diff -r f7bcb27d1940 -r eb188f9ac038 cws/cw02.tex --- a/cws/cw02.tex Thu Nov 15 03:35:38 2018 +0000 +++ b/cws/cw02.tex Thu Nov 15 14:23:55 2018 +0000 @@ -6,12 +6,12 @@ \begin{document} -\section*{Coursework 7 (DocDiff and Danube.org)} +\section*{Coursework 7 (Scala)} -This coursework is worth 10\%. The first part and second part are due +This coursework is worth 10\%. The first and second part are due on 22 November at 11pm; the third, more advanced part, is due on 21 December at 11pm. You are asked to implement Scala programs for -measuring similarity in texts and for recommending movies +measuring similarity in texts, and for recommending movies according to a ratings list. Note the second part might include material you have not yet seen in the first two lectures. \bigskip @@ -36,7 +36,7 @@ any test cases on your own computer. For example you can call Scala on the command line with the option \texttt{-cp docdiff.jar} and then query any function from the template file. Say you want to find out -what the function \texttt{occurences} produces: for this you just need +what the function \texttt{occurrences} produces: for this you just need to prefix it with the object name \texttt{CW7a} (and \texttt{CW7b} respectively for \texttt{danube.jar}). If you want to find out what these functions produce for the list \texttt{List("a", "b", "b")}, @@ -45,76 +45,101 @@ \begin{lstlisting}[language={},numbers=none,basicstyle=\ttfamily\small] $ scala -cp docdiff.jar -scala> CW7a.occurences(List("a", "b", "b")) +scala> CW7a.occurrences(List("a", "b", "b")) ... \end{lstlisting}%$ \subsection*{Hints} +\noindent +\textbf{For Part 1:} useful operations involving regular +expressions: +\[\texttt{reg.findAllIn(s).toList}\] +\noindent finds all +substrings in \texttt{s} according to a regular regular expression +\texttt{reg}; useful list operations: \texttt{.distinct} +removing duplicates from a list, \texttt{.count} counts the number of +elements in a list that satisfy some condition, \texttt{.toMap} +transfers a list of pairs into a Map, \texttt{.sum} adds up a list of +integers, \texttt{.max} calculates the maximum of a list.\bigskip +\noindent +\textbf{For Part 2 + 3:} use \texttt{.split(",").toList} for splitting +strings according to commas (similarly $\backslash$\texttt{n}), +\texttt{.getOrElse(..,..)} allows to querry a Map, but also gives a +default value if the Map is not defined, a Map can be `updated' by +using \texttt{+}, \texttt{.contains} and \texttt{.filter} can test whether +an element is included in a list, and respectively filter out elements in a list, +\texttt{.sortBy(\_.\_2)} sorts a list of pairs according to the second +elements in the pairs---the sorting is done from smallest to highest, +\texttt{.take(n)} for taking some elements in a list (takes fewer if the list +contains less than \texttt{n} elements). \newpage \subsection*{Part 1 (4 Marks, file docdiff.scala)} -It seems source code plagiarism---stealing someone else's code---is a -serious problem at other universities.\footnote{Surely, King's - students, after all their instructions and warnings, would never - commit such an offence.} Dedecting such plagiarism is time-consuming -and disheartening. To aid the poor lecturers at other universities, -let's implement a program that determines the similarity between two -documents (be they code or English texts). A document will be -represented as a list of strings. +It seems source code plagiarism---stealing and submitting someone +else's code---is a serious problem at other +universities.\footnote{Surely, King's students, after all their + instructions and warnings, would never commit such an offence. Yes?} +Detecting such plagiarism is time-consuming and disheartening for +lecturers at those universities. To aid these poor souls, let's +implement in this part a program that determines the similarity +between two documents (be they source code or texts in English). A +document will be represented as a list of strings. \subsection*{Tasks} \begin{itemize} -\item[(1)] Implement a function that cleans a string by finding all - words in this string. For this use the regular expression - \texttt{"$\backslash$w+"} and the library function - \texttt{findAllIn}. The function should return a list of - strings.\\ +\item[(1)] Implement a function that `cleans' a string by finding all + (proper) words in this string. For this use the regular expression + \texttt{$\backslash$w+} for recognising word characters and the + library function \texttt{findAllIn}. The function should return a + document (a list of + strings).\\ \mbox{}\hfill [1 Mark] -\item[(2)] In order to compute the similarity between two documents, we +\item[(2)] In order to compute the overlap between two documents, we associate each document with a \texttt{Map}. This Map represents the - strings in a document and how many times these strings occur in a + strings in a document and how many times these strings occur in the document. A simple (though slightly inefficient) method for counting - the number of string-occurences in a document is as follows: remove + the number of string-occurrences in a document is as follows: remove all duplicates from the document; for each of these (unique) strings, count how many times they occur in the original document. - Return a Map from strings to occurences. For example + Return a Map associating strings with occurrences. For example \begin{center} - \pcode{occurences(List("a", "b", "b", "c", "d"))} + \pcode{occurrences(List("a", "b", "b", "c", "d"))} \end{center} produces \pcode{Map(a -> 1, b -> 2, c -> 1, d -> 1)} and \begin{center} - \pcode{occurences(List("d", "b", "d", "b", "d"))} + \pcode{occurrences(List("d", "b", "d", "b", "d"))} \end{center} produces \pcode{Map(d -> 3, b -> 2)}.\hfill[1 Mark] -\item[(3)] You can think of the Maps calculated under (2) as efficient +\item[(3)] You can think of the Maps calculated under (2) as memory-efficient representations of sparse ``vectors''. In this subtask you need to - implement the \emph{product} of two vectors, sometimes also called - \emph{dot product}.\footnote{\url{https://en.wikipedia.org/wiki/Dot_product}} + implement the \emph{product} of two such vectors, sometimes also called + \emph{dot product} of two vectors.\footnote{\url{https://en.wikipedia.org/wiki/Dot_product}} - For this implement a function that takes two documents + For this dot product, implement a function that takes two documents (\texttt{List[String]}) as arguments. The function first calculates the (unique) strings in both. For each string, it multiplies the - occurences in each document. If a string does not occur in one of the - documents, then the product is zero. At the end you - sum all products. For the two documents in (2) the dot product is 7: + corresponding occurrences in each document. If a string does not + occur in one of the documents, then the product for this string is zero. At the end + you need to add up all products. For the two documents in (2) the dot + product is 7, because \[ \underbrace{1 * 0}_{"a"} \;\;+\;\; \underbrace{2 * 2}_{"b"} \;\;+\;\; \underbrace{1 * 0}_{"c"} \;\;+\;\; - \underbrace{1 * 3}_{"d"} + \underbrace{1 * 3}_{"d"} \qquad = 7 \] \hfill\mbox{[1 Mark]} @@ -126,34 +151,134 @@ \texttt{overlap}(d_1, d_2) = \frac{d_1 \cdot d_2}{max(d_1^2, d_2^2)} \] - This function should return a \texttt{Double} between 0 and 1. The + You can expect this function to return a \texttt{Double} between 0 and 1. The overlap between the lists in (2) is $0.5384615384615384$. - Second implement a function that calculates the similarity of - two strings, by first extracting the strings using the function from (1) - and then calculating the overlap. - \hfill\mbox{[1 Mark]} -\end{itemize} + Second, implement a function that calculates the similarity of + two strings, by first extracting the substrings using the clean + function from (1) + and then calculating the overlap of the resulting documents.\\ + \mbox{}\hfill\mbox{[1 Mark]} +\end{itemize}\bigskip + + +\subsection*{Part 2 (2 Marks, file danube.scala)} + +You are creating Danube.co.uk which you hope will be the next big thing +in online movie renting. You know that you can save money by +anticipating what movies people will rent; you will pass these savings +on to your users by offering a discount if they rent movies that +Danube.co.uk recommends. +Your task is to generate \emph{two} movie recommendations for every +movie a user rents. To do this, you calculate what other +renters, who also watched this movie, suggest by giving positive ratings. +Of course, some suggestions are more popular than others. You need to find +the two most-frequently suggested movies. Return fewer recommendations, +if there are fewer movies suggested. + +The calculations will be based on the small datasets which the research lab +GroupLens provides for education and development purposes. + +\begin{center} +\url{https://grouplens.org/datasets/movielens/} +\end{center} + +\noindent +The slightly adapted CSV-files should be downloaded in your Scala +file from the URLs: -\newpage -You are creating Danube.org, which you hope will be the next big thing -in online movie provider. You know that you can save money by -anticipating what movies people will rent; you will pass these savings -on to your users by offering a discount if they rent movies that Danube.org -recommends. This assignment is meant to calculate +\begin{center} +\begin{tabular}{ll} + \url{https://nms.kcl.ac.uk/christian.urban/ratings.csv} & (940 KByte)\\ + \url{https://nms.kcl.ac.uk/christian.urban/movies.csv} & (280 KByte)\\ +\end{tabular} +\end{center} + +\noindent +The ratings.csv file is organised as userID, +movieID, and rating (which is between 0 and 5, with \emph{positive} ratings +being 4 and 5). The file movie.csv is organised as +movieID and full movie name. Both files still contain the usual +CSV-file header (first line). In this part you are asked +to implement functions that process these files. If bandwidth +is an issue for you, download the files locally, but in the submitted +version use \texttt{Source.fromURL} instead of \texttt{Source.fromFile}. + +\subsection*{Tasks} +\begin{itemize} +\item[(1)] Implement the function \pcode{get_csv_url} which takes an + URL-string as argument and requests the corresponding file. The two + URLs of interest are \pcode{ratings_url} and \pcode{movies_url}, + which correspond to CSV-files mentioned above. The function should + return the CSV-file appropriately broken up into lines, and the + first line should be dropped (that is omit the header of the CSV-file). + The result is a list of strings (the lines in the file). In case + the url does not produce a file, return the empty list.\\ + \mbox{}\hfill [1 Mark] + +\item[(2)] Implement two functions that process the (broken up) + CSV-files from (1). The \pcode{process_ratings} function filters out all + ratings below 4 and returns a list of (userID, movieID) pairs. The + \pcode{process_movies} function returns a list of (movieID, title) pairs.\\ + \mbox{}\hfill [1 Mark] +\end{itemize} + + +\subsection*{Part 3 (4 Marks, file danube.scala)} + +\subsection*{Tasks} -To do this, you offer an incentive for people to upload their lists of -recommended books. From their lists, you can establish suggested -pairs. A pair of books is a suggested pair if both books appear on one -person’s recommendation list. Of course, some suggested pairs are more -popular than others. Also, any given book is paired with some books -much more frequently than with others. +\begin{itemize} +\item[(3)] Implement a kind of grouping function that calculates a Map + containing the userIDs and all the corresponding recommendations for + this user (list of movieIDs). This should be implemented in a tail + recursive fashion using a Map as accumulator. This Map is set to + \pcode{Map()} at the beginning of the calculation. For example + +\begin{lstlisting}[numbers=none] +val lst = List(("1", "a"), ("1", "b"), + ("2", "x"), ("3", "a"), + ("2", "y"), ("3", "c")) +groupById(lst, Map()) +\end{lstlisting} + +returns the ratings map + +\begin{center} + \pcode{Map(1 -> List(b, a), 2 -> List(y, x), 3 -> List(c, a))}. +\end{center} + +\noindent +In which order the elements of the list are given is unimportant.\\ +\mbox{}\hfill [1 Mark] +\item[(4)] Implement a function that takes a ratings map and a movieID + as argument. The function calculates all suggestions containing the + agiven movie in its recommendations. It returns a list of all these + recommendations (each of them is a list and needs to have the given + movie deleted, otherwise it might happen we recommend the same movie + ``back''). For example for the Map from above and the movie + \pcode{"y"} we obtain \pcode{List(List("x"))}, and for the movie + \pcode{"a"} we get \pcode{List(List("b"), List("c"))}.\\ + \mbox{}\hfill [1 Mark] +\item[(5)] Implement a suggestions function which takes a ratings map + and a movieID as arguments. It calculates all the recommended movies + sorted according to the most frequently suggested movie(s) sorted + first. This function returns \emph{all} suggested movieIDs as a list of + strings.\\ + \mbox{}\hfill [1 Mark] +\item[(6)] + Implement then a recommendation function which generates a maximum + of two most-suggested movies (as calculated above). But it returns + the actual movie name, not the movieID. If fewer movies are recommended, + then return fewer than two movie names.\\ + \mbox{}\hfill [1 Mark] +\end{itemize} \end{document}