I am new to Weka and I am trying to build a classifier to classify EEG data. The EEG attribute data is 5 minutes of recorded raw signals as well as other attributes. How can I specify in WEKA arff file format that my instance has a vector input of a 5 minute raw signal?
for example:
Num. -- raw -- class
1 -- [1,2,3,4,5,6] -- Relaxed
2 -- [2,3,4,5,6] --- Bored
Where raw is an attribute vector..
Think about your problem- what are you trying to classify/predict, and how can it be best represented. Chances are that you don't want to predict the next raw EEG reading, so a time-series approach probably isn't critical.
Weka can only handle instances (rows of data) with a fixed set of attributes (features, values, or in other words, a vector of a predefined length). The possible types of attributes one can have are nominal (e.g. "red","green","blue"), numeric (any integer/floating point value), string (mostly for text mining). and date. There is no way to represent a vector of raw signal as a single attribute. Here is the documentation: http://weka.wikispaces.com/ARFF+%28stable+version%29
That said, your instances could look like this:
num,class1,reading_1,reading_2,reading_3 ... reading_n,relaxed,bored
where reading_1 is the first raw reading and reading_n is the last one at the end of 5 minutes. This would be asking WEKA to predict your class based on the raw readings, and probably won't be very effective (because the readings may not line up with each other, and because this treats each reading separately, with no care for things like frequency or average which are relative).
Alternatively, you can do some pre-processing of the raw data so that it is useful for most machine learning algorithms in WEKA. In this case, you would need to decide on important features and then create them. A crude example could be:
num,class1,average,frequency,max_magnitude,standard_deviation,relaxed,bored
Where you have calculated things like average and frequency of the data before putting it into an ARFF file. Then the algorithms have a much more informative picture of the dataset on which to base their predictions.
However, still another concern is what are you representing? Is the entire 5 minute sample the same class, or is the user relaxed for part of it and bored for part of it? If this is the case, you should probably have two samples: one for when the user is bored and one for when she is relaxed.
Related
I have been searching and attempting to implement a word embedding model to predict similarity between words. I have a dataset made up 3,550 company names, the idea is that the user can provide a new word (which would not be in the vocabulary) and calculate the similarity between the new name and existing ones.
During preprocessing I got rid of stop words and punctuation (hyphens, dots, commas, etc). In addition, I applied stemming and separated prefixes with the hope to get more precision. Then words such as BIOCHEMICAL ended up as BIO CHEMIC which is the word divided in two (prefix and stem word)
The average company name length is made up 3 words with the following frequency:
The tokens that are the result of preprocessing are sent to word2vec:
#window: Maximum distance between the current and predicted word within a sentence
#min_count: Ignores all words with total frequency lower than this.
#workers: Use these many worker threads to train the model
#sg: The training algorithm, either CBOW(0) or skip gram(1). Default is 0s
word2vec_model = Word2Vec(prepWords,size=300, window=2, min_count=1, workers=7, sg=1)
After the model included all the words in the vocab , the average sentence vector is calculated for each company name:
df['avg_vector']=df2.apply(lambda row : avg_sentence_vector(row, model=word2vec_model, num_features=300, index2word_set=set(word2vec_model.wv.index2word)).tolist())
Then, the vector is saved for further lookups:
##Saving name and vector values in file
df.to_csv('name-submission-vectors.csv',encoding='utf-8', index=False)
If a new company name is not included in the vocab after preprocessing (removing stop words and punctuation), then I proceed to create the model again and calculate the average sentence vector and save it again.
I have found this model is not working as expected. As an example, calculating the most similar words pet is getting the following results:
ms=word2vec_model.most_similar('pet')
('fastfood', 0.20879755914211273)
('hammer', 0.20450574159622192)
('allur', 0.20118337869644165)
('wright', 0.20001833140850067)
('daili', 0.1990675926208496)
('mgt', 0.1908089816570282)
('mcintosh', 0.18571510910987854)
('autopart', 0.1729743778705597)
('metamorphosi', 0.16965581476688385)
('doak', 0.16890916228294373)
In the dataset, I have words such as paws or petcare, but other words are creating relationships with pet word.
This is the distribution of the nearer words for pet:
On the other hand, when I used the GoogleNews-vectors-negative300.bin.gz, I could not add new words to the vocab, but the similarity between pet and words around was as expected:
ms=word2vec_model.most_similar('pet')
('pets', 0.771199643611908)
('Pet', 0.723974347114563)
('dog', 0.7164785265922546)
('puppy', 0.6972636580467224)
('cat', 0.6891531348228455)
('cats', 0.6719794869422913)
('pooch', 0.6579219102859497)
('Pets', 0.636363685131073)
('animal', 0.6338439583778381)
('dogs', 0.6224827170372009)
This is the distribution of the nearest words:
I would like to get your advice about the following:
Is this dataset appropriate to proceed with this model?
Is the length of the dataset enough to allow word2vec "learn" the relationships between the words?
What can I do to improve the model to make word2vec create relationships of the same type as GoogleNews where for instance word pet is correctly set among similar words?
Is it feasible to implement another alternative such as fasttext considering the nature of the current dataset?
Do you know any public dataset that can be used along with the current dataset to create those relationships?
Thanks
3500 texts (company names) of just ~3 words each is only around 10k total training words, with a much smaller vocabulary of unique words.
That's very, very small for word2vec & related algorithms, which rely on lots of data, and sufficiently-varied data, to train-up useful vector arrangements.
You may be able to squeeze some meaningful training from limited data by using far more training epochs than the default epochs=5, and far smaller vectors than the default size=100. With those sorts of adjustments, you may start to see more meaningful most_similar() results.
But, it's unclear that word2vec, and specifically word2vec in your averaging-of-a-name's-words comparisons, is matched to your end goals.
Word2vec needs lots of data, doesn't look at subword units, and can't say anything about word-tokens not seen during training. An average-of-many-word-vectors can often work as an easy baseline for comparing multiword texts, but might also dilute some word's influence compared to other methods.
Things to consider might include:
Word2vec-related algorithms like FastText that also learn vectors for subword units, and can thus bootstrap not-so-bad guess vectors for words not seen in training. (But, these are also data hungry, and to use on a small dataset you'd again want to reduce vector size, increase epochs, and additionally shrink the number of buckets used for subword learning.)
More sophisticated comparisons of multi-word texts, like "Word Mover's Distance". (That can be quite expensive on longer texts, but for names/titles of just a few words may be practical.)
Finding more data that's compatible with your aims for a stronger model. A larger database of company names might help. If you just want your analysis to understand English words/roots, more generic training texts might work too.
For many purposes, a mere lexicographic comparison - edit distances, count of shared character-n-grams – may be helpful too, though it won't detect all synonyms/semantically-similar words.
Word2vec does not generalize to unseen words.
It does not even work well for wards that are seen but rare. It really depends on having many many examples of word usage. Furthermore a you need enough context left and right, but you only use company names - these are too short. That is likely why your embeddings perform so poorly: too little data and too short texts.
Hence, it is the wrong approach for you. Retraining the model with the new company name is not enough - you still only have one data point. You may as well leave out unseen words, word2vec cannot work better than that even if you retrain.
If you only want to compute similarity between words, probably you don't need to insert new words in your vocabulary.
By eye, I think you can also use FastText without the need to stem the words. It also computes vectors for unknown words.
From FastText FAQ:
One of the key features of fastText word representation is its ability
to produce vectors for any words, even made-up ones. Indeed, fastText
word vectors are built from vectors of substrings of characters
contained in it. This allows to build vectors even for misspelled
words or concatenation of words.
FastText seems to be useful for your purpose.
For your task, you can follow FastText supervised tutorial.
If your corpus proves to be too small, you can build your model starting from availaible pretrained vectors (pretrainedVectors parameter).
I am building a RNN for a time series model, which have a categorical output.
For example, if precious 3 pattern is "A","B","A","B" model predict next is "A".
there's also a numerical level associated with each category.
For example A is 100, B is 50,
so A(100), B(50), A(100), B(50),
I have the model framework to predict next is "A", it would be nice to predict the (100) at the same time.
For real life examples, you have national weather data.
You are predicting the next few days weather type(Sunny, windy, raining ect...) at the same time, it would be nice model will also predict the temperature.
Or for Amazon, analysis customer's trxns pattern.
Customer A shopped category
electronic($100), household($10), ... ...
predict what next trxn category that this customer is likely to shop and predict at the same time what would be the amount of that trxns.
Researched a bit, have not found any relevant research on similar topics.
What is stopping you from adding an extra output to your model? You could have one categorical output and one numerical output next to each other. Every neural network library out there supports multiple outputs.
Your will need to normalise your output data though. Categories should be normalised with one-hot encoding and numerical values should be normalised by dividing by some maximal value.
Researched a bit, have not found any relevant research on similar topics.
Because this is not really a 'topic'. This is something completely normal, and it does not require some special kind of network.
I know decision tree has feature_importance attribute calculated by Gini and it could be used to check which features are more important.
However, for application in scikit-learn or Spark, it only accepts numeric attribute, so I have to transfer string attribute to numeric attribute and then do one-hot encoder on that. When features are put into decision tree model, it's 0-1 encoded other than original format, my question is, how to explain feature importance for original attributes? should I avoid one-hot encoder when try to explain feature importance?
Thanks.
Conceptually, you may want to use something along the lines of permutation importance. The basic idea, is that you take your original dataset, and randomly shuffle the values of each column 1 at a time. Then, you score your perturbed data with the model and compare the performance to the original performance. If done 1 column at a time, you can assess the performance hit you take by destroying each variable, indexing it to the variable that had the most loss (which would become 1, or 100%). If you can do this to your original dataset, prior to the 1 hot encoding, then you'll be getting an importance measure that groups them together overall.
I have a set of 3-5 black box scoring functions that assign positive real value scores to candidates.
Each is decent at ranking the best candidate highest, but they don't always agree--I'd like to find how to combine the scores together for an optimal meta-score such that, among a pool of candidates, the one with the highest meta-score is usually the actual correct candidate.
So they are plain R^n vectors, but each dimension individually tends to have higher value for correct candidates. Naively I could just multiply the components, but I hope there's something more subtle to benefit from.
If the highest score is too low (or perhaps the two highest are too close), I just give up and say 'none'.
So for each trial, my input is a set of these score-vectors, and the output is which vector corresponds to the actual right answer, or 'none'. This is kind of like tech interviewing where a pool of candidates are interviewed by a few people who might have differing opinions but in general each tend to prefer the best candidate. My own application has an objective best candidate.
I'd like to maximize correct answers and minimize false positives.
More concretely, my training data might look like many instances of
{[0.2, 0.45, 1.37], [5.9, 0.02, 2], ...} -> i
where i is the ith candidate vector in the input set.
So I'd like to learn a function that tends to maximize the actual best candidate's score vector from the input. There are no degrees of bestness. It's binary right or wrong. However, it doesn't seem like traditional binary classification because among an input set of vectors, there can be at most 1 "classified" as right, the rest are wrong.
Thanks
Your problem doesn't exactly belong in the machine learning category. The multiplication method might work better. You can also try different statistical models for your output function.
ML, and more specifically classification, problems need training data from which your network can learn any existing patterns in the data and use them to assign a particular class to an input vector.
If you really want to use classification then I think your problem can fit into the category of OnevsAll classification. You will need a network (or just a single output layer) with number of cells/sigmoid units equal to your number of candidates (each representing one). Note, here your number of candidates will be fixed.
You can use your entire candidate vector as input to all the cells of your network. The output can be specified using one-hot encoding i.e. 00100 if your candidate no. 3 was the actual correct candidate and in case of no correct candidate output will be 00000.
For this to work, you will need a big data set containing your candidate vectors and corresponding actual correct candidate. For this data you will either need a function (again like multiplication) or you can assign the outputs yourself, in which case the system will learn how you classify the output given different inputs and will classify new data in the same way as you did. This way, it will maximize the number of correct outputs but the definition of correct here will be how you classify the training data.
You can also use a different type of output where each cell of output layer corresponds to your scoring functions and 00001 means that the candidate your 5th scoring function selected was the right one. This way your candidates will not have to be fixed. But again, you will have to manually set the outputs of the training data for your network to learn it.
OnevsAll is a classification technique where there are multiple cells in the output layer and each perform binary classification in between one of the classes vs all others. At the end the sigmoid with the highest probability is assigned 1 and rest zero.
Once your system has learned how you classify data through your training data, you can feed your new data in and it will give you output in the same way i.e. 01000 etc.
I hope my answer was able to help you.:)
So I read a paper that said that processing your dataset correctly can increase LibSVM classification accuracy dramatically...I'm using the Weka implementation and would like some help making sure my dataset is optimal.
Here are my (example) attributes:
Power Numeric (real numbers, range is from 0 to 1.5132, 9000+ unique values)
Voltage Numeric (similar to Power)
Light Numeric (0 and 1 are the only 2 possible values)
Day Numeric (1 through 20 are the possible values, equal number of each value)
Range Nominal {1,2,3,4,5} <----these are the classes
My question is: which Weka pre-processing filters should I apply to make this dataset more effective for LibSVM?
Should I normalize and/or standardize the Power and Voltage data values?
Should I use a Discretization filter on anything?
Should I be binning the Power/Voltage values into a lot smaller number of bins?
Should I make the Light value Binary instead of numeric?
Should I normalize the Day values? Does it even make sense to do that?
Should I be using the Nominal to Binary or Nominal to some thing else filter for the classes "Range"?
Please advice on these questions and anything else you think I might have missed...
Thanks in advance!!
Normalization is very important, as it influences the concept of distance which is used by SVM. The two main approaches to normalization are:
Scale each input dimension to the same interval, for example [0, 1]. This is the most common approach by far. It is necessary to prevent some input dimensions to completely dominate others. Recommended by the LIBSVM authors in their beginner's guide (Appendix B for examples).
Scale each instance to a given length. This is common in text mining / computer vision.
As to handling types of inputs:
Continuous: no work needed, SVM works on these implicitly.
Ordinal: treat as continuous variables. For example cold, lukewarm, hot could be modeled as 1, 2, 3 without implicitly defining an unnatural structure.
Nominal: perform one-hot encoding, e.g. for an input with N levels, generate N new binary input dimensions. This is necessary because you must avoid implicitly defining a varying distance between nominal levels. For example, modelling cat, dog, bird as 1, 2 and 3 implies that a dog and bird are more similar than a cat and bird which is nonsense.
Normalization must be done after substituting inputs where necessary.
To answer your questions:
Should I normalize and/or standardize the Power and Voltage data
values?
Yes, standardize all (final) input dimensions to the same interval (including dummies!).
Should I use a Discretization filter on anything?
No.
Should I be binning the Power/Voltage values into a lot smaller number of
bins?
No. Treat them as continuous variables (e.g. one input each).
Should I make the Light value Binary instead of numeric?
No, SVM has no concept of binary variables and treats everything as numeric. So converting it will just lead to an extra type-cast internally.
Should I normalize the Day values? Does it even make sense to do
that?
If you want to use 1 input dimension, you must normalize it just like all others.
Should I be using the Nominal to Binary or Nominal to some thing else filter for the classes "Range"?
Nominal to binary, using one-hot encoding.