Fix: Found array with 0 sample(s)

[allenyllee]

Symptom:
When using SVMSMOTE on dataset which contains a minority class which has very few samples (may be < 10), it'll raise error ValueError: Found array with 0 sample(s) (shape=(0, 600)) while a minimum of 1 is required.

Reference Issue

#742

What does this implement/fix? Explain your changes.

Root cause:
The line noise_bool = self._in_danger_noise(...) will find noise data according to kneighbors estimator's n_neighbors attribute, this value is equal to m_neighbors attribute of SVMSMOTE class. If we set a very large number to m_neighbors to initialize SVMSMOTE, for example: SVMSMOTE(m_neighbors=1000), this error will be gone. This is because the range of neighbor searches is large enough to contain another minority class data point, therefore the center data point will not be treated as noise according to this line n_maj == nn_estimator.n_neighbors - 1. But when m_neighbors is small (default is 10), and the minority class has very few sample, it may treat whole minority class data as noise data, cause returned noise_bool with all true, then in _safe_indexing(...) will remove all these data, resulted in zero number of support_vector data.

Solution:
Save support vector before trimming noise data point. When after trimmed noise data, check whether the length of support vector is zero, if true, then restore previous saved support vector, this enforce every minority data point used as support_vector.

Any other comments?

[pep8speaks]

Hello @allenyllee! Thanks for opening this PR. We checked the lines you've touched for PEP 8 issues, and found:

• In the file imblearn/over_sampling/_smote.py:

Line 557:1: W293 blank line contains whitespace
Line 559:1: W293 blank line contains whitespace
Line 566:1: W293 blank line contains whitespace
Line 569:1: W293 blank line contains whitespace
Line 612:17: W503 line break before binary operator
Line 857:89: E501 line too long (91 > 88 characters)

[glemaitre]

You will need to correct the PEP8 issue. I think that we should raise a warning as well because we are not strictly performing the algorithm which is expected (but we are in a corner case).

[glemaitre]

We will need a non-regression test (that you posted in the issue) and an entry in what's new as well since it would impact the end-user