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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Homework 3: Prediction and Classification\n",
"\n",
"Due: Thursday, October 16, 2014 11:59 PM\n",
"\n",
" Download this assignment\n",
"\n",
"#### Submission Instructions\n",
"To submit your homework, create a folder named lastname_firstinitial_hw# and place your IPython notebooks, data files, and any other files in this folder. Your IPython Notebooks should be completely executed with the results visible in the notebook. We should not have to run any code. 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",
"\n",
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"\n",
"In this assignment you will be using regression and classification to explore different data sets. \n",
"\n",
"**First**: You will use data from before 2002 in the [Sean Lahman's Baseball Database](http://seanlahman.com/baseball-archive/statistics) to create a metric for picking baseball players using linear regression. This is same database we used in Homework 1. This database contains the \"complete batting and pitching statistics from 1871 to 2013, plus fielding statistics, standings, team stats, managerial records, post-season data, and more\". [Documentation provided here](http://seanlahman.com/files/database/readme2012.txt).\n",
"\n",
"\n",
"http://saberseminar.com/wp-content/uploads/2012/01/saber-web.jpg\n",
"\n",
"**Second**: You will use the famous [iris](http://en.wikipedia.org/wiki/Iris_flower_data_set) data set to perform a $k$-neareast neighbor classification using cross validation. While it was introduced in 1936, it is still [one of the most popular](http://archive.ics.uci.edu/ml/) example data sets in the machine learning community. Wikipedia describes the data set as follows: \"The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres.\" Here is an illustration what the four features measure:\n",
"\n",
"\n",
"http://sebastianraschka.com/Images/2014_python_lda/iris_petal_sepal.png\n",
"\n",
"**Third**: You will investigate the influence of higher dimensional spaces on the classification using another standard data set in machine learning called the The [digits data set](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html). This data set is similar to the MNIST data set discussed in the lecture. The main difference is, that each digit is represented by an 8x8 pixel image patch, which is considerably smaller than the 28x28 pixels from MNIST. In addition, the gray values are restricted to 16 different values (4 bit), instead of 256 (8 bit) for MNIST. \n",
"\n",
"**Finally**: In preparation for Homework 4, we want you to read through the following articles related to predicting the 2014 Senate Midterm Elections. \n",
"\n",
"* [Nate Silver's Methodology at while at NYT](http://fivethirtyeight.blogs.nytimes.com/methodology/)\n",
"* [How The FiveThirtyEight Senate Forecast Model Works](http://fivethirtyeight.com/features/how-the-fivethirtyeight-senate-forecast-model-works/)\n",
"* [Pollster Ratings v4.0: Methodology](http://fivethirtyeight.com/features/pollster-ratings-v40-methodology/)\n",
"* [Pollster Ratings v4.0: Results](http://fivethirtyeight.com/features/pollster-ratings-v40-results/)\n",
"* [Nate Silver versus Sam Wang](http://www.washingtonpost.com/blogs/plum-line/wp/2014/09/17/nate-silver-versus-sam-wang/)\n",
"* [More Nate Silver versus Sam Wang](http://www.dailykos.com/story/2014/09/09/1328288/-Get-Ready-To-Rumbllllle-Battle-Of-The-Nerds-Nate-Silver-VS-Sam-Wang)\n",
"* [Nate Silver explains critisims of Sam Wang](http://politicalwire.com/archives/2014/10/02/nate_silver_rebuts_sam_wang.html)\n",
"* [Background on the feud between Nate Silver and Sam Wang](http://talkingpointsmemo.com/dc/nate-silver-sam-wang-feud)\n",
"* [Are there swing voters?]( http://www.stat.columbia.edu/~gelman/research/unpublished/swing_voters.pdf)\n",
"\n",
"\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Python modules"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# special IPython command to prepare the notebook for matplotlib\n",
"%matplotlib inline \n",
"\n",
"import requests \n",
"import StringIO\n",
"import zipfile\n",
"import numpy as np\n",
"import pandas as pd # pandas\n",
"import matplotlib.pyplot as plt # module for plotting \n",
"\n",
"# If this module is not already installed, you may need to install it. \n",
"# You can do this by typing 'pip install seaborn' in the command line\n",
"import seaborn as sns \n",
"\n",
"import sklearn\n",
"import sklearn.datasets\n",
"import sklearn.cross_validation\n",
"import sklearn.decomposition\n",
"import sklearn.grid_search\n",
"import sklearn.neighbors\n",
"import sklearn.metrics"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Problem 1: Sabermetrics\n",
"\n",
"Using data preceding the 2002 season pick 10 offensive players keeping the payroll under $20 million (assign each player the median salary). Predict how many games this team would win in a 162 game season. \n",
"\n",
"In this problem we will be returning to the [Sean Lahman's Baseball Database](http://seanlahman.com/baseball-archive/statistics) that we used in Homework 1. From this database, we will be extract five data sets containing information such as yearly stats and standing, batting statistics, fielding statistics, player names, player salaries and biographical information. You will explore the data in this database from before 2002 and create a metric for picking players. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 1(a) \n",
"\n",
"Load in [these CSV files](http://seanlahman.com/files/database/lahman-csv_2014-02-14.zip) from the [Sean Lahman's Baseball Database](http://seanlahman.com/baseball-archive/statistics). For this assignment, we will use the 'Teams.csv', 'Batting.csv', 'Salaries.csv', 'Fielding.csv', 'Master.csv' tables. Read these tables into separate pandas DataFrames with the following names. \n",
"\n",
"CSV file name | Name of pandas DataFrame\n",
":---: | :---: \n",
"Teams.csv | teams\n",
"Batting.csv | players\n",
"Salaries.csv | salaries\n",
"Fielding.csv | fielding\n",
"Master.csv | master"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### Your code here ###\n",
"\n",
"def getZIP(zipFileName):\n",
" r = requests.get(zipFileName).content\n",
" s = StringIO.StringIO(r)\n",
" zf = zipfile.ZipFile(s, 'r') # Read in a list of zipped files\n",
" return zf\n",
"\n",
"url = 'http://seanlahman.com/files/database/lahman-csv_2014-02-14.zip'\n",
"zf = getZIP(url)\n",
"tablenames = zf.namelist()\n",
"print tablenames"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['SchoolsPlayers.csv', 'SeriesPost.csv', 'Teams.csv', 'TeamsFranchises.csv', 'TeamsHalf.csv', 'AllstarFull.csv', 'Appearances.csv', 'AwardsManagers.csv', 'AwardsPlayers.csv', 'AwardsShareManagers.csv', 'AwardsSharePlayers.csv', 'Batting.csv', 'BattingPost.csv', 'Fielding.csv', 'FieldingOF.csv', 'FieldingPost.csv', 'HallOfFame.csv', 'Managers.csv', 'ManagersHalf.csv', 'Master.csv', 'Pitching.csv', 'PitchingPost.csv', 'readme2013.txt', 'Salaries.csv', 'Schools.csv']\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create pandas DataFrames for each of the five data sets. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"teams = pd.read_csv(zf.open(tablenames[tablenames.index('Teams.csv')]))\n",
"players = pd.read_csv(zf.open(tablenames[tablenames.index('Batting.csv')]))\n",
"salaries = pd.read_csv(zf.open(tablenames[tablenames.index('Salaries.csv')]))\n",
"fielding = pd.read_csv(zf.open(tablenames[tablenames.index('Fielding.csv')]))\n",
"master = pd.read_csv(zf.open(tablenames[tablenames.index('Master.csv')]))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can print the dimensions (i.e. number of rows and columns) for each of the DataFrames. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### Your code here ###\n",
"print \"Dimensions of teams DataFrame:\", teams.shape\n",
"print \"Dimensions of players DataFrame:\", players.shape\n",
"print \"Dimensions of salaries DataFrame:\", salaries.shape\n",
"print \"Dimensions of fielding DataFrame:\", fielding.shape\n",
"print \"Dimensions of master DataFrame:\", master.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Dimensions of teams DataFrame: (2745, 48)\n",
"Dimensions of players DataFrame: (97889, 24)\n",
"Dimensions of salaries DataFrame: (23956, 5)\n",
"Dimensions of fielding DataFrame: (166991, 18)\n",
"Dimensions of master DataFrame: (18354, 24)\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 1(b)\n",
"\n",
"Calculate the median salary for each player and create a pandas DataFrame called `medianSalaries` with four columns: (1) the player ID, (2) the first name of the player, (3) the last name of the player and (4) the median salary of the player. Show the head of the `medianSalaries` DataFrame. \n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### Your code here ###\n",
"byPlayerID = salaries.groupby('playerID')['playerID','salary'].median()\n",
"medianSalaries = pd.merge(master[['playerID', 'nameFirst', 'nameLast']], byPlayerID, \\\n",
" left_on='playerID', right_index = True, how=\"inner\")\n",
"medianSalaries.head()"
],
"language": "python",
"metadata": {},
"outputs": [
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\n",
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" \n",
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\n",
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\n",
"
\n",
" \n",
" \n",
" | \n",
" playerID | \n",
" POS | \n",
" minYear | \n",
" maxYear | \n",
" 1B | \n",
" 2B | \n",
" 3B | \n",
" HR | \n",
" BB | \n",
" OPW | \n",
" nameFirst | \n",
" nameLast | \n",
" salary | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" abreubo01 | \n",
" RF | \n",
" 1996 | \n",
" 2012 | \n",
" -0.008202 | \n",
" 0.006421 | \n",
" 0.001002 | \n",
" -0.003252 | \n",
" 0.050501 | \n",
" 104.050008 | \n",
" Bobby | \n",
" Abreu | \n",
" 9000000 | \n",
"
\n",
" \n",
" | 1 | \n",
" ackledu01 | \n",
" 1B | \n",
" 2011 | \n",
" 2013 | \n",
" -0.009270 | \n",
" -0.016605 | \n",
" -0.001974 | \n",
" -0.015274 | \n",
" 0.001597 | \n",
" 53.806003 | \n",
" Dustin | \n",
" Ackley | \n",
" 2400000 | \n",
"
\n",
" \n",
" | 2 | \n",
" adamsru01 | \n",
" SS | \n",
" 2004 | \n",
" 2009 | \n",
" -0.007867 | \n",
" -0.001289 | \n",
" 0.004160 | \n",
" -0.017533 | \n",
" 0.002672 | \n",
" 67.496507 | \n",
" Russ | \n",
" Adams | \n",
" 329500 | \n",
"
\n",
" \n",
" | 3 | \n",
" alfoned01 | \n",
" 2B | \n",
" 1995 | \n",
" 2006 | \n",
" 0.013485 | \n",
" -0.002177 | \n",
" -0.003239 | \n",
" -0.006436 | \n",
" 0.010745 | \n",
" 83.404437 | \n",
" Edgardo | \n",
" Alfonzo | \n",
" 4112500 | \n",
"
\n",
" \n",
" | 4 | \n",
" alicelu01 | \n",
" 2B | \n",
" 1988 | \n",
" 2002 | \n",
" 0.035625 | \n",
" -0.009597 | \n",
" 0.007988 | \n",
" -0.026156 | \n",
" -0.006580 | \n",
" 78.561778 | \n",
" Luis | \n",
" Alicea | \n",
" 750000 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" nameFirst | \n",
" nameLast | \n",
" POS | \n",
" 1B | \n",
" 2B | \n",
" 3B | \n",
" HR | \n",
" BB | \n",
" OPW | \n",
" salary | \n",
" minYear | \n",
" maxYear | \n",
"
\n",
" \n",
" \n",
" \n",
" | 81 | \n",
" Barry | \n",
" Bonds | \n",
" OF | \n",
" -45 | \n",
" 1 | \n",
" 1 | \n",
" 31 | \n",
" 112 | \n",
" 143 | \n",
" 8541667 | \n",
" 1986 | \n",
" 2007 | \n",
"
\n",
" \n",
" | 582 | \n",
" Edgar | \n",
" Martinez | \n",
" DH | \n",
" 1 | \n",
" 12 | \n",
" -4 | \n",
" 5 | \n",
" 53 | \n",
" 114 | \n",
" 3500000 | \n",
" 1987 | \n",
" 2004 | \n",
"
\n",
" \n",
" | 274 | \n",
" Morgan | \n",
" Ensberg | \n",
" 3B | \n",
" -33 | \n",
" -3 | \n",
" -0 | \n",
" 26 | \n",
" 48 | \n",
" 109 | \n",
" 450000 | \n",
" 2000 | \n",
" 2008 | \n",
"
\n",
" \n",
" | 475 | \n",
" Nick | \n",
" Johnson | \n",
" 1B | \n",
" -30 | \n",
" 18 | \n",
" -3 | \n",
" -0 | \n",
" 72 | \n",
" 108 | \n",
" 1450000 | \n",
" 2001 | \n",
" 2012 | \n",
"
\n",
" \n",
" | 236 | \n",
" David | \n",
" Dellucci | \n",
" LF | \n",
" -51 | \n",
" -19 | \n",
" 5 | \n",
" 24 | \n",
" 57 | \n",
" 100 | \n",
" 812500 | \n",
" 1997 | \n",
" 2009 | \n",
"
\n",
" \n",
" | 922 | \n",
" Jayson | \n",
" Werth | \n",
" RF | \n",
" -15 | \n",
" -1 | \n",
" -4 | \n",
" 12 | \n",
" 33 | \n",
" 96 | \n",
" 1700000 | \n",
" 2002 | \n",
" 2013 | \n",
"
\n",
" \n",
" | 55 | \n",
" Mark | \n",
" Bellhorn | \n",
" 2B | \n",
" -39 | \n",
" 2 | \n",
" 1 | \n",
" 5 | \n",
" 47 | \n",
" 92 | \n",
" 477500 | \n",
" 1997 | \n",
" 2007 | \n",
"
\n",
" \n",
" | 872 | \n",
" Andres | \n",
" Torres | \n",
" CF | \n",
" -39 | \n",
" 25 | \n",
" 9 | \n",
" -3 | \n",
" 6 | \n",
" 84 | \n",
" 1500000 | \n",
" 2002 | \n",
" 2013 | \n",
"
\n",
" \n",
" | 966 | \n",
" Gregg | \n",
" Zaun | \n",
" C | \n",
" -8 | \n",
" -17 | \n",
" -3 | \n",
" -11 | \n",
" 52 | \n",
" 83 | \n",
" 1000000 | \n",
" 1995 | \n",
" 2010 | \n",
"
\n",
" \n",
" | 611 | \n",
" Frank | \n",
" Menechino | \n",
" SS | \n",
" -22 | \n",
" -11 | \n",
" -2 | \n",
" -14 | \n",
" 46 | \n",
" 74 | \n",
" 314750 | \n",
" 1999 | \n",
" 2005 | \n",
"
\n",
" \n",
"
\n",
"