I am running test on data samples using the example of SVM Regression Model, in the case of the example given in this MathWorks documentation (link: https://uk.mathworks.com/help/stats/compactregressionsvm.predict.html#buvytaz) the training data needs to have the same number of rows as the predict data, this is required so far to be able to run the prediction. What can I do if my data varies from the number of rows? How can I train my support vector machine with a data that have different number of samples and still be able to predict with the consequence of having maybe bigger error?
Data sample of the training data for the model and the data that I want to use for Mdl = fitrsvm.
ans=10×2 table
Training data Data to predict
___________ ____________
14 9.4833
27 28.938
10 7.765
28
22 21.054
29 31.484
24.5 30.306
18.5
32 28.225
28
Step by step verification of what I wanted to do:
What I did was:
1. Built a model
2. Test it with YFit
3. Modify the table and it did work!.
4. I doubled the size of the table to predict and it did work!.
I did something wrong before.
You can't train your model with unlabelled data, i.e. that has no "predict" value. I would suggest you just filter out all the unlabelled data points and train the model on this subset.
From intuition this just represents the fact, that you cannot learn from these data points. If I want to learn the relationship between age -> income, it does not help me at all to ask someone JUST their age and not their income. The information is useless to answer my question.
Related
I am doing a project on an online signature verification system using RNNs LSTM. In the project, I am facing a problem while using the signatures as LSTM training data. I was using the SVC 2004 dataset where there are 40 signatures of each user. 20 are genuine and 20 are forged. among these signatures, each one is not of equal length. Some have 130 responses, some have 150 responses (rows) (the number of the column is same but the number of rows is different in each signature data). Both are signatures of the same person and I have to use both of them as training data. But each row is crucial so I can not downsample the data. also upsampling the data can affect the time dependency. Then How can I adjust this imbalance in the signatures? If anyone helps me solve this problem I will be really grateful to him/her. Thank you.
My question is about passing variables (training dataset ,Labels and test variable) as predictors and responses. What I do is that load all 3 in workspace of matlab and start session. But every time I get the error(Described in attached Image) i.e No responses selected ,select response variable.My dataset is as following:
faces [ size : 5000 * 10000 (5000 samples ,10000 features)]
TrainingLabels [ size :5000 *1]
TestVariable [ size :1*10000]
Now what should be Predictors and responses in my case and how can I use them correctly in order to make classification Learner app work?
Any Kind help regarding this matter will be highly appreciated. Thankyou.
Step 1): Prepare the data!! If you have N samples of training data and M samples of test data, then combine it together to make it MxN samples. The rows, here, represent each sample and the columns the different types of features detected from a sample.
Step 2): Add an extra column at FIRST or LAST of the data (preferably): This column should represent the desired labels for the data. So, now you will have : total no.of columns= no.of features + 1. While importing the data into the Classification Learner App, it is advised to import the data as a TABLE.
Step 3): Now, set up the data to be used by the Classification Learner App!! By default, all columns will be selected as predictors. The app will prompt you to select the responses. A response is the one which you added as an extra column (the label). So, change the label-column to make it point as a response.
Step 4): Before starting the session, you need to set up the Cross Validation strategy adopted. [ A k-fold validation divides the total MxN data into k-parts and begins by taking the first part of testing and rest k-1 parts for training. Then, again it takes the second part for testing and rest k-1 parts for training and so on. Finally, average of all the accuracies obtained is taken as final accuracy].
Step 5): Start session, select the classifier you want and the hit the training button!!!
Choose one of the columns as response that is change it from predictor to response in "Import as" dropdown.
I've read a few ideas on the correct sample size for Feed Forward Neural networks. x5, x10, and x30 the # of weights. This part I'm not overly concerned about, what I am concerned about is can I reuse my training data (randomly).
My data is broken up like so
5 independent vars and 1 dependent var per sample.
I was planning on feeding 6 samples in (6x5 = 30 input neurons), confirm the 7th samples dependent variable (1 output neuron.
I would train on neural network by running say 6 or 7 iterations. before trying to predict the next iteration outside of my training data.
Say I have
each sample = 5 independent variables & 1 dependent variables (6 vars total per sample)
output = just the 1 dependent variable
sample:sample:sample:sample:sample:sample->output(dependent var)
Training sliding window 1:
Set 1: 1:2:3:4:5:6->7
Set 2: 2:3:4:5:6:7->8
Set 3: 3:4:5:6:7:8->9
Set 4: 4:5:6:7:8:9->10
Set 5: 5:6:7:6:9:10->11
Set 6: 6:7:8:9:10:11->12
Non training test:
7:8:9:10:11:12 -> 13
Training Sliding Window 2:
Set 1: 2:3:4:5:6:7->8
Set 2: 3:4:5:6:7:8->9
...
Set 6: 7:8:9:10:11:12->13
Non Training test: 8:9:10:11:12:13->14
I figured I would randomly run through my set's per training iteration say 30 times the number of my weights. I believe in my network I have about 6 hidden neurons (i.e. sqrt(inputs*outputs)). So 36 + 6 + 1 + 2 bias = 45 weights. So 44 x 30 = 1200 runs?
So I would do a randomization of the 6 sets 1200 times per training sliding window.
I figured due to the small # of data, I was going to do simulation runs (i.e. rerun over the same problem with new weights). So say 1000 times, of which I do 1140 runs over the sliding window using randomization.
I have 113 variables, this results in 101 training "sliding window".
Another question I have is if I'm trying to predict up or down movement (i.e. dependent variable). Should I match to an actual # or just if I guessed up/down movement correctly? I'm thinking I should shoot for an actual number, but as part of my analysis do a % check on if this # is guessed correctly as up/down.
If you have a small amount of data, and a comparatively large number of training iterations, you run the risk of "overtraining" - creating a function which works very well on your test data but does not generalize.
The best way to avoid this is to acquire more training data! But if you cannot, then there are two things you can do. One is to split the training data into test and verification data - using say 85% to train and 15% to verify. Verification means compute the fitness of the learner on the training set, without adjusting the weights/training. When the verification data fitness (which you are not training on) stops improving (in general it will be noisy), and your training data fitness continues improving - stop training. If on the other hand you use a "sliding window", you may not have a good criterion to know when to stop training - the fitness function will bounce around in unpredictable ways (you might slowly make the effect of each training iteration have less effect on the parameters, however, to give you convergence... maybe not the best approach but some training regimes do this) The other thing you can do normalize out your node's weights via some metric to ensure some notion of 'smoothness' - if you visualize overfitting for a second you'll find that in the extreme case your fitness function sharply curves around your dataset positives...
As for the latter question - for the training to converge, you fitness function needs to be smooth. If you were to just use binary all-or-nothing fitness terms, most likely what would happen is that whatever algorithm you are using to train (backprop, BGFS, etc...) would not converge. In practice, the classification criterion should be an activation that is above for a positive result, less than or equal to for a negative result, and varies smoothly in your weight/parameter space. You can think of 0 as "I am certain that the answer is up" and 1 as "I am certain that the answer is down", and thus realize a fitness function that has a higher "cost" for incorrect guesses that were more certain... There are subtleties possible in how the function is shaped (for example you might have different ideas about how acceptable a false negative and false positive are) - and you may also introduce regions of "uncertain" where the result is closer to "zero weight" - but it should certainly be continuous/smooth.
You can re-use sliding window's.
It basically the same concept as bootstrapping (your training set); which in itself reduces training time, but don't know if it's really helpful in making the net more adaptive to anything other than the training data.
Below is an example of a sliding window in pictorial format (using spreadsheet magic)
http://i.imgur.com/nxhtgaQ.png
https://github.com/thistleknot/FredAPI/blob/05f74faf85d15f6898aa05b9b08d5363fe27c473/FredAPI/Program.cs
Line 294 shows how the code is ran using randomization, it resets the randomization at position 353 so the rest flows as normal.
I was also able to use a 1 (up) or 0 (down) as my target values and the network did converge.
I am trying to train a feedforward neural network, for binary classification. My Dataset is 6.2M with 1.5M dimension. I am using PyBrain. I am unable to load even a single datapoint. I am getting MemoryError.
My Code snippet is:
Train_ds = SupervisedDataSet(FV_length, 1) #FV_length is a computed value. 150000
feature_vector = numpy.zeros((FV_length),dtype=numpy.int)
#activate feature values
for index in nonzero_index_list:
feature_vector[index] = 1
Train_ds.addSample(feature_vector,class_label) # both the arguments are tuples
It looks like your computer just does not have the memory to add your feature and class label arrays to the supervised data set Train_ds.
If there is no way for you to allocate more memory to your system it might be a good idea to random sample from your data set and train on the smaller sample.
This should still give accurate results assuming the sample is large enough to be representative.
I'm having problems in understanding how K-NN classification works in MATLAB.´
Here's the problem, I have a large dataset (65 features for over 1500 subjects) and its respective classes' label (0 or 1).
According to what's been explained to me, I have to divide the data into training, test and validation subsets to perform supervised training on the data, and classify it via K-NN.
First of all, what's the best ratio to divide the 3 subgroups (1/3 of the size of the dataset each?).
I've looked into ClassificationKNN/fitcknn functions, as well as the crossval function (idealy to divide data), but I'm really not sure how to use them.
To sum up, I wanted to
- divide data into 3 groups
- "train" the KNN (I know it's not a method that requires training, but the equivalent to training) with the training subset
- classify the test subset and get it's classification error/performance
- what's the point of having a validation test?
I hope you can help me, thank you in advance
EDIT: I think I was able to do it, but, if that's not asking too much, could you see if I missed something? This is my code, for a random case:
nfeats=60;ninds=1000;
trainRatio=0.8;valRatio=.1;testRatio=.1;
kmax=100; %for instance...
data=randi(100,nfeats,ninds);
class=randi(2,1,ninds);
[trainInd,valInd,testInd] = dividerand(1000,trainRatio,valRatio,testRatio);
train=data(:,trainInd);
test=data(:,testInd);
val=data(:,valInd);
train_class=class(:,trainInd);
test_class=class(:,testInd);
val_class=class(:,valInd);
precisionmax=0;
koptimal=0;
for know=1:kmax
%is it the same thing use knnclassify or fitcknn+predict??
predicted_class = knnclassify(val', train', train_class',know);
mdl = fitcknn(train',train_class','NumNeighbors',know) ;
label = predict(mdl,val');
consistency=sum(label==val_class')/length(val_class);
if consistency>precisionmax
precisionmax=consistency;
koptimal=know;
end
end
mdl_final = fitcknn(train',train_class','NumNeighbors',know) ;
label_final = predict(mdl,test');
consistency_final=sum(label==test_class')/length(test_class);
Thank you very much for all your help
For your 1st question "what's the best ratio to divide the 3 subgroups" there are only rules of thumb:
The amount of training data is most important. The more the better.
Thus, make it as big as possible and definitely bigger than the test or validation data.
Test and validation data have a similar function, so it is convenient to assign them the same amount
of data. But it is important to have enough data to be able to recognize over-adaptation. So, they
should be picked from the data basis fully randomly.
Consequently, a 50/25/25 or 60/20/20 partitioning is quite common. But if your total amount of data is small in relation to the total number of weights of your chosen topology (e.g. 10 weights in your net and only 200 cases in the data), then 70/15/15 or even 80/10/10 might be better choices.
Concerning your 2nd question "what's the point of having a validation test?":
Typically, you train the chosen model on your training data and then estimate the "success" by applying the trained model to unseen data - the validation set.
If you now would completely stop your efforts to improve accuracy, you indeed don't need three partitions of your data. But typically, you feel that you can improve the success of your model by e.g. changing the number of weights or hidden layers or ... and now a big loops starts to run with many iterations:
1) change weights and topology, 2) train, 3) validate, not satisfied, goto 1)
The long-term effect of this loop is, that you increasingly adapt your model to the validation data, so the results get better not because you so intelligently improve your topology but because you unconsciously learn the properties of the validation set and how to cope with them.
Now, the final and only valid accuracy of your neural net is estimated on really unseen data: the test set. This is done only once and is also useful to reveal over-adaption. You are not allowed to start a second even bigger loop now to prohibit any adaption to the test set!