I have been looking for a maximum entropy classification implementation which can deal with an output size of 500 classes and 1000 features. My training data has around 30,000,000 lines.
I have tried using MegaM, the 64-bit R maxent package, the maxent tool from the University of Edinburgh but as expected, none of them can handle the size of data. However, the size of the data set doesn't seem too out of the world for nlp tasks of this nature.
Are there any techniques that I should be employing? Or any suggestion for a toolkit which I may use?
I am trying to run this on a 64-bit Windows machine with 8GB of RAM,using Cygwin where required.
Vowpal Wabbit is currently regarded as the fastest large-scale learner. LibLinear is an alternative, but I'm not sure if it can handle matrices of 3e10 elements.
Note that the term "MaxEnt" is used almost exclusively by NLP people; machine learning folks call it logistic regression or logit, so if you search for that you might find many more tools than when you search for MaxEnt.
Related
I classify clients by many little xgboost models created from different parts of dataset.
Since it is hard to support many models manually, I decided to automate hyperparameters tuning via Hyperopt and features selection via Boruta.
Would you advise me please, what should go first: hyperparameters tuning or features selection? On the other hand, it does not matter.
After features selection, the number of features decreases from 2500 to 100 (actually, I have 50 true features and 5 categorical features turned to 2 400 via OneHotEncoding).
If some code is needed, please, let me know. Thank you very much.
Feature selection (FS) can be considered as a preprocessing activity, wherein, the aim is to identify features having low bias and low variance [1].
Meanwhile, the primary aim of hyperparameter optimization (HPO) is to automate hyper-parameter tuning process and make it possible for users to apply Machine Learning (ML) models to practical problems effectively [2]. Some important reasons for applying HPO techniques to ML models are as follows [3]:
It reduces the human effort required, since many ML developers spend considerable time tuning the hyper-parameters, especially for large datasets or complex ML algorithms with a large number of hyper-parameters.
It improves the performance of ML models. Many ML hyper-parameters have different optimums to achieve best performance in different datasets or problems.
It makes the models and research more reproducible. Only when the same level of hyper-parameter tuning process is implemented can different ML algorithms be compared fairly; hence, using a same HPO method on different ML algorithms also helps to determine the most suitable ML model for a specific problem.
Given the above difference between the two, I think FS should be first applied followed by HPO for a given algorithm.
References
[1] Tsai, C.F., Eberle, W. and Chu, C.Y., 2013. Genetic algorithms in feature and instance selection. Knowledge-Based Systems, 39, pp.240-247.
[2] M. Kuhn, K. Johnson Applied Predictive Modeling Springer (2013) ISBN: 9781461468493.
[3] F. Hutter, L. Kotthoff, J. Vanschoren (Eds.), Automatic Machine Learning: Methods, Systems, Challenges, 9783030053185, Springer (2019)
I am doing kaggle inclass challege of bosten hosing prices and learnt that XGBoost is faster than RandomForest but when implemented was slower.i Want to ask when XGBoost becomes faster and when RandomForest??.I am new to machine learning and need your help.Thanking in advance
Mainly, the parameters you choose have strong impact in the speed of your algorithm, (e.g learning rate, depth of the tree, number of features etc.), there's a trade-off between accuracy and speed, so i suggest you put the parameters you've chosen for every model and see how to change it to get faster performance with reasonable accuracy.
I have a multi-class text classification/categorization problem. I have a set of ground truth data with K different mutually exclusive classes. This is an unbalanced problem in two respects. First, some classes are a lot more frequent than others. Second, some classes are of more interest to us than others (those generally positively correlate with their relative frequency, although there are some classes of interest that are fairly rare).
My goal is to develop a single classifier or a collection of them to be able to classify the k << K classes of interest with high precision (at least 80%) while maintaining reasonable recall (what's "reasonable" is a bit vague).
Features that I use are mostly typical unigram-/bigram-based ones plus some binary features coming from metadata of the incoming documents that are being classified (e.g. whether them were submitted via email or though a webform).
Because of the unbalanced data, I am leaning toward developing binary classifiers for each of the important classes, instead of a single one like a multi-class SVM.
What ML learning algorithms (binary or not) implemented in scikit-learn allow for training tuned to precision (versus for example recall or F1) and what options do I need to set for that?
What data analysis tools in scikit-learn can be used for feature selection to narrow down the features that might be the most relevant to the precision-oriented classification of a particular class?
This is not really a "big data" problem: K is about 100, k is about 15, the total number of samples available to me for training and testing is about 100,000.
Thx
Given that k is small, I would just do this manually. For each desired class, train your individual (one vs the rest) classifier, take look at the precision-recall curve, and then choose the threshold that gives the desired precision.
I am trying to develop a lightweight system that uses an unsupervised learning method that uses system parameters such as CPU, RAM utilization to train an anomaly detection system. I could not think of anything beyond a Self organizing map. Is there any other learning technique that I can consider here?
You don't have many options on this with SOM. The only think you could consider is whether you will do batch or sequential training, if of course the implementation that you will use offers both options. But this option mainly affects the training time (the first is much more quicker) and not the resulting map (in theory at least).
You could also select a distance function other than the Euclidian but the vast percentage of the bibliography doesn't bother with this.
Background
For years I've been using my own Bayesian-like methods to categorize new items from external sources based on a large and continually updated training dataset.
There are three types of categorization done for each item:
30 categories, where each item must belong to one category, and at most two categories.
10 other categories, where each item is only associated with a category if there is a strong match, and each item can belong to as many categories as match.
4 other categories, where each item must belong to only one category, and if there isn't a strong match the item is assigned to a default category.
Each item consists of English text of around 2,000 characters. In my training dataset there are about 265,000 items, which contain a rough estimate of 10,000,000 features (unique three word phrases).
My homebrew methods have been fairly successful, but definitely have room for improvement. I've read the NLTK book's chapter "Learning to Classify Text", which was great and gave me a good overview of NLP classification techniques. I'd like to be able to experiment with different methods and parameters until I get the best classification results possible for my data.
The Question
What off-the-shelf NLP tools are available that can efficiently classify such a large dataset?
Those I've tried so far:
NLTK
TIMBL
I tried to train them with a dataset that consisted of less than 1% of the available training data: 1,700 items, 375,000 features. For NLTK I used a sparse binary format, and a similarly compact format for TIMBL.
Both seemed to rely on doing everything in memory, and quickly consumed all system memory. I can get them to work with tiny datasets, but nothing large. I suspect that if I tried incrementally adding the training data the same problem would occur either then or when doing the actual classification.
I've looked at Google's Prediction API, which seem to do much of what I'm looking for but not everything. I'd also like to avoid relying on an external service if possible.
About the choice of features: in testing with my homebrew methods over the years, three word phrases produced by far the best results. Although I could reduce the number of features by using words or two word phrases, that would most likely produce inferior results and would still be a large number of features.
After this post and based on the personal experience, I would recommend Vowpal Wabbit. It is said to have one of the fastest text classification algorithms.
MALLET has a number of classifiers (NB, MaxEnt, CRF, etc). It's written Andrew McCallum's group. SVMLib is another good option, but SVM models typically require a bit more tuning than MaxEnt. Alternatively some sort of online clustering like K-means might not be bad in this case.
SVMLib and MALLET are quite fast (C and Java) once you have your model trained. Model training can take a while though! Unfortunately it's not always easy to find example code. I have some examples of how to use MALLET programmatically (along with the Stanford Parser, which is slow and probably overkill for your purposes). NLTK is a great learning tool and is simple enough that is you can prototype what you are doing there, that's ideal.
NLP is more about features and data quality than which machine learning method you use. 3-grams might be good, but how about character n-grams across those? Ie, all the character ngrams in a 3-gram to account for spelling variations/stemming/etc? Named entities might also be useful, or some sort of lexicon.
I would recommend Mahout as it is intended for handling very large scale data sets.
The ML algorithms are built over Apache Hadoop(map/reduce), so scaling is inherent.
Take a look at classification section below and see if it helps.
https://cwiki.apache.org/confluence/display/MAHOUT/Algorithms
Have you tried MALLET?
I can't be sure that it will handle your particular dataset but I've found it to be quite robust in previous tests of mine.
However, I my focus was on topic modeling rather than classification per se.
Also, beware that with many NLP solutions you needn't input the "features" yourself (as the N-grams, i.e. the three-words-phrases and two-word-phrases mentioned in the question) but instead rely on the various NLP functions to produce their own statistical model.