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COMP4650/6490 machine learning

创建日期：2024-06-03 15:16:05

所属分类：计算机computer science

标签： COMP4650

machine learning

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COMP4650/6490 Document Analysis

Assignment
Overview
In this assignment you will:
1. Develop a better understanding of how machine learning models are trained in practice, including
partitioning of datasets, evaluation, and tuning hyper-parameters.
2. Become familiar with the scikit-learn1 and gensim2 packages for machine learning with text.
3. Become familiar with the PyTorch3 framework for implementing neural network-based machine
learning models.
Throughout this assignment you will make changes to the provided code to improve or complete existing
functions. In some cases, you may write your own code to perform functionalities similar to the provided
code but using different types of features or dataset.
Submission
• You will produce an answers file with your responses to each question. Your answers file must be
a PDF file named u1234567.pdf where u1234567 should be replaced with your Uni ID.
• The answers to this assignment (including your code files) have to be submitted online in Wattle.
• You should submit a ZIP file containing all of the code files and your answers PDF file, BUT NO
DATA. A simple Python script create submission zip.py that can generate a ZIP archive with
all the required files for your submission has been provided.
• No late submission will be permitted without a pre-arranged extension. A mark of 0 will be
awarded if not submitted by the due date.
Marking
This assignment will be marked out of 100, and it will contribute 10% of your final course mark.
Your answers to coding questions (or coding parts of each question) will be marked based on the quality
of your code (is it efficient, is it readable, is it extendable, is it correct) and the solution in general (is it
appropriate, is it reliable, does it demonstrate a suitable level of understanding).
Your answers to discussion questions (or discussion parts of each question) will be marked based on how
convincing your explanations are (are they sufficiently detailed, are they well-reasoned, are they backed
by appropriate evidence, are they clear, do they use appropriate visual aids such as tables, charts, or
diagrams where necessary).
This is an individual assignment. Group work is not permitted. Assignments will be checked for
similarities.
1https://scikit-learn.org/
2https://radimrehurek.com/gensim/intro.html
3https://pytorch.org/
1
Question 1: Movie Review Sentiment Classification (40%)
For this question you have been provided with a labelled movie review dataset – the same dataset you
explored in Lab 3. The dataset consists of 50,000 review articles written for movies on IMDb, each
labelled with the sentiment of the review – either positive or negative. The overall distribution of labels
is balanced (25,000 pos and 25,000 neg). Your task is to apply logistic regression with both sparse and
dense representations to predict the sentiment label from the review text.
Part A
One simple approach to classifying the sentiment of documents from their text is to train a logistic
regression classifier using TF-IDF features. This approach is relatively straightforward to implement and
can be very hard to beat in practice.
To do this you should first implement the get features tfidf function (in features.py) that takes a set
of training sentences as input and calculates the TF-IDF (sparse) document vectors. You may want to use
the TfidfVectorizer4 in the scikit-learn package. You should use it after reading the documentation.
For text preprocessing, you could set the analyzer argument of TfidfVectorizer to the tokenise text
function provided in features.py. Alternatively, you may set appropriate values to the arguments of
TfidfVectorizer or write your own text preprocessing code.
Next, implement the search C function (in classifier.py) to try several values for the regularisation
parameter C and select the best based on the accuracy on the validation data. The train model and
eval model functions provided in the same Python file might be useful for this task. To try regularisation
parameters, you should use an automatic hyper-parameter search method presented in the lectures.
You should then run sentiment analysis.py which first reads in the dataset and splits it into training,
validation and test sets; then trains a logistic regression sentiment classifier and evaluate its performance
on the test set. Make sure you first uncomment the line with the analyse sentiment tfidf function
(which uses your get features tfidf function to generate TF-IDF features, and your search C function
to find the best value of C) in the top-level code block of sentiment analysis.py (i.e., the block after
the line “if name == ’ main ’:”) and then run sentiment analysis.py.
Answer the following questions in your answers PDF:
1. What range of values for C did you try? Explain, why this range is reasonable. Also explain what
search technique you used and why it is appropriate here.
2. What was the best performing C value?
3. What was your accuracy on the test set?
Part B
Another simple approach to building a sentiment classifier is to train a logistic regression model that uses
aggregated pre-trained word embeddings. While this approach, with simple aggregation, normally works
best with short sequences, you will try it out on the movie reviews.
Your task is to use Word2Vec5 in the gensim package to learn embeddings of words and predict the
sentiment labels of review text using a logistic regression classifier with the aggregated word embedding
features. You should use it after reading the documentation.

hyper-parameters of Word2Vec (e.g. vector size, window, negative, alpha, epochs, etc.) as well as the
regularisation parameter C for your logistic regression classifier. You should use an automatic hyper-
parameter search method presented in the lectures. (Hint: The search C function in classifier.py and
the get features w2v in features.py might be useful.)

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关键词一关键词二关键词三关键词四关键词五和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 EARN 5 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