Parallelize the fit and decision_function methods of FeatureBagging

[Shihab-Shahriar]

This PR Parallelize the fit and decision_function methods of FeatureBagging. The earlier implementation only used the n_jobs when base_estimator parameter is None. Apart from fixing that, the model level PR enables parallelism at more coarser level, thereby noticeably improving performance.

Benchmark results using n_estimators=20 and base_estimator=None, averaged over 3 runs. Values indicate fit time in seconds, the one inside bracket denote time for decision_function:

Dataset (shape)	Orig (n_jobs=1)	Orig (n_jobs=4)	This PR (n_jobs=4)
pima (768, 8)	0.19 (0.094)	2.30 (2.155)	0.64 (0.63)
vowels (1456, 12)	0.71 (0.42)	2.36 (2.17)	0.66 (0.64)
pendigits (6870, 16)	9.12 (5.02)	5.87 (4.32)	1.78 (1.42)
musk (3062, 166)	18.92 (8.32)	7.46 (5.88)	3.90 (2.79)
shuttle (49097, 9)	59.09 (38.67)	46.10 (28.11)	33.43 (18.01)

Performance can be slightly worse than single-process method for smaller datasets, but I think that is expected.

Please let me know if further changes are needed. Thanks.

[coveralls]

Pull Request Test Coverage Report for Build 1512

• 51 of 52 (98.08%) changed or added relevant lines in 2 files are covered.
• No unchanged relevant lines lost coverage.
• Overall coverage increased (+0.2%) to 95.819%

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Changes Missing Coverage	Covered Lines	Changed/Added Lines	%
pyod/models/feature_bagging.py	31	32	96.88%

Totals
Change from base Build 1507:	0.2%
Covered Lines:	4423
Relevant Lines:	4616

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💛 - Coveralls

[Shihab-Shahriar]

CC @yzhao062 for comments, in case you've accidentally missed it, I hope it's ok.

[yzhao062]

Sorry for being late. this will be the next PR to be in. It looks nice to me. Just need to run some test locally :) hang in there. Thanks!

[Shihab-Shahriar]

CC @yzhao062