(split ds)
(split ds split-type)
(split ds
split-type
{:keys [seed parallel? shuffle?] :or {shuffle? true} :as options})
Split given dataset into 2 or more (holdout) splits
As the result two new columns are added:
:$split-name
- with subgroup name:$split-id
- fold id/repetition idsplit-type
can be one of the following:
:kfold
- k-fold strategy, :k
defines number of folds (defaults to 5
), produces k
splits:bootstrap
- :ratio
defines ratio of observations put into result (defaults to 1.0
), produces 1
split:holdout
- split into two parts with given ratio (defaults to 2/3
), produces 1
split:loo
- leave one out, produces the same number of splits as number of observations:holdout
can accept also probabilites or ratios and can split to more than 2 subdatasets
Additionally you can provide:
:seed
- for random number generator:repeats
- repeat procedure :repeats
times:partition-selector
- same as in group-by
for stratified splitting to reflect dataset structure in splits.:split-names
names of subdatasets different than default, ie. [:train :test :split-2 ...]
:split-col-name
- a column where name of split is stored, either :train
or :test
values (default: :$split-name
):split-id-col-name
- a column where id of the train/test pair is stored (default: :$split-id
)Rows are shuffled before splitting.
In case of grouped dataset each group is processed separately.
See more
Split given dataset into 2 or more (holdout) splits As the result two new columns are added: * `:$split-name` - with subgroup name * `:$split-id` - fold id/repetition id `split-type` can be one of the following: * `:kfold` - k-fold strategy, `:k` defines number of folds (defaults to `5`), produces `k` splits * `:bootstrap` - `:ratio` defines ratio of observations put into result (defaults to `1.0`), produces `1` split * `:holdout` - split into two parts with given ratio (defaults to `2/3`), produces `1` split * `:loo` - leave one out, produces the same number of splits as number of observations `:holdout` can accept also probabilites or ratios and can split to more than 2 subdatasets Additionally you can provide: * `:seed` - for random number generator * `:repeats` - repeat procedure `:repeats` times * `:partition-selector` - same as in `group-by` for stratified splitting to reflect dataset structure in splits. * `:split-names` names of subdatasets different than default, ie. `[:train :test :split-2 ...]` * `:split-col-name` - a column where name of split is stored, either `:train` or `:test` values (default: `:$split-name`) * `:split-id-col-name` - a column where id of the train/test pair is stored (default: `:$split-id`) Rows are shuffled before splitting. In case of grouped dataset each group is processed separately. See [more](https://www.mitpressjournals.org/doi/pdf/10.1162/EVCO_a_00069)
(split->seq ds)
(split->seq ds split-type)
(split->seq ds
split-type
{:keys [split-col-name split-id-col-name]
:or {split-col-name :$split-name split-id-col-name :$split-id}
:as options})
Returns split as a sequence of train/test datasets or map of sequences (grouped dataset)
Returns split as a sequence of train/test datasets or map of sequences (grouped dataset)
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