Looking for advice on how to determine wether my model output data distribution is similar (and if so, then how similar) to the observed datasets distribution.
Basically I have a GBM model with mean reversion that provides seemingly good results, when I compare its distribution to observed data. You can see their PDFs side-by-side in the attached picture.
PDF of observed and model data
Both datasets are huge (~6 million datapoint), and I start to suspect that this is part of the problem...
I am looking for a way to verify that the datasets distributions are similar. I tried the two-sample Kolmogorov-Smirnov test, two-sample t-test, but for some reason both of them rejected the Null hypothesis (always, even with different Alphas). In some threads I've read that these tests are unreliable, when applied to huge datasets, but there wasn't a consensus about this.
I am using Matlab currently, but I am open to others if necessary.
Any help would be appreciated! I primarily looking for a hypothesis test for verification, but if you have a different idea don't hold it back!
Related
I have estimated a complex hierarchical model with many random effects, but don't really know what the best approach is to checking for convergend. I have complex longitudinal data from a few hundred individuals and estimate quite a few parameters for every individual. Because of that, I have way to many traceplots to inspect visually. Or should I really spend a day going through all the traceplots? What would be a better way to check for convergence? Do I have to calculate Gelman and Rubin's Rhat for every parameter on the person level? And when can I conclude that the model converged? When absolutely all of the thousends of parameters reached convergence? Is it even sensible to expect that? Or is there something like "overall convergence"? And what does it mean when some person-level parameters did not converge? Does it make sense to use autorun.jags from the R2jags package with such a model or will it just run for ever? I know, these are a lot of question, but I just don't know how to approach that.
The measure I am using for convergence is a potential scale reduction factor (psrf)* using the gelman.diag function from the R package coda.
But nevertheless, I am also quickly visually inspecting all the traceplots, even though I also have tens/hundreds of them. It can be really fast if you put them in PNG files and then quickly go through them using e.g. IrfanView (let me know if you need me to expand on this).
The reason you should inspect the traceplots is pretty well described by an example from Marc Kery (author of great Bayesian books): see "Never blindly trust Rhat for convergence in a Bayesian analysis", here I include a self explanatory image from this email:
This is related to Rhat statistics while I use psrf, but it's pretty likely that psrf suffers from this too... and better to check the chains.
*) Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).
How does word2vec create vectors for words? I trained two word2vec models using two different files (from commoncrawl website) but I am getting same word vectors for a given word from both models.
Actually, I have created multiple word2vec models using different text files from the commoncrawl website. Now I want to check which model is better among all. How can select the best model out of all these models and why I am getting same word vectors for different models?
Sorry, If the question is not clear.
If you are getting identical word-vectors from models that you've prepared from different text corpuses, something is likely wrong in your process. You may not be performing any training at all, perhaps because of a problem in how the text iterable is provided to the Word2Vec class. (In that case, word-vectors would remain at their initial, randomly-initialized values.)
You should enable logging, and review the logs carefully to see that sensible counts of words, examples, progress, and incremental-progress are displayed during the process. You should also check that results for some superficial, ad-hoc checks look sensible after training. For example, does model.most_similar('hot') return other words/concepts somewhat like 'hot'?
Once you're sure models are being trained on varied corpuses – in which case their word-vectors should be very different from each other – deciding which model is 'best' depends on your specific goals with word-vectors.
You should devise a repeatable, quantitative way to evaluate a model against your intended end-uses. This might start crudely with a few of your own manual reviews of results, like looking over most_similar() results for important words for better/worse results – but should become more extensive. rigorous, and automated as your project progresses.
An example of such an automated scoring is the accuracy() method on gensim's word-vectors object. See:
https://github.com/RaRe-Technologies/gensim/blob/6d6f5dcfa3af4bc61c47dfdf5cdbd8e1364d0c3a/gensim/models/keyedvectors.py#L652
If supplied with a specifically-formatted file of word-analogies, it will check how well the word-vectors solve those analogies. For example, the questions-words.txt of Google's original word2vec code release includes the analogies they used to report vector quality. Note, though, that the word-vectors that are best for some purposes, like understanding text topics or sentiment, might not also be the best at solving this style of analogy, and vice-versa. If training your own word-vectors, it's best to choose your training corpus/parameters based on your own goal-specific criteria for what 'good' vectors will be.
I have run across issues in developing models where the translation time (simulates quickly but takes far too long to translate) has become a serious issue and could use some insight so I can look into resolving this.
So the question is:
What are some of the primary factors that impact the translation time of a model and ideas to address the issue?
For example, things that may have an impact:
for loops vs a vectorized method - a basic model testing this didn't seem to impact anything
using input variables vs parameters
impact of annotations (e.g., Evaluate=true)
or tough luck, this is tool dependent (Dymola, OMEdit, etc.) :(
use of many connect() - this seems to be a factor (perhaps primary) as it forces translater to do all the heavy lifting
Any insight is greatly appreciated.
Clearly the answer to this question if naturally open ended. There are many things to consider when computation times may be a factor.
For distributed models (e.g., finite difference) the use of simple models and then using connect equations to link them in the appropriate order is not the best way to produce the models. Experience has shown that this method significantly increases the translation time to unbearable lengths. It is better to create distributed models in the same approach that is used the MSL Dynamic pipe (not exactly like it but similar).
Changing the approach as described is significantly faster in translational time (orders of magnitude for larger models, >~100,000 equations) than using connect statements as the number of distributed elements increases to larger numbers. This was tested using Dymola 2017 and 2017FD01.
Some related materials pointed out by others that may be useful for more information have been included below:
https://modelica.org/events/modelica2011/Proceedings/pages/papers/07_1_ID_183_a_fv.pdf
Scalable Test Suite : https://dx.doi.org/10.3384/ecp15118459
New with Matlab.
When I try to load my own date using the NN pattern recognition app window, I can load the source data, but not the target (it is never on the drop down list). Both source and target are in the same directory. Source is 5000 observations with 400 vars per observation and target can take on 10 different values (recognizing digits). Any Ideas?
Before you do anything with your own data you might want to try out the example data sets available in the toolbox. That should make many problems easier to find later on because they definitely work, so you can see what's wrong with your code.
Regarding your actual question: Without more details, e.g. what your matrices contain and what their dimensions are, it's hard to help you. In your case some of the problems mentioned here might be similar to yours:
http://www.mathworks.com/matlabcentral/answers/17531-problem-with-targets-in-nprtool
From what I understand about nprtool your targets have to consist of a matrix with only one 1 (for the correct class) in either row or column (depending on the input matrix), so make sure that's the case.
Some general Modelica advice?
We've built a model with ~2000 equations and three vectors of input from measured data. Using OpenModelica, attempts at simulation have begun to hang in the translation stage (which runs for hours where it used to take less than a minute) and now I regularly "lose connection to omc.exe." Is there perhaps something cumulative occurring that's degrading translation/compilation performance?
In general, are there any good rules of thumb for keeping simulations lighter and faster? I realize that, depending on the couplings, additional equations could be exponentially increasing the size of the resulting system of equations - could this be a problem?
Thanks for your thoughts!
It shouldn't take that long. Seems like a bug.
You can report this bug here:
https://trac.openmodelica.org/OpenModelica (New Ticket).
If your model is public you can post it there, if not you can contact the OpenModelica team privately.
I did some cleaning in the code; and got the part that repeats 12x (the module) down to ~180 equations; in the process I reduced the size of my input vectors (and also a 2D look-up table the module refers to) by quite a bit - they're both down to a few hundred values. It's working now--simulations run in reasonable time, a few minutes each.
Since all these tables were defined within Modelica functions (as you pointed out, Mr. Tiller) perhaps shrinking them helped to improve the performance. I had assumed that all that data just got spread out in a memory array, without going through any real processing, but maybe that's not the case...time to know more about what's going on under the hood in this environment (as always).
Thanks for the help!