Hello,
I have noticed a considerable difference on my results when running the exact same code in python 2.7.8 and python 3.6.8.
I have been considering results as correct when running on python 3.6.8 taking into account that the support/test for the used libraries is better for python 3.6.8 but I would like to ask whether you have fully tested your implementation for python 3.6 or i should consider python 2.7.
Does anyone come across the same issue? Or, are there specific versions of libraries that I should use?
Thanks in advance,
Redona

May I know if MISVM supports instance-level predictions?

According to the released code, however, the accuracy of MissSVM is only 40%, I have read the original paper, but have not found any difference between code and paper. So, what do you think the reason why the accuracy is only 40%? Thank you!!

I generated some synthetic data using the 20newsgroup to run experiment on mi-SVM and MI-SVM. I noticed that, if I predict the labels in instance-level (actually that happens for me on bag-level too), all predictions share the same sign (either positive or negative, depending on the data). The AUC looks good, which means the ranking is correct. I guess this might due to some issues caused by library version, or not? Does anyone come across the same issue? Or, which versions of libraries should we use? Thanks!

Hey there,
I was happy to see this extensive work on Multiple Instance Learning, but I ran into an error running your example code. Could it be that I installed it or its dependencies incorrectly? The problem is in cvxopt. Am I not using the correct version?

pcost dcost gap pres dres
0: -4.7135e+01 -1.9465e+00 3e+03 5e+01 4e-09
Traceback (most recent call last):

...

File "/Users/snoran/misvm_example/example.py", line 38, in main
classifier.fit(train_bags, train_labels)

File "/Users/snoran/cvxopt-1.1.8/src/misvm/misvm/sil.py", line 44, in fit
super(SIL, self).fit(svm_X, svm_y)

File "/Users/snoran/cvxopt-1.1.8/src/misvm/misvm/svm.py", line 67, in fit
self.verbose)

File "/Users/snoran/cvxopt-1.1.8/src/misvm/misvm/quadprog.py", line 105, in quadprog
return qp.solve(verbose)

File "/Users/snoran/cvxopt-1.1.8/src/misvm/misvm/quadprog.py", line 65, in solve
initvals=self.last_results)

File "build/bdist.macosx-10.5-x86_64/egg/cvxopt/coneprog.py", line 4468, in qp
return coneqp(P, q, G, h, None, A, b, initvals, options = options)

File "build/bdist.macosx-10.5-x86_64/egg/cvxopt/coneprog.py", line 2243, in coneqp
if iters == 0: W = misc.compute_scaling(s, z, lmbda, dims)

File "build/bdist.macosx-10.5-x86_64/egg/cvxopt/misc.py", line 285, in compute_scaling
W['d'] = base.sqrt( base.div( s[mnl:mnl+m], z[mnl:mnl+m] ))

AttributeError: 'module' object has no attribute 'div'

Hello,

I have an issue with the distribution of classes. I have 4000 negative bags and 500 positive bags and it always predicts as negative. I would like to add the class_weight parameter, but the parameters for the mi-SVM are limited.

Thanks!

Hi everyone,
I'm actually running some of my work on your library ( pretty weel designed by the way), and I'm facing some issue during fit step.
I got arround 100 000 instances split on 5000 bags.

Problem is encountered in quadprog.convert() method at line P = cvxmat(H), where H is a sample*sample matrix. I got some matrix that contains arround 10 000 000 000 values, and by the ways exceed the authorized number of values (limited to INT_MAX values).

Do someone have some clues on how to solve that?
Best regards

Here is the problem I got:

Traceback (most recent call last):
File "/Applications/PyCharm Edu.app/Contents/helpers/pydev/pydevd.py", line 1599, in
globals = debugger.run(setup['file'], None, None, is_module)
File "/Applications/PyCharm Edu.app/Contents/helpers/pydev/pydevd.py", line 1026, in run
pydev_imports.execfile(file, globals, locals) # execute the script
File "/Users/chilab/PycharmProjects/MILSVM/mis_stress.py", line 87, in
classifier.fit(X_train, y_train)
File "/Users/chilab/src/misvm/misvm/sil.py", line 45, in fit
super(SIL, self).fit(svm_X, svm_y)
File "/Users/chilab/src/misvm/misvm/svm.py", line 68, in fit
self.verbose)
File "/Users/chilab/src/misvm/misvm/quadprog.py", line 106, in quadprog
return qp.solve(verbose)
File "/Users/chilab/src/misvm/misvm/quadprog.py", line 77, in solve
raise e
ValueError: Rank(A) < p or Rank([P; A; G]) < n

Thx!

Hello, is there a way to obtain scores of the predictions instead of labels? As you may guess, I am interested in computing AUC, rather than accuracy or similar metric.
When I create the classifier object with MISVM, I can see a score(X,y) function. However, when I pass the test bags as X and test bag labels as y, it gives me error.

ValueError: Classification metrics can't handle a mix of binary and continuous targets

I wonder how to realize multi label classification task using this binary classification MISVM.

I get this error when i try to run your example code
for algorithm, classifier in classifiers.items(): classifier.fit(train_bags, train_labels)

`ValueError Traceback (most recent call last)
in ()
26 accuracies = {}
27 for algorithm, classifier in classifiers.items():
---> 28 classifier.fit(train_bags, train_labels)
29 predictions = classifier.predict(test_bags)
30 accuracies[algorithm] = np.average(test_labels == np.sign(predictions))

~/misvm/src/misvm/misvm/misssvm.py in fit(self, bags, y)
55 bs.pos_instances,
56 bs.pos_instances,
---> 57 bs.neg_instances])
58 self._y = np.vstack([np.matrix(np.ones((bs.X_p + bs.L_p, 1))),
59 -np.matrix(np.ones((bs.L_p + bs.L_n, 1)))])

~/misvm/src/misvm/misvm/util.py in getattr(self, name)
59 return self.neg_bags
60 elif name == 'neg_instances':
---> 61 self.neg_instances = np.vstack(self.neg_bags)
62 return self.neg_instances
63 elif name == 'pos_instances':

/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/numpy/core/shape_base.py in vstack(tup)
235
236 """
--> 237 return _nx.concatenate([atleast_2d(_m) for _m in tup], 0)
238
239 def hstack(tup):

ValueError: need at least one array to concatenate `

This is problem with MissSVM on example code!

When i try other classifiers , I get different errors, for example sbMIL
3 clf = misvm.sbMIL(kernel='linear', eta=0.1, C=1.0)
----> 4 clf.fit(train_bags, train_labels)
5 predictions = clf.predict(test_bags)
6 print (np.average(test_labels == np.sign(predictions)))

~/misvm/src/misvm/misvm/sbmil.py in fit(self, bags, y)
54 scale_C=self.scale_C, verbose=self.verbose,
55 sv_cutoff=self.sv_cutoff)
---> 56 initial_classifier.fit(bags, y)
57 if self.verbose:
58 print('Computing initial instance labels for sbMIL...')

~/misvm/src/misvm/misvm/smil.py in fit(self, bags, y)
49 if self.scale_C:
50 iC = float(self.C) / bs.L_n
---> 51 bC = float(self.C) / bs.X_p
52 else:
53 iC = self.C

ZeroDivisionError: float division by zero

SIL works as expected. However, for MISVM:

clf = misvm.MISVM(kernel='linear', C=1.0, max_iters=50) clf.fit(train_bags, [-1,1])

Gives me fitting and output. How does this work? There are 82 train bags and only 2 labels, why is there no "dimension mismatch" error as when i try this with SIL?

The classifier.predict() method outputs NaN values for some test bags (in case of sbMIL classifier that I checked on my dataset). Could you please advise why it would predict NaN values and how to resolve it?

Hi, thank you for your great work and enjoying your program.

In l298 of "mi_svm.py",
I think
super_args = super(MISVM, self).get_params()
should be
super_args = super(miSVM, self).get_params().
Could you check this part and please modify if it is necessary.

Thank you.

Hi,
I am trying to run missSVM and MICA but I am getting memory error. The total number of instances across all training bags is about 120,000 with dimensions 100. Is there a way to get it running on a 16Gb RAM computer ?

Hi, I'm trying to use the algorithm but I get this error :

ValueErrorTraceback (most recent call last)
in ()
1
----> 2 classifier.fit(train_bags, train_labels)

3 frames
/content/src/misvm/misvm/quadprog.py in solve(self, verbose)
75 self._ensure_pd(eps)
76 else:
---> 77 raise e
78
79 _apply_options(old_settings)

ValueError: Rank(A) < p or Rank([P; A; G]) < n

Can you help me solve this ? thank you in advance for any help

Hi, first of all thank you for the implementation of all these versions of multiple instance learning !
I used conda with python3.6 and I installed the required libraries.
However when I executed the file "example.py"

$ python example/example.py

I obtained the following message:

Traceback (most recent call last):
File "./example/example.py", line 49, in
main()
File "./example/example.py", line 40, in main
classifier.fit(train_bags, train_labels)
File "/home/piero/miniconda3/lib/python3.6/site-packages/misvm-1.0-py3.6.egg/misvm/misssvm.py", line 57, in fit
File "/home/piero/miniconda3/lib/python3.6/site-packages/misvm-1.0-py3.6.egg/misvm/util.py", line 61, in getattr
File "/home/piero/miniconda3/lib/python3.6/site-packages/numpy/core/shape_base.py", line 237, in vstack
return _nx.concatenate([atleast_2d(_m) for _m in tup], 0)
ValueError: need at least one array to concatenate

Can you suggest me what is the mistake I made?
Thanks
Piero

Getting below Warning when the data size is more than 200 rows and 25 columns
Warning: numerical instability (primal simplex, phase I)

And the optimization job does not complete