COMP90051 Statistical Machine Learning Project 2 Description
Due date: 4:00pm Thursday, 17th October 2019 Weight: 251
Multiarmed bandits MABs are a powerful tool in statistical machine learning: they bridge decision making, control, optimisation and learning; they address practical problems of sequential decision making while backed by elegant theoretical guarantees; they are relatively easily implemented, efficient to run, and are used in many industrial applications. They are neither fully supervised nor unsupervised, being partial supervised by indirect rewardsas a subroutine they employ supervised learning for predicting future rewards. Exploitation behaviour in MABs optimises shortterm rewards by acting greedily based on current knowledge; but this must be balanced against imprecision in knowledge by exploration; and when effectively balanced, MABs optimise for longterm cumulative reward. In this project, you will work individually not in teams to implement several MAB learners. Some will be directly from class, while others will be more advanced and come out of papers that you will have to read and understand yourself.
By the end of the project you should have developed
ILO1. A deeper understanding of the MAB setting and common MAB approaches, and an appreciation of how MABs are applied;
ILO2. Better understanding of how the Bayesian paradigm can support machine learning;
ILO3. Demonstrable ability to implement ML approaches in code; and
ILO4. An ability to pick up recent machine learning publications in the literature, understand their focus, contributions, and algorithms enough to be able to implement and apply them. And being able to ignore other presented details not needed for your task.
Overview
Through the 2000s Yahoo! Research led the way in applying MABs to problems in online advertising, informa tion retrieval, and media recommendation. One of their many applications was to Yahoo! News, in deciding what news items to recommend to users based on article content, user profile, and the historical engagement of the user with articles. Given decision making in this setting is sequentialwhat do we show nextand feedback is only available for articles shown, Yahoo! researchers observed a perfect formulation for MABs like those Greedy and UCB learned about in class. Going further, however, they realised that incorporating some element of userarticle state requires contextual bandits: articles are arms; context per round incorpo rates information about both user and article arm; and 0, 1valued rewards represent clicks. Therefore the per round cumulative reward represents clickthroughrate CTR which is exactly what services like Yahoo! News want to maximise to drive user engagement and advertising revenue. You will be implementing these approaches, noting that you need not necessarily complete the entire project.
Required Resources The LMS page for project 2 comprises
project2.pdf this spec;
proj2.ipynb Jupyter notebook: skeleton in Python; and
dataset.txt A textfile dataset see below for details. 1Forming a combined hurdle with project 1.
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You will implement code in Python Jupyter notebooks, which after running on your machine you will submit via LMS. Further detailed rules about what is expected with code are available towards the end of this spec. We appreciate that while some have entered COMP90051 with littleno prior Python experience, many workshops so far have exercised and built up basic Python and Jupyter knowledge.
Part 1: Implementing Greedy and UCB 3 marks total
Implement Python classes EpsGreedy and UCB for both Greedy and UCB learners as covered in class. You
should use inheritance: make your classes subclasses of the abstract MAB base class. Include components:All necessary properties for storing MAB state
init constructor methods for initialising MAB state with respective signatures:
def initself, narms, epsilon, Q0 for positive integer narms, floatingpoint probability
epsilon, realvalued Q0 taking by default numpy.inf; and
def initself, narms, rho, Q0 for positive integer narms, positive real rho, realvalued Q0
taking by default numpy.inf.
Additional methods where in your implementations context will go unused
def playself, tround, context for positive integer tround, and unused for now context. This should return an arm integer in 1, . . . , self.narms; and
def updateself, arm, reward, contextforpositiveintegerarmnolargerthanpropertyself.narms, floatingpoint reward, and unused for now context. This method should not return anything.
Tiebreaking in play should be completed uniformlyatrandom among valuemaximising arms. Part 2: The Basic Thompson Bandit 5 marks total
Your next task is to implement a third bandit learner, one that you havent seen in class. Thompson sampling is named after Thompson who discovered the idea in 1933 before the advent of machine learning, and went unnoticed by the machine learning community until relatively recently. It is now regarded as a leading MAB technique, that uses a Bayesian model of rewards. The simplest Thompson sampler models rewards in 0,1 as Bernoulli draws with different parameters per arm each starting with a common Beta prior.
In this part you are to implement a Python class for the BetaBernoulli Thompson MAB as described in Algorithm 1 Thompson Sampling for Bernoulli bandits from the paper:
Shipra Agrawal and Navin Goyal, Analysis of Thompson sampling for the multiarmed bandit problem, in Proceedings of the Conference on Learning Theory COLT 2012, 2012. http:proceedings.mlr.pressv23agrawal12agrawal12.pdf
While the COLT2012 Algorithm 1 only considers a uniform Beta1, 1 prior, you are to implement a more flexible Beta0, 0 prior for any given 0, 00. A point that may be missed on first reading, is that while each arm begins with the same prior, each arm updates its own posterior.
Your class BetaThompson should subclass abstract MAB base class with components similar to above:
All necessary properties for storing MAB state
Constructor for initialising MAB state with signature initself, narms, alpha0, beta0 for pos itive integer narms, positive reals alpha0, beta0 taking by default 1;
Additional methods where again context will go unused of play and update as above. Tiebreaking in play should again be completed uniformlyatrandom among valuemaximising arms.
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Part 3: OffPolicy Evaluation 3 marks total
A major practical challenge for industry deployments of MAB learners has been the requirement to let the learner loose on real data. Inevitably bandits begin with little knowledge about arm reward structure, and so a bandit must necessarily suffer poor rewards in beginning rounds. For a company trying out and evaluating dozens of bandits in their data science groups, this is potentially very expensive.
A breakthrough was made when it was realised that MABs can be evaluated offline or off policy. The idea being that you collect just once a dataset of uniformlyrandom arm pulls and resulting rewards. Then you evaluate any possible future bandit learner of interest on that one historical datathere is no need to run bandits online in order to evaluate them! In this part you are to implement a Python function for offlineoffpolicy evaluation.
You must implement the algorithm first described as Algorithm 3 Policy Evaluator from the paper:
Lihong Li, Wei Chu, John Langford, Robert E. Schapire, A ContextualBandit Approach to Per sonalized News Article Recommendation, in Proceedings of the Nineteenth International Conference on World Wide Web WWW 2010, Raleigh, NC, USA, 2010. https:arxiv.orgpdf1003.0146.pdf
You should begin by reading Section 4 of the WWW2010 paper which describes the algorithm. You may find it helpful to read the rest of the paper up to this point for background skipping Sec 3.2 as this also relates to Part 4. If you require further detail of the algorithm you may find the followup paper useful particularly Sec 3.1:
Lihong Li, Wei Chu, John Langford, and Xuanhui Wang. Unbiased offline evaluation of contextual banditbased news article recommendation algorithms. In Proceedings of the Fourth ACM Interna tional Conference on Web Search and Data Mining WSDM2011, pp. 297306. ACM, 2011. https:arxiv.orgpdf1003.5956.pdf
Note that what is not made clear in the pseudocode of Algorithm 3, is that after the bandit plays written as function or policyan arm that matches a given log, you should not only note down the reward as if the bandit really received this reward, but you should also update the bandit with the played arm a, reward ra, and later in the project the context x1, . . . , xK over the K arms. Bandits that do not make use of contextsuch as your Part 1 and 2 banditscan still take context as an argument even if unused.
A second point that is implied in the pseudocode, but may be missed, is that when asking the bandit to play an arm, the supplied round number should not be the current round in the log file, but instead the length of history recorded so far, plus one. That is, after playing a matching arm on the first logged event, a bandit may play different arms for events 2, 3, and 4, and on the 5th event may for a second time play a matching arm. For the function calls to play for each of events 2, 3, 4, 5 you would pass as the tround argument the value 2. You would then increment to 3 for tround from the 6th event.
Implement your function nominally outside any Python class with signature
def offlineEvaluatemab, arms, rewards, contexts, nroundsNone
for a MAB class object mab such as EpsGreedy, UCB, BetaThompson and the classes implemented in later project Parts, a numpy array arms of values in 1,,mab.narms, an array of scalar numeric rewards of the same length as arms, a numeric 2D array contexts with number of rows equal to the length of arms and number of columns equal to a positive multiple of mab.narms, a positive integer nrounds with default value None.
Here arms corresponds to the arms played by a uniformlyrandom policy recorded in a dataset of say M events. While rewards corresponds to the resulting observed M rewards. In the next Part we will consider contextual bandits, in which each arm may have a feature vector representing its statecontext and potentially
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factoring in the context of the user also. So that if each of the K arms have d features, each row of contexts will have these feature vectors flattened as the d features of arm 1 followed by the d features of arm 2, all the way up to arm K so that we have dK features a multiple of K.
Finally nrounds is the desired number of matching events we would like to evaluate bandit mab on. Once your function finds this many matching arm plays, it should stop and return the per round rewardsand not their sum as in the WWW2010 Algorithm 3. If it reaches the end of the logged dataset without reaching the required number or in the case of default None then it should return the per round rewards recorded.
Dataset: The LMS page for project 2 contains a 2 MB dataset.txt suitable for validating MAB implemen tations. You may download this file and familiarise yourself with its format:
10,000 lines i.e., rows corresponding to distinct site visits by usersevents in the language of this part;Each row comprises 102 spacedelimited columns of integers:
Column 1: The arm played by a uniformlyrandom policy out of 10 arms news articles;
Column 2: The reward received from the arm played1 if the user clicked 0 otherwise; and
Columns 3102: The 100dim flattened context: 10 features per arm incorporating the content of the article and its match with the visiting user, first the features for arm 1, then arm 2, etc. up to arm 10.
Your function should be able to run on this file where column 1 forms arms, column 2 forms rewards, and columns 3102 form contexts. On both classes youve implemented thus far. You should output the result of running
mabEpsGreedy10, 0.05
resultsEpsGreedyofflineEvaluatemab, arms, rewards, contexts, 800
printEpsGreedy average reward , np.meanresultsEpsGreedy
mabUCB10, 1.0
resultsUCBofflineEvaluatemab, arms, rewards, contexts, 800
printUCB average reward , np.meanresultsUCB
mabBetaThompson10, 1.0, 1.0
resultsBetaThompsonofflineEvaluatemab, arms, rewards, contexts, 800
printBetaThompson average reward , np.meanresultsBetaThompson
Part 4: Contextual BanditsLinUCB 5 marks total
In this part you are to implement a fourth MAB learner as a fourth Python class. This time you are to read up to and including Section 3.1 of the WWW2010 paper to understand and then implement the LinUCB learner with disjoint linear models Algorithm 1. This is a contextual banditlikely the first youve seenhowever its workings are a direct mashup of UCB and ridge regression both of which youve seen in class. Practicing reading and implementing papers is the best way to turbocharge your ML skills. Your class LinUCB should have methods
def initself, narms, ndims, alpha constructor for positive integer narms the number of arms, positive integer ndims the number of dimensions for each arms context, positive realvalued alpha a hyperparameter balancing explorationexploitation
def playself, tround, context as for your other classes. For positive integer tround, and context being a numeric array of length self.ndimsself.narms; and
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def updateself, arm, reward, context as for your other classes. For positive integer arm no larger than property self.narms, floatingpoint reward, and context as previous.
While the idea of understanding LinUCB enough to implement it correctly may seem daunting, the WWW2010 paper is written for a nonML audience and is complete in its description. The pseudocode is detailed. There is one unfortunate typo however: pg. 3, column. 2, line 3 of the linked arXiv version should read ca rather than ba. The pseudocode uses the latter correctly as shorthand for the former times the contexts.
Note also one piece of language you may not have encountered: design matrix means a feature matrix in the statistics literature.
After you have implemented your class, include and run an evaluation on the given dataset with
mabLinUCB10, 10, 1.0
resultsLinUCBofflineEvaluatemab, arms, rewards, contexts, 800
printLinUCB average reward , np.meanresultsLinUCB
Part 5: Contextual BanditsLinThompson 6 marks total
Just as LinUCB mashes up ridge regression with confidential intervals with UCB for the contextual MAB problem, we may plug Bayesian linear regression into the Thompson sampling framework to tackle contextual bandit learning. This idea is considered by the paper:
Shipra Agrawal and Navin Goyal, Thompson sampling for contextual bandits with linear payoffs, in Proc. International Conference on Machine Learning ICML 2013, pp. 127135. 2013. http:proceedings.mlr.pressv28agrawal13.pdf
In this Part you are to implement as a fifth MAB class LinThompson, the ICML2013 Algorithm 1 described in Section 2.2. Your class LinThompson should have methods
def initself, narms, ndims, v constructor for positive integer narms the number of arms, positive integer ndims the number of dimensions for each arms context, v a hyperparameter controlling exploration vs. exploitation;
def playself, tround, context as for your other classes. For positive integer tround, and context being a numeric array of length self.ndimsself.narms; and
def updateself, arm, reward, context as for your other classes. For positive integer arm no larger than property self.narms, floatingpoint reward, and context as previous.
While the ICML2013 intro does reintroduce the Thompson sampling framework explored in Part 2, it can be very much skimmed. Section 2.1 introduces the setting formallyinformation on regret and the assumptions2 are not important for simple experimentation. Section 2.2 and Algorithm 1 Thompson Sampling for Contextual bandits is the key place to find the described algorithm to be implemented. Note that the first 14 lines of Section 2.3 explains the hyperparameters ,further: the latter controls our confidence of regret being provably low and so we might imagine taking it to be 0.05 as in typical confidence intervals; while advice is given for settingwhen you know the total number of rounds to be played. All that said, R,, only feature in the expression v, while we wouldnt really know R. And so you should just use v as a hyperparameter to control exploration balance as you have in the previous part with .
After you have implemented your class, include and run an evaluation on the given dataset with
2SubGaussianity is a generalisation of Normally distributed rewards. I.e., while they dont assume the rewards are Normal, they assume something Normallike in order to obtain theoretical guarantees. R takes the role of 1 and controls how fast likelihood of extreme rewards decays. You can ignore all thisphew!
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mabLinThompson10, 10, 1.0
resultsLinThompsonofflineEvaluatemab, arms, rewards, contexts, 800
printLinThompson average reward , np.meanresultsLinThompson
Part 6: Evaluation 3 marks total
In this part you are to delve deeper into the performance of your implemented bandit learners. This parts first subpart does not necessarily require completion of Parts 4 and 5.
Part 6a 1 marks: Run offlineEvaluate on each of your Python classes just with hyperparameters as when you ran offlineEvaluate above. This time plot the running perround cumulative reward i.e. T 1 Tt1 rt,a for T1..800 as a function of round T , all on one overlayed plot. Your plot will have up to 5 curves, clearly labelled.
Part 6b 2 marks: How can you optimise hyperparameters? Devise gridsearch based strategies to select theand v hyperparameters in LinUCB and LinThompson, as Python code in your notebook. Output the result of this strategywhich could be a graph, number, etc.
Project Submission
Preserving its structure, you must 1 rename proj2.ipynb as username.ipynb using your username3 2 flesh out with your project solutions with cells, 3 run on your local machine prior to submission so that outputs and plots are preserved you are strongly recommended to open your notebook again prior to upload to double check. We may not run your notebook; given your environment might subtly differ to ours, it is your responsibility to ensure results are contained, and then 4 submit in LMS.
Marks: graders will perform code reviews of your implementations. In general a portion will be available for correctness, a portion will be available for code structure and style primarily the former. Code should have necessary commenting to understand interfaces, and major points of inner working, basic checks of wellformed input, clear variable names and readable statements.
Further Rules. You may discuss the bandit learning deck or Python at a highlevel with others, but do not collaborate on solutions. You may consult resources to understand bandits conceptually, but do not make any use of online code whatsoever. We will run code comparisons against online partial implementations to enforce these rules. You must use the environment Anaconda3, Python 3.6 or higher as used in labs. You may use your own machine of course, but we strongly recommend you check code operation on lab machines prior to submission. In case we run code. You may only use the packages already imported in the provided proj2.ipynb notebook. You should use matplotlib for plotting. Late submissions will be accepted to 4 days with 3 penalty per day.
3LMSUniMelb usernames look like brubinstein, not to be confused with email such as benjamin.rubinstein. 6
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