CITS1401 Computational Thinking with Python
Department of Computer Science and Software Engineering
CITS1401 Computational
Thinking with Python
Project 1: Computing World Happiness Index
Submission deadline: 11:59pm, Monday 29 April 2019.
Value: 15% of CITS1401.
To be done individually.
You should construct a Python 3 program containing your solution to the following problem and submit your
program electronically using cssubmit. No other method of submission is allowed.
You are expected to have read and understood the University's guidelines on academic conduct. In accordance with
this policy, you may discuss with other students the general principles required to understand this project, but the
work you submit must be the result of your own effort. Plagiarism detection, and other systems for detecting
potential malpractice, will therefore be used. Besides, if what you submit is not your own work then you will have
learnt little and will therefore, likely, fail the final exam.
You must submit your project before the submission deadline listed above. Following UWA policy, a late penalty of
10% will be deducted for each day (or part day), after the deadline, that the assignment is submitted. However, in
order to facilitate marking of the assignments in a timely manner, no submissions will be allowed after 7 days
following the deadline.
Overview
For the last few years, the United Nations Sustainable Development Solutions Network has been publishing the World
Happiness Report. Details of the 2018 report can be found here. The underlying data, which you can also download from the
latter URL, is a combination of data from specially commissioned surveys undertaken by the Gallup organisation, and
statistical and economic data from other sources. The web site linked above also provides the methodology for how the
different data have been combined to compute the final score, most dramatically called the Life Ladder.
Here is a sample:
country Life Ladder Log GDP
per capita
Social
support
Healthy life
expectancy
at birth
Freedom to
make life
choices
Generosity Confidence
in national
government
Afghanistan 2.66171813 7.460143566 0.490880072 52.33952713 0.427010864 -0.106340349 0.261178523
Albania 4.639548302 9.373718262 0.637698293 69.05165863 0.74961102 -0.035140377 0.457737535
Algeria 5.248912334 9.540244102 0.806753874 65.69918823 0.436670482 -0.194670126
4/19/2019 CITS1401
代做CITS1401作业、代写Software Engineering作业、代写Python课程设计作业、Python编程作业调试
https://lms.uwa.edu.au/bbcswebdav/pid-1254315-dt-content-rid-18654189_1/courses/CITS1401_SEM-1_2019/project1_2019.html 2/5
Argentina 6.039330006 9.843519211 0.906699121 67.53870392 0.831966162 -0.186299905 0.305430293
Armenia 4.287736416 9.034710884 0.697924912 65.12568665 0.613697052 -0.132166177 0.246900991
Australia 7.25703764 10.71182728 0.949957848 72.78334045 0.910550177 0.301693261 0.45340696
The data shown above (and discussed below) can be found in the CSV formated text file
WHR2018Chapter2_reduced_sample.csv.
The actual method used to compute the Life Ladder score is quite complicated, so the the aim of this Project, in brief, is to
test whether simpler methods can yield similar results. In particular, the Project aims to see whether any of a range of
proposed methods yields a similar ranking, when countries are ranked by Life Ladder score in descending order i.e. from
happiest on these measures, to least happy. (The Wikipedia article also discusses criticisms of the World Happiness Report
process.)
Looking at the data sample above, you can see that the column headers occupy the first row, the countries are listed in the
first column, while the Life Ladder scores that we are seeking to emulate are in the second column. The third and subsequent
columns contain the data from which you will compute your own Life Ladder scores. However, for this exercise, please
remember that the aim is not to replicate the precise Life Ladder scores, but rather to replicate the ranking of countries as a
result of the Life Ladder scores.
Eye-balling the Data
In Data Science projects, it is always a good idea to "eyeball" the data before you attempt to analyse it. The aim is to spot
any trends ("this looks interesting") or any issues. So, looking at the sample above (ignoring the first two columns), what do
you notice?
There is a difference in scale across the columns. Healthy Life Expectancy at Birth ranges from 52.3 to 72.8, but in
general is valued in 10's, while Social Support is a value in the range 0.0 to 1.0, and Freedom to Make Life Choices has
both negative and positive floating point numbers. (The problem of GDP per Capita being actually valued in the
thousands, or tens of thousands, has already been solved by the data collectors taking logs.) The issue is that you don't
want a particular attribute to appear significant just because it has much larger values than other attributes.
The other thing you may have noticed is that sometimes the data is simply missing, e.g. the score for Confidence in
National Government for Algeria. Any metric we propose will have to deal with such missing data (which is actually a
very common problem).
Specification: What your program will need to do
Input
Your program needs to call the Python function input three times to:
get the name of the input data file
get the name of the metric to be computed across the normalised data for each country. The allowed names are "min",
"mean", "median" and "harmonic_mean".
get the name of the action to be performed. The two options here are: "list", list the countries in descending order of the
computed metric, or "correlation", use Spearman's rank correlation coefficient to compute the correlation between ranks
according to the computed metric and the ranks according to the Life Ladder score.
The order of the 3 calls is clearly important.
Output
The output, printed to standard output, will be either a listing of the countries in descending order based on the computed
metric, or a statement containing the correlation value (a number between -1.0 and 1.0).
Tasks: A more detailed specification
4/19/2019 CITS1401
https://lms.uwa.edu.au/bbcswebdav/pid-1254315-dt-content-rid-18654189_1/courses/CITS1401_SEM-1_2019/project1_2019.html 3/5
Use input to read in 3 strings, representing the input file name, the metric to be applied to the data from the file
(excluding the first two columns) and the action to be taken to report to the user.
Read in the CSV formated text file. That is, fields in each row are separated by commas, e.g.
Albania,4.639548302,9.373718262,0.637698293,69.05165863,0.74961102,-0.035140377,0
.457737535
Algeria,5.248912334,9.540244102,0.806753874,65.69918823,0.436670482,-0.194670126,
Apart from the first field, all the other fields are either numbers (so converted using float(), or empty, which can be
translated to the Python object None. Each line will be transformed into a row, represented as a list, so you end up with a
list of lists.
For each column apart from the first two, compute the largest and smallest values in the column (ignoring any None
values).
Given the maximum and minimum values for each column, normalise all the values in the respective columns. That is,
each value should be normalised by transforming it to a value between 0.0 and 1.0, where 0.0 corresponds to the smallest
value, and 1.0 to the largest, with other values falling somewhere between 0.0 and 1.0. For example, the minimum Life
Expectancy years in the small dataset is 52.33952713. This is transformed to 0.0. The maximum value is 72.78334045,
which is transformed to 1.0. So, working proportionally, 69.05165863 is transformed to 0.81746645. In general, the
transformation is (score - min)/(max-min), where max and min are the respective maximum and minimum scores for a
given column, and will, of course, differ from column to column.
For each row, across all the columns except the first two, compute the nominated metric using the normalised values
(excluding None). "min", "mean" and "median" are, respectively, the minimum value (on the basis that a nation's
happiness is bounded by the thing the citizens are grumpiest about), "mean" and "median" are the arithmetic mean and
median value (discussed in lectures). The harmonic mean of a list of numbers is defined here. For harmonic mean, apart
from avoiding None values, you will also have to avoid any zeroes; the other metrics have no problem with 0. The output
from this stage is a list of country,score pairs.
The list of country,score pairs are either to be listed in order of descending score, or the Spearman's rank correlation
coefficient should be computed between the country,score list that you have computed and the Life Ladder list, when
sorted by descending score. You can assume there are no tied ranks, which means that the simpler form of the Spearman
calculation can be used. An example of how to compute Spearman's rank correlation can be found here.
Example
>>> happiness.main()
Enter name of file containing World Happiness computation data:
WHR2018Chapter2_reduced_sample.csv
Choose metric to be tested from: min, mean, median, harmonic_mean mean
Chose action to be performed on the data using the specified metric. Options are
list, correlation correlation
The correlation coefficient between the study ranking and the ranking using the
mean metric is 0.8286
>>> happiness.main()
Enter name of file containing World Happiness computation data:
WHR2018Chapter2_reduced_sample.csv
Choose metric to be tested from: min, mean, median, harmonic_mean harmonic_mean
Chose action to be performed on the data using the specified metric. Options are
list, correlation list
Ranked list of countries' happiness scores based the harmonic_mean metric
Australia 0.9965
Albania 0.5146
Armenia 0.3046
Afghanistan 0.0981
Argentina 0.0884
Algeria 0.0733
4/19/2019 CITS1401
https://lms.uwa.edu.au/bbcswebdav/pid-1254315-dt-content-rid-18654189_1/courses/CITS1401_SEM-1_2019/project1_2019.html 4/5
The complete table is in file WHR2018Chapter2_reduced.csv.
Important
You will have noticed that you have not been asked to write specific functions. That has been left to you. However, it is
important that your program defines the top-level function main(). The idea is that within main() the program calls
the other functions, as described above. (Of course, these may call further functions.) The reason this is important is that
when I test your program, my testing program will call your main() function. So, if you fail to define main(), my
program will not be able to test your program.
Assumptions
Your program can assume a number of things:
Anything is that meant to be a string (i.e. a name) will be a string, and anything that is meant to be a number (i.e. a score
for a country) will be a number.
The order of columns in each row will follow the order of the headings, though data in particular columns may be missing
in some rows.
What being said, there are number of error conditions that your program should explicitly test for and respond to. One
example is detecting whether the named input file exists; for example, the user may have mistyped the name. The way this
test can be done is to first:
import os
Then, assuming the file name is in variable input_filename, use the test:
if not os.path.isfile(input_filename) :
return(None)
and test for None in the calling function (likely main()).
Things to avoid
There are a couple things for your program to avoid.
Please do not use Python's csv module. While use of the csv module is a perfectly sensible thing to do in a production
setting, it takes away from much of the point of the first part of the project, which is about getting practice opening text
files and processing text file data.
Please do not assume that the input file names will end in .csv. File name suffixes such as .csv and .txt are not mandatory
in systems other than Microsoft Windows.
Please make sure your program has only 3 calls to the input() function. More than 3 will cause your program to hang,
waiting for input that my automated testing system will not provide. In fact, what will happen is that the marking program
detects the multiple calls, and will not test your code at all.
Submission
Submit a single Python (.py) file containing all of your functions via cssubmit.
Marking Rubric
For convenience, your program will be marked out of 20 (later scaled to be out of 15% of the final mark).
60% of the marks (12/20) will be awarded based on how well your program completes a number of tests, reflecting normal
use of the program, and also how the program handles various error states, such as the input file not being present. Other
4/19/2019 CITS1401
https://lms.uwa.edu.au/bbcswebdav/pid-1254315-dt-content-rid-18654189_1/courses/CITS1401_SEM-1_2019/project1_2019.html 5/5
than things that you were asked to assume, you need to think creatively about the inputs your program may face.
40% (8/20) will be style (5/8) — the code is clear to read — and efficiency (3/8) — your program is well constructed and
runs efficiently. For style, think about use of comments, sensible variable names, your name at the top of the program.
(Please look at your lecture notes, where this is discussed.)
Style Rubric
0 Gibberish, impossible to understand
1-2 Style is really poor
3-4 Style is good or very good, with small lapses
5 Excellent style, really easy to read and follow
For Project 1, there are not too many ways your code can be inefficient, but try to minimise the number of times your
program looks at the same data items. There are particular places where you should use readline(), but not in a loop.
Efficiency Rubric
0 Code too incomplete to judge efficiency, or wrong problem tackled
1 Very poor efficiency, addtional loops, inappropriate use of readline()
2 Acceptable efficiency, one or more lapses
3 Good efficiency, within the scope of the assignment and where the class is up to
Automated testing is being used so that all submitted programs are being tested the same way. Sometimes it happens that
there is one mistake in the program that means that no tests are passed. If the marker is able to spot the cause and fix it
readily, then they are allowed to do that and your - now fixed - program will score whatever it scores from the tests, minus
2 marks, because other students will not have had the benefit of marker intervention. Still, that's way better than getting
zero, right? (On the other hand, if the bug is too hard to fix, the marker needs to move on to other submissions.)
因为专业,所以值得信赖。如有需要,请加QQ:99515681 或邮箱:99515681@qq.com
微信:codinghelp
CITS1401 Computational Thinking with Python的更多相关文章
- windows和linux中搭建python集成开发环境IDE——如何设置多个python环境
本系列分为两篇: 1.[转]windows和linux中搭建python集成开发环境IDE 2.[转]linux和windows下安装python集成开发环境及其python包 3.windows和l ...
- 机器学习常用Python扩展包
在Ubuntu下安装Python模块通常有3种方法:1)使用apt-get:2)使用pip命令(推荐);3)easy_instal 可安装方法参考:[转]linux和windows下安装python集 ...
- Python学习资料下载地址(转)
[转]Python学习资料和教程pdf 开发工具: Python语言集成开发环境 Wingware WingIDE Professional v3.2.12 Python语言集成开发环境 Wingwa ...
- [转]Python学习资料和教程pdf
开发工具: Python语言集成开发环境 Wingware WingIDE Professional v3.2.12 Python语言集成开发环境 Wingware WingIDE Professio ...
- 转:Python语言编程学习资料(电子书+视频教程)下载汇总
开发工具: Python语言集成开发环境 Wingware WingIDE Professional v3.2.12 Python语言集成开发环境 Wingware WingIDE Professio ...
- 数学类网站、代码(Matlab & Python & R)
0. math & code COME ON CODE ON | A blog about programming and more programming. 1. 中文 统计学Computa ...
- 【深度学习Deep Learning】资料大全
最近在学深度学习相关的东西,在网上搜集到了一些不错的资料,现在汇总一下: Free Online Books by Yoshua Bengio, Ian Goodfellow and Aaron C ...
- 机器学习(Machine Learning)&深度学习(Deep Learning)资料(Chapter 2)
##机器学习(Machine Learning)&深度学习(Deep Learning)资料(Chapter 2)---#####注:机器学习资料[篇目一](https://github.co ...
- juypter-notebook安装配置
juypter-notebook安装配置 Table of Contents 1. jupyter notebook概述 2. jupyter notebook安装 3. 在jupyter noteb ...
随机推荐
- 【转】Java中的static关键字解析
一.static关键字的用途 在<Java编程思想>P86页有这样一段话: “static方法就是没有this的方法.在static方法内部不能调用非静态方法,反过来是可以的.而且可以在没 ...
- 51nod--1174 区间中最大的数 (RMQ)
题目: 1174 区间中最大的数 基准时间限制:1 秒 空间限制:131072 KB 分值: 0 难度:基础题 收藏 关注 给出一个有N个数的序列,编号0 - N - 1.进行Q次查询,查询编号i至j ...
- 使用 wordcloud 构建词云图
from wordcloud import WordCloudfrom matplotlib import pyplot as pltfrom PIL import Imageimport numpy ...
- 微信H5支付坑一--手续费未结算
简单随笔小记: 场景:在微信H5支付的过程中,无论怎么支付完成,在微信商户后台查询手续费依然未扣除,当时手续费账户月为5元. 解决方法:起初无论怎么测试都不知道代码到底问题出在哪里了,想一下手续费账户 ...
- 一次ES故障排查过程
作者:莫那鲁道 原文:http://thinkinjava.cn/#blog 某天晚上,某环境 ES 出现阻塞, 运行缓慢.于是开始排查问题的过程. 开始 思路:现象是阻塞,通常是 CPU 彪高,导致 ...
- Jmeter性能测试之Monitor监控(四)
使用Jmeter(该篇文章使用的版本最高为3.1, 3.1+的版本存在兼容性问题)做性能测试, 要监控服务器硬件资源消耗情况, 可以使用扩展插件完成. 1. 服务端插件下载agent, 点击这里 , ...
- Xilinx 7 Serial PUDC_B
PUDC_B管脚用途 Pull-Up During Configuration (bar) Active-Low PUDC_B input enables internal pull-up resis ...
- Gradle: Download 下载慢的解决办法
应该是gradle被墙了,在网上查得:使用阿里云的国内镜像仓库地址,就可以快速的下载需要的文件: 修改项目根目录下的文件 build.gradle : buildscript { repositori ...
- MyString
[摘自C++程序设计语言] MyString.h #include <cstring> #include <iostream> #include <stdexcept&g ...
- matplotlib等高线图-【老鱼学matplotlib】
等高线图是在地理课中讲述山峰山谷时绘制的图形,在机器学习中也会被用在绘制梯度下降算法的图形中. 因为等高线的图有三个信息:x, y以及x,y所对应的高度值. 这个高度值的计算我们用一个函数来表述: # ...