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COMPX523 Assignment
创建日期：2024-07-30 14:42:42
标签： COMPX523
Assignment
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Assignment COMPX523
1 Overall Description 
In this assignment, you will implement an ensemble classifier for data streams 
and rolling window features’ extractors. In general, this assignment has two 
parts, each of them graded as follows: 
1. Coding (50% of the final score); 
2. Experimentation and Analysis (20% of the final score). 
Each of these parts are detailed in the following sections. Important infor- 
mation: 
• Deadline: See Moodle 
• This assignment can be developed in groups of 1 or 2 students. 
• Parts of the assignments are mandatory for groups of 2 students and op- 
tional for single student groups. These are marked as (Groups of 2 Re- 
quired). 
• You must code your assignment using either MOA or scikit-multiflow. 
• You need to build a classifier that is compatible with either MOA or scikit- 
multiflow. In MOA the classifier class will extend the AbstractClassifier 
abstract class, and implements the MultiClassClassifier and Capabili- 
tiesHandler interfaces. 
• You need to build a filter that is compatible with either MOA or scikit- 
multiflow. 
• Your submission must contain a zip file with 3 files: 
1. a file containing the classifier implementation (a single .java or .py); 
2. a pdf report with the experiments and analysis; and 
1 
Start End 
Window with length l 
At the start of the window… 
CREATE candidate c 
At the end, either… 
- REPLACE t by c 
 
- IGNORE c 
During the window… 
c is not used for testing 
p(c) is updated 
Figure 1: The candidate c updates in a given window 
3. a README file with details about how to run your code and repro- 
duce the experiments. Optionally, the README file can be replaced 
by a jupyter notebook with the execution of the experiments. Notice 
that a jupyter notebook doesn’t replace the PDF report. 
Throughout this assignment you will find (Optional) questions and re- 
quests. These do not give you extra credit nor they influence your final score. 
They are challenges that may go beyond what was taught in the course, or 
require further programming and analysis efforts. In summary, you are en- 
couraged to try them, but they are not required. 
2 Coding 
You must create a new classifier, but you are encouraged to look at the exist- 
ing ensemble classifiers to observe how ensemble members are usually stored, 
hyperparameters conventions, and other common code. Notice that your en- 
semble classifier must be implemented in either MOA or scikit-multiflow in a 
way that it can be executed as a stand-alone classifier. 
The ensemble must contain the implementations to satisfy the requirements 
described in the following subsections. 
2.1 Ensemble Implementation 
The ensemble will have a fixed number of learners S. Updates to the ensemble 
(i.e. additions and removals) will occur every l instances, such that these can 
be identified as windows of length l instances. At the start of each window, 
a new base model should be added as a candidate c to join the ensemble. At 
the end of each window, the ensemble will either: REPLACE another learner 
e currently in the ensemble by c; or IGNORE c and discard it. This process is 
depicted on Figure 1. 
2 
The base learner for the ensemble will be the Hoeffding Tree algorithm. 
Optional: You are encouraged to think about how your ensemble could be im- 
plemented using any base learner. The majority of the ensemble algorithms 
available in MOA/scikit-multiflow do not enforce the use of a specific base 
learner. However, it would be not easy to accomplish that in this assignment 
as the diversity induction strategy assumes that we are using a Hoeffding Tree. 
Another approach is to enforce that the base learner will be a subclass of Ho- 
effding Tree, such as the Hoeffding Adaptive Tree. However, if your ensem- 
ble is designed to only accept the basic Hoeffding Tree algorithm as the base 
learner that is fine. 
The training and testing methods depend on how diversity is induced (see 
2.1.4) and the voting method (see 2.1.2). Notice that even though the ensemble 
is updated in windows of length l, the Hoeffding tree models are continuously 
updated, i.e. they should not be reset every window and they should be up- 
dated during the window as well. 
You can name the ensemble algorithm, but if you cannot find a good name, 
just call it TheEnsemble (we late refer to it using this name). 
2.1.1 Predictive performance p(e) 
The estimated predictive performance p(e) of each member e of the ensemble 
E must be stored and continuously updated. Concretely, immediately before 
using an instance xt for training an ensemble member e, xt should be used for 
assessing whether e would correctly predict it, i.e. he(xt) = yt. Notice that if 
the instance xt is first used for training e, then the estimated predictive perfor- 
mance would be misleading. By default, p(e) corresponds to the accuracy of e, 
i.e. number of correct predictions divided by total number of predictions made 
by e. (Optional): Encapsulate p(e) such that other metrics, such as Kappa T, 
could be used. 
2.1.2 Prediction - Weighted votes 
The ensemble prediction should be determined by a weighted majority vote 
strategy. The weights of each member e are determined according to the pre- 
dictive performance p(e). 
During prediction, the algorithm should sum the weighted votes assigned 
to each class label to decide the ensemble prediction. For example, given an 
ensemble with four models, such that p(g) = 0.90, p(m) = 0.85, p(n) = 0.81, 
and p(q) = 0.92, where the predictions for a given xwere hg(x) = 0, hm(x) = 0, 
hn(x) = 0, and hq(x) = 1. The weighted votes for class 0 w(0) = 0.90 + 
0.85 + 0.81 = 2.56 and for class 1 w(1) = 0.92, as a consequence, the ensemble 
prediction is class 0. 
3 
2.1.3 Dynamic updates 
After processing l instances, the algorithm should be updated by replacing the 
worst e∗ (min p(e)) by the new model c. If p(c) ≤ p(e∗), i.e. the predictive 
performance of c is worst than the minimum predictive performance observed 
in the ensemble p(e∗), then c should be discarded (i.e. IGNORED). 
2.1.4 Training - Inducing diversity 
There are multiple ways of inducing diversity into an ensemble model [3]. One 
can vary in which instances or features the models will be trained, modify the 
outputs of the training instances, or even combine several strategies. In this 
assignment, you are asked to implement a strategy that vary the hyperparam- 
eters of the base Hoeffding Trees. Concretely, implement a method that create 
new candidate trees c with random values for the following hyperparameters. 
• Grace period gp. The number of instances a leaf should observe between 
split attempts. Range of values (10; 200). Step = 10. 
• Split Confidence sc. The allowable error in split decision. Range of val- 
ues (0.0; 1.0). Step = 0.05. 
• Tie Threshold t. Threshold below which a split will be forced to break 
ties. Range of values (0.0; 1.0). Step = 0.05. 
For example, a given c may be initialized with gp = 110, sc = 0.05 and 
t = 0.55. There is not much difference between a tree trained using sc = 0.046 
and sc = 0.05, thus we define a step of 0.05 for sc and t, and a step of 10 for gp. 
2.1.5 (Groups of 2 Required) Measuring diversity 
To verify if a diversity induction strategy was successful, one approach is to 
estimate how diverse the base models are w.r.t their predictions. Intuitively, if 
model a always predict the same labels as model b, then they are homogeneous. 
To estimate ‘how’ diverse two models on cases other than the extremes (always 
agree, always disagree), several approaches were defined, such as the Yule’s Q 
statistic or the Kappa statistic. 
In this assignment, you are asked to calculate the Kappa statistic k(a, b) 
for the predictions of each pair of models u and v in the ensemble. Notice 
that you will need to keep counters for how every pair of models predicted. 
Adapted from [3]: This can be achieved by constructing a contingency table Cij , 
such that the value at the intersection of a row i and a column j stores the 
amount of instances x ∈ X where hv(x) = i and hu(x) = j. Table 2.1.5 shows 
an example of contingency table Cij for a k-class problem. The diagonal in 
matrixCij contains the concomitant decisions of the pair, thus a naive approach 
to weight their similarity is to sum its values and divide it by the amount of 
instances n, as shown in Equation 1. 
where T is the current instance index, w the window size and xi holds the 
value of feature x. The feature identifier was omitted, otherwise it would look 
something like xji , i.e. feature j at time i. 
Observation: The first values on the stream should be calculated according 
to the number of instances available. For example, the SMA of w = 5 when 
there are only 3 values available should be calculated using the 3 values, i.e. 
assuming w = 3. This is just for completeness and to avoid doubts during 
implementation. 
EMA. The Exponential Moving Average (EMA) is a moving average that 
assigns a greater weight according to α on the most recent data as shown in 
equation 5. Table 1 presents an example of EMA for feature f0. 
where the coefficient α is the degree of weighting decrease (constant be- 
tween 0 and 1). A higher α discounts older values faster. By default, let’s use 
α = 0.3. There are several ways of initializing EMA(x,w, 1, α), we are setting 
it to x0, even though other approaches are also viable, such as using the SMA 
of the first values. 
In MOA, you can extend AbstractStreamFilter.java and base your imple- 
mentation on the other classes that implement AbstractStreamFilter. The basic 
idea, is that the filter to create the EMA and SMA versions of the attributes will 
be applied to a stream, which in turn will them be used for other problems. In- 
clude options to define which features should have SMA or EMA created and 
also to control their hyperparameters w and α. 
Important: Notice how on table 1 the EMA and SMA do not include the 
current values of the feature f0, the last f0 used in any calculation is always the 
value immediately before the current instance. 
3 Evaluation and Analysis 
The results should be presented using a table format, followed by a short analy- 
sis about them guided by the questions associated with each experiment. More 
details are presented in the subsections for each experiment. At the end of this 
6 
f0 SMA(f0, 10) EMA(f0, α = 0.4) 
26 26 26 
20 26 26 
34 23 23.6 
24 26.67 27.76 
17 26 26.26 
17 24.2 22.56 
35 23 20.33 
15 24.71 26.20 
13 23.5 21.72 
19 22.33 18.23 
37 22 18.54 
28 23.1 25.92 
27 23.9 26.75 
Table 1: Calculating SMA and EMA for 13 values. Red cells indicate exceptions 
to the general equation, e.g. calculating SMA with when T < w 
section you will find more details about how to prepare, run and report your 
experiments using either scikit-multiflow or MOA. 
Evaluation framework. For all experiments, use a test-then-train evalua- 
tion approach and report the final result obtained, i.e. the result obtained after 
testing using all instances. 
Metrics. For all experiments, report accuracy, time1. 
Datasets. electricity, covertype, SEA abrupt (drift at 50000), SEA gradual 
(drift at 50000, width=10000), and RTG 2abrupt (drift at 30000 and 60000). Re- 
fer to attached materials to obtain the “.arff” and “.csv” versions. 
3.1 Experiment 1: TheEnsemble variations and sanity check 
In the first part of the evaluation, you are asked to perform experiments us- 
ing TheEnsemble and a single Hoeffding Tree. This experiment will produce 3 
Tables of results (algorithms (columns) vs datasets (rows)), where S = K rep- 
resents the number of learners, seed is the initializer to the random object2, and 
l is the length of the window. Important: you don’t need to report the time 
taken for this experiment (it would require yet another take). 
• First table (l = 1000 for all TheEnsemble variations): HoeffdingTree, 
TheEnsemble(S = 5), TheEnsemble(S = 10), TheEnsemble(S = 20), and 
TheEnsemble(S = 30); 
• Second table (S = 20 and l = 1000 for all): TheEnsemble(seed = 1), 
TheEnsemble(seed = 2), TheEnsemble(seed = 3), TheEnsemble(seed = 
1In MOA, time can be estimated by the CPU Time 
2The actual value of the random seed doesn’t matter much, as long as it is not the same for 
every experiment. 
7 
4), TheEnsemble(seed = 5) 
• Third table (S = 20 for all): TheEnsemble(l = 500), TheEnsemble(l = 
1000), TheEnsemble(l = 2000), TheEnsemble(l = 5000), TheEnsemble(l = 
10000) 
Questions: 
1. How does TheEnsemble compares against a single hoeffding tree? Also, 
does TheEnsemble improve results if more base models are available? 
Present a table with results varying from S = {5, 10, 20, 30}, where S is 
the total number of learners. 
2. (Groups of 2 Required) What is the impact of the randomization strategy 
on the diversity of the base learners? 1st repeat each experiment 5 times 
varying the random seed of the classifier and present a table with the 
average and standard deviation of each result. 2nd Present a plot, for 
each dataset, depicting the average Kappa statistic over time to support 
your claim. 
3. Is the ensemble able to recover from concept drifts? Present a plot de- 
picting the accuracy over time to support your claim. There should be a 
plot for each dataset comparing whichever version of TheEnsemble that 
produced the best results on the first table against the single hoeffding 
tree. 
4. What is the impact of the l hyperparameter? Discuss the results varying 
the l hyperparameter. 
5. (Optional) Which combination of l and S presents the best results for 
each dataset (on average). This will require another separate table where 
l and S are combined together. 
Important: To plot results, use a windowed evaluation. In MOA, Evalu- 
atePrequential with its default Evaluator should be sufficient. The code from 
the following repository can be used for the plots, but notice that any other tool 
or language can be used. 
https://github.com/hmgomes/data-stream-visualization 
3.2 Experiment 2: TheEnsemble vs. others 
Compare the best result of TheEnsemble, obtained in the previous experiment, 
against Adaptive Random Forest (ARF) [4], Leveraging Bagging (LB) [1] and 
the Dynamic Weighted Majority (DWM) [5] for each dataset. Use S = 20 for 
all the ensembles and subspacem = 60% for ARF. This will produce two tables 
with results: 
• First table: The accuracy for each ensemble and dataset. 
8 
• Second table: The processing time for each ensemble and dataset. 
Questions: 
1. How does TheEnsemble performs in terms of predictive performance 
against the others? 
2. In terms of time, is TheEnsemble more efficient than the other methods? 
3. (Optional) Create another table varying the number of learners in the 
other datasets as well. Then discuss which ensemble method scales bet- 
ter in terms of accuracy, i.e. produces better results as we increase the 
number of learners for the given datasets. 
4. (Optional) What design choice made affect the computational resources 
the most for TheEnsemble? For example, one design choice is how the 
algorithm update learners, how diversity is induced, etc. Notice that the 
value of l or S is not a design choice, it is an hyperparameter configura- 
tion. 
3.3 (Groups of 2 Required) Experiment 3: Feature engineering 
For the electricity dataset, present (at the least) 3 transformations of the stream 
to include EMA and SMA. Produce the results using the transformed datasets 
using a single Hoeffding Tree, TheEnsemble, ARF, LB, and DWM. Use the same 
configurations as in Experiment 2, for the ensembles. This will produce one 
table where columns are the ensembles and the rows are the versions of the 
dataset. 
Observation: Notice that the past values of the class labels are available for 
transformation and the creation of a feature that uses the previous class labels 
is possible. This is similar to the TemporallyAugmentedClassifier that we saw 
in class. 
Questions: 
1. Is it possible to improve the results without changing the ensemble con- 
figurations? In other words, just by transforming the data? 
2. Which transformations were the most effective? In other words, are there 
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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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