Resampling Methods¶
k-fold¶
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mlpy.
kfold
(nsamples, sets, rseed=0, indexes=None)¶ K-fold Resampling Method.
Input
- nsamples - [integer] number of samples
- sets - [integer] number of subsets (= number of tr/ts pairs)
- rseed - [integer] random seed
- indexes - [list integer] source indexes (None for [0, nsamples-1])
Output
- idx - list of sets tuples: ([training indexes], [test indexes])
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mlpy.
kfoldS
(cl, sets, rseed=0, indexes=None)¶ Stratified K-fold Resampling Method.
Input
- cl - [list (1 or -1)] class label
- sets - [integer] number of subsets (= number of tr/ts pairs)
- rseed - [integer] random seed
- indexes - [list integer] source indexes (None for [0, nsamples-1])
Output
- idx - list of sets tuples: ([training indexes], [test indexes])
Monte Carlo¶
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mlpy.
montecarlo
(nsamples, pairs, sets, rseed=0, indexes=None)¶ Monte Carlo Resampling Method.
Input
- nsamples - [integer] number of samples
- pairs - [integer] number of tr/ts pairs
- sets - [integer] 1/(fraction of data in test sets)
- rseed - [integer] random seed
- indexes - [list integer] source indexes (None for [0, nsamples-1])
Output
- idx - list of pairs tuples: ([training indexes], [test indexes])
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mlpy.
montecarloS
(cl, pairs, sets, rseed=0, indexes=None)¶ Stratified Monte Carlo Resampling Method.
Input
- cl - [list (1 or -1)] class label
- pairs - [integer] number of tr/ts pairs
- sets - [integer] 1/(fraction of data in test sets)
- rseed - [integer] random seed
- indexes - [list integer] source indexes (None for [0, nsamples-1])
Output
- idx - list of pairs tuples: ([training indexes], [test indexes])
Leave-one-out¶
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mlpy.
leaveoneout
(nsamples, indexes=None)¶ Leave-one-out Resampling Method.
Input
- nsamples - [integer] number of samples
- indexes - [list integer] source indexes (None for [0, nsamples-1])
Output
- idx - list of nsamples tuples: ([training indexes], [test indexes])
All Combinations¶
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mlpy.
allcombinations
(cl, sets, indexes=None)¶ All Combinations Resampling Method.
Input
- cl - [list (1 or -1)] class label
- sets - [integer] number of subset
- indexes - [list integer] source indexes (None for [0, nsamples-1])
Output
- idx - list of tuples: ([training indexes], [test indexes])
Manual Resampling¶
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mlpy.
manresampling
(cl, pairs, trp, trn, tsp, tsn, rseed=0)¶ Manual Resampling.
Input
- cl - [list (1 or -1)] class label
- pairs - [integer] number of tr/ts pairs
- trp - [integer] number of positive samples in training
- trn - [integer] number of negative samples in training
- tsp - [integer] number of positive samples in test
- tsn - [integer] number of negative samples in test
Output
- idx - list of pairs tuples: ([training indexes], [test indexes])
Resampling File¶
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mlpy.
resamplingfile
(nsamples, file, sep='\t')¶ Resampling file from file.
Returns a list of tuples: ([training indexes],[test indexes])
Read a file in the form:
[test indexes 'sep'-separated for the first replicate] [test indexes 'sep'-separated for the second replicate] . . . [test indexes 'sep'-separated for the last replicate]
where indexes must be integers in [0, nsamples-1].
Input
- file - [string] test indexes file
- nsamples - [integer] number of samples
Output
- idx - list of tuples: ([training indexes],[test indexes])