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COMP4318/5318 Assignment

创建日期：2024-06-03 17:11:32

所属分类：计算机computer science

标签： COMP4318

Assignment

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COMP4318/5318 Assignment 1 Overview

This assignment involves two core parts. Part 1 focuses on a classi�cation goal to identify cases of
heart disease in a Cardiovascular Disease Dataset. Part 2 focuses on a regression problem on the
Concrete Dataset.
Your tasks involve applying pre-processing techniques and writing functions to implement and
evaluate for each part. After providing these functions, you will perform grid search procedures to
tune for the best hyperparameter combinations for some of these models.
Each part has been broken down into 4 sections as follows:
1. Pre-processing techniques
2. Classi�cations/Regression methods
3. Ensemble methods
4. Hyperparameter tuning
Academic integrity
While the University is aware that the vast majority of students and sta� act ethically and honestly, it
is opposed to and will not tolerate academic integrity breaches and will treat all allegations seriously.
Part 1: Classi�cation
In Part 1 of this assignment, you will be exploring di�erent classi�cation methods on a modi�ed
version of a real dataset - the Cardiovascular Disease Dataset. This dataset has a balanced selection of
the target class - Cardiovascular disease. The features of the Cardiovascular Disease Dataset are as
follows:
Age - positive int (days)
Height - positive int (cm)
Weight - positive �oat (kg)
Systolic blood pressure - positive int
Diastolic blood pressure - positive int
Gender - categorical [F, M]
Cholesterol - categorical [normal, above normal, well above normal]
Glucose - categorical [normal, above normal, well above normal]
Smoking - categorical [No, Yes]
Alcohol intake - categorical [No, Yes]
Physical activity - categorical [No, Yes]
Cardiovascular disease - categorical [No, Yes]
Your tasks for this part involve applying pre-processing techniques and writing classi�cation
functions that can be applied to this dataset using strati�ed 10-fold cross-validation.
After providing these functions, your next task is to design and test two functions on a range of
di�erent hyperparameters and evaluate their performance with strati�ed 10-fold cross-validation for
bagging and a validation set for Adaboost. You should use the cvkfold provided for all functions when
performing cross-validation.
Although it is not always necessary to wrap your code in functions when using Jupyter Notebooks,
this allows us to test your implementations. Wherever relevant, pass a random_state argument as
instructed below to control for randomness between runs and ensure your results are reproducible.
Further instructions can be found in the sca�old notebook.
Random state clari�cation:
There are multiple ways possible to ensure randomness is controlled between runs, however please
note there are some di�erences in the instructions for Part 1 and Part 2.
In Part 1, whenever there is a need to control for random events in a model, a random_state=0
argument should be passed to the constructor of the model inside your function, and it will not be
passed again during testing.
Part 1 extra instructions
If you fail a hidden test or running your code results in an error, ensure you have handled other
possible datasets within the speci�ed parameters and your functions can handle being called
with di�erent combinations of hyperparameters. You do not need to pass all hidden tests before
moving on to the next section.
Data:
The �lename to be loaded is 'cardio_diseases.csv'. You can �nd it in the �le browser by clicking
the button that looks like this
The �rst function load_data should handle setting any invalid values to np.nan and converting
categorical strings to numbers as outlined in the sca�old. The second function process_data
should process these missing values as described in the sca�old. Note the instructions in the
sca�old regarding testing your code on other datasets.
You can assume any valid categorical attributes in other datasets will take on values [Yes, No],
[Male, Female], and [Normal, Above normal, Well above normal] like the given dataset.
However, they may occur in di�erent columns, have di�erent names, or not be present at all.
Ensure your output array has a dtype of �oat64
Set the variables currently set to None by calling your functions. The values of these variables
will be checked along with your function to ensure you can use them in the rest of the
notebook.
All non-negative values (including 0) should be considered as valid numerical data
Evaluating methods with cross validation:
For the cross validation functions, use the skeletons provided, so that algorithm
hyperparameters such as number of neighbors and power of minkowski distance can
optionally be passed in as arguments and accessed as a dictionary using the **kwargs syntax.
Note you also can pass arguments as a dictionary to functions (such as sklearn constructors)
using the **kwargs syntax (you may need to search for documentation on **kwargs if
unfamiliar).
Your functions should create an instance of the model with the correct hyperparameters set
and run 10 fold cross validation (using the fold set up above and your preprocessed data X and
y) to evaluate it. The function should return the model instance with the correct
hyperparameters and the average cross validation accuracy obtained (but please do not round
this return value).
Once de�ned, you can treat cvKFold as a global variable and use it in all cells below.
Where the sca�old asks you to test your functions, please do so in the same cell and produce
output as below.
Follow the speci�ed formats for the outputs, rounded to 2 decimal places.
For the KNN the expected output format is as for the other methods
Cross validation score: x.xx
You can assume there will not be any cases where attributes shared by both bagging and
logistic regression are passed.
Grid search:
The train_val_test_split function should stratify the training/validation/test sets.
You can ignore this comment
TODO: uncomment this code to create the initial train test split
X_train_all, X_test, y_train_all, y_test = train_test_split(X, y, random_state=0)
Please read the sca�old instructions carefully for the Adaboost grid search. You are not
performing a cross validation grid search, but a grid search using a separate validation set. We
covered this procedure brie�y in the lectures. You calculate a validation score rather than
performing cross validation at each step.

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COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 CIVL2010 ECMT2150 CS 333 CP1406 KIT308 Economics 701 ECON3740 ECE 5759 BFC3340 FINC2012 COMP4500 MATH3871 FIT3080 18-819C Economic ECO202Y5Y PHY100 FINC-780 Econ 100B EC602 IE582 EE450 COMP9517 WRDS 150B ECON 570 LECTURE 2 ECA5101 ECE 4420 ADAD9311 FIT3031 CS 544 MF 703 CS 6140 KIT501 ENGSCI 344 CSC171 IE 332 FINM8007 21S2 COSC 1114 FE545 E471 SIT102 CSC108 MAT301 IAE 101 COP3503 STAT2004 EC 303 CS1026 STAT1203 MATH3171 ISOM5160 IFN657 FIT2014 ACCT2102 DSCI550 STATS 330 ELEC421 COMP3670 QBUS6840 MEES:698B MAST30027 MIET1077 Economics IFT 6758 FIT5122 BUSN5101 CptS 111 HRMT5502 Math2018 PP 312 INFS3200 CZ3005 CSSE1001 SENG 2011 MKTG 553Q BUFN 640 COMP507 ETF5952 DATA 311 ECE4043 BANK3011 finance M3H623544 HW1 INFS 7410 AI6103 STAT3888 INFS3208 ACCT7103 ECOS3013 PSYCH 20B F79MA STAT3006 ENGG952 2X MGSC 7000 ECE4132 MSF 526 MATH 3033 CDS 511 EEEE4120 EEET2465 ECA5103 MATH2070 ELEC3104 BU51019 EEE8147 ENGR20003 COSC 1107 ETF3200 FNCE 435 MSFI 435 ISyE6420 GENG4405 HW ETC5523 IE 7315 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 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