Pseudo-code for Network-only-bayes-classifier - algorithm

I am trying to implement a classification toolkit for univariate network data using igraph and python.
However, my question is actually more of an algorithms question in relational classification area instead of programming.
I am following Classification in Networked Data paper.
I am having a difficulty to understand what this paper refers to "Network-Only Bayes Classifier"(NBC) which is one of the relational classifiers explained in the paper.
I implemented Naive Bayes classifier for text data using bag of words feature representation earlier. And the idea of Naive Bayes on text data is clear on my mind.
I think this method (NBC) is a simple translation of the same idea to the relational classification area. However, I am confused with the notation used in the equations, so I couldn't figure out what is going on. I also have a question on the notation used in the paper here.
NBC is explained in page 14 on the paper,
Summary:
I need the pseudo-code of the "Network-Only Bayes Classifier"(NBC) explained in the paper, page 14.
Pseudo-code notation:
Let's call vs the list of vertices in the graph. len(vs) is the
length. vs[i] is the ith vertex.
Let's assume we have a univariate and binary scenario, i.e., vs[i].class is either 0 or 1 and there is no other given feature of a node.
Let's assume we run a local classifier before so that every node has an initial label, which are calculated by the local classifier. I am only interested in with the relational classifier part.
Let's call v the vertex we are trying to predict, and v.neighbors() is the list of vertices which are neighbors of v.
Let's assume all the edge weights are 1.
Now, I need the pseudo-code for:
def NBC(vs, v):
# v.class is 0 or 1
# v.neighbors is list of neighbor vertices
# vs is the list of all vertices
# This function returns 0 or 1
Edit:
To make your job easier, I did this example. I need the answer for last 2 equations.

In words...
The probability that node x_i belongs to the class c is equal to:
The probability of the neighbourhood of x_i (called N_i) if x
belonged indeed to the class c; Multiplied by ...
The probability of the class c itself; Divided by ...
The probability of the neighbourhood N_i (of node x_i) itself.
As far as the probability of the neighbourhood N_i (of x_i) if x were to belong to the class c is concerned, it is equal to:
A product of some probability; (which probability?)
The probability that some node (v_j) of the neighbourhood (N_i) belongs to the class c if x belonged indeed to the class c
(raised to the weight of the edge connecting the node that is being examined and the node that is being classified...but you are not interested in this...yet). (The notation is a bit off here I think, why do they define v_j and then never use it?...Whatever).
Finally, multiply the product of some probability with some 1/Z. Why? Because all ps are probabilities and therefore lie within the range of 0 to 1, but the weights w could be anything, meaning that in the end, the calculated probability could be out of range.
The probability that some x_i belongs to a class c GIVEN THE
EVIDENCE FROM ITS NEIGHBOURHOOD, is a posterior probability. (AFTER
something...What is this something? ... Please see below)
The probability of appearance of neighbourhood N_i if x_i
belonged to the class c is the likelihood.
The probability of the class c itself is the prior probability.
BEFORE something...What is this something? The evidence. The prior
tells you the probability of the class without any evidence
presented, but the posterior tells you the probability of a specific
event (that x_i belongs to c) GIVEN THE EVIDENCE FROM ITS
NEIGHBOURHOOD.
The prior, can be subjective. That is, derived by limited observations OR be an informed opinion. In other words, it doesn't have to be a population distribution. It only has to be accurate enough, not absolutely known.
The likelihood is a bit more challenging. Although we have here a formula, the likelihood must be estimated from a large enough population or as much "physical" knowledge about the phenomenon being observed as possible.
Within the product (capital letter Pi in the second equation that expresses the likelihood) you have a conditional. The conditional is the probability that a neighbourhood node belongs to some class if x belonged to class c.
In the typical application of the Naive Bayesian Classifier, that is document classification (e.g. spam mail), the conditional that an email is spam GIVEN THE APPEARANCE OF SPECIFIC WORDS IN ITS BODY is derived by a huge database of observations, or, a huge database of emails that we really, absolutely know which class they belong to. In other words, I must have an idea of how does a spam email looks like and eventually, the majority of spam emails converge to having some common theme (I am some bank official and i have a money opportunity for you, give me your bank details to wire money to you and make you rich...).
Without this knowledge, we can't use Bayes rule.
So, to get back to your specific problem. In your PDF, you have a question mark in the derivation of the product.
Exactly.
So the real question here is: What is the likelihood from your Graph / data?
(...or Where are you going to derive it from? (obviously, either a large number of known observations OR some knowledge about the phenomenon. For example, what is the likelihood that a node is infected given that a proportion of its neighbourhood is infected too)).
I hope this helps.

Related

How is the class center for a decision attribute calculated in class center based fuzzification algorithm?

I came across class center based fuzzification algorithm on page 16 of this research paper on TRFDT. However, I fail to understand what is happening in step 2 of this algorithm (titled in the paper as Algorithm 2: Fuzzification). If someone could explain it by giving a small example it would certainly be helpful.
It is not clear from your question which parts of the article you understand and IMHO the article is written in not the clearest possible way, so this is going to be a long answer.
Let's start with some intuition behind this article. In short I'd say it is: "let's add fuzziness everywhere to decision trees".
How a decision tree works? We have a classification problem and we say that instead of analyzing all attributes of a data point in a holistic way, we'll analyze them one by one in an order defined by the tree and will navigate the tree until we reach some leaf node. The label at that leaf node is our prediction. So the trick is how to build a good tree i.e. a good order of attributes and good splitting points. This is a well studied problem and the idea is to build a tree that encode as much information as possible by some metric. There are several metrics and this article uses entropy which is similar to widely used information gain.
The next idea is that we can change the classification (i.e. split of the values into a classes) as fuzzy rather than exact (aka "crisp"). The idea here is that in many real life situations not all members of the class are equally representative: some a more "core" examples and some a more "edge" example. If we can catch this difference, we can provide a better classification.
And finally there is a question of how similar the data points are (generally or by some subset of attributes) and here we can also have a fuzzy answer (see formulas 6-8).
So the idea of the main algorithm (Algorithm 1) is the same as in the ID3 tree: recursively find the attribute a* that classifies the data in the best way and perform the best split along that attribute. The main difference is in how the information gain for the best attribute selection is measured (see heuristic in formulas 20-24) and that because of fuzziness the usual stop rule of "only one class left" doesn't work anymore and thus another entropy (Kosko fuzzy entropy in 25) is used to decide if it is time to stop.
Given this skeleton of the algorithm 1 there are quite a few parts that you can (or should) select:
How do you measure μ(ai)τ(Cj)(x) used in (20) (this is a measure of how well x represents the class Cj with respect to attribute ai, note that here being not in Cj and far from the points in Cj is also good) with two obvious choices of the lower (16 and 18) and the upper bounds (17 and 19)
How do you measure μRτ(x, y) used in (16-19). Given that R is induced by ai this becomes μ(ai)τ(x, y) which is a measure of similarity between two points with respect to attribute ai. Here you can choose one of the metrics (6-8)
How do you measure μCi(y) used in (16-19). This is the measure of how well the point y fits in the class Ci. If you already have data as fuzzy classification, there is nothing you should do here. But if your input classification is crisp, then you should somehow produce μCi(y) from that and this is what the Algorithm 2 does.
There is a trivial solution of μCj(xi) = "1 if xi ∈ Cj and 0 otherwise" but this is not fuzzy at all. The process of building fuzzy data is called "fuzzification". The idea behind the Algorithm 2 is that we assume that every class Cj is actually some kind of a cluster in the space of attributes. And so we can measure the degree of membership μCj(xi) as the distance from the xi to the center of the cluster cj (the closer we are, the higher the membership should be so it is really some inverse of a distance). Note that since distance is measured by attributes, you should normalize your attributes somehow or one of them might dominate the distance. And this is exactly what the Algorithm 2 does:
it estimates the center of the cluster for class Cj as the center of mass of all the known points in that class i.e. just an average of all points by each coordinate (attribute).
it calculates the distances from each point xi to each estimated center of class cj
looking into formula at step #12 it uses inverse square of the distance as a measure of proximity and just normalizes the value because for fuzzy sets Sum[over all Cj](μCj(xi)) should be 1

Markov chains and Random walks on top of biological data

I'm coming from biology's field and thus I have some difficulties in understanding (intuitively?) some of the ideas of that paper. I really tried my best to decipher it step by step by using a lot of google and youtube, but now I feel, it's the time to refer to the professionals in that field.
Before filling out the whole universe with (unordered) questions, let me put the whole thing down and try to introduce you to the subject while at the same time explain to you what I got so far from my research on that.
Microarrays
For those that do not have any idea of what this is, you can imagine, that it is literally an array (matrix) where each cell of it contains a probe for a specific gene. Making the long story short, by the end of the microarray experiment, you have a matrix (in computational terms) with each column representing a sample, each line a different gene while the contents of the matrix represent the expression values of the genes for each sample.
Pathways
In biology pathway / gene-set they call a set of genes that interact with each other forming a small network responsible for a specific function.These pathways are not isolated but they talk/interact with each other too. What that paper does on the first hand, is to expand the initial pathway (let us call it target pathway), by including some other genes from other pathways that might interact with that.
Procedure
1.
Let's assume now that we have a matrix G x S. Where G for genes and S for Samples. We construct a gene co-expression network (G x G) using as weights the Pearson's correlation coefficients between genes' pairs (a). This could also be represented as an undirected weighted graph. .
2.
For each gene (row OR column) we calculate the weighted degree (d) which is nothing more than the sum of all correlation coefficients of that gene.
3.
From the two previous matrices, they construct the transition matrix producing the probabilities (P) to transit from one gene to another by using the
formula
Q1. Why do they call this transition probability? Is there any intuitive way to see this as a probability in the biological context?
4.
Since we have the whole transition matrix, we can define a subnetwork of the initial one, that we want to expand it and it consisted out of let's say 15 genes. In that step, they used formula number 3 (on the paper) which transforms the values of the initial transition matrix as it says. They set the probability of 1 on the nodes that are part of the selected subnetwork because they define them as absorbing states.
Q2. In that same formula (3), I cannot understand what the second condition does. When should the probability be 0? Intuitively, in my opinion, all nodes that didn't exist in subnetwork, should have the P_ij value as a probability.
5.
After that, the newly constructed transition matrix is showed at formula (4) in the paper and I managed to understand it using this excellent article.
6.
Here is where everything is getting more blur for me and where I need the most of the help. What I imagine at that step, is that the algorithm starts randomly from one node and keep walking around the network. In order to construct a relevance function (What that exactly means?), they firstly calculate a probability called joint probability of visiting one node/edge E(i,j) and noted as :
From the other hand they seem to calculate another probability called probability of a walk of length L starting in x and denoted as :
7.
In the next step, they divide the previously calculated probabilities and calculate the number of times a random walk starts in x using the transition from i to j that I don't really understand what this means.
After that step, I lost their reasoning at all :-P.
I'm not expecting an expert to come open my mind and give me understand that procedure. What I'm expecting is some guidelines, hints, ideas, useful resources or more intuitive approaches to understanding the whole procedure. Then when I fully understand it I will try to implement it on R or python.
So any idea / critics is welcome.
Thanks.

Organize a trade event || Business "speed dating" algorithm

I'm Student of software engineering,
Right now I am working for my final project, scheduling Business matchmaking on a trade day.
The idea is to bring a seller (developer) and a buyer (A person with financial means) together. The algorithm should be like "Speed Dating".
Let's say I have 15 tables and 10 sessions.
It means that each session 15 buyers will meet 15 sellers for 20 minutes.
My question is how do I make the matching?
Suppose each person has 8 attribute that characterize him.
• I thinking creating bipartite graph (group A – Sellers, group B - Buyers)
• Then link up between a seller and buyer based on similar attributes (Should consider what is level of error). dont want to bring together people who are not related
• Then on each session look for a maximum matching.
Constraints: it's not a real time, I'll close registration a few days before the event.
I'm currently "idea blocked" on how to do the linking step (base on a person attributes).
I would appreciate your help,
Even a dialogue on the matter would help me a lot!:)
Often given multi-dimensional data that describe data points, you define a similarity or "kernel" between points. This could be the e.g. dot product after you normalize by standard deviation in each dimension for example. Or it could be a Gaussian kernel e^((-d^2)/y) where d is the dot-product between points and y is a constant bandwidth parameter. Also e.g. if certain dimensions are categorical then you could the one-dimensional dot-product to be 1 if the categorical variables agree, otherwise 0. Then you can form the overall dot-product from the multi-dimensional data after normalizing each dimension by its standard deviation. The point is, once you form a similarity or kernel between points, then you can define a weighted bipartite graph where the weight of an edge is equal to the similarity/kernel between points, and your problem is to find a maximum weight matching. This is a well-known problem with solutions in the literature e.g. the Hungarian algorithm, see e.g. http://en.wikipedia.org/wiki/Matching_%28graph_theory%29#In_weighted_bipartite_graphs .

Machine Learning Algorithm for Completing Sparse Matrix Data

I've seen some machine learning questions on here so I figured I would post a related question:
Suppose I have a dataset where athletes participate at running competitions of 10 km and 20 km with hilly courses i.e. every competition has its own difficulty.
The finishing times from users are almost inverse normally distributed for every competition.
One can write this problem as a matrix:
Comp1 Comp2 Comp3
User1 20min ?? 10min
User2 25min 20min 12min
User3 30min 25min ??
User4 30min ?? ??
I would like to complete the matrix above which has the size 1000x20 and a sparseness of 8 % (!).
There should be a very easy way to complete this matrix, since I can calculate parameters for every user (ability) and parameters for every competition (mu, lambda of distributions). Moreover the correlation between the competitions are very high.
I can take advantage of the rankings User1 < User2 < User3 and Item3 << Item2 < Item1
Could you maybe give me a hint which methods I could use?
Your astute observation that this is a matrix completion problem gets
you most of the way to the solution. I'll codify your intuition that
the combination of ability of a user and difficulty of the course
yields the time of a race, then present various algorithms.
Model
Let the vector u denote the speed of the users so that u_i is user i's
speed. Let the vector v denote the difficulty of the courses so
that v_j is course j's difficulty. Also when available, let t_ij be user i's time on
course j, and define y_ij = 1/t_ij, user i's speed on course j.
Since you say the times are inverse Gaussian distributed, a sensible
model for the observations is
y_ij = u_i * v_j + e_ij,
where e_ij is a zero-mean Gaussian random variable.
To fit this model, we search for vectors u and v that minimize the
prediction error among the observed speeds:
f(u,v) = sum_ij (u_i * v_j - y_ij)^2
Algorithm 1: missing value Singular Value Decomposition
This is the classical Hebbian
algorithm. It
minimizes the above cost function by gradient descent. The gradient of
f wrt to u and v are
df/du_i = sum_j (u_i * v_j - y_ij) v_j
df/dv_j = sum_i (u_i * v_j - y_ij) u_i
Plug these gradients into a Conjugate Gradient solver or BFGS
optimizer, like MATLAB's fmin_unc or scipy's optimize.fmin_ncg or
optimize.fmin_bfgs. Don't roll your own gradient descent unless you're willing to implement a very good line search algorithm.
Algorithm 2: matrix factorization with a trace norm penalty
Recently, simple convex relaxations to this problem have been
proposed. The resulting algorithms are just as simple to code up and seem to
work very well. Check out, for example Collaborative Filtering in a Non-Uniform World:
Learning with the Weighted Trace Norm. These methods minimize
f(m) = sum_ij (m_ij - y_ij)^2 + ||m||_*,
where ||.||_* is the so-called nuclear norm of the matrix m. Implementations will end up again computing gradients with respect to u and v and relying on a nonlinear optimizer.
There are several ways to do this, perhaps the best architecture to try first is the following:
(As usual, as a preprocessing step normalize your data into a uniform function with 0 mean and 1 std deviation as best you can. You can do this by fitting a function to the distribution of all race results, applying its inverse, and then subtracting the mean and dividing by the std deviation.)
Select a hyperparameter N (you can tune this as usual with a cross validation set).
For each participant and each race create an N-dimensional feature vector, initially random. So if there are R races and P participants then there are R+P feature vectors with a total of N(R+P) parameters.
The prediction for a given participant and a given race is a function of the two corresponding feature vectors (as a first try use the scalar product of these two vectors).
Alternate between incrementally improving the participant feature vectors and the race feature vectors.
To improve a feature vector use gradient descent (or some more complex optimization method) on the known data elements (the participant/race pairs for which you have a result).
That is your loss function is:
total_error = 0
forall i,j
if (Participant i participated in Race j)
actual = ActualRaceResult(i,j)
predicted = ScalarProduct(ParticipantFeatures_i, RaceFeatures_j)
total_error += (actual - predicted)^2
So calculate the partial derivative of this function wrt the feature vectors and adjust them incrementally as per a usual ML algorithm.
(You should also include a regularization term on the loss function, for example square of the lengths of the feature vectors)
Let me know if this architecture is clear to you or you need further elaboration.
I think this is a classical task of missing data recovery. There exist some different methods. One of them which I can suggest is based on Self Organizing Feature Map (Kohonen's Map).
Below it's assumed that every athlet record is a pattern, and every competition data is a feature.
Basically, you should divide your data into 2 sets: first - with fully defined patterns, and second - patterns with partially lost features. I assume this is eligible because sparsity is 8%, that is you have enough data (92%) to train net on undamaged records.
Then you feed first set to the SOM and train it on this data. During this process all features are used. I'll not copy algorithm here, because it can be found in many public sources, and even some implementations are available.
After the net is trained, you can feed patterns from the second set to the net. For each pattern the net should calculate best matching unit (BMU), based only on those features that exist in the current pattern. Then you can take from the BMU its weigths, corresponding to missing features.
As alternative, you could not divide the whole data into 2 sets, but train the net on all patterns including the ones with missing features. But for such patterns learning process should be altered in the similar way, that is BMU should be calculated only on existing features in every pattern.
I think you can have a look at the recent low rank matrix completion methods.
The assumption is that your matrix has a low rank compared to the matrix dimension.
min rank(M)
s.t. ||P(M-M')||_F=0
M is the final result, and M' is the uncompleted matrix you currently have.
This algorithm minimizes the rank of your matrix M. P in the constraint is an operator that takes the known terms of your matrix M', and constraint those terms in M to be the same as in M'.
The optimization of this problem has a relaxed version, which is:
min ||M||_* + \lambda*||P(M-M')||_F
rank(M) is relaxed to its convex hull ||M||_* Then you trade off the two terms by controlling the parameter lambda.

Optimal placement of objects wrt pairwise similarity weights

Ok this is an abstract algorithmic challenge and it will remain abstract since it is a top secret where I am going to use it.
Suppose we have a set of objects O = {o_1, ..., o_N} and a symmetric similarity matrix S where s_ij is the pairwise correlation of objects o_i and o_j.
Assume also that we have an one-dimensional space with discrete positions where objects may be put (like having N boxes in a row or chairs for people).
Having a certain placement, we may measure the cost of moving from the position of one object to that of another object as the number of boxes we need to pass by until we reach our target multiplied with their pairwise object similarity. Moving from a position to the box right after or before that position has zero cost.
Imagine an example where for three objects we have the following similarity matrix:
1.0 0.5 0.8
S = 0.5 1.0 0.1
0.8 0.1 1.0
Then, the best ordering of objects in the tree boxes is obviously:
[o_3] [o_1] [o_2]
The cost of this ordering is the sum of costs (counting boxes) for moving from one object to all others. So here we have cost only for the distance between o_2 and o_3 equal to 1box * 0.1sim = 0.1, the same as:
[o_3] [o_1] [o_2]
On the other hand:
[o_1] [o_2] [o_3]
would have cost = cost(o_1-->o_3) = 1box * 0.8sim = 0.8.
The target is to determine a placement of the N objects in the available positions in a way that we minimize the above mentioned overall cost for all possible pairs of objects!
An analogue is to imagine that we have a table and chairs side by side in one row only (like the boxes) and you need to put N people to sit on the chairs. Now those ppl have some relations that is -lets say- how probable is one of them to want to speak to another. This is to stand up pass by a number of chairs and speak to the guy there. When the people sit on two successive chairs then they don't need to move in order to talk to each other.
So how can we put those ppl down so that every distance-cost between two ppl are minimized. This means that during the night the overall number of distances walked by the guests are close to minimum.
Greedy search is... ok forget it!
I am interested in hearing if there is a standard formulation of such problem for which I could find some literature, and also different searching approaches (e.g. dynamic programming, tabu search, simulated annealing etc from combinatorial optimization field).
Looking forward to hear your ideas.
PS. My question has something in common with this thread Algorithm for ordering a list of Objects, but I think here it is better posed as problem and probably slightly different.
That sounds like an instance of the Quadratic Assignment Problem. The speciality is due to the fact that the locations are placed on one line only, but I don't think this will make it easier to solve. The QAP in general is NP hard. Unless I misinterpreted your problem you can't find an optimal algorithm that solves the problem in polynomial time without proving P=NP at the same time.
If the instances are small you can use exact methods such as branch and bound. You can also use tabu search or other metaheuristics if the problem is more difficult. We have an implementation of the QAP and some metaheuristics in HeuristicLab. You can configure the problem in the GUI, just paste the similarity and the distance matrix into the appropriate parameters. Try starting with the robust Taboo Search. It's an older, but still quite well working algorithm. Taillard also has the C code for it on his website if you want to implement it for yourself. Our implementation is based on that code.
There has been a lot of publications done on the QAP. More modern algorithms combine genetic search abilities with local search heuristics (e. g. Genetic Local Search from Stützle IIRC).
Here's a variation of the already posted method. I don't think this one is optimal, but it may be a start.
Create a list of all the pairs in descending cost order.
While list not empty:
Pop the head item from the list.
If neither element is in an existing group, create a new group containing
the pair.
If one element is in an existing group, add the other element to whichever
end puts it closer to the group member.
If both elements are in existing groups, combine them so as to minimize
the distance between the pair.
Group combining may require reversal of order in a group, and the data structure should
be designed to support that.
Let me help the thread (of my own) with a simplistic ordering approach.
1. Order the upper half of the similarity matrix.
2. Start with the pair of objects having the highest similarity weight and place them in the center positions.
3. The next object may be put on the left or the right side of them. So each time you may select the object that when put to left or right
has the highest cost to the pre-placed objects. Goto Step 2.
The selection of Step 3 is because if you left this object and place it later this cost will be again the greatest of the remaining, and even more (farther to the pre-placed objects). So the costly placements should be done as earlier as it can be.
This is too simple and of course does not discover a good solution.
Another approach is to
1. start with a complete ordering generated somehow (random or from another algorithm)
2. try to improve it using "swaps" of object pairs.
I believe local minima would be a huge deterrent.

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