RData computing Maximum Entropy summary Trees Now we load the summarytrees package and compute maximum entropy summary trees using the greedy algorithm for this data for (k 1, 2,., k 100). Note that these arent technically maximum entropy summary trees since the greedy algorithm does not have any performance guarantees we use it here because its much faster than the exact algorithm, and based on several experiments with real data, returns summary trees whose entropies are. Library(summarytrees) loading required package: rjsonio loading required package: rcolorBrewer loading required package: servr data(dmoz) look at the data a bit: dim(dmoz) dmoz1:10, node parent weight label Adventure_Racing Clubs 3 3 1 6 Equipment_Suppliers . Leaves: table(dmoz, "weight" 0) false true compute a set of K summary trees: t1 - sys. Time k - 100 g - greedy(node dmoz, "node parent dmoz, "parent weight dmoz, "weight label dmoz, "label k k) 1 "Running des function to prepare data" 1 " Computing node levels" 1 " Computing child indices for each parent" . Time t2 - t1 time difference.022814 secs we can look at the (k 20)-node summary tree, for example, by typing ees20 node parent weight type label top Sports baseball Equestrian football golf hockey . Height 14, units of urls print.
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Table) length(btree) There are 14,284 unique topics in the top/Sports subtree sum(btree) There are 76,535 total urls classified to this subtree separate into names weights: de - names(btree) full node names with slashes weight - btree) weights, now we have. The only complication here is that some nodes do not currently have their parent node listed in the data, because the parent node is an internal node to which zero urls are directly assigned. The following block of code starts with the nodes of the tree that have nonzero weights, looks for their parents, and if any of their parents are not in the tree already, they are added to the tree. initialize variables parent - null the vector of all parents ded - null how many new internal nodes added each resume iteration iter - 0 count iterations (just out of curiosity). Split - de the set of full names to split rent - rep(na, length(weight) parents of each round of nodes t1 - sys. Time while (sum(rent) 0) iter - iter 1 print(iter) split by slash and extract the leaf label of each node, and the 'stem' de - strsplit(to. Split, label - sapply(de, function(x) tail(x, 1) stem - sapply(de, function(x) paste(x-length(x collapse compute the parent of each node: rent - match(stem, de) if rent is na, then we have to add an internal node get unique internal nodes that must. Split - des add to the vector of parents parent - c(parent, match(stem, de) t2 - sys. Time t2 - t1, now we compute the labels, we assemble the nodes, parents, weights, and labels in a data frame, and we clean up one pseudo-node that was computed as the parent of the root. Label - sapply(strsplit(de, function(x) tail(x, 1) There should be one that is the 'parent' of the root, which is an empty node give it a label of na labelsapply(label, length) 0 - na label - unlist(label) Pull it all into a ame.
Next, we use the command line to extract just the assigned topic inventory of each of the approximately.77 million urls and write them, one per line, to a file called dmoz-topics. Grep " topic " content. Rdf.u8 sed 's/ topic /g' sed 's/ /topic /g' sed 's/ /g' dmoz-topics. Next, we read this list of topics into r and compute their frequencies. Note this chunk is not computed here, because the file is too large to include with the summarytrees R package. Raw - readLines dmoz-topics. Txt compute the frequency of each unique topic: dmoz. Table - table(raw) length(dmoz. Table) There are 595,005 unique topics with at least one url retain only those nodes that fall under "Top/Sports btree - dmoz.
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Add possibility to iterate over the whole tree in one. Use multi_index internally for indexing. Make get/put functions freestanding. Add more docs on customizing key/data types. Improve docs grammar/style in places. Break large index into smaller files. Additionally there were proposals to use runtime polymorphism internships to distinguish between leaf and branch nodes. I think this idea strays so far from current implementation/interface of the library, that it is not feasible.
bool parameter to put with enum to improve readability. Del will erase existing keys from the tree, while presenting get/put/set style interface. This is clearly missing, although not immediately obvious. Add support for array-style indexing in paths. This will probably work well with 1. Add support for serialization of ptree objects.
It also boosts performance (no more string operations on path and imho will generally improve interface of the library. Most importantly, it largely does maintain compatibility with resume existing interface. Split Traits into smaller policy classes. For example keypolicy and. Indeed, these two are mostly orthogonal, and it should be possible to replace one without touching the other. Allow use of other data types as keys/paths (for example a vector of ints). Initial support is in the library, but key is still required to support string-like interface. Add possibility to disable indexed lookup, if not required. Add possibility to specify names of "special" keys in parsers ( xmlattr, xmlcomment etc.).
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