Feature/local outlier factor

[pablomm]

• Created LocalOutlierFactor (which wraps scikit-learn multivariate version)
• Example in gallery of detection of outliers
• New real dataset employed in the example (fetch_octane)
• Test and Doctests added

[codecov]

Codecov Report

Merging #164 into develop will increase coverage by 0.32%.
The diff coverage is 100%.

@@             Coverage Diff             @@
##           develop     #164      +/-   ##
===========================================
+ Coverage    72.74%   73.06%   +0.32%     
===========================================
  Files           40       41       +1     
  Lines         3900     3947      +47     
===========================================
+ Hits          2837     2884      +47     
  Misses        1063     1063

Impacted Files	Coverage Δ
skfda/_neighbors/base.py	100% <ø> (ø)	⬆️
skfda/_neighbors/regression.py	100% <ø> (ø)	⬆️
skfda/_neighbors/classification.py	100% <ø> (ø)	⬆️
skfda/_neighbors/unsupervised.py	100% <ø> (ø)	⬆️
skfda/_neighbors/outlier.py	100% <100%> (ø)

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[vnmabus]

We have discussed this method and we are not sure if it works for the population in a functional data context (it will probably work, but there is no theory published). Thus, we will keep this PR "on hold" until we have a better understanding on the theory. If you have more info about the usage of this method in FDA, please tell us.

[vnmabus]

Brief explanation missing.

[pablomm]

We have discussed this method and we are not sure if it works for the population in a functional data context (it will probably work, but there is no theory published). Thus, we will keep this PR "on hold" until we have a better understanding on the theory. If you have more info about the usage of this method in FDA, please tell us.

I didn't do much research, I read the original paper, and then I saw others in which it was applied for time series, and as it is based on proximity I extended it in the same way as the rest of the k-nn estimators, in which it worked for the fda case.

Apart from that and the tests I did with different datasets outliers comparing the results with the other methods we have implemented I did not investigate further.

[vnmabus]

The thing is that one theoretical motivation of the nearest neighbors methods is the estimation of probability densities, which do not exist in functional data. They told me that the other nearest neighbors methods (and probably this one) can have a more rigorous foundation in FDA based in Radon-Nikodym derivatives, which sometimes do exist in FDA, and can be seen as the equivalent of a quotient of densities. But the fact is that no one has tried to extend the local outlier factor to FDA right now, so I prefer to err on the side of caution.

[pablomm]

Okay, I understand your point of view. Keep me informed of progress in this regard.

I understand that this branch will be blocked for quite some time. I want to add some enhancements to the efficiency of the knn estimators to finish the work on this module. To keep everything updated easily I had thought to add the changes but without adding this estimator to the documentation, leaving it alone in the private neighbor module, what do you think?

[vnmabus]

Okay, I understand your point of view. Keep me informed of progress in this regard.

I understand that this branch will be blocked for quite some time. I want to add some enhancements to the efficiency of the knn estimators to finish the work on this module. To keep everything updated easily I had thought to add the changes but without adding this estimator to the documentation, leaving it alone in the private neighbor module, what do you think?

Ok, as long as the LocalOutlierFactor object is private. I would also create an issue so that we will not forget about it.

[pablomm]

Ok, as long as the LocalOutlierFactor object is private. I would also create an issue so that we will not forget about it.

Done.