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COSC 2670/2738 Practical Data Science
创建日期：2024-05-14 16:59:02
标签： COSC 2670
Practical Data Science

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COSC 2670/2738 Practical Data Science (with Python)

Marks : This assignment is worth 30% of the overall assessment for this course.
Due Date : Wed, 26 October 2022, 11:59PM (Week 14), via canvas. Late penalties
apply. A penalty of 10% of the total project score will be deducted per day. No
submissions will be accepted 5 days beyond the due date.
Objective
The key objectives of this assignment are to learn how to compare and contrast
several recommendation system algorithms. There are three major components of the
assignment – a completed Jupyter notebook used to run your experiments, a written
report, and a short video presentation where you describe what you did and your key
findings.
The dataset you will use will be a sample of the Netflix Prize data. The problem is
movie recommendation, and the data is already split into a training and validation set
that you can use to run all of your experiments.
Provided files
The following template files are provided:
• SXXXXX-A3.ipynb : The primer Jupyter notebook file you should use to stage
and run all of your experiments.
• netflix-5k.movie-titles.feather : The movie title dataframe that can be used to
map a movieID to a title, as well as a list of genres.
• netflix-5k.train.feather : The training tuples for 5,000 users, where each tuple
is ⟨userID,movieID,rating⟩.
• netflix-5k.validation.feather : A predefined set of validation tuples for the
same users that can be used by you to benchmark the performance of various
algorithms.
• A3.pdf : This specification file.
Creating Your Workspace
Once again, you should rename the file SXXXXX-A3.ipynb appropriately based on
your student ID.
Creating Your Anaconda Environment
In order to create your anaconda environment for this project, you should run the
following command in a terminal shell:
conda create -n PDSA3 python=3.8
conda activate PDSA3
pip install jupyterhub notebook numpy pandas
pip install matplotlib scikit-learn seaborn
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pip install kneed scikit-surprise
Note that both kneed and scikit-surprise can be finicky on some systems. For
example on my machine, an error was thrown during the compile of scikit-surprise
(Macbook Pro M1) but the install still worked when it tried a fallback install method.
So if you find that it really fails for you using pip, then and only then resort to conda.
This would break requirements.txt, but it should work reliably for everyone albeit not
very reproducible. The magic commands would be:
conda install -c conda-forge kneed
conda install -c conda-forge scikit-surprise
You can type “pip freeze” to see a list of the packages that are correctly installed
in your environment. You can also install scikit-learn-intelex and/or psutil if
you want to use the Intel-based optimisations or debug memory management as shown
in the sample Jupyter notebook.
If you also wish the timing and other jupyter extensions to be enabled in your
notebook (optional but may be useful depending on how you decide to present your
results), you need to run the following additional commands:
pip install jupyter_contrib_nbextensions
pip install jupyter_nbextensions_configurator
jupyter contrib nbextension install --user
jupyter nbextensions_configurator enable --user
Now you just need to type “jupyter notebook” to start up jupyter correctly with
access to the libraries you just installed.
Also, recall that if you ever stop working in your environment and come back later.
You must open a terminal, run “conda activate PDSA3” and then “jupyter notebook”,
otherwise you will not be working in the virtual environment you created above, and
most things you try to do will probably start failing.
You should not use any other libraries to complete your assignment beyond the
ones shown above without written permission from the course coordinator (Shane).
1 The Jupyter Notebook Primer (5 marks)
I have included the jupyter notebook I walked through at the end of the Week 10
Lectorial. This notebook will provide you with everything you need to correctly load
the dataframe files from feather, and also includes an example of how to do both a
grid search and randomised search for parameter tuning on one of the recommendation
system algorithms included in Surprise. You should spend some time reading the API
documentation and tutorial for this library provided at https://surpriselib.com.
This will be critical information you should use to stage your experiments.
2 The Report (15 marks)
The main component of your assignment will be to carefully write up your key findings.
Your report should not be more than 5 pages using 11pt font. You may also have one
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additional Appendix page containing additional graphs or tables. Your final report
must be submitted as a PDF file. You can use Microsoft Word, but I would strongly
encourage you to consider writing up your report using LATEX (https:\overleaf.
com). Writing in LATEX may seem daunting at first, but Overleaf provides plenty of
tutorials and examples, and it is pretty easy once you get the hang of it. This spec
file was written using LATEX. Microsoft Word is not a good tool for writing technical
documents, and in fact most Computer Science Conferences all require papers to be
written in LATEX. The quality of of the presentation layer is easily discernible by most
people in Computer Science. Write a paragraph or two with a graph or diagram in
overleaf and then in MSWord and compare the two – I bet you’ll see the difference
immediately!
Regardless of which tool you use to generate your final PDF, the format of the
report should be:
1. Your name and student number at the top of page 1.
2. Introduction (usually no more than 1/2 page).
3. Methodology (usually about 1 page) - This would contain a clear description of
each recommendation system algorithm you are using and a rationale as to why
it is being used.
4. Experiments ( 3 pages) – This is the main component of your report. Here you
should document all of the parameters and algorithms used, Tables, Figures, or
Graphs that you create in order to compare and contrast all of the algorithms you
have benchmarked and a discussion about what you have discovered. There is
no reason include images of code snippets as you are submitting your Jupyter
notebook already – you should include images of graphs you create – assuming
they are important to the story you are telling.
5. Conclusion (1/2 page) – A clear summary of your key findings.
6. References (Separate page) – You can include as many references as you see fit
and need in your report, and this is not counted against the 5 page limit.
7. Appendix (Separate page) – Any additional graphs or tables that you think are
important but that you could not include into the main document because of
space constraints.
Summary – a report that has a 5 page body, 1+ pages of citations, and 1 optional
page at the end as an Appendix.
You must compare at least four different algorithms from the surprise library in
your report. Note that one algorithm (e.g. SVD) with two different parameter settings
does not count as two different algorithms. That is one algorithm with two different
parameter settings, i.e. one algorithm. You can certainly include results for multiple
parameter settings for each of the four algorithms in your experiments, just make sure
you are using 4 different algorithms in total in your shootout.
In week 8, I provided several evaluation classes you can use to compute a wide
variety of evaluation measures, and we encourage you to explore as many as you
can, as doing so will provide additional evidence that your “winning” algorithm is
really a winner. The “official” metric will be RMSE as this is the metric used in the
original competition, but you should compare the 4 algorithms using a minimum of
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three different evaluation metrics. You should aim to have at least one algorithm that
can achieve an RMSE score≤ 0.800. This should be achievable with a little parameter
tuning, if you choose good algorithms from surprise, and you may even decide that the
algorithm that got the best RMSE score is not necessarily the “best” algorithm overall
based on the experiments you have ran. Hint: Think carefully about what “good”
movie recommendations really mean to you. We all know what it is, so what do you
think is the best way to prove or disprove recommendations from Algorithm A are
clearly better than the ones you get from Algorithm B. We covered a wide variety of
evaluation measures in the Week 8 Lectorial, so go look at what was in that notebook
and think about it.
Other key hints – (1) If you have a Figure, Table, or Diagram in the report, it must
have a caption and you must reference it in your report and discuss it. By that I mean
“Table 1 contains a table of RMSE results for Algorithms A-E. We can see that ...” (2)
If you use ideas or code from somewhere else, you must include it in your references.
A typical “bibtex” citation for something taken from the web would look something
like:
@misc{StackA,
title = {{Stackoverflow Discussion on X},
howpublished = {\url{http://www.example.com}},
note = {Accessed: 2022-10-05}
The preferred referencing style for Computer Science is usually APA. See https:
//libguides.murdoch.edu.au/APA/sample for an example. There are lots of
tutorials available online on APA referencing, so if you have never seen it, just
search for tutorials on APA referencing and you’ll find more than you could ever
read/watch. (3) If it isn’t clear, you must include graphs, tables, and/or diagrams in
your experimental section, which are to be used as evidence to back any claims about
algorithm performance that you make.
3 The Video Presentation (10 marks)
Your final task it to create a 5-10 minute video recording of you presenting your
findings. You must create slides, and I would strongly encourage you to use PIP
(Picture in Picture Mode) in your video. If you have no idea what I am talking about,
have a look at this video created by our guest speaker when he was a PhD student
at RMIT – https://www.youtube.com/watch?v=jFhhL1mkziQ The video format
should be MP4. No other video formats will be accepted.
A good presentation will have visually appealing slides, a clear story line, and
the presenter will not just be reading what is on the slide or from a piece of paper –
they will be describing what the current slide is showing. If you want to know more,
check out the talk by S. P. Jones on “How to Give a Great Research Talk” – https:
//www.youtube.com/watch?v=sT_-owjKIbA
You might say to yourself, I’m not giving a research talk – but you are. You are
doing experimental research in Data Science on Recommendation Systems. So, treat it
like one.
If you have no idea how to create a PIP video of a presentation quickly and eas-
ily, check out this video – https://www.youtube.com/watch?v=rKDKniBmwe0&
feature=youtu.be. If you don’t like this technique just search for how to create a
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picture in picture presentation using Zoom or Microsoft Teams. It is surprisingly easy
to do using Microsoft Teams, Zoom, or the open source program OBS. I’m sure there
are many other ways to do it, but there are definitely simple and quick ways to do it
once you see how. The point is that I don’t want “technology” to be an impediment
in creating a great presentation. It is much more useful spending time on getting the
content right and doing a few practice runs so that you can create an interesting talk.
Submission
All submissions must be made on the canvas shell for the course. A link will be
provided within canvas, under the Assignments tab for submissions. Assignments
submitted through any other method will not be marked.
Pay close attention because the final submission must be done differently than
the first two assignments. The rationale for this is so that each of you have access
to a Turnitin report of your submission when you submit. Since you are writing a
report, this is a valuable tool for you to ensure that you do not get dinged for academic
misconduct. Believe it or not, we do take this seriously, and every semester several
students get reported to the university for failing to comply with the plagiarism rules.
So make sure you submit early enough that you can action it, otherwise, you’ll find
yourself in a really uncomfortable meeting with a university panel and you won’t get a
mark for the course until it is resolved!
This is an individual project. Do your own work.
You should submit four separate files in canvas.
[Your Student Number]-A3-notebook.ipynb
[Your Student Number]-A3-notebook.pdf
[Your Student Number]-A3-report.pdf
[Your Student Number]-A3-presentation.mp4
Do not put them in a single zip file or you will not get the plagiarism report as it
cannot run on archived files. If you do submit a single zip file, you will lose a mark,
and we will still run the Turnitin report, but you will not get a copy of it, unless you
end up getting flagged for plagiarism, and none of us wants that to happen. So for your
own sanity and ours please submit the four files individually, named as shown above.
If you do, you should be able to see the Turnitin results for your report and for your
notebook.
GETTING HELP. There is a discussion board available in the course canvas. Please
do not post code snippets in this forum. Do ask questions if you have them! We will
do our best to answer them as quickly as we can. This is the best medium to ask a
question, as there is a very strong chance that if you are confused about something,
your classmates are too. We will also be running office hours in Week 13 from 3-4PM
on Thursday in Collaborate Ultra if you want to stop by and ask a question.
Plagiarism Warning
University Policy on Academic Honesty and Plagiarism: It is University policy that
cheating by students in any form is not permitted, and that work submitted for
5
assessment purposes must be the independent work of the student concerned. Please
see the RMIT policy for more details: https://www.rmit.edu.au/students/
my-course/assessment-results/academic-integrity.
THIS IS NOT A GROUP PROJECT. Students are reminded that this assignment is
to be attempted individually. Plagiarism of any form will result in zero marks being
given for this assessment, and can result in disciplinary action. We routinely use
plagiarism software on projects! Please, please don’t do it. Be aware that paying
someone on a coding site to do it for you is a form of plagiarism. If you are submitting
work that is not your own, regardless of how you got it – you are in breach of this
policy.
Extension Policy
Individual extensions are very unlikely to be considered or granted by
the PDS team for the final assignment. We have this rule in place simply
because we set the deadline as late in the semester as we possibly could
based on the deadlines we have to submit your final grades. If you have
a strong case for needing one, you must request it 48 hours before the
deadline, and you should submit the form provided on canvas along
with supporting documentation to me. Be aware, there is no wiggle
room on the Assignment 3 deadline. We have a very short period of
time to mark 300 projects and submit the final marks to the University
Panel for approval. So, if you do get an extension from me, it will be
very short – maybe 1-2 days at most.
If you find yourself in a situation where you really need an extension
of a week or more (it can happen to any of us), you will need to apply
formally through RMIT for Special Consideration. This isn’t because
we are heartless academics, it is because we cannot change a mark for
a course after the university deadline without documentation from SC.
Even with the appropriate documentation, it can take many weeks to
get grade changes approved after the cutoff, which means in practical
terms, if you miss the cutoff, you won’t be able to graduate if it your
last semester, or you won’t be able to register for any classes that have
PDS as a prerequisite when enrolments open for next semester. So, the
moral of the story is, seriously, don’t be late on the final assignment. If
you are, there is very little we can do to turn it around and get a final
mark for you before the RMIT grade entry and approval deadline.
There is a University process in place to grant extensions. Our
preference is that you go this route if you must, as they have very
clear criteria to grant exemptions. We will always honour these –
assuming you provided the correct information to them.
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Getting Help
There is a discussion group available in the course canvas. Please do not post code
snippets in this forum. Do ask questions if you have them! We will do our best to
answer them as quickly as we can. This is the best medium to ask a question, as there
is a very strong chance that if you are confused about something, your classmates are
too. We will also be running office hours in Week 13 from 3-4PM on Thursday in
Collaborate Ultra if you want to stop by and ask a question.
• Email us.
• Use the discussion board.
• Ask a question in a Lectorial or a practical.
There is help available if you need it. We do strongly urge that you start with the
discussion board because, as mentioned above, if you are confused, there is a very
good chance someone else has exactly the same question, and it is not a great use of
the team’s time to keep answering the same question over and over.
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MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A PHIL2617 PHYS3116 MIE250 BCPM0008 ECN 620 QBUS6830 ECSE-415 ECON5102 MGMT20005 MAT 653 ECO 480 COMP 5/600 CSSE 5600 ME 571 7CCMMS61 CS 550 COMP2865 MATH3075 STATS 506 QTM 220 ISYS2120 FIT5106 STAT 153 MKTG3112 ECON2102 ELE2018 COMP3031 MSIN0010 SCC461 MATH237 CS246 CSC3065 CSE 101 ICTWEB411 COMP9313 ECOS 2025 FN620 MATH11111 MATH 3018 KE1068 FINA 6112 C++ MISM 6202 MSIN0105 CSC8631 MECO6935 COMP4121 ACCT2011 DPBS1150 EEL 3701C F19PL OPM 661 CSE 310 CS918 BIOL30001 ACCT3563 BANK3600 MET CS622 MATH367 ACCT5907 Continuing COMP5048 COMP61021 CS3334 GSOE9210 CSCI-570 COMP 5322 HW 2 ALY-6020 IEOR 4706 DATA 200 EBUS504 CGE12412 COMP4108 7SSMM800 ECMM171P MATE50002 CS225 RS 6003 BH2274 ELEC3111 MAT6669 ECON-UA 227 PHIL 1012 7SSMM603 TB028A P6 ECS708 CSC 427 CS6476 CSE 250A A1A2 ITS61504 EECS 376 model HW6 UV0010 ECON 6074 MG4F7 CMP-7030Y INFO3008 BFIP013 ECON 5068 MATH6002 CS3483 PSTAT 131 C31FM CSOR W4246 AAEC 5307 DATA ELEC362 COMSM0047 ECN6006 MA20224 BST351 MATH 323 MATH323 AA2021 COMP3811 COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 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ACCOUNTING BUSS1040 CHEM 181 MAT136H1 GY 6183 COMP3170 AGRI90089 PHYSICS 1002 ECON10003 IEOR E4004 ACS61012 MATH 40550 MKTG2113 TELE4653 ENSC1004 ECE 232E MK726 MTRX2700 SHEET 3 CSCA08 COMP30027 Math 2XX3 statistic MMAN3200 BISM7213 ECMM109 ECON 2022 FNCE 20005 GR5067 CSCB09 ME 3017 CS 5100 PHYS 2903 COMS 4771 COMP222 COMP 1046 CSC148H1 ISYS2038 FINS2615 OT5206 COMP2048 A6 MECH5770M MA826/20 CMPT 726 STA457 INFS6071 INFS588 integral ECON 216 IRDR 0017 MA323 PA 15213 PSYC2001 ENV222 Statistics ACCT5930 ACCT12 LAND2121 ME 5405 COMM1180 TELE3113 A7 ECE 568 ACCT2019 INFS1200 ENGG1340 CS 1400 ASST3 FINM7409 SWEN30006 INFO2222 FIN20150 SOSS1000 CSSE7231 FINS5513 COMS3200 CECN 702 ECON4060H MN3515 HW2 PSYA02H3 ECON 2112 COMP90051 CS 1652 MA30059 AF1605 MKTG7501 ECO-6003B COM4521 CTM ECON3350 COMP 3804 ELEC9762 BISM 7255 ST22 MATH20014 MNE3122 COMP343 MATH5165 ELE7029 ENGR30002 SEEM3500 ECON7350 RISK5002 FINM7405 COMP90059 MOS 3320B STAT 431 BISM7221 MATH3014 MTH783P EBA 35302 ACS133 BUS2206 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FN1024 MATH0094 MANG 6554 ECOS3003 ELEC6252 C2511C PHY328 EC1060 ECON32202 COMP4002-E1 MATH3063 EFIMM0124 QBUS2310 ECON4411 FUNDAMENTALS ACCT20001 18YX RPM COMP 2711 QBUS6600 BUSS5221 ST3189 FIT1047 MAT344 EGB375 MATH60013 MATH96011 COMP3557 MAST20029 ECM159 EAE1011 ENSCEN 105 SECTION A STA 141C SOSE 2022 PAM006 COMP0048 M40007 ISOM2500 CMPE 142 A06472 MCC150 ACST 8083 GEOG6087 ECON920 FINM7008 ENVENG 4110 COMPSCI 361 ENGRCEE 21 PHYS 1112 ACF2100 ECMM108 COMPS267F MAT00050M MAST30025 CSIT970 MSCI 224 INT202 PUBL0054 MSDM5004 BMAN 21020 BMAN21020 MGMT1002 MATH11150 LAWS7023 ACTL30002 CIVL3340 MATH1002 ECON 8069 COMPSCI 351 IBUS7302 STAT4528 CHEM1101 STAT3925 BUSN 7008 Comp 3120 ECOS2040 SWEN90010 ENGN6528 ECM158 ECON20022 COMP6452 Calculation CITS1401 S1889112 BUS 351 ACTL30008 FIT2091 ECON6002 IBUS6020 PHI 001 ECON30290 MATH7501 ECON10004 MFIN7017 ISYS90043 LAWS7012 ECON30025 COMP30023 COMM1190 ECOM20001 GSBS6009 STAT 337 EMET8002 ECOM90001 ECOM30001 FINC 2012 BUSINESS ENGCV512 ECO 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