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hw 3- 2014
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gistfile1.txt
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Homework 3: Prediction and Classification\n",
"\n",
"Due: Thursday, October 16, 2014 11:59 PM\n",
"\n",
"<a href=https://raw.githubusercontent.com/cs109/2014/master/homework/HW3.ipynb download=HW3.ipynb> Download this assignment</a>\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",
"![\"Sabermetrics Science\"](http://saberseminar.com/wp-content/uploads/2012/01/saber-web.jpg)\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",
"![\"iris data features\"](http://sebastianraschka.com/Images/2014_python_lda/iris_petal_sepal.png)\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": [
"cd C:\\Users\\tk\\Dropbox\\My final projects\\2014-master\\homework\\data"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"C:\\Users\\tk\\Dropbox\\My final projects\\2014-master\\homework\\data\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"teams = pd.read_csv('Teams.csv')\n",
"players = pd.read_csv('Batting.csv')\n",
"salaries = pd.read_csv('Salaries.csv')\n",
"fielding = pd.read_csv('Fielding.csv')\n",
"master = pd.read_csv('Master.csv')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"master.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"Index([u'playerID', u'birthYear', u'birthMonth', u'birthDay', u'birthCountry', u'birthState', u'birthCity', u'deathYear', u'deathMonth', u'deathDay', u'deathCountry', u'deathState', u'deathCity', u'nameFirst', u'nameLast', u'nameGiven', u'weight', u'height', u'bats', u'throws', u'debut', u'finalGame', u'retroID', u'bbrefID'], dtype='object')"
]
}
],
"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. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### Your code here ###\n",
"new_df = master[['playerID', 'nameFirst', 'nameLast']]\n",
"master_ns = salaries.groupby('playerID').median()\n",
"#medianSalaries = pd.merge(new_df, master_ns, on = 'playerID')\n",
"master_ns = master_ns.reset_index()\n",
"master_ns = master_ns.drop('yearID', 1)\n",
"medianSalaries = new_df.merge(master_ns, on = 'playerID')\n",
"medianSalaries.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>playerID</th>\n",
" <th>nameFirst</th>\n",
" <th>nameLast</th>\n",
" <th>salary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> aardsda01</td>\n",
" <td> David</td>\n",
" <td> Aardsma</td>\n",
" <td> 419000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> aasedo01</td>\n",
" <td> Don</td>\n",
" <td> Aase</td>\n",
" <td> 612500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> abadan01</td>\n",
" <td> Andy</td>\n",
" <td> Abad</td>\n",
" <td> 327000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> abadfe01</td>\n",
" <td> Fernando</td>\n",
" <td> Abad</td>\n",
" <td> 451500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> abbotje01</td>\n",
" <td> Jeff</td>\n",
" <td> Abbott</td>\n",
" <td> 255000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
" playerID nameFirst nameLast salary\n",
"0 aardsda01 David Aardsma 419000\n",
"1 aasedo01 Don Aase 612500\n",
"2 abadan01 Andy Abad 327000\n",
"3 abadfe01 Fernando Abad 451500\n",
"4 abbotje01 Jeff Abbott 255000"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 1(c)\n",
"\n",
"Now, consider only team/season combinations in which the teams played 162 Games. Exclude all data from before 1947. Compute the per plate appearance rates for singles, doubles, triples, HR, and BB. Create a new pandas DataFrame called `stats` that has the teamID, yearID, wins and these rates.\n",
"\n",
"**Hint**: Singles are hits that are not doubles, triples, nor HR. Plate appearances are base on balls plus at bats."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### Your code here ###\n",
"\n",
"teams_df= teams[(teams['yearID'] > 1947) & (teams['G'] ==162)]\n",
"\n",
"teams_df['PA'] = teams_df['AB'] + teams_df['BB']\n",
"teams_df['1B'] = teams_df['H'] - teams_df['2B'] - teams_df['HR'] - teams_df['3B']\n",
"\n",
"for i in [\"1B\",\"2B\",\"3B\",\"HR\",\"BB\"]:\n",
" teams_df[i]=teams_df[i]/teams_df.PA\n",
"\n",
"stats = teams_df[[\"teamID\",\"yearID\",\"W\",\"1B\",\"2B\",\"3B\",\"HR\",\"BB\"]].copy()\n",
"stats.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"-c:9: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_index,col_indexer] = value instead\n"
]
},
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>teamID</th>\n",
" <th>yearID</th>\n",
" <th>W</th>\n",
" <th>1B</th>\n",
" <th>2B</th>\n",
" <th>3B</th>\n",
" <th>HR</th>\n",
" <th>BB</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1366</th>\n",
" <td> LAA</td>\n",
" <td> 1961</td>\n",
" <td> 70</td>\n",
" <td> 0.147748</td>\n",
" <td> 0.035708</td>\n",
" <td> 0.003604</td>\n",
" <td> 0.030958</td>\n",
" <td> 0.111548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1367</th>\n",
" <td> KC1</td>\n",
" <td> 1961</td>\n",
" <td> 61</td>\n",
" <td> 0.164751</td>\n",
" <td> 0.035982</td>\n",
" <td> 0.007829</td>\n",
" <td> 0.014993</td>\n",
" <td> 0.096618</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1377</th>\n",
" <td> NYA</td>\n",
" <td> 1962</td>\n",
" <td> 96</td>\n",
" <td> 0.167148</td>\n",
" <td> 0.038536</td>\n",
" <td> 0.004656</td>\n",
" <td> 0.031952</td>\n",
" <td> 0.093770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1379</th>\n",
" <td> LAA</td>\n",
" <td> 1962</td>\n",
" <td> 86</td>\n",
" <td> 0.159482</td>\n",
" <td> 0.038027</td>\n",
" <td> 0.005737</td>\n",
" <td> 0.022455</td>\n",
" <td> 0.098672</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1381</th>\n",
" <td> CHA</td>\n",
" <td> 1962</td>\n",
" <td> 85</td>\n",
" <td> 0.165797</td>\n",
" <td> 0.040756</td>\n",
" <td> 0.009129</td>\n",
" <td> 0.014998</td>\n",
" <td> 0.101076</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
" teamID yearID W 1B 2B 3B HR BB\n",
"1366 LAA 1961 70 0.147748 0.035708 0.003604 0.030958 0.111548\n",
"1367 KC1 1961 61 0.164751 0.035982 0.007829 0.014993 0.096618\n",
"1377 NYA 1962 96 0.167148 0.038536 0.004656 0.031952 0.093770\n",
"1379 LAA 1962 86 0.159482 0.038027 0.005737 0.022455 0.098672\n",
"1381 CHA 1962 85 0.165797 0.040756 0.009129 0.014998 0.101076"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 1(d)\n",
"\n",
"Is there a noticeable time trend in the rates computed computed in Problem 1(c)? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 1(e) \n",
"\n",
"Using the `stats` DataFrame from Problem 1(c), adjust the singles per PA rates so that the average across teams for each year is 0. Do the same for the doubles, triples, HR, and BB rates. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
" def mean(df):\n",
" for val in [\"1B\",\"2B\",\"3B\",\"HR\",\"BB\"]:\n",
" df[val] = df[val] -df[val].mean(axis=0)\n",
" return df\n",
"\n",
"stats = stats.groupby('yearID').apply(mean)\n",
"stats.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>teamID</th>\n",
" <th>yearID</th>\n",
" <th>W</th>\n",
" <th>1B</th>\n",
" <th>2B</th>\n",
" <th>3B</th>\n",
" <th>HR</th>\n",
" <th>BB</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1366</th>\n",
" <td> LAA</td>\n",
" <td> 1961</td>\n",
" <td> 70</td>\n",
" <td>-0.008502</td>\n",
" <td>-0.000137</td>\n",
" <td>-0.002113</td>\n",
" <td> 0.007983</td>\n",
" <td> 0.007465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1367</th>\n",
" <td> KC1</td>\n",
" <td> 1961</td>\n",
" <td> 61</td>\n",
" <td> 0.008502</td>\n",
" <td> 0.000137</td>\n",
" <td> 0.002113</td>\n",
" <td>-0.007983</td>\n",
" <td>-0.007465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1377</th>\n",
" <td> NYA</td>\n",
" <td> 1962</td>\n",
" <td> 96</td>\n",
" <td> 0.001516</td>\n",
" <td> 0.002683</td>\n",
" <td>-0.002121</td>\n",
" <td> 0.008141</td>\n",
" <td> 0.005180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1379</th>\n",
" <td> LAA</td>\n",
" <td> 1962</td>\n",
" <td> 86</td>\n",
" <td>-0.006150</td>\n",
" <td> 0.002174</td>\n",
" <td>-0.001040</td>\n",
" <td>-0.001356</td>\n",
" <td> 0.010082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1381</th>\n",
" <td> CHA</td>\n",
" <td> 1962</td>\n",
" <td> 85</td>\n",
" <td> 0.000165</td>\n",
" <td> 0.004904</td>\n",
" <td> 0.002352</td>\n",
" <td>-0.008813</td>\n",
" <td> 0.012486</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
" teamID yearID W 1B 2B 3B HR BB\n",
"1366 LAA 1961 70 -0.008502 -0.000137 -0.002113 0.007983 0.007465\n",
"1367 KC1 1961 61 0.008502 0.000137 0.002113 -0.007983 -0.007465\n",
"1377 NYA 1962 96 0.001516 0.002683 -0.002121 0.008141 0.005180\n",
"1379 LAA 1962 86 -0.006150 0.002174 -0.001040 -0.001356 0.010082\n",
"1381 CHA 1962 85 0.000165 0.004904 0.002352 -0.008813 0.012486"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 1(f)\n",
"\n",
"Build a simple linear regression model to predict the number of wins from the average adjusted singles, double, triples, HR, and BB rates. To decide which of these terms to include fit the model to data from 2002 and compute the average squared residuals from predictions to years past 2002. Use the fitted model to define a new sabermetric summary: offensive predicted wins (OPW). Hint: the new summary should be a linear combination of one to five of the five rates.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"stats[stats.yearID < 2002].head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>teamID</th>\n",
" <th>yearID</th>\n",
" <th>W</th>\n",
" <th>1B</th>\n",
" <th>2B</th>\n",
" <th>3B</th>\n",
" <th>HR</th>\n",
" <th>BB</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1366</th>\n",
" <td> LAA</td>\n",
" <td> 1961</td>\n",
" <td> 70</td>\n",
" <td>-0.008502</td>\n",
" <td>-0.000137</td>\n",
" <td>-0.002113</td>\n",
" <td> 0.007983</td>\n",
" <td> 0.007465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1367</th>\n",
" <td> KC1</td>\n",
" <td> 1961</td>\n",
" <td> 61</td>\n",
" <td> 0.008502</td>\n",
" <td> 0.000137</td>\n",
" <td> 0.002113</td>\n",
" <td>-0.007983</td>\n",
" <td>-0.007465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1377</th>\n",
" <td> NYA</td>\n",
" <td> 1962</td>\n",
" <td> 96</td>\n",
" <td> 0.001516</td>\n",
" <td> 0.002683</td>\n",
" <td>-0.002121</td>\n",
" <td> 0.008141</td>\n",
" <td> 0.005180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1379</th>\n",
" <td> LAA</td>\n",
" <td> 1962</td>\n",
" <td> 86</td>\n",
" <td>-0.006150</td>\n",
" <td> 0.002174</td>\n",
" <td>-0.001040</td>\n",
" <td>-0.001356</td>\n",
" <td> 0.010082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1381</th>\n",
" <td> CHA</td>\n",
" <td> 1962</td>\n",
" <td> 85</td>\n",
" <td> 0.000165</td>\n",
" <td> 0.004904</td>\n",
" <td> 0.002352</td>\n",
" <td>-0.008813</td>\n",
" <td> 0.012486</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
" teamID yearID W 1B 2B 3B HR BB\n",
"1366 LAA 1961 70 -0.008502 -0.000137 -0.002113 0.007983 0.007465\n",
"1367 KC1 1961 61 0.008502 0.000137 0.002113 -0.007983 -0.007465\n",
"1377 NYA 1962 96 0.001516 0.002683 -0.002121 0.008141 0.005180\n",
"1379 LAA 1962 86 -0.006150 0.002174 -0.001040 -0.001356 0.010082\n",
"1381 CHA 1962 85 0.000165 0.004904 0.002352 -0.008813 0.012486"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn import linear_model\n",
"clf = linear_model.LinearRegression()\n",
"\n",
"stat_train = stats[stats.yearID < 2002]\n",
"\n",
"stat_test = stats[stats.yearID > 2002]\n",
"\n",
"X_train = stat_train[['1B','2B','3B','HR','BB']].values\n",
"y_train = stat_train['W'].values\n",
"\n",
"X_test = stat_test[['1B','2B', '3B', 'HR', 'BB']].values\n",
"y_test = stat_test['W'].values\n",
"\n",
"clf.fit(X_train, y_train)\n",
"clf.coef_\n",
"\n",
"predict = clf.predict(X_test)\n",
"np.mean((y_test - predict)**2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"82.415034727463663"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.scatter(predict, predict - y_test, c='g', s =40, alpha = 0.5 )\n",
"plt.hlines(y=0, xmin =50, xmax =100)\n",
"plt.xlabel('predicted value of x_test')\n",
"plt.ylabel('diff between prediced (X_test) and Y_test value')\n",
"plt.title('Residual plots')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"<matplotlib.text.Text at 0xe7d6830>"
]
},
{
"metadata": {},
"output_type": "display_data",
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L3A2ehR5eaX2VHad3MDC0wMhG+cQvMDKtKTARyMXWkmCflE1tAJRS1yil3lJK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\n1dZCJkomm0DEUCiEbmvGLDN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Y62h/IXek2qNP2G9ea/2QUuoP+RNJEIRUruKLli+zdQ07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\nVX0BJ8gXRbx0MnBwJNc0ZiJIhVXomcL2sxsDWaFQaNAviMhu4G7gGuB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"text": [
"<matplotlib.figure.Figure at 0x2fa9290>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Your answer here: **\n",
"\n",
"The mean squared error is 102.42"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 1(g)\n",
"\n",
"Now we will create a similar database for individual players. Consider only player/year combinations in which the player had at least 500 plate appearances. Consider only the years we considered for the calculations above (after 1947 and seasons with 162 games). For each player/year compute singles, doubles, triples, HR, BB per plate appearance rates. Create a new pandas DataFrame called `playerstats` that has the playerID, yearID and the rates of these stats. Remove the average for each year as for these rates as done in Problem 1(e). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show the head of the `playerstats` DataFrame. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### Your code here ###\n",
"players.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>playerID</th>\n",
" <th>yearID</th>\n",
" <th>stint</th>\n",
" <th>teamID</th>\n",
" <th>lgID</th>\n",
" <th>G</th>\n",
" <th>G_batting</th>\n",
" <th>AB</th>\n",
" <th>R</th>\n",
" <th>H</th>\n",
" <th>...</th>\n",
" <th>SB</th>\n",
" <th>CS</th>\n",
" <th>BB</th>\n",
" <th>SO</th>\n",
" <th>IBB</th>\n",
" <th>HBP</th>\n",
" <th>SH</th>\n",
" <th>SF</th>\n",
" <th>GIDP</th>\n",
" <th>G_old</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> aardsda01</td>\n",
" <td> 2004</td>\n",
" <td> 1</td>\n",
" <td> SFN</td>\n",
" <td> NL</td>\n",
" <td> 11</td>\n",
" <td> 11</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> aardsda01</td>\n",
" <td> 2006</td>\n",
" <td> 1</td>\n",
" <td> CHN</td>\n",
" <td> NL</td>\n",
" <td> 45</td>\n",
" <td> 43</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> aardsda01</td>\n",
" <td> 2007</td>\n",
" <td> 1</td>\n",
" <td> CHA</td>\n",
" <td> AL</td>\n",
" <td> 25</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> aardsda01</td>\n",
" <td> 2008</td>\n",
" <td> 1</td>\n",
" <td> BOS</td>\n",
" <td> AL</td>\n",
" <td> 47</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> aardsda01</td>\n",
" <td> 2009</td>\n",
" <td> 1</td>\n",
" <td> SEA</td>\n",
" <td> AL</td>\n",
" <td> 73</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 24 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
" playerID yearID stint teamID lgID G G_batting AB R H ... SB CS \\\n",
"0 aardsda01 2004 1 SFN NL 11 11 0 0 0 ... 0 0 \n",
"1 aardsda01 2006 1 CHN NL 45 43 2 0 0 ... 0 0 \n",
"2 aardsda01 2007 1 CHA AL 25 2 0 0 0 ... 0 0 \n",
"3 aardsda01 2008 1 BOS AL 47 5 1 0 0 ... 0 0 \n",
"4 aardsda01 2009 1 SEA AL 73 3 0 0 0 ... 0 0 \n",
"\n",
" BB SO IBB HBP SH SF GIDP G_old \n",
"0 0 0 0 0 0 0 0 11 \n",
"1 0 0 0 0 1 0 0 45 \n",
"2 0 0 0 0 0 0 0 2 \n",
"3 0 1 0 0 0 0 0 5 \n",
"4 0 0 0 0 0 0 0 NaN \n",
"\n",
"[5 rows x 24 columns]"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"player_ques = players[(players.AB + players.BB > 500) & (players.yearID > 1947) ].copy()\n",
"\n",
"\n",
"player_ques['1B'] = player_ques.H - player_ques[\"2B\"] - player_ques[\"3B\"] - player_ques[\"HR\"]\n",
"player_ques[\"PA\"] = player_ques.BB + player_ques.AB\n",
"\n",
"\n",
"for val in ['1B', '2B', '3B', 'HR', 'BB']:\n",
" player_ques[val] = player_ques[val]/player_ques.PA\n",
"\n",
"playerstats = player_ques[[\"playerID\",\"yearID\",\"1B\",\"2B\",\"3B\",\"HR\",\"BB\"]].copy()\n",
"\n",
"playerstats = playerstats.groupby('yearID').apply(mean)\n",
"\n",
"playerstats.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>playerID</th>\n",
" <th>yearID</th>\n",
" <th>1B</th>\n",
" <th>2B</th>\n",
" <th>3B</th>\n",
" <th>HR</th>\n",
" <th>BB</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> aaronha01</td>\n",
" <td> 1955</td>\n",
" <td> 0.001060</td>\n",
" <td> 0.018570</td>\n",
" <td> 0.005585</td>\n",
" <td> 0.011337</td>\n",
" <td>-0.027249</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> aaronha01</td>\n",
" <td> 1956</td>\n",
" <td> 0.021561</td>\n",
" <td> 0.013857</td>\n",
" <td> 0.012480</td>\n",
" <td> 0.009593</td>\n",
" <td>-0.044600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> aaronha01</td>\n",
" <td> 1957</td>\n",
" <td> 0.004817</td>\n",
" <td>-0.002034</td>\n",
" <td> 0.000466</td>\n",
" <td> 0.037093</td>\n",
" <td>-0.013166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> aaronha01</td>\n",
" <td> 1958</td>\n",
" <td> 0.018367</td>\n",
" <td> 0.011015</td>\n",
" <td>-0.002219</td>\n",
" <td> 0.015398</td>\n",
" <td>-0.007762</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> aaronha01</td>\n",
" <td> 1959</td>\n",
" <td> 0.016261</td>\n",
" <td> 0.025762</td>\n",
" <td> 0.002743</td>\n",
" <td> 0.028368</td>\n",
" <td>-0.022898</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
" playerID yearID 1B 2B 3B HR BB\n",
"8 aaronha01 1955 0.001060 0.018570 0.005585 0.011337 -0.027249\n",
"9 aaronha01 1956 0.021561 0.013857 0.012480 0.009593 -0.044600\n",
"10 aaronha01 1957 0.004817 -0.002034 0.000466 0.037093 -0.013166\n",
"11 aaronha01 1958 0.018367 0.011015 -0.002219 0.015398 -0.007762\n",
"12 aaronha01 1959 0.016261 0.025762 0.002743 0.028368 -0.022898"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 1(h)\n",
"\n",
"Using the `playerstats` DataFrame created in Problem 1(g), create a new DataFrame called `playerLS` containing the player's lifetime stats. This DataFrame should contain the playerID, the year the player's career started, the year the player's career ended and the player's lifetime average for each of the quantities (singles, doubles, triples, HR, BB). For simplicity we will simply compute the avaerage of the rates by year (a more correct way is to go back to the totals). "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"player_avg = playerstats.groupby('playerID').apply(lambda x: x.mean()) #I am calculating the life time average for each player. \n",
"player_avg = player_avg.reset_index()\n",
"player_avg.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>playerID</th>\n",
" <th>yearID</th>\n",
" <th>1B</th>\n",
" <th>2B</th>\n",
" <th>3B</th>\n",
" <th>HR</th>\n",
" <th>BB</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> aaronha01</td>\n",
" <td> 1964.105263</td>\n",
" <td>-0.007157</td>\n",
" <td> 0.006539</td>\n",
" <td>-0.000270</td>\n",
" <td> 0.027850</td>\n",
" <td> 0.009447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> abramca01</td>\n",
" <td> 1953.000000</td>\n",
" <td> 0.013463</td>\n",
" <td>-0.023915</td>\n",
" <td> 0.002384</td>\n",
" <td> 0.003842</td>\n",
" <td> 0.019455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> abreubo01</td>\n",
" <td> 2004.384615</td>\n",
" <td>-0.008202</td>\n",
" <td> 0.006421</td>\n",
" <td> 0.001002</td>\n",
" <td>-0.003252</td>\n",
" <td> 0.050501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> ackledu01</td>\n",
" <td> 2012.000000</td>\n",
" <td>-0.009270</td>\n",
" <td>-0.016605</td>\n",
" <td>-0.001974</td>\n",
" <td>-0.015274</td>\n",
" <td> 0.001597</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> adairje01</td>\n",
" <td> 1963.666667</td>\n",
" <td> 0.011933</td>\n",
" <td> 0.003286</td>\n",
" <td>-0.002139</td>\n",
" <td>-0.012934</td>\n",
" <td>-0.037229</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
" playerID yearID 1B 2B 3B HR BB\n",
"0 aaronha01 1964.105263 -0.007157 0.006539 -0.000270 0.027850 0.009447\n",
"1 abramca01 1953.000000 0.013463 -0.023915 0.002384 0.003842 0.019455\n",
"2 abreubo01 2004.384615 -0.008202 0.006421 0.001002 -0.003252 0.050501\n",
"3 ackledu01 2012.000000 -0.009270 -0.016605 -0.001974 -0.015274 0.001597\n",
"4 adairje01 1963.666667 0.011933 0.003286 -0.002139 -0.012934 -0.037229"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show the head of the `playerLS` DataFrame. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"playerLs = master[['playerID', 'debut', 'finalGame']].merge(player_avg, on ='playerID')\n",
"playerLs.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>playerID</th>\n",
" <th>debut</th>\n",
" <th>finalGame</th>\n",
" <th>yearID</th>\n",
" <th>1B</th>\n",
" <th>2B</th>\n",
" <th>3B</th>\n",
" <th>HR</th>\n",
" <th>BB</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> aaronha01</td>\n",
" <td> 1954-04-13</td>\n",
" <td> 1976-10-03</td>\n",
" <td> 1964.105263</td>\n",
" <td>-0.007157</td>\n",
" <td> 0.006539</td>\n",
" <td>-0.000270</td>\n",
" <td> 0.027850</td>\n",
" <td> 0.009447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> abramca01</td>\n",
" <td> 1949-04-20</td>\n",
" <td> 1956-05-09</td>\n",
" <td> 1953.000000</td>\n",
" <td> 0.013463</td>\n",
" <td>-0.023915</td>\n",
" <td> 0.002384</td>\n",
" <td> 0.003842</td>\n",
" <td> 0.019455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> abreubo01</td>\n",
" <td> 1996-09-01</td>\n",
" <td> 2012-10-02</td>\n",
" <td> 2004.384615</td>\n",
" <td>-0.008202</td>\n",
" <td> 0.006421</td>\n",
" <td> 0.001002</td>\n",
" <td>-0.003252</td>\n",
" <td> 0.050501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> ackledu01</td>\n",
" <td> 2011-06-17</td>\n",
" <td> 2013-09-29</td>\n",
" <td> 2012.000000</td>\n",
" <td>-0.009270</td>\n",
" <td>-0.016605</td>\n",
" <td>-0.001974</td>\n",
" <td>-0.015274</td>\n",
" <td> 0.001597</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> adairje01</td>\n",
" <td> 1958-09-02</td>\n",
" <td> 1970-05-03</td>\n",
" <td> 1963.666667</td>\n",
" <td> 0.011933</td>\n",
" <td> 0.003286</td>\n",
" <td>-0.002139</td>\n",
" <td>-0.012934</td>\n",
" <td>-0.037229</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
" playerID debut finalGame yearID 1B 2B \\\n",
"0 aaronha01 1954-04-13 1976-10-03 1964.105263 -0.007157 0.006539 \n",
"1 abramca01 1949-04-20 1956-05-09 1953.000000 0.013463 -0.023915 \n",
"2 abreubo01 1996-09-01 2012-10-02 2004.384615 -0.008202 0.006421 \n",
"3 ackledu01 2011-06-17 2013-09-29 2012.000000 -0.009270 -0.016605 \n",
"4 adairje01 1958-09-02 1970-05-03 1963.666667 0.011933 0.003286 \n",
"\n",
" 3B HR BB \n",
"0 -0.000270 0.027850 0.009447 \n",
"1 0.002384 0.003842 0.019455 \n",
"2 0.001002 -0.003252 0.050501 \n",
"3 -0.001974 -0.015274 0.001597 \n",
"4 -0.002139 -0.012934 -0.037229 "
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"playerLs['debut'] = pd.to_datetime(playerLs.debut)\n",
"playerLs['finalGame']= pd.to_datetime(playerLs.finalGame)\n",
"playerLs['minYear'] = map(lambda x: x.year, playerLs.debut)\n",
"playerLs['maxYear'] = map(lambda x: x.year, playerLs.finalGame)\n",
"playerLw= playerLs.drop(['debut', 'finalGame'], axis =1)\n",
"\n",
"playerLw.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>playerID</th>\n",
" <th>yearID</th>\n",
" <th>1B</th>\n",
" <th>2B</th>\n",
" <th>3B</th>\n",
" <th>HR</th>\n",
" <th>BB</th>\n",
" <th>minYear</th>\n",
" <th>maxYear</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> aaronha01</td>\n",
" <td> 1964.105263</td>\n",
" <td>-0.007157</td>\n",
" <td> 0.006539</td>\n",
" <td>-0.000270</td>\n",
" <td> 0.027850</td>\n",
" <td> 0.009447</td>\n",
" <td> 1954</td>\n",
" <td> 1976</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> abramca01</td>\n",
" <td> 1953.000000</td>\n",
" <td> 0.013463</td>\n",
" <td>-0.023915</td>\n",
" <td> 0.002384</td>\n",
" <td> 0.003842</td>\n",
" <td> 0.019455</td>\n",
" <td> 1949</td>\n",
" <td> 1956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> abreubo01</td>\n",
" <td> 2004.384615</td>\n",
" <td>-0.008202</td>\n",
" <td> 0.006421</td>\n",
" <td> 0.001002</td>\n",
" <td>-0.003252</td>\n",
" <td> 0.050501</td>\n",
" <td> 1996</td>\n",
" <td> 2012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> ackledu01</td>\n",
" <td> 2012.000000</td>\n",
" <td>-0.009270</td>\n",
" <td>-0.016605</td>\n",
" <td>-0.001974</td>\n",
" <td>-0.015274</td>\n",
" <td> 0.001597</td>\n",
" <td> 2011</td>\n",
" <td> 2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> adairje01</td>\n",
" <td> 1963.666667</td>\n",
" <td> 0.011933</td>\n",
" <td> 0.003286</td>\n",
" <td>-0.002139</td>\n",
" <td>-0.012934</td>\n",
" <td>-0.037229</td>\n",
" <td> 1958</td>\n",
" <td> 1970</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
" playerID yearID 1B 2B 3B HR BB \\\n",
"0 aaronha01 1964.105263 -0.007157 0.006539 -0.000270 0.027850 0.009447 \n",
"1 abramca01 1953.000000 0.013463 -0.023915 0.002384 0.003842 0.019455 \n",
"2 abreubo01 2004.384615 -0.008202 0.006421 0.001002 -0.003252 0.050501 \n",
"3 ackledu01 2012.000000 -0.009270 -0.016605 -0.001974 -0.015274 0.001597 \n",
"4 adairje01 1963.666667 0.011933 0.003286 -0.002139 -0.012934 -0.037229 \n",
"\n",
" minYear maxYear \n",
"0 1954 1976 \n",
"1 1949 1956 \n",
"2 1996 2012 \n",
"3 2011 2013 \n",
"4 1958 1970 "
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ls = list(playerLw)\n",
"my_order = [ 0, 7,8, 1,2,3,4,5,6]\n",
"my_list = [ls[i] for i in my_order]\n",
"my_list"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"['playerID', 'minYear', 'maxYear', 'yearID', '1B', '2B', '3B', 'HR', 'BB']"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"playerLw= playerLw[my_list]\n",
"playerLw = playerLw.drop('yearID', axis =1)\n",
"playerLw.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>playerID</th>\n",
" <th>minYear</th>\n",
" <th>maxYear</th>\n",
" <th>1B</th>\n",
" <th>2B</th>\n",
" <th>3B</th>\n",
" <th>HR</th>\n",
" <th>BB</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> aaronha01</td>\n",
" <td> 1954</td>\n",
" <td> 1976</td>\n",
" <td>-0.007157</td>\n",
" <td> 0.006539</td>\n",
" <td>-0.000270</td>\n",
" <td> 0.027850</td>\n",
" <td> 0.009447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> abramca01</td>\n",
" <td> 1949</td>\n",
" <td> 1956</td>\n",
" <td> 0.013463</td>\n",
" <td>-0.023915</td>\n",
" <td> 0.002384</td>\n",
" <td> 0.003842</td>\n",
" <td> 0.019455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> abreubo01</td>\n",
" <td> 1996</td>\n",
" <td> 2012</td>\n",
" <td>-0.008202</td>\n",
" <td> 0.006421</td>\n",
" <td> 0.001002</td>\n",
" <td>-0.003252</td>\n",
" <td> 0.050501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> ackledu01</td>\n",
" <td> 2011</td>\n",
" <td> 2013</td>\n",
" <td>-0.009270</td>\n",
" <td>-0.016605</td>\n",
" <td>-0.001974</td>\n",
" <td>-0.015274</td>\n",
" <td> 0.001597</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> adairje01</td>\n",
" <td> 1958</td>\n",
" <td> 1970</td>\n",
" <td> 0.011933</td>\n",
" <td> 0.003286</td>\n",
" <td>-0.002139</td>\n",
" <td>-0.012934</td>\n",
" <td>-0.037229</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
" playerID minYear maxYear 1B 2B 3B HR \\\n",
"0 aaronha01 1954 1976 -0.007157 0.006539 -0.000270 0.027850 \n",
"1 abramca01 1949 1956 0.013463 -0.023915 0.002384 0.003842 \n",
"2 abreubo01 1996 2012 -0.008202 0.006421 0.001002 -0.003252 \n",
"3 ackledu01 2011 2013 -0.009270 -0.016605 -0.001974 -0.015274 \n",
"4 adairje01 1958 1970 0.011933 0.003286 -0.002139 -0.012934 \n",
"\n",
" BB \n",
"0 0.009447 \n",
"1 0.019455 \n",
"2 0.050501 \n",
"3 0.001597 \n",
"4 -0.037229 "
]
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 1(i)\n",
"\n",
"Compute the OPW for each player based on the average rates in the `playerLS` DataFrame. You can interpret this summary statistic as the predicted wins for a team with 9 batters exactly like the player in question. Add this column to the playerLS DataFrame. Call this colum OPW."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"predict_rates = playerLw[['1B', '2B', '3B', 'HR', 'BB']]\n",
"playerLw['OPW']= clf.predict(predict_rates)\n",
"playerLw.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>playerID</th>\n",
" <th>minYear</th>\n",
" <th>maxYear</th>\n",
" <th>1B</th>\n",
" <th>2B</th>\n",
" <th>3B</th>\n",
" <th>HR</th>\n",
" <th>BB</th>\n",
" <th>OPW</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> aaronha01</td>\n",
" <td> 1954</td>\n",
" <td> 1976</td>\n",
" <td>-0.007157</td>\n",
" <td> 0.006539</td>\n",
" <td>-0.000270</td>\n",
" <td> 0.027850</td>\n",
" <td> 0.009447</td>\n",
" <td> 108.696139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> abramca01</td>\n",
" <td> 1949</td>\n",
" <td> 1956</td>\n",
" <td> 0.013463</td>\n",
" <td>-0.023915</td>\n",
" <td> 0.002384</td>\n",
" <td> 0.003842</td>\n",
" <td> 0.019455</td>\n",
" <td> 92.575472</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> abreubo01</td>\n",
" <td> 1996</td>\n",
" <td> 2012</td>\n",
" <td>-0.008202</td>\n",
" <td> 0.006421</td>\n",
" <td> 0.001002</td>\n",
" <td>-0.003252</td>\n",
" <td> 0.050501</td>\n",
" <td> 104.050008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> ackledu01</td>\n",
" <td> 2011</td>\n",
" <td> 2013</td>\n",
" <td>-0.009270</td>\n",
" <td>-0.016605</td>\n",
" <td>-0.001974</td>\n",
" <td>-0.015274</td>\n",
" <td> 0.001597</td>\n",
" <td> 53.806003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> adairje01</td>\n",
" <td> 1958</td>\n",
" <td> 1970</td>\n",
" <td> 0.011933</td>\n",
" <td> 0.003286</td>\n",
" <td>-0.002139</td>\n",
" <td>-0.012934</td>\n",
" <td>-0.037229</td>\n",
" <td> 56.395050</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
" playerID minYear maxYear 1B 2B 3B HR \\\n",
"0 aaronha01 1954 1976 -0.007157 0.006539 -0.000270 0.027850 \n",
"1 abramca01 1949 1956 0.013463 -0.023915 0.002384 0.003842 \n",
"2 abreubo01 1996 2012 -0.008202 0.006421 0.001002 -0.003252 \n",
"3 ackledu01 2011 2013 -0.009270 -0.016605 -0.001974 -0.015274 \n",
"4 adairje01 1958 1970 0.011933 0.003286 -0.002139 -0.012934 \n",
"\n",
" BB OPW \n",
"0 0.009447 108.696139 \n",
"1 0.019455 92.575472 \n",
"2 0.050501 104.050008 \n",
"3 0.001597 53.806003 \n",
"4 -0.037229 56.395050 "
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 1(j)\n",
"\n",
"Add four columns to the `playerLS` DataFrame that contains the player's position (C, 1B, 2B, 3B, SS, LF, CF, RF, or OF), first name, last name and median salary. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from collections import defaultdict\n",
"\n",
"def fun(df):\n",
" df1 = df.POS\n",
" result = defaultdict(int)\n",
" \n",
" for val in df1:\n",
" result[val] += 1\n",
" d= max(result.iteritems(), key = lambda x: x[1])\n",
" return d[0]\n",
"\n",
"fielding_data = fielding.groupby('playerID').apply(fun)\n",
"fielding_data = fielding_data.reset_index()\n",
"\n",
"fielding_data = fielding_data.rename(columns = {0: 'POS'})\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"merged = fielding_data.merge(playerLw, on = 'playerID')\n",
"playerLw_merged = merged.merge(medianSalaries, on = 'playerID')\n",
"playerLw_merged.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>playerID</th>\n",
" <th>POS</th>\n",
" <th>minYear</th>\n",
" <th>maxYear</th>\n",
" <th>1B</th>\n",
" <th>2B</th>\n",
" <th>3B</th>\n",
" <th>HR</th>\n",
" <th>BB</th>\n",
" <th>OPW</th>\n",
" <th>nameFirst</th>\n",
" <th>nameLast</th>\n",
" <th>salary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> abreubo01</td>\n",
" <td> RF</td>\n",
" <td> 1996</td>\n",
" <td> 2012</td>\n",
" <td>-0.008202</td>\n",
" <td> 0.006421</td>\n",
" <td> 0.001002</td>\n",
" <td>-0.003252</td>\n",
" <td> 0.050501</td>\n",
" <td> 104.050008</td>\n",
" <td> Bobby</td>\n",
" <td> Abreu</td>\n",
" <td> 9000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> ackledu01</td>\n",
" <td> 1B</td>\n",
" <td> 2011</td>\n",
" <td> 2013</td>\n",
" <td>-0.009270</td>\n",
" <td>-0.016605</td>\n",
" <td>-0.001974</td>\n",
" <td>-0.015274</td>\n",
" <td> 0.001597</td>\n",
" <td> 53.806003</td>\n",
" <td> Dustin</td>\n",
" <td> Ackley</td>\n",
" <td> 2400000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> adamsru01</td>\n",
" <td> SS</td>\n",
" <td> 2004</td>\n",
" <td> 2009</td>\n",
" <td>-0.007867</td>\n",
" <td>-0.001289</td>\n",
" <td> 0.004160</td>\n",
" <td>-0.017533</td>\n",
" <td> 0.002672</td>\n",
" <td> 67.496507</td>\n",
" <td> Russ</td>\n",
" <td> Adams</td>\n",
" <td> 329500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> alfoned01</td>\n",
" <td> 2B</td>\n",
" <td> 1995</td>\n",
" <td> 2006</td>\n",
" <td> 0.013485</td>\n",
" <td>-0.002177</td>\n",
" <td>-0.003239</td>\n",
" <td>-0.006436</td>\n",
" <td> 0.010745</td>\n",
" <td> 83.404437</td>\n",
" <td> Edgardo</td>\n",
" <td> Alfonzo</td>\n",
" <td> 4112500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> alicelu01</td>\n",
" <td> 2B</td>\n",
" <td> 1988</td>\n",
" <td> 2002</td>\n",
" <td> 0.035625</td>\n",
" <td>-0.009597</td>\n",
" <td> 0.007988</td>\n",
" <td>-0.026156</td>\n",
" <td>-0.006580</td>\n",
" <td> 78.561778</td>\n",
" <td> Luis</td>\n",
" <td> Alicea</td>\n",
" <td> 750000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
" playerID POS minYear maxYear 1B 2B 3B HR \\\n",
"0 abreubo01 RF 1996 2012 -0.008202 0.006421 0.001002 -0.003252 \n",
"1 ackledu01 1B 2011 2013 -0.009270 -0.016605 -0.001974 -0.015274 \n",
"2 adamsru01 SS 2004 2009 -0.007867 -0.001289 0.004160 -0.017533 \n",
"3 alfoned01 2B 1995 2006 0.013485 -0.002177 -0.003239 -0.006436 \n",
"4 alicelu01 2B 1988 2002 0.035625 -0.009597 0.007988 -0.026156 \n",
"\n",
" BB OPW nameFirst nameLast salary \n",
"0 0.050501 104.050008 Bobby Abreu 9000000 \n",
"1 0.001597 53.806003 Dustin Ackley 2400000 \n",
"2 0.002672 67.496507 Russ Adams 329500 \n",
"3 0.010745 83.404437 Edgardo Alfonzo 4112500 \n",
"4 -0.006580 78.561778 Luis Alicea 750000 "
]
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 1(k)\n",
"\n",
"Subset the `playerLS` DataFrame for players active in 2002 and 2003 and played at least three years. Plot and describe the relationship bewteen the median salary (in millions) and the predicted number of wins. [Forbes article on log scale](http://www.forbes.com/sites/naomirobbins/2012/01/19/when-should-i-use-logarithmic-scales-in-my-charts-and-graphs/)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"active = playerLw_merged[(playerLw_merged[\"minYear\"] <= 2002) & \\\n",
" (playerLw_merged[\"maxYear\"] >= 2003) & \\\n",
" (playerLw_merged[\"maxYear\"] - playerLw_merged[\"minYear\"] >= 3) ]\n",
"\n",
"plt.scatter(active.salary/ 10**6, active.OPW, c='g' )\n",
"plt.xscale('log')\n",
"plt.xlabel('salary in millions on log')\n",
"plt.title('Relationship between Salary and Predicted Number of Wins')\n",
"plt.ylabel('OPW')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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1pbFi9leNIxrCbI2oxDUwa8GtTH5jEjyDD9ID8AF6o5/ONp8hf+nKyavAnsUd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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xe75f2b0>"
]
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 1(l)\n",
"Pick one players from one of each of these 10 position C, 1B, 2B, 3B, SS, LF, CF, RF, DH, or OF keeping the total median salary of all 10 players below 20 million. Report their averaged predicted wins and total salary."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### Yout6r code here ###\n",
"def mean_norm(df):\n",
" temp = df[['resid']] \n",
" df[['resid']]=temp-temp.median(axis=0)\n",
" return df\n",
"\n",
"active['resid'] = active['OPW']\n",
"active = active.groupby('POS').apply(mean_norm)\n",
"active.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>playerID</th>\n",
" <th>POS</th>\n",
" <th>minYear</th>\n",
" <th>maxYear</th>\n",
" <th>1B</th>\n",
" <th>2B</th>\n",
" <th>3B</th>\n",
" <th>HR</th>\n",
" <th>BB</th>\n",
" <th>OPW</th>\n",
" <th>nameFirst</th>\n",
" <th>nameLast</th>\n",
" <th>salary</th>\n",
" <th>resid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> abreubo01</td>\n",
" <td> RF</td>\n",
" <td> 1996</td>\n",
" <td> 2012</td>\n",
" <td>-0.008202</td>\n",
" <td> 0.006421</td>\n",
" <td> 0.001002</td>\n",
" <td>-0.003252</td>\n",
" <td> 0.050501</td>\n",
" <td> 104.050008</td>\n",
" <td> Bobby</td>\n",
" <td> Abreu</td>\n",
" <td> 9000000</td>\n",
" <td> 16.510691</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> alfoned01</td>\n",
" <td> 2B</td>\n",
" <td> 1995</td>\n",
" <td> 2006</td>\n",
" <td> 0.013485</td>\n",
" <td>-0.002177</td>\n",
" <td>-0.003239</td>\n",
" <td>-0.006436</td>\n",
" <td> 0.010745</td>\n",
" <td> 83.404437</td>\n",
" <td> Edgardo</td>\n",
" <td> Alfonzo</td>\n",
" <td> 4112500</td>\n",
" <td> 10.674691</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> allench01</td>\n",
" <td> LF</td>\n",
" <td> 1999</td>\n",
" <td> 2005</td>\n",
" <td> 0.023014</td>\n",
" <td>-0.009664</td>\n",
" <td> 0.000201</td>\n",
" <td>-0.016584</td>\n",
" <td>-0.031390</td>\n",
" <td> 58.526131</td>\n",
" <td> Chad</td>\n",
" <td> Allen</td>\n",
" <td> 240000</td>\n",
" <td>-14.466879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td> alomaro01</td>\n",
" <td> 2B</td>\n",
" <td> 1988</td>\n",
" <td> 2004</td>\n",
" <td> 0.022145</td>\n",
" <td> 0.001698</td>\n",
" <td> 0.002653</td>\n",
" <td>-0.009386</td>\n",
" <td> 0.004503</td>\n",
" <td> 90.800847</td>\n",
" <td> Roberto</td>\n",
" <td> Alomar</td>\n",
" <td> 5466667</td>\n",
" <td> 18.071102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td> aloumo01</td>\n",
" <td> OF</td>\n",
" <td> 1990</td>\n",
" <td> 2008</td>\n",
" <td> 0.004058</td>\n",
" <td> 0.001022</td>\n",
" <td>-0.000406</td>\n",
" <td> 0.009994</td>\n",
" <td> 0.000737</td>\n",
" <td> 92.104556</td>\n",
" <td> Moises</td>\n",
" <td> Alou</td>\n",
" <td> 5135000</td>\n",
" <td> 13.800993</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
" playerID POS minYear maxYear 1B 2B 3B HR \\\n",
"0 abreubo01 RF 1996 2012 -0.008202 0.006421 0.001002 -0.003252 \n",
"3 alfoned01 2B 1995 2006 0.013485 -0.002177 -0.003239 -0.006436 \n",
"5 allench01 LF 1999 2005 0.023014 -0.009664 0.000201 -0.016584 \n",
"7 alomaro01 2B 1988 2004 0.022145 0.001698 0.002653 -0.009386 \n",
"9 aloumo01 OF 1990 2008 0.004058 0.001022 -0.000406 0.009994 \n",
"\n",
" BB OPW nameFirst nameLast salary resid \n",
"0 0.050501 104.050008 Bobby Abreu 9000000 16.510691 \n",
"3 0.010745 83.404437 Edgardo Alfonzo 4112500 10.674691 \n",
"5 -0.031390 58.526131 Chad Allen 240000 -14.466879 \n",
"7 0.004503 90.800847 Roberto Alomar 5466667 18.071102 \n",
"9 0.000737 92.104556 Moises Alou 5135000 13.800993 "
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"active.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"(318, 14)"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Y = active.resid.values\n",
"X = np.log(active[[\"salary\"]])\n",
"\n",
"clfl = linear_model.LinearRegression()\n",
"clfl.fit(X,Y)\n",
"\n",
"active['resid'] = Y - clfl.predict(X)\n",
"active.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>playerID</th>\n",
" <th>POS</th>\n",
" <th>minYear</th>\n",
" <th>maxYear</th>\n",
" <th>1B</th>\n",
" <th>2B</th>\n",
" <th>3B</th>\n",
" <th>HR</th>\n",
" <th>BB</th>\n",
" <th>OPW</th>\n",
" <th>nameFirst</th>\n",
" <th>nameLast</th>\n",
" <th>salary</th>\n",
" <th>resid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> abreubo01</td>\n",
" <td> RF</td>\n",
" <td> 1996</td>\n",
" <td> 2012</td>\n",
" <td>-0.008202</td>\n",
" <td> 0.006421</td>\n",
" <td> 0.001002</td>\n",
" <td>-0.003252</td>\n",
" <td> 0.050501</td>\n",
" <td> 104.050008</td>\n",
" <td> Bobby</td>\n",
" <td> Abreu</td>\n",
" <td> 9000000</td>\n",
" <td> 3.926400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> alfoned01</td>\n",
" <td> 2B</td>\n",
" <td> 1995</td>\n",
" <td> 2006</td>\n",
" <td> 0.013485</td>\n",
" <td>-0.002177</td>\n",
" <td>-0.003239</td>\n",
" <td>-0.006436</td>\n",
" <td> 0.010745</td>\n",
" <td> 83.404437</td>\n",
" <td> Edgardo</td>\n",
" <td> Alfonzo</td>\n",
" <td> 4112500</td>\n",
" <td> 4.525845</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> allench01</td>\n",
" <td> LF</td>\n",
" <td> 1999</td>\n",
" <td> 2005</td>\n",
" <td> 0.023014</td>\n",
" <td>-0.009664</td>\n",
" <td> 0.000201</td>\n",
" <td>-0.016584</td>\n",
" <td>-0.031390</td>\n",
" <td> 58.526131</td>\n",
" <td> Chad</td>\n",
" <td> Allen</td>\n",
" <td> 240000</td>\n",
" <td> 2.729780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td> alomaro01</td>\n",
" <td> 2B</td>\n",
" <td> 1988</td>\n",
" <td> 2004</td>\n",
" <td> 0.022145</td>\n",
" <td> 0.001698</td>\n",
" <td> 0.002653</td>\n",
" <td>-0.009386</td>\n",
" <td> 0.004503</td>\n",
" <td> 90.800847</td>\n",
" <td> Roberto</td>\n",
" <td> Alomar</td>\n",
" <td> 5466667</td>\n",
" <td> 9.583405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td> aloumo01</td>\n",
" <td> OF</td>\n",
" <td> 1990</td>\n",
" <td> 2008</td>\n",
" <td> 0.004058</td>\n",
" <td> 0.001022</td>\n",
" <td>-0.000406</td>\n",
" <td> 0.009994</td>\n",
" <td> 0.000737</td>\n",
" <td> 92.104556</td>\n",
" <td> Moises</td>\n",
" <td> Alou</td>\n",
" <td> 5135000</td>\n",
" <td> 5.827588</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
" playerID POS minYear maxYear 1B 2B 3B HR \\\n",
"0 abreubo01 RF 1996 2012 -0.008202 0.006421 0.001002 -0.003252 \n",
"3 alfoned01 2B 1995 2006 0.013485 -0.002177 -0.003239 -0.006436 \n",
"5 allench01 LF 1999 2005 0.023014 -0.009664 0.000201 -0.016584 \n",
"7 alomaro01 2B 1988 2004 0.022145 0.001698 0.002653 -0.009386 \n",
"9 aloumo01 OF 1990 2008 0.004058 0.001022 -0.000406 0.009994 \n",
"\n",
" BB OPW nameFirst nameLast salary resid \n",
"0 0.050501 104.050008 Bobby Abreu 9000000 3.926400 \n",
"3 0.010745 83.404437 Edgardo Alfonzo 4112500 4.525845 \n",
"5 -0.031390 58.526131 Chad Allen 240000 2.729780 \n",
"7 0.004503 90.800847 Roberto Alomar 5466667 9.583405 \n",
"9 0.000737 92.104556 Moises Alou 5135000 5.827588 "
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"active = active[active.resid >= 0]\n",
"active.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
"(148, 14)"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"active.POS.unique()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"array(['RF', '2B', 'LF', 'OF', 'SS', '1B', 'CF', '3B', 'C', 'DH'], dtype=object)"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.scatter(X,Y)\n",
"plt.plot(X, clfl.predict(X))\n",
"plt.xlabel('salaries')\n",
"plt.ylabel('predicted residuals')\n",
"plt.title('Visualizing the linear regression')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"<matplotlib.text.Text at 0xe3b94b0>"
]
},
{
"metadata": {},
"output_type": "display_data",
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KQ1K8FNzFt0JMQ/J64D7xxNk9/Fbu0nL66Q+wevVg95W5ON0UvePel8tFXhYv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\n",
"text": [
"<matplotlib.figure.Figure at 0xe201870>"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"active = active[active.resid >= 0]\n",
"np.unique(active.POS.values)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
"array(['1B', '2B', '3B', 'C', 'CF', 'DH', 'LF', 'OF', 'RF', 'SS'], dtype=object)"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def MinSalary(s):\n",
" return s[\"salary\"].min()\n",
"\n",
"minSalaryByPos = active.groupby('POS').apply(MinSalary)\n",
"minSalaryByPos.sort(ascending=False)\n",
"#np.sum(minSalaryByPos)\n",
"minSalaryByPos"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
"POS\n",
"RF 1700000\n",
"CF 1500000\n",
"DH 1000000\n",
"3B 432500\n",
"1B 375000\n",
"C 350000\n",
"OF 315000\n",
"SS 314750\n",
"2B 275000\n",
"LF 195000\n",
"dtype: float64"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"milliondollar = 20*10**6\n",
"milliondollar"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
"20000000"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"posleft = list(minSalaryByPos.index)\n",
"posleft"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": [
"['RF', 'CF', 'DH', '3B', '1B', 'C', 'OF', 'SS', '2B', 'LF']"
]
}
],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"posleft"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": [
"['RF', 'CF', 'DH', '3B', '1B', 'C', 'OF', 'SS', '2B', 'LF']"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"milliondollar - sum([minSalaryByPos[x] for x in posleft])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 40,
"text": [
"2303583.0"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"list_of_values = []\n",
"\n",
"for val in range(len(posleft)):\n",
" \n",
" maxmoney = milliondollar - sum([minSalaryByPos[x] for x in posleft])\n",
" \n",
" \n",
" indices = [True if val in posleft else False for val in active.POS.values]\n",
" new_active = active[indices &((active.salary <= maxmoney))]\n",
" \n",
" outstanding = new_active['resid'].argmax()\n",
" \n",
" list_of_values.append(outstanding)\n",
" \n",
" posleft.remove(new_active.loc[outstanding].POS)\n",
" \n",
" milliondollar = milliondollar - new_active.loc[outstanding].salary\n",
" \n",
"\n",
"topPicks=active.loc[list_of_values,:]\n",
"topPicks=topPicks.sort([\"OPW\"],ascending=False)\n",
"print list_of_values\n",
"topPicks"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[81, 274, 236, 55, 611, 475, 966, 872, 922, 848]\n"
]
},
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>playerID</th>\n",
" <th>POS</th>\n",
" <th>minYear</th>\n",
" <th>maxYear</th>\n",
" <th>1B</th>\n",
" <th>2B</th>\n",
" <th>3B</th>\n",
" <th>HR</th>\n",
" <th>BB</th>\n",
" <th>OPW</th>\n",
" <th>nameFirst</th>\n",
" <th>nameLast</th>\n",
" <th>salary</th>\n",
" <th>resid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>81 </th>\n",
" <td> bondsba01</td>\n",
" <td> OF</td>\n",
" <td> 1986</td>\n",
" <td> 2007</td>\n",
" <td>-0.045184</td>\n",
" <td> 0.001342</td>\n",
" <td> 0.001377</td>\n",
" <td> 0.030717</td>\n",
" <td> 0.112071</td>\n",
" <td> 143.101202</td>\n",
" <td> Barry</td>\n",
" <td> Bonds</td>\n",
" <td> 8541667</td>\n",
" <td> 52.642834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>274</th>\n",
" <td> ensbemo01</td>\n",
" <td> 3B</td>\n",
" <td> 2000</td>\n",
" <td> 2008</td>\n",
" <td>-0.033243</td>\n",
" <td>-0.003037</td>\n",
" <td>-0.000346</td>\n",
" <td> 0.026321</td>\n",
" <td> 0.047626</td>\n",
" <td> 108.584662</td>\n",
" <td> Morgan</td>\n",
" <td> Ensberg</td>\n",
" <td> 450000</td>\n",
" <td> 48.943930</td>\n",
" </tr>\n",
" <tr>\n",
" <th>475</th>\n",
" <td> johnsni01</td>\n",
" <td> 1B</td>\n",
" <td> 2001</td>\n",
" <td> 2012</td>\n",
" <td>-0.030406</td>\n",
" <td> 0.018102</td>\n",
" <td>-0.002523</td>\n",
" <td>-0.000333</td>\n",
" <td> 0.072075</td>\n",
" <td> 107.537577</td>\n",
" <td> Nick</td>\n",
" <td> Johnson</td>\n",
" <td> 1450000</td>\n",
" <td> 23.763780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>236</th>\n",
" <td> delluda01</td>\n",
" <td> LF</td>\n",
" <td> 1997</td>\n",
" <td> 2009</td>\n",
" <td>-0.050673</td>\n",
" <td>-0.018869</td>\n",
" <td> 0.004528</td>\n",
" <td> 0.024153</td>\n",
" <td> 0.057238</td>\n",
" <td> 100.483979</td>\n",
" <td> David</td>\n",
" <td> Dellucci</td>\n",
" <td> 812500</td>\n",
" <td> 34.667274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>922</th>\n",
" <td> werthja01</td>\n",
" <td> RF</td>\n",
" <td> 2002</td>\n",
" <td> 2013</td>\n",
" <td>-0.015169</td>\n",
" <td>-0.000742</td>\n",
" <td>-0.003539</td>\n",
" <td> 0.012239</td>\n",
" <td> 0.032909</td>\n",
" <td> 95.991832</td>\n",
" <td> Jayson</td>\n",
" <td> Werth</td>\n",
" <td> 1700000</td>\n",
" <td> 9.562527</td>\n",
" </tr>\n",
" <tr>\n",
" <th>848</th>\n",
" <td> sweenmi01</td>\n",
" <td> DH</td>\n",
" <td> 1995</td>\n",
" <td> 2010</td>\n",
" <td> 0.017613</td>\n",
" <td> 0.013491</td>\n",
" <td>-0.004271</td>\n",
" <td> 0.006983</td>\n",
" <td>-0.004961</td>\n",
" <td> 95.303910</td>\n",
" <td> Mike</td>\n",
" <td> Sweeney</td>\n",
" <td> 1450000</td>\n",
" <td> 6.266458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55 </th>\n",
" <td> bellhma01</td>\n",
" <td> 2B</td>\n",
" <td> 1997</td>\n",
" <td> 2007</td>\n",
" <td>-0.039044</td>\n",
" <td> 0.002241</td>\n",
" <td> 0.001326</td>\n",
" <td> 0.005377</td>\n",
" <td> 0.046554</td>\n",
" <td> 91.598047</td>\n",
" <td> Mark</td>\n",
" <td> Bellhorn</td>\n",
" <td> 477500</td>\n",
" <td> 30.412329</td>\n",
" </tr>\n",
" <tr>\n",
" <th>872</th>\n",
" <td> torrean02</td>\n",
" <td> CF</td>\n",
" <td> 2002</td>\n",
" <td> 2013</td>\n",
" <td>-0.038587</td>\n",
" <td> 0.025363</td>\n",
" <td> 0.009017</td>\n",
" <td>-0.003139</td>\n",
" <td> 0.006371</td>\n",
" <td> 83.881250</td>\n",
" <td> Andres</td>\n",
" <td> Torres</td>\n",
" <td> 1500000</td>\n",
" <td> 13.450094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>966</th>\n",
" <td> zaungr01</td>\n",
" <td> C</td>\n",
" <td> 1995</td>\n",
" <td> 2010</td>\n",
" <td>-0.008357</td>\n",
" <td>-0.016634</td>\n",
" <td>-0.003284</td>\n",
" <td>-0.010903</td>\n",
" <td> 0.052494</td>\n",
" <td> 82.532772</td>\n",
" <td> Gregg</td>\n",
" <td> Zaun</td>\n",
" <td> 1000000</td>\n",
" <td> 15.380639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>611</th>\n",
" <td> menecfr01</td>\n",
" <td> SS</td>\n",
" <td> 1999</td>\n",
" <td> 2005</td>\n",
" <td>-0.021680</td>\n",
" <td>-0.011214</td>\n",
" <td>-0.001739</td>\n",
" <td>-0.014485</td>\n",
" <td> 0.046071</td>\n",
" <td> 73.581041</td>\n",
" <td> Frank</td>\n",
" <td> Menechino</td>\n",
" <td> 314750</td>\n",
" <td> 25.534135</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
" playerID POS minYear maxYear 1B 2B 3B HR \\\n",
"81 bondsba01 OF 1986 2007 -0.045184 0.001342 0.001377 0.030717 \n",
"274 ensbemo01 3B 2000 2008 -0.033243 -0.003037 -0.000346 0.026321 \n",
"475 johnsni01 1B 2001 2012 -0.030406 0.018102 -0.002523 -0.000333 \n",
"236 delluda01 LF 1997 2009 -0.050673 -0.018869 0.004528 0.024153 \n",
"922 werthja01 RF 2002 2013 -0.015169 -0.000742 -0.003539 0.012239 \n",
"848 sweenmi01 DH 1995 2010 0.017613 0.013491 -0.004271 0.006983 \n",
"55 bellhma01 2B 1997 2007 -0.039044 0.002241 0.001326 0.005377 \n",
"872 torrean02 CF 2002 2013 -0.038587 0.025363 0.009017 -0.003139 \n",
"966 zaungr01 C 1995 2010 -0.008357 -0.016634 -0.003284 -0.010903 \n",
"611 menecfr01 SS 1999 2005 -0.021680 -0.011214 -0.001739 -0.014485 \n",
"\n",
" BB OPW nameFirst nameLast salary resid \n",
"81 0.112071 143.101202 Barry Bonds 8541667 52.642834 \n",
"274 0.047626 108.584662 Morgan Ensberg 450000 48.943930 \n",
"475 0.072075 107.537577 Nick Johnson 1450000 23.763780 \n",
"236 0.057238 100.483979 David Dellucci 812500 34.667274 \n",
"922 0.032909 95.991832 Jayson Werth 1700000 9.562527 \n",
"848 -0.004961 95.303910 Mike Sweeney 1450000 6.266458 \n",
"55 0.046554 91.598047 Mark Bellhorn 477500 30.412329 \n",
"872 0.006371 83.881250 Andres Torres 1500000 13.450094 \n",
"966 0.052494 82.532772 Gregg Zaun 1000000 15.380639 \n",
"611 0.046071 73.581041 Frank Menechino 314750 25.534135 "
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"topPicks['salary'].sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 37,
"text": [
"17696417.0"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"round(topPicks['OPW'].mean())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 38,
"text": [
"98.0"
]
}
],
"prompt_number": 38
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 1(m)\n",
"What do these players outperform in? Singles, doubles, triples HR or BB?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def round1000(x):\n",
" return np.round(x*1000)\n",
"\n",
"topPicks[[\"1B\",\"2B\",\"3B\", \"HR\",\"BB\"]] = topPicks[[\"1B\",\"2B\",\"3B\", \"HR\",\"BB\"]].apply(round1000)\n",
"topPicks[[\"OPW\"]] = np.round(topPicks[[\"OPW\"]])\n",
"topPicks[[\"nameFirst\",\"nameLast\",\"POS\",\"1B\",\"2B\",\"3B\", \"HR\",\"BB\",\"OPW\",\"salary\",\"minYear\",\"maxYear\"]].head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>nameFirst</th>\n",
" <th>nameLast</th>\n",
" <th>POS</th>\n",
" <th>1B</th>\n",
" <th>2B</th>\n",
" <th>3B</th>\n",
" <th>HR</th>\n",
" <th>BB</th>\n",
" <th>OPW</th>\n",
" <th>salary</th>\n",
" <th>minYear</th>\n",
" <th>maxYear</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>81 </th>\n",
" <td> Barry</td>\n",
" <td> Bonds</td>\n",
" <td> OF</td>\n",
" <td>-45</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 31</td>\n",
" <td> 112</td>\n",
" <td> 143</td>\n",
" <td> 8541667</td>\n",
" <td> 1986</td>\n",
" <td> 2007</td>\n",
" </tr>\n",
" <tr>\n",
" <th>274</th>\n",
" <td> Morgan</td>\n",
" <td> Ensberg</td>\n",
" <td> 3B</td>\n",
" <td>-33</td>\n",
" <td> -3</td>\n",
" <td> 0</td>\n",
" <td> 26</td>\n",
" <td> 48</td>\n",
" <td> 109</td>\n",
" <td> 450000</td>\n",
" <td> 2000</td>\n",
" <td> 2008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>475</th>\n",
" <td> Nick</td>\n",
" <td> Johnson</td>\n",
" <td> 1B</td>\n",
" <td>-30</td>\n",
" <td> 18</td>\n",
" <td>-3</td>\n",
" <td> 0</td>\n",
" <td> 72</td>\n",
" <td> 108</td>\n",
" <td> 1450000</td>\n",
" <td> 2001</td>\n",
" <td> 2012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>236</th>\n",
" <td> David</td>\n",
" <td> Dellucci</td>\n",
" <td> LF</td>\n",
" <td>-51</td>\n",
" <td>-19</td>\n",
" <td> 5</td>\n",
" <td> 24</td>\n",
" <td> 57</td>\n",
" <td> 100</td>\n",
" <td> 812500</td>\n",
" <td> 1997</td>\n",
" <td> 2009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>922</th>\n",
" <td> Jayson</td>\n",
" <td> Werth</td>\n",
" <td> RF</td>\n",
" <td>-15</td>\n",
" <td> -1</td>\n",
" <td>-4</td>\n",
" <td> 12</td>\n",
" <td> 33</td>\n",
" <td> 96</td>\n",
" <td> 1700000</td>\n",
" <td> 2002</td>\n",
" <td> 2013</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 36,
"text": [
" nameFirst nameLast POS 1B 2B 3B HR BB OPW salary minYear \\\n",
"81 Barry Bonds OF -45 1 1 31 112 143 8541667 1986 \n",
"274 Morgan Ensberg 3B -33 -3 0 26 48 109 450000 2000 \n",
"475 Nick Johnson 1B -30 18 -3 0 72 108 1450000 2001 \n",
"236 David Dellucci LF -51 -19 5 24 57 100 812500 1997 \n",
"922 Jayson Werth RF -15 -1 -4 12 33 96 1700000 2002 \n",
"\n",
" maxYear \n",
"81 2007 \n",
"274 2008 \n",
"475 2012 \n",
"236 2009 \n",
"922 2013 "
]
}
],
"prompt_number": 36
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Best players all outperformed in BB."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Problem 2: $k$-Nearest Neighbors and Cross Validation \n",
"\n",
"What is the optimal $k$ for predicting species using $k$-nearest neighbor classification \n",
"on the four features provided by the iris dataset.\n",
"\n",
"In this problem you will get to know the famous iris data set, and use cross validation to select the optimal $k$ for a $k$-nearest neighbor classification. This problem set makes heavy use of the [sklearn](http://scikit-learn.org/stable/) library. In addition to Pandas, it is one of the most useful libraries for data scientists! After completing this homework assignment you will know all the basics to get started with your own machine learning projects in sklearn. \n",
"\n",
"Future lectures will give further background information on different classifiers and their specific strengths and weaknesses, but when you have the basics for sklearn down, changing the classifier will boil down to exchanging one to two lines of code.\n",
"\n",
"The data set is so popular, that sklearn provides an extra function to load it:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#load the iris data set\n",
"iris = sklearn.datasets.load_iris()\n",
"\n",
"X = iris.data \n",
"Y = iris.target\n",
"\n",
"print X.shape, Y.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(150L, 4L) (150L,)\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 2(a) \n",
"Split the data into a train and a test set. Use a random selection of 33% of the samples as test data. Sklearn provides the [`train_test_split`](http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.train_test_split.html) function for this purpose. Print the dimensions of all the train and test data sets you have created. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### Your code here ###\n",
"X_train, X_test, Y_train, Y_test = sklearn.cross_validation.train_test_split(X, Y, test_size = 0.33, random_state = 42)\n",
"\n",
"print X_train.shape\n",
"print X_test.shape\n",
"print Y_train.shape\n",
"print Y_test.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(100L, 4L)\n",
"(50L, 4L)\n",
"(100L,)\n",
"(50L,)\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 2(b)\n",
"\n",
"Examine the data further by looking at the projections to the first two principal components of the data. Use the [`TruncatedSVD`](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html) function for this purpose, and create a scatter plot. Use the colors on the scatter plot to represent the different classes in the target data. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### Your code here ###\n",
"svd = sklearn.decomposition.TruncatedSVD(n_components=2)\n",
"\n",
"X_train_norm = X_train - np.mean(X_train, axis =0)\n",
"X_2d = svd.fit_transform(X_train_norm)\n",
"\n",
"sns.set_style('white')\n",
"\n",
"plt.scatter(X_2d[:,0], X_2d[:,1], c= Y_train,s = 50, cmap=plt.cm.prism)\n",
"\n",
"plt.xlabel('PC1')\n",
"plt.ylabel('PC2')\n",
"plt.title('First two PCs using iris data')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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rrldfkaHclgJ1k7o6bXv612nFiv2R+Or9GdDqEYoUcUxRK+RXBGsi+AQ638Nk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\nc06b6oBjbMA1w1nMMWBLAX1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l3UWWUlJSUEHr8Uk3Kin5KviXA12mbW/1RjgUuozY2Fgm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\ni4iIuND/A/LZSBWChToCAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x3696048>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 2(c) \n",
"\n",
"In the lecture we discussed how to use cross validation to estimate the optimal value for $k$ (the number of nearest neighbors to base the classification on). Use ***ten fold cross validation*** to estimate the optimal value for $k$ for the iris data set. \n",
"\n",
"**Note**: For your convenience sklearn does not only include the [KNN classifier](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html), but also a [grid search function](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html#sklearn.grid_search.GridSearchCV). The function is called grid search, because if you have to optimize more than one parameter, it is common practice to define a range of possible values for each parameter. An exhaustive search then runs over the complete grid defined by all the possible parameter combinations. This can get very computation heavy, but luckily our KNN classifier only requires tuning of a single parameter for this problem set. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"k = np.arange(20)+1\n",
"\n",
"parameters = {'n_neighbors': k}\n",
"knn = sklearn.neighbors.KNeighborsClassifier()\n",
"clf= sklearn.grid_search.GridSearchCV(knn, parameters, cv= 10)\n",
"\n",
"clf.fit(X_train, Y_train)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"GridSearchCV(cv=10,\n",
" estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_neighbors=5, p=2, weights='uniform'),\n",
" fit_params={}, iid=True, loss_func=None, n_jobs=1,\n",
" param_grid={'n_neighbors': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
" 18, 19, 20])},\n",
" pre_dispatch='2*n_jobs', refit=True, score_func=None, scoring=None,\n",
" verbose=0)"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 2(d)\n",
"\n",
"Visualize the result by plotting the score results versus values for $k$. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Verify that the grid search has indeed chosen the right parameter value for $k$."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf.best_params_"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"{'n_neighbors': 4}"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_test.shape[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"50L"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 2(e)\n",
"\n",
"Test the performance of our tuned KNN classifier on the test set."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_test.shape[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"50L"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### Your code here ###\n",
"def compute_test(x_test, y_test, clf, cv):\n",
" KFolds = sklearn.cross_validation.KFold(x_test.shape[0], n_folds =cv)\n",
" \n",
" scores =[]\n",
" \n",
" for i,j in KFolds:\n",
" test_set = x_test[j]\n",
" test_labels = y_test[j]\n",
" scores.append(sklearn.metrics.accuracy_score(test_labels, clf.predict(test_set)))\n",
" return scores\n",
"\n",
"scores = compute_test(x_test = X_test, y_test = Y_test, clf = clf, cv= 5)\n",
"np.mean(scores), np.std(scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"(0.98000000000000009, 0.039999999999999994)"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Discussion for Problem 2\n",
"\n",
"*Write a brief discussion of your conclusions to the questions and tasks above in 100 words or less.*\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"K nearest neighbours is a simplest machine learning algorithim and it is a lzay algorithim, it doesnt run computations on data set until you give it a new data point you are trying to test. We will use KNN for supervised learning. Lets say you have several apples and several oranges and you have an unclassified fruit. in this case K -=3 we are going to look at 3 nearest neighbours, we are going to identify our mystery fruit. Now our fruit is orange, whenever you set k, ensure k as odd. majority votes. Two ways in which you can use majority vote in -> each KNN has eaqual weight, next thing is distance weight in which each knn vote is based on distance. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use Iris data set is a classical data set. Our job is to classify which flower. Blue are training data and The pink green and lylica is where your decision lines are located. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Problem 3: The Curse and Blessing of Higher Dimensions\n",
"\n",
"In this problem we will investigate the influence of higher dimensional spaces on the classification. The data set is again one of the standard data sets from sklearn. The [digits data set](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) 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",
"First we again load our data set."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"digits = sklearn.datasets.load_digits()\n",
"\n",
"X = digits.data \n",
"Y = digits.target\n",
"\n",
"print X.shape, Y.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(1797L, 64L) (1797L,)\n"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 3(a) \n",
"\n",
"Start with the same steps as in Problem 2. Split the data into train and test set. Use 33% of the samples as test data. Print the dimensions of all the train and test data sets you created. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_train, X_test, Y_train, Y_test = sklearn.cross_validation.train_test_split \\\n",
"(X, Y, test_size = 0.33, random_state = 42)\n",
"\n",
"X_norm = np.mean(X_train, axis=0)\n",
"\n",
"X_train_norm = X_train - X_norm\n",
"\n",
"X_test_norm = X_test - X_norm\n",
"\n",
"print X_train_norm.shape\n",
"print X_test_norm.shape\n",
"print Y_train.shape\n",
"print Y_test.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(1203L, 64L)\n",
"(594L, 64L)\n",
"(1203L,)\n",
"(594L,)\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 3(b) \n",
"\n",
"Similar to Problem 2(b), create a scatter plot of the projections to the first two PCs. Use the colors on the scatter plot to represent the different classes in the target data. How well can we separate the classes?\n",
"\n",
"**Hint**: Use a `Colormap` in matplotlib to represent the diferent classes in the target data. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### Your code here ###\n",
"\n",
"svd = sklearn.decomposition.TruncatedSVD(n_components=2)\n",
"\n",
"\n",
"X_2dl = svd.fit_transform(X_train)\n",
"\n",
"sns.set_style('white')\n",
"\n",
"plt.scatter(X_2dl[:,0], X_2dl[:,1], c= Y_train, s = 50, cmap=plt.cm.Paired)\n",
"\n",
"plt.colorbar()\n",
"\n",
"plt.xlabel('PC1')\n",
"plt.ylabel('PC2')\n",
"plt.title('First two PCs')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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GqxyJWCmbtetGs/XO8dz1t7/9V+sPNbMFiXNR7LjIYDqPimfhxgxSTxfxdIcq\nQtOZKeUZvj9MfhP2zOKO9hHkOFzM2n6a1vXt1G9opHint1LxdLeuYywLec9+8UVG7T+AVDbfXN9k\nYkJUFDPz8xgd5tuW7LjLRXJhAdle/2+Yzk4n63/4kbHPP3fR9y8Q/LdIGJBqmY9a2/41IcTyOmf5\n1Kk43n6HviW+UmolCxfz9Zy5jJ7yBTEBFutLksSAsWNh7Ngqxzy0fS39IwMXcpRyj1VvUKNG6Nt3\nlouTR9eRJalcKCvaoezZyy/FxQwymvyOe3WdjcVFdLPG+J2j6zp5HTv6CSXAxnnfMjjGzU/fnWR0\nqRXKTB+OkcwfpzMvNpabJ070G2fxN9+Ss3IFlJRCYhMGPv54wPcLoMsdt6POnk3+8WLo5GuLiwpm\n1IiGJO/LDDTde/YmAVg0/Ss65C0jpoXP7vrmYEZHBDNr+2lu7BDDV3sP8YDXVr4URC/z9u959FFy\nc3MJC1CsASDRbOGk20W8yUwDs5kUVylOS+WC9Hhr+UgvEFxiZJOE0Vg78ZP/iwfcQAixvI5xOByk\nfziZAaXO8i/mYIOBm48dZ/Gbb3LP++9f1Liy2YqnUMMoB9gqylh9Mk3/iRNZum49N2ZmAVCkaYQH\n2HIKfNWAQq1WlhQWEiHLJFrMnHC5OeVxMzTEzteREQzMySVekjgK7GrdinFv/aPSOKbiDDYey6a3\nw1wpolnHYGDHosWU3ncfJSUlhIeH8/Vzz9FlwSLanRWnXbv5dtkyQps3x3oqHa/JRF5SEuOee5a4\nuDgSW7Rg34MPIH3yEfNXpzGsVxxHzzj4dWMGBw4WUNjbxa096leyS6+jIEkSOXtX0S3Aw8fAltFs\nPpxHywENWWa7AeOBA6BpGNq05o5nnsFms3H69GkiHY6AoeoYo5EzHg/xZYf620L5Ij+bXLOHMIOM\noawObfuRt1TzPyYQXH6MRhmjqXYbMRirmJ64aBsu6WiCa4plP/zADbl5EMBr03fsuOhx+48aT/Jr\ncxkQ7+8ylbq9GOOrnq8EqBsfT69PPmbZe++j7dlDfkEBJSXFBFqqv99iJqZdW0bu3kOB18sxl4tm\nFgvdbTaSY6J5ZtEidqxbx/q9+zBHRhB57BjLPvqYtrfcjNK2bfk4HlMIeTlOOsmB/xyK9u9nZp9+\nBDtLUCMi6XQmg8gK79kJt5u49NP0K3SwsqgQl6bT9sABVs+dS3GvXox443Vufvxxjg0dxtIpX/Bm\n8lpaHD6bA4wjAAAgAElEQVRDcWEJz4RFs2lfKduic+nYzLecxOPV+HGPk+Ev+rxZ2VkQ0K4QixGn\nV0OPbMaEv72OruscOXIEi8VCeNnG1w0aNGB9/fo0D1Dbd29pKT0qZPK6dR2nS+Og00mWx4NXMmC9\n524GtWxZ6VyB4LdENhowmmrpWeoiDCu4RHhKnZiqClV4Lj70FhYWRuyNj/DVN28RfiYXkweyTeBt\n14Mnn3q6xvMbKQqN/m9y+etZ77xL9hdTqLh9sVvXOdWtK2NfeIH5Dz/CwJNptAkORtd1NliDaPzE\nk1gsFroNGMCJTZsxfv4FN7jdSJLEvh9/YsstN3H3668D0LDLEFL3rSXNU0B9Y+VKQabiEvqZfO25\nhw/TKtS/JN3u0hJG2MPY4HCQaLaQUDZX2hzQN29h5sTHeGTWLzRsmsgDb7zB9xMfJeJAGqFBwWwu\ndhAsGShamMHba9NJDAtGyvPQu8TEyomP4n37bTRbDHC0kl2n80s5UWLh5pGPsHbJHI6umEojTlGq\nySy3NKLzuCdo0aYjYaNHk/bJp9SvsPQny+3Gpet+c53Ligp5IPpchuxJTeNMgwbk5OQQFhZ2WbZY\nEwiuFYRYXsf0GjuGdVO+pHtp5VIXRXE1r/GrjsJjGXTYU0Jzry/Gp+s6y91HOHpApVmb2mWYjvzD\n08zSNLYvWEDM6TPkhIXi7tmT8a+/TlBQEHfNnsXizz/HfeQoelgYN9w7gfhGjQDYumoV9b6fShNd\nLw81t9Q0ImfO5uuICOxWKzGJiYTfeC/zd7zLQ27db37vmNNJ1HnFEHTdv4/xbM1Vr8fPUwOfl97j\n4EHWzJ9Pn5tu8p2fepBNDgfNLEF0sVop1jSSCwvpqwfTxm0GzGCC4eln+ObhRyDGzs6kYto1CPGz\nYYbq4aF/zSAz/STuNR8xvK4OWMt6pLPom78R9/J33PLE4yy22dg/dy6cPoPLHsqh9FM8WCaKuq6z\noaSYKNnot451b5CTkhUfs+nQD+Rhx9ikB2Mefg5DFWFxgeByYTQZah+GFZ6l4FIRExuLd9xYTnz9\nDQkVvgBXFBUStP8Aq3/5hT4XsWlwUVERhV9/TYcKiSGSJHFjVjbL3nuPZp9/VqvxJEli9LN/xPP0\nU2RkZBAREUFwhW2urFYrowNU0QFInTOXvucVUyjWNNYU5NPq089paTJxWtfZ26wpQ97/gin/eIPG\nJ09Rx6Ox32wktsjtV/GnQ1Aw3+XmEG00YZB8CUhFXl/lHGMVXnqMJJG6fz+UieWxwiIGhobS1OKb\nv7XLMvdERrKssJBcr4eICuHgbllZyNk55B7RmRWfTXjDEJzB4bhj2/D4B38mMjKKld//hyHRlbOE\nBtZzsmzGV9z6wFMMuf8+uP++8mMlJSUs+vxzPIePoB4+zOB9+0ioULRhcVAp/cc1IMx61tMupTB/\nKT9NdnHHE38JeJ+Xm9On0lgx/TMM+cfR5SDszboxbNzvxE4p1wHyRcxZypqYsxRcQsb86XlemTWL\nBmmnkCVfeLNDsJU4JBZ/8im9Ro2qtSexYvp0uuflgRQgwWf3bjRNuyjvxGg0EhcXV6tzpABec3Jh\nIaPDwsvFra4kcXPqIeZ/8CEvL1xMQUEB+fn5OJOX0uJf//I7d0tJMQNDQqlXFmrVdZ1phbmkOEvx\nVlHhKFvTiExsWv5aNhjKhbIi/UJCSC4qLN//EiDSaOK4y0UX3Yp+XKfgqINNI/ow4cVzy2/kkhwI\nsL+zUTagOzID2hQcHMzoJ54A4Nc1a/A8ci7bt1jTsCq2CkLpIzRIxnRiA0VFRX5LhSridDpRVZVN\nC6ZizT2IwetEC2tAm6F307ZLz4DnXAhpx4+y8sOnGRpXghTm+3/LPZTCN/86wITn36zhbMG1jvEi\n5iyN3ur7K4oyAbi37GUw0A6IVVU1YJKAiKdc55w8eZIe+fkMs9sZHGpnhD2MuLLqNc2PHmX3tm01\njFCZtGPHqqyM5ygq+k09gaBWLSmpUADdpWvYDIaAXmDz/QfYtXkLdrudhIQEhtw9ntVx57JU09wu\n6hmN5UIJPq/3LnskC7ViHJLE6QA1ttY1aUzfUSPLX8fXjQ1oqyxJlSqzrvMWU89kLL9WmCxjOpjq\n18cbXLnOLIBX08EaGfBYRTr26sWuls3LX6d5XDRNDLxVWGKQg8OpKZXadV1nxqdvM+sv4yie9ih1\nTizDkX6QHnVKGWY/zKlZr7Fn2+YAI14Yq6Z/wrD6pX6fnQirkWaFW9i3c/tFjyu4NjCaDZjMcq1+\njObq5U1V1a9VVe2vqmp/YCvwRFVCCUIsr3tkWUYP4AECeCQJ+UK2rzqP4CIHaxyOgMeyZPk3Fcsh\n99/PomZN0crU26FphFaxjUmcpnHq8LkaqmazmZ7/eov5TRpxUtOYW1pIx2BrwHObhwbh7NeEuUlN\nmStJnHK72S1JLGzTmuHvv+fnSUtxlZeJgG8rMbmCXJ70uglvb2dHiH+ylX5esYfmfUaxN6vy08my\nNBP9b7034LUqIkkSI95+m3mtWnIQsEkyR9KLA/ZNKzUTF1+5ms+cbybTLmshg+q7SKobQp+kKG7t\nUI8Fu84A0DVGY9fi72u0pSoM2YcCtjePNrJv49KLHldwbXA2G7Y2P/IFrstUFKUz0EpV1c+r6yfC\nsNc5cXFxJLdsCfv3k6I5ORQJpnATboeHk1IUf2lX/VKPQAQDkkFiV0kJbcvmFr26zqLCAsLbta3U\n//jRI6Ts3UFSq/Y0aNT4v70lP4KCgrj7u2+Z989/oe/chaZ5yDx+gm4BKgHstlrp0a+vX1vzjh1R\n5sxh7o/TMH3+d7RTgYvL6ZJE/wZegp9/k4aNGvP1629gOXIYW1AQm2bNJvrRieXzrO0n/I7tm7fQ\n4bwHipl6IbZ4M0tKSvAGG4hKCmFIhxjm5R2DdF8fp6Zh6uzbO1TXdb574w2yFy4guySXxbEGujQJ\npk6YjVQSaH/Ho5UKMFRFXMOGPPTTj+zdto2Tqalk7VqGVzuKXOHBQtN0MsNaEh0d7X/vuk7R/lVE\n1fP/OjEYJNol2Ek5U0RSbAiGglMXZEsgdEPgrypd15GqWPIjEFwgLwGTauokPmUCOj7zNN8//Rit\nOwQxquW5BRqHc1zMn/opN41/pFbjRXbpTL35C8h2u1hYUIAsgVeHHlYbuzqf2yTa4XDw0zsvkFC8\nn6QISF0nsSxIYdwz/6hyTuxisNvt3FWhYPqizz/nxHsfkFAhPOvQNPIH9Cembt1K50uSRGlWKnf0\nr8f674/RG3/vUtd19FgT+R6ZKFsI3z39NIPWrie0bKmFa/NmpqxdS+dnnmDRf94k9OBxCvIdbPHq\nNLeY8Rhhj8HFXeMSqR8dXOn6mtNnZ5ausaZTBx585hm8Xi9/HT6AO49lcNjoIayxmZatwylxeVl3\nykD7sWOwR9crz9wtLCxE13XsdjuappGamorVaiU+3n/zrlYdO9KqY0eKhg/np7f/RBOXSlKExMFc\nnUOmZox95u+V7HM6nQS7cgn0ddK8bgiL9maSFBuCZg7slV8Ihnqt8DhXVSp0sTFdp+dTt130uIJr\ng4sqSuCuub+iKOFAkqqqq2ocr1ZXF/xP0qZnT7YOaEHXejl+7U0izRzZOZfi0fdgtdb8RbdhxUIO\nr/4FQ8EpdrcNJlQtYag51Lffpa4zPyGeMWVJJQDT3/sLN4WkINt9H8P2sdBGO8iM917m3j//56Lv\nR9d1Zn39IcUH12NwFqLZYmjQcyS9h/jmDYc++CDJJhP7f5mJ4dQptIhwrP36c/cLf6pyTIPLQWSI\nGblDKNu3Oehg8CXoODQv3+mF/K5fM1YXN8SwfTs3rNtQLpQAZsnAzWoK70y8l/s8wcTIJgjx1WQ9\n6HSyKtKNrcDA+p+OYbYZkROCGNavPrJBIjXbTYbSgzXR9ajTrSu/HzkSg8HAF29PosOxk2RIMqH9\nI+nV9Ny8ZdsGMHvGKziTg0k21CWvVCPJWoSEzp5cE3VsBlqFFFLslVluaUz3258m6bzi9iEhIdz/\nt8kcSU1h5+4dJLVqS9+k5gTCYrFQbAoDKofeD2Y4aBxlpaDUi7VJ1wv7DwxA12F38NHflnJ3SwMR\nVl8YevsZL1L726hXRVhb8L+DfBFLR+QLSwjqAyy7kI5CLAXk5+cT7TlNoJTKrlGlrF+xmIEjRlc7\nxrrkuehrPmRYlAThQINoHJ3C+XhpHi1CGiK1aM7op54iMsrnuebm5hKVtwfZ5v+Blg0SdfL3kp2d\nTVRUlN8xj8fDigWzKMo5TaNWnejQJfCWX1Pfm8QNzvXYY87+cZ3i4IbJrHS76HfTOAAGTZgAEyZU\nWjN5Fl3XSZ46lezlK6C0FJVCenfS6N+1LocaOFiyKxvJrWOOMlNXDmfWpiwGTfon+76ZSvMAmb6a\nrtOsSCfG5j/f2Mxi4cBJJwNDQgk2GsAJxSka0wuOEN+lCXqrUTz/6hOVxju8chETDcEsiXAxqmnl\nBJ+b2saweG8mY9oUszolmyahNg5nFtE1zEjHhuGAbz1oR9KZ/+VfiZv0fUBvvnHTJBo3TQr4Pp9F\nkiSsSb3Iz1xAWPC5LzRd19l+vIDmDWJZJXXinnsmVjNK1cyc8h5mdQETO5hYl5rDoVwvnshExj75\nKk0VUV3oeuCismEvbM4yCQg8IX7+eLW6uuB/EpPJhLuKj4LDpWENCZwZWZGja3/xCWUFbBYj/TqF\n0/Sht2jUpKnfsbSTJ4izlAKVPda4ICdpJ475ieWBPTvY8NXf6ReVT5jVyOH5M/lkdhPueek9P6/3\nzJnThKdvxB7n/xTaLMLAwg2z0EeM9RPHqpKNvnn+T3Sav4A2Zcc7eT18V2zmvn71SKxrI7GuT2zm\nbD6FZ2sBN2Flz8p1UEWVm+NuF+2rqIvbLiiIIy4XLct2erEaDDQ8bSRm1N/o0jPwA4HVopGjeTHZ\nA/+/yQbpbA0GejeL5Ictp8grdjOxX6NKfQfVKyF5xteMvvexgGNdCKPvf5qf/q8Y47F1JFkdnCox\n8mtuMPHdx9Ng8Ej6V+GV1sSmNcuJPz6XxDgjINFXiaYvcCArj8L8vIu2V3BtIZsuYp3lBfRXVfXf\nFzreby6WiqKYgClAQ8ACvAbsB74CNGAP8JiqqlXtxSC4xFitVhzhSUBqpWNbi6K4u/f5O0z6o2ka\nhoI0nHbIKnQSFWImqOyD2iFWZvW65ZXEsnGTRBaWhtAYrdJ4R0psDK7gzei6zoZv3mRkgoOzH9km\nESYaeI8x8+PXGf/M6+V9N69eRtdYHSotwoCQ0tMUFBSQdvw41tBQGpVV+amIruvs2ryZBosWE1VB\nSENlI333l/Lm6RTaNQjBZYAzmSWYT7u4BRshsoz75EkO5B6nicdF/Hll84IkA6malyYB3r8zHg8N\nz8tw7WiQ2bh9W5Vi2aBlSzYcWoOpIPAXgserUez0kp5fSl27hWKnl8bRgUPpZqMBrSjweswLRZIk\nbn/szxQVFZGyfy9t6icwpJZrYgNxdNNiBkUEmAuNNpK8bj4dul782k3BtcNl9CwvmCuxdGQ8kKmq\nah9gKDAZeBt4qaxNAkZWc77gMtD/nj8y84SNYpdvmYJX01l6wkCrWx6tsYCAruscOJHDSjWLbIeb\n1Sk5zN5xGo9XI7fES0SdyqXzbDYbpXFdKCjx3z+xyOnBUa8LISEh6LrO+uRk3nn+jzT0nKg0hlE2\nIKXvQquQqBNbP4GMosB1bfcdzGf2uNvIGTOOg8NH8OXYcRwoW0eqaRo/f/Y2378wljl/uo/mWmUR\nry+bCT1aQnZGCb1aRfHIzYncfb/CpjZGdumlpOaf5s4mWSyNdHPS7So/L9fr5bMgM2qbTuX7U1Z8\n7064XeWbVJ8lX9OwRkeTlpZGSUlJJVsa9hhJROcY0vJK2Z6S63cso9DJJ6uOEWUzkZ7vZO7OM5zI\nLcHtDfz86fZqSLaogMdqS0hICB27dKt18YiqMHgr33v5MU/VxwSCS82VCMNOB2aU/W4A3EBHVVVX\nl7UtBAYDs66AbdcV6adOkXbkCElt2hDfsDHjX/ue5F++xZWTBrYwBr40oXzpwZHUFLatXoRsstD/\nljsICwsrH+enya/zQNdwQiy+j1PbeCh2eZm3KwMpphkTBo8IeH1nSibLNh7BkmilTmwwmWdKKDnk\nwNinFYf27mP5iy/QOfUwdwI7NCc/1c/m1hEN/TIiLVoJbrcbi8VXqq1Lj958NSeW+mH+yUrbUnLo\neNBBW4MTyooutN5/gMV/eIb4+fOYO+UdehSvxh5nZH6EAT0j8FymM87M3SOblB8zyQaG94rjq+Jj\n1K9vZeWaNJpmaKxyO7AVF+PSdVS7Rswjb1C/RXs+/edfGXriEA0lA8d1nelejfEBkqcWWM3EbJmK\nuv1jcrzBlMS0ZdwTk8o35e4zbDQrNS9FkT8zb9OvbD1VSO/mkRSVetl0JJdH+zcqt7FjgzAaRweT\ncsbB/vRCWtQL9bvWD79mEdZwE9Ne24OtcSduuvNBVs7/mdPbFmMozUOzRpHQdXh5gtRvSng83mLV\nbwkL+ATeEFl5vafgfxOj0YCpttmwl9iz/M3FUlVVB4CiKKH4hPNloGLcuAgIC3Cq4BKRm5PDz88+\nS91ft1GvpJTFkRG4bxzAHX//Ozff9ZBfX13X+f69V6iXsY5+MQafx/naLCJ6TWDAyDtwOByYT24k\npL7/R8lqlrGYTTQb+XhAzzQzM5PITZvo7rLi3qeTv7sQDx48kUYydy/lk0fW8lhOKeayggkd5CCa\nn9KYvyKNkQMTyscpscaVCyX4woG9J7zErCmv0DcihwibidSMYlasTOdxQ3glO/pnZjLnww8JztmA\nvewe2reJYuv+NNpIFpbZnJgSgpAtBkqzXFhM5oAienPPuvx7xa8kOdw0HhZDx7gQ9h/J58TOPG7O\ngo1GE6GR0SS9OZlVv26kKPUAtkSF+HrxLPz8n/Q6dJhmuk6h18t8q4Xm3cx0T3Dim6nQcHu3MfXf\nf+L+l98rv2a/EWPpN2IsTqcTo9FIaupBVn01mTu77K1kY5v4MA5nlZDjcDN35xlaxYXgcHpZdyiX\nAUoUSbFZQBYFJw/zt/tnMTbRTfvws19ORRza8CHJRfkMGvO7Svd+ORl420PMfn0DtySc8yJ1XWde\nWgi3PfTAb2qL4MpxttBAbc+5pDZc0tEuEEVREoBfgMmqqk5TFOWfFQ6HAmLm/jIy46mnGPHrdt8X\nqslEXGERRb/MYnpwMLe//LJf38W/fE/X4rVEx/o+KkZZon+8zob1U9jTuDlLf/menqZCzmZXVqR1\nPSt6UOV2gOOpqSQUFYHJjEmS2Gp10XJwXXrE+bweh9PDrAXHGHbGhM0gs7nYQabHg3OXzqzTqdgS\nbcQnxdCgZ+Us3aYtWtP4Hz/w1nMPEpuynaZ1rDSzBkGAojRmycDRjeu4tXMpPmGC+lHB7G5tZWpa\nEQ+Na+rnyW4+nMvOEwW0S/BPegoLNmFzFTDhtqYYyrygnq3r4GwexdwfDpO7bTV064skSTTo3AM6\n++YiD25Yyks/z2D/9u1sWLsOa2wMkdum0z3BP8Rokg00cOzl2JFDNGyc6HfMYrFw7Mgh9qxdBAWn\niawboFAskF/spr8Shc1iJOVMEYv25/DcoEZ+fc7kl2LMOcYeUwg7NZ0Qi5G+SZEkRsgs2DybA626\nUrduXcLDKz94XA4iIiIY9OS7LP7pI8j0ldnT6yQx4o9PEhoaWsPZgv8VLqqQuvEaL6SuKEossAR4\nVFXVFWXN2xVF6Vu2MHQYF7juRVB71F27aLp9ZyXPI8RgwLlsBd4XX/TbtzBv/zqiQyt/TLrXk5j8\n9wcY3yGMXZmlNI2pLIonSyx0r6IiT2KLFiwND6e+o5ijHicNb6xDs7hzX342i5E7RjZh7reHCMvx\nEms00dVado1iyNzhJtkaxx+HBV7S4vV6aWLMYXAHX5GB9d5TQOVsVJeuUeAu5JRDJqbs8vvTCzkj\ne4lsYmNtai69mkaUC2bXJhHM3nG6kljOSSllYPuocqE8i8VowN4shIh96zm0di6JvW4uP5aWsoeG\nNiM/PPEk+v79YDKR2bw59S35BFrG0yrawPbtWyqJ5Y//9wYn500jIrOE4hInjoYKNkvgP+2FezKw\nB5vIdnhpkOAfxjx4xsGRLAdP3HguDSmj0DfneUv7ujTkNLv/czcp9khyI1oz5olXL2nxiKqo36AR\ndz771mW/juDqxZfgc52FYfGVFgoD/qooyl/L2p4C3lcUxQzs49ycpuASk7J9O+01rXxvx4qE5Obg\ncDiw288Jgaso1+frn4ckSTS2e8kocHIsuxiPN8LPA/NqOpnhrSutlSwf1+Vke4NI8vdn4g6G2xIr\neyqyQcJYz0JeZiE9bf5fynVkmYR9KRQUFPjZe5aMjAxiDfmcFUgpzsL+w05aSBa/fslyCS3a1uOo\n2U47/SiL9mbSOMrKhJ6+UG9+sZsft5xiTKd65Rm+3vNK5Z3Kd5JhTaJf9GECERMTREZBPr2yp7Pq\nm5UccYURkZBE747tyHjtc1pnnGGH7MKk6fQ/foJlHa2QUKfSOIfzNBKb++8FunLRbDLnfMfgozrR\ncjBOzcLiNae4daC/EKblldKkjo2cYhfDWscA8P0eFxVFef/pQm5p51/BKCbUQmSImWmb0ih2eWlZ\nL4Qe9Q14tb38+O/neGDSRwHvWSC4lMgXEYa9wKIEF8yVmLN8Cp84nk+/39iU65KWXbtywCjT2ls5\n27MwOtrPU9A0jYO/psBNMZX65hW7SNmcQ+OSYhrh4b20VHq0j6Z5vRD2phezJjWPBnWy+fyxgQQ1\n7szQCc8QXcc3zuyvP8C4bx5/uAG4oRkfrzhWpb0edGLPyxQ9S7u8fLauWMGAkZUTT6KiotjgtQG+\nzNj4ujYKo2DhrnyaFckUo3MsUqdd33ocDo1k4IN/5bNJE+kcWkDzeufegzCridu6xLF4byY3tfXt\nFuLVdObsPI1J9s3h5pfqDPvjRFJ+fJHuAaLO6qkibukVhz3YRGRQFu+uzqCFS+fXmTPIzzxOSP96\njGjTgBK3xvKN6WSq2ZR0jCTY7L/Af/VpE2327iC+YWNsZZtMH928mIgTTqJlX5KQxWCgRaqLGaVH\nadMximi7hW3H89F0nSGtYli4JwPwJcjkm2NxebIxlz2Bm+TAXy43JEawsMTDnd3qo54uYu7OM9zc\nLpZm7oOo+3aTfuI4BXnZ3DBwRJUPRwLBtY4oSnCdkdiiBcs7daLFps3IFbzLXE0jZMhgv2ScVbNm\n0/d4EUs2eBnc49zyD03T+WLeEe51h5AleWhjDKZbjoFvV5xmR9MgrBYTLw1tVN5f17cz85+PM/7V\nr9m1dQOxqb+QFHfOwxvQKpp9pwppGefvwuq6TuqJQiRv4MIBZzQvMefVNj1LcHAwrriOOD2bsBgN\n9GgSzq/H8rnld03ZdCiXA6cLqRsRzM4zDhxmmajoOjRt242OzhWVxjLJhvJVm0VODyFBxnLvDGDj\nSTcJDRqyK7I9Ts8OLBXCP3nFLsxWGXuwCbdX45Npx3nSZcZ6LAuAAnM4i9R8eraOxmqWualPPIss\n6UxeW0rP5lZahLk5nOth45F8bm0bQdH2T/h4wSe0GHIvw+94gFPHTnKzx+z3l9zEYKZxms6C1JNY\nBkTQv3kdrGZ/r3jlKSO/f+UDpr/zPDeGHCPaZkILUFwewOXVMcm+d0CpG4KOL1SteXUWv/skwxsb\nsAfJrHtrKs7G/Rn7++fFhsyCS8pF1Ya91ucsBVeeuz78gJ+ef57QzVuIycvnZL26GIcOYdyzz/r1\nyzmYQk+jhWM7ncw8eRhzrAXdo+M8VUpEuoutve0kJISzLb2YnANFjCiw8VF2Ma+M8Z+nlCSJ4bG5\nLPrpK/au+JlHO/iHQpvXDeGXbelE2EzUC/OFTT1ejZ8XH2e8y8ZSb1HAsnTLjW4mdepU5X2Oe+wv\nTHv3Zepk7EQJgzy3zMfrzxAfAvf2alA+XkHpfr76x3PYw6v3ijYdLeBEdhG3dvBfN5plSyQ6Opo7\n//Aa0yf/HfPJbUTJDo7k65zILmZU8zD2nill0S4PD7rMWCs8kNhlmcFZZtbszKRvB58AD+pSl5Qt\nZro9+wVLZs/AkDaNIYqdjYdyaRZro3+8kV8XfsiU9DRC4hqTwX6iz/tTliQJe5CRrknR5UJ5ONNB\ndIiJpSck4odMJDo6mkde+5xVi+ewK3U7R9lctjG3//u84kAWfZLOvTfN64Ywa3s6Ogbu7nAuBN6r\nPmTkLGXxz/UYOrb6rNnDR48xO3kVBW4d0Ak3G7jjlqHExlSOYtSGo4cOsmXZbNA02vYehtKqzX81\n3rHDqWyc9z2Gkmy04Ai6Dh9fY/k/waVHNkkXEYa9tA9sQiyvQ2w2G/dNnkx+fj4ZGRl0TUgoX79X\nkcikJLI1jYYGEw1zgVzw1YwIZl4cDOlej1yHi+PHiwhqFMyiQ0XEhwUF9CqCTDIZBzYSo2UBlXf2\nGN2hLq/PPEpbJAxmCW+mm4GlZkJkmcGhdj5x5THSGEI92USe18vs/2fvvOOjqtI+/r13WjLpvZGe\nMEnoEFrovUkRUAQUFXtbd1ddy7rVXdeyurprWbGgoIJIk957IPSWkARI771Nps/c94+BhGGGEuRd\nfffN7/Ph8yH3nnPuuXdm7u88z3me3yNpETReGAwGl3MHez3K+198i6qqSnIzTzFxfhL7V/ybScpT\nDu283WQkV5/gKMPJN5mJ83N0+0qSRJEyntCx81HvXozFZuDQqWrqLmjRakXMMUref/IpwrtEMHjW\nvYRH/5a6ujqGXXrxZ54+SZiPL93ffBt1QYbTPH1lMppL9NDH/rcAWEwWdrz0ChXnz+Lnr+Vks4y7\nUtsT/WMC1WRV7sQw+GkOrt9CylU1pyVJIlNpJtUmYTBb2ZVTy5EyE/6RSUSpVZQcXMv3hReZtuBJ\nRnZ0hFcAACAASURBVE2aAcygsbGB715/ivF+1fio5UiSxP4L9fipFW2EexnZFS08N95RlQkg2FPG\niax9cB2yrKquZsnG3WjSxrZ9EyRJ4sNvVvPSYwtuSrTfFVZ/9g+88jYyOsReMzXr260cCxrGvGf/\ncEuW7vH03VSsf5uxoVYENwEkyPjiKFXjnmXQqAm3NMdO3BoUclmH8ywVnZZlJ24XfHx8HMQFrsaI\n6dP59IvFTMsvcDhebDYRnOzN9oPlCGdbGY69rNQeMxwtc130GaC2upoucgGrTWpLMm/Wm1HIROQy\nATe3ACaUtlxqLW8rHKkSBDSp/uS7y1lf3kxIgDvegg9KmciKz/7FgqdfuO59hoSEEhIy0T5qUxE4\nx86gCVaRseQblnuqeXxCEP6XBM9tNol1ZV48/uePCAwKpnXkeN5deB9TTpcySHaJpPMKOHo2C3dR\nIHPJEpZE+5A0pCfuMX25Y/6j9Em1V9s4dhPva0mS+HLlRRbUqfCSlQJgrhNY7aaltYfFIcq1W6g7\nW7P2M/Ffi/j82aeZrrMQKJNTZjFxyMfCvKlxZJa1YLLYGN41gAp9IwtTtAiC/TMyNZfwxZ/O8uhr\nnyKTyfD19eOh15ewc/0KtGXnyTpzklkRHoT5Oi5GKpsMeLsr2/Y6r4bM1OLy+GX8sHUnXQePcTgm\nCAIJaeNYs3EL8++aeeMHdRVOHE4nvHgjXUOveD7BCgJaDrB78xpGT+7YmJIkcW7TZ0wJs3GldOKg\nEBsbty1m4Mjxna7m/yD+X6aOdOL/DkRRZOoHH7D51VcJPn0aP52e3NAQWjxr6eOvwrpZS5Ks3QoY\no/QkTGtg+6kq4iM8ySxvQSETsVglDCjwCu3LcC8TG85U0cXPjbJGA/5qJUaLlRPFzbjL5FywSiTK\nHC27nUoDo3pGs/1cLU9Pind4SX19fC319Qvx97e7CSVJIn3XFqqyD2MTZKQMnUy3Xu2uWptw7SLC\nvlaYU2Pjvc+ySYn2psVNRnNYMk++swg/P7uSkV6vp1t+KWFXCab3V6vZ2NzEFDd3AooaMXQ9Q6Jb\nAV++nsNDv7MLCXgOHIh2fzqeV/Wts1rwi7E/x0NZdYypVeB1xQ9dIQjMMqjZfaiSO0Y67tGK+nr6\nDxtBv2On2bd+PSfPnKHq7HoWpvkhCALB3naia9CZifJwdGUr5SITvIrZuX4l42fMAUAulzPhznn2\nz7OxgVWvP85078Y212yr0cLOnDp6RnhR32rC38M5xcXq6ew5uBKtZgkvF0SjUKqo05tc9LgxLmZs\nZmyA82cb6iXnbNYB6CBZFhUVEW0tBZzri3aVVZCTfY7klG63NNdOdBwy0YpMdC1jeb0+txOdZNmJ\n6yI8OoqFS5dQUVFBfW0tMyMj2fHXeeSfKmCCTOXUPkXpxvtHa5C7yRzSEC7UGjmm8KDFLOCrlmO1\nSQ7nRycF8t3Rcir7iOSc1dLHKKdJLueQ0sa48aEcLWxkRp9Qp9X8vJ6e7PjuU+5+4iWsViufv/Ys\nQ+TZpHjZCTf7+z0s3jkMT7U7NBSTU1zJaD/BKfLzYGYtvfVyBLnAAoUP5wuM9FepOJh/louZ5+g/\nbKi93Q8/MKBV5zL1xk0QsUgScTIl23Kb6ZPgR29dFicOp9N34BAmPvAAn+4/wKgjR/G5tG9Za7Xy\nbYA73bwC+eGCjXMZzfxa7vxc5YKAVG10Om5T2xcJoigycvp0mD6d/Vt7sm37IkaGmVHKBE5V29hX\nYOAXAwOd+vuq5TQXngbsZNnQ0IDVaiUwMBBtUyNWlTcfHsjBVyVhMlkQBViQFokALD9aztwB4Q6f\nyckagZSpdzld50rIrmOQybi1+gmCxXTNt5locX5uPwadFR7+f6KTLDtxUwgLCyMszB7YYgjrC0eu\nXQJOLYmMSnJ8MScGqigpyyHdoxuylt3M7ucotC0IApO6B3OypJnpC+P5MkfN7OffYnBEBJ/+5Xl0\ntXuY0M3Z7SeKAjQWA7Bx2WdMUuegN0tsyaxGpRCJD/RAf2I1U/uFIaoFhvYU+DK9hBl9QgnyUiFJ\nEsdyG2g90kSo3G5FBMjlZNiakA/zo3+4J5lLf8OFExOY8/Sr1DbW02qz4e2iFJcZqa0ygXBJHz7W\nT87uk/voO3AIcrmcR7/4nB3ffktDxmEQRQKHDeWvs2eTeegQ+c+/QHKjEbydyRJAuopksuskEsZP\nc2o3bMJ0WoeOZde6FZiNrfS5Yzy1K/6JIDhXlbGPKycn8xTHVn2EnzYPEYkKRQSt9VXMTxZheHtA\n0+H8BvJrdCSGeDC5RzBrT1ZiEwTUbioqWwVsQYnc6ePvdI2GhgY+Wb6aosZWWqor6B8YRXCXGIc2\npefPMaFvT5dzvBHcwzXoSk867a1abRL4x7judB1ER0dzQB5Jd2qdzl20hTM/ubOO5n8Sos2AaOuY\ncL5oM9zWOXSSZSc6jDnP/IE/7c5gbE2lQ/oJgFWSkFyUVAIYFGQiM3EIxdU5Ls/7qBUYzFYUMpFo\nXxvxXbtSWlRASLA/OeddB/EA2GR2ktMVnmR3QR1+agXjU4LQma2sP11F1xCPNjeiSi7y0NAovs4o\npb5MT6xeJEUro6e83d1WbDYyemwEA5LsVltUgDta435WffImnmqB/SojUyyOQSiSJGG0SYiCgN5m\nI7daS8TFBnrH+2IT293KMpmMCffdB/fd59D/9KefMra5hdMKBUUmE9FKR/em3mYj12bieFEjHio5\nueZgKktFIg69Ra7hT0gJCfR8+CF6DRkC2IO4ps59sK1/dkJ/mvNz8HZ3/Gwu1JsJHpbGia9+x+Qu\nZvC1z3Vb1jkmJDqT3sA4P9adriQ+SE1WeQvuShnHipp4bLgfQV4qoIaMJc/TOPk5Uofa9yXLyit4\n8p1FNIQlIah8IDKc4g3rGda7FykDhoIkkX8qg+RgT3p2vzXX5riZ9/LlqzuZFV7X9llLksT6Mi9m\nvvrIDXo7QxAEkic/xIF1bzMk1IogCEiSREaVSOLEhZ37lf9hiJIemc2FXuUN+tzWOdzW0Trx/wIK\nhYLnl65kU7BzpMwmuY6oRNcSaAazDXcPTzxDY1yeN1lsZF1ooqrJgFXlS0lhPvs//CUTxOOMS1CT\nXeEcOFLSZCGij73eZl5+Pv1jfBmS4I8oCniq5MwdEEFDq5mG1va9MFEUuG9wF5TuMpJ0MqKuqD0p\nSRLblQaiwjxYf7qKLZnVrD9dRXWzEWv+QSREYtMC2EkrlkvlthotFr5vamS4pwc2SWJDcxMPW71R\n7Whk0YZS0ibdfeOHmpMLQC93d9YZmykyt7sOG61W1vkZ+eXsroT7upFe48b5ffnMP5NDakkxkRUV\npGUcpvjXz5GZcbitn81mo6amBoPBwMTZ97HT2pOSpvaw2ewaM0Wh46jOO8u4cMe9QqskOYgiXAmz\nVWL50XI0oZ5M7B7MixPjOV7UxOmSZsAeBJOz9au29v/8dhWNEd0QxPbxBE1/dmflUndsO0LhcZ6a\nOY7Z01xXp7kZqFQq5v7u3+xUDGVbbQDbqv3YIQ5i6m8+vGUd235po+j9+AdsF9PYaUxih5hGt4f/\nycAR4255np24NYhWA6JV38F/nZZlJ34G8PX1ZcqXi9nx9t+Rzp6lsbYSt2AYPCySoyVNLvtkNPky\nd/QE9ui1lJ3+lAhvx5fx1oMVPFCp5NiyEuqn9uLA6s+YEGEnjZRwL7afq6G+1czgOD8EAY5X2WiM\nGsvd4+4AwF20EuLChTkqKZCtWTVM7tGewydJEBvjRWWIRHZWCyGtUGY0U2k20fPOcLLKW7ijZ3Cb\nBXG0sJHa8kIa8uT4dPUi7V5P9h6tQd9ioqjWgJ9J4HBrK1Zgopc3bqJIPErKyq2sf+ddfKqqkdRq\ngsaMZuzcuU6WiaRUAVouWkyMnR7FvsJGAnRm5IKAdxd35veKRCYKhPm4UVZQxJ0NFtbrDYTK5YQr\nFGS0tqLXtlD/+Wd0HzSQzcs/p/7UFgJttTTb3NEHdWfOs3/m3Klj7D69D5sgo9tdkxnSqw/fv/Mb\nZPKrPAQ2CZtNcsq5BDhfa+bFsZFt5xQykYndg1l3upKUcE8UMpFgYwlVVVWEhISQU9MMIc5BP/LY\n7lhkOubNnnnpM5HYtPxzGnMOIDO1YPUIRTNyFqlDR7v8Pl0Nb29v5jz9u5tqe7OIiokj6hd/uK1j\ndqLjEG3GW3DD3t696k6y7MQtIyImhvs+/AAAo9HI2s/+ztnS0xjcFCw+Ws/c3j64KWSYLTbSK0Xi\nJz+MTCZjzLS7WVNbzoWcbfQPNNGkM3PoSDUJhTY8ZUpGImfrkbPY0oIhpP1641KCqGkxsvVcDXli\nLA++8g6R0TFt50OCg3BVsEYmClz9zs8oaCA12ocgLxXWvkF8ur8I3/7z8C0oJ7/mEA8MiXRo3z/G\nl4vlWuZnn+dzrQ8eAz2YPCICsKe/rF1aykQX4ufDENm6ajUjve0pOrUZGSw9fYYFb77h0E7o1wdp\n63byvG1MS/AjOcaHjWeqmNrXUQDhQq2RQKORszozd/r4IF4i3Rilimarla/TD7Jt5RIizi9jYJic\nyyW+rLbTbVquA4aOdBhTUnkjWRwjZQfH+bE7t5YxyY7egwu1ZqL83V2S6ChNIAcu1jNKE4hFEpDL\n7a+X6zksxSuu+d0HfyFVuxd//8uvpTzObn2LQyYjg0dPus4onfhvh3ALe5ZC555lJ36OUKlUzHnq\nt21/63Q6Pv/77zEVHsVfbkKm9uHi0T0k9xmMr68vdy78JY2ND/DOiDQSWnXIPWUUuwnIWo0kylUM\nqazkm4sSk0IcLcUgLxUTuwWxW53mQJQAol8krsiyUWdGfsXL/UyZloy8BloMFqw20BktKCN78dBz\nv0Wn07HrVdcv5lHdAjl3ooTZZc3kdLmXCnMNotWMR2IK/qGLobLK9cO5ghACEQjfvIULC+4jsVv7\n/tzU3/+ebwsKUFRl2Z+nXKRvlA+rT1QQF6TG113B4VI9TR6xeHmVk6gUHIgG7GpAgTodVcc3kxrq\n+NOWiQLJ1gucO32SlF59HM4NnnovBz9KZ0h4e5xngKeSi3UWyg9XMCnFHw+VnP0X6jhQpGNwpHM6\nBYCnSobBbNccrvOIa9OJTQ72Id2FApNYVcDsufcCUF1dhVfpAfzDHefdIxA27/2ukyw78ZOjkyw7\n8aNRWFiIrlWLJim5rbxXXvZZNMZzpPZy53KumiSdYvnrT/PoG0sQRREPDw9kShuhacGkaux5gWcL\nm/h2dyWzbWpKKw0YLQoHrVWAjHIbac/OcZpH74nzyFj2KoNC2vOrJElia20gwQNnsLO+CJvCnbgZ\n4+mTdBJj6VlEBAIiurFg/mOoVCp0Oh0KlWsyEAQ4JRpJFdQUtOiY9cIf284tPXAMqaLSiRCO6HT0\ndXccr5vVysH1GxzIUu3hQZdJk9i7oo6qZgMh3m7EBKqJCVRTUq+nWW/Gq9cUgr180dbloHFz/dMN\ntVmpKTkPoc77yUmBcvaeOepElpHRMZRO/AUbt3xOb486FCIca/bByz+QOUlyjhY2ojdZGRjri1Wy\ni7C7QvrFenp18WZLqYL+9z/ddvzX999D7hv/oiZYg3hJFN9WX8FdKRF0CbdHRR/atZm0a6RnGivO\ns/i7VTw4Z5brBj8xTCYT21Z/jb48F+RuJA2dQo++A37qaf1XQSbpkdlc12m9Xp/biU6y7MQtIyfz\nFIeX/4MoSxEeconvLMGEDZnNqKlzyNy+nAnBji9VQRAY41vJni1rGT15JufOnKBbPy9qLRYy8hsZ\nGOtLjxgfgmepeGdDHn3KWvjzJl+eHBFEhI8CSZI4XG5DljqP8AhnAfXkHn0w6V9hy9alCA0FWAUF\nUkgyc377UptowWX0Txvm8p78/Py40CxnqItzxwqbCI/ypKHEim9MjMO58c8/z/qsc0woLUN+iTAv\nmEw02awEy53l86QrUk8qSkpY98ijjC0qpp8gsGKTgXtmx7UtErr4ubFeF8Ad859ElMlZlr6GbGsZ\nvUVnUtfZbCiaXL8kypothPWxS9TtX7ee0s2boVUL0TGMefIJBo5YwfGMdJpNRmJsEv7pf0MmCgyK\n8wOg2WDBSyXHz0PBkYIGBsT6tY1d22Jke5GNET2nMnH2g/j7t0fSBgUG8PWff8MXK9aQV1ONm1xg\n6rTBpA1IbWvjGxBMY76VAA/nV1KDTcGac7W0LlnG0wvmury3nwrNzc0s+8vjTA6stqsrSZC9Op0f\nzs5g+hULhk78ONhTRzpGlp2pI534WUCr1XJ08e+ZGmXics3IBFrIPraYo37BiC0VrsRPCPCQc7Y4\nF6vVyur3X2FIgIx+0f7UtZpZe6qSHhHeJIZ4kNo9EEOlkZQGHXvE/gQJbuTsOUJkixXV0aV8vf0Q\nfR99hJT+/R3G7zVgCL0GDMFmsyEIwi2F+CvDNOzKzmBUUkBb/+NFjQR4KqlTm0mPi2LhTEdFmJCI\nCO5e+T1bP/4Y28WL2NQeFNfXce/xkxzV6ai12BP6bRK4+fszev68tr7bXvsL00pK4ZJQwcwmFVuX\n5FEf4014z24IQYlMfv6xthJnAx/8PWtPP0VPveTgim2xWpEQUJWaaNZbnNJEjrSGsnDYKFa8/jqx\n3y5j5CWvq3TsBFsOHGDkok/aFhHbN6zGU+Vo0XupZDTozAxN9CenUsu605UoZSIWm0S1TuA3H64h\nMiraoc/5Cxc5fuoMMZFdeOr+edf8PIaNmciS7YuZ5uEY8WyzSRwzhyDzCWDjmRweMRpRqVznof4U\n2PjlP5gVVocotj/r5CA5h8/9QFnpdCK6RF6ndyduFvZoWNel+q7X57bO4baO1on/N9i0/DPGhTt/\nGZP94eKBH7CpXFSMxu7CE9y8WfP5ezzcXSA1xhdBEAj0VDKzbxiZZc1YL+UrThoRTrXOTCBNNB4v\n4MHcIiZXVDGmuoYxBw+R+4tnyT5+3OV1RFG85Vy4XsMmEOzjxoYz1W2pIz7uCgbF+VFocWfsu++2\nuZuvhLe3N3e9+CJ3fvghqj4JhMQoeF3XRKhcxiRvbyZ4eTPOy4ucpkYKzp0D7C48xanTDuMoBZEJ\nFjXjcwzkHqklJXUcwVdEk6YOG8Ovtx/kfcnGodZWik0mdmtb2NuqZYKXF+OMbize10p6mUSLwUJO\njYkfasKY8MRrVJSW4r5yFV2ukKERBIFx1TXse/+fbccGjxzP0VpHUhIEAU+VjOJ6PUmhnkzrFcrE\n7sGMTQ5Eq/Dj/Nnj2Gx2b4LRaOTNDxbxw4k8pPj+nGiQ+PP7iyguKXX5zEVRpN+c51h9EfQmuxu9\nrNHIy0cEijX2lJJ6N38ys87d7Mf4H4FUleMy2GlAmEjGls4a9rcLomRAZtN36J8odVqWnfgZIO/w\nNsZ2d73WEnT1+PYYS+2FJQRe5VbbXa5g8sP3sf5vj+AR6vz1G50UyIELdVhtEh4qOTJPGSX5RYw8\nWoxScLzeoOYWdn76GcnXKdN1Kxg27g4+2b2KWT0q2gTfAfaX2pj9xifEJiUBdkLYsfZbDPXluAd0\nYeyMuYiiyOd/eJzpASVUWA2EubkReYUsoFwQeFztweJHH0e95EsS+/ZFYTY7zQHAUxRJzMmh8Fe/\nRnj/PboPGtR2zt/fn94PP0zBl++T4yHhL1cQVC8hYJdjSx44iUFPPs6ZowcJj4rjwe52ZZxV773H\nYIPRpVyflJnZfm1PT+TdJlJSvI7IK1J8/AKCOKToTlZVCYrmUhpbWlEINp7oHULz6Y9YdGAdC373\nEV8sX0n4gHHIFXZrwD80Av/QCL5as4nf/eJRl/fbo98gfIIWce/zL+Lj6U69Rzik9mpza8tNOoKC\nnCX7fkoINtf6o4IgINlc7+12ouOwu2E7Ztt1umE78bOAvqkOk8XHZeWJsroW5sy+jxUfluBZvJeB\nYQJak5V9tZ6kzHgGb29vZGaty3F91AqOFDby6LBoWo0WFF5yDHVWIq9lJebn/6j7KK+o5JPv11JQ\n32ovEp0YyYN3z+K+Vz/kh0/fRCrPRLAYkfxj6HnPfST36AtAXk4W+z97lbEhWtRKGa2FFr5+ZR1i\n9ABszRV8Ie9FvVVFYPdCrLlZBBj0+MtkeMrs5aPCzGbOLFpEryVL0Gu6wtlMp7kd0rXS312Np7aV\nHZ995kCWleVlVGev4/6FSW2WTUWDgQ3rSpD5x5E8chj71i5FUKjoNaB9B1YQZdhoK+jiCNHx6PT7\nn2HPpi6cO7kd0diM1SOY7jPuZnTqIGpra9n5l7nM7N2ujhTgITLTrZy1n71NrSKWAIWz28w7OpkT\np07Tt3cvl59HVFQUCSl9OO8d55RyonGzEhX583Jr2gITgBNOxzOrLfRe0BnB+9+ETrLshEvU19eT\nsWcr/kGhDBw60smlGRviw9asGqb2CnE4fq68GVlQCoIgMOfp31JVuZADOzei9vZj3qTpbbl3Fs9Q\noMzputnlLQxN8MdHrWDx3iJiumkwe6diufB9m4VxJSQPj1u+x+LSMp567wuawpIQvO0WS3ZeK+fe\neI+/v/wr5v3yz9fse+CbvzMtUs9l2vFQyZkR2cqv0w8y7Im3iff2Jf5S26N7thGy6F18dFoarFYm\neNkLJgs5udhsNro9+gjHX36Fftr28mYlJhMWibYKJUKe46Lgs78+yxPdVQ6fS5ifG1HDgzktBcPO\nvzIqwC5Yv//N9XilLWDsnfMZMX8eu5cuZYjOMQhIkiSEXs66rCMn3wmT73Q6vm/DckZHi04uSLlM\nxFqWiTypu8vn5h8WQVFJ9jXJEuD3j9zLc+99SrlXJDJPbyytTYQ1l/L7ZxZes89PhTFzn2L9P55h\nSnhr27OobLFQEjSU4ZpO/djbBbtl2bFtlZuxLDUazcvAVEABfJCbm/vVtdp2kmUnHCBJEiv//SbK\nwr0MCLZSn2Xhqw3/pv/cF+jWuz16URUcR4JBz5oTFcQFeeChkpFdoaXVImP67x9vaxcSGsaM+Q87\nXSdu2Cyy975PckD7D8BstbHiZA2isiurst0Q1D0YOuMZknr0ZNPmLYxsdrRGdTYb6ktaqLeCj1f8\nQHN4soMFI3PzIKO+hSMnTjKgbx+X/QoK8om2FHA5sOkyShpNJIy9H7W3o7xa15HjuXj0ANNPH8Uq\nSaxpasRNFLEplQiCQL/Rozn30Yd8+fLLhGTnIAEBMjljva7Y971iUWAymfA1VyIIzrKCjToTc+Kq\n2oJ7ZKLAiC5w8NBXlA8YQVh4BPlDhqLaspXUS5VXDDYbW2Ojmf3ySzf/8CwmBxf1lZALNiR9s8tz\npblnWTCmv8tzlxEZEc7yN3/Hhu07ySurJC4lkqnjH0QUbz3Eorm5mZXrN9NotCIgER3oy4w7Jv2o\nMQFCwyO446VF7FzxKdQXIcndCOw9lHlTfp5pLv9XYRcl6Hif60Gj0YwEBufm5qZpNBoP4DfXa99J\nlp1wwJbvv6Rnww6CwuWASJi3kmneTWz4+i8kJH/XFomYPG4eNev+xp19PSht0KMzWRmWGMB+WSqx\nCV1veJ20MZNZV1vDG2s/J9pTotEq54QtnOLuE4gtqmdY/GAkSWL14kMs+FUAkS++xK633mJoYyNK\nQeS8KHBh+Age/NUvb/leL9Q2Q2Cw03GZfyg7j5y6Jlm2NDXio3D+5R6pVRA31VXSCRhiEuH0UWSC\nQIxSiZsgUNGnT5tlmNKvHzFr17J6/ATGNDgKK+htNlRpaW1/FxcXI1e4JiqTxeoUBQswOFxg14Zv\nOVyv4JBfMj9MCSciMwO1QYfBQ8Gi5Uvx9rZbvBaLhaXfr6GiSY9VEFALEiP796Jfn3ZrsNvgcZz7\ndhMpwc7Xkvzj6BYVSkVFGf5hEW3HjXo97vp6IsLDnfpcDVEUmTbBWYO1tbUVk8mEn5+fi16u0djY\nyDtfLCNp+OS2OqT1zY288/FnPP/kIz9aFD0gIJC7n3j5R43RietDZjMgs3asOJrsxnJ344GzGo1m\nLeANXLeKfCdZdsIBDVn7CPJ3EXgT0sqOtcuYMucBAPoMGs4ZmZzN25chM5Zjk6upDB/Effc9cdPX\nOlDcTEbqM2RINhDs0atqoEivo0drPb4e/nTxTOGHb3fwyuu/oPfYMez8+mssLS1oRo1mRGo/tFot\nWq2W4ODgDlsJ8mtYRpIkUVtedM1+Kd178v2yAKJxdGV6iBaMeh1uamfXsKhrt4p7uat5392N3/7R\nUXNUrVYT+cLzrHjtr0zWavGUybgoQPbQISz8TfvvODAwkCqbu0vt1laz6+W3IAgU5+dxQOyGsqWU\nEF9vrP0GcqGlFULj+PaHTTx+3z0A/GPRlwT1HkHsFWIKO8+dRJIkUvv2BkDTrQcZPqmE64/j696+\n17mjXMXghx8mNlHDmo1byDqUjVlUIFhMRPi48fRCx2orN4uSwnx2L30Hz8bzqAQrjeooEsbMvylR\n8xXrN5M88g4HUvTw9kUf24NDh4+QNmjgLc2pE/852N2wHTMtRdsNC4kHAZHAHUAcsA5IulbjTrLs\nhANkJufKHgBqpQxjc43DsZ790+jZP81l+5tBTk0zQmgYCI6BJaqEJLLSTzHEYzAAtSVabDYbnp6e\nTH/c7uKtr6/jq7/+Eq+GbLxlJqrEYEIGTGPsnfPbxskvLGLJuq3U6U0EuCu5f/pEYqOj2s7H+6go\ns1oQZVf9DApOEeV2bbKUy+V49ZpM7qmv0ES0u0IjRR3frFrCiKsWDDarFa/Mk21/59psLPzsU/wD\n2oUSzGYzKz54DWXFCeKHqVhVKWCQh3Pn0y/x6EDHl7mvry+BmjS+ubCHmdFyezI8kF2jJ6vJ9U+6\nqsVMZpNAmGcTsx9YiHjJwjIZ9Hzz9ZecsNnnkpl1DllILKqrVIciU/qw5+iuNrIEWPDC39i0YjHN\n5zMQLQYkv2iGPPEgkTFxANw5ZSJ3Xro3uVx+yxacwWBg+wcvMCOyFbxl2PeJKzmx/V0yvX3p04LT\nXQAAIABJREFU3uf6bt1GgxUvF9cODIskMyu9kyz/D0C06RFtlg72cR1lfgVqgezc3FwLcF6j0Rg0\nGk1gbm6ucxFTOsmyE1fBog4GnCNVq1vM+Cddc9HlBEmSMJlMKC/ty3UYDnmAzmOvfOtXzAqtQvAU\nsIuFN5F35iv2urkzYtJMduxL540NBzCFxCEoBCSzRPqHy3hl+nBGDbGTcM9AJXlbvqG0+wxkHnah\nc1tJFneaMwhWyLDZbE7W6unjhynIOkGj1kjtD6XkRLgh95RjbrEQUmUlTb6JdN9ABk6cjlyhpL6s\nmNLP/8nDZUUgikiSREG/vozr4+ji/fbd3zJMOs57lUrOuvVDHxGGV10+RV98zhcDBjg9w3sf/BVL\nFlv5ri6HwFYz9VoT+eZoIu+Zw9ojHzIjtn0Vbrba2KOLRi/zZPY9C9qIEkDp5s7MmXezbaU9ruFE\nZjZhXV2Th87qOAdBEJgyZyFw/cAbhYuo2I5g++qlpHrW8VW+P3rvWASrkbDWi9wRY2P7jhU3JMvr\nffskqWOuvU78NBBtBmQdJssbtj8APAu8q9FowgEPoO5ajTvJshMO6DpyFlnb3qJbYPsrRpIk9jaH\n8ej4O25qjB1rvqHq2EbcDNUYRA9kUanMevwlp5dmcrAPh1wIbBsv5tA9UNP2d2CUpwNpHdqzncHq\nMgTBcbx4X5FNGRuQJt7Jos17MYd2bXtRCoKAKTSeTzbuYWTaIARBwC8gmNd761iR8yVZikS8Q2Lw\nVuuRW70wSiaHebW2tvLNG7+kl5DP8AAFlS1mNkW7MbJYIkImAAoQFGC1ULp6KTtqK1D5BqA7dphR\nBefR2qzkKhSU9+3D7Hf+7jDvqqpKAutP8UaZGzm9FiDIZKgAU3g857VNvPPZEp5/5H4kSeL08aPo\ndFpSBw1lzvxnee1fH7O9upFmrZ6e3XvSRdODBs8X+fjQCvz1pehbdXTpM5oHf/8r8v/xbweivAwf\n/wDCLsnTqVVKWg16lG7O8ksitydvsKmpieMnTxERHo6ma+IN21cXneeILY3k+59o+0x0zY188v2f\nSAisvmH/QE8lFrMJucJRLq08L4exvV1H7Xbivx+5ubkbNRrNcI1GcwS7QM+Tubm511w9dZJlJxzQ\nf9gYDuhb2bjve3wMFRhQYAjsxl0vvXJTFuL21V8TcPZL+gbLsX+9jBiM+/j67QYefOVdh7bPzp9N\nzt//TUNoMsKll7ixspTYaiM+XfyxWi2U6TN56peO+1yV+dkk+bi2VmS6WnLPX6BYUuNKFC3fpOD9\nufMI8fRE3qMHqwpUmHtPZ/LIdvk6i9nE/q/e4f4r+q364E/c6V+MTLRfN9RLwcIpMaxYV0BEZXs7\nSZIITe3Hy++/yfkLF7DOnUZoSAjnT5+mZ3w8k6OiuBq5madQSzqyA8c5kZnc04edWZmMPrSPzHUf\n0V1Zhb8CFn/vxjJdV2wpQxF8BZRApkFP+dIvmHf/w/iEPs+Fg9t5ZPYUIrvYg2xCr5PQn5wQC8Ad\nE8fxt8+Xk5Q21uG82WQk2KNj2pxXQ5IkFi1dTo1JJCQhhZNnili5dQ/3zZhEVKSz1u9lZNVD/3lP\nOhxTe/viOeZx8vd+ecPrzps5nb998Cld+o/Cw8vuQaguKcBbX0OvHmNv0LsTPweIkvFm3KpX9bnx\n4i43N/fFmx2vkyw74YSh46cxZNxUWlpacHNzQ6m8uZekJElUH99Ev6siJN0UMiLrzlBUkEd0bHzb\n8fCwUL7+43MsWr6Kwnot7nKRHn0iaAz0wGwowydIze/m/6ItSvMyvEO60FBlxs/DmTBtbj52ofJr\nELsgSQw4fpxYpQp9+kE29urDhJGOOq9yhZJedyxg/8FDDB+ShsFgwL0mE1mk85g9+wezd3UlIxRu\nFABne3bn7rfeRBAENF3bo4IHjRp1zecWk5DEok9t0Me1ldUkKDnxzV+ZmXjZ5Qx5FyVsPYY6LGDk\nbu7UeAZx5Iel9E5K4IWH5nLqbBYrt+7CaBOoKi+lWThC0qWKGMUXsqkuK0bbWM/MoXaxBTc3N8an\nprD94A5i+w1FqXKjuqSA1sIsnn/8x+U5frNyLbLoHsRfSq3x9PGD+CS+WLWBPzz72DUXY/5x3Vwe\nD47RkJt941xGlUrFq88+zsZtOygrzETAxqDkrgycdm1R9qrqar5YtZ5anYlAtZKFs6YSEuwcOd2J\n/wzs2rAdzLPsYPTsjdBJlp1wCUEQnEjqRjAajbjrq3H1teoTImN/xl4HsgTw8vLiuUce6NB1Rk2a\nwVf7vmOGh2MuX22rBd/kESRpuhLFKlw56MKzjhCjUFJntVCIhYjACBet7PJs3yxfxPAhaWi1WrxE\nA7go7hzpr+LMww9wyMOXmF69eGyo69SR6yEqOgZVUCxSTTGEJzidl2lrmaJx1N0pkAW7JBeVfwh+\nKhX333MX6zZvo8CoJLSvnaijgcKcsxzbvQV9aytx3XqROnICNquVwycPYdm2kynjxzBk4AD69OjO\nhi3b0ZstDEmII3Xa407X6igKa5uIT/B1Oh6cksreA+mMHOb62clduI7BvjiLjI5DkiROnjpDfVMT\nQwcNwM3NzamtXC5n+uSJNzXP9CPH+dOKLRhCExFEEanVxu63PuUPd09kyIDbK634U8NkMpF5Lpug\nAH8if2bqSFdCsOlvIc8SbifFdZJlJ24blEolBpkacA7ZLmu2EjHAmQhuBTKZjORpz/DeJ39kQpSN\ncE+R/ZUiR7RBPDVrOIIg8ND4Yby9JQNLSGxbP/FsBrOKctgYKhE/vAv9k4NIP+06CMBmtZLVaGLZ\nDxuYM3UydbIgoMmp3a5CGw2BaspLDZy5eJSTZwtY8PA9HQ5qeea1D9h4/2OIYfEOJGg1GYnQV6BS\nOBKG7DouJrkoYrFYOFVQTuJgRzdjTFIPzuzbzrj5j+LuYY/kFWUyElOHcvLkIfpVVREaEoJarebu\nmdM7dA83gllyTXp+QaGUXTyCTqdj8/adGEwmRg1NIzwsDIBgDyUWs7lNZ/YyirNPk+Lvy9yXX6dY\n5oOkdMd3w36m9U7giXud653eLD78YRvGcE37frcoYgzX8MHabf9VZPnR0uVsPH2RGpUfCrOerm4W\nXl04l/iY6Bt3/g9DJhmQ2TqYZykJgLNwx62is+pIJ24bRFFEiEzFaHF+kR/Th5I62HUNyVvBvrMX\nSP71Vxzu/TJf+z+AefY/GfL026zavg+AiaOG8eFDdzJcrKWbsZzQ9DW8dmgThmAr9/06lZGDIwny\ndSNZXY5B1+o0fsa+ndgiNew6cx5RFAnqN5nCJkfR7PNVBg5W9MKzKZEgQUOALZGmTA9ee/mdDkdZ\nWq02Ro+eiEdJFsbyfEzaJkxFOYQ2lhDu5e70TLsLFdgszns4UlURM8cOJzPrHN6Rrt26fhHRbUR5\nJWJ7DWTzrn0dmveVMJlMpB8+TFZ2jsvzKsG16HhNSSFNTU28/dUqtCHJCPED+Gr7YT5dugyA+bNn\nkJ++CW1jQ1uf0vNZBEutfLIjg4pgDYqAUJRePujCNSy/2MjqTVtv6R7y8vLIN7suAVZgVXExL++W\nxr2dqK2tZf2W7dd8zjeDZT9sYFleE9rwJNwDQpCHxpDvm8ALHyzGanX9Of2UsOdZ6jv4r1NIvRM/\nY8x6/CW+efs3RLdm0StYpKzZylFdCOMe++OPVkq5jLq6OsxudndeWEIK0L5v5RYSQ+75C2i6JpLU\nNYHXutqt2b3r1sELJwjtH4LyCittbl8V7236G/ScQ5euPbBaLBzcvZ0z1Q0owuNoqrM7c8fPvp+d\nP6jIPrYJWWs1VpU3GaWxdI8a4zA3hVyJsimMPbv2MWrMiJu+p62799JtyBh6DJdRU1FOTUUZkcMG\n4OXrR+beTWwt3XxJi9aOh1NkHNy3hIa+s1FeCloxlRcwp1sYSYkJ5OXnY9JXubzWtcQbRFHEZLaw\nbecuVEoVQ9MGuSxFBqDT6Whubm4Tg/h8+SpWH8+mzj0IwaTHrebf9IwOJzQ0HG+FwPTxo+gWHUbp\nVao+NpuN6pzjuAWG03XQ4LbjMT1SaaqtYv2WbUydOJ7fPfsEW3fspjjrHIIEk/v1ZP+JBpqDE51X\n/N4BbD6WyczJE27iyTvCbLE4FOa+EpIgx2TqWJDJ7YTNZuN3733MwYoWTL5hCHuziBdX89enHqRL\neFiHxtpyIhvRJ9bpeJVvNGs2bWX21Mm3a9r/NfjJyFKj0QwE3sjNzR2l0WgSgC8BG5AJPHW9EN5O\n/HyhVCp58LfvUZh3gf2H9xPeP56FacOdiPL4yVOcPHceSbIxtH9fkjU3lsi7jJaWFpRq1+4VtY8v\n9fX1TseHT53K69+vZFq8o+iCTCby3DDYceZj/rw5BsnLH8LikIfbE+vDvNtTKMZMvwem39P294Vn\n/+FyDj7qAHJO5XWILAVBaEstDQoLJyisXRJOoVAy4rE32PDNO/hr81DJrOwpsdLi0wP/yvN4mIKR\ny0SCw/xprK3DarUSHxeHefNuSEx2ulZloWvrqL6qnDPZF5HF9MRiNLHn468Y2SeJYYMH0dLSgqen\nJ3q9nvWL38DHeBF/NwsHDV5k6buw3RKOEJbUFoFsC+7C8YuneXjsQFTu7ny+ZiuPzp6E6cgxsg9l\no/ILwahtwsOmJ9TPG//eg5zm4xMYwvnj2YCdyCeNd1yYrNl7CFHhel+90dixnLzL0HTtSqS0khrA\najRgNRlReHojCAJd0JKcpLnhGP9beGvRl+wzeiOGhdhf3O4eFAIv/utzvvnbqx0aq05nAh/n43K1\nF4WVNc4nfmLYLcuOWbyizfWi51bxk5ClRqP5DXAv7dnv7wKv5Obm7tNoNB8D04G1P8XcOnFtnM/J\nIvPAOmSSAcEzgrHT70WtVrts2yU6lrLSEiRk9ooWl8hSkiTeX/QlhMYTkmJX/9lwKouME6d5cO5d\nNzWPqKgojJv2cKVFeRm1+dn0ecB5HEEQ+NUn/2bj23Pp5rygplFvxRrfG6VH+8tXVlPM3NnXjmIV\n5YCLd7IkSQjym7OiN+/ex5r0Y1Q26mjcnE7fXr0ZOHSkQxtflUhsoobYPy6ivr6ez7/9nm1yC8H1\nRQSERRDo50+/wcMQZTIMula+XbmW++bMQhMRxO4NKxk4fioKpQqT0cCxPduw1uRzYcd3JI5t39ez\nmE0c3bWZ8fcsbPusvNPGsWTFYnYcy0Lp5YdkaKX23D7enmBBoWj/3NeuyUfQOFcREaKTObhvJ6Mm\n3EFi2ljWbtnFEw/Mw2q1UllZia+vLx4eHnzy9Ypreh2s0rWfY1SQH5bzWuQuckKD1K5dqTeCIAjM\n7J/Chl0/MDDZl0AfN07mt3C6XOT+WTPa5llVXcOiFWsobdLjLhcY368nk8eOvKVr3gwkSSI9rxwx\nzFkYpEgRwIGMIwwdNOCmxwtUq3Cl1WVpbSZO0zEr9T8BQTIjSh1bAAnS7aW3n8qyvAjMBJZe+rtv\nbm7u5c2SzdgFbjvJ8meE7T98jWfpOmbE2y06o6mQlf84yISH3yA4JNSh7eaV33J69WeE6kuwSgK7\nvGIYuuCXDBs3mXWbt6JO7IeXn39b+y5du1FVlMfRYyfon9r3hnMRRZFeceEUFOcRHNUeXVtfUUZC\nkLfLaEiwa68SkYZWn4mne3uwiMFkocWjGwPMzWSWlGKWRGK8lSyYPIRB/dqVdsxmMzqdDi8vL/bv\nSaemqZyqWi1do3ohSRKVtTuICKhBRIfSlEhRwUWiY68d1PTduk18fDQfyS8K1CCEw5GaWlo2r2Ps\npGlYLRYuHNrBwzPb6yL6+/tzsaqOltwCFL2HoFV7crGpheOff8KsaTMICg2nqNG+B1tU00jqmMmc\nzdiPJNloaWpC7eNH7zvuI+rEB5R+cwadj10RJzuvhElP/8mBtE7u30n/cdPw8W/Pz4zqncYnW/7C\n01eoHBoUrq18UaGktdkuZi0IAi1mu+0sk8mIiGh3xYb4eVGnbUHt6eU0hrvs2g6mudPvYM3Lr1Mf\n7phaItZXMHvizRPHlZAkiYayo7z+9OC2ZzG0P1zIq+YyJ1/ML+TZD5fSEp6E4G7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DnKS4pY/NfHCWkvwlsFO9a9zbqIofz6Dy/T3XoU6P8DbaisxFfthIYN8FBomOTRS2dvDRZRRn5T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\n8iW5AKSlZzD/2gf4btVSutoacLhcVJYVMXfykH6eq9k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"text": [
"<matplotlib.figure.Figure at 0x371aeb8>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create individual scatter plots using only two classes at a time to explore which classes are most difficult to distinguish in terms of class separability. You do not need to create scatter plots for all pairwise comparisons, but at least show one. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### Your code here ###\n",
"\n",
"ind = np.logical_or(Y_train ==3, Y_train ==2)\n",
"plt.scatter(X_2dl[ind,0], X_2dl[ind, 1], c = Y_train[ind], s = 50, cmap = plt.cm.Paired)\n",
"\n",
"plt.colorbar()\n",
"\n",
"plt.xlabel('PC1')\n",
"plt.ylabel('PC2')\n",
"plt.title('First two PCs using digits data (Only two classes)')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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3YdDbPD7GmyhlqlD4gPz8fD74ciZXLTr05kAMlgK6p7ZlzIih9dL/xp17aJnR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\n7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Tab6XnvAB596X8MefZFl+bukiTR8t5HSDAV3F89avWlw4Mjr6nviKDMDAaSVVXtAvQE\nvr50QFEUHfAe0B1ncOxTiqKEXGkxYZleZ6QkJ/Hz+JfRjm7F157FPP9aRHe5p0BvzMtZ+sdMdi+Z\nhTn5BHr/aoS37Mp/Rr9SqqjRywkPC+O1Jx9h7uIlpGVZkDU7teyZ1DWexd2+m1fqcVJSUgi7zNqU\nZPdfT7tDw+BV9D5Tk5atadLyhzLJf7W5dcAjLHt3A/V0zlQNWZKw2t0/8Bw2+zC4HKolfffzDORa\ncUS0bgQ4lesPc/7iP3ffRkyd2thsduKCdejkgu4zg04mY98asrKy8PNz7YBTFmRZ5tnhQ0lKSmLT\n1u3EVPfBcX4PoW6qBdpDal/R2jabzfz47qtk7F9HVtpF4i06qlWvQUzLm7ljwFDimpauOL6g4jFh\nR1f+RRtmA5fCwmXIv4CqqnZFURqpqupQFCUc503ripaJUKbXEZqm8f1rT9E0Yy+Sr4QzpTeJtOUT\nmevnT79Cxb//+n0GJ2e+h2K0gQ9gz8S0aSoT/pvMqHe+KLUcdrud6d98wtkda7DnpOMTEUPHfsPw\nCVRYP1/Gz00VQYvBF3//gq68wNhWaEf+drEWDjqq89z9Rbs0rwcaN29J+lPv8e/M77GfPkBmtomU\nLCv7krJpEuabNy8tx4alblsiakaW6XoJR46S4RVMdPXLHmYkidiOPZj3z0qeGz6EhIPx1CALd7cN\nP9MFzp07V2HBO2FhYfS5yxncpP57J5k75+B/WdT2OYue5vcOLPBd0TSN9atWcGT3ZrwDgzm6dwf1\nT64m2ihBhFMbZ1rTsAbXEIq0imPCiuxh397iApZUVc0CyK0PPxt4rdBxh6Io9+G0WBcC2S6LXIZw\n815HbFyzkojz+1yUTzUjHFq9oMCYpmnsWTKT8EJBLt56Gfv+VZw4llhqOb5+YzReaybRyJRAEymZ\neue2sOWrF8i8kEJaaGOX+Q5Nwzu2Db6+vgXGHxj1Grv0dTHbHHkyHzZ50+KBp13mXo906Nqdsd/O\n4olf1uFTK5Z74qoT4KXj3+PpbD6VwYYTGRxNMxNeM6rM11q3eWuBXNfLSbc4LeLYuCakyK4FMAAy\nfUKpWbPknWDKwohX30Hq/iTx3g3YZwvhULVmxAx5k7seeDhvTk5ODu8/PZj4rxOAsF0AACAASURB\nVJ8haNMvSEs+I3nTYk6kF6wq5W+QOL1paV5zdEHV5FI0r2f9TIu3ZBVFiQZWAL+oqjqz8HFVVf8A\nonDuqf7nSmsJy/Q6IjF+L6HuU+2wpiUV+NlsNmNPOQ5udFJdLzMbVv1D9NDiq9UcTzzK6gW/o2kO\nuvbpj9ViRT6wCh+fgs9ptQ05bPhjCv1fGM/Md58nJiuBQC+ZZDOcrdGcp19+z2XtsPAIXvrxT+ZN\nn8y5E4eQvf25b8B/iKnXoFi5rieCgoIw6g1ghtpBXtQOKvghny+HNney5D5gDODSUGhoKIZGnbAe\nXpZXPQrAZHMQ0rJbpaV4SJLEwJHPwcjnipzzy6dv0+jCdnReTjmNOpnOtf3593g6tYO80F/2O/PO\nSuL8+fNERLhPwxFcfUySLf+L6Mk5VyDXfbsMeEpV1ZWFjgUCC4DbVVW1KIqSBVf2Gwtleh0R3bAx\nh5ZpVPdy/dIZggpGvhqNRjSjP+4ad6eaJRrWqVvs9X758gPOr5pBPS/nGnNXzuCYbx26+bj/Euec\nTqCB0ohXpyxk+aJ5JJ08SuPGrXi0y61FBn54eXnxwKMV2/z6WiCwfnMcu1SXaN7jOTq69yw677ak\n9LytC98vWEX9VgULkTkcDoIv+z6N/O/H/PDOy2TvX4ef6QKZvmFUa9GNx156u8wylCep+zZR081D\nRquafuw9l03Lmvl7u2afEIKDgytTPIGHmLAVkdBUNCWwTF/F2bXsTUVR3swdmwj4qao6UVGUacAa\nRVGswC5g2pUWE8r0OuKWW7uzenocIVkHCiini1ao26NXgbmyLFOt6c3Y9s8v8JQOcDqoAcO6XjkP\nbsPqlZhW/UJ9bweX0hPq+Vg4dXwn2RHe+Bpcg4x0Pn551779CoUXBK48+NSLfD5qJ41zDubVyU0y\ngXfH/sQ1ce+e9YTQ0FCUGj4kHtxLrYZNATBlZ3F04z+MfSzffWo0Gnnm7U/JyMjg7NmzREVFVUmX\nu92c5baovI9exmzPjxq32B0EN+uCl1cRLh3BdYuqqs/hbANa1PGJOJVriRDK9DpCkiSGv/01U99/\nGf2x7QRgJsU7nMhOfej/iKt19+i4t/ny5RT8j2+mlredixaN4/71eeCl94uviLNiHpFuel12jPJl\nbZJGt0IeM5tDI6RZB5f5FUVGRgaLZv6MJSudhq070qFLt0q7dkUQGBjIuO9mM2/aJE4l7EHSe9H4\n1rvocoXKR54yoG9vDqgqazf/i12D0CA/Xn/2cbd9ZwMCAggIcL9/WhXwjWoAKdtdxtVUKzofXy6a\nbJwjAK8mXRlZxaxqgStmbGge2qaW67kFm6DiiYiM4sUvp3Lq1CnOnj5N46ZNi9zL8vb2Ztznk9m7\nawe7N/1LZHQdBt95d4kKezty80cLo5cl/KIasNuageI4i5de5pxJIiWyDc+/8F+355Q3a5ctYt3E\nd2gop+Krk9i7ZiqrZrdlzMcTr2kLxNvbmwcfe7pCrxGnKMQ5Ozpd09zcfzibvoon5rL+odk2Bz5t\n+zLw8dEcPaTSq3krapRT1SZBxWLCht1DZWoVylRQHkRFRREVVbIoz6YtWtG0RSuP1g+IjsWauLpA\nIAo4c0Bj23fioZFjWDx7BulpKbRs3YG2N3f2aP3Skp2dzdqJ42lqSOOS+znMG4KTNjP1i/E8Vsl9\nVT0lfv8+MjLSadm6zXVT/DwrK4uZEz4i7dAONIeDwLpNuX/k81SvUXGKrH2X25B1n7J+zhTMSceR\nfQKo2aoLT48cjSzLRNeuU2HXFpQ/ZsmGXXL1hF0Jm4fzy4pQpoJScd8jT/LJpn9oYUvMcwlrmsZe\nfW1GP/o0Xl5e9Hv4kWJWKX8Wz5pOQzmFwllfBp3E+X2byrR2Tk4O86b+yMXjB5G8/bi5zwPFNrou\nKbu3bWHxt+8SkByPNzaW+0UTd9fD3PPw8HJZ/2phsVj4dNTDNM06QGju3ry27xDfjN7OqAmzPCpp\n6Cltb+lK21sqtn+uoHIwYUXnYSanHaFMBdcAfn5+PPXpL8z+5iPSE3YhAQH1mjFi5AvlvpeWlZXF\nvKkTyTl/Bq/gMO4ZMqLIa5gyLxKoc/9Hp5mvmHN9RVKSk/jm+f/Q2HKUmrnrr9q+mIN9n+a+YU+U\nel2AzMxMFnw0lmZyMvhJgIFQznL2z89ZHVaTrnfcVap1NU1jzs8TObVzLZrVhE9kLPcOf5bwiMrJ\nBwVYMPMXlIz96PT5n4kkSTS1HWPu5AkMG1MxZQgF1xea5Hx5ek5lIpSpoNTUCA3jyf9+VKHXiN+7\nmznvjKKRdgZ/nbPt2Jer/6TPuE9pflNbl/ktO3dnzYop1PJ23S/xjSp9furML8c7rfDLFHVtbyvx\nCyZyoe8AQkKuWLbzisyf9iONtCQoVOElwmhj19LfS61Mv3h1FGGHl1HX4JRZu7CHH5/fyCMfTiGy\nVu1Sy+sJKQd3UVPv+nCjkyUyjsdXigwCQWUgKiAJqjQLJrxLM/kchlwlppclmulSWPzdeLfzm7Zo\nhTm2CyZbQRfPEVsAXR58vNRyZCTschvhHGvIZOmcGaVeF8CUmuyy93wJW8b5Uq25bdMGfNXl+BsK\nWoTNOMP8SV+5PcdqtTLrz/l8/fNMJkz5ldXr/i3VtQtgKDrg62JWNhcvXiz7NQTXPQadvlSvykRY\npoIqy+nTp9Gd3A1uPLr+5/ajxh8gYd8uEreuxmGzUCO2Jf2GjmDU+K+Y8c2nHN+9DocpC++IunR7\n8DFatGkPOAsRLP79V07sWg9AreYduXvAoCtGMWsOh9tHT1kCR24PTLPZjHpgP6HhER6V1vMLi8Js\nc+DlxoLTB7m2mSsJu9cuI8LbffRjhptuPDk5OXzwzWTqdOhB9TrOfOA9Z06yb/JUnnp0SKlkAGjV\noy87dv1FzUJpVKk5NtIS1/P9o93xadyJJ9740G0KTnmSnZ3NuXPnqFmzZl5/U8G1gUHWofew+YZN\nFtG8AgHgvMEbsZFpkUjJslLDz4C/0fkHZcTG1E+cJeNqeTmVkOX4Oj7avJKxX01nyLMvAi+6rGm3\n2/n4hRHUOrGOKKPzvHT1Hz7auJIXPv6hyG45AXWbwLEkl/EEsw8D7xnAjG8/5fjquQRnnSZL9sZe\nuxUPv/weNaOii32ffQc9wifL59Cc0wXGT1m9aNdnYLHnu0PWG4osDyjrXJXWr3/Mp37nu9BfFkFc\nvWYtzlnMbN+5i9YtS1cIvt0tXdi/eTDH1vxKndzKWMfTTCSmWehcJxBJysJ6cAnf/w+e/d/npbpG\ncVitVn5873Uu7lmNv+kCWT6hBLe6jeHj3irweS+ZM5N9y//AmpqEPqgGyq196PPQULdrpqens3jW\nVKw52bS9rReNmjStENkFTvQ6PQZPO1kV4e2pKISbV1BliYqKYmeaTMIFEz4G579rEtOx2jX2WKpR\n/8IOAr3yv8JGnUzjzP3Mmlh0D99Fs6cTfXIdAcb88wKMMtEn17Fo9vQiz+s9fDR7tbACfV9TzBIh\nnfuzecUSrCt+pLGcQs0AIw38HCjntzHpjadL1CfWx8eHB9/8ioMhrTicpeNYho39xhjqD3qVDl2u\nXImqKLrdO5AjZlcXq82hEazc5DJ+PsdaQJFeIrxOfbbuVUslwyWGjXmVnu/N4myz/iw6ZgZJoktM\nYJ6iN+gkcvatJS0trZiVSsf3/3uZ0H1/0thwkdoBOuL0FwjaPotJH7yZN+ePKd9z6td3iE3bQ2Pp\nHA3T93H+9w/49bvPXNZbNncWEx69HeM/XxG0YTL/vDGQL98YU+49gQX5GGUdRlnv4atsbSQ9RVim\n1xAXL17k1y/Hk354F5rmwL9OY+4dMYao6OszZ+7H8a/QKxIMOqfbMdzfiNWusfxYFsENFarbDrqc\no5cl0g7vKnLNk7s2UMvg+gzpb5A5uWs9POS+MUTdBg0Z/tlM5k/5hpyzR9F5+RHX9S5uu+sePh7R\nj4YG1xtp9MWDrFq6mG497y72vcY2aswLX88gKSmJrKwsYmJiytSounZMXSJ7DufYXxPzLMJ0i4PE\n6i144RlXi/3KeqDsSiK2URyWPg+grZ9BzQBXyzjYlsbRhARa3eSq6MtCamoqOftXYzQW/Mx9DDKJ\nO1aQnZ2N0Wjk4N+/0dhY0C0YYtTYv+oPTMOezHMLnzt7lp3TPqSJVxaXAsZqe9vIiv+L2ZNjeWD4\nU+Uqv8CJXvZ8D1SrZFNRKNNrBLPZzBejH6a56RCRl26yCSeY8tJunvhiJjVCS7e3VlVJT08ne986\nDF6u+aJRNYKoVqcuJLgqUyhGMWilzz0Lj6jJ4y//z2XcknoO3NRXCPaSOJUQDxSvTC9xeXP0svLg\niFEcuKUb6xfMwmE1EdW4DYP6DXC7NxzsrcPhcLgcSzl1jLYNnT1KE48cZuPKvwmNiqbbHXeVqFLW\n5UTXrsNFrxBqkulyLM0QTN369T1aryQcjN9PdVsaGF0/IP+cZE6dOgVAQPpxCHCdE5pzhn17dnFT\nW+d++5LfptDQmEnhyGs/g0TC9tUglOkNi1Cm1wjzZ/xEXNZB5EJBKk200/w5eQKPvVR1K/ucPnmC\nhT9/Q/bpI8jevtRr1527HxhcpOWVdO4cUyZ8TpDlAni5WjFhcg7edeJI3r/UpeWc1a5RXSm6mlPN\nph3ITljpUog/22onsml7z98cYAwOh8xUl/FUs0adBo1KtWZ5EdekWYkK4T90b28+/OEXGnbuhd7g\n/J1npJ7HciKetr2G8b+nH8YrYSNKsJ6TZjsvfPEmw9/7kSYtS160IjAwEN/GnbGqi/Kis8H5mXnH\n3UK1atU8f4PFULd+LOvkAGpgcjmW5R1CREQEJpOJHNkH3HQZycRY4EHVbsp26dxzCYcpq9zkFhTE\noNNj1HlWEUyrXC+v2DO9VrhwdL/baE9Zksg+dfgqSFQyjh4+yM8vPkz43rnUS91NzJmNpP7+Lt+/\n+5rb+dO++pCpT/Wi1v7fOZtpcTsnzRBMn/4PktW4J6nmfDPUZHNwIKg5Ax57tkh5+jw0hCPh7cm2\n5rv0sq12joS3p/eDpYtabdi1DxcKiappGieClFLniFY2AQEBvPLkIziObOfsjjUk7VhNTfM5nh/5\nKJ+9PoZGyVtoFGJAkiSCvfXcGpzDd2MGYbd7FjH5xBsfcEa5m3izH2czLcSb/TjdsCdPvFkx+cph\nYWFIDdphdxR0V1jtGj5xNxMQEEBoaCjEuH8AM0c1p05MfjvC2k1ak2Z27/rwiSx/y1rgRKTGCMoN\nyVh042XZ26/IY1ebxT99SROpYEGCIAOkbl9IwqFh1I9tmDe+aukizKt/JtbLAejJtDiw2B15LccA\nrHYHPk06U61aNZ59+1OWL+rKoY3L0exWwhrdxEuDhl2xpq1er2fc5z/x20/fs+LvP/EyetOud1/G\nDX4Evb50fw59Bz/KzIx09q+eS3DmSbJkHxwxrRk+bnyZ9j0rG19fX4Y+1N9l/MymZTSMcH2Qaxfs\n4KcJn/PYqLElvobBYOCZtz/l4sWLbNm0ER8fH9q271ChaTEj3/qM7/87Bu3wRmqQTbIUgE7pyJOv\nf5A3Z9CL7zLpjaeIST9IkJdMhsXBEd96DBn7ToG1brurL+MXzCDg4m50l7UuPOQIps+g0ucxC66M\nQXIGIHmC43quzasoShDOBqsBgBF4XlXVjYqi9AM+Ak7kTv2vqqprKlO2qk6Huwawdttiogs13r5g\nhvodb79KUhVP5rEDbsfreFlYv2Qe9WPzg2H2rVxAtDH/D6BjdAAbTmQQYNQR7m/gojEY3yadGZF7\nE5QkiR6976VH73s9kmnWD19ycuVsOnAeU7bEwRV/sCu2ETeVoRj/QyNHY3n0KQ6q8dQIDSMiIqL4\nk64R9PYcwLVnaZC3jlVb1gL5ylTTNDatW8Oxw/E0b3ezWxfzsaNHmPXx6+hP7sRPM7PWL5p63fvz\nwGPPVIj8vr6+jPnoe86eOUOCeoCecU0ICw8vMCeyVjSvTZrHPwv/5FziIarXqsur99zvkiolyzLP\nfzaF6V+MJzV+Cw6rGb9ohV6DR9IwTqTHVBT6UliaNt11rEyBMcDfqqp+qShKQ+BX4Kbc1zhVVf+o\nZHmuGZq3bsOBXo9zaMlPNPDKQZIkjuXoMba7h179Blxt8YpG1uGu3rRDc+ZCFhgrtOeklyU61wkk\n22rnQEgbxn70HYGBgWUSZ8nc3zCvmESc0QHo8DVAiDWRpZ+/gtJ8Cf7+/qVe22g00rRZ8zLJVxXJ\n1txb+sfSTETcFJv388njx/jl7TGEpx4g1AtWLviahbXb8sx7E/LaANrtdn556xlaWI+CH4CRCM5x\nfsm3/BUUTK8BgyvsfUTUrEnEFYppyLLMHX3vK3YdX19fHn/lnWLnCcoPo06H0UNlatdVbtGGyt4z\n/Qz4Iff/BuBSU8ybgEcVRVmjKMrHiqJU8tbxtcGDI0YxeMIi0m8ZTlqHYfR6fzZPVPE/6mqxrXG4\nCa89ZPHjzv4Fb5xe4bXd5uoZZImGLduVWZECxK9eRHWjq3ZXpBTmT/uxzOtfjwQqbTieVjCAx2rX\n2JFi54kXXs8bmzb+RZpmH8gLCqvlbaPB2fVMei+/mP2y+XOol5Xgco3qRgcHVs6vmDcgEFQCFWaZ\nKooyHBhdaHiYqqrbFEWJAKYCz+WOLwPmqqqaqCjKd8BIYEJFyXYtE1EzkiFu8gSrKg+NeoUvRh+g\nYeaBvAjaoyYjDfuNdGnM3GfoU0wZu5YmJOeNaZrGfkMMY4eOKBd5HG6ibsGZcpN+sXR1cK933vhq\nCmMfvIOjiQn4GWSsDgcpNgP9xr6b10Jt7+6dBJ3bA4W29nWyRMaBjZjNZk4kHuXvmZPwScrC36ij\ncahPgT1la9o5j2VLTkpi2ZwZaGh069OfqFrFV5wSXHvoJT0GD/dMrdJ1Uk5QVdVJwKTC44qiNMPp\n3h2rqura3OHJqqpeqng9D7i/ouQSVC5BQUG88sPvLJo1jXOH9yB5+dLrnkE0jGvsMrdmZBQD3vqe\nJT99QfqRvSDLBNVvyePPvIyvr+ueXWkwVK8J2a7Rz9lWO8HRIhrTHd7e3nz952pWLlnE0V0b0fn4\nM+ahRwrsO544kkCI3oE7Z5fRks6M7z7j4qoZ3OpjhVoBpObYWH7kIl1jgvKK/BuqOddzOBz8+v3n\nnNmxBnt2Br4169Gp/yO0an9zgXV/m/gVJ5dMob4xCwmY9ffPBHd+QLR1uw4xlMLNa9W5pjpVJJUd\ngNQYmA0MUFV1T+6YBOxSFOUWVVVPAT2ArZUpl6BiMRgM3Du4ZI3CGyhxPPP+dxUmS8d7hrDps61E\nG3IKjB/0rs8rFbhfV96sWDSXnYt/w3L+FLJfNaLb9uChJ0ZVWPSwJEnc1qs39Ort9vhNHTsxdaof\nseS4HEvWhSCv+pX6Pta8sWAfPV1jAtl2JpMOtQJIscg07u4MJPv6zTGEHVxCw0upYKdOs+aTndie\n+ziv2ff2zRs5/9cPxHrbuBQpXt/bTPK6aaxu0tJtSpLVamXGt59yfv9mNJsVv2iFvsOfrbR2dILS\nU5pUl8pOjansPdPxOKN4v1QUZaWiKHNVVdWA4cAcRVFWAV7AxEqWS3CD0ObmzjR77G1U/0YczDKw\nP8eXI5G38Nh7P1wxpaYqsWzubxz66b80SN1FYzmFRjmH0ZZ/yw/j3efuVgZh4eF4N7vNpfXdeYuE\nFlCDet7mvLEcq4MjF0xkWhyYbQ72E0Fgr6foef9ADh7Yj7R/Bb6Fcqrr6jP59/d8R9eWJb9Ty9vV\n8gj10ti3epHLuKZpfDTmUfz/nUyD9P3EZh8iUl3IlHHDOHPqZFnfvqCCMchOy9STl+F6rs2rqqrb\nHAZVVZcDyytTlusJq9XKnJ++Izl+K5pDI6RhS/o/+pRoM1UEXe7sTZc7e5OamoqXl1e5uZArA03T\n2LlwGo2M1gLj/gaJM9uXkpw0mtByLEnoCSPf+IBfPg/i8PaVkJWGLrgmSq9+NDp7AmlHPA5NY8OJ\nDHz0MtFBXpzNtJCKNy99NjPPZbxl1TLquFGSANknD+X9/0rVhhw5rsdWLllIrbNbMBhdK4jNm/wV\nI9/4wOUcQdVBL3u+Z6ovZr6iKAZgMlAHpxH3jqqqCy47PhBnXI8N2AM8lWv8ub+eR9IJqhw2m40P\nnxtKw5Rt1Ml9mree2cRH2/9l3IQZeHkV3Zz5Ric4OPhqi+AxmZmZkHIsN62kIDH6LP5dsZR7Hyp9\n/9GyoNPpeGTsG2ja69jt9rwiGBvXrGTvhlkcTcmkZYQffrlt9EL9DDSqoTH57TG8PMHZYN2vWnCR\nvV1lr/yHnoDohlgTVro0VXdoGj6RdQufytGdGwg1urrAJUkqoKQFNxSDgWRVVYcoihIM7AQWACiK\n4gP8D2iqqqpJUZQZQO9Lx90hygle4yyaPZ16SVsL3HwMOpm4jL38OVWkelxveHt7Yze6r3h10QoR\nJeifWtFIklSgmlSHLt24ENMJm0PLU6SXzw06s5O9u3YA0LPfgxyUChZUgNzWcY075P3c7z8j2G+M\ncUml2ktN7h32tKtMemeFpaQsK5tOZrDtdCbmXJe0bKjYpuSCsnMpAMkjN2/x/U9nA5f68MkULM5s\nAjqqqnopJ0wPbgICLkMo02uc03s3u9ygwNnbM1ndfhUkElQkBoMBv9g2bnN3zwbF0r5T16sgVfGM\nfOsT/PzcPwTU9HJwYNc2wPmwcOuI19hrDcFqd77HFDPEh7TmP2Pyc1r9/Px44uMpnIztxQE5kv1E\ncKLe7Qx9/yeq16jhco0u9wxkYUImF0022kX50yzcl51ns9hzLovwZje7zBdULQxyKWrzFuPmVVU1\nS1XVTEVRAnAq1tcuO6apqpoMoCjKs4Cfqqr/XGk94ea9xpGkKzx9SeJZ6XrkkVfG8+ULSUSk7CbU\nCzKtDhK86tD/+XeqbC3ggIAADKG1wXHK5dhpk45uN+V37Lml+520aH8Li377hayMVJQW7Xnk1u4u\n7y0sPIKn33Zt3u2OHetW0jXaN68pvFEn0b5WAJvOWene76EyvDNBZWCQPa/NW5IAJEVRooE/gAmq\nqs4sdEwGPgQaUIJ0TaFMr3Hqt+/GmQNLqVao72em1UGtlp2uklSCiiQgIIBXv5vJ+tUrWLNwDheS\nzxLbsBE5OVf0Ql1VJEmidsdeZKyaSIAhXyk6NI2MWq1davj6+/vzYDn2Bj2xdSUNjK4Pl21C9Syd\n9fM1VQjlRqQ0tXn1xcxXFCUcZ8Ggp1RVXelmyvc43b39rhR4lHc9j6QTVDl69L6XLzauwn5gGdVz\ng3fTzBpnY7ryQiUFouzYspl9m9bgF1yDu/oPFEFPlcSWv+cTemQlTbyBfQfYvHUem9vey8hX373a\norll0FPP87PFxP5NSwjMPE2mIRBjbHuefP3DYs91OBwsnfc7x7avA00jutUt9Or3QIkblGsm14bk\n4KzQZM9xf0xQdTDKnhdtMBZvmb4KBAFvKopyae90Is7wvq3Ao8AaYIWiKABfqKr6Z1GLCWV6jSNJ\nEs+98zmrli7m0KblaJpGvZu6MKT3vRXu8rNYLHw+biSBxzYR6ePAZHPw8cIp3PHM/+Ul1wsqhkWz\nZ1AjfgmB3vnKJNLHQfK2P1izvAtdut95FaVzjyRJDBvzGibTWI4fP05oaGiJIqodDgefjHuSiKOr\niMz1wKTGL+Pjdf8w9uPvXTq7uMMYHgPHjriMp5k1ohq19Pi9CK59VFV9jvyStu7wKFFVKNPrAEmS\n6Nbzbrr1vLtSr/vLZ+9Q/8y/GHycNzhvvUwzzrH067do2W7ZNVME4Vrk2LbVRHu5WmWhXnBgzZIq\nqUwv4e3tTcOGDYufmMtfc3+jZuIqAi97v4FGGfnkWhb8No17Bw0tdo3bHnqMpeO30UB3MW/M7tA4\nFtKUIR628BNUPhXh5i1vRISKoNSc37cRg871KxRrO82SubOugkQ3DprNUqpj1yLHt68j0M1+p79B\n5tSu9SVao0mLVnR/8XMSa3Zkny2E/VJNzjW5h+c//7nKBm0J8rkUgOTJ67qugCS4vrDnZDgb6RXC\nxyCTfj7Z9YCg3KhWrym2UxvRywUVQY7VQVijViVex2w2M2/6ZM4f3o2kMxLXpSddb+9VKpk0TWPd\nir85c+I4N3e/g1rR5VPzVnNcqclzsXEhebRo04EWbToUP1FQ5ZDQI3morjydX1aEMhWUGp+a9SDF\nNZf1pEnPrZ1vuwoS3Tjc98hTfLR5JS0sCci5lpXNoXEwqBkvP1S82xMgKyuLT54dTFzmAaJyi34c\n3r+M+K3/etwn98CeXfzxyatEZSRQzQiz53+Nvml3nn7roxIHCRVFZLP2ZB9entfC7xI5VgfhjduW\naW3BtYHFJmG2eeZBsHg4v6wIN6+g1LTvN4xj1oINLM02B6b6nWjctPlVkurGwNfXlzFf/cqFmwaR\nENSUhOBmZHQcxgtfTS3xXvXMbz+mWXZ8gepZoV5g3zKXPTu3lVgWu93OnI/G0cxyhBAvCVmSqO9t\nJjR+IdO+/sjj91aY3g88zJHw9uRY8y1Us81BQo2buGfQsDKvL6j6WG0SFqvs0ctaycpUWKaCUnNz\nt9uRZZkNf0zBdDYRnW8AYW07MerZcVdbtBuCwMBAHn3hzeInFkHaoV2Eyq43nFreDrb+vYBmLW8q\n0Tp/L5xL3exEKLSv6auXOb59FfBSqWUE0Ov1jPv8J/6cNpnj+zYBEBbXhheHPCaC3ARVBqFMBWWi\nQ9fudOja/WqLISgVRe83ag57iVc5f/pkXmWhwtiy0jyWyh16vZ7+w0aQnv4Q83/5gYvHDzF9wsf0\nGTKC6tWrl8s1BFUXq03GYvPMkWr1cH5ZEW5egeAGJbBuM7c1fs+YZFp2K3maVdxNHThrcu9S8wot\nv8L7+3Zt5+sRffBb9yPhB/8iaOPPTHyyD9vWry23awiqJma7jNnm4csuyCjsTAAADZlJREFUlKlA\nIKgEHnhyLLuNDbA58hVqmkXD2rwXrduVPOq1dbsOXIi6CbujoGI+YzHQ8q7yq3u7+LsPaConoct1\nTetkicb6VP7+8UOX7jGC6wurh/ulFquM1Vq56k24eQWCG5SgoCDGfvMbf/z0LRcT9yMbjNRr350h\n9/T3eK3nPvyBnz54g7T9G5Esmehq1KHlgEF0731fuch69uxZ9Kf2gr/rsaDzKgf27RFBb9cxVrvk\nuZvXLgKQBAJBJeHn58eQZ14o8zo+Pj489dbHOBwOLBYL3t7e5SBdPmazGZ1mw50zzSBpZGdnl+v1\nBFULi1XCbPUwNcbD+WVFuHkFAkG5IctyuStSgNq1a5NTI9btsRT/2rS6SeSbCq4uQpkKBIIqjyRJ\ntBswwiWv+ZTVSPN7HilRsXvBtYvV7ozm9eRlreQAJOHmFQgE1wTd7upLcGg46/+chiXtHPrAGrS7\n+0HadRIdiq53LDYZg4cBRZ7usZYVoUwFAsE1Q8u27WnZtv3VFkNQyVhsMnoPlaNQpgKBQCAQXIaI\n5hUIBAKBoIxYrRI6D6NzrSKaVyAQVDU0TWPLxvX8s3iBSEMRCNwgLFOBQHBFdm3ZwKIJ7xB68TB+\nOo2vptSg9m0DGDhyzNUWTXCDYLHLSJ7umYpoXoHgxsNkMvHn1B9JSzyAbPSm1e330vbmzldbLLKy\nslj08Us01SWDj/PmFEcq55dPYkl4FD37PXCVJRTcCFisMpKn0bzXczlBRVH8gBlANcACDFVV9bSi\nKB2AzwEbsExV1bcrUy6B4GqSlprKl6OHEJdziMjc3qLbdv1FfLdhDLnK7ewWzpiMwjkK7whVN9iJ\nX7VAKFNBpWC1Sx5bppUdgFTZe6aPAVtUVe0KTAMu3Sm+AwaqqtoJaK8oSstKlksguGrM+uZDWlgO\nF2jSHemtkbJiOseOHrmKkkF2ajIGnfvbhD3jQiVLI7hRsVhlzB6+KtsyrdSrqar6BTA+98c6QKqi\nKAGAUVXVo7njS4EelSmXQHA1uXh4F5Lk+hRdz9vMqgWzroJE+YRE1yfb6r63qaF6zUqWRnCj4kyN\n8ex13aTGKIoyHBhdaHiYqqrbFEVZDjQF7gCCgPTL5mQA9SpKLoGgyqE53A5LkgQO98cqi7v6D+L9\nxTNpYU8sMH7S6k37voOvjlACQTmgKIoBmIzTsPMC3lFVdUGhOb7A38CjqqqqV1qvwpSpqqqTgElF\nHOuuKIoCLAJaAQGXHQ4E0ipKLoGgquFftylafKKLdXrMpOfOO++9SlI5MRgMPDr+O2Z99n9Yju5A\n77Bgq1GfNoOG0b5zt6sqm+DGwWKTcXjotrUVv8c6GEhWVXWIoijBwE4gT5kqitIG5xZkJFBsw9zK\nDkB6BTipqupUIAuwqaqaoSiKRVGUesBRnNbqW5Upl0BwNbnvibH8MHYXTe0n8hpfp1rAq+29NIxr\nfJWlg6joOoz5dDKZmZnk5ORQo0YNt25pgaCisNhk7B4GIJVg/mzg99z/yzgDYC/HCNwLTC3J9So7\nNWYS8LOiKI8COuCR3PGRwPTcsaWqqm6pZLkEgqtGeERNnp0wmz9/mkDmiUPIXj40vOVOevS+ulZp\nYfz9/fH3d9OdWyCoYKx2yWNl6ihmz1RV1SyA3Lid2cBrhY6vzz1eoutVqjJVVTUJ6OVmfBPQsTJl\nEQiqEtWqVWPYmNeKnygQ3IBYS5FnqpVgvqIo0cAfwARVVWeWTjonomiDQCAQCKo0FrsMnnaBsctX\nTFdRFCUcWAY8parqyjKIBwhlKhAIBIIbk1dxZpO8qSjKm7ljEwE/VVUnerqYUKYCgUAgqNLY7BJ4\nmjdqlzBe4bCqqs8BzxW3jKqqJQpbF8pUIBAIBFUao06CIipxFYnuOinaIBAIBAJBeWDQyUh6DwOQ\ndDLWCpLHHUKZCgQCgaBKY9DLyB4qU4deKFOBQCAQCPLQ62R0HipTu6du4TJS2V1jBAKBQCC47hCW\nqUAgEAiqNEadjE6n8+icyrZMhTIVCAQCQZXGoJPRe+jmtQllKhAIBAJBPga9jMFDZepp9G9ZEcpU\nIBAIBFUagyxj9NTSlIUyFQgEAoEgD4Ne8tgy1fSVW7RBRPMKBAKBQFBGhGUqEAgEgiqNQSdjLEUF\npMpEKFOBQCAQVGkMOs/3TB1CmQoEAoFAkI9B77llahfRvAKBQCAQ5GPUSZ4rU9E1RiAQCASCfEpj\nmdoq2TIV0bwCgUAgEJQRYZkKBAKBoEqjl2UMHgYU6UXRBoFAIBAI8jGWws1rFQFIAoFAIBDkYyhF\nAJJFBCAJBAKBQJBPafJMPXULlxWhTAUCgUBQpSlNBaTKVqYimlcgEAgEgjJSqZapoih+wAygGmAB\nhqqqelpRlH7AR8CJ3Kn/VVV1TWXKJhAIBIKqSWkCkIqbryiKAZgM1AG8gHdUVV1w2fE+wBuADZis\nquqPV1qvsi3Tx4Atqqp2BaYB43LHbwLGqaraLfclFKlAIBAIANDrpLwG4SV96YsPQBoMJKuq2gXo\nCXx96UCuov0UuB3oCv/f3v2H2l3XcRx/ztn8MaYE2lIQDaoXQpAZ/lGWTpaKYgn9oGBkG5qGBi2o\n5DZQC1JkTBhDpsw5w4yo0PlbJyKkEmmW5ApfWw0JZ1MaeckxQbf1x+d73fHcc69nfe+559yPrweM\ne8/3e+53nzdvznmf7/fzOe8vl0v60LRjbBXhIbK9VtJEAT8Z+E/z++nAaZJWAs8AV9veN5tji4iI\n0TSgBUi/AX7b/H4Y5Qx0wqnA322PA0h6Cjir4/mTDKyYSroUWNm1ebnt5yQ9DnwCOK/Z/hhwj+2X\nJN0CfAe4eYpDzwfYtWvXAEYdERGHouO9eP6g/o+947sPeUHR3vHd0+63vQdA0iJKYV3VsfsYYLzj\n8X+BY6c73sCKqe2NwMYp9i2VJOBB4KPAJtuvN7vvBb4yzaFPAFi2bNkMjjYiIlo6AfjHIA686bru\n87KZIekk4G7gZtu/6tg1DizqeLyIg1dSe5rtBUhjwMu27wT2cPC0+nlJZ9reCXwB+OM0h3kW+Dzw\nLyCXgiMihms+pZA+O4Bjvwx8ZAaOMYmkxcAW4ErbT3TtfhH4mKQPUmrVWZRFslOad+DAgZbj7F8z\ngftz4EhKAq62/XtJS4GfAW8CW4HvZc40IiIGRdJa4GuAOzZvABba3iDpIuAaynzqRtvrpzverBbT\niIiIGqVpQ0REREspphERES2lmEZERLQ08o3uJc2nTAp/HDhA+Q7qAuABYFvztPW2fz2cEQ5Gs1jr\nOWApsB+4o/m5FbjKdjWT3V2xLqTi3Er6Ewe/v7YDuIFKc9sj1nWUr8PVmtsx4IvAByjddJ6m3tx2\nx/oXKn7d9mPkiylwEbDf9ucknU1Z9Xs/sMb2TcMd2mA0raxupSzJnkdpa/Vj27+TtB64GNg8xCHO\nmB6xfppKcyvpSADb53Rsu48KcztFrJdRb26XAJ+x/dmmB/mPgC9TZ26XMDnWw6k0t/0a+WJq+15J\nDzQPTwFep7zhStLFwHZgpe03hjTEQVgNrAfGmsend/QrfpjSOWrOvygbk2Kl3tx+Ejha0qOU194q\n6s1tz1ipN7fnAS9I2kzpnvND4NJKc9szVurNbV/mxJyp7X2S7gDWAndR+vf+oGmYvwO4dojDm1GS\nllOaL29pNs1r/k14g/doazVX9IgVKs4t5ex7te3zKdMVd3Xtrya3TI71F5RL+bXm9njKh/yvUuL9\nJZW+bukd6x+oN7d9mRPFFMD2ckCU+dMttv/c7NoMfGpY4xqAFcC5kp4ATqM0uTi+Y/8iytl5DXrF\n+nDFud1GU0Btbwd2A4s79teU216xPlpxbv9NeV962/Y2SgOazuJZU267Y90LPFRxbvsy8sVU0jeb\nyW4oSdsP3C3pjGbbUqZvPzin2D7b9pJmrul54BLgkWa+GOACoIpb1PWI9VvA5lpzS/nwsAZA0omU\nN9gtNeaWybEeA9xTcW6fotzGayLeo4HHK81td6wLgQcrzm1fRr4DkqSjKCviPkxZOXYD8E/KXWXe\novTovbzG6/PNGdsVlFXMGyirmP8GfLuWVYETOmI9ikpzK+lwYBPl9oNQFm7spsLcThHrXirNLYCk\nG4FzKCcpY8BLVJhb6Bnra1Sc236MfDGNiIgYdSN/mTciImLUpZhGRES0lGIaERHRUoppRERESymm\nERERLaWYRkREtDTyvXkj5ipJp1A6Af2V8l3hBcArwArbOyVdAnyX8v3pw4DbbK/rOsZPgX22fzKb\nY4+IQ5NiGjFYO22/01pN0vXAOkmPUJpUXGj7VUnHUroh7bF9e/P4JuAbwI1DGXlE9C2XeSNm15OU\ne/OuAr5v+1UA2+OUdopbm+d9iXJWu4Z3N0yPiBGUM9OIWdLcu/XrlDtsrGh+vsP2ix2/39n8zfvu\n7hsRc1GKacRgnShp4m4aR9DcqopSTHPGGVGJFNOIwXqlc850gqQdwBmUy74T25YA59se635+RIy2\nzJlGDMdqYI2kxQCSjmu2bR/qqCLi/5Iz04jB6nlbJtu3SloAPCZpP+WD7S22b+/3GBExOnILtoiI\niJZymTciIqKlFNOIiIiWUkwjIiJaSjGNiIhoKcU0IiKipRTTiIiIllJMIyIiWkoxjYiIaOl/HTf6\n1l6Zyb8AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x167feb00>"
]
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Give a brief interpretation of the scatter plot. Which classes look like hard to distinguish? Do both feature dimensions contribute to the class separability? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Your answer here: **\n",
"The two classes mentioned above are not clearly seperable. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 3(c) \n",
"\n",
"Write a **ten-fold cross validation** to estimate the optimal value for $k$ for the digits data set. *However*, this time we are interested in the influence of the number of dimensions we project the data down as well. \n",
"\n",
"Extend the cross validation as done for the iris data set, to optimize $k$ for different dimensional projections of the data. Create a boxplot showing test scores for the optimal $k$ for each $d$-dimensional subspace with $d$ ranging from one to ten. The plot should have the scores on the y-axis and the different dimensions $d$ on the x-axis. You can use your favorite plot function for the boxplots. [Seaborn](http://web.stanford.edu/~mwaskom/software/seaborn/index.html) is worth having a look at though. It is a great library for statistical visualization and of course also comes with a [`boxplot`](http://web.stanford.edu/~mwaskom/software/seaborn/generated/seaborn.boxplot.html) function that has simple means for changing the labels on the x-axis."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def compute_test(x_test, y_test, clf, cv):\n",
" KFolds = sklearn.cross_validation.KFold(x_test.shape[0], n_folds =cv)\n",
" \n",
" scores =[]\n",
" \n",
" for i,j in KFolds:\n",
" test_set = x_test[j]\n",
" test_labels = y_test[j]\n",
" scores.append(sklearn.metrics.accuracy_score(test_labels, clf.predict(test_set)))\n",
" return scores"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"k = np.arange(20)+1\n",
"print k\n",
"parameters = {'n_neighbors': k}\n",
"parameters"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20]\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"{'n_neighbors': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
" 18, 19, 20])}"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"k = np.arange(20)+1\n",
"parameters = {'n_neighbors': k}\n",
"\n",
"knearest = sklearn.neighbors.KNeighborsClassifier()\n",
"clf = sklearn.grid_search.GridSearchCV(knearest, parameters, cv =10)\n",
"\n",
"\n",
"accuracy =[]\n",
"params =[]\n",
"\n",
"no_of_dimensions = [1,2,3,4,5,6,7,8,9, 10]\n",
"\n",
"for d in no_of_dimensions:\n",
" svd =sklearn.decomposition.TruncatedSVD(n_components =d)\n",
" \n",
" if d<64:\n",
" X_fit = svd.fit_transform(X_train)\n",
" X_fit_atest = svd.transform(X_test)\n",
" \n",
" else:\n",
" X_nl = X_train\n",
" X_nl1 = X_test\n",
"\n",
" clf.fit(X_fit, Y_train)\n",
" \n",
" accuracy.append(compute_test(x_test = X_fit_atest, y_test = Y_test, clf = clf, cv =10))\n",
" params.append(clf.best_params_['n_neighbors'])\n",
" \n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in acc:\n",
" print i"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 0.08333333 0.15 0.11666667 0.16666667 0.15254237 0.20338983\n",
" 0.13559322 0.13559322 0.11864407 0.13559322]\n",
"[ 0.48333333 0.38333333 0.48333333 0.58333333 0.40677966 0.45762712\n",
" 0.45762712 0.44067797 0.30508475 0.38983051]\n",
"[ 0.66666667 0.75 0.75 0.73333333 0.61016949 0.66101695\n",
" 0.6779661 0.6440678 0.6440678 0.66101695]\n",
"[ 0.83333333 0.78333333 0.81666667 0.88333333 0.83050847 0.83050847\n",
" 0.83050847 0.81355932 0.77966102 0.79661017]\n",
"[ 0.91666667 0.86666667 0.86666667 0.9 0.79661017 0.88135593\n",
" 0.88135593 0.79661017 0.88135593 0.91525424]\n",
"[ 0.95 0.95 0.91666667 0.93333333 0.86440678 0.91525424\n",
" 0.94915254 0.89830508 0.93220339 0.91525424]\n",
"[ 0.95 0.96666667 0.9 0.91666667 0.93220339 0.91525424\n",
" 0.98305085 0.93220339 0.89830508 0.96610169]\n",
"[ 0.93333333 0.95 0.95 0.95 0.94915254 0.94915254\n",
" 0.98305085 0.93220339 0.94915254 0.98305085]\n",
"[ 0.96666667 0.98333333 0.95 0.95 0.91525424 0.96610169\n",
" 1. 0.94915254 0.98305085 0.98305085]\n",
"[ 0.98333333 0.98333333 0.95 0.98333333 0.98305085 0.96610169\n",
" 1. 0.94915254 0.98305085 1. ]\n"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### Your boxplot code here ### \n",
"acc = np.asarray(accuracy) \n",
"sns.boxplot(acc.T,names = [np.str(i) for i in acc])\n",
"plt.ylabel(\"Accuracy\")\n",
"plt.xlabel(\"Number of Dimensions\")\n",
"plt.title('Accuracy as a function of Number of Dimensions')\n",
"plt.show()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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gHsC/AccDe5VSvlVKeUJHg5MkSdKI2CM7QUXEJvIpM18rpcwjx77/G/CtjgYm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\ngoKCFoIivBcUFBQUFBQUFBS0EPwXH8FQmC3mzLUAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1665bef0>"
]
}
],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Write a short interpretation of the generated plot, answering the following questions:\n",
"\n",
"* What trend do you see in the plot for increasing dimensions?\n",
"\n",
"* Why do you think this is happening?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Your answer here: **"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Problem 3(d) \n",
"\n",
"**For AC209 Students**: Change the boxplot we generated above to also show the optimal value for $k$ chosen by the cross validation grid search. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### Your code here ### "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Write a short interpretation answering the following questions:\n",
"\n",
"* Which trend do you observe for the optimal value of $k$?\n",
"\n",
"* Why do you think this is happening?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Your answer here: **"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Discussion for Problem 3\n",
"\n",
"*Write a brief discussion of your conclusions to the questions and tasks above in 100 words or less.*\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Submission Instructions\n",
"\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"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
}
],
"metadata": {}
}
]
}
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