Sentiment prediction based on document vectors works pretty well, as examples show:
https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/doc2vec-IMDB.ipynb
http://linanqiu.github.io/2015/10/07/word2vec-sentiment/
I wonder what pattern is in the vectors making that possible. I thought it should be similarity of vectors making that somehow possible. Gensim similarity measures rely on cosine similarity. Therefore, I tried the following:
Randomly initialised a fix “compare” vector, get cosine similarity of the “compare” vector with all other vectors in training and test set, use the similarities and the labels of the train set to estimate a logistic regression model, evaluate the model with the test set.
Looks like this, where train/test_arrays contain document vectors and train/test_labels labels either 0 or 1. (Notice, document vectors are obtained from genism doc2vec and are well trained, predicting the test set 80% right if directly used as input for the logistic regression):
fix_vec = numpy.random.rand(100,1)
def cos_distance_to_fix(x):
return scipy.spatial.distance.cosine(fix_vec, x)
train_arrays_cos = numpy.reshape(numpy.apply_along_axis(cos_distance_to_fix, axis=1, arr=train_arrays), newshape=(-1,1))
test_arrays_cos = numpy.reshape(numpy.apply_along_axis(cos_distance_to_fix, axis=1, arr=test_arrays), newshape=(-1,1))
classifier = LogisticRegression()
classifier.fit(train_arrays_cos, train_labels)
classifier.score(test_arrays_cos, test_labels)
It turns out, that this approach does not work, predicting the test set only to 50%....
So, my question is, what “information” is in the vectors, making the prediction based on vectors work, if it is not the similarity of vectors? Or is my approach simply not possible to capture similarity of vectors correct?
This is less a question about Doc2Vec than about machine-learning principles with high-dimensional data.
Your approach is collapsing 100-dimensions to a single dimension – the distance to your random point. Then, you're hoping that single dimension can still be predictive.
And roughly all LogisticRegression can do with that single-valued input is try to pick a threshold-number that, when your distance is on one side of that threshold, predicts a class – and on the other side, predicts not-that-class.
Recasting that single-threshold-distance back to the original 100-dimensional space, it's essentially trying to find a hypersphere, around your random point, that does a good job collecting all of a single class either inside or outside its volume.
What are the odds your randomly-placed center-point, plus one adjustable radius, can do that well, in a complex high-dimensional space? My hunch is: not a lot. And your results, no better than random guessing, seems to suggest the same.
The LogisticRegression with access to the full 100-dimensions finds a discriminating-frontier for assigning the class that's described by 100 coefficients and one intercept-value – and all of those 101 values (free parameters) can be adjusted to improve its classification performance.
In comparison, your alternative LogisticRegression with access to only the one 'distance-from-a-random-point' dimension can pick just one coefficient (for the distance) and an intercept/bias. It's got 1/100th as much information to work with, and only 2 free parameters to adjust.
As an analogy, consider a much simpler space: the surface of the Earth. Pick a 'random' point, like say the South Pole. If I then tell you that you are in an unknown place 8900 miles from the South Pole, can you answer whether you are more likely in the USA or China? Hardly – both of those 'classes' of location have lots of instances 8900 miles from the South Pole.
Only in the extremes will the distance tell you for sure which class (country) you're in – because there are parts of the USA's Alaska and Hawaii further north and south than parts of China. But even there, you can't manage well with just a single threshold: you'd need a rule which says, "less than X or greater than Y, in USA; otherwise unknown".
The 100-dimensional space of Doc2Vec vectors (or other rich data sources) will often only be sensibly divided by far more complicated rules. And, our intuitions about distances and volumes based on 2- or 3-dimensional spaces will often lead us astray, in high dimensions.
Still, the Earth analogy does suggest a way forward: there are some reference points on the globe that will work way better, when you know the distance to them, at deciding if you're in the USA or China. In particular, a point at the center of the US, or at the center of China, would work really well.
Similarly, you may get somewhat better classification accuracy if rather than a random fix_vec, you pick either (a) any point for which a class is already known; or (b) some average of all known points of one class. In either case, your fix_vec is then likely to be "in a neighborhood" of similar examples, rather than some random spot (that has no more essential relationship to your classes than the South Pole has to northern-Hemisphere temperate-zone countries).
(Also: alternatively picking N multiple random points, and then feeding the N distances to your regression, will preserve more of the information/shape of the original Doc2Vec data, and thus give the classifier a better chance of finding a useful separating-threshold. Two would likely do better than your one distance, and 100 might approach or surpass the 100 original dimensions.)
Finally, some comment about the Doc2Vec aspect:
Doc2Vec optimizes vectors that are somewhat-good, within their constrained model, at predicting the words of a text. Positive-sentiment words tend to occur together, as do negative-sentiment words, and so the trained doc-vectors tend to arrange themselves in similar positions when they need to predict similar-meaning-words. So there are likely to be 'neighborhoods' of the doc-vector space that correlate well with predominantly positive-sentiment or negative-sentiment words, and thus positive or negative sentiments.
These won't necessarily be two giant neighborhoods, 'positive' and 'negative', separated by a simple boundary –or even a small number of neighborhoods matching our ideas of 3-D solid volumes. And many subtleties of communication – such as sarcasm, referencing a not-held opinion to critique it, spending more time on negative aspects but ultimately concluding positive, etc – mean incursions of alternate-sentiment words into texts. A fully-language-comprehending human agent could understand these to conclude the 'true' sentiment, while these word-occurrence based methods will still be confused.
But with an adequate model, and the right number of free parameters, a classifier might capture some generalizable insight about the high-dimensional space. In that case, you can achieve reasonably-good predictions, using the Doc2Vec dimensions – as you've seen with the ~80%+ results on the full 100-dimensional vectors.
Related
while reading the paper :" Tactile-based active object discrimination and target object search in an unknown workspace", there is something that I just can not understand:
The paper is about finding object's position and other properties using only tactile information. In the section 4.1.2, the author says that he uses GPR to guide the exploratory process and in section 4.1.4 he describes how he trained his GPR:
Using the example from the section 4.1.2, the input is (x,z) and the ouput y.
Whenever there is a contact, the coresponding y-value is stored.
This procedure is repeated several times.
This trained GPR is used to estimate the next exploring point, which is the point where the variance is maximum at.
In the following link, you also can see the demonstration: https://www.youtube.com/watch?v=ZiLq3i-BJcA&t=177s . In the first part of video (0:24-0:29), the first initalization takes place where the robot samples 4 times. Then in the next 25 seconds, the robot explores explores from the corresponding direction. I do not understand how this tiny initialization of GPR can guide the exploratory process. Could someone please explain how the input points (x,z) from the first exploring part could be estimated?
Any regression algorithm simply maps the input (x,z) to an output y in some way unique to the specific algorithm. For a new input (x0,z0) the algorithm will likely predict something very close to the true output y0 if many data points similar to this was included in the training. If only training data was available in a vastly different region, the predictions will likely be very bad.
GPR includes a measure of confidence of the predictions, namely the variance. The variance will naturally be very high in regions where no training data has been seen before and low very close to already seen data points. If the 'experiment' takes much longer than evaluating the Gaussian Process, you can use the Gaussian Process fit to make sure you sample regions where you are very uncertain of your answer.
If the goal is to fully explore the entire input space, you could draw a lot of random values of (x,z) and evaluate the variance at these values. Then you could perform the costly experiment at the input point where you are most uncertain in y. Then you can retrain the GPR with all the explored data so far and repeat the process.
For optimization problems (Not the OP's question)
If you wish to find the lowest value of y across the input space, you are not interested in doing the experiment in regions that you know give high values of y, but you are just uncertain of how high these values will be. So instead of choosing the (x,z) points with the highest variance, you might choose the predicted value of y plus one standard deviation. Minimizing values this way is named Bayesian Optimization and this specific scheme is named Upper Confidence Bound (UCB). Expected Improvement (EI) - the probability of improving the previously best score - is also commonly used.
I am trying to Classify Document Vector pairs (Doc2Vec, 300 Features per Document) as similar/not similar. I tried Distance Messures (Cosine etc.) with additional Features like document size etc. but did not achieve perfect results, especially because I suspect, that only some of the features are meaningful for my problem.
What is the simple, but effective way to feed two vectors to a Classifier (LogisticRegression, SVM etc.)
I already tested the subtraction of one vector from the other and use the absolute result as feature vector: abs(vec1 -vec2) but this was worse than distance messures
I also tried the concatenation of both vectors, also with worse results. I suspect the doubling of dimension will increase the need of training samples, at least for some classifiers?
Is there a state-of-the-art way to classify similaritys or relationships between feature vectors? Or if there are concurent methods, which one is to prefer for which problem/classifier?
Generally, you'd aim for your vectorization of the documents (eg via Doc2Vec) to give vectors where the similarities between vectors are a useful continuous similarity measure. (Most often this is cosine-similarity, but in some cases euclidean-distance may be worth trying as well.)
If the vectors coming out of the Doc2Vec stage don't already exhibit that, the first thing to do would be to debug and optimize that process. That could involve:
double-checking everything, including logged output of the process, for errors
tweaking document preprocessing, to perhaps ensure salient document features are retained and noise discarded
tuning Doc2Vec meta-parameters and modes, to ensure the resulting vectors are sensitive to the kinds of similarity that are important in your end-goals.
It'd be hard to say more about improving that step without more details about your data size and character, Doc2Vec choices/code so far, and end-goals.
How are you deciding whether two documents are "similar enough" or not? How much such evaluative data do you have to help score different Doc2Vec models in a repeatable, quantitative way. (Being able to do such automated scoring will let you test far more Doc2Vec permutations.) Are there examples of doc pairs where simple doc-vector cosine-similarity is working well, or not working well?
I see two red flags in the word you've chosen so far:
"did not achieve perfect results" - getting "perfect" results is an unrealistic goal. You want to find something close to the state of the art, given the resources & tolerance-for-complexity of your project
"300 Features per Document" - Doc2Vec doesn't really find "300 Features" that are independent. It's a single 300-dimensional "dense" "embedded" vector. Every direction – not just the 300 axes – may be meaningful. So even if certain "directions" are more significant for your needs, they're unlikely to be fully correlated with exact dimension axes.
It's possible a classifier on the (v1 - v2) difference, or (v1 || v2) concatenation, could help refine a "similar enough or not" decision, but you'd need a lot of training data, and perhaps a very sophisticated classifier.
i was reading about Classification Algorithm KNN and came across with one term Distance Sensitive Data. I was not able to Found what exactly is Distance Sensitive Data wha are it's classifications, How to say if our Data is Distance-Sensitive or Not?
Suppose that xi and xj are vectors of observed features in cases i and j. Then, as you probably know, kNN is based on distances ||xi-xj||, such as the Euclidean one.
Now if xi and xj contain just a single feature, individual's height in meters, we are fine, as there are no other "competing" features. Suppose that next we add annual salary in thousands. Consequently, we look at distances between vectors like (1.7, 50000) and (1.8, 100000).
Then, in the case of the Euclidean distance, clearly salary feature dominates height and it's almost like we are using the salary feature alone. That is,
||xi-xj||2 ≈ |50000-100000|.
However, if the two features actually have similar importance, then we are doing a poor job. It is even worse if salary is actually irrelevant and we should be using height alone. Interestingly, under weak conditions, our classifier still has nice properties such as universal consistency even in such bad situations. The problem is that in finite samples the performance is our classifier is very bad so that the convergence is very slow.
So, as to deal with that, one may want to consider different distances, such that do something about the scale. Commonly people standardize (set the mean to zero and variance to 1) each feature, but that's not a complete solution either. There are various proposals what could be done (see, e.g., here).
On the other hand, algorithms based on decision trees do not suffer from this. In those cases we just look for a point where to split the variable. For instance, if salary takes values in [0,100000] and the split is at 40000, then Salary/10 would be slit at 4000 so that the results would not change.
I have a collection of documents related to a particular domain and have trained the centroid classifier based on that collection. What I want to do is, I will be feeding the classifier with documents from different domains and want to determine how much they are relevant to the trained domain. I can use the cosine similarity for this to get a numerical value but my question is what is the best way to determine the threshold value?
For this, I can download several documents from different domains and inspect their similarity scores to determine the threshold value. But is this the way to go, does it sound statistically good? What are the other approaches for this?
Actually there is another issue with centroids in sparse vectors. The problem is that they usually are significantly less sparse than the original data. For examples, this increases computation costs. And it can yield vectors that are themselves actually atypical because they have a different sparsity pattern. This effect is similar to using arithmetic means of discrete data: say the mean number of doors in a car is 3.4; yet obviously no car exists that actually has 3.4 doors. So in particular, there will be no car with an euclidean distance of less than 0.4 to the centroid! - so how "central" is the centroid then really?
Sometimes it helps to use medoids instead of centroids, because they actually are proper objects of your data set.
Make sure you control such effects on your data!
A simple method to try would be to employ various machine-learning algorithms - and in particular, tree-based ones - on the distances from your centroids.
As mentioned in another answer(#Anony-Mousse), this won't necessarily provide you with good or usable answers, but it just might. Using a ML framework for this procedure, E.g. WEKA, will also help you with estimating your accuracy in a more rigorous manner.
Here are the steps to take, using WEKA:
Generate a train set by finding a decent amount of documents representing each of your classes (to get valid estimations, I'd recommend at least a few dozens per class)
Calculate the distance from each document to each of your centroids.
Generate a feature vector for each such document, composed of the distances from this document to the centroids. You can either use a single feature - the distance to the nearest centroid; or use all distances, if you'd like to try a more elaborate thresholding scheme. For example, if you chose the simpler method of using a single feature, the vector representing a document with a distance of 0.2 to the nearest centroid, belonging to class A would be: "0.2,A"
Save this set in ARFF or CSV format, load into WEKA, and try classifying, e.g. using a J48 tree.
The results would provide you with an overall accuracy estimation, with a detailed confusion matrix, and - of course - with a specific model, e.g. a tree, you can use for classifying additional documents.
These results can be used to iteratively improve the models and thresholds by collecting additional train documents for problematic classes, either by recreating the centroids or by retraining the thresholds classifier.
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I am using LibSVM to classify some documents. The documents seem to be a bit difficult to classify as the final results show. However, I have noticed something while training my models. and that is: If my training set is for example 1000 around 800 of them are selected as support vectors.
I have looked everywhere to find if this is a good thing or bad. I mean is there a relation between the number of support vectors and the classifiers performance?
I have read this previous post but I am performing a parameter selection and also I am sure that the attributes in the feature vectors are all ordered.
I just need to know the relation.
Thanks.
p.s: I use a linear kernel.
Support Vector Machines are an optimization problem. They are attempting to find a hyperplane that divides the two classes with the largest margin. The support vectors are the points which fall within this margin. It's easiest to understand if you build it up from simple to more complex.
Hard Margin Linear SVM
In a training set where the data is linearly separable, and you are using a hard margin (no slack allowed), the support vectors are the points which lie along the supporting hyperplanes (the hyperplanes parallel to the dividing hyperplane at the edges of the margin)
All of the support vectors lie exactly on the margin. Regardless of the number of dimensions or size of data set, the number of support vectors could be as little as 2.
Soft-Margin Linear SVM
But what if our dataset isn't linearly separable? We introduce soft margin SVM. We no longer require that our datapoints lie outside the margin, we allow some amount of them to stray over the line into the margin. We use the slack parameter C to control this. (nu in nu-SVM) This gives us a wider margin and greater error on the training dataset, but improves generalization and/or allows us to find a linear separation of data that is not linearly separable.
Now, the number of support vectors depends on how much slack we allow and the distribution of the data. If we allow a large amount of slack, we will have a large number of support vectors. If we allow very little slack, we will have very few support vectors. The accuracy depends on finding the right level of slack for the data being analyzed. Some data it will not be possible to get a high level of accuracy, we must simply find the best fit we can.
Non-Linear SVM
This brings us to non-linear SVM. We are still trying to linearly divide the data, but we are now trying to do it in a higher dimensional space. This is done via a kernel function, which of course has its own set of parameters. When we translate this back to the original feature space, the result is non-linear:
Now, the number of support vectors still depends on how much slack we allow, but it also depends on the complexity of our model. Each twist and turn in the final model in our input space requires one or more support vectors to define. Ultimately, the output of an SVM is the support vectors and an alpha, which in essence is defining how much influence that specific support vector has on the final decision.
Here, accuracy depends on the trade-off between a high-complexity model which may over-fit the data and a large-margin which will incorrectly classify some of the training data in the interest of better generalization. The number of support vectors can range from very few to every single data point if you completely over-fit your data. This tradeoff is controlled via C and through the choice of kernel and kernel parameters.
I assume when you said performance you were referring to accuracy, but I thought I would also speak to performance in terms of computational complexity. In order to test a data point using an SVM model, you need to compute the dot product of each support vector with the test point. Therefore the computational complexity of the model is linear in the number of support vectors. Fewer support vectors means faster classification of test points.
A good resource:
A Tutorial on Support Vector Machines for Pattern Recognition
800 out of 1000 basically tells you that the SVM needs to use almost every single training sample to encode the training set. That basically tells you that there isn't much regularity in your data.
Sounds like you have major issues with not enough training data. Also, maybe think about some specific features that separate this data better.
Both number of samples and number of attributes may influence the number of support vectors, making model more complex. I believe you use words or even ngrams as attributes, so there are quite many of them, and natural language models are very complex themselves. So, 800 support vectors of 1000 samples seem to be ok. (Also pay attention to #karenu's comments about C/nu parameters that also have large effect on SVs number).
To get intuition about this recall SVM main idea. SVM works in a multidimensional feature space and tries to find hyperplane that separates all given samples. If you have a lot of samples and only 2 features (2 dimensions), the data and hyperplane may look like this:
Here there are only 3 support vectors, all the others are behind them and thus don't play any role. Note, that these support vectors are defined by only 2 coordinates.
Now imagine that you have 3 dimensional space and thus support vectors are defined by 3 coordinates.
This means that there's one more parameter (coordinate) to be adjusted, and this adjustment may need more samples to find optimal hyperplane. In other words, in worst case SVM finds only 1 hyperplane coordinate per sample.
When the data is well-structured (i.e. holds patterns quite well) only several support vectors may be needed - all the others will stay behind those. But text is very, very bad structured data. SVM does its best, trying to fit sample as well as possible, and thus takes as support vectors even more samples than drops. With increasing number of samples this "anomaly" is reduced (more insignificant samples appear), but absolute number of support vectors stays very high.
SVM classification is linear in the number of support vectors (SVs). The number of SVs is in the worst case equal to the number of training samples, so 800/1000 is not yet the worst case, but it's still pretty bad.
Then again, 1000 training documents is a small training set. You should check what happens when you scale up to 10000s or more documents. If things don't improve, consider using linear SVMs, trained with LibLinear, for document classification; those scale up much better (model size and classification time are linear in the number of features and independent of the number of training samples).
There is some confusion between sources. In the textbook ISLR 6th Ed, for instance, C is described as a "boundary violation budget" from where it follows that higher C will allow for more boundary violations and more support vectors.
But in svm implementations in R and python the parameter C is implemented as "violation penalty" which is the opposite and then you will observe that for higher values of C there are fewer support vectors.