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EXP on [ "_Naive", "_Smote", "_TunedLearner , "_TunedSmote"] #9
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Why tune_smote so slowly
theoretically, we need run 10x10x10 =1000 SMOTE+Fitting learner+ Predicting. For HPCbased on previous experience, 4 cores will run immediately. and 8 will wait for quite long time until have to kill jobs. |
Have you tried multiprocessing? It might be the answer to our time issues. Specifically:
Try to parallelize the 10 crossvals on 10 threads. Each crossval can occur concurrently so, this will produce a massive speed up (if you use HPCs, you may even get x10 speedups..)
A parallel DE might be a better option, we have a working version of a parallel DE, it is ridiculously fast on HPCs
There are several work arounds to this.
Combine Multiprocessing with HPCs to get massive speedups. |
Results
NB_TunedSmote_1000_mean_weighted means:
Time: Obvservation
|
0.8 is great. Two things: 1. I prefer unweighted_mean of F-measure, 2. do you have the result of oversampling rate after tuned? |
@WeiFoo With multiprocessing? Or without? Also, Parallel DE? |
Run
SMOTE params:
neighbors: k=[2,15], default is 5
over-sampling size for each minority(multiple classes): num = [10, max_num_majority],
in SMOTE paper, num can be 2X~5X original size. Here I set the range from 10 to the number of majority class instances.
NB params:
alpha: [0.0, 1.0], Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing). default:1.0
fit_prior: [False, True] Whether to learn class prior probabilities or not. If false, a uniform prior will be used.
DE:
Dataset:
anime.txt
Job submitted
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