + "vote : dict\n", + " The JSON-decoded dictionary for that vote\n", + " \n", + "Examples\n", + "--------\n", + ">>> get_senate_vote(11)['bill']\n", + "{u'congress': 113,\n", + " u'number': 325,\n", + " u'title': u'A bill to ensure the complete and timely payment of the obligations of the United States Government until May 19, 2013, and for other purposes.',\n", + " u'type': u'hr'}\n", + "\"\"\"\n", + "#your code here\n", + "\n", + "def get_senate_vote(vote):\n", + " url = 'http://www.govtrack.us/data/congress/113/votes/2013/s%i/data.json' % vote\n", + " page = requests.get(url).text\n", + " return json.loads(page) " + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "get_all_votes\n", + "\n", + "Scrapes all the Senate votes from http://www.govtrack.us/data/congress/113/votes/2013,\n", + "and returns a list of dicts\n", + "\n", + "Parameters\n", + "-----------\n", + "None\n", + "\n", + "Returns\n", + "--------\n", + "votes : list of dicts\n", + " List of JSON-parsed dicts for each senate vote\n", + "\"\"\"\n", + "#Your code here\n", + "\n", + "def get_all_votes():\n", + " page = requests.get('https://www.govtrack.us/data/congress/113/votes/2013/').text\n", + " dom = web.Element(page)\n", + " votes = [a.attr['href'] for a in dom.by_tag('a') \n", + " if a.attr.get('href', '').startswith('s')]\n", + " n_votes = len(votes)\n", + " return [get_senate_vote(i) for i in range(1, n_votes + 1)]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#vote_data = get_all_votes()\n", + "vote_data = json.load(open('vote_data.json'))" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Problem 2\n", + "\n", + "Now, turn these data into a NetworkX graph, according to the spec below. For details on using NetworkX, consult the lab materials for November 1, as well as the [NetworkX documentation](http://networkx.github.io/)." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "vote_graph\n", + "\n", + "Parameters\n", + "----------\n", + "data : list of dicts\n", + " The vote database returned from get_vote_data\n", + "\n", + "Returns\n", + "-------\n", + "graph : NetworkX Graph object, with the following properties\n", + " 1. Each node in the graph is labeled using the `display_name` of a Senator (e.g., 'Lee (R-UT)')\n", + " 2. Each node has a `color` attribute set to 'r' for Republicans, \n", + " 'b' for Democrats, and 'k' for Independent/other parties.\n", + " 3. The edges between two nodes are weighted by the number of \n", + " times two senators have cast the same Yea or Nay vote\n", + " 4. Each edge also has a `difference` attribute, which is set to `1 / weight`.\n", + "\n", + "Examples\n", + "--------\n", + ">>> graph = vote_graph(vote_data)\n", + ">>> graph.node['Lee (R-UT)']\n", + "{'color': 'r'} # attributes for this senator\n", + ">>> len(graph['Lee (R-UT)']) # connections to other senators\n", + "101\n", + ">>> graph['Lee (R-UT)']['Baldwin (D-WI)'] # edge relationship between Lee and Baldwin\n", + "{'difference': 0.02, 'weight': 50}\n", + "\"\"\"\n", + "#Your code here\n", + "\n", + "def _color(s):\n", + " if '(R' in s:\n", + " return 'r'\n", + " if '(D' in s:\n", + " return 'b'\n", + " return 'k'\n", + " \n", + "def vote_graph(data):\n", + " \n", + " senators = set(x['display_name'] for d in data for vote_grp in d['votes'].values() for x in vote_grp)\n", + " weights = {s: {ss: 0 for ss in senators if ss != s} for s in senators}\n", + " \n", + " for d in data:\n", + " for grp in ['Yea', 'Nay']:\n", + " if grp not in d['votes']:\n", + " continue\n", + " vote_grp = d['votes'][grp]\n", + " for i in range(len(vote_grp)):\n", + " for j in range(i + 1, len(vote_grp)):\n", + " sen1 = vote_grp[i]['display_name']\n", + " sen2 = vote_grp[j]['display_name'] \n", + " weights[min(sen1, sen2)][max(sen1, sen2)] += 1\n", + " \n", + " g = nx.Graph()\n", + " for s in senators:\n", + " g.add_node(s)\n", + " g.node[s]['color'] = _color(s)\n", + " \n", + " for s1, neighbors in weights.items():\n", + " for s2, weight in neighbors.items():\n", + " if weight == 0:\n", + " continue\n", + " g.add_edge(s1, s2, weight= weight, difference = 1. / weight)\n", + " \n", + " return g\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "votes = vote_graph(vote_data) " + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How (and how not) to visualize networks\n", + "\n", + "Network plots often *look* impressive, but creating sensible network plots is tricky. From Ben Fry, the author of the Processing program:\n", + "
\n", + "Usually a graph layout isn\u2019t the best option for data sets larger than a few dozen nodes. You\u2019re most likely to wind up with enormous spider webs or balls of string, and the mess seen so far is more often the case than not. Graphs can be a powerful way to represent relationships between data, but they are also a very abstract concept, which means that they run the danger of meaning something only to the creator of the graph. Often, simply showing the structure of the data says very little about what it actually means, even though it\u2019s a perfectly accurate means of representing the data. Everything looks like a graph, but almost nothing should ever be drawn as one.\n", + "
\n", + "\n", + "Let's look at bad and better ways of visualizing the senate vote network.\n", + "\n", + "First, consider the \"default\" plot from networkx." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#this makes sure draw_spring results are the same at each call\n", + "np.random.seed(1) \n", + "\n", + "color = [votes.node[senator]['color'] for senator in votes.nodes()]\n", + "\n", + "#determine position of each node using a spring layout\n", + "pos = nx.spring_layout(votes, iterations=200)\n", + "\n", + "#plot the edges\n", + "nx.draw_networkx_edges(votes, pos, alpha = .05)\n", + "\n", + "#plot the nodes\n", + "nx.draw_networkx_nodes(votes, pos, node_color=color)\n", + "\n", + "#draw the labels\n", + "lbls = nx.draw_networkx_labels(votes, pos, alpha=5, font_size=8)\n", + "\n", + "#coordinate information is meaningless here, so let's remove it\n", + "plt.xticks([])\n", + "plt.yticks([])\n", + "remove_border(left=False, bottom=False)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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37emrBjB516+vAk4BjgN6EgplMGqUCYfDQWpqKtnZ2djtdqxWK1lZWQzq3RvL\nMcdgWrCAuYEAFcACoDdw5a5XXLPrVW8BTgX6AZcCQWAO0A04HhgN2AHTHv56ddfP4qSTTqJHjx5k\nZWUl2mkmk2m3v2w2G1arlczMTOx2O4sWLcLtdrN+/XqmTJnSrJ+fiCQPBR6RVip+4unTTz/liSee\n4JVXXtnrce+GYcgwDJ58snY7TeOWAoOBY4GXgC+Ar4FtQBVgw2Kxc/jhh+N2uwGw2x1UVlbSLy8P\nC7VB5Rngn8AiIAso3PXqg4F/AE8BnwFeoADwAZ8CnYAHgU+A8K7nNLYjKFbn/TQW+Br+TCKRCNFo\nFI/HQzgc5pJLLiE7O5sLL7yQiooKunXrRn5+PjfddNOefjAikoTU0hJppVasWMFjjz1GYWEhixYt\nIisrKzFor7q6moEDB/L4448zZ84c8vPzKSws5LrrruOll17CZDIxePBxPPhgNYHAXKAUmAr8fder\nL6e2ttIZ6Ah8v+vz11BbU/En1mGz2QiHwxxoDmqrPXFWoGnnuJrHarUSiURwuVz07duXvLw8jj/+\neHbu3Mlbb71F//79D8J3FZHWRhUekVbKMAyWLFnCWWedRVZWFgAPPPAAt912G8uWLSMtLY0vvvgC\nk8lE//79Wbx4MTk5OYTDYd59910mTPg9gcA3u17tLeASYC61dZjRuz6/kx/DDsB86oYd4KCEHagf\nduDghB2oHWZotVrx+/1s3boVn89HOBwmJyeHO++88yB9VxFpbRR4RFopk8nE9OnTWbVqFYsXLwZg\n3bp13HrrrYwePZply5axfft2AIYNG5Z4TvzXlZUAI4BV1Dad+gP3UlvlGVL3Ox2S99NSgsEgkV0z\ngGKxGBkZGXTq1IkdO3bwzTff7OPZIpIsNHtdpBWzWq289tprjB8/nt69ezNgwAB++ctfJkJNNBrl\nu+++q3c0O/7rtWuXA38E/kNtVecPgAd4jx93xkDt5uXkVbdCFQwGGTZsGBUVFdhsNqqrq1twZSJy\nKKnCI9KKmUwmsrKyWLBgAb/85S+59dZbueeeezjjjDMYN24cBQUFAHz77bfYbDYqKysxmUw89dRT\n3HzzaKAntduJv6V2CzHAX4D4jJz29b+AaDRK165dE1OidbGoSPuhTcsibcTy5cu58sor6dOnD+Fw\nmHnz5jFgwIDE12bPns20adPo27cvY8aMISsri2CwP17vF/xYxTFTv7rT/px22mkYhkFxcTFWq5W1\na9e29JLFdRizAAAgAElEQVRE5BBoX3+8E2nDTCYTV155JR9//DFz587lqaeeAmrDzvz58xkzZgwv\nvPACN9xwAxaLBa/XC6ynfsuq/YYdq9WK2+0mEolQU1ODxWJh+PDhLb0sETlEtIdHpA0xDIPly5dz\nySWX4Ha7+f777zn99NN5//338Xg8ic258OMFn+2dyWTCarVis9nYsWMHRx11FOPGjWPlypXMmTOn\npZcnIoeIAo9IG2EYBgsWLODVV1+lpKSEzp07s23bNu65556DdnS8LUtNTSUSiWCxWBg5ciQejwe7\n3c7YsWO54YYbGDBgAN27d2/pZYrIIaI9PCJtxIoVK1i6dCkdOnTgkUceISsrizFjxvCXv/yFWKy2\nVWUymZp9/UQyGTBgAOvXr8fpdHL66afz5ZdfMnToUC666CIGDBjA2LFjW3qJItJCVOERaUPi1yuU\nlJSwfft2duzYgdVqJRQKJb7enhUVFQG1wwY9Hg+nn346EydOZMWKFcycObOFVyciLUmBR6QNWbBg\nATabjWAwiN1up6KigsMOO4wtW7a09NJahT59+rBu3TrMZjMej4ecnBwmTZrEtdde29JLE5EWplNa\nIq1Y3RvTR44cSW5uLjNnzuSUU07huOOOY/r06RQVFSUu97TZbC253BY3dOhQsrOzGTp0KIZh8M9/\n/hOXy9XSyxKRVkCBR6QV29tgvCFDhvDxxx9jNpvp0aMHcPDuvWqNnE5nvY+7dOnC/PnzqampYcCA\nATz22GMttDIRaY0UeETamJUrV/Kf//yH+fPn87///Y/bbrut3t4Vi8XSwitsGpPJVO9KjKY8Pq7u\nlGSHw4HJZGLWrFl8//33dOnShaqqKm1QFpF6tIdHpI1ZsGABVquVxYsX89lnn3HHHXfw4YcfsnLl\nSnJzc/nDH/7AWWedxdSpU9m8eTM5OTmccMIJOJ1ODMPgf//7HzfddBP/93/34/f3BhxAFbVDCkcC\nK4FSai8edQJ9gdnANGA7tfeaB4HO1N62/nOgH/A3au/qGgW8AfwSOBr4f0AlkA1U0b3raQwf4cJq\ntWIYBpmZmdx9993ceuut3HXXXUyZMoXDDz+cLVu2UFRURHV1NZ06dUpMRz7mmGP46quvGDJkCF98\n8QXTp0/nlFNO4eKLL2bKlClcfPHF3HPPPYfiH4WItCEKPCJtiGEY3HPPPaxYsYJoNMpRRx2129cB\nLr30Uh577DFycnIAKCgooF+/flgsFrp27crkyZNZvHgxN930Eb/6lZlt28bsegUz4KM26AwB5gLn\nUBt40qmd2twL2AC8S20Ymg5cBswBbgfOpPay0kXAZOBwYDngxWr9ORs3v4PLVVvZGTt2LC+//DLj\nxo3DMIzEXqRnn32WOXPm0KNHD1auXMmCBQsYMWIE3377LQCdO3emZ8+eTJgwgdmzZyfe/3PPPXcA\nfsoikowUeETaiFgsRkFBAatWrWLVqlUsWbKEhQsXAhAIBAD47rvvEo+Pz+QJBAIMHjyYt956KzGM\nz2qt/U//tNOq+fe/wxx9tEF5efyZq4ETgDygE9Cf2gtHu1B7NYUJuIYfO+KHA2uAAbv+fladVb8E\nnAesBY6ma1ew22tD2YoVK8jOzsZsNrN06dJ679VkMjFjxgzOOussBg8evNvPYvDgwbz44ovN/RGK\nSDumwCPSyr3yyit8/vnnxGIxbr/9dtLT0znjjDMYPHhwYh/L2WefzWmnncaJJ56Y+Nzxxx/PxIkT\nufHGG7nnnns499xzMQyDDh068MorrxAOhzEMg86dc8jONtcJPG8Av6E21NiAe3f9Orjr47carPBa\n4ELgecCy6zFQWw0qA84GttK169EcdlhtVcdqtdK1a1cef/zxPb7vLl26MGjQoMTH8ff1/fffc9JJ\nJ+3Pj1JE2jFNWhZp5fx+P4Zh4HK59npqq65AIEA0GsXlctXbGGwYBlVVVYRCIdLT07Hb7Xi9Xr7/\nvoiRI/sQCu3Phuf4/0JM1La/ngECQFfABfhwOH7L558/zbHH7sfLA8FgkAkTJrB06VJuu+02Zs6c\nqWshRKRZFHhEWqlYLIbf78dqteJwOJr0nGg0SiAQaPQ5wWCQqqoq7HY76enpRKNRSkpKyM/Pp6io\niF/8IkQgcMl+rLQa+Bm1IWcQ8DRgT3z12GPh//0/OPLI/XhpwOPxMH78eG6++WYuvvji/XsREWn3\nFHhEWqFwOEwoFMLhcCT22+xLMBgkEonsdjS9blUnIyMDm81GdXU1BQUFlJSU8PDDD/Pee+8BgzCb\n/0Ms1rRwFWexwGmnwaefQvyydrsdLroIfvUrGD68WS8nInJQKPCItDLNbWHFYjECgQBms3m3YXwN\nqzqRSIQdO3aQn5/PunXr+O1vf0tNTQ2dOnVi/vz5bNt2MjNnZhONNq11ZjLBvHlwzTUQi4HHU/u5\njIzav4uItBbatCzSSuxPCysUChEOh3erBDWs6litViorK8nPz6e0tJRbbrmFr7/+GoCZM2cyffp0\nunbtitVqpXPnCFdeaaO6eu/f2+WC55+HSy+t/dhshqys/XrrIiIHnSo8Iq1Ac1tY8aqOyWTC6XTW\nqwQFg0E8Hg8ul4vU1FSCwSBFRUUUFBSwePFi7r33XgD69evHvHnzOPLII3G73YmgVXvxJrz4Ijzx\nBPzvf/W/d58+cP31tVWdDh0O6I9BROSgUeARaWHNbWGFw2GCwSAOh6PeZaGGYeDxeAiHw4mqTkVF\nBXl5eezYsYOpU6dSWFiI1WrloYceYuLEiWRmZmKz2fYatNasgeJiMAzIyYGhQ9WuEpG2R4FHpIU0\nt4UVHyLYWDgKBAJ4PB5SUlJITU0lEAhQUFBAUVER8+bNY8GCBUDtjesPPvggvXv3xuFwYLfbm9w+\nExFpyxR4RFpAc1tYkUiEYDCIzWbDbv/xyHcsFsPj8RCNRsnIyMBsNlNaWkpubi6bNm1ixowZeDwe\nMjMzefrppxk+fDhpaWlYrVacTmezLu8UEWnLFHhEDrHmtLAMwyAYDBKLxXYLKH6/n6qqKlJSUkhL\nS6OmpoZt27ZRVFTE3XffzapVqwC45ppruPnmm+nUqRMOh2O3VpiISHugwCNyiDS3hbWnIYJ1qzqZ\nmZkAlJSUsHXrVlauXMldd91FOBymR48eLFiwILEpOd6+auq0ZhGRZKLAI3IINLeFtachgn6/n+rq\nalJSUnC73VRXV5Obm0tRUREzZsxg8+bNANx3331ceumlZGVlYbfbd3sdEZH2RoFH5CALBALEYrEm\ntbDiVR2LxVJviGDDqo5hGBQVFbF161YWLlzIM888A8Bxxx3H008/Tc+ePXE6nYmNySIi7Z0Cj8hB\n0twWVryq07AK1LCqU1FRwdatW8nLy2Pq1KmUlZWRkpLCM888w8iRI0lPT8dms+02n0dEpD1T4BE5\nCJrTwqp7NUTdPTaxWIzKykpisRiZmZlEo1EKCgrIz8/nnnvuYdmyZQBccsklzJkzhy5duiT26TT1\n/i0RkfZC/1cUOcDiLayUlJR9Vlj2dDWEz+ejpqYGt9uNy+WivLyczZs38/XXX3PjjTcSCATo0qUL\nCxcuZODAgfU2JYuIyO5U4RE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+ "text": [ + "" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The spring layout tries to group nodes with large edge-weights near to each other. In this context, that means it tries to organize the Senate into similarly-voting cliques. However, there's simply too much going on in this plot -- we should simplify the representation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Problem 3\n", + "\n", + "Compute the `Minimum Spanning Tree` of this graph, using the `difference` edge attribute as the weight to minimize. A [Minimum Spanning Tree](http://en.wikipedia.org/wiki/Minimum_spanning_tree) is the subset of edges which trace at least one path through all nodes (\"spanning\"), with minimum total edge weight. You can think of it as a simplification of a network.\n", + "\n", + "Plot this new network, making modifications as necessary to prevent the graph from becoming too busy." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Your code here\n", + "plt.figure(figsize=(15, 10))\n", + "np.random.seed(5)\n", + "mst = nx.minimum_spanning_tree(votes, weight='difference')\n", + "pos = nx.spring_layout(mst, iterations=900, k=.008, weight='difference')\n", + "\n", + "mst_edges = list(nx.minimum_spanning_edges(votes, weight='difference'))\n", + "\n", + "nl = votes.nodes()\n", + "c = [votes.node[n]['color'] for n in nl]\n", + "nx.draw_networkx_edges(votes, pos, edgelist=mst_edges, alpha=.2)\n", + "nx.draw_networkx_nodes(votes, pos, nodelist = nl, node_color = c, node_size=60)\n", + "\n", + "for p in pos.values():\n", + " p[1] += .02\n", + " \n", + "nx.draw_networkx_labels(votes, pos, font_color='k', font_size=7)\n", + "\n", + "plt.title(\"MST of Vote Disagreement\", fontsize=18)\n", + "plt.xticks([])\n", + "plt.yticks([])\n", + "remove_border(left=False, bottom=False)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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tW7bk5ZdfNrfdoUMHli1bxkcffUT+/Pl59tln6dq1K59//jkmkwmA6dOnU758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+ "text": [ + "" + ] + } + ], + "prompt_number": 68 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Problem 4\n", + "\n", + "While this graph has less information, the remaining information is easier to digest. What does the Minimum Spanning Tree mean in this context? How does this graph relate to partisanship in the Senate? Which nodes in this graph are the most and least bi-partisan?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*The edges of a minimum spanning tree trace a path of low resistance through the network. In the present context, this has the effect of moving bipartisan Senators like Hagan towards the center of the graph -- it is much easier to connect Hagan to a Republican node than, say, a partisan Democrat like Al Franken. Partisan Senators are pushed away from the center of the graph and deeper into the party cliques. *\n", + "\n", + "*This scheme also moves outlier senators to the outside of the graph. For example, John Kerry cast very few votes before becoming Secretary of State. Most of the edges connected to John Kerry have large difference values, so the fewest possible number of edges (1) remain in the MST.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Problem 5\n", + "\n", + "(For this problem, use the full graph for centrality computation, and not the Minimum Spanning Tree)\n", + "\n", + "Networkx can easily compute [centrality](https://en.wikipedia.org/wiki/Centrality) measurements. \n", + "\n", + "Briefly discuss what ``closeness_centrality`` means, both mathematically and in the context of the present graph -- how does the centrality relate to partisanship? Choose a way to visualize the `closeness_centrality` score for each member of the Senate, using edge `difference` as the distance measurement. Determine the 5 Senators with the highest and lowest centralities. \n", + "\n", + "Comment on your results. In particular, note the outliers John Kerry (who recently resigned his Senate seat when he became Secretary of State), Mo Cowan (Kerry's interim replacement) and Ed Markey (Kerry's permanent replacement) have low centrality scores -- why?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*The closeness centrality measures the average difference between a Senator and all other Senators. Bipartisan voters will have more shared votes with the members of the opposite party, which tends to increase their centrality. However, these senators also vote less often with their own party, which can decrease centrality*\n", + "\n", + "*Centrality scores are also small for people who haven't cast many votes (like John Kerry, Mo Cowan, and Ed Markey). This says nothing about bipartisanship*" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Your code here\n", + "\n", + "bet = nx.closeness_centrality(votes, distance='difference')\n", + "bipartisans = sorted(bet, key=lambda x: -bet[x])\n", + "\n", + "print \"Highest closeness\"\n", + "for senator in bipartisans[:5]:\n", + " print \"%20.20s\\t%0.3f\" % (senator, bet[senator])\n", + "print\n", + "print \"Lowest closeness\"\n", + "for senator in bipartisans[-5:]:\n", + " print \"%20.20s\\t%0.3f\" % (senator, bet[senator])\n", + " \n", + "\n", + "plt.figure(figsize=(15, 4))\n", + "x = np.arange(len(nl))\n", + "y = np.array([bet[n] for n in nl])\n", + "c = np.array([votes.node[n]['color'] for n in nl])\n", + "\n", + "ind = np.argsort(y)\n", + "y = y[ind]\n", + "c = c[ind]\n", + "\n", + "plt.bar(x, y, color=c, align='center', width=.8)\n", + "\n", + "remove_border(left=None, bottom=None)\n", + "ticks = plt.xticks(x, [nl[i] for i in x[ind]], \n", + " rotation='vertical', fontsize=7)\n", + "limits = plt.xlim(-1, x[-1] + 1)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Highest closeness\n", + " Collins (R-ME)\t98.685\n", + " Manchin (D-WV)\t95.837\n", + " Pryor (D-AR)\t95.008\n", + " Donnelly (D-IN)\t93.965\n", + " Hagan (D-NC)\t93.940\n", + "\n", + "Lowest closeness\n", + " Markey (D-MA)\t42.925\n", + " Chiesa (R-NJ)\t39.446\n", + " Lautenberg (D-NJ)\t25.260\n", + " Booker (D-NJ)\t11.866\n", + " Kerry (D-MA)\t3.945\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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ECK/tT09PlySlp6dr8ODBju3z/P169OjhWP+IESM0Y8YM5c6dW9OnT/da/tCh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\nd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+ "text": [ + "" + ] + } + ], + "prompt_number": 111 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Problem 6\n", + "\n", + "Centrality isn't a perfect proxy for bipartisanship, since it gauges how centralized a node is to the network as a whole, and not how similar a Democrat node is to the Republican sub-network (and vice versa).\n", + "\n", + "Can you come up with another measure that better captures bipartisanship than closeness centrality? Develop your own metric -- how does it differ from the closeness centrality? Use visualizations to support your points." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#your code here\n", + "\n", + "\"\"\"\n", + "Here, we compute the mean weight for the edges that connect a Senator\n", + "to a node in the other party (we consider Independents to be Democrats\n", + "for this analysis).\n", + "\n", + "This only considers how similarly a Senator votes with the other party.\n", + "\n", + "The scatter plot shows that the betweenness centrality and bipartisan score\n", + "correlate with each other. However, the betweenness centrality judges Democrats\n", + "to be more bipartisan as a whole. Part of this is a bias due to the fact\n", + "that Democrats are the majority party in the Senate right now, so their\n", + "votes are considered more \"central\" due to their bigger numbers.\n", + "\"\"\"\n", + "def bipartisan_score(graph, node):\n", + " party = graph.node[node]['color']\n", + " other = 'r' if party != 'r' else 'b'\n", + " return np.mean([v['weight'] for k, v in graph[node].items() if graph.node[k]['color'] == other])\n", + "\n", + "bp_score = {node: bipartisan_score(votes, node) for node in votes.nodes()}\n", + "bp2 = sorted(bp_score, key=lambda x: -1 * bp_score[x])\n", + "\n", + "print \"Most Bipartisan\"\n", + "for senator in bp2[:5]:\n", + " print \"%20.20s\\t%0.3f\" % (senator, bp_score[senator])\n", + "\n", + "print\n", + "print \"Least Bipartisan\"\n", + "for senator in bp2[-5:]:\n", + " print \"%20.20s\\t%0.3f\" % (senator, bp_score[senator])\n", + "\n", + " \n", + "senators = bp_score.keys()\n", + "x = [bet[s] for s in senators]\n", + "y = [bp_score[s] for s in senators]\n", + "c = [votes.node[s]['color'] for s in senators]\n", + "\n", + "plt.scatter(x, y, 80, color=c, \n", + " alpha=.5, edgecolor='white')\n", + "plt.xlabel(\"Betweenness Centrality\")\n", + "plt.ylabel(\"Bipartisan Score\")\n", + "remove_border()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Most Bipartisan\n", + " Collins (R-ME)\t153.089\n", + " Murkowski (R-AK)\t132.875\n", + " Manchin (D-WV)\t118.500\n", + " Pryor (D-AR)\t112.913\n", + " McCain (R-AZ)\t112.393\n", + "\n", + "Least Bipartisan\n", + " Chiesa (R-NJ)\t39.038\n", + " Markey (D-MA)\t23.435\n", + " Lautenberg (D-NJ)\t9.289\n", + " Booker (D-NJ)\t3.659\n", + " Kerry (D-MA)\t2.235\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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k11+3XrtTpywzFhXZCPDixdbT16+fLfj97ncJeTg/gh4AAOezdav0/vuWvioc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\n68ELD7ch227dLPQNHCh9/bU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Hqn///nrhhRcqj3Xv3l033XSTfvOb31Qeo2By48jLs5p2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\n5BFoQgghhBBt1P/cNXpCCCGEEOLJSKEnhBBCCNF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+ "text": [ + "" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Leadership in the Senate\n", + "\n", + "There are many metrics to quantify the leadership in the Senate.\n", + "\n", + " 1. Senate leaders sponsor and co-sponsor lots of bills\n", + " 2. Leaders sit on many committees, as well as more important committees\n", + " 3. Leaders usually have been in office for a long time\n", + " \n", + "Another approach uses the philosophy behind how Google ranks search results. The core idea behind Google's PageRank algorithm is:\n", + "\n", + "1. A \"good\" website (i.e. one to rank highly in search results) is linked to by many other websites\n", + "2. A link found on a \"good\" website is more important than a link found on a \"bad\" website\n", + "\n", + "The PageRank algorithm thus assigns scores to nodes in a graph based on how many neighbors a node has, as well as the score of those neighbors.\n", + "\n", + "This technique can be adapted to rank Senate leadership. Here, nodes correspond to Senators, and edges correspond to a senator co-sponsoring a bill sponsored by another Senator. The weight of each edge from node A to B is the number of times Senator A has co-sponsored a bill whose primary sponsor is Senator B. If you interpret the PageRank scores of such a network to indicate Senate leadership, you are then assuming:\n", + "\n", + "1. Leaders sponsor more bills\n", + "1. Leaders attract co-sponsorship from other leaders\n", + "\n", + "### Problem 7\n", + "\n", + "Govtrack stores information about each Senate bill in the current congress at http://www.govtrack.us/data/congress/113/bills/s/. As in problem 1, write two functions to scrape these data -- the first function downloads a single bill, and the second function calls the first to loop over all bills." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "get_senate_bill\n", + "\n", + "Scrape the bill data from a single JSON page, given the bill number\n", + "\n", + "Parameters\n", + "-----------\n", + "bill : int\n", + " Bill number to fetch\n", + " \n", + "Returns\n", + "-------\n", + "A dict, parsed from the JSON\n", + "\n", + "Examples\n", + "--------\n", + ">>> bill = get_senate_bill(10)\n", + ">>> bill['sponsor']\n", + "{u'district': None,\n", + " u'name': u'Reid, Harry',\n", + " u'state': u'NV',\n", + " u'thomas_id': u'00952',\n", + " u'title': u'Sen',\n", + " u'type': u'person'}\n", + ">>> bill['short_title']\n", + "u'Agriculture Reform, Food, and Jobs Act of 2013'\n", + "\"\"\"\n", + "#your code here\n", + "def get_senate_bill(bill):\n", + " url = 'http://www.govtrack.us/data/congress/113/bills/s/s%i/data.json' % bill\n", + " page = requests.get(url).text\n", + " return json.loads(page)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 11 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "get_all_bills\n", + "\n", + "Scrape all Senate bills at http://www.govtrack.us/data/congress/113/bills/s\n", + "\n", + "Parameters\n", + "----------\n", + "None\n", + "\n", + "Returns\n", + "-------\n", + "A list of dicts, one for each bill\n", + "\"\"\"\n", + "#your code here\n", + "def get_all_bills():\n", + " page = requests.get('http://www.govtrack.us/data/congress/113/bills/s/').text\n", + " dom = web.Element(page)\n", + " links = [a.attr['href'] for a in dom.by_tag('a') \n", + " if a.attr.get('href', '').startswith('s')]\n", + " return [get_senate_bill(i) for i in range(1, len(links) + 1)]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#bill_list = get_all_bills()\n", + "bill_list = json.load(open('bill_list.json'))" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 13 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Problem 8\n", + "\n", + "Write a function to builded a Directed Graph (DiGraph) from these data, according to the following spec:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\"\"\"\n", + "Function\n", + "--------\n", + "bill_graph\n", + "\n", + "Turn the bill graph data into a NetworkX Digraph\n", + "\n", + "Parameters\n", + "----------\n", + "data : list of dicts\n", + " The data returned from get_all_bills\n", + "\n", + "Returns\n", + "-------\n", + "graph : A NetworkX DiGraph, with the following properties\n", + " * Each node is a senator. For a label, use the 'name' field \n", + " from the 'sponsor' and 'cosponsors' dict items\n", + " * Each edge from A to B is assigned a weight equal to how many \n", + " bills are sponsored by B and co-sponsored by A\n", + "\"\"\"\n", + "#Your code here\n", + "\n", + "def bill_graph(data):\n", + " \n", + " sp = nx.DiGraph()\n", + "\n", + " for bill in data:\n", + " sponsor = bill['sponsor']['name']\n", + " sponsor_data = bill['sponsor']\n", + " \n", + " cosponsors = [cs['name'] for cs in bill['cosponsors']]\n", + " \n", + " if sponsor not in sp:\n", + " sp.add_node(sponsor, **sponsor_data)\n", + " \n", + " for cosponsor in bill['cosponsors']:\n", + " if cosponsor['name'] not in sp:\n", + " sp.add_node(cosponsor['name'], **cosponsor) \n", + " cosponsor = cosponsor['name']\n", + " \n", + " try:\n", + " w = sp[cosponsor][sponsor]['weight'] + 1\n", + " except KeyError:\n", + " w = + 1\n", + " sp.add_edge(cosponsor, sponsor, weight=w) \n", + "\n", + " return sp" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 14 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "bills = bill_graph(bill_list)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 15 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Problem 9\n", + "\n", + "Using `nx.pagerank_numpy`, compute the PageRank score for each senator in this graph. Visualize the results. Determine the 5 Senators with the highest\n", + "PageRank scores. How effective is this approach at identifying leaders? How does the PageRank rating compare to the degree of each node?\n", + "\n", + "Note: you can read about individual Senators by searching for them on the [govtrack website](https://www.govtrack.us/)." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Your code here\n", + "\n", + "pagerank = nx.pagerank_numpy(bills)\n", + "names = np.array(pagerank.keys())\n", + "vals = np.array([pagerank[n] for n in names])\n", + "\n", + "ind = np.argsort(vals)\n", + "names = names[ind]\n", + "vals = vals[ind]\n", + "\n", + "print \"Highest Scores\"\n", + "for n, v in zip(names, vals)[-5:][::-1]:\n", + " print \"%20.20s\\t%0.3f\" % (n, v)\n", + "\n", + "print\n", + "print \"Lowest Scores\" \n", + "for n, v in zip(names, vals)[:5]:\n", + " print \"%20.20s\\t%0.3f\" % (n, v)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Highest Scores\n", + " Brown, Sherrod\t0.030\n", + " Harkin, Tom\t0.030\n", + " Reid, Harry\t0.027\n", + "Lautenberg, Frank R.\t0.026\n", + " Menendez, Robert\t0.026\n", + "\n", + "Lowest Scores\n", + " Chiesa, Jeff\t0.001\n", + " Kerry, John F.\t0.001\n", + " Cowan, William M.\t0.001\n", + " Sessions, Jeff\t0.001\n", + " Scott, Tim\t0.001\n" + ] + } + ], + "prompt_number": 16 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Your code here\n", + "\n", + "deg = nx.degree(bills)\n", + "\n", + "plt.scatter([deg[n] for n in bills.nodes()],\n", + " [pagerank[n] for n in bills.nodes()], 80, alpha=.8, \n", + " color='k', edgecolor='white')\n", + "\n", + "labels = ['Reid, Harry', 'Lautenberg, Frank R.', 'Menendez, Robert', 'Harkin, Tom']\n", + "for lbl in labels:\n", + " plt.annotate(lbl, (deg[lbl], pagerank[lbl] + .002), fontsize=10, rotation=10)\n", + " \n", + "plt.xlabel(\"Degree\")\n", + "plt.ylabel(\"PageRank\")\n", + "remove_border()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": 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x5MgRfHx86Nu3L8bGxko7rm+Lubk5vr6+bNu2jR07dvDXX38xduxYHn30Uc6e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\nGsLMzEw0bdpUHD9+v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+ "text": [ + "" + ] + } + ], + "prompt_number": 17 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*The PageRank approach does seem to be effective at identifying influential Senators like Tom Harkin and Harry Reid (the Majority Leader). We see in particular that Harry Reid's PageRank score is relatively higher than his degree -- he seems to sponsor fewer bills overall, but those bills appear to be more important. This makes sense, since he is the figurehead of the Democratic party in the Senate, and thus probably focuses on the highest-profile legislation.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interactive Visualization\n", + "\n", + "Producing a good node link layout is not quite so simple. Nevertheless, we will give it a try. \n", + "\n", + "We will use [Gephi](https://gephi.org/) for interactive graph visualization. Gephi supports a wide variety of graph file formats, and NetworkX exports to several of them. We'll use the Graph Exchange XML Format (GEXF)." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "nx.write_gexf(votes, 'votes.gexf')" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 18 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Problem 10: Analysis with Gephi\n", + "\n", + "Download and install [Gephi](https://gephi.org/). See the [lab](http://goo.gl/SzHioP) for a brief introduction. Load the exported votes file. Try to produce a layout that clearly separates Democrats from Republicans (hint: filter on edge weight and re-layout once you filtered). Run PageRank and some other statistics and try encoding them with node color and node size. Run the \"Modularity\" statistic and encode the results in color.\n", + "\n", + "Include a screenshot of your \"best\" visualization and embed the image here with `IPython.display.Image`. Make sure to include this image in your submission.\n", + "\n", + "Explain your observations. Is the network visualization very helpful? Try to visualize your LinkedIn network (see the lab) or the one provided in the lab. Which dataset is more suitable for visualization and why is there a difference?\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*David Chouinard did a very nice job with this -- go look at [his page](http://static.davidchouinard.com/congress/). Notice how it is generally difficult to study the details of very complex networks -- we can see bipartisan structure, but not much else. The LinkedIn graphs are easier to digest and interpret*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to Submit\n", + "\n", + "To submit your homework, create a folder named lastname_firstinitial_hw5 and place this notebook file in the folder. Double check that this file is still called HW5.ipynb, and that it contains your solutions. Also include any Gephi screenshots. Compress the folder (please use .zip compression) and submit to the CS109 dropbox in the appropriate folder. If we cannot access your work because these directions are not followed correctly, we will not grade your work." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "*css tweaks in this cell*\n", + "" + ] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/README.md b/README.md index 1529206..a886335 100644 --- a/README.md +++ b/README.md @@ -4,12 +4,12 @@ Welcome to CS109: Data Science ## Assignments -* [Homework 0](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW0.ipynb): Hello, world -* [Homework 1](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW1.ipynb): Which of two things is larger? -* [Homework 2](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW2.ipynb): Desperately Seeking Silver -* [Homework 3](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW3.ipynb): Bayesian Tomatoes -* [Homework 4](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW4.ipynb): Do We Really Need Chocolate Recommendations? -* [Homework 5](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW5.ipynb): Networks and Congress +* [Homework 0](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW0.ipynb): Hello, world ([solutions](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW0_solutions.ipynb)) +* [Homework 1](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW1.ipynb): Which of two things is larger? ([solutions](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW1_solutions.ipynb)) +* [Homework 2](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW2.ipynb): Desperately Seeking Silver ([solutions](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW2_solutions.ipynb)) +* [Homework 3](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW3.ipynb): Bayesian Tomatoes ([solutions](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW3_solutions.ipynb)) +* [Homework 4](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW4.ipynb): Do We Really Need Chocolate Recommendations? ([solutions](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW4_solutions.ipynb)) +* [Homework 5](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW5.ipynb): Networks and Congress ([solutions](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW5_solutions.ipynb)) ## Lecture Supplements