I am trying to build a deep neural networks that takes in a set of documents and predicts the category it belongs.
Since number of documents in each collection is not fixed, my first attempt was to get a mapping of documents from doc2vec and use the average.
The accuracy on training is high as 90% but the testing accuracy is low as 60%.
Is there a better way of representing a collection of documents as a fixed length vector so that the words they have in common are captured?
The description of your process so far is a bit vague and unclear – you may want to add more detail to your question.
Typically, Doc2Vec would convert each doc to a vector, not "a collection of documents".
If you did try to collapse a collection into a single vector – for example, by averaging many doc-vecs, or calculating a vector for a synthetic document with all the sub-documents' words – you might be losing valuable higher-dimensional structure.
To "predict the category" would be a typical "classification" problem, and with a bunch of documents (represented by their per-doc vectors) and known-labels, you could try various kinds of classifiers.
I suspect from your description, that you may just be collapsing a category to a single vector, then classifying new documents by checking which existing category-vector they're closest-to. That can work – it's vaguely a K-Nearest-Neighbors approach, but with every category reduced to one summary vector rather than the full set of known examples, and each classification being made by looking at just one single nearest-neighbor. That forces a simplicity on the process that may not match the "shapes" of the real categories as well as a true KNN classifier, or other classifiers, could achieve.
If accuracy on test data falls far below that observed during training, that can indicate that significant "overfitting" is occurring: the model(s) are essentially memorizing idiosyncrasies of the training data to "cheat" at answers based on arbitrary correlations, rather than learning generalizable rules. Making your model(s) smaller – such as by decreasing the dimensionality of your doc-vectors – may help in such situations, by giving the model less extra state in which to remember peculiarities of the training data. More data can also help - as the "noise" in more numerous varied examples tends of cancel itself out, rather than achieve the sort of misguided importance that can be learned in smaller datasets.
There are other ways to convert a variable-length text into a fixed-length vector, including many based on deeper learning algorithms. But, those can be even more training-data-hungry, and it seems like you may have other factors to improve before trying those in-lieu-of Doc2Vec.
I'm tackling an interesting machine learning problem and would love to hear if anyone knows a good algorithm to deal with the following:
The algorithm must learn to approximate a function of N inputs and M outputs
N is quite large, e.g. 1,000-10,000
M is quite small, e.g. 5-10
All inputs and outputs are floating point values, could be positive or negative, likely to be relatively small in absolute value but no absolute guarantees on bounds
Each time period I get N inputs and need to predict the M outputs, at the end of the time period the actual values for the M outputs are provided (i.e. this is a supervised learning situation where learning needs to take place online)
The underlying function is non-linear, but not too nasty (e.g. I expect it will be smooth and continuous over most of the input space)
There will be a small amount of noise in the function, but signal/noise is likely to be good - I expect the N inputs will expain 95%+ of the output values
The underlying function is slowly changing over time - unlikely to change drastically in a single time period but is likely to shift slightly over the 1000s of time periods range
There is no hidden state to worry about (other than the changing function), i.e. all the information required is in the N inputs
I'm currently thinking some kind of back-propagation neural network with lots of hidden nodes might work - but is that really the best approach for this situation and will it handle the changing function?
With your number of inputs and outputs, I'd also go for a neural network, it should do a good approximation. The slight change is good for a back-propagation technique, it should not have to 'de-learn' stuff.
I think stochastic gradient descent (http://en.wikipedia.org/wiki/Stochastic_gradient_descent) would be a straight forward first step, it will probably work nicely given the operating conditions you have.
I'd also go for an ANN. Single layer might do fine since your input space is large. You might wanna give it a shot before adding a lot of hidden layers.
#mikera What is it going to be used for? Is it an assignment in a ML course?
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There are some data structures around that are really useful but are unknown to most programmers. Which ones are they?
Everybody knows about linked lists, binary trees, and hashes, but what about Skip lists and Bloom filters for example. I would like to know more data structures that are not so common, but are worth knowing because they rely on great ideas and enrich a programmer's tool box.
PS: I am also interested in techniques like Dancing links which make clever use of properties of a common data structure.
EDIT:
Please try to include links to pages describing the data structures in more detail. Also, try to add a couple of words on why a data structure is cool (as Jonas Kölker already pointed out). Also, try to provide one data-structure per answer. This will allow the better data structures to float to the top based on their votes alone.
Tries, also known as prefix-trees or crit-bit trees, have existed for over 40 years but are still relatively unknown. A very cool use of tries is described in "TRASH - A dynamic LC-trie and hash data structure", which combines a trie with a hash function.
Bloom filter: Bit array of m bits, initially all set to 0.
To add an item you run it through k hash functions that will give you k indices in the array which you then set to 1.
To check if an item is in the set, compute the k indices and check if they are all set to 1.
Of course, this gives some probability of false-positives (according to wikipedia it's about 0.61^(m/n) where n is the number of inserted items). False-negatives are not possible.
Removing an item is impossible, but you can implement counting bloom filter, represented by array of ints and increment/decrement.
Rope: It's a string that allows for cheap prepends, substrings, middle insertions and appends. I've really only had use for it once, but no other structure would have sufficed. Regular strings and arrays prepends were just far too expensive for what we needed to do, and reversing everthing was out of the question.
Skip lists are pretty neat.
Wikipedia
A skip list is a probabilistic data structure, based on multiple parallel, sorted linked lists, with efficiency comparable to a binary search tree (order log n average time for most operations).
They can be used as an alternative to balanced trees (using probalistic balancing rather than strict enforcement of balancing). They are easy to implement and faster than say, a red-black tree. I think they should be in every good programmers toolchest.
If you want to get an in-depth introduction to skip-lists here is a link to a video of MIT's Introduction to Algorithms lecture on them.
Also, here is a Java applet demonstrating Skip Lists visually.
Spatial Indices, in particular R-trees and KD-trees, store spatial data efficiently. They are good for geographical map coordinate data and VLSI place and route algorithms, and sometimes for nearest-neighbor search.
Bit Arrays store individual bits compactly and allow fast bit operations.
Zippers - derivatives of data structures that modify the structure to have a natural notion of 'cursor' -- current location. These are really useful as they guarantee indicies cannot be out of bound -- used, e.g. in the xmonad window manager to track which window has focused.
Amazingly, you can derive them by applying techniques from calculus to the type of the original data structure!
Here are a few:
Suffix tries. Useful for almost all kinds of string searching (http://en.wikipedia.org/wiki/Suffix_trie#Functionality). See also suffix arrays; they're not quite as fast as suffix trees, but a whole lot smaller.
Splay trees (as mentioned above). The reason they are cool is threefold:
They are small: you only need the left and right pointers like you do in any binary tree (no node-color or size information needs to be stored)
They are (comparatively) very easy to implement
They offer optimal amortized complexity for a whole host of "measurement criteria" (log n lookup time being the one everybody knows). See http://en.wikipedia.org/wiki/Splay_tree#Performance_theorems
Heap-ordered search trees: you store a bunch of (key, prio) pairs in a tree, such that it's a search tree with respect to the keys, and heap-ordered with respect to the priorities. One can show that such a tree has a unique shape (and it's not always fully packed up-and-to-the-left). With random priorities, it gives you expected O(log n) search time, IIRC.
A niche one is adjacency lists for undirected planar graphs with O(1) neighbour queries. This is not so much a data structure as a particular way to organize an existing data structure. Here's how you do it: every planar graph has a node with degree at most 6. Pick such a node, put its neighbors in its neighbor list, remove it from the graph, and recurse until the graph is empty. When given a pair (u, v), look for u in v's neighbor list and for v in u's neighbor list. Both have size at most 6, so this is O(1).
By the above algorithm, if u and v are neighbors, you won't have both u in v's list and v in u's list. If you need this, just add each node's missing neighbors to that node's neighbor list, but store how much of the neighbor list you need to look through for fast lookup.
I think lock-free alternatives to standard data structures i.e lock-free queue, stack and list are much overlooked.
They are increasingly relevant as concurrency becomes a higher priority and are much more admirable goal than using Mutexes or locks to handle concurrent read/writes.
Here's some links
http://www.cl.cam.ac.uk/research/srg/netos/lock-free/
http://www.research.ibm.com/people/m/michael/podc-1996.pdf [Links to PDF]
http://www.boyet.com/Articles/LockfreeStack.html
Mike Acton's (often provocative) blog has some excellent articles on lock-free design and approaches
I think Disjoint Set is pretty nifty for cases when you need to divide a bunch of items into distinct sets and query membership. Good implementation of the Union and Find operations result in amortized costs that are effectively constant (inverse of Ackermnan's Function, if I recall my data structures class correctly).
Fibonacci heaps
They're used in some of the fastest known algorithms (asymptotically) for a lot of graph-related problems, such as the Shortest Path problem. Dijkstra's algorithm runs in O(E log V) time with standard binary heaps; using Fibonacci heaps improves that to O(E + V log V), which is a huge speedup for dense graphs. Unfortunately, though, they have a high constant factor, often making them impractical in practice.
Anyone with experience in 3D rendering should be familiar with BSP trees. Generally, it's the method by structuring a 3D scene to be manageable for rendering knowing the camera coordinates and bearing.
Binary space partitioning (BSP) is a
method for recursively subdividing a
space into convex sets by hyperplanes.
This subdivision gives rise to a
representation of the scene by means
of a tree data structure known as a
BSP tree.
In other words, it is a method of
breaking up intricately shaped
polygons into convex sets, or smaller
polygons consisting entirely of
non-reflex angles (angles smaller than
180°). For a more general description
of space partitioning, see space
partitioning.
Originally, this approach was proposed
in 3D computer graphics to increase
the rendering efficiency. Some other
applications include performing
geometrical operations with shapes
(constructive solid geometry) in CAD,
collision detection in robotics and 3D
computer games, and other computer
applications that involve handling of
complex spatial scenes.
Huffman trees - used for compression.
Have a look at Finger Trees, especially if you're a fan of the previously mentioned purely functional data structures. They're a functional representation of persistent sequences supporting access to the ends in amortized constant time, and concatenation and splitting in time logarithmic in the size of the smaller piece.
As per the original article:
Our functional 2-3 finger trees are an instance of a general design technique in- troduced by Okasaki (1998), called implicit recursive slowdown. We have already noted that these trees are an extension of his implicit deque structure, replacing pairs with 2-3 nodes to provide the flexibility required for efficient concatenation and splitting.
A Finger Tree can be parameterized with a monoid, and using different monoids will result in different behaviors for the tree. This lets Finger Trees simulate other data structures.
Circular or ring buffer - used for streaming, among other things.
I'm surprised no one has mentioned Merkle trees (ie. Hash Trees).
Used in many cases (P2P programs, digital signatures) where you want to verify the hash of a whole file when you only have part of the file available to you.
<zvrba> Van Emde-Boas trees
I think it'd be useful to know why they're cool. In general, the question "why" is the most important to ask ;)
My answer is that they give you O(log log n) dictionaries with {1..n} keys, independent of how many of the keys are in use. Just like repeated halving gives you O(log n), repeated sqrting gives you O(log log n), which is what happens in the vEB tree.
How about splay trees?
Also, Chris Okasaki's purely functional data structures come to mind.
An interesting variant of the hash table is called Cuckoo Hashing. It uses multiple hash functions instead of just 1 in order to deal with hash collisions. Collisions are resolved by removing the old object from the location specified by the primary hash, and moving it to a location specified by an alternate hash function. Cuckoo Hashing allows for more efficient use of memory space because you can increase your load factor up to 91% with only 3 hash functions and still have good access time.
A min-max heap is a variation of a heap that implements a double-ended priority queue. It achieves this by by a simple change to the heap property: A tree is said to be min-max ordered if every element on even (odd) levels are less (greater) than all childrens and grand children. The levels are numbered starting from 1.
http://internet512.chonbuk.ac.kr/datastructure/heap/img/heap8.jpg
I like Cache Oblivious datastructures. The basic idea is to lay out a tree in recursively smaller blocks so that caches of many different sizes will take advantage of blocks that convenient fit in them. This leads to efficient use of caching at everything from L1 cache in RAM to big chunks of data read off of the disk without needing to know the specifics of the sizes of any of those caching layers.
Left Leaning Red-Black Trees. A significantly simplified implementation of red-black trees by Robert Sedgewick published in 2008 (~half the lines of code to implement). If you've ever had trouble wrapping your head around the implementation of a Red-Black tree, read about this variant.
Very similar (if not identical) to Andersson Trees.
Work Stealing Queue
Lock-free data structure for dividing the work equaly among multiple threads
Implementation of a work stealing queue in C/C++?
Bootstrapped skew-binomial heaps by Gerth Stølting Brodal and Chris Okasaki:
Despite their long name, they provide asymptotically optimal heap operations, even in a function setting.
O(1) size, union, insert, minimum
O(log n) deleteMin
Note that union takes O(1) rather than O(log n) time unlike the more well-known heaps that are commonly covered in data structure textbooks, such as leftist heaps. And unlike Fibonacci heaps, those asymptotics are worst-case, rather than amortized, even if used persistently!
There are multiple implementations in Haskell.
They were jointly derived by Brodal and Okasaki, after Brodal came up with an imperative heap with the same asymptotics.
Kd-Trees, spatial data structure used (amongst others) in Real-Time Raytracing, has the downside that triangles that cross intersect the different spaces need to be clipped. Generally BVH's are faster because they are more lightweight.
MX-CIF Quadtrees, store bounding boxes instead of arbitrary point sets by combining a regular quadtree with a binary tree on the edges of the quads.
HAMT, hierarchical hash map with access times that generally exceed O(1) hash-maps due to the constants involved.
Inverted Index, quite well known in the search-engine circles, because it's used for fast retrieval of documents associated with different search-terms.
Most, if not all, of these are documented on the NIST Dictionary of Algorithms and Data Structures
Ball Trees. Just because they make people giggle.
A ball tree is a data structure that indexes points in a metric space. Here's an article on building them. They are often used for finding nearest neighbors to a point or accelerating k-means.
Not really a data structure; more of a way to optimize dynamically allocated arrays, but the gap buffers used in Emacs are kind of cool.
Fenwick Tree. It's a data structure to keep count of the sum of all elements in a vector, between two given subindexes i and j. The trivial solution, precalculating the sum since the begining doesn't allow to update a item (you have to do O(n) work to keep up).
Fenwick Trees allow you to update and query in O(log n), and how it works is really cool and simple. It's really well explained in Fenwick's original paper, freely available here:
http://www.cs.ubc.ca/local/reading/proceedings/spe91-95/spe/vol24/issue3/spe884.pdf
Its father, the RQM tree is also very cool: It allows you to keep info about the minimum element between two indexes of the vector, and it also works in O(log n) update and query. I like to teach first the RQM and then the Fenwick Tree.
Van Emde-Boas trees. I have even a C++ implementation of it, for up to 2^20 integers.
Nested sets are nice for representing trees in the relational databases and running queries on them. For instance, ActiveRecord (Ruby on Rails' default ORM) comes with a very simple nested set plugin, which makes working with trees trivial.
It's pretty domain-specific, but half-edge data structure is pretty neat. It provides a way to iterate over polygon meshes (faces and edges) which is very useful in computer graphics and computational geometry.
The problem statement:
Given a set of integers that is known in advance, generate code to test if a single integer is in the set. The domain of the testing function is the integers in some consecutive range.
Nothing in particular is known now about the range or the set to be tested. The range could be small or huge (but a solution can reject problems that are to big but higher limits are better). It could be that very few of the values in the allowed range are in the set or most of them are or anything in between. The set may be uniformly distributed or clustered. There may be large sections of only contained/not-contained values or there may be at least a few of each type of value in most swaths. (sort of like the assumption made about items to be sorted when analyzing sorting algorithms)
The objective is a procedure for generating effective code for running the test.
Partial solutions that come to mind include
perfect hash function (costly for large sets)
range tests: foreach(b in ranges) if(b.l <= v && v <= b.h) return true;
trees/indexes (more costly than others in some cases)
table lookup (costly for large sets)
the inverse of any of these these (kodos to Jason S)
It seems that an ideal solution would be able to pick what option is best or if none work well, use a tree to break down the full range into sections and then switch to other options for subsection that are better suited to them.
Topic(s) that might be useful include:
Huffman coding
Note: this is not homework. if it was issued as homework below the doctoral level the prof should be shot with a Nerf gun (if you don't get that then re-read the problem, it is very much non trivial)
Note: This is a problem that occurred to me a few days a go and I've been puzzling over it off and on. I have no direct use for this but thought it would be a cool problem to attack. The reason that I wan to generate the code is because generated code will be no slower than general code (it can be the same thing if needed) and might be faster in some/many cases.
I'm posting this question as much to clarify my thoughts as anything. If I can come up with any reasonable or cool solutions I plan on implementing them as a template meta program (the other reason for generated code)
some people have noted that the problem is very general. That is the point. I'm hoping to generate a system that would work an a very general domain: sets of integers in some range.
a previous question on dictionary/spellchecking had a number of responses that mentioned Bloom filters; maybe that would help.
I would think that testing for large sets is going to be expensive no matter what.
let's pretend, for a moment, that this is a real question:
there are no limits on the size of the base set or the input set
this makes the "problem" unrealistic, underspecified, and un-solvable in any practical sense
if someone wants to posit a solution, here's some unit test cases:
unit test 1:
the base set is all integers between -1,000,000,000,000 and +1,000,000,000,000 except for 100,000,000,000 randomly-removed values
the input set is 100,000,000,000 randomly-generated integers in the same range
unit test 2:
the base set is the Fibonacci series
the input set is 1T randomly-generated integers in the range 0..infinity
there's also boost::dynamic_bitset, not sure how it scales for time, or in space with respect to distribution of original numbers. (e.g. if the bits are stored in chunks of 8/16/32/64, then sparse bitsets are inefficient)
or perhaps this (compressed bit set) or this (bit vector) webpage (I googled for "large sparse bit sets" and "compressed bit sets")