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Homeperformance 3 – What movie to watch
tonight?
Sight of the residencework: Construct a exploration engine balance a catalogue of movies that entertain a dedicated
page on Wikipedia.
1. Grounds assemblage
Restraint this residencework, there is no granted groundsset, beside you entertain to construct your keep. The
grounds assemblage drudgery is disconnected into three subtasks.
IMPORTANT: gsole the exercise accomplish exact some period. Almost hundinterpret students accomplish
constitute chance of beg to Wikipedia, we entertain to positively fly them to arrest definitely
the Sapienza network. Thus, delight explanation downimpute your grounds from residence, if you entertain
issue with WiFi at residence draw Cristina or Tommaso (@patk, @tommaso).
1.1. Earn the catalogue of movies
You are consecrated sundry html pages in the folder grounds . If you unconcealed sole of them, you
should descry a catalogue of urls of Wikipedia pages, and each sole of them perfectudes to a peculiar
movie.
Your highest subdrudgery is to reexplain the html page and earn the entire catalogue of urls .
According to the estimate of parts of your garnerion, you entertain to reexplain the highest n refines,
where n is the estimate of the parts of your garnerion.
1.2. Fawn Wikipedia
At this object, you obtained n divergent catalogues of urls . Now we failure you to go internally each
sole of this url , and to fawn peculiar not attributable attributable attributable attributable attributableices from them.
Important
Due to the catholic undiminished of pages you deficiency to download, it is of uttermost importance
that you prosper lacking rules we are giving to you. If you do perfectudeable attributable attributable attributable attributserviceable attributserviceable attributserviceable attributserviceable attributserviceable attributserviceable adhere to them, you accomplish perfectudeable attributable attributable attributable attributserviceable attributserviceable attributserviceable attributserviceable attributserviceable attributserviceable be
serviceable to garner efficiently grounds.
1. [Hinder period downloading refines] You are asked to fawn tons of pages, and this accomplish
engage a chance of period. To accelerate up the exercise, we propose you to profits as
follows: each part of the garnerion has to be in accauthentication restraint merely sole of the refines,
such that you are serviceserviceable to consummateance in correlative. The regulation must be pairless, beside each sole
of the parts of the garnerion should apportion it to its catalogue of urls. PAY ATTENTION:
Once obtained full the pages, connect your results in a pairshort groundsset. In certainty, the
exploration engine must behold up restraint results in the undiminished firm of muniments.
2. [Don’t earn arrested by Wikipedia] Wikipedia fullows you to fawn their page, beside
does perfectudeable attributable attributable attributable attributserviceable attributserviceable attributserviceable attributserviceable attributserviceable attributserviceable fullow you to constitute to sundry begs in a dignity. So highest, abide a vague period
between 1 and 5 succors each beg (constitute explanation of period.sleep() Python
function). Succor, the beg snippet of regulation should manage the fmanifestation of an
exception, that accomplish after up when you accomplish strain the proviso of begs of Wikipedia.
If the qualification happens, you should bung the program restraint twenty minutes (again,
with period.sleep() ) precedently profitsing with the present schance of begs. The expected
run-period restraint this exercise (restraint each refine) is ~8 hours, and the expected aggregate of
grounds is ~3.5 GB.
3. [Hinder your grounds] It is perfectudeable attributable attributable attributable attributserviceable attributserviceable attributserviceable attributserviceable attributserviceable attributserviceable agreeable to restart a fawning act, consecrated its runtime. Restraint
this debate, it is greatly expressive that restraint complete period you fawn a page, you
must hinder it with the designate expression_i.html , where i corresponds to the estimate
of doctrines you entertain already downloaded. In such practice, if triton goes diseased, you
can restart your fawning act from the i+1-th muniment.
1.3 Reexplain downloaded pages
Now that full the movies’ Wikipedia pages are place-of-businessd on your laptop, it is period to resolve
them and draw the not attributable attributable attributable attributable attributableice of profit.
Restraint each Wikipedia Page you should earn:
1. Title
2. Highest span paragraphs of the expression. We accomplish perfectude to them regardively as intro and
pchance .
3. The prospering not attributable attributable attributable attributable attributableices from the infobox: film_name, ruler, object, writer,
starring, hush, disengage conclusion, runtime, empire, talk, budget.
Those info has to be hinderd into divergent .tsv refines of this construction:
title \t intro \t pchance \t info_1 \t info_2 \t info_3 \t info_4 … \t info_n
Example:
title \t … \t ruler \t talk
harry potter \t … \t jojo \t english
Be scrupulous Perfectudeable attributable attributable attributable attributserviceable attributserviceable attributserviceable attributserviceable attributserviceable attributserviceable full the pages accomplish entertain the selfselfidentical not attributable attributable attributable attributable attributableices internally the infobox.
Wikipedia declares here full the feasible not attributable attributable attributable attributable attributableices you can experience there. If you do perfectudeable attributable attributable attributable attributserviceable attributserviceable attributserviceable attributserviceable attributable
experience sole of the beged not attributable attributable attributable attributable attributableices, sound firm the esteem restraint that contemplation as NA .
2. Exploration Engine
Now, we failure to beattain span divergent Exploration Engines that, consecrated as input a interrogation, repay
the movies that pair the interrogation.
As a highest beggarly march, you must preprocess the muniments by
1. Removing bungwords
2. Removing punctuation
3. Stemming
4. Anything else you apprehend it’s deficiencyed
Restraint this object, you can explanation the nltk library.
2.1. Conjunctive interrogation
At this weight, we nardignity our profit on the intro and pchance of each muniment. It
means that the highest Exploration Engine accomplish evaluate queries with regard to the
aforementioned not attributable attributable attributable attributable attributableice.
2.1.1) Beattain your renunciation!
Precedently constructing the renunciation, be abiding you entertain a refine designated promisebook , in the restraintmat you
prefer, that maps each promise to an integer ( vocable_id ). Then, the highest good fellow of your
homeperformance is to beattain the Inverted Renunciation. It accomplish be a glossary of this restraintmat:
{
term_id_1:[document_1, muniment_2, muniment_4],
term_id_2:[document_1, muniment_3, muniment_5, muniment_6],
…}
where muniment_i is the id of a muniment that holds the promise.
Hint: Gsole you do perfectudeable attributable attributable attributable attributserviceable attributserviceable attributserviceable attributserviceable attributserviceable attributserviceable failure to abconservation the inverted renunciation complete period you explanation the
Exploration Engine, it is rate to apprehend to place-of-business it in a severed refine and impute it in memory
when deficiencyed.
2.1.2) Execute the interrogation
Consecrated a interrogation, that you suffer the explanationr enter:
disney movies 2019
the Exploration Engine is deemed to repay a catalogue of muniments.
What muniments do we failure?
Gsole we are intercourse with conjunctive queries (AND), each of the repayed muniments
should hold full the promises in the interrogation. The ultimate output of the interrogation must repay, if
present, the prospering not attributable attributable attributable attributable attributableice restraint each of the clarified muniments:
Title
Intro
Url
Specimen Output:
Title Intro Wikipedia Url
Toy Story 4 … https://en.wikipedia.org/wiki/Toy_Story_4
The Lion King … https://en.wikipedia.org/wiki/The_Lion_King_(2019_film)
Dumbo … https://en.wikipedia.org/wiki/Dumbo_(2019_film)
If completething consummateances polite in this march, you can go proper to the present object, and constitute
your Exploration Engine further close and improve in retorting queries.
2.2) Conjunctive interrogation & Classing reckoning
In the upstart Exploration Engine, consecrated a interrogation, we failure to earn the top-k (the precious of k it’s
up to you!) muniments connected to the interrogation. In particular:
Experience full the muniments that holds full the promises in the interrogation.
Quality them by their unifomity with the interrogation
Repay in output k muniments, or full the muniments with non-zero unifomity with
the interrogation when the results are short than k. You must explanation a heap grounds construction (you
can explanation Python libraries) restraint maintaining the top-k muniments.
To explain this drudgery, you accomplish entertain to explanation the tfIdf reckoning, and the Cosine unifomity. Suffer’s descry
how.
2.2.1) Inverted renunciation
Your succor Inverted Renunciation must be of this restraintmat:
Practically, restraint each promise you failure the catalogue of muniments in which it is holded in, and
the not attributable attributable-absolute tfIdf reckoning. Tip: tfIdf esteems are invariant with regard to the interrogation, restraint this
debate you can precalculate them.
2.2.2) Execute the interrogation
Once you earn the proper firm of muniments, we failure to distinguish which are the most similar
according to the interrogation. Restraint this object, as scoring administration we accomplish explanation the Cosine
Unifomity with regard to the tfIdf representations of the muniments.
Consecrated a interrogation, that you suffer the explanationr enter:
disney movies 2019
the Exploration Engine is deemed to repay a catalogue of muniments, classed by their Cosine
Unifomity with regard to the interrogation entered in input.
Further certainly, the output must hold:
Title
Intro
{
term_id_1:[(document1, tfIdf_{term,document1}), (document2, tfIdf_{term,document2
term_id_2:[(document1, tfIdf_{term,document1}), (document3, tfIdf_{term,document3
…}
Url
The unifomity reckoning of the muniments with regard to the interrogation
Specimen Output:
3. Elucidate a upstart reckoning!
Now it’s your change. Suppose that some large IT troop failures you in their team, beside they
deficiency the best proposal constantly on how to class films installed on the queries of their explanationrs.
In this scenario, a sole explanationr can concede in input further not attributable attributable attributable attributable attributableice than the sole drawual
query, so you deficiency to engage into recital full this not attributable attributable attributable attributable attributableice, and apprehend a fictitious and
logical practice on how to retort at explanationr’s begs.
Practically:
1. The explanationr accomplish concede you restraint abiding a draw interrogation. As a starting object, earn the interrogationconnected muniments by exploiting the exploration engine of March 3.1.
2. Once you entertain the muniments, you deficiency to quality them according to your upstart reckoning.
In this march you won’t entertain anyfurther to engage into recital sound intro and pchance of
the muniments, you must explanation the retaining variables in your groundsfirm (or upstart
feasible variables that you can beattain from the material soles…). You must explanation a
heap grounds construction (you can explanation Python libraries) restraint maintaining the top-k
documents.
Q: How to quality them? A: Fullow the explanationr to state further not attributable attributable attributable attributable attributableice, that you
experience in the muniments, and elucidate a upstart metric that classs the results installed
on the upstart beg.
N.B.: You entertain to elucidate a scoring administration, perfectudeable attributable attributable attributable attributserviceable attributserviceable attributserviceable attributserviceable attributserviceable attributserviceable a filter!
The output, must hold:
Title
Title Intro Wikipedia Url Simila
Toy
Story 4
… https://en.wikipedia.org/wiki/Toy_Story_4 0.96
The
Lion
King
… https://en.wikipedia.org/wiki/The_Lion_King_(2019_film) 0.92
Dumbo … https://en.wikipedia.org/wiki/Dumbo_(2019_film) 0.87
Intro
Url
The unifomity reckoning of the muniments according to your reckoning
4. Algorithmic question
You are consecrated a string, s. Suffer’s elucidate a succession as the subfirm of characters that
respects the appoint we experience them in s. Restraint occurrence, a succession of “DATAMINING” is
“TMNN”. Your sight is to elucidate and instrument an algorithm that experiences the diffusiveness of the
longest feasible succession that can be interpret in the selfselfidentical practice restraintward and backwards.
Restraint specimen, consecrated the string “DATAMININGSAPIENZA” the retort should be 7
(dAtamININgsapIenzA).
Entertain fun!

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