Commit Graph

1 Commits

Author SHA1 Message Date
Nicolas Goix 788a458bba [MRG+2] LOF algorithm (Anomaly Detection) (#5279)
* LOF algorithm

add tests and example

fix DepreciationWarning by reshape(1,-1) one-sample data

LOF with inheritance

lof and lof2 return same score

fix bugs

fix bugs

optimized and cosmit

rm lof2

cosmit

rm MixinLOF + fit_predict

fix travis - optimize pairwise_distance like in KNeighborsMixin.kneighbors

add comparison example + doc

LOF -> LocalOutlierFactor
cosmit

change LOF API:
-fit(X).predict() and fit(X).decision_function() do prediction on X without
 considering samples as their own neighbors (ie without considering X as a
 new dataset as does fit(X).predict(X))
-rm fit_predict() method
-add a contamination parameter st predict returns a binary value like other
 anomaly detection algos

cosmit

doc + debug example

correction doc

pass on doc + examples

pep8 + fix warnings

first attempt at fixing API issues

minor changes

takes into account tguillemot advice

-remove pairwise_distance calculation as to heavy in memory
-add benchmarks

cosmit

minor changes + deals with duplicates

fix depreciation warnings

* factorize the two for loops

* take into account @albertthomas88 review and cosmit

* fix doc

* alex review + rebase

* make predict private add outlier_factor_ attribute and update tests

* make fit_predict take y argument

* fix benchmarks file

* update examples

* make decision_function public (rm X=None default)

* fix travis

* take into account tguillemot review + remove useless k_distance function

* fix broken links :meth:`kneighbors`

* cosmit

* whatsnew

* amueller review + remove _local_outlier_factor method

* add n_neighbors_ parameter the effective nb neighbors we use

* make decision_function private and negative_outlier_factor attribute
2016-10-25 11:53:51 -04:00